The Ledger, every show

“And I don't think we're headed to that world largely because of the fact that the models rely so much on large scale test time compute. In order to achieve their greatest intelligence. If you, if it requires so much test time compute to unlock the full capabil…”

“What we're seeing today with the modern models is that 5.5 and other models can think for, if you scaffold them reasonably well, can think for weeks even before having performance plateau on some of these benchmarks.”

“I use it day to day for a lot of this kind of stuff, and I think they're at a point now where They've actually been at a point for a while now where I feel like I can just trust the outputs, arguably more than I could trust the output from a human.”

“I think it's pretty clear that as these models get bigger, they have a world model, and that world model becomes better with scale. So they are implicitly developing a world model, and I don't think it's something that you need to explicitly model.”

“If these models become smart enough, they develop things like theory of mind. They develop an understanding that there are other agents that like can take actions and have motives and all this stuff. And these models just develop that implicitly with scale and…”

“This is why I'm interested in the reasoning direction, because I think there's this whole other dimension. That people are not scaling right now, which is the amount of compute at inference time.”

“And so I think the proper way to, and so my claim is the proper way to evaluate the models now is you either have some kind of budget for the benchmark, whether it's tokens or cost or time or whatever, or you plot the performance as a function of the amount of…”

“It's really easy to show you can do much better than previous benchmarks or previous, previous models on benchmarks by just, for example, scaffolding a bunch of models together. So if you say, okay, well, we're going to, instead of just running this model once…”

“And I wouldn't be surprised if, you know, six months or a year from now, the model is able to do zero shot an entire poker solver, basically my entire PhD thesis in one go.”

“The preparedness frameworks and responsible scaling policies, they don't really account for the amount of tests I'm computed. They just say, okay, well, what's the capability of the model? The problem is we're in a world now where the capability of the model i…”

“We used an internal model at OpenAI a few weeks ago to disprove the unit Erdos unit distance conjecture.”

“The model release cycle is every, every couple months we put out a new model that's even more powerful, and so the cost of disproving the Erdos unit distance gesture drops by, like, 10 or a hundred x with every model release cycle. Probably, in some cases, mor…”

“We are trying to encourage people to not spend all their time just, like, Going through all the mathematical open problems, physics problems, and just seeing, pushing the models to their limits to see what they can prove or disprove. Because we really think th…”

“Go ahead and like look at all the published work and synthesize that and then try to come up with something novel and it's not able to do it. And I can give it a lot of time and it's still not able to do it.”

“I would talk to researchers about we, it makes sense to show the benchmarks with an x-axis, whether it's tokens or cost or time, there should be an x-axis, and everybody would say, like, yeah, that makes sense, we should do that, but. Well, really, their respo…”

“So at this point, like, you know, the truth is, you know, GPT-IV-O and like O-III, these models are like passing the Turing test.”

“And that is very clearly a domain where you don't have an easily verifiable metric for success. It's very like, what is the best research report that you could generate? And yet these models are doing extremely well at this domain. So I think that's like an ex…”

“The ideal harness is no harness. Right. I think harnesses are like a crutch that eventually we're going to be able to move beyond.”

“We've said pretty openly that we want to move to a world where there is a single unified model. And in that world, you shouldn't need a router on top of the model. So I think that the router issue Will eventually be solved also.”

“Like, we're gonna scale it, sure, we're gonna scale these things up by a few more orders of magnitude, they're gonna become more capable, but we're not gonna see superintelligence from just that. And like, yes, if we had a quadrillion dollars to train these mo…”

“And so if you have an AI model that's, like, actually really aligned, To you and your preferences, then that could end up doing a way better job than a human could. Well, not, not that it's doing a better job than a human could, but like it's doing a better jo…”

“And I think that if you're able to have them cooperate and compete with billions of AIs over a long period of time and build up a civilization essentially, the things that they would be able to Produce and answer would be far beyond what is possible today with…”

“I think that a lot of the approaches that have been taken have been very heuristic and haven't really been following like the bitter lesson approach to scaling and research.”

“Basically, when you're playing, like, the zero-sum games, like, poker, Game Theory Optimal works really well. When you're playing a game like Diplomacy, where there's, like, you need to collaborate and compete, and you need, there's room for collaboration, the…”

“My point is that, like, this is where the AlphaGo analogy breaks down. And, not necessarily breaks down, but, like, it's not going to be as easy as self-play was in AlphaGo.”

“These models are becoming more efficient in the way they're thinking, as they're able to do more with the same amount of test time compute, and I think that's a very underappreciated point, that it's not just that we're getting these models to think for longer…”

“I think the Turing test is no longer really a useful measure the way it was intended to be. Certainly Just because we have bots that can, I wouldn't say they can pass the Turing test, but I mean, like they're getting close enough that it's no longer that usefu…”

“It's not clear that data really is the bottleneck on performance here. And I've talked to AI researchers about this, and I think there isn't as much of a worry about this as people might think. Probably that's because there's a lot more data that's out there t…”

“Like we also found in chess and go, we actually ran this experiment. If you do. Just pure supervised learning on a giant data set of human chess and go games. The bot that you get out from that is not an expert chess or go player. Even if it's like conditioned…”

“I think the major challenge with Using things like reinforcement learning for trading is that it's a non-stationary environment. So you can have all this historical data, but it's not a stationary system and it's gonna like the markets respond to world events,…”

“I think if you were to look at constrained domains certain negotiation tasks, I think that AIs could probably do better than humans in that today. I mean, I'm trying to think of like specific examples, but things like you know, if you wanted to negotiate over …”

“You know, it doesn't seem crazy to me that you could have a model that can prove the Riemann hypothesis within the next five years. If you can solve the reasoning problem in a truly general way.”

“It turned out that 5.5 is just much more efficient with its thinking. If you run it at max settings, 5.4 is thinking for a lot longer. It takes longer to get back a response than 5.5. And once you control for the amount of thinking time, actually you can see t…”

“Actually the AISI in their evaluations has shown that the models continue to improve at A hundred million tokens. You know, if you run them for a hundred million tokens, they're still improving at beyond that point.”

“This idea that the models, you just let them think for a week or whatever, and then they respond, it's, it sounds nice, and yes, the benchmarks look great, but it's not very practical when working because like, okay, you ask the model a question, and then you …”

“We're seeing now with the most recent models that you can actually scaffold, for example, 5.5 into doing a series of experiments that can run for weeks, for months.”

“The model release cycle is, look, we're releasing new models, like, every two or three months at this point, and so a model comes out, it takes two or three months to push it to its limits, and then you have another model come out, and so nobody actually knows…”

“After we announced the results, A bunch of people found that you could get the answer out of 5.5 as well. If, now, it's not as simple as just asking 5.5, hey, here's the Irish unit distance conjecture. What's the disproof? You had to scaffold it a bit. You had…”

“There are some benchmarks where the models will just not improve if they have more inference budget. So I think a lot of factual factual retrieval kind of questions fall into this category of if you ask a person when was Abraham Lincoln born and they don't kno…”

“We're not seeing that with AI models today. They kind of, they're born into a world for, and they exist for a very short context window, and then they just, like, disappear. And yeah, there are things that you can kind of do to, like, continue them, but it's v…”

“I think that we're going to continue to see, as I said before, that we're going to see this paradigm continue to progress rapidly. And I think that that's true even today, that we saw that with like going from O-one preview to O-one to O-three, consistent prog…”

“One thing that I think is underappreciated is that the models, the pre-trained models need a certain level of capability in order to really benefit from this, like, extra thinking.”

“How far does O three get without any harness? How far does it get playing Pokemon? And the answer is like, not very far, you know?”

“I think the difference is that like for reinforcement fine tuning, you're collecting data that's going to be useful As the models improve as well. So if we come out with, like, future models that are even more capable, you could still fine tune them on your da…”

“One of OpenAI's big success was betting on the scaling paradigm. It is just kind of odd because, you know, they were not the biggest lab, you know, it was, like, difficult for them to scale. Back then, it was much more common to do, like, a lot of small experi…”

“Like I've been using it day to day. It's basically replaced Google search for me. Like I just use it all the time.”

“I think that Oh, three is not where the technology will be in six months.”

“The team, in many ways, is actually a misnomer, because we're working on more than just multi-agent. Multi-agent is one of the things we're working on. Some other things we're working on is just like being able to scale up test time compute by a ton. So how, y…”