Recursive self improvement

Aug 25, 2026

Dylan Patel – Two labs will soon control most of the world's workforce

A detailed discussion on the rapid centralization of AI compute power within frontier labs like OpenAI and Anthropic, driven by their superior monetization of compute. The conversation explores the massive CapEx requirements for AI infrastructure, the potential for a sovereign debt crisis due to rising interest rates, and the impact of regulation on AI progress. It also delves into the strategic shift from inference to R&D within labs and the implications of exponential growth in

Aug 15, 2026

Exo: Harnesses should see their own code and logs — Alex Krentsel

An introduction to Exo, a fully recursive AI agent harnessing a novel three-layer architecture (Executor, Harness, Sandbox) designed for autonomous self-improvement. It delves into how Exo surpasses current agent limitations by allowing the agent to edit its own code and policy at runtime, ensuring protected state and isolated execution, and discusses practical implications and the underlying systems philosophy enabling this paradigm shift.

Aug 06, 2026

Chasing Trillion-Dollar Companies, Founder Ambition, Token Budgets, & Regulatory Capture

Elad and Sarah discuss the rapid rise of AI's multi-trillion-dollar companies, debating whether this growth is sustainable or an anomaly. They explore how founder ambition is shaped by fear of AI labs, optimal strategies for startup exits, and the psychological impact of impending AGI on researchers. Key bottlenecks like compute power, the emergence of an oligopoly, and the growing threat of regulatory capture—exemplified by California's tax policies—are also analyzed, highlighting the critical societal trade-off between safety and technological progress.

Jul 30, 2026

First Steps Toward Automated AI Research — Richard Socher, CEO Recursive AI

Richard Socher introduces the "Eureka machine," a concept for automating scientific research and invention through AI. Inspired by open-ended evolution and Karl Popper's philosophy of science, he argues that AI can compress the timeline of scientific discovery, overcoming human-centric bottlenecks. The machine relies on four pillars (knowledge, data, simulations, physical labs) orchestrated by an agent swarm, requiring a rethinking of existing infrastructure. Recursive Self-Improvement (RSI), where AI improves its own code and addresses its shortcomings, is presented as the path forward, with early proof points in model optimization, training speed, and GPU kernel efficiency.

Jul 15, 2026

Recursive Model Improvement — Lee Robinson, Cursor, SpaceXAI

Lee Robinson of Cursor outlines a comprehensive strategy for recursive AI model improvement, centered on a two-loop training framework. He details how Cursor enhances both user-feedback-driven outer loops and high-quality evaluation inner loops, introducing novel methods like textual feedback and addressing reward hacking. The discussion extends to scaling compute infrastructure through partnerships with SpaceX, Colossus, and Terafab, and leveraging agent-based automation to streamline research and foster a future where models continuously train and improve themselves.

Jul 10, 2026

Podcast Crossover: AIE, AGI, frontier lab strategy with ​ ⁨@matthew_berman⁩ and @swyxtv

Matt, the organizer of the AI.engineer conference, shares insights into its origin, the challenges of early adoption, and its current value as a neutral ground for AI labs. He delves into AI hardware trends, discussing specialized chips like Etched, and gives a nuanced take on Anthropic's Fable, addressing performance concerns and compute limitations. The conversation then explores OpenAI's rumored equity offer to the US government, discussing implications for regulation and societal involvement. Matt shares his perspective on AI existential risk and alignment, emphasizing the need for pragmatic engineering solutions. Finally, he outlines the limitations of current LLMs, the critical need for data efficiency, and offers strategic advice for "Agent Labs" navigating the "model capability overhang" versus multi-model agnosticism.