An audio version of my blog post, Thoughts on AI progress (Dec 2025)

Dwarkesh Patel· Host0:00

I'm confused why some people have super short timelines, yet at the same time are bullish on scaling up reinforcement learning atop LLMs. If we're actually close to a human-like learner, then this whole approach of training on verifiable outcomes is doomed. (static buzz) Now, currently, the labs are trying to bake in a bunch of skills into these models through mid-training. There's an entire supply chain of companies that are building RL environments which teach the model how to navigate a web browser or use Excel to build financial models. Now, either these models will soon learn on the job in a self-directed way, which will make all this pre-baking pointless, or they won't, which means that AGI is not imminent. Humans don't have to go through this special training phase where they need to rehearse every single piece of software that they might ever need to use on the job. Barron Milledge made an interesting point about this in a recent blog post he wrote. He writes, quote, "When we see frontier models improving at various benchmarks, we should think not just about the increased scale and the clever ML research ideas, but the billions of dollars that are paid to PhDs, MDs, and other experts to write questions and provide example answers and reasoning targeting these precise capabilities. You can see this tension most vividly in robotics. In some fundamental sense, robotics is an algorithms problem, not a hardware or a data problem. With very little training, a human can learn how to teller-operate current hardware to do useful work. So if we actually had a human-like learner, robotics would be, in large part, a solved problem. But the fact that we don't have such a learner makes it necessary to go out into a thousand different homes and practice a million