1 00:00:00,090 --> 00:00:02,490 The following content is provided under a Creative 2 00:00:02,490 --> 00:00:04,030 Commons license. 3 00:00:04,030 --> 00:00:06,330 Your support will help MIT OpenCourseWare 4 00:00:06,330 --> 00:00:10,690 continue to offer high-quality educational resources for free. 5 00:00:10,690 --> 00:00:13,320 To make a donation or view additional materials 6 00:00:13,320 --> 00:00:17,260 from hundreds of MIT courses, visit MIT OpenCourseWare 7 00:00:17,260 --> 00:00:18,210 at ocw.mit.edu. 8 00:00:20,717 --> 00:00:22,800 WILLIAM BONVILLIAN: So Matt was putting a question 9 00:00:22,800 --> 00:00:24,178 on the table for us. 10 00:00:24,178 --> 00:00:25,720 So why don't you go and lay that out. 11 00:00:25,720 --> 00:00:27,010 And we'll get to the next-- 12 00:00:27,010 --> 00:00:29,725 AUDIENCE: This idea of how you distribute your talents 13 00:00:29,725 --> 00:00:32,510 across R&D sectors. 14 00:00:32,510 --> 00:00:35,850 The economic model that stands now 15 00:00:35,850 --> 00:00:39,437 is treat technology as [INAUDIBLE] a box and say this. 16 00:00:39,437 --> 00:00:41,020 Put this much capital into technology, 17 00:00:41,020 --> 00:00:42,478 and you'll get this kind of growth, 18 00:00:42,478 --> 00:00:44,980 where it doesn't consider how you distribute 19 00:00:44,980 --> 00:00:46,392 your capital within that. 20 00:00:48,997 --> 00:00:50,580 WILLIAM BONVILLIAN: I'll just give you 21 00:00:50,580 --> 00:00:55,390 an example because you're right in identifying the problem. 22 00:00:55,390 --> 00:00:58,300 About seven or eight years ago, there 23 00:00:58,300 --> 00:01:01,540 were a whole series of major studies of climate 24 00:01:01,540 --> 00:01:04,803 and how are we going to develop the necessary energy 25 00:01:04,803 --> 00:01:06,720 and related technologies that were going to be 26 00:01:06,720 --> 00:01:09,550 needed to tackle the problem. 27 00:01:09,550 --> 00:01:14,710 And interestingly, all of those studies got to this moment 28 00:01:14,710 --> 00:01:17,560 where they were going to talk about the technologies. 29 00:01:17,560 --> 00:01:19,970 And they all basically read the same way. 30 00:01:19,970 --> 00:01:25,900 They all said, yes, we're going to need a lot of technologies. 31 00:01:25,900 --> 00:01:28,690 And then we'll spend a lot of money on them. 32 00:01:28,690 --> 00:01:31,360 We'll spend a lot of money doing research and development, 33 00:01:31,360 --> 00:01:32,322 was their conclusion. 34 00:01:32,322 --> 00:01:34,030 And then we need to spend this much money 35 00:01:34,030 --> 00:01:34,930 on research and development. 36 00:01:34,930 --> 00:01:36,100 Then we need to spend this much. 37 00:01:36,100 --> 00:01:38,558 But we'll spend a lot of money on research and development. 38 00:01:38,558 --> 00:01:43,660 There was never an attempt to get inside that black box, 39 00:01:43,660 --> 00:01:47,830 as you well put it, Matt, of how that innovation 40 00:01:47,830 --> 00:01:52,900 system is actually organized to optimize the results. 41 00:01:52,900 --> 00:01:54,940 That is our problem. 42 00:01:54,940 --> 00:01:59,380 That is the problem of this class, 43 00:01:59,380 --> 00:02:02,590 getting inside that black box and figuring out 44 00:02:02,590 --> 00:02:05,390 how these innovation systems actually work 45 00:02:05,390 --> 00:02:07,657 and how you can optimize the organization of them. 46 00:02:07,657 --> 00:02:09,490 And we're going to look at a bunch of models 47 00:02:09,490 --> 00:02:12,080 and a bunch of problems, exactly along those lines. 48 00:02:12,080 --> 00:02:14,380 But if we do nothing else, you're 49 00:02:14,380 --> 00:02:17,332 going to get deep inside that black box and, I think, 50 00:02:17,332 --> 00:02:19,290 start to understand some of the elements you've 51 00:02:19,290 --> 00:02:22,350 got to think about when you design your way out of it. 52 00:02:22,350 --> 00:02:24,230 Does that help? 53 00:02:24,230 --> 00:02:25,270 Because you're right. 54 00:02:25,270 --> 00:02:30,680 It's been a classic problem in science and tech policy 55 00:02:30,680 --> 00:02:33,620 that it hasn't really faced the innovation organization 56 00:02:33,620 --> 00:02:34,120 problems. 57 00:02:34,120 --> 00:02:36,230 It hasn't understood how important they are. 58 00:02:36,230 --> 00:02:37,522 But I think they're really key. 59 00:02:37,522 --> 00:02:39,985 And we really jump into that in the next class. 60 00:02:42,520 --> 00:02:43,020 All right. 61 00:02:43,020 --> 00:02:48,070 So we're now at our third great growth economist, 62 00:02:48,070 --> 00:02:51,880 Dale Jorgenson, who teaches up the street at Harvard. 63 00:02:51,880 --> 00:02:55,450 And Jorgenson I put in here basically 64 00:02:55,450 --> 00:02:58,390 because he improves the model. 65 00:02:58,390 --> 00:03:07,120 He takes a look at the 1990s IT boom, that period 66 00:03:07,120 --> 00:03:10,450 of rapid growth, and shows that that 67 00:03:10,450 --> 00:03:14,560 is driven by technological and related innovation. 68 00:03:14,560 --> 00:03:16,240 And the resurgence in the US economy 69 00:03:16,240 --> 00:03:22,450 in that '93 to 2001 period really outran all expectations. 70 00:03:22,450 --> 00:03:27,460 And his view is that there is the rapid decline 71 00:03:27,460 --> 00:03:32,380 in IT prices for a technology that inherently increases 72 00:03:32,380 --> 00:03:37,060 productivity in important ways and costs ever less. 73 00:03:37,060 --> 00:03:41,110 That's a huge driver and was key to the surge 74 00:03:41,110 --> 00:03:44,650 in the 1990s growth. 75 00:03:44,650 --> 00:03:51,670 And he argues that the core technology in the IT wave 76 00:03:51,670 --> 00:03:54,480 was semiconductors. 77 00:03:54,480 --> 00:03:56,090 So let me just walk through this. 78 00:03:56,090 --> 00:03:57,620 You all probably know a fair amount 79 00:03:57,620 --> 00:04:00,530 of the semiconductor history. 80 00:04:00,530 --> 00:04:05,060 Bell Labs in 1947 with Bardeen, Brattain, and Shockley, 81 00:04:05,060 --> 00:04:09,350 they developed the transistor from semiconductor materials. 82 00:04:09,350 --> 00:04:12,560 And it's an electrical switching device. 83 00:04:12,560 --> 00:04:18,170 It enables essentially the whole follow on of digital technology 84 00:04:18,170 --> 00:04:18,680 innovation. 85 00:04:18,680 --> 00:04:21,110 It's that digital form that's key. 86 00:04:21,110 --> 00:04:23,960 And then following on that comes the integrated circuit. 87 00:04:23,960 --> 00:04:26,390 And that really evolves in 1958. 88 00:04:26,390 --> 00:04:30,640 And it's developed in two different sites, two 89 00:04:30,640 --> 00:04:32,980 different great groups frankly. 90 00:04:32,980 --> 00:04:35,860 Jack Kilby was the leader of a team at Texas Instruments. 91 00:04:35,860 --> 00:04:37,585 Robert Noyce is the leader of a team 92 00:04:37,585 --> 00:04:38,710 at Fairchild Semiconductor. 93 00:04:38,710 --> 00:04:40,855 And Fairchild later morphs into Intel. 94 00:04:43,510 --> 00:04:48,820 And they develop the integrated circuit. 95 00:04:48,820 --> 00:04:53,170 Gordon Moore, who's working with Noyce at Fairchild 96 00:04:53,170 --> 00:04:56,290 Semiconductor, watching what's going on, 97 00:04:56,290 --> 00:04:58,750 he develops Moore's law, which is a good way of describing 98 00:04:58,750 --> 00:05:01,960 what Jorgenson is onto. 99 00:05:01,960 --> 00:05:05,590 The integrated circuit, every two years, 100 00:05:05,590 --> 00:05:08,230 is going to double the number of transistors per chip. 101 00:05:08,230 --> 00:05:12,610 And the cost per transistor is going to decline by half 102 00:05:12,610 --> 00:05:15,820 in that same two years-- and sometimes, it's 18 months-- 103 00:05:15,820 --> 00:05:16,800 kind of time period. 104 00:05:16,800 --> 00:05:21,790 So in other words, you have much greater capability 105 00:05:21,790 --> 00:05:24,490 at ever lower cost. 106 00:05:24,490 --> 00:05:27,950 That's the phenomena that's at the core of this innovation 107 00:05:27,950 --> 00:05:28,450 wave. 108 00:05:28,450 --> 00:05:32,020 And that is often what's happening. 109 00:05:32,020 --> 00:05:36,070 You'll have a core technology with a declining cost base 110 00:05:36,070 --> 00:05:40,210 that gets evermore capable at a lower cost. 111 00:05:40,210 --> 00:05:43,600 So you introduce, in effect, a deflationary factor 112 00:05:43,600 --> 00:05:47,740 into your technology advanced process. 113 00:05:47,740 --> 00:05:52,150 So in '68, Noyce, Moore, and Andy Grove found Intel. 114 00:05:52,150 --> 00:05:55,690 And they move over to microprocessors or logic chips, 115 00:05:55,690 --> 00:06:03,220 microchips, which is a bundle of transistors playing out 116 00:06:03,220 --> 00:06:05,320 this Moore's law theory. 117 00:06:05,320 --> 00:06:07,780 So Jorgenson looks at semiconductors 118 00:06:07,780 --> 00:06:09,700 as the core technology advance. 119 00:06:09,700 --> 00:06:12,760 And under Moore's law, it's coming down by half every two 120 00:06:12,760 --> 00:06:15,520 years in cost. 121 00:06:15,520 --> 00:06:20,470 Communications equipment cost is also coming down driven by, 122 00:06:20,470 --> 00:06:22,640 in large part, cheaper semiconductors. 123 00:06:22,640 --> 00:06:26,080 So all kinds of transmission technologies 124 00:06:26,080 --> 00:06:27,640 are occurring at this time period. 125 00:06:27,640 --> 00:06:31,240 I've listed some of them there that are coming down in price. 126 00:06:31,240 --> 00:06:34,600 And the result is a technology that's 127 00:06:34,600 --> 00:06:38,680 creating major new productivity gains in the economy. 128 00:06:38,680 --> 00:06:40,690 In other words, you're producing more 129 00:06:40,690 --> 00:06:43,990 for less with less labor input. 130 00:06:43,990 --> 00:06:47,800 Therefore, you're creating a real gain, 131 00:06:47,800 --> 00:06:50,860 which amounts to a new real wealth in the society, which 132 00:06:50,860 --> 00:06:51,760 you can distribute. 133 00:06:51,760 --> 00:06:53,650 That's the pattern here. 134 00:06:53,650 --> 00:06:57,260 And the result is a growth resurgence. 135 00:06:57,260 --> 00:07:01,030 So there's price declines in computing and communications 136 00:07:01,030 --> 00:07:03,310 equipment, computing equipment. 137 00:07:03,310 --> 00:07:07,900 Between '90 and 1995, Jorgenson traces a 15%-a-year price 138 00:07:07,900 --> 00:07:15,430 decline, between '95 and 2000, a 32% decline per year. 139 00:07:15,430 --> 00:07:19,060 In other words, this thing is costing evermore 140 00:07:19,060 --> 00:07:23,500 capable and costing a lot less in relatively short time 141 00:07:23,500 --> 00:07:24,970 periods. 142 00:07:24,970 --> 00:07:28,030 Now, the outlier here is software. 143 00:07:28,030 --> 00:07:30,970 And this is how Bill Gates gets rich 144 00:07:30,970 --> 00:07:34,480 because the decline in price in software is only 1.6% 145 00:07:34,480 --> 00:07:40,060 between '90 and '95 and only 2.4% between '95 and 2000. 146 00:07:40,060 --> 00:07:42,820 So in other words, you can still charge a premium 147 00:07:42,820 --> 00:07:45,130 for your software even if the hard technology cost 148 00:07:45,130 --> 00:07:47,500 is coming down significantly. 149 00:07:47,500 --> 00:07:52,270 So that's why Gates and Microsoft get bigger than IBM 150 00:07:52,270 --> 00:07:57,880 because IBM bet on the wrong category. 151 00:07:57,880 --> 00:08:00,393 And we still haven't tackled this cost of software problem. 152 00:08:00,393 --> 00:08:02,185 It's still too much of an art form frankly. 153 00:08:04,900 --> 00:08:09,700 But there's big growth in the '90s in this area, 154 00:08:09,700 --> 00:08:12,850 much higher than any other kind of capital good. 155 00:08:12,850 --> 00:08:14,920 And it becomes pervasive in the economy. 156 00:08:14,920 --> 00:08:15,760 It's in homes. 157 00:08:15,760 --> 00:08:17,050 And it's in every business. 158 00:08:17,050 --> 00:08:20,950 And it's in government. 159 00:08:20,950 --> 00:08:22,885 And the productivity numbers. 160 00:08:25,840 --> 00:08:31,030 From 1945 to 1973, the productivity growth rate 161 00:08:31,030 --> 00:08:35,650 in the US is in the 2% range. 162 00:08:35,650 --> 00:08:43,750 And then from '73 to '93, productivity rate 163 00:08:43,750 --> 00:08:47,910 falls much lower into the 1% range. 164 00:08:47,910 --> 00:08:53,020 And then from '95 to 2000, productivity rate is 3.5%. 165 00:08:55,540 --> 00:08:59,680 And economic growth, in turn, is 4.2%. 166 00:08:59,680 --> 00:09:01,780 These are astonishing numbers. 167 00:09:01,780 --> 00:09:04,620 Historical US growth rate is 3%. 168 00:09:04,620 --> 00:09:07,810 In that '73 to '93 period, it falls down to 2%. 169 00:09:07,810 --> 00:09:11,590 Productivity falls down to the 1% range. 170 00:09:11,590 --> 00:09:13,270 That's a grim period. 171 00:09:13,270 --> 00:09:15,460 That is a grim period in the US. 172 00:09:18,490 --> 00:09:22,270 And getting out of that grim period really 173 00:09:22,270 --> 00:09:24,370 felt pretty amazing. 174 00:09:24,370 --> 00:09:25,900 Guess what? 175 00:09:25,900 --> 00:09:29,020 Our current growth rate is in the 2% range. 176 00:09:29,020 --> 00:09:32,895 And our current productivity rate is in the 1% range. 177 00:09:32,895 --> 00:09:35,860 It doesn't feel robust and dynamic. 178 00:09:35,860 --> 00:09:39,430 A lot of people are getting left behind. 179 00:09:39,430 --> 00:09:40,750 What we need? 180 00:09:40,750 --> 00:09:43,150 New innovation-based growth wave. 181 00:09:43,150 --> 00:09:45,130 That's what Jorgenson is writing about. 182 00:09:45,130 --> 00:09:50,572 That's what turned these numbers around in the 1990s. 183 00:09:50,572 --> 00:09:52,030 And again, I want you to understand 184 00:09:52,030 --> 00:09:56,300 the relationship between growth and productivity gains. 185 00:09:56,300 --> 00:09:58,210 Again, productivity gains are what 186 00:09:58,210 --> 00:10:01,180 create that real gain in society that you can distribute. 187 00:10:01,180 --> 00:10:05,400 And they get driven by technological advance. 188 00:10:05,400 --> 00:10:07,470 Productivity gains get driven predominantly 189 00:10:07,470 --> 00:10:08,670 by technological advance. 190 00:10:11,370 --> 00:10:14,190 So that's Jorgenson's picture. 191 00:10:16,800 --> 00:10:19,020 He essentially proves the model. 192 00:10:19,020 --> 00:10:21,900 He basically shows, by looking at a period of a big innovation 193 00:10:21,900 --> 00:10:25,470 wave, that Solow has got it right. 194 00:10:25,470 --> 00:10:27,000 It's technological-based innovation 195 00:10:27,000 --> 00:10:30,720 that's driving that amazing period of economic growth 196 00:10:30,720 --> 00:10:33,610 in the 1990s. 197 00:10:33,610 --> 00:10:37,515 So questions about this. 198 00:10:37,515 --> 00:10:40,510 AUDIENCE: For that dip in the '70s to '90s, 199 00:10:40,510 --> 00:10:45,396 is that mostly attributed to policy or demographic change? 200 00:10:45,396 --> 00:10:51,497 What was going on there that was resulting in lower innovation? 201 00:10:51,497 --> 00:10:53,830 WILLIAM BONVILLIAN: There's a number of things going on. 202 00:10:53,830 --> 00:10:56,720 It's not so much demographics change in that period of time. 203 00:10:56,720 --> 00:10:58,930 Now, we've got much more significant demographics 204 00:10:58,930 --> 00:11:00,460 issues. 205 00:11:00,460 --> 00:11:03,730 And the cost of an aging demographic because you're 206 00:11:03,730 --> 00:11:06,180 going to have to pay for me. 207 00:11:06,180 --> 00:11:09,430 And I'm going to take a lot of your real wealth. 208 00:11:09,430 --> 00:11:11,140 It's an intergenerational transfer now. 209 00:11:11,140 --> 00:11:12,932 We didn't really have much of that going on 210 00:11:12,932 --> 00:11:14,830 in that time period. 211 00:11:14,830 --> 00:11:17,500 It was the ascendancy of a baby boom, which in some ways 212 00:11:17,500 --> 00:11:19,570 is the opposite. 213 00:11:19,570 --> 00:11:24,005 But in that period, that's when the competition with Japan 214 00:11:24,005 --> 00:11:24,505 hits. 215 00:11:27,370 --> 00:11:29,500 And we'll talk more about this. 216 00:11:29,500 --> 00:11:32,770 But in the post World War II period, 217 00:11:32,770 --> 00:11:37,810 the US organizes its innovation system. 218 00:11:37,810 --> 00:11:40,280 And it organizes its innovation system 219 00:11:40,280 --> 00:11:46,270 around leading these innovation waves. 220 00:11:46,270 --> 00:11:50,230 And it ends up leading almost all of them in that 221 00:11:50,230 --> 00:11:54,310 late-20th-century, second-half-of-the-20th-century 222 00:11:54,310 --> 00:11:57,180 period. 223 00:11:57,180 --> 00:12:00,180 And it gets very rich because it's 224 00:12:00,180 --> 00:12:02,820 getting the first mover advantage of these innovation 225 00:12:02,820 --> 00:12:04,080 waves. 226 00:12:04,080 --> 00:12:06,150 So we create the richest nation on Earth. 227 00:12:09,580 --> 00:12:12,200 And then it misses one. 228 00:12:12,200 --> 00:12:15,470 And we'll talk more about this in class 3. 229 00:12:15,470 --> 00:12:22,070 But Japan figures out quality manufacturing. 230 00:12:22,070 --> 00:12:23,570 It's an innovation wave. 231 00:12:23,570 --> 00:12:25,700 It's a whole new way of doing production. 232 00:12:25,700 --> 00:12:29,180 And out of that, Japan is able to capture very large parts 233 00:12:29,180 --> 00:12:31,640 of consumer electronics and the auto sector, 234 00:12:31,640 --> 00:12:34,790 which had been core sectors in the US economy. 235 00:12:34,790 --> 00:12:39,290 So a fair amount of what's going on between '73 and '93 236 00:12:39,290 --> 00:12:41,720 is that the US misses an innovation wave. 237 00:12:41,720 --> 00:12:43,880 Now, interestingly, then what happens? 238 00:12:43,880 --> 00:12:47,210 Then we have this boom time. 239 00:12:47,210 --> 00:12:48,870 What did we do? 240 00:12:48,870 --> 00:12:51,430 We brought on another innovation wave. 241 00:12:51,430 --> 00:12:52,430 This is the IT wave. 242 00:12:52,430 --> 00:12:54,860 That's what Jorgenson is telling us. 243 00:12:54,860 --> 00:12:59,610 And fascinatingly, Japan missed it. 244 00:12:59,610 --> 00:13:03,977 So there's a lot of lessons here that we'll 245 00:13:03,977 --> 00:13:06,060 talk about when we get to the manufacturing class. 246 00:13:06,060 --> 00:13:08,340 But go ahead, Max. 247 00:13:08,340 --> 00:13:11,007 AUDIENCE: [INAUDIBLE] I mean, if you've got companies 248 00:13:11,007 --> 00:13:13,550 like Sony that did very well. 249 00:13:13,550 --> 00:13:15,300 WILLIAM BONVILLIAN: I'll describe for you, 250 00:13:15,300 --> 00:13:17,630 Matt-- let me interrupt since you mentioned Sony. 251 00:13:17,630 --> 00:13:22,020 So I'm in Japan in January of 2006 252 00:13:22,020 --> 00:13:25,710 speaking at a big conference organized by the US National 253 00:13:25,710 --> 00:13:30,510 Academies and similar organizations in Japan. 254 00:13:30,510 --> 00:13:35,550 And the meeting is on innovation systems actually. 255 00:13:35,550 --> 00:13:45,270 And the headline in the major Japanese newspaper-- 256 00:13:45,270 --> 00:13:49,560 and there's a major Japanese English language newspaper. 257 00:13:49,560 --> 00:13:52,830 The headline is-- bold headline across the front-- 258 00:13:57,020 --> 00:14:04,040 Apple Sells 14 Million iPods, semicolon, where is Sony, 259 00:14:04,040 --> 00:14:05,360 question mark, question mark. 260 00:14:08,210 --> 00:14:15,800 Apple took back a large part of that consumer innovation wave. 261 00:14:15,800 --> 00:14:19,070 Snatched it because of defeat at the hands of great companies 262 00:14:19,070 --> 00:14:20,330 like Sony. 263 00:14:20,330 --> 00:14:21,890 So the US was-- 264 00:14:21,890 --> 00:14:25,820 US quote because Apple doesn't make anything here-- 265 00:14:25,820 --> 00:14:27,990 was able to take back leadership of 266 00:14:27,990 --> 00:14:30,320 an important because of its leadership of a computing 267 00:14:30,320 --> 00:14:31,880 revolution. 268 00:14:31,880 --> 00:14:33,590 In effect, it adds a computing revolution 269 00:14:33,590 --> 00:14:36,650 onto what that had been consumer electronics 270 00:14:36,650 --> 00:14:39,320 and then leads in that territory. 271 00:14:39,320 --> 00:14:42,740 And that's what's playing out in this time 272 00:14:42,740 --> 00:14:44,440 period of the innovation wave. 273 00:14:47,350 --> 00:14:50,230 But we'll dive into this when we get to the manufacturing class. 274 00:14:53,880 --> 00:14:55,710 So anything else on Jorgenson? 275 00:14:55,710 --> 00:14:56,460 What do you think? 276 00:14:56,460 --> 00:14:59,010 Is he right? 277 00:14:59,010 --> 00:15:00,100 Is this the right picture? 278 00:15:05,940 --> 00:15:07,740 I think the economic evidence is pretty 279 00:15:07,740 --> 00:15:09,360 overwhelming on this one. 280 00:15:09,360 --> 00:15:13,500 It looks really pretty clear what happened. 281 00:15:13,500 --> 00:15:21,810 And to this model of a core invention, apps pile on it, 282 00:15:21,810 --> 00:15:27,000 semiconductors, apps pile around it like the internet. 283 00:15:27,000 --> 00:15:30,180 And then it becomes innovation. 284 00:15:30,180 --> 00:15:33,600 And then it becomes an innovation wave. 285 00:15:33,600 --> 00:15:36,510 And then it spreads throughout the economy. 286 00:15:36,510 --> 00:15:39,120 And then it creates big productivity gains. 287 00:15:39,120 --> 00:15:42,720 And then you can translate those productivity gains 288 00:15:42,720 --> 00:15:44,820 into a real gain in your society, which 289 00:15:44,820 --> 00:15:46,710 means a real new wealth in your society 290 00:15:46,710 --> 00:15:47,752 which you can distribute. 291 00:15:47,752 --> 00:15:51,270 That looks very clear that that happens. 292 00:15:51,270 --> 00:15:56,370 So a major issue in the class is how do you how do you do these? 293 00:15:56,370 --> 00:15:58,020 Can we do these? 294 00:15:58,020 --> 00:15:59,130 Can we speed the pace? 295 00:15:59,130 --> 00:16:02,160 I mean, typically, they happen about every 40 or 50 years. 296 00:16:02,160 --> 00:16:04,232 Could you reduce the interim period? 297 00:16:04,232 --> 00:16:05,940 One way of looking at what's going on now 298 00:16:05,940 --> 00:16:08,550 is we're just waiting for a wave. 299 00:16:08,550 --> 00:16:12,330 It's like we're surfers paddling around waiting for that wave 300 00:16:12,330 --> 00:16:14,730 to kind of take us. 301 00:16:14,730 --> 00:16:17,700 And I don't know what that wave is going to be. 302 00:16:17,700 --> 00:16:20,967 But it could be very interesting because we 303 00:16:20,967 --> 00:16:22,800 seem to do these about every 40 or 50 years. 304 00:16:22,800 --> 00:16:26,730 But then the question is, are we going to lead it? 305 00:16:26,730 --> 00:16:29,100 So if the next big wave worldwide is energy, 306 00:16:29,100 --> 00:16:31,650 and there's certainly a case that it might be, 307 00:16:31,650 --> 00:16:33,630 are we going to have a first-mover advantage 308 00:16:33,630 --> 00:16:37,500 in a lot of these technologies and get some of the gains? 309 00:16:37,500 --> 00:16:38,947 Or not? 310 00:16:38,947 --> 00:16:40,530 I think that's a pretty open question. 311 00:16:46,086 --> 00:16:47,358 AUDIENCE: Chime in. 312 00:16:47,358 --> 00:16:48,900 WILLIAM BONVILLIAN: Martin, go ahead. 313 00:16:48,900 --> 00:16:50,470 AUDIENCE: Yeah, so in general, that's 314 00:16:50,470 --> 00:16:53,220 the classic thing of, if we miss the next wave, your economy 315 00:16:53,220 --> 00:16:54,090 will plummet too. 316 00:16:54,090 --> 00:16:56,210 And that's probably what happened in Japan. 317 00:16:56,210 --> 00:16:58,430 The big concern, though, is like most like Schwinger, 318 00:16:58,430 --> 00:17:02,190 who's the nuclear scientist in the 50's, 60's, talked 319 00:17:02,190 --> 00:17:04,900 about how, if we do nuclear, it wouldn't be possible in the US, 320 00:17:04,900 --> 00:17:07,319 It would probably have to be somewhere in Asia 321 00:17:07,319 --> 00:17:09,329 because we're just kind of close-minded to it. 322 00:17:09,329 --> 00:17:11,079 And then if you look into recent examples, 323 00:17:11,079 --> 00:17:14,013 like Bill Gates funded a fission company that's 324 00:17:14,013 --> 00:17:15,180 doing a new kind of reactor. 325 00:17:15,180 --> 00:17:17,638 But the policy just so hard here that they're just doing it 326 00:17:17,638 --> 00:17:19,950 in China because it's faster. 327 00:17:19,950 --> 00:17:22,151 And I think Romer also talks about charter cities 328 00:17:22,151 --> 00:17:23,234 where you have less rules. 329 00:17:23,234 --> 00:17:26,040 So it's easier to innovate quickly. 330 00:17:26,040 --> 00:17:27,470 So that's another thing. 331 00:17:27,470 --> 00:17:30,150 There's like this saying where, if you outlaw innovation, 332 00:17:30,150 --> 00:17:31,545 only outlaws will evolve. 333 00:17:31,545 --> 00:17:33,660 If you make it really hard to innovate, 334 00:17:33,660 --> 00:17:36,415 people are just going to go somewhere else. 335 00:17:36,415 --> 00:17:37,790 WILLIAM BONVILLIAN: I think those 336 00:17:37,790 --> 00:17:38,998 are important points, Martin. 337 00:17:38,998 --> 00:17:45,810 And this is a legacy sector problem. 338 00:17:45,810 --> 00:17:46,860 In other words, the US-- 339 00:17:46,860 --> 00:17:49,210 and we'll talk about this a lot when we get to energy. 340 00:17:49,210 --> 00:17:50,370 And we'll read about it. 341 00:17:50,370 --> 00:17:55,170 But the US is pretty good at creating 342 00:17:55,170 --> 00:17:58,830 these kind of new frontier territories, 343 00:17:58,830 --> 00:18:01,560 standing up these new areas. 344 00:18:01,560 --> 00:18:05,160 We're not good at bringing innovation back 345 00:18:05,160 --> 00:18:06,330 into established sectors. 346 00:18:06,330 --> 00:18:08,100 We'd just rather do the next big thing. 347 00:18:08,100 --> 00:18:10,920 And that's not bad. 348 00:18:10,920 --> 00:18:13,920 But when a lot of your social problems 349 00:18:13,920 --> 00:18:17,280 are tied to the legacy sectors, then it 350 00:18:17,280 --> 00:18:19,560 becomes a big disadvantage. 351 00:18:19,560 --> 00:18:21,900 Now, that's not to say that countries in general 352 00:18:21,900 --> 00:18:23,720 are good at innovating in legacy sectors. 353 00:18:23,720 --> 00:18:24,220 They're not. 354 00:18:24,220 --> 00:18:26,670 There's a problem for everybody. 355 00:18:26,670 --> 00:18:29,580 But I think we're particularly bad at it. 356 00:18:29,580 --> 00:18:33,750 And think of what the big societal problems are. 357 00:18:33,750 --> 00:18:37,410 Energy efficiency and moving to a new generation 358 00:18:37,410 --> 00:18:38,370 of energy technologies. 359 00:18:38,370 --> 00:18:39,550 We've got to figure out how to do that. 360 00:18:39,550 --> 00:18:42,008 But that means learning how to innovate in a legacy sector. 361 00:18:42,008 --> 00:18:44,820 We've got a terrible problem with health care delivery, 362 00:18:44,820 --> 00:18:47,040 terrible. 363 00:18:47,040 --> 00:18:48,420 We do the new thing. 364 00:18:48,420 --> 00:18:49,240 We'll do biotech. 365 00:18:49,240 --> 00:18:50,490 We'll create the new frontier. 366 00:18:50,490 --> 00:18:53,070 We just don't go back and fix the health care delivery system 367 00:18:53,070 --> 00:18:54,940 because it's tough. 368 00:18:54,940 --> 00:18:58,450 We'd rather do biotech. 369 00:18:58,450 --> 00:19:00,630 So a lot of our societal problems 370 00:19:00,630 --> 00:19:01,980 are tied up in this stuff. 371 00:19:01,980 --> 00:19:04,663 And as we'll talk about in a couple of weeks, 372 00:19:04,663 --> 00:19:06,330 manufacturing has played a critical role 373 00:19:06,330 --> 00:19:09,990 in creating a very deep inequality problem in society, 374 00:19:09,990 --> 00:19:11,790 the failure of that sector. 375 00:19:11,790 --> 00:19:15,870 And again, we're not good at going back and bringing 376 00:19:15,870 --> 00:19:17,700 innovation, i.e. the next generation 377 00:19:17,700 --> 00:19:21,580 of advanced manufacturing, into an established legacy sector. 378 00:19:21,580 --> 00:19:23,640 So this legacy sector problem actually 379 00:19:23,640 --> 00:19:24,750 turns out to be a big one. 380 00:19:24,750 --> 00:19:27,230 And that'll be one of the themes of the class. 381 00:19:27,230 --> 00:19:29,980 I'm stealing my own thunder for a few weeks now. 382 00:19:29,980 --> 00:19:33,810 But you get an advance snapshot. 383 00:19:33,810 --> 00:19:35,380 Chloe? 384 00:19:35,380 --> 00:19:39,480 AUDIENCE: Are we at a new point in our history of innovation 385 00:19:39,480 --> 00:19:42,881 because we have too many next big things that we're 386 00:19:42,881 --> 00:19:45,214 having problems following through creating an innovation 387 00:19:45,214 --> 00:19:46,188 wave? 388 00:19:46,188 --> 00:19:48,848 We have health care and space and energy and nuclear. 389 00:19:48,848 --> 00:19:49,890 WILLIAM BONVILLIAN: Yeah. 390 00:19:49,890 --> 00:19:53,780 I don't think it's bad to have a big menu to get waves out of. 391 00:19:53,780 --> 00:19:54,640 I think that's fine. 392 00:19:54,640 --> 00:19:58,060 And the other thing I should say is not all waves are big waves. 393 00:19:58,060 --> 00:20:01,110 In other words, waves are of different size here. 394 00:20:01,110 --> 00:20:03,840 Biotech is not a big a wave as an IT sector. 395 00:20:03,840 --> 00:20:06,360 But it sure is an important wave. 396 00:20:06,360 --> 00:20:08,130 And you don't want to miss it. 397 00:20:08,130 --> 00:20:10,830 So we're going to have different waves of different sizes. 398 00:20:10,830 --> 00:20:13,560 So having a lot of candidates for waves, I think, 399 00:20:13,560 --> 00:20:16,740 is actually a pretty important thing. 400 00:20:16,740 --> 00:20:21,180 Because it's hard to predict where 401 00:20:21,180 --> 00:20:24,536 the big innovation waves will actually go. 402 00:20:24,536 --> 00:20:25,670 Does that answer that? 403 00:20:29,190 --> 00:20:32,340 Any other thoughts? 404 00:20:32,340 --> 00:20:39,840 All right, let's do this Merrill Lynch piece. 405 00:20:44,510 --> 00:20:48,350 So here's the question that this-- this is a little Merrill 406 00:20:48,350 --> 00:20:53,690 Lynch report that is sent just before 9/11, 407 00:20:53,690 --> 00:20:57,860 interestingly, when the markets went bust. 408 00:20:57,860 --> 00:21:00,200 This was done on September 4, 2001. 409 00:21:00,200 --> 00:21:03,950 And disaster is only days ahead. 410 00:21:03,950 --> 00:21:10,700 But this is that they're riding the IT innovation wave. 411 00:21:10,700 --> 00:21:16,430 And they're trying to assess what innovation waves are 412 00:21:16,430 --> 00:21:20,930 and how their investors can get rich off of them. 413 00:21:20,930 --> 00:21:24,800 And in a capitalist economy, that's not an unimportant task. 414 00:21:24,800 --> 00:21:30,620 So how do investors look at potential technology 415 00:21:30,620 --> 00:21:31,640 breakthroughs? 416 00:21:31,640 --> 00:21:36,000 Do they believe that they drive growth? 417 00:21:36,000 --> 00:21:39,050 So what does the Merrill report out to its investors, 418 00:21:39,050 --> 00:21:40,910 tell its investors? 419 00:21:40,910 --> 00:21:43,880 Well, they say, yes. 420 00:21:43,880 --> 00:21:46,550 Innovation drives growth so you want to get on this. 421 00:21:46,550 --> 00:21:49,430 You really want to get on these innovation waves. 422 00:21:49,430 --> 00:21:52,490 And then Norman Piore, who I think 423 00:21:52,490 --> 00:21:54,620 is related to MIT's Michael Piore, who's 424 00:21:54,620 --> 00:21:59,720 a wonderful professor of entrepreneurship and technology 425 00:21:59,720 --> 00:22:03,647 advances at Sloan, has done important work 426 00:22:03,647 --> 00:22:05,105 on a lot of things, including DARPA 427 00:22:05,105 --> 00:22:08,670 and including manufacturing. 428 00:22:08,670 --> 00:22:12,080 So I think Norman and Michael are related. 429 00:22:12,080 --> 00:22:13,730 But I haven't asked him. 430 00:22:13,730 --> 00:22:16,790 But Norman Piore, who's the chief economist 431 00:22:16,790 --> 00:22:18,500 for Merrill Lynch at the time, he 432 00:22:18,500 --> 00:22:23,680 said, yes, innovation-based growth drives the economy. 433 00:22:23,680 --> 00:22:26,590 And by the way, it drives the stock market. 434 00:22:26,590 --> 00:22:28,840 And then he goes on to say-- 435 00:22:28,840 --> 00:22:30,640 and where he gets this, I don't know. 436 00:22:30,640 --> 00:22:35,440 But he says, it takes 28 years for widespread acceptance 437 00:22:35,440 --> 00:22:38,410 of a new technology. 438 00:22:38,410 --> 00:22:40,780 That's-- is it always 28 years? 439 00:22:40,780 --> 00:22:42,310 Maybe it's 27. 440 00:22:42,310 --> 00:22:44,190 I don't know. 441 00:22:44,190 --> 00:22:49,210 Then it takes 56 years for rapid growth to evolve. 442 00:22:49,210 --> 00:22:52,840 And then it takes 112 years measured 443 00:22:52,840 --> 00:22:58,840 from here for technological maturity to occur. 444 00:22:58,840 --> 00:23:00,880 And then after that, growth in that sector 445 00:23:00,880 --> 00:23:04,240 is going to resemble growth in population rates. 446 00:23:04,240 --> 00:23:06,610 Well, I don't know where he gets the 28 447 00:23:06,610 --> 00:23:08,350 years, 56 years, 112 years. 448 00:23:08,350 --> 00:23:09,880 I don't know where that comes from. 449 00:23:09,880 --> 00:23:13,630 But this is the chief economist of Merrill Lynch saying, 450 00:23:13,630 --> 00:23:15,110 yes, this is how it works. 451 00:23:15,110 --> 00:23:17,290 This is how it happens. 452 00:23:17,290 --> 00:23:20,440 That's interesting. 453 00:23:20,440 --> 00:23:22,720 It's not just an obscure economist conjury 454 00:23:22,720 --> 00:23:23,660 of growth wave. 455 00:23:23,660 --> 00:23:26,560 This is advice going out from, then, 456 00:23:26,560 --> 00:23:31,690 the largest stock, broad-based investment company in the US. 457 00:23:31,690 --> 00:23:35,560 Then they hit on, what's the wave we're going to hit? 458 00:23:35,560 --> 00:23:37,600 So they think nano. 459 00:23:37,600 --> 00:23:41,440 That's the one, nanotechnology. 460 00:23:41,440 --> 00:23:45,732 So they describe an interesting pattern here, 461 00:23:45,732 --> 00:23:47,440 which I think is actually very important. 462 00:23:47,440 --> 00:23:50,020 It's really, I think, very perceptive. 463 00:23:50,020 --> 00:23:52,900 I don't know where the 28 year comes from although the time 464 00:23:52,900 --> 00:23:54,970 frames are roughly right. 465 00:23:54,970 --> 00:23:57,040 But this, I think, is really intriguing. 466 00:23:57,040 --> 00:24:00,070 And I want you to think about this. 467 00:24:00,070 --> 00:24:03,420 So they say, let's look at nanotechnology. 468 00:24:03,420 --> 00:24:05,860 And as you know, that's fabrication 469 00:24:05,860 --> 00:24:07,960 of the mark of the scale. 470 00:24:07,960 --> 00:24:12,910 And first, for a technology to evolve and get 471 00:24:12,910 --> 00:24:20,830 into range of being innovation and maybe an innovation wave, 472 00:24:20,830 --> 00:24:22,930 first, there's got to be a vision. 473 00:24:22,930 --> 00:24:27,580 Somebody has to present a vision of what this technology could 474 00:24:27,580 --> 00:24:29,170 accomplish. 475 00:24:29,170 --> 00:24:32,950 And they argued that the first vision about nanotechnology 476 00:24:32,950 --> 00:24:37,840 was from physicist Richard Feynman, a famous physicist who 477 00:24:37,840 --> 00:24:40,750 went to school here, of course, and was in the Manhattan 478 00:24:40,750 --> 00:24:44,230 Project and then unfortunately taught at an obscure California 479 00:24:44,230 --> 00:24:47,200 school called Caltech. 480 00:24:47,200 --> 00:24:52,450 But Feynman, in a 1959 piece, argued, 481 00:24:52,450 --> 00:24:54,370 in the physics community, there's 482 00:24:54,370 --> 00:24:56,630 plenty of room at the bottom. 483 00:24:56,630 --> 00:24:58,720 In other words, at the really small scales 484 00:24:58,720 --> 00:25:02,860 were quantum effects can occur, some really interesting things 485 00:25:02,860 --> 00:25:04,160 can happen. 486 00:25:04,160 --> 00:25:05,290 And that's the nano scale. 487 00:25:05,290 --> 00:25:07,570 So he gives a vision that we could 488 00:25:07,570 --> 00:25:09,280 do some really interesting stuff when 489 00:25:09,280 --> 00:25:13,840 you're operating outside of Newtonian physics 490 00:25:13,840 --> 00:25:15,200 at a smaller scale. 491 00:25:15,200 --> 00:25:21,030 So the second piece that has to occur they 492 00:25:21,030 --> 00:25:23,610 refer to as enablers. 493 00:25:23,610 --> 00:25:28,290 So there's got to be, in effect, tool sets and instruments 494 00:25:28,290 --> 00:25:32,970 that enable this vision to get played out. 495 00:25:32,970 --> 00:25:37,050 And the key tool set in that time period 496 00:25:37,050 --> 00:25:43,320 was when IBM did the scanning tunneling electron microscope. 497 00:25:43,320 --> 00:25:45,150 And you probably don't remember this. 498 00:25:45,150 --> 00:25:50,730 But there was a moment when IBM, before there 499 00:25:50,730 --> 00:25:54,010 was much of an internet, distributed photographs 500 00:25:54,010 --> 00:25:59,260 of stacks of molecules spelling out the IBM logo 501 00:25:59,260 --> 00:26:03,600 and to civilians like me, I said, oh, that looks very nice. 502 00:26:03,600 --> 00:26:08,580 But to scientists seeing an ability 503 00:26:08,580 --> 00:26:11,220 to structure molecules in that kind of way 504 00:26:11,220 --> 00:26:13,500 was just absolutely amazing. 505 00:26:13,500 --> 00:26:17,280 So the scanning tunneling electron microscope 506 00:26:17,280 --> 00:26:19,740 is not only an observing system. 507 00:26:19,740 --> 00:26:26,250 It's a device by which you can move molecules around. 508 00:26:26,250 --> 00:26:30,540 So it becomes a huge capability in allowing 509 00:26:30,540 --> 00:26:34,110 measurement and manipulation in nanoscale systems. 510 00:26:34,110 --> 00:26:36,420 So that's when this article was written. 511 00:26:36,420 --> 00:26:38,460 That was 20 years before. 512 00:26:38,460 --> 00:26:42,990 The third piece that this Merrill Lynch piece talks 513 00:26:42,990 --> 00:26:45,560 about, which I also think is really important, 514 00:26:45,560 --> 00:26:49,290 is this is straight out of Romer. 515 00:26:49,290 --> 00:26:52,050 You've got to create research mass. 516 00:26:52,050 --> 00:26:54,940 In other words, you got to put talent on the problem. 517 00:26:54,940 --> 00:26:57,630 This is human capital engaged in research. 518 00:26:57,630 --> 00:27:04,320 So Eric Drexler, from MIT, in a 1981 journal article, 519 00:27:04,320 --> 00:27:09,480 begins to describe the kind of physics and possibilities 520 00:27:09,480 --> 00:27:10,320 in nanotechnology. 521 00:27:10,320 --> 00:27:11,910 That's the first one. 522 00:27:11,910 --> 00:27:18,900 By 2000, there are 1,800 journal articles on nano. 523 00:27:18,900 --> 00:27:22,493 And by the way, that's a similar phenomena 524 00:27:22,493 --> 00:27:23,910 to the number of articles starting 525 00:27:23,910 --> 00:27:27,930 to write about the internet in the 1990s. 526 00:27:27,930 --> 00:27:30,900 In other words, this journal article total 527 00:27:30,900 --> 00:27:32,640 signals research mass. 528 00:27:32,640 --> 00:27:34,080 It signals prospectors. 529 00:27:34,080 --> 00:27:38,010 It signals human capital engaged in research. 530 00:27:38,010 --> 00:27:41,520 So these are, I think, really interesting conceptual points 531 00:27:41,520 --> 00:27:42,750 here. 532 00:27:42,750 --> 00:27:45,720 And you have to go through these to get to your innovation slash 533 00:27:45,720 --> 00:27:48,000 innovation wave. 534 00:27:48,000 --> 00:27:50,550 You need the vision. 535 00:27:50,550 --> 00:27:52,260 You need the enabling tools. 536 00:27:52,260 --> 00:27:56,930 And then you need talent on the task, this research mass. 537 00:27:56,930 --> 00:28:00,930 And you've got to go through all three steps to get there. 538 00:28:00,930 --> 00:28:03,360 That's interesting. 539 00:28:03,360 --> 00:28:06,360 And then they come to, how are we 540 00:28:06,360 --> 00:28:08,730 going to tell our investors to get rich off this? 541 00:28:08,730 --> 00:28:11,650 Because we've gone through the three stages for nano. 542 00:28:11,650 --> 00:28:16,110 We've got 1,800 journal articles the previous year. 543 00:28:16,110 --> 00:28:17,330 We've got research mass. 544 00:28:17,330 --> 00:28:20,730 How are we going to get rich? 545 00:28:20,730 --> 00:28:22,620 And then they issue this warning. 546 00:28:22,620 --> 00:28:26,040 Although the futuristic market is fascinating, 547 00:28:26,040 --> 00:28:28,720 it is not inevitable. 548 00:28:28,720 --> 00:28:30,780 So just because you're approaching the future, 549 00:28:30,780 --> 00:28:32,780 it doesn't mean you're always going to get rich. 550 00:28:32,780 --> 00:28:34,200 So how are you going to get rich? 551 00:28:34,200 --> 00:28:36,810 They acknowledge that nanotechnology in 2001 552 00:28:36,810 --> 00:28:40,200 is starting to get close to commercial markets. 553 00:28:40,200 --> 00:28:43,440 And then they review key markets, 554 00:28:43,440 --> 00:28:46,620 which we would view as hilariously short term. 555 00:28:46,620 --> 00:28:49,500 So zero to two years, short term, 556 00:28:49,500 --> 00:28:52,740 zero to five years, mid-term. 557 00:28:52,740 --> 00:28:54,790 Five-plus years is long term. 558 00:28:54,790 --> 00:28:59,235 Now, remember Norman Piore? 559 00:28:59,235 --> 00:29:02,730 It takes 28 years to do this and takes 56 years to do this. 560 00:29:02,730 --> 00:29:06,190 It takes 112 years to do this. 561 00:29:06,190 --> 00:29:09,072 Those are long periods of time. 562 00:29:09,072 --> 00:29:10,530 These guys are talking about trying 563 00:29:10,530 --> 00:29:14,100 to get rich in what we would view as extremely 564 00:29:14,100 --> 00:29:15,420 short-term periods. 565 00:29:18,180 --> 00:29:20,220 And they give you a good perspective 566 00:29:20,220 --> 00:29:22,560 as to how far out investors are even willing to look. 567 00:29:25,260 --> 00:29:27,360 And they argued that the keys to nanotechnology 568 00:29:27,360 --> 00:29:28,890 are manufacturing and communication. 569 00:29:28,890 --> 00:29:31,500 If you can't build it in volume, then there's 570 00:29:31,500 --> 00:29:32,850 not much you can do with it. 571 00:29:36,270 --> 00:29:39,350 So they look at a series of opportunities with that idea 572 00:29:39,350 --> 00:29:40,790 behind. 573 00:29:40,790 --> 00:29:43,280 They look at opportunity number 1-- 574 00:29:43,280 --> 00:29:44,360 instrumentation. 575 00:29:44,360 --> 00:29:48,260 And they note that, as a new technology is advancing along, 576 00:29:48,260 --> 00:29:52,310 the first class of winners are the tool makers. 577 00:29:52,310 --> 00:29:57,410 So we'll go back to our California Gold Rush in 1849. 578 00:29:57,410 --> 00:30:00,320 The first people to get really rich in California 579 00:30:00,320 --> 00:30:03,230 are those that are supplying those miners that are 580 00:30:03,230 --> 00:30:05,150 headed off to the gold fields. 581 00:30:05,150 --> 00:30:08,540 The tool makers tend to be an early class. 582 00:30:08,540 --> 00:30:11,480 So for heaven's sakes, let's invest in the tool makers 583 00:30:11,480 --> 00:30:14,060 because they're critical to everybody else. 584 00:30:14,060 --> 00:30:16,010 Everybody is going to need their stuff. 585 00:30:16,010 --> 00:30:18,440 Let's invest in that stuff. 586 00:30:18,440 --> 00:30:20,750 Then they look at semiconductors. 587 00:30:20,750 --> 00:30:23,810 And they note that, within 10 years, if you're 588 00:30:23,810 --> 00:30:25,310 going to stay on Moore's law, you're 589 00:30:25,310 --> 00:30:27,600 going to have to get deep into nanotechnology, 590 00:30:27,600 --> 00:30:30,350 which, of course, turns out to be the case. 591 00:30:30,350 --> 00:30:35,180 But that could take 10 years. 592 00:30:35,180 --> 00:30:36,622 So forget that. 593 00:30:36,622 --> 00:30:38,330 They're not going to advise any investors 594 00:30:38,330 --> 00:30:40,910 to get into that business. 595 00:30:40,910 --> 00:30:43,100 Instead, they really want to focus 596 00:30:43,100 --> 00:30:46,370 on stuff that can evolve in two years or less. 597 00:30:49,250 --> 00:30:54,710 So if Norman is right and technology development takes 598 00:30:54,710 --> 00:30:59,300 a really long time measured in decades 599 00:30:59,300 --> 00:31:02,750 and Merrill is only willing to invest for what it refers 600 00:31:02,750 --> 00:31:05,810 to as the short-term zero to two years, 601 00:31:05,810 --> 00:31:09,350 there is a big disconnect in our ability 602 00:31:09,350 --> 00:31:13,290 to stand up technologies. 603 00:31:13,290 --> 00:31:16,950 We're just not operating at a plausible frame. 604 00:31:16,950 --> 00:31:19,500 And who is going to fund the rest of it? 605 00:31:22,260 --> 00:31:25,350 I don't think Norman's 28 years is necessarily right. 606 00:31:25,350 --> 00:31:28,440 But it's a long time. 607 00:31:28,440 --> 00:31:29,760 And who is going to fund that? 608 00:31:29,760 --> 00:31:33,210 Who's going to carry this stuff for that extended period 609 00:31:33,210 --> 00:31:35,820 if Merrill and the boys are only willing to do 610 00:31:35,820 --> 00:31:37,320 a two-year timetable? 611 00:31:42,700 --> 00:31:46,590 This is a big gap in the innovation system. 612 00:31:49,510 --> 00:31:52,840 And when we were talking a few minutes ago about the problems 613 00:31:52,840 --> 00:31:56,810 we've got in standing up hard technologies, 614 00:31:56,810 --> 00:31:59,950 investment at scale, if it's only going to focus 615 00:31:59,950 --> 00:32:01,700 on a two-year time frame-- in other words, 616 00:32:01,700 --> 00:32:08,800 you've got to get to production in the short term to really 617 00:32:08,800 --> 00:32:12,427 have this investable, to work as an investment, 618 00:32:12,427 --> 00:32:15,010 so you're not going to want to invest in technologies that are 619 00:32:15,010 --> 00:32:19,510 more than two years out from production-- 620 00:32:19,510 --> 00:32:22,510 then you're taking an enormous amount of interesting things 621 00:32:22,510 --> 00:32:24,290 off the table here. 622 00:32:24,290 --> 00:32:26,960 So it's a really big structural problem. 623 00:32:26,960 --> 00:32:29,613 The answer that the US has come up with historically 624 00:32:29,613 --> 00:32:32,030 is that the federal government will play a long-term role. 625 00:32:32,030 --> 00:32:33,940 The federal government will provide the long-term patient 626 00:32:33,940 --> 00:32:34,440 capital. 627 00:32:37,840 --> 00:32:41,080 But that's not a perfect model either because of what 628 00:32:41,080 --> 00:32:43,990 we'll talk about next week, this valley of death problem. 629 00:32:46,610 --> 00:32:47,660 So you following me? 630 00:32:50,460 --> 00:32:54,150 So this is a classic kind of innovation system problem. 631 00:32:54,150 --> 00:32:57,060 So when you're thinking about innovation systems, 632 00:32:57,060 --> 00:32:59,580 you're going to have to think about the actors 633 00:32:59,580 --> 00:33:02,740 in that system, the handoffs between the actors, 634 00:33:02,740 --> 00:33:05,250 and then the time frames that the actor is 635 00:33:05,250 --> 00:33:07,140 going to need support. 636 00:33:07,140 --> 00:33:09,090 So this is another way of helping 637 00:33:09,090 --> 00:33:13,560 us think about how to look at innovation systems. 638 00:33:30,092 --> 00:33:31,300 Any questions flow from this? 639 00:33:31,300 --> 00:33:33,970 Because this is a big challenge here. 640 00:33:37,590 --> 00:33:40,270 The venture capital time frame is basically organized 641 00:33:40,270 --> 00:33:42,595 for everything except biotech when 642 00:33:42,595 --> 00:33:45,220 looking at technologies that are no more than a couple of years 643 00:33:45,220 --> 00:33:47,970 out from production. 644 00:33:47,970 --> 00:33:52,120 So the US created this amazing venture capital system, 645 00:33:52,120 --> 00:33:54,730 which, in many ways, is the envy of the world. 646 00:33:54,730 --> 00:33:57,135 And it works very nicely for IT. 647 00:33:57,135 --> 00:33:58,760 And it works for very different reasons 648 00:33:58,760 --> 00:34:01,300 that we'll talk about for biotech. 649 00:34:01,300 --> 00:34:04,030 But it's not working in these hard technology sectors 650 00:34:04,030 --> 00:34:06,070 because the timetables are just too long 651 00:34:06,070 --> 00:34:08,440 and the risks are too high and the uncertainties 652 00:34:08,440 --> 00:34:10,030 are too great. 653 00:34:10,030 --> 00:34:14,889 So this is a big gap in the innovation system. 654 00:34:14,889 --> 00:34:17,889 Merrill Lynch doesn't look at it as a gap in the innovation 655 00:34:17,889 --> 00:34:18,429 system. 656 00:34:18,429 --> 00:34:21,363 But that's really what they're showing us exists. 657 00:34:21,363 --> 00:34:22,780 And then the other important thing 658 00:34:22,780 --> 00:34:27,550 to remember about what they present us with 659 00:34:27,550 --> 00:34:32,230 is I think this very perceptive notion that you need to go 660 00:34:32,230 --> 00:34:33,760 through these three stages. 661 00:34:33,760 --> 00:34:38,350 You need to move a technology from invention and discovery 662 00:34:38,350 --> 00:34:39,770 to innovation-- 663 00:34:39,770 --> 00:34:43,330 vision, the enabling technologies, and then 664 00:34:43,330 --> 00:34:46,590 the research mass, the human capital engaged in research, 665 00:34:46,590 --> 00:34:47,380 the talent base. 666 00:34:52,080 --> 00:34:54,469 AUDIENCE: So are innovation systems, 667 00:34:54,469 --> 00:34:56,730 like Bell Labs, just unsustainable? 668 00:34:56,730 --> 00:35:01,350 Or why haven't we seen anything like that? 669 00:35:01,350 --> 00:35:03,180 WILLIAM BONVILLIAN: Yeah, I mean, we'll 670 00:35:03,180 --> 00:35:04,570 deal with this a bit next week. 671 00:35:04,570 --> 00:35:08,940 And you should, Beth, bring me back to this next week 672 00:35:08,940 --> 00:35:11,735 for sure. 673 00:35:11,735 --> 00:35:13,110 The short story-- and we'll spend 674 00:35:13,110 --> 00:35:15,810 a little more time on this. 675 00:35:15,810 --> 00:35:18,750 Bell Labs was tied and was able to have 676 00:35:18,750 --> 00:35:23,640 very patient long-term supportive technology 677 00:35:23,640 --> 00:35:26,760 because it was tied to a government-guaranteed monopoly 678 00:35:26,760 --> 00:35:27,930 model. 679 00:35:27,930 --> 00:35:32,490 And when we broke up AT&T and the segments that 680 00:35:32,490 --> 00:35:38,730 survived were placed in a highly competitive situation with one 681 00:35:38,730 --> 00:35:42,540 another plus an IT revolution, which created an entirely 682 00:35:42,540 --> 00:35:45,120 different communication system around the internet, 683 00:35:45,120 --> 00:35:49,650 was descending, then Bell Labs' ability 684 00:35:49,650 --> 00:35:53,070 to sustain a model of long-term patient 685 00:35:53,070 --> 00:35:57,390 investment in breakthrough technology advance, it's gone. 686 00:35:57,390 --> 00:36:04,210 And by and large, with some exceptions, 687 00:36:04,210 --> 00:36:10,690 the model of the large industry-supported, basic and 688 00:36:10,690 --> 00:36:15,880 applied research laboratory has largely gone. 689 00:36:15,880 --> 00:36:17,330 There's still some pieces left. 690 00:36:17,330 --> 00:36:20,190 IBM, although that's under a lot of economic pressure 691 00:36:20,190 --> 00:36:22,640 as it tries to shift to a service-based business 692 00:36:22,640 --> 00:36:25,610 and wonders why it's doing hard technology research. 693 00:36:28,970 --> 00:36:31,670 That's a remaining piece. 694 00:36:31,670 --> 00:36:33,890 But it's not what it was a decade ago in terms 695 00:36:33,890 --> 00:36:35,150 of its basic research support. 696 00:36:35,150 --> 00:36:36,410 So there's few of these left. 697 00:36:36,410 --> 00:36:40,100 And the model of incredible global competition 698 00:36:40,100 --> 00:36:43,550 has really eroded the ability of a company 699 00:36:43,550 --> 00:36:50,690 to take a long-term risk over highly hypothetical research 700 00:36:50,690 --> 00:36:53,330 advances that are very high risk. 701 00:36:53,330 --> 00:36:54,950 It's just really eroded this. 702 00:36:54,950 --> 00:36:57,650 So what's been happening? 703 00:36:57,650 --> 00:37:02,480 So universities are increasingly being 704 00:37:02,480 --> 00:37:05,600 asked to take this job on. 705 00:37:05,600 --> 00:37:10,940 So universities have gone from a 19th-century model of education 706 00:37:10,940 --> 00:37:16,190 as their dominant driver to a mid-20th-century model 707 00:37:16,190 --> 00:37:21,620 of adding research to education and merging those to, 708 00:37:21,620 --> 00:37:27,350 in recent decades, playing an economic role, which they never 709 00:37:27,350 --> 00:37:30,260 played before, and trying to figure out 710 00:37:30,260 --> 00:37:32,000 how to play that role. 711 00:37:32,000 --> 00:37:36,620 Because, in effect, they're the surrogate piece 712 00:37:36,620 --> 00:37:40,240 to substitute for the Bell Labs model 713 00:37:40,240 --> 00:37:45,290 to sustaining longer-term, higher-risk technology 714 00:37:45,290 --> 00:37:47,880 development. 715 00:37:47,880 --> 00:37:50,220 And they're just beginning to figure out how to do this. 716 00:37:50,220 --> 00:37:53,920 So the engine at MIT is a really interesting model, 717 00:37:53,920 --> 00:37:55,890 which we'll talk about. 718 00:37:55,890 --> 00:37:57,750 And if anybody has any ties over the engine, 719 00:37:57,750 --> 00:37:59,910 we probably ought to go over there and take a look 720 00:37:59,910 --> 00:38:04,202 at what they're up to, wander down the street, 721 00:38:04,202 --> 00:38:07,635 and talk to them. 722 00:38:07,635 --> 00:38:09,600 AUDIENCE: Just to clarify-- 723 00:38:09,600 --> 00:38:11,100 WILLIAM BONVILLIAN: Go ahead, Max. 724 00:38:11,100 --> 00:38:11,490 Go ahead. 725 00:38:11,490 --> 00:38:12,698 AUDIENCE: What is the engine? 726 00:38:15,383 --> 00:38:17,050 WILLIAM BONVILLIAN: We'll talk about it. 727 00:38:17,050 --> 00:38:23,670 But MIT is attempting to create a place for startups 728 00:38:23,670 --> 00:38:26,280 that are right up against this wall of not getting venture 729 00:38:26,280 --> 00:38:31,650 support doing primarily hard technologies where they can 730 00:38:31,650 --> 00:38:40,700 live and have access to advanced equipment, advanced 731 00:38:40,700 --> 00:38:43,340 technologies, and a lot of know-how. 732 00:38:43,340 --> 00:38:47,720 So in effect, it's substituting a place for the kind of stuff 733 00:38:47,720 --> 00:38:50,392 that they had to carry out with venture funding before. 734 00:38:50,392 --> 00:38:52,100 So if you throw a group of them together, 735 00:38:52,100 --> 00:38:53,232 they have shared assets-- 736 00:38:53,232 --> 00:38:54,440 in effect, Creative Commons-- 737 00:38:54,440 --> 00:38:59,750 rely on this campus but also secondary nodes like Lincoln 738 00:38:59,750 --> 00:39:01,190 Labs, like some companies that are 739 00:39:01,190 --> 00:39:08,680 interested in this model to help them scale up 740 00:39:08,680 --> 00:39:09,830 their technologies. 741 00:39:09,830 --> 00:39:12,250 So there's a lot of incubators in this neighborhood. 742 00:39:12,250 --> 00:39:14,822 We have eight incubators very close to MIT. 743 00:39:14,822 --> 00:39:16,030 And they're very interesting. 744 00:39:16,030 --> 00:39:17,280 And some of them are terrific. 745 00:39:19,990 --> 00:39:22,840 The incubator tends to focus on getting your business 746 00:39:22,840 --> 00:39:25,780 plan together and kind of perfecting 747 00:39:25,780 --> 00:39:27,970 your initial prototype. 748 00:39:27,970 --> 00:39:29,950 This is an attempt to do something 749 00:39:29,950 --> 00:39:32,980 that goes to much later stages to help 750 00:39:32,980 --> 00:39:41,140 you do the advanced prototype, late-stage development, 751 00:39:41,140 --> 00:39:43,870 demonstration, test bed, and maybe even 752 00:39:43,870 --> 00:39:46,910 pilot production, which is typically what 753 00:39:46,910 --> 00:39:48,890 you get venture funding for. 754 00:39:48,890 --> 00:39:51,530 But the timetables don't work for venture funding. 755 00:39:51,530 --> 00:39:55,468 Maybe by substituting space for venture capital, 756 00:39:55,468 --> 00:39:57,260 you could create a different kind of model. 757 00:39:57,260 --> 00:40:00,890 So MIT is busy taking this amazing adventure 758 00:40:00,890 --> 00:40:01,790 trying to do this. 759 00:40:01,790 --> 00:40:03,403 And that's just a classic example 760 00:40:03,403 --> 00:40:04,820 of how university is going to have 761 00:40:04,820 --> 00:40:09,800 to wrestle with their new economic role 762 00:40:09,800 --> 00:40:13,970 to get around this Merrill Lynch-identified problem 763 00:40:13,970 --> 00:40:15,800 for us. 764 00:40:15,800 --> 00:40:18,380 AUDIENCE: Also, there's sufficient capital-- 765 00:40:18,380 --> 00:40:19,460 WILLIAM BONVILLIAN: Yes. 766 00:40:19,460 --> 00:40:20,420 Right. 767 00:40:20,420 --> 00:40:21,920 And part of this-- 768 00:40:21,920 --> 00:40:24,322 pure space doesn't always solve the problem. 769 00:40:24,322 --> 00:40:26,030 You're going to need some bridge funding. 770 00:40:26,030 --> 00:40:28,610 So part of this is raising some money. 771 00:40:28,610 --> 00:40:29,585 And could you raise-- 772 00:40:32,450 --> 00:40:34,940 the return on venture capital is designed to be high. 773 00:40:34,940 --> 00:40:37,387 And it is designed to be pretty short term 774 00:40:37,387 --> 00:40:39,470 to get your money back in a pretty reasonable time 775 00:40:39,470 --> 00:40:43,730 period in these venture funds. 776 00:40:43,730 --> 00:40:47,120 This is not going to happen with these hard technologies, 777 00:40:47,120 --> 00:40:49,940 a number of which will be coming out of Martha's world of energy 778 00:40:49,940 --> 00:40:51,402 technology development. 779 00:40:53,708 --> 00:40:55,250 Is there a community out there that's 780 00:40:55,250 --> 00:41:01,280 prepared to tolerate a much lower-level-return, 781 00:41:01,280 --> 00:41:08,240 higher-risk, and long-term focus just for societal well-being. 782 00:41:08,240 --> 00:41:09,190 Stefania? 783 00:41:09,190 --> 00:41:10,040 AUDIENCE: Estefania. 784 00:41:10,040 --> 00:41:10,860 WILLIAM BONVILLIAN: Estefania. 785 00:41:10,860 --> 00:41:11,660 AUDIENCE: There you go. 786 00:41:11,660 --> 00:41:12,050 WILLIAM BONVILLIAN: All right. 787 00:41:12,050 --> 00:41:14,480 You're going to get me to do this right. 788 00:41:14,480 --> 00:41:15,210 I promise. 789 00:41:15,210 --> 00:41:16,280 AUDIENCE: I think that's precisely why it's 790 00:41:16,280 --> 00:41:18,620 so interesting to study the nonprofit development model 791 00:41:18,620 --> 00:41:20,120 and why it's important to understand 792 00:41:20,120 --> 00:41:22,970 how to rebrand science and technology innovation processes 793 00:41:22,970 --> 00:41:25,430 and systems in particular because nonprofits, 794 00:41:25,430 --> 00:41:26,930 for a very long time, and especially 795 00:41:26,930 --> 00:41:28,820 community-led and local nonprofits, 796 00:41:28,820 --> 00:41:31,370 have understood exactly the consequences of resource 797 00:41:31,370 --> 00:41:34,040 scarcity no matter how good their ideas are. 798 00:41:34,040 --> 00:41:36,210 And that's why precisely I appreciate it 799 00:41:36,210 --> 00:41:38,810 on her articulation of the ways in which-- 800 00:41:38,810 --> 00:41:42,907 was it the Manhattan Project in Kansas, the great teams model? 801 00:41:42,907 --> 00:41:44,490 WILLIAM BONVILLIAN: Great groups, yes. 802 00:41:44,490 --> 00:41:46,650 AUDIENCE: This model that we'll talk about later. 803 00:41:46,650 --> 00:41:49,330 But I do think it's important to have 804 00:41:49,330 --> 00:41:52,010 a sort of critical literacy of funding models in the way that 805 00:41:52,010 --> 00:41:55,850 does impact the kind of research that you can pursue 806 00:41:55,850 --> 00:41:58,400 and also how that funding model will impact 807 00:41:58,400 --> 00:42:01,822 the ways in which you'll have to construct 808 00:42:01,822 --> 00:42:03,530 your narrative of success, whether that's 809 00:42:03,530 --> 00:42:05,140 through a publication or the product 810 00:42:05,140 --> 00:42:06,950 that you actually end up producing. 811 00:42:06,950 --> 00:42:08,450 Because it turns out that there's 812 00:42:08,450 --> 00:42:10,220 different kinds of venture models. 813 00:42:10,220 --> 00:42:14,120 And I'm sure that MIT is very cognizant of the ways in which 814 00:42:14,120 --> 00:42:18,620 donor-specified funding plays a role in shaping what you can 815 00:42:18,620 --> 00:42:20,037 and cannot pursue as a researcher. 816 00:42:20,037 --> 00:42:21,078 WILLIAM BONVILLIAN: Yeah. 817 00:42:21,078 --> 00:42:22,970 And interestingly, exactly as you say, 818 00:42:22,970 --> 00:42:26,330 we're starting to play around with different mechanisms 819 00:42:26,330 --> 00:42:29,000 including a number of nonprofit mechanisms, that 820 00:42:29,000 --> 00:42:32,780 would help us to kind of fill this gap in the innovation 821 00:42:32,780 --> 00:42:33,760 system. 822 00:42:33,760 --> 00:42:36,770 And in effect, Martin, back to your point, 823 00:42:36,770 --> 00:42:38,360 enable us to have a lot more kitchens. 824 00:42:42,950 --> 00:42:43,830 All right. 825 00:42:43,830 --> 00:42:50,100 Let me see if I can draw some conclusions here. 826 00:42:50,100 --> 00:42:56,010 This class has talked about two direct innovation 827 00:42:56,010 --> 00:43:00,000 factors, things that I would argue 828 00:43:00,000 --> 00:43:01,470 that you can't do without. 829 00:43:01,470 --> 00:43:04,680 So if Solow, which its growth is driven 830 00:43:04,680 --> 00:43:07,150 by technological and related innovation, is right-- 831 00:43:07,150 --> 00:43:11,020 it's responsible for 2/3 of historic US economic growth-- 832 00:43:11,020 --> 00:43:14,880 then having an R&D system is a pretty critical pillar. 833 00:43:14,880 --> 00:43:17,397 And you need to invest in that system. 834 00:43:17,397 --> 00:43:18,480 So that's factor number 1. 835 00:43:18,480 --> 00:43:19,897 Let's look at some of the numbers. 836 00:43:19,897 --> 00:43:22,620 And this is-- one of the readings this week was 837 00:43:22,620 --> 00:43:23,910 from NSF indicators. 838 00:43:23,910 --> 00:43:27,690 And NSF indicators comes out every other year. 839 00:43:27,690 --> 00:43:33,150 It is the great collection of data on the US innovation 840 00:43:33,150 --> 00:43:37,140 system with lots of comparative international assessments 841 00:43:37,140 --> 00:43:39,120 as well. 842 00:43:39,120 --> 00:43:45,480 And it's just a great tool set for you 843 00:43:45,480 --> 00:43:47,130 as you look at innovation systems 844 00:43:47,130 --> 00:43:49,140 and need to make comparative analysis. 845 00:43:49,140 --> 00:43:50,700 So don't miss it. 846 00:43:50,700 --> 00:43:53,040 I don't want you to read it. 847 00:43:53,040 --> 00:43:53,970 Just glance at it. 848 00:43:53,970 --> 00:43:57,030 See the kinds of things that they 849 00:43:57,030 --> 00:43:59,700 that they can collect data for you on 850 00:43:59,700 --> 00:44:01,800 and what those plots look like. 851 00:44:01,800 --> 00:44:05,160 And I'm pulling a few of my favorite charts, 852 00:44:05,160 --> 00:44:09,660 sometimes innovators but almost always from NSF-developed data. 853 00:44:09,660 --> 00:44:10,990 Federal research funding. 854 00:44:10,990 --> 00:44:14,310 So this is federal R&D outlays as a percentage 855 00:44:14,310 --> 00:44:18,540 of total federal government discretionary spending 856 00:44:18,540 --> 00:44:22,590 between '62 and 2008. 857 00:44:22,590 --> 00:44:28,110 And you see that discretionary spending on R&D 858 00:44:28,110 --> 00:44:32,910 began to approach it's like 17% in 1965. 859 00:44:32,910 --> 00:44:35,880 And then it comes down to well below 10%, 860 00:44:35,880 --> 00:44:45,570 and actually barely above 8% by 2010. 861 00:44:45,570 --> 00:44:49,600 So that's another way of looking at that same curve. 862 00:44:49,600 --> 00:44:53,740 So that blue curve is a slightly different measurement. 863 00:44:53,740 --> 00:44:56,010 But that's essentially the decline 864 00:44:56,010 --> 00:44:58,950 of federal government support. 865 00:44:58,950 --> 00:45:03,630 But then you see, in the purple, it's an interesting x curve. 866 00:45:03,630 --> 00:45:09,135 So you see an increase in industry support for R&D 867 00:45:09,135 --> 00:45:10,260 over that same time period. 868 00:45:10,260 --> 00:45:11,927 By the way, you can extrapolate this out 869 00:45:11,927 --> 00:45:13,335 and the same phenomenon occurs. 870 00:45:17,050 --> 00:45:21,330 And this x curve is a very famous curve. 871 00:45:21,330 --> 00:45:24,600 So you think, oh, well, it's too bad that government 872 00:45:24,600 --> 00:45:27,960 is pulling out of R&D. But isn't it nice 873 00:45:27,960 --> 00:45:29,520 that industry is taking over. 874 00:45:29,520 --> 00:45:31,590 And great. 875 00:45:31,590 --> 00:45:33,210 Problem solved. 876 00:45:33,210 --> 00:45:38,880 Because we're essentially at the same percentage of GDP 877 00:45:38,880 --> 00:45:43,680 that goes to R&D as we were back in the 1960s. 878 00:45:43,680 --> 00:45:46,200 So no problem. 879 00:45:46,200 --> 00:45:48,450 But then you figure out that these two different lines 880 00:45:48,450 --> 00:45:52,680 are measuring-- it's apples and oranges. 881 00:45:52,680 --> 00:45:56,400 Government supports predominantly research. 882 00:45:56,400 --> 00:46:01,310 Industry supports predominantly development. 883 00:46:01,310 --> 00:46:03,797 Is there a relationship between research and development? 884 00:46:03,797 --> 00:46:06,380 Well, yes, of course, there is a relationship between research 885 00:46:06,380 --> 00:46:08,030 and development. 886 00:46:08,030 --> 00:46:10,340 To a significant extent, research 887 00:46:10,340 --> 00:46:14,030 is going to drive development over an extended period 888 00:46:14,030 --> 00:46:16,020 of time. 889 00:46:16,020 --> 00:46:19,850 So then this picture becomes very problematic. 890 00:46:19,850 --> 00:46:25,520 In a way, the government investment back here 891 00:46:25,520 --> 00:46:29,060 enabled this big industry build-up 892 00:46:29,060 --> 00:46:31,390 in development that followed afterwards 893 00:46:31,390 --> 00:46:35,160 over decades afterwards. 894 00:46:35,160 --> 00:46:41,440 But if you're bringing down your research, over time, 895 00:46:41,440 --> 00:46:44,950 that's going to affect your ability to do development. 896 00:46:44,950 --> 00:46:50,500 So this x curve, it's a very problematic curve 897 00:46:50,500 --> 00:46:54,310 for the future of US innovation because what you want 898 00:46:54,310 --> 00:46:56,590 are two parallel rising lines. 899 00:46:56,590 --> 00:46:59,470 You want rising research, which in turn is going 900 00:46:59,470 --> 00:47:02,260 to enable rising development. 901 00:47:02,260 --> 00:47:05,950 You don't want either or. 902 00:47:05,950 --> 00:47:06,730 It doesn't work. 903 00:47:09,135 --> 00:47:10,510 I mean, there's a phenomena here. 904 00:47:10,510 --> 00:47:13,630 You have to understand why was this government level so high. 905 00:47:13,630 --> 00:47:14,710 Imagine that. 906 00:47:14,710 --> 00:47:17,530 2/3 of R&D is being spent by the federal government 907 00:47:17,530 --> 00:47:18,430 here in the 1960s. 908 00:47:18,430 --> 00:47:20,620 What's going on? 909 00:47:20,620 --> 00:47:21,490 Space race. 910 00:47:21,490 --> 00:47:23,620 Cold War. 911 00:47:23,620 --> 00:47:25,540 And hot war. 912 00:47:25,540 --> 00:47:27,640 So we've got simultaneously a hot war, Cold War, 913 00:47:27,640 --> 00:47:28,750 and space race. 914 00:47:28,750 --> 00:47:33,610 That does wonders for science investment. 915 00:47:33,610 --> 00:47:36,940 And obviously, fortunately, we haven't replicated that, 916 00:47:36,940 --> 00:47:39,497 to the extent anyway, that it occurred back 917 00:47:39,497 --> 00:47:40,330 in that time period. 918 00:47:43,480 --> 00:47:44,410 That's the x curve. 919 00:47:44,410 --> 00:47:46,471 Let me go back to this. 920 00:47:46,471 --> 00:47:48,640 This is investment in development, 921 00:47:48,640 --> 00:47:51,940 which, as you can look, you can see the x curve there. 922 00:47:51,940 --> 00:47:54,160 And that's investment in basic research. 923 00:47:54,160 --> 00:47:56,960 So government dominates basic research. 924 00:47:56,960 --> 00:48:01,640 The blue industry dominates development. 925 00:48:01,640 --> 00:48:03,490 Why percentage of GDP? 926 00:48:03,490 --> 00:48:06,070 Is that real? 927 00:48:06,070 --> 00:48:07,600 That's the best measure for showing 928 00:48:07,600 --> 00:48:11,290 what the societal commitment is to research. 929 00:48:11,290 --> 00:48:15,520 How much of your society's wealth are you spending on R&D? 930 00:48:15,520 --> 00:48:17,200 That's probably the best measure that 931 00:48:17,200 --> 00:48:19,640 shows the societal commitment. 932 00:48:19,640 --> 00:48:21,790 This is where it goes. 933 00:48:21,790 --> 00:48:23,170 So health is the blue. 934 00:48:23,170 --> 00:48:24,670 And that's been the big expanding 935 00:48:24,670 --> 00:48:27,250 area in federal R&D funding. 936 00:48:30,080 --> 00:48:35,380 And that doubles starting in 1988. 937 00:48:35,380 --> 00:48:37,450 And you can see that take off, whereas the rest 938 00:48:37,450 --> 00:48:39,970 is fairly stagnant. 939 00:48:42,760 --> 00:48:44,920 Here's another way of looking at that. 940 00:48:44,920 --> 00:48:47,740 Federal non-defense research and development trends as a share 941 00:48:47,740 --> 00:48:49,510 of GDP-- 942 00:48:49,510 --> 00:48:50,590 health now dominates. 943 00:48:53,170 --> 00:48:56,140 Other nations are obviously working hard 944 00:48:56,140 --> 00:48:59,380 on building their R&D capability. 945 00:48:59,380 --> 00:49:04,992 And you can see that red line of the US kind of stagnating. 946 00:49:04,992 --> 00:49:06,700 A lot of other countries aren't following 947 00:49:06,700 --> 00:49:11,110 that model, particularly China on the bottom. 948 00:49:11,110 --> 00:49:14,820 They understand the need for R&D investment. 949 00:49:14,820 --> 00:49:19,050 So these are some ways of looking at that Solow thing. 950 00:49:19,050 --> 00:49:20,640 What are we doing on R&D? 951 00:49:20,640 --> 00:49:25,410 These are ways of quantifying the investment levels 952 00:49:25,410 --> 00:49:29,040 around R&D, both on the governmental side, which 953 00:49:29,040 --> 00:49:31,890 tends to dominate research, and the industry side, which 954 00:49:31,890 --> 00:49:33,667 tends to dominate development. 955 00:49:36,590 --> 00:49:41,750 And the pictures are not ones that we really want to have. 956 00:49:41,750 --> 00:49:44,780 Let's look at our second direct innovation factor, the Romer 957 00:49:44,780 --> 00:49:47,420 factor, the talent factor. 958 00:49:47,420 --> 00:49:54,080 So if Romer is right, human capital engagement research 959 00:49:54,080 --> 00:49:59,870 is the critical input for the follow 960 00:49:59,870 --> 00:50:02,000 on technological innovation. 961 00:50:02,000 --> 00:50:05,600 It's a prerequisite for running your R&D system. 962 00:50:05,600 --> 00:50:10,010 And that's the prospector theory and the talent-based theory 963 00:50:10,010 --> 00:50:11,970 of growth. 964 00:50:11,970 --> 00:50:13,640 So then talent development becomes 965 00:50:13,640 --> 00:50:18,500 another key pillar for looking at the strength 966 00:50:18,500 --> 00:50:20,900 of your innovation economy and looking at your innovation 967 00:50:20,900 --> 00:50:21,400 capable. 968 00:50:27,310 --> 00:50:30,150 So let's look at some of this. 969 00:50:30,150 --> 00:50:32,200 That's natural science and engineering doctorates 970 00:50:32,200 --> 00:50:33,700 in selected countries. 971 00:50:37,840 --> 00:50:44,710 And you see particularly this incredible rise 972 00:50:44,710 --> 00:50:47,717 in degrees coming out of China but a lot of growth 973 00:50:47,717 --> 00:50:49,300 in a number of other countries as well 974 00:50:49,300 --> 00:50:52,930 and a fair amount of stagnation in the US. 975 00:50:52,930 --> 00:50:55,570 This US innovation depends on the presence 976 00:50:55,570 --> 00:50:58,150 of foreign-born scientists and engineers. 977 00:50:58,150 --> 00:51:02,410 That's a very important part of our system. 978 00:51:02,410 --> 00:51:05,380 And that's how this curve has taken off. 979 00:51:05,380 --> 00:51:10,730 So this is very important input into the US innovation system. 980 00:51:10,730 --> 00:51:15,370 That's why immigration is such a huge concern for the 400 tech 981 00:51:15,370 --> 00:51:19,615 companies that filed the amicus brief for today's argument. 982 00:51:22,450 --> 00:51:24,610 This is the annual growth rate on numbers 983 00:51:24,610 --> 00:51:26,080 of researchers by country. 984 00:51:29,350 --> 00:51:32,890 I mean, the US has the largest research pool by far. 985 00:51:32,890 --> 00:51:34,380 But the growth rate is much lower. 986 00:51:34,380 --> 00:51:41,240 You see the rising growth rates in a series of Asian economies. 987 00:51:41,240 --> 00:51:44,410 This is world share of natural science and engineering 988 00:51:44,410 --> 00:51:47,050 publications. 989 00:51:47,050 --> 00:51:50,530 You actually see a decline in world share from the US 990 00:51:50,530 --> 00:51:53,550 and, obviously, a huge rise in world share from China, 991 00:51:53,550 --> 00:51:57,460 but also countries like South Korea. 992 00:51:57,460 --> 00:51:59,883 Significant increases in first university degrees 993 00:51:59,883 --> 00:52:01,300 in natural science and engineering 994 00:52:01,300 --> 00:52:04,140 in China against the rest of the world. 995 00:52:04,140 --> 00:52:06,850 China, rest of the world, right? 996 00:52:06,850 --> 00:52:09,220 They get an innovation-based growth model. 997 00:52:09,220 --> 00:52:10,720 That's what they're after. 998 00:52:10,720 --> 00:52:12,280 They understand its importance. 999 00:52:12,280 --> 00:52:15,730 They are a developing economy that's 1000 00:52:15,730 --> 00:52:17,140 become an emerging economy that's 1001 00:52:17,140 --> 00:52:22,140 using an innovation-based growth model for its growth. 1002 00:52:22,140 --> 00:52:25,830 So these are ways of looking at talent and the strength 1003 00:52:25,830 --> 00:52:27,030 of your innovation system. 1004 00:52:27,030 --> 00:52:29,010 And the NSF indicators can tell us 1005 00:52:29,010 --> 00:52:35,220 a lot about the second pillar, this Paul Romer 1006 00:52:35,220 --> 00:52:38,460 pillar, this talent pillar. 1007 00:52:38,460 --> 00:52:40,260 Now, where did these direct innovation 1008 00:52:40,260 --> 00:52:43,530 system factors come from, these two? 1009 00:52:43,530 --> 00:52:52,890 Well, on the direct governmental role, 1010 00:52:52,890 --> 00:52:56,100 federal government funding of university research 1011 00:52:56,100 --> 00:52:58,350 is obviously key. 1012 00:52:58,350 --> 00:53:02,790 And government dominates research investment as we saw. 1013 00:53:02,790 --> 00:53:06,030 Things like government labs are obviously government dominated. 1014 00:53:06,030 --> 00:53:07,740 The education and training system, these 1015 00:53:07,740 --> 00:53:08,940 are Romer-type factors. 1016 00:53:08,940 --> 00:53:12,750 The federal government dominates higher education support 1017 00:53:12,750 --> 00:53:15,870 for science and engineering. 1018 00:53:15,870 --> 00:53:18,282 Support for industry R&D, that's pretty 1019 00:53:18,282 --> 00:53:19,740 significant on the government side, 1020 00:53:19,740 --> 00:53:21,858 particularly through the Defense Department. 1021 00:53:25,440 --> 00:53:27,150 And sometimes some other agency missions, 1022 00:53:27,150 --> 00:53:28,360 but DoD is the big one. 1023 00:53:28,360 --> 00:53:30,450 So this is the government playing 1024 00:53:30,450 --> 00:53:35,730 a role in direct innovation factors, both education and R&D 1025 00:53:35,730 --> 00:53:37,020 investment. 1026 00:53:37,020 --> 00:53:40,680 But then the private sector plays a significant role 1027 00:53:40,680 --> 00:53:43,130 in these two direct innovation factors as well. 1028 00:53:43,130 --> 00:53:46,020 So industry R&D, which as we talked about, 1029 00:53:46,020 --> 00:53:48,295 it's primarily development. 1030 00:53:48,295 --> 00:53:50,670 Industry takes things through the engineering prototyping 1031 00:53:50,670 --> 00:53:53,430 and production stages. 1032 00:53:53,430 --> 00:53:57,390 The training system is dominated by industry. 1033 00:53:57,390 --> 00:54:00,270 So that's a Romer-like factor. 1034 00:54:00,270 --> 00:54:04,020 So you see the division between public sector 1035 00:54:04,020 --> 00:54:07,930 and private sector roles around these two innovation system 1036 00:54:07,930 --> 00:54:08,430 pillar. 1037 00:54:13,390 --> 00:54:15,174 Any questions about this data stuff? 1038 00:54:20,330 --> 00:54:21,710 So let's do some wrap-up. 1039 00:54:21,710 --> 00:54:27,350 Robert Solow, the key to economic growth 1040 00:54:27,350 --> 00:54:29,510 is technology and related innovation. 1041 00:54:29,510 --> 00:54:32,180 And for shorthand-- although it's not a fair summary, 1042 00:54:32,180 --> 00:54:37,280 but in shorthand, we could say that you've got to do R&D. Then 1043 00:54:37,280 --> 00:54:39,530 we talked about Paul Romer. 1044 00:54:39,530 --> 00:54:43,310 Behind that technological and related innovation 1045 00:54:43,310 --> 00:54:48,200 factor is what he calls human capital engaged in research. 1046 00:54:48,200 --> 00:54:49,503 It's your talent base. 1047 00:54:49,503 --> 00:54:51,170 But again, it's got to be in the system. 1048 00:54:51,170 --> 00:54:52,340 It can't be driving cabs. 1049 00:54:52,340 --> 00:54:56,540 It's gotta be in that system. 1050 00:54:56,540 --> 00:55:01,700 And Dale Jorgenson tells us that the key to 1990s growth 1051 00:55:01,700 --> 00:55:04,430 actually was technological and related innovation, 1052 00:55:04,430 --> 00:55:07,070 that Solow is absolute right. 1053 00:55:07,070 --> 00:55:08,973 That's how the economy grew. 1054 00:55:08,973 --> 00:55:10,640 And we saw the picture that he presented 1055 00:55:10,640 --> 00:55:14,480 for us of a core technology, semiconductors, 1056 00:55:14,480 --> 00:55:18,320 applications pile around it, it enters the economy 1057 00:55:18,320 --> 00:55:21,560 at significant scale and starts to affect many sectors. 1058 00:55:21,560 --> 00:55:23,330 That enables productivity gains. 1059 00:55:23,330 --> 00:55:25,653 The productivity gains is a real gain 1060 00:55:25,653 --> 00:55:27,320 in the society, which can be distributed 1061 00:55:27,320 --> 00:55:29,360 to improve societal well-being. 1062 00:55:29,360 --> 00:55:33,200 That's the picture he uses to show how these innovation 1063 00:55:33,200 --> 00:55:36,290 waves actually operate. 1064 00:55:36,290 --> 00:55:40,550 And then from the Merrill Lynch piece that we read, 1065 00:55:40,550 --> 00:55:43,940 we learn that investors are only prepared to tackle 1066 00:55:43,940 --> 00:55:47,840 a pretty small slice of that long-term technology 1067 00:55:47,840 --> 00:55:50,570 development time frame that's needed. 1068 00:55:50,570 --> 00:55:52,550 They acknowledge that it's long term. 1069 00:55:52,550 --> 00:55:56,270 They're going to focus on a very short part of it. 1070 00:55:56,270 --> 00:55:58,748 So you've got automatically a big gap in the US innovation 1071 00:55:58,748 --> 00:56:00,790 system that's going to have to get wrestled with. 1072 00:56:03,480 --> 00:56:05,400 And then the last piece we went through 1073 00:56:05,400 --> 00:56:08,760 were these direct innovation factors, R&D and talent, 1074 00:56:08,760 --> 00:56:11,190 and the different roles that both government and private 1075 00:56:11,190 --> 00:56:13,750 sector play in these two. 1076 00:56:13,750 --> 00:56:16,290 And we looked at the NSF indicators 1077 00:56:16,290 --> 00:56:19,950 as a good source for you for terrific data 1078 00:56:19,950 --> 00:56:22,480 on how to look at innovation, the US innovation systems 1079 00:56:22,480 --> 00:56:26,840 but also comparative analysis with other nations as well.