 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. 小時候我對這個世界最不理解的事情之一，就是表現所帶來的回報往往呈現超線性增長的程度。 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. 老師和教練們總是隱晦地告訴我們回報是線性的。「付出多少，」我聽過無數次，「就會得到多少。」他們本意是好的，但現實很少如此。如果你的產品只有競爭對手一半好，你不會只得到一半客戶。你會完全失去客戶，最終倒閉。 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] 很明顯，商業領域中表現的回報確實呈現超線性增長。有些人認為這是資本主義的缺陷，認為只要改變規則就能避免這種情況。但超線性回報是這個世界的特性，而非我們所制定規則的產物。我們在名聲、權力、軍事勝利、知識甚至對人類的貢獻等領域，都能看到相同的模式——富者愈富。[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. 若不理解「超線性回報」的概念，就無法真正理解這個世界。尤其對懷抱雄心壯志的人來說，這更是必須掌握的關鍵，因為這將是引領你乘風破浪的趨勢。 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. 表面上看來，超線性回報似乎有許多不同類型，但根據我的觀察，它們都可歸結於兩個根本原因：指數型成長與臨界點效應。 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. 最明顯的超線性回報案例，就是當你從事具有指數成長特性的事務時。例如培養細菌菌落——只要開始生長，就會呈現指數擴張。但培養過程極需技巧，這意味著熟練者與生手之間的最終成果差距會非常懸殊。 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. 新創公司同樣可能實現指數成長，我們在此也觀察到相同模式。少數企業能達成高速成長，多數則否。其結果將產生質變般的差異：高成長率的公司往往成為極具價值的巨頭，而成長遲緩者甚至可能無法存活。 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 鼓勵創辦人專注於成長率而非絕對數字。這能避免他們在初期因絕對數字仍低而感到氣餒。同時也有助於決定該聚焦什麼：你可以將成長率視為指南針，指引公司如何進化。但最主要的好處是，專注於成長率往往能帶來指數級的增長。 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 並未明確告訴創辦人「投入多少就成長多少」這個關於成長率的道理，但這說法離事實不遠。如果成長率與表現成正比，那麼表現 p 隨時間 t 獲得的回報將與 p t 成正比。 Even after decades of thinking about this, I find that sentence startling. 即使思考這個問題數十年，我仍覺得這句話令人震驚。 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. 每當你的表現取決於過往表現時，就會產生指數級成長。但無論是我們的 DNA 或習俗，都未為此做好準備。沒有人覺得指數成長是自然的；每個孩子第一次聽到這個故事時都會感到驚訝——有人向國王要求第一天給一粒米，之後每天加倍。 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. 對於那些我們天生無法理解的事物，我們會發展出習俗來應對，但關於指數級增長，我們也沒有太多習俗，因為在人類歷史上這種情況實在太少了。原則上，畜牧業本應是一個例子：你擁有的動物越多，牠們繁衍的後代就越多。但實際上，牧場才是限制因素，而且當時也沒有讓牧場面積呈指數級增長的計劃。 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. 或者更準確地說，沒有普遍適用的計劃。確實存在一種讓領土呈指數級增長的方法：透過征服。你控制的領土越多，你的軍隊就越強大，征服新領土也就越容易。這就是為什麼歷史上充滿了帝國。但真正創建或經營帝國的人少之又少，以至於他們的經驗對習俗影響不大。皇帝是一個遙遠而可怕的存在，而非人們生活中可以借鑒的榜樣。 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] 在前工業時代，指數增長最常見的案例大概就是學術研究了。你知道得越多，學習新事物就越容易。無論古今，結果就是某些人對特定主題的知識量會驚人地超越其他人。但這對社會習俗的影響依然有限。雖然思想帝國可以相互重疊，因此能產生更多「思想帝王」，但在工業化之前，這類帝國幾乎沒有實際影響力。[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. 這種情況在過去幾個世紀已經改變。如今的思想帝王能設計出擊敗領土帝王的炸彈。但這個現象仍新穎到我們尚未完全消化。就連參與者中也很少有人意識到自己正受益於指數增長，或思考能從其他指數增長案例中學到什麼。 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] 超線性回報的另一個來源體現在「贏家通吃」這句話中。在體育比賽裡，表現與回報的關係呈現階梯函數：無論贏得輕鬆或險勝，獲勝隊伍都只能記上一勝。[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，階躍函數的源頭並非競爭本身，而是結果中存在門檻值。你不需要競爭也能達到這些門檻。即使你是唯一的參與者，例如證明定理或達成目標時，情境中仍可能存在門檻值。 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，具有單一超線性回報來源的情境，往往也同時具備另一種來源。跨越門檻會帶來指數級成長：戰役中獲勝的一方通常承受較少損傷，這使他們未來更可能獲勝。而指數級成長能幫助你跨越門檻：在具有網絡效應的市場中，成長足夠快的公司可以阻擋潛在競爭者。 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. 名氣是結合兩種超線性回報來源的有趣案例。名氣會呈指數成長，因為現有粉絲會為你帶來新粉絲。但名氣如此集中的根本原因在於門檻值：一般人腦海中的 A 級名單容量有限。 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. 結合兩種超線性回報來源最重要的案例可能就是學習。知識呈指數級增長，但其中也存在門檻效應。以學習騎自行車為例。某些門檻就像工具機一樣：一旦學會閱讀，你就能更快地掌握其他知識。但最重要的門檻莫過於那些代表新發現的突破點。知識似乎具有分形特性——當你在某個知識領域的邊界持續深耕時，有時會發現一個全新的領域。若能如此，你就能搶先探索該領域所有待發現的新事物。牛頓做到了，杜勒和達爾文也是如此。 Are there general rules for finding situations with superlinear returns? The most obvious one is to seek work that compounds. 是否存在尋找超線性回報情境的通用法則？最顯而易見的原則就是尋找具有複利效應的工作。 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. 工作可以透過兩種方式產生複利效應。第一種是直接複利，也就是在一個週期表現出色會讓你在下個週期表現更好。例如當你正在建設基礎設施、培養受眾或品牌時就會發生這種情況。第二種是透過學習產生複利，因為學習本身具有複利效應。第二種情況特別有趣，因為在過程中你可能會覺得自己表現不佳。你可能無法達成眼前目標。但如果你學到很多，你實際上仍在獲得指數級成長。 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. 這就是矽谷如此寬容失敗的原因之一。矽谷人並非盲目容忍失敗。只有當你從失敗中學習時，他們才會繼續押注於你。但如果你確實如此，你其實是個好賭注：也許你的公司沒有如預期般成長，但你自己成長了，這終將帶來成果。 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. 確實，那些不涉及學習的指數型成長形式，往往與學習交織在一起，以至於我們應該將其視為常態而非例外。這也衍生出另一條經驗法則：永遠保持學習。如果你沒有在學習，那麼你可能沒有走在通往超線性回報的道路上。 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. 但不要過度優化你所學習的內容。不要將自己局限於學習已知有價值的東西。你正在學習；你還無法確定什麼最終會具有價值，如果太過嚴格，你將會剔除那些潛在的異數。 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] 關於階梯函數呢？是否存在像「尋找臨界點」或「尋找競爭」這類有用的啟發式方法？這裡的情況就比較棘手了。臨界點的存在並不保證這場遊戲值得參與。如果你玩一輪俄羅斯輪盤賭，確實會遇到一個臨界點，但即使在最佳情況下，你也沒有變得更好。「尋找競爭」同樣沒什麼用處；如果獎品根本不值得競爭呢？足夠快速的指數增長能同時保證回報曲線的形狀和幅度——因為增長速度夠快的東西，即使最初微不足道，最終也會變得龐大——但臨界點只能保證形狀。[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] 要善用臨界點的原則，必須包含一個測試來確保這場遊戲值得參與。這裡有個可行的方法：如果你遇到某個平庸卻仍受歡迎的東西，取代它可能是個好主意。例如，如果一家公司生產的產品人們不喜歡卻仍在購買，那麼假設你做出更好的替代品，他們應該會買單。[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. 如果能找到有潛力的知識疆界突破點就太棒了。有沒有方法能辨識出哪些問題背後藏著全新領域？雖然我懷疑我們永遠無法確切預測，但這個獎賞實在太珍貴了，即使預測工具只比隨機猜測好一點點也很有價值，而我們確實有望找到這類工具。某種程度上，我們可以預測哪些研究問題不太可能帶來新發現：就是那些看似合理但枯燥乏味的問題。反之，真正能開創新發現的問題往往顯得神秘莫測，卻又看似無關緊要。（如果既神秘又明顯重要，早就會成為眾人鑽研的著名未解難題了。）因此這裡有個啟發法：讓好奇心而非功利心驅動你——盡情釋放你的好奇心，別只做那些「該做」的研究。 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. 對懷抱雄心的人而言，績效帶來超線性回報的前景令人振奮。這方面還有個好消息：這個領域正在雙向擴張。現在有更多類型的工作能獲得超線性回報，而且回報本身也在持續增長。 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. 這背後有兩個原因，雖然它們緊密交織到幾乎像是一個半：技術的進步，以及組織重要性的降低。 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，要參與雄心勃勃的專案，加入組織是更為必要的。那時這是獲取所需資源的唯一途徑、擁有同事的唯一方式，也是取得市場通路的唯一方法。因此在 1970 年代，你的聲望多半取決於所屬組織的聲望。而這種聲望是準確的預測指標，因為若不屬於某個組織，你很可能難以有所成就。當然也有少數例外，最明顯的是藝術家和作家，他們獨自工作，使用成本低廉的工具，並擁有個人品牌。但即使是這些人，要觸及觀眾仍得仰賴組織的力量。[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. 一個由組織主導的世界壓抑了表現回報的變異性。但在我有生之年，這個世界已大幅瓦解。如今有更多人能享有 20 世紀藝術家與作家所擁有的自由。有許多雄心勃勃的專案不需要大量初始資金，也有許多新方法可以學習、賺錢、尋找夥伴並觸及受眾。 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. 舊世界仍有許多殘留，但以歷史標準來看，變革速度已相當驚人。尤其考慮到其中涉及的利害關係。很難想像還有什麼比表現回報的改變更根本的變革。 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] 若沒有機構的緩衝作用，結果的差異性將會更大。這並不意味著每個人都會過得更好：表現優異的人會更加出色，但表現不佳的人則會更糟。這是需要牢記的重要觀點。追求超線性回報並不適合所有人。對多數人而言，留在群體中會是更好的選擇。那麼誰該追求超線性回報呢？有兩類野心勃勃的人：一類是深知自己足夠優秀，能在差異更大的世界中獲得淨收益；另一類則是（特別是年輕人）有能力承擔風險去嘗試並驗證的人。[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. 這種脫離機構的轉變，不僅僅是現有成員的出走。許多新贏家將是機構原本絕不會接納的人。因此，機會的民主化將比機構內部可能構想的任何溫和版本更廣泛且更真實。 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? 並非所有人都對這種雄心壯志的大解放感到欣喜。它威脅到某些既得利益，也與某些意識形態相悖。[8]但如果你是懷抱野心的人，這對你來說是個好消息。你該如何把握這個機會呢？ 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. 要從超線性回報中獲取績效優勢，最顯而易見的方式就是做出卓越的工作。在曲線的極端端點，邊際努力會帶來超值回報。這現象之所以更加顯著，不僅因為要做到極致本身就很困難，更因為多數人光想到要追求卓越就心生畏懼，導致競爭者寥寥無幾。這意味著不僅卓越成果本身具有超值效益，就連嘗試追求卓越都是划算的投資。 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. 影響工作品質的變數眾多，若想成為頂尖者，就必須掌握幾乎所有關鍵要素。舉例來說，要將某件事做到極致，你必須對其懷抱熱忱。僅靠勤奮是不夠的。因此在超線性回報的世界裡，了解自身興趣所在並找到實踐途徑變得更加珍貴。[9] 選擇符合個人處境的工作也至關重要。比方說，若某類工作本質上需要耗費大量時間精力，趁著年輕尚未育有子女時投入這類工作，其價值將與日俱增。 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. 要做出卓越的工作，其實需要相當多的技巧，這不僅僅是努力嘗試就能達成的。我將試著用一段話來提供一個方法。 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] 選擇你天生擅長且深感興趣的工作。培養投入個人專案的習慣，只要這些專案讓你感到振奮且充滿抱負，內容是什麼並不重要。在不耗盡精力的前提下盡可能努力工作，這最終會引領你來到知識的邊疆。從遠處看這些領域似乎很平順，但近看卻充滿了缺口。留意並探索這些缺口，如果幸運的話，其中一個缺口可能會擴展成一個全新的領域。承擔你所能負擔的最大風險；如果你不曾偶爾失敗，那可能表示你太過保守。尋找最優秀的同事合作。培養良好的品味，並向最佳範例學習。保持誠實，尤其是對自己。注意運動、飲食和睡眠，並避免使用危險藥物。當有疑問時，跟隨你的好奇心。它從不說謊，而且對於值得關注的事物，它比你知道得更多。[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. 當然，你還需要另一樣東西：運氣。運氣永遠是個因素，但當你獨自工作而非作為組織一員時，它更顯關鍵。雖然有些格言說得好，像是「運氣是準備遇上機會」之類的，但確實存在你無法掌控的純粹偶然成分。解決方法就是多嘗試幾次——這也是為什麼要及早開始冒險的另一個原因。 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. 最能體現超線性回報的領域或許就是科學。它透過學習呈現指數級成長，同時在表現的極限處——也就是知識的邊界——存在著門檻效應。 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] 這導致科學發現領域的不平等程度，甚至讓最階級分明的社會中的財富不平等相形之下都顯得溫和。牛頓的發現可以說比他所有同代人的總和還要偉大。[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. 這一點看似顯而易見，但仍有必要明確指出：超線性回報意味著不平等。回報曲線越陡峭，結果的差異就越大。 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. 事實上，超線性回報與不平等之間的關聯性如此強烈，以至於它提供了另一種尋找這類工作的經驗法則：觀察那些少數大贏家遠勝其他人的領域。如果某種工作類型中每個人的表現都差不多，那就不太可能產生超線性回報。 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] 哪些領域會出現少數大贏家遠勝其他人的情況？以下是一些顯而易見的例子：體育、政治、藝術、音樂、表演、導演、寫作、數學、科學、創業和投資。在體育領域，這種現象源於外部設定的門檻；你只需要比別人快幾個百分點就能贏得每場比賽。在政治領域，權力的增長方式與帝王時代如出一轍。而在其他某些領域（包括政治），成功很大程度上由名氣驅動，而名氣本身就是超線性增長的來源。但當我們排除體育、政治和名氣效應後，一個驚人的模式浮現：剩下的清單恰好與那些需要獨立思考才能成功的領域完全一致——在這些領域中，你的想法不僅要正確，還必須具有新穎性。[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. 這在科學領域顯然是如此。你不能發表別人已經說過的東西。但在投資領域也同樣適用，舉例來說，只有當大多數投資者不看好某家公司時，你相信它會表現良好才有意義；如果其他人都認為這家公司會表現良好，那麼它的股價早已反映了這一點，就沒有賺錢的空間了。 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. 我們還能從這些領域學到什麼？在所有這些領域中，你必須先投入初步的努力。超線性回報起初看起來很小。按照這個速度，你會覺得自己永遠不會有所成就。但因為回報曲線在遠端急劇上升，所以值得採取非凡的措施來達到那個階段。 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. 在創業界，這個原則被稱為「做那些無法規模化的事」。如果你對最初那一小群客戶投入過度的關注，理想情況下，你就能通過口碑引發指數級增長。但這個原則同樣適用於任何呈指數增長的事物。例如學習。當你剛開始學習某樣東西時，你會感到迷茫。但值得付出初步努力來站穩腳跟，因為你學得越多，就會變得越容易。 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. 在那些具有超線性回報的領域清單中，還蘊含著一個更微妙的啟示：不要將工作與職業畫上等號。對 20 世紀的大多數人來說，這兩者幾乎是相同的，因此我們繼承了一種將生產力等同於擁有職業的觀念。即使到了現在，對多數人而言，「你的工作」這個詞仍意味著他們的職業。但對作家、藝術家或科學家來說，它代表著他們當前正在研究或創造的事物。對這些人而言，他們的工作是隨著職業轉換而隨身攜帶的東西——如果他們真的有職業的話。這些工作可能是為雇主完成的，但它們同時也是個人作品集的一部分。 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. 進入一個由少數大贏家主宰的領域確實令人望而生畏。有些人會刻意選擇這樣的領域，但你並不需要這麼做。如果你擁有足夠的天賦，並且能持續追隨自己的好奇心到足夠遠的地方，你終將置身其中。你的好奇心不會讓你對無聊的問題產生興趣，而有趣的問題往往會創造出具有超線性回報的領域——如果它們尚未成為這類領域的一部分的話。 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. 超線性回報的領域絕非靜止不變。事實上，最極端的回報來自於擴展這個領域。因此，雖然抱負和好奇心都能帶你進入這個領域，但好奇心可能是兩者中更強大的力量。抱負往往會讓你攀登現有的高峰，但如果你緊跟一個足夠有趣的問題，它可能會在你腳下成長為一座高山。 Notes 筆記 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. 在區分努力、表現與回報之間，其實存在著一定的模糊地帶，因為它們在現實中並非涇渭分明。對某人而言的回報，可能對另一人來說只是表現。儘管這些概念的界線模糊，卻並非毫無意義。我已盡可能精確地描述它們，同時避免陷入謬誤。 [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] 進化本身或許是表現帶來超線性回報最普遍的例證。但我們很難對此感同身受，因為我們並非受益者——我們就是那些回報本身。 [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] 當然，在工業革命之前，知識確實已產生實際影響。農業的發展徹底改變了人類生活。但這類變革源自技術廣泛而漸進的改良，而非少數學識淵博者的發現。 [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] 用「超線性」來描述階梯函數在數學上並不精確，但當一個從零開始的階梯函數用來描述理性行動者的努力回報曲線時，其運作方式確實類似超線性函數。若起點為零，階躍前的部分會低於任何線性增長的回報，而階躍後的部分必須高於該點的必要回報，否則沒人會願意投入。 [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] 追求競爭或許是個不錯的啟發法，畢竟有些人能從中獲得動力。這也多少能指向有潛力的問題，因為這表示其他人也認為這些問題值得投入。但這個訊號非常不完美：經常可見一大群人爭相追逐某個問題，最終卻被默默耕耘另一個問題的人超越。 [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] 不過並非總是如此。對這條規則要謹慎以對。當某件事物平庸卻廣受歡迎時，背後往往存在隱藏原因。可能是壟斷或法規使得競爭困難，也可能是顧客品味不佳或採購決策流程有缺陷。世上存在大量因這類理由而生的平庸事物。 [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. [6] 二十多歲時，我曾想成為藝術家，甚至去藝術學校學習繪畫。主要是因為我熱愛藝術，但不可忽視的是，部分動機來自藝術家似乎最不受組織束縛這個事實。 [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] 原則上每個人都能獲得超線性回報。學習具有複利效應，每個人在生命歷程中都在學習。但實際上，很少有人將這種日常學習推進到回報曲線真正陡峭的程度。 [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] 倡導「公平」的人究竟意指為何尚不明確。他們內部似乎也存在分歧。但無論他們的意思是什麼，很可能都與以下世界觀相衝突：機構對結果的控制力減弱，而少數異數者表現遠優於其他人。 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. 這個概念誕生於世界正朝相反方向轉變的時刻，看似運氣不佳，但我認為這並非巧合。我認為它此刻興起的原因之一，是其信奉者對績效差異快速擴大感到威脅。 [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] 推論：那些逼迫孩子從事像醫學這類光鮮亮麗工作，即使孩子毫無興趣的家長，將會比過去更嚴重地拖累孩子的發展。 [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] 本段原始版本是《如何成就偉大工作》的初稿。當我寫完後立即意識到，這比超線性回報更重要，因此暫停本文寫作，將這段擴充成獨立文章。原始版本幾乎沒保留下來，因為完成《如何成就偉大工作》後，我根據該文重寫了這部分。 [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] 工業革命前，人們致富方式通常像帝王：奪取某種資源使其更強大，進而能奪取更多。如今則可像科學家般，透過發現或創造獨特價值來致富。多數致富者會混合使用新舊方法，但在最先進經濟體中，僅過去半世紀裡，發現式致富的比例已大幅提升。 [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] 如果獨立思考是造成不平等的主因之一，那麼傳統思維者會厭惡不平等也就不足為奇了。但這不僅僅是因為他們不願見到別人擁有自己得不到的東西。事實上，傳統思維者根本無法想像擁有新穎點子是什麼感覺。因此，對他們來說，表現差異懸殊的現象顯得極不自然，當他們遇到這種情況時，往往會認定這必定是作弊或某種惡意外力影響所致。 Thanks to Trevor Blackwell, Patrick Collison, Tyler Cowen, Jessica Livingston, Harj Taggar, and Garry Tan for reading drafts of this. 感謝 Trevor Blackwell、Patrick Collison、Tyler Cowen、Jessica Livingston、Harj Taggar 和 Garry Tan 閱讀本文草稿。 |