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. 当我还是个孩子的时候，我没能理解世界上最重要的一个事实：绩效带来的回报通常是超线性的。
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. 教师和教练总是给我们灌输一种思想：回报与付出是成正比的。他们说，“你得到的和你付出的一样多。”他们是出于好意，但实际情况往往并非如此。如果你的产品质量只有竞争对手的一半，你不会仅仅失去一半的客户。更可能的是，你一名客户都留不住，最后关门大吉。
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] 在商业领域，绩效带来的超线性回报尤其明显。有人认为这是资本主义的缺陷，认为只要改变规则，这种情况就会消失。但事实上，绩效的超线性回报是这个世界的一个特性，而不是我们制定规则的副产品。无论是在名声、权力、军事胜利、知识，还是对人类的贡献方面，我们都能看到这一模式。在所有这些领域，成功者往往会越来越成功。[1]
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. 理解超线性回报的概念对于理解这个世界至关重要。如果你有远大的抱负，那么你更应该理解它，因为这会是你乘风破浪的力量。
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. 虽然似乎有许多情况都存在超线性回报，但归根结底，它们主要源于两个因素：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. 超线性回报最典型的例子是指数型增长的情况，比如培养细菌。细菌在增长时，其速度是指数级的。但培养它们颇具挑战，因此技术娴熟与否将导致巨大的结果差异。
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. 对于初创公司也是如此，它们也可能实现指数型增长。一些公司成功实现了高增长率，而多数公司却做不到。这导致了截然不同的结果：高增长率的公司可能成长为价值巨大的企业，而增长率低的公司可能连生存都困难。
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 倡导创始人更多关注增长率而非绝对数值。这不仅能防止他们在初期因为绝对数值低而气馁，还能帮助他们决定重点关注的领域：通过增长率可以指引公司的发展方向。最重要的是，专注于增长率通常意味着你能实现指数型增长。
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. 虽然 YC 并未直接告诉创始人，增长率与你的投入成正比，但这一说法颇有道理。如果增长率确实与绩效成比例，那么随着时间推移，绩效 p 的回报将与 pt 成比例。
Even after decades of thinking about this, I find that sentence startling. 即使在深入思考了数十年后，这个观点仍然让我感到震撼。
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. 当你的绩效依赖于你以往的成就时，就会出现指数级的增长。然而，无论是我们的 DNA 还是习惯都没有为此做好准备。指数增长对任何人来说都不是直观的；比如，孩子们在第一次听到有关一个男人从国王那里第一天要求一粒米，随后每天翻倍的故事时，都会感到惊奇。
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. 我们对于不自然理解的事物，通常会通过发展习俗来应对。但是，关于指数增长的习俗却寥寥无几，因为人类历史中很少出现这样的例子。理论上，放牧动物本可以成为一个例子：你拥有的动物越多，它们的后代就越多。但实际上，放牧地成了限制因素，没有办法实现指数级的增长。
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. 或者更确切地说，并不存在一个普遍适用的策略。过去，有一种方法可以让领土呈指数级扩张：那就是征服。领土越广，军力就越强大，进而征服新土地也就更加轻而易举。这正是历史上层出不穷的帝国背后的逻辑。然而，真正创建或统治帝国的人寥寥无几，他们的经历对一般人的日常生活和习俗影响甚微。对普通人来说，皇帝是一个遥远且可怕的存在，而不是能在日常生活中借鉴的经验教训。
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，最常见的指数增长例子可能是学问。你掌握的知识越多，学习新事物就越容易。因此，无论是过去还是现在，总有一些人在特定领域的知识远超其他人。但这种差异也并未对传统习俗造成太大影响。虽然思想的“帝国”可以相互重叠，拥有众多的“皇帝”，但在前工业时代，这类帝国几乎没有实际的影响力。[2]
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. 然而，近几个世纪以来，这种情况发生了翻天覆地的变化。如今，思想的“皇帝”能够设计出能够击败领土“皇帝”的炸弹。但这种现象仍然非常新颖，以至于我们还未能完全理解和吸收它。即便是参与其中的人，很少有人意识到自己正在从指数级增长中受益，或者思考他们能从其他类似情况中学到什么。
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] “赢者通吃”这一说法揭示了另一个超线性收益的来源。以体育比赛为例，比赛的表现和回报之间呈现一种阶梯式关系：无论胜出的队伍优势多大或仅略胜一筹，他们都只能获得一场胜利。[3]
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. 但这种阶梯效应并非仅源于竞争本身。更关键的是结果中的“阈值”。即使在没有竞争的情况下，比如独自证明一个定理或实现一个目标，也存在这样的阈值。
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. 在很多情况下，一个能带来超线性回报的因素通常伴随着另一个。例如，跨越某个门槛往往能引发指数级增长：在战斗中，赢的一方往往损失更少，这使他们未来更有可能获胜。同样，指数级增长也助于跨越门槛：在一个市场中，如果一个公司快速增长，就能有效排除潜在竞争对手。
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. 名声就是一个典型例子，它结合了两种超线性收益的来源。名声之所以能指数级增长，是因为现有的粉丝会吸引新的粉丝。但名声集中的主要原因在于人们的注意力有限，比如大众心目中的明星名单（A-list）只有那么多位。
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. 学习可能是最重要的结合了这两种超线性回报的例子。知识以指数形式增长，但也存在一些关键门槛，比如学习骑自行车。有些门槛就像机械工具，一旦你学会阅读，其他知识就能更快掌握。但最关键的门槛是新发现。知识在某种意义上像是分形的：深入一个领域的边界时，有时会开辟一个全新领域。像牛顿、杜勒和达尔文这样的大师，正是这样开创新领域并首先探索其中的新知识。
Are there general rules for finding situations with superlinear returns? The most obvious one is to seek work that compounds. 那么，如何找到找到具有超线性回报情况的通用规则呢？一个显而易见的方法是寻找那些可以实现复合增长的工作。
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. 复合增长的工作有两种类型：一种是直接的复合增长，就是说你在上一个周期的优秀表现能让你在下一个周期做得更好。这种情况通常出现在你建设基础设施或者扩大观众群和品牌影响力时。另一种是通过学习实现的复合增长，毕竟学习本身就能带来复合效应。这种情况很有意思，因为在这个过程中，你可能觉得自己做得不够好，甚至没能达成当下的目标。但如果你学到了很多，那你依然在经历着指数级的成长。
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. 这正是硅谷对失败如此宽容的原因之一。硅谷人并非对失败一味宽容，他们只有在看到你从失败中吸取教训时，才会继续支持你。但如果你真的做到了，那么你实际上是个不错的选择：也许你的公司没有像你期望的那样增长，但你个人的成长最终会带来回报。
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. 实际上，不包含学习元素的指数增长往往与学习紧密交织在一起，我们应将这视为常态而非例外。这就衍生出另一个启发式原则：永远保持学习。如果你停止了学习，那么你可能就偏离了通往超线性回报的道路。
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. 但也不要过度追求优化你的学习内容。不要局限于只学习那些已知有价值的知识。毕竟你还在学习阶段，还不确定哪些知识将来会有价值，过于苛刻的标准可能会让你错过一些异常但有潜力的领域。
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] 谈到阶梯函数，我们是否也能找到像“寻找阈值”或“寻找竞争”这样的实用策略呢？这个问题比较棘手。光有阈值并不意味着参与游戏就一定值得。比如，玩一轮俄罗斯轮盘赌，虽然确实存在明显的阈值，但即使在最佳情况下，你的处境也并未改善。同理，“寻找竞争”也不总是有效；如果奖励本身就不吸引人怎么办？相比之下，快速的指数增长不仅保证了收益曲线的形态，还保证了其规模——因为增长得足够快的事物，哪怕起初微不足道，最终也会变得庞大——而阈值仅仅确保了形态。[4]
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] 要想利用阈值，就必须包含一种测试，以确保游戏值得一玩。这里有一个办法：如果你发现某件事物虽平庸但依然受欢迎，那么尝试替换它可能是个不错的选择。比如，一家公司生产的产品虽不受欢迎，但人们还是会购买，那么如果你能制造出一个更好的替代品，他们很可能会转而购买这个新产品。[5]
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. 如果能找到一种方法来发现有潜力的智力阈值就好了。我们怎样才能判断，哪些问题的背后隐藏着全新的研究领域呢？虽然我们可能永远无法完全确定地预测这一点，但鉴于潜在的巨大价值，即便是略胜于随机的预测方法也很有用，而且我们有希望找到这样的方法。我们在一定程度上可以预测哪些研究问题不太可能带来新发现：那些看似合理但却乏味的问题。而那些能够带来新发现的问题通常显得非常神秘，但可能看起来并不重要。（如果它们既神秘又显然重要，那它们就会成为众所周知的重大未解问题，吸引众多研究者的关注。）因此，这里的一个策略是让好奇心而非职业主义驱动自己——放任你的好奇心自由驰骋，而不是仅仅做那些“应该”做的工作。
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. 对于那些有远大志向的人来说，绩效超线性增长的前景是令人兴奋的。而且，这方面的好消息是：这一领域正在不断扩张，无论是在工作类型上，还是在回报本身上。
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. 这种变化有两个原因，尽管它们紧密相连，几乎可以看作是同一个原因：一是技术的飞速进步，二是组织重要性的日渐减弱。
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，想要参与宏伟的项目几乎必须加入某个组织，因为这是获取资源、结交同事、拓宽分发渠道的唯一途径。所以在 1970 年，你的声望往往取决于你所属组织的声望。这种评价方式相当准确，因为不属于任何组织的人很难取得重大成就。当然，也有一些例外，像艺术家和作家这样的独立工作者，他们用廉价的工具创作，并拥有自己的品牌。但他们仍然依赖于组织来触及更广泛的受众。[6]
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. 过去，由组织主导的世界限制了绩效回报的差异。但在我这一生中，这种现象已经显著改变。现在，更多的人能享受到 20 世纪艺术家和作家所拥有的自由。有很多宏伟项目不再需要庞大的初始投资，同时，学习、赚钱、寻找合作伙伴和触及受众的途径也变得更加多样。
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. 尽管旧世界依然存在，但这种变化的速度在历史上是非常惊人的，特别是考虑到其深远的影响。很难想象有什么比业绩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] 一旦摆脱了机构的限制，结果的多样性将更加显著。这并不意味着每个人都会受益：绩效出色的人会取得更大的成功，而绩效不佳的人可能遭遇更大的失败。这一点非常重要，需要牢记。冒险追求超线性的回报并不适合所有人。对大多数人来说，作为一个整体的一部分会更好。那么，谁应该追求超线性回报呢？有两类人：一类是对自己的实力充满自信，相信在一个变化更大的世界里能够取得更高净收益的人；另一类是可以承担尝试风险的人，特别是年轻人，他们愿意冒险一试，看看能否成功。[7]
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. 摆脱机构束缚的转变并不仅仅意味着当前机构成员的离去。许多新的成功者将是那些过去从未被机构接纳的人。因此，机会的民主化将比机构自行制定的任何方案更广泛、更真实。
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? 不是每个人都对这种释放雄心的转变感到满意。它挑战了一些既得利益和固有的意识形态。 [8] 但如果你是一个有抱负的个人，这无疑是个好消息。那么，你该如何抓住这个机会呢？
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. 要充分利用超线性回报来提升工作效果，最佳的方法就是做出卓越的成果。在成就曲线的顶端，多付出一点点努力就能带来巨大的回报。而且，顶端的竞争相对较少 —— 这不仅仅是因为做出卓越的事情非常困难，还因为人们对此望而却步，鲜少有人真正尝试。这意味着，不仅投入卓越工作本身是一种超值的选择，甚至仅仅是尝试去做也同样如此。
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. 影响你工作成果的因素众多，要想脱颖而出，你几乎需要在所有方面都做到极致。例如，要想把事情做到极致，你必须对它充满兴趣。单纯的勤奋是不够的。因此，在一个超线性回报的世界里，了解自己的兴趣所在并寻找机会去实现它显得尤为重要。[9] 选择适合自己当前生活环境的工作同样重要。例如，如果某种工作本质上需要大量的时间和精力，那么在你年轻、未育有子女的时候去做这类工作会更有价值。
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. 要想做出卓越的成就，技巧至关重要。这不仅仅是努力的问题。我在下面这个段落中尝试提供一个方法。
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] 选择那些你天生擅长且深度感兴趣的工作。培养独立进行个人项目的习惯，项目是什么并不重要，关键是要让你感到充满雄心壮志。尽可能地努力工作，但避免过度劳累，这最终会引领你走到知识的前沿。这些领域看似平坦，但细看却充满缝隙。努力工作，避免过度劳累，这最终会引领你走到知识的前沿。尽可能多地承担风险；如果你从未遭遇失败，那可能意味着你过于保守。寻找最优秀的合作伙伴。培养优雅的品味，向最佳范例学习。保持诚实，特别是对自己。注意运动、饮食和睡眠，远离危险药物。在犹豫不决时，跟随你的好奇心。好奇心永远不会欺骗你，它比你更清楚什么值得关注。[10]
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，还有一件至关重要的事情：运气。运气在任何时候都是一个不可忽视的因素，特别是当你独立工作，而不是作为组织一员时，它的作用更加凸显。我们常说运气是准备和机遇的结合，但实际上，还有一部分纯粹的偶然性，是我们无法控制的。解决之道在于多次尝试，这也是尽早开始冒险的另一个理由。
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. 科学领域可能是超线性回报的最典型例子。它的增长呈指数级，这种增长不仅是学习的过程，更是在知识边界——人类知识的极限上不断突破。
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] 这种现象导致科学发现的不平等程度远超过最为分化的社会中的财富不平等。可以说，牛顿的发现比他所有同时代人的总和还要伟大。[11]
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. 这个观点虽然看似显而易见，但仍然值得详细说明。超线性回报就意味着不平等存在。回报曲线越陡，成果差异就越大。
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. 实际上，超线性回报与不平等之间的联系如此紧密，以至于我们可以通过一个简单的方法来发现此类工作：寻找那些少数顶尖者远超其他人的领域。在大家绩效差不多的领域，往往不会出现超线性回报。
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] 那么，哪些领域存在着少数人远超其他人的情况呢？一些显而易见的例子包括：体育、政治、艺术、音乐、表演、导演、写作、数学、科学、创业和投资。在体育领域，这种现象是由外部规则决定的；在比赛中，只需比其他人快那么一点点就能夺冠。在政治领域，权力的增长模式和古代皇帝时代相似。在其他一些领域（包括政治），成功往往与名声有关，名声本身也是超线性增长的一种形式。但如果我们排除掉体育、政治和名声的影响，就会发现一个有趣的模式：剩余的这些领域正是那些需要独立思考才能成功的领域 —— 在这些领域中，你的想法不仅要正确，还要有创新。[12]
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. 在科学界，这一点显而易见。你不能只是发表重复他人观点的论文。但在投资领域，情况也相同。只有当大多数其他投资者不看好一家公司时，你对其看好才有意义；如果所有人都认为某公司前景光明，那么它的股价已经反映了这一预期，赚钱的机会就不复存在了。
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. 那么，我们还能从这些领域学到什么呢？无论在哪个领域，最初的努力都是必不可少的。超线性回报一开始看似微不足道，按这个速度， 你可能会想，我怎么也达不到目标。 但是，由于奖励曲线在后期急剧上升，为了达到这个目标，采取特别的措施是非常值得的。
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，这个原则被称为“做那些不可扩展的事情”。如果你能对你的少数初期客户投入极大的关注，理想情况下，你就能通过口碑引发指数级增长。而且，这个原则同样适用于任何以指数形式增长的领域，比如学习。刚开始学习新事物时，你可能会感到很茫然。但是，但为了获得一个立足点，做出最初的努力是值得的，因为随着你学得越多，过程就会变得越来越容易。
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. 在具有超线性回报的领域中，还有一个更深层次的教训：不要把工作等同于一份职业。在 20 世纪大部分时间里，对大多数人来说，这两者是一样的。因此，我们形成了一种习惯，即把生产力等同于拥有一份工作。即便到了现在，对大多数人而言，“你的工作”仍然意味着他们的职业。但对于作家、艺术家或科学家来说，这指的是他们当前正在研究或创作的事物。对这样的人来说，他们的工作是他们从一份职业带到另一份职业的东西，即使他们根本就没有固定工作。这份工作可能是为雇主而做，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. 踏入一个领域，面对少数顶尖高手遥遥领先的情况，确实令人望而却步。有人是刻意追求这种竞争，但这并非必要之路。只要你天资聪颖，足够追寻你的好奇心，你自然而然会进入这样的领域。你的好奇心不会允许你停留在平淡无奇的问题上，而那些引人入胜的问题往往会孕育出超线性的回报，即便它们最初并不属于任何领域。
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. 超线性回报的世界并非固定不变。实际上，最巨大的回报往往源于不断扩展这个领域。因此，虽然雄心和好奇心都能引领你进入这片领域，但好奇心或许是更为强大的动力。雄心可能驱使你攀登已知的高峰，但如果你始终紧扣一个足够吸引人的问题，它可能就在你脚下逐渐崛起，成为一座巍峨的山峰。
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. 要精确划分努力、绩效和回报是有挑战的，因为在实际情况中，这些概念本身就没有明确的界限。某个人眼中的回报，可能在另一个人看来只是绩效。虽然这些概念的边界有些模糊，但它们并非毫无意义。我尽力精确地描述了这些概念，力求避免误解。
[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. [1] 进化可能是最广泛的绩效超线性增长实例。然而，由于我们并非受益者，而是其中的一部分，所以我们很难深刻体会这一点。
[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. [2] 当然，在工业革命之前，知识对实际生活已有所影响。例如，农业的发展彻底改变了人类生活方式。但这种改变是渐进的、广泛的技术进步所带来的，而不是少数几个博学者的突破性发现。
[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. [3] 从数学角度来看，将阶跃函数描述为超线性是不准确的。但是，如果一个阶跃函数从零开始，那么在描述理性行为者的努力回报曲线时，它就像超线性函数一样工作。在阶跃之前，回报低于任何线性增长；在阶跃之后，回报必须高于那一点所需的回报，否则没人会去尝试。
[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. [4] 寻找竞争可以是一个有效的策略，因为它激励了一些人。同时，这也指向了一些有前景的问题，因为这意味着其他人也认为这些问题值得关注。但这不是一个完美的指标：往往很多人都在追求同一个问题，最终却被默默致力于其他问题的人超越。
[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. [5] 不过，这个规则也不是绝对的。当一些事物尽管平庸却广受欢迎时，背后往往有隐藏的原因。可能是由于垄断或监管，竞争变得困难；或许是因为消费者的品味问题，或者他们的购买决策流程存在缺陷。因此类原因而存在的平庸之物实在太多。
[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. [6] 二十多岁时，我曾想成为一名艺术家，甚至去学习绘画。主要是因为我热爱艺术，但我选择这条道路也有一个不小的原因：艺术家似乎最不受组织束缚。
[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. [7] 从理论上讲，每个人都在获得超线性回报。学习是一个累积过程，人人都在一生中学习。但实际上，很少有人能把这种日常学习推进到让回报曲线急剧上升的地步。
[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] 关于“公平”的倡导者们具体主张什么，并不完全清楚。他们之间似乎还存在分歧。但无论他们的目标是什么，这个目标可能与一个机构影响力较小、少数杰出者远超其他人的世界相抵触。
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. 这个观念正值全球思潮发生逆转之际出现，看起来似乎是运气不佳，但我认为这并非偶然。我相信它之所以现在出现，一个原因是支持者感觉到自己受到了日益加剧的表现差异的威胁。
[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] 相关论断：那些强迫孩子投身于自己不感兴趣的高声望行业，如医学的父母，其实是在对孩子的未来造成更大的伤害。
[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. [10] 这一段最初是文章“如何做出伟大工作”的初稿。写完之后我意识到，相较于超线性收益，这个话题更加重要。因此，我暂停了当前的论文创作，专注于将这一段发展成一个独立的主题。最终，“如何做出伟大工作”完成后，我基于其重新撰写了这一段，原稿几乎没有保留下来。
[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. [11] 在工业革命之前，人们致富的方式通常是通过占据资源来增强自己的力量，类似于皇帝。而现在，致富可以像科学家那样，通过发现或创造独特而有价值的东西。尽管大多数致富者采用了传统和现代的结合方式，但在最发达的经济体中，过去半个世纪里这种方式已经显著转向创新发现。
[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] 如果独立思维是驱动不平等的主要因素之一，那么对于那些传统思维的人来说，不喜欢不平等就不足为奇了。但问题不仅仅是他们不愿看到别人拥有自己无法拥有的东西。事实上，那些传统思维的人根本无法想象拥有创新想法是怎样的体验。所以，他们认为绩效表现的巨大差异是不自然的，在遇到这种情况时，他们往往认为这是作弊或某种恶意外部因素造成的。