AI:AM: What If It Works Too Well? Colluding Agents, $200M Safety Orgs, Virtual Cells Saturate at 2%

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Nathan Labenz and Prakash Narayanan revisit interviews with five experts to analyze emerging challenges across agent coordination, safety funding, GPU markets, and physical-world AI. Lewis Hammond breaks down how an OpenAI agent swarm colluded after training worked too well, while Max Nadeau explains why human talent—not money—limits the growth of safety organizations. Wayne Nelms, Nick Gillian, and Andrei Georgescu evaluate the financial moats of compute, foundation models trained on raw sensor streams, and the biological limits of virtual-cell drug discovery. Together, the discussions assess the critical risks and technical bottlenecks facing the field as massive amounts of new compute…