He's been vibecoding some stuff himself personally, on one of his scuba projects. You could take people as actually believing in the things they do and say.
Are you aware of how productivity has increased over the past century in general? That didn't lead to 100x wage increases or more free time. Labour is a market commodity and follows market rules. Increased productivity means more gets done in less time. It doesn't mean you spend less time working
You get AIs to prove their code is correct in precisely the same ways you get humans to prove their code is correct. You make them demonstrate it through tests or evidence (screenshots, logs of successful runs).
Tao's broad project, which he has spoken about a few times, is for mathematics to move beyond the current game of solving individual theorems to being able to make statements about broad categories of problems. So not 'X property is true for this specific magma' but 'X property is true for all possible magmas', as an example I just came up with. He has experimented with this via crowdsourcing problems in a given domain on GitHub before, and I think the implications of how to use AI here are obvious.
Does a system being deterministic really matter if it's complex enough you can't predict it? How many stories are there about 'you need to do it in this specific way, and not this other specific way, to get 500x better codegen'?
It's an extension of how I've noticed that AIs will generally write very buttoned-down, cross-the-ts-and-dot-the-is code. Everything gets commented, every method has a try-catch with a log statement, every return type is checked, etc. I think it's a consequence of them not feeling fatigue. These things (accessibility included) are all things humans generally know they 'should' do, but there never seems to be enough time in the day; we'll get to it later when we're less tired. But the ghost in the machine doesn't care. It operates at the same level all the time
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