Nathan Lambert

speaker

1,814 appearances 3 recordings 2 series first heard Feb 2025 last heard 1 Feb

Nathan Lambert’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 2 in all, peaking in Feb 2026 with 1.

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

On the character training thing, I think this research is built on fine-tuning about 7 billion parameter models with LoRa, which is essentially you only fine-tune a small subset of the weights of the model.

I don't know exactly how many GPU hours that would take.

But it's doable.

not doable for every academic so the situation for some academics is like so dire that the only work you can do is doing inference where you have closed models or open models and you get completions from them and you can look at them and understand the models and that's very well suited to evaluation which you become you want to be the best at creating representative problems that the models fail on or show certain abilities which i think that you can break through with this so i've like i think that the

top end goal for a researcher working on evaluation if you want to have career momentum is the frontier labs pick up your evaluation so it's like you don't need to have every project do this but if you go from a small university with no compute and you figure out something that Claude struggles with and then the next Claude model has it in the blog post like there's your career rocket ship I think that that's hard but it's like if you want to scope the maximum possible impact with minimum compute it's something like that which is just get very narrow

And it takes learning of where the models are going.

So you need to like build a tool that tests where not cloud 4.5 will fail.

If you're going to do a research, if I'm going to start a research project, I need to think where the models in eight months are going to be struggling.

But what about developing totally novel ideas?

This is a trade-off.

I think that if you're doing a PhD, you could also be like, it's too risky to work in language models.

I'm going way longer term, which is like, what is the thing that's going to define language model development in 10 years?

Which I think that I end up being a person that's pretty practical.

I mean, I went to my PhD where it's like, I got into Berkeley, worst case, I get a master's and then I go work in tech.

I'm very practical about it.

So I'm like,

The life afforded to people to work at these AI companies, the amount of, like, OpenAI's average compensation is over a million dollars in stock a year for an employee.

Any normal person in the U.S.

to get into this AI lab is transformative for your life.

So I'm pretty practical of, like...

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