저자: RayDalio · 외국인
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결론: 레이 달리오는 이 영상에서 투자 판단 방식과 조직 운영 원칙을 설명할 뿐, 특정 자산의 가격 방향을 예측하지 않는다. 개인 이력: 12세에 주식 투자를 시작했고, 초기 매수 종목 노스이스트 항공(Northeast Airlines)으로 돈을 세 배로 늘린 경험을 소개했다. 실패 경험: 1970년대 후반 신흥국 부채 위기를 예견했으나, 1982년 멕시코 디폴트 이후에도 증시와 경제는 오히려 상승해 큰 손실을 봤고 직원 대부분을 해고했다. 교훈: 이 실패로 '내가 맞다'가 아니라 '내가 맞다는 것을 어떻게 아는가'를 묻는 겸손함을 얻었다고 말한다. 의사결정 원칙: 최고의 아이디어가 이기는 '아이디어 메리토크라시'를 지향하며, 이를 위해 극단적 진실성과 투명성을 강조한다. 대화를 녹음해 공개하고, 실제 이메일 피드백 사례도 소개했다. 원칙의 체계화: 실수에서 얻은 교훈을 원칙으로 적고, 이를 알고리즘에 내장해 컴퓨터가 사람과 병행해 의사결정하도록 했다고 설명한다. 도구 소개: 회의에서 참석자들이 서로를 1~10점으로 평가하는 'Dot Collector' 도구를 소개한다. 평가 데이터로 사람의 성향을 파악해 업무 배정과 의사결정 가중치(신뢰도, believability)에 활용한다. 결론적으로 다수결이나 독재가 아니라 구성원의 신뢰도를 반영한 알고리즘 기반 의사결정을 지향한다는 점이 핵심 메시지다. 이 영상은 2020년 6월 1일 게시된 것으로 보이며, 자산별 1~3개월 가격 전망 발언은 없다.
다듬은 글
since I was a kid I've had a terrible Road memory and I didn't like following instructions I was no good at following instructions but I love to figure out how things work for myself when I was 12 I hated school but I fell in love with trading the markets I carried at the time earned about five dollars a bag and I took my catting 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 and I figured I could buy more shares and if we went up I'd make more money so there 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 that 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 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 Nate and so in parallel we would make these decisions and I could see how those decisions been compared with my own decisions and I could see that those decisions were a lot better and that was because the computer could make decision 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 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 and 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 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 my for myself and for my clients that I had to shut down my operation pretty much I had to let almost everybody go these were like extended family and it was heartbroken and I had lost so much money that I had to borrow $4,000 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 experience of my life because it changed my attitude about decision-making rather than thinking I'm right I started ask myself how do I know I'm right I gained a humility that I needed in order to balance with 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 in 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 meritocratic meritocracy in order to have a idea meritocracy we have to let people speak and say what they want just to give you an example this is an email from Jim Haskell who somebody works with me and this was available to everybody in the company ray you have deserve a d-minus for your performance today in the meeting you did not prepare it all well because there is no way you could have been that disorganized 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 of meritocracy so for the 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 that attribute and provider rating from 1 to 10 for example as the meeting began a researcher named Jen rated me of 3 in other words badly for not showing a good balance of open-mindedness and assertiveness as the meeting transpired Jen's assessments of people added up 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 and 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 pointillist 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 was 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 you
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