extremely high snr from this podcast...the only one i've been able to watch in full in one sitting. <> the only way we don't get rsi is if we fall into some regulatory capture (which seems to be trending at present) <> we're nowhere near the ceiling of how well you can do research <> all thinking can do is update your posterior based on the knowledge you’ve gained since you formed your prior. you can’t gain any new knowledge from just thinking <> you can spend an equivalent amount [7 figures] of compute in AI agents to get a century’s worth of thinking, a century's worth of theory, before every training run <> taste is just behavior that works in the long run, and can be baked in a longer context window <> creativity is just solving hard search problems, and can also be baked in a longer context window you should follow everyone here, especially @oneill_c, i think it takes a special talent to be able to not only develop deep technical competency, but to also be able to use that to consistently make accurate predictions about the future (i think this was literally François Chollet's definition of intelligence in last year's YC event). my only nit here: @dwarkesh_sp should have pushed on two quite conflicting statements from @oneill_c and @BerenMillidge. on one hand: <> “anything that can be learned through RL can be distilled very easily”. this is the justification as to why open source models (chinese) can catch up to closed source models (american). fair... 5 minutes later <> “Opus 5 could not distill / generalize well from Fable [despite anthropic having the live deployment, access to logits, and obviously a good prompt distribution]”. can only have one or the other, imo. also lol at the timeline: - ai will dominate top human experts in 3-4 years - it will take 5-10 years to automate ai research k i n o