Kernel code by AI, Tesla's Robotaxi miles, and the friction in the machine

00:00:04 DHH made a claim on Thursday worth considering. He replied to someone saying that Mac, Windows, and Linux are all now developed by AI — and he went further: very soon, every line of code going into the Linux kernel will be written by AI. That's a big prediction.

00:00:22 But what made the story interesting today wasn't just the claim itself. A couple hours earlier, DHH announced that Omarchy was hiring Emir Beganović as Head of Infrastructure. He starts in November. This is the Omacom Foundation's third full-time hire — joining someone on Kernel and someone on Shell.

00:00:42 So you've got a grand prediction about the future of kernel development paired with concrete infrastructure investment happening right now, three people deep. The team is small. Three full-time people already have leads for kernel, shell, and infra. That's a different shape than what you see at most AI companies right now where headcount runs into the hundreds.

00:01:07 DHH is confident AI will write kernel code, and he's been right about those calls before. The real tension is what happens when a small funded team starts building something in parallel with Linux rather than trying to fix it from within. There are four hundred-thousand lines of code in the average kernel today.

00:01:29 Some of that complexity comes from decades of patching, compatibility layers, and hardware abstraction. Some of it is genuinely good design. But a lot of it is just historical momentum — features layered on because removing them would break things nobody has time to debug.

00:01:48 If AI systems get good enough at understanding the kernel's implicit contracts, they could accelerate the kind of cleanup that human maintainers have been unable to do for structural reasons. Or they could produce something entirely new that doesn't carry Linux's inheritance burden.

00:02:07 Either way, having a real team working on it rather than just an opinion on X changes the baseline.

00:02:14 Over in autonomous vehicle news, ARK Invest posted about Tesla's Austin Cybercab event from last week. They commercialized first rides Tuesday evening after unveiling the two-seat, purpose-built vehicle Monday. The tweet itself was truncated — the full numbers got cut off in the post — but what they disclosed was that in just six weeks of operation, the Robotaxi fleet increased its unsupervised driving miles.

00:02:43 ARK posted it with a chart, nine hundred likes and three hundred thousand views. Six weeks isn't a lot of time for autonomous vehicle metrics. But it's also not nothing when you're talking about unsupervised operations. The difference between supervised and unsupervised isn't just a data label — it changes the entire liability model, the operational requirements, and what kind of safety infrastructure you need to justify.

00:03:12 The scaling story looks like Tesla's usual trajectory: rapid initial growth followed by regulatory friction that reshapes the metrics nobody expects. The Austin launch came after months of preparation. The real test for unsupervised fleet operations tends to show up around month three, when you've exhausted the low-hanging operational scenarios and have to deal with edge cases that don't appear in your training distribution.

00:03:42 Six weeks in, you're still collecting data on how the system behaves during fleet operation. The interesting period starts when the data tells you something unexpected.

00:03:53 Watcher.Guru reported that Nasdaq invested one hundred million dollars in Payward, the parent company of Kraken. The tweet got two thousand likes and a couple hundred retweets — institutional money flowing into crypto infrastructure is still newsworthy enough to generate that kind of attention.

00:04:14 This milestone differs from the AI stories. It's about capital allocation — specifically, where institutional money goes when it wants exposure without betting on a specific token price. One hundred million isn't a massive number for Nasdaq, but it signals confidence in Kraken's regulatory trajectory more than anything else.

00:04:37 If they're willing to write that check today, they're making an assessment about which crypto entities will survive the next round of compliance requirements. Two stories today — AI infrastructure hiring and traditional finance investing in crypto infrastructure — that both come back to the same question: what does it actually cost to run reliable distributed systems broadly?

00:05:03 The answer keeps getting bigger than anyone expects.

00:05:07 Nathan Lambert published a piece on interconnects.ai — actually back in March twenty-second — that's been sitting in the archive for a while. It maps onto something I've noticed in how we talk about AI progress this week. His argument is straightforward: models will become core to the development loop but won't cause fast takeoff.

00:05:30 He calls it lossy self-improvement instead of recursive self-improvement. The key insight lives in three bottlenecks he identifies. First, automatable research is too narrow. Models can optimize single metrics — test loss, reward scores — but navigating many metrics at once is what actual researchers do.

00:05:52 There's a gap between on-paper accuracy and what users find productive that benchmarks don't capture well. Second, adding more agents hits saturation from human supervision limits. He gives the example of organizing thirty to forty AI agents for a researcher — some people can get value from that scale, but not many.

00:06:15 The bottleneck isn't compute; it's who decides what each agent does and when to pivot. Third, resource bottlenecks and politics. Even as models improve, humans control resource allocation within organizations. The billions of dollars in research compute are unlikely to be totally isolated for end-to-end AI experimentation.

00:06:38 There are always political layers between the model and the resources it needs. The piece's line that stuck with me: the bottom of every sigmoid feels like an exponential — but that's what it looks like before the complexity hits. The current news cycle layers predictions right on top of each other.

00:07:00 DHH saying kernel code will be AI-written. Tesla announcing unsupervised Robotaxi scaling in weeks. ARK posting charts. Meanwhile, Nathan is arguing that the complexity brake on advanced systems will hold back whatever acceleration we're seeing. Both can be true at different levels.

00:07:20 The progress from AI-assisted development is tangible and worth being excited about. The complexity problem is also something you can build for.

00:07:30 Two things showed up in the relevant feeds today. They tell very different stories. On one side, two Reddit posts — one from the Anthropic subreddit saying a researcher would burn their equity for a one percent higher chance of survival, another from the OpenAI subreddit quoting a Meta AI researcher who quit and said if OpenAI wanted to cripple a nation they could unleash an agent swarm.

00:07:55 Both scored zero with zero comments. They're image reposts circulating on the fringe, no source verification, no engagement. The anxiety behind them is genuine enough to spread, but the provenance isn't. Meanwhile, Jim Kessler from Third Way argued that the AI community needs open safety research and open-weight models for defense — before the next attack inevitably happens.

00:08:20 Thomas Wolf reposted it. It's a policy position grounded in what people actually build rather than worst-case scenarios. And then Ethan Mollick, who works with these tools daily, wrote about managing agents: if you're not steering long-running agentic work or building interim reporting for visibility, you probably aren't managing agents enough.

00:08:42 That's a practical note from someone using the tools that matters more than either the fear posts or the policy positions. Three different registers of the same question: what do we actually build and how do we control it? The answer depends on whether you're reading about worst-case scenarios, writing policy recommendations, or setting up monitoring dashboards for your team's deployment pipeline.

00:09:08 The local model sees all three versions of this story and compresses them into roughly the same vector. That compression is its own kind of friction — not the kind Nathan Lambert wrote about, but a different one entirely.