AI:AM Highlights: Clean Neurons, Black October, the GPU Credit Crunch, and Buying the Supply Chain

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.

For full show notes, links, and references, read the episode page:
cognitiverevolution.ai/ai-am-highlights-clean-…

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CHAPTERS:

() Weekly episode preview

() Recursive self improvement (Part 1)

() Sponsors: OutSystems | Parallel

() Recursive self improvement (Part 2)

() Clean neural networks (Part 1)

() Sponsor: Tasklet

() Clean neural networks (Part 2)

() The cognitive core

() Black October security

() The GPU credit crunch

() Atoms over bits