 October 2023 2023 年 10 月 One of the most important things I didn't understand about the world when I was a child is the degree to which the returns for performance are superlinear. 我小时候对世界最重要的一个理解是，表现的回报程度是超线性的。 Teachers and coaches implicitly told us the returns were linear. "You get out," I heard a thousand times, "what you put in." They meant well, but this is rarely true. If your product is only half as good as your competitor's, you don't get half as many customers. You get no customers, and you go out of business. 老师和教练们隐含地告诉我们，回报是线性的。“你付出多少，就会得到多少，”我听过无数次。他们的出发点是好的，但这很少是事实。如果你的产品只比竞争对手的好一半，你不会得到一半的客户。你会得不到任何客户，最终倒闭。 It's obviously true that the returns for performance are superlinear in business. Some think this is a flaw of capitalism, and that if we changed the rules it would stop being true. But superlinear returns for performance are a feature of the world, not an artifact of rules we've invented. We see the same pattern in fame, power, military victories, knowledge, and even benefit to humanity. In all of these, the rich get richer. [1] 在商业中，业绩的回报显然是超线性的。有些人认为这是资本主义的缺陷，如果我们改变规则，这种情况就会停止。但业绩的超线性回报是世界的一个特征，而不是我们发明的规则的产物。我们在名声、权力、军事胜利、知识，甚至对人类的益处中都看到了同样的模式。在所有这些方面，富人变得更富。 You can't understand the world without understanding the concept of superlinear returns. And if you're ambitious you definitely should, because this will be the wave you surf on. 你无法理解这个世界，除非你理解超线性回报的概念。如果你有雄心壮志，你绝对应该这样做，因为这将是你所乘风破浪的浪潮。 It may seem as if there are a lot of different situations with superlinear returns, but as far as I can tell they reduce to two fundamental causes: exponential growth and thresholds. 看起来似乎有很多不同的超线性回报情况，但据我所知，它们归结为两个基本原因：指数增长和阈值。 The most obvious case of superlinear returns is when you're working on something that grows exponentially. For example, growing bacterial cultures. When they grow at all, they grow exponentially. But they're tricky to grow. Which means the difference in outcome between someone who's adept at it and someone who's not is very great. 超线性回报最明显的例子是当你从事某种以指数方式增长的事物时。例如，培养细菌文化。当它们生长时，它们是以指数方式增长的。但它们的培养很棘手。这意味着，擅长此事的人与不擅长的人之间的结果差异非常大。 Startups can also grow exponentially, and we see the same pattern there. Some manage to achieve high growth rates. Most don't. And as a result you get qualitatively different outcomes: the companies with high growth rates tend to become immensely valuable, while the ones with lower growth rates may not even survive. 初创公司也可以实现指数级增长，我们在这里看到相同的模式。有些公司能够实现高增长率，但大多数则无法做到。因此，结果会有质的不同：高增长率的公司往往变得极具价值，而低增长率的公司可能甚至无法生存。 Y Combinator encourages founders to focus on growth rate rather than absolute numbers. It prevents them from being discouraged early on, when the absolute numbers are still low. It also helps them decide what to focus on: you can use growth rate as a compass to tell you how to evolve the company. But the main advantage is that by focusing on growth rate you tend to get something that grows exponentially. Y Combinator 鼓励创始人关注增长率而非绝对数字。这可以防止他们在早期阶段因绝对数字仍然较低而感到沮丧。它还帮助他们决定关注的重点：你可以将增长率作为指南，告诉你如何发展公司。但主要的优势在于，专注于增长率往往会带来指数级的增长。 YC doesn't explicitly tell founders that with growth rate "you get out what you put in," but it's not far from the truth. And if growth rate were proportional to performance, then the reward for performance p over time t would be proportional to pt. YC 并没有明确告诉创始人，增长率“你付出多少就能得到多少”，但这并不远离真相。如果增长率与表现成正比，那么在时间 t 内，表现 p 的回报将与 p t 成正比。 Even after decades of thinking about this, I find that sentence startling. 即使经过几十年的思考，我仍然觉得这句话令人震惊。 Whenever how well you do depends on how well you've done, you'll get exponential growth. But neither our DNA nor our customs prepare us for it. No one finds exponential growth natural; every child is surprised, the first time they hear it, by the story of the man who asks the king for a single grain of rice the first day and double the amount each successive day. 每当你的表现好坏取决于你过去的表现时，你就会获得指数增长。但无论是我们的基因还是我们的习俗都没有为此做好准备。没有人觉得指数增长是自然的；每个孩子第一次听到这个故事时都会感到惊讶，故事讲的是一个人向国王请求第一天一粒米，接下来的每一天都翻倍。 What we don't understand naturally we develop customs to deal with, but we don't have many customs about exponential growth either, because there have been so few instances of it in human history. In principle herding should have been one: the more animals you had, the more offspring they'd have. But in practice grazing land was the limiting factor, and there was no plan for growing that exponentially. 我们自然会对不理解的事物发展出习俗，但关于指数增长的习俗也不多，因为人类历史上出现过的实例非常少。原则上，放牧应该是一个例子：你拥有的动物越多，它们的后代就会越多。但实际上，放牧土地是限制因素，并没有计划以指数方式增加土地。 Or more precisely, no generally applicable plan. There was a way to grow one's territory exponentially: by conquest. The more territory you control, the more powerful your army becomes, and the easier it is to conquer new territory. This is why history is full of empires. But so few people created or ran empires that their experiences didn't affect customs very much. The emperor was a remote and terrifying figure, not a source of lessons one could use in one's own life. 更准确地说，没有普遍适用的计划。扩展领土的方式是通过征服。你控制的领土越多，你的军队就越强大，征服新领土也就越容易。这就是为什么历史上充满了帝国。但创造或管理帝国的人寥寥无几，他们的经历对习俗的影响并不大。皇帝是一个遥远而可怕的形象，而不是可以在自己生活中借鉴的教训来源。 The most common case of exponential growth in preindustrial times was probably scholarship. The more you know, the easier it is to learn new things. The result, then as now, was that some people were startlingly more knowledgeable than the rest about certain topics. But this didn't affect customs much either. Although empires of ideas can overlap and there can thus be far more emperors, in preindustrial times this type of empire had little practical effect. [2] 在前工业时代，最常见的指数增长案例可能是学术。你知道得越多，学习新事物就越容易。因此，结果和现在一样，有些人在某些主题上比其他人显得惊人地更有知识。然而，这对习俗的影响也不大。尽管思想的帝国可以重叠，因此可以有更多的皇帝，但在前工业时代，这种类型的帝国几乎没有实际影响。 That has changed in the last few centuries. Now the emperors of ideas can design bombs that defeat the emperors of territory. But this phenomenon is still so new that we haven't fully assimilated it. Few even of the participants realize they're benefitting from exponential growth or ask what they can learn from other instances of it. 在过去几个世纪，这种情况发生了变化。现在，思想的皇帝可以设计出击败领土皇帝的炸弹。但这一现象仍然如此新颖，以至于我们尚未完全吸收它。甚至很少有参与者意识到他们正在从指数增长中受益，或者询问他们可以从其他实例中学到什么。 The other source of superlinear returns is embodied in the expression "winner take all." In a sports match the relationship between performance and return is a step function: the winning team gets one win whether they do much better or just slightly better. [3] 超线性回报的另一个来源体现在“赢家通吃”这一表达中。在体育比赛中，表现与回报之间的关系是一个阶梯函数：获胜的队伍无论表现得多好或稍微好一点，都会获得一场胜利。 The source of the step function is not competition per se, however. It's that there are thresholds in the outcome. You don't need competition to get those. There can be thresholds in situations where you're the only participant, like proving a theorem or hitting a target. 阶梯函数的来源并不是竞争本身，而是结果中的阈值。你并不需要竞争来获得这些阈值。在你是唯一参与者的情况下，比如证明一个定理或达到一个目标，也可以存在阈值。 It's remarkable how often a situation with one source of superlinear returns also has the other. Crossing thresholds leads to exponential growth: the winning side in a battle usually suffers less damage, which makes them more likely to win in the future. And exponential growth helps you cross thresholds: in a market with network effects, a company that grows fast enough can shut out potential competitors. 令人惊讶的是，拥有一种超线性回报的情况往往也会有另一种。跨越门槛会导致指数增长：在战斗中，胜利的一方通常遭受的损失较少，这使得他们在未来更有可能获胜。而指数增长也有助于你跨越门槛：在具有网络效应的市场中，快速增长的公司可以排除潜在竞争对手。 Fame is an interesting example of a phenomenon that combines both sources of superlinear returns. Fame grows exponentially because existing fans bring you new ones. But the fundamental reason it's so concentrated is thresholds: there's only so much room on the A-list in the average person's head. 名声是一个有趣的例子，它结合了超线性回报的两种来源。名声以指数方式增长，因为现有的粉丝会为你带来新的粉丝。但它如此集中根本原因在于阈值：在普通人的脑海中，A-list 的空间是有限的。 The most important case combining both sources of superlinear returns may be learning. Knowledge grows exponentially, but there are also thresholds in it. Learning to ride a bicycle, for example. Some of these thresholds are akin to machine tools: once you learn to read, you're able to learn anything else much faster. But the most important thresholds of all are those representing new discoveries. Knowledge seems to be fractal in the sense that if you push hard at the boundary of one area of knowledge, you sometimes discover a whole new field. And if you do, you get first crack at all the new discoveries to be made in it. Newton did this, and so did Durer and Darwin. 结合超线性回报的两个来源中最重要的案例可能是学习。知识以指数方式增长，但其中也存在阈值。例如，学习骑自行车。这些阈值中的一些类似于机器工具：一旦你学会了阅读，你就能够更快地学习其他任何东西。但最重要的阈值是那些代表新发现的阈值。知识似乎是分形的，因为如果你在某个知识领域的边界上用力推进，有时会发现一个全新的领域。如果你这样做了，你就能率先获得在该领域中进行的新发现。牛顿就是这样做的，杜勒和达尔文也是如此。 Are there general rules for finding situations with superlinear returns? The most obvious one is to seek work that compounds. 寻找超线性回报的情况是否有一般规则？最明显的一条是寻找能够复利的工作。 There are two ways work can compound. It can compound directly, in the sense that doing well in one cycle causes you to do better in the next. That happens for example when you're building infrastructure, or growing an audience or brand. Or work can compound by teaching you, since learning compounds. This second case is an interesting one because you may feel you're doing badly as it's happening. You may be failing to achieve your immediate goal. But if you're learning a lot, then you're getting exponential growth nonetheless. 工作可以以两种方式复合。它可以直接复合，意味着在一个周期中表现良好会导致下一个周期表现更好。例如，当你在建设基础设施、扩大受众或品牌时，就会发生这种情况。或者，工作也可以通过学习来复合，因为学习是会积累的。第二种情况很有趣，因为在这个过程中你可能会觉得自己表现不佳。你可能未能实现你的短期目标。但如果你学到了很多东西，那么你仍然会获得指数级的增长。 This is one reason Silicon Valley is so tolerant of failure. People in Silicon Valley aren't blindly tolerant of failure. They'll only continue to bet on you if you're learning from your failures. But if you are, you are in fact a good bet: maybe your company didn't grow the way you wanted, but you yourself have, and that should yield results eventually. 这就是硅谷对失败如此宽容的原因之一。硅谷的人们并不是盲目地容忍失败。他们只会在你从失败中学习的情况下继续支持你。但如果你确实在学习，那么你实际上是一个不错的投资：也许你的公司没有按照你想要的方式成长，但你自己却成长了，这最终应该会带来成果。 Indeed, the forms of exponential growth that don't consist of learning are so often intermixed with it that we should probably treat this as the rule rather than the exception. Which yields another heuristic: always be learning. If you're not learning, you're probably not on a path that leads to superlinear returns. 确实，不包含学习的指数增长形式常常与学习交织在一起，因此我们应该将其视为规则而非例外。这又得出了另一个启示：永远保持学习。如果你不在学习，你可能并不在通往超线性回报的道路上。 But don't overoptimize what you're learning. Don't limit yourself to learning things that are already known to be valuable. You're learning; you don't know for sure yet what's going to be valuable, and if you're too strict you'll lop off the outliers. 但不要过度优化你所学习的内容。不要仅限于学习那些已经被证明有价值的东西。你在学习；你还不确定什么会是有价值的，如果你过于严格，就会排除掉那些边缘案例。 What about step functions? Are there also useful heuristics of the form "seek thresholds" or "seek competition?" Here the situation is trickier. The existence of a threshold doesn't guarantee the game will be worth playing. If you play a round of Russian roulette, you'll be in a situation with a threshold, certainly, but in the best case you're no better off. "Seek competition" is similarly useless; what if the prize isn't worth competing for? Sufficiently fast exponential growth guarantees both the shape and magnitude of the return curve — because something that grows fast enough will grow big even if it's trivially small at first — but thresholds only guarantee the shape. [4] 关于阶梯函数呢？是否也存在“寻找阈值”或“寻找竞争”的有用启发式？这里的情况更复杂。阈值的存在并不保证游戏值得参与。如果你玩一轮俄罗斯轮盘赌，你肯定会处于一个有阈值的情况，但在最好的情况下，你并没有更好。“寻找竞争”同样没有用；如果奖品不值得竞争，那又如何呢？足够快的指数增长保证了回报曲线的形状和幅度——因为足够快的增长即使一开始微不足道也会变得很大——但阈值只保证了形状。 A principle for taking advantage of thresholds has to include a test to ensure the game is worth playing. Here's one that does: if you come across something that's mediocre yet still popular, it could be a good idea to replace it. For example, if a company makes a product that people dislike yet still buy, then presumably they'd buy a better alternative if you made one. [5] 一个利用门槛的原则必须包括一个测试，以确保游戏值得参与。这里有一个原则：如果你遇到一些平庸但仍然受欢迎的事物，替换它可能是个好主意。例如，如果一家公司生产一种人们不喜欢但仍然购买的产品，那么可以推测，如果你制造了一个更好的替代品，他们会购买。 It would be great if there were a way to find promising intellectual thresholds. Is there a way to tell which questions have whole new fields beyond them? I doubt we could ever predict this with certainty, but the prize is so valuable that it would be useful to have predictors that were even a little better than random, and there's hope of finding those. We can to some degree predict when a research problem isn't likely to lead to new discoveries: when it seems legit but boring. Whereas the kind that do lead to new discoveries tend to seem very mystifying, but perhaps unimportant. (If they were mystifying and obviously important, they'd be famous open questions with lots of people already working on them.) So one heuristic here is to be driven by curiosity rather than careerism — to give free rein to your curiosity instead of working on what you're supposed to. 如果能找到有前景的智力门槛，那就太好了。有没有办法判断哪些问题背后有全新的领域？我怀疑我们是否能以确定的方式预测这一点，但这个奖赏是如此珍贵，以至于即使是比随机更好一点的预测工具也是有用的，并且有希望找到这些工具。在某种程度上，我们可以预测一个研究问题不太可能导致新发现的情况：当它看起来合法但无聊时。而那些确实会导致新发现的问题往往显得非常神秘，但可能并不重要。（如果它们既神秘又显然重要，那它们就会是著名的开放性问题，已经有很多人在研究。）因此，这里有一个启发式的方法是，应该被好奇心驱动，而不是职业主义——放任你的好奇心，而不是去做你应该做的事情。 The prospect of superlinear returns for performance is an exciting one for the ambitious. And there's good news in this department: this territory is expanding in both directions. There are more types of work in which you can get superlinear returns, and the returns themselves are growing. 超线性回报的前景对有抱负的人来说是令人兴奋的好消息。在这一领域，有一个好消息：这个领域正在双向扩展。可以获得超线性回报的工作类型越来越多，而回报本身也在增长。 There are two reasons for this, though they're so closely intertwined that they're more like one and a half: progress in technology, and the decreasing importance of organizations. 这有两个原因，尽管它们紧密相连，更像是一个半：技术进步和组织的重要性下降。 Fifty years ago it used to be much more necessary to be part of an organization to work on ambitious projects. It was the only way to get the resources you needed, the only way to have colleagues, and the only way to get distribution. So in 1970 your prestige was in most cases the prestige of the organization you belonged to. And prestige was an accurate predictor, because if you weren't part of an organization, you weren't likely to achieve much. There were a handful of exceptions, most notably artists and writers, who worked alone using inexpensive tools and had their own brands. But even they were at the mercy of organizations for reaching audiences. [6] 五十年前，参与一个组织在从事雄心勃勃的项目时显得更加必要。这是获取所需资源的唯一途径，也是拥有同事的唯一方式，更是获得分发的唯一方法。因此，在 1970 年，您的声望在大多数情况下是您所属组织的声望。而声望是一个准确的预测指标，因为如果您不属于任何组织，您很可能不会取得太大成就。只有少数例外，最显著的是艺术家和作家，他们独自工作，使用廉价工具，并拥有自己的品牌。但即便如此，他们在接触观众时也依赖于组织。 A world dominated by organizations damped variation in the returns for performance. But this world has eroded significantly just in my lifetime. Now a lot more people can have the freedom that artists and writers had in the 20th century. There are lots of ambitious projects that don't require much initial funding, and lots of new ways to learn, make money, find colleagues, and reach audiences. 一个被组织主导的世界抑制了绩效回报的变化。但在我这一生中，这个世界已经显著地改变了。现在，更多的人可以享有 20 世纪艺术家和作家的自由。有许多雄心勃勃的项目不需要太多的初始资金，还有许多新的方式来学习、赚钱、寻找同事和接触观众。 There's still plenty of the old world left, but the rate of change has been dramatic by historical standards. Especially considering what's at stake. It's hard to imagine a more fundamental change than one in the returns for performance. 旧世界仍然有很多，但按历史标准来看，变化的速度是惊人的。尤其是考虑到所涉及的利益。很难想象还有比绩效回报的变化更根本的改变。 Without the damping effect of institutions, there will be more variation in outcomes. Which doesn't imply everyone will be better off: people who do well will do even better, but those who do badly will do worse. That's an important point to bear in mind. Exposing oneself to superlinear returns is not for everyone. Most people will be better off as part of the pool. So who should shoot for superlinear returns? Ambitious people of two types: those who know they're so good that they'll be net ahead in a world with higher variation, and those, particularly the young, who can afford to risk trying it to find out. [7] 没有机构的阻尼效应，结果的变异性会更大。这并不意味着每个人都会过得更好：表现良好的人会更好，而表现不佳的人会更糟。这是一个重要的观点。让自己暴露于超线性回报并不适合每个人。大多数人作为整体会过得更好。那么，谁应该追求超线性回报呢？有两种类型的雄心勃勃的人：那些知道自己非常优秀，在一个变异性更高的世界中会有净收益的人，以及那些，尤其是年轻人，能够承担风险去尝试以找出答案的人。 The switch away from institutions won't simply be an exodus of their current inhabitants. Many of the new winners will be people they'd never have let in. So the resulting democratization of opportunity will be both greater and more authentic than any tame intramural version the institutions themselves might have cooked up. 转向非机构化的过程不仅仅是当前居民的迁出。许多新的赢家将是那些他们从未允许进入的人。因此，随之而来的机会民主化将比机构本身可能策划的任何温和的内部版本更为广泛和真实。 Not everyone is happy about this great unlocking of ambition. It threatens some vested interests and contradicts some ideologies. [8] But if you're an ambitious individual it's good news for you. How should you take advantage of it? 并不是每个人都对这种雄心的巨大解放感到高兴。这威胁到一些既得利益，并与某些意识形态相矛盾。[8] 但如果你是一个有抱负的人，这对你来说是个好消息。你应该如何利用这一点呢？ The most obvious way to take advantage of superlinear returns for performance is by doing exceptionally good work. At the far end of the curve, incremental effort is a bargain. All the more so because there's less competition at the far end — and not just for the obvious reason that it's hard to do something exceptionally well, but also because people find the prospect so intimidating that few even try. Which means it's not just a bargain to do exceptional work, but a bargain even to try to. 利用超线性回报来提升表现的最明显方式就是做好工作。在曲线的最远端，增量努力是个便宜的买卖。更何况，在最远端竞争较少——不仅仅是因为做得特别好很难，还有因为人们觉得这个前景令人畏惧，以至于很少有人尝试。这意味着，做好卓越的工作不仅是个便宜的买卖，甚至尝试去做也是如此。 There are many variables that affect how good your work is, and if you want to be an outlier you need to get nearly all of them right. For example, to do something exceptionally well, you have to be interested in it. Mere diligence is not enough. So in a world with superlinear returns, it's even more valuable to know what you're interested in, and to find ways to work on it. [9] It will also be important to choose work that suits your circumstances. For example, if there's a kind of work that inherently requires a huge expenditure of time and energy, it will be increasingly valuable to do it when you're young and don't yet have children. 影响你工作质量的变量有很多，如果你想成为一个例外，你需要几乎把所有这些变量都掌握好。例如，要把某件事情做到极致，你必须对它感兴趣。单靠勤奋是不够的。因此，在一个超线性回报的世界里，了解自己的兴趣并找到相应的工作方式变得更加重要。[9] 选择适合自己情况的工作也将是重要的。例如，如果某种工作本质上需要大量的时间和精力投入，那么在年轻且尚未有孩子的时候去做这项工作将变得越来越有价值。 There's a surprising amount of technique to doing great work. It's not just a matter of trying hard. I'm going to take a shot giving a recipe in one paragraph. 做出优秀作品需要相当多的技巧。这不仅仅是努力工作的问题。我将尝试在一段话中给出一个方法。 Choose work you have a natural aptitude for and a deep interest in. Develop a habit of working on your own projects; it doesn't matter what they are so long as you find them excitingly ambitious. Work as hard as you can without burning out, and this will eventually bring you to one of the frontiers of knowledge. These look smooth from a distance, but up close they're full of gaps. Notice and explore such gaps, and if you're lucky one will expand into a whole new field. Take as much risk as you can afford; if you're not failing occasionally you're probably being too conservative. Seek out the best colleagues. Develop good taste and learn from the best examples. Be honest, especially with yourself. Exercise and eat and sleep well and avoid the more dangerous drugs. When in doubt, follow your curiosity. It never lies, and it knows more than you do about what's worth paying attention to. [10] 选择你有自然天赋和深厚兴趣的工作。养成独立开展项目的习惯；无论项目是什么，只要你觉得它们充满挑战和激情就可以。尽可能努力工作，但不要过度疲惫，这最终会将你带到知识的前沿。这些从远处看起来光滑，但近看却充满了空隙。注意并探索这些空隙，如果幸运的话，其中一个会扩展成一个全新的领域。尽可能承担风险；如果你偶尔不失败，那你可能过于保守。寻找最优秀的同事。培养良好的品味，并向最佳范例学习。要诚实，尤其是对自己。锻炼、饮食和睡眠要良好，避免更危险的药物。当有疑问时，跟随你的好奇心。它从不撒谎，并且比你更了解值得关注的事物。 And there is of course one other thing you need: to be lucky. Luck is always a factor, but it's even more of a factor when you're working on your own rather than as part of an organization. And though there are some valid aphorisms about luck being where preparedness meets opportunity and so on, there's also a component of true chance that you can't do anything about. The solution is to take multiple shots. Which is another reason to start taking risks early. 当然，还有一件事你需要：运气。运气始终是一个因素，但当你独自工作而不是作为组织的一部分时，它的影响更为显著。尽管有一些关于运气的有效格言，比如运气是准备与机会相遇的地方等等，但也有一种真正的偶然性是你无法控制的。解决方案是多尝试几次。这也是尽早冒险的另一个理由。 The best example of a field with superlinear returns is probably science. It has exponential growth, in the form of learning, combined with thresholds at the extreme edge of performance — literally at the limits of knowledge. 科学可能是一个超线性回报领域的最佳例子。它以学习的形式呈现出指数增长，并结合了在性能极限边缘的阈值——字面上处于知识的边界。 The result has been a level of inequality in scientific discovery that makes the wealth inequality of even the most stratified societies seem mild by comparison. Newton's discoveries were arguably greater than all his contemporaries' combined. [11] 结果是科学发现中的不平等程度，使得即使是最分层社会的财富不平等相比之下显得温和。牛顿的发现可以说超过了他所有同时代人的总和。 This point may seem obvious, but it might be just as well to spell it out. Superlinear returns imply inequality. The steeper the return curve, the greater the variation in outcomes. 这一点可能看起来显而易见，但也许明确说明一下更好。超线性回报意味着不平等。回报曲线越陡，结果的变异性就越大。 In fact, the correlation between superlinear returns and inequality is so strong that it yields another heuristic for finding work of this type: look for fields where a few big winners outperform everyone else. A kind of work where everyone does about the same is unlikely to be one with superlinear returns. 事实上，超线性回报与不平等之间的关联如此强烈，以至于它提供了另一种寻找此类工作的启发式方法：寻找一些大赢家表现优于其他人的领域。那种每个人的表现大致相同的工作不太可能具有超线性回报。 What are fields where a few big winners outperform everyone else? Here are some obvious ones: sports, politics, art, music, acting, directing, writing, math, science, starting companies, and investing. In sports the phenomenon is due to externally imposed thresholds; you only need to be a few percent faster to win every race. In politics, power grows much as it did in the days of emperors. And in some of the other fields (including politics) success is driven largely by fame, which has its own source of superlinear growth. But when we exclude sports and politics and the effects of fame, a remarkable pattern emerges: the remaining list is exactly the same as the list of fields where you have to be independent-minded to succeed — where your ideas have to be not just correct, but novel as well. [12] 哪些领域是少数大赢家超越其他所有人的？以下是一些显而易见的领域：体育、政治、艺术、音乐、表演、导演、写作、数学、科学、创业和投资。在体育中，这种现象是由于外部施加的门槛；你只需快几个百分点就能赢得每一场比赛。在政治中，权力的增长方式与帝王时代相似。在其他一些领域（包括政治）中，成功在很大程度上是由名声驱动的，而名声本身具有超线性增长的特性。但当我们排除体育、政治和名声的影响时，一个显著的模式浮现出来：剩下的列表与成功所需的独立思考的领域完全相同——在这些领域中，你的想法不仅要正确，还必须是新颖的。 This is obviously the case in science. You can't publish papers saying things that other people have already said. But it's just as true in investing, for example. It's only useful to believe that a company will do well if most other investors don't; if everyone else thinks the company will do well, then its stock price will already reflect that, and there's no room to make money. 这在科学领域显然是如此。你不能发表已经被其他人说过的观点。但在投资方面同样如此。只有在大多数其他投资者不看好一家公司时，相信这家公司会表现良好才有意义；如果其他人都认为这家公司会表现良好，那么其股价已经反映了这一点，就没有赚钱的空间。 What else can we learn from these fields? In all of them you have to put in the initial effort. Superlinear returns seem small at first. At this rate, you find yourself thinking, I'll never get anywhere. But because the reward curve rises so steeply at the far end, it's worth taking extraordinary measures to get there. 我们还能从这些领域学到什么？在所有这些领域中，你都必须付出最初的努力。超线性回报起初似乎很小。以这样的速度，你会想，我永远也无法取得进展。但由于奖励曲线在远端急剧上升，采取非凡的措施去达到那里是值得的。 In the startup world, the name for this principle is "do things that don't scale." If you pay a ridiculous amount of attention to your tiny initial set of customers, ideally you'll kick off exponential growth by word of mouth. But this same principle applies to anything that grows exponentially. Learning, for example. When you first start learning something, you feel lost. But it's worth making the initial effort to get a toehold, because the more you learn, the easier it will get. 在创业界，这一原则被称为“做一些不具规模的事情”。如果你对最初的小规模客户给予极大的关注，理想情况下，你将通过口碑推动指数级增长。但这个原则同样适用于任何以指数方式增长的事物，比如学习。当你刚开始学习某样东西时，你会感到迷茫。但付出初步的努力以获得立足点是值得的，因为你学得越多，事情就会变得越容易。 There's another more subtle lesson in the list of fields with superlinear returns: not to equate work with a job. For most of the 20th century the two were identical for nearly everyone, and as a result we've inherited a custom that equates productivity with having a job. Even now to most people the phrase "your work" means their job. But to a writer or artist or scientist it means whatever they're currently studying or creating. For someone like that, their work is something they carry with them from job to job, if they have jobs at all. It may be done for an employer, but it's part of their portfolio. 在超线性回报的领域列表中还有一个更微妙的教训：不要将工作等同于职业。在 20 世纪的大部分时间里，这两者对几乎每个人来说是相同的，因此我们继承了一种习俗，将生产力与拥有一份工作等同起来。即使在现在，对大多数人来说，“你的工作”这个短语意味着他们的职业。但对作家、艺术家或科学家来说，这意味着他们当前正在研究或创作的任何东西。对于这样的人来说，他们的工作是他们从一份工作带到另一份工作的东西，如果他们有工作的话。它可能是为雇主完成的，但它是他们作品集的一部分。 It's an intimidating prospect to enter a field where a few big winners outperform everyone else. Some people do this deliberately, but you don't need to. If you have sufficient natural ability and you follow your curiosity sufficiently far, you'll end up in one. Your curiosity won't let you be interested in boring questions, and interesting questions tend to create fields with superlinear returns if they're not already part of one. 进入一个少数大赢家超越其他所有人的领域，确实让人感到畏惧。有些人故意这样做，但你并不需要。如果你有足够的天赋，并且足够深入地追随你的好奇心，你最终会进入一个这样的领域。你的好奇心不会让你对无聊的问题感兴趣，而有趣的问题往往会创造出超线性回报的领域，除非它们已经是某个领域的一部分。 The territory of superlinear returns is by no means static. Indeed, the most extreme returns come from expanding it. So while both ambition and curiosity can get you into this territory, curiosity may be the more powerful of the two. Ambition tends to make you climb existing peaks, but if you stick close enough to an interesting enough question, it may grow into a mountain beneath you. 超线性回报的领域绝非静态。实际上，最极端的回报来自于扩展这个领域。因此，尽管雄心和好奇心都能让你进入这个领域，但好奇心可能更为强大。雄心往往使你攀登现有的高峰，但如果你紧紧围绕一个足够有趣的问题，它可能会在你脚下成长为一座山。 Notes There's a limit to how sharply you can distinguish between effort, performance, and return, because they're not sharply distinguished in fact. What counts as return to one person might be performance to another. But though the borders of these concepts are blurry, they're not meaningless. I've tried to write about them as precisely as I could without crossing into error. 在努力、表现和回报之间的区分是有限的，因为它们实际上并没有明确的界限。对一个人来说，回报可能对另一个人来说就是表现。尽管这些概念的边界模糊，但它们并不是毫无意义的。我尽量准确地写出这些内容，而不陷入错误。 [1] Evolution itself is probably the most pervasive example of superlinear returns for performance. But this is hard for us to empathize with because we're not the recipients; we're the returns. 进化本身可能是超线性回报在表现上的最普遍例子。但这对我们来说很难产生共鸣，因为我们不是接受者；我们是回报。 [2] Knowledge did of course have a practical effect before the Industrial Revolution. The development of agriculture changed human life completely. But this kind of change was the result of broad, gradual improvements in technique, not the discoveries of a few exceptionally learned people. 知识在工业革命之前确实产生了实际影响。农业的发展彻底改变了人类生活。但这种变化是广泛、渐进的技术改进的结果，而不是少数博学之士的发现。 [3] It's not mathematically correct to describe a step function as superlinear, but a step function starting from zero works like a superlinear function when it describes the reward curve for effort by a rational actor. If it starts at zero then the part before the step is below any linearly increasing return, and the part after the step must be above the necessary return at that point or no one would bother. 将阶梯函数描述为超线性在数学上并不正确，但从零开始的阶梯函数在描述理性行为者的努力奖励曲线时表现得像一个超线性函数。如果它从零开始，那么阶梯之前的部分低于任何线性递增的回报，而阶梯之后的部分必须高于那个点所需的回报，否则没人会去做。 [4] Seeking competition could be a good heuristic in the sense that some people find it motivating. It's also somewhat of a guide to promising problems, because it's a sign that other people find them promising. But it's a very imperfect sign: often there's a clamoring crowd chasing some problem, and they all end up being trumped by someone quietly working on another one. 寻求竞争可能是一种良好的启发式，因为有些人会觉得这很有激励性。这在某种程度上也是一个有前景问题的指引，因为这表明其他人认为这些问题有前景。但这也是一个非常不完美的标志：通常会有一群人争先恐后地追逐某个问题，而他们最终都被一个默默研究另一个问题的人超越。 [5] Not always, though. You have to be careful with this rule. When something is popular despite being mediocre, there's often a hidden reason why. Perhaps monopoly or regulation make it hard to compete. Perhaps customers have bad taste or have broken procedures for deciding what to buy. There are huge swathes of mediocre things that exist for such reasons. 并不总是如此。你必须对这个规则保持谨慎。当某样东西尽管平庸却仍然受欢迎时，通常有一个隐藏的原因。也许是垄断或监管使竞争变得困难。也许顾客的品味不好，或者在决定购买什么时程序出现了问题。由于这些原因，存在大量平庸的事物。 [6] In my twenties I wanted to be an artist and even went to art school to study painting. Mostly because I liked art, but a nontrivial part of my motivation came from the fact that artists seemed least at the mercy of organizations. 在我二十多岁的时候，我想成为一名艺术家，甚至去艺术学校学习绘画。主要是因为我喜欢艺术，但我动机中有相当一部分来自于艺术家似乎最不受组织控制的事实。 [7] In principle everyone is getting superlinear returns. Learning compounds, and everyone learns in the course of their life. But in practice few push this kind of everyday learning to the point where the return curve gets really steep. 原则上，每个人都在获得超线性回报。学习是累积的，每个人在生活中都会学习。但在实践中，很少有人将这种日常学习推向一个使回报曲线变得非常陡峭的程度。 [8] It's unclear exactly what advocates of "equity" mean by it. They seem to disagree among themselves. But whatever they mean is probably at odds with a world in which institutions have less power to control outcomes, and a handful of outliers do much better than everyone else. [8] “公平”的倡导者究竟是什么意思尚不清楚。他们似乎在彼此之间存在分歧。但无论他们的意思是什么，可能都与一个机构控制结果的权力较小，而少数异类表现远超其他人群的世界相悖。 It may seem like bad luck for this concept that it arose at just the moment when the world was shifting in the opposite direction, but I don't think this was a coincidence. I think one reason it arose now is because its adherents feel threatened by rapidly increasing variation in performance. 这个概念的出现似乎与世界正朝着相反的方向转变的时刻相悖，似乎是个坏运气，但我认为这并非巧合。我认为它现在出现的一个原因是，它的支持者感到快速增加的表现差异带来了威胁。 [9] Corollary: Parents who pressure their kids to work on something prestigious, like medicine, even though they have no interest in it, will be hosing them even more than they have in the past. [9] 推论：那些逼迫孩子去从事一些有声望的职业，比如医学，尽管他们对此毫无兴趣的父母，将会比以往更加伤害他们。 [10] The original version of this paragraph was the first draft of "How to Do Great Work." As soon as I wrote it I realized it was a more important topic than superlinear returns, so I paused the present essay to expand this paragraph into its own. Practically nothing remains of the original version, because after I finished "How to Do Great Work" I rewrote it based on that. 这一段的原始版本是《如何做出伟大的工作》的初稿。当我写下它时，我意识到这是一个比超线性回报更重要的话题，因此我暂停了当前的文章，将这一段扩展为独立的内容。原始版本几乎没有保留，因为在我完成《如何做出伟大的工作》后，我根据那篇文章进行了重写。 [11] Before the Industrial Revolution, people who got rich usually did it like emperors: capturing some resource made them more powerful and enabled them to capture more. Now it can be done like a scientist, by discovering or building something uniquely valuable. Most people who get rich use a mix of the old and the new ways, but in the most advanced economies the ratio has shifted dramatically toward discovery just in the last half century. 在工业革命之前，富人通常像皇帝一样致富：获取某种资源使他们更强大，从而能够获取更多。现在，富人可以像科学家一样，通过发现或创造一些独特的有价值的东西来致富。大多数富人使用传统与现代方法的结合，但在过去的半个世纪里，在最发达的经济体中，这种比例已经显著向发现倾斜。 [12] It's not surprising that conventional-minded people would dislike inequality if independent-mindedness is one of the biggest drivers of it. But it's not simply that they don't want anyone to have what they can't. The conventional-minded literally can't imagine what it's like to have novel ideas. So the whole phenomenon of great variation in performance seems unnatural to them, and when they encounter it they assume it must be due to cheating or to some malign external influence. [12] 如果独立思考是导致不平等的最大驱动力之一，那么传统思维的人不喜欢不平等也就不足为奇了。但他们并不仅仅是不希望别人拥有他们无法拥有的东西。传统思维的人根本无法想象拥有新颖想法是什么样的。因此，表现的巨大差异对他们来说似乎是不自然的，当他们遇到这种情况时，他们会认为这一定是由于作弊或某种恶劣的外部影响所致。 Thanks to Trevor Blackwell, Patrick Collison, Tyler Cowen, Jessica Livingston, Harj Taggar, and Garry Tan for reading drafts of this. |