The popular story about AI and companies is about headcount: smaller teams, fewer workers, the coming one-person business. That is the surface. Underneath sits a ninety-year-old question about why companies exist at all, and AI is the largest shock to its answer in a generation, though not in the single direction the hype assumes.
Anthropic's biggest developer event of the year shipped no new base model, and was still a major month for AI capability. That isn't a contradiction. It's the clearest sign yet that the frontier has moved from the model's weights to the architecture built around them.
For forty years, the thing that made software the best business in the world was a quirk: serving one more user cost almost nothing. AI inference puts a meter back on every answer, and that quietly unwinds the economics that scale, freemium, and 80% margins were all built on.
AI tools have made writing code faster and cheaper. They have not made software development easier, and by lowering the cost of creating complexity, they may have made it harder.
SWE-bench improvements get read as a developer tools story — better Cursor, better Claude Code. That's real. It's also the smaller of the two effects. Better coding means better agents, and the gap between those is wider than it looks.
Non-profit research organizations occupy a structural position in AI that no commercial institution can replicate: technically serious, independently positioned, and optimized for knowledge rather than product. METR and Transluce are among the clearest examples of what that independence enables.
A note on what separates founders who hire well from those who don't — and why the difference rarely shows up in the interview itself.
Scattered observations from a few years of trying — what worked, what was theater, and what I would do differently.