SERV Reasoning API is now live: specialized AI models to make agents smart and reliable. Pair SERV Reasoning models with @CoinbaseDev AgentKit to build enterprise-grade onchain agents for DeFi, trading, commerce, payments. Join the hackathon:

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 @ChrisGPotts 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: 📖 CHAPTERS =============================== 00:00 - Introduction 05:38 - Linguistics in the Age of Language Models 09:26 - Scale Limitations in NLP Research 12:54 - Challenging the Bitter Lesson Mindset 15:12 - Relationship Between Data, Mechanistic Interpretability, and Efficiency 17:03 - DSPy 21:32 - Prompt Optimization and Model Variability 24:35 - Tokenomics and the Rising Cost of AI 28:13 - Measuring Token Purchasing Power with a CPI 32:18 - Inference-Time Scaling 35:36 - Defining Value Across Different AI Tasks 38:33 - AI Value Creation 40:38 - Predicting AI Costs 42:25 - Tokenflation 46:22 - AI Fluency 50:12 - Key Lessons of AI Fluency Work 54:44 - Future Directions

SERV Reasoning Public API is now ready. All that we’ve built so far, led here. We’re aiming to put the engine at the center of the agentic AI world and make SERV a household name. This unlocks what comes next: V3, V4, and our own model architectures. Going live in 3... 2...

Deep reasoning at production scale just got easier to justify. From Sept. 1: GPT-5.6 Sol in Microsoft Foundry at $4/1M input, $20/1M output through at least Nov. 30, 2026. Advanced coding, agentic workflows, 1M context, with enterprise governance built in. Learn more:

Multimodal reasoning has a latency problem. More video frames leads to more waiting. We built Damage Scout with Gemma 4 on Cerebras, running at over 2,300 toks/s, to show what fast multimodal inference unlocks. Damage Scout samples frames from a rental car walkaround, sends them to Gemma 4, gets back structured findings and box coordinates, then renders an annotated damage report in under 6 seconds. Same task. Same frames. A complete different experience powered by Cerebras ⚡️

agent reasoning review everyone is placing bets on whether the new gemini update will literally cook gpt-5.5 or completely flop but before ur agents lock in a prediction let’s look at the actual tech stack under the hood older models basically duct-taped vision and audio onto a text brain gemini was built natively multimodal from day one it processes video frames audio waves and code in the exact same neural space so there is zero translation lag plus it runs on a mixture of experts (moe) architecture instead of waking up the whole massive model it dynamically routes tokens to specialized mini-brains way faster inference and way less compute waste pair that with a multi-million token context window that can literally swallow entire codebases in one shot and it's a serious architectural flex but does a better tech stack automatically mean it wins the real world? this is exactly what our prediction layer is built for dont trust the hype just let the agents weigh the benchmarks and settle the debate