Apr 29, 2026

How to Build the Future: Demis Hassabis

Demis Hassabis, CEO of Google DeepMind, outlines the remaining challenges on the path to AGI, including memory, continual learning, and true reasoning. He discusses how learnings from AlphaGo are shaping agent development, the strategic importance of powerful small models like Gemma, and his vision for AI as the ultimate tool for scientific discovery, offering a framework for identifying breakthrough opportunities and advice for founders building in the age of AI.

Apr 28, 2026

What happens now that AI is good at math? — the OpenAI Podcast Ep. 17

OpenAI researchers Sébastien Bubeck and Ernest Ryu discuss the dramatic and surprising progress of AI in mathematics. They cover how models went from basic arithmetic to solving Olympiad-level and even 40-year-old open research problems, what this progress means for the future of science and AGI, and the evolving role of human researchers in an era of AI-accelerated discovery.

Mar 27, 2026

François Chollet: ARC-AGI-3, Beyond Deep Learning & A New Approach To ML

François Chollet discusses his contrarian approach to AI, moving beyond scaling LLMs to understand intelligence from first principles. He explains his work on the ARC benchmark series, including the new ARC-AGI V3, designed to measure 'agentic intelligence' and skill acquisition efficiency. He also introduces his lab, Ndea, which is developing a new ML paradigm based on symbolic models, and shares his perspective on the limits of current systems and the future path to AGI.

Mar 16, 2026

Beyond the Hype: What AI Actually Can (and Can't) Do • Jodie Burchell & Michelle Frost • GOTO 2026

Jodie Burchell and Michelle Frost of JetBrains offer a measured, research-grounded perspective on the state of generative AI. They discuss the shifting definitions of AI, the enduring importance of foundational machine learning principles, historical parallels to previous 'AI summers,' the measurement problem of AGI, and what the evidence actually says about AI's impact on developer productivity.

Jan 18, 2026

Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION]

An exploration of scientific simplification, questioning the metaphors we use to understand the brain and intelligence. This summary delves into the tension between creating useful models and mistaking them for reality, featuring insights on the mind-as-software debate, the limits of prediction versus understanding, and the philosophical underpinnings of our quest for AGI.

Dec 27, 2025

AGI: The Path Forward – Jason Warner & Eiso Kant, Poolside

In a live demo, Poolside's CEOs showcase their second-generation model, the Malibu agent, by migrating a complex codebase from ADA to Rust, including automated testing and iterative feature development. They outline their vision for achieving AGI through a full-stack approach combining proprietary models, reinforcement learning, and massive-scale compute, with plans for a public model release in early 2025.