The greatest living mathematician just said something that reframes the entire AI debate (Save this).
Terence Tao, Fields Medal winner, UCLA professor, and by most measures the most accomplished pure mathematician alive speaking at an OpenAI Forum event in March 2026, and the observation he made is deceptively simple but profound in its implications.
"We lived in a world with cognitive friction until very recently, where every task required us to use our brain. So we didn't really think about it, we just thought this was the cost of doing something intellectual. But now we have AI and the other technologies that can bring these frictions down to zero."
To understand why this matters so much, you have to understand what most research time actually looks like.
Most research time is spent checking cases, chasing references, translating intuition into computation, testing a path, finding it false, and deciding whether the failure taught you anything useful.
As Tao puts it the lower cost of exploration that AI enables means he can now try crazier things and that makes all the difference.
The reason unconventional ideas in science are often abandoned is because the bookkeeping, coding, or literature search needed to even test them is too expensive for what is ultimately just a hunch.
This is where cognitive friction becomes scientific friction, and lowering it does not make taste, judgment, or proof disappear, it makes more weak signals cheap enough to inspect before they are abandoned.
AI is making hesitation less expensive, and that is often where discovery begins.
Tao now uses AI to search literature, write code, make plots and figures, run calculations, and test whether a possible approach is even worth chasing and he declared AI ready for primetime in March 2026 after confirming that in math and theoretical physics, it now saves more time than it wastes.
He had previously called early AI models mediocre but not entirely inept graduate students and then watched as they passed the threshold where the value of acceleration exceeded the cost of correction.
Years ago, Tao predicted that 2026-level AI, when used properly, will be a trustworthy co-author in mathematical research and by his own assessment this year, that prediction came in on schedule.
A 23-year-old used ChatGPT to solve Erdős Problem #1196 , a problem that had gone unsolved for 60 years in just over 80 minutes.
OpenAI's GPT-5.2 Pro resolved another open Erdős problem, with OpenAI President Greg Brockman posting about it in January 2026.
And OpenAI's Chief Research Officer Mark Chen articulated the institutional goal in terms that every investor should internalize, "We care less about winning a Nobel Prize or a Fields Medal, and more about enabling 100 mathematicians out there to do that for themselves."
If AI is genuinely collapsing the cost of scientific exploration not just in mathematics but in drug discovery, materials science, climate modeling, and theoretical physics then the companies building the compute infrastructure that makes that acceleration possible are not just selling chips and cloud capacity.
They are selling the raw material of compounding human discovery, and that is a demand curve with no visible ceiling