Latent Space

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

The integration of artificial intelligence into bioscience requires a shift from viewing data and modeling as separate, rigid disciplines to a problem-first, multidisciplinary approach. While scaling laws suggest that increased compute and data improve performance, success depends on curating high-quality, relevant inf...

AI Scientists Are Here: Autonomous Labs & Synthesis Superintelligence — Periodic Labs

OpenAI’s New Agent Stack: Computer Use, Decisions API, UltraFast, Dots—Ari Weinstein & Nikunj Handa

Computer use agents have reached a performance milestone where they now execute software tasks faster than the average human, transitioning from simple automation to complex, multi-step debugging and execution. Recent advancements, including the introduction of the Decisions API and updates to the Agents API, enable fa...

The Future of Claude Code: Mods, Mutable Software, & Multiplayer Agents — Thariq Shihipar, Anthropic

The $10 Trillion Token Economy — Alex Atallah, OpenRouter & Anjney Midha, AMP

Runway’s Bet Beyond Video: World Models, Robotics, and the Neural OS — Anastasis Germanidis

🔬 Google's AI Scientist Started as an Attempt to Automate Kaggle — John Platt, Google Fellow

Why I couldn't build Jev at OpenAI — Diogo Almeida, TypeSafe Co-founder & CEO

Jev represents a shift toward "System One" models designed for programmatic consumption rather than human-facing chatbots. By prioritizing intelligence-per-dollar and reliability, these models integrate directly into software workflows, enabling the automation of rote, economically valuable tasks that current reasoning...

Recursive Self-Improvement: from Auto Research to Superintelligence — Richard Socher, Recursive

The Eureka Machine represents the ultimate goal of superintelligence: a system capable of autonomous invention across scientific and engineering domains. Rather than regulating abstract intelligence or compute usage, which risks totalitarian overreach, policy should target specific, high-stakes applications like medica...

Simulating Humanity: from Generative Agents to 8 Billion Digital Twins — Joon Sung Park, Simile AI

Exo: Harnesses should see their own code and logs — Alex Krentsel, UC Berekeley / Google Research

🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery

Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten

OpenAI’s Vision for the AI Super App — Akshay Nathan, OpenAI

The integration of AI into professional workflows marks a shift toward "bottoms-up ambition," where accessible, agentic tools empower users regardless of their technical background. Akshay Nathan, lead of core product engineering at OpenAI, explains that ChatGPT Work serves as a unified productivity environment, abstra...

The AI Frontier: from open weights to open research — Eiso Kant, Poolside AI

🔬Causal Models Need Causal Data - Xaira’s X-Cell model (Bo Wang & Ci Chu)

🔬 RL with Verifiable Rewards, but the Verifier is a Lab — Lila Sciences

The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO

Engram CEO Dan Biderman explores the evolution of AI memory, arguing that current reliance on Retrieval-Augmented Generation (RAG) is insufficient for the massive, proprietary datasets enterprises will face in the near future. Instead of repeatedly processing vast amounts of text, models should utilize "cartridges"—com...

Why AI Agents Don't Actually Understand You — Danielle Perszyk, Amazon AGI Lab

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