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My working thesis about the generalized impact of “AI” as it is currently deployed can be summed up in the observation that the arc of AI bends toward demoralization.
And so everything is lazy and deferred and functional and reusable inside the compiler. And it's a very different way of writing compilers than than what the textbooks will traditionally teach you.
So the funny thing, my funny answer to this is I don't actually think a lot of the fast growing companies are using data well at this point. And the main reason why is because traditionally things just didn't grow that fast.
I think it's actually the opposite which is that everyone's kind of using the same data which is you need such an enormous amount of generalized text that the amount that Google has or that meta has is not actually enough to move to be a kind of fundamental difference in what you can train with.
I don't think all parts of the economy can absorb intelligence equally. So let's just say we develop fairly generalized super intelligence. I always use the analogy like you can invent a lot of drugs, but if clinical trials still take a long time, you're not necessarily going to get new therapies rapidly.
I think for me, the frontier in modern physics, where the new physics lies is not in high energy particle physics, it’s not in quantum gravity, it’s not in any of these sort of traditionally sold, “This is going to be the newest deepest insight we have into the nature of reality.” It is going to be in studying the problems of life and intelligence and the things that are sort of also our current existential crises as a civilization or a culture that’s going through an existential trauma of inventing technologies that we don’t understand right now.
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