“If you look at things like how much energy is there in the country, how much how many chips can TSMC produce and what fraction of them are already being used by AI. Even if you look at like raw GDP, like how much money does the world have? How much wealth does the world have by all those metrics? It seems like this pace of four X a year, which is like a 160 X in four years, right? Like this cannot continue beyond 20, 28.”
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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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