How to build a company where the best ideas win | Ray Dalio

Transcript

0:12
Whether you like it or not,
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radical transparency and algorithmic decision-making is coming at you fast,
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and it’s going to change your life.
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That’s because it’s now easy to take algorithms
0:23
and embed them into computers
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and gather all that data that you’re leaving on yourself
0:28
all over the place,
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and know what you’re like,
0:31
and then direct the computers to interact with you
0:34
in ways that are better than most people can.
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Well, that might sound scary.
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I’ve been doing this for a long time and I have found it to be wonderful.
0:43
My objective has been to have meaningful work
0:46
and meaningful relationships with the people I work with,
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and I’ve learned that I couldn’t have that
0:51
unless I had that radical transparency and that algorithmic decision-making.
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I want to show you why that is,
0:58
I want to show you how it works.
1:00
And I warn you that some of the things that I’m going to show you
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probably are a little bit shocking.
1:05
Since I was a kid, I’ve had a terrible rote memory.
1:09
And I didn’t like following instructions,
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I was no good at following instructions.
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But I loved to figure out how things worked for myself.
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When I was 12,
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I hated school but I fell in love with trading the markets.
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I caddied at the time,
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earned about five dollars a bag.
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And I took my caddying money, and I put it in the stock market.
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And that was just because the stock market was hot at the time.
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And the first company I bought
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was a company by the name of Northeast Airlines.
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Northeast Airlines was the only company I heard of
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that was selling for less than five dollars a share.
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(Laughter)
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And I figured I could buy more shares,
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and if it went up, I’d make more money.
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So, it was a dumb strategy, right?
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But I tripled my money,
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and I tripled my money because I got lucky.
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The company was about to go bankrupt,
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but some other company acquired it,
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and I tripled my money.
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And I was hooked.
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And I thought, “This game is easy.”
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With time,
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I learned this game is anything but easy.
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In order to be an effective investor,
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one has to bet against the consensus
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and be right.
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And it’s not easy to bet against the consensus and be right.
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One has to bet against the consensus and be right
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because the consensus is built into the price.
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And in order to be an entrepreneur,
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a successful entrepreneur,
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one has to bet against the consensus and be right.
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I had to be an entrepreneur and an investor —
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and what goes along with that is making a lot of painful mistakes.
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So I made a lot of painful mistakes,
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and with time,
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my attitude about those mistakes began to change.
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I began to think of them as puzzles.
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That if I could solve the puzzles,
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they would give me gems.
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And the puzzles were:
3:00
What would I do differently in the future so I wouldn’t make that painful mistake?
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And the gems were principles
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that I would then write down so I would remember them
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that would help me in the future.
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And because I wrote them down so clearly,
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I could then —
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eventually discovered —
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I could then embed them into algorithms.
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And those algorithms would be embedded in computers,
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and the computers would make decisions along with me;
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and so in parallel, we would make these decisions.
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And I could see how those decisions then compared with my own decisions,
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and I could see that those decisions were a lot better.
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And that was because the computer could make decisions much faster,
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it could process a lot more information
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and it can process decisions much more —
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less emotionally.
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So it radically improved my decision-making.
4:00
Eight years after I started Bridgewater,
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I had my greatest failure,
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my greatest mistake.
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It was late 1970s,
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I was 34 years old,
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and I had calculated that American banks
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had lent much more money to emerging countries
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than those countries were going to be able to pay back
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and that we would have the greatest debt crisis
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since the Great Depression.
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And with it, an economic crisis
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and a big bear market in stocks.
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It was a controversial view at the time.
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People thought it was kind of a crazy point of view.
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But in August 1982,
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Mexico defaulted on its debt,
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and a number of other countries followed.
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And we had the greatest debt crisis since the Great Depression.
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And because I had anticipated that,
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I was asked to testify to Congress and appear on “Wall Street Week,”
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which was the show of the time.
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Just to give you a flavor of that, I’ve got a clip here,
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and you’ll see me in there.
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(Video) Mr. Chairman, Mr. Mitchell,
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it’s a great pleasure and a great honor to be able to appear before you
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in examination with what is going wrong with our economy.
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The economy is now flat —
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teetering on the brink of failure.
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Martin Zweig: You were recently quoted in an article.
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You said, “I can say this with absolute certainty
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because I know how markets work.”
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Ray Dalio: I can say with absolute certainty
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that if you look at the liquidity base
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in the corporations and the world as a whole,
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that there’s such reduced level of liquidity
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that you can’t return to an era of stagflation.”
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I look at that now, I think, “What an arrogant jerk!”
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(Laughter)
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I was so arrogant, and I was so wrong.
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I mean, while the debt crisis happened,
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the stock market and the economy went up rather than going down,
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and I lost so much money for myself and for my clients
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that I had to shut down my operation pretty much,
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I had to let almost everybody go.
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And these were like extended family,
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I was heartbroken.
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And I had lost so much money
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that I had to borrow 4,000 dollars from my dad
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to help to pay my family bills.
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It was one of the most painful experiences of my life …
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but it turned out to be one of the greatest experiences of my life
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because it changed my attitude about decision-making.
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Rather than thinking, “I’m right,”
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I started to ask myself,
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“How do I know I’m right?”
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I gained a humility that I needed
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in order to balance my audacity.
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I wanted to find the smartest people who would disagree with me
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to try to understand their perspective
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or to have them stress test my perspective.
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I wanted to make an idea meritocracy.
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In other words,
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not an autocracy in which I would lead and others would follow
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and not a democracy in which everybody’s points of view were equally valued,
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but I wanted to have an idea meritocracy in which the best ideas would win out.
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And in order to do that,
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I realized that we would need radical truthfulness
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and radical transparency.
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What I mean by radical truthfulness and radical transparency
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is people needed to say what they really believed
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and to see everything.
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And we literally tape almost all conversations
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and let everybody see everything,
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because if we didn’t do that,
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we couldn’t really have an idea meritocracy.
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In order to have an idea meritocracy,
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we have let people speak and say what they want.
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Just to give you an example,
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this is an email from Jim Haskel —
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somebody who works for me —
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and this was available to everybody in the company.
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“Ray, you deserve a ‘D-‘
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for your performance today in the meeting …
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you did not prepare at all well
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because there is no way you could have been that disorganized.”
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Isn’t that great?
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(Laughter)
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That’s great.
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It’s great because, first of all, I needed feedback like that.
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I need feedback like that.
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And it’s great because if I don’t let Jim, and people like Jim,
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to express their points of view,
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our relationship wouldn’t be the same.
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And if I didn’t make that public for everybody to see,
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we wouldn’t have an idea meritocracy.
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So for that last 25 years that’s how we’ve been operating.
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We’ve been operating with this radical transparency
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and then collecting these principles,
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largely from making mistakes,
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and then embedding those principles into algorithms.
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And then those algorithms provide —
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we’re following the algorithms
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in parallel with our thinking.
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That has been how we’ve run the investment business,
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and it’s how we also deal with the people management.
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In order to give you a glimmer into what this looks like,
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I’d like to take you into a meeting
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and introduce you to a tool of ours called the “Dot Collector”
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that helps us do this.
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A week after the US election,
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our research team held a meeting
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to discuss what a Trump presidency would mean for the US economy.
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Naturally, people had different opinions on the matter
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and how we were approaching the discussion.
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The “Dot Collector” collects these views.
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It has a list of a few dozen attributes,
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so whenever somebody thinks something about another person’s thinking,
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it’s easy for them to convey their assessment;
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they simply note the attribute and provide a rating from one to 10.
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For example, as the meeting began,
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a researcher named Jen rated me a three —
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in other words, badly —
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(Laughter)
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for not showing a good balance of open-mindedness and assertiveness.
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As the meeting transpired,
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Jen’s assessments of people added up like this.
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Others in the room have different opinions.
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That’s normal.
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Different people are always going to have different opinions.
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And who knows who’s right?
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Let’s look at just what people thought about how I was doing.
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Some people thought I did well,
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others, poorly.
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With each of these views,
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we can explore the thinking behind the numbers.
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Here’s what Jen and Larry said.
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Note that everyone gets to express their thinking,
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including their critical thinking,
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regardless of their position in the company.
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Jen, who’s 24 years old and right out of college,
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can tell me, the CEO, that I’m approaching things terribly.
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This tool helps people both express their opinions
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and then separate themselves from their opinions
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to see things from a higher level.
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When Jen and others shift their attentions from inputting their own opinions
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to looking down on the whole screen,
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their perspective changes.
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They see their own opinions as just one of many
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and naturally start asking themselves,
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“How do I know my opinion is right?”
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That shift in perspective is like going from seeing in one dimension
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to seeing in multiple dimensions.
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And it shifts the conversation from arguing over our opinions
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to figuring out objective criteria for determining which opinions are best.
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Behind the “Dot Collector” is a computer that is watching.
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It watches what all these people are thinking
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and it correlates that with how they think.
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And it communicates advice back to each of them based on that.
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Then it draws the data from all the meetings
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to create a pointilist painting of what people are like
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and how they think.
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And it does that guided by algorithms.
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Knowing what people are like helps to match them better with their jobs.
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For example,
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a creative thinker who is unreliable
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might be matched up with someone who’s reliable but not creative.
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Knowing what people are like also allows us to decide
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what responsibilities to give them
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and to weigh our decisions based on people’s merits.
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We call it their believability.
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Here’s an example of a vote that we took
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where the majority of people felt one way …
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but when we weighed the views based on people’s merits,
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the answer was completely different.
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This process allows us to make decisions not based on democracy,
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not based on autocracy,
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but based on algorithms that take people’s believability into consideration.
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Yup, we really do this.
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(Laughter)
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We do it because it eliminates
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what I believe to be one of the greatest tragedies of mankind,
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and that is people arrogantly,
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naïvely holding opinions in their minds that are wrong,
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and acting on them,
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and not putting them out there to stress test them.
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And that’s a tragedy.
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And we do it because it elevates ourselves above our own opinions
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so that we start to see things through everybody’s eyes,
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and we see things collectively.
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Collective decision-making is so much better than individual decision-making
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if it’s done well.
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It’s been the secret sauce behind our success.
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It’s why we’ve made more money for our clients
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than any other hedge fund in existence
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and made money 23 out of the last 26 years.
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So what’s the problem with being radically truthful
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and radically transparent with each other?
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People say it’s emotionally difficult.
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Critics say it’s a formula for a brutal work environment.
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Neuroscientists tell me it has to do with how are brains are prewired.
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There’s a part of our brain that would like to know our mistakes
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and like to look at our weaknesses so we could do better.
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I’m told that that’s the prefrontal cortex.
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And then there’s a part of our brain which views all of this as attacks.
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I’m told that that’s the amygdala.
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In other words, there are two you’s inside you:
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there’s an emotional you
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and there’s an intellectual you,
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and often they’re at odds,
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and often they work against you.
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It’s been our experience that we can win this battle.
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We win it as a group.
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It takes about 18 months typically
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to find that most people prefer operating this way,
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with this radical transparency
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than to be operating in a more opaque environment.
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There’s not politics, there’s not the brutality of —
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you know, all of that hidden, behind-the-scenes —
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there’s an idea meritocracy where people can speak up.
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And that’s been great.
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It’s given us more effective work,
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and it’s given us more effective relationships.
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But it’s not for everybody.
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We found something like 25 or 30 percent of the population
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it’s just not for.
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And by the way,
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when I say radical transparency,
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I’m not saying transparency about everything.
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I mean, you don’t have to tell somebody that their bald spot is growing
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or their baby’s ugly.
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So, I’m just talking about —
15:19
(Laughter)
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talking about the important things.
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So —
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(Laughter)
15:28
So when you leave this room,
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I’d like you to observe yourself in conversations with others.
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Imagine if you knew what they were really thinking,
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and imagine if you knew what they were really like …
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and imagine if they knew what you were really thinking
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and what were really like.
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It would certainly clear things up a lot
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and make your operations together more effective.
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I think it will improve your relationships.
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Now imagine that you can have algorithms
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that will help you gather all of that information
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and even help you make decisions in an idea-meritocratic way.
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This sort of radical transparency is coming at you
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and it is going to affect your life.
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And in my opinion,
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it’s going to be wonderful.
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So I hope it is as wonderful for you
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as it is for me.
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Thank you very much.
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(Applause)