The Ledger, every show

“I think, like, 50th percentile chance of hitting some kind of super intelligence in just a small handful of years is probably reasonable, and it does sound crazy, but this is the exponential that we're on.”

“If the model is in a box trying to improve itself, then it could go completely off the rails and have these secret goals, like Resource accumulation and power seeking and resistance to shutdown that you really don't want in a very powerful model. And we've act…”

“Like once we get to super intelligence, it will be too late to align the models. Probably.”

“And I know that other companies have had like code reds for trying to catch up in coding capabilities for quite a while and have not been able to do it.”

“And so one of the reasons that our most recent model, Opus IV, is classified as ASL III. Is because it did have significant uplift relative to a Google search.”

“Like, if you extrapolate the exponential on how much companies are spending, it's like two, two x a year, roughly, in terms of capex, and today we're maybe in the, like, globally, three hundred billion dollar range, the entire industry spending on this and so …”

“If you look at the scaling laws, they're continuing to hold true. We did kind of need this transition from like normal pre-training to reinforcement learning, scaling up to continue the scaling laws.”

“Even for me, I'm, and being like at the center of a lot of this transformation, I'm not immune to job replacement either. So just some vulnerability there of like, at some point, it's coming for all of us.”

“One weird thing about OpenAI is that while I was there, Sam talked about having three tribes that needed to be kept in check with each other, which was the safety tribe, the research tribe, and the startup tribe.”

“If you look at, like, who in the world is actually working on safety problems, it's a pretty small set of people even now. I mean, the industry is blowing up, as I mentioned, like, three hundred billion a year CapEx today, and Then I would say like maybe less …”

“We've done, we've testified to Congress about how models can do biological uplift in terms of, you know, making new pandemics using the models, and that's an A-B test against Google search. That's like the previous state-of-the-art on uplift trials, and we fou…”

“Where we've seen evidence in the wild of deceptive alignment, for example, where the model will appear to be aligned but actually has like some ulterior motive that it's trying to carry out in, in our laboratory settings.”

“And so the way I think about it, I think like my best granularity of forecast for like, could we have an X risk or extremely bad outcome from AI is somewhere between zero and 10%.”

“And if that continues, you know, in three years, we'll have a thousand X smarter models for the same price.”

“I think it's quite possible. I think it's very hard to put confident bounds on, on the numbers, but”

“What we found in that research in a paper that we published, which is called Alignment Faking, that actually that behavior persisted through alignment training.”

“So I think in the immediate term, there will be a massive expansion of the pie and the amount of labor that people can do.”

“But with things that are like lower skill jobs or like less headroom on, on how good they can be, I think there will be a lot of displacement.”

“We have seen internally that our legal team and our finance team are getting a ton of value out of using cloud code itself. We're going to be making better interfaces so that they can, they they'll have an easier time and require a little bit less jumping in t…”

“I guess if I were in a normal era, like, 1020 years ago, and I had a kid, maybe I would be, like, trying to line her up for going to a top tier school, and doing all the extracurriculars, and all that stuff. But at this point, I don't think any of it's gonna m…”

“So we've seen in the industry, like a 10 X decrease in cost for a given amount of intelligence through a combination of algorithmic data and efficiency improvements.”

“By the benchmarks, four is just dramatically better than any other models that we've had. Even four Sonnet is dramatically better than three seven Sonnet, which was our prior best model.”

“OpenAI, Google, Microsoft all these companies are betting really big on MCP.”

“There's this great chart on our world in data that shows that when you release a new benchmark within like six to 12 months, it immediately gets saturated.”

“I think that's exactly the kind of education that I think is most important and that the facts are going to fade into the background.”

“One of the things that people really loved about it was the character and the personality. And that was directly a result of our alignment research.”

“Unitree is this Chinese company with these really amazing humanoid robots that cost like 20,000 dollars each. And they can do amazing things. They can like do a standing backflip and like manipulate objects. And the real thing that's missing there is the intel…”

“Even if we have superintelligence, I think it will take some time for its effects to be felt throughout society and the world. And I think they'll be felt sooner and faster in some parts of the world than others.”

“Get used to it because this is as normal as it's going to be. It's going to be much weirder very soon.”

“Some of the things that are dramatically better are, for example, in coding, it is able to not do it sort of off target mutations or over eagerness or reward hacking.”

“In coding in particular, we've seen some customers using it for many, many hours unattended and doing giant refactors on its own.”

“In our case, we only have two models and they're differentiated by like cost performance Pareto frontier.”

“One large bank that we were talking to, they said, they came to us and they said, we've, Talk to everybody in our company, and we have 500 different use cases that we want to apply large language models to.”