Is it true that you almost exclusively hire people with zero background in finance? Yes.

We find it much easier to teach mathematicians about the markets than it is to teach mathematics and programming to people who know about the markets. Also everything we do, we figured out for ourselves and I really like it that way. So unlike some of our competitors, we try to avoid hiring people who have been at other financial firms.

In that case, what do you actually look for in

applicants? Math ability, programming ability, a love for data, a work ethic, and most importantly, the ability and desire to work well in a collegial environment.

How do you actually assess those qualities?

I think probably the same way other firms do. Once we get resumes, those who look promising, we give them phone interviews and we ask them for references that those pan out, then we invite the promising applicants to give research talks, talks like if you're applying for a job at university or something like that. And then we put them through a grueling day of solving problems in math, physics, statistics, computer science and so forth, that a

blackboard. All right, so now is it also true that your staff had to install mirrors in the corners of the office to prevent you from flying into people as you rode a unicycle around the office?

Where did you get all these questions from? Yes, it's true, although I don't ride a unicycle anymore because at one point I crashed and the unicycle broke.

So it was true. And there's one thing we've got to ask before we stop, which is harkening back to your days at IBM. I recently heard that in a talk you gave in Harvard Business School, you mentioned that you had a role in starting up the deep blue project at IBM. Can you tell us about that? Wow.

Okay, I had been at IBM for a year or two and I was standing in the men's room one day when the vice president of computer science, a man named Abe Pelett walked up next to me. I thought to myself, now's my chance. I turned to him and said, Dr. Pelett, do you realize that for a million dollars, we could build a chess machine that would defeat the world champion? Think of the advertising value to IBM. He turned to me, looking kind of annoyed and said, what's your name? So I told him and then he said, could you please let me finish up here? And so I thought, wow, I made a big mistake. So I apologized and I high-tailed it out of there as fast as I could, hoping he'd forget my name even faster. But a half hour later, he called me in my office and told me that if I wanted to build a chess machine, he'd put up a million dollars. I told him that I was occupied with speech recognition. I have three friends from graduate school who could build it. He said, okay, hire them. So we did. They built the machine. I named it Deep Blue. In the first match, the IBM machine was a very weak machine, weak physically. You know, I think only one special purpose chip in it and we lost. The final match, however, was a different story. IBM had a much, much stronger machine with hundreds of special purpose chess chips. IBM won that match and IBM stock jumped two billion dollars afterwards. Of course, it fell back down later. Now, a few years ago, I was asked to speak at the Harvard Business School. And when I arrived outside the auditorium, I could see all these protesters. And I thought, oh, no, why are they protesting? What have we done? Is this something I'm not aware of? I really didn't want to do that. But as I got closer, I could see they're all holding signs about investing in Puerto Rico. And I thought, what is this all about? I was totally confused because I don't think we had any to do with Puerto Rico. And it turned out that the speaker before me was some guy named Seth Clareman from some firm named Bao Post. Evidently, that firm had some investments in Puerto Rico and the protesters were investing him. So I wanted to see Clareman's talk, or at least the end of Clareman's talk, to find out what all the hullabaloo was about. And at the end of his talk, someone asked him his thoughts on quantitative investing. I suppose it was a setup for my talk. I don't know. And I carefully noted his answer, which was, to do what I do takes a certain amount of creativity in finesse that a computer will never have. And all those Harvard Business School MBAs seem to really like that response. So when it was my time to speak, right after him, I began by pointing out that after defeating Deep Blue in the first match, Casparov was elated and gave a press conference at which he said, to play chess at my level takes a certain amount of creativity in finesse that a computer will never have. Oh boy. I then went on to point out that two years later we crushed him. Now, I'm not sure that's how things will evolve, but whether it's in speech recognition, machine translation, or building large language models or chess, or making investment decisions, I continued to love the process of showing that human intelligence, intuition, creativity, and finesse are nothing more than computation.

Peter, that's an excellent point to conclude on. It's always such a pleasure to speak with you.

Thanks a lot, Raj.

And thank you for listening to this special episode of Goldman Sachs Exchanges, Great Investors. This podcast was recorded on July 27, 2023. If you enjoyed the show, we hope you'll follow us on Apple Podcasts, Spotify, or Google Podcasts, or wherever you listen to your podcasts and leave us so rating in a

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