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Do AI Tokenomics Matter More Than Model Benchmarks? with Chris Potts - #776

2026-09-09 · 59 min · episode 776 · 13 entities

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    Do AI Tokenomics Matter More Than Model Benchmarks? with Chris Potts - #776
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Entities found in this episode

concepts 5

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companys 4

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persons 3

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    Do AI Tokenomics Matter More Than Model Benchmarks? with Chris Potts - #776
  • mentioned Sam Charrington person
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    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
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    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
As reasoning models consume more tokens and AI systems become more expensive to run, understanding what those tokens actually buy is becoming increasingly important. In this episode, Stanford professor and Big Spin co-founder Chris Potts joins us to discuss AI tokenomics and his research into “tokenflation”—the possibility that token usage is growing faster than the measurable value those tokens produce. We explore how to measure the return on AI spending, why benchmarks alone provide an incomplete picture of model progress, and what inference-time scaling means for the economics of increasingly capable models. Chris also explains why expert AI users tend to get better results by challenging and iterating with models, how AI fluency affects outcomes, and why more efficient architectures could change the underlying economics. We also discuss DSPy, interpretability, the limits of today’s transformer architectures, and where Chris sees opportunities for more fundamental innovation in AI. 🗒️ Full show notes: ⁠⁠https://twimlai.com/go/776.