Do AI Tokenomics Matter More Than Model Benchmarks? with Chris Potts - #776
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In this podcast episode, Stanford professor Chris Potts discusses the concept of "tokenflation" — the idea that AI models use more and more tokens (small units of text that the model processes) without providing corresponding increases in value or performance.
Potts argues that when evaluating how good an AI model is, we often focus only on benchmarks — test results — but overlook what it actually costs to run the model. He explains that users skilled at iterating and challenging models get better results, and that more efficient architectures could change the economics of AI spending.
Listen to the episode at TWIML AI →
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