← Home
Odd Lots · June 19, 2026 · 1h 6m

Anthropic's Co-Founder and Top Economist on Doing Research at the AI Frontier

Jack Clark (co-founder and head of public benefit at Anthropic) and economist Peter McCrory discuss how the AI safety company approaches existential risk, labor market impacts, and government regulation. They cover recursive self-improvement, the hiring priorities for engineers building frontier models, and why Clark transitioned from Bloomberg to the early AI industry.

This summary was generated from show notes and public descriptions, not from a full transcript review. Details may contain inaccuracies.

Highlights

The Trump administration recently forced Anthropic to block foreign access to its two leading models, Mythos and Fable, raising questions about government control of frontier AI systems.
Existential Risk as Legitimate Business and Policy Concern
Anthropic frames existential risk from AI as a primary concern alongside labor market impacts and government regulation, not a fringe theoretical concern.

Editorial

Recursive Self-Improvement as Engineering Challenge
Anthropic is actively preparing engineering strategies for scenarios where AI systems can recursively improve themselves, requiring different safety architectures than current frontier models.
AI Labor Market Impact as Central Research Question
Peter McCrory, Anthropic's head economist, is actively researching how frontier AI systems will disrupt labor markets and which workers face the highest risk of displacement.
Early-Stage AI Industry as Alternative to Established Finance
Jack Clark's transition from Bloomberg to Anthropic during the early AI era illustrates how frontier research attracts talent away from traditional power structures in media and finance.

Misc

Trump administration forced Anthropic to block foreign access to its two leading models (Mythos and Fable) just before this episode
Clark left Bloomberg to enter early AI industry — a significant career pivot
Anthropic is actively preparing for recursive self-improvement scenarios
Was this useful?