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

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

Author: Sam Charrington September 9, 2026 Duration: 59:29
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.

Hosted by industry analyst and commentator Sam Charrington, The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) serves as a vital conduit between cutting-edge research and its real-world implications. This isn't just a series of technical lectures; it's a series of conversations that unpack how AI and machine learning are actively reshaping industries and societal structures. Each episode connects you directly with leading researchers, engineers, and innovative thinkers who are defining the frontiers of the field. The discussions go beyond abstract theory to explore the practical challenges, ethical considerations, and business transformations driven by these technologies. Whether you're a data scientist deep in the code, a tech-savvy leader strategizing implementation, or simply fascinated by the future of intelligent systems, this podcast provides the context and depth needed to stay informed. By focusing on the people behind the algorithms and the ideas powering the platforms, Sam creates a resource that is both intellectually substantive and genuinely engaging, building a thoughtful community around one of the most significant technological shifts of our time.
Author: Language: English Episodes: 50

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Podcast Episodes
How to Engineer AI Inference Systems with Philip Kiely - #766 [not-audio_url] [/not-audio_url]

Duration: 54:51
In this episode, Philip Kiely, head of AI education at Baseten, joins us to unpack the fast-evolving discipline of inference engineering. We explore why inference has become the stickiest and most critical workload in AI…
The Race to Production-Grade Diffusion LLMs with Stefano Ermon - #764 [not-audio_url] [/not-audio_url]

Duration: 1:03:18
Today, we're joined by Stefano Ermon, associate professor at Stanford University and CEO of Inception Labs to discuss diffusion language models. We dig into how diffusion approaches—traditionally used for images—are bein…
Intelligent Robots in 2026: Are We There Yet? with Nikita Rudin - #760 [not-audio_url] [/not-audio_url]

Duration: 1:06:37
Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion Robotics to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains…