Superlinear Returns --- 超线性回报
Superlinear Returns --- 超线性回报
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.
当我还是个孩子的时候，我对这个世界不了解的最重要的事情之一就是绩效回报的超线性程度。
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.
老师和教练含蓄地告诉我们，回报是线性的。 “你把什么放进去，”我听过一千遍了，“你就拿出什么。”他们的本意是好的，但事实却很少如此。如果您的产品只有竞争对手的一半，那么您就不会获得竞争对手的一半数量。你没有顾客，你就倒闭了。
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]
显然，商业中的绩效回报是超线性的。一些人认为这是资本主义的缺陷，如果我们改变规则，它就不再是真实的。但超线性绩效回报是世界的一个特征，而不是我们发明的规则的产物。我们在名誉、权力、军事胜利、知识甚至人类利益方面都看到了同样的模式。在所有这些方面，富人变得更富。 [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.
如果不理解超线性回报的概念，你就无法理解世界。如果你雄心勃勃，你绝对应该这样做，因为这将是你冲浪的浪潮。
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.
似乎有很多不同的情况具有超线性回报，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情况是当你从事呈指数增长的事情时。例如，培养细菌培养物。当它们真正成长时，它们会呈指数级增长。但它们的生长很困难。这意味着擅长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.
初创公司也可以呈指数级增长，我们也看到了同样的模式。有些成功实现了高增长率。大多数人没有。结果，你会得到截然不同的结果：增长率高的公司往往会变得非常有价值，而增长率较低的公司甚至可能无法生存。
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不是绝对数字。当绝对数字仍然很低时，这可以防止他们在早期就灰心丧气。它还可以帮助他们决定关注什么：您可以使用增长率作为指南针来告诉您如何发展公司。但主要优点是，通过关注增长率，您往往会得到指数级增长的东西。
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，随着增长率的增长，“你投入什么，就会得到什么”，但这与事实相差不远。如果增长率与绩效成正比，那么随着时间 t 的绩效 p 的奖励将与 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.
每当你做得有多好取决于你做得有多好时，你就会得到指数级的增长。但我们的 DNA 和习俗都没有为此做好准备。没有人认为指数增长是理所当然的；每个孩子第一次听到这个故事时都会感到惊讶，这个故事是这样的：一个人第一天向国王要一粒米，之后的每一天都会加倍。
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.
对于我们不理解的事情，我们自然会发展出习惯来处理，但我们也没有太多关于指数增长的习惯，因为人类历史上这样的例子太少了。原则上，放牧应该是这样一种：你拥有的动物越多，它们的后代就越多。但实际上，牧场是限制因素，而且没有计划以指数级增长。
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.
或者更准确地说，没有普遍适用的计划。有一种方法可以使领土呈指数级增长：通过征服。你控制的领土越多，你的军队就越强大，也就越容易征服新的领土。这就是为什么历史上充满了帝国。但创建或经营帝国的人太少了，他们的经历并没有对习俗产生太大影响。皇帝是一个遥远而可怕的人物，不是一个人可以在自己的生活中使用的教训来源。
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]
前工业时代指数增长最常见的例子可能是学术。你知道的越多，学习新事物就越容易。无论当时还是现在，其结果是，有些人对某些主题的了解比其他人要多得多。但这也没有对海关产生太大影响。尽管思想帝国可以重叠，从而可以有更多的皇帝，但在前工业时代，这种类型的帝国几乎没有实际作用。 [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.
在过去的几个世纪里，情况发生了变化。现在，思想皇帝可以设计炸弹来击败领地皇帝。但这种现象仍然很新，我们还没有完全吸收它。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例子中学到什么。
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“赢者通吃”这一表述中。在体育比赛中，表现和回报之间的关系是阶跃函数：获胜的球队无论表现好得多还是稍微好一点，都会获得一场胜利。 [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.
然而，阶跃函数的来源并不是竞争本身。就是结果有门槛。你不需要竞争就能获得这些。在您是唯一参与者的情况下，例如证明定理或达到目标，可能会存在阈值。
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.
值得注意的是，具有一种超线性回报来源的情况常常也具有另一种超线性回报来源。跨越门槛会导致指数级增长：战斗中获胜的一方通常受到的伤害较小，这使得他们更有可能在未来获胜。指数增长可以帮助您跨越门槛：在具有网络效应的市场中，增长足够快的公司可以将潜在的竞争对手拒之门外。
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.
名声是结合了超线性回报的两种来源的现象的一个有趣的例子。名气成倍增长，因为现有的粉丝给你带来了新的粉丝。但它如此集中的根本原因是门槛：普通人的头脑中一线名单上的空间有限。
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种来源的最重要的情况可能是学习。知识呈指数增长，但也有门槛。例如，学习骑自行车。其中一些阈值类似于机器工具：一旦你学会阅读，你就能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，因为如果你努力突破一个知识领域的边界，有时你会发现一个全新的领域。如果你这样做了，你就能首先了解其中的所有新发现。牛顿这样做了，丢勒和达尔文也这样做了。
Are there general rules for finding situations with superlinear returns? The most obvious one is to seek work that compounds.
是否有寻找超线性回报情况的一般规则？最明显的一个是寻找复合的工作。
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.
工作有两种复合方式。它可以直接复合，从某种意义上说，在一个周期中表现出色会导致您在下一个周期中表现更好。例如，当您构建基础设施或扩大受众或品牌时，就会发生这种情况。或者工作可以通过教你来复合，因为学习是复合的。第二种情况很有趣，因为当它发生时你可能会觉得自己做得很糟糕。您可能无法实现眼前的目标。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.
这是硅谷如此容忍失败的原因之一。硅谷人不会盲目容忍失败。只有当你从失败中吸取教训时，他们才会继续押注于你。但如果你是，那么你实际上是一个不错的选择：也许你的公司没有按照你想要的方式发展，但你自己却做到了，这最终应该会产生结果。
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.
事实上，不包含学习的指数增长形式经常与学习混合在一起，因此我们应该将其视为规则而不是例外。这就产生了另一个启发：永远学习。如果你不学习，你可能就走不到一条带来超线性回报的道路。
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.
但不要过度优化你正在学习的内容。不要将自己限制在学习已知有价值的东西上。你正在学习；你还不确定什么是有价值的，如果你太严格，你就会剔除异常值。
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]
那么阶跃函数呢？是否还有“寻求门槛”或“寻求竞争”形式的有用启发法？这里的情况比较棘手。门槛的存在并不能保证游戏值得玩。如果您玩一轮俄罗斯轮盘赌，您肯定会遇到一个有门槛的情况，但在最好的情况下，您的情况也不会更好。 “寻求竞争”同样毫无用处；如果奖品不值得竞争怎么办？足够快的指数增长保证了回报曲线的形状和幅度——因为增长足够快的东西即使一开始很小也会变得很大——但阈值只能保证形状。 [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]
利用阈值的原则必须包括测试以确保游戏值得玩。确实如此：如果您遇到一些平庸但仍然流行的东西，那么替换它可能是个好主意。例如，如果一家公司生产了一种人们不喜欢但仍然购买的产品，那么如果你生产了一种产品，他们可能会购买更好的替代品。 [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.
如果有一种方法可以找到有前途的智力门槛，那就太好了。有没有办法知道哪些问题有超越它们的全新领域？我怀疑我们是否能够准确地预测这一点，但是这个奖项是如此有价值，以至于拥有比随机更好一点的预测器将会很有用，并且有希望找到这些预测器。我们可以在某种程度上预测一个研究问题何时不太可能带来新的发现：何时它看起来合法但无聊。而确实带来新发现的那种往往看起来非常神秘，但也许并不重要。 （如果它们很神秘并且显然很重要，那么它们就会成为著名的开放性问题，很多人已经在研究它们。）因此，这里的一个启发是由好奇心而不是野心驱动——自由发挥你的好奇心而不是工作关于你应该做的事情。
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.
对于雄心勃勃的人来说，超线性绩效回报的前景是令人兴奋的。这个部门有一个好消息：这个领域正在向两个方向扩展。可以获得超线性回报的工作类型越来越多，而且回报本身也在增长。
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.
造成这种情况的原因有两个，尽管它们紧密地交织在一起，更像是一个半的原因：技术的进步，以及组织重要性的下降。
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，成为组织的一部分来开展雄心勃勃的项目变得更加必要。这是获得所需资源的唯一途径，是拥有同事的唯一途径，也是获得分配的唯一途径。所以在 1970 年，你的声望在大多数情况下就是你所属组织的声望。声望是一个准确的预测因素，因为如果你不是某个组织的一员，你就不可能取得多大成就。有少数例外，尤其是艺术家和作家，他们使用廉价工具单独工作并拥有自己的品牌。但即便如此，他们在吸引受众方面也受到组织的摆布。 [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.
由组织主导的世界抑制了绩效回报的变化。但就在我有生之年，这个世界已经被严重侵蚀了。现在，更多的人可以享受 20 世纪艺术家和作家所拥有的自由。有很多雄心勃勃的项目不需要太多的初始资金，也有很多新的学习、赚钱、寻找同事和接触受众的方法。
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.
旧世界仍然存在，但以历史标准来看，变化的速度是惊人的。特别是考虑到什么是利害攸关的。很难想象还有比绩效回报更根本的变化了。
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]
如果没有制度的阻尼作用，结果将会有更大的差异。这并不意味着每个人都会过得更好：做得好的人会做得更好，但做得不好的人会做得更糟。这是需要牢记的重要一点。并不是每个人都适合接受超线性回报。作为游泳池的一部分，大多数人都会过得更好。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的人，他们将在一个更加多样化的世界中处于绝对领先地位，以及那些有能力冒险尝试找出答案的人，尤其是年轻人。 [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.
远离机构不仅仅是现有居民的外流。许多新的赢家将是他们从未让他们进来的人。因此，由此产生的机会民主化将比机构本身可能炮制的任何温和的内部版本更大、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?
并不是所有人都对这种雄心壮志的伟大释放感到高兴。它威胁到一些既得利益并与一些意识形态相矛盾。 [8] 但如果您是一个雄心勃勃的人，这对您来说是个好消息。你应该如何利用它？
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.
利用超线性绩效回报的最明显方法是出色地完成工作。在曲线的远端，渐进的努力是划算的。更重要的是，因为远端的竞争较少——这不仅是因为很难把某件事做得特别好，而且还因为人们发现这种前景如此令人生畏，以至于很少有人去尝试。这意味着，不仅做出色的工作是划算的，甚至尝试这样做也是划算的。
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.
有很多变量会影响你的工作质量，如果你想成为一个异常值，你需要把几乎所有的变量都做好。例如，要把某件事做得特别好，你必须对它感兴趣。仅仅勤奋是不够的。因此，在一个超线性回报的世界中，了解自己感兴趣的内容并找到解决问题的方法就更有价值了。 [9] 选择适合您情况的工作也很重要。例如，如果有一种工作本质上需要花费大量的时间和精力，那么在你还年轻、还没有孩子的时候去做它会变得越来越有价值。
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.
完成伟大的工作需要大量令人惊讶的技术。这不仅仅是努力的问题。我打算尝试在一个段落中给出一个食谱。
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]
选择你有天赋和浓厚兴趣的工作。养成为自己的项目工作的习惯；只要你发现它们雄心勃勃，它们是什么并不重要。尽你所能地努力工作，不要精疲力尽，这最终会把你带到知识的前沿之一。从远处看，它们看起来很光滑，但近距离观察，它们充满了缝隙。注意并探索这些差距，如果幸运的话，您将扩展到一个全新的领域。承担尽可能多的风险；如果你不是偶尔失败，那么你可能太保守了。寻找最好的同事。培养良好的品味并从最好的例子中学习。诚实，尤其是对自己。锻炼身体，吃好睡好，避免使用更危险的药物。如有疑问，请跟随您的好奇心。它从不说谎，而且它比你更了解什么是值得关注的。 [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，你还需要一件事：幸运。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，它就更重要了。尽管有一些格言说运气是做好准备与机遇相遇的地方等等，但真正的机遇中也有一部分是你无能为力的。解决办法就是多拍几张。这是尽早开始冒险的另一个原因。
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.
具有超线性回报的领域的最好例子可能是科学。它以学习的形式呈指数级增长，并结合了性能极限的阈值——实际上是知识的极限。
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]
结果是科学发现中存在一定程度的不平等，相比之下，即使是分层最严重的社会，财富不平等也显得温和。牛顿的发现可以说比他同时代的所有发现的总和还要伟大。 [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.
这一点似乎是显而易见的，但最好还是把它说清楚。超线性回报意味着不平等。回报曲线越陡，结果的变化就越大。
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，超线性回报和不平等之间的相关性如此之强，以至于它产生了寻找此类工作的另一种启发：寻找少数大赢家表现优于其他所有人的领域。如果一种工作中每个人都做同样的事情，那么它不太可能获得超线性回报。
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
to succeed — where your ideas have to be not just correct, but novel as well.
[12]
在哪些领域，一些大赢家的表现优于其他所有人？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、创办公司和投资。在体育运动中，这种现象是由于外部强加的阈值造成的。你只需要快几个百分点就能赢得每场比赛。在政治上，权力的增长与皇帝时代一样。在其他一些领域（包括政治），成功很大程度上是由名声驱动的，名声有其自身的超线性增长源泉。但是，当我们排除体育、政治以及名誉的影响时，就会出现一个显着的模式：剩下的列表与你必须具有独立思想才能成功的领域列表完全相同——你的想法必须不仅仅是正确的，但也新颖。 [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.
科学上显然就是这样。你不能发表说别人已经说过的话的论文。但在投资领域也是如此。只有当大多数其他投资者表现不佳时，相信一家公司会表现出色才有意义。如果其他人都认为该公司会做得很好，那么它的股价已经反映了这一点，并且没有赚钱的空间。
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？在所有这些中，你都必须付出最初的努力。超线性回报乍一看似乎很小。按照这个速度，你会发现自己在想，我永远不会有任何进展。但由于奖励曲线在远端急剧上升，因此值得采取非常措施来实现这一目标。
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，这一原则的名称是“做无法规模化的事情”。如果您对最初的一小部分客户给予了极大的关注，那么理想情况下，您将通过口碑开启指数级增长。但同样的原则也适用于任何呈指数增长的事物。例如，学习。当你第一次开始学习某样东西时，你会感到迷失。但为获得立足点而付出最初的努力是值得的，因为你学得越多，就越容易。
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列表中还有另一个更微妙的教训：不要将工作与工作等同起来。在 20 世纪的大部分时间里，这两者对几乎每个人来说都是相同的，因此我们继承了一种将生产力等同于有工作的习惯。即使现在对大多数人来说，“你的工作”这句话也意味着他们的工作。但对于作家、艺术家或科学家来说，这意味着他们当前正在研究或创造的任何东西。对于这样的人来说，如果他们有工作的话，他们的工作就是他们从一个工作到另一个工作的随身携带的东西。这可能是为雇主做的，但这是他们投资组合的一部分。
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胜过其他所有人的领域是一个令人生畏的前景。有些人是故意这样做的，但你不需要这样做。如果你有足够的天赋，并且足够强烈地追随你的好奇心，你最终就会成为其中之一。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具有超线性回报的领域（如果它们还不是其中的一部分）。
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不是静态的。事实上，最极端的回报来自于扩张。因此，虽然野心和好奇心都可以让你进入这个领域，但好奇心可能是两者中更强大的。野心往往会让你攀登现有的高峰，但如果你足够接近一个足够有趣的问题，它可能会在你脚下长成一座山。
Notes 笔记
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.
努力、绩效和回报之间的区分是有限的，因为事实上它们并没有明确区分。对一个人来说算作回报的东西对另一个人来说可能就是表现。尽管这些概念的界限很模糊，但它们并非毫无意义。我试图尽可能准确地描述它们，而不会犯错误。
[
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.
[1] 进化本身可能是超线性绩效回报最普遍的例子。但这对我们来说很难产生共鸣，因为我们不是接受者；而是我们。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.
[2] 在工业革命之前，知识当然确实具有实际作用。农业的发展彻底改变了人类的生活。但这种变化是技术广泛、逐步改进的结果，而不是少数博学多才的人的发现。
[
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.
[3] 将阶跃函数描述为超线性在数学上是不正确的，但从零开始的阶跃函数在描述理性参与者努力的奖励曲线时就像超线性函数一样。如果它从零开始，那么该步骤之前的部分低于任何线性增加的回报，并且该步骤之后的部分必须高于该点所需的回报，否则没有人会打扰。
[
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] 寻求竞争可能是一个很好的启发，因为有些人发现它具有激励作用。它在某种程度上也是对有前途的问题的指南，因为它表明其他人认为这些问题很有前途。但这是一个非常不完美的迹象：经常有一群吵闹的人群在追逐某个问题，而他们最终都被默默致力于另一个问题的人所击败。
[
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] 但并非总是如此。你必须小心这个规则。当某样东西尽管平庸却很受欢迎时，往往有一个隐藏的原因。也许垄断或监管会导致竞争变得困难。也许顾客的品味很差，或者决定购买什么的程序不正确。由于这些原因，存在大量平庸的事物。
[
6
] In my twenties I wanted to be an
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] 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，但我的动机的一个重要部分来自这样一个事实：艺术家似乎最不受组织的摆布。
[
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] 原则上每个人都获得超线性回报。学习是复合的，每个人都在一生中学习。但在实践中，很少有人将这种日常学习推向回报曲线变得非常陡峭的地步。
[
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] 目前还不清楚“公平”的倡导者到底是什么意思。他们之间似乎意见不一。但无论他们的意思是什么，都可能与一个机构控制结果的权力较小、少数异常者比其他人做得更好的世界相矛盾。
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.
这个概念出现在世界正向相反方向转变的时刻，这似乎是运气不好，但我不认为这是巧合。我认为它现在出现的原因之一是它的追随者感到性能变化的迅速增加带来了威胁。
[
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：父母如果强迫孩子从事一些有声望的事情，比如医学，即使他们对此不感兴趣，也会比过去更加严厉地对待他们。
[
10
] The original version of this paragraph was the first draft of "
." 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]本段的原始版本是《如何做伟大的工作》的初稿。当我写完这篇文章时，我意识到这是一个比超线性回报更重要的话题，所以我暂停了这篇文章，将这一段扩展为自己的内容。原来的版本几乎没有留下任何东西，因为在我完成“如何做伟大的工作”之后，我在此基础上重写了它。
[
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
toward discovery just in the last half century.
[11]在工业革命之前，富人通常会像皇帝一样：夺取一些资源使他们变得更加强大，并使他们能够夺取更多资源。现在，它可以像科学家一样通过发现或构建具有独特价值的东西来完成。大多数人致富的方式都是新旧方式相结合，但在最发达的经济体中，这一比例在过去半个世纪里已经急剧转向发现。
[
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] 如果独立思想是不平等的最大驱动因素之一，那么思想传统的人不喜欢不平等也就不足为奇了。但这不仅仅是他们不想让任何人拥有他们不能拥有的东西。传统观念的人根本无法想象拥有新颖想法会是什么样子。因此，表现差异巨大的整个现象对他们来说似乎是不自然的，当他们遇到这种情况时，他们认为这一定是由于作弊或某些恶意的外部影响。