High Signal Podcasts Evidence ledger
Method
Browse
← Back to evidence

Evidence receipt / prediction

Published · transcript-backed

Nat Friedman: prediction

22 Mar 2023 Dwarkesh Podcast Nat Friedman (Github CEO) — Reading ancient scrolls, open source, & AI

“I think some of these labs potentially have 50, 100, 200 percent training efficiency improvement techniques and so there's just a lot of low-hanging fruit on the technique side of things.”

— Nat Friedman

Source trail

Everything needed to verify it.

Speaker
Nat Friedman
Attribution
Verified speaker
Claim type
prediction
Recorded
22 Mar 2023
Publisher
Dwarkesh Podcast

Transcript context

…No, that's why I asked you to make the bear case because I know about you. I want to ask you about these foundation models. What is the stable equilibrium you think of how many of them will there be? Will it be an oligopoly like Uber and Lyft where…? I think there will probably be wide-scale proliferation. And if you asked me, what are the structural forces that are pro proliferation and the structural forces that are pro concentration? I think the pro proliferation case is a bit stronger. The pro proliferation case is – They're actually not that hard to train. The best practices will promulgate. You can write them down on a couple sheets of paper. And to the extent that secrets are developed that improve training, those are relatively simple and they get copied around easily. Number one, number two. The data is mostly public, it's mostly data from the internet. Number three, the hardware is mostly commodity and the hardware is improving quickly and getting much more efficient. I think some of these labs potentially have 50, 100, 200 percent training efficiency improvement techniques and so there's just a lot of low-hanging fruit on the technique side of things. We're seeing it happen. I mean, it's happening this weekend, it's happening this year. We're getting a lot of proliferation. The only case against proliferation is that you'll get concentration because of training costs. And I don't know if that's true. I don't have confidence that the trillion dollar model will be much more valuable than the 100 billion dollar model and that even it will be necessary to spend a trillion dollars training it. Maybe there will be so many techniques available for improving efficiency. How much are you willing to spend on researchers to find techniques if you're willing to spend a trillion on training? That's a lot of bounties for new techniques and some smart people are going to take those bounties. How different will these models be? Will it just be sort of everybody chasing the same exact marginal improvement leading to the same marginal capabilities or will they have entirely different repertoire of skills and abilities?…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.

Search evidence