How to build a company where the best ideas win | Ray Dalio
Summary
TLDRIn this enlightening talk, Ray Dalio advocates for radical transparency and algorithmic decision-making, explaining how it has transformed his life and work. He shares his journey from a young investor to the founder of Bridgewater Associates, emphasizing the power of embracing mistakes and learning from them. Dalio illustrates how incorporating algorithms and principles into decision-making processes has led to better outcomes and fostered an 'idea meritocracy' within his company, ultimately attributing this approach to their success and resilience in the market.
Takeaways
- π Radical transparency and algorithmic decision-making are becoming integral parts of our lives due to the ease of embedding algorithms into computers and the vast amount of data we generate.
- π The speaker advocates for the benefits of radical transparency, stating that it has improved his decision-making by allowing for more objective and less emotionally driven outcomes.
- π‘ The concept of 'algorithmic decision-making' involves embedding principles into algorithms that computers can use to make decisions alongside humans, leading to better outcomes.
- π The speaker's early experience with the stock market and his initial success with Northeast Airlines taught him about the complexities of investing and the importance of contrarian thinking.
- π A major mistake in predicting a debt crisis led to significant financial loss and a shift in the speaker's approach to decision-making, emphasizing the need for humility and open-mindedness.
- π€ The idea of an 'idea meritocracy' is introduced, where the best ideas win out, regardless of who they come from, and this requires radical truthfulness and transparency.
- π£οΈ The company culture encourages open feedback, even to the CEO, fostering an environment where everyone's opinions are valued and can be part of the decision-making process.
- π The 'Dot Collector' tool is used to gather and assess the opinions of individuals during meetings, providing a quantitative way to understand and compare perspectives.
- π§ The process of collective decision-making, informed by algorithms and individual 'believability', leads to more effective outcomes than either democracy or autocracy alone.
- π€ Embracing radical transparency can lead to more effective work and relationships, though it may be challenging for some due to the emotional difficulty of confronting one's weaknesses.
- π The speaker's company, Bridgewater, has found success through these methods, achieving high performance in investment and client satisfaction over many years.
Q & A
What is the main concept introduced by the speaker in the transcript?
-The main concept introduced by the speaker is 'radical transparency' and 'algorithmic decision-making', which he believes will change lives by improving decision-making processes through the use of algorithms and data collection.
How does the speaker describe his initial experience with the stock market?
-The speaker describes his initial experience with the stock market as accidental and lucky. He invested his caddying money into Northeast Airlines, a company he chose simply because it was selling for less than five dollars a share, and he tripled his money due to a lucky acquisition of the company.
What was the speaker's approach to dealing with mistakes in his early career?
-The speaker's approach to dealing with mistakes was to view them as puzzles. He would try to understand what he would do differently to avoid making the same mistake in the future, and he would write down the principles he learned from these experiences.
How did the speaker's perspective on mistakes evolve over time?
-Over time, the speaker's perspective on mistakes evolved from seeing them as failures to viewing them as opportunities to learn and improve. He began to see mistakes as puzzles that, when solved, would yield valuable principles to guide his future actions.
What was the speaker's greatest failure mentioned in the transcript?
-The speaker's greatest failure was in the late 1970s when he misjudged the economic impact of a debt crisis. He believed there would be a major economic downturn due to over-lending by American banks to emerging countries, but instead, the economy and stock market went up.
How did the speaker's failure in the late 1970s affect his approach to decision-making?
-The failure led the speaker to adopt a more humble and questioning approach to decision-making. Instead of assuming he was right, he began to ask 'How do I know I'm right?' and sought out the perspectives of others, especially those who disagreed with him.
What is the concept of an 'idea meritocracy' as described by the speaker?
-An 'idea meritocracy' is a system where the best ideas win out, regardless of who they come from. It's not an autocracy where one person leads and others follow, nor is it a democracy where all opinions are equally valued. It requires radical truthfulness and transparency for effective functioning.
How does the speaker describe the process of collective decision-making in his organization?
-The speaker describes collective decision-making as a process that involves gathering diverse opinions, weighing them based on individuals' merits or 'believability', and using algorithms to guide the decision-making process. This approach is intended to be more effective than decisions made by a single person or by majority vote.
What is the 'Dot Collector' tool mentioned in the transcript?
-The 'Dot Collector' is a tool used in the speaker's organization to collect and assess the views of individuals during meetings. It allows participants to rate each other on various attributes, providing a quantitative measure of performance and thinking styles.
How does the speaker address the emotional difficulty of radical transparency?
-The speaker acknowledges the emotional difficulty of radical transparency but argues that it can be overcome. He suggests that it takes about 18 months for most people to prefer this way of operating, as it eliminates politics and hidden agendas, leading to more effective work and relationships.
What is the speaker's final message about the impact of radical transparency and algorithmic decision-making?
-The speaker's final message is that radical transparency and algorithmic decision-making are trends that are coming and will affect everyone's life. He believes that embracing these concepts will lead to improved decision-making and better relationships, and he hopes that others will find it as wonderful as he does.
Outlines
π Embracing Radical Transparency and Algorithmic Decision-Making
The speaker introduces the concept of radical transparency and algorithmic decision-making, asserting their transformative impact on life and work. Despite initial apprehensions, the speaker's personal journey has been positive, emphasizing the importance of these tools for meaningful work and relationships. The narrative begins with the speaker's early experiences with the stock market, highlighting a serendipitous yet formative investment in Northeast Airlines. It then transitions to the speaker's realization of the complexities of investing and entrepreneurship, the necessity of challenging the consensus, and the painful yet enlightening process of learning from mistakes. The speaker outlines the development of principles from these experiences, embedding them into algorithms to improve decision-making processes, which the speaker found to be superior due to their speed, information processing capacity, and reduced emotional bias.
π The Pivotal 1982 Debt Crisis and a Paradigm Shift in Decision-Making
This section recounts the speaker's significant professional setback in the late 1970s, where a misjudgment of the economic impact of the debt crisis led to substantial financial losses and the collapse of his business operations. The speaker reflects on this period as a pivotal moment that instilled humility and prompted a reevaluation of his approach to decision-making. The narrative shifts towards the establishment of an 'idea meritocracy,' a system that prioritizes the best ideas through radical truthfulness and transparency. The speaker illustrates this concept with examples of company-wide feedback mechanisms and the use of technology to facilitate open communication and objective decision-making, as demonstrated by the 'Dot Collector' tool used in a post-US election meeting to gauge team perspectives on economic implications.
π€ The Power of Collective Opinions and the 'Dot Collector' in Decision-Making
The speaker delves into the dynamics of diverse opinions and the use of the 'Dot Collector' tool to aggregate and analyze team perspectives. The tool allows for real-time feedback and rating of individuals' contributions, fostering an environment where all voices are heard and considered. The speaker explains how this method of collective feedback and analysis helps to elevate the conversation from a debate of personal opinions to an objective evaluation based on established criteria. The 'Dot Collector' also serves to create a data-driven profile of individuals' thinking patterns, aiding in job matching and decision-making weighted by 'believability.' The speaker provides an example of how this process led to a decision that contradicted the majority vote but aligned with the collective merits of the team, emphasizing the effectiveness of algorithm-aided decision-making in their investment success.
π§ Navigating Emotional and Intellectual Conflicts in Radical Transparency
In the final paragraph, the speaker addresses the emotional challenges of radical transparency and the potential for it to create a harsh work environment. Drawing on neuroscientific insights, the speaker discusses the internal conflict between the desire for self-improvement and the defensive reaction to criticism. Despite these challenges, the speaker argues that the benefits of transparency and open feedback outweigh the emotional discomfort, leading to more effective work and relationships. The speaker acknowledges that this approach is not universally accepted, with a significant minority finding it unsuitable. The narrative concludes with an encouragement for the audience to consider the potential of radical transparency and algorithmic assistance in their own lives, predicting a positive impact similar to the speaker's own experience.
Mindmap
Keywords
π‘Radical Transparency
π‘Algorithmic Decision-Making
π‘Consensus
π‘Mistakes as Puzzles
π‘Idea Meritocracy
π‘Humility
π‘Believability
π‘Dot Collector
π‘Stress Testing
π‘Collective Decision-Making
π‘Neuroscience
Highlights
Embracing radical transparency and algorithmic decision-making for better life outcomes.
Algorithms can be embedded into computers to gather personal data for better interaction.
The speaker's personal journey towards meaningful work and relationships through transparency and algorithms.
The importance of solving the 'puzzles' of mistakes to extract valuable 'gems' of principles.
Principles can be written down and embedded into algorithms for improved decision-making.
The computer's ability to make faster, more informed, and less emotional decisions.
The speaker's greatest failure and the pivotal shift in his approach to decision-making.
The concept of an 'idea meritocracy' where the best ideas win, regardless of hierarchy.
The necessity of radical truthfulness and transparency for an effective idea meritocracy.
The practical application of radical transparency in company communications and operations.
Introduction of the 'Dot Collector' tool for capturing and analyzing team members' assessments.
How the 'Dot Collector' helps in shifting perspectives from personal opinions to collective insights.
The use of algorithms to match individuals with jobs based on their thinking patterns and reliability.
Weighing decisions based on individuals' 'believability' rather than majority rule or hierarchy.
The success of the investment business attributed to collective decision-making with algorithms.
The emotional challenge of radical transparency and the brain's response to it.
Overcoming the emotional resistance to transparency and the benefits of an idea meritocracy.
The speaker's vision of radical transparency's impact on personal and professional life.
Transcripts
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,
naΓ―vely 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)
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