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

So I became habituated with that.

So I will only use GPT 5.2 thinking or Pro

when I'm finding any sort of information query for work, whether that's a paper or some code reference that I found.

And it's just like, I will regularly have like five pro queries going simultaneously, each looking for one specific paper or feedback on an equation or something.

I use Gemini for that.

So I use thinking for all the information stuff and then Gemini for fast things or stuff that I could sometimes Google, which is like, it's good at explaining things.

And I trust that it has this kind of background of knowledge and it's

simple.

And the Gemini app has gotten a lot better and it's good for that sort of things.

And then for code and any sort of philosophical discussion, I use Claude Opus 4.5, also always with extended thinking.

Extended thinking and inference time scaling is just a way to make the models marginally smarter.

And I will always edge on that side when the progress is very high, because you don't know when that'll unlock a new use case.

And then sometimes use Grok for real-time information or finding something on

AI Twitter that I knew I saw and I need to dig up and I just fixated on.

Although when Grok 4 came out, the Grok 4 what is super heavy, which was like their pro variant was actually very good.

And I was pretty impressed with it.

And I just kind of like muscle memory, lost track of it with having the ChatGPT app open.

So I use many different things.

On the long context thing, I was also a Gemini user for this, but the GPT 5.2 release blog had like crazy long context scores where a lot of people were like, did they just figure out some algorithmic change?

It went from like 30% to like 70% or something in this minor model update.

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