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The key economic logic here is automation does indeed displace workers who are doing work that got automated,
这里关键的经济学逻辑是，自动化确实让很多工人因为自己所在的岗位被自动化而丢了饭碗
but it doesn't actually affect the total number of jobs in the economy because of these offsetting effects.
然而，正是由于有这些抵消效应，所以自动化并没有影响国民经济的总体岗位数目。
Warnings about the “end of work” tend to focus on this part and not all of this -- like a widely cited study from 2013,
那些关于“工作终结”的警告通常侧重上面这一部分而不是下面这一部分——2013年就有这样一项研究，还被大量引用了。
“According to research conducted by Oxford University, nearly half of all current jobs in America --”
“据牛津大学的研究，目前美国将近半数的岗位……”
“47 percent of all our jobs--”
“47%的岗位……”
“47 percent of US jobs in the next decade or two, according to researchers at Oxford, will be replaced by robots.”
“牛津大学研究员表示，在未来一二十年，美国将近47%的岗位都会被机器人取代。”
That study assessed the capabilities of automation technology,
该研究评估了自动化将能够带来的变化，
it didn’t attempt to estimate the actual “extent or pace” of automation or the overall effect on employment.
却没有考虑自动化的实际“程度或速度”，也没有考虑自动化对就业的总体影响。
Now, all this doesn’t mean that the new jobs will show up right away
上述内容并非意味着新的岗位马上就会出现，
or that they’ll be located in the same place or pay the same wage as the ones that were lost.
也不代表新的岗位就会填补当前的空缺，或者有着跟过去同等的薪资。
All it means is that the overall need for human work hasn’t gone away.
只是意味着世界对人工劳动的总体需求并没有消失。
Technologists and futurists don’t deny that’s been true historically,
过去，技术专家和预言家门并不否认这一观点属实，
but they question whether history is a good guide of what’s to come.
他们只是怀疑历史是否对未来有很好的参考价值。
Fundamentally the argument is that this time it’s different. That’s what I think.
这一争论的主要论点是，今时不同往日了。至少我是这么认为的。
Imagine a form of electricity that could automate all the routine work.
试想，如果有一种电力能让日常工作都实现自动化，
I mean, that’s basically what we are talking about here,
我们现在讨论的基本上就是这个问题，
so It’s going to be across the board.
那么这样一来，所有的工作岗位都会被淘汰。
And it is easy to underestimate technology these days.
何况如今我们很容易轻视科技的力量。
In a 2004 book, two economists assessed the future of automation
在2004年的一本书里，两位经济学家评估了未来的自动化水平，
and concluded that tasks like driving in traffic would be “enormously difficult” to teach to a computer.
声称在车流中开车之类的工作对计算机而言是“无比困难”的。
That same year, a review of 50 years of research concluded that “human level speech recognition has proved to be an elusive goal.”
同年，在回顾了过去50年来科技的发展后，另一项研究指出“人类语音识别可望而不可即”
And now?
现在呢？
“Ok Google. How many miles has google’s autonomous vehicle driven?”
“谷歌，谷歌的无人驾驶汽车里程已经多少英里了？”
“According to Recode, that’s because the company announced its self-driving car project, which was created in 2009,
“据Recode网站报道，该公司2009年推出的自动驾驶汽车
has racked up over two million miles of driving experience.”
驾驶里程累计已经有200万英里了。”
This is the textbook chart of advancement in computer hardware —
作者在这张图中展示了计算机硬件方面的技术进步——
it’s the number of transistors that engineers have squeezed onto a computer chip over time.
也就是工程师在一个电脑芯片上嵌入的传感器数目的增长曲线。
Already pretty impressive, but notice that this isn’t a typical scale:
虽然很了不起，但要注意的是，图上并没有一个具有代表性的数值：
these numbers are increasing exponentially.
这些数字都呈几何级数方式增长。
On a typical linear scale it would look more like this.
在典型的线性坐标系里变化曲线则是这样。
It really is hard to imagine this not being massively disruptive.
真的很难想象这种变化不会造成巨大的破坏。
And as the authors of The Second Machine Age point out,
正如《第二个机器时代》作者指出的那样，
processors aren’t the only dimension of computing that has seen exponential improvement.
处理器并不是计算领域见证了指数增长模式的唯一一个方面，
The idea of acceleration in your daily life when do you encounter that?
加速的概念你在日常生活中遇到过吗？
Maybe in a car for a few seconds? In an airplane for seconds again?
比如在几秒内给汽车加速？在几秒内给飞机加速？
The idea that something can accelerate for decades literally just continuously,
需要几十年才能实现的加速概念就这样连续实现了，
it is just not something that we deal with, I mean, we think in straight lines.
它压根儿就不是我们这个时代会遇到的问题，因为我们都是线性思维的人
But even though there’s been all this innovation, it’s not showing up in the data.
然而，尽管我们有这些发明创造，但它们并没有体现在数据里。
If we were seeing this big increase in automation,
如果我们看到了自动化过程中的这一巨大进步，
we would see productivity growing much more rapidly now than it usually does,
我们会发现，现在的生产力进步的速度比以往快得多，
and we are instead seeing the opposite.
但我们看到的恰好是技术进步的另一面。
Labor productivity is a measure of the goods and services we produce divided by the hours that we work.
劳动生产率是用来衡量我们单位时间生产的产品，提供的服务的标准。
Over time it goes up - we do more with less labor. We’re more efficient.
随着时代的进步，劳动生产力逐步提高，也即事半功倍，效率更高。
If we were starting to see a ton of labor-saving innovation, you’d expect this line to get steeper,
如果我们有无数劳动力节约型发明，这条曲线就会更陡，
but when you look at productivity growth,
但就生产率增长而言，
you can see that it has been slowing down since the early 2000s, and not just for the US.
可以看出，自21世纪初以来，生产率增长速度是在逐步下降的，而且这一情况并非美国独有。
It’s possible that new technologies are changing our lives without fundamentally changing the economy.
这可能是因为新技术改变了我们的生活，但并没有深刻改变整个国家的经济。
So will this all change? Will today’s robots and AI cause mass unemployment?
这一切会改变吗？如今的机器人、人工智能也会造成大规模失业吗？
There’s reason to be skeptical, but nobody really knows.
虽然我们有理由对此表示怀疑，但并没有人真正知道答案。
But one thing we do know is that the wealth that technology creates, it isn’t necessarily shared with workers.
然而，我们知道的是，工人并没能享受到技术带来的财富。
When you account for inflation, the income of most families has stayed pretty flat as the economy has grown.
如果把通胀考虑进去，虽然经济发展了，但大多数家庭的收入基本还是过去的水平。
One of the problems we've seen over the last 40 years is that we have seen all of this rising productivity growth
从过去40年的发展中我们看到了很多问题，其中之一就是我们看到生产力虽然在不断提高，
but actually hasn't been broadly shared,
但实际上受益的人群并不大，
it's been captured by a thin slice of people at the top of the income distribution.
受益的都是那些位于财富分配金字塔顶端的一小部分人。
Even if unemployment stays low, automation might worsen economic inequality,
即便能把失业率维持在较低水平，自动化还是有可能造成贫富差距的恶化，
which is already more extreme in the US than it is in most other advanced countries.
而在美国，这一问题已经比大多数发达国家都要严峻。
But technology isn’t destiny.
然而，科技并非宿命。
Governments decide how a society weathers disruptions, and that worries people on both sides of the debate about the future of work.
一个社会如何度过困难时期是由政府说了算，而对此大家，无论他们对人类工作的未来持何观点，都表示很忧虑。
You know, we’ve adopted policies that instead of really trying to counteract the trend caused by technology and globalization and other things,
你知道吗，我们政府采纳的政策不仅没有努力遏止技术、全球化等带来的变化趋势，
we’ve in many cases, you know, exacerbated them.
很多时候反而是在助长这些趋势。
We’ve put a wind in the back of them and made them more extreme.
在背后煽风点火，让情况变得更加极端。
And that’s a big problem.
这才是大问题。
We will probably always be fascinated by the prospect of robots taking our jobs.
我们可能还是会经常被机器人代替劳力的前景吸引，
But if we focus on things we can’t really control, we risk neglecting the things we can.
但如果把目光都集中在我们控制不了的因素上，那我们就会忽视掉那些我们真正能够掌控的方面。
