Automation anxiety has been spreading lately, a fear that in the future, many jobs will be performed by machines rather than human beings, given the remarkable advances that are unfolding in artificial intelligence and robotics. What's clear is that there will be significant change. What's less clear is what that change will look like. My research suggests that the future is both troubling and exciting. The threat of technological unemployment is real, and yet it's a good problem to have. And to explain how I came to that conclusion, I want to confront three myths that I think are currently obscuring our vision of this automated future.
近期,自動化焦慮一直在散佈, 它是種恐懼,害怕在未來 許多工作會由機器來進行, 而不是人類, 因為現在已可以看到在人工智慧 和機器人學領域的驚人進步。 很清楚的一點是, 將來會有顯著的改變。 比較不那麼清楚的是, 改變會是什麼樣的。 我的研究指出,未來 既讓人困擾又讓人興奮。 科技造成失業的威脅是真的, 但,能有這種問題也是件好事。 為了解釋我如何得到這個結論, 我想要來正視三項迷思, 我認為這些迷思 目前遮掩了我們的視線,
A picture that we see on our television screens,
讓我們看不清自動化的未來。
in books, in films, in everyday commentary is one where an army of robots descends on the workplace with one goal in mind: to displace human beings from their work. And I call this the Terminator myth. Yes, machines displace human beings from particular tasks, but they don't just substitute for human beings. They also complement them in other tasks, making that work more valuable and more important. Sometimes they complement human beings directly, making them more productive or more efficient at a particular task. So a taxi driver can use a satnav system to navigate on unfamiliar roads. An architect can use computer-assisted design software to design bigger, more complicated buildings.
我們在電視上、書中、電影中、 每天的評論中所看到的描繪, 通常是機器人大軍湧入工作場所, 心中只有一個目標: 在工作上取代人類。 我稱這個想法為「終結者迷思」。 是的,在特定的工作任務上, 機器會取代人類, 但它們不會就這樣代替人類。 它們在其他工作任務上會補足人類, 讓工作更有價值、更重要。 有時,它們會直接補足人類, 讓人類在特定的工作任務上 能更有生產力或更有效率。 計程車司機在不熟悉的路上 可以用衛星導航系統來協助導航。 建築師可以用電腦輔助的設計軟體 來設計更大、更複雜的建築物。
But technological progress doesn't just complement human beings directly. It also complements them indirectly, and it does this in two ways. The first is if we think of the economy as a pie, technological progress makes the pie bigger. As productivity increases, incomes rise and demand grows. The British pie, for instance, is more than a hundred times the size it was 300 years ago. And so people displaced from tasks in the old pie could find tasks to do in the new pie instead. But technological progress doesn't just make the pie bigger. It also changes the ingredients in the pie. As time passes, people spend their income in different ways, changing how they spread it across existing goods, and developing tastes for entirely new goods, too. New industries are created, new tasks have to be done and that means often new roles have to be filled. So again, the British pie: 300 years ago, most people worked on farms, 150 years ago, in factories, and today, most people work in offices. And once again, people displaced from tasks in the old bit of pie could tumble into tasks in the new bit of pie instead.
但科技進步並不只會直接補足人類。 它也會用間接方式補足人類, 間接的方式有兩種。 第一,如果我們把 經濟想成是一塊派, 科技進步會讓派變更大。 隨著生產力增加, 收入會增加,需求會成長。 比如,英國的派 與三百年前相比,現在超過百倍大。 在舊派工作被取代的人, 能在新派中找到工作。 但科技進步並不只會讓派變大。 它也會改變派的成分原料。 隨時間演進,人會以 不同的方式花費他們的收入, 改變既有商品花費上的分配, 並也會發展出對於全新商品的品味。 新的產業會被創造出來, 有新的工作任務需要被完成, 那就意味著有新角色要有人扮演。 所以,再回到英國的派: 三百年前,大部分的人在農場工作, 一百五十年前,在工廠工作, 現今,大部分的人在辦公室工作。 再提一次,在老派工作被取代的人, 可能會在新派當中 發現可以做的工作任務。
Economists call these effects complementarities, but really that's just a fancy word to capture the different way that technological progress helps human beings. Resolving this Terminator myth shows us that there are two forces at play: one, machine substitution that harms workers, but also these complementarities that do the opposite.
經濟學家把這些效應稱為互補性, 但那只是個很炫的詞,其實意思就是 科技進步用不同的方式在協助人類。 解開這個終結者迷思之後, 會發現有兩股力量在運作: 第一:機器代替,這會傷害到工人, 但也會有第二股力量, 互補性,反而會幫助工人。
Now the second myth, what I call the intelligence myth. What do the tasks of driving a car, making a medical diagnosis and identifying a bird at a fleeting glimpse have in common? Well, these are all tasks that until very recently, leading economists thought couldn't readily be automated. And yet today, all of these tasks can be automated. You know, all major car manufacturers have driverless car programs. There's countless systems out there that can diagnose medical problems. And there's even an app that can identify a bird at a fleeting glimpse.
再來,第二項迷思, 我稱之為「智慧迷思」。 以下這些工作任務: 駕駛一台車、做出醫療診斷, 及快速一瞥就辨識出 一隻鳥,有何共通性? 這些工作任務都是直到最近 仍被經濟學家認為不能 自動化的工作任務。 然而,現今,所有這些 工作任務都能被自動化。 所有大型汽車製造商都有 無人駕駛汽車的計畫。 外面有數不清的系統 都能夠診斷醫療問題。 甚至有個應用程式能用來辨識鳥類, 只要快速一瞥。
Now, this wasn't simply a case of bad luck on the part of economists. They were wrong, and the reason why they were wrong is very important. They've fallen for the intelligence myth, the belief that machines have to copy the way that human beings think and reason in order to outperform them. When these economists were trying to figure out what tasks machines could not do, they imagined the only way to automate a task was to sit down with a human being, get them to explain to you how it was they performed a task, and then try and capture that explanation in a set of instructions for a machine to follow. This view was popular in artificial intelligence at one point, too. I know this because Richard Susskind, who is my dad and my coauthor, wrote his doctorate in the 1980s on artificial intelligence and the law at Oxford University, and he was part of the vanguard. And with a professor called Phillip Capper and a legal publisher called Butterworths, they produced the world's first commercially available artificial intelligence system in the law. This was the home screen design. He assures me this was a cool screen design at the time.
這並不是經濟學家運氣不好的情況。 他們錯了, 而他們為什麼會錯的原因很重要。 他們陷入了智慧迷思中, 相信機器必須要複製人類 思考和推理的方式, 才能夠表現得比人類好。 當這些經濟學家在試圖想出 機器無法勝任哪些工作任務, 他們想像,將工作任務自動化的 唯一方式就是和人類坐下來, 讓人類解釋他們如何執行工作任務, 再試著分析他們的解釋, 轉換成一組指令,讓機器照著做。 在人工智慧領域,這種觀點 曾在某個時點很流行過。 我知道這點,因為理查薩斯金, 他是我爸爸也是我的共同作者, 在八〇年代,在牛津大學 寫了一篇關於人工智慧 與法律的博士論文, 他是先鋒部隊之一。 和一位名叫菲利普卡波的教授, 以及一間法律出版社 叫做 Butterworths, 他們合作製作出了 世界上第一個商業用的 法律人工智慧系統。 這是首頁的畫面設計。 他向我保證,在當時 這是很酷的畫面設計。
(Laughter)
(笑聲)
I've never been entirely convinced. He published it in the form of two floppy disks, at a time where floppy disks genuinely were floppy, and his approach was the same as the economists': sit down with a lawyer, get her to explain to you how it was she solved a legal problem, and then try and capture that explanation in a set of rules for a machine to follow. In economics, if human beings could explain themselves in this way, the tasks are called routine, and they could be automated. But if human beings can't explain themselves, the tasks are called non-routine, and they're thought to be out reach. Today, that routine-nonroutine distinction is widespread. Think how often you hear people say to you machines can only perform tasks that are predictable or repetitive, rules-based or well-defined. Those are all just different words for routine. And go back to those three cases that I mentioned at the start. Those are all classic cases of nonroutine tasks. Ask a doctor, for instance, how she makes a medical diagnosis, and she might be able to give you a few rules of thumb, but ultimately she'd struggle. She'd say it requires things like creativity and judgment and intuition. And these things are very difficult to articulate, and so it was thought these tasks would be very hard to automate. If a human being can't explain themselves, where on earth do we begin in writing a set of instructions for a machine to follow?
我從來沒有被說服。 他用兩張軟碟片的形式將之出版, 在那個時代,軟碟片真的是軟的, 而他的方式就和經濟學家一樣: 和一名律師坐下來, 讓她向你解釋如何解決法律問題, 接著就試著把她的解釋 轉成一組指令給機器執行。 在經濟上,如果人類能夠用 這種方式解釋自己做的事, 這種工作任務就叫做例行事務, 是可以被自動化的。 但如果人類無法解釋出怎麼做, 這種工作任務叫做非例行事務, 應該是不能自動化的。 現今,將事務區別為例行 與非例行是處處可見的。 想想看,你有多常聽到別人對你說 機器能進行的工作任務 只有可預測的、重覆性的、 以規則為基礎的,或定義清楚的。 那些詞只是例行事務的不同說法。 回到我一開始提到的三個案例。 那些案例是典型的非例行事務。 比如,去問一位醫生 如何做醫療診斷, 她可能會給你少數經驗法則, 但最終,她會很掙扎。 她會說,你還需要創意、 判斷,以及直覺才行。 這些東西是很難明確表達的, 所以這些工作任務就會 被認為很難自動化。 如果人類無法解釋他們自己的做法, 我們究竟要從何開始寫指令 給機器遵循?
Thirty years ago, this view was right, but today it's looking shaky, and in the future it's simply going to be wrong. Advances in processing power, in data storage capability and in algorithm design mean that this routine-nonroutine distinction is diminishingly useful.
三十年前,這個觀點是對的, 但現今,它很不穩固, 在未來,它將會是錯的。 處理能力、資料儲存容量, 以及演算法設計都在進步, 這就表示例行與非例行事務間的區別 越來越沒有用了。
To see this, go back to the case of making a medical diagnosis. Earlier in the year, a team of researchers at Stanford announced they'd developed a system which can tell you whether or not a freckle is cancerous as accurately as leading dermatologists. How does it work? It's not trying to copy the judgment or the intuition of a doctor. It knows or understands nothing about medicine at all. Instead, it's running a pattern recognition algorithm through 129,450 past cases, hunting for similarities between those cases and the particular lesion in question. It's performing these tasks in an unhuman way, based on the analysis of more possible cases than any doctor could hope to review in their lifetime. It didn't matter that that human being, that doctor, couldn't explain how she'd performed the task.
要了解這點,我們 回到醫療診斷的案例。 今年早些時候, 史丹佛的一個研究者團隊 宣佈他們發展出了一個系統, 它能告訴你一個斑點是否為惡性的, 正確率不輸給頂尖皮膚科醫生。 它怎麼做到的? 它並不是嘗試複製 醫生的判斷或是直覺。 它對於醫學是一竅不通。 反之,它進行的是模式辨識演算法, 在 129,450 個個案當中, 獵尋那些個案與欲探究的損害 之間有哪些相似性。 它是用非人類的方式 在進行這些工作任務, 且是以大量案例的分析來當依據, 案例數多到是醫生 一輩子都看不完的。 無所謂人類,也就是醫生, 是否能解釋她如何進行此工作任務。
Now, there are those who dwell upon that the fact that these machines aren't built in our image. As an example, take IBM's Watson, the supercomputer that went on the US quiz show "Jeopardy!" in 2011, and it beat the two human champions at "Jeopardy!" The day after it won, The Wall Street Journal ran a piece by the philosopher John Searle with the title "Watson Doesn't Know It Won on 'Jeopardy!'" Right, and it's brilliant, and it's true. You know, Watson didn't let out a cry of excitement. It didn't call up its parents to say what a good job it had done. It didn't go down to the pub for a drink. This system wasn't trying to copy the way that those human contestants played, but it didn't matter. It still outperformed them.
有些人老是會想著 這些機器被建立時 沒有依循我們的形象。 以 IBM 的「華生 」為例, 那是台超級電腦,2011 年參加 美國的益智節目《危險邊緣》, 在節目中它打敗了兩位人類冠軍。 它獲勝之後的隔天, 《華爾街日報》刊了一篇 哲學家約翰希爾勒的文章, 標題是〈華生不知道 它自己贏了《危險邊緣》 〉。 是的,這篇文章很聰明也沒說錯。 華生並沒有興奮地放聲大叫。 它沒有打電話給它的父母 說它的表現多棒。 它沒有去酒吧喝酒慶祝。 這個系統並沒有試圖複製 那些人類參賽者比賽的方式, 但那無所謂。 它仍然表現得比人類好。
Resolving the intelligence myth shows us that our limited understanding about human intelligence, about how we think and reason, is far less of a constraint on automation than it was in the past. What's more, as we've seen, when these machines perform tasks differently to human beings, there's no reason to think that what human beings are currently capable of doing represents any sort of summit in what these machines might be capable of doing in the future.
解開這個智慧迷思之後, 看到的是雖然我們對於 人類智慧、對我們如何 思考推理的方式了解有限, 但這個限制對於自動化的影響 已經遠比過去小很多。 此外,如我們所見, 當這些機器用和人類不同的 方式來執行工作任務時, 沒有理由認為 人類目前能夠做到的事 就代表了一種上限, 在未來機器能夠達成的事 都不可能超過這個上限。
Now the third myth, what I call the superiority myth. It's often said that those who forget about the helpful side of technological progress, those complementarities from before, are committing something known as the lump of labor fallacy. Now, the problem is the lump of labor fallacy is itself a fallacy, and I call this the lump of labor fallacy fallacy, or LOLFF, for short. Let me explain. The lump of labor fallacy is a very old idea. It was a British economist, David Schloss, who gave it this name in 1892. He was puzzled to come across a dock worker who had begun to use a machine to make washers, the small metal discs that fasten on the end of screws. And this dock worker felt guilty for being more productive. Now, most of the time, we expect the opposite, that people feel guilty for being unproductive, you know, a little too much time on Facebook or Twitter at work. But this worker felt guilty for being more productive, and asked why, he said, "I know I'm doing wrong. I'm taking away the work of another man." In his mind, there was some fixed lump of work to be divided up between him and his pals, so that if he used this machine to do more, there'd be less left for his pals to do. Schloss saw the mistake. The lump of work wasn't fixed. As this worker used the machine and became more productive, the price of washers would fall, demand for washers would rise, more washers would have to be made, and there'd be more work for his pals to do. The lump of work would get bigger. Schloss called this "the lump of labor fallacy."
第三項迷思, 我稱之為優越迷思。 常見的說法是,有些人會 忘記了科技進步的幫助面, 忘記過去的互補性, 這些人所犯的,就是 所謂的「勞動總合謬誤」。 問題是,勞動總合謬誤本身 就是個謬誤, 我把它稱為 「勞動總合謬誤的謬誤」, 簡寫為「LOLFF」。 讓我解釋一下。 勞動總合謬誤是個很古老的想法。 這個名稱是 1892 年由英國 經濟學家大衛許洛斯取的。 有件事讓他百思不解, 他遇到一個碼頭工人, 這個工人開始用機器來製造墊圈, 墊圈是小型的金屬圓盤, 固定在螺絲底端。 這個碼頭工人對於自己的 高生產力有罪惡感。 通常,我們預期的是相反的反應, 生產力不高才會讓人感到罪惡, 你知道的,工作時 花太多時間滑臉書或推特。 但這個工人對於 太有生產力感到罪惡, 問他原因,他說:「我知道我做錯了。 我搶走了另一個人的工作。」 在他的認知中,勞動總合是固定的, 要由他和他的伙伴來分攤, 所以如果他用機器多做一點, 他伙伴能做的就變少了。 許洛斯看到了這個錯誤。 勞動總合並不是固定的。 當這個工人用機器提高生產力, 墊圈的價格會下降, 對墊圈的需求會提高, 就得要做出更多的墊圈, 他的伙伴反而會有更多要做。 勞動總合變更大了。 許洛斯稱之為「勞動總合謬誤」。
And today you hear people talk about the lump of labor fallacy to think about the future of all types of work. There's no fixed lump of work out there to be divided up between people and machines. Yes, machines substitute for human beings, making the original lump of work smaller, but they also complement human beings, and the lump of work gets bigger and changes.
現今,在思考有各類工作的未來時, 會聽到人們談到勞動總合謬誤。 沒有固定的勞動總合 要讓人類與機器瓜分。 是的,機器會取代人類, 讓原本的勞動總合變少, 但它們也會補足人類, 勞動總合會變更大並且改變。
But LOLFF. Here's the mistake: it's right to think that technological progress makes the lump of work to be done bigger. Some tasks become more valuable. New tasks have to be done. But it's wrong to think that necessarily, human beings will be best placed to perform those tasks. And this is the superiority myth. Yes, the lump of work might get bigger and change, but as machines become more capable, it's likely that they'll take on the extra lump of work themselves. Technological progress, rather than complement human beings, complements machines instead.
但,LOLFF。 錯誤是這樣的: 認為科技進步會讓 要做的勞動總合變大, 這點是沒錯的。 有些工作任務變得較有價值。 有新工作任務需要完成。 錯的地方在於,認為安排人類 來做那些工作任務一定是最好的。 這就是優越迷思。 是的,勞動總量可能 會變大也會改變, 但隨著機器變得更有能力, 很有可能它們會自己去接下 那些額外的勞動總量。 科技進步就不是在補足人類了, 反而是補足機器。
To see this, go back to the task of driving a car. Today, satnav systems directly complement human beings. They make some human beings better drivers. But in the future, software is going to displace human beings from the driving seat, and these satnav systems, rather than complement human beings, will simply make these driverless cars more efficient, helping the machines instead. Or go to those indirect complementarities that I mentioned as well. The economic pie may get larger, but as machines become more capable, it's possible that any new demand will fall on goods that machines, rather than human beings, are best placed to produce. The economic pie may change, but as machines become more capable, it's possible that they'll be best placed to do the new tasks that have to be done. In short, demand for tasks isn't demand for human labor. Human beings only stand to benefit if they retain the upper hand in all these complemented tasks, but as machines become more capable, that becomes less likely.
可以回頭看駕駛汽車的 工作任務來了解這點。 現今,衛星導航系統直接補足人類。 它讓一些人類變成更好的駕駛。 但在未來, 軟體會取代坐在駕駛座上的人類, 這些衛星導航系統 就不是在補足人類了, 而單純就是在讓這些 無人駕駛汽車更有效率, 改而協助機器。 或也可以回到 我剛提過的間接互補性。 經濟的派可能會變更大, 但隨著機器更有能力, 有可能所有符合新需求的商品都適合 由機器而不是由人類來製造。 經濟的派可能會改變, 但隨著機器變得更有能力, 有可能它們最適合運用在 新工作任務中,那些必須解決的事。 簡言之,對工作任務的需求 並非對人類勞動力的需求。 人類只有在仍然能支配 這些補足性工作任務的 情況下才有可能受益, 但隨著機器變得更有能力, 那就更不可能發生。
So what do these three myths tell us then? Well, resolving the Terminator myth shows us that the future of work depends upon this balance between two forces: one, machine substitution that harms workers but also those complementarities that do the opposite. And until now, this balance has fallen in favor of human beings. But resolving the intelligence myth shows us that that first force, machine substitution, is gathering strength. Machines, of course, can't do everything, but they can do far more, encroaching ever deeper into the realm of tasks performed by human beings. What's more, there's no reason to think that what human beings are currently capable of represents any sort of finishing line, that machines are going to draw to a polite stop once they're as capable as us. Now, none of this matters so long as those helpful winds of complementarity blow firmly enough, but resolving the superiority myth shows us that that process of task encroachment not only strengthens the force of machine substitution, but it wears down those helpful complementarities too. Bring these three myths together and I think we can capture a glimpse of that troubling future. Machines continue to become more capable, encroaching ever deeper on tasks performed by human beings, strengthening the force of machine substitution, weakening the force of machine complementarity. And at some point, that balance falls in favor of machines rather than human beings. This is the path we're currently on. I say "path" deliberately, because I don't think we're there yet, but it is hard to avoid the conclusion that this is our direction of travel.
所以,這三項迷思告訴我們什麼? 解開終結者迷思之後, 我們知道工作的未來還要 仰賴兩股力量間的平衡: 第一:機器代替,這會傷害到工人, 但也會有第二股力量, 互補性,反而會幫助工人。 直到目前,這平衡是對人類有利的。 但解開了智慧迷思之後, 我們知道,第一股力量,機器代替, 正在聚集實力。 當然,機器並非什麼都能做, 但它們能做的很多, 能更深進入到人類所進行之 工作任務的領域中。 此外,沒有理由去認為 人類目前已經能做到的事, 就表示是某種終點線, 等到機器和我們一樣有能力時 就會禮貌地在終點線前停下來。 這些都無所謂, 只要機器和人類在工作上 能相得益彰就好。 但解開了優越迷思之後, 我們了解到,工作任務侵佔的過程 不僅是強化了機器代替的那股力量, 也會耗損那些有助益的互補性。 把這三項迷思結合起來, 我想,我們就能對 讓人困擾的未來有點概念。 機器持續變得更有能力, 比以前更深入人類進行的工作任務, 強化機器代替的那股力量, 弱化機器互補性的那股力量。 在某個時點,那平衡 會變得對機器有利, 而非人類。 我們目前就在這條路上。 我刻意用「路」這個字, 因為我們還沒有到達那裡, 但無可避免,結論會是: 這就是我們行進的方向。
That's the troubling part. Let me say now why I think actually this is a good problem to have. For most of human history, one economic problem has dominated: how to make the economic pie large enough for everyone to live on. Go back to the turn of the first century AD, and if you took the global economic pie and divided it up into equal slices for everyone in the world, everyone would get a few hundred dollars. Almost everyone lived on or around the poverty line. And if you roll forward a thousand years, roughly the same is true. But in the last few hundred years, economic growth has taken off. Those economic pies have exploded in size. Global GDP per head, the value of those individual slices of the pie today, they're about 10,150 dollars. If economic growth continues at two percent, our children will be twice as rich as us. If it continues at a more measly one percent, our grandchildren will be twice as rich as us. By and large, we've solved that traditional economic problem.
那是讓人困擾的部分。 現在讓我說明為什麼我認為 有這個問題是件好事。 大部分的人類歷史中, 主導的都是這一個經濟問題: 如何讓經濟的派夠大, 確保每個人都得以維生。 回到西元一世紀, 如果用全球的派當作例子, 將它切成相同的等分, 分給全世界的人, 每個人可能得到幾百美元。 幾乎每個人都是在 貧窮水平線上下過生活。 如果你再向前轉一千年, 大致上也是一樣的。 但在過去幾百年間,經濟成長起飛。 這些經濟的派在尺寸上都爆增。 全球的人均生產總值, 也就是現今每個人分到的那片派, 價值約 10,150 美元。 如果經濟成長率維持 2%, 我們的孩子會比我們富有兩倍。 如果成長率低一點,維持在 1%, 我們的孫子會比我們富有兩倍。 總的來說,我們解決了 傳統的經濟問題。
Now, technological unemployment, if it does happen, in a strange way will be a symptom of that success, will have solved one problem -- how to make the pie bigger -- but replaced it with another -- how to make sure that everyone gets a slice. As other economists have noted, solving this problem won't be easy. Today, for most people, their job is their seat at the economic dinner table, and in a world with less work or even without work, it won't be clear how they get their slice. There's a great deal of discussion, for instance, about various forms of universal basic income as one possible approach, and there's trials underway in the United States and in Finland and in Kenya. And this is the collective challenge that's right in front of us, to figure out how this material prosperity generated by our economic system can be enjoyed by everyone in a world in which our traditional mechanism for slicing up the pie, the work that people do, withers away and perhaps disappears.
如果真的因為科技進步而造成失業, 從一種奇怪的角度來看, 那會是一種成功的象徵, 它能夠解決一個問題 ──如何讓派變大── 但卻用另一個問題取代它── 如何確保每個人得到一片派。 如其他經濟學家注意到的, 解決這個問題並不容易。 現今,對大部分人而言, 他們的工作就是在 經濟晚餐餐桌上的席位, 在一個更少或甚至沒工作的世界裡, 沒人知道他們如何得到自己的那片派。 比如,有很多的討論都是 關於全體基本收入的各種形式, 這是種可能的方式, 且在美國、芬蘭, 及肯亞都有試驗正在進行中。 這是我們要面臨的集體挑戰, 要想出我們的經濟體制 所產生出的物質繁榮要如何 讓每個人都享受到, 而且在這個世界中, 我們的傳統切派機制, 瓜分人們所做的工作的機制, 在衰弱且也許在消失中。
Solving this problem is going to require us to think in very different ways. There's going to be a lot of disagreement about what ought to be done, but it's important to remember that this is a far better problem to have than the one that haunted our ancestors for centuries: how to make that pie big enough in the first place.
若要解決這個問題,我們 得要用很不同的方式思考。 對於該做什麼事, 必定會有很多異議, 但很重要的是要記住, 有這個問題其實算好事, 比我們的祖先煩惱了 幾世紀的問題要好多了, 他們煩惱的是: 一開始要如何讓派變大。
Thank you very much.
非常謝謝各位。
(Applause)
(掌聲)