On productivity: I feel the baseline for productivity has moved up. As a personal anecdote, I had a startup before with 25+ engineers. Now we are a startup of 2 co-founders and a couple of interns, and if I step back, I feel we can produce a surprising amount of stuff. But producing is just the input side, which brings me to 'care'.
On somebody needs to care enough: I meant that AI agents by design have no intrinsic motivations, so (stating the obvious) they do not really care if the product works or actually solves the user's problem. This puts the onus on the human to care for the problem to really solve it, vs just building something. With increased AI output there are too many things to care about which the AI won't, and I believe that's why people might be working more and not less
I think we are talking about the same thing and reaching wildly different conclusions. If "producing is just the input side" and you still need people to check if the product really solves the user's problems, then I get to conclude that the bottleneck has never been the "generating the code" part of the work, and actual productivity - i.e, delivering things that actually solves the customer's problem - is pretty much the same.
I think we agree on the diagnosis: generating code was never the whole bottleneck. Where we differ is the conclusion. Work is implementation plus verification (specing, testing, checking it actually solves the problem). Pre-AI, implementation was so slow that verification hid inside its schedule - you verified while the next thing was being built. AI collapsed implementation time, so verification is now the exposed bottleneck - and the cheapest way teams absorb that today is more human hours. So: output per person is up, hours up too, and the delivered-value gain is real but smaller than the raw output gain suggests. Which I think explains both our observations.