EP 21 Kimi k2 Thinking, The AI Bubble, Nvidia’s Future, and LangChain Experiments

Description

In Episode 21, Tom Spencer and Cameron Rohn break down the current state of AI — from market hype to hardcore engineering practice. Topics include: * Michael Burry’s short on Nvidia and Palantir * Is there really an AI bubble — or just a new kind of economy? * Breaking Nvidia’s CUDA lock-in with modular AI * Google’s “Nested Learning” and Anthropic’s interleaved thinking * Building AI copilots and MCP servers * LangSmith experiments, evaluators, and continuous optimization * Microsoft’s Copilot Studio and enterprise automation * What real AI engineering looks like in production 🎧 Subscribe for weekly deep dives into AI products, agent frameworks, and research. *00:00* – Airport stories, brisket, and warm-up banter *03:00* – MCP servers and Polygon data experiments *05:00* – Minimax and Anthropic’s interleaved thinking *07:00* – Google’s “Nested Learning” paper and continual optimization *08:30* – NeurIPS, AI research culture, and the VC invasion *09:30* – Is there an AI bubble? Michael Burry’s Nvidia short *11:00* – Palantir, Nvidia, and the tech bubble debate *14:00* – CapEx growth and the “AI money loop” *17:00* – Are AI companies actually profitable? *19:00* – Free users, monetization, and ChatGPT’s economics *20:30* – The real differences from the dot-com era *22:00* – Nvidia’s margins, chip efficiency, and modular AI challengers *25:00* – Breaking CUDA lock-in and the rise of hardware portability *27:00* – Local inference, hybrid models, and agentic operating systems *33:00* – Chrome OS, MCP in browsers, and local AI *34:00* – Anthropic Excel plugin and Kimi Thinking model benchmarks *37:00* – MCP server demos and architecture discussion *43:00* – Building an AI options trading copilot *46:00* – Visualizing strategies, composable components, and LangGraph *50:00* – How MCP connects data and trading logic *55:00* – Skill systems, consistency, and reproducibility in LLM apps *58:00* – LangChain documentation and developer experience *1:00:00* – Combining MCP data for richer insights *1:03:00* – Converting trading logic into agentic workflows *1:06:00* – Building autonomous trading systems on LangGraph *1:08:00* – LangSmith experiments, datasets, and evaluators *1:13:00* – Backtesting AI outputs and customer feedback optimization *1:20:00* – Comparing models and evaluators in LangSmith *1:24:00* – Microsoft Copilot Studio and Power Automate for enterprise AI *1:29:00* – Wrapping up: AI compliance, tooling, and what’s next