Seems like there's a good argument to be made that we'll have plenty of opportunities for valuable growth and learning, just about different things. Just like it's always been with technology. The machine does some of the stuff I used to do so now I do some different stuff.

IMO the more salient point is that bugs requiring multiple weeks of human work aren't going away! Claude has actually not been trained on, say, a mystifying and still poorly-explained Java concurrency bug I experienced in 2012, which cost a customer $150,000. Now in 2026 we have language-side tooling that mitigates that bug and Claude can actually help a lot with the rewrite. But we certainly don't have language tooling around the mysterious (but now perfectly well-explained) bug I experienced in 2017 around daylight saving's time and power industry peak/off-peak hours. I guess I haven't asked, but I can almost guarantee Claude would be no help there whatsoever.

Just so many confusing things go wrong in real-world software, and it is asinine to think that Mythos finding a ton of convoluted memory errors in legacy native code means we've solved debugging. People should pay more attention to the conclusion of "Claude builds a C compiler" - eventually it wasn't able to make further progress, the code was too convoluted and the AI wasn't smart enough. What if that happens at your company in 2027, and all the devs are too atrophied to solve the problem themselves?

I don't think we're "doomed" like some anti-AI folks. But I think a lot of companies - potentially even Anthropic! - are going to collapse very quickly under LLM-assisted technical debt.