Interview with Ken Griffin founder of Citadel.

You can decide that at the end of this hour.

Wow. So, this brings back memories. I was with my son and we were updating the Xbox. He was about 13. This is a few years ago. He looks at me, he goes, 'You were alive before the internet.' And in the stone age of the 80s, you used static dishes to get real-time stock quotes. There was no internet. So, there was not ready access to real-time pricing. And I needed real-time pricing to engage in arbitrage strategies between common stocks and related derivatives, convertible bonds, convert preferred securities, or warrants. I had a background in software engineering, a passion for mathematics, and a real interest in how to use those skills in the realm of finance. That was a personal interest of mine. And I think one of my friends who will leave name aside because he's never told me I can use his story, put this succinctly: the entrepreneurs of any moment in time are the people that have the skill set that is relevant to solve the problems of that moment in time. I think that's a very succinct understanding of what drives many of your great entrepreneurial success stories is having the skills that are relevant at that moment in time to solve the problem of that moment in time. I mean, what's interesting is all the work that we did in pricing derivatives in the late 80s and early 90s as I built the firm is all taught in like it's like a chapter today in your textbooks. It's like just in passing let's talk about Black-Scholes and in passing let's talk about derivative securities. It's just part of the foundation today of what's taught in finance. It's no longer on the cutting edge or revolutionary. And so that was the story of the start of my career was a passion for finance, an ability to use my skills in computer science, my interest in mathematics, and to solve the pricing in derivatives. Now, I never thought I'd do this for a living. I wanted to do private equity. God forbid. I still really haven't had a chance to do what I set out to do. It's a bit frustrating sometimes, but it's been good. It's been very impressive.

Was it 22 or 21? I wonder about that. So, here's... Yeah, I just can't help myself but share the story. The group that funded me in Chicago was a Chicago-New York based firm. The person in Chicago I just had a personal fondness for. The guy in New York was like central casting Wall Street, right? He had the perfect suit, the perfect pink shirt, the perfect tie, the perfect gray hair. The guy in Chicago looked like your high school physics teacher. All right, I need to say nothing more. And I went to Chicago to work with the partner in Chicago because I had a personal affinity for him. And it was very simple. They would back me. They had seen my track record and what I'd done in college. He'd tell you the reason that they backed me was that, you know, if you can do this in college, you're pretty resourceful. We'll take a bet on this. And if the bet doesn't work, well, you can go back to business school. That was his sales pitch to me. And I haven't come back to business school yet, but I have really enjoyed my journey in finance. So that's how I set out. Let me just be to the point. Like right now, most of you don't have children, you don't have a mortgage, you're at a point in your life to take risk. Like when you're in your 20s, what's your worst case scenario? It's not that bad. If you're in your 40s and you go to start a venture, there's more downside. And I really decided that in my 20s I would take risk in my career. Why not? I have nothing to lose.

I mean, if I had gotten admitted here is another question that's not as obvious. It's not as obvious.

We have an hour. Okay. So, you know, let me walk through. Actually, I'm not going to answer that question. All right? Because I think what's more interesting is how did you actually build the business? Because everyone's going to have to navigate setbacks and setbacks are all going to be different. What I've learned over the years is we spend way too much time analyzing what goes wrong rather than thinking about what goes right. All right? And what goes right is like that's the flywheel that's going to drive your business. So you need to spend, you know, if you sell a product, what did I do right in that sales process, like what did I nail in getting the customer to say yes? Whereas if you focus all your time on the no's, you're not focusing on what it takes to win, if that makes any sense. All right. So what did we get right? The first thing that I got right was I hired really... So I'm in my early 20s. I'm not going to get the 20-year veteran from Wall Street. Like who's kidding who? But I can attract really bright undergraduates and graduate students to work for me. And to be clear, I paid better than Wall Street and lots of promises of responsibility. And there's a whole bunch of people that were either interns or worked at Citadel in the early 1990s that have gone on to really big jobs on Wall Street from running, you know, from chief operating officer at JP Morgan to running foreign exchange at Credit Suisse. I mean, really big jobs out of that initial group. And of course, a number of those people were and to this day are partners at Citadel. So, we hired really bright, ambitious people. We gave them a tremendous amount of responsibility and then we would selectively bring in people with expertise that would help to seed the ground with wisdom and knowledge about what it took to be successful. And so that combination of really bright raw talent, great athletes, and where we could find it, people who we could attract that had experience and wisdom, that created an environment in which we could prosper and succeed. The other thing we had as an advantage was Wall Street was very skeptical of the use of quantitative analytics in the early days. You know, I hired a Russian rocket scientist. Like, he actually built rockets for the Russian government. And one of my friends called me and said, like, what the hell are you thinking? Like, you spent a bunch of money to hire a Russian rocket scientist? My friend worked at Bear Stearns. All right. We had to do things differently to compete against the incumbents. Like I can't go head-to-head against a JP Morgan or Goldman Sachs in most of what we do and win. They have such incredible institutional capabilities. You need to think about how to be to the left or to the right of them. How to get underneath the radar to succeed. You got to do things differently than they do if you're going to compete. And that's an important element for a startup. Like you don't want to just go on the frontal assault. You're going to lose. But how do you get under the radar of your competition? How do you solve a problem with a customer that they're not paying attention to? You need to think outside of the box in building a startup and I think we really did that quite well. And but this is the naivety of youth and just take a step back like strategy 101. How are we going to win? So we did it with really bright people. We hired very good communicators, lots of innovation, lots of idea generation, lots of debate within our four walls about what to do and how to do it. Like an incredibly high rate of learning. And to this day, that's the essence of what makes our culture so successful is the rate of learning at Citadel is just extraordinary. And then, you know, the asset management business is actually fairly straightforward. If you deliver alpha, you should be able to sell that product. So, it's actually like one of the easier products in the world to sell because it's fairly concrete. You have a track record. You've got your sales pitch and you can sell that. And so we're all clear, let me tell you how much I enjoyed selling when I was 20. That would be a donut. All right? And my physics teacher, who was my partner in Chicago, when he retired, he said, 'You can have whatever you want from my office. You can have.' I want the $10 plaque that was behind your desk that said, 'If we're all gonna eat, someone has to sell.' Because when I was like 20 years old and looked at that plaque, it was like a wakeup moment. Like, of all the things that this man could have around him, he's got like this cheesy $10 plaque that says, 'If we're all going to eat, someone has to sell.' And it speaks to the importance of what it takes to build a business. You're always selling. You're selling to candidates, you're selling to vendors, you're selling to counterparties, you're selling to customers. And you just have to get comfortable with the fact that if you're always selling, you know what you're going to hear a lot? No. Not going to do that. Not interested, no interest. You know, 1994, I was in Switzerland. We had a rough year in '94. We lost about 4% of our capital in '94. It was one of our only losing years in the history of the firm. And I'm in Switzerland. I mean, it was a rough day. My lunch. I sat down at lunch. This person sits down. 'Oh, you're not John Griffin.' 'No, I'm Ken Griffin.' He goes, 'Oh, I thought you were John Griffin from Fenchurch, another firm.' He goes, 'I got to go.' Like, great. I flew all the way to Switzerland for my lunch date to get up and leave the table. And then around 3:00 or 4:00 in the afternoon, I was with another Swiss banker in his office. His office was like almost the square footage of the stage. Beautiful furniture. Goes, 'Do you mind if I smoke?' Takes out a big cigar. He's smoking this cigar. And we're talking for about 45 minutes. 'Such a pity that such a bright young man so picked the wrong career.' Like, well, that's the most graceful no I've gotten today. But you just have to tolerate. You're going to hear no a lot, but you need to become accustomed to having to market your ideas and market what you represent and what you stand for. Again, whether it's the people that you want to have work for you or people who are trying to give you capital or customers, you need to get comfortable with the art of selling.

Well, so the interesting part of that question is like what would be my theory as to my initial competitive advantage? So like job one would be to take a huge step back and go, okay, what do we think we can do better or differently than those that we're going to compete against? Because if I can't establish what our competitive advantage is going to be, there's no point starting the journey. So that would be the real challenge and I can't tell you how much we just... It's like if you were in our meetings every year where we go through business strategy, it is strategy. It's how do you build a competitive advantage? What are your moats? How do you strengthen the moat? What are our competitors doing? Which of those ideas are clever that we should be copying? Which of those can we improve upon? Strategy is so underrated and so necessary for success in a business. So the first question I would ask is well what are we going to do that we can have a competitive advantage in doing? Once we establish that question then the rest of the problem comes into focus and of course, you know, a strategy is going to come down to what product can we solve or create that solves the need of a consumer. Like all businesses come down to the following. Can you create a product that meets the needs of a consumer and can you do it at a price that when you sell it to the consumer leaves you with a reasonable margin with which to run your business? Right? So the product that epitomizes this. I see one right there. I see an iPhone in her hands. Everyone's got an iPhone in this room. Like Apple has like one of the absolute killer products in the world. It has made them one of the most valuable companies in the world. They sell a product that solves a huge number of the problems that you face in day-to-day life. How do you stay in contact with friends? How do you keep track of your schedule? Like it's amazing. How do you take pictures? I mean I don't need to give you Apple sales pitch. You live it every day of your life. What is your killer app? What is your product that looks like the iPhone?

I would love that. Okay. High frequency trading. So, one of my partners was a string theorist. Actually went to school down the street here at a school called Berkeley. Okay. I think I've heard it. Yeah. But he studied string theory at Berkeley. You want to hear an irony? The government ended the funding of the super collider in Texas around the time of his graduation. And without that government funding, we never completed the super collider in the United States. There's CERN in Switzerland, but the United States does not have a competitive offering. And his dreams of proving his theories in string theory literally just came to an end. So he decides to pursue a career in finance. He ran... Isn't that a wild story, isn't it? By the way, there's a whole generation of derivatives traders on Wall Street that all look like my string theorist colleague. Like you'd sit down at a lunch in Canada at some derivatives conference and oh I studied string theory and I did string theory and I did string theory. Like what is it about string theory that draws you to finance? But to make a long story short, he ran our equity set business which he started from scratch and he viewed that as a long-term equity set up strategy. And he goes, I want to be engaged in a shorter horizon trading strategy. So I'll call that high frequency trading. And that's how that word actually took root in the industry was my colleague the string theorist chose to call our short-term trading high frequency trading in contrast to the longer frequency or longer holding cycle of what he did traditionally day in and day out. So bringing us to AI, look, generative AI has just gripped the world both in mind share, impact, and some degree hype. Let's go a little bit back through the history books. Generative AI is another branch in the holistic machine learning tree. And machine learning has been around since at least the 90s. I mean when I was younger, you know, we took a look at this in the 90s. Let's be right to the point, what an interesting curiosity, not nearly enough computational power to actually create machine learning models of any note and the whole area just sort of disappeared into the dust bins of history until Google. Google harnessing roughly the computational power of the fifth largest country in the world, got that one firm, fifth largest country in the world, decided to revisit machine learning for purposes of optimizing the search monetization for Google users. And it worked and from that came TensorFlow which Google gifted to the world and it's one of the greatest gifts ever given to humanity. It was the reintroduction of machine learning at large to the broader population. I remember my colleagues were in my office when TensorFlow the day it was announced, they were so excited about this. They're like, 'This is going to change the world.' And about two weeks later, the most senior partner in the room, I called, I said, 'So, like, any thoughts on this whole machine learning thing yet?' He's like, 'Thoughts? It's in production. We use it to trade equities every day today.' And I'm like, it's been two weeks. He goes, it only took 10 days. But in 10 days, it took 10 days from TensorFlow to be a part of the decision-making in almost one in four trades that happens every day in the US stock market. All right. And I give you this bit of history because generative AI is just another step in the journey of the use of machine learning technologies by modern society. Do we use it in our investment business? A little bit. A little bit. I can't say it's been game-changing. It's saved some time. It's a productivity enhancement tool. It's nice. It won't... I don't think it's going to revolutionize most of what we do in finance. Why is it? These models are based on what has happened thus far in the history of humanity. And investing is about understanding what's going to unfold tomorrow or next year or in two years. That's not really the basis by which machine learning models holistically are trained. Machine learning models are really good at when you have an underlying data set that the relationships within that data set represent what happens holistically today and tomorrow and so on and so forth in the future. So I'll give you a simple mental model. Self-driving cars historically have worked really well in the south, clear skies, no snow. Got that? Snow wreaks havoc with self-driving cars because it changes the contours, the lines of what the car has to recognize in terms of staying on the road. All right, so machine learning models work really well with problems that are more static in nature, reading a radiological report. But investing is about understanding how the future's going to unfold. And that's where these models really struggle. All right? They work great in short-term trading. And short-term I mean as in like the next five minutes, but when you think about the next year or two years, they really start to fall apart. Like that's just not what they do. So having said that, it's going to change the world around you. It's going to change the world around you in a lot of profound ways. Call centers as we know them today will be extinct in five to 10 years like gone. I mean I have friends who have businesses that have big call centers. Today you call, the human on the other side talks to you but every prompt is now coming from a generative AI model and over time it's getting better and better and better and they're going to push their customer base into Slack and email as the mode of communication. They're going to try to take the human out of the loop and it's going to have a profound change. For example, call centers. Could you imagine doing language translation? So a friend of mine has a multinational. They sell products in 10 and some countries. They have roughly 8,000 people in documentation. They think it'll be a thousand within three years. And those are really good high-paying white collar jobs. And I got to tell you, it's going to be painful for them to find employment from the skills that they have now, which is highly specialized, highly valued. They're going to have to pivot midway through their careers or even late in career to other roles. So machine learning is going to come with a cost to society. A cost that we need to understand how do we help these people land on their feet so we don't end up with a backlash against AI and machine learning. Other areas that it'll come into play, I mean marketing to one, you know, a friend of mine in his 20s ran a pet insurance business. All right? And they can look at your Facebook page and when you post a photo of a new golden retriever, they can hit you with a direct ad. Congratulations on the new golden retriever that's in your life. But nothing's more heartbreaking if that dog gets sick and we have the insurance solution to meet your needs. Got that? Marketing to one, like it's game-changing and one of the fastest growing pet insurance business in the world and they sold it for a lot of money because of this innovative use of generative AI marketing to one. So I think the really interesting generative AI stories are going to be when people think about how to use these tools in radically different ways than we currently use software and those will be many of the game-changing businesses of the next 10 to 20 years. So it's going to be incredibly exciting. It's going to come with a cost to society. As a society we need to think about how to manage the cost for those who are adversely affected and those people will be very adversely affected in some cases. And it also highlights the point that, you know, there's often a mindset in America like I finished college or I finished graduate school. I've finished my education. You've just started your education. Like you've just started. I hope that the most important thing that you've learned at Stanford is to learn how to learn because you will have to develop entirely new toolkits in your journey of life if you want to be relevant and in the middle of this world for the 40 to 50 years you have ahead of you in your career. And I say 40 to 50 years because of machine learning and generative AI, we're all going to live longer and healthier lives. And you're going to have to really work hard to acquire skill sets that are current and relevant in that much longer, much healthier life.

I got to tell you, when I was most involved in stock selection, it was just a very different world. It was phenomenally less competitive than it is today. And one of the reasons that the market today is so competitive is we pushed very hard a model of deep sector expertise. Now, we weren't the first to do this, but I think we did it in the boldest, most recognized way in the marketplace. So, we've got several dozen teams of absolute world-class portfolio managers with great analysts and great associates that know their companies cold. I mean, just cold. The way that I would have done my research back in the 90s, I would just be writing these guys checks as in I would be on the wrong side of every trade with them. They know these businesses so well. And others who have seen the impact of just how successful this model has become have really copied this across a number of the large hedge funds. Like we unfortunately, like we did change the world, we made the world more competitive. That's good for society as a whole, it means capital's allocated more efficiently. But it means that everything I know about picking stocks in the 90s is frankly not that relevant today and it was actually a kind of a fun time. I mean there was a stock I was looking at and like, God, these guys are just going to run out of money. Like, their cash flow is terrible. Like, they're going to run out of money soon. I called the company. I'm talking to the CFO. I'm like, I don't see how you're going to make it through the next quarter. And the stock's up a lot on this theory that your company's going to get bought. He goes, 'Yeah, if we're going to get bought, it's going to be bought out of bankruptcy.' Okay, this is definitely short. I got it. Right. That call just doesn't happen today. Doesn't happen. Just I mean it's a great story. It's a 30-year-old story. It doesn't happen today.

So, first of all, you know, I think the key is that Citadel is a team sport. Like I am so fortunate to have extraordinarily strong partners and that helps to create resilience because everyone has a down day, a down week, a down month. Having really good partners who have really good judgment helps to... it's a huge centering force in moments of dislocation. And part of being a good investor is to get over emotional burdens to making the appropriate and rational decision. I mean I do think I have a few people that work for me who are just completely rational all times. I'm not one of them. All right. I get emotional and they walk in my office and go like, 'Okay, Ken, so you're married to this position and you're dead wrong and let me tell you why.' And you're like, 'Oh, it's going to be a long conversation.' But they're often right, right? And you want to have people around you who are really thoughtful in ways that you're not so that you can get to a reasoned discourse, a reasoned debate, and you can get to a better position. I think one of the things that I'm very good with my colleagues on is my willingness. Okay, if we think we're right, we're going for it. Like, if you can walk me through the argument, the merits of our investment, like we're willing to go for it. You know, I'm very proud of the culture of our firm. We're a risk-taking firm and we're not shy about it and we attract people who want to be risk-takers. You know, there's a lot of people in the money management business who ultimately when it's all said and done, they actually don't want to be risk-takers. They enjoy hiding behind I'm a fiduciary and you know I'm a steward and I'm here in a credential role and you know the people that give us their capital they want us to earn a return on it. They want us to outthink, to out-hustle, and to outwork the competition and when we have a winning idea they want us to bet on that idea. Like that's what they want us to do. And I think that's an important part of our culture which is a culture that takes pride in winning. Like we have no shame. Like we go to work, we go to work to win. Like we're here to win. And I, you know, I'm actually happy to see the pendulum in America swinging back towards that ethos because while it's swung away from that ethos, the rest of the world was still focused on winning. And it's good to see. It's a positive change in the United States. This sort of like we're in it to win it again. So, you know, to be clear, great investors. Great investors. Okay. Have great clarity of thought as to where they have a competitive advantage. They do great research to create those differentiated viewpoints, then they're willing to commit capital against your viewpoints with conviction. And equally, when they're wrong, they're able to let go of their position to acknowledge their error and to move on to the next idea, frankly, with no tears. Like we'll triage what we got wrong, what we can learn from that, but we're moving on. And when they get it right, it's like what happened in... what allowed that right moment to happen and how do we find more of those moments? They're really good at getting past sunk cost fallacies. All right? And they view their wins and their losses as just the tuition bill of being professional investors. And by the way, I've paid a lot of tuition. All right. The only good news is on the winning side of the balance sheet, I've made far more in wins than I've endured in losses. But I've paid a lot of tuition.

Okay. It's 24:47 on the clock. I indirectly or indirectly, I've been wrong like I don't know probably three million times since we sat down. Like we're about 52% in our market making business on win-loss ratio and we'll do a few million trades in an hour. No one in this room is more wrong than I am today.

Well, let's broaden the aperture on that. It is really important to build relations with those who you can learn from. All right. So, when I was in college, you know, I would trade convertible bonds, one of the up-and-coming proprietary traders at Bear Stearns. 12 and 227-24498, the phone number. Like in the dark ages, we used to have phone numbers we'd memorize. But I would talk to them three or four times a day and walk through trades and explain to me this and what do you think about that? You want to find people who will teach you in life. Like one of the most important decisions you'll make when you graduate is what learning environment will you place yourself into. Okay? If you're going to work at a firm and you're the smartest person in the room, you have so screwed up your Stanford MBA. Like what a mistake that is. All right? I will walk into a room at Citadel and on any given topic I will guarantee you I am not the smartest guy in the room. Like not a chance. I mean, you know, there's one of my colleagues is probably one of the top 10 in his generation in stats. He's brilliant at math. We have a tough math problem. He goes, 'I'm not sure how we're going to solve this.' Goes over to another guy who works for us in Europe, comes back with a solution the next day. Like, I can't imagine how smart that guy is. Like, when the smartest guy that I know goes, I don't know how to solve it, but I know who will. The other guy must be really smart. All right? So you don't want to be the smartest person in the room. You want people that you will learn from. You will learn from your colleagues at work. You will learn from your customers. You will learn from your competitors. And one of the great resources in America is you can learn from those who are older than you who will take the time to teach you. The American culture is very generationally kind and generous. I cannot tell you how many people have spent time with me over my career telling me things I needed to know and coaching me and helping me not out of pecuniary interest but just because as a culture we're very kind and generous culture and frankly everybody in this room needs to carry that culture forward with them. It's part of the magic of America is that we are willing to across generations share insights, ideas, observations, and lessons learned.