As it turns out, when tens of millions of people

處於失業或是低度就業的狀況發生時

are unemployed or underemployed,

就會有不少人會對科技如何影響勞工這個議題有興趣

there's a fair amount of interest in what technology might be doing to the labor force.

而當我開始檢視這個議題, 赫然發現

And as I look at the conversation, it strikes me

大家關切的主題是正確的

that it's focused on exactly the right topic,

但又同時全然的地忽視了關鍵要點。

and at the same time, it's missing the point entirely.

在這個主題上所提出的問題, 是關於

The topic that it's focused on, the question is whether or not

這些數位科技是否影響了人們謀生的能力?

all these digital technologies are affecting people's ability

或者, 換個說法就是

to earn a living, or, to say it a little bit different way,

機器人是否正在搶走人類的工作機會?

are the droids taking our jobs?

有一些證據顯示的確如此

And there's some evidence that they are.

大蕭條(2008~2012)結束時, 美國的 GDP 恢復了

The Great Recession ended when American GDP resumed

緩慢步調的上昇, 其他的一些

its kind of slow, steady march upward, and some other

經濟指標也開始反彈，看起來

economic indicators also started to rebound, and they got

比較健康也比較迅速了。企業的獲利

kind of healthy kind of quickly. Corporate profits

是相當高的。事實上，如果把銀行業也包含進來

are quite high. In fact, if you include bank profits,

這些數值比以往任何時候都來得高。

they're higher than they've ever been.

企業在工具與設備的投資

And business investment in gear, in equipment

還有硬體和軟體方面, 都處於歷史新高。

and hardware and software is at an all-time high.

所以企業都在拿出支票本花錢投資

So the businesses are getting out their checkbooks.

但是他們並沒有真正的擴大招募員工

What they're not really doing is hiring.

這條紅線是就業人口的比率，

So this red line is the employment-to-population ratio,

換句話說，就是處於就業年齡的美國人

in other words, the percentage of working age people

真的有工作的比例

in America who have work.

我們可以看到這個比例在大蕭條時萎靡

And we see that it cratered during the Great Recession,

但是到現在都還沒有開始反彈回來

and it hasn't started to bounce back at all.

但是這個故事並不只是關於大蕭條

But the story is not just a recession story.

十年來，我們剛剛經歷了持續性的

The decade that we've just been through had relatively

相對低落的就業增長，尤其是當我們

anemic job growth all throughout, especially when we

與過去的幾個十年進行比較時, 2000年這個十年

compare it to other decades, and the 2000s

是唯一的一次我們經歷到,

are the only time we have on record where there were

在十年期間的結束時的工作人口, 比十年剛開始的時候

fewer people working at the end of the decade

還少的狀況. 這不是大家樂見的

than at the beginning. This is not what you want to see.

當你用潛在就業人口的數據

When you graph the number of potential employees

來對照國內工作數量作圖，您會看到之間的差距

versus the number of jobs in the country, you see the gap

隨著時間越來越大，,

gets bigger and bigger over time, and then,

而在大蕭條的時候差距特別顯著

during the Great Recession, it opened up in a huge way.

我做了一些簡單的計算。我把過去的 20 年的國內生產總值增長

I did some quick calculations. I took the last 20 years of GDP growth

和同一期間的勞動生產率的增長

and the last 20 years of labor productivity growth

用相當簡單直接的方式

and used those in a fairly straightforward way

嘗試預測維持經濟持續成長

to try to project how many jobs the economy was going

所需要工作機會的數量, 而這是我算出的數據畫出的線

to need to keep growing, and this is the line that I came up with.

這是好事還是壞事？來看看政府預測的數據

Is that good or bad? This is the government's projection

關於就業人口的未來預測

for the working age population going forward.

所以如果這些預測是準確的, 這個差距不會被弭平

So if these predictions are accurate, that gap is not going to close.

問題是，我不認為這些預測是準確的。

The problem is, I don't think these projections are accurate.

明白地說，我認為我的預測是太樂觀的

In particular, I think my projection is way too optimistic,

因為當我做預測時, 我假設了未來應該會

because when I did it, I was assuming that the future

跟過去是相像的

was kind of going to look like the past

在關於勞動生產力的成長方面，這是我不相信的會成立的假設

with labor productivity growth, and that's actually not what I believe,

因為當我環顧四周，我認為我們並未考慮到那些

because when I look around, I think that we ain't seen nothing yet

關於技術對勞動力市場的衝擊。

when it comes to technology's impact on the labor force.

只是在過去的幾年中，我們已經看到數位工具

Just in the past couple years, we've seen digital tools

顯示的技能和能力，遠超過以往

display skills and abilities that they never, ever had before,

而且從某種角度來說, 已經吃進了人類的賴以為生的

and that, kind of, eat deeply into what we human beings

就業領域. 讓我舉幾個例子。

do for a living. Let me give you a couple examples.

在過去的所有的歷史年代，如果你想要把某個文章

Throughout all of history, if you wanted something

從一種語言翻譯成另一種，

translated from one language into another,

必須要靠人類來做

you had to involve a human being.

現在我們有了多國語言的，即時的

Now we have multi-language, instantaneous,

自動翻譯服務, 還是免費的

automatic translation services available for free

經由我們使用的終端裝置, 直接在智慧手機就能用到

via many of our devices all the way down to smartphones.

而如果有使用過這些翻譯服務，我們就會知道，

And if any of us have used these, we know that

做得並不是完美, 但也夠得體了。

they're not perfect, but they're decent.

在過去的所有的歷史年代，如果你想要寫下一些東西，

Throughout all of history, if you wanted something written,

比如一份報告或一篇文章，你必須透過人來做

a report or an article, you had to involve a person.

不再是這樣了。這裡有一篇文章，

Not anymore. This is an article that appeared

不久前發表在富比世雜誌上, 是關於蘋果公司的收益的

in Forbes online a while back about Apple's earnings.

這篇文章是用演算法寫出來的

It was written by an algorithm.

寫的不止是得體而已, 而是到了完美

And it's not decent, it's perfect.

很多人看到這些事情會說, "那又怎樣?

A lot of people look at this and they say, "Okay,

這些都只是非常特定、 狹窄領域的任務，

but those are very specific, narrow tasks,

大多數的知識工作者實際上是通才，

and most knowledge workers are actually generalists,

他們做的是, 坐擁一個由專業技能和知識組成的

and what they do is sit on top of a very large body

龐然巨物, 這些人運用龐大的技能與知識

of expertise and knowledge and they use that

來隨時對無法預測的要求, 馬上做出反應

to react on the fly to kind of unpredictable demands,

這是非常、 非常難以自動化的工作"

and that's very, very hard to automate."

就以一個最令人印象深刻的知識工作者

One of the most impressive knowledge workers

大家可能記得最近有一個人, 名叫肯恩 詹寧斯。

in recent memory is a guy named Ken Jennings.

他在益智問答節目 "Jeopardy!" 連續贏了74次

He won the quiz show "Jeopardy!" 74 times in a row,

把 300 萬美金的獎金帶回家。

took home three million dollars.

在右邊的就是 肯恩, 比數是 三比一,

That's Ken on the right getting beat three to one by

在與 IBM 的超級電腦 華生(Watson) 進行的 "Jeopardy!" 遊戲中被打敗了

Watson, the "Jeopardy!"-playing supercomputer from IBM.

所以當我們在看技術會怎樣影響到

So when we look at what technology can do

一般知識工作者的時候，我開始思考

to general knowledge workers, I start to think

也許所謂的通才的特殊之處並不存在

there might not be something so special about this idea

尤其是當我們開始能夠做到例如

of a generalist, particularly when we start doing things

把 Siri (蘋果手機的語音助理) 連結到 華生 (IBM的超級電腦)

like hooking Siri up to Watson and having technologies

並且逐漸發展一些技術, 能了解人類說話內容

that can understand what we're saying

並且用人類語音回答我們

and repeat speech back to us.

現在，Siri 還撐不上完美, 我們也常拿它的一些差錯

Now, Siri is far from perfect, and we can make fun

來開玩笑，但是我們仍應該記住，

of her flaws, but we should also keep in mind that

如果像 Siri 和 華生 這樣的技術的改進

if technologies like Siri and Watson improve

是沿著 摩爾法則 的預測軌跡，他們將

along a Moore's Law trajectory, which they will,

在六年中，這些技術將不只是進步兩倍

in six years, they're not going to be two times better

或進步四倍，他們會比現在進步 16 倍。

or four times better, they'll be 16 times better than they are right now.

所以我開始覺得, 很多知識工作都將會受到技術的影響

So I start to think that a lot of knowledge work is going to be affected by this.

而且 數位技術不只影響知識工作而已

And digital technologies are not just impacting knowledge work.

它們也開始在實體世界大展身手了

They're starting to flex their muscles in the physical world as well.

前一陣子我有機會坐上了 Google 的自動駕駛汽車

I had the chance a little while back to ride in the Google

它坐起來跟聽起來一樣的酷

autonomous car, which is as cool as it sounds. (Laughter)

我可以做證, 它能夠處理走走停停的路況

And I will vouch that it handled the stop-and-go traffic

在101號公路上面, 開得非常平穩

on U.S. 101 very smoothly.

總共大概有 350萬的人

There are about three and a half million people

在美國這裡, 以開卡車為職業謀生

who drive trucks for a living in the United States.

我想這些人中, 有一部份會受到這項科技的影響

I think some of them are going to be affected by this

在目前, 人形機器人仍然還

technology. And right now, humanoid robots are still

非常的原始。它們會做的事情不多

incredibly primitive. They can't do very much.

但是它們發展得很快, 而且 DARPA,

But they're getting better quite quickly, and DARPA,

就是國防部的投資部門,

which is the investment arm of the Defense Department,

一直試著讓他們的發展更加速。

is trying to accelerate their trajectory.

所以，簡單地說，對啦，機器人就要來搶我們的工作了。

So, in short, yeah, the droids are coming for our jobs.

在短期內，我們可以刺激就業增長

In the short term, we can stimulate job growth

透過鼓勵創業, 還有投資在基礎建設上

by encouraging entrepreneurship and by investing

因為機器人目前仍然不是

in infrastructure, because the robots today still aren't

很擅長修復橋樑。

very good at fixing bridges.

但在不用太久，我想在場的各位

But in the not-too-long-term, I think within the lifetimes

在有生之年，我們將會經歷到

of most of the people in this room, we're going to transition

經濟型態的轉變, 一種非常具有生產力

into an economy that is very productive but that

但是不需要許多的人類工作者的狀況

just doesn't need a lot of human workers,

而如何管理這個轉變的發生, 將會是

and managing that transition is going to be

我們的社會所面臨的最大挑戰。

the greatest challenge that our society faces.

伏爾泰總結了其中的原因。他說，"工作讓我們避開了

Voltaire summarized why. He said, "Work saves us

三個魔鬼： 無聊、 墮落, 和需要。"

from three great evils: boredom, vice and need."

縱使有這樣的挑戰，至少就我個人來說，

But despite this challenge, I'm personally,

我仍然是個超級的數位樂觀主義者，我也同時

I'm still a huge digital optimist, and I am

十分自信地認為，我們現在發展的數位技術

supremely confident that the digital technologies that we're

將會帶領我們進入一個烏托邦的未來，

developing now are going to take us into a utopian future,

而不是一個 反烏托邦式的未來。要解釋為什麼，

not a dystopian future. And to explain why,

我想要丟出一個有些過度誇張大的問題。

I want to pose kind of a ridiculously broad question.

我想問的是, 在人類歷史上

I want to ask what have been the most important

最重要的發展是什麼?

developments in human history?

現在，我想分享一些我所找到的答案

Now, I want to share some of the answers that I've gotten

來回答這個問題。這是一個很棒的問題

in response to this question. It's a wonderful question

一問了就會展開無窮無盡的爭論

to ask and to start an endless debate about,

因為有些人會搬出

because some people are going to bring up

西方和東方的哲學的系統，

systems of philosophy in both the West and the East that

這些的確改變了很多人看待世界的方式

have changed how a lot of people think about the world.

然後其他人會說："才不是這樣，真正重大的

And then other people will say, "No, actually, the big stories,

關鍵的發展, 是世界上主要宗教的建立

the big developments are the founding of the world's

宗教改變了各地的文明

major religions, which have changed civilizations

也改變並影響了無數人的一生如何度過

and have changed and influenced how countless people

然後一些其他人會說，

are living their lives." And then some other folk will say,

"其實，改變文明的，改變人們觀點的,

"Actually, what changes civilizations, what modifies them

改變人們生活的

and what changes people's lives

其實是帝國，在人類歷史上的重大發展

are empires, so the great developments in human history

主要是關於征服與戰爭的故事"

are stories of conquest and of war."

然後一些愛開玩笑的人就會跟著提出說

And then some cheery soul usually always pipes up

"嘿，別忘了還有那些瘟疫。"（笑聲）

and says, "Hey, don't forget about plagues." (Laughter)

對這個問題，有一些樂觀的答案

There are some optimistic answers to this question,

比如有些人會提出的是 探索的年代(十五世紀)

so some people will bring up the Age of Exploration

對整個世界的開拓

and the opening up of the world.

其他人則將提出: 智慧方面的成就

Others will talk about intellectual achievements

在一些學科, 例如 數學, 就幫助人類對於

in disciplines like math that have helped us get

世界有更好的理解, 還有一些人會提出

a better handle on the world, and other folk will talk about

那個 藝術與科學 深度繁榮發展

periods when there was a deep flourishing

的時期。所以像這樣的辯論可以一直談下去

of the arts and sciences. So this debate will go on and on.

這個辯論談不完, 也不會有結論

It's an endless debate, and there's no conclusive,

也沒有唯一的答案。但如果你像我一樣，是個阿宅工程師

no single answer to it. But if you're a geek like me,

你會問，"嗯，有沒有實際的資料, 資料怎麼說？"

you say, "Well, what do the data say?"

那你就會開始做一些我們有興趣的事情, 像是畫圖表

And you start to do things like graph things that we might

比方全世界的人口總數，

be interested in, the total worldwide population, for example,

或是某些社會發展的數據，

or some measure of social development,

或是社會進步的狀態

or the state of advancement of a society,

然後你開始繪製這些資料，因為，通過這樣的方式，

and you start to plot the data, because, by this approach,

整個故事的全貌，在人類歷史上的大發展

the big stories, the big developments in human history,

應該會是那些造成這些圖表曲線變彎很多的

are the ones that will bend these curves a lot.

所以當你這樣做了，把資料畫出圖表了

So when you do this, and when you plot the data,

你很快就會得到一些奇怪的結論

you pretty quickly come to some weird conclusions.

你做出的結論是，事實上，前面講的這些答案

You conclude, actually, that none of these things

沒有一個是真正重要的。（笑聲）

have mattered very much. (Laughter)

這些答案根本對這些圖表曲線沒有影響。（笑聲）

They haven't done a darn thing to the curves. (Laughter)

事實上只有一個故事, 一項發展

There has been one story, one development

在人類的歷史上, 真正折彎了那些曲線, 而且彎了

in human history that bent the curve, bent it just about

將近90 度，這個故事, 就是 技術。

90 degrees, and it is a technology story.

像是蒸汽引擎, 還有其它的相關技術

The steam engine, and the other associated technologies

帶動了工業革命, 改變了整個世界

of the Industrial Revolution changed the world

對人類歷史產生的重大的影響

and influenced human history so much,

套用 歷史學家 伊恩 · 莫里斯 (Ian Morris) 的話說，

that in the words of the historian Ian Morris,

這項發展讓先前發生的其它事情都變得微不足道了

they made mockery out of all that had come before.

這項發展, 把我們的肌肉力量 放大了無窮倍

And they did this by infinitely multiplying the power

克服了人類身體肌肉的限制

of our muscles, overcoming the limitations of our muscles.

而現在, 我們正經歷著

Now, what we're in the middle of now

超越人類個別大腦的限制的時機

is overcoming the limitations of our individual brains

將我們的心智能力放大無窮多倍的時候

and infinitely multiplying our mental power.

這必然也是一個至少 跟克服人類的肌肉力量限制

How can this not be as big a deal as overcoming

一樣重大的發展吧?

the limitations of our muscles?

所以請原諒我又再重覆了，當我觀察到

So at the risk of repeating myself a little bit, when I look

這段期間內數位科技的發展

at what's going on with digital technology these days,

我們離這段期間的終點還很遠

we are not anywhere near through with this journey,

而當我看到所發生的事情, 對我們經濟

and when I look at what is happening to our economies

還有社會所發生的影響, 我的唯一結論是

and our societies, my single conclusion is that

我們還沒看到重大的里程碑, 最好的日子還在未來。

we ain't seen nothing yet. The best days are really ahead.

讓我舉幾個例子。

Let me give you a couple examples.

經濟體並不是靠能源運作的, 也不是靠資本

Economies don't run on energy. They don't run on capital,

也不是靠勞力。經濟體的運行靠的是想法。

they don't run on labor. Economies run on ideas.

所以創新的工作, 產生新的想法的工作

So the work of innovation, the work of coming up with

是人類所能做的 多種 最強大的

new ideas, is some of the most powerful,

最基本的 工作之一，這些工作是人類在經濟體裡

some of the most fundamental work that we can do

能做的。而這也是我們過去如何創新的方式

in an economy. And this is kind of how we used to do innovation.

我們會發現一大群看起來相當類似的人

We'd find a bunch of fairly similar-looking people

— — （笑聲） — —

— (Laughter) —

我們帶他們離開原本的精英的機構，把他們放到

we'd take them out of elite institutions, we'd put them into

另一個精英的機構，然後等著創新的發生

other elite institutions, and we'd wait for the innovation.

現在 — — （笑聲） — —

Now — (Laughter) —

作為一個在麻省理工學院還有哈佛度過整個職涯的白種人

as a white guy who spent his whole career at MIT

我對這沒有什麼問題。（笑聲）

and Harvard, I got no problem with this. (Laughter)

但一些其他人遇到了問題，他們有點像是

But some other people do, and they've kind of crashed

搞砸了派對, 而且放鬆了創新應有的規範

the party and loosened up the dress code of innovation.

（笑聲）

(Laughter)

這裡是一些 頂尖程式員寫程式大賽的優勝者

So here are the winners of a Top Coder programming challenge,

我向你保證沒有人在意

and I assure you that nobody cares

這些孩子是在哪裡長大, 在哪裡念書,

where these kids grew up, where they went to school,

或是他們的長相。所有人只會在意

or what they look like. All anyone cares about

他們工作產出的品質, 他們的點子的品質。

is the quality of the work, the quality of the ideas.

一次又一次的，我們看到這種情況發生

And over and over again, we see this happening

在這個科技推動的世界

in the technology-facilitated world.

創新的工作越來越開放，

The work of innovation is becoming more open,

更具包容性、 更透明、 和更以志業為基礎，

more inclusive, more transparent, and more merit-based,

這會繼續下去, 不管 麻省理工學院和哈佛大學

and that's going to continue no matter what MIT and Harvard