> The bifurcation is real and seems to be, if anything, speeding up dramatically. I don't think there's ever been a time in history where a tiny team can outcompete a company one thousand times its size so easily.

Slightly overstated. Tiny teams aren't outcompeting because of AI, they're outcompeting because they aren't bogged down by decades of technical debt and bureaucracy. At Amazon, it will take you months of design, approvals, and implementation to ship a small feature. A one-man startup can just ship it. There is still a real question that has to be answered: how do you safely let your company ship AI-generated code at scale without causing catastrophic failures? Nobody has solved this yet.

> There is still a real question that has to be answered: how do you safely let your company ship AI-generated code at scale without causing catastrophic failures? Nobody has solved this yet.

It's very simple. You treat AI as junior and review its code.

But that awesomely complex method has one disadvantage, having to do so means you can't brag about 300% performance improvement your team got from just commiting AI code to master branch without looking.

> how do you safely let your company ship AI-generated code at scale without causing catastrophic failures? Nobody has solved this yet.

Ultimately, it's the same way you ship human-generated code at scale without causing catastrophic failure: by only investing trust in critical systems to people who are trustworthy and have skin in the game.

There are two possibilities right now: either AI continues to get better, to the point where AI tools become so capable that completely non-technical stakeholders can trust them with truly business-critical decision making, or the industry develops a full understanding of their capabilities and is able to dial in a correct amount of responsibility to engineers (accounting for whatever additional capability AI can provide). Personally, I think (hope?) we're going to land in the latter situation, where individual engineers can comfortably ship and maintain about as much as an entire team could in years past.

As you said, part of the difficulty is years of technical debt and bureaucracy. At larger companies, there is a *lot* of knowledge about how and why things work that doesn't get explicitly encoded anywhere. There could be a service processing batch jobs against a database whose URL is only accessible via service discovery, and the service's runtime config lives in a database somewhere, and the only person who knows about it left the company five years ago, and their former manager knows about it but transferred to a different team in the meantime, but if it falls over, it's going to cause a high-severity issue affecting seven teams, and the new manager barely knows it exists. This is a contrived example, but it goes to what you're saying: just being able to write code faster doesn't solve these kinds of problems.

I swear in a month at a startup I used to build what takes a year at my current large corp job. AI agents don't seem to have sped up the corporate process at all.