Evidence receipt / belief
Published · transcript-backedNathan Lambert: belief
1 Feb 2026 Lex Fridman Podcast #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI
“I think OpenAI’s definition is somewhat related to that—an AI that can do a certain number of economically valuable tasks—which I don’t really love as a definition, but it could be a grounding point.”
Source trail
Everything needed to verify it.
- Speaker
- Nathan Lambert
- Attribution
- Verified speaker
- Claim type
- belief
- Recorded
- 1 Feb 2026
- Publisher
- Lex Fridman Podcast
Transcript context
…Let’s talk about timelines specifically: timelines to AGI or ASI. Is it fair, as a starting point, to say that nobody really agrees on the definitions of AGI and ASI? I think there’s a lot of disagreement, but I’ve been getting pushback where people say it is something that could reproduce most digital economic work. The remote worker is a fairly reasonable example. I think OpenAI’s definition is somewhat related to that—an AI that can do a certain number of economically valuable tasks—which I don’t really love as a definition, but it could be a grounding point. Language models today, while immensely powerful, are not this remote worker drop-in. There are things an AI could do that are way harder than remote work, like solving a… …finding an unexpected scientific discovery that you couldn’t even posit, which would be an example of something people call an artificial superintelligence problem. Or taking in all medical records and finding linkages across certain illnesses that people didn’t know or figuring out that some common drug can treat a niche cancer. They would say that is a superintelligence thing. So these are natural tiers. My problem is that it becomes deeply entwined with the quest for meaning in AI and these religious aspects. There are different paths you can take. And I don’t even know if remote work is a good definition. I liked the originally titled AI2027 report. They focus more on code and research taste, so the target there is the superhuman coder. They have several milestone systems: superhuman coders, superhuman AI researcher, then superintelligent AI researcher, and then the full ASI. After you develop the superhuman coder, everything else follows quickly. The task is to have fully autonomous, automated coding, so any kind of coding you need to do in order to perform research is fully automated. From there, humans would be doing AI research together with that system, and they will quickly be able to develop a system that actually can do the research for you. That’s the idea. Initially, their prediction was 2027 or ’28, and now they’ve pushed it back by three to four years to 2031, mean prediction. My prediction is probably even beyond 2031, but at least you can think concretely about how difficult it is to fully automate programming.…
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