The key to growth? Race with the machines
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0:01 - 0:03Growth is not dead.
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0:03 - 0:04(Applause)
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0:04 - 0:08Let's start the story 120 years ago,
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0:08 - 0:12when American factories began to electrify their operations,
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0:12 - 0:15igniting the Second Industrial Revolution.
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0:15 - 0:16The amazing thing is
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0:16 - 0:19that productivity did not increase in those factories
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0:19 - 0:22for 30 years. Thirty years.
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0:22 - 0:26That's long enough for a generation of managers to retire.
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0:26 - 0:28You see, the first wave of managers
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0:28 - 0:31simply replaced their steam engines with electric motors,
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0:31 - 0:34but they didn't redesign the factories to take advantage
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0:34 - 0:37of electricity's flexibility.
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0:37 - 0:41It fell to the next generation to invent new work processes,
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0:41 - 0:44and then productivity soared,
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0:44 - 0:47often doubling or even tripling in those factories.
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0:47 - 0:52Electricity is an example of a general purpose technology,
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0:52 - 0:54like the steam engine before it.
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0:54 - 0:58General purpose technologies drive most economic growth,
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0:58 - 1:01because they unleash cascades of complementary innovations,
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1:01 - 1:05like lightbulbs and, yes, factory redesign.
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1:05 - 1:08Is there a general purpose technology of our era?
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1:08 - 1:11Sure. It's the computer.
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1:11 - 1:13But technology alone is not enough.
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1:13 - 1:16Technology is not destiny.
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1:16 - 1:18We shape our destiny,
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1:18 - 1:20and just as the earlier generations of managers
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1:20 - 1:23needed to redesign their factories,
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1:23 - 1:25we're going to need to reinvent our organizations
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1:25 - 1:27and even our whole economic system.
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1:27 - 1:31We're not doing as well at that job as we should be.
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1:31 - 1:32As we'll see in a moment,
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1:32 - 1:35productivity is actually doing all right,
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1:35 - 1:39but it has become decoupled from jobs,
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1:39 - 1:43and the income of the typical worker is stagnating.
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1:43 - 1:46These troubles are sometimes misdiagnosed
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1:46 - 1:49as the end of innovation,
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1:49 - 1:52but they are actually the growing pains
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1:52 - 1:57of what Andrew McAfee and I call the new machine age.
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1:57 - 1:59Let's look at some data.
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1:59 - 2:02So here's GDP per person in America.
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2:02 - 2:05There's some bumps along the way, but the big story
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2:05 - 2:07is you could practically fit a ruler to it.
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2:07 - 2:11This is a log scale, so what looks like steady growth
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2:11 - 2:14is actually an acceleration in real terms.
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2:14 - 2:16And here's productivity.
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2:16 - 2:19You can see a little bit of a slowdown there in the mid-'70s,
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2:19 - 2:22but it matches up pretty well with the Second Industrial Revolution,
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2:22 - 2:25when factories were learning how to electrify their operations.
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2:25 - 2:29After a lag, productivity accelerated again.
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2:29 - 2:32So maybe "history doesn't repeat itself,
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2:32 - 2:34but sometimes it rhymes."
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2:34 - 2:37Today, productivity is at an all-time high,
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2:37 - 2:39and despite the Great Recession,
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2:39 - 2:44it grew faster in the 2000s than it did in the 1990s,
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2:44 - 2:48the roaring 1990s, and that was faster than the '70s or '80s.
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2:48 - 2:51It's growing faster than it did during the Second Industrial Revolution.
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2:51 - 2:53And that's just the United States.
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2:53 - 2:56The global news is even better.
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2:56 - 2:59Worldwide incomes have grown at a faster rate
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2:59 - 3:01in the past decade than ever in history.
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3:01 - 3:06If anything, all these numbers actually understate our progress,
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3:06 - 3:08because the new machine age
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3:08 - 3:10is more about knowledge creation
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3:10 - 3:12than just physical production.
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3:12 - 3:15It's mind not matter, brain not brawn,
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3:15 - 3:17ideas not things.
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3:17 - 3:20That creates a problem for standard metrics,
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3:20 - 3:23because we're getting more and more stuff for free,
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3:23 - 3:26like Wikipedia, Google, Skype,
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3:26 - 3:29and if they post it on the web, even this TED Talk.
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3:29 - 3:32Now getting stuff for free is a good thing, right?
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3:32 - 3:34Sure, of course it is.
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3:34 - 3:38But that's not how economists measure GDP.
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3:38 - 3:44Zero price means zero weight in the GDP statistics.
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3:44 - 3:46According to the numbers, the music industry
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3:46 - 3:49is half the size that it was 10 years ago,
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3:49 - 3:52but I'm listening to more and better music than ever.
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3:52 - 3:55You know, I bet you are too.
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3:55 - 3:57In total, my research estimates
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3:57 - 4:02that the GDP numbers miss over 300 billion dollars per year
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4:02 - 4:05in free goods and services on the Internet.
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4:05 - 4:07Now let's look to the future.
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4:07 - 4:09There are some super smart people
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4:09 - 4:14who are arguing that we've reached the end of growth,
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4:14 - 4:18but to understand the future of growth,
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4:18 - 4:21we need to make predictions
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4:21 - 4:24about the underlying drivers of growth.
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4:24 - 4:28I'm optimistic, because the new machine age
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4:28 - 4:33is digital, exponential and combinatorial.
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4:33 - 4:35When goods are digital, they can be replicated
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4:35 - 4:40with perfect quality at nearly zero cost,
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4:40 - 4:44and they can be delivered almost instantaneously.
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4:44 - 4:46Welcome to the economics of abundance.
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4:46 - 4:50But there's a subtler benefit to the digitization of the world.
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4:50 - 4:55Measurement is the lifeblood of science and progress.
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4:55 - 4:57In the age of big data,
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4:57 - 5:01we can measure the world in ways we never could before.
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5:01 - 5:05Secondly, the new machine age is exponential.
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5:05 - 5:11Computers get better faster than anything else ever.
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5:11 - 5:15A child's Playstation today is more powerful
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5:15 - 5:19than a military supercomputer from 1996.
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5:19 - 5:22But our brains are wired for a linear world.
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5:22 - 5:26As a result, exponential trends take us by surprise.
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5:26 - 5:28I used to teach my students that there are some things,
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5:28 - 5:30you know, computers just aren't good at,
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5:30 - 5:33like driving a car through traffic.
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5:33 - 5:35(Laughter)
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5:35 - 5:38That's right, here's Andy and me grinning like madmen
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5:38 - 5:41because we just rode down Route 101
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5:41 - 5:44in, yes, a driverless car.
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5:44 - 5:47Thirdly, the new machine age is combinatorial.
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5:47 - 5:51The stagnationist view is that ideas get used up,
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5:51 - 5:53like low-hanging fruit,
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5:53 - 5:56but the reality is that each innovation
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5:56 - 5:59creates building blocks for even more innovations.
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5:59 - 6:03Here's an example. In just a matter of a few weeks,
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6:03 - 6:05an undergraduate student of mine
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6:05 - 6:09built an app that ultimately reached 1.3 million users.
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6:09 - 6:10He was able to do that so easily
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6:10 - 6:12because he built it on top of Facebook,
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6:12 - 6:14and Facebook was built on top of the web,
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6:14 - 6:16and that was built on top of the Internet,
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6:16 - 6:18and so on and so forth.
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6:18 - 6:23Now individually, digital, exponential and combinatorial
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6:23 - 6:25would each be game-changers.
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6:25 - 6:28Put them together, and we're seeing a wave
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6:28 - 6:29of astonishing breakthroughs,
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6:29 - 6:32like robots that do factory work or run as fast as a cheetah
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6:32 - 6:35or leap tall buildings in a single bound.
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6:35 - 6:37You know, robots are even revolutionizing
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6:37 - 6:39cat transportation.
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6:39 - 6:41(Laughter)
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6:41 - 6:44But perhaps the most important invention,
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6:44 - 6:49the most important invention is machine learning.
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6:49 - 6:52Consider one project: IBM's Watson.
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6:52 - 6:54These little dots here,
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6:54 - 6:59those are all the champions on the quiz show "Jeopardy."
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6:59 - 7:01At first, Watson wasn't very good,
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7:01 - 7:07but it improved at a rate faster than any human could,
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7:07 - 7:10and shortly after Dave Ferrucci showed this chart
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7:10 - 7:11to my class at MIT,
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7:11 - 7:15Watson beat the world "Jeopardy" champion.
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7:15 - 7:19At age seven, Watson is still kind of in its childhood.
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7:19 - 7:24Recently, its teachers let it surf the Internet unsupervised.
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7:24 - 7:30The next day, it started answering questions with profanities.
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7:30 - 7:32Damn. (Laughter)
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7:32 - 7:35But you know, Watson is growing up fast.
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7:35 - 7:39It's being tested for jobs in call centers, and it's getting them.
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7:39 - 7:43It's applying for legal, banking and medical jobs,
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7:43 - 7:45and getting some of them.
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7:45 - 7:46Isn't it ironic that at the very moment
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7:46 - 7:49we are building intelligent machines,
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7:49 - 7:52perhaps the most important invention in human history,
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7:52 - 7:56some people are arguing that innovation is stagnating?
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7:56 - 7:59Like the first two industrial revolutions,
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7:59 - 8:02the full implications of the new machine age
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8:02 - 8:04are going to take at least a century to fully play out,
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8:04 - 8:07but they are staggering.
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8:07 - 8:11So does that mean we have nothing to worry about?
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8:11 - 8:14No. Technology is not destiny.
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8:14 - 8:17Productivity is at an all time high,
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8:17 - 8:20but fewer people now have jobs.
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8:20 - 8:23We have created more wealth in the past decade than ever,
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8:23 - 8:27but for a majority of Americans, their income has fallen.
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8:27 - 8:29This is the great decoupling
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8:29 - 8:32of productivity from employment,
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8:32 - 8:35of wealth from work.
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8:35 - 8:38You know, it's not surprising that millions of people
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8:38 - 8:41have become disillusioned by the great decoupling,
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8:41 - 8:42but like too many others,
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8:42 - 8:45they misunderstand its basic causes.
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8:45 - 8:48Technology is racing ahead,
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8:48 - 8:52but it's leaving more and more people behind.
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8:52 - 8:55Today, we can take a routine job,
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8:55 - 8:58codify it in a set of machine-readable instructions,
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8:58 - 9:01and then replicate it a million times.
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9:01 - 9:03You know, I recently overheard a conversation
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9:03 - 9:05that epitomizes these new economics.
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9:05 - 9:09This guy says, "Nah, I don't use H&R Block anymore.
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9:09 - 9:12TurboTax does everything that my tax preparer did,
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9:12 - 9:16but it's faster, cheaper and more accurate."
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9:16 - 9:18How can a skilled worker
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9:18 - 9:21compete with a $39 piece of software?
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9:21 - 9:23She can't.
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9:23 - 9:26Today, millions of Americans do have faster,
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9:26 - 9:28cheaper, more accurate tax preparation,
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9:28 - 9:30and the founders of Intuit
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9:30 - 9:32have done very well for themselves.
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9:32 - 9:37But 17 percent of tax preparers no longer have jobs.
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9:37 - 9:39That is a microcosm of what's happening,
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9:39 - 9:43not just in software and services, but in media and music,
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9:43 - 9:47in finance and manufacturing, in retailing and trade --
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9:47 - 9:51in short, in every industry.
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9:51 - 9:54People are racing against the machine,
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9:54 - 9:57and many of them are losing that race.
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9:57 - 10:01What can we do to create shared prosperity?
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10:01 - 10:04The answer is not to try to slow down technology.
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10:04 - 10:07Instead of racing against the machine,
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10:07 - 10:10we need to learn to race with the machine.
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10:10 - 10:13That is our grand challenge.
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10:13 - 10:16The new machine age
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10:16 - 10:19can be dated to a day 15 years ago
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10:19 - 10:22when Garry Kasparov, the world chess champion,
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10:22 - 10:25played Deep Blue, a supercomputer.
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10:25 - 10:27The machine won that day,
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10:27 - 10:30and today, a chess program running on a cell phone
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10:30 - 10:33can beat a human grandmaster.
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10:33 - 10:36It got so bad that, when he was asked
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10:36 - 10:39what strategy he would use against a computer,
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10:39 - 10:43Jan Donner, the Dutch grandmaster, replied,
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10:43 - 10:44"I'd bring a hammer."
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10:44 - 10:48(Laughter)
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10:48 - 10:53But today a computer is no longer the world chess champion.
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10:53 - 10:55Neither is a human,
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10:55 - 10:59because Kasparov organized a freestyle tournament
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10:59 - 11:01where teams of humans and computers
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11:01 - 11:03could work together,
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11:03 - 11:06and the winning team had no grandmaster,
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11:06 - 11:08and it had no supercomputer.
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11:08 - 11:13What they had was better teamwork,
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11:13 - 11:18and they showed that a team of humans and computers,
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11:18 - 11:21working together, could beat any computer
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11:21 - 11:24or any human working alone.
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11:24 - 11:26Racing with the machine
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11:26 - 11:28beats racing against the machine.
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11:28 - 11:31Technology is not destiny.
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11:31 - 11:33We shape our destiny.
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11:33 - 11:34Thank you.
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11:34 - 11:39(Applause)
- Title:
- The key to growth? Race with the machines
- Speaker:
- Erik Brynjolfsson
- Description:
-
As machines take on more jobs, many find themselves out of work or with raises indefinitely postponed. Is this the end of growth? No, says Erik Brynjolfsson -- it’s simply the growing pains of a radically reorganized economy. A riveting case for why big innovations are ahead of us … if we think of computers as our teammates. Be sure to watch the opposing viewpoint from Robert Gordon.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 11:56
Morton Bast edited English subtitles for The key to growth? Race with the machines | ||
Thu-Huong Ha edited English subtitles for The key to growth? Race with the machines | ||
Thu-Huong Ha approved English subtitles for The key to growth? Race with the machines | ||
Thu-Huong Ha edited English subtitles for The key to growth? Race with the machines | ||
Morton Bast accepted English subtitles for The key to growth? Race with the machines | ||
Morton Bast edited English subtitles for The key to growth? Race with the machines | ||
Joseph Geni added a translation |