Dwarkesh Patel
speaker
19,288 appearances
62 recordings
3 series
first heard Feb 2024
last heard 6d ago
Dwarkesh Patel’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 — 27 in all, peaking in Jun 2026 with 6.
Appearances
It does suggest that there's an enormous amount of value from bringing into distribution these kinds of common tasks, even if we can't replicate whatever is making human learning so special.
And it might be more inefficient to train AIs to do these kinds of tasks than it is to train humans, but so what?
Human lifespan simply does not allow for the quantity and the breadth of training that these models experience.
If you, as a human, had some weird learning disability where you needed to read through every public repository on GitHub before you could be a competent software engineer, then it would simply not make sense to train you up.
You'd be on Social Security by the early stages of your education, and even once you were trained, you would only be able to work on one project at a time.
But AIs can learn these skills by fire hosing gigawatts of training at a time.
And what they learn can be amortized across billions of sessions at once.
So we can be ludicrously inefficient in training them up and still be wildly in the green.
And then there's a question of, well, how much out of distribution thinking do white collar employees need to do that you simply can't train for in advance?
This is more a question about the nature of different jobs than it is a question about AI research.
And it also depends on which job you're talking about.
Some jobs are so mechanical and predictable that we were able to automate them long before the modern era of AI.
For example, bank tellers or travel agents.
But there are other jobs which require dealing on a daily basis with problems that are quite distant from the data distribution.
I think software engineering is probably one such.
This is the job that AIs are supposed to take first, but I would be willing to bet that there's overall more demand for human software engineers in 2027 than there is right now, largely due to the complementary input of AI.
The last plans for this latter category of jobs is first to automate AI research and then have the automated AI researchers solve the sample efficiency problem.
So then the question is, can AIs, which do not have human-level sample efficiency, nonetheless solve the remaining research problems that stand on the way of human-like intelligence and learning?
This is a very complicated question, and I'll have to address it in a much longer future blog post.
But just to tease it a bit, I think that the way that people currently think about an intelligence explosion is very clumsy, because either people dismiss the possibility of AI speeding up AI progress altogether, or they assume that some kind of god pops out the other end.
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