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 monthsRecordings per month over the last 12 months — 2 in all, peaking in Feb 2026 with 1.
Appearances
So like the remote worker is a fairly reasonable example.
And I think OpenAI's definition is somewhat related to that, which is like an AI that can do a certain number of economically valuable tasks, which I don't really love as a definition, but I think it could be a grounding point because...
Language models today, while immensely powerful, are not this remote worker drop-in.
And there are things that you could think of that could be done by an AI that are way harder than remote work, which are like finding an unexpected scientific discovery that you couldn't even posit, which would be an example of something that somebody says is like an artificial superintelligence problem or like...
taking in all medical records and finding linkages across certain illnesses that people didn't know or figuring out that some common drug can treat some niche cancer.
Like they would say that that is like a super intelligence thing.
So these are kind of natural tiers.
My problem with it is that it
becomes deeply entwined with like the quest for meaning of AI and this religious aspects to it.
So there's kind of different, there's different paths you can take it.
Yeah, I disagree with some of their presumptions and dynamics on how it would play out.
But I think they did good work in the scenario defining milestones that are concrete and to tell a useful story, which is why the reach for this AI 2027 document well transcended Silicon Valley is because they told a good story and they did a lot of rigorous work to do this.
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.
So I think that
when they're close to this automated software engineer.
What it will be good at is that traditional ML systems and front-end, the model is excellent at, 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 is just getting amplified.
And then it's kind of messier in these trade-offs.
And then there's like, how do you think AI research works and so on.
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