About this episode

Ted Moskovitz discusses the empirical science of scaling AI models with Nathan Benaich.

Ted Moskovitz leads the Science of Scaling team at Anthropic, the group that works out how to turn compute into smarter models. In this RAAIS 2026 fireside with Air Street Capital's Nathan Benaich, he argues that frontier scaling has become an empirical science - a discipline for cutting uncertainty before spending the compute, not just buying more of it. They get into the honest measure of AI acceleration (it's the counterfactual, not the benchmark), why a bigger model can be cheaper than splitting a task across small ones, whether a model can have research taste, and why safety and capability turn out to be the same axis. Plus the highest-leverage AI work to do in 2026, and why Anthropic's London office no longer feels like a satellite. Recorded live at RAAIS 2026 in London. Timestamp: 00:00 - Meet Ted Moskovitz and the Science of Scaling team 00:45 - What "the science of scaling" actually means 01:18 - Why scaling is a science, not an art 02:55 - Big labs vs the new "neo labs" 04:47 - How a research finding reaches the product 06:44 - What neuroscience carries over to AI (and what doesn't) 09:12 - "When AI builds itself" and the real measure of…

People in this episode

Host: Nathan Benaich

Guest: Ted Moskovitz

Topics covered

• AI scaling
• machine learning
• neuroscience in AI
• AI safety
• AI research
• compute efficiency

Keywords

• AI acceleration
• compute
• model scaling
• research taste
• safety
• alignment

Mentioned in this episode

Organizations: Anthropic, Air Street Capital

Places: London

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