Terence Tao Used AI Agents to Finish a 1999 Project He Abandoned — in Two Hours
On July 11, Terence Tao — Fields Medal 2006, widely considered the world’s greatest living mathematician — published a blog post describing how he used AI coding agents to do something he could not do himself in 1999: finish a special relativity visualization tool he started and abandoned when the code complexity became unmanageable.
The post, on his WordPress blog, is methodical. No hype. Just a working mathematician documenting what actually happened.
What He Did
In the past few days, Tao migrated his old web materials to a new repository. As a test, he asked an AI coding agent to port his collection of Java 1.0 applets — written in 1999 for complex analysis and linear algebra courses — to modern JavaScript.
Result: all 24 applets functional, including two that had been broken for years. Time: hours. The agent also added color to his monochrome Besicovitch set applet without being asked.
Code quality held. Tao found one minor bug — incorrect drag behavior outside a canvas boundary. The agent independently flagged two bugs in his original 1999 code. Net change: negative one defect.
The 1999 Project
The more striking part is the spacetime tool. In 1999, Tao had an idea: Inkscape, but in Minkowski space — a visualization for special relativity that would let users manipulate spacetime diagrams interactively. He started writing Java, hit a complexity wall, and dropped it.
Twenty-seven years later, a couple of hours of “vibe coding” with an AI agent produced what he originally envisioned. The app is now live. A full transcript of the agent conversation is published alongside it.
He then applied the same approach to a Gilbreath conjecture visualization — a mathematical structure he wrote about in a paper the same day — and had a working interactive tool in another few hours.
Why This Is a Signal
Tao is not an AI booster. He has been engaged with machine-assisted mathematics since at least 1999. His assessment is direct: the agent session was “painless enough” to make him consider adding interactive visualizations to future papers as standard supplements.
His framing on acceptable risk is precise: because these visualizations are secondary aids rather than load-bearing mathematical arguments, the residual error rate from AI-generated code is acceptable. He is not claiming the agents are correct; he is claiming the quality bar fits the use case.
That distinction — domain expert using AI tools where the error tolerance matches the output stakes — is what most AI productivity claims fail to make. Tao made it explicitly.
Key Numbers
- Applets ported: ~24 (Java 1.0 to JavaScript)
- Bug count: agent introduced 1, found 2 pre-existing
- Time to complete the 1999 spacetime project: “a couple of hours”
- Time to build the Gilbreath visualization from scratch: “a few hours”
- Years the spacetime project sat unfinished: 27
The transcripts are published. The apps are live. This is reproducible evidence, not a press release.