Podbit · Dwarkesh Podcast
Serving personalized AI weights efficiently requires batching thousands of sequences simultaneously — the optimal batch size for a sparse model like DeepSeek V3 exceeds 2,400. Individual users running batch size 1 face more than 100x worse compute efficiency, meaning the economics of personalized AI strongly favor large organizations.
Where this was said
At 7:32 · chapter starts 6:45
Prediction seven explores the incentive dynamics that continual learning creates for how AI labs structure their pricing and access policies. If deployment experience is the primary driver of model improvement, then every session a user runs generates valuable training data. Labs will therefore have a strong financial incentive to subsidize — or even give away — access to users who consent to training on their sessions [1] — Dwarkesh Patel "If real-world usage becomes the primary driver of model improvement, AI labs have every incentive to subsidize users who let them train on …" 06:45 . Patel observes this is already visible in the generous deals offered to new users of coding products. The deeper analogy is Google Search: Google gives it away for free because the real product is the behavioral data generated at scale. Conversely, labs may respond to enterprises that refuse training consent by restricting them to inferior models. Both the carrot and the stick point in the same direction: maximizing the flow of real-world experience back into the model.
If real-world usage becomes the primary driver of model improvement, AI labs have every incentive to subsidize users who let them train on sessions — exactly like Google giving away free search. Enterprises that refuse may find themselves locked out of the best models.
AI labs may subsidize or give preferential access to enterprises that allow their sessions to be used for training, similar to how Google gives away search.
AI lab revenues are increasing far faster than their compute costs, evidence of large economies of scale already present in AI training.
Back-of-the-envelope math suggests the optimal inference batch size for a sparse model like DeepSeek V3 is more than 2,400 concurrent sequences to avoid underutilizing compute.
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