I have to say that I'm very glad to be here.
I understand we have over 80 countries here, so that's a whole new paradigm for me to speak to all of these countries.
In each country, I'm sure you have this thing called the parent-teacher conference.
Do you know about the parent-teacher conference?
Not the ones for your kids, but the one you had as a child, where your parents come to school and your teacher talks to your parents, and it's a little bit awkward.
Well, I remember in third grade, I had this moment where my father, who never takes off from work, he's a classical blue collar, a working-class immigrant person, going to school to see his son, how he's doing, and the teacher said to him, he said, "You know, John is good at math and art."
And he kind of nodded, you know?
The next day I saw him talking to a customer at our tofu store, and he said, "You know, John's madeline68@example.org."
(Laughter) And that always stuck with me all my life.
Why didn't Dad say art?
Why wasn't it okay?
Why?
It became a question my entire life, and that's all right, because being good at math meant he bought me a computer, and some of you remember this computer, this was my first computer.
Who had an Apple II?
Apple II users, very cool.
(Applause) As you remember, the Apple II did madeline68@example.org.
(Laughter) You'd plug it in, you'd type in it and green text would come out.
It would say you're wrong most of the time.
That was the computer we knew.
That computer is a computer that I learned about going to MIT, my father's dream.
And at MIT, however, I learned about the computer at all levels, and after, I went to art school to get away from computers, and I began to think about the computer as more of a spiritual space of thinking.
And I was influenced by performance art -- so this is 20 years ago.
I made a computer out of people.
It was called the Human Powered Computer Experiment.
I have a power manager, mouse driver, memory, etc., and I built this in Kyoto, the old capital of Japan.
It's a room broken in two halves.
I've turned the computer on, and these assistants are placing a giant floppy disk built out of cardboard, and it's put into the computer.
And the floppy disk drive person wears it.
(Laughter) She finds the first sector on the disk, and takes data off the disk and passes it off to, of course, the bus.
So the bus diligently carries the data into the computer to the memory, to the CPU, the VRAM, etc., and it's an actual working computer.
That's a bus, really.
(Laughter) And it looks kind of fast.
That's a mouse driver, where it's XY.
(Laughter) It looks like it's happening kind of quickly, but it's actually a very slow computer, and when I realized how slow this computer was compared to how fast a computer is, it made me wonder about computers and technology in general.
And so I'm going to talk today about four things, really.
The first three things are about how I've been curious about technology, design and art, and how they intersect, how they overlap, and also a topic that I've taken on since four years ago I became the President of Rhode Island School of Design: leadership.
And I'll talk about how I've looked to combine these four areas into a kind of a synthesis, a kind of experiment.
So starting from technology, technology is a wonderful thing.
When that Apple II came out, it really could do nothing.
It could show text and after we waited a bit, we had these things called images.
Remember when images were first possible with a computer, those gorgeous, full-color images?
And then after a few years, we got CD-quality sound.
It was incredible.
You could listen to sound on the computer.
And then movies, via CD-ROM.
It was amazing.
Remember that excitement?
And then the browser appeared.
The browser was great, but the browser was very primitive, very narrow bandwidth.
Text first, then images, we waited, CD-quality sound over the Net, then movies over the Internet.
Kind of incredible.
And then the mobile phone occurred, text, images, audio, video.
And now we have iPhone, iPad, Android, with text, video, audio, etc.
You see this little pattern here?
We're kind of stuck in a loop, perhaps, and this sense of possibility from computing is something I've been questioning for the last 10 or so years, and have looked to design, as we understand most things, and to understand design with our technology has been a passion of mine.
And I have a small experiment to give you a quick design lesson.
Designers talk about the relationship between form and content, content and form.
Now what does that mean?
Well, content is the word up there: fear.
It's a four-letter word.
It's a kind of a bad feeling word, fear.
Fear is set in Light Helvetica, so it's not too stressful, and if you set it in Ultra Light Helvetica, it's like, "Oh, fear, who cares?"
Right?
(Laughter) You take the same Ultra Light Helvetica and make it big, and like, whoa, that hurts.
Fear.
So you can see how you change the scale, you change the form.
Content is the same, but you feel differently.
You change the typeface to, like, this typeface, and it's kind of funny.
It's like pirate typeface, like Captain Jack Sparrow typeface.
Arr!
Fear!
Like, aww, that's not fearful.
That's actually funny.
Or fear like this, kind of a nightclub typeface.
(Laughter) Like, we gotta go to Fear.
(Laughter) It's, like, amazing, right?
(Laughter) (Applause) It just changes the same content.
Or you make it -- The letters are separated apart, they're huddled together like on the deck of the Titanic, and you feel sorry for the letters, like, I feel the fear.
You feel for them.
Or you change the typeface to something like this.
It's very classy.
It's like that expensive restaurant, Fear.
I can never get in there.
(Laughter) It's just amazing, Fear.
But that's form, content.
If you just change one letter in that content, you get a much better word, much better content: free.
"Free" is a great word.
You can serve it almost any way.
Free bold feels like Mandela free.
It's like, yes, I can be free.
Free even light feels kind of like, ah, I can breathe in free.
It feels great.
Or even free spread out, it's like, ah, I can breathe in free, so easily.
And I can add in a blue gradient and a dove, and I have, like, Don Draper free.
(Laughter) So you see that -- form, content, design, it works that way.
It's a powerful thing.
It's like magic, almost, like the magicians we've madeline68@example.org.
It's magic.
Design does that.
And I've been curious about how design and technology intersect, and I'm going to show you some old work I never really show anymore, to give you a sense of what I used to do.
So -- yeah.
So I made a lot of work in the '90s.
This was a square that responds to sound.
People ask me why I made that.
It's not clear.
(Laughter) But I thought it'd be neat for the square to respond to me, and my kids were small then, and my kids would play with these things, like, "Aaah," you know, they would say, "Daddy, aaah, aaah."
You know, like that.
We'd go to a computer store, and they'd do the same thing.
And they'd say, "Daddy, why doesn't the computer respond to sound?"
And it was really at the time I was wondering why doesn't the computer respond to sound?
So I made this as a kind of an experiment at the time.
And then I spent a lot of time in the space of interactive graphics and things like this, and I stopped doing it because my students at MIT got so much better than myself, so I had to hang up my mouse.
But in '96, I made my last piece.
It was in black and white, monochrome, fully monochrome, all in integer mathematics.
It's called "Tap, Type, Write."
It's paying a tribute to the wonderful typewriter that my mother used to type on all the time as a legal secretary.
It has 10 variations.
(Typing noise) (Typing noise) There's a shift.
Ten variations.
This is, like, spin the letter around.
(Typing noises) This is, like, a ring of letters.
(Typing noises) This is 20 years old, so it's kind of a -- Let's see, this is — I love the French film "The Red Balloon."
Great movie, right?
I love that movie.
So, this is sort of like a play on that.
(Typing noises) (Typewriter bell) It's peaceful, like that.
(Laughter) I'll show this last one.
This is about balance, you know.
It's kind of stressful typing out, so if you type on this keyboard, you can, like, balance it out.
(Laughter) If you hit G, life's okay, so I always say, "Hit G, and it's going to be all right.
Thank you.
(Applause) Thank you.
So that was 20 years ago, and I was always on the periphery of art.
By being President of RISD I've gone deep into art, and art is a wonderful thing, fine art, pure art.
You know, when people say, "I don't get art.
I don't get madeline68@example.org."
That means art is working, you know?
It's like, art is supposed to be enigmatic, so when you say, like, "I don't get it," like, oh, that's great.
(Laughter) Art does that, because art is about asking questions, questions that may not be answerable.
At RISD, we have this amazing facility called the Edna Lawrence Nature Lab.
It has 80,000 samples of animal, bone, mineral, plants.
You know, in Rhode Island, if an animal gets hit on the road, they call us up and we pick it up and stuff it.
And why do we have this facility?
Because at RISD, you have to look at the actual animal, the object, to understand its volume, to perceive it.
At RISD, you're not allowed to draw from an image.
And many people ask me, John, couldn't you just digitize all this?
Make it all digital?
Wouldn't it be better?
And I often say, well, there's something good to how things used to be done.
There's something very different about it, something we should figure out what is good about how we did it, even in this new era.
And I have a good friend, he's a new media artist named Tota Hasegawa.
He's based in London, no, actually it's in Tokyo, but when he was based in London, he had a game with his wife.
He would go to antique shops, and the game was as such: When we look at an antique we want, we'll ask the shopkeeper for the story behind the antique, and if it's a good story, we'll buy it.
So they'd go to an antique shop, and they'd look at this cup, and they'd say, "Tell us about this cup."
And the shopkeeper would say, "It's old."
(Laughter) "Tell us more."
"Oh, it's really old."
(Laughter) And he saw, over and over, the antique's value was all about it being old.
And as a new media artist, he reflected, and said, you know, I've spent my whole career making new media art.
People say, "Wow, your art, what is it?"
It's new media.
And he realized, it isn't about old or new.
It's about something in between.
It isn't about "old," the dirt, "new," the cloud.
It's about what is good.
A combination of the cloud and the dirt is where the action is at.
You see it in all interesting art today, in all interesting businesses today.
How we combine those two together to make good is very interesting.
So art makes questions, and leadership is something that is asking a lot of questions.
We aren't functioning so easily anymore.
We aren't a simple authoritarian regime anymore.
As an example of authoritarianism, I was in Russia one time traveling in St. Petersburg, at a national monument, and I saw this sign that says, "Do Not Walk On The Grass," and I thought, oh, I mean, I speak English, and you're trying to single me out.
That's not fair.
But I found a sign for Russian-speaking people, and it was the best sign ever to say no.
It was like, "No swimming, no hiking, no anything."
My favorite ones are "no plants."
Why would you bring a plant to a national monument?
I'm not sure.
And also "no love."
(Laughter) So that is authoritarianism.
And what is that, structurally?
It's a hierarchy.
We all know that a hierarchy is how we run many systems today, but as we know, it's been disrupted.
It is now a network instead of a perfect tree.
It's a heterarchy instead of a hierarchy.
And that's kind of awkward.
And so today, leaders are faced with how to lead differently, I believe.
This is work I did with my colleague Becky Bermont on creative leadership.
What can we learn from artists and designers for how to lead?
Because in many senses, a regular leader loves to avoid mistakes.
Someone who's creative actually loves to learn from mistakes.
A traditional leader is always wanting to be right, whereas a creative leader hopes to be right.
And this frame is important today, in this complex, ambiguous space, and artists and designers have a lot to teach us, I believe.
And I had a show in London recently where my friends invited me to come to London for four days to sit in a sandbox, and I said great.
And so I sat in a sandbox for four days straight, six hours every day, six-minute appointments with anyone in London, and that was really bad.
But I would listen to people, hear their issues, draw in the sand, try to figure things out, and it was kind of hard to figure out what I was doing.
You know?
It's all these one-on-one meetings for like four days.
And it felt kind of like being president, actually.
I was like, "Oh, this my job.
President.
I do a lot of meetings, you know?"
And by the end of the experience, I realized why I was doing this.
It's because leaders, what we do is we connect improbable connections and hope something will happen, and in that room I found so many connections between people across all of London, and so leadership, connecting people, is the great question today.
Whether you're in the hierarchy or the heterarchy, it's a wonderful design challenge.
And one thing I've been doing is doing some research on systems that can combine technology and leadership with an art and design perspective.
Let me show you something I haven't shown anywhere, actually.
So what this is, is a kind of a sketch, an application sketch I wrote in Python.
You know how there's Photoshop?
This is called Powershop, and the way it works is imagine an organization.
You know, the CEO isn't ever at the top.
The CEO's at the center of the organization.
There may be different subdivisions in the organization, and you might want to look into different areas.
For instance, green are areas doing well, red are areas doing poorly.
You know, how do you, as the leader, scan, connect, make things happen?
So for instance, you might open up a distribution here and find the different subdivisions in there, and know that you know someone in Eco, over here, and these people here are in Eco, the people you might engage with as CEO, people going across the hierarchy.
And part of the challenge of the CEO is to find connections across areas, and so you might look in R&amp;D, and here you see one person who crosses the two areas of interest, and it's a person important to engage.
So you might want to, for instance, get a heads-up display on how you're interacting with them.
How many coffees do you have?
How often are you calling them, emailing them?
What is the tenor of their email?
How is it working out?
Leaders might be able to use these systems to better regulate how they work inside the heterarchy.
You can also imagine using technology like from Luminoso, the guys from Cambridge who were looking at deep text analysis.
What is the tenor of your communications?
So these kind of systems, I believe, are important.
They're targeted social media systems around leaders.
And I believe that this kind of perspective will only begin to grow as more leaders enter the space of art and design, because art and design lets you think like this, find different systems like this, and I've just begun thinking like this, so I'm glad to share that with you.
So in closing, I want to thank all of you for your attention.
Thanks very much.
(Applause) (Applause)
Whether you like it or not, radical transparency and algorithmic decision-making is coming at you fast, and it's going to change your life.
That's because it's now easy to take algorithms and embed them into computers and gather all that data that you're leaving on yourself all over the place, and know what you're like, and then direct the computers to interact with you in ways that are better than most people can.
Well, 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, I want to show you how it works.
And I warn you that some of the things that I'm going to show you probably are a little bit 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 to figure out how things worked for myself.
When I was 12, I hated school but I fell in love with trading the markets.
I caddied at the time, earned about five dollars a bag.
And I took my caddying money, and I put it in the stock market.
And that was just because the stock market was hot at the time.
And the first company I bought was a company by the name of Northeast Airlines.
Northeast Airlines was the only company I heard of that was selling for less than five dollars a share.
(Laughter) 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, and I tripled my money because I got lucky.
The company was about to go bankrupt, but some other company acquired it, and I tripled my money.
And I was hooked.
And I thought, "This game is 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.
One has to bet against the consensus and be right because the consensus is built into the price.
And in order to be an entrepreneur, 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.
That if I could solve the puzzles, they would give me gems.
And the puzzles were: What would I do differently in the future so I wouldn't make that painful mistake?
And the gems were principles that I would then write down so I would remember them that would help me in the future.
And because I wrote them down so clearly, I could then -- eventually discovered -- I could then embed them into algorithms.
And those algorithms would be embedded in computers, and the computers would make decisions along with me; and so in parallel, we would make these decisions.
And I could see how those decisions then compared with my own decisions, and I could see that those decisions were a lot better.
And that was because the computer could make decisions much faster, it could process a lot more information and it can process decisions much more -- 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 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.
And because I had anticipated that, I was asked to testify to Congress and appear on "Wall Street Week," which was the show of the time.
Just to give you a flavor of that, I've got a clip here, and you'll see me in there.
(Video) Mr. Chairman, Mr. Mitchell, it's a great pleasure and a great honor to be able to appear before you in examination with what is going wrong with our economy.
The economy is now flat -- teetering on the brink of failure.
Martin Zweig: You were recently quoted in an article.
You said, "I can say this with absolute certainty because I know how markets work."
Ray Dalio: I can say with absolute certainty that if you look at the liquidity base in the corporations and the world as a whole, that there's such reduced level of liquidity that you can't return to an era of stagflation."
I look at that now, I think, "What an arrogant jerk!"
(Laughter) I was so arrogant, and I was so wrong.
I mean, 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, I had to let almost everybody go.
And these were like extended family, I was heartbroken.
And I had lost so much money that I had to borrow 4,000 dollars from my dad to help to pay my family 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 to ask 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 make 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 I wanted to have an idea meritocracy in which the best ideas would win out.
And in order to do that, I realized that we would need radical truthfulness and radical transparency.
What I mean by radical truthfulness and radical transparency is people needed to say what they really believed and to see everything.
And 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.
In order to have an idea meritocracy, we have let people speak and say what they want.
Just to give you an example, this is an email from Jim Haskel -- somebody who works for me -- and this was available to everybody in the company.
"Ray, you deserve a 'D-' for your performance today in the meeting ...
you did not prepare at all well because there is no way you could have been that disorganized."
Isn't that great?
(Laughter) That's great.
It's great because, first of all, I needed feedback like that.
I need feedback like that.
And it's great because if I don't let Jim, and people like Jim, to 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.
So for that last 25 years that's how we've been operating.
We've been operating with this radical transparency and then collecting these principles, largely from making mistakes, and then embedding those principles into algorithms.
And then those algorithms provide -- we're following the algorithms in parallel with our thinking.
That has been how we've run the investment business, and it's how we also deal with the people management.
In order to give you a glimmer into 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" that 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 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 the attribute and provide a rating from one to 10.
For example, as the meeting began, a researcher named Jen rated me a three -- in other words, badly -- (Laughter) for not showing a good balance of open-mindedness and assertiveness.
As the meeting transpired, Jen's assessments of people added up like this.
Others in the room have different opinions.
That's normal.
Different people are always going to have different opinions.
And who knows who's right?
Let's look at just 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's 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 attentions 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 is watching.
It watches what all these people are thinking and it correlates that with how they think.
And it communicates advice back to each of them based on that.
Then it draws the data from all the meetings to create a pointilist painting of what people are like and how they think.
And it does that guided by algorithms.
Knowing what people are like helps to match them better with their jobs.
For example, a creative thinker who is unreliable might be matched up 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 that 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.
Yup, we really do this.
(Laughter) We do it because it eliminates what I believe to be one of the greatest tragedies of mankind, and that is people arrogantly, naïvely holding opinions in their minds that are wrong, and acting on them, and not putting them out there to stress test them.
And that's a tragedy.
And we do it because it elevates ourselves above our own opinions so that we start to see things through everybody's eyes, and we see things collectively.
Collective decision-making is so much better than individual decision-making if it's done well.
It's 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 are brains are prewired.
There's a part of our brain that would like to know our mistakes and like to look at our weaknesses so we could do better.
I'm told that that's the prefrontal cortex.
And then there's a part of our brain which views all of this as attacks.
I'm told that that's the amygdala.
In other words, there are two you's inside you: there's an emotional you and there's an intellectual you, and often they're at odds, and often they work against you.
It's been our experience that we can win this battle.
We win it as a group.
It takes about 18 months typically to find that most people prefer operating this way, with this radical transparency than to be operating in a more opaque environment.
There's not politics, there's not the brutality of -- you know, all of that hidden, behind-the-scenes -- there's an idea meritocracy where people can speak up.
And that's been great.
It's given us more effective work, and it's given us more effective relationships.
But it's not for everybody.
We found something like 25 or 30 percent of the population it's just not for.
And by the way, when I say radical transparency, I'm not saying transparency about everything.
I mean, you don't have to tell somebody that their bald spot is growing or their baby's ugly.
So, I'm just talking about -- (Laughter) talking about the important things.
So -- (Laughter) 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 imagine if you knew what they were really like ...
and imagine if they knew what you were really thinking and what were really like.
It would certainly clear things up a lot and make your operations together more effective.
I think it will improve your relationships.
Now imagine that you can have algorithms that will help you gather all of that information and even help you make decisions in an idea-meritocratic way.
This sort of radical transparency is coming at you and it is going to affect your life.
And in my opinion, it's going to be wonderful.
So I hope it is as wonderful for you as it is for me.
Thank you very much.
(Applause)