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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

“Anthropic, I think, bought thousands of books and scanned them and was cleared legally for that because they bought the books, and that is going through the system.”

— 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

…Mm-hmm. And on that note, there’s also the distinction between the licensing. Some people, like you said, just purchase the license. Let’s say they buy an Amazon Kindle book or a Manning book, and then use that in the training data; that is a gray zone because you paid for the content and you might want to train on it. But then there are also restrictions where even that shouldn’t be allowed. That is where it gets a bit fuzzy. And I think that is still a hot topic right now. Big companies like OpenAI approached private companies for their proprietary data, and private companies are becoming more and more protective of their data because they know, “Okay, this is going to be my moat in a few years.” And I do think that’s the interesting question. If LLMs become more commoditized, and a lot of people learn about LLMs, there will be a lot more people able to train them. Of course, there are infrastructure challenges. But if you think of big industries like pharmaceuticals, law, or finance, I do think they at some point will hire people from other frontier labs to build their in-house models on their proprietary data, which will be another unlock with pre-training that is currently not there. Because even if you wanted to, you can’t get that data—you can’t get access to clinical trials most of the time and these types of things. So I do think scaling in that sense might still be pretty much alive if you look at domain-specific applications, because right now we are just looking at general-purpose LLMs like ChatGPT, Anthropic, and so forth. They are just general purpose. They’re not even scratching the surface of what an LLM can do if it is really specifically trained and designed for a specific task. I think on the data thing, this is one of the things where, like, this happened in 2025 and we totally forget it: Anthropic lost in court and owed $1.5 billion to authors. Anthropic, I think, bought thousands of books and scanned them and was cleared legally for that because they bought the books, and that is going through the system. And then on the other side, they also torrented some books, and I think this torrenting was the path where the court said that they were then culpable to pay these billions of dollars to authors, which is just such a mind-boggling lawsuit that kind of just came and went. Like, that is so much money- … from the VC ecosystem. These are court cases that will define the future of human civilization because it’s clear that data drives a lot of this, and there’s this very complicated human tension. I mean, you can empathize. You’re both authors. And there’s some degree to which, I mean, you put your heart and soul and your sweat and tears into the writing that you do. It feels a little bit like theft for somebody to train on your data without giving you credit.…

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