Most journalists will say they’re not good at mathematics, or, as our U.K. friends put it, “the maths.” In high school I went to the state scholastic fair to compete in calculus, an experience that disabused me of several notions about my intellectual abilities, beginning with the idea that I knew anything about calculus.
I also competed in French, a language I seem to have studied forever without acquiring the ability to conduct a basic conversation. I can read it reasonably well, provided nobody expects me to pronounce anything or answer a question.
Still, I’ve been to Paris and am not afraid of numbers. I retain a certain affection for the elegance of mathematics. But the scholastic fair introduced me to students who revealed how much I had to learn.
Recently I read a column by British mathematician Hannah Fry in New Scientist about the distress artificial intelligence is causing among her colleagues. Fry, whose doctoral work involved the Navier-Stokes equations (I don’t know what those are), reports that OpenAI has deployed thousands of AI agents to attack mathematical problems that have resisted human solution for generations. One reported result involves those equations, which describe the movement of fluids (I can use Google) and contain an unresolved question about whether certain mathematical solutions can break down.
Most interesting is Fry’s account of mathematicians’ reactions. Instead of celebrating the apparent resolution of an old problem, many seem to be having professional conniptions.
That’s because almost nobody understands the proof, which runs to 166 pages, accompanied by an immense quantity of computer code. Whether it withstands scrutiny or resolves the precise problem for which the Clay Mathematics Institute has offered a million-dollar prize requires independent mathematical judgment. But Fry’s larger point doesn’t depend on the outcome. Our machines are producing purported answers faster than we can make sense of them.
And mathematicians want to understand why things are true. They admire an elegant proof the way a musician admires a perfectly resolved phrase or a writer admires a keenly balanced sentence. There is a difference between knowing and understanding.
Fry seems particularly offended by OpenAI’s competitive attitude toward mathematics, its apparent indifference to collaboration with people who have devoted their lives to these problems. She calls AI, at its worst, an engine of extraction. Her suggestion is that the company has appropriated not merely the work but the prestige of mathematical discovery.
I don’t know enough about OpenAI’s internal deliberations to assign motives. Companies tend to be interested in prestige, especially when it can be monetized. I’m more interested in what happens to human ambition when aspirations can be accomplished without us.
There have always been people who could run faster, calculate more quickly, write better sentences, or hit a golf ball farther than the rest of us. The existence of Tiger Woods never persuaded me to give up golf; reading Richard Ford never persuaded me to stop writing. Comparison may be the thief of joy, but there is utility in knowing one’s limitations. Most of us accommodate ourselves to superior talent. We establish modest goals, invent handicapping systems, and take satisfaction in small improvements. Some of us manage to be gracious about the achievements of others.
You can admire the gift, envy its possessor, wonder about the advantages of upbringing and temperament, and console yourself with the thought that everybody has deficiencies. But a frickin’ machine doesn’t have to hit range balls, practice scales, learn multiplication tables or endure the humiliation of a statewide high school calculus competition. It simply performs.
A machine that does in seconds what you spent years learning to do has not merely surpassed your achievement. It has called into question the wisdom of your investment. Yet I don’t believe that’s a sufficient reason to stop trying.
After all these years I still care considerably more than is good for me about whether a paragraph works. I spend unreasonable amounts of time jiggering words, pruning sentences, trying to get the rhythm right. Sometimes the improvement is real. I don’t much care if nobody notices. I notice.
Ambition is often unattractive, especially in others. It can make us competitive, petty, vain, and incapable of enjoying what we have. It persuades us our accomplishments are inadequate. The distance between what we’ve made and what we want to make can become a permanent irritation.
Every once in a while I wish I didn’t care. I suspect I’d sleep much better, might become one of those agreeable people who finish their work at a reasonable hour and spend the evening doing whatever agreeable people do. Perhaps they watch television without simultaneously thinking about something they should have written differently.
The uncomfortable truth is that I’m not sure I’d want to become one.
The satisfaction of doing something