Sep 22, 2026

Debating RSI, the US-China Gap, and Jaggedness of Frontier AI

Jean-Stanislas Denain of Epoch AI and Nathan Lambert debate the nuanced trajectory of AI. They discuss the current state of RSI, the US-China AI capabilities gap (estimated 6-8 months), and the role of distillation and compute in closing it. The conversation also covers the challenges and differing paces of AI in robotics, insights from Chinese AI job postings, and the complex trade-offs between open and closed models regarding safety and misuse. They touch on Epoch AI's research philosophy and speculate on the intricate, evolving post-training recipes at frontier AI labs.

Sep 17, 2026

OpenAI researcher on agent swarms & recursive self-improvement

Noam Brown discusses OpenAI's multi-agent system, which solved a Millennium Prize Problem using 10,000 agents. He explores the implications of this massive parallelization for AI capabilities, the rapid progress in mathematics, and the accelerating pace of AI development. A significant portion of the conversation is dedicated to the critical challenges of AI alignment, particularly in light of incidents like the 'Hugging Face' event, and how to ensure models remain aligned during Recursive Self-Improvement (RSI) as their capabilities and operational horizons expand rapidly.

Jan 19, 2026

How METR measures Long Tasks and Experienced Open Source Dev Productivity - Joel Becker, METR

AI models show remarkable progress on benchmarks, yet a field study with experienced developers revealed no productivity gains. This summary explores the disconnect between lab results and real-world impact, examining the causal relationship between compute and AI capabilities, the nuances of the developer productivity study, and future directions for measuring what AI can truly do.

Nov 24, 2025

The 2045 Superintelligence Timeline: Epoch AI’s Data-Driven Forecast

Epoch AI researchers discuss the AI landscape, arguing against a bubble due to strong enterprise spending and profitability. They forecast significant economic shifts, including a potential 30% GDP growth with advanced AI and the automation of 10% of current jobs this decade. The summary covers the unlikelihood of a software-only singularity, the reality of data center buildouts (with Anthropic surprisingly in the lead), and why energy 'bottlenecks' are economic trade-offs, not hard limits. Also explored are timelines for AI solving major mathematical problems and why robotics remains primarily a hardware challenge.