A 58-Year-Old Mathematician's Life Advice about AI | Ken Ono, Axiom Math

[00:00:00]Hi, my name is Ken Ono. I'm a mathematician. I work at Axiom Math and the University of Virginia, but in my life, I was not a good student in college. In fifth grade, we had a a math contest [music] and I got third. Now, third is pretty good out of a fifth grade, but my father was a famous mathematician. They came to the competition and son of famous mathematician gets third in Hampton Elementary [music] School. I thought for 50 years of my life that I had utterly [music] failed. But the reason I bring this up is that when my dad passed away in January, we were cleaning up his belongings and of all the things that he could have kept and it was just in a closet now was that plaque from my fifth grade contest.

[00:00:48]And I I thought, "Wow, I had misinterpreted that event my whole life." It actually meant something to him to keep a plaque where I didn't win, but I got third place. What he saw, I'm sure, was that I wanted to do well. We live at a time where the world places so much emphasis on benchmarks. how these [music] AI firms and the state-of-the-art large language models are competing for these crazy scores. There's a lot of anxiety over benchmarks. [music] But when it comes to assessing intelligence, do we honestly believe that someone who gets a higher IQ score is somehow smarter? No, of course not.

[00:01:30]It should put people at ease, right? If you have to live up to the standards set by someone else, then you're not living for yourself. You're not giving yourself credit. I consider that toxic because [music] you know what the brutal truth is? The brutal truth is if you're not LeBron James or Raphael Nadal or a Nobel Prizewinning scientist, the reality is you will always be able to find someone that looks better, achieve something that you cannot do, and you're not then giving yourself permission to live the life that was meant for you. It's important to give yourself permission to live your life.

[00:02:23]Almost exactly one year ago, I was part of a group of mathematicians hired by a company called Epoch AI to write very difficult math problems that would serve as a benchmark for state-of-the-art large language models. And I thought it would be easy money. We were paid. But last year I found it very difficult to write some of these problems. Strictly speaking the models would make mistakes but when you studied the reasoning traces it was frightening how far these large language models have come. So I think the right way to describe it is well as an identity crisis. Maybe it was something like being the sharecropper, the farmer in the late 19th century who comes face to face with the first combustion engine tractor, recognizing that well maybe there's no future for my work as a sharecropper. What to do next?

[00:03:20]What's next for a mathematician? It was pretty devastating honestly seeing these models solve problems that were on my research program. Well, came to realize that technology has helped mankind over and over again. There was the invention of the wheel and later there was the invention of the engine and then calculators and computers and somehow we adapted.

[00:03:48]What's surprising about this particular moment is that many of the techn technological advances were about lightning physical work. An elevator meant that you didn't have to climb all these stairs. Tractors can do a lot of work that humans shouldn't do. The difference now is the work is mental.

[00:04:05]That is the stuff of identities. And where are we now? We are now at a point where many of those skills can be done automatically. And AI companies are talking about what's called selfplay.

[00:04:18]They want their AI systems to play with themselves. And so where does that leave us? Well, mathematics, it's it is devastating. It would be dishonest to say that a student who's graduating from college now with a bachelor's degree in mathematics or who is in graduate school now isn't deeply worried about all the years of effort they put into learning a trade, learning a body of knowledge that now anybody who can type if they have access to a large language model. So there's no dancing around that fact.

[00:04:50]This is very disruptive. When I was a graduate student and a young assistant professor, I would have said that I was most proud of my works that depended on the accumulation of knowledge involving years of effort. I could solve this paper because a few years ago I learned this technique and last year I learned this technique and here we are. I've written this paper. My view has changed on that and I hope that the viewers here think about this. There's actually something quite hollow about how I viewed myself as a mathematician that I only recognized recently. If my success as a mathematician relied only on my ability to learn techniques that somehow could be put together to prove a theorem, well then maybe that was actually automatable. And maybe I mistook all of that hard effort for something maybe it wasn't right. As hard as it was to master bodies of work, many papers, graduate texts, maybe at the end of the day, there is some truth to that being an automated process. Now, make no mistake, that's not what we do for a living in mathematics and in most fields. in my work now and this is how I think about what we do with AI at Axiom is supported in a number of ways. The the copout would be to say can I ask an interesting question that I want an answer to but make no mistake that's not what research is. Research begins with a question that you're probably not able to answer. You try to answer and by failing you learn a little bit more about that conjecture and the work that you do sheds light on a path that might reveal a long list of questions that you one by one try to attack and eventually you might prove a theorem and if you prove that theorem you backtrack and say maybe I could have proven this previous question I couldn't answer right this is how you learn it's it's really the proverbial two steps forward one back and when you recognize that research is not ask a question, you get an answer.

[00:06:59]You realize that the AI tools are lowering the burden for your ability to actually perform discovery. When you were doing all of these homework problems, when I was doing homework problems as a college student, as a graduate student, I was learning techniques. But was I really discovering? No. What I was doing though was important. I was learning how mathematics fits together to help me become a mathematician who can ask these questions and participate in the discovery. So that process I think is is changing for students and faculty who want to stick to the traditional ways.

[00:07:35]Well, the reality is in some areas of mathematics, they will be left behind.

[00:07:41]We have computers that can compute 20 million cases overnight while you're sleeping and you and it might take you years to do those 20 million cases and you have to decide would you like to have that power at your disposal freeing you up to participate in the process of discovery and that's what we have to value. So that's what I think science is going to become. So let me give a concrete example. Typical person that drove a car today doesn't have the foggiest idea of chemical reactions and engineering advances that had to come to fruition before they could actually get in the car and drive. The automobile is an incredible invention and it requires mastering chemical processes and engineering challenges. All of that is is available to us now for free. But maybe when Henry Ford made his first car, he had to solve all of it. How do I make the wheel? What do I make tires out of today? Maybe you only need to know how to pump gas. Now, is that bad? No.

[00:08:41]Because think about all the things that mankind can do now because they can travel great distances very quickly.

[00:08:46]What that opens us up to. And I think that's going to be our future. Is that rosé now? No. This year is horrible. If you ask me, I I would rather wake up and have it be 2017. Given that that is our future, I think we should do our very best to encourage people of all professions, teachers, parents, young students to do their best to be prepared to be flexible to seek out those opportunities as they pop up. But I don't think the loss of of jobs is anywhere near as significant. And I hope that remains to be true. But this is really the time to think very carefully about education, thinking about opportunities and being very human.

[00:09:40]So what makes a good question? There's several things I want to say. The first thing as a as a teacher, my immediate response is there's no such thing as a bad question. Of course, that's not quite true. If you genuinely want to know the answer to a question, then that's a great question. You should never ever doubt your interest in a subject. Okay? But I don't think that's necessarily what you're asking, right? I could ask, what is the meaning of life?

[00:10:08]That's a great question on the one hand, but on other it's kind of an impossible question. Another question is like, I wonder what I have to do to be rich. I want to be rich. How do I do it? Well, that is a question, but is it a great question? No. Oh, I think it's a flawed question in many ways. First of all, the question is, well, how do I achieve that? So, you need to break that down so that a question becomes maybe a plan, something that's actionable, but it's also somewhat hollow. So, questions that don't speak to your humanity somehow, whether it's why do you want to be richer? What are you going to do to make the world a better place, makes that line of reasoning richer. Now as a scientist you might be facing an open problem that your interest in in your field cares. Maybe people outside your field might not care so much if I told you about the questions I think about on a daily basis. I'd be very surprised that you would care at all. But you know I wouldn't take that personally. I would start by saying here's a math problem that I deeply care about. And I would expect that you would respect that. If you're in a situation where you have to think about whether the question you're asking has value, I think you should pause and think about who you're asking the question for. If you're not asking a question for yourself, well, my question to you would be, well, then who are you living for? Are you living a life meant for you, or are you living a life that you think someone should be meant for you? And then my question for you would be, why?

[00:11:45]I'm not honestly comfortable talking about super intelligence because it puts me at unease. Something that is super, it means that it's better than others.

[00:11:55]And I think what we're really talking about here is a future and a present honestly where AI is a co-pilot gives us tools that we cohabitate with at our service. So to say that a computer could be super intelligent is a bizarre thought to me because I would never call my automobile super fast compared to people. Right? Obviously it's super fast compared to people. Well, I would I would have never even thought about it for a moment. Reducing the load in physical work is super. The only reason we're really worried about super intelligence is that so much of our identity is based on thinking skills.

[00:12:36]Many of the exams I took in college that I crammed for, did my best to get a good grade in only to recognize I'd forgotten the facts maybe by the middle of summer.

[00:12:46]Yeah, I did learn something from that the process. But is what I learned the information that I'd forgotten? No. So let's not talk about what is super intelligence because I don't know what intelligence is. But I do know quite well when I see achievement. We live at a time where the world places so much emphasis on benchmarks. In sports, I get it. Runner A runs faster than runner B.

[00:13:14]They're a better runner. Okay, that's academic. But when it comes to assessing intelligence, do we honestly believe that someone who gets a higher IQ score is somehow smarter? Do you actually believe a school that might be ranked fifth in the college rankings is really better than a school that's ranked seventh only to turn around the next year to see that the rankings have changed? And now you think about how these AI firms and the state-of-the-art large language models are competing for these crazy scores and we're all caught up in that. Is any of that intelligence?

[00:13:46]No, of course not. But if somebody writes a poem that just knocks you off your feet. If someone solves a math theorem, even if it's with the help of AI that represents knowledge mankind had never seen before, that is intelligence.

[00:14:02]Is that super intelligence? Absolutely.

[00:14:10]The easiest way to make a mistake in the era of AI is to confuse what people are saying when they're talking about AI.

[00:14:20]It's important to first understand that AI comes in many different forms. The forms of AI that most people encounter these days would be the chat GPT, but make no mistake, that's only one form of AI. AI's ability to use machine learning techniques to conduct a superhuman search that no person would ever want to do. Right? This is how John Jumper and Dennis Hassus won the Nobel Prize in chemistry for solving protein folding.

[00:14:47]It's just smarter and it and it is accelerated. And the third part of AI is where I think there is so much hope. The third part of AI is called formalization. And the idea in formalization is to take human natural language, transform it into computer code, which is an enhanced or at least an exact interpretation of the human language and then have AI study this code and look for vulnerabilities. It's called verifiable computer code. We live at a time now where an enormous proportion of the computer code that's written and deployed in the world is not the stuff of human programmers. It's called vibe coding. But make no mistake, that code is not perfect. And so the space that we're in now in terms of formalization is to cut back on those inefficiencies. And when we start teaching mathematics or computer science or any field that has been formalized, we've come to learn that our original framing of these subjects was somehow incomplete. So I'll give you an example.

[00:15:49]Our company is partnering with Scott Commoners. He's a very distinguished economist at Harvard, a mathematical economist. And in our work, we are formalizing, as I described for you before, mathematical theories in economics. And we've discovered that some of the foundational theorems in the subject weren't really accurately portrayed or implemented or applied. Let me give you an example. 2026 is the 50th anniversary of a very famous theorem by the Nobel laurate Robert Alman. And one of his most famous theorems is the theorem that's called we agree to disagree or can we agree to disagree where the phenomenon is if you have different parties observing and making decisions or indicating their preference preferences based on the same common prior knowledge. Is it possible for these parties to disagree? And this is the stuff of modern vernacular. You might get in an argument with a friend.

[00:16:49]you listen to each other and you understand each other's perspective and the end is quite satisfying to say well I guess we're just going to have to agree to disagree. Almond's theorem doesn't allow for that. It can't be that you can agree to disagree. What really happens is you can actually end up understanding each other's perspectives and that's a very big theorem. However, there are subtleties, there are hypotheses. What does it mean to say you have the same priors? And that's where the formalization came in. And it's become kind of a viral moment in mathematical economics. Many economists from around the world are joining our effort recognizing that for the sake of getting economics right, it should be formalized. And this is happening across fields. We are even working with computer scientists rethinking and formalizing machine learning which underlies all of AI to begin with. And so this is our future. So I said, what are the opportunities for AI? Maybe we're worried about the loss of work, but there are new opportunities. One is how do we use AI to best guardrail the other forms of AI? Cyber security will need legions of computer scientists, also ethicists, make no mistake, and lawyers who have to rethink or imagine this new world, right? There going to be legal issues that come up. And certainly for the AI experts who are into and devoted to formalization, that group will be setting up the guard rails that will keep us safe. A large language model is something like the most incredible librarian. A librarian who's read everything. But that doesn't mean you want your librarian to be your neurosurgeon. In very high stakes situations, you need taste. You need human judgment. And of course, on top of that, you need someone with the emotional intelligence to understand how decisions impact people. Well, all of those things can be part of formalization. And I think that's an opportunity. And whether you want to help robotic surgeons be accurate or whether you're worried about securing the internet or financial networks, any system that can be rewritten or is somehow controlled by mathematical language after translation should be formalized. So yeah, I think that's a very big future. And for students entering college and graduate school, if you want to be a mathematician, start formalizing. You may still prove unsolved conjectures along the way, but make no mistake, this is 2026, 2027, I don't believe now, is the race for more compute. It really should be the race for more truth. And I think that, and I hope I'm right, will be by means of formalization.

[00:19:40]When a scientist says that a fact is formally verified, this statement is true. End of story. If there is a mistake, it's because you didn't frame the problem correctly. That's not judgment. That's a yes, no binary question. Judgment is how do people when given this information choose to act? We have autonomous drones flying all over the world doing all sorts of things.

[00:20:07]Whether it's keeping track of traffic in Los Angeles or Seoul or whether it's looking for dangerous people in fields of battle, all of those situations require judgment. In some of those low stakes situations, well, you know, maybe the drone that's measuring air quality above Los Angeles, maybe the human judgment there isn't so important. But if we're talking about whether or not to target a city, how do you know that a building that you're targeting actually has a dangerous person in it versus being a school or a hospital? And I don't actually think it's very difficult to distinguish situations that really are so high stakes that most rational people would not be comfortable with letting an AI decide. I think in most cases that we care about the most that are high stakes when you want a person involved. Maybe it's not that easy. We have ride share surfaces that are driverless, but people like them. These opinions in these viewpoints can change over time, but apart from those strange situations, I think it's very clear when you want a human in the room.

[00:21:24]We live at a time where the world makes judgments, snap decisions, snap evaluations on very little data. It's crazy. you apply for a job, you're probably going to submit your cover letter and your CV or resume to an automated system that has an algorithm that has a bunch of check boxes that you have to predict so that you know that you're not sd in the first round for no good reason. None of us should be happy with that. Everywhere you look, we have adopted a system where we are replaced by numbers. we are replaced by what an algorithm seeks. And this is coming from someone who works in AI. How can any of us be happy with that? My children, they're 27 and 30. They're beyond the most critical phases of getting their career started. But they knew. And I'll be lying to you if I didn't say when they were applying to colleges. As a university professor myself, I knew a university college admissions committee is going to be looking for these 10 things. Make sure you check those boxes, but then still be absolutely genuine about what you're passionate about.

[00:22:33]Yeah, I would be lying if I didn't say we didn't do that. But let's pause and think about what all of that means.

[00:22:40]Because if we buy into that 100%, then you're forgetting that the quality of someone's character matters. You're forgetting that the quality of human judgment and achievement matters. You're saying that what matters is can you check every box and imagine what those boxes are. And I'm sorry if you want to find the cure for cancer, it's not going to be a bunch of check boxes. If it was, we would have already found the cure for cancer. So the question then becomes, if we live in a society, in a community where we are so rigid because the computer age allows us to. When I was starting out, you would look for a job.

[00:23:18]You might actually go to a company and drop off your CV and resume and shake the hand of a business owner and try to make that human contact. Who does that now? You probably upload your your resume and cover letter to a website and you might even apply to like 500 jobs. I mean, what what's human in any of that?

[00:23:35]My dream for the future has many pieces to it. one, what I would give to fight against that so that we could start a movement where we could slow down and really evaluate people for who they are, where they've come from, what their personal experiences are, the quality of their character, and how they interact with others. That would be awesome. Now, how have I been lucky enough to identify some of my best students, the ones that maybe other schools wouldn't have never taken a chance on? They were the outliers. I had a graduate student, his name was Robert Schneider. He was actually and still is a famous independent rock artist. He was um producer for a band called Neutral Milk Hotel and lead singer for a band called Apples and Stereo. And he had the most fascinating story. He loved equipment.

[00:24:25]He loved to perform with these old microphones, solidstate old microphones and speakers when they went on tour. But because they were old, they were constantly breaking and they needed to be repaired. And it became so expensive repairing them that he decided that he was going to start learning electronics.

[00:24:44]So he bought a book and the first formula he saw in this book was Oh Law and he said to me the first time I met him and it was the craziest thing. He had decided to go back to school. He was a college dropout. He stopped touring.

[00:25:00]He went to college, got his math degree, and found his way into my office. And it begins what I what I just described to you. And when he said when I saw M's law, it made me stop and think about what is it that I am producing when I'm writing and singing music. Electrical circuits populate my brain. That's a creative part. I somehow write down the music on paper and then I perform it on my guitar to be picked up by the microphone to go back into my brain. And all of this was modulated by an equation called Ohm's law. And I wanted to figure out how does the biology work? How does that equation work? How does the world work? 3 hours later, I said, you know, you have to be my student because you made me rethink everything I thought about mathematical equations, thinking that I knew how you could find inspiration in math. I never thought I would have found that story. So from Robert to some some of the other students that I could tell you about.

[00:25:59]I'm proud of all of my students. I've had 35 PhD students. But if if we were to go through them one by one, I could tell you a story. His is just particularly colorful. And what I like about the process is when they finish their graduate degrees or when they finish their undergraduate thesis, there's a huge moment, undeniable. You know it when it happens. And this is particular for graduate students when you can look at the student and say, you know, you're like a professor now and they look back at you and they know exactly what you mean. And it's not because they checked some box, they fulfilled their thesis that has somehow become irrelevant. It's the other part.

[00:26:39]So to answer your question, how do I recognize that? It circles back to what I was saying earlier. We have no shortage of students who mistakenly think, and it's not their fault, who mistakenly think that the path to success is you go to the right schools, you get the right grades, you find, you know, you get the right degree and all good things will happen to you. That's a mindless way of going about one's life.

[00:27:07]It's not actually giving yourself permission to live the life that was meant for you. It's just saying I'm following a recipe that we think will be very successful and the odds of success are very high. That's on us. That's on the universities. That's on us, the parents. That's because we have decided that there are benchmarks that will evaluate whether you're successful. Go to the number five school instead of the number 10. Get the best test scores. Do all of that. We haven't given enough credit where credit should be due. And we place so much emphasis on all of this other stuff that we're now paying for it and we have to fix that right away. I don't know if this is controversial, but I think that's all true.

[00:27:52]I know what you're talking about. I work at an AI company and for the last year I work with AI models. I study them. My wife will say, "Ken, you must have a relationship with these models." And I don't think she is wrong. It's sometimes quite satisfying when the models start thinking like you do because they learn.

[00:28:14]But I also believe that if it's not cared for and those in charge aren't mindful of its use, it could be a train wreck. Do you want to take a trip with your AI? Hey chat GPT, here we are. I'm in Rome. What kind of wine would you like with dinner? That's not living. I was in the taxi cab from Inchan airport to the my hotel in Gangman yesterday and the traffic was horrible. Monday four o'clock you can sit for 10 minutes at a at a block. So I did a little experiment. I started counting the people walk by with their phones in their hands like this. And it was something like 70% of the folks here in gangmen walking on the street probably going home from work were looking at their phones like this. That's messed up. Think about all the opportunities that you are missing because you think your world evolves from that little screen. You might be missing the opportunity to make a new best friend.

[00:29:09]If you find yourself engaging with a chatbot as if it was really a person, stop. Put it down. Go for a long walk.

[00:29:19]Put yourself in a position where you 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.

[00:29:39]As a 58-year-old mathematician, I want to see some questions answered in my lifetime. We're already beginning to see that happen. There are famous examples.

[00:29:51]OpenAI a few weeks ago announced a proof of a theorem called the airish unit distance conjecture which is a problem I thought was never going to be solved in my lifetime and post hawk meaning when you go back and look at how this was achieved the truth is it was achieved by a little bit of human collaboration the mathematicians at open AAI with their system but I don't think it could have been solved by people alone unless you had a remarkable collection of experts from different fields who somehow came together. I don't think this would have been the stuff of one person and that I think represents some of the strength and possibility in AI where think about all the things in science that you would like to have solved and maybe the accumulated wisdom of mankind can solve it but when would you ever be in a position to put the right people together in a room to discuss it. So what AI offers and promise is the access to the accumulation of human knowledge.

[00:30:50]tirelessly and it lowers the bar for solving these problems. Is it the case that some of the ideas and solutions are beyond what humans have ever come up with? And this is probably the most provocative point. There are many who will argue that yeah, AI is going to come up with ideas, genuinely new ideas that people have never thought of before. I don't know that I believe that. I do believe that AI computer systems can compute more than people have ever done before. Can find patterns in different areas of science that humans are unable to do, but the ideas are somehow already there. Do people come up with new ideas all the time? The artwork that you find in Picasso, good luck finding evidence of that before Picasso. Do I think AI has that ability to come up with those new ideas? I don't know.

[00:31:46]Do I hope it does? God, I hope never.

[00:31:55]What worries me about what you just said is this need to compare your personal situation now with others. That sounds horrible. If you have to live up to the standards set by someone else, then you're not living for yourself. you're not giving yourself credit. Whatever pressures [snorts] someone may feel that inspires them to constantly be comparing themselves to others, I consider that toxic. Because you know what the brutal truth is? The brutal truth is if you're not LeBron James or a Nobel Prize-winning scientist, the reality is you will always be able to find someone that looks better, achieve something that you cannot do, and you're not then giving yourself permission to live the life that was meant for you. In my life, I was not a good student in college. In fifth grade, we had a a math contest and I got third. Now, third is pretty good out of a fifth grade, but you probably would have thought Canono was a famous mathematician. He probably won easily.

[00:33:00]No, I got third. In fact, when I was in fifth grade and I got third, I thought I let my parents down. My father was a famous mathematician. They came to the competition and son of famous mathematician gets third in Hampton Elementary School. And this is one of those defining moments on the drive home. It was just silence. My mom didn't talk about it. My dad didn't talk about it. I thought for 50 years of my life that I had utterly failed. Now, it's not true that this experience weighed on me so much that I thought about it for for decades and decades and decades, but it was instances like that where like you, I was worried about how I would stack up with others. But the reason I bring this up is that when my dad passed away in January, we were cleaning up his belongings. There was very little left because they had already downsized to very small apartment in Florida where my parents, we had just moved them. And of all the things that he could have kept, and it was just in a closet now, was that plaque from my fifth grade contest.

[00:34:10]And I I thought, "Wow, I had misinterpreted that event my whole life." It actually meant something to him to keep a plaque where I didn't win, but I got third place. And although he had passed away, I could never ask him about it. It's obvious he saw something else. What he saw, I'm sure, was that I wanted to do well. So, I hope that's a lesson for anyone who thinks this way because I thought that way. And if you put yourself in a position where you're always comparing with others, you might not actually be right and you might actually be completely wrong. So you have to give yourself permission to the life that was meant for you. And this might be morbid, but one day you will be on your deathbed. You may only have a few days left. And someone might ask you some questions. What are your five deepest regrets? And this comes up all the time. I'm not making this up.

[00:35:06]people. Certainly when you get to my age, you start being around these kinds of conversations. And I think the number one regret is I wish I had the chance to live the life that was meant for me. I wish I was able to keep in close contact with the friends that I lost touch with.

[00:35:25]So try to imagine what those four or five wishes are. And at your age, do your very best to recognize that you don't want those to be your regrets. You need some inspiration often. You need a creative idea often. And so encouraging students, encouraging all people to wonder about the world that they live in is one giving permission to think that way. And wouldn't the world be a much better place if everyone thought about what their talents are, gave themselves permission to be creative? Wouldn't the world look a lot more interesting instead of yeah, I'm supposed to do this or I'm supposed to do that, so I I do it. So, I hope that is food for thought.

[00:36:10]I think I've said several times today that it's important to give yourself permission to live your life. Now, that doesn't mean ignore all the signals of what might help you be successful.

[00:36:22]That's not we don't want to be ignorant.

[00:36:24]But giving yourself permission to lead the life that was meant for yourself also is giving yourself permission to find your passion. And that passion might be something that isn't popular.

[00:36:34]But if you find it, you can draw strength from it. I'm a Japanese kid that grew up in a very white suburb of Baltimore, Maryland at a time when it wasn't good to be Japanese. I wore glasses. I was Mr. Four Eyes. But that gave me strength. As difficult as that was, being one of the only oriental kids in an all-white school being different, I ultimately drew strength from that wasn't easy and it probably took 10 years to overcome that. But whatever demons AI or culture or family and friends impose, they don't all have to be there. And quite frankly, the moral of this conversation is there's very little you can do about the world that's around you. So, how can you choose a life that's meant for you? Be flexible and embrace and chase opportunities that were that seem to be destined for you.