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This podcast features a nuanced debate between Nathan Lambert and Epoch AI's JS Denain regarding the trajectory of US-China model gaps, the efficacy of distillation, and safety trade-offs between open and closed systems. The discussion highlights significant uncertainty about whether current data on recursive self-improvement (RSI) indicates imminent acceleration or full automation in AI capabilities.
Debating RSI, the US-China Gap, and Jaggedness with JS Denain of Epoch AI
Podcast #19
This episode is with Jean-Stanislas “JS” Denain of Epoch AI, who leads their Insights Team and is one of the people I find myself debating the state and trajectory of AI with more and more. We’ve had follow-on discussions of many of my favorite recent posts online and/or in private, so I wanted to dig into the nuance in a public episode. A big takeaway of this podcast is how JS and I both have so much uncertainty with exactly where we are heading, and this was our best effort at stating our observations today. 24:20 How far behind are Chinese models? 24:20 How far behind are Chinese models? 27:39 Does distillation explain the gap? 27:39 Does distillation explain the gap? 48:13 Are open or closed models safer? 48:13 Are open or closed models safer? Listen on Apple Podcasts, Spotify, and where ever you get your podcasts. For other Interconnects interviews, go here. 00:00:00 Nathan Lambert: I’m here with JS Denain, who is a senior researcher at Epoch AI. He leads the insights team. He is one of the people who I feel like I get the best feedback on my writing from, whether it’s from US-China AI capabilities, now…
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