Episode Details

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-acceleratio