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Former Anthropic pre-training researcher and Core Automation co-founder Rohan Anil explains how compute islands allow decentralized GPU clusters to train frontier AI models without colocation. He details why current scaling hits test-time learning bottlenecks, how butterfly mixing handles asynchronous training noise, and sets a 2027 timeline for models that continuously learn on the fly. Check out our sponsor Lovable: https://lovable.dev. Turn ideas into software people love.
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Timestamps:
(00:00) Introduction
(00:50) Leaving Anthropic for Core Auto
(01:51) Training on Compute Islands
(07:50) Labs Designing Their Own Chips
(09:24) How Butterfly Mixing Works
(11:38) Training Frontier Models in Space
(15:44) Real Bottlenecks to AGI Timelines
(17:55) Alignment Risks of Continual Learning
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Note: This podcast is not investment, legal, or tax advice, and is intended for informational and entertainment purposes only. Hosts and guests may hold positions in the companies and securities discussed; do your own research before acting on anything you hear.
