DHH's new way of writing code
Six months ago, David Heinemeier Hansson was openly skeptical of AI coding tools on Lex Fridman's podcast. Then, over winter break, something changed dramatically—he went «agent first» on everything. What triggered this complete reversal from one of the most opinionated and experienced builders in tech? And if a developer who values craft, aesthetics, and beautiful code above all else is now letting AI write most of his software, what does that signal about the shift hitting the entire industry?
Pontos-chave
DHH's stance on AI didn't change philosophically—the tools simply became good enough. Opus 4.5 in late November 2024 was the inflection point where agents could consistently produce merge-worthy code at DHH's exacting quality bar.
Senior developers are seeing 5–10x productivity gains because they can validate agent output and redirect when needed. Junior developers, unable to assess quality at that level, are becoming more vulnerable as pure implementation work gets automated.
The real unlock isn't efficiency on existing work—it's tackling projects that would never have been started before. DHH's team is now optimizing the fastest 1% of requests (P1) and building features in days that previously would have taken months.
Ruby on Rails is experiencing a renaissance because it's one of the most token-efficient ways to build web apps, making it ideally suited for agent workflows. DHH believes aesthetics is truth: when code is beautiful, it's likely to be correct.
The constraint is shifting from implementation to product judgment. Developers who can't also do product thinking, talk to customers, and exercise taste will struggle, while those who combine technical depth with business sense become vastly more valuable.
Em resumo
We may have reached peak software engineer: AI agents are now good enough that senior developers can 5–10x their output, shifting the constraint from implementation to taste, judgment, and deciding what to build—making those who care deeply about craft more valuable, not less.
The Inflection Point: From Skeptic to Agent-First
Opus 4.5 in November 2024 changed everything for DHH overnight.
DHH's transformation wasn't philosophical—it was empirical. On Lex Fridman's podcast six months earlier, he had dismissed AI autocomplete tools as infuriating, like someone constantly interrupting mid-sentence. The models weren't good enough, and the ergonomics were terrible. Then Anthropic released Claude Opus 4.5 on November 27, 2024, and everything shifted.
The combination of a frontier model that could consistently produce merge-worthy code and agent harnesses that gave AI access to terminal tools created a new paradigm. DHH describes it less like managing a team and more like stepping into a «super mech suit» where he suddenly has twelve arms and can look at seven screens simultaneously. He's still the one doing the work—just hyper-accelerated.
Within weeks, DHH went from code-first (opening his editor, writing code, occasionally consulting AI) to agent-first (starting every project by instructing agents, reviewing their output, and making alterations only when needed). The speed of this shift shocked even him. What took months or years to contemplate—like optimizing the fastest 1% of requests—now happens in days as side projects.
The Unix Philosophy Validated
CLIs and agent harnesses prove the 1971 Unix philosophy was right all along.
The Unix Philosophy Validated
DHH realized that building CLIs for products like Basecamp, Hey, and Fizzy wasn't just about making them easier for agents to use—it was about validating the fundamental Unix philosophy from 1971. Small tools that interoperate with pipes can now be orchestrated by agents across GitHub, Sentry, Basecamp, and more. An agent can check errors in Sentry, post a write-up to Basecamp, create a GitHub pull request, and comment back—all autonomously. The 50-year-old design pattern is suddenly the cutting edge of AI orchestration.
How DHH Works Today
Two models at different speeds, constant review, and git diffs.
Start with agents DHH opens a split-screen layout: NeoVim editor on the left, OpenClaw running Kimmy K25 on top right, Opus in ClaudeClaw on bottom right, and a terminal strip at the bottom. He tells one or both agents what he wants.
Review the diff He switches to NeoVim and uses LazyGit to review the changes. If the code looks correct and matches his aesthetic standards, he commits immediately. If not, he either instructs the agent to revise or edits the code himself.
Iterate or merge The ratio of agent-generated code he merges as-is has skyrocketed. Early on, he spent hours writing code himself. Now, agents produce the first draft, and he supervises. The speed is intoxicating, but he's careful not to treat it like a limited sale—it's sustainable, not a sprint.
Run multiple agents in parallel For complex problems, DHH sometimes has two frontier models—Opus and another—ping-pong on a plan, critiquing each other's approach. Once the plan is solid, he kicks off execution. This is how he tackled adding dual-boot support to his Linux distribution, Omachi.
The 100 Pull Requests in 90 Minutes
Claude reviewed a backlog that would have taken a week in under two hours.
“I went into GitHub and we had I don't know 250 PRs pending and I kind of just sighed a little bit… I just asked Claude to review URL and the URL is the issue or is the PR. In 90 minutes, I think it was, I processed 100 PRs. And it wasn't that I merged all of them. In fact, I'd say I merged a small minority. Maybe 10% got merged as is. Then maybe 20% got merged but with Claude's implementation… This would have been a week's worth of work, days at the very least. What the heck? And even more than that, Claude's analysis of at least half the issues pertained to things I knew nothing about where it was undeniably a smarter, better reviewer, programmer that I could ever dream to be.”
The Projects That Would Never Have Started
Senior vs. Junior: The Widening Gap
Senior developers are 5–10xing productivity while juniors face an uncertain future.
Aesthetics Is Truth: Why Craft Still Matters
Beautiful code is likely correct code—taste is becoming more valuable, not less.
DHH has always believed that aesthetics and correctness are inseparable. «When something is beautiful, it's likely to be correct,» he says, citing mathematics, physics, and software as domains where this principle holds. This philosophy is why he fell in love with Ruby—it produces the most beautiful code in his view, with an elegant balance of expressiveness and pragmatism.
In the age of AI agents, this philosophy becomes even more critical. Agents can generate working code, but can they generate beautiful, maintainable, coherent code that fits the grain of the system? Not yet—at least not without supervision. DHH won't merge sloppy agent output any more than he'd merge sloppy code from a junior developer. His bar remains sky-high, and that bar is what makes agent acceleration work for him.
This is why DHH believes standout designers and engineers who care deeply about craft will become more in demand, not less. As implementation gets commoditized, taste, judgment, and the ability to shape systems with intention become the differentiators. The constraint is no longer «can we build it?» but «should we build it, and what's the right way?» That's a deeply human question, and one that requires aesthetic sensibility as much as technical skill.
Designers as Product Managers and Implementers
At 37signals, designers figure out what to build and how to implement it.
Most companies treat designers as people who make specs look pretty. At 37signals, designers are product managers, implementers, and decision-makers rolled into one. They figure out what should be built, how it should work, and increasingly—with agent acceleration—how to implement it in CSS, HTML, and even JavaScript or Ruby.
DHH argues that when you combine these three roles—product thinking, visual design, and implementation—you get someone who understands the materials they're working with. They know how CSS stretches, how HTML structures, how the web wants to behave. It's like a jewelry designer knowing the properties of gold or an architect understanding load-bearing structures. Working natively in the medium produces better, more coherent results.
With agents, this model is becoming even more powerful. Designers at 37signals can now use AI to produce full working prototypes, not just as they'll be merged, but as a demonstration of the final shape and interaction. DHH recently hired a designer, Zoltan, who embodies this philosophy—someone who can think, design, and build fluidly across the entire stack. DHH believes this convergence of skills, now accelerated by AI, is the future of how great software gets made.
Peak Software Engineer: The Argument
We may have seen peak demand for programmers as pure implementers.
Peak Software Engineer: The Argument
DHH's provocative thesis: we've likely reached «peak software engineer» in terms of the guild-like scarcity that commanded high salaries simply for knowing how to code. Before, programmers were the constraint—you couldn't ship without them. Now, agents are loosening that constraint rapidly. More software than ever will be produced (Jevons' paradox), but that doesn't mean all programmers are safe. Anywhere software is a cost center—which is most places—there will be intense pressure to cut headcount. The developers who survive and thrive will be those who combine technical depth with taste, business sense, and the ability to validate and shape what agents produce. Pure implementation is being automated. Judgment is not.
Ruby on Rails and Token Efficiency
The Intoxication and the Trap
Working with agents is so effective it's dangerously addictive.
“When you can be this effective and impactful on an hour of supervision of these agents, it's really intoxicating. And I need to go, do you know what? This is not like a limited sale. AI is going to be here next month and the months after that. I cannot just operate as though it is a limited sale and I need to get all the dopamine harvested within the next two weeks.”
Key Numbers from the Conversation
Data points that illustrate the scale and impact of DHH's AI shift.
What Drives DHH: Love of Computers
Wealth and success never changed the mission—building with computers is the goal itself.
DHH has been financially independent for years, yet he works as hard as ever. Why? Because he genuinely loves computers. He's been obsessed since age five, whether playing video games, building software, or tinkering with Linux distributions. For him, wealth was never a checkpoint or a finish line—it was a byproduct of doing work he found deeply satisfying.
He rejects the notion that leisure is the ultimate goal. Psychological research consistently shows that purpose and mission are what make people happy, not passive consumption of free time. DHH has seen this pattern play out with every entrepreneur who sells their company, sits on a beach for three weeks, and then immediately jumps back into building something new.
Right now, DHH is more excited about computers than he's been in decades. Agent acceleration feels like the early 2000s when he first discovered Ruby—a time of pure creative possibility. He wakes up every morning intensely curious about what's happening in AI, what's possible today that wasn't yesterday. And while he's careful not to sacrifice sleep, health, or family time, he's leaning in hard. The mission remains the same: make things with computers, make them beautiful, and share them with the world.
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Glossário
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