Feb 24
Loading video player...
“2026 in AI for Science is going to look a lot like 2025 for Software Engineering” — Kevin Weil From building *Crixet* in stealth (so stealthy Kevin had to hunt down Victor on Reddit to explore an acquisition) to launching *Prism (*https://openai.com/prism/) as OpenAI's free AI-native LaTeX editor, *Kevin Weil* (VP of OpenAI for Science) and *Victor Powell* (Product Lead on Prism) are embedding frontier reasoning models like GPT 5.2 directly into the scientific publishing workflow—turning weeks of LaTeX wrestling into minutes of natural language instruction, and accelerating the path from research breakthrough to published paper. We discuss: * What *Prism* is: a free AI-native LaTeX editor with *GPT-5.2 embedded directly into the workflow* (no copy-pasting between ChatGPT and Overleaf, the AI has full context on all your files) * The *origin story:* Kevin found Victor's stealth company Cricket on a Reddit forum, DMed him out of the blue, and brought the team into OpenAI to build the scientific collaboration layer for AI acceleration * *Live demo highlights:* proofreading an introduction paragraph-by-paragraph, converting a whiteboard commutative diagram photo into TikZ LaTeX code, generating 30 pages of general relativity lecture notes in seconds, and verifying complex symmetry equations in parallel chat sessions * Why *LaTeX is the bottleneck:* scientists spend hours aligning diagrams, formatting equations, and managing references—time that should go to actual science, not typesetting * The *software engineering analogy:* just like 2025 was the year AI moved from "early adopters only" to "you're falling behind if you're not using it" for coding, 2026 will be that year for science * Why *collaboration is built-in:* unlimited collaborators for free (most LaTeX tools charge per seat), commenting, multi-line diff generation, and Monaco-based editor infrastructure * The *UI evolution thesis:* today your document is front and center with AI on the side, but as models improve and trust increases, the primary interface becomes your conversation with the AI (the document becomes secondary verification) * *OpenAI for Science's mission:* accelerate science by building frontier models _and_ embedding them into scientific workflows (not just better models, but AI in the right places at the right time) * The *progression from SAT to open problems:* two years ago GPT passed the SAT, then contest math, then graduate-level problems, then IMO Gold, and now it's solving open problems at the frontier of math, physics, and biology * Why *robotic labs are the next bottleneck:* as AI gets better at reasoning over the full literature and designing experiments, the constraint shifts from "can we think of the right experiment" to "can we run 100 experiments in parallel while we sleep" * The *in silico acceleration unlock:* nuclear fusion simulations, materials science, drug discovery—fields where you can run thousands of simulations in parallel, feed results back to the reasoning model, and iterate before touching the real world * *Self-acceleration and the automated researcher:* Jakub's public goal of an intern-level AI researcher by September 2026 (eight months away), and why that unlocks faster model improvement and faster science * The vision: *not to win Nobel Prizes ourselves, but for 100 scientists to win Nobel Prizes using our technology*—and to compress 25 years of science into five by making every scientist faster — Prism * Try Prism: https://prism.openai.com (free, log in with your ChatGPT account) * OpenAI for Science: https://openai.com/science 00:00:00 Introduction: OpenAI Prism Launch and the AI for Science Mission 00:00:42 Why LaTeX Needs AI: The Scientific Writing Bottleneck 00:03:13 The Cricket Acquisition Story: From Reddit to OpenAI 00:05:50 Live Demo: AI-Powered LaTeX Editing with GPT-5.2 00:17:13 Engineering Challenges: Monaco, WebAssembly, and Backend Rendering 00:18:19 The Future of Scientific UIs: From Document-First to AI-First 00:15:51 Collaboration Features and Notebooks: The Next Integration 00:21:02 AI for Science: From SAT Tests to Open Research Problems 00:23:32 The Wet Lab Bottleneck: Robotic Labs and Experimental Acceleration 00:33:08 Self-Acceleration and the Automated AI Researcher by September 2026