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Whether you like it or not, radical transparency and algorithmic decision-making are coming at you fast, and they are going to change your life. That's because it is now easy to take algorithms, embed them into computers, gather all the data you are leaving on yourself everywhere, know what you're like, and then direct the computers to interact with you in ways that are better than most people can. That might sound scary. I've been doing this for a long time, and I have found it to be wonderful. My objective has been to have meaningful work and meaningful relationships with the people I work with, and I've learned that I couldn't have that unless I had that radical transparency and that algorithmic decision-making. I want to show you why that is, show you how it works, and warn you that some of what I'm going to show you is probably a little shocking.
Since I was a kid, I've had a terrible rote memory, and I didn't like following instructions. I was no good at following instructions, but I loved figuring out how things work for myself. When I was 12, I hated school, but I fell in love with trading the markets. At the time I earned about five dollars a bag caddying, and I took my money and put it in the stock market, just because the stock market was hot at the time. The first company I bought was Northeast Airlines. Northeast Airlines was the only company I had heard of that was selling for less than five dollars a share, and I figured I could buy more shares, and if it went up I'd make more money. So it was a dumb strategy, right? But I tripled my money. I tripled my money because I got lucky: the company was about to go bankrupt, but another company acquired it, and I tripled my money. I was hooked, and I thought this game was easy. With time I learned this game is anything but easy. In order to be an effective investor, one has to bet against the consensus and be right, and it's not easy to bet against the consensus and be right, because the consensus is built into the price. And in order to be a successful entrepreneur, one has to bet against the consensus and be right. I had to be an entrepreneur and an investor, and what goes along with that is making a lot of painful mistakes. So I made a lot of painful mistakes, and with time my attitude about those mistakes began to change. I began to think of them as puzzles, and if I could solve the puzzles, they would give me gems. The puzzles were: what would I do differently in the future so I wouldn't make that painful mistake again? The gems were principles that I would then write down so I would remember them and they would help me in the future. Because I wrote them down so clearly, I eventually discovered I could embed them into algorithms, and those algorithms could be embedded in computers, and the computers would make decisions along with me. In parallel, we would make these decisions, and I could see how those decisions compared with my own. Those decisions were a lot better, because the computer could make decisions much faster, could process a lot more information, and could process decisions much less emotionally. So it radically improved my decision-making.
Eight years after I started Bridgewater, I had my greatest failure, my greatest mistake. It was in the late 1970s. I was 34 years old, and I had calculated that American banks had lent much more money to emerging countries than those countries were going to be able to pay back, and that we would have the greatest debt crisis since the Great Depression, and with it an economic crisis and a big bear market in stocks. It was a controversial view at the time; people thought it was kind of a crazy point of view. But in August 1982, Mexico defaulted on its debt, and a number of other countries followed, and we had the greatest debt crisis since the Great Depression. Because I had anticipated that, I was asked to testify to Congress and to appear on Wall Street Week, which was the show of the time. To give you a flavor of that, I've got a clip here, and you'll see me in it.
[Clip from Wall Street Week]: "Mr. Chairman, Mr. Mitchell, it's a great pleasure and a great honor to be able to appear before you and discuss what is going wrong with our economy. The economy is now flat, teetering on the brink of failure. You were recently quoted in an article saying, 'I can say this with absolute certainty because I know how markets work. I can say with absolute certainty that if you look at the liquidity base in the corporations and the world as a whole, there's such a reduced level of liquidity that you can't return to an era of stagflation.'"
Looking at that now, I think: what an arrogant jerk I was. I was so arrogant, and I was so wrong. While the debt crisis happened, the stock market and the economy went up rather than going down, and I lost so much money for myself and for my clients that I had to shut down my operation pretty much entirely. I had to let almost everybody go, and these were like extended family, and it was heartbreaking. I had lost so much money that I had to borrow $4,000 from my dad to help pay my family's bills. It was one of the most painful experiences of my life, but it turned out to be one of the greatest experiences of my life, because it changed my attitude about decision-making. Rather than thinking "I'm right," I started asking myself, "How do I know I'm right?" I gained a humility that I needed in order to balance my audacity. I wanted to find the smartest people who would disagree with me, to try to understand their perspective, or to have them stress-test my perspective. I wanted to create an idea meritocracy, in other words, not an autocracy in which I would lead and others would follow, and not a democracy in which everybody's points of view were equally valued, but an idea meritocracy in which the best ideas would win out. In order to do that, I realized we would need radical truthfulness and radical transparency. What I mean by radical truthfulness and radical transparency is that people needed to say what they really believed and to see everything. We literally tape almost all conversations and let everybody see everything, because if we didn't do that, we couldn't really have an idea meritocracy. To have an idea meritocracy, we have to let people speak and say what they want. To give you an example, this is an email from Jim Haskins, who works with me, and it was available to everybody in the company: "Ray, you deserve a D-minus for your performance today in the meeting. You did not prepare it at all well, because there was no way you could have been that disorganized." Isn't that great? It's great because, first of all, I needed feedback like that. And it's great because if I don't let Jim and people like Jim express their points of view, our relationship wouldn't be the same, and if I didn't make that public for everybody to see, we wouldn't have an idea meritocracy.
For the last 25 years, that's how we've been operating: with this radical transparency, collecting principles largely from making mistakes, and then embedding those principles into algorithms. Those algorithms provide, and we follow them, in parallel with our own thinking. That's how we've run the investment business, and it's how we also deal with people management.
To give you a glimmer of what this looks like, I'd like to take you into a meeting and introduce you to a tool of ours called the Dot Collector, which helps us do this. A week after the US election, our research team held a meeting to discuss what a Trump presidency would mean for the US economy. Naturally, people had different opinions on the matter, and on how we were approaching the discussion. The Dot Collector collects these views. It has a list of a few dozen attributes, so whenever somebody thinks something about another person's thinking, it's easy for them to convey their assessment. They simply note that attribute and provide a rating from 1 to 10. For example, as the meeting began, a researcher named Jen rated me a three, in other words badly, for not showing a good balance of open-mindedness and assertiveness. As the meeting transpired, Jen's assessments of people added up. Others in the room had different opinions, which is normal. Different people are always going to have different opinions, and who knows who's right? Let's look at what people thought about how I was doing. Some people thought I did well, others poorly. With each of these views, we can explore the thinking behind the numbers. Here's what Jen and Larry said. Note that everyone gets to express their thinking, including their critical thinking, regardless of their position in the company. Jen, who is 24 years old and right out of college, can tell me, the CEO, that I'm approaching things terribly. This tool helps people both express their opinions and then separate themselves from their opinions to see things from a higher level. When Jen and others shift their attention from inputting their own opinions to looking down on the whole screen, their perspective changes. They see their own opinions as just one of many, and naturally start asking themselves, "How do I know my opinion is right?" That shift in perspective is like going from seeing in one dimension to seeing in multiple dimensions, and it shifts the conversation from arguing over our opinions to figuring out objective criteria for determining which opinions are best.
Behind the Dot Collector is a computer that watches what all these people are thinking, correlates that with how they think, and communicates advice back to each of them based on that. Then it draws the data from all the meetings to create a pointillist painting of what people are like and how they think, guided by algorithms. Knowing what people are like helps us match them better with their jobs. For example, a creative thinker who was unreliable might be matched with someone who's reliable but not creative. Knowing what people are like also allows us to decide what responsibilities to give them and to weigh our decisions based on people's merits. We call it their believability. Here's an example of a vote we took, where the majority of people felt one way, but when we weighed the views based on people's merits, the answer was completely different. This process allows us to make decisions not based on democracy, not based on autocracy, but based on algorithms that take people's believability into consideration.
We frequently do this, because it eliminates what I believe to be one of the greatest weaknesses of mankind: people arrogantly and naively holding opinions in their minds that are wrong and acting on them, without putting them out there to stress-test them. That's a tragedy. We do it because it elevates us above our own opinions, so that we start to see things through everybody's eyes. Collective decision-making is so much better than individual decision-making, if it's done well. It has been the secret sauce behind our success. It's why we've made more money for our clients than any other hedge fund in existence, and made money 23 out of the last 26 years.
So what's the problem with being radically truthful and radically transparent with each other? People say it's emotionally difficult. Critics say it's a formula for a brutal work environment. Neuroscientists tell me it has to do with how our brains are pre-wired. There's a part of our brain that would like to know our mistakes and look at our weaknesses so we could do better; I'm told that's the prefrontal cortex. And there's a part of our brain that views all of this as attacks; that's the amygdala. In other words, there are two yous inside you: an emotional you and an intellectual you, and often they're at odds, and often they work against you. It has been our experience that we can win this battle, and we win it as a group. It typically takes about 18 months to find that most people prefer operating this way, with this radical transparency, to operating in a more opaque environment. There's no politics, no brutality hidden behind the scenes. There's an idea meritocracy where people can speak up, and that has been great. It has given us more effective work and more effective relationships. But it's not for everybody. We've found that something like 25 or 30 percent of the population just isn't suited to it. And by the way, when I say radical transparency, I'm not saying transparency about everything. You don't have to tell somebody that their walls are growing or that their baby is ugly. We're talking about the important things.
So when you leave this room, I'd like you to observe yourself in conversations with others. Imagine if you knew what they were really thinking, and what they were really like. Imagine if they knew what you were really thinking and what you were really like. It would certainly clear things up a lot and make your operations together more effective, and I think it would improve your relationships. Now imagine having algorithms that help you gather all that information and even help you make decisions in an idea meritocratic way. This sort of radical transparency is coming at you, it is going to affect your life, and in my opinion it's going to be wonderful. I hope it is as wonderful for you as it is for me. Thank you very much.