Ken Ono, mathematician at Axiom Math and the University of Virginia, on why 2026 shouldn’t be the race for more compute. It should be the race for more truth.
He watched AI solve problems that were on his own research program. He calls it devastating, and he works at an AI company anyway.
In this conversation Ken hands over a working map of where AI actually leaves human work: the three distinct forms of AI most people confuse for one thing, why “formalization” is where the next generation of jobs (and safety) will come from, what separates a good question from a hollow one, and why benchmarks like IQ, school rankings, and LLM leaderboards are a toxic way to measure a person. It ends where his career started: a fifth-grade math plaque he misread as failure for 50 years.
What you’ll learn:
– The three forms of AI (chatbots, superhuman search, formalization) and which one is hiring
– Why students who want to be mathematicians should start ‘formalizing’ now
– The test for a good question vs. a hollow one, and how to tell who you’re really asking it for
– How he spots outlier talent that a box-checking admissions system throws away
– Why comparing yourself to LeBron or a Nobel laureate guarantees you lose
00:00 Intro
02:13 Q1) Have you ever felt obsolete because of AI?
09:33 Q2) What makes a good question?
11:35 Q3) What does ‘Superintelligence’ mean?
14:04 Q4) What’s the biggest AI misconception?
19:34 Q5) What judgement can’t AI replace?
21:13 Q6) How do I get past AI filters?
27:45 Q7) Is it bad that I prefer talking to AI over humans?
29:30 Q8) AI is smarter than most of us. What should we do?
31:46 Q9) As a junior, how can I catch up with seniors in the AI era?
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