“If you look at the ELO score of chess bots since the 80s, there's just a very linear increase in ELO over time — but there's this huge discontinuity as they cross the human range: human experts always win against AIs, and then human experts never win against AIs. So far AI capabilities haven't been that big a deal in terms of their end economic impact, but that's because they're slowly rising in ELO relative to humans.”

Yeah, the only hope really is if deep learning just can't get us to an AI which is at least can dominate human research and human development, including the human ability to come up with new paradigms and so forth. Or like, I don't know, maybe humans would also never have discovered the next learning architecture, but to the extent humans could have discovered it eventually. But it just seems like, I don't know, if you just look at the progress that's happened in 2012 till now, and you just continue that on. I mean, I know if that's been powered by huge amounts of compute scaling and so forth, but it would be weird if like it just didn't get to the point where it could dominate humans, at least in RD, especially over the next few years, there's going to be. Ryan Greenboff was on the podcast recently, and he made this point that you could imagine as AIs get more and more capable and are capable of making progress on simulations which incentivize getting better at not only AI RD, but generally at science. So this is a thing that all the labs are targeting, many startups are targeting. Or another intuition pump is if you look at the ELO score of chess bots since the 80s, there's just like a very linear…