Nathan Labenz: commitment

Transcript context

…Welcome to the cognitive revolution, and to a new experiment we're calling AI in the AM. Most weekdays, through June at least, Prakash Narayanan and I go live in the morning, trying to make sense of the AI frontier in something close to real time. Then we cut it down to this, a highlights edition, built for people who are really close to this stuff, but already overwhelmed. And I'll be up front, this whole thing is an experiment. The studio we broadcast from, Prakash vibe coded it. The booking, the research, the clipping, those are AI skills we refine as we go, and we plan to publish them in all sorts of artifacts as this matures. Which it turns out is the story of the week. The frontier labs are running away with everything, and increasingly, they seem a little scared of their own progress. OpenAI is publicly asking for independent review of models. At a closed-door event on recursive self-improvement, People from multiple labs agreed a coordinated slowdown might one day be necessary. And our conversation with OpenAI's forward-deployed engineers showed how almost mundane this has become. Walk into a tax firm, stand up a thin scaffold, capture where it's wrong, and let the model rewrite its own scaffolding, correction by correction. That's the whole loop, and it climbs the hill astonishingly fast. So when the harness is that cheap to build, the real question becomes, What, around the core intelligence, is still safe? That's the lens for this week. And please, tell us what's working and what isn't. We mean it. This only gets good with your feedback. Start with a day I spent inside a closed-door event, full of people from the Frontier Labs, all of whom think self-improvement is close and is the plan. Here's the honest version of what they believe and what they don't. So this event was called recursive. It was premised on the idea that recursive self-improvement seems to be coming pretty soon. It is increasingly the explicit plan of at least Anthropic and OpenAI and Google DeepMind to some extent, although they kind of waffle on it a little bit more. Whereas, you know, OpenAI has publicly put forward timelines of later this year for an ML research intern and early 2028 for the full AI R&D researcher that they hope will perform on the level of their human researchers. So the kind of basic theory of change there is a pretty obvious one, but we're stating that today they may have 1000 or a couple thousand people that they would really consider to be top-notch ML researchers if they can get that same level of performance from models on chips, then they're only limited by the amount of compute that they can throw at it. And obviously they're building out a lot of compute. So presumably they could throw a million human researcher equivalents at problems. And they also, by the way, you may have noted they run faster and they run 24 7. ey're building out a lot of compute. So presumably they could throw a million human researcher equivalents at problems. And they also, by the way, you may have noted they run faster and they run 24 7. So the hope is that this will allow them to move much faster than they have moved and kind of pull away from the competition. I would say most people at that event thought that was very credible. There was not too much debate around like, will this level off?…

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