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 acceleration

10:33 - Trust, bypass mode, and the latest model jumps

11:42 - One big model vs many small ones

13:13 - Can a model have research taste?

15:39 - How safety research makes products better

17:36 - Emergent misalignment and the alignment race

19:14 - The highest-leverage AI work in 2026

20:21 - Inside Anthropic's London office

21:34 - Audience Q&A