Evidence receipt / belief
Published · transcript-backedDaniel Kokotajlo: belief
3 Apr 2025 Dwarkesh Podcast AI 2027: month-by-month model of intelligence explosion — Scott Alexander & Daniel Kokotajlo
“I think I’m imagining something maybe similar happening with algorithmic progress.”
Source trail
Everything needed to verify it.
- Speaker
- Daniel Kokotajlo
- Attribution
- Verified speaker
- Claim type
- belief
- Recorded
- 3 Apr 2025
- Publisher
- Dwarkesh Podcast
Transcript context
…Is there some intuition pump from history where there’s been some output and because of some really weird constraints, production of it has been rapidly skewed along one input, but not all the inputs that have been historically relevant and you still get breakneck progress. Possibly the Industrial Revolution. I’m just extemporizing here, I hadn’t thought about this before, but as Scott’s famous post that was hugely influential to me a decade ago talks about, there’s been this decoupling of population growth from overall economic growth that happened with the Industrial Revolution. And so in some sense, maybe you could say that’s an example of previously these things grew in tandem. More population, more technology, more farms, more houses, et cetera. Your capital infrastructure and your human infrastructure was going up together, but then we got the industrial revolution and they started to come apart. And now all the capital infrastructure was growing really fast compared to the human population size. I think I’m imagining something maybe similar happening with algorithmic progress. And again with population, population still matters a ton today. In some sense progress is bottlenecked on having larger populations and so forth. But it’s just that the population growth rate is just inherently kind of slow and the growth rate of capital is much faster. And so it just comes to be a bigger part of the story. Maybe the reason that this sounds less plausible to me than the 25x number implies is that when I think about concretely what that would look like, where you have these AIs and we know that there’s a gap in data efficiency between human brains and these AIs. And so somehow there’s a lot of them thinking and they think really hard and they figure out how to define a new architecture that is like the human brain or has the advantages of the human brain. And I guess they can still do experiments, but not that many. Part of me just wonders, what if you just need an entirely different kind of data source that’s not like pre-training for that, but they have to go out in the real world to get that. Or maybe it needs to be an online learning policy where they need to be actively deployed in the world for them to learn in this way. And so you’re bottlenecked on how fast they can be getting real world data. I just think it’s hard……
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