Why Reinforcement Learning Matters Now

Sholto Douglas, a top AI researcher at Anthropic, discusses the breakthroughs behind Claude Sonnet 4.5—the world's leading coding model—and why we might be just 2-3 years from AI matching human-level performance on most computer-facing tasks.

You'll discover why RL on language models suddenly started working in 2024, how agents maintain coherency across 30-hour coding sessions through self-correction and memory systems, and why the "bitter lesson" of scale keeps proving clever priors wrong.

Sholto shares his path from top-50 world fencer to Google's Gemini team to Anthropic, explaining why great blog posts sometimes matter more than PhDs in AI research. He discusses the culture at big AI labs and why Anthropic is laser-focused on coding (it's the fastest path to both economic impact and AI-assisted AI research). Sholto also discusses how the training pipeline is still "held together by duct tape" with massive room to improve, and why every benchmark created shows continuous rapid progress with no plateau in sight.

Bold predictions: individuals will soon manage teams of AI agents working 24/7, robotics is about to experience coding-level breakthroughs, and policymakers should urgently track AI progress on real economic tasks. A clear-eyed look at where AI stands today and where it's headed in the next few years.

Anthropic

Website - https://www.anthropic.com

Twitter - https://x.com/AnthropicAI

Sholto Douglas

LinkedIn - https://www.linkedin.com/in/sholto

Twitter - https://x.com/_sholtodouglas

FIRSTMARK

Website - https://firstmark.com

Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)

LinkedIn - https://www.linkedin.com/in/turck/

Twitter - https://twitter.com/mattturck

(00:00) Intro

(01:09) The Rapid Pace of AI Releases at Anthropic

(02:49) Understanding Opus, Sonnet, and Haiku Model Tiers

(04:14) Shelto's Journey: From Australian Fencer to AI Researcher

(12:01) The Growing Pool of AI Talent

(16:16) Breaking Into AI Research Without Traditional Credentials

(18:29) What "Taste" Means in AI Research

(23:05) Moving to Google and Building Gemini's Inference Stack

(25:08) How Anthropic Differs from Other AI Labs

(31:46) Why Anthropic Is Laser-Focused on Coding

(36:40) Inside a 30-Hour Autonomous Coding Session

(38:41) Examples of What AI Can Build in 30 Hours

(43:13) The Breakthroughs That Enabled 30-Hour Runs

(46:28) What's Actually Driving the Performance Gains

(47:42) Pre-Training vs. Reinforcement Learning Explained

(52:11) Test-Time Compute and the New Scaling Paradigm

(55:55) Why RL on LLMs Finally Started Working

(59:38) Are We on Track to AGI?

(01:02:05) Why the "Plateau" Narrative Is Wrong

(01:03:41) Sonnet's Performance Across Economic Sectors

(01:05:47) Preparing for a World of 10–100x Individual Leverage