Oct 23, 2026

How OpenAI Built ChatGPT for Bankers and Analysts — Joseph Kim, OpenAI

Joseph Kim, who leads ChatGPT for Financial Services, explains the capability overhang, the gap between what models can do and the value professionals get from them at work. He covers the product built with Morgan Stanley and Evercore, why the team licensed and indexed financial data instead of relying on MCP connectors, how firm templates, charts, and citation viewers sit on the ChatGPT plugin platform, and his view of model progress, the threshold of usefulness, and three bets for finance in 2027.

Oct 18, 2026

A mind-expanding conversation on how models actually work | Emmanuel Ameisen (Anthropic)

Emmanuel Ameisen, an interpretability researcher at Anthropic, explains how the team looks inside models like Claude to understand how they actually think. The conversation covers why next-token prediction hides real planning and conceptual representation, why models are grown rather than built, how intermediate activations reveal structure, why anthropomorphic framing can be useful, how smooth scaling produces sudden capability thresholds, and how interpretability ties into alignment, safety, and the broader question of what these systems are.

Oct 15, 2026

AI Can Automate 60% of Tasks... But There's a Catch

MIT FutureTech director Neil Thompson revisits his group's 2020 computational limits paper and discusses his newer research on how AI automates tasks across the economy, covering the crashing-waves versus rising-tides survey, adoption bottlenecks, fixed-cost economics, apprenticeship risks, AI safety, and the open versus closed model debate.

Oct 10, 2026

Why AlphaFold Didn't Solve Protein Folding — Pushmeet Kohli, Google DeepMind & Sal Candido, Biohub

A panel with Google DeepMind's Pushmeet Kohli and Biohub's Sal Candido, moderated by Brandon Anderson, examines how AI should be built for biology. Topics include scaling laws and data quality, the bitter lesson applied to data, AlphaFold's handcrafted design and its limits, protein dynamics, cryo-EM as a richer data source, trust and calibration versus interpretability, and why curing disease requires 10x breakthroughs.

Oct 07, 2026

Why Reflection AI Is Making the Open Intelligence Bet - and What It Means for Deploying Models

Joe Spisak of Reflection discusses why open models and open intelligence matter, covering RL-driven customization, environments as the new dataset, the OpenEnv project, PyTorch and Llama lessons, inference scaling, and the case for ecosystem control and open safety.

Sep 25, 2026

Why LLM Recommenders Will Be AI's Biggest Consumer App — Devansh Tandon, Meta

Devansh Tandon, who leads Meta Recommendations Research, argues that recommendation systems scale like LLMs, that the field is early on that curve, and that LLM recommenders will become one of AI's biggest consumer applications. He walks through four S-curves of recommendation, a three-step recipe built on semantic IDs, and why content feeds can be up to 100x cheaper per hour of engagement than AI chat apps.