AI:AM Highlights: Recursive Self-Improvement, Rushed and Vibe-Coded?
About this episode
The episode discusses the evolving nature of AI work and the implications of recursive self-improvement in AI systems.
In this AI:AM Highlights episode, Nathan Labenz and Prakash revisit three live mornings with Louis Kirsch and Damon Falck of Inherent Laboratories, Vercel CTO Malte Ubl, Genesis Molecular AI CTO Sergey Edunov, Arm’s Mohamed Awad, David Li of Shenzhen Open Innovation Lab, and Q.ANT CEO Michael Förtsch. The central thread is that the meaningful unit of AI work is becoming a division of labor between models, from Inherent’s 27B “scientist” model using GPT-5.5 Codex for implementation to production pipelines that route taste, execution, security, and hardware coordination across different systems. The episode weighs why that shift matters for recursive self-improvement, verification of discoveries humans may not be able to check, model refusals in security work, and the infrastructure needed for agents that can act in the world. Its sharpest stake is whether frontier labs are scaling reinforcement learning and agentic workflows on top of reward environments and vendor pipelines that may be too rushed, noisy, or gameable to support the institutional self-improvement they are pursuing. For full show notes, links, and references, read the episode…
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