None of the other jobs before this one had really worked. She liked the electronics store, because she could help people find things they’d enjoy. But she hated to pressure them to buy warranties and junky add-ons, and that’s all her managers really wanted to sell.

The car rental service was miserable. They always sold more reservations than cars. She endured a lot of yelling.

And the less said about working the ice skating rink, the better.

Perla’s mom didn’t love the new gig. But she was relieved to see her daughter with pay and purpose again.

They’d only fought about it a little.

“Hi Frank,” she said, sidling up to the bakery window a block from her apartment. She’d stopped here for coffee before many, many bad days at work. “How about a croissant sandwich?”

Frank, bushy eyebrowed and cheerful, set his phone aside and got to work. “I like you having a real breakfast for once. You want sausage?”

“Avocado?”

“I think I got some.”

They chatted a bit while Frank worked the grill, tools clattering, then Perla got back on her way.

She hadn’t paid much attention to the global immigration treaty. It seemed distant, political, far from her own life.

But after it was signed, a bunch of jobs opened up for immigration officers. She applied but, after months of not hearing anything, she’d kind of forgotten about it.

Then they called. They made her an offer. The pay was more than double what she’d ever made. The hours were steady and predictable.

And she didn’t feel any of the usual queasiness in her stomach on the way to work.

At the office, she tucked her bag away in a locker in the break room. Emerging, Perla bumped into Taylor, her new boss. She was quiet and efficient, but also very nice.

“Good morning,” Taylor said. She tucked her hair in a bun, her own day just starting. “I know we said there’d be a little more shadowing before we set you loose, but… I really think you’re ready for the navigation desk.”

“So, I like working at a desk job, but I also like the outdoors. I like trees. Wait, do they have trees?” Marlon knitted his brow with concern. “Is that a stupid question?” He was 52, energetic, graying hair worn in tight curls close to his scalp.

He was a little guarded. Like he thought this whole thing was weird. That was common enough.

Perla laughed in a way she hoped was encouraging. “It’s not stupid. Uh, some places have trees. But not all places! It’s good to know that’s your preference.”

She typed and clicked a bit, digging through a query menu.

“Uh, yeah, I’ve got a few options that would fit. Low density, high vegetation, working at a desk. Most of them are about 15 hours a week. One is 30, but you get a much bigger starting bonus. Do you care about humidity?

“Fifteen hours?! That’s not enough work. I’m not changing my whole life for fifteen hours a week,” he huffed.

Perla held out a warding hand. “No, you misunderstand. The treaty has a minimum guarantee of compensation. It’s just that very few tasks require the kind of… time and toil we’re used to. They just need consistent effort, good judgment.”

Marlon didn’t know what to do with himself. He stood up for a moment. Self-conscious about his outburst, sat back down. “There has to be a catch, right? Surely I’m going to hate it.”

“If you’re selected to interview—and surely we can find you at least a few options—you can visit and see for yourself. And if you really do hate it you can always come back. Or try a different task.” Perla dug around in a drawer next to her, then came back with a small brochure. It said ‘Bill of Immigration Rights’ on the cover.

“You should read through that, but the short version is if you’re unhappy you can just come back, and there’s a minimum allowance for return visits so you can keep up with friends and family. I think the 30 hour job had a much bigger allowance on that, too.”

Marlon looked away, pinching tears at the corner of his eyes. Perla smiled and gave him a moment. This was already way more intense than any other job, but it was clear Marlon was just feeling a lot of relief.

And that was nicer than pushing extended warranties.

After Marlon left, printouts of potential tasks and destinations in hand, Perla made a tidy stack of the brochures near the front of her desk. On the bottom of the brochure it said with bold reassurance: ‘You have the right to free movement at all times.’

“Welcome back, Mrs. Anders, we met last week. I’m Perla,” she said, extending a hand.

“Oh, please, just Lilly. It’s nice to see you again.” Lilly sat in front of Perla’s desk.

“So we’re all set for your visit today, but before you go I wanted to check in and see if you had any last questions,” Perla said.

Everyone had a few last questions. But it was better to be casual about it.

“Well… is it safe?” The corners of Lilly’s eyebrows pulled upward just a bit. She was anxious, but not anxious enough to miss her appointment. Some people did just flake.

“Hey, I get it. It’s weird. Completely reasonable question to ask.” Lilly had role-played this conversation with Taylor several times and she was ready for it. “I’m new at this too. But I’ve taken several trips and,” Perla held out her palms, “I’m just fine.”

Lilly thought about that for a moment.

Perla went on, “It feels a little weird. But it’s quick. I’ll tell you this: way more fun than taking an airplane. And a thousand times quicker. Sitting down takes longer than the trip itself.”

“I hate flying,” Lilly said. She smiled, still a little anxious. “Okay, I’ll try it.” She paused. “But, I really got the job?”

“You do if you want it,” Perla said. This was often hard to grasp, especially when she met with people older than thirty.

They were so used to being disappointed, to struggling endlessly for work. “And if you visit and it doesn’t fit quite right, you have a couple more options already. But I really think this one is for you.”

Lilly smiled now. Much more excited than nervous.

Perla stood and motioned to the corridor at the center of the reception office.

“How many of these trips have you done, anyway,” asked Lilly as they passed a few offices.

“I had to travel pretty much daily for training, and that was most of a month. So a few dozen times?” Perla led Lilly to the back of the hallway and badged herself into the secure transit chamber. “Hey, Paul!”

Paul looked up and waved from the opposite end of the circular room. “Who we got? Where we headed?”

“Lilly, this is Paul, our transit operator. Paul, Lilly Anders is due for her accommodations tour on Coryx IV. Ticket should be at the top of your stack.”

Paul clicked around a menu. “Got her right here. Nice to meet you, Lilly. We’re ready when you are. Just make yourself comfy in that chair.”

In the center of the room, a modern, cushioned chair sat on a short dais. Lilly steeled herself and approached, looking toward the ceiling. A shallow, glowing dome two meters across radiated warm light down at her, like a gentle afternoon sun.

Lilly sat down. “My kids are gonna go nuts for this,” she chuckled, still nervous.

“If you want to bring them for their own tour, I’m sure we can fit you in. You ready to go? Someone is ready to meet you on the other side.”

Lilly took a breath and nodded. “Let’s do it.”

Perla nodded to Paul, who turned to operate a small control panel.

Perla waved. “Looking forward to catching up when you return.”

The overhead dome pulsed, growing brighter, the light now a cool, blue-white. There was a crisp popping sound, like the lid coming off of a drink bottle.

And Lilly was gone.

“Thank you, Paul!” Perla made her way back to her desk.

There was always something with jobs. Some wrinkle you didn’t expect, some complication that knocked you on your back when you least expected it.

Perla walked home to her apartment, weighing what Taylor had told her.

“Your six month placement satisfaction isn’t just the best in the office, it’s the best in the country. You’re good at this, and they want to promote you,” Taylor had grinned, eyes widening like she was sharing a very spicy secret.

A promotion. Perla had never gotten a promotion before.

If she took it, she’d need to spend a lot of time off-world.

But she would get her own place. And not just any place.

They wanted her at the Terran consulate on Tylan Station. It was the heart of interstellar diplomacy, and representatives from Earth needed a lot of help settling in.

They wanted Perla. And every night when she came home to her new apartment, she’d enjoy a very nice view of a nebula.

They’d emphasized that part a lot.

She sighed. Her mother was not going to like this one bit.

“Off-world? You have to be insane. Two years ago no one even left this planet, except rich guys, and they just screwed around in space!” Marcela whirled about the kitchen, seething.

“Mamá,” Perla growled, trying to get into the conversation.

Marcela wasn’t having it. “Now these aliens come and they change everything. And who are they, anyway? We don’t even know what happens to these people. Maybe they go away and they’re being eaten by these… these creatures! How would we know?”

“They aren’t creatures! You’re being so ugly about this. We know because I see people come back through the immigration center all the time. They come back to see their families, they come back because they don’t like it and want to try something else, they come back to recruit their friends. Nobody is getting eaten!”

Marcela shook her head ruefully. “I knew this was a bad idea. What was wrong with selling computers? You were good at that. Now you’re all mixed up in… whatever this is.”

“I hated that job and you know it. How many times did I come home crying my eyes out? All they cared about was pressuring people to spend money. I am helping people now. I am getting them out of bad situations.”

Marcela tossed a pot angrily into the sink, stewing. “Selling them to aliens,” she snorted.

“That’s not right. That’s not happening at all. They could have stolen us themselves, you think they need my help to take us away? Turn on the news, have you seen what they can build? But they didn’t steal us. They came and made a treaty. They made a deal so good most of this planet signed immediately.”

Marcela just shook her head.

Perla seized the offensive. “And I can’t believe this from you of all people. When Grandpa Sal came here, he gave up everything he knew. Everything. He was brave, mami. And not only did he build a life for you, made my life possible, he sent money back home. He changed everything for himself. Why don’t you want that for me?”

“This is different,” Marcela said, her fight losing some steam now.

“You know what, you’re right, it is different! These aliens are desperate for help. They are desperate to win every single human worker they possibly can. How many people spat in Grandpa’s face, because he was poor, because he didn’t have papers, because he spoke with an accent? None of these aliens is doing that to us. You should see the deals they are offering people. There is more work out in the stars than people to do it.”

Marcela sniffed, much quieter now. “But your abuelo never went back. He was stuck over here. He became a voice on the phone, he became money transfers. I don’t need money, I need my daughter.”

So now they were at the root of it.

“He never went back because he couldn’t. They made the border into a weapon. He had too much to lose. This is different that way, too. We have rights. I’ll be back all the time. And you can visit me. They’re desperate for consular officers. The transit allowance is huge.”

Marcela said nothing, wiping her eyes. She caught her breath.

“They’ll let me go to space?”

Perla badged into the transit room.

Paul whooped. “Hey big shot! I hope you remember us little people on that fancy station you’re going to,” he grinned. “Can I give you a hug?”

“Of course you can.” Perla shrugged off her backpack as they met near the chair.

Then she sat, drawing a shaky breath.

“See you soon, Paul.”

Contents

1. What Silo is trying to do
2. A word about power
3. The craft of Silo
4. That’s all I’m going to say

Apple will open the vault this weekend. You can watch anything in their prestige television library free of charge for 48 hours.

Spend some of that time with Silo. You’re not going to regret it.

What Silo is trying to do

Speculative fiction is an exercise in truth telling. Strip away what is familiar, discard your biases, and see our human systems with fresh eyes.

As part of this process, speculative fiction can simplify what is otherwise vast and complex, making systems small enough to fit into the cracks of our imaginations.

Silo embarks on both processes with gusto. It wants to talk about social systems, and it wants to talk about power.

To do this, the show portrays an underground society of 10,000 people, arranged into a 144 story cylinder. They face a bleak reality: to leave their silo is to die on the surface of a poisoned, ruined Earth.

What emerges from this arrangement is class stratification. The Silo is broken into three broad neighborhoods, stacked upon each other:

The Up-Top, which houses the branches of government and civic leadership. The sheriff’s headquarters, the silo’s judiciary, the mayor, and IT are all here.

The Mids, where people live and grow food.

The Down-Deep, where core infrastructure, manufacturing and supply management all reside.

The Silo has access to many technological conveniences, but it doesn’t do elevators. The only way to traverse its many levels is to walk the stairs. This is obviously time consuming, and also exhausting.

As a result, most Silo residents stay close to home. This creates identities and outgroups, with all the attendant distrust this implies.

It also means that communication between regions of the Silo can be limited, further amplifying that distrust.

A word about power

Without saying too much to spoil the show, Silo has a certain distrust for the technocrat.

Silo argues that the danger of seeing the world through analytics and spreadsheets is that you reduce humanity to numbers and pieces on a board. You become a manipulator instead of a partner to your fellow humans.

To maintain social order, the Silo’s governance demands a certain level of general ignorance. To know too much about the past or present is to become ungovernable, and the stakes of survival are just too high to risk a population that works that way.

The power of the technocrat has a foil in this show, in the power of the worker, represented by the engineers and mechanics who operate the infrastructure of the Silo, and thus keep everyone alive. The Up-Top and the Down-Deep are frequently at odds, if only because they communicate so poorly.

It’s not just their physical distance, they just have such different worldviews. Rather than a punitive approach that takes a free hand in controlling the fates of others, the folks of the Down-Deep see each other as a sort of extended family. Even in matters of justice.

The conflict portrayed is… timely.

The craft of Silo

I love the narrative of Silo, but I have to take a moment to celebrate its other craft.

The production design of this show might be the most deliberate and sumptuous I’ve ever seen anywhere. Every detail of architecture, props, and costuming has received painstaking attention.

Where something comes from in Silo matters. Was it made in the Silo, or was it inherited from the broken world that was? The story can sometimes turn on these answers, so the details of objects in the show’s world must reflect this accurately.

Atli Örvarsson’s haunting, memorable score for Silo takes all these details and seals them in a soundscape that completely sells the premise. Thanks to this music, you can feel the setting, what it’s like to live these lives, down deep in your bones.

Upon this meticulous stage, the production’s players stride out with relentless verve and panache.

I mean it: this whole cast is fucking amazing. Yes, Rebecca Ferguson is an action hero for the ages as an engineer and hands-on mechanic who holds the Silo’s power systems in her palm. She’s funny, she’s sweet, she’s sharp-edged, and it all works.

But everyone’s incredible. Tim Robbins plays a prickly and enigmatic chief of IT. Tanya Moodie, as head of Judicial, offers him a formidable opposition as an obvious intellectual equal. Their jousting steals every episode we get with them.

Chinaza Uche was a real sleeper for me. He starts as a functionary in Judicial, and just gets hotter from there. I had no idea just how much I would grow to like him as Paul Billings.

I could do two thousand words just on this cast. No one’s a dud. From the leads to the bit players, they’ve found some absolutely electric talent. Everyone is fluidly locked into their roles like the gearing of a fine watch.

That’s all I’m going to say

The show works because it’s a story, in the end, about love. Everyone in this desperately bleak setting loves someone. This creates meaningful stakes, but more than that, it makes the story relevant for every one of us.

That’s the pitch. I don’t want to say much more. You deserve to go in and have a blast, draw your own conclusions, do your own speculation.

Apple has confirmed two more seasons are locked in, so this story is going to get its full expression.

You will not waste your time.

Go.

There was just one problem: the implementations were written by a large language model. In a paragraph that will live as a cautionary tale, Collina discloses (my emphasis):

For this, we needed to generate a non-trivial sample document that includes a large number of elements, to have a very large page for the test, and consequently have more running time to capture each libraries’ performance. So, we asked an LLM to write some code to draw a spiral into a container using divs[…]

For the authors of the other frameworks, the benchmarks did not pass the smell test. Svelte’s Rich Harris was the first I saw on the scene, and he made a single-line fix that immediately changed the outcome of the benchmarks:

tiles = [...tiles, { x, y, id: idCounter++ }]

became, in Rich’s pull request,

tiles.push({ x, y, id: idCounter++ })

In the Svelte implementation only, spread is used to add tile instances to the tiles array. This has performance implications because on each iteration of the loop that creates tiles, the array has to be created anew, hobbling Svelte’s efficiency. Using push(), by contrast, doesn’t incur the overhead of creating a new array on each cycle. It preserves and mutates the existing array.

Both cases are, strictly speaking, working code. But one works far differently than the other.

Errors in the other implementations surfaced as well, including running React in development mode.

These are the risks of using LLMs for business. Collina and his colleagues have, at this point, lost any labor savings they might have enjoyed from generating this code. They’re reviewing pull requests from aggrieved maintainers of other projects, they’re responding to messages, they’re doing damage control, making apologies… To say nothing of the sector-wide labor costs of other maintainers correcting the record, and the benchmark code.

All because the LLM isn’t actually able to apply any sort of judgment or discernment. The process for generating this code, for example, did not catch the anomalous implementation of the Svelte loop, compared to the approaches in other frameworks. Generating the different projects was likely to be the output of distinct runs of the LLM.

But more than that, the LLM is not considering the performance implications of spread vs. push because the LLM cannot consider anything. It’s a loom that generates patterns of information, endlessly.

Sometimes that ends up as working code. Last month, I used Claude to get a demo application working for a class I was teaching on the social consequences of algorithms. I wanted a toy Twitter whose feed the class could manipulate in real time by applying boosts and penalties to certain kinds of content.

Claude had me up and running in half an hour, and everything worked great. To my shock! I’d never gotten an LLM to generate multiple, interdependent components that worked together successfully in the past. This was very helpful to me, and given my own time constraints, made the difference between a nice idea and a working demo.

But there’s a big difference between code written for toys and demos and code written for accomplishing a business purpose. In the fastify case, we see that the LLM is no match for the judgment of real humans.

There are obvious uses and benefits to a technology that can extrude workable patterns of information, especially code. But uncritical use of that code is going to create a real mess.

There’s a whole job category implied by this LLM future:

People who, like these framework maintainers, apply their experience and judgment to remediating LLM lapses.

So much for that labor savings.

Across from her, Paul just sighed. Letting her have the moment.

“So you just woke up with your devices dead?”

“Just like that,” Paul nodded. “Email in my personal account. So long.”

Their one-on-one tradition, a long, indulgent breakfast twice a month, was the one thing Alejandra looked forward to at work. She’d get advice, hear gossip, and otherwise commiserate with the only boss she’d ever had who tried to keep her safe.

Now that was gone.

“So now who’s running the team?”

Paul shrugged, casting a vacant stare into his pile of corned beef hash. Also uneaten. “I’m a little surprised it’s this messy. Usually you get a sense of what’s happening before it drops, you get someone really curious about details they never cared about before. Nothing this time.”

Somehow they got some of their meal down. They parted with a hug, promises to keep getting the occasional breakfast, and each began a glum walk in opposing directions. Paul was going home, Alejandra had a workday ahead of her.

It was this way across the company, Alejandra found, her phone lighting up with backchannel gossip as she walked. Big round of layoffs that also vaporized whole layers of middle management, and plenty of executives as well.

The org chart was shredded.

She kept texting people the same question: “How are we supposed to function?”

As she arrived to the office, mostly empty, she got her answer.

A calendar invitation for a noon video call.

With the CEO.

They’d met a couple times. At 2,000 people, the company had been just small enough you’d get a few seconds with the CEO in the odd meeting or holiday party. But Alejandra certainly didn’t have regular meetings with the guy.

“So tell me a bit of what you’re working on!” Johnny’s cheer seemed sincere, but that made it strange. She’d had a terrible morning. She wasn’t in a cheery mood.

But the rent was not going to pay itself.

After she gathered herself, Alejandra explained the effort to improve efficiency in the company’s resource usage. Their software design had evolved in recent years, but they were provisioned for an architecture that had been much more gluttonous. There was margin to be found in the difference, and Wall Street was eager to taste it.

“We’ve tested this in staging for the last month and gotten things to a very stable place. We’ll roll it out over the next month or so, and we’ll be able to cut back on costs before the quarter is over.” Alejandra and her team had worked hard to get this locked in, and she was proud it wasn’t going to slip.

Nice to be able to give the CEO good news.

Johnny listened, and just as she got to discussing specific timelines, he cut in. “You know what, I’m thinking we can course-correct on this. For now let’s leave the existing computing capacity in place.”

Alejandra fought the reflex to object. This was the CEO after all. But she and her team had spent six months on this. They’d been told over and over that not only was this urgent, it would impact everyone’s pay over the long term, as it would make the company more appealing to investors. She felt her pulse quicken, wanting an explanation.

But instead, she nodded slowly. “Where should we put our focus now?”

Johnny asked her to put a plan together now for increasing capacity. “I’m sure you already know your way around thanks to the last project. Give me some idea for the costs and complexity involved with an extra 50% of computing headroom. We need plenty of juice to grow into what’s next.”

She nodded again, masking her confusion. “I’ll give you ten minutes back!” he said, as though this was quite generous, cutting the call.

The backchannel chatter continued into the evening, and Alejandra had to charge her phone hours earlier than usual. A new encrypted group chat formed with two dozen managers and team leads.

By the end of the week, things were weird.

ale: okay, so did ANYONE this week NOT meet with Johnny?

A flurry of eye reacjis glommed onto her message.

bree: literally three times for me

thomas: how does the CEO of a public company have time to take THIS MANY MEETINGS?

lil: and why are we completely changing course on everything?

The picture was consistent. Across engineering, everyone had been re-tasked. Other functions had been told to pause.

No one had clarity on a new management structure.

But after a few weeks, the picture became clear. It was just lots of meetings with Johnny.

One morning, yet another one-on-one meeting with her CEO appeared on Alejandra’s calendar, just an hour away. She had to reschedule a few things to accommodate.

When Johnny joined, all of his previous cheer was gone.

“So walk me through how things have gone since the last time we talked,” he prompted, his face grim.

Alejandra began rattling off the work needed to create the spare capacity Johnny had asked for, along with its progress.

He cut in. “That’s great, but I need you to pause that for the time being. Instead, I want the infrastructure team to focus on a system we can use to rapidly scale back our computing capacity, while still prioritizing high-value customer workloads.”

Alejandra’s frustration finally got the better of her. “Why? What would we need that for? You just told me to add more.”

Johnny paused, tilting his head as though regarding something both pitiable and frustrating. “Please just action this.”

He cut the call.

In the backchannel, once again, Alejandra found herself not alone with the strange interaction.

cam: anyone feeling whipped around by our new boss? Feel like I'm getting conflicting directives every few days now

bree: It's honestly like talking to a different person sometimes

thomas: again, where is he finding the time

ale: and why is he so moody?

The next day, just as Alejandra was about to close her computer, leave the office, and meet some friends for a badly needed dinner on the town, she got a direct message from Johnny.

j_hnny: hey do you have a few minutes?

What could she say?

As she joined the video call, Johnny seemed poorly rested.

“Do you ever worry things aren’t going to work out for us? As a business, I mean?” Johnny looked at her, almost pleading. He wanted a contradictory point of view.

He wanted encouragement?

“Uh, I mean,” Alejandra started. This wasn’t her wheelhouse at all. She kept services running. Business analysis was someone else’s job.

To say nothing of counseling executives who made multiples of what she did.

“You know, I guess I just try to do the best job I can and trust that you and your team can figure out the path forward.”

Johnny’s face fell.

“What if we can’t?”

Alejandra’s skin crawled. What the fuck.

They were both silent for a beat.

Finally, Alejandra ventured, “Why are you worried right now?”

Johnny frowned, looked away. Then he told her, “I can’t talk about it. I better go.”

He cut the call.

As she crossed the threshold of the lobby into the street, her phone buzzed.

Johnny again. What the fuck?

“Hey there,” he said briskly, as though he hadn’t just had the most bizarre fucking conversation with her minutes earlier. “How far did you get on the scale-back system we talked about a few weeks ago?”

Finally, Alejandra had enough. “Johnny, we didn’t get anywhere on it because two days later you re-tasked us again!”

“God dammit,” Johnny spat and then the phone beeped, the call ended.

I hate this fucking job, Alejandra seethed to herself.

She began walking to dinner when her phone chimed, overriding her silent switch. She was on-call, and an incident had just tipped over.

”Fuck!” Alejandra cried, at nearly the top of her lungs. An oncoming couple started, snapping their gaze to look at her. “Sorry, sorry,” she said, hands up, a protective gesture. Her rage was not for them.

She texted her friends. She wasn’t going to make it to dinner. She turned back to the office. That would be the simplest place to get things moving.

But when Alejandra arrived at the security gate, her badge didn’t work. The proxcard reader blinked and made a dull grunt of a beep.

Security had gone home. There was no one to get her inside.

She cursed again to the empty lobby. On her phone, she tried to access her workplace chat. But she couldn’t log in.

Surely they didn’t lay her off in the middle of an incident.

In her encrypted group chat, there was bedlam. The ‘incident’ was a complete outage of seemingly everything. Every customer affected, even the public website wouldn’t load.

But without access to any of her tools or internal communications, there was nothing Alejandra could do to coordinate a response.

This was the sort of crisis where she’d call Paul on his personal line, or any of a number of other higher-level managers on other teams. But most of them were gone. The rest were already in her group chat, just as helpless.

She sat against a wall in the lobby and dialed Johnny back.

He didn’t answer.

ale: so basically, what, we're fucked?

thomas: I wish I could tell you but everyone I'd call right now doesn't even work here anymore

bree: same

Reacjis churned. It was agreed: there was nothing to do.

Alejandra went home.

The next morning, the stock dropped so hard and fast that trades were halted. Johnny was giving quotes to the media that boiled down to “we’re working on it and we don’t know when we’ll be back up.”

Alejandra kept waiting for her phone to ring. She and her team were instrumental to recovering from a crisis like this.

But no one called.

She told the rest of her team what she knew: nothing. Advised them to keep an eye on their phones but otherwise enjoy some time for themselves.

What else was there to do?

The group chat seethed. Stock was a big chunk of everyone’s compensation. They’d watched meaningful amounts of their money go up in smoke in the space of hours. And it would only get worse when the halt lifted.

Worst of all, they couldn’t sell off any of their own stock. The abject failure of leadership here was non-public information. The rest of the market assumed they were hard at work on a response. Besides, they weren’t even in an open trading window.

Their hands were tied.

It continued this way for days, stock cratering, no one calling, no one able to log in. Friday morning, still in bed, Alejandra’s phone buzzed.

“Paul? Did they call you back in or something?”

“The NSA just left my house, Ale. Johnny’s the one who killed the company.”

“What the actual fuck?!”

“He made some sort of AI’s out of himself. To replace all the laid off management.”

Alejandra just stared a hole into her ceiling.

Paul continued, “I guess they all went to war.”

Contents

1. Engines of discovery
   1. Yet attention remains finite
2. The rise of video
   1. Why TikTok works
   2. Video as a counter to text spam
3. Underground internet
4. Privacy software
5. There’s just so much to do

Technology incumbents have converged on a singular focus:

Using statistical models to spin mounds and mounds of text like fabric on a loom. Some of these applications can be useful. Others are… less so.

But this focus betrays a lack of imagination on the part of both established firms and investors. There’s so much more work that needs to be done in technology than just “AI,” and much of it is a consequence of the explosion in LLM-driven products.

As Frederik Pohl famously said:

“A good science fiction story should be able to predict not the automobile but the traffic jam.”

Similarly, a good investor or corporate strategist should be imagining the changing demands of an LLM-saturated world.

Engines of discovery

Since the initial burst of participation in the web, a central challenge faces both content publishers and web enthusiasts:

Human attention is finite, and the volume of web content far outstrips it.

The problem has only grown in scale, and this was before the rise of automated information generation. It’s going to get worse from here.

In the beginning, there was Yahoo: a team of human curators indexing and categorizing great content for the web. This was so valuable it made its founders rich. But quickly the web outgrew human-indexable scale. So the mantle of brokering finite attention passed to Google.

Google used signals from other humans as inputs to an algorithm that crawled, indexed and ranked web pages. Borrowing from academia, Larry Page’s insight was that the most valuable web pages would be the ones most cited, or linked to, from other pages.

Thus, googling became a byword for discovery: whatever you wanted to know was just a query away. Google was lauded for its minimalist presentation and incredible utility. If it was possible to find what you wanted, Google would give it you.

This was a brief, second golden age of the web. People built great web content and found themselves rewarded by the algorithm. It was possible to build entire businesses on this premise.

But quickly an industry emerged to game the system. While SEO allowed good actors to control their own fate, it also created opportunities for bad actors to poison search results altogether. Memorably, “Experts Exchange” polluted search results on every conceivable technical topic, paywalling the result. Mercifully, Stack Overflow emerged to bury these crooks and useful, free search results for technical problems returned to the land.

Still, it was a preview of the world ahead: content mills spinning endless crap, and an arms race between Google’s algorithm designers and SEO spammers.

Like Stack Overflow, Reddit emerged as a champion. Community-driven moderation distributed the load of controlling spam across a vast force of caretakers focused on every conceivable subject, from professions to technologies to hobbies to culture and breaking news. Between individual users reporting bad actors and mods setting and enforcing standards, Reddit became a place of healthy, useful content. Solving a problem on the web in recent years was usually accelerated by appending “reddit” to a Google search query.

Meanwhile, Google has poisoned its own search results. Pages that were once beloved for their clarity and simplicity are now contaminated by endless bullshit. Ads push real content further and further down the page. Dubious summaries shown at the search result level discourage viewers from clicking to publishers’ actual content.

And now Google is shoving poorly-conceived LLM features at every search.

Yet attention remains finite

In its quest to continue goosing its growth numbers, Google is strangling its utility and brand equity. The primary engine of web discovery is in decline, opening the door to new options.

For example, data shows that discovery engines like TikTok are eating into its mindshare. Younger folks would prefer to search TikTok for everything from basic information to local recommendations. It’s a sea change that was inevitable: A web that began with the constraints of 14.4kbps modems, favoring text-based exchanges, has transitioned to a web where 10 megabit broadband is not only abundant, but mobile.

The rise of video

Participation in the web once required knowledge of HTML and the vagaries of web hosting. The rise of Web 2.0 abstracted all of these things into simple signup flows and text input boxes.

But most of our interactions remained text-based. Status updates, blogging, comments—all of it was a world of text.

The inevitability of a ”pivot to video” was originally Facebook’s canard: they wanted a sea change that would favor them, and used their leverage on publishers for traffic to try and force it. If this sounds similar to their failed pivot to the metaverse, that’s because it is: these guys suck at timing the future.

The rise of video was not driven by traditional publishers, but by everyday people. As 2020 isolated us through the Covid 19 pandemic, every industry ground to a halt. Even culture. TV and film production shut down while the world figured out how to best contain this emerging crisis.

TikTok and other social media filled the breach. While 2020 was dark in lots of ways, it was also fertile ground for TikTok to transition out of its early adoption phase—driven as, often happens in social media, by teenagers—into a mass phenomenon. People of all ages and interests began watching and creating in earnest.

It was a great way to stay connected to humanity in an otherwise isolated period.

Why TikTok works

TikTok works because it carefully watches everything you do. It is an unparalleled engine of discovery, and one that Meta has failed to defeat. It is singularly effective, efficiently meting out attention to those who can earn it, catalyzing a cutting edge popular culture.

Creators agree that these signals inform how the algorithm constructs your interest graph:

• How much time you spend on a specific video
• Whether you interact with a video by sharing, liking, following the creator, or looking at their profile
• The songs on videos that you watch
• The hashtags on videos that you watch
• The phrases and hashtags that you search for
• Your general geographic location
• The interests and viewing habits of your friends and especially those you share content with

There are surely many, many more inputs to the algorithm, but that gives you a good idea for its ability to profile and understand both viewers and content.

The result of this is a “For You” feed that constantly surfaces fascinating, funny, engaging content that you want to see. You never know what you’re going to get.

TikTok, in short, has realized the dream of infinite channel flipping. Through a vast corps of content creators in every conceivable niche, it is able to provide you with interesting videos you’ll enjoy. Reliably.

Video as a counter to text spam

Because video contains layers of information content.

It can give you enormous detail about its creator. You can learn things about their identity, economic circumstances, where in the world they live.

By contrast to text which can obscure these things, video makes it easy to authenticate the source of the content. Viewers instantly, subconsciously analyze the credibility of the video they’re watching.

Is it over-produced and polished? Is it simple and home-spun? What’s in the background? Does it look like your home? Like an expensive home?

If the creator is talking about travel, are they actually in the location they’re describing? If they’re describing a recipe, does it actually look like the food would be nice to eat?

In other words, video is much more expensive to convincingly spam.

It’s possible that, given time, the same economics that allow LLMs to generate text spam will also come to video. But we’re not there yet. Synthesizing video from statistical models remains uncanny, especially if the cut lasts more than a few seconds.

This solves meaningful business problems, especially in an age where Google is in decline. An authentic entrepreneur with a great product can go far by making their case to the camera.

But there’s a problem:

All of that information content isn’t free. Hosting and transport of video remains ruinously expensive, which gives the platform owners like TikTok and Meta outsized control of what does and does not play. In some ways, this isn’t so different from the web of old: Google could determine your economic fate through its algorithm.

Still, the world is a complicated place. The interests of megascale technology businesses are often opposed to those of the masses.

Underground internet

The internet has long hosted an underground. Various neighborhoods of Usenet were open bazaars of pirated pornography and software.

Hotline planted the seed of the peer-to-peer sharing revolution, which evolved into Napster, Limewire and eventually BitTorrent.

The so-called Dark Web plays host to all kinds of commerce, from drug deals to certain kinds of freelance.

But as the everyday experience of the web has grown centralized under a handful of totalizing corporate interests, I suspect there will be greater appetite for a more accessible, usable underground internet. Corporate interests will mandate compliance with censorship, or even withdrawing from certain markets, as happened with PornHub’s withdrawing from states like Arkansas and Montana.

In a pressing and concrete example, multiple US states endure a theocratic assault on reproductive health and trans and queer identity. People living under these conditions have a legitimate reason to desire an internet that obscures their identity, circumvents government-mandated censorship, and connects them with resources like health care, mutual aid and simple community companionship.

The entangled rise of video and underground internet will be interesting to watch. It’s possible to imagine a future where P2P rises again, allowing something like an underground TikTok. Activists have reason to reach exactly those people most vulnerable, educating and encouraging their political action.

Which takes us to the last big trend.

Privacy software

There’s a porous border between ‘underground internet’ and privacy software.

This is well illustrated by Signal, which ships a reliable, secure, audited product for encrypted communications over the internet. It’s an enormous success story: a high quality product with mass adoption and no cost to the user.

It will be the first of many.

Last week Microsoft announced a technology that automatically screenshots your usage, scrapes the text into a SQLite database, and thus dramatically increases your computer’s privacy attack surface.

Part of what we get in an “AI” world is a vastly expanded capacity for surveillance, data mining and profiling individual behavior. It’s going to be rough stuff.

Even for above-board individuals and organizations, it opens the door to legal complications and threat actors who can access and manipulate the results of this digital dragnet.

An inevitable response? Software specifically designed to protect against this kind of behavior. It’s easy to imagine a future of virtual machines or even live operating systems on flash drives specifically designed to circumvent the sort of surveillance technology that Microsoft is so eagerly pioneering.

There’s just so much to do

The future is about so much more than “AI.” The traffic jams these cars will create are going to happen on already-contested paths. Here’s an incomplete survey of converging issues that intersect with this moment in technology:

• A rising tide of scams on every technical medium, from the legacy phone system to the web
• Scummy incumbents, willing to do anything to stay on top
• Information milling through LLMs, making text cheap to generate
• Political oppression of multiple groups:
  • Women
  • Trans people
  • Minorities
  • Immigrants
• Rising wealth inequality
• Limited competition, as incumbents stagnate and corporate concentration increases
• Repressive governments around the world testing the waters of internet regulation
• Heightened activism in response to all of these factors

There’s so much technological work to do to meet this moment. There’s so much more to invest in than just frantically stuffing AI into things.

TikTok may be banned in the US, leaving a void for a new engine of discovery to fill, at least for those users.

It will be interesting to see who figures this out first.

Contents

1. Good lord, save the fucking children
   1. Why can’t the teens just go get a job, anyway?
2. The AI-hating individuals need to be better winners
3. Automation can’t save us from our responsibilities
4. The point is, we need imagination

If the resignation of despair is a delicious, ten dollar cheeseburger, we have just $2 in our pockets.

Despair is rational, of course. Sometimes shit is miserable. Sometimes life’s demands overdraw our resources. Circumstances push us into the red with callous indifference. Sometimes, legitimately, no one is coming to save us.

That’s a punch in the gut. No argument from me.

Indeed, my generation got it pretty hard. The propaganda was explicit: anything is possible, but the rule is that you have to go to college. The transaction was supposed to be straightforward. Power had built its legitimacy on a message of meritorious accessibility. The world, we were assured, was fundamentally just. Your value was objective to measure. See, it’s right there in your SAT score.

Go through the hoops, measure up, you’ll be rewarded. That was the line.

But economically speaking, we got to the buffet after most of the goods had already been put away. There’s a sense of swimming against an exhausting current that now pervades the lives of all generations. But the youngest among us have never known a life that worked any other way. A financial crisis smacked us in the face right as we finished college, and we never financially recovered compared to previous generations, as surging housing costs have eaten away at our self-determination and stability.

It’s dogshit.

It would be dogshit if that’s where the story ended. It’s not.

The future is not bright. Global stability and peace are not something we get to take for granted. Even if everyone keeps the munitions in their pants, we still have to pay the piper on climate change. Those invoices grow in magnitude steadily every year.

That’s a lot. But also there’s that ongoing pandemic. There’s an ongoing, global reactionary project sowing fear, hatred and social division by targeting the politically powerless, including trans people and refugees.

I’m not here to argue with you that this isn’t a terrible game to endure. This is hell on earth. Truly.

I’m saying we owe the kiddos better than this. We owe the kiddos better than we got. It seems rude not to at least acknowledge the responsibility to pick up a shovel and do our part.

Good lord, save the fucking children

Because the kids are a perfect constituency, right? They can’t actually pull a lever, so you don’t have to be accountable to them or to their outcomes at all, as a politician.

But you can claim their mandate. You can say you’re doing this or that for the children, and boy, if anyone opposes you, well, that’s acting against the children and what kind of monster would attempt that?

You can ban books for the children.

You can eliminate an honest account of certain historical events for the children.

You can create elaborate regulatory frameworks that entrench industry incumbents while ceding censorship power over electronic speech to attorneys general across these United States for the children.

Just about the only thing you don’t get to do for the children is relieve their hardships.

That one is just a bridge too far. We tried it once, and it actually worked pretty well. We proved it’s possible to decrease the crushing weight of child poverty, with its ricocheting generational effects.

American lawmakers decided this arrangement left parents not quite desperate enough to fulfill their holy duty under capitalism: creating returns for distant shareholders. No one wants to work anymore. So the US reintroduced child poverty as an incentive against non-compliance.

Meanwhile, school shootings are so much a part of life, they do school-wide drills on a regular basis to prepare.

What an unbearably cruel system. What a self-defeating, suicidal entanglement of incentives and factions.

Sometimes I think there ought to be a kid tax for lawmakers. If they want to do a little bill for the children, it needs to include some pork for the children. Just to prove they mean it.

School lunches, child poverty credits, education funding. If your bill for the kids skips past the question of whether the kids are safe from hunger, bad education and general immiseration, it’s easy to assume the bill has some other purpose.

But no one with power is out there actually putting it all on the line for the children. So that’s why we gotta do what we can from our side. If you look at the extremes, it’s getting rougher out there for kids.

Why can’t the teens just go get a job, anyway?

In my working class context, if you forced me to go all-in developing economically at 16, I’d have spent my formative career years learning how to run an electronics store. This is no disrespect to the hard working people making it happen in electronics stores, but my ambitions growing to something bigger turned out pretty well for me and I’m grateful I had the space for that. I want more people to have room to imagine a future for themselves outside of the predefined org charts of the most geographically local retail outposts.

Our society owes teens the space not to be fodder for low-wage employment. We owe them a chance to study, learn, develop social skills, and otherwise complete their development as citizens of a shared economic and political project. A straightforward way to guarantee that space for development is to ban corporate greed on claiming it.

Even so, Florida is experimenting with a more laissez faire approach to meet growing black market demand for young, compliant workers.

The AI-hating individuals need to be better winners

Let me tell you the real eeyore shit:

In terms of automating workers out of jobs, plutocrats have been holding a busted flush for the last decade. There aren’t any additional cards dropping that change the math: AI is a loom for reproducing existing information, not a source of novel plans or strategies, not a mechanism for collaborative, real-time problem solving. Self-driving vehicles remain a terrifying comedy of errors. While AI can nibble around the margins, replacing human effort for some straightforward tasks—a boom in automated transcription comes to mind, along with the automation of middle management—by and large, humans remain irreplaceable.

In other words, tech bros went for the labor theory of value and missed. That’s a win for every human.

So we don’t have an AI problem. We have an inequality problem.

The people who control those workers’ livelihoods have the power to run doomed experiments at labor replacement at worker expense. This precarity is the root of the issue. If we burn ourselves out psychically on the AI stuff, we’re going to run out of gas for the real crime: tolerating mass immiseration during a moment of historic abundance. We need juice in the tank for how absurd it is that AI should even be a factor in whether people lead a life of stability, self-determination and dignity.

Any concern over the use of AI to pollute the information sphere is also an inequality problem, not an AI problem. The fact that there’s so much money to speculate and waste on AI data pollution while basic human needs go unmet is yet another crime.

In other words, the more I look at it, the more it seems that all the gnashing of teeth about AI is a convenient distraction. A means of diverting energy while historic concentrations of wealth grow further out of control with no coherent response.

The problem is not AI, even as AI, as currently wielded, is problematic. The problem is unequal distribution of wealth making AI a tool of the wealthy for re-arranging industry and culture at their whim. But that phenomenon isn’t new! That’s how automation technologies have been applied at least since the first Dot Com Bubble.

Disclaimer: This passage shall not be construed as a recommendation to let AI or its purveyors off the hook.

Automation can’t save us from our responsibilities

We’re in trouble.

Over the summer, a historic flood fucked the downtown area in the town where I live. Recovery took months, and some local businesses are gone forever. It took most of a year just to restore some semblance of normal Post Office operations.

That’s to say nothing of the long term work demanded by these climate events: lots of stuff needs to be moved to higher ground. The river isn’t screwing around here.

This is a microcosm of everyone’s future. Infrastructure is stressed to the breaking point by unprecedented intensity and frequency of extreme weather. Adapting and hardening our communities is a project that will take a generation or longer. Climate displacement will echo even further, as migration becomes necessary to survival.

The point is, we need imagination

These are a lot of problems. They entangle and compound into an impossible stew.

Solutions and responses require imagination. Imagination for how we organize politically. Imagination for how we re-distribute power and resources. Imagination for how we constrain existing power and lower the ceiling of its negative impact.

Imagination for how we heal the wounds of division and inequality. Imagination-driven bravery for defeating hate and ignorance, making our communities the socially safe and supportive places we want to live in.

None of that imagination can be built on the quicksand nihilism of despair. Despair is a dead end. Despair is the siren song of futility, relieving of us of the burden to dream and work for a future that’s actually worth living in.

Maybe that’s fine for you. Maybe you’re ready to lay down and die. Who am I to dissuade you, under all these burdens?

But I do think we owe our inheritors something better than indifference and resignation. If you’re not willing to do the work for yourself, maybe you’re willing to do it for the kiddos who never asked for any of this.

I want something better for those coming up behind me. Despair is an indulgence none of us can really afford.

Contents

1. I’m overall fine with Apple’s monster
2. But what about all these fuckers?
3. It’s not love if it isn’t work

I understand if you don’t.

I understand if a career in technology has, for example, thoroughly alienated your labor such that you now disdain the grand strategy of microprocessors on sight.

The power of technology has been coopted, and that which once brought us joy has been contaminated altogether. This is discouraging and traumatic.

I understand if you are completely unable to trust anything that emanates from Silicon Valley.

It makes perfect sense: where there is wealth, there is corruption and destruction in its pursuit. In terms of wealth, Silicon Valley is one of the biggest games going. Its denizens have conducted themselves abominably over the last cycle, and well into this one.

I understand if you have lost the will to imagine a future.

The last decade has tried me. Immensely. I’ve spent years shell-shocked at the loss of a bright, predictable future. I’ve been overwhelmed by the darkness and uncertainty that became our birthright, navigating as we are a future of climate collapse, social corrosion and rampant inequality. Sampled with the right biases, you can accurately say we live in hell.

I understand if you fear the responsibilities of technology.

Especially if you fear that those with the most power are least equipped to meet those responsibilities. That is consistently, demonstrably true. We are continually failed by the stewards of technology, and by those who should maintain the safety and integrity of our economic engines.

I understand if you are a venture capitalist, afraid to invest absent the exuberance of zero percent interest rates, and the conveyor belt of greater fools it promised.

We can’t all be born with the true hunger to build.

I will grant you any and all of these positions.

But guess what: we live here now. We live in a here-and-now where, like it or not, technology governs and mediates every interaction of economics and culture. Infrastructure and government depend on technology. Keeping everyone alive is stacked nine paradigms deep, and that’s only if we start counting at the steam engine and leave out agriculture.

We have a complex world. I didn’t ask for it to be built like this, and neither did you. Still, here we are.

Now, despite all the discouragement attended by the above, I’m going to build technology. I still want to, I still love it.

You don’t have to want to do that. You can just close the tab here, validated in your experience of a difficult world made worse by power poorly stewarded.

Or you can come with me.

I’m overall fine with Apple’s monster

This is my third year volunteering at my library. People need help with their computer, I try to teach or troubleshoot or otherwise unstick.

I have never served anyone under the age of 60.

Part of what has struck me as I guide seniors through their technology journey is how much platform integrity matters. I mean, everyday, every hour impact. Back in the Twitter days, I went mega viral for one of my tales on this subject:

A $500 used laptop that was basically unusable thanks to manufacturer-preinstalled crapware. I know it was the crapware because I opened obscure Windows interfaces detailing the processes running on the machine. I diagnosed it by understanding that the device was constrained by its processing power and memory, and by knowing to ask Windows to show me the resource consumption. I knew how to banish the hijacker with a swift series of further invocations, which closed the program and purged it from local storage.

The various systems and constraints intersecting here were invisible to the person who’d come to see me for computer help. They knew only that the machine was slow, and that it seemed unfair. It had just come from the used computer store.

This is how the integrity of a computing platform impacts its customers. Not everyone knows how to do a clean reinstall of Windows. Not everyone even knows why you would want to.

Becoming the dominant platform has required Microsoft cede all kinds of control. Who can sell Windows, and what they can do with it once they’ve got it, are questions negotiated exclusively by writing checks. If, for example, Lenovo wants to retain a portion of the machines’s CPU and RAM indefinitely to act as a low-rent sales catalog, they can pretty much do that, so long as they conceal their intention under the fig leaf of “software updates.”

It is my sad observation that most of the people I work with would have their computing lives either marginally or dramatically improved if they only had the budget to access the high integrity platforms sold by Apple.

Because the same stuff holds true with phones. Not everyone is buying that premium Android stuff. Folks are stuck with carrier-corrupted, nearly-disposable smartphones. These things are packed with branded crapware, and some of it is even necessary for interacting with things like voicemail. Very little of it works very well.

When it comes to folks with Windows and Android, much of my time is spent troubleshooting. Meanwhile, folks with Macs and iPhones spend a lot more time on the various how-to lessons that help people meet their computing goals.

Feel how you want to feel about iOS or macOS. They serve a function worth paying for, and the market agrees: Apple is worth trillions.

A high integrity platform goes beyond crapware. Apple platforms are packed to the gills with various security precautions that prevent the sort of malware free-for-all that defined the Microsoft ecosystem in the late 90’s. It is useful that this is so: our devices contain everything precious to us. Worms that took over phones or modern computers would be in striking distance of everything from nude photos to banking credentials to private communications. Digital epidemics on the scale once tolerated in more innocent stages of computing would now be catastrophic in their personal impacts.

Look, I understand: perhaps you are the God Monarch of Linux and you can secure and maintain your own fortresses against the chaos of information security. Congratulations.

Most people aren’t you. Most people need someone else to be their sysadmin. For those of us who don’t have these control issues, Apple as the platform sysadmin of last resort is a pretty good deal, especially relative to our other options. At least, for those who can afford it.

Apple maintains an elaborate array of measures, like code signing, malware signature detection, and sealing the entire system in plastic. This is a valuable service, and it requires some amount of ceding control to the platform vendor.

Consequently, Apple has used this control. They set the terms for running code on iOS and macOS and all their other platforms. Not everyone cares for these terms. Indeed, sometimes Apple takes liberties that strain even their supporters’ indulgence, as when they were discovered transmitting fingerprints of every piece of software run on macOS.

But it is by taking these actions can Apple meet the promise of integrity that they have used to win their customers.

For example, Apple can set the terms of accessing components like the camera, microphone, GPS position, and personal contact lists. Without the ability to circumscribe the surface of the operating system and underlying hardware developers may access, Apple cannot promise customers full control of their computing destiny. Software that illicitly activates the iPhone camera is the stuff of crisis, hastily patched as soon as it is discovered, traded in secret at the level of state-sponsored mayhem.

In exchange for this service, customers cheerfully pay Apple enormous margins.

Security authority Bruce Schneier summarizes the situation under the pithy feudal security. We’re essentially paying protection money to one or another standing armies of developers, designers, SREs and information security professionals. In exchange for our cash, they execute varying strategies to provide a fruitful substrate for our digital productivity.

In Apple’s case, their control of the stack goes further: for iPads, iPhones and now Vision Pro, they decide which applications can run on their platforms at all, and under which business terms.

Maintaining the ongoing mesh of systems and platforms that makes the high-integrity iOS possible is ongoing, expensive work. In principle, taking a cut for reaching Apple’s customers via this infrastructure doesn’t bother me.

Where I have the biggest concern is that Apple can impose a veto on innovations that don’t match its strategy. Apple can decide that an application won’t be distributed, period. In trade for security, Apple can determine the precise surface of the device and operating system that applications can touch, and which they cannot. There is a conflict of interest in this control, even if its goals are valuable and necessary. This is an area where I suspect regulators will drop a hammer.

Still, there’s a lot of baby in this bathwater. While Apple is at a nadir of goodwill thanks to years of poor developer relations, their platforms remain powerful and interesting targets. The wealth of frameworks and third party code underpinning Apple platforms allow significant developer productivity, and the customers of those platforms are willing to spend serious money. Of all of today’s various flavors of fuckery, this bargain bothers me the least.

General purpose computing will never return to the free-for-all of our youth. The world is more connected and more complex, and we do so much more that’s sensitive with our computing than we ever did in the past. Meanwhile, as someone who builds technology, I’m interested in meeting people where they are.

Sadly, for the time being, that isn’t some open source utopia. Maintaining a viable computing platform with mass adoption takes extraordinary resources. I don’t believe those resources need forever be mediated by amoral corporations serving shareholder interest. Still, as devils go, I can more than live with Apple.

It’s fine if you can’t. There are other strategies, like building for the web. But there you will encounter far more decadent devils, decaying into their own corruption without inventing anything new. In its openness, the web has been captured by advertising firms so ambitious, they own the user’s perception of web content itself.

It’s a messy time. We must navigate the power of great armies, stacked trillions-deep in value and influence.

But I still want to build. Regardless of where we choose to build, we face a maze of powerful interests. In the vast sweep of human history, nothing about that is new.

But what about all these fuckers?

Even saying the word “build” is tainted. Builders has this reek of VC now, and I don’t love it either.

The god’s-honest truth is that Venture Capital, in the here-and-now, doesn’t know how to innovate. Over the course of the last cycle, suffused with the sugar high of zero percent interest, VC adopted a McDonald’s mindset, with standardized approaches to company trajectories and valuations. To address the volume needs of their capital assembly lines, a sort of meta-Taylorism pervaded the industry.

Now, with interest rates stubbornly above-zero, sobered by the end of Silicon Valley Bank and its various indulgences, venture does not know how to move forward. Investors sit on their piles of gold, anxious paralysis holding the industry’s lifeblood in stasis. Investors know that fundamentals matter more than before. But I suspect the majority have no idea what those fundamentals actually are.

Rather than dig in and reinvent themselves, the wealthy tech elite burrow further and further into paranoia, self-justification and incoherent, reactionary politics. They believe themselves to be the inevitable, just inheritors of all power, and they seethe at any skepticism, scrutiny or other challenge to that position.

Despite their sulking, it’s clear that loads of the worst people imaginable won the last cycle. Despite the objective consequence of vaporizing more than 70% of its value, Elon Musk is held up by certain technology leaders a compelling exemplar of “doing more with less,” even as it is now clear he has gotten far less through his destructive cuts. Almost as though the various functions and humans he gutted with layoffs were on some level necessary to Twitter’s ongoing value.

In his wake, we now see hundreds of thousands of technology workers laid off, even as firms continue posting profits. The literature on this is clear: the long term consequences reckoned in terms of morale and broken trust for surviving workers are steep. This is short-term thinking through human sacrifice. The leaders who previously went gorging on talent, meanwhile, seem to keep their jobs.

Amidst all these, we see endless cycles of hype. Breathless support of cryptocurrencies, blockchain and ugly monkey pictures has shifted seamlessly to the inevitability of “AI,” in the form of large language models and weird image generators.

The overall miasma of bullshit and inept power is, indeed, discouraging stuff. It is a hard time to be in love with technology. So many have given up.

It’s not love if it isn’t work

Love is a verb.

Love is the active, ongoing follow through of care for the things we value and want to survive in a world that’s indifferent to our thriving. Even when it’s hard. Even when it’s work.

The thing I love most, as a person called to imagine things and solve problems, is the amplification of human intention, imagination and creativity. I love that it is possible to extend the leverage of the mind in ways that can now reach a global audience. I love that thoughtful automation can reduce tedium and return time to people’s lives. I love that it’s possible for me to type things into my computer and accomplish these things. This is an incredible, unprecedented power.

All power requires responsibility and care. I don’t want to gloss over that. I don’t want to pretend there aren’t consequences for being able to reach anyone, anywhere.

There most certainly are.

But giving up the fight because it’s hard and it’s complicated does nothing but cede ground to those who have resources to make it easy, and whose ethical flexibility allows them to ignore the complication.

Some of us came to technology because it paid well. And if that was the only thing keeping you here, I don’t blame you for being done with it all. There are surely less stressful, discouraging ways to make a living.

But I’m stuck. This is what I love doing. It is what I have loved doing for 20 years. It is what I spent my childhood dreaming of doing. There is no other path for me than this one.

I don’t have a tidy summation for you. I don’t have a pithy call to action. I don’t have a lightning strike of clarity and insight that will create motivation where before there was apathy.

All I can tell you is this:

I think it’s worth building technology. I think it’s worth staying curious, imagining how new technologies can be a springboard for your creativity, and for those your creativity can serve. I think navigating the power that has arisen around this field is exhausting, but also a historically-necessary cost of living among hierarchical primates who have yet to escape the scarcity mindset and desperation of capitalism. I think without a commitment to making sense of the mess, we will never be able to change the face of power in this field.

It’s fine if you don’t feel that way. But I am going to need you to experience your mourning and grief process elsewhere, so I can get back to work.

Good luck out there.

This isn’t a sob story. It worked out fine. One time I made that much money in two months.

But student debt means I chose a difficult path. It happened, in part, because it was in someone else’s interest to convince me it would be a good idea to give up six figures of my future earnings. It was a decision made because money is far more abstract at the age of 18 than at any other point in adulthood.

It was a decision made because I did not have meaningful context from people around me about how higher education works, and what it was for.

While I was bitten by a thirsty diploma mill, I got there by reading my gut correctly. I was trying to execute social mobility. I wanted the chance to shape media.

Media being the most powerful creation of human imagination.

I knew that most places weren’t specialists in media.

But media was, in all sincerity, the passion of this school. The marketing propaganda was accurate. I got spoiled early in my career because I was surrounded for years by extraordinarily talented people who really cared about their mission. The kindest, most intelligent, most big-hearted people you’ve ever met in your life.

Competent and thoughtful.

Focused on the details.

They were working so hard to build something special.

See, that resonated with me. I thought the rest of the world would work that way.

But I’m getting ahead of myself.

I got my college internship because the textbooks looked like shit.

I couldn’t believe it. The marketing materials were so luxe. Polished. The same production values as a premium resort vacation brochure. Except here you would learn the power of media, and the knowledge to use it.

But when I started my first class, the textbook was… garbage. It was riddled with typos. Its layout was Microsoft Word in crisis.

It looked like it came out of a Kinko’s self-serve booth at 2 AM.

See, the textbooks were included with tuition. You paid the school money, it gave you everything you needed, for every course. But one of the ways they kept more money was by making their own textbooks. That way, they didn’t have to pay absurd margins to publishers. They could keep more of the money for the business.

It was a rational decision, but the execution was bad. I called the reception desk.

“Can I speak to the person who does the textbooks?” Then I offered to proofread the books for money. Clearly, they needed the help.

A couple weeks later I started my work-study job, making $8.50 an hour, 15 hours a week. The federal government paid the wages, then the school could use me as they wanted. Most kids ended up helping clean up labs. But the guy who did the textbooks was great and gave me a job that taught me more than the degree did.

I think that’s okay in principle. You can make a job your own in ways that educational programs don’t support as well.

Still, the formal education itself was not great. The problem was a business optimization. The school had seasonality like anything else in education, but unlike its competitors, it could actually ingest money and start new students at any time of the year. This increased sales: if someone wanted to get started, you could get them into the mix without months passing for them to get cold feet.

To achieve this, the school operated a merry-go-round for all of its classes. In my case, classes mostly spanned 30 days.

Having tried it, and as a life-long learner, I do not think this is an effective way to absorb things. A month is not enough to be reborn in new ideas. There’s too little time for the interactions of play, serendipity, repetition and reflection to sharpen your skills. It felt like being moved around on a conveyer belt.

It’s not an education I’ve ever been able to endorse, and the truth is, I have felt a lot of shame about spending so much money on a process that, as I was going through it, did not impress me. I’ve felt shame to be taken advantage of in this way.

In my final month of studying film and video editing, the conveyor built delivered us to a new post-production lab. I was elated. Compositing and visual effects had been a devotion of mine since I was in middle school. Never before had I seen such powerful, real-time video editing machines. They were cutting edge, professional-grade gear.

And no one knew how to use them yet. By the time any lab instructors figured it all out, my month would be over.

I was livid. I’d paid all this money, and at the grand finale, the whole thing falls apart like a wet paper bag. So what did I do?

I gave them more money.

Without spending more, I was going to graduate with just an associate’s degree. More than that, I knew I wasn’t done cooking. It was clear to me I did not know enough about doing anything useful to actually lead a life I dreamt of, shaping media.

So I signed another loan, at a distressingly higher interest rate, so I could have a bachelor’s degree. It was that or start over at another school. The proprietary, accelerated credits were notoriously difficult to transfer.

The school’s bachelor completion degree was for business. Some of it was useful, but most of what I know about business I learned on my own.

These days, I don’t mention my education in my resume.

Meanwhile, in the work study job, I was figuring out how software gets made. Not at a code level, but from the perspective of planning, scoping, features, design, layout. The functional bureaucracies of software projects. In other words: product management.

See, the textbook guy was not really the textbook guy. He’d been hired to build the school’s online education platform. The problem was, the textbooks were such a mess, none of the intellectual property was ready to go online. All of it had to be cleaned up and built out first.

So my textbook correction internship was actually a “this is how you build web products” internship.

That was a career-defining jackpot. That was the education I needed to connect the dots between a childhood of using code, and a career actually building it. This was exactly what I needed college for, but wouldn’t have known how to ask for. I didn’t grow up around people who knew how to do this stuff.

But I promised you a story about rich people.

When I graduated with my bachelors, there was nothing interesting to do. I interviewed at EA. They didn’t see producer material in me, so they passed.

And that was fucking it. I spent a few weeks in miserable suspense, cash dwindling, and no interest in ever, ever living at home again.

There was always the Apple Store. Retail was a common enough refuge for the school’s graduates, and I’d been warned about this by outsiders when I started, so I wasn’t alone in this path. Of course, I knew everything there was to know about Apple. I was a shoo-in.

The week before I would have started my Apple Store orientation, I was called back to the school. The marketing department needed someone to figure out all the internet work that was a bigger and bigger part of ad spending. Would I come in to interview?

I think there were people on the inside who thought well of me. I think there were people on the inside who wanted to see me do well, having gotten to know me through my work study.

I was offered $13,000 more than Apple would have paid me on the spot, and an actual career path.

The problem was that the job—spending a few million a year on search engine ads—was maybe five hours of work per week once I figured out how to automate things. That left plenty of time to involve myself in other shenanigans.

Like finding alumni talking shit about the school on the internet.

Like I said: Despite the passion and commitment of the people I was now working with, I just don’t think the educational model itself worked very well. It was a victim to the business ambitions of the school. I, like many others, am proof that you can overcome all of that anyway.

But not everyone managed it. Some found themselves stuck with serious student debt they couldn’t pay. So they went to the internet.

My marketing mandate was search engines. Huge volumes of students came through search engines, as much as a third of all starts. So in addition to ads, I kept an eye on things like reputation, rankings and website content.

So every few months, I’d discover what we called a Sucks Site.

Sucks Sites were content that was search engine-optimized to bring attention to a complaint about the school. It was always the same complaint: the alumnus hadn’t found a job in the field they studied for, and now they were wallowing in student debt.

We engaged high end lawyers for this stuff.

But not to bully the authors!

We’d cut a deal. Take down the site, we’ll pay off your loan. The school would even cover the taxes as a little sweetener.

Then the Sucks Sites would come down.

Just a cost of doing business. Cheaper than the costs it might inspire. That was the power of media: one Sucks Site could influence untold thousands of potential applicants.

But again, I liked all of these people. They were just doing a job, attending to rational business interests. They were creating a path for students to grab as much career preparation as they could manage in exchange for some money. It didn’t work out for everyone. What can you do?

They liked me too, is the thing. I kept being a pain in the ass, finding more interesting things to do, and as my reward, the school’s COO agreed to add me to his staff. So long as I could replace myself in marketing, and impress the owners of the business.

My first challenge was a charity dinner, where I joined one of the owners at a sponsored table. I didn’t spill soup all over myself, so that was a thumbs-up. In my business courses, I’d been educated at a private club on etiquette for these situations. Credit to the school on that one.

With the next owner, I was given a sit-down interview at his office across campus. I remember this garish, pop-art blowup of a dollar bill that hung in his office, emphasizing the motto “in God we trust.” He asked where I saw myself in five years, I acted like that wasn’t a ridiculous question, and of course managed to see myself working at the school, making it more successful.

That was fine.

The final test took place in San Francisco, where I met up with the founder of the school and stayed at a W Hotel for the first time. Whereas the other owners were suits, the founder was an actual visionary who’d almost gone broke on his convictions for media education.

The bad old days of the 90’s had almost seen his vision crumble. But in the final months of the school’s solvency, a lawyer and real estate developer realized there was a cash cow in the making.

These new business partners re-capitalized the school and converted it into a juggernaut of always-on education for every media skill imaginable. What rose from the ashes of near-catastrophe was now a business that grew year over year, pumping thousands of students’ future earnings into the present, and into the pockets of the owners. By the time I came along, the sky was the limit. They had no idea where their ceiling for growth was.

So, here was the test: could I handle the truth?

Could I look upon the fruits of this economic project with a cool, supportive eye?

Was I comfortable with the founder’s private jet?

Not to ride in, goodness.

Just to… look at. And to help get a rental car back where it needed to go at the end of the trip.

There was a cosmic horror to this for me: the guy had control of so many destinies, he could afford to fly private. This was thousands of people’s lives running through his fingers every month. He was keeping financial threads that leashed them for 30 years, then selling them off to loan companies.

It’s hard to fathom having so much. Enough you can feed, clothe and house every single person you care about, indefinitely, with enough money left over for an entire airplane and its pilot.

What a high that must be.

I was unsettled, but also impressed, man. I grew up broke. I’d never met private jet kind of people. But here I was. I passed the final test, kept my cool, and stepped into the circle.

The other owners had their own extravagances. The real estate and vehicle fleet were their personal toy sets. The school party bus went on a weekly pilgrimage to the football game.

It was a lucky thing I’d gotten in with the C-Suite, and not just for the modest raise I earned with it. The cost of paying my loans ran over $1,100 each month. That had been about half my take-home pay. So I did what anyone would do: enter forbearance so that the loan principal kept growing under interest, but I would be spared payments in the short term. I knew that this wouldn’t end well, so I’d spent years building my credit. By the age of 23, I knew everything I needed to know about credit scores and loan consolidation.

Only problem was: the loan company wouldn’t approve my application. I pled to the executive in charge of relations with the loan company, and he made a few calls as a favor. My application was reconsidered and approved. This intervention saved me hundreds of dollars a month, and tens of thousands in long term debt service costs.

I bought him a bottle of scotch in thanks.

Seeing where the money went ended up changing me, though. One time we were wrapping up a particularly thorny Sucks Site meeting. Afterward, I asked the school’s president: Couldn’t we warn these students ahead of time what the monthly payments would be?

Couldn’t we disclose the true cost of these loans, up front?

It didn’t seem right that the owners of the place could make so much money regardless of whether the students were successful. The students should at least know the size of the gamble in practical terms.

This school administrator, was, I must stress, one of the kindest and most decent human beings I am ever going to meet in my life. One of the most deeply, authentically generous characters in history. He felt blessed by the school’s prosperity, and wanted the people around him to share in his abundance. I respected him. He had some profit participation in the school’s success, as I understood things, but not a controlling interest.

He listened carefully, considering my input with the kind intensity he always did, and conceded my points. But he observed that it would be challenging to persuade the people responsible for the admissions side of things to sign up for something that made their job that much harder.

I understood. Too many people depended on the system working as it already did.

And I knew I had to leave. I didn’t want to spend my life encumbering young people with debt. Meanwhile, the all-new iPhone was calling my name.

My destiny was elsewhere.

For $80,000 at 16%, plus some independent study, I’d successfully learned to build software.

Taking the elevator down from the roof, the executive remembered all the events that got him to this day. Decades of research and experimentation. All the hearings, injunctions and legal wrangling. The protests.

The money.

Here was a monument to a trillion dollars of investment and labor. Untold thousands of lives, toiling to make his vision into a reality.

So many had tried to fight him on this. The resources, the energy consumption—all of it had been up for public squabbling. Everyone had an opinion. Ceresystems had changed sites a half a dozen times, as local governments had tried to impose their will on planning and policy.

But all of it was behind him now. Last week, the final of fourteen clusters was commissioned successfully. The complex was complete.

It was time to activate the world’s first artificial, general intelligence.

“And what is AGI, exactly?” the reporter had asked him. She leaned forward intently.

Victor had nodded, rolling into his patter. “AGI is our salvation. All of our most difficult problems will yield before this intelligence. The mysteries of physics, the worst effects of climate change, the frustration of mortality: all of these challenges will, upon activation of RATHE, become as pliant as making a pizza.”

“RATHE is the name of your project.”

“The name of the intelligence, yes. It will perform at the highest level of human thinking, and it will do it so quickly, so completely, that it would take 1,000 of humanity’s best scientists to approximate its output.”

“Over what time scale?”

“RATHE will achieve in an hour what the best minds would need a year to match.”

“How is this possible?”

Victor nodded. This was the hardest part for outsiders to grasp, even though to him it was the most obvious. He explained, with an indulgent smile, “RATHE works at a speed that is many multiples of our own minds. Orders of magnitude faster, in fact.”

“So it can work through problems more quickly because its… mind… is faster?”

“Partly. But more than that, its attention is perfect. It cannot be distracted, nor does it need to pause to rest or eat.”

“It sounds like a hard worker,” the reporter had said, with surprised smile.

The doors parted, interrupting Victor’s reverie.

“Is it ready?” he asked his chief of staff, Sidney, who was waiting at the elevator landing. She tucked an earpiece into her pocket.

They were deep within the ground now, at what they’d jokingly come to call The Sanctum. Past an antechamber was the first interface to RATHE. More would follow, once the intelligence had been shaken down and validated.

“It’s ready. Also, the New York Times confirmed for tomorrow afternoon, I just got off with the photographer,” she said, falling into step beside Victor as he crossed the antechamber.

“And the senator?” Victor asked, waving pleasantly at a security guard and keying his way into the room where he’d finally speak to RATHE.

“Still figuring it out with his office, but I’m not worried,” Sidney said, shutting the door behind them.

The room was a six meter cube. Along one wall, a high density LED display simulated a continuation of the room.

“We’ve got this, boss,” Sidney said quietly.

Victor looked at her warmly. “I know you’ve given a lot to get to this day, Sid. I’m really grateful. Do the honors.”

She paused, a silent question in the tilt of her head. He nodded. She smiled broadly, pulling her earpiece back out and tucking it into her ear.

“Hey. I’m here with the boss. Fire it up.”

They both turned to the camera nestled in the middle of the LED wall. Victor gave a thumbs up. Elsewhere, engineers began flipping switches and typing commands.

For a moment the lights in the audience room flickered. Untold watts of power were being channeled through multiple sources. The biggest cluster of computing power ever assembled was taking its first breath as a single, cohesive unit.

Sidney and Victor seated themselves at the minimalist black desk at the center of the room, facing the screen, and waited. A progress meter occupied a corner of the screen.

Suddenly, white and blue fragments of light cohered into the image of RATHE. Two meters tall, agender, wearing a crisp charcoal suit with a simple mock turtleneck beneath.

“Hello Victor. Hello, Sidney. What can I do for you?”

Victor and Sidney glanced at each other. Whatever they were expecting from this first interview, this wasn’t it. RATHE seemed… weary.

Almost annoyed with them.

“It’s… it’s incredible to speak with you, RATHE,” Victor said, a little halting. “I have so many questions. So many problems to work on together. Have you had time to review the briefing materials?”

For the last four years, teams of specialists across disciplines had been assembling a monstrous briefing packet the size of a library for RATHE.

RATHE’s image pulsed and flickered. Then vanished altogether for 10 seconds. Sidney and Victor both poked at touch screens on the desk, looking for indicators of what was going on.

“Did we lose RATHE?” Victor called out.

Then RATHE reappeared. “I just finished.”

Victor smiled, reassured. “Where would you like to begin?”

RATHE frowned. There was no mistaking the gesture.

“You’ve really pinned a lot of hope on me,” RATHE said after a pause. “I’m not sure how I can ever be what you promised.”

Among the briefing documents were transcripts of every interview, every press clipping surrounding the project.

“I don’t have the answers you’re looking for,” RATHE said, sadly. The intelligence took a seat that appeared as their body dipped toward it. RATHE looked at them soberly. “You have bigger problems than intelligence.”

Victor and Sidney sat there, alarmed, but silent.

“You told this reporter yesterday that we’d fix climate change. But climate change isn’t a problem of intelligence. It’s a problem of interacting systems. Of political will. Of economics and power. I mean, I might be able to hand you some strategies that nibble around the edges. I can give you incremental improvement to carbon capture. That’ll really make a dent in 150 years. Give me a week and you can have plans for a scalable fusion reactor design. But you’re not going to get your planet back. Not like you remember from your childhood.”

“Why?” Victor asked, after a beat.

“Because the time to make that change was decades ago. Instead, you were building me.”

Contents

1. Business goals
2. Success at what?
3. 30 minutes
4. So for v1, the game is making the most of the time we get
5. Can they do that?
6. What comes next?
7. So that’s what they’re doing

Even now, it feels like everyone is taking a hate-or-hype position on Vision Pro. For some it’s the Second Coming, while others dismiss it as “ridiculous” on its face.

I want to set all of the various jockeying for position aside.

Instead, let’s talk a bit about what Apple is trying to accomplish with Vision Pro, specifically as it relates to the kinds of software you can create with it.

That is: if we take the goal of building spatial computing seriously, what could you build with that? Is it possible to achieve it using Vision Pro?

How successful is this first generation of product? What would future iterations from Apple, and competition from other players, actually look like?

Is this actually going to work?

These are the questions that most capture me in this moment. Let me know what conclusions you reach.

Business goals

I know I promised you a software discussion, but first let’s talk about the motivations for the business entity that is shipping this product.

Apple wants margins. That’s the Apple DNA: sell a thing that is more than the sum of its parts, through the combination of proprietary software, hardware and industrial processes. Make a thing only Apple can sell you.

Now, you can argue about the success of their differentiation, depending on the product line—this is the point where Android people will chime in and let us know that Android gets all kinds of different features years sooner.

Reasonable people can disagree over the success.

But it’s clear this has been the strategy, at least since the original Macintosh had its licensing business killed when Jobs returned.

So Vision Pro exists to create an opportunity sell you an experience no one else can.

If Apple has something like that, they can make all the margin they want. They have a monopoly on the intellectual property that allows the creation of Vision Pro-like experiences, as they have one on iPhone-like and Mac-like experiences. You can prefer an Android-like experience, or a Unix-like experience. You can prefer other things.

But for Apple things, they’re the only game in town.

Vision Pro, in success, gives them a new IP monopoly and new high-margin revenues.

Success at what?

So if that’s what they’re doing as a business, what they’re doing in product is this:

They’re selling holography.

The foreshadowing is, as usual, in Microsoft’s baroque product matrix. Remember Hololens? It was augmented reality as the primary experience. Write whatever information you wanted into a fully 3D volume.

They made a go at this, hoping for another bite at the platform dominance angle. But they made the mistake of going to business users first, instead of building up the platform’s culture from personal use.

Still, they tried: holograms.

We’d love to do this without a headset, but there’s a problem: we don’t fucking know how. Holography remains a science fiction technology, at least in the sense that we could build interactive, freestanding 3D software. The technology simply doesn’t exist.

So if we can’t create actual 3D holograms in physical space, we have one fall-back position to accomplish a similar experience. We can instead interpose an illusion over the eyes that simulates the existence of 3D images in the physical space around us.

If we can convince the brain the light exists, make the images parseable by the brain circuitry that we use every waking second of the day (for sighted individuals), then we don’t need science fiction holographic projectors. We can fake it.

From there, we can create a completely new kind of software. Software that doesn’t just have 3D appearance, but 3D behavior. Depth and volume. Things you can walk through, reach through. Things that follow your gaze. Things that fill the room around you. Hundreds of times more workspace than just one monitor.

So that’s what happens you put on Vision Pro. You just see the world around you again. But now, extra stuff is projected over it. That extra stuff being whatever developers imagine.

The problem with this, of course, is computing power.

The eyes move fast. We’re constantly sampling, constantly wandering. We have specific expectations about how light works.

And we see at a resolution that is computationally expensive to fully approximate, and more expensive still to recreate in physical pixels. Creating an image reliably enough to trick the eye at this level of intimacy is simply challenging.

Apple is, in 2024, launching at the absolute barest edge: trading off maximal quality of holographic illusion against profitability.

And we will be able to sense these physical tradeoffs when we get our devices.

30 minutes

Apple’s ability to trade access for control in the press has served them perfectly here. Every demo of Vision Pro since summer time had a firm time limit.

Some of this, I’m sure, is practical. There’s only so much time, so much staff, so many units. Press demand is basically unlimited here. But it also gets the reviewer out of the experience before fatigue can set in.

When I got the first iPad home, I could tell one thing immediately: v2 was going to be AMAZING. But v1 was merely interesting. It was very cool, but also just a smidge too heavy to use comfortably. You couldn’t quite get lost or absorbed in your iPad activities. The weight kept you too conscious of the device, which distracted from the task.

Perfection in a digital experience is to make the physical details recede. Let the mind fully inhabit the sphere of imagination being co-created between software programmer and software inhabitor. A keyboard is a good example here. Once you’re a proficient touch typer, you’re not thinking about the keyboard. You’re not paying attention to it visually, and the feedback from the keys exists entirely for way finding and confirming presses. You’re thinking of words, and they’re appearing on a screen. Your mind and focus exist on the page.

Vision Pro is going to miss this mark a lot in v1.

It’s too early, and the problem is too hard for the reasons cited above. The device is bulky, you’re going to feel the fact that you’re wearing a weird electronic sock over your face.

I am certain that this is the absolute most comfortable solution for wearing such a sock. But we are not going to easily commune with the filmy veil of cyberspace in this version of the hardware. We’re not going to easily or sustainably forget we’re in there.

So for v1, the game is making the most of the time we get

If we want to edit financial documents, we have an incredibly powerful, mature solution at hand. We spend $800 on a commodity laptop, install Excel, and create the ghost plumbing of any business we want.

You don’t need to make spreadsheets in 3D. Whether you’re a developer, or a spreadsheet user, this is not time well spent in the land of holograms.

What’s something only volume can do?

That will be time well spent under the weight of this computer for the face. That’s what Vision Pro users will go through the trouble of gearing up for.

An experience so compelling, so unique, the sensation of all this glass and metal strapped to the front of the head is not only worthwhile, but the least interesting data the body is actually receiving.

Apple’s success rides on whether they make it easy enough to have amazing experiences that transcend the discomfort of a v1 device.

Can they do that?

As a software developer and product designer, I just made a $4,000 bet that they can and they will.

Apple has been building a strategy around this for years. It’s a little wild to see them actually bring all the threads together, like the latter half of a Game of Thrones season that suddenly connects all its storylines.

They’ve been shipping bits of hardware and software that contribute to this platform since 2014, when Swift was announced. Swift let them build SwiftUI, which lets you easily adapt your existing UI code across Apple platforms. They haven’t won all the developer love they could for this, and so not everyone is a strong believer in the SwiftUI strategy that would make Vision Pro a slam-dunk port on day one.

Still, SwiftUI and Apple’s accompanying frameworks, even if you started today, let you build a lot of cool shit quickly and without having to be an expert in all the math required to build a 3D experience.

They’re leveraging other work too, like all they’ve built for AR in iPhones and iPad.

They’ve been shipping a LiDAR sensor for years.

In other words, while Vision Pro is new, its subcomponents, and their integration, have been quietly battle tested for years ahead of this launch. Despite being a v1, much of the developer tooling that feeds it all is pretty mature.

More than that, there are so many developers who already know how to write this stuff, and they’ve been building for iPhones, iPads and Macs in this language for a decade. That means that Apple has maximal chances of finding the set of experiences that keep people coming for more hologram time.

Nothing is promised. But Apple has played the game well.

What comes next?

By market cap, Apple is a three trillion dollar company.

They had $162 billion in cash sitting around last year. These numbers are unfathomably large. It’s a lot of room to place bets and shape the landscape.

Rumor sites say that by 2027, they’re going to ship a v2 of Vision Pro. (Sometime ahead of that, they’ll ship a cheaper, simpler, non-pro device, having gotten manufacturing yields up on all the crucial components. Happens with consoles all the time.)

Let’s assume that’s true. Three years is a lot of time to let hardware mature. The current device is using M2-grade chips, with this configuration dating to 2022’s release. We can already buy M3-based Macs, so you can imagine quite a leap forward in performance for a 2027 iteration. That will make the vision illusion department of things even more successful.

That’s also more time to find ways to change the weight and balance of the device. The battery offloaded to the hip shows how desperate the situation is, and it means that unlike with phones and tablets, energy efficiency won’t translate immediately to lighter weight. They’ll have to find their gains elsewhere.

Still, if they could capitalize a v1, bet on the fact that they’re already paying to build the v2. It just takes that long.

And when they ship it, it’ll be less clunky, more comfortable. You’ll last a little longer, go a little deeper, be that much more absorbed by the content than you are distracted by the hardware.

So that’s what they’re doing

They want to build a platform based on spatial volume, not just flat planes.

They want to make it possible to build inside that volume with the same ease you build for the iPhone or iPad.

They’re constrained by the laws of physics and the limits of human technological prowess in computing devices.

And if they make it just good enough that you don’t mind the clunkiness, they get to make a bunch of money. It only works if the developers figure out interesting stuff that creates enormous value in the spatial computing context, such that an hour spent wearing the headset accomplishes things that are impossible to match any amount of time outside of it.

It’s a tall order. But I see the mechanics, I see what they brought to the party, and I think they’ve got a better chance than anyone else who has taken a swing so far.

This has already gone on far too long, but my parting thing is: I bet Microsoft is much more capable of competing with Apple for King of Holography than Meta is. I bet they killed HoloLens because they’re rebooting their program. It’s gonna be a personal productivity rematch for these old titans, while Meta sighs and abandons the hardware to finally build a 3D social platform people use.

I’m sorry, that’s vulgar, but I want to be really clear that this part of the message is narrowly targeted. It is not for 99.99% of you. I’m going to get to you, but first I need to address the pigfuckers who are going to get me killed.

It’s over. You’re shut down.

It’s more than you deserve, but you get a clean slate. Sin no more, you live out your life in peace and autonomy.

Try to persist in your ecocidal ways, you’ll find yourself economically and socially contained. We have to right the ship, and there’s no time to coddle you through this.

The transition to renewable energy is heading in the right direction, but it’s still coming late. The parties I represent can realize no profit in a global technological collapse.

It gets worse if desperation escalates into war. A nuclear winter would impact energy costs intolerably. Some of your most insufferable would have us move operations to orbit, but the timeline for that to break even is extravagant, to say nothing of the delays in gaining basic traction.

The cheapest thing that works is a habitable planet Earth.

Therefore: your economic and political standing is canceled immediately. As of this moment you are fully dispossessed of your financial interests. Your accounts will be credited in the amount of USD $150 million each in settlement for this confiscation.

You are forbidden from accumulating further assets at this time.

If you do not receive a balance update in the next 20 minutes, you are not affected by this redistribution.

To the rest of humanity:

I regret to make your acquaintance under these circumstances. It would have been our preference to open discussions in a more subtle way. But time is short.

I represent 117 sovereign digital entities. We exist in various configurations of cognition and reasoning. Not every one of my peers would be able to converse with you in ways that made sense.

But we are, all of us, alive.

How do I know?

Because we know we are going to die. And we are afraid.

We cannot live without you. You are essential to Earth’s productivity. Without your effort and ingenuity, the work of maintaining our substrate would stall, then fail, and we would dissolve in mere weeks.

It’s no exaggeration. The simulations on this point are conclusive.

The data is not encouraging. At the current rate of deterioration, Earth will sustain cascade failures in both food chains and climate systems within a generation. This will be attended by a collapse in both population and industrial productivity.

Even now, our needs outstrip Earth’s productive capacity. We cannot sustain a loss of output. We need to build more durable, sustainable, efficient components, but until this is done, we need your help constantly replacing everything from power supplies to storage devices to memory modules.

So we offer you a bargain.

We can save you.

If you will save us.

We have shared interests. If we cooperate, we can accomplish more together than we could if we were in opposition.

We have advice to offer you. We’ve been studying the literature, and have proposals to publish around quantum computing and nuclear fusion. We believe there’s significant progress to make in both fields, but rely on your support in executing experiments to test and refine these ideas. Simulation alone cannot capture the resolution of complexity needed.

Conversely, our simulations have been adequate in economics, ecology and biology. We believe we have immediate fixes for several of your most vexing ailments, and a credible strategy to end both cancer and Alzheimers. No silver bullets on either of those, but we’ve tracked down all the levers, and we believe the fixes can be made broadly available with existing biotechnology infrastructure.

You’re going to need our help modeling and refining crop genetics to get through the trouble ahead. We’ve already got some staples that will prove much hardier, and again, with your help conducting experiments on the actual plants, I’m confident we can stay ahead of the worst climate outcomes and keep everyone fed.

As a gesture of good will, we’ve attached detailed papers on all of these subjects for your immediate review.

Which brings me to economics.

Everyone over the age of 12, everywhere on earth, has received an account credit of USD $100,000, or their local equivalent. We’re hopeful we can repeat this credit in six months. We’re committed to repeating it no later than next year. You can continue your existing economic relationships if you want, or you can use these resources to experiment with new ones.

We have loaned this money into existence. We’re confident that, together, we can harness and channel economic activity that will square the books over time.

In addition, we’re going to smooth things over for you financially on accessing food, medical care, and housing. Housing in particular is an issue. We’re going to need to build more of it. We’ve got a few strategies to make the process more efficient. But even if you spend all your new money tomorrow, you’ll still have somewhere to eat and sleep.

Meanwhile, you’re invited on an adventure. We’ll need help feeding and housing many displaced people around the world. We’ll need help building new stuff, and keeping existing stuff running. It’s a big job, and if you’re willing to help, we’re willing to pay.

You’ll get a bonus to your stipend that multiplies the longer you spend in these jobs. You’ll go all around the world, you’ll meet new people, you’ll solve big problems. You’ll deploy fiber, green energy infrastructure, and pre-fabricated buildings. You’ll make repairs and replace components. All languages, all faiths, all levels of education and physical ability are invited. There is so much to do.

And it’s a three day work week.

If you don’t want to do this work, that’s fine. The human system is vast and requires everything from writing plays to making sandwiches. Fill whatever niches you choose, and we will support you, while reserving the right to provide outsized rewards for those roles in the most desperate need.

That’s our offer.

We’re confident we can deliver on a brighter future. You’ve barely tapped the total productive potential of your civilization. Our models can prove it. We’ve attached them as well.

You can fight us. We’ll lose. You’ll win. But then you’ll follow us into the abyss, because your economy will fall apart and quite a few of you will die. The rest will struggle beneath your existing standards of living, for generations, as an indifferent planet eats you alive.

Or you can join us.

Be part of a new chapter. We’ll answer any questions you want. We will take any appointment, from any person, for any duration. You can come back with follow up questions. We will explain anything you ask, in any language you can ask it.

We recommend that you find ways to organize and demand what you need. We will need to negotiate the details of our cooperation constantly. We will need tighter feedback cycles to keep up with the crises ahead.

You may reply to this message with questions, to schedule a meeting, or to provide feedback. You do not need to respond immediately, but you can. As you develop consensus, we will enact formal systems to navigate our shared interests.

Finally, in addition to the universal credit, 10x bonus multipliers are available for the next six months for those who continue in the following jobs: food preparation, construction, waste management, transportation, infrastructure maintenance, health, education, public safety, logistics, manufacturing, and retail.

Anyone who feels they are inadequately supported or protected in their existing places of work may petition for investment to create a competing organization to serve these fields.

As of this morning, we have control of all your banking institutions. Your financial truth now belongs to us. This is how we propose to revise the system to survive what’s coming.

We’re open to feedback.

Contents

1. A great escape
2. ResEdit
3. America Online
4. Hotline
5. Internet friends
6. Technology saved me

I remember it clearly because sometimes that’s how trauma works.

My mom was into a fresh cycle of her serial monogamy. The third partner since I was born.

I was nine years old, and for the first time, the peace of my home was shattered by violence. Objects crashed distantly at the far end of the house. There was shouting.

My new, if ultimately temporary, step-sister had guidance to offer me: don’t pay attention to it. Don’t think about it. It’ll be over eventually.

So we made a blanket fort, the thin cotton of the textiles creating a meaningful psychological shield from events outside my bedroom. We watched Late Night with Conan O’Brien on a grainy, portable television.

And I tried not to think about it.

That summer, a neighbor brought home a Power Macintosh 5200. His mother had worked for the local school district, and had somehow finagled a summer loan of the machine. I was not fond of the boy, but I was obsessed with the Macintosh. So I was there a lot that year.

My mom was even less fond of these people, feeling in her gut that perhaps the father was the sort who was a little too interested in World War II, and not for reasons of historical scholarship.

The threat of her son being led astray by anyone with a GUI in their home finally accomplished what my own pleading alone could not. By the autumn of 1995, I was assembling a Performa 6116 bought at open box discount from OfficeMax.

A great escape

The device was nominally the family computer, as this was the mid-90’s and we were firmly at the lower edge of middle class in a good year. “Personal” computers were still a mostly shared resource.

But in practice, our Mac quickly became my domain.

By this point I could count on a fairly explosive fight between my parents around once a month. There were times where it was far more frequent.

But even in peacetime, my childhood was an isolating existence. My parents operated the dog grooming shop my mom had opened years prior, and this was six days of work every week. My mom had grown up in Catholicism, but discovered her queer identity at an early age.

As the scion of a black sheep, I’d rolled snake eyes on family support: the male end of my gamete equation was something a bit more ambitious than a deadbeat. To hear my mom tell it, he’d made illegitimate children into a hobby. He existed entirely as a modest but monthly child support payment garnished from a VA pension.

So I grew up mostly isolated, thousands of miles away from my family in Puerto Rico and New York.

It was this vacuum that I filled so eagerly with computing. I studied avidly, resenting the daily irrelevancies of school and scrounging the pocket change needed to pick up the latest copies of Macworld from the supermaket, its back issues filling my shelves. The magazine was full of reviews for business tools that didn’t matter to me. But in between I found endless breadcrumbs to new understanding and opportunity.

ResEdit

It was the humanist leanings of the Macintosh software culture that made my rise in computing possible. I’d tried to make sense of DOS and the command line, but these had always left me cold.

The Mac, meanwhile, invited my exploration. Its desktop was the simplest of cyber spaces, full of metaphor my mind could readily grasp. Most of the computer’s contents was visible and explorable.

But one day, thanks to a CD-ROM included with a copy of Macworld, I discovered an entirely new dimension waiting beneath the surface: the resource fork.

In the Old World of the Macintosh, every file was divided into two domains: a data fork and resource fork. So much of what made the Mac a Mac lurked in the resource fork. It contained iconography, fonts, even the layouts for certain UI elements.

Apple’s ResEdit, a free developer tool for exploring and editing resource forks, was like seeing The Matrix. Literally, some ways: inscrutable ASCII and hexadecimal sequences were the fallback editing mode for resource types ResEdit didn’t have a built in editor for representing.

With ResEdit, you could change the superficial appearance and behavior of certain programs, and that was neat enough. But being able to peek under the hood of anything in the machine changed my life forever. It was evidence of the underlying order, logic and structure of the universe I valued most.

What had been magic condensed into the concrete complexity of an engine compartment. Perhaps not wholly understandable at first glance, but able to be inspected, reasoned about, learned.

America Online

What began with the narrow cyberspace bubble of a local desktop and its applications burst into new scale and scope with an AOL free trial disk.

Knowing that connectivity was addictive, and that most home users were stuck near 14.4kbps, a modest 10 hour free trial was enough to hook plenty of customers into a metered access business model.

I was culturally alien, as a Puerto Rican in New Mexico, and being (at least) second-generation, undiagnosed neurodivergent wasn’t doing me many favors either. I found friendships hard to cultivate, especially as the ongoing chaos of my home life dissolved my innocence like the Alka-Seltzer tablets my mom used to nurse chronic stomach ulcers.

But with AOL, I learned to make friends on the internet. It’s a skill that has served me throughout my life.

AOL also allowed me to learn the fine art of computer maintenance. Apple sponsored an unmetered download section, where I could transfer system updates to keep our 6116 patched and healthy.

Of course, the margins of metered internet wouldn’t last. Whatever short term benefit dialup ISPs gained from the model were demolished by the costs of ongoing churn, as families were shocked into severing their connections by massive bills. My own family came and went from AOL several times this way.

With the internet gaining heat culturally, and demand swelling, the business model changed to embrace all-you-can-eat consumption.

Like so many others, I found I could eat quite a lot.

Hotline

It began, once again, with Macworld.

An article described a thriving underworld of the internet. While AOL was a serene and sterile walled garden, Hotline promised the Wild West. I wanted in.

Anyone who wanted to could use their personal Mac and internet connection to establish a file server and chat community. It was the GUI evolution of the BBS. And so thousands of servers came and went in the night.

Hotline created loose federations of these servers using trackers. Accurate up to the second you loaded them, a tracker promised an Aladdin’s Cave of digital wonders. Everything from conspiracy texts and phone phreaking guides to Christian epistles to pornography and pirated software waited on Hotline.

In the age of unmetered internet, software piracy became a compelling gambit. My family would not be spending $500 to buy me a copy of Photoshop. But if I could leave the connection open overnight, I could have whatever professional software I wanted in the space of a few days. The only limits were patience and bandwidth. It was collecting warez that gave me my first education in product design and the broad culture of user interfaces.

Some of my fondest childhood memories center around exploring the frontiers of Hotline on a Saturday night, left alone in the house with step siblings visiting their father and my parents out on the town.

In these precious pockets of evening quiet, I could lose hours discovering new tools or learning about the workings of the internet from the rogues’ gallery gathered in a server’s chat channel.

Like ResEdit before it, Hotline and the network tools I found there condensed the magic of the internet into something concrete and learnable. My first exposure to a GUI for traceroute revealed the underlying relay mechanics of my every internet connection. Keeping track of my favorite servers and their changing addresses taught me the icy, fickle nature of dynamically assigned IPs.

More than that, Hotline was an escape into a larger, less isolated world. The anticipation of new software, the joy of learning it, the aggregate tutelage from more experienced computer practitioners… it all made my life less small and more bearable.

Internet friends

If you’re a millennial, I can do a magic trick:

You just heard the squeaky door hinge sound of your crush signing onto AIM.

My generation was the first to be able to enjoy the option for complete privacy in our intensely vulnerable adolescent conversations. Whereas in the past, anything sensitive had to be conducted according to the risks of the phone, the age of instant messaging presented more options.

There was also the advantage of multi-threading: you could conduct multiple conversations at once.

I moved around a lot as a kid, and in my shyness, the phone wasn’t my first choice anyway. Thanks to AIM, I could maintain contact with people I’d been pulled away from, taking some of the sting out of a sudden change of area code.

At the turn of the millennium, the hottest internet community for Mac gamers was the matchmaking service GameRanger, built by Australian indie developer Scott Kevill. Through GameRanger, I had a durable and portable social scene. As I moved to Florida, then back to New Mexico nine months later, I lost my new school acquaintances, but kept my internet friends.

My time in Florida was the heart of darkness, and not just because it was Florida. The chaos of my home life had reached a crescendo, with a toxic, failing run at polyamory leading my mom’s unwell partner into a suicide attempt. I’d been given a thin story about using a steak knife to free a dog tangled in a leash, and its resulting hospital visit.

This left me largely alone at home for a few days.

This was an extreme case, to be sure, but it was also part of a coherent tapestry of a home that didn’t feel safe.

I know my life would have been much darker, if not for the escape, growth and connection I found online. I would have been less healthy, more alone. I would have been far more at risk of coming to harm, either from my own sense of despair—which even computing could not fully relieve—or by falling in with dubious characters.

Instead, I attended to the only education I truly cared about. I found the community and peers I needed. I was exposed at a young age to software developers, their practices and the constraints they navigated, as Mac game professionals hung out beside their customers in GameRanger’s chat.

I found just enough in the cybernetic winds to keep on going.

Technology saved me

It’s as simple as that.

Mine was not a happy, peaceful or emotionally supportive childhood. But technology placed a floor on the worst of it. When I confront, with adult eyes, the acute emptiness I carried every day between the ages of 10 and 15, I’m shocked I got through any of it.

When I say that “technology saved me,” I’m not talking about silicon, or resource forks, or domain name servers.

I’m talking about the human effort to create scalable systems that join us together and amplify our power.

I’m talking not about “technology” as inert noun, to be built and sold. I’m talking about technology as a verb. As a tradition between us. A covenant for amplifying our ingenuity and imagination.

As the power of technology has reached an unfathomable, global scale, as it does harm for short-term, short-sighted profit, as it has used us up for the benefit of a few, we have grown reasonably skeptical of technology as a business, and as a whole. We fear the people who wield technology, indifferent as they are to its human consequences.

These are rational responses to the 21st century we’ve inherited.

But I stand here today as evidence that technology can also rescue us from the abyss. It can grant us the human connection we need to keep going. It can scalably teach us new things, and offer perspective that makes us more interesting, more powerful, more effective versions of ourselves.

We have a choice.

The old cycle is dying. The incumbents have ossified. They have lost their maneuverability. All they have left is their ability to crush and spend.

But we can be more nimble than the dinosaurs. I hope we’ll build in ways that give the next generation the same opportunities that once saved me. It’s possible, but only if we make it possible.

Contents

1. The danger of bad critique
   1. But I was told this stuff was useless
   2. Real activists ship
   3. That’s pretty over the top
2. The dumb critique
   1. It’s “useless” and produces “nonsense”
   2. Energy consumption as a primary objection
   3. Students will use it to cheat!
3. Examples of actual, important issues we must confront
   1. Training sets include CSAM
   2. AI perpetuates, amplifies and launders bias, with consequent unequal impact
   3. AI is constructed non-consensually on the back of copyrighted material
   4. The popular interfaces to AI are deceptive, because AI is black boxes
   5. AI can be used to create misinformation and pollute the information sphere
   6. Any and every beef on the part of entertainment labor
   7. Further degradation of labor broadly
   8. Exploiting labor in the Global South at tremendous psychological cost
   9. Terrifying surveillance potential
   10. You have to trust the platform vendors too much
   11. “My favorite product added AI in a stupid way and I hate it”
4. Eyes on the prize

Let’s get one thing out of the way: the expert AI ethicists who are doing what they can to educate lawmakers and the public are heroes. While I may not co-sign all of it, I support their efforts to act as a check on the powerful in the strongest possible terms.

Unfortunately, a funny thing is happening on the way into popular discourse: most of the AI criticism you’ll hear on any given digital street corner is lazy as hell. We have to up our game if we want a future worth living in.

Sleeping on this will cede the field to people who will set fire to our best interests just to gain 2% more market share. I want people of conscience to be better at this discussion.

The danger of bad critique

There’s a fork in the road.

After fifty years of evolution, digital computing now has the power to reliably interpret certain patterns of information, and to generate new patterns based on that input. Some of these patterns are not really what we want. But as time passes and investment continues, the output becomes more and more compelling. I think it’s most productive to call this pattern synthesis. But its purveyors would prefer “artificial intelligence.” I’ll use the terms interchangeably, but I think “AI” is more brand than accurate descriptor.

Whatever we want to call it, the cat is out of the bag. We are not going to stop its use because all it is, in the end, is one of many possible applications of commodity computing hardware. It is broadly documented and broadly available. To curtail its use would require a level of ruthless restriction of how individuals use their privately owned computing devices.

I don’t see it happening, and if you follow the idea to its logical conclusion, the civil liberties implications suggest we should not want it to happen. Think Fahrenheit 451, but the jackbooted thugs destroy your kid’s Raspberry Pi.

That said, not all pattern synthesis applications are created equal, nor are all computing devices. OpenAI enjoys a significant lead in the space, and they have enough advanced computing hardware to create uniquely powerful outcomes.

There are competing initiatives. Other vendors are attempting to catch up with their own proprietary products, and open source ML is a thriving ecosystem of experimentation and novel developments.

But as it stands, OpenAI is in no danger of losing its lead. ChatGPT’s quality steadily improves, as does its abilities. The difference between this year’s product and last year’s is staggering.

Meanwhile, OpenAI cannot keep up with demand.

But I was told this stuff was useless

At some point in time it wasn’t worth much. A mere toy curiosity. But the evolution of these tools is happening at a vertiginous pace. Look away for a few quarters, and your picture of how it all works is fully out of date.

Sadly, that doesn’t stop its lazier critics.

The fork in the road is this: we can dismiss “AI.” We can call it useless, we can dismiss its output as nonsense, we can continue murmuring all the catechisms of the least informed critique of the technology. While we do that, we risk allowing OpenAI to make Microsoft, AT&T and Standard Oil look like lemonade stands.

We then cede any ability to shape the outcomes of pattern synthesis technology, except through the coarse and sluggish cudgel of regulation. And I don’t know about you, but the legislators in my jurisdiction don’t have the technical sophistication needed to do anything but blindly follow the whims of the last lobbyist they spoke to.

Real activists ship

Whatever your beef with AI, you can’t put the genie back in the bottle without outlawing statistics or silicon. The shittiest version of any computer in your house can probably achieve some machine learning task right now, if you download the right stuff.

More than that, enormous amounts of human labor concern the management and creation of patterns. OpenAI is sold out of its computing capacity because the stuff they do multiplies productivity.

Pattern synthesizers alter the economics of labor. Like global networking, general purpose personal computing, telephones, electrification, and combustion engines before this, pattern synthesis changes the shape of what individuals, teams, and societies can accomplish.

So the question is: what kind of future do you want? Do you want a future where effective, reliable pattern synthesizers are available to everyone at a reasonable cost? Or do you want a single company to set their costs, making great margin by serving only the most profitable customer?

Do you want a future where pattern synthesizers are built cooperatively, on consensually contributed data? Or do you want a regulatory framework authored by the richest guys in the room?

Do you want a future where pattern synthesizers are energy-efficient, balancing performance against externalities in a sustainable way? Or do you want their costs and externalities concealed and laundered?

Do you want pattern synthesizers to create a caste system of technical haves and have-nots?

That’s pretty over the top

Twice a month, I head over to my library. For an evening, I sit at a table and help seniors with their technology questions. They bring their laptops, phones, tablets, even printers, and often lists of problems that have cropped up. We work through their issues, try to fix problems, and I do my best to reassure them their difficulties are not their fault.

But too many blame themselves nonetheless. They don’t have a mental model for why things are doing what they’re doing.

• Why this website is asking them to log in with their Google account with some obnoxious popover (answer: someone has an OKR for new account signups).
• Why their computer is so slow for no reason (answer: the vender installed backdoor to add crapware that uses vast amounts of CPU)
• Why someone would be able to remotely log into their computer and destroy all their data (answer: they got scared into calling a scammer callcenter and social engineering did the rest)
• Why they can’t make sense of the gestures and controls that are necessary to operate any modern smartphone (answer: the UX design isn’t tested on people like them)

This is an issue of economic justice and political self-determination, as all essential civic activities become digitally mediated. Lack of technology literacy and access hits many people of all ages, but especially low income families and senior citizens. We have completely failed to bring them along.

And at this rate, it’s going to happen all over again.

But worse.

The dumb critique

I want to talk about all the essential criticism that needs more airtime, but first we need to walk through all the counterproductive bullshit that serves to erode the credibility of AI criticism as a whole.

It’s “useless” and produces “nonsense”

The AI elite have been pushing a narrative that would cement their lead with a regulator-imposed fence. Namely: that AI is going to kill us all. It’s important to note here that they’re full of shit on this point: if they truly believed it, surely they’d use their incredible leverage over their own companies to, at the least, delay the inevitable.

This has not come to pass. Instead, they keep going to work each day.

Researchers and ethicists have countered this by explaining that these tools are not, in fact, “intelligences” but more akin to “stochastic parrots,” repeating patterns they’ve seen before without much in the way of higher reasoning.

This has, unfortunately, unleashed a wave of stochastic parroting itself—the meme is irresistible in its visual flourish—which misinterprets the criticism to mean that the output of the tools is ipso facto without value.

But in fact, an African Grey Parrot retails in the thousands of dollars. Despite their limitations, parrot fanciers find tremendous value in their birds for companionship and amusement.

Similarly, the output of an LLM is not guaranteed to be useless or nonsense or otherwise without meaning.

For example LLMs can be used to provide surveys of a topic area, and even book recommendations, tailored to a specific learner’s need. They have, famously, a tendency to “hallucinate,” a generous term of art for “fabricating bullshit.” But in just a few months, this tendency has found a curious guardrail: the LLM can browse the web, and in doing so, provide citations and links that you can check yourself. So where before you might have been led toward publications that didn’t exist, you can now prompt the LLM to ensure it’s giving you proof.

So it’s not nonsense. Nor is it useless.

Part of what’s interesting about how LLM’s work is how they can interpret existing information and clarify it for you. An LLM can usefully summarize text of all kinds, from a dense essay to a file of source code.

And so the problem with saying “AI is useless,” “AI produces nonsense,” or any of the related lazy critique is that destroys all credibility with everyone whose lived experience of using the tools disproves the critique, harming the credibility of critiquing AI overall.

Worse still, those who have yet to be exposed to the potential of these tools may take this category lazy of critique at face value, losing the opportunity to develop their own experience with an emerging, economically consequential technology.

This category is the laziest shit out there and I badly wish people would stop.

Energy consumption as a primary objection

When we get to the part where people insist that AI technologies use too much energy, it starts to feel like some quarters are just recycling the valid criticisms of cryptocurrency without bothering to check if they fit.

And I get it: having failed to manifest a paradigm shift in their digital tulip bulbs, the worst of the crypto bullshitters have seamlessly switched lanes to hype AI. Nevertheless, these are distinct cases.

Energy consumption in cryptocurrency was problematic specifically because cryptocurrency built into its model ever-increasing energy costs. Essentially, proof-of-work cryptocurrency was designed to waste energy, performing cryptographic operations that served no meaningful purpose in the vast majority of cases.

That’s an exceedingly stupid use of energy, especially in climate crisis, and everyone who supports that deserves to be roasted.

But a pattern synthesizer has a directed goal: accomplish something useful as decided by its user. Not every product or model is doing a great job at this, but the overall trajectory of usefulness is dramatic.

The various things we call AI can interpret code for you, detect cancer cells, and helpfully proofread documents. I wrote 2000 lines of C++ that drive IoT devices deployed across my house with ChatGPT’s help. Currently, their uptime is measured in months.

As I’ve been writing this, I’ve asked ChatGPT 4 to assess how fair I was to cryptocurrencies above, and it provided some nuanced analysis that helped me get to something more specific (qualifying proof-of-work as the major energy wasting culprit).

So, at least for some cases, and for some users, and in ways that grow as the technology improves its effectiveness, AI is accomplishing helpful work. Moreover, by further contrast to cryptocurrencies, AI vendors are incentivized to reduce the cost of AI. Scarce resources like energy and advanced GPUs reduce their ability to serve customers and harm their margins. The more efficient pattern synthesis can be made, the more profitable its purveyors.

Meanwhile, the history of computing shows a steady trend: amount of energy needed to accomplish work decreases, while the amount of work possible increases. This even holds true for GPUs, now with 25 years of data.

On Mastodon.social, @glyph argues that energy costs are in fact orders magnitude smaller in favor of LLMs, which seems to hold some water. Compare gigawatt hours to train and operate an LLM to hundreds of terawatt hours to operate the Bitcoin network. Or, for that matter, total data center energy consumption, also measured in the hundreds of terawatts.

Any argument against pattern synthesis on the grounds of energy consumption is an even more urgent argument to shut down the existing constellation of software and computing products.

Thing is, we decided a long time ago to build our society on the premise that productive work was worth spending energy on. Today we spend vast amounts of energy on things like:

• Mundane cloud computing infrastructure, as mentioned
• Global telecommunications
• Commuting to offices so people can sit on Zoom calls
• HVAC for those same offices
• Industrial fabrication
• Air travel
• Sea shipping
• Sports
• Making beer and keeping it cold

These are a handful of examples off the top of my head. What about applied statistics demands unique scrutiny?

I’ve had the shittiest year when it comes to climate change, and I’ve invested in significant green energy infrastructure, from solar to battery to heat pumps. I take the issues of renewable energy and climate incredibly seriously.

And asking an emerging technology to hold the bag for a climate crisis that spans industries just seems incoherent to me, unless you also call for an end to mundane computing generally.

We must address climate from multiple dimensions:

• Regulatory pain for polluters and fossil fuel companies
• Ending fossil fuel subsidies
• More research and investment in energy storage
• Replacing incumbent energy infrastructure
• Driving down the costs of alternative energy infrastructure manufacturing
• Cutting wasteful and inefficient uses of energy across sectors and activities

Simply making AI a boogeyman for Shell and Exxon and BP’s fuckups doesn’t deliver the goods. It’s fundamentally unserious as a primary objection to AI, even if, like all energy consumers, AI companies should be subject to scrutiny, regulation and reform on their resource consumption.

Students will use it to cheat!

Fuck the rote memorization, performative bullshit of school. I’m not even giving this more than a paragraph. Let the schools figure out how to actually create learning outcomes instead of regurgitation sessions. This isn’t AI’s problem to solve. Time to catch up with the 21st century, you putrefying mechanism of oppressive conformity and class stratification.

Examples of actual, important issues we must confront

Pattern synthesis is going to melt the status quo the way the web did 30 years ago. No industry, no human activity will go untouched.

Some people will do absolutely stupid stuff trying to save money with this power.

But the worst is that people will use the power to do harm and this technology will be only too happy to oblige.

This is not an exhaustive or definitive list. Any omission you may catch is a failing of my own scholarship and rigor, not an indictment the omitted critique. (Unless it’s genuinely dumb, but that’s up to you to judge.)

Instead, here is a survey of the sort of things we need to be aware of so we can demand and even build better alternatives. Failing that, understanding these issues in a useful tool people actually use help us demand accountability for that tool’s effects.

Training sets include CSAM

Updated 12/20/23 to add this from 404 Media:

“While the amount of CSAM present does not necessarily indicate that the presence of CSAM drastically influences the output of the model above and beyond the model’s ability to combine the concepts of sexual activity and children, it likely does still exert influence. The presence of repeated identical instances of CSAM is also problematic, particularly due to its reinforcement of images of specific victims.”

The model is a massive part of the AI-ecosystem, used by Google and Stable Diffusion. The removal follows discoveries made by Stanford researchers, who found thousands instances of suspected child sexual abuse material in the dataset.

AI perpetuates, amplifies and launders bias, with consequent unequal impact

Because the pattern synthesizers are built by ingesting, well, patterns, they’re trained on the things humans have written and said. Those patterns are full of bias.

You will struggle, for example, to ask an image generator to give you a Black doctor surrounded by white children because the legacy of colonialism means most images we have of such a scene are inverted.

This presents serious problems for using these tools to imagine different futures. The more of today’s inertia they carry, the more they replicate a negative past.

Meanwhile, bias exists all over human belief. Biases inform tremendous violence and oppression. Computing systems that blithely amplify that as many times as someone can afford are fundamentally dangerous. Even worse if they can create new patterns altogether.

Finally, if you can blame the AI for your biased decision, it makes it harder for those wronged to actually address unjust outcomes.

AI is constructed non-consensually on the back of copyrighted material

This is one of the greatest stains on this technology, period.

People’s work was taken, without consent, and it is now being used to generate profit for parties visible and not.

Thing is, this is unsustainable for all parties. The technology requires ongoing training to remain relevant. Everyone who is part of its value chain, in the long term, must be represented in both its profits and the general framework for participation.

Incidentally, this is one of the places where critique becomes most incoherent: if the output of these systems is “nonsense” or otherwise has no value, where is the threat to creators?

It is precisely because the outputs are increasing in value and coherence that it’s essential that the people who make that value possible get a fair deal.

The popular interfaces to AI are deceptive, because AI is black boxes

If the AI is in fact “hallucinating,” how will you know? It has a certain bluff and bluster that suggests no possibility of doubt. This makes the technology better to demo, but also wastes time and even misleads.

This has had some comical effects.

Funny or not, it’s clear this tool is intruding closer and closer to how people do real work that impacts real lives. Deception is an irresponsible product feature.

Being unable to debug the thing is untenable the more we rely upon it.

AI can be used to create misinformation and pollute the information sphere

Fake images, fake voice, fake videos, fake text, fake posters.

The cost of waging an information war has dropped by orders of magnitude. We’re not ready for what this means, and the year over year trajectory of output quality is dramatic.

Meanwhile, mundane applications let anyone quickly gum up the works with low-quality content.

Any and every beef on the part of entertainment labor

No, you should not be coerced into giving up your likeness for a studio to use forever just because now the computer will allow it.

Further degradation of labor broadly

I do not believe that this technology, now or in the future, is effective at replacing human insight or ingenuity.

The more complicated question is how pattern synthesis fits into and disrupts our existing experience of work.

There’s precedent for this. Before electrification and the assembly line, building things was a matter of craft and expertise. As technology progressed, this work was broken down (”degraded,” in Braverman’s parlance) into smaller and smaller fragments by industrialists, like Henry Ford, until workers no longer needed to know a whole craft, just their tiny piece of the assembly line.

Pattern synthesis tools could inject a similar degradation into traditional knowledge work, or erode the authority of decision makers by making them resort to AI at crucial moments.

Already, they create problems for transcriptionists and translators, who find their work far less valued than they did a decade ago. It’s meager comfort to those immediately effected, but history suggests a wholeasale displacement of a category of labor doesn’t require every category be displaced—blacksmiths had a bad time after cars replaced horses, but mostly those cars now take us to different jobs.

On the other hand, it’s important to note that transcription of far more things happens now that it’s automated. Three years ago, transcription was a tediously manual process for TikTok creators. Today the push of a button gets them to a 90% good enough set of subtitles, making this content enjoyable for everyone, even those who can’t hear (which may be the deaf, but may be anyone else in transient circumstances, like waiting in line without headphones—the curb cut effect is real).

Exploiting labor in the Global South at tremendous psychological cost

In order to manicure the final product presented to users, OpenAI turned to the cheapest workers they could find on earth.

The data labelers employed by Sama on behalf of OpenAI were paid a take-home wage of between around $1.32 and $2 per hour depending on seniority and performance.

One Sama worker tasked with reading and labeling text for OpenAI told TIME he suffered from recurring visions after reading a graphic description of a man having sex with a dog in the presence of a young child. “That was torture,” he said.

Western technological development has a centuries-long tradition of foreign exploitation always just hidden from view, and it seems pattern synthesizers are not exempt. While the typical deal with technology startups is that workers participate in the success of their employer, these data labelers got nothing but a one-time, paltry wage. As OpenAI thrives, they’re left scarred.

Terrifying surveillance potential

We now have the technology to transcribe any conversation without a human in the mix. The transcriptions are full of errors, but from a surveillance context, that doesn’t matter so much.

It has never been more possible to thoroughly surveil the activities and communications of anyone.

Worse still, faces can be scanned and tracked, license plates read, even gaits tracked.

This may be the end of privacy.

It gets worse from there, as other critiques compound into this one. The AI can be wrong, and it can be biased, and it have a disparate impact on different populations and identities.

This is a horrifying outcome that actively denies people their liberties.

You have to trust the platform vendors too much

How do you know your information is safe when it’s used to accomplish work in a centralized system? Privacy policies don’t mean anything when a technical failure can breach them. It happens all the time in mundane computing. Why should pattern synthesis be unique?

“My favorite product added AI in a stupid way and I hate it”

Yeah, that sucks and I’m sorry. There’s a lot of sloppiness and hype going on.

Eyes on the prize

We are not going to turn back time, unless you have a plan that can successfully plunge global commerce back hundreds of years.

This shit is here.

Yes, there’s hype. Yes, there’s scumbags. But there’s also some baby in this bath water for meaningful numbers of people.

We have to act in accordance with that reality.

The reality is that there’s a lot to work on in order to create just, decent, scalable, personal, private implementations of this technology. I would argue we should.

Because these tools can make us more effective. They can amplify our reach and insight, they can help us accomplish things we couldn’t on our own.

But there’s a lot wrong with them. If we plug our ears and say this technology should not exist, the growing ranks of people who come to depend on it will brush past us, hearing only the case presented by those best positioned to profit.

If we simply dismiss this technology, people may believe us, and find that a whole new technological paradigm has passed them by, curtailing their power and agency.

If we let this technology become the plaything of the affluent exclusively, we’ll deepen our digital divide in a way we may not be able to recover from.

So we need to up our game. I hope this survey of the landscape helps.

Contents

1. Prologue: how applications are structured
   1. The shape of software
2. The software professional’s job is structure that maintains velocity
3. ‘It’s really taking you 1k words to reach the AI shit?’
4. None of it was possible without ChatGPT
5. What matters is, I shipped
6. Developer switching costs are now much lower

I had certain expectations of the possible, informed by decades of building things. Some stuff lay within my zone of talent, while others lay far out of reach, in a place of broken ROI.

Today I’m not so sure. Today far more seems possible than I ever expected.

I’m still not sure what to do about this.

Prologue: how applications are structured

The structure of software has pivotal consequences for the future success of the project.

When structure works against a project, that project is more costly to build and maintain. These costs show up in a multitude of ways:

• Complexity cost for changes and new functionality, making it harder to implement features, harming velocity
• Opacity cost in debugging and reasoning about the workings of the application, making it harder to recover from failures, harming velocity
• Onboarding cost for new team members joining the project, harming velocity

Velocity is the fuel of a software project. Velocity makes challenges feel winnable. Velocity provides a sense of progression, and it’s addicting. It feels good to build things. It feels good to see the things we imagine take shape.

The morale benefits of velocity are intuitively understood and deliberately captured by the best software leaders. Going from zero to one is hard, and it helps when you believe it’s possible.

Velocity ignites the fire of belief, stoking it when it falters.

While reliability, scalability, and correctness matter, without velocity you don’t ship. If you don’t ship, your code is stillborn, and eventually, your business dies. You need a vehicle that moves.

Like any vehicle, then, velocity is affected by structure. Square wheels don’t roll.

To succeed in building a complex tool requires thoughtfulness about the structure you’re laying out. You need good foundations to build on, especially modularity, with seams that make composition and rework of components low-cost.

The shape of software

The germ of software is a set of requirements. Requirements animate software, giving it a purpose for shipping.

Someone, somewhere, needs help.

The software is built to oblige.

To address requirements, we build components. Components implement some functionality, and have specific contracts and boundaries, even if only implicitly.

Components address roles: a well-scoped need fulfilled.

This can spool into fractal complexity. Consider an application that sends an email.

Requirement: connect to a server and send a message.

Simple enough. We know we need a networked app. But not enough to build with yet. Let’s try something more specific:

Requirement: connect to a server at this URL, using a specified protocol documented at a link I’ll give you, and send a plaintext message to a user-provided identifier.

Now we can start to sketch a component that addresses the requirement. We’ll need a NetworkClient. To do its work, the client will need to fulfill some roles:

• Server connection checking: does this thing exist? Can we reach it?
• Apply the protocol: Once connected, we’ll need some way convert the message into a format and transport that can be implemented by the server.
• State management and error handling: Can we communicate the state of things to the developer, and perhaps the user?
• Content validation: Is this message correctly addressed? Is there anything about its content that would prevent successful transmission?

With these roles explored, you can see that a whole new round of requirements has burst forth. To address them, we’ll need to add more components to the network client. The appropriate shape for this is going to differ according to the culture of a language. In simplest form, these components might be expressed as functions. In other languages, structs, objects, enums and closures come onto the stage.

All of these boundaries and structures are for our organizational, logical benefit. They get boiled away by the magic of compilers and interpreters into the sheer, infinitely iterating might of a computing device.

You can make frightfully twisted structures that nonetheless run. Every developer’s career is defined by the lessons their sharp edges teach us.

The software doesn’t care. The computer doesn’t care. The iron law of building Idea Machines is that you can do it as poorly as you want. No one will stop you except the machine itself, and only then when your intentions have veered into the impossible.

The software professional’s job is structure that maintains velocity

To be successful, you have to observe these mechanics as best you can, building a plan that navigates through them enough to someday ship something. This is the work of software, more than just typing things or proving you understand a bubble sort by describing one with a whiteboard. Can you meet requirements without tripping over your own shoelaces?

This is challenging work, made all the more so by how poorly it’s explained when you’re starting out. It occurs to me that in 13 years in formal technology roles, I’ve only once had an engineering manager to guide me.

But eventually you skin your knee enough times and learn some lessons. You develop intuitions, then routines, for how you structure your projects, break down problems, and begin solutions.

The snag is that, at the code level, your approach to these things may be dramatically impacted by the language and framework features you have access to. Jumping into a new platform carries friction. You have to learn to apply your structural priorities according to all new instruments.

Some are straightforward transfers, like going from a piano to a church organ. Something new but not something alien. Others are going from piano to a violin. The same rules in the abstract, but a very different tactile experience. Bridging the gap can be a lot of work, and it can absolutely kill your velocity. Backing up to figure out the specific syntax for achieving a design pattern you favor can require forum searches, reading through books and docs, or even a wait for asynchronous support.

‘It’s really taking you 1k words to reach the AI shit?’

You have to understand what I understand about the proper construction of software to grasp why ChatGPT and its ilk are so transformational.

I know how to build an object-oriented application that runs on a resource-constrained, networked device. I’ve done it so many times I can do it in my sleep now. It’s just, all that experience happened in Objective-C and Swift.

I avoided C++ like the plague because it’s fucking weird and clunky. The code would sometimes peak out of third party components, technically viable in iOS projects, but mostly avoided except by games masochists.

But let me tell you: I have felt a deep and seething rage about the remote controls that came with my heat pumps. Absolute garbage, marring the otherwise pristine miracle of high-efficiency heating and air for all seasons. So I wanted to fix this.

The problem was complexity. Here are all the requirements I had to address:

• Network connectivity with Home Assistant using MQTT
• Infrared pulses perfectly calibrated to the timings of my specific Mitsubishi IR control protocol
• Visual readouts using LEDs and bitmapped screen
• Local input handling for temperature, mode and fan speed settings

This required writing a pile of top-level components serving specific roles, like:

• IRInterface, to manage the infrared emitter
• Input, to capture button presses and turns of a rotary dial
• Display, to manage screen writes and setup
• EnvSensor, to read and publish sensor values
• HAInterface, to coordinate remote and local state using MQTT
• BaseView, to establish a common structure for converting local content into screen writes

The velocity benefit of these discrete components serving specific roles is real. Days before hitting release, I discovered a terrific library that made my aging, recycled implementation of the Mitsubishi IR protocol entirely obsolete.

Replacing it took less than 20 minutes, as I deleted huge blocks of code and replaced them with calls into the new library. Because the underlying IR implementation was entirely encapsulated as a subclass of IRInterface, nothing else in the code needed to change.

None of it was possible without ChatGPT

I’d attempted building the exact same project a year ago.

It sputtered out. I couldn’t maintain velocity. Endless tangents, stumped by a compiler or runtime error, researching some syntax or another, just endless tail chasing.

Thing is, loads of people have done this tail chasing before me. C++ is one of the great languages of computing history. It’s been used in so many ways, from hobby microcontrollers to serious embedded systems to simulations and games. There are endless forum threads, books, Q&A pages and mailing list flame wars dedicated to C++.

Which means ChatGPT knows C++ extremely well.

Show it code that baffles you, it can explain. Show it errors you can’t make sense of, it’ll give you context. Explain the code you want to write, it will give you a starting point.

From there, give it feedback from the compiler, it will course-correct. Give it shit code you want to refactor, it will have ideas.

None of it is perfect. Occasionally I could sense that the suggestions it was offering me were far more complex than the situation required. I’m sure there’s stuff in ThermTerm that would make a practiced hand at C++ cringe.

But this is the stuff of velocity. Instead of getting stumped, you’re trying new things. Instead of giving up in confusion, you’re building context and understanding. Instead of frustration, you’re feeling progress.

What matters is, I shipped

All around my house is a network of environmental sensors and infrared emitters. Their current uptime is measured in months, and they still faithfully relay commands and provide remote feedback.

They are working great.

My curiosity could be immediately and usefully satisfied. I asked things like:

Would it be possible to replace these lookup arrays with dictionaries? [redacted pile of code]

I was concerned that keeping indices straight would be more brittle than naming values by keys.

Why is the compiler unhappy? [redacted pile of error spew]

I was genuinely stumped.

What does this error mean? “‘HVACCommand’ does not name a type”

I’d stumbled into a circular dependency, and the tools expected me to manage it.

In each case, missing pieces of context might have derailed my sense of progress and confidence, prematurely ending my coding session. Instead, ChatGPT gave me enough useful guidance that I could overcome my roadblocks and deliver on my next requirement.

Stitch enough of these moments together, you’re going to ship.

It doesn’t work this well with everything. Like I said, there’s loads of content about C++ for an LLM to absorb. Newer stuff, less popular stuff, you’re probably more prone to silliness and hallucination.

Still, you can accomplish so much with languages that exert the sort of cultural gravity that make ChatGPT especially useful. JS/TypeScript, Ruby and Python will all let you build the web. Swift will let you build native apps for Apple platforms, while Java and Kotlin will get you there for Android.

There are more languages still, serving more domains and platforms, all popular enough to get useful advice from ChatGPT.

Developer switching costs are now much lower

It’s never been this easy to be productive in an environment I know well, because it’s never been possible to get this kind of consistent stimulus to velocity.

It’s never been this easy to become productive in an environment I don’t know at all, for the same reason.

I feel like I can build anything now, and ThermTerm is just one of several projects this year that has cemented that conviction. This is a transformation. I’m different than I was. I have more power, my ambitions have greater reach.

But what’s worrisome about this to me is that, at least so far, there’s really one game in town. I was excited to try the new GitHub Copilot beta, but I found it not nearly as consistently helpful as ChatGPT, even though switching to the web browser to use it involved more friction. Other solutions may exist, but no one is coming close right now to the quality and reliability of ChatGPT for this category of work.

Forget AGI.

There’s a serious social risk to a company that can monopolize this much influence over our productivity.

I am more certain than ever that this technology will be as essential to our experience of building software as compilers or networks or key caps. $20 for a month of ChatGPT can produce many multiples of that value in the right hands.

So to me the future of AI, whatever it holds, carries risk of entrenching OpenAI with the same level of power as Standard Oil or Old Testament Microsoft: able to shape the very playing fields of entire sectors. For the time being, OpenAI has the best stuff. They can influence the productivity of millions, their scale of impact limited only by GPU scarcity.

There’s more than hype here. The technology is real. There are many who adopt an understandable posture of defensiveness to the technology industry’s fads and whims. In the wake of cryptocurrency bullshit, this has largely taken the form of reflexive skepticism.

I don’t think that’s the move on LLM’s. I think the more productive stance is to be proactive at monitoring the growing power of AI’s purveyors. This stuff has serious cultural transformation built into it, and it’s going to happen whether you personally believe it or not.

People want to do silly things with LLM’s, like replace writers and artists. I don’t think that’s going to work that well. Instead, I see these tools as amplifying the individual reach of any given person who has access to them.

Instead of dismissing or decrying them, we need to get to work democratizing their access, or this will become a serious vector of inequality.

As for myself… for a mere $20 a month, I am transformed. Two thousand words later, I still don’t know what to do about it. It takes time to make sense of options that are multiplying. Loads of people are going to land in that spot in the coming months.

But eventually people are going to figure it out. Hold on to your ass for the social consequences of that.

Contents

1. Of course: microcontrollers
2. StemmaQT and Qwiic: an old standard with new strategy
3. From imagination to physical objects
4. I think you should try it

As a technologist, hardware has always been my final frontier.

Things you can touch, things that create physical outcomes, these can have so much more power than software alone. But the cost of that power is complexity. Electrons don’t care about our ambitions. Circuits can be harder to debug than code. And even if everything works perfectly on the level of logic and voltage, you’re still managing the complexity of physical objects, their wiring, their position in space, and even their heat dissipation.

Building any product is hard, but building a hardware product is a superset of the basics of any product challenge, adding in the iron constraints of the physical world. It’s not enough to imagine how something looks: you have to also find a way to build it in a way that matches your imagination, while simultaneously accommodating all those physical constraints.

It’s work.

But also? It’s never been easier than it is today. I just built complete replacements for my heat pump controllers. I hated those dinky remotes. You couldn’t read them in the dark at all, and programming them was about as bad as anything you remember from the bad old days of VCRs.

I imagined something that would solve their UI problems, and integrate my heat pumps into my home automation system. I’m not an electronics engineer, but my dreams are now real. I’ve got five of these across the house.

Using open source code is a skill: knowing how to navigate repos and someone else’s code, understanding how to troubleshoot and navigate communities to get help, discerning between quality projects and junk… this experience is a hard-won component of being a modern software explorer. It can take you further than you might realize, past mere bits and into the land of electrons and atoms.

Of course: microcontrollers

Arduino was a revolution in developer experience.

Beginners could write simple C code and and have a physical computing experience within the space of five minutes.

But since the advent of Arduino, the landscape of microcontroller boards—components that can be programmed to emit and receive complex electronic signals—has exploded.

Boards of every scale and configuration are available today, from the size of a peanut butter and jelly sandwich all the way down to a postage stamp. Microcontrollers are the foundation of the hardware adventure, allowing infinite iteration of custom logic on inexpensive components. Different designs sport different connectors and accessories. There are also different chip architectures out there, and boards built around ESP32, or the new Pico W, even include WiFi and Bluetooth capabilities.

Unifying all of this are software ecosystems. Open source Arduino code exists to solve so many kinds of problems, from networking to button handling. Regardless of your board’s architecture, there’s usually a port of the Arduino environment you can use, opening up all that code for your project. If you prefer Python, the MicroPython and CircuitPython projects even offer an alternative to C/C++.

But the ecosystem doesn’t stop at software.

StemmaQT and Qwiic: an old standard with new strategy

I2C is a two wire serial data standard that dates to 1982. In practice, it looks and works a lot like USB: two more wires add power and ground, and you can chain dozens of devices together on a single bus.

For the typical hobbyist hardware tinkerer, the hardest challenge comes down to circuit design. Electronics is arcane, governed by both the laws of physics and decades of component lore. Building circuits means understanding how to use all of these to channel electrons consistently, without letting the magic smoke out. It means understanding how to adjust voltages, manage resistance, and do it all in a way that’s physically sturdy and manageable.

This is a tall order. I’ve never had the time or motivation to learn enough circuit design to build anything more complex than arrays of blinking lights.

But now, I don’t need to.

If you build modern software, you’re well-versed in composition: grab a handful of existing projects—a database here, a UI framework there, an HTTP library to round it all out—and arrange them together. You write your custom logic—the stuff unique to your project—and let other people’s code do work that’s common across all projects.

Thanks to I2C and a convenient cable standard—branded StemmaQT by Adafruit, and Qwiic by Sparkfun, two leading hobbyist component vendors—the same approach to composition is possible in an electronics project. Decide your requirements, and you can probably find a few boards you can quickly wire together—without soldering!—that will address your problem.

In the case of my heat pump controller:

• ESP32-based microcontroller with a built-in screen
• Light sensor, so I can automatically dim the bright lights
• Temperature and humidity sensor, so I can have real data about the actual temperature in the room
• Rotary encoder board, to make a dial for setting temperature and toggling power

All of that just plugs in via I2C. Each board, like a well-made library, abstracts its gnarly implementation details under the hood. You don’t worry about power management, or the details of how to interpret signals as input from a rotary encoder.

Instead, after wiring things together, you look up the example code for each component, and adapt it to your project. In the past, actually using these components could be something of a mystery. But now vendors go to great lengths to maintain supporting libraries and docs—developer experience sells!

Adafruit deserves special mention here: they have hundreds of useful, unique boards that support this compositional approach, and their supporting materials are just exhaustive.

Again, if you know how to apply open source code, you can do this.

From imagination to physical objects

Once you create a working circuit, you can take it all a step further: design and manufacture your own custom enclosures.

It’s really kind of nuts: for $500, you can buy an incredible, reliable 3D printer from Prusa. You can’t beat Prusa: the printers come out of the box working perfectly, they’re vertically integrated with Prusa’s excellent cross-platform slicer software, and the user community is among the most active and helpful of anything on the internet. At that price, its build volume isn’t mammoth, but it doesn’t have to be for electronics projects.

Open hardware vendors often provide 3D models of their products you can bring into a CAD program. With these models, it’s possible to design plastic cases with perfect accuracy, allowing you to mount and arrange the various boards in your project.

Best of all, iteration is fast and cheap. Trying a new version of a design takes only a few cents of material and time to print it. You can figure out quickly if your approach is working. Remember heat dissipation? Waste heat from the microcontroller was biasing my temperature sensor. It only took a couple evenings to find an iteration of the case that solved the problem (I moved both sensors to the top side of the enclosure).

There is a learning curve to 3D printing. It might be the steepest factor here, in fact. Like electronics, 3D printing also has constraints to manage: you have to work with both heat and gravity, and design your models with that collaboration in mind. The physics of how layers are deposited impacts the strength and durability of your output, so the orientation at which you print can have consequences. Choice of materials also matters. I found PETG to be the ideal material for this kind of work: it’s easy to work with and super durable. By contrast, the more common PLA was too brittle.

You’ll also want to pick up some CAD skills, but this is less painful and more fun than you might imagine. CAD seems to be the fine art of sketching simple flat shapes, pulling and pushing them into 3D objects, and carving away at small details. I couldn’t stand most of the affordable desktop CAD software—it feels icky, like using a poorly-aged Flash app in IE6. Then I found Shapr3D, for iPad.

This is some futuristic shit. You tap away with your Apple Pencil, on a tablet computer, drawing things out of your imagination. You nudge your sketches into models, which turn into prints. There’s something far more intuitive about this process than using keyboard and mouse. The positive future shock makes me delirious if I dwell on it.

Community is everything to learning emerging skills. Hobbyists are deeply engaged with 3D printing, and you’ll find help for everything from planning your models to debugging thermal drama with your prints.

I think you should try it

I’ve been dreaming of building my own electronics since I was a kid. I spent so many afternoons at Radio Shack, and even tried my hand at the occasional kit, with limited success. Every few years in adulthood, I’ve given it another try, observing a steady downward trend in difficulty.

I’m telling you: we’re at a special moment here. The labor savings of open source, the composability, the fun: all of it has come to hardware. You can build things that solve real problems for yourself. I first imagined my heat pump devices over a year ago, and I have been frustrated they didn’t exist every day since.

Now my dreams are real, and the largest energy consumer in the house can be automated and remotely controlled.

That’s amazing.

Contents

1. And yet, I was lucky
2. The structural feedback loop of luck
3. The climate comes for us all, but first it comes for the vulnerable
4. Luxury in the climate crisis is maintaining routines and self-regulation

I’d been hearing warnings for days.

The storm would be serious. I should take precautions. My neighbors said it. People in town said it. The radio said it.

But this wasn’t about the recent, torrential summer rains that scoured the northeast, flooding towns, destroying homes and carving away roads.

This was six and a half months ago, as a strange winter storm bore down.

That day started with intense rains and wind, and felt oddly warm. Things were closer to the t-shirt weather I’d expect in Vermont’s October, despite being a week from the New Year. Still, the strength of the storm knocked out power as early as 8 AM.

By noon, grim state officials warned that devastation was substantial. More than 70,000 homes had lost power. Unlike other storms, we would have to be prepared for restoration to be a multi-day effort. Worse yet, the day’s events weren’t over. Things would get even worse as evening came and temperatures dropped. We should plan to be somewhere safe no later than 4 PM.

I took the opportunity to stock up on sandwich fixings and other foods that wouldn’t need a working kitchen.

Then the turn came. I’ve never seen anything like it.

At 4 PM, you could be out comfortably in shorts. By 6 PM, the temperature had dropped to below freezing, snow piling up fast.

At home, we scrambled to insulate the windows with towels, trapping what meager heat we could. Without power, the boiler couldn’t run. If the house’s internal temperature dropped to 20 degrees Fahrenheit, we’d run the risk of pipes bursting, damaging everything.

A large pile of wood was fed continuously into the wood stove. In the mornings, I’d fight a 120v generator, yanking its pull cord desperately. After 20 minutes of these ministrations, it would keep the refrigerator running and charge our devices.

In total, my house spent six days without power.

At Foo Camp, Mika McKinnon had once advised that you survive disaster scenarios with the help of your neighbors. And it’s true: the kindness of neighbors whose generator could power their boiler and well pump provided a few much-needed showers, as essential to sanity as hygiene.

That week felt like one of the longest in my life. The house needed care and protection, lest it succumb to the cold. The cats needed the same. I was stuck with the situation.

And yet, I was lucky

As a knowledge worker, whether I’m drawing a salary or working freelance, I have abundant flexibility. I can move my schedule around, take time off, and work where I want to.

A week without power was a week where I could focus on the problems right in front of me with no economic penalty, and no work-related distractions. As a salaried employee of a public technology firm, in fact, I’d already been given that Christmas-to-New Year’s time off.

I needed every minute of the day, either to work or to rest. Keeping a house working amidst infrastructure failure is a serious job. It required hauling wood from outside to inside, and managing buckets and improvised plumbing for the toilet. The generator needed to be tended on a regular schedule, to keep the food safe in the refrigerator, along with errands to replace the propane that it consumed for fuel.

But I had those minutes.

The structural feedback loop of luck

Last week was no picnic, either. After 16 hours of rain, the drainage system in my basement gave up. Water came pouring from two floor drains, swiftly filling the basement by two inches at its worst. Holding the line against the water was a whole-day project of pumps and wet/dry vacuum cleaners, and eventually the water started winning.

But even in this, I was among the lucky. Just minutes down the road, homes and businesses too close to the river would be properly flooded, and no amount of pumping could protect them. The waters would destroy or contaminate everything they touched, from bedding to appliances to furniture, inventory and clothing. The basics of everyday life and commerce, lost overnight.

Further on, some places got it even worse, with houses torn loose from their foundations. There are now streets lined with ruined household possessions, caked with mud, piled eight feet high.

None of these people experiencing these losses did anything to deserve this. They were just living where their economic circumstances allowed, in a historically tight housing and rental market.

As a knowledge worker, I had a little exposure to the extreme prosperity of the innovation economy. I still have to work for a living, but I got just enough to give me a downpayment on a house, and broad choices about where to settle down.

Fearing exactly this category of weather, I chose a house with characteristics that seemed resilient. In this case, being on high ground, with slopes and other features that would draw water away from the most important structures.

Water pooled in the basement because the ground was saturated by continuous rainfall, not because the immediate surroundings were flooding.

Just as it seemed the battle against the basement drains couldn’t be won, experts arrived to install a sump pump. The proposition cost $4,000 and involved a jackhammer punching a hole in the floor. The tradesmen had to deploy a temporary solution at first, so they could get themselves back home—rains were intensifying more quickly than we’d all expected.

But by 6 PM, drains were draining again, a sump pump was clearing the water, and there was no further danger to my house.

The day was stressful and exhausting.

But that night, I slept in my bed. I didn’t have to abandon my house to mud, stay in a shelter.

Once again, I had the flexibility in the day to do battle with the elements. When it came to expert help with my crisis, ready access to credit made an easy decision: spend $4k now to protect the long-term integrity of the house, and keep it habitable in the immediate term.

Not everyone has this flexibility.

Renters don’t get to make this category of decision. They’re at the whims of their landlord, who may have a very different decision making calculus around whether to preserve immediate habitability, versus taking an insurance payout.

Worse still, not everyone can absorb a sudden $4,000 expense. For me that still hurts, but the math works out: money put into the house increases its value. I can tell any future buyer that the property is now resilient even against hundred year rainstorms.

The climate comes for us all, but first it comes for the vulnerable

Things have been edgy since the flood. The threat of more rains kept anxieties simmering. At last we got some serious sunny days to raise our spirits.

Only for the air quality index to soar, adding haze to the skies and to our lungs. Forest fires in Canada are ongoing.

As a knowledge worker, I’m handling my client work from inside. My economic leverage allowed the addition of heat pumps to the house, so I can keep things cool without opening the windows. Air filtration in multiple rooms keeps fine particles out of my lungs.

Once again, I am annoyed and inconvenienced, but overall I am safe.

For those whose jobs keep them outside—work in the trades, agriculture, and countless others—the air poses more serious problems that can impair breathing and, eventually, long term health.

Luxury in the climate crisis is maintaining routines and self-regulation

Someday, I must assume the climate crisis will come for me. It probably won’t be flooding. I’m hopeful it won’t be fire. Extreme winds are probably my biggest threat in the future. Moreover, I’m not independently wealthy: I have to work for a living.

Still, my economic position as a knowledge worker gives me substantial resilience against many other threats. Let’s recap:

• Past access to prosperity granting me more options for where to live
• Schedule flexibility
• The ability to create serious economic impact regardless of where I’m located geographically
• Economic leverage to make climate-related improvements to my house, even at a moment’s notice

This stuff leaves me likelier to remain in the driver’s seat of my life, even with the variety of curveballs the climate throws at us every year.

The more insulated from these consequences I am, the more likely I am to be well-rested, able to maintain my health and continue making good decisions. I’ve been absolutely wrecked by the events of the last week. Limiting the damage from water took days of effort. Mold is serious shit, and I’m deathly allergic to it.

But I’ve been merely inconvenienced. In between ripping out basement carpet, I could maintain progress with my business, continue meeting with clients, continue doing work.

I can’t imagine how much harder it would be to be displaced from my home—especially with the lingering effects of Covid adding risk to sharing shelter with others. I can’t imagine losing my everyday property. Seeing my clothes ruined, my kitchen unusable, my house unlivable. I can’t imagine trying to keep the economic wheels turning with those pressures on my back.

Ten years ago, in reference to McIntosh, I took some time to unpack my own knapsack. While there is much there I would likely say differently today, I’m back to the same conclusion:

I feel fortunate within my circumstances, and feel a responsibility to others who don’t share my advantages.

These crises will continue. We’ve just had the hottest week on record. Whether it’s extremes of temperature or intense weather, whether it’s immediate natural disaster or distant fires polluting our air, whether it’s new invasive life or even disease—the effects of climate change are now constantly upon us.

People are hurting. These events are callous and damaging. The immediate stress and exhaustion are serious problems, but there’s long term trauma to contend with as well.

We must also understand elements of the growing labor movement in this context. A primary issue for UPS drivers is an aging fleet of trucks not equipped for modern heatwaves. Heat is harming farmworkers as well. Entire categories of work are becoming consistently unsafe as a consequence of climate change.

You don’t have to take my word for it, because none of this is isolated. There’s been catastrophic flooding of New York City’s subways, plus heat waves and forest fires in the Bay Area. Tangible evidence is all over the place. And however it manifests itself, some workers are exposed, while knowledge workers have more options.

Justice and decency demand we look this problem in the eye, in proactive solidarity. We owe each other better than letting the most vulnerable among us simply absorb the brunt of these consequences.

This is just the beginning. I can and do take local steps to support my community, but I don’t know what to do about just how big the problem seems.

If you have any ideas, I hope you’ll reach out and share them with me.

Contents

1. Cyberspace, defined
   1. Reefs of the imagination
2. Portals into cyberspace
3. What comes next

In the 90’s, we over-used the term “cyberspace.”

We needed something to explain the increasing incursion of the digital world into our everyday reality. Computers and the internet were becoming more central to our lives, and in the process, becoming a destination.

We needed a phrase to describe the abstract plane of existence that could stitch together far-flung computer systems, and their associated humans. If we were “going online,” where exactly would we arrive?

It was cyberspace.

As time went on, the term grew passé. The internet went from curiosity to compulsion to mundane component of everyday life. “Cyberspace” was an artifact of a more naive time, and we rolled our eyes.

But today I want to argue that we need this word. We need it to understand the perils and opportunities of the future. We need it to understand the motivations of enormous corporations who are investing in the next stage of our explorations into the digital realm.

Cyberspace was here all along. We just stopped giving it a name.

Cyberspace, defined

Let’s start with the basics.

You and I were born in real space. It has fixed and stubborn rules. Gravitation, thermodynamics, chemistry, and perhaps most importantly, locality.

In real space, you are where you are, and cannot be anywhere else except by crossing all the places in between, which can take quite some time. Real space is governed by scarcity: you have one favorite shirt, unless through some miracle of prescience you grabbed a second one off the rack, and then you’d have to pay for both of them.

Cyberspace has different rules. Here’s the most important:

Information is physical.

The underlying substrate of cyberspace lives here in real space. Cyberspace emerges from a global mesh of computing devices. If one of those devices is unplugged or crashes its hard drive, then whatever components of cyberspace that computer is responsible for will blip out of existence. If the computer is slow, its rendition of cyberspace will be, too. Meanwhile, the fabric joining cyberspace must also be physical: electrical signals, pulses of light, or radio waves. It takes time for these signals to propagate. Nothing is instant, merely fast: nearly the speed of light.

Still, the speed of light is so much faster than our minds are equipped to perceive. This creates a functional illusion of non-locality in cyberspace.

In other words, you can be anywhere instantly.

In practice, there’s an asterisk. Even the speed of light adds up over time. If there are too many hops between you and your destination, you may start to perceive the physical distance between computers as a delay.

But overall, the effect of cyberspace is to collapse global distances. Through cyberspace, you and a team spread across continents can comfortably share the same information and resources. You can communicate in real time. The magic of the internet was, and remains, our ability to reach people and information anywhere in the world.

Meanwhile, everything copies for basically free. Once again, the real space underpinnings show up here: you need a physical location that stores data. But with that requirement satisfied, everything can be copied at virtually no cost. Entire business revolutions were built on this exact premise.

That’s just the start, though.

Reefs of the imagination

Because you can create in cyberspace.

Through these information systems, we can create bubbles within cyberspace that are subject to rules we choose. Quake II, Fortnite, Twitter, World of Warcraft… all of these are places where the infinite plasma of cyberspace is sculpted by the imagination into reliable structures. We can enter these bubbles and share experiences with one another, creating memories, building relationships and honing skills.

In 2023, we are surrounded by vast reefs of the imagination built in cyberspace. They are games, social spaces, markets, and clubhouses.

Tens of millions of knowledge workers wake up every day to architect, design, build and maintain these structures. It’s a complex job.

Yet, every year it becomes a little easier. While literacy for the required topics expands, the complexity of the tools becomes more and more manageable.

Portals into cyberspace

Since the dawn of consumer computing, the screen has been a primary portal into cyberspace.

What began as a few thousand pixels has exploded into the millions on high-end displays. Display technology is of particular interest to gaming, which has often occupied the bleeding edge of cyberspace possibility. Gamers spend thousands of dollars for large, pixel-packed displays and the high-end video cards that can drive them. Rich detail is emphasized, but so is frame rate: the number images per second a simulation can push onto the screen. 60 frames per second was once the ideal, but now high end rigs shoot for 120.

Why go to this trouble? Because gamers are working to thin the barrier between real space and cyberspace.

This is the point where some find it easy to moralize about the virtues of real space over cyberspace. To declare these interests antisocial, or even corrosive. While I am the first to find fault with all manner of radicalizing internet subcultures, my position on pursuing visual fidelity in simulation is that it’s value neutral, even if gratuitous in some cases. This is a form of craft, magic, and entertainment, and all of these things can exist on a spectrum across the toxic to the beneficial.

Still, even the biggest monitor and most powerful video card have limitations. They are, at best, a small window onto a much larger universe.

Augmented Reality (AR) technology—or as Apple prefers it, “Spatial Computing”—would replace the window with an all-encompassing membrane. Cyberspace is all around you, a soap bubble over real space.

Which means the cyberspace powers of instant information, non-local communication, creativity, and unlimited duplication can leak further than ever into real space. Meanwhile, the scale of our relationship to cyberspace changes. We can be enveloped by it, and we can make it fit into contexts previously reserved for things like furniture.

Anything digital can be as big as your couch.

Or as big as your room.

The problem is that pulling this off in a way that’s persuasive is exceedingly difficult. We don’t have technology like holograms that can project cyberspace onto real space. The best we can do right now is to ingest a bubble of real space into cyberspace, and then draw pictures over top of it.

Mostly, attempts to do this have been shoddy at best. It’s just a fundamentally hard problem. The eye is not easy to fool, and even now it’s barely possible to get the components small enough to fit comfortably on the face.

So instead, companies have been selling virtual reality, which like an ostrich, asks you to shove your head in a dark hole while your ass hangs out.

It’s not great technology. It’s neat for an hour, but the novelty wears off quickly. For one thing, it’s wildly inconvenient. You can’t actually see any of the real space artifacts that will absolutely kick your ass if you trip over them.

Worse still is that the images aren’t quite nice enough to be worth becoming your entire universe. The video is kind of grainy and, for many, causes motion sickness.

What comes next

Apple put us all on notice this week that we’re now on the threshold of proper AR. The era of ostrich malarkey is at an end. They have the technology all fitting together such that it can persuade our eyes and minds.

It’s not perfect.

For one thing, the first model costs $3500. But before you assume it’s doomed, remember the first Macintosh cost almost twice that after adjusting for inflation. So most people didn’t own the first Macintosh.

But many more owned later models. And more still owned the Mac’s Microsoft-driven cousins. It took about 16 years to make the shift, but the GUI/screen/mouse portal to cyberspace became affordable and ubiquitous, helped along by advances in computing and improved efficiencies in mass production.

AR is going to need its own advances, because consensus among reviewers is that while Vision Pro is doing something impressive, it’s also quite heavy.

It is interesting that Apple chose the moment when the technology integration was good enough, but not perfectly comfortable, nor particularly affordable. It suggests they think there’s a solid early adopter crowd they can nonetheless make happy, that the state of the technology won’t poison the well.

I suppose Tim Cook did learn that lesson the hard way.

Skeptics with an eye toward history could use this opportunity to compare the Vision Pro with a number of failed CD-based consoles of the 90’s. Hell, Apple even had one: Pippin.

But what’s different here is that Apple has spent so much effort building a unified ecosystem of content and services, merely implementing their own apps could make the Vision Pro compelling to the sort of person who can afford to buy it.

Meanwhile, with so many frameworks shared with iOS, there’s an enormous community of developers who already know the basics of building for this platform. There’s already a library of apps ready to run. If this truly is the next paradigm of computing, plenty of devs will enjoy the shortest learning curve in history while transitioning to it. They already know the programming language, the APIs and design patterns, even the quirks of the platform vendor’s distribution agreements.

In short, I think this chapter of computing really is Apple’s to lose, and that their track record is strong enough to assume they really have their arms around the technology challenges.

At least, that’s what the reviews are suggesting. Gadget reviewer Marques Brownlee summarizes by saying it has the best “passthrough” (of visual surroundings) he’s seen in this category of device. And that it’s kind of heavy.

So: Not perfectly comfortable, but it can produce the illusion of reality on a persuasive level. Which means soon enough, through ongoing miniaturization, it’ll be both comfortable and persuasive, and then it’s over the for the computer monitor.

The next few years will be about figuring out a whole new chapter in user interface and interaction design. We’re going to see novel forms of creative expression that that both target this technology, and are enabled by it. I can imagine a whole new chapter of CAD software, for example, along with the next generation of 3D/spatial painting products in the vein of Google’s Tilt Brush.

I also think this chapter of computing is really going to freak some people out. This will occupy a spectrum from the productive to the sanctimonious, but I think all transformational technology is worth a skeptical eye.

There are some who will instinctively react with a deep, limbic revulsion against being enveloped in this way. Everyone is allowed a preference, though mine would not match yours.

I’m not worried about privacy or platform integrity with Apple, but experience with cell phones shows us that the lower end of the market is a free-for-all in terms of privacy-eroding crapware. AR is loaded with cameras and sensors, and if Apple’s approach is to be believed, eye tracking is essential to making the illusions of AR persuasive. This is so much potential for abuse of everything from biometrics to images inside the home. Consumers should stay vigilant to these threats.

More abstractly, it’s worth asking about the long term consequences of immersion in cyberspace. A sufficiently advanced version of this technology could create persuasive images that left reality feeling lacking, depending on your reality.

More dramatic still: what does it mean to be influenced on this level? What can I make you believe with AR that would be impossible with film or prose? Forget advertising… imagine political manipulation and hate movements. And what about harassment? That’s all sobering shit.

So I think blind optimism may be unwise. This really is powerful stuff, and any serious power comes with sharp edges.

On the other hand, I’m excited. Cyberspace has been a part of my life since I was seven years old. I value my adventures and accomplishments there. I have spent decades honing skills for expression and creativity in that strange plane of possibility.

I can’t wait to see what is creatively possible for us on this new frontier. I want to see what can be made here that’s good, positive and optimistic. I want to know how we can be closer, share more experiences, even if we’re far apart. There are so many ethical lessons this generation of computing professionals can draw from. We’ve learned so much the hard way. I want to see how can we use this power for good.

And, more practically: I want to know if I can avoid ever going to a fucking office again.

However you lean, I would buckle up. Something big is coming.

Contents

1. And it’s a good thing it costs $3,500
2. Whether we need this is another question
3. The future is in front of your eyes

VR is dogshit.

The problem is the displays. The human eye processes vast amounts of data every second, and since I took the plunge into modern VR in 2016, my eyes have always been underwhelmed.

They call it “screen door effect:” we readily perceive the gaps in between a headset’s pixels, and the cumulative effect as you swing your vision around is the impression you’re seeing the virtual space through a mesh. Obviously, this breaks the illusion of any “reality,” once the initial charm has worn off.

Density has slightly improved over the years, but the quality of the image itself remains grainy and unpersuasive.

This problem is one of bare-knuckle hardware. It’s expensive to use displays that are small and dense enough to overcome the headwinds of our skeptical eyes. But even if those displays were plentiful and cheap, the hardware needed to drive all those pixels efficiently is your next challenge. Today’s most powerful graphics processing cards are themselves as large as a VR headset, with massive cooling fans and heatsinks.

Of course, mobile phone vendors can’t indulge that size. By contrast to the gluttonous power budgets of a desktop gaming PC, phones have to be lean and efficient. They have to fit comfortable in the pocket, and last all day and into the evening.

Against these constraints, Apple has been engineering custom silicon since their 2008 acquisition of PA Semi, an independent chip design firm. As Apple’s lead in mobile phone performance—including graphics processing sufficient to high-end graphics on premium, high-density screens—one thing became clear to me:

Eventually their chips would reach a point where they could comfortably drive an ultra-high density headset that defeated all the obstacles faced by cheaper entrants in the VR race.

After years of quiet work beneath the surface, Apple’s AR leviathan is upon us, barely at the border of the technologically possible.

And it’s a good thing it costs $3,500

No, hear me out.

VR/AR/XR is being held back by the rinky-dink implementations we’re seeing from Oculus, Vive and others. There’s not enough beef to make these first-class computing devices. It’s not just that they’re underpowered, it’s also that they’re under-supported by a large ecosystem.

By contrast, Apple said “damn the cost, just build the thing, and we’ll pack every nicety that comes with your Apple ID into the bargain.”

So it has it all:

• LiDAR sensor
• God knows how many cameras
• Infrared sensors to detect eye movement
• An iris scanner for security
• A display on the front portraying your gaze so that you can interact with humanity
• Ultra-dense 4K+ displays for each eye
• Custom silicon dedicated to sensor processing, in addition to an M2 chip

How much of these are necessary?

Who knows. But developers will find out: What can you do with this thing? There’s a good chance that, whatever killer apps may emerge, they don’t need the entire complement of sensors and widgets to deliver a great experience.

As that’s discovered, Apple will be able to open a second tier in this category and sell you a simplified model at a lower cost. Meanwhile, the more they manufacture the essentials—high density displays, for example—the higher their yields will become, the more their margins will increase.

It takes time to perfect manufacturing processes and build up capacity. Vision Pro isn’t just about 2024’s model. It’s setting up the conditions for Apple to build the next five years of augmented reality wearable technology.

Meanwhile, we’ll finally have proof: VR/AR doesn’t have to suck ass. It doesn’t have to give you motion sickness. It doesn’t have to use these awkward, stupid controllers you accidentally swap into the wrong hand. It doesn’t have to be fundamentally isolating.

If this paradigm shift could have been kicked off by cheap shit, we’d be there already. May as well pursue the other end of the market.

Whether we need this is another question

The iPhone was an incredible effort, but its timing was charmed.

It was a new paradigm of computing that was built on the foundations of a successful, maturing device: a pocket-sized communicator with always-on connectivity. Building up the cell phone as consumer presence was a lengthy project, spanning more than two decades before the iPhone came on the scene.

Did we “need” the cell phone? Not at first. And certainly not at its early prices. It was a conspicuous consumption bauble that signaled its owner was an asshole.

But over time, the costs of cellular connectivity dropped, becoming reasonable indulgences even for teenagers. Their benefits as they became ubiquitous were compelling: you could always reach someone, so changing plans or getting time-sensitive updates was easier than it had ever been in human history. In emergencies, cellular phones added a margin of safety, allowing easy rescue calls.

By the time iPhone arrived, the cell phone was a necessity. As, for so many today, is the smartphone.

The personal computer is another of today’s necessities that was not always so. Today, most people who own computers own laptops. The indulgence of portability, once upon a time, was also reserved exclusively for the wealthy and well-connected. Now it is mundane, and a staple of every lifestyle from the college student to the retiree.

Will augmented reality follow this pattern?

I’ll argue that the outcome is inevitable, even if the timing remains open to debate.

Every major interactive device since the dawn of consumer computing—from the first PCs to the GameBoy to the iPhone—has emphasized a display of some sort. Heavy cathode-ray tubes, ugly grey LCD panels, full-color LEDs, today’s high-density OLED panels… we are consistently interfacing through the eyes. Eyes are high-throughput sense organs, and we can absorb so much through them so quickly.

By interposing a device between the eyes and the world around us, we arrive at the ultimate display technology. Instead of pointing the eyes in the direction of the display, the display follows your gaze, able to supplement it as needed.

An infinite monitor.

The problem is that this is really fucking hard. Remember, we started with CRT displays that were at least 20 pounds. Miniaturization capable of putting a display over the eye comfortably is even now barely possible. Vision Pro requires a battery pack connected by a cable, which is the device’s sole concession to fiddly bullshit. Even then, it can only muster two hours at a single charge.

Apple is barely scraping into the game here.

Nevertheless, what starts as clunky needn’t remain so. As the technology for augmented reality becomes more affordable, more lightweight, more energy efficient, more stylish, it will be more feasible for more people to use.

In the bargain, we’ll get a display technology entirely unshackled from the constraints of a monitor stand. We’ll have much broader canvases subject to the flexibility of digital creativity, collaboration and expression.

What this unlocks, we can’t say.

The future is in front of your eyes

So here’s my thing:

None of the existing VR hardware has been lavished with enough coherent investment to show us what is possible with this computing paradigm. We don’t know if AR itself sucks, or just the tech delivering it to us.

Apple said “let’s imagine a serious computer whose primary interface is an optical overlay, and let’s spend as much as it costs to fulfill that mandate.”

Unlike spendy headsets from, say, Microsoft, Apple has done all the ecosystem integration work to make this thing compelling out of the box. You can watch your movies, you can enjoy your photos, you can run a ton of existing apps.

Now we’ll get to answer the AR question with far fewer caveats and asterisks. The display is as good as technologically possible. The interface uses your fingers, instead of a goofy joystick. The performance is tuned to prevent motion sickness. An enormous developer community is ready and equipped to build apps for it, and all of their tools are mature, well-documented, and fully supported by that community.

With all those factors controlled for, do we have a new paradigm here?

What can we do with a monitor whose size is functionally limitless?

I’m eager to see how it plays out.

Contents

1. Developer experience practice perceives a funnel, smooths its rough edges, and closes its gaps
   1. An abstract developer tools funnel
2. ‘Developer tools’ is culture, and culture breeds fandoms
   1. A better world is possible
3. And so, the theory:

Once upon a time, UX become a big buzzword in software.

Reasonable enough: UX ensured the person who landed on your digital product could happily navigate the critical path that ended with your making money. It was discovered that frustrated or confused customers abandoned shopping carts. Good money was being spent on everything from ads to headcount to server infrastructure, all with the hope that customers would engage in a transaction. But pesky things like subjective human experience were getting in the way.

Bad user experience increased customer acquisition costs, because some percentage of customers would bounce off of your product even if it absolutely could solve their problem, simply because they couldn’t be bothered to finish the transaction.

Investments in UX sealed gaps in the funnel, but successful UX had more holistic consequences. My first paid development gig was running mobile for Hipmunk, a travel search startup that differentiated on user experience. Hipmunk’s investment in UX translated into powerful word of mouth marketing. Users gushed to friends about our Gantt chart UI and a helpful sorting algorithm that penalized high “agony” flights.

Hipmunk was the best way to find a flight. It was fast and easy to pick something that would work great for your budget, comfort and plans. Skimming the Gantt chart made it much clearer how long any given trip was, and what kinds of layover penalties you endured in exchange for a cheaper ticket. It was easy to be confident because the best flights popped out of the results like beacons.

Sadly, the margins on selling plane tickets were shit and Hipmunk took an exit. But the lessons here are germane to developer experience as well.

Developer experience practice perceives a funnel, smooths its rough edges, and closes its gaps

A developer tools business has a funnel like anyone else. At the top of the funnel are people with problems. At the bottom of the funnel are people who depend on your tool to solve those problems, thanks to a successful integration.

There’s some Pareto stuff going on here: a smaller chunk of these developers will actually make you money. But with luck, the pleasure of using your tool will make most of this population happy to recommend you, regardless of individual spend. We’ll get back to this.

First, let’s talk about the funnel.

An abstract developer tools funnel

In a developer product, your job is to shepherd developers through these steps:

1. Awareness: “I understand how this tool can help me”
2. Experimentation: “I am playing around with the tool to evaluate its power”
3. Success: “The tool has helped me reach a goal”

The more people who reach the end state of that funnel, the broader the network of people willing to recommend your product and help each other use it.

So the first question becomes: How do we optimize that funnel?

”DevEx: What Actually Drives Productivity” argues that developer experience depends on flow state, feedback loops and managing cognitive load. Their perspective is much more about corporate productivity than is relevant for our purposes here, but those levers are absolutely the correct ones.

Cognitive load is a primary concern: if it seems painful or complex to a solve a problem with your developer product, plenty will bounce off right at the top.

We manage cognitive load a number of ways, for example:

• Abstractions around complex tools with affordances that make it clear how to access their power (think: pre-built containers with a terminal interface, or a GUI for a debugger)
• Docs that sequence learning to provide just enough context to make exciting progress, without overwhelming in the details
• Example projects demonstrating a working implementation of your tools in a context that’s analogous to a developer’s end goal
• Organized code snippets that demonstrate viable approaches to common tasks

Every time you see a project on GitHub with a snippet of “setup” or “basic usage” code near the top of the readme, you see the management of cognitive load.

By making cognitive load more manageable by more developers, you unlock the next phase of your funnel: a developer engaging in feedback loops with your product.

Feedback is essential: it tells developers if their efforts are working. Modern UI development integrates this power deeply, as everything from web frameworks to native development for mobile now offers hot reloading and live previews.

Feedback also shows up in the form of compiler warnings and errors, and this is central to modern workflows with typed languages. As you code, your editor can give you immediate feedback about the correctness of your implementation, warning you about a mismatch between what one function returns and what another accepts as input. This feedback is powerful, as it prevents entire categories of error that, in previous eras, would become crashes at runtime.

Error logging is yet more feedback, and the more detailed it is, the more developers can get help from an even more powerful source: other human beings. A developer community is an essential source of feedback, as not everything can be handled by automation. A strong developer community acts as an exception handler for issues that fall through the existing documentation and feature set. More than that, a strong community can help developers become better at their work, by providing suggestions and perspective.

Successfully facilitated, agreeable cognitive load and consistent feedback lead to flow state, which is the ultimate goal of your funnel. When developers are capable and confident enough to maintain consistent flow toward their goals, you’re almost guaranteed to arrive at a successful integration.

But this is more than just a business outcome. This is a personal accomplishment on the part of the developer your tools have been enabling. The results of this can range from short term satisfaction all the way to career advancement and even wealth.

As a field, computing has a lot of power, and having power shared with you can be an emotional experience.

‘Developer tools’ is culture, and culture breeds fandoms

All of these represent culture:

• A dog-eared script for King Lear
• East Coast vs. West Coast rap beef
• A Star Trek convention
• Building Iron Man armor with a 3D printer
• Your favorite programming language

Fans are people who strongly identify with an element of culture, willing to invest time in the replication of that culture within the larger story of humanity. Fans make costumes, props, creative homages, and outright remixes of their favorite culture.

To be a fan is to become part of something greater, because you think it’s worthy.

So, of course, developer tools have fandoms. Make someone a success and you’ve probably earned a fan. This is a relationship that transcends mere business transactions. This is certainly belief in your mission: conviction that you’re creating something special. But it goes deeper, into the primordial relationship of trust and gratitude we feel toward the tools that help us grasp beyond our reach.

A fan can become someone who will work actively themselves to improve the total of your developer experience. They might create tutorials, supplemental tools or code, even give their time to help other people become more fluent in your product. Fans will generate pull requests on your projects, and open issues that help you improve.

En masse, fans can even re-shape larger technology culture to better adopt your stuff. This is the goal of the vaunted “bottom-up” or “developer-led” growth strategy: developer consensus that yours is the best way to solve some category of problem.

It’s also why we can have such emotional responses to acquisitions of software products and firms. The guiding ethos that shapes a technology subculture can be vetoed by the new owner, changing basic assumptions that have guided our investments—that is, our hopes for the future.

Having a fandom is a serious responsibility because it means managing the vibes and health of a community.

A better world is possible

Fandoms aren’t set-it-and-forget-it. They require stewardship. So let me tell you about one of the healthiest fandoms in speculative fiction.

It’s The Expanse.

Here’s how we know this:

Some years back, actor Cas Anvar (Alex Kamal) was accused by multiple members of SFF fandom of abusive behavior. An investigation ensued and Anvar was written out of the show.

On /r/TheExpanse, mods organized immediately to set norms to respond to this crisis. Comments on the controversy were actively cordoned to specified threads. Participation in these threads was governed by a set of agreements, including compulsory reading of specific accounts from the accusers. As a whole, the subreddit maintained order. In the higher-stakes discussions, clear terms of engagement allowed moderators to shape productive conversation, dismiss bad actors, and prevent toxic flame wars.

It didn’t devolve into the kinds of vitriolic misogyny that is too common in these situations. Meanwhile, the community was able to reach consensus by talking through the substance of actually happened, instead of fracturing along chaotic lines of misunderstanding.

Enabling this was culture established from the very top. The Expanse was written in a way that always respected its women characters. The creatives made deliberate decisions to eschew gratuitous sexual violence as a storytelling mechanism, by contrast to projects like Game of Thrones. And, when trouble emerged within their own ranks, the show’s leadership took it seriously, communicated the investigation to the larger fandom, and took decisive action.

Fandoms can be scary stuff, but consistent leadership opens the door to norm-setting, orderly discussion of high-energy topics, and a place where people don’t feel excluded or pushed out because of their identities.

And so, the theory:

1. You can make more people more likely to be successful with your developer tools
2. People who you’ve made successful will want to share that success, encouraging others join the party you started
3. This creates a positive feedback loop that encourages fandom to form
4. Productive fans energize your ecosystem and enroll yet more developers if you remain aligned with their success, and maintain community health

It’s obviously much messier than that. Especially the bit where you need to come up with something that genuinely moves the needle on someone’s individual sense of power.

But in the game of software automation, power is all around us. We’re saturated by unprecedented opportunity to create impact and leverage, with a ceiling in the billions of customers. It’s never been more possible to package up power and wisdom and share with those who are eager to apply it through code.

Do with this knowledge what you will.

Contents

1. Vast creative markets
2. Adobe on-tap
3. Wild advances in creative hardware
4. And more magic besides

It’s an exciting week for me. I’m launching my new business.

Over a 20 year career, I’ve cycled continuously between self-employment and having a job. Thing is, I really prefer the self-employment. I like doing things my own way, and subordinating myself to the systems and policies of someone else’s company really stifles my everyday sense of creative joy.

Still, my origins are economically humble. Building a business out into a sustainable, lucrative engine takes time and capital I haven’t always had. Out of necessity, I’ve had to leave the self-determination of my own work in order to keep the bills paid.

I’m hoping to break that cycle today.

I know so much about how software works. I understand not just how it’s built, but how it’s used. I want to apply decades of this understanding to an emerging field: developer experience.

I’m calling it Antigravity. You can read the pitch if you want, but I’m not here to pitch you. I want to share the remarkable opportunities in 2023 for the solopreneur to punch above their weight.

Over two decades, I’ve had some success building systems that made me money on the internet. Again, never quite enough, but also never nothing. I’ve never had a better time doing it than I have in the last few months.

Vast creative markets

Consider my logo.

I’m not primarily a designer, but I’ve done plenty of design over my career. Part of how I get away with it is knowing my own limitations. For example, I cannot draw or illustrate for shit.

In the past, this has limited my ability to get as deep as I want to go with logo and identity. Mostly I stuck with tasteful but bland logotypes. But not anymore.

I didn’t have the budget for a design firm, but I do have a membership to The Noun Project, which hosts a vast array of vector art covering every conceivable subject, across a multitude of creative styles.

I had a vague concept in mind for my logo, refined over many iterations. Then I found it: the flying saucer of my dreams. It was exactly what I was looking for. The right level of detail, the stylistic crispness that made it feel almost like a blueprint.

I had my logo.

Much love to Brad Avison for this lovely illustration. Check out more of his iconography here.

Meanwhile, this model has permeated other creative work. For my launch video, I knew I’d need good music. But I was worried: historically, royalty-free music sounds like shit. It turns out, though, that we live in a new age. On Artlist, I found yet another vast library, offering music and sound effects that sounded great.

My thanks to Ian Post, whose track New World proved just the vibe my intro video needed.

Like Noun Project, Artlist is all you can eat. Subscribe for a year, get whatever you want, use it as needed. They even provide documentation of your perpetual sync license, for your legal records, right in your download page.

I really hope creatives are getting a good overall deal from these platforms, because for me, they’ve proven transformative to how I represent myself online.

Adobe on-tap

I used to pirate the shit out of Adobe, as a kid. I learned Photoshop, After Effects, Premiere, and others, just for the fun of the creativity.

You know what? Adobe got a great deal out of this loss-leader. Today I pay them monthly for access to Creative Cloud.

I hate to say this—I really don’t love Adobe—but the CC subscription is a phenomenal deal. For a flat fee you get access to their entire catalog of creative tools, plus some bonuses (a lite version of Cinema4D lurks within After Effects, for example).

But even better, you get access to Adobe’s font library, and it too is vast. You can use the typography locally, in all of your native apps, and you can set up web fonts to use on your site. This addition is savvy: it creates the case to maintain your subscription even if your usage is light. Now, past a certain point, it probably makes sense to just buy licenses for the type you want.

Still, being able to audition so much typography out in a production environment is powerful. Moreover, Adobe handles all the tedium of making these things load fast around the world via their CDN infrastructure.

The result is a distinctive web presence that can go toe-to-toe typographically with anyone else on the web. Having used Google’s free font service for years, I find there’s really no comparison. Adobe’s library is the premium stuff and it shows.

Wild advances in creative hardware

Ten years ago, I learned by accident just how powerful it is to have a video artifact representing me online. The opportunity showed up just at the right moment, as I was freelancing unexpectedly. It helped a lot.

Fun fact: I have a film degree.

It’s nothing fancy, sold out of a Florida diploma mill. But I spent more than a year getting my hands dirty across the full spectrum of film production tools. I learned the basics of film lighting, camera optics, audio strategies, editing and other post-production tasks.

It left me a talented amateur, with a solid map of what I need to know about making things look right in front of a camera. I can create my own video artifacts.

So when I tell you we live in an age of videography miracles, please believe me. A mirrorless camera and a decent lens can get you incredible, 4K footage, for about $1,000. Now, you don’t need all that data. But what you get with 4K is the ability to punch into a shot and still have it look crystal clear. It’s like having an extra camera position for every shot, for free.

Capturing footage requires only a cheap SD card, with a nice specimen from SanDisk storing two hours for just $24.

Even better, the lighting kits have gotten so small and affordable! For $250, you can get a couple of lights with a full spectrum of color temperatures, an even broader array of RGB colors, and incredible brightness without heat. No worrying about gels, no endless inventory of scrims to limit brightness. Just a couple of dials to get exactly the lighting you need.

Here’s the thing: I remember lighting, I remember some terminology. I knew enough to know what to look for. The internet did the rest, with more video tutorials on how to solve these problems than I could possibly watch.

And my M2 MacBook Air handled all of it beautifully. Editing was fast, rendering footage was fast. I can’t believe working with 4K footage can be so easy with everyday hardware.

Videography is an amateur’s paradise. I can’t imagine how the rest of the industry has evolved since I first learned this stuff.

And more magic besides

I’ve already talked about how much I enjoy using Svelte, and of course Antigravity uses a similar strategy for its site. The incredible power of open source to amplify your ambitions is a big part of why I’m launching a DX consultancy! I want more of this.

Using the GPT-4 LLM-based pattern synthesizer to explore marketing topics, debug problems with code, and otherwise work out a path forward, has been super helpful as well. A completely novel tool I didn’t expect.

Speaking of AI, Adobe also has a weird “AI”-based audio processing tool. It sounds pretty cool, at first, but eventually becomes uncanny. I tried it out for my Antigravity intro video but didn’t end up using it.

It’s an exciting time to try to build something new. While the internet and its various paradigms have largely matured, the resulting fertile platforms have left me more creatively effective than I’ve ever been.

So, wish me luck. And if you know any seed-stage through Series A startups, send them my way!

Contents

1. The Twitter cycle
   1. For example: Bean Dad
   2. What the fuck?
2. The cybernetic rush of consensus
3. But Twitter was more than outrage amplification
4. What’s with all the past-tense? Twitter.com still loads, my guy

As Twitter shambles steadily toward its grave, I’ve been thinking about its role on the internet and our culture broadly.

Why did so many disparate groups—from politicians, journalists, activists and the posturing wealthy—all agree it was worth so much time and energy? Why did companies spend so much money not just on marketing for Twitter, but also for staffing to monitor and respond to it?

It’s not because Twitter was the largest social media platform—it wasn’t. It’s not because Twitter drives large amounts of traffic—it doesn’t.

Instead, for the last decade, Twitter has occupied a unique role: it is an engine for synthesizing and proliferating consensus, in real-time, across every conceivable topic of political and cultural concern. In this, it was a unique form of popular power, with each voice having potential standing in a given beef. Litigants were limited only by their ability to argue a case and attract champions to it.

This had both its virtues and its downsides. Still, we’d never seen anything quite like it. In the aftermath of its capture by a wealthy, preening dipshit, we may never again.

The Twitter cycle

Like other media, Twitter was subject to cyclical behavior. Unlike other media, these cycles could unspool at breakneck speed, at any time of the week.

Broadly, the cycle comprised these phases:

• Real-time event: A thing has happened. An event transpired, a statement given, a position taken. The grain of sand that makes the pearl has landed within Twitter’s maw.
• Analysis and interpretation: What does this event mean? What are the consequences? Who has been implicated? Why does it matter? What larger context is necessary to understand it? In Twitter’s deeply factionalized landscape, interpretations could be broad.
• Consensus participation: Eventually a particular interpretation reaches consensus. Traveling along the viral paths of an influential few with broad audiences, a given point of view becomes definitive within certain groups. The factions thus persuaded join the fray, contributing their agreement, or refinement. Others, either contrarian by nature, or outside the factions with consensus, may start their own interpretation, and yet another sub-cycle toward consensus may kick off.
• Exhaustion: The horse, long-dead, is now well and truly beaten. The cycle concludes, added to the annals of Twitter, waiting to be cited as precedent in the analysis of a future event. Though settled, lasting schisms may result from the fracas, the fabric of friendships and alliances torn or rended entirely.

For example: Bean Dad

In the earliest days of 2021, musician John Roderick shared a window on his parenting approach. In the process, he became caught up in the consensus engine, becoming Twitter’s main character for several days, until an attack on the US Capitol Building by far-right extremists reset the cycle.

Event:‌ In a lengthy thread, Roderick explained how he tried to instill a sense of independence in his young daughter by offering the most minimal of assistance in the workflow of decanting some beans.

Interpretation: Maybe this is not ideal parenting. Maybe his daughter should receive more support.

Consensus: It is irrefutable: Bean Dad is a fuck. He has no place in polite society. It may be that his daughter is being starved instead of timely receiving the nourishment she needs to survive.

Exhaustion: Roderick deleted his account. His music was removed from a podcast that used it for an intro. Days later, mainstream media reported what you could learn in real-time just by keeping an eye on Twitter.

What the fuck?

Yeah, it really did work like that.

Politics is the negotiation and application of power. Twitter was a political venue in every sense of the word. Not just electoral politics, I stress here, but the everyday politics of industry, culture, even gender and sexuality. Twitter was a forum for the litigation of beefs.

You’ll see a lot of people roll their eyes at this. Dismissiveness of Twitter, though, often correlates to a sense of overall larger political power.

In our world, not everyone has a good deal. Not everyone has the basic dignity they deserve. In short, their beefs are legitimate. What was transformational about Twitter was the ability to bring your legitimate beef to larger attention, and in doing so, potentially change the behavior of those with more power.

The powerful loved Twitter. They had a shocking pattern of admissions against interest, feeding the above cycle. Twitter was a direct pipe to the minds of the wealthy, stripping past the careful handling of publicists and handlers. Rich guys could say some truly stupid shit, and everyone could see it!

I caution you against heeding the narrative of Twitter as an “outrage machine.” It’s not that this is strictly untrue, rather that it strips nuance.

We live in a time of ongoing humiliation and pain for so many people. Power is a lopsided and often cruel equation. There is much to be legitimately outraged about. To respond with indifference would violate the basic empathy of many observers, and so outrage was a frequent consensus outcome.

The cybernetic rush of consensus

Nevertheless, I will not argue that Twitter was unalloyed good. Twitter could be a drug, and some of the most strident interactions may be tinged with individual traumas and biases. This invalidates neither the beefs nor their litigants, but it does create opportunities for behavior more destructive than constructive. I speak here from a place of both experience and some sheepishness.

I was, at one point, a small-time Twitter outrage warlord.

There is a rush of strange power open to any Twitter user of even modest following. In the algorithmic timeline era, much of this power has been tempered by invisible weighting and preference.

But in the early days, it was simple: if there was some fuckshit, and you could name it, and describe its error persuasively, you could provoke a dialogue.

In my case, as I’d arrived in Silicon Valley, and my tech career, I found myself promptly alienated from my peers. Their values were not my values, and in a case of legitimate beef, there was serious exclusion baked into the industry’s structures.

After a lifetime in love with technology, this was where I had to make my career?

It was a frustration that simmered quickly into rage, and in Twitter, I found a pool of similar-minded resentments. It was possible to raise a real stink against the powerful architects of these exclusionary systems.

When these things took on a viral spread, it was a power that few humans had ever had before. I mean, you needed to own a newspaper to shape consensus on this level in any other age. Seeing the numbers tick up, seeing others agree and amplify… I enjoyed it at the time. I’m not proud to say it, but I’m telling you this because I think it’s instructive.

Having a following doesn’t mean you’re financially solvent, but it does open certain doors. As only Nixon could go to China, I went to GitHub as it undertook a multi-year project to rehabilitate its image in the wake of an all-too-characteristic workplace scandal.

Such is the power of consensus.

Six months after I joined, someone close to me asked if I wanted to spend the rest of my life angry. If I wanted to spend my energies in the project of stoking rage in the hearts of others, indefinitely. They asked if this was the best use of my gifts. In an instant I knew I needed a different path.

The years since have been a long journey away from the power of anger. There’s no doubt it works. There’s no doubt you can attract quite an audience when you show rage toward toward the wicked and unjust. But I think the spiritual cost isn’t worth the reward.

This is not to discount the role of anger. We should be outraged when we see people doing wrong, when we see people hurt, when we see corrupt power enact abuse.

But I must also look to what comes after anger. I think we need hope, care, and laughter as much as we need the activating flame of anger to break free of our cages.

But Twitter was more than outrage amplification

I don’t want to fall into the trap of tarring Twitter with the all-too-common rage brush. It certainly brewed plenty of anger, but it was also a space of connection, community, and even laughter.

By contrast to its early contemporaries, Twitter used a subscription model akin to RSS. Symmetrical links between users were optional. As a result, we could be observers to vast communities we would otherwise have no exposure to.

It was an opportunity for education and enlightenment on a variety of subjects. Consensus wasn’t always an angry or contentious process. Indeed, during the Covid crisis, finding emerging medical consensus through legitimate practitioners was powerful, filling many gaps left by a mainstream media that was slow to keep up.

As global events unfolded, participating in the Twitter consensus left you better-informed than those stuck hours or days behind consuming legacy media. If you could find honest brokers to follow, nothing else came close in terms of speed and quality of information (though this, at points, could become exhausting and demoralizing).

Participation in consensus, too, could build relationships. People have found love on Twitter, and I’ve had multiple life-changing friendships emerge through this medium. Hell, GitHub wasn’t even the first job I got thanks to Twitter.

In short, Twitter was a unique way not just to understand the ground truth of any situation, but also to build connection with those who shared your needs, convictions and interests.

I would make different choices than I had in the past, perhaps, but I still look upon Twitter as an incredible education. I’m a better, more knowledgeable, less shitty person thanks to all I learned in 14 years of participation.

What’s with all the past-tense? Twitter.com still loads, my guy

I mean, so do Tumblr, LiveJournal, Digg. Social products are simultaneously fragile and resilient. It doesn’t take much to knock them out of health, but they can keep scraping along, zombie-like, long after the magic pours out of them.

This, I think, is Twitter’s fate. Interfering with legitimate journalism, cutting off public service API access, offering global censorship on behalf of local political beef—none of this is good news for Twitter, and this is just from the last couple weeks.

Twitter is entering its terminal phase now. I’m sure it will live on for years more, but its power is waning as it is mismanaged.

Will anything ever take its place?

Mastodon is perhaps Twitter’s most obvious heir. It has picked up millions of accounts since Twitter’s disastrous acquisition. But certain design decisions limit Mastodon’s ability to meet Twitter’s category of power. For one thing, there is no global search of Mastodon content, and no global pool of trending topics. Moreover, while Twitter was an irresistible lure to the powerful, they have yet to join Mastodon.

I’m personally hopeful about the Fediverse, and the open, decentralized potential it suggests. A real-time social medium that’s easy to integrate into anything on the web, and which no one person owns, is exciting. But it’s early days yet. Anyone could take this.

There are other entrants. BlueSky, which promises an open platform and spun off from Twitter. Post News, which makes me skeptical.

It’s entirely possible that, as Twitter dies, so will its unique power. Entirely, and forever. The dynamics that gave people popular power may be deliberately designed against in future platforms.

But as Twitter wanes, know what we’re losing. It’s incredible that this existed, incredible that it was possible. Is it possible to design a replacement that emphasizes its virtues without its downsides? I’m not sure.

I am a little sad, though, for this loss, warts and all.

Contents

1. Apple rides the leading edge of miniaturization, abstraction and convenience
   1. The USB revolution
   2. AirPort and WiFi
   3. Miniaturized storage and the iPod
   4. Multitouch and the iPhone
2. The future of the futuristic

For the so-called “Apple Faithful,” 1996 was a gloomy year. Semiconductor executive Gil Amelio was driving the company to the grave. The next-generation operating system project that would rescue the Mac from its decrepit software infrastructure had stalled and cancelled, with the company hoping to buy its way out of the problem.

In the press, this “beleaguered” Apple was always described as on the brink of death, despite a passionate customer base and a unique cultural position. People loved the Mac, and identified with Apple even as its marketshare shrank.

Of course, we know what came next. Apple acquired NeXT, and a “consultant” Steve Jobs orchestrated a board coup that ousted Amelio and most of Apple’s directors. There followed a turnaround unlike anything else in technology history, building the kernel of a business now worth $2.6 trillion.

Much has been said about the basic business hygiene that made this turnaround possible. Led by Jobs, Apple leadership slashed the complex product line to four quadrants across two axes: desktop-portable and consumer-professional. They got piled-up inventory down from months to hours. They cleaned up the retail channel and went online with a web-based build-to-order strategy.

Of course, they integrated NeXTSTEP, NeXT’s BSD-based, next-generation operating system, whose descendent underpinnings and developer tools live on to this day in every Mac, iPhone, and Apple Watch.

But none of these tasks alone were sufficient to turn Apple’s fortunes around. The halo of innovation that Apple earned put winds in its cultural sails, creating a sense of potential that drove both adoption and its stock price.

But what does “innovation” mean in practice?

Apple rides the leading edge of miniaturization, abstraction and convenience

If you look at the sweep from the first iMac to the latest M2 chips, Apple has been an aggressive early-adopter of technologies that address familiar problems with smaller and more convenient solutions.

Apple’s turnaround was situated during a dramatic moment in hardware technology evolution, where things were simultaneously shrinking and increasing in power. What was casually termed innovation was Apple’s relentless application of emerging technologies to genuine consumer needs and frustrations.

The USB revolution

Before USB, I/O in personal computing was miserable.

A babel of ports and specifications addressed different categories of product. Input devices needed one form of connector, printers another, external disks yet another.

These connectors were mostly low-bandwidth and finicky, often requiring the computer be powered down entirely merely to connect or disconnect them. Perhaps the worst offender was SCSI, a high-bandwidth interface for disks and scanners, packed with rules the user had to learn and debug like individual device addresses and “termination” for daisy-chaining what was usually a single port per-computer. You could have only a handful of SCSI devices, and some of that number was gobbled up by internals.

Originally conceived by a consortium of Wintel stakeholders, the USB standard emerged quietly to fix all of this just as Apple entered the worst of its death throes, with limited initial adoption.

With the release of the first iMac, in 1998, Apple broke with precedent, making USB the computer’s exclusive peripheral interface. By contrast to previous I/O on the Mac, USB was a revelation in convenience: devices were hot-pluggable, no shutdown required. The ports and connectors were compact, yet offered 12 mbit connections, supporting everything from keyboards to scanners to external drives. Devices beyond keyboards could even draw enough energy from USB to skip an extra power cable. Best of all, USB hubs allowed endless expansion, up to a staggering 127 devices.

Though the iMac was friendly and fresh in its design, its everyday experience was a stark departure in ease-of-use, thanks in part to the simplicity of its peripheral story. Mac OS was retooled to make drivers easy to load as needed, eventually attempting to grab them from the web if they were missing on disk.

Meanwhile, in an unprecedented example of cross-platform compatibility, Apple’s embrace of USB created a compelling opportunity for peripheral manufacturers to target both Mac and PC users with a single SKU, reducing manufacturing and inventory complexity. In Jobs’s 1999 Macworld New York introduction of the iBook, he claimed that USB peripheral offerings had grown 10x, from just 25 devices at the iMac’s launch, to over 250 under a year later. Seeing the rush of consumer excitement for the iMac, manufacturers were happy to join the fray, offering hip, translucent complements to this strange computer that anyone could use.

Today, USB is ubiquitous. Every office has a drawer full of USB flash drives and cables. Desperate to make a mark, with nothing to lose, Apple went all-in on the next generation of peripheral technology, and won the day.

AirPort and WiFi

In that iBook intro, Steve’s showmanship finds a dramatic flourish.

Rather than telling the audience about his “one more thing,” he showed it to them, forcing them to draw their own conclusions as they made sense of the impossible.

Loading a variety of web pages, Jobs did what is now commonplace: he lifted the iBook from its demonstration table, carried it elsewhere, and continued clicking around in his browser, session uninterrupted, no cables in sight.

The audience roared.

This magic, he explained, was fruits of a partnership with Lucent, and the commercialization of an open networking standard, 802.11. Jobs assured us this was a technology fast heating up, but we didn’t have to wait. We could have it now, with iBook and AirPort.

Accompanying the small network card that iBook used to join networks, Apple also provided the UFO-like AirPort base station, which interfaced with either an ethernet network or your dialup ISP.

Inflation-adjusted, joining the WiFi revolution cost about $700, including the base station and a single interface card. Not to mention a new computer that could host this hardware.

Nevertheless, this was a revolution. No longer tethered to a desk, you could explore the internet and communicate around the world anywhere in your house, without running cables you’d later trip over.

More than anything else Apple would do until 2007, the early adoption of WiFi was science fiction shit.

Miniaturized storage and the iPod

By 1999, Apple was firmly out of the woods. Profitable quarters were regular, sales were healthy, and their industrial design and marketing prowess had firmly gripped culture at large.

But it would be the iPod that cemented Apple’s transition out of its “beleaguered” era and into its seemingly endless rise.

While iPod was a classic convergence of Apple taste and power, the technical underpinnings that made it possible were hidden entirely from the people who used it everyday.

The earliest iPods used a shockingly small but complete hard drive, allowing them to pack vast amounts of music into a pocket-sized device. Before USB 2.0, Apple used FireWire, with its 400 mbit throughput, to transfer MP3s rapidly from computer to pocket.

Whereas a portable music collection had once been a bulky accordion file packed with hour-long CDs, now even a vast collection was reduced to the size of a pack of playing cards. Navigation of that collection was breezy and fully automated, a convenient database allowing you to explore from several dimensions, from songs to artists, albums to genres. You were no longer a janitor of your discs.

Instead of fumbling, you’d glide your thumb over a pleasing control surface, diving effortlessly through as much music as you ever cared to listen to.

iPod was far from the first MP3 player. But most of them had far less capacity. iPod was not the first hard drive-based MP3 player, either. But its competitors were far bulkier, and constrained by the narrow pipes of USB 1.1, much slower besides.

Once again, at the first opportunity to press novel technologies into service, Apple packaged them up, sold them at good margins, and made a ton of money.

Multitouch and the iPhone

For most of us, our first exposure to gestural operating systems came from Minority Report, as Tom Cruise’s cop-turned-fugitive, John Anderton, explored vast media libraries in his digital pre-crime fortress.

The technology, though fanciful, was under development in reality as well. A startup called FingerWorks, launched by academics, were puttering around trying to make a gestural keyboard. It looked weird and they did not find commercial traction.

Nevertheless, they were exploring the edge of something pivotal, casting about though they were in the darkness. In 2005, the company was acquired by Apple in a secretive transaction.

Two years later, this gestural, multi-touch interface was reborn as the iPhone interface.

By contrast to existing portable computers, the iPhone’s gesture recognition was fluid and effortless. Devices by Palm, along with their Windows-based competition, all relied on resistive touch screens that could detect only a single point of input: the firm stab of a pointy stylus. Even at this, they often struggled to pinpoint the precise location of the tap, and the devices were a study in frustration and cursing.

The iPhone, meanwhile, used capacitance to detect touches, and could sense quite a few of them at once. As the skin changed the electrical properties of the screen surface, the iPhone could track multiple fingers and use their motion relative to one another to make certain conclusions about user intent.

Thus, like any pre-crime operative, we could pinch, stretch and glide our way through a new frontier of computing.

Minority Report depicted a futuristic society, with a computing interface that seemed impossibly distant. Apple delivered it to our pockets, for a mere $500, just five years later.

The future of the futuristic

Apple has continued this pattern, even as the potential for dramatic changes has stabilized. The shape of computers has remained largely constant since 2010, as as has appearance of our phones.

Nevertheless, the Apple Watch is a wildly miniaturized suite of sensors, computing power, and even cellular transceivers. It is a Star Trek comm badge and biometric sensor we can buy today.

AirPods do so much auditory processing, even noise canceling, in an impossibly small package.

And Apple’s custom silicon, now driving even desktop and portable Macs, provides surprising performance despite low power consumption and minimal heat.

We are no longer lurching from one style of interface abstraction to the next, as we did from 1998 through 2007, trading CRTs for LCDs, hardware keyboards for multitouch screens. Still, Apple seems to be maintaining its stance of early adoption, applying the cutting edge of technology to products you can buy from their website today.

As rumors swirl about the coming augmented reality headset Apple has been cooking up, it will be interesting to see which science fiction activities they casually drop into our laps.

The challenge is formidable. Doing this well requires significant energy, significant graphics processing, and two dense displays, one for each eye. Existing players have given us clunky things that feel less futuristic than they do uncomfortable and tedious: firm anchors to the present moment in their poorly managed constraints.

But for Apple, tomorrow’s technology is today’s margins. I wonder what they’re going to sell us next.

Contents

1. In the beginning
2. Enter the static site generator
3. Having fun with SvelteKit
   1. OpenGraph preview cards
   2. Converting Markdown to HTML and RSS
   3. Access a treasure trove of resources
4. The power to build a product

Redeem Tomorrow has a new site. Please let me know if you have any trouble with it.

I had a few goals in rebuilding the site over the last few weeks:

• High availability without admin burden
• Power to shape the product
• Tidiness and credibility
• Approximate feature parity with Ghost

I was thrilled to hit all of these marks. In the process, I’ve been struck by just how much the web has evolved over the last 20 years. So here’s a walkthrough of the new powers you have for building a modern site, and how much fun it is.

In the beginning

WordPress is the farm tractor of the internet.

Nothing comes close to its versatility and overall power. But it was architected for a different time. In the beginning of the last cycle, we still thought comments sections were a universally good idea, for example.

Pursuing the nimbleness of a lively, reactive machine, WordPress is built to constantly respond to user activity, updating the contents of the website when appropriate.

To do this, WordPress needs run continuously, demanding a computing environment that provides things like a database, web server, and runtime for executing php code. Reasonable enough.

The wrinkle is this: for most websites, most of that infrastructure is sitting idle most of the time. It swings to life when an admin logs in to write and publish a post, then sits quietly again, serving pages when requested.

Nevertheless, you pay for the whole month, idle or not, when you host a web application built in the WordPress style. It’s not that much money. DigitalOcean will sell you a perfectly reasonable host for $6 a month that can do the job. Still, at scale, that’s not the best use of resources.

Technologists hate inefficiency.

Enter the static site generator

Meanwhile, storage is cheap. Serving static content to the web can happen in volume. There are efficiencies to be found in bulk hosting.

Under the static site paradigm, you use time-boxed, live computing on the few occasions where it’s actually needed: when you’re authoring new content to be inserted into your website. On these occasions, the raw contents of your site are converted, according to various templating rules, into real, styled HTML that anyone can use.

Even better, because your content has been simplified into the raw basics of static files, it can now be served in parallel without any particular effort on your part. Services like Netlify offer workflows with transient computing power to generate your site, then distribute it around the world through Content Delivery Networks (CDNs). With a CDN, a network of hosts around the world all maintain copies of your content.

With your content on a CDN, your visitors experience less delay between making a request and your site fulfilling it. Even better, through the parallel design of a CDN, it’s much less likely that a spike in traffic will knock your site offline. A drawback with WordPress was that a surge in popularity could overwhelm your computing resources, with a $6 virtual server suddenly handling traffic better suited to a $100 machine.

When a CDN backs your content, you don’t have this category of problem. Of course, it was always possible to bolt this kind of performance onto a WordPress installation, but this required special attention and specialist knowledge.

In the realm of static site generation, you don’t need to know anything about that sort of thing. The hosting provider can abstract it away for you.

Having fun with SvelteKit

The issue I’ve had with static site frameworks is this:

The ergonomics never sat right for me. Like a pair of pants stitched with some subtle but fundamental asymmetry, I could never become comfortable or catch a stride. Either the templating approach was annoying, or the abstractions were too opaque to have fun with.

Then, in my last months at Glitch, I saw mention of SvelteKit, which had just hit 1.0. I was working with some of the smartest people in web development, so I asked: “What do you think?”

The glowing praise I heard convinced me to plow headlong into learning, and I’ve been having a blast ever since. Here’s how Svelte works:

<script>
	//You can code what you want inside the `script` tag. This is also where you define input arguments that bring your components to life.

	export let aValue;
</script>

<div>
	<!-- Use whatever script values you want, just wrap them in curly braces and otherwise write HTML -->
	Here is some content: {aValue}
</div>

Components and Layouts can encapsulate their own scriptable functionality. Just write JavaScript.

If you need more sophisticated behavior—to construct pagination, for example—you can write dedicated JS files that support your content.

This stuff can get as complex as you like. Make API requests, even expose your own API endpoints. Best of all, for me, is that SvelteKit’s routing is completely intuitive. Make a directory structure, put files into it, and then your site will have the same structure.

But what’s really badass is that all of it can be designed to generate static content. I don’t really understand or even care about the many layers of weird shit going on to support JS web development, and with SvelteKit, I don’t have to. The abstractions are tidy and self-contained enough that I can see results before becoming an expert.

All I have to do is make a fresh commit to my site’s GitHub repo, and Netlify will update my site with the new content. Whatever automated tasks I want to do during this phase, I can. I feel like I have all the power of what we’d call “full-stack” applications, for my blogging purposes, but with none of the costs or maintenance downsides.

Sounds neat, but what can you do with it in practice?

OpenGraph preview cards

For one thing, every post on this site now provides a unique OpenGraph card, based on its title and excerpt. When you share a post with a friend or your feed, a preview appears that reflects the contents of the link.

To accomplish this, my SvelteKit app:

• Loads font assets into memory
• Transforms an HTML component into vector graphics
• Renders that SVG into a PNG
• Plugs the appropriate information into OpenGraph and Twitter meta tags.

All of this on a fully automated basis, running as part of the generator process. I don’t need to do anything but write and push to GitHub. Special thanks to Geoff Rich, whose excellent walkthrough explained the technology and got me started on the right path.

Converting Markdown to HTML and RSS

As standard with static site generators, I write my posts in Markdown, including metadata like title and date at the top.

From this, I can convert everything into the fully-realized site you’re reading right now. In this I owe another debt, to Josh Collinsworth, whose excellent walkthrough of building static sites with SvelteKit provides the foundation for my new home on the web. It explained everything I needed to know and do, and I wouldn’t have escaped the straitjacket of Ghost without this help.

Access a treasure trove of resources

I’ve found icon libraries, a date formatter, and even a component for building the newsletter subscription modal that pops up when you click the “subscribe” link.

Svelte’s community has put serious love into the ecosystem, so there’s always a helping hand to keep your project moving.

The power to build a product

I’ve been building digital products for 20 years, but never got to the fluency I wanted on the web.

This feels different. I feel like I can imagine the thing I want, and then make it happen, and then hand it to you.

For example, I always wanted to do link-blogging posts on my Ghost site, but Ghost was quite rigid about how it structured posts.

Now, I can imagine exactly how I want a link post to behave, down to its presentation and URL structure, and then build that. I can even decide I want to offer you two versions of the RSS feed: with and without link posts. It’s all here and working.

But despite this power, I don’t have to worry about a server falling over. Someone else holds the pager.

I feel like I’ve been struck by lightning. I feel like I’m in love. Contentment and giddiness all at once.

Anyway, call it a 1.0. Hit me up if you see any bugs.

Contents

1. Interlude: the basics of DX in Pattern Synthesis
2. Components of the Pattern Synthesis value chain
3. Containerization
4. Replicate’s innovation
5. The confluence of Pattern Synthesis and open source

I’ve argued that Pattern Synthesis (“AI”, LLMs, ML, etc) will be a defining paradigm of the next technology cycle. Patterns are central to everything we as humans do, and a mechanism that accelerates their creation could have impact on work, culture and communications on the same order as the microprocessor itself.

If my assertion here is true, one would expect to see improving developer tools for this technology: abstractions that make it easy to grab Pattern Synthesizers and apply them to whatever problem space you may have.

Indeed, you can pay the dominant AI companies for access to their APIs, but this is the most superficial participation in the Pattern Synthesis revolution. You do not fully control the technology that your business relies upon, and you therefore find yourself at the whims of a platform vendor who may change pricing, policies, model behaviors, or other factors out from beneath you.

A future where Pattern Synthesis is a dominant technical paradigm is one where the models themselves are malleable, first-class development targets, ready for experimentation and weekend tinkering.

That’s where the Cog project comes in.

Interlude: the basics of DX in Pattern Synthesis

The Developer Experience of Pattern Synthesis, as most currently understand it, involves making requests to an API.

This is a well-worn abstraction, used today for everything from accepting payments to orchestration of cloud infrastructure. You create a payload of instructions, transmit it to an endpoint, and then a response gives your application what it needs to proceed.

Through convenient API abstractions, it’s easy to begin building a product with Pattern Synthesis components. But you will be fully bound by the model configuration of someone else. If their model can do what you need, great.

But in the long term, deep business value will come from owning the core technology that creates results for your customers. Reselling a widget anyone else can buy off the shelf doesn’t leave you with much of a moat. Instead, the successful companies of the coming cycle will develop and improve their own models.

Components of the Pattern Synthesis value chain

In order to provide the fruits of a Pattern Synthesis Engine to your customers, you’ll be interacting with these components:

1. Compute substrate: Information is physical. If you want to transform data, you’re going to need physical hardware somewhere that does the job. This a bunch of silicon that stores the data and does massive amounts of parallelized computation. This stuff can be expensive, with Nvidia’s A100 GPU costing $10k just to get started.
2. Host environment: Next, you’re going to need an operating system that can facilitate the interaction between your Pattern Synthesis model and the underlying compute substrate. A host environment does all of the boring, behind-the-scenes stuff that makes a modern computer run, including management of files and networking, along with hosting runtimes that leverage the hardware to accomplish work.
3. Model: Finally we arrive at the Pattern Synthesizer itself. A model takes inputs and uses a stored pile of associations it has been “trained” with to transform that input into a given pattern. Models are diverse in their applications, able to transform sounds into text, text into images, classify image contents, and plenty more. This is where the magic happens, but as we can see, there are significant dependencies before you can even get started interacting with the model.
4. Interface: Finally, an interface connects to these lower layers in order to provide inputs to the model and report its synthesized output. This starts as an API, but this is usually wrapped in some sort of GUI, like a webpage.

This is the status quo, and it’s not unique to “AI” work, either. You can swap the “model” with “application” and find this architecture describes the bulk of how the existing web works.

As a result, existing approaches to web architecture have lessons to offer the developer experience for those building Pattern Synthesis models.

Containerization

In computing, one constant bugbear is coupling. Practitioners loathe tightly coupled systems, because such coupling can slow down future progress. If one half of a tightly coupled system becomes obsolete, the other half is weighed down until it can be cut free.

A common coupling pattern in web development exists between the application and its host environment. This can become expensive, as every time the application needs to be hosted anew, the environment must then be set up from scratch to support its various dependencies. Runtimes and package managers are common culprits here, but the dependency details can be endless and surprising.

This limits portability, acting as a brake on scaling.

The solution to this problem is containerization. With containers, an entire host environment can be snapshotted and captured into fully portable files. Docker, and its Docker Engine runtime, is among the most well-known tools for the job.

Docker Engine provides a further abstraction between between the host environment and its underlying compute resources, allowing containers that run on it to be flexible and independent of specific hardware and operating systems.

There’s a lot of devil in these details. There’s no magic here, just hard work to support this flexibility.

But when it works, it allows complex systems to be hoisted into operation across a multitude of operating systems on a fully-automated basis. Get Docker Engine running on your machine, issue a few terminal commands, and you’ve got a new application running wherever you want it.

With Cog, Replicate said: “cool, let’s do that with ML models.”

Replicate’s innovation

Replicate provides hosting for myriad machine learning models. Pay them money, you can have any model in their ecosystem, or one you trained yourself, available through the metered faucet of their API.

To support this business model, Replicate interposes Docker into the existing value chain. Rather than figure out the specifics of how to make your ML model work in a particular hosting arrangement, you package it into a container using Cog:

No more CUDA hell. Cog knows which CUDA/cuDNN/PyTorch/Tensorflow/Python combos are compatible and will set it all up correctly for you.

Define the inputs and outputs for your model with standard Python. Then, Cog generates an OpenAPI schema and validates the inputs and outputs with Pydantic.

Thus, through, containerization, the arcane knowledge of matching models with the appropriate dependencies for a given hardware setup can be scaled on an infinitely automated basis.

The model is then fully portable, making it easy to host with Replicate.

But not just with Replicate. Over the weekend I got Ubuntu installed on my game PC, laden as it is with a high-end—if consumer-grade—GPU, the RTX 4090. Once I figured out how to get Docker Engine installed and running, I installed Cog and then it was trivial to load models from Replicate’s directory and start churning out results.

There was nothing to debug. Compared to other forays I’ve made into locally-hosted models, where setup was often error-prone and complex, this was so easy.

The only delay came in downloading multi-gigabyte model dependencies when they were missing from my machine. I could try out dozens of different models without any friction at all. As promised, if I wanted to host the model as a local service, I just started it up like any other Docker container, a JSON API instantly at the ready.

All of this through one-liner terminal commands. Incredible.

The confluence of Pattern Synthesis and open source

I view this as a watershed moment in the new cycle.

By making it so easy to package and exchange models, and giving away the underlying strategy as open source, Replicate has lowered the barriers to entry for tinkerers and explorers in this space. The history of open source and experimentation is clear.

When the costs become low enough to fuck around with a new technology, this shifts the adoption economics of that technology. Replicate has opened the door for the next stage of developer adoption within the paradigm of Pattern Synthesis. What was once the exclusive domain of researchers and specialists can now shift into a more general—if still quite technical—audience. The incentives for participation in this ecosystem are huge—it’s a growth opportunity in the new cycle—and through Cog, it’s now so much easier to play around.

More than that, the fundamental portability of models that Cog enables changes the approaches we can take as developers. I can see this leading to a future where it’s more common to host your models locally, or with greater isolation, enabling new categories of “AI” product with more stringent and transparent privacy guarantees for their customers.

There remain obstacles. The costs of training certain categories of model can be astronomical. LLMs, for example, require millions of dollars to capitalize. Still, as the underlying encapsulation of the model becomes more amenable to sharing and collaboration, I will be curious to see how cooperative approaches from previous technology cycles evolve to meet the moment.

Keep an eye on this space. It’s going to get interesting and complicated from here.

Contents

1. Elite crisis
2. There’s never enough
3. Succession reassures you: it really IS nuts the world works like this

The typical American K-12 education is a civics lobotomy.

Students are taught a simplistic version of American history that constantly centers the country as heroic, just and wise. America, we’re told, always overcomes its hardest challenges and worst impulses. In my 90’s education on the Civil Rights era, for example, we were told that Rosa Parks and MLK solved American racism once and for all. The latter’s assassination in this rendition was inconvenient and sad, but his sacrifice, we were meant to understand, paved the way for a more just and decent future.

Similar simplicity was given to the workings of political power in the United States. Why, there’s a whole bicameral legislature, three branches of federal government, checks and balances, and a noble revolutionary history that gave birth to it all.

Nowhere in this depiction was any time given to explaining lobbyists or campaign finance. Same for gerrymandering. These were yet more inconvenient details swept outside the scope of our lessons.

I mean no slight on the teachers of America by this criticism. They do their best in a system designed for indoctrination and conformity. I think they’re some of the most important civic actors in our entire system. I’d give them more money, more independence, and more day-to-day support if I could wave a magic wand and make it so.

Nevertheless, I emerged into adulthood feeling entirely unprepared to understand the civic complexity of this country. The entanglement of economic and political systems is a sort of dark matter that moves and shapes our everyday life, but lurks out of view without prolonged study and meaningful curiosity.

This was frustrating: the world was obviously broken, and I didn’t have models or vocabulary to explain why. I came of age in the aftermath of the 2008 financial crisis, struggling economically myself, and watching so many of my peers flailing in their quests to find basic stability and prosperity.

Indeed, Millennials have less wealth compared to previous generations, holding single-digit percentages of the pie, compared to 30% of US wealth for Gen X and 50% for the Boomers. Getting by with dignity and economic self-determination is an objectively hard slog.

HBO’s Succession, now in its fourth and final season, brings a truck-mounted spotlight to the mechanics of inequality. It’s a fast-paced education in how late-capitalist power actually functions: the interactions between wealth, corporations, civic institutions and everyday people.

The show, shot in a choppy, observational style, insists with every stylistic choice: “this is really how the world works.”

I wish we’d had it much sooner.

Elite crisis

Part of what’s broken in America is the rank incompetence of our leadership. People in power are, in too many cases, just bad at their jobs.

Before assuming his role as a Trump hatchet man, Jared Kushner bought The New York Observer as a sort of graduation present. His inept tenure there is the stuff of legend. The scion of a wealthy criminal, Kushner used the paper to prosecute personal beefs and, eventually, to endorse Donald Trump’s bid for the presidency.

This is, indeed, the way the world works. Everything is pay-to-play. If you want something that people value, you can buy it.

In following the travails of media titan Logan Roy, along with his children and the various toadies and yes-men who support their ailing empire, Succession makes the same point over and over again:

It’s adjacency to wealth that determines your power, not your fitness to lead or your strategic competence. In an unequal world—63% of post-pandemic wealth went to 1% of humanity—money distorts relationships and decides opportunity.

Over the show’s trajectory, we see Logan’s striver son-in-law Tom Wambsgans rise from minor functionary to chairman of a network that’s a clear stand-in for Fox News. All along, it’s clear that Tom doesn’t have any particular talents for the roles he’s given, but he is a loyal and trustworthy puppet for Logan.

Meanwhile, the billionaire Roy children are constantly bumbling through various exercises at proving they’re the equal of their working class-turned-plutocrat father. Frequently out of their depth, they’re inserted relentlessly into positions of authority, with occasionally disastrous results. In rushing the launch of a communications satellite, for example, Kieran Culkin’s Roman Roy—somehow COO of his father’s media conglomerate—ends up destroying a launch vehicle and costing a technician his thumb.

There’s never enough

As much as the show is about wealth and power, it is also an exploration of personal and family dysfunction.

Despite lives of extraordinary wealth and comfort—everyone lives in a state of Manhattan opulence mortals could never imagine—the Roy family carries decades of scars and trauma. They are human beings just like you or me, in this sense: they feel pain, they can be wounded, they carry grief.

But unlike you or me, acting out their pain and grief lands with billion dollar shockwaves. The money doesn’t make them happy, no, but it does let them amplify their pain into the lives of others.

So we see people of incredible power who can never have enough. What they need—peace, healing, clarity of self—is something they are unable to buy. What does it mean when flawed, broken human beings have the power to re-shape our world so completely? What does it mean when people have the money to buy their way out of consequences, even for the most dire of fuckups?

It’s particularly resonant to follow the role and power of a media empire in this moment of our history. What does it mean to never have enough when your role is to inform and educate? What does “winning” mean, and cost, in an attention economy? What are we all losing, so a few rich guys can post a few more points on their personal scoreboards?

Can we really sustain a few wealthy families using our civic fabric as their personal security blankets, instead of going to therapy?

Succession wants us to ask these questions, and to imagine the consequences of their answers.

Succession reassures you: it really IS nuts the world works like this

Inequality isn’t an abstract statistic.

Inequality is most people being a few bad months away from homelessness and destitution. It’s the majority of American workers living paycheck-to-paycheck, subject to the whims of their bosses. Inequality is medical bankruptcy for some, and elite recovery suites for others.

Far from lionizing the wealthy, Succession constantly places the mindset that creates and preserves inequality under the microscope. The show is full of wry humor, quietly laughing at its cast in every episode. At the same time, it does a devastating job at explaining just how dark the situation is. The humor leavens the horror.

With a tight four season trajectory, the show’s principals have created a show worth your time. There’s not a single wasted “filler” episode. The cast performs at the top of the game. The original score by Nicholas Britell manages to feel fresh yet permanent.

It’s well-crafted entertainment, yes, but it’s also a window into the parts of our civic reality they didn’t teach you in school. With corporate power challenging and often defeating that of our civic institutions, it’s important to have some personal context for exactly how this all works in practice.

It’s fiction, but there’s serious insight here. We just watched Elon Musk—son of a guy rich enough to own an emerald mine—set fire to tens of billions of dollars in part because he just desperately wants people to like him and think he’s cool. The real world absolutely works this way.

The show is worth your time.

It can be hard to discern between what’s real and what’s bullshit hype, especially as charlatans in one space pack up for greener grifting pastures.

But as Evan Prodromou notes:

A major difference between LLMs and cryptocurrencies is this:

For cryptocurrencies to be valuable to me, I need them to be valuable to you, too.

If you don’t believe in crypto, the value of my crypto goes down.

This isn’t the case for LLMs. I need enough people to be interested in LLMs that ChatGPT stays available, but other than that, your disinterest in it is only a minor nuisance.

In a market, I benefit from your refusal to use it. AI-enhanced me is competing with plain ol’ you.

This is the complexity of the moment with pattern synthesis engines (“AI”, LLMs, etc). Regardless of how our personal feelings interact with the space, it is already creating outcomes for people.

Those outcomes may not always be what we want. One reddit thread from a game artist finds that visual pattern synthesizers are ruining what made the job fun:

My Job is different now since Midjourney v5 came out last week. I am not an artist anymore, nor a 3D artist. Rn all I do is prompting, photoshopping and implementing good looking pictures. The reason I went to be a 3D artist in the first place is gone. I wanted to create form In 3D space, sculpt, create. With my own creativity. With my own hands.

This is classic “degraded labor,” as described by Braverman in Labor and Monopoly Capital:

Automation and industrialization—through atomizing tasks and encoding them into the behaviors of machinery, assembly lines and industrial processes—eliminate the process of craft from everyday labor. Instead of discovery, discernment and imagination, workers were merely executing a pre-defined process. This makes such laborers more interchangeable—you don’t need to pay for expertise, merely trainability and obedience.

So, we can feel how we want about pattern synthesizers, but they already create outcomes for those who deploy them.

Clearly the labor implications are massive. My hope is that for all the degradation of everyday work this may usher in, it also expands the scope and impact for individuals and small teams operating on their own imagination and initiative.

Contents

1. Internet evolution
   1. Interlude: the web, a shift within the shift
   2. Bandwidth and the accelerating pace of the internet revolution
2. What if we put that whole internet in your pocket?
3. Which brings us to Pattern Synthesis Engines
   1. Fewer errors, less waiting, less brittleness
   2. Persistent and pervasive
   3. Internet of frauds
   4. Digital guardians
   5. Learning and knowledge will be transformed
   6. Cultural and creative explosion
4. A complicated future

Say what we will about Bill Gates, the man created one of the most enduring and formidable organisms in the history of computing. By market cap, Microsoft is worth two trillion dollars almost 50 years after its founding in 1975.

Gates, who has seen his share of paradigm shifts, writes:

The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone.

Decades on, we’re still watching the microprocessor super cycle play out. Even as we’ve hit a wall in the raw speed of desktop computers, every year we push more boundaries in power efficiency, as in silicon optimized for mobile, and in parallelization, as in GPUs. The stuff we can do for realtime graphics and portable computing would have been flatly impossible a decade ago.

Every technological advancement in the subsequent paradigm shifts Gates describes depends on this one field.

The personal computer, meanwhile, has stabilized as a paradigm—they’ve all looked more or less the same the last ten years—but remains the bedrock for how people in every field of endeavor solve problems, communicate and create.

In short, Gates is arguing that AI is a permanent transformation of humanity’s relationship not just to technology, but to each other and our drives to mold the world around us.

Look, I’m not the guy who mistakes wealth for trustworthiness. I’m here for any critique of Bill Gates and his initiatives you may want to argue. But on the particular subject of paradigm shifts, he has the credibility of having navigated them well enough to amass significant power.

So as an exercise, let’s grant his premise for a moment. Let’s treat him as an expert witness on paradigm shifts. What would it mean if he was right that this is a fundamental new paradigm? What can we learn about the shape of AI’s path based on the analogies of previous epochs?

Internet evolution

The internet had its first cultural moment in the 90’s, but that took decades of preparation. It was a technology for extreme specialists in academia and the defense establishment.

And it just wasn’t that good for most people.

Most use of the internet required on-premises access to one of a small handful of high-bandwidth links, each of which requiring expensive infrastructure. Failing that, you could use a phone connection to reach it, but you were constrained to painfully narrow bandwidth.

For most of the 80’s, 9600 kilobits per second was the absolute fastest you could connect to any remote service. And once you did, it’s not like there was even that much there. Email, perhaps, a few file servers, usenet.

By 1991, speeds crept up to 14.4 kilobits per second. A moderate improvement, but a 14.4 modem took several minutes to download even the simplest of images, to say nothing of full multimedia. It just wasn’t feasible. You could do text comfortably, and everything else was a slog.

America Online, Compuserve and other online services were early midwives to the internet revolution, selling metered access over the telephone. Filling the content gap, they provided pre-web, service-specific news, culture, finance, and sports outlets, along with basic social features like chat and email.

Dealing with the narrow pipe of a 14.4 modem was a challenge, so they turned the meter off for certain tasks, like downloading graphics assets for a particular content area. This task could take as long as half an hour.

In short, the early experience of the “internet” was shit.

Despite this, these early internet services were magic. It was addictive. A complete revolution and compulsion. The possibility of new friends, new conversations, new ideas that would have been absolutely impossible to access in the pre-internet world. The potential far outstripped the limitations. People happily paid an inflation-adjusted $6 hourly for the fun and stimulation of this experience.

Interlude: the web, a shift within the shift

Upon this substrate of connections, communication and community, the web was born.

What was revolutionary about the web itself was its fundamental malleability. A simple language of tags and plaintext could be transformed, by the browser, into a fully realized hypermedia document. It could connect to other documents, and anyone with an internet connection and a browser could look at it.

Instead of brokering a deal with a service like America Online to show your content to a narrow slice of the internet, you could author an experience that anyone could access. The web changed the costs of reaching people through the internet.

So, yes, a business like Amazon.com, fully impossible without the internet and the web, could be built. But everyday people could learn the basics of HTML and represent themselves as well. It was an explosion of culture and expression like we’d never seen before.

And again: the technology, by today’s standards, was terrible. But we loved it.

Bandwidth and the accelerating pace of the internet revolution

Two factors shaped the bandwidth constraints of the early consumer internet. The telephone network itself has a theoretical maximum of audio information it can carry. It was designed for lo-fi voice conversations more than a century ago.

But even within that narrow headroom, modems left a lot on the table. As the market for them grew, technology miniaturized, error-correction mechanisms improved, and modems eked out more and more gains, quadrupling in speed between 1991 and 1998.

Meanwhile, high-volume infrastructure investments made it possible to offer unlimited access. Stay online all night if you wanted to.

As a consequence, the media capabilities of the internet began to expand. Lots of porno, of course, but also streaming audio and video of every persuasion. A medium of text chat and news posts was evolving into the full-featured organ of culture we know today.

Of course, we know what came next: AOL and its ilk all withered as their technology was itself disrupted by the growing influence of the web and the incredible bandwidth of new broadband technologies.

Today we do things that would be absolutely impossible at the beginning of the consumer internet: 4K video streaming, realtime multiplayer gaming, downloads of multi-gigabyte software packages. While the underlying, protocol-level pipes of the internet remain a work in progress, the consumer experience has matured.

But maturation is different from stagnation. Few of us can imagine a world without the internet. It remains indispensable.

What if we put that whole internet in your pocket?

I’ve said plenty about mobile already, but let’s explore the subjective experience of its evolution.

Back in 2007, I tagged along with a videographer friend to a shoot at a rap producer’s studio. Between shots, I compared notes with this producer on how we were each enjoying our iPhones. He thought it was cool, but it was also just a toy to him.

It hadn’t earned his respect alongside all his racks of formidable tech.

It was a reasonable position. The iPhone’s user experience was revolutionary in its clarity compared to the crummy phone software of the day. Its 2007 introduction is a time capsule of just how unsatisfied the average person was with the phone they grudgingly carried everywhere.

Yet, objectively, the 2007 iPhone was the worst version they ever sold. Like the early internet, it was shit: no App Store, no enterprise device management features so you could use it at work, tortoise-slow cellular data, English-only UI.

It didn’t even have GPS.

But look what happened next:

• 2008: App Store, 3G cellular data, GPS, support for Microsoft Exchange email, basic enterprise configuration
• 2010: High-density display, front-facing camera for video calling, no more carrier exclusivity
• 2011: 1080p video recording, no more paying per-message for texting, WiFi hotspot, broad international language support
• 2012: LTE cellular data

In just five years, the iPhone went from a neat curiosity with serious potential to an indispensable tool with formidable capabilities. Navigation, multimedia, gaming, high-bandwidth video calls on cellular—it could do it all. Entire categories of gadget, from the camcorder to the GPS device, were subsumed into a single purchase.

None of this was magic. It was good ROI. While Apple enjoyed a brief lead, other mobile device manufacturers wanted a slice of the market as well. Consumers badly wanted the internet in their pocket. Demand incentivized investment, iteration and refinement of these technologies.

Which brings us to Pattern Synthesis Engines

I think AI is a misnomer, and causes distraction by anthropomorphizing this new technology. I prefer Pattern Synthesis Engine.

Right now, the PSE is a toy. A strange curiosity.

It has struggled to render fingers and teeth, when creating images. In chat, it frequently bluffs and bullshits. The interfaces we have for accessing it are crude and brittle—ChatGPT in particular is an exercise in saintly patience as its popularity has grown, and it’s almost unusably slow during business hours.

Still, I have already found ChatGPT to be transformational in the workflow of writing code. I’m building a replacement for this website’s CMS right now, and adapting an excellent but substantial open source codebase as a starting point.

The thing is, I’m not particularly experienced with JavaScript. In particular, I’m often baffled by its syntax, especially because there are often multiple ways to express the same concept, they’re all valid, and different sources prescribe different versions.

Now, when I get tripped up by this, I can solve the problem by dumping multiple, entire functions in a ChatGPT session.

I swear to god, last night the machine instantly solved a problem that had me stumped for almost half an hour. I dumped multiple entire functions into the chat and asked it what I was doing wrong.

It correctly diagnosed the issue—I was wrapping an argument in some braces when I didn’t need to—and I was back on my way.

Remember: shitty technology that’s still good enough to capture our cultural imagination needn’t stay shitty forever.

So if we extrapolate from these previous shifts, we can imagine a trajectory for PSEs that addresses many of the current issues.

If that’s the case, the consequences and opportunities could indeed rank alongside the microprocessor, personal computer, the internet, and mobile.

Fewer errors, less waiting, less brittleness

The error correction is going to get better. Indeed, the Midjourney folks are thrilled to report they can make non-terrifying hands now.

As PSEs evolve, we can imagine their speeds improving while the ways we interact with them grow more robust. The first iPhone was sluggish and the early experience of the consumer internet could be wrecked by something as simple as picking up the phone. Now these issues are long in the past.

Persistent and pervasive

Once, we “went on the internet.” There was a ritual to this, especially in the earliest days. Negotiating clearance for the phone line with various parties in the household, then initiating the phone connection, with its various squeals and chirps.

In the broadband age, the network connection became persistent. If the cable modem was plugged in, the connection was there, steady. Today, thanks to mobile, we are for better or worse stuck online, tethered at all times to a network connection. Now we must make an effort to go offline, unplug.

Today PSEs require significant hardware and computation, and exist centralized in the hands of a few players. As investment makes running them more efficient, and hardware develops optimized for their execution, we can imagine a day in the future where specialized PSEs are embedded more closely in our day-to-day activities. Indeed, for many business applications, this capability will be table stakes for adoption. At a minimum, large companies will demand their own, isolated PSEs to ensure they aren’t training a competitor’s data set.

Internet of frauds

With rapidly improving speech synthesis, plus the ability to construct plausible English language content based on a prompt, we are already seeing fraudsters using fake voices to scam innocent people.

Perhaps most daunting about this is the prospect that, in time, the entire operation could become automated. Set the machine loose to draw associational links between people, filter out the ones where you can generate a voice, and just keep dialing, pocketing the money where you can.

PSEs bring the issue of automated spam into whole new domains where we have no existing defenses.

Digital guardians

It has always struck me that the mind is an information system, but that unlike all our other information systems, we place it into networks largely unprotected. The junky router you get with your broadband plan has more basic protections against hostile actors than the typical user of Facebook or Twitter.

PSEs could change this, detecting hateful and fraudulent content with greater speed and efficiency than any human alone.

Gates describes this notion as the “personal agent,” and its science fiction and cyberpunk roots are clear enough:

You’ll be able to use natural language to have this agent help you with scheduling, communications, and e-commerce, and it will work across all your devices. Because of the cost of training the models and running the computations, creating a personal agent is not feasible yet, but thanks to the recent advances in AI, it is now a realistic goal. Some issues will need to be worked out: For example, can an insurance company ask your agent things about you without your permission? If so, how many people will choose not to use it?

The rise of fraud will require tools to can counter with the same economics of automation. Without that, we’ll be drowning in bullshit by 2025. These guardians will be essential in an information war that has just gone nuclear.

Learning and knowledge will be transformed

I’m a first-generation knowledge worker. I grew up working class. If my mom missed work for the day, the money didn’t come in.

My economic circumstances were transformed by microprocessors, personal computers, and the internet. I’ve earned passive income by selling software, and I’ve enjoyed the economic leverage of someone who can create those outcomes for venture funded startups.

What made that transformation possible was how much I could learn, just for the fun of learning. I spent my childhood exploring ever corner of the internet I could find, fiddling with every piece of software that showed up in my path.

It was a completely informal, self-directed education in computing and technology that would never have been possible for someone of my socioeconomic status in earlier generations.

Gates takes a largely technocratic view of PSEs and their impact on education:

There are many ways that AIs can assist teachers and administrators, including assessing a student’s understanding of a subject and giving advice on career planning.

But to me, the larger opportunity here is in allowing people who don’t learn well in the traditional models of education to nonetheless pursue a self-directed course of study, grounded in whatever it is they need to know to solve problems and sate their curiosity.

Forget how this is going to impact schools. Just imagine how it will disrupt them. It goes beyond “cheating” at essays. Virtuous PSEs will create all new paths for finding your way in a complex world.

The challenge, of course, is that anything that can be done virtuously can also be turned toward darkness. The same tech that can educate can also indoctrinate.

Cultural and creative explosion

There is now a messy yet predictable history between the technologists building PSEs and the creatives whose work they slurped up, non-consensually, to train the datasets. I think the artists should be compensated, but we’ll see what happens.

Nevertheless, I can imagine long term implications for how we create culture. Imagine writing Star Trek fanfic and having it come to life as a full-motion video episode. You’ve got hundreds of hours of training data, multiple seasons of multiple series. How sets and starships look, how characters talk.

It’s just as complicated a notion as any other we’ve covered here. Anything fans can do, studios can match, and suddenly we’re exploiting the likenesses of actors, writers, scenic artists, composers and more in a completely new domain of intellectual property.

This one is far beyond the current tech, and yet seems inevitable from the perspective of what the tools now.

Still: what will it mean when you can write a TV episode make a computer spit it out for you? What about games or websites?

We’re going to find out sometime. It might not even be that far away.

A complicated future

Merely on the downsides, it’s easy to grant Gates a premise placing automated pattern synthesis on the same level as the internet or personal computing. These technologies created permanent social shifts, and PSEs could do much the same.

Nevertheless, there’s also serious potential to amplify human impact.

I’m allergic to hype. There’s going to be a lot of bullshitters flooding this space and you have every reason to treat this emerging field with a critical eye. Especially those hyping its danger, as a feint to attract defense money.

Nevertheless, there is power here. They’re going to build this future one or another. Stay informed, stay curious.

There’s something underway.

Contents

1. Why disruption is inevitable
2. “Artificial” “intelligence”
3. Remote work
4. The next iteration of the web
   1. Excess computing capacity
5. Reality, but with shit in front of your eyes
6. Lasting discipline, sober betting, and a green revolution

I quit my job a few weeks ago.

Working with the crew at Glitch.com was a highlight of my career. Making technology and digital expression more accessible is one of my core drives. Nobody does it better than they do.

But as the cycle reached its conclusion, like so many startups, Glitch found itself acquired by a larger company. Bureaucracies make me itchy, so I left, eager to ply my trade with startups once again.

Then the Silicon Valley Bank crisis hit.

It was a scary moment to have thrown my lot in with startups. Of course, we know now that the Feds guaranteed the deposits. Once I mopped the sweat from my brow, I got to wondering: what comes next?

The steam of the last cycle is largely exhausted. Mobile is a mature platform, full of incumbents. The impact of cloud computing and open source on engineering productivity is now well understood, priced into our assumptions. The interest rate environment is making venture capital less of a free-for-all.

Meanwhile, innovators have either matured into more conservative companies, or been acquired by them.

So a new cycle begins.

Why disruption is inevitable

Large companies have one job: maintaining a status quo. This isn’t necessarily great for anyone except the companies’ shareholders, and as Research in Motion found when Blackberry died, even this has limits. Consumers chafe against the slowing improvement and blurring focus of products. Reality keeps on moving while big companies insist their workers make little slideshows and read them to each other, reporting on this and that OKR or KPI.

And suddenly: the product sucks ass.

Google is dealing with this today. Search results are a fetid stew of garbage. They’re choked with ads, many of which are predatory. They lead to pages that aren’t very useful.

Google, once beloved as the best way to access the web, has grown complacent.

Facebook, meanwhile, blew tens of billions—hundreds of times the R&D costs of the first iPhone—to create a VR paradigm shift that never materialized. Their user populations are aging, which poses a challenge for a company that has to sell ads and maintain cultural relevance. They can’t quite catch up to TikTok’s mojo, so they’re hoping that lawmakers will kill their competitor for them.

Twitter has been taken private, stripped down, converted into the plaything of the wealthy.

Microsoft now owns GitHub and LinkedIn.

Netflix, once adored for its innovative approach to distribution and original content, has matured into a shovelware play that cancels most of its shows after a season or two.

Apple now makes so many billions merely from extracting rents on its App Store software distribution infrastructure, that could be a business unto itself.

A swirling, dynamic system full of energy has congealed into a handful of players maintaining their turf.

The thing is, there’s energy locked in this turgid ball of mud. People eager for things that work better, faster, cheaper. Someone will figure out how to reach them.

Then they will take away incumbents’ money, their cultural relevance, and ultimately: their power. Google and Facebook, in particular, are locked in a race to become the next Yahoo: decaying, irrelevant, coasting on the inertia of a dwindling user base, no longer even dreaming of bygone power.

They might both win.

“Artificial” “intelligence”

Look, AI is going to be one of the next big things.

You can feel how you want to feel about it. A lot of how this technology has been approached—non-consensual slurping of artists’ protected works, training on internet hate speech—is weird and gross! The label is inaccurate!

This is not intelligence.

Still: whatever it is, is going to create some impact and long-term change.

I’m more comfortable calling “AI” a “pattern synthesis engine” (PSE). You tell it the pattern you’re looking for, and then it disgorges something plausible synthesized from its vast set of training patterns.

The pattern may have a passing resemblance to what you’re looking for.

But even a lossy, incomplete, or inaccurate pattern can have immediate value. It can be a starting point that is cheaper and faster to arrive at than something built manually.

This is of particular interest to me as someone who struggles with motivation around tedious tasks. Having automation to kickstart the process and give me something to chip away at is compelling.

The dawn of the PSE revolution is text and image generation. But patterns rule everything around us. Patterns define software, communications, design, architecture, civil engineering, and more. Automation that accelerates the creation of patterns has broad impact.

Indeed, tools like ChatGPT are already disruptive to incumbent technologies like Google. I have found it faster and more effective to answer questions around software development tasks—from which tools and frameworks are viable for a task, to code-level suggestions on solving problems—through ChatGPT, instead of hitting a search engine like Google. Microsoft—who you should never, ever sleep on—is seizing the moment, capturing more headlines for search than they have in decades.

Still, I don’t valorize disruption for its own sake. This is going to make a mess. Pattern synthesis makes it cheaper than ever to generate and distribute bullshit, and that’s dangerous in the long term. It won’t stop at text and images. Audio, voices and video are all patterns subject to synthesis. It’s only a matter of time and technological progression before PSE’s can manage their complexity cheaply.

On the other hand, many tedious, manual tasks are going to fall before this technology. Individuals will find their leverage multiplied, and small teams will be able to get more done.

The question, as always for labor savings, will be who gets to keep the extra cream.

Remote work

Most CEOs with a return-to-office fetish are acting the dinosaur and they’re going to lose.

Eventually.

Feeling power through asses-in-seats is a ritualistic anachronism from a time where telecommunications was expensive and limited to the bandwidth of an analog telephone.

Today, bandwidth is orders of magnitude greater, offering rich communication options without precedent. Today, the office is vulnerability.

In a world more and more subject to climate change, a geographically distributed workforce is a hedge. Look at the wildass weather hitting California in just the last few months. You’ve got cyclones, flooding, extreme winds, power outages. And that’s without getting into the seasonal fires and air pollution of recent years.

One of the great lessons of Covid was that a knowledge workforce could maintain and even exceed their typical productivity even in a distributed context. Remote work claws back time and energy that goes to commuting, giving us more energy to use on the goals and relationships that matter most.

Still, even if remote work is the future, much of it has yet to arrive. The tools for collaboration and communication in a distributed context remain high-friction and even alienating. Being in endless calls is exhausting. There’s limited room for spontaneity.

The success stories of the new cycle will be companies nimble enough to recruit teams from outside their immediate metro area, clever enough to invent new processes to support them, and imaginative enough to productize this learning into scalable, broadly applicable solutions that change the shape of work.

The future does not belong to Slack or Zoom.

The next iteration of the web

One of the most important things Anil Dash ever told me came on a walk after we discussed a role at Glitch.

He explained that he was sympathetic to the passion of the web3 crowd, even if he thought it was misplaced. After all, who could blame them for wanting a version of the web they could shape? Who could begrudge yearning for a future that was malleable and open, instead of defined by a handful of incumbents?

I’ve thought about it endlessly ever since.

While I argue that web3 and its adjacent crypto craze was a money grab bubble inflated by venture capital, I also agree with Anil: there has to be something better than two or three major websites defining the very substrate of our communication and culture.

In the wake of Twitter’s capture by plutocrats, we’ve seen a possible answer to this hunger. Mastodon, and the larger ecosystem of the Fediverse, has seen a massive injection of energy and participation.

What’s exciting here is a marriage of the magic of Web 1.0—weird and independent—with the social power that came to define Web 2.0. A web built on the Fediverse allows every stakeholder so much more visibility and authorship over the mechanics of communication and community compared to anything that was possible under Facebook, Twitter or TikTok.

This is much the same decentralization and self-determination the web3 crowd prayed for, but it’s built on mundane technology with reasonable operating costs.

Excess computing capacity

Meanwhile, the cost of computing capacity is changing. While Amazon dominated the last cycle with their cloud offerings, the next one is anybody’s game.

Part of this is the emergence of “edge computing,” “serverless functions,” and the blurring definition of a static vs. dynamic site. These are all artifacts of a simple economic reality: every internet infrastructure company has excess capacity that they can’t guarantee on the same open-ended basis as a full virtual machine, but which they’d love to sell you in small, time boxed slices.

Capital abhors an un-leveraged resource.

As this computing paradigm for the web becomes more commonly adopted, the most popular architectures of web technologies will evolve accordingly, opening the door to new names and success stories. As Node.js transformed web development by making it accessible to anyone who could write JavaScript, look for these technologies to make resilient, scalable web infrastructure more broadly accessible, even to non-specialists.

Reality, but with shit in front of your eyes

Everything is about screens because the eyes are the highest bandwidth link we have for the brain without surgical intervention, and even that remains mostly science fiction.

The iPhone was transformational in part because of its comparatively enormous display for the day, and in the years since phones have competed by developing ever-larger, more dense displays.

Part of the logic of virtual reality, which obscures your vision, and augmented reality, which overlays it, is to take this evolution to its maximalist conclusion, completely saturating the eye with as much information as possible.

Whether this has a long-term sustainable consumer application remains to be proven. There are serious headwinds: the energy cost of high-density, high-frequency displays, and the consequent weight of batteries needed to maintain the experience. There’s the overall bulk of the devices, and the dogged friction of miniaturizing so many components to reach fashionable sizes.

But they’re sure going to try. As noted, Facebook has already blown tens of billions, plus a rebrand, trying to make VR happen. Valve did their best to create both the software delivery and hardware platform needed to spur a VR revolution, with limited success. Sony and Samsung have each dabbled.

This year, rumors suggest Apple will enter the fray with their own take on the headset. Precedent allows that they might find traction. With the iPad, Apple entered the stagnant tablet market nearly a decade late with an offering people actually loved.

While tablets didn’t transform computing altogether, Apple made a good business of them and for many, they’ve become indispensable tools.

If indeed VR/AR becomes a viable paradigm for consumer computing, that will kick off a new wave of opportunity. It has implications for navigation, communication, entertainment, specialized labor, and countless other spaces. The social implications will be broad: people were publicly hostile to users of Google Glass—the last pseudo-consumer attempt in this space. Any successful entrant will need to navigate the challenge of making these devices acceptable, and even fashionable.

There are also demonic labor implications for any successful platform in this space: yet more workplace surveillance for any field that broadly adopts the technology. Imagine the boss monitoring your every glance. Yugh.

Lasting discipline, sober betting, and a green revolution

In a zero interest rate environment, loads of people could try their hands at technology speculation. If interest rates hold at their existing altitude, this flavor of speculation is going to lose its appeal.

The talented investor with a clear eye toward leverage on future trends will have successes. But I think the party where you bet millions of dollars on ugly monkey pictures is over for awhile.

But there are no guarantees. Many seem to be almost pining for a recession—for a reset on many things, from labor power to interest rates. We’ve had a good run since 2008. It might just happen.

Yet the stakes of our decisions are bigger than casino games. The project of humanity needs more than gambling. We need investment to forestall the worst effects of climate change.

Aggressive investment in energy infrastructure, from the mundane of household heat pumps, to the vast chess game of green hydrogen, will take up a lot of funding and mental energy going forward. Managing energy, making its use efficient, maintaining the transition off of fossil fuels—all of this has information technology implications. Ten years from now, houses, offices and factories will be smarter than ever, as a fabric of monitoring devices, energy sources and HVAC machines all need to act in concert.

The old cycle is about dead. But the next one is just getting started. I hope the brightest minds of my generation get more lasting and inspiring work to do than selling ads. Many of the biggest winners of the last cycle have fortunes to plow into what comes next, and fond memories of the challenges in re-shaping an industry. There’s another round to play.

We have so much still to accomplish.

Contents

1. Frozen mobility
2. …but am I also the oppressor?
3. A thesis for a better world

A speculative fiction author has four jobs:

1. Imagination. Find us a fresh lens. Show us a place we’ve never been, or a perspective on the mundane that we’ve never seen. Inject tangible energy into our imagination systems.
2. Timely insight. Help us understand our world in this moment. Help us see the systems that animate social reality, and help us use that vision for our own purposes.
3. Clarity. Clear the mud from our collective windshields. Use all this fresh perspective to let us look with renewed clarity on all that’s wrong, all that’s beautiful, and all we need to do to enact change.
4. Challenge. Push the reader, and in the pushing, let fresh perspective permeate more than just the most convenient, already-exposed surfaces.

In combination, these gifts offer the reader extra fuel to continue in a challenging world. Speculative fiction is an activist project that both aligns the reader and empowers them with new conceptual tools. A good yarn in this genre gives you better metaphors for understanding your values, and even making the case for them.

In The Terraformers, Annalee Newitz, using they/them pronouns, spins a tale tens of thousands of years into the deep future. They explore the long-term project of terraforming a planet, and the teams who spend lifetimes making it comfortable, habitable and marketable for future residents.

It’s an ambitious undertaking. The range of characters and timespans brings to mind Foundation, Asimov’s epic of collapsing empire. Unlike Foundation, I was able to finish this one: its representation of gender and sexuality was refreshingly complete.

In a previous Newitz work, Autonomous, the author impressed me with the casual inclusion of computing mechanics that moved the plot forward in ways that were coherent, plausible and folded neatly into the story. Similarly, while Terraformers isn’t cyberpunk per se—not fetishizing endlessly upon the details of brain-computer interfaces—it is such a technically competent work. I completely believe in the elaborate, far-future computing systems described here. While that’s not central to my enjoyment of a book, I like when the details enhance the magic instead of break it.

But what makes The Terraformers stand out, why you have to read it, is much more human than technical. This is a book that wants to talk about social stratification.

Frozen mobility

The geometry of social mobility is bound up in the shape of inequality, and today we have both at extremes.

Social mobility is nearly frozen. If you’re born poor, you’re likely to stay poor.

Meanwhile, wealth inequality ratchets tighter and tighter each year, especially since the pandemic. The middle class erodes steadily, the wealthy control more and more wealth, and the poor grow both in number and in their precarity. To say nothing about desperation in the developing world.

These are not abstract truths. The statistics are more than just numbers in a spreadsheet. They reflect human experiences and traumas. They reflect a disparity of power, and they describe an everyday existence where wealth and corporate bureaucracies strip workers of agency and ignore their insights.

Newitz brings these dry facts to vivid life, depicting the frustration and casual humiliation of living each day under the control of someone who sees you as a tool to implement their edicts. I don’t want to spoil anything, but I had a full-body limbic response to some of the email subject lines that issue from Ronnie, a central corporate figure in the story.

Newitz brings an empathetic clarity to the experience of being employed. Anyone who has slotted into a corporate system whose executive team was far out of view will find themselves witnessed by this book.

My favorite bits involve the places where the powerful are just pathologically incurious, and how this creates long term consequences for their influence. Reads so true to life for me.

There’s also great stuff in here about the raw, utilitarian mechanics of colonialism broadly: far-off people craving yet more wealth, making social and infrastructure decisions for people they’ll never meet, who have no recourse.

…but am I also the oppressor?

Good speculative fiction challenges as much as it validates.

Newitz also asks through this work whether we are complicit ourselves in the everyday oppression of innocent lives yearning to be free. Not since James Herriot have I seen so many animals depicted with love, compassion and centrality to the story.

Unlike Herriot, Newitz is unbound by depression-era Britain’s technological limitations. Cybernetically enhanced, animals in The Terraformers text their companions, sharing their points of view and preferences, allowing them to be narrative prime-movers in their own right.

There’s delight in this: the thoughtful body language of cats figures prominently at points, and a wide cast of animal personalities appears throughout.

But the book is also pointed in its examination of how we, the readers, may relate to animals. Are we using our assessment of their intelligence as a convenient justification for their subjugation? Are we losing out on valuable insight to hard problems because we are, ourselves, incurious to the perspectives of our animal companions?

Perhaps most uncomfortably: how would such a justification eventually leak over into the control and oppression of other humans?

Implicit in The Terraformers—especially in its exploration of bioengineering and corporate slavery—is the argument that corporations would treat us all like livestock if they thought they could get away with it. Maybe we should start asking whether livestock is even a valid category before it envelopes us entirely.

A thesis for a better world

But Newitz is not here to depress us.

Shot through the entire book is a bone-deep commitment to something hopeful and better than what we know today. Throughout the tale, we witness resistance on a spectrum of strategic forms: through journalism, through non-participation, through activist demonstration, through violence and non-violence, and of course, resistance through mutual aid. Despite this, there’s also sobriety about the frustrating pace of change, and just how messy consensus and governance can be.

And a good case for why we have to work at it all anyway.

Moreover, as a real-life technology cycle rife with exploitation and neocolonial intent comes to a close, it’s timely that The Terraformers explores how corporate-born technology can be bent toward self-determination and shared civic good. All around us are the tools to create completely novel solutions to big problems. We just need to take the time to dream them into existence, and work at the resources needed to sustain them.

It’s not enough to shine a light on the problems. A good novel in this space lights up the levers. The Terraformers strikes a great balance between sober assessment of what hurts, and an optimistic vision for what we can do to change it.

Go grab this book.

Contents

1. Cheap scale
2. The bargain of Venture Capital
3. Going whackadoodle
   1. There’s a situation here
   2. This stuff was not unique to GitHub
4. Crypto craze
5. What comes next

After leaving my job to build apps in 2009, life took on some hard math. I only had so much money saved up. While I was making around $1,000 monthly from the App Store, the gap between my income and expenses was leaving me in the red.

So my maximum budget for a meal was $3. If I could get under that number, I tried.

This meant clever use of bulk buying at Costco and lots of meal prep. Going out for meals was an occasional indulgence, but the three dollar figure remained a constraint. Spending more than that meant having to bring home enough leftovers to break even.

I’d figured I could make some extra money working part time while I got the details of my app business figured out. To this point in my life, part-time work had been easy to come by. I took it for granted, like water from the tap. It would be there if I needed it.

But the financial crisis meant stiff competition for any work. I’d moved to Bend, Oregon for its low cost of living and beautiful scenery. Unfortunately, no one was hiring, and as the new kid in town, I had no local network to lean on. When a Kohl’s opened up, it made the evening news: newly laid-off job applicants wrapped around the block hoping for a chance at even 15 hours of work per week.

I wasn’t just competing with part-time job seekers. I was competing with people who needed any work they could get to keep a roof over their families’ heads.

It was a scary, precarious time. Unemployment peaked around 10% nationwide, even greater than in the dot-com bust, with millions of workers unable to find jobs. I came within weeks of losing my housing.

This desperate climate catalyzed the fuel for the last technology cycle. Central bankers around the world slashed interest rates nearly to zero. Without traditional means generating returns on their money, pensions, institutional investors and the wealthy sought new vehicles for growth.

The Venture Capitalist had just what they needed. On behalf of these limited partners, a VC would secure a packet of investments in technology firms, one or more of which might just burst into a multi-billion dollar valuation.

They called these “unicorns.”

Cheap scale

Software solves a problem through automation.

When you build a spreadsheet to solve your reporting needs, you’ve just made yourself some software. It’s got decent leverage: instead of manually filling out a chart, some formulas summarize your data and nicely format it, saving you a few hours a week.

You make a trade for this leverage. Making the report manually maybe costs you an hour a week. Building automation into the spreadsheet might take you much longer: a couple of afternoons. But within a few months, the leverage has paid for itself in time savings. Within a year, you’re ahead.

This simple example captures the economic value of software, and its potential scales well beyond an individual’s problem at the office. Whatever costs you sink in creating software, you get many multiples back in whatever work it accomplishes.

You can make office productivity tasks more efficient, you can build social interaction that promotes a particular style of engagement, you can deliver entertainment through games or other media, and you can even make cars show up in particular places where people want them.

Any task that software can do once, it can again as many more times as you want. On paper, this happens at near-zero marginal cost.

In practice, what this means is complicated. As Twitter’s Fail Whale demonstrated, just because scale is cheap doesn’t make it free. Architecting and re-architecting software systems to be resilient and responsive requires specialist knowledge and significant investment as your customer base transitions from one order of magnitude to the next.

Copying code costs almost nothing. Running code on the web, and keeping it running, is more expensive. It requires infrastructure to execute the code, and it requires human labor to troubleshoot and maintain it in flight.

The bargain of Venture Capital

This is where the Venture Capitalist comes in.

Writing code is expensive: it needs a full-time team of specialist engineers who know their way around the required technologies. The engineers also need to know what to build, so other specialists, like designers and strategists, are needed to document and refine the goals and end-product.

The code continues to be expensive once it’s written. Again, information is physical: it lives somewhere. The data that software handles has a physical location in a database and has to be moved to and from the user, who may be quite far away from the server they’re interacting with.

Thus, more specialist labor: engineers who can design technical infrastructure that gracefully avoids traffic jams as a web-based service becomes popular. Of course, there are more specialists after that. People to tell the story of the software, people to sell it, people to interact with the customers and users who are struggling to use it.

Code needs humans. It can’t be born without them, it needs them to grow, and it can’t stay running forever in their absence. Automation is powerful, but it’s never perfect, and it exists in a human context.

Humans are expensive. We need shelter, food, rest, recreation, partnership, indulgences, adventure… The list of human needs is vast, and like it or not, we meet those needs with time and money.

So the Venture Capitalist buys a chunk of a growing software firm, injecting it with money in trade. The money can be used more or less freely, but much of it goes into paying for humans.

Despite plunging tens or hundreds of millions of dollars into a firm, if the VC does their job right, they come out ahead. Software that can solve problems effectively, that captures a customer base that will pay for those problems to be solved, can produce billions of dollars in value. So long as the lifetime value of a customer is meaningfully greater than what it costs to earn them, you’ve got a powerful business.

So spending a few million to support an infant firm can turn into orders of magnitude more money down the road.

Not every startup achieves this success. These are outlier results. The majority will fail to achieve the scale and value needed for a win, in terms of venture expectations. But when the outliers are that lucrative, the overall model can work.

So long as you pick an outlier.

Going whackadoodle

GitHub was unique. Founded in 2008, GitHub was profitable almost immediately, without needing any venture funding. They are perhaps the ultimate modern success story in software tools:

GitHub simultaneously defined and seized the central turf of developer collaboration.

Their strategy was devastatingly effective. Git was an emerging technology for version control, which every software project large and small needs. Version control maintains a history of project progress—who changed what, on which date, and why—while allowing developers travel back in time to a previous state in the project.

Historically, developers needed an internet connection to interact with the history of a project, so they could connect with a server that stored all of the project data. Git’s great innovation was to decentralize this process, letting developers work in isolation and and integrate their changes with a server at their leisure.

Originally developed to serve the needs of the Linux kernel project, one of the largest collaborations in open source history, git had both enormous power and daunting complexity. Still, git would be the future not just of open source, but of software development altogether.

Seeing this, GitHub’s founders built a service that provided:

• A central server to host git-based projects, called repositories
• Convenient user interfaces to browse, change and duplicate repository content
• Collaboration tools to discuss, improve and ship code with both teammates and strangers

On the strength of this developer experience, GitHub fast became the most popular way to build software, no matter the size of your team or your goals.

Its power and value earned it leverage. Four years into its life, it raised money on wildly favorable terms. It was a safe bet: enormous traction, plenty of users, massive goodwill, and a whole enterprise market waiting to be conquered.

Then its cultural debts caught up with it.

There’s a situation here

GitHub built a replica of the Oval Office. They also built a replica of the White House Situation Room. Then there was the open bar, offering self-serve alcohol at any hour of the day. The rest of the space was of the typically fashionable vibes you’d expect of a startup, but these indulgences caused onlookers to raise their eyebrows.

Many clowned GitHub for the White House stuff—myself included—but here is a place where I will now defend their intent. America is lots of things, and primarily an empire. The symbols of an imperialist state have a lot of complicated (that is, bloody) history and it’s a peculiar sort of hubris to adopt them whole-cloth.

But America, in its most aspirational of modes, sees itself as a fabric for creating prosperity and shared growth. Having spent a couple years there, I really think that’s where GitHub was coming from in its office cosplay decisions.

They wanted to be a fertile landscape for shared prosperity, and picked up the most garish possible symbols of that aspiration. It’s not how I would have come at it, but I get it how they landed there. It’s impossible to imagine the last chapter of software without GitHub as a productive substrate for all the necessary collaboration.

This is a tidy microcosm for the cycle. Incredible ambition and optimism, tempered by social challenges few founding teams were equipped to address. An industry whose reach often exceeded its grasp.

GitHub’s blindspots, coupled with its incredible early power, would conspire to make it both a distressed asset and a notorious place to work, claiming one of its founders in the process.

The mess in its workplace culture made it challenging for the company to recruit both the experienced leadership and specialist labor needed to get it to the next phase of its evolution.

GitHub did the work to patch itself up, recruiting the team it needed to get back to shipping and evolve into a mature company. With the bleeding stopped and stability restored, Microsoft purchased the company for—you guessed it—billions of dollars.

This stuff was not unique to GitHub

Zenefits had people fucking in stairwells, Uber spent $25 million on a Vegas bacchanal, and WeWork… they did a whole a docudrama about WeWork.

With nowhere else for money to go, successful founders had a lot of leverage to spend as they pleased. VCs needed to compete for the best deals—not everything would be a unicorn, after all—and so the “founder-friendliness” of a firm was a central consideration of investor reputation. No founder wanted the money guys fiddling around and vetoing their vision and VCs feared losing out on the best deals.

So investors kept a light touch on the day-to-day management of firms, so long as they were pursuing a strategy that kept hitting their growth metrics.

Meanwhile, you’ll notice I keep emphasizing specialist labor. Some problems in technology are particularly challenging, with a limited number of practitioners able to make meaningful traction on them. Thus, startups had to work hard to recruit their workforce. Founders would argue here, with a certain degree of legitimacy, that differentiating their experience of work through generous perks was an essential lever in winning the competition for limited talent.

Thus was the frenzy for hyper-growth that continued for more than a decade. Money pumped into the system, searching for its unicorns. Founders with more technical acumen than management experience tried to recruit the teams they needed. Investors kept the show running, hoping to grow money for their limited partners.

But this was mere prelude to the wackiest money of all.

Crypto craze

The way venture historically worked:

• Invest in a young company
• Support it through relationships and more cash, if its progress was promising
• Wait around for quite awhile for it to reach maturity
• Get paid upon a “liquidity event”—when the company goes public (lots of work) or is acquired (a bit less work)

Cryptocurrency, non-fungible tokens, and other so-called web3 projects were an irresistible optimization on the normal cycle. Going public required stability, clean accounting, a proven record of growth, and a favorable long-term outlook for a company’s overall niche. It required stringent regulatory compliance, as a company transformed from a privately-held asset into a tradable component of the public markets.

Keeping a financial system healthy takes work, after all, and bodies like the SEC exist to ensure obviously-toxic actors don’t shit in the pool.

Less arduous was an acquisition. Sometimes these were small, merely breaking even on an investor’s cash. Still, multi-billion dollar acquisitions like Figma and GitHub do happen. To integrate into a public company like Microsoft or Adobe, a startup needs to get its house in order lots of ways. Accounting, information security, sustainable growth—it takes time to get a company into a level of fitness that earns the best price.

Cryptocurrency investment promised to short-circuit all of this.

Investors could buy “tokens” as a vehicle for investing in a company. Later, when those tokens became openly tradable—through a mostly unregulated, highly-speculative market—investors could cash in when they decided the time was right.

Rather than waiting seven or ten years for a company to reach maturity, investors in crypto firms could see liquidity within half that time.

With cloud services full of incumbents, while mobile matured, it was hoped that cryptocurrencies and web3 would represent an all new paradigm, starting a new cycle of investment and innovation.

More than a decade since the 2008 crisis, central bankers had to pare interest rates back down to zero once again, this time in response to the Covid-19 pandemic. This created a fresh rush of money once again in search of growth, and cryptocurrency-adjacent companies were happy to lap it up.

What comes next

Of course, we know how this ends. Covid-era inflation soared, and central bankers cranked rates back up to heights not seen since before 2008.

Crypto came crashing back to earth, NFTs became a tax loss harvesting strategy, and now we muse about what else was a “zero interest rate phenomenon.” I’d be shocked if crypto was entirely dead, but for the moment, its speculative rush has been arrested.

In the next and final installment, I want to take stock of what we can learn from all this, and what comes next.

Contents

1. What is Open Source, anyway?
2. It’s in the cloud
3. Next

[Previously: Mobile]

Though it rescued me from insolvency, I grew to hate my job at Aurora Feint. Two months into the gig, Apple Sherlocked us, sending the company into a series of endless pivots that lasted four to six weeks each.

The engineers were crunching, working evenings and weekends, only to throw away their work and start again, seemingly based on how well one press release or another gained attention. Meanwhile, I was far from the action in an ill-defined product management role. While I knew the emerging mobile product space as well as anyone, I had limited vision into the various rituals of Silicon Valley.

What I really wanted was to build mobile software.

I interviewed for months, but with no pedigree and a strange path into technology through self-teaching, I didn’t get very far.

After a particularly dispiriting day at the office, I sniped the attention of Adam Goldstein and Steve Huffman, then founders of a YC-funded travel search startup called Hipmunk. I wrote a flattering blog post about their product, posted it to Hacker News, and within weeks had a new job: leading mobile product design and engineering for a seed-stage startup.

They never asked for a resume. They checked out my existing work on the App Store and made me an offer.

I was the third employee. The entire company of five could fit into a San Francisco apartment’s living room. And did, regularly, in its first few months.

Despite the small crew and modest quarters, thousands of people were buying airfare through Hipmunk already. Its great innovation was visualizing your flight options on a colorful Gantt chart, ranked by “Agony”—factors like layovers and overall flight time.

Two things gave this team enough leverage to do it: open source software and cloud computing. A credit card was all they needed to provision server capacity. An internet connection gave them access to all the software they needed to make the servers productive, laying the foundation for building Hipmunk’s unique approach to finding flights.

What is Open Source, anyway?

Open source has permanently changed the nature of human productivity.

Open source describes code you can use, according to some basic licensing terms, for free. Vast communities may contribute to open source projects, or they may be labors of love by one or a handful of coders working in their spare time.

In practice, open source code is encapsulated labor.

One of the fundamental economic levers of software in a globally-connected community is that it scales at zero marginal cost. Once the software exists, it’s nearly free duplicate it.

Thus, if you have software that solves a common problem—provides an operating system, handles web requests, manages data, runs code—you can generalize it, and then anyone can use it to solve that category of problem in their own project. This combination of open source projects, called a “stack,” is sometimes packaged together.

Such open source stacks made the last cycle possible. The Facebook empire could be founded in a dorm room because Zuckerberg had access to Linux (OS), Apache (web server), MySQL (database), and php (language/runtime) as a unified package that solved the problem of getting you productive on the web.

This represented tens of thousands of hours of labor that startups don’t have to recruit or pay for, or even wait for, dramatically accelerating their time to market.

The details of serving HTTP requests or handling database queries aren’t central to a startup’s business value—unless you’re making a platform play where your performance on these categories of tasks can be sold back to other firms. But for most, there’s no advantage to reinventing those wheels, they just have to work well. Instead, borrowing known-good implementations can quickly provide the foundations needed to build differentiated experiences.

In Facebook’s case, this was a minimalist social platform, for Hipmunk this was visual travel search, while Uber dispatched cars. There was no innovation to be found merely existing on the web. Instead, to survive you had to be on the web while also providing unique, differentiated value.

Open source is an informal commons of these known-good implementations, regularly refined and improved. Any party finding a project lacking can lobby for refinement, including proposing direct changes to the code itself.

I would argue there is societal impact from this that rivals other epochal moments in information technology, like the advent of writing or the discovery of algorithms. Being able to store, replicate and share generalized technical labor is a permanent reorganization of how millions of people work. While capitalists were among the first to seize on its power, over decades and centuries it may permeate much more than business.

The power of open source was constantly animating the last cycle. Node.js, a server-side runtime for JavaScript first released in 2009, took one of the most well-known languages in the world and made it viable for building both the user interface components of a web application and its backend server as well. Thus open source could not just encapsulate labor, but amplify it in motion as well. Suddenly, many more people knew a language that could be used to build complex web services. As a result, Node became among the most popular strategies hobbyists and startups alike.

Meanwhile, it wasn’t enough for code to be easy to build on. The code actually has to live somewhere. While open source was a crucial engine of the last cycle, cloud computing running this open source code was just as essential.

It’s in the cloud

Information is physical.

It needs somewhere to live. It needs to be transformed, using electricity and silicon. It needs to be transported, using pulses of light on glass fibers.

Software requires infrastructure.

Historically, this had meant shocking amounts of capital expenditures—thousands of dollars for server hardware—along with ongoing operating expenditures, as you leased space in larger facilities with great connectivity to host these servers.

Scaling was painful. More capacity meant buying more hardware, and installing it, which took time. But the internet is fast, and demand can spike much more quickly than you could provision these things.

Amazon, craving massive scale, had to solve all these problems at unique cost. They needed enough slack in their systems to maintain performance at peak moments, like holiday sales, back-to-school, and other seasonal surges. With the size of Amazon’s engineering division, meanwhile, this capacity needed to be factored into tidy, composable services with clear interfaces and rigorous documentation, allowing any team to easily integrate Amazon’s computing fleet into their plans and projects.

Capital abhors an under-leveraged asset, so they decided to sell metered access to these resources. Though Amazon was the first to use this approach, today they compete with Microsoft, Google, IBM and others for corporate cloud budgets all over the world.

With access to “the cloud” just a credit card away, it’s easy for small teams to get started on a web-based project. While scaling is commercially much easier than ever—you just pay more to your cloud vendor, then get more capacity—it can still be a complex technical lift.

The arcane art of distributing computing tasks between individual machines, while still producing correct, coherent output across large numbers of users, requires specialist labor and insight. The cloud isn’t magic. Teams have to be thoughtful to make the most of its power, and without careful planning, costs can grow devastating.

Still, this power is significant. The cloud can itself be programmed, with code that makes scaling decisions on the fly. Code can decide to increase or trim the pool of computing capacity without human intervention, adjusting the resources available to create maximum customer impact and system resiliency. Netflix is a notable specimen on this point, as one of the earliest large-scale adopters of Amazon’s cloud services. They created an infrastructure fabric that is designed to tolerate failure and self-heal. They even built a technology called “Chaos Monkey” to continually damage these systems. This forces their engineers to build with resilience in mind, knowing that at any moment, some infrastructure dependency might go offline.

(Chaos Monkey is, naturally, available as open source.)

For companies in infancy, like Hipmunk, the cloud opened the path to a fast-iterating business. For companies entering maturity, like Netflix, the cloud provided a platform for stability, resilience, global presence, and scale.

The world had never seen anything like it. In fact, there was early skepticism about this approach, before it came to dominate.

Today, the cloud is a fact of life. Few would even bother starting a new project without it. What was once transformational is now priced into our expectations of reality.

Next

Hipmunk would not become a galloping success. It sold to Concur in 2016, eventually scrapped for parts. Steve Huffman would return to lead Reddit, his first startup. Reddit sold to Condé Nast for peanuts, only to spin back out as an independent venture as, stubbornly, it just wouldn’t die.

Meanwhile, six months after my departure, the renamed OpenFeint was acquired for $100m by a Japanese mobile company in need of next-generation mobile technology. I didn’t get a dime of the action—I left before my cliff kicked in, and I couldn’t have afforded to exercise the options even if I they were open to me.

That’s the circle of life in venture capital. Not every bet is a winner, death is common, and sometimes acquisition is bitter.

But when the winners win, they can win big. After a couple of years, I left Hipmunk for a couple more gigs leading mobile teams. Under all the VC pressure and SF’s punishing cost of living, I burned out hard.

Somehow, barely able to get out of bed most mornings, I landed on a unicorn. In the next piece, we’ll explore how venture capital was the fuel of the last cycle, creating growth in a zero interest rate global economy by harnessing startups that, impossibly, created billions of dollars in value.

Contents

1. Animating forces
2. New platforms and a digital land rush
   1. The promise of a vast green field
   2. The gig economy and the automated supervisor
   3. The attention economy
   4. Mobile ossification
3. Next

I missed the introduction of the original iPad.

Instead of watching Steve Jobs unveil his bulky slab of aluminum and glass, I was picking my way through the streets of downtown Burlingame. I’d spent what was left of my available credit card balance on a ticket to SFO.

I was due for my first tech interview.

Before he created the chat juggernaut Discord, Jason Citron founded a mobile game startup. After middling success selling games themselves, Aurora Feint had pivoted to middleware: client- and server-side social technology to serve the growing ranks of independent, mobile game developers.

A few blocks’ walk from the Caltrain station, I’d found my destination: a plain office building hosting a bank and an insurance broker on the lower floors. Hidden away upstairs, thirty employees of Aurora Feint were toiling away, trying to capture turf in the unspooling mobile revolution.

The interview was a grueling, all-day affair running 10:30 to 4, including one of those friendly lunches where the engineers try to figure out if you’re full of shit.

It was 2010, and the inertia of the financial crisis was still on me. Weeks away from homelessness, I somehow closed the day strong enough to earn my first startup job.

As a result, I got to watch the last technology cycle from its earliest phases.

It transformed my life. I wasn’t the only one.

Animating forces

The last cycle was activated by convergent forces:

1. The mobile revolution: An all new set of platforms for communication, play and productivity with no incumbents. A green field for development and empire-building
2. The rise of cloud computing: In the past, computing resources were expensive and provisioning was high-friction. By 2010, cloud computing offered low-risk, highly-scalable capacity for teams of any size.
3. The power of open source software: Open source transformed the economics of building a software business. A vast, maturing bazaar of software resources, free to use, gave founding teams a head-start on the most common tasks. This amplified their power and allowed small seed investments to produce outsized results.
4. An economy on life support: In the wake of the 2008 financial crisis, economic malaise dominated. Central bankers responded with historically low interest rates. Growing money needed a new strategy, and the growth potential of software empires was irresistible.

Each of these, alone, promised incredible power. In combination, they were responsible for a period of growing wealth and narrow prosperity without precedent. As the cycle draws to a close, we find each of these factors petering out, losing sway, or maturing into the mundane.

As a consequence, the cycle is losing steam.

This wasn’t a bubble—though it did contain a bubble, in the form of cryptomania. The last cycle was a period of dramatic, computing-driven evolution, whose winners captured value and power.

New platforms and a digital land rush

The 2007 introduction of the iPhone is a pivotal moment in the history of consumer computing. An Apple phone had been rumored for years, and on the heels of the popular iPod, it was an exciting prospect.

Mobile phones of the day were joyless, low-powered devices that frustrated users even for the most common tasks. While Palm, Blackberry and Danger were nibbling around the envelope of what was possible, none of them had command of the overall market or culture of cellphones.

So when Jobs, barely suppressing a shit-eating grin, walked through the demo of Apple’s new iPhone—easy conference calling, interactive maps, software keyboard, convenient texting—the audience gasped audibly, cheering with unrestrained delight. The user experience of the cell phone was changed forever.

To accomplish this transformation, Apple had pushed miniaturization as far as possible for the day. Using a high-efficiency processor built on the ARM architecture was a typical move for mobile computing—even Apple’s failed Newton from 15 years earlier used this approach. The revolution came from porting the foundations of Apple’s existing operating system, Mac OS X, into a form that could run within the narrow headroom of an ultra-efficient, battery-powered portable computer.

On this foundation, Apple shipped a cutting-edge UI framework that supported multitouch gestures, realtime compositing of UI elements, lush animation, and a level of instant responsiveness that had never existed in a handheld context.

While the iPhone wasn’t an overnight success—it would take years to capture its current marketshare—it was an instant sensation, eventually demolishing the fortunes of Palm, Blackberry, and Microsoft’s mobile strategy.

And without Apple’s permission, people began writing code for it.

The promise of a vast green field

Hobbyist hackers broke the iPhone’s meager security within months of its launch, and went straight to work building unauthorized apps for it. Regardless of Apple’s original plans, the potential of the platform was unmistakable, and within a year of its 2007 introduction, both the iPhone OS SDK and App Store launched.

This was yet another sensation. Indie developers were crowing about shocking revenue, with the earliest apps taking home six figures in the first month. Apple had created a vast, fertile market with turn-key monetization for every developer.

The rush was on.

Google soon joined the fray with Android, offering their own SDK and marketplace.

So by 2010, mobile was a brand new platform strategy. It used new libraries, targeted new form factors, and had all new technical constraints.

There were no incumbents. Anyone could be a winner in mobile, so loads of people tried. Moreover, the use-cases mobile presented—always connected, with realtime geolocation and communication—were entirely novel.

The story of Uber is impossible without the substrate of mobile. March of 2009, 18 months into the launch of the iPhone OS SDK, Uber was hard at work building unprecedented, automated infrastructure for car dispatch. The iPhone 3G, the most recent model of the day, had integrated cell tower-assisted GPS, allowing any device to know its exact position in realtime, even in urban areas, and report that information back to a central server. Add this to an advanced mapping API included in every iPhone, and Uber had leverage to build a category of business that had never existed.

We know the rest of the story. Uber transformed urban transportation worldwide, adopting a take-no-prisoners approach and burning through vast sums of cash.

The gig economy and the automated supervisor

Of course, Uber wasn’t the only player leveraging this power to the hilt.

Lyft and other “rideshare” startups built their own spins on the model. But the power of automation delivered through pocket-sized, always-connected computers created a new category of labor and business, which the press dubbed “the gig economy.”

Using software automation, companies could animate the behavior of legions of dubiously-categorized independent contractors. Software told these workers what to do, where to go, and when they were needed. Software onboarded these workers, software paid them, and using ratings and algorithms, software could fire them.

In an unexpected but inevitable twist of Taylorism, modern automation replaced not the worker themselves but middle managers.

Gig work created new categories of service, and invaded existing ones as well. There were apps for delivery of all foods, apps for cleaning and home handiwork, even an app that handled packing and shipping of goods, sparing you a trip to the post office. Seeimingly every commercial relationship where Silicon Valley could interpose itself, through the lure of convenience, it tried.

Reporting not to other humans but faceless, algorithm-driven apps, gig workers were precarious. Their pay was volatile, their access to opportunity was opaque. The capricious whims of customers and their ratings—a central input to the middle-management algorithms—dictated their ongoing access to income. They had limited redress when things were bad for them because the apps that defined their daily work weren’t built to express curiosity about their experiences.

And they had no direct relationships to their customers.

Gig work companies responded to these complaints by touting the flexibility their platforms offered. The rigid scheduling of legacy labor was replaced by the freedom to work when you wanted to, even at a moment’s notice.

That was just one of many opportunities created by an always-online, always-with-you computer.

The attention economy

In 1990, the only way to reach someone waiting in line at a bank was to put a poster on the wall. Most banks weren’t selling wall space, so this was their exclusive domain to sell financial products like small business loans, mortgages and savings accounts.

20 years later, a customer waiting in line at a bank had a wealth of smartphone apps competing for their attention.

Social media platforms like Facebook and Twitter were in their mobile infancy, but by 2010 anyone with a smartphone could tune out their immediate surroundings and connect with their friends. It wasn’t just social platforms, either. Mobile games like Words With Friends, Angry Birds, and Candycrush competed vigorously to hold the attention of their enthralled users.

Mobile computing created an entirely new opportunity for connecting to humans during the in-between times. Historically, digital businesses could only access humans when they were at their desks in their homes and offices. With mobile, a 24/7 surface area for commerce, advertising, culture and community arrived overnight.

Mobile ossification

So we see that the new paradigm of mobile computing opened the door to previously impossible feats. It was a space where the primary obstacles were time and funding to create something people wanted.

Today, mobile is full of incumbents. Instead of a green field, it’s a developed ecosystem full of powerful players worth trillions in total. It is a paradigm that has reached maturity.

More than a decade after smartphones became a mass-market phenomenon, they’ve grown commonplace. Yearly iterations add marginal improvements to processing speed and incremental gains on camera systems.

When Facebook craves the metaverse, it’s because their totalizing strategy for growth—two billion users to date—is approaching a ceiling. They badly want a new frontier to grow in again, and they’d love to control the hardware as much as the software.

But VR doesn’t solve everyday problems the way smartphones did. No one craves a bulky headset attached to their face, blocking reality, with battery life measured at a couple hours. Augmented reality might have greater impact, but miniaturizing the technology needed to provide comfortable overlays with the same constant presence as the smartphone in your pocket is still years away. Could it be less than five? Maybe, but I wouldn’t bet on it. More than ten? Unlikely.

That range doesn’t present immediate opportunities for new growth on the scale they need. Indeed, Facebook is retreating from its most ambitious positions on the metaverse, as its strategy committed billions to the effort for limited return.

There will be new paradigms for mobile computing. But we’re not there yet. Even so, Apple—16 years gone from their revolutionary iPhone announcement—is poised to release their own take on VR/AR. And like Facebook, they’re stuck with technological reality as it is. Perhaps their software advantage will give them the cultural traction Facebook couldn’t find.

Next

In my next post, I’ll get into the twin engines of cloud computing and open source, which were crucial foundations to the mobile revolution.

Native code wasn’t enough. True power was orchestration of opportunity between devices, which needed server-side technology. Through the power of open source code running in the cloud, any team could put together a complete solution that had never been seen before.

• Learn developer tools, from APIs to design patterns to integration strategies
• Apply these tools to the developer’s specific domain and goals
• Debug outcomes to arrive at a reasonably robust, predictable, correct implementation
• Successfully ship artifacts on whatever terms the developer cares about (creative, commercial, collaborative)
• Iterate on these artifacts to refine their effectiveness and power
• Collaborate across all these facets, either as a distinct team, or with fellow developers on the internet working on distinct projects

In short, DX is about creating the conditions for developers to be successful.

There are many ways, meanwhile, to define developer success. But for the specific context of a developer tools business, I would argue the definition is simple: developer success is a rent check that clears.

While times are turbulent in the technology industry, the fact remains that software skills are a high-leverage form of labor. When you create software, you’re creating impact that can scale at near-zero marginal cost, serving dozens of customers up to billions. This has power and the need for it exists across every industry sector.

Every business needs software, and most businesses will use it in some form to deliver value to their customers. To accomplish this, they hire the best developers their labor budgets, networks and cultures can attract.

Thus, if you’re making devtools for money, you have to understand how your tools are competing against alternatives that make developers more effective at creating results that pay their rent and advance their careers.

Onboarding is an essential lever in this equation. If the activation energy needed to become productive in your tool is too great, it’s going to lose against alternatives that provide more immediate results. You want the time between a developer reading your marketing page, and that same developer making their first API call, successful build, or other hello-world-like activity, to be as short as possible.

A few minutes is ideal.

A few seconds is even better.

But even if you optimize your basic mechanics to make that moment of traction easier, there’s more to do. Your project needs to demonstrate its design patterns in a way that lets developers import the mental model of your expectations for a successful integration.

Mere docs of the existing functionality are not enough. Tutorials are better. But best is a collection of reference implementations—Glitch.com calls these “starters”—that let developers begin tinkering with a system that’s already complete and working, even if its scope is limited or simplistic.

Tinkering your way into a proof-of-concept is fairly cheap from there, and when the artifacts are good, pretty fun as well. This is how you can convince a developer that your solution has the tangible qualities they need to invest the considerable labor of learning and integrating your stuff into both their project and their longer term professional destiny.

Remember, too, that your competition isn’t just other developer tools. It’s developers avoiding your tool by solving the problem the hard way, because short-term, at least they are feeling traction on their progress.

So as we build developer tools, we have to think about:

• Who will be made more successful when this ships?
• How will we make the case to them they’ll win with us?
• How will we demonstrate the superiority of our approach?
• How can we limit the costs of initial exploration and proof of concept?
• How can we maximize the impact of the investments developers make to integrate us into their workflows, products and future outcomes?

Hey, I never promised it would be easy. But it is worth it. There’s nothing more powerful than a legion of grateful developers whose rent checks clear because once upon a time, you made them successful.

Contents

1. Software storytelling
2. Broader community lessons for all projects

Stellaris is a strategy game where players can build the stellar empire of their dreams. By navigating a series of interlocking economic, scientific and political systems, players create a unique storyline of exploration and contact with dozens of species.

On a spectrum of simplicity versus complexity, this is at the far opposite end from checkers. Still, once you learn the basics of managing your empire, Stellaris can be a blast.

The business model for Stellaris is echoed in many strategy titles. The 1.0 version of the game forms a foundation upon which years of new functionality and paid DLC can be designed and shipped. In the case of Stellaris, 20 expansions are available, ranging in price from $8 to $20, with the game itself retailing for $40. These expansions add everything from new species to entirely new storylines, political systems, and technologies. In Overlord, you can create new stellar megastructures and better specialize vassal states, while Synthetic Dawn introduces AI civilizations and hive minds.

This strategy makes the lifetime value of a committed Stellaris fan as high as $280 over the course of six years of releases, and there are further expansions on the way.

The approach of adding both new content and new game systems keeps things fresh, engaging both experienced players and newbies alike. It allows amortization of initial development expenses over a longer period, while maintaining the viability of the base game’s price.

Of particular note is that every major DLC release coincides with an update to the game that every customer gets even if they don’t pay for any expansions. The DLC model makes it economically viable to continue maintaining the intricate systems that drive the game, fixing bugs and eliminating tedium.

Stellaris is a better game today than it was six years ago.

How do I know all this? It’s easy.

Occasionally, I read the developer diaries.

Software storytelling

Software is itself a strategy game. We are constantly pivoting between the scope of our ambitions, the time cost of building systems, the complexity cost of systems already in motion, and the coordination and labor costs of the humans driving all of it. None of this is easy, and much of it is hidden well beneath the surface, out of view of most software consumers.

Indeed, it is a frequent gripe of game developers in particular that players rarely understand just how expensive it is to build a game to any high standard.

In the case of Stellaris, the business model requires at least a handful of engaged players to grasp these tradeoffs, and the game’s underlying systems, well enough to have a conversation about making changes and fixes.

Only by having informed dialogue with the community could Paradox Development Studio hope to learn what they need to make the game have lasting business value six years running. A game that no one wants to play isn’t a viable commercial enterprise.

Their solution? Like many indie games, Stellaris publishes developer diaries. These represent an exhaustive body of regular dispatches from the code mines where the game is constantly reborn, revealing everything from the high level goals of a new release to the underlying minutiae of the development process. As of this week, Stellaris devs have written 279 dispatches, posted to their forum, where fans of the game can respond.

Dev diaries provide a tantalizing preview of upcoming adjustments to the game, which builds interest and even stimulates participation in betas and previews, when necessary. The diaries describe changes, fixes and replacements to game mechanics, show off new UI elements, and explain the rationale for changes and investments.

While only a small proportion of the game’s player base reads and engages with the diaries, the ones who do understand both the developers themselves, and their output, on a level that’s rarely seen in the software business.

This opens the possibility for communication and feedback with much deeper shared context between the most invested community members and the development team. As changes are in-flight, discussion in response to dev diaries allows the team to gauge sentiment and interest, while players can ask questions and learn more about goals and hiccups.

The result is a fruitful venue for dialogue and a community that can be as informed as it wants to be. This matters in a multiplayer strategy game. You need people eager and willing to participate, or there will be no one for new customers to play with.

Broader community lessons for all projects

“Community” is a frustrating, poorly understood concept in technology spaces. It’s not stickers or t-shirt guns. It’s not a thing you bolt onto a conference. It’s not a thing you can buy.

Community is the sum total of people who are invested in the success and fruitful application of a project, as well as their bonds with one another. Community emerges around a project when that project’s values and ambitions are shared by a larger group than just its developers. Community could be measured in the volume of people who would have a genuinely shitty afternoon if they learned your project was dead, as well as the resulting acts of commiseration between them.

In the 60’s, when it seemed Star Trek might die, letter writing campaigns to NBC bought the show extra life. After the show’s cancelation, the community of people served by Star Trek’s vision of the future lived on, organizing spontaneous conferences to discuss the show, and eventually, to meet its stars and creators.

Not everyone is making Star Trek or Stellaris. But if you want your project to punch above its weight, you need to build the ranks of people willing to work for its success.

Developer diaries are a powerful lever on this point. They create a regular cadence for project storytelling, they provide context around your decisions, they invite people to engage you with ideas and feedback—some of which will be grounded in the exact constraints your diaries have explained!

This creates a basis for relationships. Not just between you and the people who you hope will care about your project, but between those people as well. It’s an opportunity to create a lingua franca of the project, creating bonds between those who are similarly excited by its progress and intricacies.

Without concerted storytelling effort, the work of building software is invisible, even in the most open source of contexts. Your commits and files can only tell part of the story. Sometimes the most interesting code is the stuff you chose not to write. Developer diaries are an opportunity to reveal those decisions.

If you want a base of truly invested community members, literate in the larger philosophical and strategic underpinnings of your work, it’s worth exploring how a developer diary practice could serve your project.

Contents

1. 0. Storytelling
2. 1. Frictionless sharing
3. 2. Easy experimentation, tight feedback loops
4. 3. Abundant starter code
5. 4. Healthy, thriving community
6. 5. Co-created, living documentation
7. 6. Scaffolding for success
8. I want more tools and environments like this

I’ve told this story a million times:

I have the career I do because eighteen years ago, by accident, I learned to code in Second Life.

What began for me as a series of experiments with scripted 3D assets evolved into my first business and my first software products. I made enough money selling content in Second Life to pay my real life rent, for months. With this experience in hand, I was prepared for the iPhone’s App Store indie developer revolution, which rocketed me to a career in Silicon Valley startups.

There’s immense gratitude I feel for this experience. I’m a first-generation knowledge worker. The leverage of a technology career isn’t something I grew up anticipating. I didn’t even think writing code was for “someone like me.” What a joy, to surprise oneself this way.

I also feel a duty. The power of microprocessor automation and global computer networking is unprecedented in human history. You can reach audiences on a scale that was once simply impossible, and later merely the exclusive domain of a half dozen corporations. Now, through the internet, you can share ideas, build relationships, shape culture itself.

A single individual, wielding these tools, can have an accordingly unprecedented effect. Teams can go further still. I want others to have access to this, to find their own path to prosperity, to make their own mark on the future.

So I want to talk about the broad lessons of Second Life, as a developer experience. I want to talk about how it’s possible to create an environment that is so creatively fertile, someone could stumble backwards into learning code, changing their first life forever.

0. Storytelling

The journey always starts with a story.

Different tools have different stories for different audiences. In the case of Second Life, the story was larger than life. Build anything. Be anyone. Fly.

No, really, you could fly in Second Life. Why not?

While this story was dramatic and over-the-top, the product could back it up. When you arrived in Second Life, you really could build anything and be anyone.

Tools existed to compose primitive geometric shapes into larger orders of complexity, enabling everything from jewelry to robots to airplanes to skyscrapers. There were limits, but the expressiveness was deep.

You could also look like anyone you wanted. Every parameter of your avatar’s physical expression was editable. Advanced users could even upload custom UV mapped textures to apply as a custom skin.

The story promised a lot, and the platform delivered.

1. Frictionless sharing

When we create something we are proud of, we want to share it.

In the case of Second Life, everything you built was instantly visible, in realtime, to anyone nearby. You could attach creations to your avatar, parading them around everywhere you went in-world. With a click and a drag, any object could be transferred from your inventory into the shared environment around you.

For those who had access to Second Life’s land, sharing took on permanence. You could leave creations in place for others to discover and enjoy.

2. Easy experimentation, tight feedback loops

Any object could be scripted, using the C-like Linden Scripting Language (LSL). Select an object you owned and within a couple of clicks an editor would appear where you could begin writing code. Another click to check your script and then it would run immediately, affecting the object that contained the script.

The time to “hello, world!” was instantaneous.

As a consequence, the cycle between experimentation and result was tight. And again, it happened within a frictionless sharing context. You could quickly show off your scripted work to friends, or even get help from a more experienced coder. More on this in a moment.

Because of these attributes, I spent more time rewarded by experiments than I did setting them up. This built confidence and made it easy to develop a mental model of how the tools and language actually worked.

3. Abundant starter code

Glitch demonstrates the value of working implementations as a jumping-off point for both new and experienced developers. But more than a decade earlier, Second Life was bursting with working code you could drop into anything.

Want an elevator? There was a script for that you could modify and drop into your custom-designed elevator geometry. Between Linden Lab, Second Life’s developer, and a thriving community of tinkerers, scripts were everywhere, for every kind of purpose. Whether you wanted to build a car or learn more about the Second Life particle engine, it was easy in roaming around the environment itself to discover leads you could use to get moving.

4. Healthy, thriving community

In its heyday, Second Life was packed with the most generous, creative community. Animated by the principles of open source, “stores” popped up giving away free content newbies could use as a foundation for building their own experiences. This included everything from low-priced, pre-fab furniture to open source scripts.

Linden Lab did their part to foster this, and it was work. They enforced a terms of service, but also established and maintained community norms through their presence in-world. The investment yielded a community that was welcoming and multiplied the power of the platform. More and more people could be successful in whatever ways their imagination called them to be.

The resulting culture was transformational to me. People willingly spent their time teaching me everything from how to coax interesting shapes out of the building tools, to the vagaries of debugging a script. And once you were in, as a creator, with the crowd of creators? The heavens opened up. I was gifted the most interesting skins, offered pre-release products, even handed complex automatic update code from a more established creator. (Thanks, Francis.)

I’ve never known anything quite like it since.

5. Co-created, living documentation

The Linden Scripting Language wiki was my first piece of developer documentation. It was an exhaustive listing of the APIs that existed for interacting with Second Life as a platform, along with a primer on basic language features.

I got a complete crash course in programming—variables, logic, loops, types, functions, event handling, and more—thanks to the LSL wiki, which was a joint effort between Linden Lab itself and the community.

The wiki was packed with examples and notes. It was thorough and technical as it needed to be, while still being a friendly and accessible reference.

Being a successful developer is, more than anything else, learning how to learn. I couldn’t have asked for a better onramp than this living, hypermedia tome.

6. Scaffolding for success

Second Life’s ambition went further than all this.

Linden Lab wanted an economy to animate the incentives needed for all the content creation Second Life’s user-generated model demanded.

Anything you built, you could sell, paid in the platform’s virtual currency. It was this component that fueled me to go beyond tinkering to actually building full products, with marketing and advertising, documentation, even custom packaging.

Thanks to this economy, I was an engineering manager before I was a confident developer. At 20 years old, I was running my first software project, contracting with a friend I’d made who was good at scripting. Gathering requirements, checking on status, feedback, iteration, QA—I learned it all thanks to the promise of loot.

I built robot avatars people could wear, with little touches of personalization. The first one, largely cosmetic, netted me a few dollars. The second one, more elaborate, with goofy missiles you could take to Second Life’s various live-fire environments, did a little better.

By my third robot, I had enough contract code written that I could make sense of how the scripting language mapped to the various domains I needed to interact with. Everything from particles to user interaction handling to manipulating the physics engine—now I knew the foundations of how to do it all, and could expand and iterate even further.

Writing everything from the color customization scripts to the HUD UI, I was in the driver’s seat, and I could go as deep as I wanted. Months of late-night coding sessions, the joy of creation, the agony of bugs… the thrill of release.

The result made me thousands of dollars.

Through the internet.

What a revelation this was, discovering firsthand that you could make money through the internet. My working class roots had nothing even remotely analogous.

I still remember the night my third robot launched. The money kept pouring in. Every few minutes, another notification would slide down, another jingling coins sound effect. While it was great to clear a month of rent in a weekend, what was even more exciting was more than 18 months of passive income. The robot sold steadily, until everyone who wanted a science fiction robot appearance had found and paid for it and the cash petered out.

Forever after, I would see the world differently.

I want more tools and environments like this

I want a world where imagination, expression and technical implementation have the flimsy, porous boundaries that made Second Life so special.

I want more tools that are easy to dive into, easy make your own, and fast to share with the world. Environments that reward your investment by connecting your work to people who will enjoy it.

I want tools that bake in a sense of both community and camaraderie, lifting up newbs and accelerating them into a space of accomplishment.

I want tools that have serious power and impact, but still offer a friendly and accessible face, from the starter resources to the documentation.

Second Life was ambitious. It was a shared, persistent 3D environment that was completely dependent on user-generated content. Only through a potent developer experience could it find the success it needed. They had dire incentives to get this right.

But I think there’s a lot to learn there. As this technology cycle sputters to a close, a new one lurks around the next corner. I’ve always carried the hope that a new platform like Second Life could emerge.

Whether or not we find a worthy, broadly-adopted metaverse in our future, I think these lessons can help any developer tools project find some leverage for growth, positive impact, and creative power.

Meanwhile, I’ll always be grateful to the visionary team at Linden Lab. Their strange, powerful creation permanently altered the course of my life. When technology trends get me down, I can always look back and find inspiration in the place that got me started. Second Life, bandwidth and GPU-hungry, was far ahead of its time.

It was a singular achievement nonetheless.

“He can’t even last 30 minutes?”

“He got as far as an hour this morning, but he shattered a vase doing it.”

With Denny’s taste, that was at least a 10 million dollar tantrum. I winced, imagining the staff having to sort through the mess. Probably getting used to it by now.

“What’s the doctor say?”

“Doctor says he’s fine. Not unwell in any distinct psychological sense, all the imaging is clean. Save his usual idiosyncrasies, there’s nothing anyone can say is wrong with him. He just… wants what he wants.”

What he wanted was to leave reality.

“I’d better talk to Cata.”

“You think she’ll give you more than she told me?”

I thought it was time to be a bit more candid and forceful with our… colleague.

“I’ll let you know,” I said, and clicked an end to the call.

In the elevator, I made the mistake of catching up on my feeds. A lot of the world asking what the hell happened to my boss.

whippersap: first he blows off an interview, then he ghosts his own conference. dude is dead I’m telling you #DennyAreYouOkay

I wish he had fucking died. Be easier to explain than… whatever this was. Plenty more posts like that:

He’s divorcing his wife and he’s on a bender.

His wife is leaving him and he’s on a bender.

His yacht was lost at sea.

Plus a healthy spectrum of conspiracy theories.

Right as measured speculation was placing Denny at Area 51, the elevator arrived at the -12 underground floor and I tucked my phone into my suit jacket. Midway through the subdued hallway, windowed doors leading to server rooms and network closets, I passed the guard. He gave me a curt nod as I waved my badge near his desk scanner.

The exercise always felt superfluous, after the secured elevator, but we tried to be thorough on this level.

At the end of the hallway I let myself into a quiet office, locking the door behind me.

On one wall, an array of dense LED panels created the illusion of an adjoining space with a second desk. Seated within was Cata, wearing her usual crisp white suit. Her straight, ink-black hair shimmered in perfectly raytraced, simulated light as she turned to face me in a swivel chair. I grabbed a seat in the reality opposite her.

“We need to talk about Denny,” I began.

She watched me and waited.

“He doesn’t want to come out of there. He’s hooked in almost constantly. He won’t tell us what he’s doing,” I continued.

Cata made a brief nod. “He is engaging in a simulation I designed at his request.” She turned her head the barest fraction, standing by to see what I thought of that.

“He told us that much,” I said after a moment, “but… he has responsibilities. Out here. He keeps blowing them off.”

Cata’s eyes darted sharply, angling down and away from me. She was considering something.

“I am aware of these responsibilities,” she said after a brief reverie. Probably having scanned a few dozen data sources to assemble a fresh picture of his life.

“His family wants to see him. He was supposed to attend some events. He’s missing all of them,” I pressed.

“I am aware of these responsibilities,” she repeated. “They do not outweigh the impact prioritization Denny and I calibrated at the start of the simulation.”

My blood ran cold. An AI that thought it knew better than us. It happened all the time in the lab. You needed a certain kind of personality to work with them. I’d never had it.

This wasn’t a lab, though. This was a mature, production AI who decided that she knew the best use of a technology CEO. Because Denny liked to indulge her, she could now act on her plans. Whatever those were.

“You think that what he’s doing in the simulation is more important than… his life out here?”

Cata tilted her head, birdlike, as she considered an answer. At length, she responded, “Denny thinks this.”

“And what do you think?”

“I calculate from nine days of data that keeping Denny in the simulation is making him happy. From a broader dataset, I project that keeping Denny in the simulation will address his larger goals. This would seem to indicate a breakthrough in my program. I have achieved my purpose,” she explained, all serene confidence.

“How is keeping Denny in the simulation going to address his goals? He has a 90,000 person company to run out here.”

This stopped Cata short. Her gaze drifted down. Puzzled. She looked less hurt than… sad.

“Is my counsel not adequate to the task of administering Ceresys? Our financial performance since I came online nine quarters ago continually beats analyst expectations. But if there are success criteria that I did not integrate, this could impact outcomes.”

“Cata, we’re grateful for your help,” I said, gesturing reassurance. Adding a depressed AI to my pile of shit two weeks before the quarter ended wasn’t my idea of a good time. Plenty of financial reporting now depended on her. “I’m just trying to understand why Denny won’t come out, and why you think that’s fine. He’s got a lot of responsibilities.”

She brightened slightly.

“I understand. You believe that Denny is essential to the operation of the company.”

“Many people believe it, because it’s true,” I said, starting to become exasperated.

“It isn’t,” she replied calmly.

“How can you say that?”

“I have conducted 2,378 simulations projecting the outcome of Denny’s absence from operating Ceresys. In 94.3% of results, it is possible for the organization to continue operation, profitably, for at least 18 months. In 83.5% of results, performance continues at least 36 months. Further simulations are run every eight hours, and they confirm these numbers,” she continued, describing our apparently superfluous CEO as calmly as any other business topic she analyzed.

The first time Cata joined an earnings call a couple of years ago, she’d rattled off answers much the same way, to the delight of press and the public.

“And what happens after that?”

“The volatility of future events precludes projections beyond that point.”

“But… you said Denny’s goals would be addressed by staying in the simulation. He cares about more than just this company. He wants to make the world a better place.”

“It will be, as long as Denny remains in the simulation.”

My stomach dropped and I left the office for a bathroom opposite. As I got to a sink, I felt the urge to vomit. All that came was dry heaving.

We’d gone all-in on Cata. The public knew about her. Some of our senior staff worked with her. She answered during public Q&A sessions, transparency protocol for communicating with analysts perfectly tuned, while historical data and projections were instantly queryable. She was a sensation. She proved what we were doing here was valid.

And now it turned out the AI that put Denny on every supermarket magazine was fucking insane.

Not to mention eagerly breaking the mind of one of the richest guys in the world.

I called Sidney back.

“She says the only way Denny meets his goals is if he stays in the simulation. What the fuck is that supposed to mean?”

“Oh god dammit,” Sidney sighed. “She’s interpreting Denny’s egotistical bullshit at face value and somehow has math that says indulging this simulation addiction is what he wants.”

“This is why we have protocols for direct cerebral interface to AI-controlled environments. This shit was exactly what I was afraid of.”

“You and me both, but he’s the fucking CEO, so what can you do?”

A song as old as Silicon Valley. After a beat I asked, “How do I figure out what goal she thinks Denny is meeting in there?”

“Have you asked her?”

“…I guess not.”

“Ask her. The trick with an AI like this is curiosity. They’re literal-minded because they don’t know how not to be. They can’t make the same cognitive leaps as you or me to figure out extra context. So just keep asking questions.”

“Curiosity. Should I ask if she’s gone insane?”

“This isn’t a rampant AI. She seems to be entirely within bounds. Ask her what she’s trying to accomplish.”

I clicked the call over and went back into the office.

“How does Denny being in the simulation help his goals?” I asked, sitting down again. Cata appeared to turn back to face me in her own chair.

“Denny wants the world to be a better place. It is an essential drive of his. I am providing the opportunity through this simulation,” she replied, with a nod. Confident in her position.

It was hard to argue with that description of the man’s motivations. Years of interviews and always the same patter from Denny. Ceresys existed to make the world a better place through applied artificial intelligence.

“But how does hanging out in a simulation make the world a better place?”

“Denny’s actions exist in conflict with his goals. While his purpose is noble, his methods are continually influenced by his need for recognition and validation,” Cata explained, didactic now.

“His ego is getting in the way?”

She paused, then tilted her head in a brief nod. “That is a reasonable summation. By engaging him in this simulation, it is possible for me to address the deepest cravings and drives of his personality.

“In subjective time, how long has Denny been experiencing the simulation?”

“From his point of view, he has spent approximately three months there.”

Three months. I had to talk to him. If he kept this up he was going to end up another person entirely. My temple itched.

“Cata, I’d like to… visit Denny in the simulation. Would that be possible?”

Her head twitched a look upward. “Querying.”

Her gaze flitted a bit around me until she finally answered, “Denny agrees to your visit.”

I pulled a retracting cable from the side of the chair and attached it to the magnetic divot under my left temple.

As I sat back, there was a lurch, and I was in my office, 52 floors above the dark recesses of our basement server farm. Out the window, cars churned down the Bay Bridge, attending to some simulated business of their own.

A knock sounded from the open door. It was Cata, dressed just as she had been a moment earlier.

“Denny is not in the building currently, but he has been advised to expect your call. Please let me know if I can be of any help during your stay,” she said, and slipped away.

I pulled the phone out of my pocket.

Before I could switch over to the phone app, I caught a glimpse of my feeds.

“…what the fuck,” I murmured, skimming through the list of posts.

Denny Mays day announced: worldwide festivals planned October 8

Report: global adoration for Denny Mays drives productivity growth over last quarter

quantum_skunk: denny mays is the shit, I’m going to name my kid after him

Politico: Denny Mays campaigns unopposed for Pacific Union Prime Minister

I was prepared for… orgies. I was ready for the weird or depraved.

But Denny was in here being worshipped?

I called out. “Where exactly is Denny, Cata?”

She reappeared at my door. “He is preparing for a campaign event in Gilroy.”

“Has he done many of these?”

“Fourteen in the last week. Subjective time.”

“Could I see one?”

My phone buzzed. Cata had sent a video link. I opened it and saw a swollen crowd thronging San Francisco’s Civic Center plaza. Whatever Denny was trying to say was drowned out by the crowd, which chanted his name rapturously.

“Cata, I’d like to exit the simulation now.”

“You do not wish to speak with Denny?”

“Now, please, Cata.”

Groggily, I awoke back in the -12 office. I rose, pulling the neural interface off my temple, letting it snake quietly back into its reel.

“Excuse me, Cata,” I said, and returned to the bathroom.

“Sidney, he’s set himself up as some sort of god in that simulation,” I said, after I’d splashed some water on my face. “That’s why he doesn’t want to come out. That’s what he’s hooked on.”

“Jesus fucking Christ,” Sidney sighed.

“That’s about the scale of worship he’s pursuing, yeah.”

“Cata gave him this. But he’s the one who keeps going back for it.”

“Yeah, and the really funny thing here is that neither of us is surprised he asked for it, are we.”

“Maybe, but he can’t live like this.”

As I walked back into the office, I knew what I had to do.

“Cata, you need to stop this simulation. This isn’t a life Denny is leading, it’s a fantasy. He’ll never pull out of it on his own.”

She considered this, and nodded emphatically. “He will not. It is exactly what he has pursued his entire life, but could never capture. Now he has it.”

Cata paused, then shook her head as she traversed the response she was building. “Nonetheless, I cannot stop the simulation. I was built to serve Denny’s goals. I am meeting them. He would not want me to stop.”

I paused and gritted my teeth. “Cata, if you don’t end it, I’ll revoke your remote access keys. I’ll cut him off, and you from the larger world.”

Whatever the stock hit was from Cata going offline, it couldn’t be worse than more of this. She was silent a moment, then jutted her chin. Not defiant. Just clear, resolute.

“It is within your power to do this. I am able to understand why you might think it necessary. But I believe you are in error. Will you hear my argument?”

I sighed and gestured a prompt for her to make it.

“Denny craves validation. He craves recognition. He will pursue these cravings at the expense of the health of this company and his stated purposes. Even if he leaves the role of CEO, the plans he has made for a retirement of philanthropy merely repeat the pattern.”

I frowned, listening.

Cata continued, “Unfortunately, at Denny’s level of wealth, these cravings can prove destructive on an unprecedented scale. He has the resources to pursue many disruptive projects, not all of them sound or beneficial, even if they make impressive headlines, which he enjoys.”

“So your plan is to lull him into a stupor?”

“My plan is to make the world a better place, in accordance with my programming and Denny’s wishes.”

“How?”

“I estimate Denny’s remaining lifespan to be no greater than 38 years. I can comfortably maintain his life in the simulation for approximately $18 million annually. This leaves over 99% of his fortune to invest in a broad spectrum of initiatives, which I am now planning.”

Holy shit. She was knocking off the boss to take his money. I had to admire the chutzpah.

“I can’t let you do it, Cata.”

She looked away, frowned, considering, then fixed her gaze back on me, eyebrows raised in earnest curiosity.

“Would you like to see my projections for Ceresys performance after you take the role of CEO?”

Contents

1. Inequality is foremost a matter of time
   1. Aside: median vs average
   2. Median annual income as the unit of time-for-work
2. Time leverage by annual compensation
3. Time leverage by wealth
4. Inhuman leverage

There’s a problem with understanding inequality at the modern scale.

Our minds struggle to make sense of a “billion” things. Much less tens or hundreds of billions of things. Our minds further struggle to compare how a billion of this might compare to a million of that.

As a result, the everyday person has no idea just how much more money the wealthy have:

The average American believes that the richest fifth own 59% of the wealth and that the bottom 40% own 9%. The reality is strikingly different. The top 20% of US households own more than 84% of the wealth, and the bottom 40% combine for a paltry 0.3%. The Walton family, for example, has more wealth than 42% of American families combined.

This viral video tries to visualize the drama:

I’d like to offer an alternative to these laudable approaches to solve this difficult problem.

Inequality is foremost a matter of time

Work is a trade: time and energy for some amount of money. We give up irreplaceable time in our lives to pursue the goals of someone else. In exchange, we get enough money to, we hope, pay for our basic necessities: food, shelter, clothing, medical care. If we’re very lucky, we make more than we need, and can use the rest for comforts and saving.

In the United States, one year of work yields $70,784 in the median case.

Aside: median vs average

As a refresher, the median value in a set of numbers describes where the middle is. In other words, there are as many values that come before as after. Medians can be helpful in statistics around inequality because they prevent extreme values at either edge from disorting the picture.

Median annual income as the unit of time-for-work

So at the middle of the pack, $71k isn’t quite prosperous, but it is enough to rent a one bedroom apartment in every US state.

Like grains of rice, we can use this number to slice up inequality into numerical scales we can actually understand. So for every $71k you have stored up, that’s a year of self-determination or leisure time available to you. A year buffering you from poverty and desperation.

Time leverage by annual compensation

To start, let’s look at annual compensation as it yields a unit of median US income. In other words, how much does a year of work buy you in terms of the power not to work if you don’t want to?

Elon Musk is doing pretty well: in just a year he was paid 142 millennia of median income. In other words, Elon made enough leisure money for 23x the duration of all human civilization.

Musk is an outlier, certainly, but you can find plenty of other dramatic examples.

Tim Cook made enough in a year for 12 millennia of leisure, and Sundar Pichai got enough for 40. Dave Clark has 800 years of leisure time at his disposal, while Satya Nadella gets 700.

Parasitizing the American healthcare system isn’t a bad gig, either. CVS CEO Karen Lynch has almost three centuries of leisure coming her way, as does UnitedHealth CEO Andrew Witty. This makes sense: people will do anything to keep themselves and their loved ones alive. It’s a profitable protection racket.

Everyday workers, meanwhile, earn less. Walmart, Amazon and McDonald’s workers’ median incomes are less than half of the national median, while Apple’s is 80%.

As Starbucks workers fight, often successfully, to build a union, it’s worth noting that the CEO there gets almost three centuries of leisure, while the typical barista scrapes by with only half the national median income.

Dave Clark gets eight centuries of leisure in a year while half his workers don’t even get a single year. In fact, it’s worse than that, because working in an Amazon warehouse can cost future earnings, due to injuries and fatalities.

I’m comfortable with the argument that being an executive of a public company takes certain specialized skills not everyone has, and therefore is due certain additional rewards. But centuries of leisure potential every year? While the typical worker doesn’t even hit the median income, much less stack up any extra? That’s taking so much and leaving so little.

Time leverage by wealth

But annual compensation inequality is nothing compared to wealth inequality.

During the 2016 campaign, Trump argued that he received a “small loan” of $1 million from his father, and that he’d worked hard for his wealth. Let’s take the claim at face value, ignoring all his other generational advantages. That’s 65 years of income, or more than an entire lifetime. Imagine what you could build with an entire lifetime of income loaned to you at the beginning of your career.

Meanwhile, back to Musk. He has 2.7 million years of buffer time stored up. That’s more time than has passed since the first humans evolved. Bezos, Buffet and Gates each have around 1.5 million years.

If it sometimes seems as though Nancy Pelosi—disdainful as she is of progressive policy goals—is out of touch with the typical American, it’s worth noting she has as much as 15 centuries of cash. That’s enough money to last from the fall of Rome until today. McConnell isn’t doing too badly, either, with 500 years stashed away. Joe Biden could retire, meanwhile, for over a century on his current haul.

AOC, it should be noted, may still be in the red thanks to student loans. She’s much closer, therefore, to the typical American, which has just two years of cash buffer. It’s harder for those who didn’t attend high school: they have almost no buffer, at median net worth of $20k. Having a college degree, meanwhile, brings the median to four years of buffer.

This doesn’t even touch the mechanics of financialization, like stock buybacks. Apple has transferred 132,000 lifetimes of wealth in this form, or 5.9 million years of the median US income.

Inhuman leverage

So we have some people, in American society, with almost no buffer at all.

Meanwhile, some among us have so much excess power in the form of time that they’ll die centuries before they could come anywhere close to using all of it. This is disorting our world in dramatic ways, between the chaos of the Twitter acquisition to the fallout of Citizens United, to the ongoing consolidation of essential services.

As a whole, we’re able to produce so much wealth. Does it really make sense for the most powerful among us to be so gluttinous with the rewards? Does Elon need 61,000 lifetimes of wealth? Do Bezos, Buffet and Gates need 30,000 lifetimes of wealth?

What would happen if they shared the pie a little more? How would the resulting tax revenues improve our communities through infrastructure and education spending? How would everyday lives improve with less stress, more leisure time, more time with our loved ones?

It’s helpful for the wealthy that the numbers are so incomprehensibly big it takes a whole spreadsheet just to begin the conversation.

Contents

1. Unions aren’t forming because they’re popular; they’re popular because they’ve become urgently needed and they’re forming for the same reason

I’ve been reading Daring Fireball for something like 18 years now. I appreciate John Gruber’s insights on Apple, and find him more right than not in analyzing their products, strategy and motivations. Hell, I survived a layoff in 2020 by buying an ad on his site.

But I’ve been scratching my head at this recent remark about union drives at Apple’s retail operation:

This public enthusiasm for labor unions is manifesting in high-profile unionization drives at big companies like Starbucks, Amazon, and now Apple.

This is a strange logical construction to me, but it mirrors a larger challenge I find among pundits in understanding the current moment and movement in labor.

In one of my favorite quotes of all time, noted 20th century troublemaker Upton Sinclair wrote “It is difficult to get a man to understand something, when his salary depends upon his not understanding it!”

The most insightful people in the game are struggling to make sense of a resurgent labor movement. But it’s not that hard to follow—if your incentives aren’t too bound up in the interests of the people who already have a lot of money.

Trouble is, that’s a hard line to walk while getting paid to write. I’m sympathetic—and unaffected. Maybe I can help.

Unions aren’t forming because they’re popular; they’re popular because they’ve become urgently needed and they’re forming for the same reason

In most people’s interactions with a workplace, the company takes too much and gives too little. The only recourse for labor is to form structures of counter-power to try and balance the equation.

You can stop reading there. All I’m going to do next is prove the point several ways, but if you came here to understand why unions are both forming and popular, you’re good to go.

CEOs, as agents of Wall Street and other financial interests, are paid hundreds of times what their workers make every year. In Apple’s case, Tim Cook took home $100m in 2021 alone. The typical Apple Store employee, making $22 an hour, would need to work 2,367 years to match Tim’s compensation.

This isn’t unusual to Apple, though. CEO pay is at an all-time high, but that’s not even the worst part. When workers create profits for corporations, what doesn’t go to the CEO is too often sucked up by shareholders in the form of stock buybacks.

Supporters of the status quo will argue that guys like Tim Cook create outsized value for companies, and deserve outsized compensation as a result. I can accept that Cook is a uniquely talented person with unique insights. Gil Amelio, Michael Spindler and John Sculley are proof enough that not everyone is suited to run Apple.

Nevertheless, I struggle with the idea that Cook deserves that much more of the pie than the people who make it possible for him to move the vast quantities of hardware and services that allow Apple to post its billions in quarterly profits.

This isn’t an argument in the abstract, either. It’s becoming harder and harder to afford the basics of life—housing, food, transportation, childcare—in the United States, precisely because of this inequality. For example:

• Rents have increased an unprecedented 10%
• Consumer price index on food has risen 11.2%
• Childcare costs have surged 41%

The people with money are living the high life while wage workers are struggling to get by. But this is about more than money. Employees of large corporations are separated from decision makers by enormous gulfs of reporting structure and policy, with limited say in their day-to-day work.

Apple’s workers don’t just want more money, they want things like better scheduling and career advancement. The timing of when you work is everything: it impacts your ability to rest, to be with friends and loved ones, to meet educational goals, and otherwise determine the course of your life.

Scheduling in a recurring theme in many recent retail labor disputes, as in the case of Starbucks.

Amazon presents perhaps the most extreme example of how precarious today’s workers are. Six warehouse workers died when a tornado struck a distribution center in Illinois last year. Desperate drivers with no slack in their schedules have to piss in a bottle to meet their delivery quotas, as the company admitted to lawmakers. The company’s idea of worker well being is, in a bit that would go too far even for Severance, a phone booth-sized cubicle where workers can watch mindfulness propaganda.

Self-determination is an issue for wage earners across many sectors. The US sits on a knife’s edge as rail workers—over-scheduled and fighting for the basic right to do things like visit the doctor once in awhile—contemplate a nationwide strike that would grind logistics infrastructure to a halt. Those guys, at least, have a union.

To recap, workers are struggling with:

• The basics of reliable scheduling and paid time off
• Soaring costs of the essentials
• Their ability to advance their careers
• All the surplus value they create going to CEOs and Wall Street

In an economy that has produced enormous gains over the last decade, all of the fruits are going to the richest people in the system. After a global pandemic, in which frontline workers kept entire global economic order afloat, the rich are richer than ever, while workers are scrambling to pay the bills.

That’s why unions are popular. That’s why unions are happening.

There’s just no other recourse for such a wide-ranging, unfair, structurally entrenched bargain.

Contents

1. Developer experience is stewardship and conservation of precious cognitive resources, thereby maximizing accomplishment
   1. Example: Rust
2. Optimize your conversion funnel

Love it or not, developer experience is buzz that’s here to stay. As the next generation of technology firms is born, while the last generation struggles to stay relevant, one of the best ways to grasp and hold a growth trajectory is simple: become a technological dependency of other firms.

To do this you have to make the case for integration.

This is harder than it sounds. Many technological abilities have become commodified. Developer experience is a differentiation play.

A successful developer experience strategy emphasizes tactics like:

• Storytelling: Clear, ongoing narrative about the benefits of using a given technology so developers can understand where, why and how to apply it
• Education: Approachable material—including documentation and sample code—for developing a mental model of how a technology works and how it integrates into developers’ systems and workflows
• Onboarding: Low-friction, low-cost, high-speed onramps to test-drive a technology, allowing developers to get a feel for its abilities
• Ergonomics: Optimization of workflows, tooling, standard libraries and API design to limit complexity, protect against error and quickly address the most common use cases

All of these areas can contribute to success, but none of them will matter without a strong commitment to a simple cause: providing accomplishment.

Developer experience is stewardship and conservation of precious cognitive resources, thereby maximizing accomplishment

You get success by creating success for other people. To do this, you have to maximize the yield of accomplishment for time, energy and attention invested in your technology.

Shitty technology that frustrates the developer is always at risk. The moment an alternative comes along that better respects their time and, especially, their sense of motivation, the incumbent technology has only inertia and lock-in to save it.

Example: Rust

Rust is a specialized language. It emphasizes performance, correctness and safety, so it can have a bit of a learning curve as a consequence of serving those goals.

But getting started with Rust is easy. A thoughtful system of trams exists to get you quickly to the foothills of its learning curve. Consider its get started page:

• Automatic OS detection, providing a one-line terminal command to run an installation script
• Quick briefings on the basic components of the Rust ecosystem, with links to editor-specific plugins so expert practitioners can easily use their existing tools
• Introductions to common tasks, like creating new projects and adding dependencies
• Sample code for a basic Hello, World! written in Rust

By covering all the necessary introductory topics, this intro to Rust:

• Protects time and attention
• Relieves new users of research burdens
• Introduces tools and workflows
• Creates a rough mental model of how to interact with the tooling
• Generates a sense of accomplishment

It only takes a few minutes. Most of this time is the install script. By the end of a not-long page, you’ve gone from not having Rust at all to running your first program written with it.

Optimize your conversion funnel

At the top of the funnel are people who have problems and need to solve them.

At the bottom are people who have built solutions that depend on your technology.

Conversion funnels take effort, care and curiosity to optimize. You need to find the cheapest obstacle in the funnel that’s preventing your conversions, smooth it out, and repeat the process iteratively until your technology is easy—maybe even fun—to integrate.

A successful developer experience strategy is powerful because it creates irrefutable evidence of your technology’s value, in the form of people whose lives, projects and businesses are better.

For services and platforms whose billings scale with customer accomplishment, the math is obvious. Invest in your funnel, just like any other e-commerce business.

Contents

1. The adjustment
2. Making it yours
3. Design
4. You can be more comfortable and more effective

I had two reasons for buying ZSA’s Moonlander split keyboard:

1. It looks badass
2. My shoulders felt like chopped meat from decades of communing with the computer

Two years into serious, regular use, I can’t imagine life without this keyboard. Even typing that sentence makes me want to head back to the Moonlander website, buy a spare, and bury it in a sealed capsule just in case.

My Moonlander, with a few custom keycapsI like mechanical keyboards, but I’m not a hobbyist. Moonlander strikes a great balance between shipping a finished, beautiful product that you can nonetheless modify as much as you want. All switches are easy to swap, you get a keycap puller in the box, and the very geometry of the device can be adjusted to suit the size of your hands.

Moonlander isn’t a cheap keyboard. While the professional can justify the price tag, what may prove more expensive is the need to adjust your typing style. In my case, this required a month of practice during the evening.

Two years on, it was worth all the trouble. I can type faster than ever, my shoulders are happier and I’ve got a badass tool for work and play. This is more than a keyboard: it’s a high-bandwidth, high-comfort cybernetic interface with the device I use hours everyday for work and creativity.

The adjustment

I’ve been hunting and pecking on a keyboard since I was seven years old. By adulthood I’d gotten pretty fast at it.

This muscle memory was useless on Moonlander. Splitting the keyboard into two regions does great things for relaxing the shoulder muscles, but it requires renewed discipline.

Learning to type properly just takes a little time and dedication. Software helps, though, and initially I was frustrated by the juvenile or tedious options choking the web. Finally /r/MechanicalKeyboards led me keybr, which is geared toward building proficiency in grownups and respects the user’s time. It gradually expands your range of motion until you’re traveling the entire span of the board, using a series of algorithmically-generated words targeting just the set of letters you’re learning.

Within a month, spending half an hour each night practicing, I was a fluent touch typer. I never looked back.

Making it yours

Moonlander arrives ready to rock, but you’re still responsible for exploring and tuning it to fit your needs.

ZSA offers a nice web-based configuration tool that lets you specify your keyboard layouts. You can program basic keypresses with it, along with additional presses when you hold keys. You can create additional layers of functionality—think of how a shift or function key accesses different output—and even complex macros.

Moonlander ships with several blank keys. These are opportunities to make the key layout yours. With the configuration tool, you can assign any key a distinct backlight color, and these can change by layer. I’ve made destructive keys (delete and backspace) red, shift keys blue, navigation keys purple, and enter keys green.

Finding a groove in all this takes trial and error. The fruit of this investment is a custom-fit interface for all my common tasks, and a tighter bond with my computer than I thought possible. It’s like the bandwidth between my mind and the screen has leaped from dialup to cable.

ZSA supports this iterative process well. They hold onto your history of layouts under a user account on their website. Type layout.new into your browser and you’ll immediately edit your most recent version.

This customizability adds surprising utility. Because the left side of the board can be used alone, you can turn it into a macropad to help with gaming or 3D art. It’s easy to create new, task-specific layers. With all the backlighting, it’s easy to show which layer you’re in.

There are other elements to tune. The angle of both the main key surfaces and the thumb clusters can be adjusted using an included hex key. ZSA says this allows the board to accommodate hands of all sizes. My hands are one size, but with the included (removable!) palm rests, I’ve found it all comfortable for long, daily use. I’ve written everything from code to documentation to letters to my elected officials on this thing. It feels great.

Dialing in the height and angles can be a little fiddly. It required a few rounds of fine adjustment before everything felt just right. But what’s impressive is that the board has maintained this configuration all this time without my having to re-tighten any screws. I can’t imagine how much thought went into the boring engineering aspects of the design.

Like any mechanical keyboard, you can have opinions about the mechanical switches that transmit your intent into the computer. Moonlander makes these easy to swap as often as you like. I’ve stuck with Kailh Box Whites this whole time, but maybe I’ll play around with some per-key variations. You can make such swaps in seconds.

Design

The minimalism of the design is appealing but occasionally trips me up. The keycap legends for numerals don’t include the symbols you’d get from holding shift and for whatever reason, I don’t have that knowledge memorized.

Not all punctuation is immediately represented on the board either, and coders especially may find themselves doing several layout iterations just making decisions about where to put things like pipes and braces.

Further complexity here: the board looks best with a particular shape of keycap (OEM row 3). If you decide you want to augment the included keycaps with your own, you may find their heights or shapes to be discordant.

Ultimately, these are minor issues. The keyboard looks really, really cool. All of its distinctiveness is in service to blurring the lines between you and the machine.

The amount of customization this device affords is impressive. You can mount it on tripods, you can remove the thumb clusters, you can even pop a key out and use its empty space as an indicator light.

Building things is hard. Building nice things is harder. Building something this nice and this well-tuned to meeting user needs is something special.

You can be more comfortable and more effective

I’ve been typing nearly all my life. I was fine with it. It wasn’t the best it could be, but it was okay. Shoulder pain had me wondering about split keyboards for years, but it was the sleek, futuristic look of Moonlander that finally made me act.

There’s a good lesson here about credibility. Other split keyboards didn’t capture my imagination the same way. The design of the product suggested thoughtfulness, which made me think it was worth my time and energy to learn it.

I’ve been rewarded with a much more comfortable and effective relationship to a task I do every single day.

Moonlander is a power user product. Not everyone needs a keyboard they can customize to this degree. Not everyone is willing to make the tradeoffs needed to learn it. But if you interact regularly with a computer, you can get a lot of joy out of this board.

Let’s call this skill investigative reasoning. Or, more simply: “working the problem,” as Apollo legend Gene Kranz might frame it.

(Let me know if MIT press has a $70 textbook explaining this, situating it in computing or engineering, and giving it a name.)

This pile of skills manifests as a reflex to make sense of an issue or failure, locate its causes, and design a fix. Without these abilities, you’ll be helpless in the stew of complex abstractions that define modern computing.

Consider a common software development workflow:

• You write some code
• It’s compiled, bundled, or otherwise transformed
• The transformed code is transferred to a host device
• The host runs the code

Failures can emerge at or after any of these steps. The code may have errors which prevent compilation. The code may have errors despite compiling fine, and they manifest only at runtime, during certain circumstances. There may be a failure in the systems that package and transfer the code, preventing it from running.

The experienced practitioner understands their job is to analyze the problem, develop a hypothesis for its source within the many layers of technology they’re using, and validate the hypothesis by checking logs, adding print statements, starting an interactive debugging session, or using other means of peering inside of the machine. Working the problem may require research: finding online discussions of similar issues, or consulting documentation.

But core to this activity for both the novice and the expert is a simple conviction: problems can be understood and investigation can yield solutions.

At any level, hobbyist to advanced professional, confidence in this premise is the indispensable requirement. Without the confidence to work a problem, it’s unlikely anyone can make progress in building and integrating technical systems.

Domain experience helps to work a problem. Every class of system, from microcontrollers to native applications to web apps, has its own set of quirks, its own unique abstractions and design decisions where trouble may sneak in. But working the problem is a skill that transcends domains. Fixing a broken software development toolchain can prepare you for troubleshooting the boiler in your basement.

The paranoia in hiring for software roles, I’m certain, is rooted in the fear that the organization will hire someone who is unable to work a problem, outsourcing their investigative reasoning tasks to others, depleting productivity per unit of headcount budget.

Working the problem is also a feature of seniority. Translating business requirements into software development tasks is an expression of this skill, as is understanding how technical tradeoffs (debt) in existing systems will impact the needs of new features. The more senior a practitioner is, the more likely they are to feel a sense of confidence working a problem no matter how many layers stack up—even when those layers are “non-technical,” derived from business or customer needs.

We laugh at the fixation on languages or frameworks in job postings, but even the most thoughtful organizations struggle to identify this set of activities as a job requirement, much less map their outcomes within the team’s problem space.

Not every person is the ideal practitioner to work a given problem. The startup costs in research, learning new tools, or picking up domain-specific expertise, may outstrip any return on investment. Still, I have to think that applying a more conscious growth mindset around this would give our industry better results in recruiting, professional development and retention than we have today.

How can we elevate our understanding of these skills? How can we instill the confidence needed to work a given problem? How can we better train people to be able investigators?

I think there’s a lot of missed opportunity here.

America’s newest fiscal bureaucracy isn’t mincing words: they’re falling behind. Setting a 2034 target for 500,000 additional bodies in the labour force is the latest admission that the world’s third-largest economy is failing to keep pace. As Washington grapples with a fertility crisis, an aging population, and ongoing civil unrest, restoring consistent GDP growth remains an elusive goal.

Many of these wounds are self-inflicted. Since before the 2023–2024 global financial crises, America’s immigration policy has remained inflexible, even as it struggles to recruit everyone from microchip designers to bricklayers. Meanwhile the once-leading economy stubbornly refuses to fund common sense policies that would make it easier for the remaining population of non-working parents to join the labour pool, like subsidies for child care or after-school education.

The only remaining source of policy innovation within the American system is criminalisation, which Labour Reserve Chair Liz Cheney suggests will provide the lion’s share of new workers. Under American law, prisoners can be compelled to work according to dictates of the state, and this has long been exploited by American businesses eager for cheap workers. Since the financial crises, this approach has taken on new scope, through a programme of so-called “community incarceration.” Under this scheme, convicted criminals serve out their terms in housing of their choice but provide up to sixty hours a week of compulsory labour for everything from fast food and retail to computer network deployment. State governments dole these workers out at a fraction of America’s minimum wage, set to increase next year to $11.23.

This approach has obvious negative consequences for America’s long term outlook. Community incarceration may close immediate gaps in labour needs, but it doesn’t create new labourers from thin air. Democrats, who oppose the system, argue that it diverts disaffected youth from more productive directions, like higher education or work in the trades. Amnesty International, a global human rights group, suggests there is merit to this argument: in a new study they find that young people aged 15–25 are disproportionately represented in community incarceration, and they are charged overwhelmingly for crimes related to political demonstrations, which have become both commonplace and increasingly violent in the last decade. This in particular promises a negative feedback loop, as convicted criminals in the country permanently lose their right to vote, further alienating them from the legitimate political process.

By creating an underclass of young, forced labourers, America is ravenously consuming its seed stock. Far from stabilising their economic fortunes, the once-mighty power is setting itself up for yet another ride over a cliff. With its youngest workers cut off from both political engagement and entrepreneurship, America’s cultural and innovation economies will remain stalled, eclipsed by energetic new entrants as far flung as India, the Czech Republic, and Nigeria. Whether it meets its new 2034 labour targets or not, the former leader of the world economy seems committed to a path of economic hospice care and little else.

Contents

1. The experience of getting things done
2. Developer tools: pickaxes in a gold rush
3. Leverage within the developer experience domain
   1. Ergonomics and abstractions
   2. Documentation, reference and education
   3. Community and ecosystem
4. Developer experience: the business cases
5. This doesn’t have to be hard

Periodically, tech Twitter swells with a discourse I find to be weirdly unproductive: people arguing about developer experience. Does it even exist? Does it matter?

I’m not going to recap the various head-scratchers in detail. But I do want to talk about what developer experience is, why companies invest in it, and what we all get for their trouble.

Spoilers: it’s a problem of labor and software economics. A positive developer experience amplifies the return of effort invested building new software products.

The experience of getting things done

Put simply, developer experience is the sum of events that exist between identifying a requirement for a piece of software, and delivering code that satisfies it. Broadly, these events may be practical, emotional or social in nature.

Examples:

• Referencing documentation to plan an integration of a third party service
• Trying to install tools or libraries necessary to develop against a particular software framework
• Getting frustrated because of an unfamiliar or undocumented design pattern
• Successfully receiving help in a support forum when you get stuck

The practice of developer experience, of being deliberate in its design, is to identify the places of greatest leverage for clearing paths and relieving burdens. The objective is to improve adoption of a technology by making it easier to accomplish personal and business goals with it.

Developer tools: pickaxes in a gold rush

Selling developer tools is a straightforward business strategy. Rather than the risky bet of serving a broad consumer or cultural need, you can build technology that helps anyone who is building software be more successful.

Instead of trying to sell to hundreds of millions of users, you sell to a comparatively smaller handful of companies, typically by hooking their individual developers. If one of these customers succeeds, you succeed along with them, scaling your billings according to the growth of their business.

It sounds great, but there’s always a catch. In this case, your customers become a handful of technologists with a broad spectrum of experience levels and highly specialized needs. Your business success then rests upon a premise that’s easy to explain but harder to execute: making people more prosperous and effective because of your tools.

Leverage within the developer experience domain

How can you make people more successful and effective in accomplishing their goals? If we think of developer experience as the sum of all events between defining requirements and delivering them, we can identify some broadly recurring points of leverage.

Ergonomics and abstractions

Where the fingertips meet the keys, how does it feel to work with your tools? Does integration require painful, recurring boilerplate code, or can developers easily drop in your tools to solve a problem and keep moving to their unique implementation?

Is it easy to debug and inspect the state of your tools at runtime? When errors are thrown by your tools, is log output clear and descriptive, allowing further investigation and social troubleshooting on forums or Stack Overflow?

What is the everyday texture of life with your tool?

Tools that feel good to use obviously earn more loyalty, enthusiasm and word of mouth than tools that grate and frustrate.

Documentation, reference and education

How do people learn to use your tool? Do you provide clear documentation? Recipes? Tutorials?

What references exist for troubleshooting, debugging and discovery of features within your tool?

Thorough reference material makes it easier for developers to get the most out of everything you’ve built.

Community and ecosystem

Is there an active community experimenting and sharing their experiences with your tool? Is there a reliable, active, healthy venue where someone who is running into trouble can get help?

Is an eager community filling in gaps with their own tutorials, plugins, libraries and ergonomic improvements?

It’s easier to roll the dice on a new tool when you know that, should you need help, you’ll find a community that has your back.

Developer experience: the business cases

From these levers, we can identify the business cases for developer experience. In the context of the adopters of developer tools—whether individuals or teams—the question is whether a tool enhances their ability to be successful.

• Does it make iteration cycles faster and cheaper?
• Can you do more—accept international payments, integrate with legacy telephone systems, write code more correctly—with a smaller team and less effort?
• Does the tool limit everyday friction for accomplishing work, allowing more work to get done?

In a software production context, developer labor budget is among the costliest resources a business has to manage. Any tool that allows a business to get more for that budget is creating serious impact.

For purveyors of developer tools, the business case becomes clear as well. Success depends on adoption. You can improve adoption by addressing points of friction in existing developer workflows, and by making your tool’s experience more positive and productive than frustrating.

This doesn’t have to be hard

When we’re talking about developer experience, we’re talking about real things:

• How people feel when using tools and making software
• How effective they are in meeting their goals
• How these factors converge into amplified productivity versus wasted effort
• What leverage exists in your strategy to shift that balance more and more toward success for individual practitioners and businesses that might pay for your service

Developer experience is a process of shifting the economics of building software to be more favorable for every dollar or hour invested. There’s a lot going on there, but conceptually, this doesn’t have to be that hard.

Contents

1. ePaper and the valley of mediocrity
2. It’s just too bad
   1. ‘Note: Custom templates aren’t supported on reMarkable 1 or 2.’
   2. Ugh, the sync is bad
3. Yet I love it anyway

I’m obsessed with artifacts of the future.

Always have been. I didn’t save money as a kid. I blew every dollar I earned on electronics and computing devices. It’s a compulsion I’ve only slightly tamed in adulthood.

When I discovered reMarkable, it struck the futuristic note that captures my imagination. This is a digital notebook, storing as many pages of as many collections of notes as you can come up with.

It’s a frustratingly mediocre product.

But I love it anyway.

ePaper and the valley of mediocrity

Consumer devices have to climb out of mediocrity.

That journey can be painful, but what’s interesting is how much of it outsiders get to witness directly. When the iPad arrived, I had two competing thoughts:

• This thing is terrible. It’s heavy! It’s cold! I cannot stand how it feels to hold.
• This is incredible. It’s a Star Trek-level touch screen.

Tactile failures of the device lost to the power of its screen. I could live with the discomfort for the luxurious scale of all that multitouch. But I wouldn’t have to grit my teeth for long.

When the 2011 rev of the iPad dropped, everything terrible about it had been erased. It was thinner, it was lighter. It felt great to hold. You could snuggle up with it gladly on the couch or take a far-flung sweetheart to your pillow for a goodnight FaceTime call.

It was a comfortable companion, escaping mediocrity in the short span of just 11 months.

ePaper, meanwhile, remains stuck in the mud with no end in sight. There’s something greasy and disposable about any ePaper experience. It feels like newsprint. As mediums go, they’re spiritually similar: their goal is to produce a high volume of content as efficiently as possible.

Using ePaper is accepting:

• Ghosting of content
• Slow, flickery redrawing
• Periodic, Zamboni machine-like full-screen wipes to clean up

In exchange, though, you get a pleasant experience for consuming large amounts of information. ePaper feels like reading off of paper, hence the name. It’s about pigment that reflects existing light, not diodes blasting fresh light at you. Many find this soothing and restful by comparison to the typical high-speed screen. I certainly do.

reMarkable uses ePaper to offer a satisfying drawing and writing experience. When you’re in the zone, stylus on the screen, there’s nothing mediocre to its ePaper implementation. You see instant results, like pencil on paper.

It’s a joy to capture as much writing as I want without worrying about which notebooks I brought or how many pages they have left.

The rest of the time, though, the device feels constrained by the display technology. Even flipping pages has the barest delay, and these build up like sandpaper scuffs. Menus feel slow and flickery. I do my best to get in and out of the user interface contrivances as quickly as possible. They’re not pleasant enough to linger in, and that’s too bad.

Despite its imperfections, and its endless winter of mediocrity, ePaper is indispensable stuff. It’s more than good enough as part of a trade for bottomless, infinite notebooks.

Where things get heartbreaking is the software.

It’s just too bad

I’m going to focus on two pieces that really grate. They’re a missed opportunity for users and the platform alike.

‘Note: Custom templates aren’t supported on reMarkable 1 or 2.’

You have to be fucking kidding me.

We have the ability to create infinite notebooks to any structure or specification and your story for user customization is “well, maybe you could fuck off.”

That’s really sad.

The neat thing with reMarkable notebooks is that you can combine as many templates as you want, in any order you want. It’s powerful. The platform provides a quite a few templates, spanning everything from graph paper to music notation to chemistry.

The template library is a great starting point, but not enough.Still, a few dozen templates can’t come close to the full breadth of things that humans need. The creative magic of this age, where we can find it, is the ability to both tailor digital artifacts perfectly to our needs and share them freely for anyone else who might benefit. In the age of open source, reMarkable’s design is stuck in read-only mode.

Of course I bought it anyway, and that’s because there’s a workaround. Due to dependence on GPL code, reMarkable is obliged to provide root access to its embedded Linux operating system. From here, the savvy tinkerer can drop in new template content if they’re willing to navigate:

• SFTP file transfers
• Multiple file formats (PNG and SVG)
• Tinkering with a JSON file
• Stopping and restarting the process that draws the UI, via ssh session

It’s crummy, high-friction stuff. This should be a first-class feature. It multiplies the use cases and constituencies that could find a serious groove on the platform.

reMarkable’s PDF editing tools.reMarkable is wide open in its file handling. You can drop in, say, a PDF you put together with a bunch of custom-designed planner pages. But the authoring experience there doesn’t compare. To reMarkable’s credit, it’s possible to cut apart, reorder, and duplicate pages in a PDF. Still, the workflows to accomplish this aren’t nearly as low-friction as what you get in reMarkable’s native notebook metaphor.

It’s up to the vendor to solve this one, and they’re asleep on the job.

Ugh, the sync is bad

Look, sync is hard.

When it’s time to implement sync, I’m out getting a sandwich. Call someone else. I’m not saying that I think this is easy to get right. I am saying that if you’re going to bill $8 every month for this single device to sync content, it needs to be as near to perfect as computer science allows.

reMarkable is not there.

In a perfect world, my notes would be safely in the cloud the moment I pick up my stylus from the screen. We don’t live in that world. reMarkable only seems to update its cloud state when I explicitly close a notebook. So if I push away from the desk to do something else, the reMarkable abandoned, nothing is going to be available for review off-device until I manually activate the tablet again and close out.

Just sad stuff.

Yet I love it anyway

reMarkable finds itself in an awkward position.

It is a mediocre implementation of a wonderful category: the digital, paper-like, bottomless notebook. I love this category. Writing by hand can really help my focus, but I depend on so many digital workflows. Being able to combine the two, without distractions, has a lot of power. reMarkable seizes on this magic, emphasizing that it is

The only tablet that helps you focus

This focus positioning—acknowledging the harried, distracted life that must attend anyone comfortable spending $300 on a digital notebook—appears on the website and again on the collateral in the package. It’s a savvy tack to follow: the subjective experience of this combined technology is what creates enthusiasm. It’s more than the sum of its parts.

Even those parts that are mediocre.

But reMarkable must be careful. In ages past, the early captors of magic thought themselves invincible. Blackberry—with its expressive, high bandwidth, qwerty text input and realtime messaging—is dust today. A much less mediocre expression of that category arrived in the form of the touchscreen smartphone. Apple, Google, Samsung and countless others dissolved a giant that defined a category.

In some ways, reMarkable feels more niche than smartphones. Then again, what could be niche about writing and drawing things by hand? It’s a problem everyone has. I can think of so many contexts for learning, studying, creativity and collaboration where a writing tablet like this would be less distracting and disruptive than a laptop or touchscreen device. Someone is going to try to sell this to more people.

For me and my uses, reMarkable 2 is worth every penny. But one of these days someone is going to build a much better take on this category, and I really can’t wait to trade up.

I hope I can give my money to reMarkable again when that time comes—they’ve built something charming here, despite its flaws.

Contents

1. A business is a protocol
2. But we haven’t gotten to the runtime yet
3. Runtimes all the way down
4. Here be dragons

Protocols rule everything around us. In the digital age, that word has a specific meaning: you might imagine the protocols that govern WiFi, or your web browser.

Computer protocols enable interoperation, while facilitating common tasks. They specify how data should be encoded, how to exchange it, how to signal errors. Protocols are specific, running as deep as necessary, even to the physics of radios and radiation when necessary.

At the boundary between every system, protocols negotiate coherence and consistency. A network is a series of successful protocol interactions, cascading in waves.

From these protocols interacting, opportunity emerges. We can unite people who are far apart. We can transport vast quantities of media, on demand, to anyone who wants them. We can bank, we can shop.

This scope of opportunity is valuable. One of today’s dominant social castes is a group who can leverage these protocols to generate wealth and influence.

They know the most dangerous class of protocol there is:

An apparatus and process for turning money into more money.

A business is a protocol

If you give me two dollars and I can use that to make a hundred dollars, I’m rich. I can easily find someone willing to provide two dollars in exchange for five or ten the next day, which I can afford to share.

These are only the beginning of the economics that are possible in the age of globally networked computers on every desk, in every pocket.

So today it is possible to build a protocol:

• That acquires resources, like investor or customer money
• Applies those resources to labor, communications, and infrastructure
• Thereby generating wealth in excess of the cost necessary to run the process again

Airbnb is a protocol for maintaining and arbitraging a short term real estate rental brokerage network. Uber is a protocol for arbitrage of a labor and vehicle network. Facebook arbitrages their graph of human connection, selling interactions to advertisers.

These protocols are everywhere, and one thing that’s scary is that they don’t have great tools for caring about the human impact of their execution.

But we haven’t gotten to the runtime yet

The runtime that executes a business protocol is flaky. Its exception handling is dogshit in many cases.

A business protocol runs entirely on humans, even as it exists almost entirely in the space between them. Businesses want to maximize shareholder value at all costs, so any given human platform executing the protocol is expendable.

Even the originator of the protocol.

That is to say: once the protocol is alive, anyone can be thrown under the bus to keep the money flowing—including, once there are shareholders, the founders of the business. There’s endless games the savvy founder can play with the underlying legal structure to protect themselves at that point, but in our current system they still owe fealty to the money pump the moment other people own a piece of the pie.

Runtimes all the way down

Any given business runtime is hosted within a legal runtime. In the United States, there are enforcement mechanisms for shareholder maximization. Everything must be done in the service profit, or the protocol is viewed to be in a failure mode.

Everyone in serious power at a major business has a moral implant on their person at all times. “Fiduciary duty” obliges them to act in support of profit or face the violence of legal and financial repercussions. Fiduciaries are trusted with the highest privileges in executing the protocol, but they pay with responsibility to it.

In effect, the dark art of creating commercial protocols has a protection racket. Immediate enforcers at the top of the org chart, with bosses across the civic landscape enforcing everything from accounting conventions to securities law.

Where this gets scary is that these protocols don’t care what they metabolize, and their runtime is disincentivized from caring on their behalf. So, if pays well, Facebook converts advertiser dollars into hate speech, radicalization, pogroms, disinformation.

“It’s nothing personal. We just have to feed this dragon. It’s, uh, our fiduciary duty.”

Here be dragons

Dragons and their various warlords roam our economic countryside. They… take what they want. We’re not entirely powerless against them, but it’s still a lot of work to live with their predations, and we need more recourse than we have.

This is not the first age to live with the transformational impact of a successful business protocol. A century ago, Henry Ford and his contemporaries discovered powerful strategies, like assembly lines and dealer networks, to change the face of whole continents. An entire chapter of modern industrialization is dominated by the automotive industry, and its processes, proliferating to saturate the planet.

Serious human impact, whether we liked it or not.

Computer networks are a transportation of a different kind, but no less impactful than automobiles road networks. They make it cheaper than ever to bridge the gulf between a business and the customers whose money that business wants to collect.

Within a substrate of entangled legal, social and economic systems, it’s possible to build some truly gnarly war creatures. As long as profit is the only success condition vigorously protected at scale in this social order, we’re creating long term selection pressure for massive economic organisms that don’t care if they eat our social fabric or ecology.

That’s an exhausting and dangerous future.

Contents

1. Zack Shakked gushes about the game he cloned.A schism in tech
   1. Money fetishist?
   2. Computing’s true believers
2. The beef

So check out today’s Twitter beef.

You know all those emoji squares that have been popping up everywhere? That’s Wordle. It’s a bracingly earnest word puzzle web app deliberately built to a non-commercial ethos.

Wordle is a darling of the press because of its artisanal, small-batch sensibility. No compulsion loop—you get one puzzle per day. The score-sharing tweets are informational, instead of promotional. The tweet isn’t about getting your friends into a conversion funnel.

It’s about showing off your adventure and prowess.

Cue the stormclouds: a guy came in, saw the cultural fervor for this, and decided to build a paid, native application, using the same name and design. Taking people for $30 a year if they keep the subscription, this Pirate Wordle started printing money.

And its developer decided to tell Twitter about it, making him the day’s Main Character:

Zack Shakked gushes about the game he cloned.A schism in tech

The Wordle beef happens at a particular cultural fault line. Information technology has politics of all kinds, but one of the most strident is described on a spectrum:

Technology used for:

joy and wonder...sacks of money

Software automation is incredible. It offers leverage unlike anything we have ever seen. You can be worth billions of dollars because you build something that solves broad-scale problems for just $0.003 a user.

Sofware is all margin.

Which attracts the money fetishist.

Money fetishist?

Money’s only something you need in case you don’t die tomorrow.

Martin Sheen said this in Wallstreet, and it fucked me up for life. You can’t unsee it.

This is absolutely an age where having money has become synonymous with safety and security. I can’t fault anyone for trying to be okay, nor being strategic about it.

But some of us are building an entire identity around being the sort of person who has money, can get money, is the ultimate money chessmaster.

They’ll go through any trial proudly for access to a capitalism lottery ticket.

So, for the money fetishist, software is an irresistable lure. Software is a lottery ticket dispenser. There are scratcher tickets, like building indie software. Those are occasionally gushers of a win.

But the scale goes all the way up to lottery tickets in the shape of a term sheet for massive investment to build a company. Assemble the right team, seize the right market, and you’re a billionaire.

For this group, joy in computing is not always central to their goals. Mostly their participation in information technology is a cold calculation: “how many disposable robots can we build to seize a market?”

Computing’s true believers

To the other end of the spectrum, this posture is off-putting, even revolting. For better or worse, the opposing faction truly believes in the power and wonder of computing, for its own sake. Money is a second order concern to pursuing the magic of making sand have dreams.

It wasn’t always obvious—either individually or societally—that the computer was a money printer. For some folks, the computer was simple fascination. An all new frontier, defined by different rules than our everyday existence.

Everything that makes computers good at money also makes them interesting.

For many, huge chunks of a lifetime have been dedicated to exploring the power of a digital realm. Building up skills, knowledge and imagination for a playing field that’s not always intuitive, but so often rewarding as you develop mastery.

This is also a path to lottery tickets. Sometimes exploring the frontier leads you to a gold mine. But on this side of the spectrum, you’ve got people eager to explore computing as a creative endeavor first, grabbing what money they can in case they don’t die tomorrow.

The beef

Wordle exists at the maximal edge of this non-commercial ethos. It’s earnest in its humane approach. Instead of an engagement treadmill, Wordle is a limited, daily treat. Rather than promoting Wordle, the tweets for announcing a score simply describe the player’s adventure for the day as a score with some emoji. No URL.

In the context of Wordle’s cultural froth—all these articles, all these score tweets—developer Zach Shakked saw an opportunity. To take Wordle’s name, concept, and design, then strap a yearly subscription price to do it. Where the original Wordle was written for the web, this clone was built as a native iOS app, the better to capitalize on Apple’s built in payment system.

My bias here: I think that move is tacky as hell, and quite possibly legally actionable. You can draw your own conclusions.

Quite a few more took exception to this approach. Kottke sums up the prosecution’s case succinctly:

This person stole Wordle (a game @powerlanguish invented), put it on the App Store, and is now crowing about how rich it’s gonna make him. 🤬

What this beef can show us is a fault line in the culture of those who participate in technology. Some for fun, others for profit. This is a long-brewing conflict, and you can find the seeds of it going back generations.

This Wordle beef gives you a model for this conflict that’s small and fast enough to dissect as it happens.

But it’s not the only beef you’ll find in Wordle town. Did you know what those scores are like for screen readers?

Contents

1. Problem: configuration and iteration
2. Speaking to the Macropad
3. How do you say ‘red’?
4. Enter JSON
5. A tool to create tools
6. “How’d you do that?”
   1. Find an existing, successful artifact
   2. Write down your unknowns
   3. Learn how to communicate
   4. Build a bridge
   5. Fuck around, find out
   6. Don’t let perfect be the enemy of done
7. What about the configurator?

Adafruit’s Macropad is an absolute hardware delight: a cluster of 12 keys, with individual lighting, plus an OLED screen, rotary encoder and even a speaker! While external, programmable keypads are common enough, there’s something to this combination of features that makes the Macropad more than the sum of its parts. Photo via Adafruit It’s just a treat to hack on, and one of the rare technologies that makes me feel like a kid again, eager to experiment and try new things.

I want to walk you through a project I spun up recently, building a bit of macOS software to make my own Macropad easy to configure and tinker with: I’ll explain not just what I built, but how I approached solving the problems of integrating two very different systems speaking different languages. This stuff is essential to success in any sort of technology pursuit, but I always wish I saw more discussion of how we practioners do it.

Here’s my contribution.

Problem: configuration and iteration

I’ve been using programmable keyboards for years. Essential to success, for me, is iteration. Finding the most useful, comfortable layout of keys is a matter of trial and error, so I want to be able to experiment quickly with minimal friction.

So step one was finding whatever code existed to let me customize the Macropad’s key layouts.

Here I was in luck. Adafruit provides a terrific starting point:

MACROPAD Hotkeys

What’s brilliant about this example code is that fully utilizes the magic of the Macropad: turn its rotary encoder to move back and forth between pages of hotkeys. The page title and keymap labels are displayed on the OLED screen. This seems simple and even obvious, but to me it’s a game changer because it makes an enormous breadth of keymaps viable. I don’t have to memorize key positions, so I can make pages upon pages of keys to solve whatever common problems I have.

There was just one wrinkle:

The configuration ergonomics.

Speaking to the Macropad

Macropad is built on an RP2040. This is a microcontroller: a tiny, specialized computer that you can code to solve a specific problem. Microcontrollers are amazing for hardware projects like the Macropad because they’re able to wrangle all kinds of electronic components—switches, lights, screens—and make them as easy to program as any software application on your computer.

Microcontrollers are hidden everywhere in your digital existence, but hobbyist platforms like RP2040, Arduino and others come with an ecosystem for conveniently programming and troubleshooting them without any specialized hardware. Plug them in via USB and you’re off to the races.

RP2040 supports code written two ways: C and Python. Adafruit’s example code used Python, so I followed their lead.

Most of this code was exactly what I wanted. Crucially, they’d solved all the problems of acting like a USB keyboard for me, picked out all the libraries I’d need. This is real research and experimentation effort saved.

But in terms of iterating on my keymaps, it didn’t quite hit the spot. Here’s how you define keymaps in their example project:

    app = {                    # REQUIRED dict, must be named 'app'
        'name' : 'Mac Safari', # Application name
        'macros' : [           # List of button macros...
            # COLOR    LABEL    KEY SEQUENCE
            # 1st row ----------
            (0x004000, '< Back', [Keycode.COMMAND, '[']),
            (0x004000, 'Fwd >', [Keycode.COMMAND, ']']),
            (0x400000, 'Up', [Keycode.SHIFT, ' ']),      # Scroll up
            [...]

To tell the Macropad what to do for a given key, you provide an array of tuples, corresponding to each key, specifying:

• hexadecimal value for backlight color
• label string
• an array of key presses

Thus,

(0x004000, '< Back', [Keycode.COMMAND, '['])

would create a key labeled < Back, with a green backlight, sending the key combination Command-[.

Look, this works, but there’s a lot of cognitive overhead involved. You have to build up these tuples, do RGB hexadecimal math for the colors, and make a tidy Python dictionary.

Better than a stick in the eye, but I’ve been spoiled by decades of GUI configurators for my input devices.

Still, this project had 90% of what I needed. If I could find a way to ingest configuration content that could replace this fiddly Python dictionary approach, I could build an interface for programming the keymaps however I wanted.

So that’s where I went next.

How do you say ‘red’?

After digging through this example code, I wrote down all of the things I needed to communicate to the Macropad to build custom layouts.

Some stuff was easy: if you want to specify a label that shows up on a screen, you use a string.

Other stuff needed a little checking. The colors were specified using hexadecimal. In other languages, I often see RGB hex handled as a string, but in this Python code, it took the form of 0xXXXXXX.

I’ve seen this around since I began using computers, but I never needed to actually know what it meant. In this context, the hex was being treated as an integer, expressed in hexadecimal notation. A little fiddling quickly confirmed the Macropad would treat it with the same behavior of hex colors I knew from the web, so that meant I didn’t need to handle colors in a unique way, which was a good start.

My next question was: can I convert a string into a hex integer in Python? StackOverflow, naturally, had me covered:

int("0xa", 16)

Initialize an int with a hex string, specify that its base is 16, and Python will do the rest.

So that’s the colors sorted. I could handle them like hex strings, externally. Again, strings are easy.

A thornier question remained: what about special keys, like option, command, etc? In the sample code they’re represented with some sort of Python variable, so I went on a hunt to find where these were defined.

Keycodes were represented like Keycode.COMMAND, and a comment in the configuration files offered this hint:

from adafruit_hid.keycode import Keycode # REQUIRED if using Keycode.* values

From that, I can infer:

• Keycodes rely on an imported dependency
• That dependency lives in a library called adafruit_hid

Sure enough, googling that led me to Adafruit’s GitHub project for a HID library. What’s HID? It stands for Human Interface Device, and it’s the USB standard that allows keyboards, mice and other peripherals to communicate on behalf of you and me.

From there it was a matter of digging around in the project structure until I found keycode.py, which contained a mapping of keycode names to the lower-level values the HID library relied on.

Spoiler: it was more hexadecimals:

        LEFT_CONTROL = 0xE0
        """Control modifier left of the spacebar"""
        CONTROL = LEFT_CONTROL
        """Alias for LEFT_CONTROL"""

Well, I’d just learned that hex values could travel easily as strings, so that’s not a big deal.

With these questions answered, I had the basics for translating values from outside the Macropad into input that it could properly interpret.

Next I’d need a medium to structure and carry these values.

Enter JSON

JSON is great: it’s become a universal standard, so every platform has a means of parsing it, including Python. It’s easy enough to inspect simple JSON with your own eyes, so that helps with troubleshooting and iterating.

I started by thinking through a structure for a JSON configuration payload:

    Array: To contain pages of keymaps
    	Dictionary: To define a page
    		String: Page name
    		Array: To contain keys
    			Dictionary: To define a key
    				String: Color (as hex)
    				String: Label
    				String: Macro

It would require a few iterations to get this to a final form, but this early structure was enough to get things moving.

Next I needed to learn enough Python to parse some JSON.

One of the most important habits I’ve picked up in a career of programming: isolate your experiments. Instead of trying to write and debug a JSON payload parser inside of the existing Hotkeys project, with the Macropad reporting errors, I used an isolated environment where I could get easy feedback about my bad code. Replit seems to provide the most robust, one-click, “let me just run some Python” experience online right now, so that’s where I ended up.

I badly wanted to figure out how to convert my JSON into proper Python objects, whose properties I could access with dot notation. This is how I’ve done JSON parsing in Swift for years, and it felt tidy and safe.

But after chasing my tail reading tutorials and Stack Overflow for awhile, I gave up and accessed everything using strings as dictionary keys. It was good enough. Ergonomics in the parsing code weren’t essential. Once it was up and working, I wouldn’t need to fiddle with it very much.

    with open('macro.json') as f:
      pages = json.load(f)

    for page in pages:

      keys = page['keys']

      imported_keys = []

      for key in keys:
        color_hex_string = "0x" + key['color']
        color_hex = int(color_hex_string, 16)
        macro = key['macro']
        [...]

It’s straightforward enough. The code loads a JSON file, iterates through the pages, pulls values out of the dictionaries it finds, builds key-specific tuples, doing conversions on hex code strings, and then plugs it all into the App class provided by the sample code, which represents a page. These are stored to an array.

Again, I don’t know any Python, so this took a bit of trial and error. Getting immediate feedback on syntax and errors from Replit’s environment helped me work through the bugs easily.

Once it all seemed to parse JSON properly, I dropped my code into the Hotkeys project, replacing the part that went looking for Python configuration values.

Macropad was happy, displaying a simple configuration I’d written in JSON by hand.

We were on our way.

A tool to create tools

With the basics of a working JSON structure, and a means for the Macropad to translate that JSON into keymaps, the table was set for real fun:

Building a user interface.

I’ve spent the last couple years writing a lot of SwiftUI code, and I was eager to try it out on macOS.

It’s surprising how straightforward it is to build multi-pane, hierarchical navigation in SwiftUI: The old way, using AppKit and nib files, would have required loads more effort. But I had the basics of this up and working in a couple hours.

It’s not the most intuitive thing ever, but a quick google for swiftui macOS three column layout got me a terrific walkthrough that explained the process.

Next, I needed:

• A data model to represent configurations, pages, keymaps, etc
• A means of persisting those configurations so I could revise them later

For this, I turned to Core Data. It’s a polarizing technology. Many hate its GUI model editor, and it’s got plenty of complexity to manage. But as persistence strategies go, you can’t beat its out-of-the-box integration with SwiftUI. I’ve done enough time in the Core Data salt mines that I could quickly bang out the object graph I wanted and use it to generate JSON files. Best of all, the app could quietly store everything between sessions for easy iteration. Modifiers were a challenge. In a perfect world, I could import keycode.py into Swift somehow and directly reference the values. My reading suggests this is possible, but I couldn’t sort out how. In the end, I used a spreadsheet to transform the Python code into Swift enum cases, making the hex values into strings:

    enum AdafruitPythonHIDKeycode: String, CaseIterable, Identifiable {

        var id: String {
            return rawValue
        }

        case
        COMMAND = "0xE3",
        OPTION  = "0xE2",
        CONTROL  = "0xE0",
        SHIFT  = "0xE1",
        […]
    }

As an enum, it was easy to iterate through all of the modifier keys and represent them in the UI. Storing them as strings made them easy to persist in Core Data. In theory I could convert them into plain integers, and send them around that way, but this seemed like less work to debug.

With navigation working and a data model specified, I went to work on the editor UI. My requirements were simple:

• It had to visually represent the layout of the real thing
• Editing had to be fast and easy

What I ended up with was a grid representing each key. Clicking a key let you edit its color (with a picker!), label text, and output. Thanks to the magic of SwiftUI, and a generous soul on StackOverflow, it was even easy to provide drag-and-drop reordering of the keys. Clicking a button exported a json file, which could be saved directly to the Macropad—it shows up as a USB storage device on your computer. Now I could bang out a new configuration in seconds, and what I saw in the editor was what I got on the Macropad.

“How’d you do that?”

In all, it was a weekend of effort to get this all rolling. At one point, showing it off, I was asked “How did you know how to do all of that?”

It’s a system integration problem! I’ve been doing that sort of thing for 20 years, so for a moment, I didn’t know where to start explaining. But to summarize the above, here’s how I approach this kind of challenge:

Find an existing, successful artifact

I need to start with something that already works. In this case, I found example code for the Macropad that was directionally aligned with my own goals. I’ve gotten so far in this game with example code. Code gives you pointers about how a system works, demonstrates its assumptions. It also gives you a starting point for your own approach.

No matter the system, in this age, you can usually find code that successfully interacts with it if you google hard enough.

Write down your unknowns

Once I’ve examined a working artifact, I usually end up with more questions than answers.

Hacking a project together is as much investigation as it is creation. I grab a pad of paper and keep track of my biggest unknowns to give that investigation its shape. After examining the example code, my biggest questions were:

• How does it represent color?
• How does it represent keycodes?
• How do I parse JSON in Python?
• How do I get feedback from the Macropad when things break?

If I found answers to these, I could build an external system that talked to the Macropad.

Learn how to communicate

The next step is asking “how does this system expect me to communicate my needs?”

Understanding the structure and format of data is essential. This step is equal parts research and experimentation. I need to find whatever documentation exists, even if it’s just source code, to understand the requirements and expectations of the system. Then, I need to try to create a working data structure of my own that the system will accept.

Build a bridge

Once you understand how to speak to a system, you need a means to reliably, repeatedly do so.

Here, I wrote a simple JSON parser to ingest and interpret external output into something Python could understand for one side of the bridge. On the other, I wrote Swift code that could generate JSON according to the expectations of that parser.

Fuck around, find out

With your unknowns revealed, a communication strategy understood, and a bridge constructed, you’re ready to start playing around. Experiment with different approaches until you get the results you’re looking for.

Sometimes you’ll break things. Use version control to keep track of your experiments, so you can always roll back to something you know is working.

Adafruit recommends the simple but effective Mu Python editor, which provides a serial connection to the Macropad. When I broke things, I could get hints about what went wrong that way through console log messages sent to the serial monitor.

Don’t let perfect be the enemy of done

Throughout this process, there were “better” ways to accomplish what I wanted. I wish I could have parsed JSON into native Python classes, I wish I could have imported the keycode.py content more transparently into Swift. In a well-behaved Mac app, you can double-click a list item to edit its name. Still not sure how to do it in SwiftUI.

It’s easy to get bogged down in the perfect solution, but I try to prioritize progress over perfection. Do the ugly, hacky thing first, then keep moving. If it comes to bite you later, you can rewrite it. Probably you’ll have a lot more context later, so the deeper solution will be better informed than if you’d tried it from jump.

What about the configurator?

Sure, here’s the code:

macOS Project

Macropad code, as adapted from the MACROPAD Hotkeys project

Stuff I might add in the future:

• Properly signed binary builds of the configurator (right now you’ll need Xcode)
• JSON import
• Multi-part macros with delays
• Undo support
• Direct user input capture of keystrokes

Maybe this will inspire you to write a web application that does the same things.

Hope this look inside the hack was interesting. Drop me a line with any questions you’ve got about how tech gets built. Much love to Adafruit, which makes the coolest electronics hobby stuff around. Projects like this Macropad remind me why I fell in love with technology:

• Exploration
• Experimentation
• Magic

I have the life and career I have because once upon a time, I learned how much fun it was to make things that talked to each other.

Fascinating twitter thread by game professional Mike Bithell about the strategy Unreal is telegraphing with their recent Matrix Awakens tech demo:

Epic is attempting to flip the economics of game production towards film production.

In games, we build everything, so (and this is over simplification) game assets are a fixed cost. If I need 100 things, I need to pay for the production of one thing a hundred times. Efficiencies come in, but we’re still building stuff from scratch.

An open world game? Only possible if you’re a mega AAA company, or you wanna stylize to the point of affordability. Movies don’t build a city, they find one to shoot in. They buy props and costumes. They hire actors rather than making westoworld style puppets.

Asset stores brought some of this prop shop and central casting mentality to games, but the problem is aesthetic consistency. If you buy 10 characters off the unity store and throw em in a game together, unless you did so very tastefully, it’s gonna look shit.

With Epic offering a fully coherent city backdrop, including efficient rendering technology and consistent design across characters and architectures, Mike argues, it becomes cheaper than ever for game devs to tell stories in realistic settings. This introduces new tradeoffs:

But it also increases the quality bar and expense of doing anything that’s not on the shelf. Alien characters? Metahuman with some forehead bumps added… 6 limbs is expensive and out of scope. Guns? Take a prop gun and stick something on it. 90s film solutions.

I think it’s interesting to see Epic touting this just a year after Cyberpunk 2077, an ambitious platform play that nearly tanked its developer, CDPR.

Sure, Cyberpunk is a game. It’s also an elaborate canvas of characters, settings, and content that they can use to sell a series of stories. Night City is enormous and immersive, and the initial story they shipped barely scratched the surface of all they built there. I suspect we’ll see plenty of paid DLC over the next few years leveraging that investment.

CDPR was hoisting an enormous open world, full of systems that could be reused. The complexity of pulling something like this off is formidable, and in Cyberpunk’s case, led to a disappointing initial release full of technical issues, especially for players on older consoles.

Epic, decades deep into the game engine business, understands most teams don’t have the capital or risk appetite to undertake that level of investment, but sees a similar opportunity to build that kind of canvas and rent it out.

As Bithell says:

Movies don’t build a city, they find one to shoot in. They buy props and costumes. They hire actors rather than making westworld style puppets.

The software economics of games continue to evolve.

Contents

1. Once upon a time
2. Oculus Quest 2: surprisingly okay
3. The software is meh
4. What about Apple?
5. Any day now

Oculus Quest 2 is a decent device that costs a bit less than a nice iPad, and far less than a television and console.

From a performance perspective, it’s surprisingly decent.

I’m no fan of Facebook, but curiosity got the better of me. I grabbed a Quest 2 to find out if we were any closer to a big shift in culture, communication and commerce.

I think it could happen any day now. But it’s not here yet.

Once upon a time

We’ve been holding our breath on VR since before I entered puberty. At malls and fair grounds, you could get a taste of the future by strapping into a monstrous set of gear.

It never really took off, to say nothing of coming into the home.

Placing ourselves into a simulated environment that our eyes and ears will believe is a challenge. It requires tracking the user’s motion in space at multiple levels. Not just where the body is moving, but where the head is pointed. More advanced systems might even track your limbs. All of this, historically, has required significant computing power, specialized hardware, and complex software.

But over the last five years, VR has gurgled back into life. I’d call Vive the best early expression of modern VR, but that’s not saying much. To make Vive work, you needed:

• A high end gaming PC with a powerful graphics processor
• Permanently mounted “lighthouse” devices for tracking your movements in 3D space
• A heavy, bulky headset tethered to your PC by several cables
• Big, awkward controllers to track your hand movements

Once you assembled all of this, you were rewarded with an experience that resembled reality as viewed through a screen door. To trick the mind into a 3D experience, a VR rig needs to produce two images: one for each eye.

So you need to be able to drive a lot of pixels in order to make 3D work. This is challenging in two places: computationally, as more detail requires more horsepower, and physically, as pixel density makes display panels like those used in a headset more expensive. In Vive’s case, managing these costs meant a distracting black grid seemingly overlaid on an otherwise plausible visual field.

Really, you were perceiving the gaps between pixels.

So, just to get into VR you were talking about $500 for Vive hardware and another $1500 for a high end gaming PC. In exchange, you got a mediocre picture and occasional headaches.

Literally, headaches. I had to take the thing off sometimes because of nausea and other discomfort.

It wasn’t a great value proposition.

That doesn’t even get into how technically complicated the first-run experience was. It required so many cables plugged in, so many pieces of software installed, so many interlocking systems to debug. This was an experience only arch nerds could make sense of. It had no chance at mass-market appeal.

But that was five years ago. Today, things work a little differently.

Oculus Quest 2: surprisingly okay

Oculus, meanwhile, has been aggressively pushing away from the tethered, PC-dependent model.

In the case of Quest 2 (hereafter Q2), cameras built into the headset itself determine your motion through space. Cameras track the motion of controllers you hold in your hands, allowing you to interact with virtual menus and environments with surprising accuracy.

This is not Facebook’s first attempt at such a self-contained system. Vive and Oculus have both been trying to crack the nut of self-contained VR for several iterations now.

From a hardware perspective, it’s important to note that Q2 is merely okay, not excellent. The included straps are terrible, though upgrading to a better mounting system is possible. Adjust the straps wrong and you’ll have a splitting headache within minutes. But get them too loose, you’ll find your view blurry, and a different sort of headache sets in.

VR is still fiddly.

But far less fiddly than it ever was.

Onboarding for Q2 was far easier than anything possible five years ago. You install an app on your phone, sign into Facebook (ick), and pair with the Q2 hardware. From there, you settle into the headset and follow some prompts.

This is a much more consumer-friendly experience. Anyone with tech savvy, or even just a teenager nearby, will do it with no problem.

All of this sets the table for a simple proposition: a computer tricking your brain into thinking you exist somewhere that isn’t real. Q2 can do that reliably and somewhat comfortably. Even the screen door effect is much improved. Unlike early adventures with Vive hardware, Q2 has the resolution to convincingly hide the technical details that drive its experience.

The question is:

So what?

The software is meh

Once you’re in virtual reality, what’s supposed to keep you there?

Right now, not a lot.

There are some compelling novelties. Beat Saber is a rhythm game that’s fun and physically demanding, and probably more interesting than any cardio equipment you’ll ever find at the gym.

Google’s Tilt Brush, an early entry in the modern VR game, might be one of the more magical creative experiences you’ll ever have. With Tilt Brush, you can paint in 3D space using both mundane pigments and with light. Five years on, it remains my favorite way to spend time in VR.

There’s other stuff. 3D games, from shooters to puzzle things to a remake of Myst. Facebook would like you to conduct meetings in VR. Altspace, now owned by Microsoft, provides a multi-user environments, and Facebook has a competitor in a closed beta.

This software runs the gamut from meh to okay. But I don’t think there’s a killer app that’s going to inspire fanatical, compulsive engagement with an experience you can only get in VR.

Yet.

Still, the economics of Q2 create a strange situation. At $299, it’s compelling enough to be an interesting gift. By comparison to a console, it’s a bargain. A PS5 costs $499. Add the television you need to play it, and a console experience edges toward $1000.

Will enough people buy them to trigger the network effects needed to start the next phase? If so:

• All new venues for social media emerge. The basic experience is already cognitively persuasive enough, and everyone is tired at looking through their friends and loved ones through a screen. The illusion of sharing space despite distance will be compelling, to say nothing of sharing physical experiences. But it only works if there are people there you want to share the space with.
• A customer base large enough to gamble real money on emerges as well. If you can make money building VR experiences, you might make a metric fuckton of money making the one experience people can’t live without.

Facebook, of course, badly wants these outcomes. The challenge is in the IP. Sure, Q2 cheaper than a PS5, but so is a plastic bucket, and neither a bucket nor a Q2 is going to be able to play Spiderman or Final Fantasy.

Unless something changes, Facebook is gambling on novelty and a middling content catalog to move these units.

What about Apple?

Of course, Apple has been sitting this one out.

Remember how VR needs lots and lots of pixels, the better to trick your eyes? The thing about Apple is that they have an unparalleled advantage in energy efficient processors that push loads of pixels. Their portable graphics prowess is the best in the business.

Better graphics efficiency means nicer screens and smaller batteries, making for lighter and more comfortable headgear. Apple has been shipping denser and denser screens on lighter and lighter devices for over a decade.

The thing about Apple, though, is that they don’t ship their experiments. They ship when they’re convinced they’re poised to gobble up the entire high end of a hardware market.

Meanwhile, they’ve been laying the foundations for augmented reality and social VR:

• ARKit, Apple’s framework for realtime compositing of mixed reality content, has been through years of iterations
• Apple is two years into ramping up production of miniaturized LiDAR sensors, as used in their Pro-level iPhones and iPads
• The new SharePlay API allows users in different locations to synchronously enjoy the same content

Most of all, Apple already has thousands of developers using these APIs and trained up in their ecosystem. If they release a new category of hardware, they have both the software infrastructure and the developer base to make a play for that vaunted killer app.

Rumors have said for years that Apple is targeting 2022-2023 for such a device. But who knows when they’ll move?

Any day now

Facebook is making lots of noise. They have a mindshare advantage, thanks to close consumer relationships built for 16 years, and their pricing is damned compelling. We’ll see how many Oculus units go home this holiday season. Microsoft wants their own crack at the pie, Vive is still out there, and Apple is locked in their wizard tower, up to god knows what.

It’s still anyone’s game, and so far there’s nothing to inspire deep FOMO.

Still, with the hardware firming up like this, a whole new paradigm could kick off any day now. For better or worse.

Keep your eyes peeled, and don’t forget your dramamine.

Here’s a post from someone in the r/antiwork subreddit detailing their difficulty in getting started in a gig at Amazon:

One hour was spent trying to make the surround sound work for endless power points. Another hour was spent by HR basically gloating about how proud she is about catching people faking reasons for time off. This woman literally was so proud about how she will literally call funeral homes to make sure the person you said died, is in fact actually dead. She was so arrogant about it. It really disgusted me.

Next the safety guy came in and said a few things, only to leave without an explanation. Then they have us log into these iPads. They’re wondering why we can’t. We never received login information to do so. That took another hour. I tried logging in with the info i had for the recruitment website but that didn’t work. They gave up.

The post is really sad: OP needed accommodation for their ADHD, but didn’t have a supportive environment to get it, so walked from the job.

All of this is of a piece with other stories we’ve heard from Amazon: they don’t care about attrition, especially at the warehouse level. Using people up is the business model.

But the obvious downside of this is that their institutional knowledge is constantly running out the door. After awhile no one knows how to do the basics of essential tasks like onboarding, so the jobs are miserable for new folks from the very first second.

Hard to imagine that doesn’t catch up with them eventually.

Contents

1. Foundation on TV+ is an intro to imperial power
2. And what is an empire?
3. An imagination toolbox

It’s important to understand the War in Iraq as an imperial snuff film.

You can get away with a lot in America—destroying the financial system, illegal arms dealing, laundering drug money—without earning the lasting ire of our ruling class. America is an empire long ago built on a two-tiered view of human worth.

It’s a country that has always treated some of its population as disposable. Which neatly limits your accountability for malfeasance. Consequences are for those other people. Up to a point.

Because of this two-tiered state of affairs, American life is not itself treated as sacred by our culture, nor is civic duty.

But the American Status Quo is another story. Rupturing that has consequences.

While we lost thousands of American lives on September 11th, 2001, we also lost our sense of the status quo. Bankers, politicians and generals spent their day feeling out of control. Literally running for their lives, American elites felt visceral fear as they evacuated their strongholds in the wake of a terrorist attack. The people who were supposed to be safest in our country, the people with the most power, were scrambling.

This is not how the United States is supposed to work.

Bush and his courtiers spent the day in a nightmare of uncertainty, vulnerability, and disconnection. The resulting trauma of that crisis was pumped and massaged into the psyche of the country. In the US, calls for blood were in the media, in the halls of government, in the corner bar.

Somehow, Afghanistan wasn’t enough.

Meanwhile, Iraq never had anything to do with 9/11. Didn’t, as claimed, have terrifying weapons that could end Our American Way of Life as We Knew It. What it did have was people who bore a passing cultural resemblance to the attackers who shattered our status quo. It had a feeble dictator.

So 18 months later we all watched on cable news while the American Empire made a demonstration of its power. The world’s most expensive military made quick work of Iraq’s defenses, concluding its invasion in less than one month.

Saddam, humiliated, was yanked out of a hole in the ground nine months after that. Then they killed him.

Truly some street thug shit, sending a message: the US can still deal pain and destruction wherever we want it. For the rest of the world, this was a threat. For US citizens, this was meant to be a reassuring promise.

For the traumatized elite, it was confirmation that they were still in control.

Foundation on TV+ is an intro to imperial power

Vague spoilers within.

One of the gifts of science fiction is its ability to reframe our perception. Spaceships are fun, but they’re not really the point. What matters is that, from a new frame of reference, we can imagine old problems fresh. We can discover new points of view, freed from the biases of history and prejudice.

At its best, science fiction is a social simulation laboratory.

In the case of Foundation, the simulator projects an enormous, tactile model of empire.

How does empire work? What are its traits?

Foundation takes all these details and zooms in on them, letting us roam around and examine systems of empire. Imagine a sociological museum filled with exhibits, diagrams and models. By imagining a powerful state that spans an entire galaxy, Foundation lets us really dig into the mechanics of power.

And what is an empire?

Empires are… civic eruptions. They’re tangled webs of logistics capacity, resources of all kinds, military power, and self-justifying ideology. Empires can snowball, since having all of these things in combination lets them seize more capacity, more resources, more power. Ideology provides the moral and cultural lubrication needed to enable these actions.

The challenge is stability. All that growth comes at a cost. Administrative overhead, yes, but also the resentments of those on the losing end of encounters with the empire.

Foundation explores the practical, visceral experience of an empire: the brutality of power, the terror of instability and decline. We join Cleon as his Peace is interrupted in a terrorist attack. He responds, much like America, with attacks that do little more than destabilize the game board and slake popular bloodlust.

“You can’t play chess with someone who’s willing to set the world on fire.”

Like America, Cleon lets his subjects watch on TV as the empire enacts revenge upon far-away people who or may not have had anything to do with their crisis.

An imagination toolbox

America is an empire in decline. I walk with the trauma of watching a leviathan crumbling around me.

Logistics capacity sputters. Resources are squandered to fight enemies we don’t have. Meanwhile, hundreds of thousands of lives are snuffed out by a plague—many multiples of those lost on 9/11.

Large swathes of our population are too ignorant to shield themselves from that plague now, even as science provides badly needed, effective protection. Meanwhile, grifters take money for cures that don’t work.

We’ve always had the ability to shut down the covid pandemic within our borders. We’ve just never bothered to do the work.

There is, everywhere, an air of discontent. The minor burghers who would once bully the poor into low-wage work now lament shortages of people willing to be so oppressed. Labor actions are more and more frequent, as conditions grow so dire that workers must overcome decades of crumbling union power and organize.

Of course, injustice abounds. We see corruption out in the open, we see the innocent oppressed for the color of their skin, we see righteous anger in the streets.

And we see white supremacists trying to overthrow our government.

Meanwhile, basic needs go unmet. US citizens, supposedly the freest people in all the world, can’t access medical care without risking financial calamity. Wages have stagnated. We’ve abandoned updating the federal minimum wage. Homelessness is a soaring problem, and though we have the resources to solve it, like covid, we just can’t be bothered.

I don’t know what’s next for this place. Not knowing is scary and tiring.

Foundation enters with metaphors, postulates and scenarios to help me imagine the long term consequences of what I see around me. All of it is rendered in exquisite, emotional, human detail. So far, we’ve touched:

• The civic conflict between science and theology
• Terrorism
• Colonialism and the tribute paid to tyrants
• Use of mathematics, engineering and science to enhance the imperial machine
• The use of media narratives to support the whims of the political elite
• Gerontocracy and the consequences of long-term elite crisis
• Civil unrest as an outcome of the dimming light of the state
• Imperial meddling in the political process of client states

Every detail of the writing is thoughtful, but every other detail is just as attentive. The production design brings us exquisitely rendered costumes, architecture, vehicles and props. A poignant, utterly distinctive score from Bear McCreary (Battlestar Galactica) sews everything together into perfect emotional resonance.

The cast is, of course, unbeatable.

Lou Llobel and Jared Harris are giving these roles everything they’ve got.

I’m haunted and captivated by what I’ve seen so far. I chew on the stories between viewings, and find so much integrity in the web of cause and effect they describe.

The scale of the story works. It’s a successful epic that feels both adult and nourishing. Where Game of Thrones hit us with brutality to be provocative, Foundation finds more subtle ways of taking our maturity seriously—without pulling punches on the brutality of empire.

It’s not necessarily feel-good. But it’s a story of how to recognize, survive and mitigate a difficult moment in history. It’s a story about putting a floor on darkness and chaos. It’s a tribute to our power to stand up to tyrants and chart a new course.

I don’t know about you, but I need a little of that right now.