Ken Ono on Why Competing With AI on Knowledge Is the Wrong Race
EO Korea
Ken Ono is a mathematician, a professor at the University of Virginia currently on leave, and the founding mathematician at Axiom Math, a company working in AI for math. His talk starts from his own moment of crisis, when large language models began answering math problems he had designed to stump them. He argues that trying to "stay ahead of AI" is a losing goal. Knowledge has become cheap, he says, and what matters now is a deeper kind of intelligence: creating concepts, connecting ideas across fields, and asking the right questions. He then traces that view back to his own story, which runs through the Indian mathematician Srinivasa Ramanujan, and ends with a critique of how schools train students for perfection and test scores instead of wonder.
From "natural intelligence" to devastation
Ono first heard the term "artificial intelligence" in 1993. When a faculty member told him they worked in AI, he replied that he specialized in "natural intelligence." He calls that remark cocky and says he would later have his "butt handed to" him many times for talking that way.
The turning point came, by his account, exactly one year before the talk. At the time he was a contented university professor writing papers at the University of Virginia. Then he joined the FrontierMath program. Epoch AI, a company based in Berkeley, hired professional mathematicians from around the world to assemble very difficult problems, with the aim of measuring the capabilities of large language models as they improved. For the first time, Ono says, he struggled to write questions that ChatGPT would get wrong.
He describes himself as "devastated." He was one of the few scientists with access to these state-of-the-art models, so he had to wait several months before the wider world recognized what he had seen: these models know more facts than any human you could find. For those months, his question was how he could stay ahead of AI.
Why "staying ahead of AI" is the wrong question
Ono now thinks that question is misguided. If the goal is always to stay ahead of AI, he says, "we're going to lose." His analogy: nobody wants to watch Usain Bolt race a motorcycle over a mile, because it isn't a fair race. People still watch the Olympics anyway. Society has accepted that machines outperform humans physically. In Ono's view it is only now coming to terms with the fact that computers have caught up in the brain and in deep inquiry.
He suggests thinking of large language models as "the most extraordinary librarian the world has ever seen." If something has been written down, the model has probably seen it. If it's on YouTube, the model has probably been trained on it. A newspaper article published in the morning has probably been absorbed by the afternoon. Competing with that ability to collect information is hopeless, he says. Knowledge is now cheap. What has become more expensive is how you use that knowledge and how you verify it.
He pushes the librarian image further to show where human judgment still matters. Would you want your librarian to be your neurosurgeon, or the air traffic controller tracking hundreds of planes over North America or Korea? "No way," he says, because human judgment is important in those roles.
Redefining intelligence: "deep intelligence"
Ono says the experience changed both his identity and his view of intelligence. Speed no longer matters to him. Whether you reason quickly or slowly, the question is whether you can make proper inferences, create a new concept, generate ideas, and string concepts together in a deep way. That, he says, is intelligence, as opposed to "the regurgitation of facts." He adds that schools are not good at teaching it.
He describes several forms of this "deep intelligence":
- Designing systems from scratch. This means setting the dials to build something new, whether a gadget in industry, a computer program, or a whole new area of science. He notes this form is rarely recognized in schools at any level.
- Transferring patterns between disciplines. This is noticing a pattern in one area of thought and carrying it to another so that the second field moves forward. Five years ago, Ono says, he would have called this luck, being in the right place at the right time. He now considers that unfair, because someone still has to make the observation. Recognizing "a target of opportunity" is genius, and he says he doesn't use that word lightly.
- Persistent niche expertise. A student or worker who becomes an expert in a narrow field by learning something new about it every day, with hard-nosed commitment, also shows intelligence and "a kind of genius that we need to recognize."
A family plan and a near escape
Ono then turns to his personal story. He is the son of a mathematician and was considered gifted in math as a child. His parents had a plan for all three sons. The oldest would be a pianist, and he became one. The youngest, Ken, would be a mathematician. The middle son, whom they judged neither good at math nor musically gifted, should "just go work in a bank" and make a living.
According to Ono, that assessment drove the middle brother, Santa, to prove it wrong. He went on to become a distinguished scientist and president of the University of Michigan.
For Ken, the plan nearly backfired. He dropped out of high school. The last thing he wanted was to become whatever his parents wanted. He resented being "the one Asian kid in class" expected to be good at math while other kids had lives, and he says he couldn't play baseball, the all-American pastime. By April 1984 he had resolved to run away from home, never see his parents again, and strike out on his own.
The letter from Ramanujan's widow
That same month, a letter arrived for his father. It was on yellowed paper that looked a hundred years old, and it came from Janaki Ammal, the widow of Ramanujan. She was thanking Ono's father for a small donation toward a statue commissioned in her husband's memory.
Ono had never seen his father cry. He describes him as a man with almost no visible emotion, but the letter brought him to tears, and afterward he brought it to his son to explain what it meant. That day Ono learned that Ramanujan was a mystic and an autodidact. Ramanujan believed his goddess gave him formulas in visions, and he wrote them down in his notebooks. Because he cared only about mathematics, he neglected his other courses and flunked out of college twice. Ono's father was studying the three notebooks of formulas Ramanujan left behind.
Ono later learned why the story meant so much to his father. His father wanted to be a mathematician but went to college while the world was at war, and he loved mathematics as an escape from long lines for food. After the war, the United States sent some of its best mathematicians to Japan to rebuild universities and train mathematicians. At a conference, Ono's father was discovered by a Princeton professor who invited him to study at Princeton, and that launched his career. Ono says it was at that conference that his father first learned about Ramanujan. For a Japanese mathematician after the war, Ramanujan represented hope.
Ramanujan died at 32 and was nearly forgotten. Mathematicians around the world contributed small gifts so that Janaki Ammal could finally have the statue the government had promised her in 1920. For Ono's father, the letter and her photograph of the statue were a reminder of his own struggle and the moment he got his chance.
For the teenage Ken, the meaning was different. It was the first time he had heard his parents admire someone who hadn't gone to Harvard or Princeton and wasn't a perfect student. His father's hero was a two-time college dropout, and Ono says, "I needed that."
Following Ramanujan
Ono says he was a "horrible student" at the University of Chicago. Just before his senior year, while flipping channels, he came across a PBS documentary about Ramanujan, whom he hadn't thought about in years. Now he could see the full story rather than the vague outline his father had given him, and it "jump-started" him. He had a lot of catching up to do, but he became a good student. Around the same time the biography The Man Who Knew Infinity was published. "Maybe it was a sign," he says.
He chose a thesis based on Ramanujan's work. By the end of his PhD he was working on the theory of Galois representations, which he describes as a tool meant to study Ramanujan's "backwater mathematics." Then in 1993 came what he calls the bombshell of late-20th-century mathematics, the proof of Fermat's Last Theorem, and that proof depended on Galois representations. Ono says following Ramanujan every time he appeared has been the best decision of his life, while acknowledging that each of those turns could have gone differently.
Finding the other Ramanujans
Ono keeps returning to one question: where would all of us be if Ramanujan had never been discovered? He says he cannot imagine that world. It leads him to believe there must be other Ramanujans on the planet, possibly from outside privileged backgrounds. How do we find them, and how do we nurture them once we do?
For several years he ran a program called the Spirit of Ramanujan, which searched for undiscovered talent. One of its first recipients was Karina Hong, who had studied with him in a research program and who is now, as he puts it, "my boss," at Axiom Math. He wonders where she would be today without the fellowship, and he is sure many other undiscovered people are still out there.
His belief is that the potential to be like Ramanujan, or at least to be creative in a productive way, "resides in us all." Students of any age need two things: the courage to act on their curiosity, and a system that embraces it when they do.
Education, checkboxes, and lost wonder
Ono is critical of how students are shaped by that system. He says the best students in Korea, the United States, and elsewhere are stressed in high school and even in middle school, worrying about the right schools and the right test scores. Pursuing those goals only because they are checkboxes is, in his words, "messed up." He doesn't tell students to opt out, since the system will decide their college prospects. He asks them to pause and recognize that they are participating in it.
For him, education should begin with inspiring people to learn about the world and its cultures and to appreciate what is different elsewhere. That, he says, is why he went to college and why he traveled. He points to small children playing with building blocks: "play for children is science." They stack blocks, knock them over, giggle, and do it again. They are not formally studying gravity, yet they are learning about it, with no worry about their future or reputation.
He wants students to leave his classes saying the subject is beautiful. He also admits he is "not a fool." He knows students are thinking about whether they'll get an A and how their GPA will affect admission to graduate school, medical school, or law school. He says he "utterly" hates this because it is an opportunity lost.
He tells how, as an undergraduate instructor, he asked students what they would do to make the world a better place. Students found the question shocking, and some came to office hours asking what he wanted them to write. He answered that this was exactly what he didn't want them to ask. He wanted them to tell him what they would do. He says the question also reminds him, as a professor, that among his dozens of students each semester some will go on to do amazing things. He knows that will happen, but he wants the students themselves to know it could. He also mentions a Korean high school student being told they might cure cancer or write the next great novel. He suspects most would respond, "that would never be me," and he finds that "deeply depressing."
AI as the great librarian for learners
Ono says this is also how he moved from devastation to optimism. AI has already read his papers and, he says, understands them better than he remembers them. He can ask it about any adjacent area of mathematics, and it won't laugh at him. Like a great librarian, it will dutifully answer.
He qualifies the access: it applies if you are privileged enough to have internet access and to afford a large language model. For those who are, knowledge quickly became cheap. He notes that a year at a U.S. university can cost $80,000, and shares what he calls a "dirty secret": he believes he could learn everything a student learns academically, "book wise," from a large language model at his own pace, probably faster. What he would miss is human access: how the right questions were derived and what the next questions in a field might be. That, he says, is why people still go to college and why professors are still needed. Tutoring and precision learning are areas where AI can help.
Perfection, speed, and who owns your identity
Ono says the world isn't doing its best at educating children, and he stresses that as an educator himself he doesn't mean this as criticism of educators. He loves visiting kindergarten and first-grade classes on bring-your-parent-to-school days, when children announce things like "I know all the prime numbers" or "I'm really good at adding." He wishes he could bottle that wonder, and he asks where we would be if people kept the energy children have when everything is new.
He argues that the best scientists must still see the world as wondrous, and the best doctors must practice from a place of benevolence. As a counterexample he describes a university professor with a clinical practice whose motive is writing articles about their patients, which he calls "messed up." If schools value perfection and speed in routine test-taking so heavily, he asks, how are they training the next Einstein, or the professor who wonders aloud in the lab whether something might be true?
For his own children, he wants passion for the world. Passion, in his view, brings real worry about the climate and about conflict between cultures, and he suggests the people we admire most may be "the oddballs."
He closes with the cost of choosing a path for the wrong reasons. In the United States, a student can graduate with $150,000 in debt, take on another $200,000 in professional school, and discover three years later that they can't stand the sight of blood. By then the loans keep them from leaving. He calls that "purgatory," a life of going to work only because it pays the bills. His answer to the question that frames the talk, "Who owns your identity?", is that you do, and so you should give yourself permission to pursue your passion.
How did it become okay for ordinary people to feel like they were part of a system that they couldn't really participate in shaping? A high school student in Korea who might be told you should go into science because you might find the cure for cancer, or you might want to think about writing the next great novel. Maybe some would love hearing that and be excited. But I think it's probably more common that they'll say, "Oh, that would never be me." And I find that deeply depressing.
Who owns your identity? You do. So, you should give yourself permission to pursue your passion. And somehow that is often lost.
When I taught undergraduate classes, I asked this question: What are you going to do to make the world a better place? And that is shocking. What am I, this one single person, going to do to make the world a better place? And I even had students come to office hours saying, "What do you want me to write?" I said, "That's exactly what I don't want you to ask. I want you to tell me what you are going to do to make the world a better place."
It's a reminder to me as the professor that I have the luxury of having dozens of students every semester in class who will go off to do amazing things. I know it's going to happen, but I need you as a student to know that that could happen. If we could just inspire that kind of thinking in all of our classes, maybe the world would be a better place.
My name is Ken Ono. I'm a mathematician and I also work in the space called AI for math. I'm a professor at the University of Virginia on leave, and I'm the founding mathematician at Axiom Math.
The very first time I heard the term artificial intelligence was in 1993, and I met a faculty member who said, "I work in artificial intelligence." My very first words were, "Oh, that's interesting. I specialize in natural intelligence." And I was cocky. I would have my butt handed to me many times for using words like that, but that's where I came from.
Exactly 1 year ago, I was a happy-go-lucky university professor writing my papers, enjoying life at the University of Virginia, and then there was a dramatic change. I came face to face with large language models at work, the FrontierMath program, where this company based in Berkeley called Epoch AI hired professional mathematicians from around the world to assemble very difficult math problems, and their goal was to assess the capabilities of large language models as they improve. For the first time, I struggled to assemble questions that ChatGPT would get wrong.
I was devastated. At the time, I was one of the few scientists who had been given access to these state-of-the-art models, and I had to be patient for a few months to go by before the world did recognize that yeah, these models know so much. These models know more facts than any human you would ever find. So, for a few months, I was devastated, just thinking, "How am I going to stay ahead of AI?"
But I actually think that's the wrong question. If our goal is to always stay ahead of AI, then I think we're going to lose. Nobody would be interested in watching Usain Bolt race against a motorcycle in the 1-mile run. It's not a fair race, but we still watch the Olympics. We as a society now know how to accept that machines can outperform humans in every physical way, but we're still now coming to grips with the fact that the brain, deep inquiry, computers have caught up.
The large language models should be thought of as the most extraordinary librarian the world has ever seen. If it has been written down, the large language model has probably seen it. If it's on YouTube, the large language model has probably been trained on it. If you read a newspaper article, by the afternoon the large language model has probably seen it. Good luck with competing with that ability to collect information.
Information, knowledge is now cheap. But how you use it and how you verify it has become more expensive. Do you want your librarian to be your neurosurgeon? Do you want your librarian to be your air traffic controller, somehow keeping an eye on the hundreds of planes that are flying over North America or Korea? No way, because that human judgment is important.
My identity has changed. My view on intelligence now has changed quite a bit. The ability to reason, make proper inferences, whether you can do it quickly or slowly, it doesn't matter. But can you create a new concept? Can you generate ideas? Can you string concepts together in a deep way? That is intelligence. That is not the regurgitation of facts. And we're not good at teaching that.
Are you good at setting the dials to design a system from scratch that was going to produce some gadget, whether it's in industry or a computer program, or perhaps a whole new area of science? That's deep intelligence. And it rarely is the form that is recognized in schools at any level.
Do you have the ability to recognize patterns in areas of thought that can be transferred from one discipline to another so that you can propel another area forward? I would have said 5 years ago, "Oh, that's just being in the right spot at the right time." But that's unfair. Being in the right spot at the right time, well, you still have to make that observation. So, there's an element of recognizing a target of opportunity. That is genius. And I don't use genius very lightly.
The student, the worker who becomes an expert in their niche field because they plug away and learn something new about that field every day is so hard-nosed and is so committed that that is also intelligence and a kind of genius that we need to recognize.
I have a very unique personal story. I'm a son of a mathematician. When I was a child, I was considered gifted in mathematics. And my parents decided at an early age that I was going to be a mathematician.
My parents actually had a plan for all three of the boys. My oldest brother was going to be a pianist. He did it. I, the youngest, was going to be a mathematician. And the middle son, who they said wasn't good at math and wasn't gifted in music, well, he should just go work in a bank. Just make a living. Which inspired him to do great things. My brother Santa has gone on to become the president of the University of Michigan. He's a very distinguished scientist. And he was literally driven by this need to prove that his early assessment was incorrect.
For me, it almost went in a very bad way. I dropped out of high school. The last thing I wanted to be in high school was anything my parents wanted me to be. I didn't want to be the one Asian kid in class that was expected to be good at math when all the other kids had lives. I couldn't play baseball, you know, the all-American pastime. And I hated it.
In April of 1984, I was of the mindset that I'm going to run away from home. I'm never going to see my parents again, and I don't care about that. I'm going to strike out on my own. In April 1984, a letter came to the house addressed to my father. It was on this yellowed piece of paper that looked like it might as well have been 100 years old. It was a letter written by Janaki Ammal, who was the widow of the Indian mathematician Ramanujan. And she thanked my father for making a small gift to help commission a statue in memory of Ramanujan. And me seeing my dad cry, and he never cried. He was very, almost no emotions. This letter brought him to tears, and he brought this letter to me afterwards, so I had to tell someone what this is about.
So Ramanujan, it turned out, I learned that day, was a mystic, an autodidact. He had visions of mathematics; his goddess, he believed, gave him gifts of formulas that he would write down in his notebooks. Because of his passion for mathematics, he didn't study in any of his other courses, so he ended up flunking out of college twice. Here's my dad talking about someone who was a two-time college dropout but had left behind three notebooks filled with formulas that he was studying himself.
And I only learned later one of the reasons that my father was so in love with the story of Ramanujan is because Ramanujan had actually represented hope for him, his one chance in life as a Japanese mathematician post-World War II.
My father wanted to be a mathematician, but he went to college at a time when the world was at war. He loved mathematics as a way of escaping from long lines waiting for food. In the aftermath of World War II, the United States sent some of their best mathematicians to Japan to rebuild the universities and train the mathematicians. My father first learned about Ramanujan at the conference where he was discovered by a Princeton professor who invited him to study with him at Princeton, launching his career.
Ramanujan died at a very early age, at 32. He was nearly forgotten, and the mathematicians of the world contributed small gifts to give Mrs. Ramanujan the statue that the government had promised her in 1920. That letter and a photograph that she shared with us of the statue, it represented him remembering what it was like for him to struggle in the moment where he got his chance.
So what did Ramanujan mean to me that day? It gave me hope in the following. It was the first time I heard my parents speak of and look up to, like a hero, someone who hadn't gone to Harvard or Princeton and was a perfect student. On the contrary, it turned out my father's hero was a two-time college dropout.
And I needed that. Later at the University of Chicago, I was a horrible student. But right before my senior year, flipping through the channels on the television, I saw a Public Broadcasting Service documentary about Ramanujan, who I hadn't thought about in years, and I was fascinated because here, in color on TV, was more than the vaguest of outlines about Ramanujan that my dad told me. There was the whole story, and it kind of jump-started me. I had a lot of catching up to do there. I became a good student, and it was then when the biography of Ramanujan came out called The Man Who Knew Infinity. Maybe it was a sign. Maybe I was meant to follow Ramanujan. So, I started to work on a thesis based on his work, and I'm so glad I made that choice because by the end of my PhD, what I worked on was called the theory of Galois representations, which was meant to study Ramanujan's backwater mathematics.
But by 1993, the bombshell news in mathematics for the end of the 20th century was a proof of Fermat's Last Theorem. And the proof of Fermat's Last Theorem depended on these Galois representations. I don't know what it is. Following Ramanujan every time he appeared has been like the best decision I've ever made in my life, and every one of those could have gone a different way.
So, one important theme about Ramanujan for me is, where would we be? And I don't mean just me as a mathematician. Where would we all collectively be had he not been discovered? That is a world I cannot fathom. And because of that, you're left wondering, there must be other Ramanujans walking planet Earth. Maybe they don't come from privilege. How do we find them?
And how do we nurture them when we find them? I was lucky enough for a number of years to run a program called The Spirit of Ramanujan, where we looked for undiscovered talent. And what's interesting about this: my boss, my former student Carina Hong, studied with me in a research program. She was one of our first recipients. We discovered her. It makes me wonder where she would be today had she not received this Spirit of Ramanujan Fellowship. There are, I am sure, many, many undiscovered folks that we need to find. I think the idea, the ability and the potential to be someone like Ramanujan, or at least creative in a productive way, I think it resides in us all. You just have to give students of all ages the opportunity to, A, be brave enough to act on their curiosity, and then offer them a system that embraces it.
Some of your best students in Korea, the best students in the United States, best students worldwide, they're stressed out in high school. They're probably even stressed out in middle school, worrying about how do I get into the right high school? How do I get into the right college? Will I get the right test scores? If you're motivated to participate in those just because they are check boxes, that's messed up. And I'm not saying that you shouldn't do that, because I don't want to be ignorant and say don't participate in the system that will ultimately decide your fate with regard to college, but pause and recognize you're participating in that system.
Education starts with inspiring people to want to know more about the world in which they live, wanting to know more about the cultures of the world because we share the world together, and appreciate what is different in other parts of the world and in other cultures, because that's why I went to college, right? I wanted to learn about those things. That's why I traveled the world.
If you have children that are infants, how wonderful is it to play with them, say with a stack of boxes or building blocks. Play for children is science. They're not really learning about gravity, but they're really learning about gravity. They may pile blocks on top of the other and knock them over and giggle, and they'll do it again. Think about how wonderful the world is when you get to learn about it without worrying about what your future and what your reputation will be.
I want my students, when they're in my class, to say, "This is a wonderful class, Professor Ono, because the subject is beautiful." And I do my very best to try to get that across. But I'm not a fool. I know that I'm participating in a system where at the end of the day the students are worried, am I going to get an A or not, and how will that impact my ability to go to graduate school in math or medical school or law school, because that GPA is so important. And I hate that. I utterly hate that. Why? It's an opportunity lost.
What I like about AI, and this is actually how I transitioned from being devastated by AI, is it has already read my papers. It understands my papers better than I remember them. Think of all the subjects in adjacent areas of mathematics that I could just ask AI about, and AI is not going to laugh at me, and as a great librarian it will dutifully answer any question I would ask. The access to knowledge, if you are privileged enough to have access to the internet and you are privileged enough to be able to afford access to a large language model, knowledge quickly became cheap.
In the United States it could cost $80,000 to spend one year attending a university, and here's the dirty secret. I could learn everything that you would learn book-wise, academically, from a large language model at my own pace, probably accelerated with a large language model. What I would not get would be the human access, how the right questions were derived, what the next questions in the field might be. That's why we still go to college, and that's why we still need professors. But all of the other stuff, the tutoring, the precision learning, that AI can help with.
I actually believe that we in this world aren't doing the best we can at educating our children. And I don't say that to be critical of educators. I am an educator. But it's always a treat to visit a kindergarten class, a first grade class, when it's bring-your-parent-to-school day so they can talk about what they do. "Oh, I know all the prime numbers," or "I'm really good at adding." That wonder, and I want to just bottle up this energy. Because if we could maintain that wonder in the world, and the energy that children have when everything around them is new, think about where we would be today.
So, go find your passion. The best scientists in the world need to still view the world as a wondrous thing. The best doctors in the world still need to recognize that what they practice is supposed to come from a place of benevolence, not "I have this practice, but I'm a university professor that happens to have a clinical practice, and I'm going to write articles about my patients." I think that's messed up.
If we pay so much attention to and value so much perfection and speed in ordinary test taking, then how are we training someone to be the next Einstein, or the next, you name it, famous professor who was just wondering out loud in their lab, "I wonder if such and such is true." For my
children, I want them to be passionate about the world that they live in. And if you're passionate about the world that you're living, then you're deeply worried about the climate. You're deeply worried about the conflict that exists between different cultures, which this war is all over the place. How can that happen?
Who are the people that you really look up to the most? Maybe they are the oddballs.
In my country, the United States, where education is so expensive, you could come out of college with $150,000 in debt. And then you may go on to a professional school accruing another $200,000 in debt, and then 3 years later discovering, "I can't stand the sight of blood, but now I can't leave my profession because I have all of these loans." That is purgatory.
You then are stuck. And that's when your life is, "Well, I go to work because it pays the bills." Who owns your identity? You do.
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