“It’s so economically valuable, and sufficiently easy to collect data on all of these different jobs, these white collar job tasks… we should expect to see them automated within the next five years.” — Trenton Bricken 2:03:15
New episode with my good friends Sholto Douglas & Trenton Bricken. Sholto focuses on scaling RL and Trenton researches mechanistic interpretability, both at Anthropic. We talk through what’s changed in the last year of AI research; the new RL regime and how far it can scale; how to trace a model’s thoughts; and how countries, workers, and students should prepare for AGI. (00:00:00) – How far can RL scale? (00:16:27) – Is continual learning a key bottleneck? (00:31:59) – Model self-awareness (00:50:32) – Taste and slop (01:00:51) – How soon to fully autonomous agents? (01:15:17) – Neuralese (01:18:55) – Inference compute will bottleneck AGI (01:23:01) – DeepSeek algorithmic improvements (01:37:42) – Why are LLMs ‘baby AGI’ but not AlphaZero? (01:45:38) – Mech interp (01:56:15) – How countries should prepare for AGI (02:10:26) – Automating white collar work (02:15:35) – Advice for students
“Even if AI progress totally stalls, it’s sufficiently easy to collect data on all these different white collar job tasks that we should expect to see them automated within the next 5 years.” pic.twitter.com/DNtxIwd46R
— Dwarkesh Patel (@dwarkesh_sp) May 22, 2025
.@TrentonBricken and @_sholtodouglas are back
Timestamps:
00:00:00 – Claude 4 & how far RL can scale
00:16:27 – Is continual learning a key bottleneck?
00:31:59 – Model self-awareness
00:50:32 – Taste and slop
01:00:51 – How soon to fully autonomous agents?
01:15:17 – Neuralese… pic.twitter.com/9O9clYeSte— Dwarkesh Patel (@dwarkesh_sp) May 22, 2025