Dwarkesh Patel: belief

Transcript context

…The thing which I was referring to not feeling different is, okay, such and such company announced some difficult-to-comprehend dollar amount of investment. I don’t think anyone knows what to do with that. But I think the impact of AI is going to be felt. AI is going to be diffused through the economy. There’ll be very strong economic forces for this, and I think the impact is going to be felt very strongly. When do you expect that impact? I think the models seem smarter than their economic impact would imply. Yeah. This is one of the very confusing things about the models right now. How to reconcile the fact that they are doing so well on evals? You look at the evals and you go, “Those are pretty hard evals.” They are doing so well. But the economic impact seems to be dramatically behind. It’s very difficult to make sense of, how can the model, on the one hand, do these amazing things, and then on the other hand, repeat itself twice in some situation? An example would be, let’s say you use vibe coding to do something. You go to some place and then you get a bug. Then you tell the model, “Can you please fix the bug?” And the model says, “Oh my God, you’re so right. I have a bug. Let me go fix that.” And it introduces a second bug. Then you tell it, “You have this new second bug,” and it tells you, “Oh my God, how could I have done it? You’re so right again,” and brings back the first bug, and you can alternate between those. How is that possible? I’m not sure, but it does suggest that something strange is going on. I have two possible explanations. The more whimsical explanation is that maybe RL training makes the models a little too single-minded and narrowly focused, a little bit too unaware, even though it also makes them aware in some other ways. Because of this, they can’t do basic things. But there is another explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. When you do pre-training, you need all the data. So you don’t have to think if it’s going to be this data or that data. But when people do RL training, they do need to think. They say, “Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.” From what I hear, all the companies have teams that just produce new RL environments and just add it to the training mix. The question is, well, what are those? There are so many degrees of freedom. There is such a huge variety of RL environments you could produce. One thing you could do, and I think this is something that is done inadvertently, is that people take inspiration from the evals. You say, “Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?” I think that is something that happens, and it could explain a lot of what’s going on. If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance, which is something that we don’t today even understand, what we mean by that.…

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