I don’t wholeheartedly embrace AI, for I think it will be the death of liberal education. In both the humanities and science, I fear that students will lose any ability they have to write, and will not improve their writing because they’ll be using bots. This will degrade their ability to communicate. (Scientists too need to communicate, and if they rely solely on bots, which can write papers for them, they’ll also degrade their ability to think.) Take-home assignments will vanish (AI can do them, and are doing them now), and all that’s left are in-class verbal participation and in-class exams. This is fine for students who just think of college as a way to purchase accreditation and not a chance to glory in the joys of learning, but so be it.
However, AI is good for some things, including analyzing data, doing statistics, doing preliminary literature searches, and, in the article from the WSJ screenshot below, solving difficult math problems. The article shows that a problem posed by the famous and eccentric Hungarian mathematician Paul Erdös—the “unit distance problem” has been solved by AI. Open AI, which created the program that did it, describes it this way—but it’s not that simple:
For nearly 80 years, mathematicians have studied a deceptively simple question: if you place n points in the plane, how many pairs of points can be exactly distance 1 apart?
This is the planar unit distance problem, first posed by Paul Erdős in 1946. It is one of the best-known questions in combinatorial geometry, easy to state and remarkably difficult to resolve. The 2005 book Research Problems in Discrete Geometry, by Brass, Moser, and Pach, calls it “possibly the best known (and simplest to explain) problem in combinatorial geometry.” Noga Alon, a leading combinatorialist at Princeton, describes it as “one of Erdős’ favorite problems.” Erdős even offered a monetary prize for resolving this problem.
The “distance 1” thing confused me, and Wikipedia explains it a different way:
A problem posed by Paul Erdős known as the unit distance problem asks for the maximum possible number of unit-distance pairs determined by n points in the Euclidean plane; equivalently, it asks for the maximum number of edges in a unit distance graph on n vertices.
It gives a figure described as “a unit distance graph with 16 vertices and 40 edges”.

Wikipedia describes such unit distance graphs this way:
“In mathematics, particularly geometric graph theory, a unit distance graph is a graph formed from a collection of points in the Euclidean plane by connecting two points whenever the distance between them is exactly one.”
That’s what is confusing me, for if the theorem deals only with points in a two-dimensional plane, why aren’t unconnected dots not joined that are closer than some connected dots? (Look at the four dots around the center of the graph above. None of them are connected to each other, though more distant one are.) I presume some math-savvy reader will enlighten us.
Anyway, Open AI and the WSJ tells us that the problem has been solved by AI. If you want to see the solutions, open AI says this:
The proof is available here (opens in a new window). The companion paper by leading external mathematicians is available here (opens in a new window). You can find an abridged version of the model’s chain of thought here (opens in a new window).
But the WSJ gives more comprehensible details. Click screenshot to read (if you subscribe):
An excerpt:
“If you are a mathematician,” one of the world’s leading mathematicians recently wrote, “you may want to make sure you are sitting down before reading further.”
And you’ll definitely need to sit down if you’re not a mathematician.
Because a famous math problem that stumped humans for the better part of a century has finally been toppled—by AI.
Not long ago, the most advanced AI models couldn’t do basic math. By last year, they were performing at gold-medal levels at the International Mathematical Olympiad. Now they are solving classic problems in combinatorial geometry using algebraic number theory. In no time at all, artificial intelligence has gone from stupid to frighteningly smart.
But even mathematicians were astonished when OpenAI announced that one of its models resolved a puzzle known as the unit distance problem without the help of any humans scribbling a bunch of equations on chalkboards.
It was fed this prompt:
And produced this proof, giving the maximum number of unit-distance pairs:
Apparently the proof was accepted by mathematicians. More from the WSJ:
And everyone in math lost their minds.
For those who aren’t fluent in numbers, OpenAI helped translate its findings by presenting them alongside 19 pages of companion remarks from prominent mathematicians.
. . .Just looking at formulas is enough to hurt my brain, but I wanted to know more about what the AI found, how we humans missed it—and why this breakthrough matters to those of us who would like to permanently distance ourselves from math problems.
When I spoke with OpenAI employees, they told me this result would have sounded completely bananas one year ago.
“Forget one year ago,” researcher Sebastien Bubeck said. “A month ago.”
There are endorsements by mathematicians, and a history of the problem, which Erdös considered quite difficult. So difficult, in fact, that he offered what was then a pretty hefty sum for anybody who could solve it: $500. I think the money will be given to the OpenAI team.
OpenAI’s researchers were stunned. They had given this Erdős problem to an internal model as a test of its capabilities—to find out whether it was better than previous models. They found out how much better it was once they took a peek at the solution. “I initially didn’t believe it,” said Mehtaab Sawhney, a Columbia mathematician at OpenAI. So they searched for errors, verified the results with outsiders and checked the AI’s work using the company’s AI coding agent. “With enough reading and enough Codexing,” Sawhney said, “it seemed believable—and pretty remarkable.”
Long before AI, mathematicians who solved Erdős problems often framed their checks instead of cashing them. For them, the money was worth less than the glory. When I asked OpenAI researchers about their plans for the prize, they hadn’t given it much thought.
But they did have lots of thoughts about my next question: Why did AI succeed where humans failed?
The first explanation is that this particular solution happens to be highly counterintuitive.
Most people who tackled this problem tried to prove Erdős’s conjecture, rather than disprove it. Only by defying conventional wisdom and experimenting with seemingly improbable strategies did the model find an unexpected path forward.
The second is that humans specialize while AI synthesizes.
While mathematicians tend to focus on their specific areas of expertise, AI models use their vast knowledge to spot connections that we couldn’t possibly see ourselves. In this case, that meant pulling from both algebraic number theory and discrete geometry, which have about as much in common as the marathon and pole vault.
The third explanation is that AI has time, attention, patience, focus and the persistence to stick with methods that humans might abandon—and the solution to this Erdős problem demanded it.
“It’s the kind of idea that you try for a bit, it doesn’t work, and you think maybe you were just too hopeful,” said Mark Sellke, a Harvard statistician at OpenAI. “So you give up and move on.”
AI doesn’t move on. It keeps plugging away without taking breaks to eat, sleep, answer emails, pick the kids up from school and watch the Knicks.
And it can think coherently for so long that even an abridged version of the model’s “chain of thought” ran more than 75,000 words—the length of the first “Harry Potter” book.
Was it an elegant proof? Well, the article implies “no,” but it’s apparently a proof:
“It’s fair to say that we haven’t seen yet the spark of genius that you could attribute to some of the grandest proofs in the history of humanity,” Bubeck told me.
And how long did the computation take? Less than a day and a half:
After reading it, a former OpenAI researcher did some back-of-the-envelope math and estimated it took less than 32 hours and $1,000 in tokens, a bargain for a result of this caliber. The researchers wouldn’t confirm the exact amount of time and compute, but Bubeck described the costs as “really nothing crazy at all.”
At any rate, this is what AI is good for, and I wonder if, say, it could solve Fermat’s Last Theorem, which took Andrew Wiles eight years of work to solve (he was knighted for it). And I wonder if there are any seemingly intractable math problems that can’t be solved by AI, especially if they were or will be solved by humans.
Now I don’t think there are any practical implications of this results, but that’s true of much mathematical theory. I’m just amazed at what AI can do.











