Nathan Labenz: prediction

Transcript context

…Yeah, I don't think it's that concentrated. I also don't want it to be that concentrated. I think we've seen over the last couple of days what you have when we've just seen the start of power concentration and we've sort of seen some of its more unwelcome effects. So I don't like, I both don't think that it's true that basically machine learning is now over and all we need to do is like write the checks. And I also don't want it to be true. Because I think there's still kind of a synthesis there that I don't mean to suggest that machine learning is over, but the analysis I've come to kind of time and again is like people may invent new techniques that are enough to change the field, change what's possible, accelerate things, maybe make dramatic improvements to the safety profiles. But it seems unlikely that anybody's going to create such a breakthrough that scale isn't still a hugely important factor. But if I guess if you don't agree with that, then that would maybe imply that you would expect that like perhaps new entrants to the top tier could emerge. And I think that would be like fairly surprising to at least me and probably a lot of people. Yeah, that's exactly what I think. I mean, this is often like at any given moment, the incumbent looks incredibly dominant until they don't. Like IBM looked like an unstoppable force in computing at some point. Intel was like the dominant the sole provider almost of computing power. at any point, the big companies have some have the advantage of scale, they have many disadvantages, but I think the sort of lesson of history is more that, although things look like immediately unstoppable, sometimes, although to be honest, I don't think that many people are really trying. In the end, like it doesn't, it really doesn't work out that way.…

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