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 PalantirIs there really an AI bubble — or just a new kind of economy?Breaking Nvidia’s CUDA lock-in with modular AIGoogle’s “Nested Learning” and Anthropic’s interleaved thinkingBuilding AI copilots and MCP serversLangSmith experiments, evaluators, and continuous optimizationMicrosoft’s Copilot Studio and enterprise automationWhat 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
