“More importantly, I think the future economy, once we do have these AI workers will be denominated in compute, right? Cause if computer's labor, right now, if you just think about like GDP per capita, because the individual worker is such an important component of production that you have to like split it, split up national income by person. That will be true of AI's in the future, which means that like, it'll be like compute is your population size.”
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More from Dwarkesh Patel
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Patel: Individuals will eventually train superintelligences in basements
“The cost of training, the systems is declining so fast that Literally you will be able to train a super intelligence in a basement at some point in the future.”
Dwarkesh: AGI requires further algorithmic progress, not just current model scaling
“I don't think we're just right around our corner from AGI and it's just a little additional dash of something. That's all it's going to take. I think, you know, people often ask if all AI progress stopped right now and all you could do is collect more data or …”
Dwarkesh: Lack of continual learning prevents LLMs from replacing human labor
“I think a big bottleneck these models have is their inability to learn on the job, to have continual learning. Their entire memory is extinguished at the end of a session. There's a bunch of reasons why I think this actually makes it really hard to get human-l…”
Patel: Online continual learning is not imminent for current AI architectures
“And the reason I don't think that's around the corner is just because there's not, there's no obvious way, at least as far as I can tell, to just slot in this online learning into the models as they exist right now.”
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