1
00:00:00,519 --> 00:00:03,271
As it turns out, when tens of millions of people

2
00:00:03,271 --> 00:00:05,599
are unemployed or underemployed,

3
00:00:05,599 --> 00:00:09,726
there's a fair amount of interest in what technology might be doing to the labor force.

4
00:00:09,726 --> 00:00:12,445
And as I look at the conversation, it strikes me

5
00:00:12,445 --> 00:00:15,397
that it's focused on exactly the right topic,

6
00:00:15,397 --> 00:00:18,375
and at the same time, it's missing the point entirely.

7
00:00:18,375 --> 00:00:21,383
The topic that it's focused on, the question is whether or not

8
00:00:21,383 --> 00:00:25,038
all these digital technologies are affecting people's ability

9
00:00:25,038 --> 00:00:28,058
to earn a living, or, to say it a little bit different way,

10
00:00:28,058 --> 00:00:30,336
are the droids taking our jobs?

11
00:00:30,336 --> 00:00:32,304
And there's some evidence that they are.

12
00:00:32,304 --> 00:00:36,657
The Great Recession ended when American GDP resumed

13
00:00:36,657 --> 00:00:40,086
its kind of slow, steady march upward, and some other

14
00:00:40,101 --> 00:00:43,035
economic indicators also started to rebound, and they got

15
00:00:43,035 --> 00:00:45,897
kind of healthy kind of quickly. Corporate profits

16
00:00:45,897 --> 00:00:49,173
are quite high. In fact, if you include bank profits,

17
00:00:49,173 --> 00:00:51,285
they're higher than they've ever been.

18
00:00:51,285 --> 00:00:54,557
And business investment in gear, in equipment

19
00:00:54,557 --> 00:00:57,664
and hardware and software is at an all-time high.

20
00:00:57,664 --> 00:01:01,045
So the businesses are getting out their checkbooks.

21
00:01:01,045 --> 00:01:03,306
What they're not really doing is hiring.

22
00:01:03,306 --> 00:01:07,007
So this red line is the employment-to-population ratio,

23
00:01:07,007 --> 00:01:10,388
in other words, the percentage of working age people

24
00:01:10,388 --> 00:01:12,279
in America who have work.

25
00:01:12,279 --> 00:01:15,979
And we see that it cratered during the Great Recession,

26
00:01:15,979 --> 00:01:18,843
and it hasn't started to bounce back at all.

27
00:01:18,843 --> 00:01:21,350
But the story is not just a recession story.

28
00:01:21,350 --> 00:01:24,347
The decade that we've just been through had relatively

29
00:01:24,347 --> 00:01:27,740
anemic job growth all throughout, especially when we

30
00:01:27,740 --> 00:01:30,675
compare it to other decades, and the 2000s

31
00:01:30,675 --> 00:01:32,965
are the only time we have on record where there were

32
00:01:32,965 --> 00:01:36,168
fewer people working at the end of the decade

33
00:01:36,168 --> 00:01:39,228
than at the beginning. This is not what you want to see.

34
00:01:39,228 --> 00:01:42,867
When you graph the number of potential employees

35
00:01:42,867 --> 00:01:46,471
versus the number of jobs in the country, you see the gap

36
00:01:46,471 --> 00:01:50,049
gets bigger and bigger over time, and then,

37
00:01:50,049 --> 00:01:52,449
during the Great Recession, it opened up in a huge way.

38
00:01:52,449 --> 00:01:56,859
I did some quick calculations. I took the last 20 years of GDP growth

39
00:01:56,859 --> 00:02:00,155
and the last 20 years of labor productivity growth

40
00:02:00,155 --> 00:02:02,897
and used those in a fairly straightforward way

41
00:02:02,897 --> 00:02:05,523
to try to project how many jobs the economy was going

42
00:02:05,523 --> 00:02:09,182
to need to keep growing, and this is the line that I came up with.

43
00:02:09,182 --> 00:02:12,628
Is that good or bad? This is the government's projection

44
00:02:12,628 --> 00:02:16,481
for the working age population going forward.

45
00:02:16,481 --> 00:02:21,252
So if these predictions are accurate, that gap is not going to close.

46
00:02:21,252 --> 00:02:24,653
The problem is, I don't think these projections are accurate.

47
00:02:24,653 --> 00:02:28,009
In particular, I think my projection is way too optimistic,

48
00:02:28,009 --> 00:02:31,365
because when I did it, I was assuming that the future

49
00:02:31,365 --> 00:02:33,813
was kind of going to look like the past

50
00:02:33,813 --> 00:02:37,252
with labor productivity growth, and that's actually not what I believe,

51
00:02:37,252 --> 00:02:41,011
because when I look around, I think that we ain't seen nothing yet

52
00:02:41,011 --> 00:02:44,296
when it comes to technology's impact on the labor force.

53
00:02:44,296 --> 00:02:48,294
Just in the past couple years, we've seen digital tools

54
00:02:48,294 --> 00:02:52,700
display skills and abilities that they never, ever had before,

55
00:02:52,700 --> 00:02:56,488
and that, kind of, eat deeply into what we human beings

56
00:02:56,488 --> 00:02:59,744
do for a living. Let me give you a couple examples.

57
00:02:59,744 --> 00:03:01,755
Throughout all of history, if you wanted something

58
00:03:01,755 --> 00:03:04,679
translated from one language into another,

59
00:03:04,679 --> 00:03:06,343
you had to involve a human being.

60
00:03:06,343 --> 00:03:09,759
Now we have multi-language, instantaneous,

61
00:03:09,759 --> 00:03:13,977
automatic translation services available for free

62
00:03:13,977 --> 00:03:17,366
via many of our devices all the way down to smartphones.

63
00:03:17,366 --> 00:03:19,750
And if any of us have used these, we know that

64
00:03:19,750 --> 00:03:23,071
they're not perfect, but they're decent.

65
00:03:23,071 --> 00:03:26,222
Throughout all of history, if you wanted something written,

66
00:03:26,222 --> 00:03:29,637
a report or an article, you had to involve a person.

67
00:03:29,637 --> 00:03:31,889
Not anymore. This is an article that appeared

68
00:03:31,889 --> 00:03:35,119
in Forbes online a while back about Apple's earnings.

69
00:03:35,119 --> 00:03:37,646
It was written by an algorithm.

70
00:03:37,646 --> 00:03:40,901
And it's not decent, it's perfect.

71
00:03:40,901 --> 00:03:43,863
A lot of people look at this and they say, "Okay,

72
00:03:43,863 --> 00:03:46,212
but those are very specific, narrow tasks,

73
00:03:46,212 --> 00:03:48,845
and most knowledge workers are actually generalists,

74
00:03:48,845 --> 00:03:51,374
and what they do is sit on top of a very large body

75
00:03:51,374 --> 00:03:54,030
of expertise and knowledge and they use that

76
00:03:54,030 --> 00:03:57,103
to react on the fly to kind of unpredictable demands,

77
00:03:57,103 --> 00:03:59,591
and that's very, very hard to automate."

78
00:03:59,591 --> 00:04:01,568
One of the most impressive knowledge workers

79
00:04:01,568 --> 00:04:03,977
in recent memory is a guy named Ken Jennings.

80
00:04:03,977 --> 00:04:09,035
He won the quiz show "Jeopardy!" 74 times in a row,

81
00:04:09,035 --> 00:04:11,663
took home three million dollars.

82
00:04:11,663 --> 00:04:15,513
That's Ken on the right getting beat three to one by

83
00:04:15,513 --> 00:04:20,317
Watson, the "Jeopardy!"-playing supercomputer from IBM.

84
00:04:20,317 --> 00:04:22,181
So when we look at what technology can do

85
00:04:22,181 --> 00:04:25,054
to general knowledge workers, I start to think

86
00:04:25,054 --> 00:04:27,653
there might not be something so special about this idea

87
00:04:27,653 --> 00:04:30,541
of a generalist, particularly when we start doing things

88
00:04:30,541 --> 00:04:34,529
like hooking Siri up to Watson and having technologies

89
00:04:34,529 --> 00:04:36,425
that can understand what we're saying

90
00:04:36,425 --> 00:04:38,506
and repeat speech back to us.

91
00:04:38,506 --> 00:04:41,344
Now, Siri is far from perfect, and we can make fun

92
00:04:41,344 --> 00:04:44,363
of her flaws, but we should also keep in mind that

93
00:04:44,363 --> 00:04:47,039
if technologies like Siri and Watson improve

94
00:04:47,039 --> 00:04:50,820
along a Moore's Law trajectory, which they will,

95
00:04:50,820 --> 00:04:53,404
in six years, they're not going to be two times better

96
00:04:53,404 --> 00:04:58,222
or four times better, they'll be 16 times better than they are right now.

97
00:04:58,222 --> 00:05:01,905
So I start to think that a lot of knowledge work is going to be affected by this.

98
00:05:01,905 --> 00:05:05,459
And digital technologies are not just impacting knowledge work.

99
00:05:05,459 --> 00:05:09,451
They're starting to flex their muscles in the physical world as well.

100
00:05:09,451 --> 00:05:11,900
I had the chance a little while back to ride in the Google

101
00:05:11,900 --> 00:05:17,426
autonomous car, which is as cool as it sounds. (Laughter)

102
00:05:17,426 --> 00:05:20,453
And I will vouch that it handled the stop-and-go traffic

103
00:05:20,453 --> 00:05:23,358
on U.S. 101 very smoothly.

104
00:05:23,358 --> 00:05:25,323
There are about three and a half million people

105
00:05:25,323 --> 00:05:27,532
who drive trucks for a living in the United States.

106
00:05:27,532 --> 00:05:29,961
I think some of them are going to be affected by this

107
00:05:29,961 --> 00:05:33,213
technology. And right now, humanoid robots are still

108
00:05:33,213 --> 00:05:36,471
incredibly primitive. They can't do very much.

109
00:05:36,471 --> 00:05:39,052
But they're getting better quite quickly, and DARPA,

110
00:05:39,052 --> 00:05:42,203
which is the investment arm of the Defense Department,

111
00:05:42,203 --> 00:05:43,868
is trying to accelerate their trajectory.

112
00:05:43,868 --> 00:05:48,551
So, in short, yeah, the droids are coming for our jobs.

113
00:05:48,551 --> 00:05:52,431
In the short term, we can stimulate job growth

114
00:05:52,431 --> 00:05:55,375
by encouraging entrepreneurship and by investing

115
00:05:55,375 --> 00:05:58,423
in infrastructure, because the robots today still aren't

116
00:05:58,423 --> 00:06:00,163
very good at fixing bridges.

117
00:06:00,163 --> 00:06:03,528
But in the not-too-long-term, I think within the lifetimes

118
00:06:03,528 --> 00:06:07,097
of most of the people in this room, we're going to transition

119
00:06:07,097 --> 00:06:10,033
into an economy that is very productive but that

120
00:06:10,033 --> 00:06:12,837
just doesn't need a lot of human workers,

121
00:06:12,837 --> 00:06:14,392
and managing that transition is going to be

122
00:06:14,392 --> 00:06:17,131
the greatest challenge that our society faces.

123
00:06:17,131 --> 00:06:19,893
Voltaire summarized why. He said, "Work saves us

124
00:06:19,893 --> 00:06:25,170
from three great evils: boredom, vice and need."

125
00:06:25,170 --> 00:06:27,741
But despite this challenge, I'm personally,

126
00:06:27,741 --> 00:06:30,790
I'm still a huge digital optimist, and I am

127
00:06:30,790 --> 00:06:33,977
supremely confident that the digital technologies that we're

128
00:06:33,977 --> 00:06:37,533
developing now are going to take us into a utopian future,

129
00:06:37,533 --> 00:06:40,566
not a dystopian future. And to explain why,

130
00:06:40,566 --> 00:06:43,088
I want to pose kind of a ridiculously broad question.

131
00:06:43,088 --> 00:06:45,438
I want to ask what have been the most important

132
00:06:45,438 --> 00:06:47,761
developments in human history?

133
00:06:47,761 --> 00:06:50,494
Now, I want to share some of the answers that I've gotten

134
00:06:50,494 --> 00:06:52,671
in response to this question. It's a wonderful question

135
00:06:52,671 --> 00:06:54,838
to ask and to start an endless debate about,

136
00:06:54,838 --> 00:06:57,159
because some people are going to bring up

137
00:06:57,159 --> 00:07:00,619
systems of philosophy in both the West and the East that

138
00:07:00,619 --> 00:07:03,752
have changed how a lot of people think about the world.

139
00:07:03,752 --> 00:07:06,588
And then other people will say, "No, actually, the big stories,

140
00:07:06,588 --> 00:07:09,011
the big developments are the founding of the world's

141
00:07:09,011 --> 00:07:12,293
major religions, which have changed civilizations

142
00:07:12,293 --> 00:07:14,932
and have changed and influenced how countless people

143
00:07:14,932 --> 00:07:17,936
are living their lives." And then some other folk will say,

144
00:07:17,936 --> 00:07:21,463
"Actually, what changes civilizations, what modifies them

145
00:07:21,463 --> 00:07:23,626
and what changes people's lives

146
00:07:23,626 --> 00:07:27,538
are empires, so the great developments in human history

147
00:07:27,538 --> 00:07:30,373
are stories of conquest and of war."

148
00:07:30,373 --> 00:07:32,963
And then some cheery soul usually always pipes up

149
00:07:32,963 --> 00:07:38,651
and says, "Hey, don't forget about plagues." (Laughter)

150
00:07:38,651 --> 00:07:41,554
There are some optimistic answers to this question,

151
00:07:41,554 --> 00:07:43,451
so some people will bring up the Age of Exploration

152
00:07:43,451 --> 00:07:45,399
and the opening up of the world.

153
00:07:45,399 --> 00:07:47,501
Others will talk about intellectual achievements

154
00:07:47,501 --> 00:07:49,776
in disciplines like math that have helped us get

155
00:07:49,776 --> 00:07:53,086
a better handle on the world, and other folk will talk about

156
00:07:53,086 --> 00:07:54,783
periods when there was a deep flourishing

157
00:07:54,783 --> 00:07:58,585
of the arts and sciences. So this debate will go on and on.

158
00:07:58,585 --> 00:08:01,424
It's an endless debate, and there's no conclusive,

159
00:08:01,424 --> 00:08:04,676
no single answer to it. But if you're a geek like me,

160
00:08:04,676 --> 00:08:07,574
you say, "Well, what do the data say?"

161
00:08:07,574 --> 00:08:10,385
And you start to do things like graph things that we might

162
00:08:10,385 --> 00:08:14,488
be interested in, the total worldwide population, for example,

163
00:08:14,488 --> 00:08:17,129
or some measure of social development,

164
00:08:17,129 --> 00:08:19,640
or the state of advancement of a society,

165
00:08:19,640 --> 00:08:23,473
and you start to plot the data, because, by this approach,

166
00:08:23,473 --> 00:08:26,090
the big stories, the big developments in human history,

167
00:08:26,090 --> 00:08:28,951
are the ones that will bend these curves a lot.

168
00:08:28,951 --> 00:08:30,863
So when you do this, and when you plot the data,

169
00:08:30,863 --> 00:08:33,661
you pretty quickly come to some weird conclusions.

170
00:08:33,661 --> 00:08:36,584
You conclude, actually, that none of these things

171
00:08:36,584 --> 00:08:41,536
have mattered very much. (Laughter)

172
00:08:41,536 --> 00:08:45,562
They haven't done a darn thing to the curves. (Laughter)

173
00:08:45,562 --> 00:08:49,146
There has been one story, one development

174
00:08:49,146 --> 00:08:51,752
in human history that bent the curve, bent it just about

175
00:08:51,752 --> 00:08:55,798
90 degrees, and it is a technology story.

176
00:08:55,798 --> 00:08:58,757
The steam engine, and the other associated technologies

177
00:08:58,757 --> 00:09:01,688
of the Industrial Revolution changed the world

178
00:09:01,688 --> 00:09:04,112
and influenced human history so much,

179
00:09:04,112 --> 00:09:06,195
that in the words of the historian Ian Morris,

180
00:09:06,195 --> 00:09:10,272
they made mockery out of all that had come before.

181
00:09:10,272 --> 00:09:13,185
And they did this by infinitely multiplying the power

182
00:09:13,185 --> 00:09:16,322
of our muscles, overcoming the limitations of our muscles.

183
00:09:16,322 --> 00:09:18,844
Now, what we're in the middle of now

184
00:09:18,844 --> 00:09:21,763
is overcoming the limitations of our individual brains

185
00:09:21,763 --> 00:09:24,836
and infinitely multiplying our mental power.

186
00:09:24,836 --> 00:09:28,536
How can this not be as big a deal as overcoming

187
00:09:28,536 --> 00:09:31,064
the limitations of our muscles?

188
00:09:31,064 --> 00:09:34,442
So at the risk of repeating myself a little bit, when I look

189
00:09:34,442 --> 00:09:37,271
at what's going on with digital technology these days,

190
00:09:37,271 --> 00:09:40,097
we are not anywhere near through with this journey,

191
00:09:40,097 --> 00:09:42,771
and when I look at what is happening to our economies

192
00:09:42,771 --> 00:09:45,424
and our societies, my single conclusion is that

193
00:09:45,424 --> 00:09:48,952
we ain't seen nothing yet. The best days are really ahead.

194
00:09:48,952 --> 00:09:50,708
Let me give you a couple examples.

195
00:09:50,708 --> 00:09:54,936
Economies don't run on energy. They don't run on capital,

196
00:09:54,936 --> 00:09:58,716
they don't run on labor. Economies run on ideas.

197
00:09:58,716 --> 00:10:01,236
So the work of innovation, the work of coming up with

198
00:10:01,236 --> 00:10:03,662
new ideas, is some of the most powerful,

199
00:10:03,662 --> 00:10:05,477
some of the most fundamental work that we can do

200
00:10:05,477 --> 00:10:09,493
in an economy. And this is kind of how we used to do innovation.

201
00:10:09,493 --> 00:10:13,271
We'd find a bunch of fairly similar-looking people

202
00:10:13,271 --> 00:10:16,682
— (Laughter) —

203
00:10:16,682 --> 00:10:19,211
we'd take them out of elite institutions, we'd put them into

204
00:10:19,211 --> 00:10:22,157
other elite institutions, and we'd wait for the innovation.

205
00:10:22,157 --> 00:10:26,167
Now — (Laughter) —

206
00:10:26,167 --> 00:10:28,748
as a white guy who spent his whole career at MIT

207
00:10:28,748 --> 00:10:35,114
and Harvard, I got no problem with this. (Laughter)

208
00:10:35,114 --> 00:10:37,730
But some other people do, and they've kind of crashed

209
00:10:37,730 --> 00:10:40,266
the party and loosened up the dress code of innovation.

210
00:10:40,266 --> 00:10:41,190
(Laughter)

211
00:10:41,190 --> 00:10:44,834
So here are the winners of a Top Coder programming challenge,

212
00:10:44,834 --> 00:10:47,736
and I assure you that nobody cares

213
00:10:47,736 --> 00:10:51,330
where these kids grew up, where they went to school,

214
00:10:51,330 --> 00:10:53,818
or what they look like. All anyone cares about

215
00:10:53,818 --> 00:10:56,639
is the quality of the work, the quality of the ideas.

216
00:10:56,639 --> 00:10:58,805
And over and over again, we see this happening

217
00:10:58,805 --> 00:11:01,151
in the technology-facilitated world.

218
00:11:01,151 --> 00:11:03,607
The work of innovation is becoming more open,

219
00:11:03,607 --> 00:11:07,385
more inclusive, more transparent, and more merit-based,

220
00:11:07,385 --> 00:11:10,354
and that's going to continue no matter what MIT and Harvard

221
00:11:10,354 --> 00:11:14,034
think of it, and I couldn't be happier about that development.

222
00:11:14,034 --> 00:11:16,484
I hear once in a while, "Okay, I'll grant you that,

223
00:11:16,484 --> 00:11:19,871
but technology is still a tool for the rich world,

224
00:11:19,871 --> 00:11:22,585
and what's not happening, these digital tools are not

225
00:11:22,585 --> 00:11:25,940
improving the lives of people at the bottom of the pyramid."

226
00:11:25,940 --> 00:11:28,604
And I want to say to that very clearly: nonsense.

227
00:11:28,604 --> 00:11:32,042
The bottom of the pyramid is benefiting hugely from technology.

228
00:11:32,042 --> 00:11:34,682
The economist Robert Jensen did this wonderful study

229
00:11:34,682 --> 00:11:37,850
a while back where he watched, in great detail,

230
00:11:37,850 --> 00:11:41,231
what happened to the fishing villages of Kerala, India,

231
00:11:41,231 --> 00:11:44,244
when they got mobile phones for the very first time,

232
00:11:44,244 --> 00:11:46,975
and when you write for the Quarterly Journal of Economics,

233
00:11:46,975 --> 00:11:49,872
you have to use very dry and very circumspect language,

234
00:11:49,872 --> 00:11:52,344
but when I read his paper, I kind of feel Jensen is trying

235
00:11:52,344 --> 00:11:55,365
to scream at us, and say, look, this was a big deal.

236
00:11:55,365 --> 00:11:59,418
Prices stabilized, so people could plan their economic lives.

237
00:11:59,418 --> 00:12:03,541
Waste was not reduced; it was eliminated.

238
00:12:03,541 --> 00:12:06,012
And the lives of both the buyers and the sellers

239
00:12:06,012 --> 00:12:08,510
in these villages measurably improved.

240
00:12:08,510 --> 00:12:12,226
Now, what I don't think is that Jensen got extremely lucky

241
00:12:12,226 --> 00:12:14,580
and happened to land in the one set of villages

242
00:12:14,580 --> 00:12:17,092
where technology made things better.

243
00:12:17,092 --> 00:12:19,695
What happened instead is he very carefully documented

244
00:12:19,695 --> 00:12:22,387
what happens over and over again when technology

245
00:12:22,387 --> 00:12:25,651
comes for the first time to an environment and a community.

246
00:12:25,651 --> 00:12:29,615
The lives of people, the welfares of people, improve dramatically.

247
00:12:29,615 --> 00:12:31,971
So as I look around at all the evidence, and I think about

248
00:12:31,971 --> 00:12:34,447
the room that we have ahead of us, I become a huge

249
00:12:34,447 --> 00:12:37,271
digital optimist, and I start to think that this wonderful

250
00:12:37,271 --> 00:12:40,326
statement from the physicist Freeman Dyson

251
00:12:40,326 --> 00:12:44,904
is actually not hyperbole. This is an accurate assessment of what's going on.

252
00:12:44,904 --> 00:12:47,350
Our digital -- our technologies are great gifts,

253
00:12:47,350 --> 00:12:50,511
and we, right now, have the great good fortune

254
00:12:50,511 --> 00:12:54,036
to be living at a time when digital technology is flourishing,

255
00:12:54,036 --> 00:12:55,694
when it is broadening and deepening and

256
00:12:55,694 --> 00:12:59,035
becoming more profound all around the world.

257
00:12:59,035 --> 00:13:02,253
So, yeah, the droids are taking our jobs,

258
00:13:02,253 --> 00:13:06,066
but focusing on that fact misses the point entirely.

259
00:13:06,066 --> 00:13:09,319
The point is that then we are freed up to do other things,

260
00:13:09,319 --> 00:13:11,977
and what we are going to do, I am very confident,

261
00:13:11,977 --> 00:13:15,040
what we're going to do is reduce poverty and drudgery

262
00:13:15,040 --> 00:13:17,704
and misery around the world. I'm very confident

263
00:13:17,704 --> 00:13:20,736
we're going to learn to live more lightly on the planet,

264
00:13:20,736 --> 00:13:24,217
and I am extremely confident that what we're going to do

265
00:13:24,217 --> 00:13:27,138
with our new digital tools is going to be so profound

266
00:13:27,138 --> 00:13:30,029
and so beneficial that it's going to make a mockery

267
00:13:30,029 --> 00:13:31,762
out of everything that came before.

268
00:13:31,762 --> 00:13:34,500
I'm going to leave the last word to a guy who had

269
00:13:34,500 --> 00:13:36,278
a front row seat for digital progress,

270
00:13:36,278 --> 00:13:38,843
our old friend Ken Jennings. I'm with him.

271
00:13:38,843 --> 00:13:40,204
I'm going to echo his words:

272
00:13:40,204 --> 00:13:44,175
"I, for one, welcome our new computer overlords." (Laughter)

273
00:13:44,175 --> 00:13:47,104
Thanks very much. (Applause)