You were once smarter than AI at math. Yes, even you.

Even if you’re allergic to math, you can probably handle this simple arithmetic. As OpenAI’s researchers put it: “A bright middle-school student should be able to solve every problem.”

But what about a student in elementary school—or an AI model?

When the company gave this exam to a group of kids between the ages of nine and 12, they scored 60%. The primitive AI system was given the same test and scored 55%. And the researchers were elated.

“I still remember the feeling of jumping up and down,” OpenAI chief research officer Mark Chen told me, “for things that we would laugh at now.”

Never had a failing grade on a math exam felt so good. At the time, the models were lousy at reasoning through problems step by step. Since then, AI has become more powerful in far less time than even the researchers expected.

It’s no longer struggling to do fourth-grade homework. Now it’s doing mathematics that no human has ever done.

In the past few weeks alone, an OpenAI internal model solved one of the legendary Millennium Prize Problems and then several hundred more open problems, which seemed inconceivable even a few months ago. This explosion of discoveries has left the field reeling—proof that math will never be the same.

Math has become the easiest way to understand how dramatically AI’s capabilities have advanced and how badly people have underestimated them. So now what? If the models went from being stumped by long division to cracking the quasi-Riemann hypothesis in five years, how will the next five years play out? And if AI can master a field that turns most of our brains to mush, is it only a matter of time before it comes for the rest of us?

These are much harder questions than counting unicorns and Janet’s egg sales—and mathematicians didn’t think they would have to answer them first. Not long ago, in fact, math was thought to be one of the last fields AI would transform.

But as the companies used math to train their models, those models became much, much better at math.

In 2023, OpenAI’s top model breezed through the grade-school math exam that previous machines had flunked. That rapid improvement meant it was ready for a more grueling test of mathematical knowledge.

It was time for AI to take the SAT.

The model scored 700 of 800 on the SAT’s math section—a sign of how far these systems had come and how far they still had to go. Even then, AI was outperformed by teenagers who would go to college and major in English so they wouldn’t have to think about algebra.

Around this time, AI tools were still famously bad at answering one math question that wouldn’t appear on any standardized test: How many r’s are in the word strawberry? (Hint: not two.)

But inside the labs, researchers could see that the next generation of models wouldn’t have any trouble computing the number of letters in fruit.

AI went from not working very well to working so well it’s scary partly because of the nature of math itself. Unlike messier domains, math has been governed by precise rules of logic for millennia. It’s also verifiable. Answers are right or wrong, so models can learn from their mistakes, just like 9-year-olds.

Next up in their math education: problems from the high-school competitions that many AI researchers had once entered themselves.

At first, the models stumbled on tricky problems that required complex multistep reasoning. But like other math benchmark tests, this one would eventually get obliterated.

In mid-2023, OpenAI’s top model scored 23% on the test’s hardest problems. In 2024, the company rolled out its first reasoning model—code-named “Strawberry”—and it was particularly strong in math and coding. By the end of 2024, OpenAI’s signature model hit 95% on that exam.

When he was informed the models had surpassed his peak rating as a competitive programmer, Chen suddenly empathized with Lee Sedol, one of the world’s top Go players, who was defeated by AlphaGo in a defining event of AI history.

“That was a real emotional moment for me,” he said. “I feel like people who have undergone moments like that have a greater appreciation for what the models are able to do in the future.”

Chen had another reason to appreciate their perfect score on the notoriously difficult American Invitational Mathematics Examination. When he was a high-school senior, 8,586 students from all over the world took the AIME. Nine got perfect scores. He was one of them.

Once the AI models aced the AIME and won gold medals at the math Olympics, they graduated from high-school math to open problems whose solutions had eluded humans for decades.

I asked Chen what he would have said if I had told him in 2021 about the velocity of the next five years.

“I would have said that’s crazy,” he said. “But the harsh lesson here is that people are really bad at internalizing exponentials—even people inside OpenAI.”

When I called Scott Aaronson and Dana Moshkovitz, a married couple of theoretical computer scientists at the University of Texas, I learned why it’s so hard to predict the pace of progress.

“Things happen faster than you expect,” Aaronson said, “even when you update on the last thing happening faster than you expected.”

In the before times otherwise known as August 2026, Moshkovitz was talking with a colleague about the Unique Games Conjecture, a highly abstract problem that had occupied her mind for more than a decade. When she floated the idea that AI might solve it one day, the suggestion was so preposterous that he thought she was joking. Two months later, she was proven right when the solution appeared in OpenAI’s blizzard of papers.

Moshkovitz broke the news to her son when she picked him up from school. As it happens, he is 9 years old—the same age as the youngest participants in OpenAI’s study.

They are no longer smarter than AI at solving math problems, but 9-year-olds are still pretty wise.