I've been using Claude Code heavily lately, and while doing so, I've been casually watching the OpenClaw codebase evolve. What I've witnessed mirrors a pattern I've seen play out with every agent framework before it — and it's worth talking about. OpenClaw is a remarkable project. It went from zero to one of the most-starred repos on GitHub in under a week. And now, with AI agents actively contributing to its own development, the codebase is doing something extraordinary: it's expanding at a pace no human team could match — or meaningfully oversee. A month ago, the repo sat around 400k lines of code. Now it's pushing 1 million. Daily commits are holding steady above 500. There's even a lean fork — nanobot — that replicates the core functionality in roughly 4,000 lines, advertising itself as "99% smaller." That contrast alone tells you something important about what's happening to the original. From a software engineering standpoint, this is not a sign of health. Velocity without comprehensibility is just entropy with good PR. What we're witnessing is a codebase that has crossed a threshold: it is no longer humanly maintainable. No engineer can meaningfully review these commits. No architect can hold the system model in their head. Technical debt isn't accumulating — it's compounding, at AI speed, every single day. This raises a question I can't stop thinking about: Does there exist any project in the world that can grow sustainably — maintaining architectural clarity while continuously expanding functionality — with zero meaningful human involvement? Not "AI assists humans," but genuine autonomous stewardship of a living codebase? If that's possible, then what kinds of projects still can't be fully AI-maintained today? Is it complexity? Ambiguity in requirements? The need for taste and restraint? And the deepest question: will we eventually reach a point where every software project can be fully maintained by AI — including the AI systems doing the maintaining? My instinct is this: AI is extraordinarily good at local optimization. Write this function. Fix this bug. Add this feature. But "keeping a system simple" is not a local problem. It requires global aesthetic judgment — the ability to say "we could add this, but we shouldn't." That kind of restraint might be the last genuinely human contribution to software engineering. Or maybe I'm wrong. Maybe future AI systems will develop something like taste. Maybe they'll learn that the most important code is often the code you don't write. I genuinely don't know. But watching a codebase grow from 400k to 1M lines in a single month, driven almost entirely by agents, makes me feel like we're all about to find out — whether we're ready or not.