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

Summary

In 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

• 🌐 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.
• 🔍 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.
• 💡 The concept of 'algorithmic decision-making' involves embedding principles into algorithms that computers can use to make decisions alongside humans, leading to better outcomes.
• 🎓 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.
• 📉 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.
• 🤔 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.
• 🗣️ 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.
• 📊 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.
• 🧠 The process of collective decision-making, informed by algorithms and individual 'believability', leads to more effective outcomes than either democracy or autocracy alone.
• 🤝 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.
• 🚀 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

00:00

🌐 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.

05:00

📉 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.

10:02

🤔 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.

15:04

🧠 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.

Keywords

💡Radical Transparency

Radical transparency refers to the practice of being completely open and honest, especially in a business setting, to the point where all information and processes are visible to all stakeholders. In the video, the speaker advocates for radical transparency as a means to improve decision-making by allowing for the open sharing of ideas and feedback, as exemplified by the company's practice of taping conversations and making them accessible to everyone.

💡Algorithmic Decision-Making

Algorithmic decision-making is the process of using algorithms to make decisions, often by analyzing data and identifying patterns. The speaker discusses embedding principles derived from personal experiences and mistakes into algorithms. These algorithms then assist in making decisions alongside human judgment, leading to improved outcomes due to their speed, information processing capacity, and reduced emotional bias.

💡Consensus

Consensus in the context of the video refers to the general agreement or collective opinion among a group of people, often reflected in market expectations or economic forecasts. The speaker emphasizes the importance of betting against the consensus to be successful in investing and entrepreneurship, as the consensus is already factored into the market prices, and finding value requires differing insights.

💡Mistakes as Puzzles

The concept of viewing mistakes as puzzles is a way of reframing errors into opportunities for learning and growth. The speaker describes how he began to analyze his mistakes to understand what he could do differently, turning these lessons into principles or 'gems' that guide future actions. This approach is central to the video's theme of continuous improvement and learning from failure.

💡Idea Meritocracy

An idea meritocracy is a system where the best ideas, regardless of their source, are given the most weight in decision-making. The speaker contrasts this with autocracy and democracy, advocating for a culture where the value of ideas is judged on their merit rather than the status of the person proposing them. This concept is integral to the video's narrative on fostering an environment that encourages open discussion and critical evaluation of ideas.

💡Humility

Humility, in the video, is portrayed as a crucial trait for effective decision-making. The speaker recounts his greatest failure and how it instilled in him a sense of humility, prompting him to question his own certainty and seek out diverse perspectives. Humility is presented as a balance to audacity, allowing for a more open and objective evaluation of ideas.

💡Believability

Believability, as used in the video, refers to the credibility or trustworthiness of an individual's opinions or judgments, often based on their past performance or demonstrated expertise. The speaker discusses how weighing decisions based on individuals' believability can lead to more accurate outcomes than simply following the majority opinion or hierarchical authority.

💡Dot Collector

The 'Dot Collector' is a tool mentioned in the video that facilitates the collection and analysis of opinions during meetings. It allows participants to rate each other on various attributes, contributing to a collective assessment of the discussion. This tool exemplifies the video's theme of using technology to enhance the decision-making process by incorporating diverse viewpoints and feedback.

💡Stress Testing

Stress testing, in the context of the video, involves subjecting one's ideas or perspectives to rigorous examination and challenge to identify potential weaknesses or blind spots. The speaker seeks out those who disagree with him to stress test his views, which is part of the video's broader message on the importance of critically evaluating and refining one's beliefs.

💡Collective Decision-Making

Collective decision-making is the process of making choices as a group, often involving the aggregation of individual opinions or preferences. The speaker argues that when done well, collective decision-making can be superior to individual decision-making, as it benefits from a diversity of perspectives and reduces the risk of biased or uninformed choices.

💡Neuroscience

Neuroscience is the scientific study of the nervous system and brain functions. The speaker references neuroscience to explain the emotional reactions people may have to radical transparency and criticism, such as the amygdala's response to perceived attacks and the prefrontal cortex's role in recognizing and learning from mistakes. This reference to neuroscience provides a deeper understanding of the psychological challenges associated with the video's themes.

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,

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)