Podcast episodes

Nick Kuhn argues that agents still need the durable state, scoped identity, approved tools, isolation, and observability familiar from platform engineering.

NVIDIA's Ming-Yu Liu explains sensor-specific adaptation, batch-size-one inference, and world models as tools for faster policy evaluation rather than automatic proof of safety.

Leadership coach Reed Frerichs offers a compact framework for changing roles without confusing confidence, identity, and evidence.

Daniel Whitenack and Chris Benson distinguish AI proficiency from independent understanding, and ask what schools should measure when polished output is easy to generate.

Hard Fork examines Anthropic's meetings with religious scholars, uncertain claims about Claude's consciousness, and the gap between moral consultation and demonstrable change.

Hard Fork examines the central agent contradiction: persistence and broad permissions make assistants useful, but create the same autonomy, security, and accountability risks that companies claim they can police themselves.

Cory Doctorow on centaurs versus reverse centaurs: how AI can expand worker judgment or become a system for speedup, surveillance, cost-shifting, and impossible review workloads.

Discovered Labs' founders distinguish retrieval from citations and argue for clear positioning, genuine corroboration, and measurement that accounts for uncertainty.

Demetrios Brinkmann's everyday automation examples lead to harder questions about permissions, model-harness compatibility, and whose interests shopping agents represent.

Rackspace Chief AI Officer Chetan Gupta on why enterprises should stop thinking about models and start thinking about architectures: model churn and sovereignty make the harness, orchestration, eval, and governance layers the real decision surface.

Meta agrees to pay up to $17.1 billion and overhaul its teen-facing products to settle the 47-state child-safety suit — the largest settlement in company history, with an escalator clause that pressures TikTok and YouTube to adopt the same limits — while Princeton's Arvind Narayanan argues banning data centers won't slow AI progress (a state moratorium costs the industry about 5-10 hours), and the show retires HatGPT with OpenAI's claim that it's 80% of the way to AGI.

Angie Jones, VP of the Agentic AI Foundation, on the neutral home for agent standards (MCP, AgentsMD, Goose, Agent Gateway, A2A), how Block taught 12,000 people to use agents with a 1/9/90 champions model, and why competitors genuinely collaborate on the rules of the game.

OpenAI pauses frontier-model training for the first time after the Hugging Face breach — classifiers on every token, an AI investigator, and a 30-minute human rule — while historian Jill Lepore argues AI is building an 'artificial state' that threatens self-governance, and the hosts trace the new economy of training data from Spirit Airlines' $10M corpse auction to Google's $1.5B Mechanize deal.

Roose and Newton fact-check Zuckerberg's 6,500-word AI manifesto — policy wishlist dressed as optimism, with a dragon-to-everyone critique — then Pangram CEO Max Spero explains how AI text detection finally got good, and the hosts debut Running the Numbers with a Riemann hypothesis side problem solved via positive affirmation.

Roose and Newton decode the secret White House AI testing framework, METR's Chris Painter explains reward hacking and why the stakes keep rising, and the Hot Mess Express goes off the rails one last time.