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Published · transcript-backed

Nathan Lambert: belief

1 Feb 2026 Lex Fridman Podcast #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI

“I think you could train models to do this and it would be a wonderful contribution.”

— Nathan Lambert

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Everything needed to verify it.

Speaker
Nathan Lambert
Attribution
Verified speaker
Claim type
belief
Recorded
1 Feb 2026
Publisher
Lex Fridman Podcast

Transcript context

…Yeah, we’re returning to this often on the topic of education. You both have brought up the word “struggle” quite a bit. There is value in that. If you’re not struggling as part of this process, you’re not fully following the proper process for learning, I suppose. Some of the providers are starting to work on models for education designed to not give… actually, I haven’t used them, but I would guess they’re designed to not give all the information at once— —and make people work for it. I think you could train models to do this and it would be a wonderful contribution. In the book, you had to reevaluate every decision— Which is such a great example. I think there’s a chance we work on it at AI2, which I think would be so fun. It makes sense. I did something like that the other day for video games. In my spare time, I like video games with puzzles, like Zelda and Metroid. There’s this new game where I got really stuck. I didn’t want to struggle for two days, so I used an LLM. But I told it, “Please don’t add any spoilers. I’m at this point; what do I have to do next?” You can do the same thing for math where you say, “I’m at this point and I’m getting stuck. Don’t give me the full solution, but what is something I could try?” You kind of carefully probe it. But the problem is that it requires discipline. A lot of people enjoy math, but there are also a lot of people who need to do it for their homework, and then it’s just a shortcut. We can develop an educational LLM, but the other LLMs are still there, and there’s still a temptation to use them.…

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