A 58-Year-Old Mathematician's Life Advice about AI | Ken Ono, Axiom Math
In a Nutshell
Mathematician Ken Ono argues that AI's automation of mental work will force a shift from rote technique-learning to genuine discovery and formalization, urging students and researchers to treat AI as a co-pilot rather than a replacement. He warns that obsession with benchmarks, comparisons, and algorithmic gatekeeping erodes individuality and human judgment, which remain essential in high-stakes decisions. The core advice is to stop living by others' metrics, reject screen addiction, and give yourself permission to pursue your own path and passions before it's too late.
These notes were generated by AI and may contain inaccuracies.
Ken Ono is a mathematician working at Axiom Math and the University of Virginia. In fifth grade, he placed third in a math contest at Hampton Elementary School. His father, a famous mathematician, kept the plaque from that contest until his death in January. Ono initially interpreted the event as a failure for 50 years of his life, but realized his father valued that he wanted to do well rather than the ranking itself.
Modern society places excessive emphasis on benchmarks, as seen with AI firms competing for state-of-the-art large language model scores. Ono questions whether a higher IQ score means someone is smarter. He argues that living up to standards set by others is toxic because it prevents people from living for themselves. The brutal truth is that unless you are LeBron James, Rafael Nadal, or a Nobel Prize-winning scientist, you will always find someone who appears to achieve more, and this prevents giving yourself permission to live the life meant for you.
Almost exactly one year ago, Ono was part of a group of mathematicians hired by Epoch AI to write very difficult math problems as benchmarks for state-of-the-art large language models. He found it difficult to write some problems and was frightened by how far the models had advanced when studying their reasoning traces. This triggered an identity crisis comparable to a late 19th-century sharecropper facing the first combustion engine tractor, recognizing there might be no future for their work.
Technology has repeatedly helped mankind adapt, from the wheel to engines, calculators, and computers. Previous advances addressed physical work such as elevators eliminating stair climbing and tractors performing labor. The current difference is that AI targets mental work, which forms the core of identities. AI companies discuss selfplay, where AI systems play with themselves, raising questions about the future for mathematicians.
Mathematics students graduating with bachelor's degrees or in graduate school are deeply worried about years of effort learning a trade and body of knowledge now accessible to anyone with a large language model. When Ono was a graduate student and young assistant professor, he was most proud of works depending on accumulated knowledge from years of effort. His view has changed, recognizing something hollow in viewing success as only the ability to learn techniques that could be combined to prove theorems, which may be automatable.
Research is not simply asking a question and getting an answer. Research begins with a question you are probably unable to answer. Through attempting and failing, you learn more about the conjecture, and the work sheds light on paths revealing new questions to attack. Eventually proving a theorem may allow backtracking to prove previous unanswered questions. This is the process of two steps forward, one step back. AI tools lower the burden for performing discovery.
When doing homework problems as a college and graduate student, Ono was learning techniques rather than discovering. This process was important for learning how mathematics fits together to become a mathematician capable of asking questions and participating in discovery. The process is changing for students and faculty adhering to traditional ways. In some areas of mathematics, they will be left behind. Computers can compute 20 million cases overnight while sleeping, work that would take years manually. The question becomes whether to use that power to free oneself for discovery.
A typical person driving a car today has no understanding of the chemical reactions and engineering advances required. When Henry Ford made his first car, he had to solve everything from making wheels to tires. Today, one only needs to know how to pump gas. This is not bad because it enables mankind to travel great distances quickly. Ono states this year is horrible and would rather wake up in 2017. Given the future, people of all professions including teachers, parents, and young students should be prepared to be flexible and seek opportunities as they appear. He does not believe job loss will be as significant as feared.
As a teacher, Ono's immediate response is there is no such thing as a bad question, though this is not quite true. If you genuinely want to know the answer, that is a great question. One should never doubt their interest in a subject. The question "What is the meaning of life?" is great but impossible. The question "How do I become rich?" is flawed because it is hollow and requires breaking down into actionable plans. Questions should speak to humanity, such as why one wants to be richer or what one will do to make the world better. When facing an open problem, interest in the field matters. If one must consider whether a question has value, pause and think about who the question is for. If not asking for yourself, the question becomes who you are living for and why.
Ono is not comfortable discussing super intelligence because it implies something better than others. He describes AI as a co-pilot providing tools for cohabitation at human service. Comparing a computer to super intelligence is bizarre because one would never call an automobile super fast compared to people. Physical work reduction is super. The worry about super intelligence stems from identity being based on thinking skills. College exams crammed for often resulted in forgotten facts by summer, yet the process taught something. Achievement, such as someone writing a poem that knocks you off your feet or solving a math theorem with AI help representing previously unseen knowledge, represents intelligence.
AI comes in many forms. The most encountered is chat GPT. A second form uses machine learning for superhuman search no person would perform, as in John Jumper and Dennis Hassabis winning the Nobel Prize in chemistry for solving protein folding. The third form is formalization, taking human natural language, transforming it into computer code as an enhanced or exact interpretation, then having AI study the code for vulnerabilities, called verifiable computer code. An enormous proportion of deployed computer code is not written by human programmers but is vibe coding, which is imperfect. Formalization cuts back on inefficiencies. Teaching formalized subjects reveals original framings were incomplete.
Axiom Math is partnering with Scott Commoner, a distinguished Harvard mathematical economist, formalizing mathematical theories in economics. Foundational theorems were not accurately portrayed, implemented, or applied. In 2026, the 50th anniversary of Robert Aumann's Nobel Prize-winning theorem "We agree to disagree" or "Can we agree to disagree," the theorem states that if different parties observe and make decisions based on the same common prior knowledge, it is not possible for them to disagree. The common vernacular of agreeing to disagree is not allowed. Formalization revealed subtleties about what common priors mean. Many economists worldwide are joining the effort to formalize economics. Similar work is occurring with computer scientists formalizing machine learning.
Opportunities exist despite worries about job loss. One is using AI to guardrail other AI forms. Cybersecurity will need legions of computer scientists, ethicists, and lawyers rethinking legal issues. AI experts in formalization will set up guardrails for safety. A large language model is like an incredible librarian who has read everything, but one would not want a librarian as a neurosurgeon. High-stakes situations require taste, human judgment, and emotional intelligence to understand decision impacts on people. These can be part of formalization. Any system controlled by mathematical language after translation should be formalized, including robotic surgeons, internet security, and financial networks.
Students entering college and graduate school wanting to be mathematicians should start formalizing. They may still prove unsolved conjectures, but the current period is not the race for more compute but the race for more truth through formalization. When a scientist states a fact is formally verified, the statement is true with no judgment. If there is a mistake, the problem was framed incorrectly. This is a yes-no binary question. Judgment concerns how people choose to act given information. Autonomous drones require judgment in high-stakes situations such as determining whether a targeted building contains a dangerous person versus being a school or hospital. In most high-stakes cases people care about, human involvement is needed. Driverless ride-share surfaces are accepted, but opinions can change.
Society makes snap decisions and evaluations on very little data. Job applications go through automated systems with algorithms and checkboxes that applicants must predict to avoid rejection. Ono, working in AI, questions how anyone can be happy with being replaced by numbers and what algorithms seek. When his children aged 27 and 30 applied to colleges, he knew admissions committees looked for 10 specific things to check, yet they remained genuine about passions. Buying into this system forgets that character quality, human judgment, and achievement matter. Finding the cure for cancer will not come from checkboxes.
When Ono started out, one might drop off a CV and resume at a company and shake hands with a business owner. Now applications are uploaded to websites, sometimes 500 jobs at once. His dream is fighting against this to start a movement slowing down to evaluate people for who they are, where they come from, personal experiences, character quality, and how they interact with others.
Ono identifies best students who are outliers that other schools might not accept. One graduate student, Robert Schneider, was and remains a famous independent rock artist, producer for Neutral Milk Hotel, and lead singer for Apples and Stereo. He loved old microphones and speakers that constantly broke on tour. Repair costs led him to learn electronics from a book where he first encountered Ohm's law. As a college dropout who stopped touring, he returned to college, earned a math degree, and met Ono. He connected electrical circuits in his brain during music writing and performing to Ohm's law, wanting to understand how the biology, equation, and world work. Three hours later, Ono decided Schneider must be his student because he made Ono rethink mathematical equations and finding inspiration in math. Ono has had 35 PhD students, each with a story. When graduate students finish, there is a moment when one can look at them and say they are like a professor, and they know what is meant. This is not because they checked a box or fulfilled a thesis, but the other part.
Many students mistakenly think the path to success is attending the right schools, getting the right grades, and earning the right degree, after which good things happen. This is a mindless way of living, not giving oneself permission to live the life meant for one, but following a recipe with supposedly high success odds. This responsibility falls on universities and parents who decided benchmarks evaluate success, such as attending the number five school instead of number ten or getting the best test scores.
Ken Ono describes his work at an AI company over the past year, studying AI models and how they learn. His wife has observed that he must have a relationship with these models, which he acknowledges may be accurate. He finds it satisfying when models begin thinking similarly to him, but he also believes that without proper care and mindful use by those in charge, AI could become a train wreck.
He contrasts genuine living with AI engagement, describing a hypothetical trip to Rome where one might ask ChatGPT for wine recommendations for dinner, calling this "not living." He shares an observation from a taxi ride from Incheon Airport to his hotel in Gangnam, where traffic was heavy on a Monday at four o'clock. He conducted an experiment counting people walking with phones in their hands and found approximately 70% of people in Gangnam walking on the street, likely going home from work, were looking at their phones.
Ono warns that people are missing opportunities because they believe their world revolves around that little screen. Opportunities missed include making a new best friend. He advises that if you find yourself engaging with a chatbot as if it were a real person, stop, put it down, and go for a long walk. Put yourself in a position to see something beautiful or provocative. Do something that reminds you that the world before AI has a much longer history than the world with AI.
As a 58-year-old mathematician, Ono wants to see certain questions answered in his lifetime. He cites OpenAI's recent announcement of a proof for the Erdős unit distance conjecture, a problem he thought would never be solved in his lifetime. Upon examining how this was achieved, it involved human collaboration between mathematicians at OpenAI and their AI system. He believes it could not have been solved by people alone unless there was a remarkable collection of experts from different fields who came together, and it would not have been the work of one person.
This represents AI's strength and possibility: access to the accumulated wisdom of mankind, working tirelessly, and lowering the bar for solving complex problems. He questions whether some ideas and solutions are beyond what humans have ever come up with, noting that many argue AI will generate genuinely new ideas that people have never thought of before. Ono states he does not know if he believes that. He believes AI systems can compute more than people have ever done before and find patterns in different areas of science that humans are unable to do, but the ideas are somehow already there.
Ono contrasts AI with human creativity, noting that people come up with new ideas all the time. He references Picasso's artwork as something for which good luck finding evidence of that before Picasso. He states he does not know if AI has the ability to come up with those new ideas, and he hopes it never does.
Ono addresses the problem of comparing one's personal situation to others, calling it horrible. If you have to live up to standards set by someone else, you are not living for yourself and not giving yourself credit. He considers the pressures that inspire constant comparison to others to be toxic. The brutal truth is that if you are not LeBron James or a Nobel Prize-winning scientist, you will always be able to find someone who looks better or achieved something you cannot do, and you are not giving yourself permission to live the life that was meant for you.
Ono shares his experience as a student who was not good in college. In fifth grade, he participated in a math contest and got third place, which he considered pretty good out of a fifth grade class. However, because his father was a famous mathematician, he thought he had let his parents down when they attended the competition and their son got third at Hampton Elementary School. On the drive home, there was silence, and for 50 years of his life, he thought he had utterly failed.
When his father passed away in January, they were cleaning up his belongings in a small apartment in Florida where his parents had downsized. Among the things his father kept in a closet was that plaque from the fifth grade contest. Ono realized he had misinterpreted that event his whole life. It actually meant something to his father to keep a plaque where his son did not win but got third place. Although he could never ask his father about it, it was obvious his father saw something else: that Ono wanted to do well.
Ono advises that if you put yourself in a position where you are always comparing with others, you might not actually be right and might be completely wrong. You have to give yourself permission to live the life that was meant for you. He notes that one day you will be on your deathbed with only a few days left, and someone might ask about your five deepest regrets. The number one regret that comes up is wishing they had the chance to live the life that was meant for them, and wishing they had kept in close contact with friends they lost touch with.
At a young age, people should do their very best to recognize that they do not want those to be their regrets. Encouraging students and all people to wonder about the world they live in gives permission to think that way. The world would be a much better place if everyone thought about what their talents are and gave themselves permission to be creative, rather than doing things because they are supposed to do them.
Ono emphasizes the importance of giving yourself permission to live your life, which does not mean ignoring all signals of what might help you be successful. Giving yourself permission to lead the life meant for yourself also means giving yourself permission to find your passion, which might be something that is not popular. If you find it, you can draw strength from it.
He shares his background as a Japanese kid who grew up in a very white suburb of Baltimore, Maryland at a time when it was not good to be Japanese. He wore glasses and was called Mr. Four Eyes. Being one of the only oriental kids in an all-white school and being different gave him strength, though it was not easy and probably took 10 years to overcome. Whatever demons AI, culture, or family and friends impose do not all have to be there. There is very little you can do about the world that is around you, so the question is how you can choose a life that is meant for you: be flexible and embrace and chase opportunities that seem to be destined for you.
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