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 that when they’re close to this automated software engineer, what it will be good at is traditional ML systems and front end—the model is excellent at those—but the distributed ML, the models are actually really quite bad at because there’s so little training data on doing large-scale distributed learning and things.”
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
…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. Yeah, I disagree with some of their presumptions and dynamics on how it would play out, but I think they did good work in defining concrete milestones to tell a useful story. That’s why the reach of this AI 2027 document well transcended Silicon Valley—because they told a good story and did a lot of rigorous work. I think the camp that I fall into is that AI is so-called jagged, which will be excellent at some things and really bad at some things. I think that when they’re close to this automated software engineer, what it will be good at is traditional ML systems and front end—the model is excellent at those—but the distributed ML, the models are actually really quite bad at because there’s so little training data on doing large-scale distributed learning and things. And this is something that we already see, and I think this will just get amplified. And then it’s kind of messier in these trade-offs, and then there’s how you think AI research works and so on. So you think basically a superhuman coder is almost unachievable meaning, because of the jagged nature of the thing, you’re just always going to have gaps in capabilities?…
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