Dwarkesh Patel
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3 November
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From one piece Dwarkesh Podcast 2 beliefs, in the piece's order there
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Their words
So the economics of serving personalized weights strongly favor big organizations.
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Their words
Continual learning will create a clear moat for leading AI labs that they currently lack.
3 August
29 July
From one piece Dwarkesh Podcast 3 beliefs, in the piece's order there
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korrents.com
The compute supply is much less elastic and less capable of absorbing demand shocks than the extraction of metals.Their words
I'm guessing the Simon-Ehrlich basket of commodities is not the correct reference class for compute, because compute supply is much less elastic, and much less capable of absorbing large demand shocks or being accommodated by different substitutes, than the extraction of different metals is.
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Their words
But if we believe what standard economics says about labor, then the marginal value of labor (and thus the marginal price of compute) should stay astonishingly high.
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Their words
as AI models become smarter, they'll better monetize the same amount of compute.
10 July
1 July
30 June
26 June
From one piece Dwarkesh Podcast 3 beliefs, in the piece's order there
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korrents.com
Future AI progress may be driven by models learning from their deployment experience through continual learning.Their words
By this point, the main way that AI gets better is not through the training received before the model is released to the public. Rather, it's from all this experience that they are accumulating from being broadly deployed through the world and engaging in so many different kinds of tasks.
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korrents.com
The ability to make progress in a domain depends on the ability to build a replayable training target.Their words
unless you can build a very replayable training target for a domain, the models will struggle to make much progress.
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korrents.com
Training AIs on millions of verifiable tasks in diverse RL environments will lead to AGI.Their words
if we train AIs to accomplish millions of verifiable tasks across thousands of diverse RL environments, then we'll basically have built AGI.
19 June
From one piece Dwarkesh Podcast 3 beliefs, in the piece's order there
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korrents.com
There will be more demand for human software engineers in 2028 than there is currently.Their words
I would be willing to bet that there's overall more demand for human software engineers in 2028 than there is now, largely due to the complementary input of AI.
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Their words
Humans are somewhere between thousands to millions of times more sample efficient than these models. Scaling of current models simply can't make up for that discrepancy.
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Their words
I think the reason it is relatively easy for open source and previous laggards to catch up to within months of the frontier is that data is the real driver of progress.
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