The Ledger, every show

“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…”

“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 …”

“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.”

“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 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.”

“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.”

“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.”