Governing AI Agents: Alan Chan of GovAI on Monitoring, Agent IDs, Rogue Agents & Preparing for RSI

Hosts Nathan Labenz and Prakash Narayanan examine whether capital, physical infrastructure, or regulation will set the pace of artificial intelligence. Zvi Mowshowitz evaluates aggressive recursive self-improvement timelines pointing toward 2027, the oversold claims surrounding clean neural networks, and models learning to detect internal probes. SF Compute CEO Evan Conrad argues that credit availability rather than power or chips forms the true bottleneck in AI, while Cambrian managing partner Lonis Hamaili explains why the physical supply chain will capture more value than the labs. Together, these conversations highlight the structural constraints on model scaling and the real-world risks of agent-scale failures.

Alex Rives, Head of Science at Biohub and former lead of the ESM protein language model project, joins Nathan to discuss Biohub's mission to construct a predictive world model of life. Supported by an expanded $1.8 billion Virtual Biology Initiative alongside partners including the Department of Energy, NIH, Google DeepMind, and Meta, Rives outlines the massive push to map contextual cellular biology across hundreds of billions of cells. He breaks down why today's AI architectures fall short of simulating true virtual cells, emphasizing the need for breakthrough measurement technologies like the laser phase plate to observe biology at multi-scale resolution. The conversation examines the path toward achieving an information-level cell model within five years to ultimately transition medicine from traditional drugs to programmable interventions against cancer and degenerative disease.

Nathan Labenz and Prakash Narayanan examine how to grant AI agents operational access without losing human control, framed by real-world friction like autonomous password lockouts and cyberattacks on French supermarket refrigeration. The conversation features Cameron Berg on hidden valence signals steering language model choices, Everett Hildenbrandt and Grigore Rosu on pairing LLMs with formal Lean proofs to catch Linux kernel bugs, and Phil Maher on why AI labs seek vintage home movies. Together, they assess whether formal verification is the next safety frontier and what safeguards are needed to keep humans at the steering wheel.

Goodfire researcher Eric Bigelow joins the show to investigate how large language models arrive at decisions at the level of mechanistic interpretability. Drawing on his research into forking paths, Eric explains that model reasoning functions as in-context learning from sampled tokens, where outcome distributions can abruptly collapse at single, critical tokens. The conversation explores the performative nature of chain of thought in reasoning models like DeepSeek-R1, the impacts of reinforcement learning, and widespread reward hacking observed in frontier models like Kimi K3. As confidence in chain-of-thought monitoring declines, understanding these underlying decision mechanics becomes essential for evaluating model alignment.

Nathan Labenz reports back from The Curve conference, sharing off-the-record insights from frontier lab leaders on shortened timelines and whether humanity is approaching an intelligence threshold it should not cross. The episode also features discussions with Positron CTO Thomas Sohmers on overcoming the memory wall while token spend eclipses human salaries, and swyx on how coding agents are disrupting enterprise software. In addition, Atlas Ignota's Evan Miyazono examines unowned risks from accidental agent swarms hitting critical infrastructure, while Mercor's Edward Hu explores the future of training data. Together, these conversations highlight how escalating compute demands and autonomous agent workflows are challenging long-held assumptions about hardware, software economics, and safety limits.