1 00:00:00,090 --> 00:00:02,430 The following content is provided under a Creative 2 00:00:02,430 --> 00:00:03,820 Commons license. 3 00:00:03,820 --> 00:00:06,030 Your support will help MIT OpenCourseWare 4 00:00:06,030 --> 00:00:10,120 continue to offer high quality educational resources for free. 5 00:00:10,120 --> 00:00:12,660 To make a donation or to view additional materials 6 00:00:12,660 --> 00:00:16,620 from hundreds of MIT courses, visit MIT OpenCourseWare 7 00:00:16,620 --> 00:00:17,992 at ocw.mit.edu. 8 00:00:21,632 --> 00:00:23,840 WILLIAM BONVILLIAN: So I'm going to combine these two 9 00:00:23,840 --> 00:00:28,388 pieces together because they're obviously 10 00:00:28,388 --> 00:00:29,305 have a lot of overlap. 11 00:00:32,020 --> 00:00:36,358 AUDIENCE: Would you mind talking about innovation? 12 00:00:39,640 --> 00:00:41,390 WILLIAM BONVILLIAN: Isn't that the course? 13 00:00:41,390 --> 00:00:43,615 Wait a minute. 14 00:00:43,615 --> 00:00:44,490 AUDIENCE: Good point. 15 00:00:51,835 --> 00:00:54,080 So Berger and the Jonas Nahm reading 16 00:00:54,080 --> 00:00:55,730 talked about how learning takes place 17 00:00:55,730 --> 00:00:57,620 at all points in the design cycle 18 00:00:57,620 --> 00:01:01,000 and how we're not really linking games. 19 00:01:01,000 --> 00:01:04,269 Well, I know you're about to talk about this now, but-- 20 00:01:04,269 --> 00:01:05,918 WILLIAM BONVILLIAN: I promise. 21 00:01:05,918 --> 00:01:09,280 AUDIENCE: --we talked about how basically the United 22 00:01:09,280 --> 00:01:13,086 States didn't concede with innovation as existing 23 00:01:13,086 --> 00:01:14,390 in manufacturing. 24 00:01:14,390 --> 00:01:15,858 Will you hit on that now? 25 00:01:15,858 --> 00:01:16,900 WILLIAM BONVILLIAN: Yeah. 26 00:01:16,900 --> 00:01:20,800 I mean I can do that now, and it's not really 27 00:01:20,800 --> 00:01:22,270 in either of these readings. 28 00:01:22,270 --> 00:01:29,920 Briefly in the decline of US manufacturing reading. 29 00:01:29,920 --> 00:01:33,160 We'll talk about this in depth next week 30 00:01:33,160 --> 00:01:37,090 when we dive into the origins of the US innovation system 31 00:01:37,090 --> 00:01:38,470 where it came from. 32 00:01:38,470 --> 00:01:41,090 Where did the R&D agencies come from? 33 00:01:41,090 --> 00:01:45,380 But in essence, when the US is constructing, 34 00:01:45,380 --> 00:01:47,380 its innovation system in the course of World War 35 00:01:47,380 --> 00:01:49,180 II and the immediate aftermath. 36 00:01:49,180 --> 00:01:52,510 And we'll read Vannevar Bush and talk about him next week. 37 00:01:55,690 --> 00:02:00,430 Before the Second World War, the US wasn't all that 38 00:02:00,430 --> 00:02:03,655 strong a science power. 39 00:02:03,655 --> 00:02:05,530 There were countries like Germany and Britain 40 00:02:05,530 --> 00:02:07,150 that were considerably further ahead 41 00:02:07,150 --> 00:02:12,270 by general world view in science capability than the US. 42 00:02:12,270 --> 00:02:17,665 And Vannevar Bush worked hard during World War II 43 00:02:17,665 --> 00:02:22,210 to build up that early stage research capability 44 00:02:22,210 --> 00:02:24,610 and then link it to more applied stations 45 00:02:24,610 --> 00:02:26,120 of the course of the war. 46 00:02:26,120 --> 00:02:28,510 At the end of the war, he has a choice. 47 00:02:28,510 --> 00:02:30,000 What do we save? 48 00:02:30,000 --> 00:02:32,570 And he decides as the war is winding down-- 49 00:02:32,570 --> 00:02:34,630 and we'll talk about this next week. 50 00:02:34,630 --> 00:02:38,530 --he decides to save that early stage research 51 00:02:38,530 --> 00:02:41,350 capability because that was what the problem was. 52 00:02:41,350 --> 00:02:45,370 The United States was king of manufacturing. 53 00:02:45,370 --> 00:02:48,280 Manufacturing production capability in the United States 54 00:02:48,280 --> 00:02:52,180 dwarfed every other country, including our two major postwar 55 00:02:52,180 --> 00:02:55,670 or pre-war competitors, Germany and Japan. 56 00:02:55,670 --> 00:03:00,490 We just had much bigger production capability. 57 00:03:00,490 --> 00:03:02,530 We had developed mass production we would read. 58 00:03:02,530 --> 00:03:06,040 No one else had figured out, in part because we could sell 59 00:03:06,040 --> 00:03:07,660 into a continent size economy. 60 00:03:07,660 --> 00:03:12,970 We created the first continent sized industrial economy, 61 00:03:12,970 --> 00:03:14,300 and we could sell into that. 62 00:03:14,300 --> 00:03:17,380 So production wasn't the problem. 63 00:03:17,380 --> 00:03:19,180 We completely dominated production. 64 00:03:19,180 --> 00:03:21,417 It never occurred to anybody in the US 65 00:03:21,417 --> 00:03:23,500 that there might come a challenge to US production 66 00:03:23,500 --> 00:03:24,550 capability. 67 00:03:24,550 --> 00:03:28,030 Instead the problem Vannevar Bush is trying to solve 68 00:03:28,030 --> 00:03:31,580 is building this early stage research capability, 69 00:03:31,580 --> 00:03:34,950 which we didn't really have that strong 70 00:03:34,950 --> 00:03:37,330 a system prior to the war. 71 00:03:37,330 --> 00:03:39,400 So the US innovation system when it gets 72 00:03:39,400 --> 00:03:41,410 created in that post-war period-- 73 00:03:41,410 --> 00:03:44,050 and we'll go into this next week. 74 00:03:44,050 --> 00:03:49,780 --doesn't focus on production because it's not a problem. 75 00:03:49,780 --> 00:03:52,600 In countries like Germany and Japan-- 76 00:03:52,600 --> 00:03:56,260 which by the way, we had just bombed their production system. 77 00:03:56,260 --> 00:03:58,540 They've got to restore all that. 78 00:03:58,540 --> 00:04:00,160 They've got to rebuild that. 79 00:04:00,160 --> 00:04:05,440 They develop innovation systems that are focused on production. 80 00:04:05,440 --> 00:04:09,100 That's where they focus the innovation systems. 81 00:04:09,100 --> 00:04:10,340 Not like the US does. 82 00:04:10,340 --> 00:04:12,730 We think R&D is king. 83 00:04:12,730 --> 00:04:16,540 Other countries assume this link between research and production 84 00:04:16,540 --> 00:04:18,550 and focus on the production stage 85 00:04:18,550 --> 00:04:21,103 because it is indeed highly innovative. 86 00:04:21,103 --> 00:04:23,270 We just are organizing our economy very differently. 87 00:04:23,270 --> 00:04:27,670 We miss that, and the results are painful. 88 00:04:30,680 --> 00:04:33,537 Does that answer more or less? 89 00:04:33,537 --> 00:04:35,620 You can keep pushing on that too as we go further. 90 00:04:35,620 --> 00:04:35,980 Martin? 91 00:04:35,980 --> 00:04:37,360 AUDIENCE: First, I was going to ask, you spend a lot of time 92 00:04:37,360 --> 00:04:38,740 watching each other. 93 00:04:38,740 --> 00:04:41,008 How willing are politicians to listen 94 00:04:41,008 --> 00:04:43,510 to you or people that are more educated on the matter? 95 00:04:43,510 --> 00:04:43,870 WILLIAM BONVILLIAN: Oh. 96 00:04:43,870 --> 00:04:46,050 They're on the phone all the time to me Martin. 97 00:04:46,050 --> 00:04:48,520 Believe it, it's unbearable. 98 00:04:48,520 --> 00:04:49,870 It's constant. 99 00:04:49,870 --> 00:04:53,530 You heard my cell phone going off through the entire class. 100 00:04:53,530 --> 00:04:54,940 No. 101 00:04:54,940 --> 00:04:56,950 This is only a set of realizations 102 00:04:56,950 --> 00:05:01,000 that the political system is starting to come to. 103 00:05:01,000 --> 00:05:04,510 And it's been a painful set of lessons. 104 00:05:08,730 --> 00:05:10,230 AUDIENCE: So what's the line period? 105 00:05:10,230 --> 00:05:12,313 So politicians are just starting to figure it out. 106 00:05:12,313 --> 00:05:16,510 When did you figure, or like people that are better-- 107 00:05:16,510 --> 00:05:19,570 WILLIAM BONVILLIAN: Well, I was lucky enough to hang out in MIT 108 00:05:19,570 --> 00:05:22,300 and get educated by people like Suzanne Berger 109 00:05:22,300 --> 00:05:27,973 and a series of other terrific MIT folks who began teaching me 110 00:05:27,973 --> 00:05:29,140 what had been going on here. 111 00:05:29,140 --> 00:05:34,810 So I was a beneficiary of the MIT fire hose. 112 00:05:34,810 --> 00:05:37,670 I get to stand in it for a significant period of time. 113 00:05:37,670 --> 00:05:41,950 And so my next book will be on manufacturing. 114 00:05:41,950 --> 00:05:44,260 So a lot of the snippets that you're seeing here 115 00:05:44,260 --> 00:05:46,810 will be carried over into that book which 116 00:05:46,810 --> 00:05:48,497 MIT Press is publishing. 117 00:05:51,430 --> 00:05:55,180 I mean why it takes a year to process a book even though you 118 00:05:55,180 --> 00:05:57,200 submit it online is beyond me. 119 00:05:57,200 --> 00:06:00,825 But it will come out around December of this coming year. 120 00:06:00,825 --> 00:06:02,200 But let's go back into our story. 121 00:06:06,110 --> 00:06:08,870 I should add one more thing, Martin. 122 00:06:08,870 --> 00:06:12,940 Donald Trump just won the election on these issues. 123 00:06:12,940 --> 00:06:15,070 This is a lesson that is now being learned 124 00:06:15,070 --> 00:06:16,733 by the political system. 125 00:06:16,733 --> 00:06:19,150 Are they going to come to the right conclusions about what 126 00:06:19,150 --> 00:06:21,170 to do about the right policies? 127 00:06:21,170 --> 00:06:22,730 I don't know. 128 00:06:22,730 --> 00:06:29,770 But this is now embedded into the political learning lessons. 129 00:06:29,770 --> 00:06:31,210 So hollowing out. 130 00:06:31,210 --> 00:06:34,450 I argue that's what the story has been on employment, 131 00:06:34,450 --> 00:06:36,520 and manufacturing was down almost a third 132 00:06:36,520 --> 00:06:39,410 in the decade of the 2000s. 133 00:06:39,410 --> 00:06:45,950 It fell to 11.5 or 11.8 I guess, and it's now back at 12.3, 134 00:06:45,950 --> 00:06:47,320 but that's not that far back. 135 00:06:47,320 --> 00:06:51,700 It was 17 before all this started, millions of jobs. 136 00:06:51,700 --> 00:06:52,390 Investment. 137 00:06:54,970 --> 00:06:59,140 Manufacturing fixed capital investment declined 138 00:06:59,140 --> 00:07:00,675 in the 2000s for the first time when 139 00:07:00,675 --> 00:07:03,790 we started collecting the data, an actual decline in that time 140 00:07:03,790 --> 00:07:05,058 period. 141 00:07:05,058 --> 00:07:09,340 So in part, as a result, output is down. 142 00:07:09,340 --> 00:07:12,040 If you're not investing in capital planning equipment, 143 00:07:12,040 --> 00:07:14,530 what's going to happen to output? 144 00:07:14,530 --> 00:07:17,440 So output was down in, depending on how 145 00:07:17,440 --> 00:07:20,470 he counts, either 15 or 16 of the manufacturing sectors 146 00:07:20,470 --> 00:07:22,390 that the government measures. 147 00:07:22,390 --> 00:07:24,220 And then, if output is lower than assumed, 148 00:07:24,220 --> 00:07:27,100 then productivity is going to be lower than assumed-- 149 00:07:27,100 --> 00:07:28,370 as we discussed before. 150 00:07:28,370 --> 00:07:31,570 So this is the documentation of the very sharp decline 151 00:07:31,570 --> 00:07:34,210 in manufacturing employment. 152 00:07:34,210 --> 00:07:37,120 In that time period, from here to here. 153 00:07:37,120 --> 00:07:38,170 So you see what happened. 154 00:07:40,810 --> 00:07:44,510 This is national R&D intensity. 155 00:07:44,510 --> 00:07:45,010 Right? 156 00:07:45,010 --> 00:07:49,240 So this is all related to your investments in innovation, 157 00:07:49,240 --> 00:07:51,740 although the lag time is significant. 158 00:07:51,740 --> 00:07:56,247 So here's the R&D intensity of the US economy. 159 00:07:59,260 --> 00:08:02,230 GDP expenditures as a percentage of-- 160 00:08:02,230 --> 00:08:06,850 gross R&D expenditures as a percentage of GDP. 161 00:08:06,850 --> 00:08:08,530 This is-- we saw this earlier-- 162 00:08:08,530 --> 00:08:11,410 this is the trade balance for high technology 163 00:08:11,410 --> 00:08:14,380 goods versus all manufactured products. 164 00:08:14,380 --> 00:08:17,530 Huge deficit in all manufactured goods in the US. 165 00:08:17,530 --> 00:08:21,460 Increasing deficit in advanced technology goods. 166 00:08:24,100 --> 00:08:26,680 Are you going to make this up on services? 167 00:08:26,680 --> 00:08:29,770 Well, services have been growing. 168 00:08:29,770 --> 00:08:35,309 But look at that growth rate compared to the fall 169 00:08:35,309 --> 00:08:37,330 out in the goods balance. 170 00:08:37,330 --> 00:08:38,990 Now, this good balance gets better 171 00:08:38,990 --> 00:08:42,610 because, in the recession, we're not producing anything. 172 00:08:42,610 --> 00:08:46,130 So, therefore, we're we aren't buying anything. 173 00:08:46,130 --> 00:08:48,820 But that's the real number, and we're back to that range 174 00:08:48,820 --> 00:08:50,070 again-- 175 00:08:50,070 --> 00:08:50,990 in this time period. 176 00:08:50,990 --> 00:08:54,190 So as you can see, even if we're staggeringly 177 00:08:54,190 --> 00:08:57,010 successful in services, it's not going 178 00:08:57,010 --> 00:09:00,640 to offset the balance in goods. 179 00:09:00,640 --> 00:09:03,120 AUDIENCE: What's that random blip that [INAUDIBLE] 180 00:09:03,120 --> 00:09:04,284 AUDIENCE: That's 9/11. 181 00:09:04,284 --> 00:09:05,867 WILLIAM BONVILLIAN: Yeah, that's 9/11. 182 00:09:05,867 --> 00:09:09,922 AUDIENCE: Yeah, why did the services increase? 183 00:09:09,922 --> 00:09:11,380 WILLIAM BONVILLIAN: I have no idea. 184 00:09:11,380 --> 00:09:13,940 I'd have to look at that. 185 00:09:13,940 --> 00:09:16,410 We certainly weren't flying many airplanes that week. 186 00:09:16,410 --> 00:09:17,290 AUDIENCE: Yeah. 187 00:09:17,290 --> 00:09:20,360 WILLIAM BONVILLIAN: I'll have to take a look. 188 00:09:20,360 --> 00:09:22,730 So we'd been assuming, as I said earlier, 189 00:09:22,730 --> 00:09:24,730 that we'd been losing manufacturing jobs because 190 00:09:24,730 --> 00:09:25,720 of productivity gains. 191 00:09:25,720 --> 00:09:29,530 But that just really has not been the story. 192 00:09:29,530 --> 00:09:33,850 And this means, then, that we're not just 193 00:09:33,850 --> 00:09:35,710 going through a normal business cycle here. 194 00:09:35,710 --> 00:09:38,410 There's real structural effects here. 195 00:09:38,410 --> 00:09:39,040 Right? 196 00:09:39,040 --> 00:09:42,130 And we're not going to get out of this by just coming out 197 00:09:42,130 --> 00:09:43,510 of a normal business cycle. 198 00:09:43,510 --> 00:09:46,120 In fact, we didn't get out of it by coming out 199 00:09:46,120 --> 00:09:47,770 of a normal business cycle. 200 00:09:47,770 --> 00:09:51,340 These manufacturing jobs are not coming back-- 201 00:09:51,340 --> 00:09:53,950 they're gone. 202 00:09:53,950 --> 00:09:57,070 Therefore, you've got not a business cycle problem, 203 00:09:57,070 --> 00:10:00,400 but a structural problem. 204 00:10:00,400 --> 00:10:04,780 This is percentage loss in manufacturing jobs between 2000 205 00:10:04,780 --> 00:10:07,810 and 2010, by state. 206 00:10:07,810 --> 00:10:11,890 So the green states lost 30% to 40% 207 00:10:11,890 --> 00:10:14,410 of their manufacturing jobs. 208 00:10:14,410 --> 00:10:18,880 The purple states lost over 40% of their manufacturing jobs. 209 00:10:18,880 --> 00:10:22,630 So think of what was going on in textiles and furniture 210 00:10:22,630 --> 00:10:27,160 in North Carolina, or the auto sector in Michigan. 211 00:10:27,160 --> 00:10:30,470 But the overall picture-- 212 00:10:30,470 --> 00:10:34,430 it's not a pretty picture, nationwide. 213 00:10:34,430 --> 00:10:37,760 So we've had, kind of, an American Brexit here, 214 00:10:37,760 --> 00:10:39,660 with a lot of social disruption. 215 00:10:39,660 --> 00:10:41,990 So the manufacturing decline tended 216 00:10:41,990 --> 00:10:45,920 to create societal decline. 217 00:10:45,920 --> 00:10:51,110 So when we lost a third of the manufacturing jobs-- 218 00:10:51,110 --> 00:10:54,110 historically, manufacturing was an important middle-class 219 00:10:54,110 --> 00:10:59,720 pathway for, particularly, high school educated males. 220 00:10:59,720 --> 00:11:03,140 But full employment-- full-year employment 221 00:11:03,140 --> 00:11:06,980 for men with high school, but not college, degrees 222 00:11:06,980 --> 00:11:13,100 went from 76% in 1990 down to 68% in 2013. 223 00:11:13,100 --> 00:11:15,890 In other words, we may have unemployment 224 00:11:15,890 --> 00:11:20,450 now back to 4.9%, which is terrific news. 225 00:11:20,450 --> 00:11:24,890 But we don't count this structural decline 226 00:11:24,890 --> 00:11:30,860 of people who, in effect, have left the workforce-- 227 00:11:30,860 --> 00:11:33,740 which is still there, as a deep structural problem. 228 00:11:33,740 --> 00:11:35,900 So the share of men that did not work at all 229 00:11:35,900 --> 00:11:38,510 in that time period-- 230 00:11:38,510 --> 00:11:44,190 with that level of education-- went from 11% in 1990 231 00:11:44,190 --> 00:11:47,380 to 18% in 2013, which is a pretty staggering number. 232 00:11:50,560 --> 00:11:53,020 Most importantly, you can't measure this stuff 233 00:11:53,020 --> 00:11:55,270 by average income. 234 00:11:55,270 --> 00:11:57,010 You've got to measure it by median income 235 00:11:57,010 --> 00:11:59,470 because the gains of the upper middle class 236 00:11:59,470 --> 00:12:08,080 disguise the decline in the other income quintiles. 237 00:12:08,080 --> 00:12:10,930 So you've got to look at median income. 238 00:12:10,930 --> 00:12:13,780 Median income of men, with no high school diploma, 239 00:12:13,780 --> 00:12:17,620 fell 20% between 1990 and 2013. 240 00:12:17,620 --> 00:12:20,020 And men with a high school diploma, or some college, 241 00:12:20,020 --> 00:12:22,270 fell 13%. 242 00:12:22,270 --> 00:12:25,390 So there's a growing income split between college 243 00:12:25,390 --> 00:12:28,450 and non-college educated-- 244 00:12:28,450 --> 00:12:32,720 and a major accompanying rise in income inequality. 245 00:12:32,720 --> 00:12:39,670 So David Otter's picture of this is of a barbell. 246 00:12:39,670 --> 00:12:41,070 And one of the bells-- 247 00:12:41,070 --> 00:12:43,270 and Beth could tell us this-- 248 00:12:43,270 --> 00:12:46,090 but one of the bells is growing. 249 00:12:46,090 --> 00:12:48,370 That's this upper middle class bell. 250 00:12:48,370 --> 00:12:51,790 And that community is doing quite well. 251 00:12:51,790 --> 00:12:53,420 But then there's a thinned out middle. 252 00:12:53,420 --> 00:12:54,920 And a lot of that thinned out middle 253 00:12:54,920 --> 00:12:57,100 has been push towards the other bell, which 254 00:12:57,100 --> 00:13:01,460 is a growing lower end, lower paid services sector. 255 00:13:01,460 --> 00:13:06,880 So we're thinning out the middle and, therefore, 256 00:13:06,880 --> 00:13:10,930 creating this big economic inequality problem. 257 00:13:10,930 --> 00:13:13,540 I mean, this is in a country that 258 00:13:13,540 --> 00:13:15,460 created a democracy that delivered 259 00:13:15,460 --> 00:13:18,940 staggering social mobility to its population. 260 00:13:18,940 --> 00:13:21,640 Staggering, unprecedented in the world, right? 261 00:13:21,640 --> 00:13:24,700 And we're now bringing that to a close. 262 00:13:24,700 --> 00:13:25,900 We're shutting that down. 263 00:13:25,900 --> 00:13:27,760 What are we doing? 264 00:13:27,760 --> 00:13:32,020 And the social consequences of this are actually profound. 265 00:13:32,020 --> 00:13:36,220 So the election just told us-- 266 00:13:36,220 --> 00:13:38,680 completely disrupted the Republican Party, 267 00:13:38,680 --> 00:13:41,190 and caused huge disruption across the board 268 00:13:41,190 --> 00:13:41,815 of all parties. 269 00:13:44,380 --> 00:13:47,530 It's an incredible message, that just got delivered here 270 00:13:47,530 --> 00:13:49,150 in the United States. 271 00:13:49,150 --> 00:13:52,060 In wrestling with this and trying to understand and figure 272 00:13:52,060 --> 00:13:53,740 out what to do about it-- 273 00:13:53,740 --> 00:13:58,490 I think it's pretty fundamental to the future of the democracy. 274 00:13:58,490 --> 00:14:00,980 We will avoid this problem at our peril. 275 00:14:03,570 --> 00:14:06,140 So we've got a loss to middle income ranks, growing 276 00:14:06,140 --> 00:14:09,770 social inequality, and a whole post-industrial backlash that's 277 00:14:09,770 --> 00:14:11,250 going on here. 278 00:14:11,250 --> 00:14:13,970 And the question is, can this idea 279 00:14:13,970 --> 00:14:15,650 of advanced manufacturing, in some way, 280 00:14:15,650 --> 00:14:17,480 speak to some of this? 281 00:14:17,480 --> 00:14:21,090 So manufacturing remains a big sector. 282 00:14:21,090 --> 00:14:24,890 It's $1.7 trillion of a $15 trillion US economy 283 00:14:24,890 --> 00:14:30,980 and employs 12 million in a workforce of 150 million. 284 00:14:30,980 --> 00:14:34,010 It tends to dominate the innovation system. 285 00:14:34,010 --> 00:14:39,050 So-- approaching 64% of scientists and engineers 286 00:14:39,050 --> 00:14:41,700 are employed by industrial companies. 287 00:14:41,700 --> 00:14:43,700 AUDIENCE: I thought that back-- you had shown us 288 00:14:43,700 --> 00:14:47,200 before, said that manufacturing had like two thirds of. 289 00:14:47,200 --> 00:14:48,408 What was it two thirds of? 290 00:14:48,408 --> 00:14:49,200 The two thirds of-- 291 00:14:49,200 --> 00:14:52,070 WILLIAM BONVILLIAN: No, it's not it's-- 292 00:14:52,070 --> 00:14:57,060 it's of fortune-- of Standard and Poor's 500 companies. 293 00:14:57,060 --> 00:14:58,380 It's their-- in other words-- 294 00:14:58,380 --> 00:15:00,838 the companies that make the money. 295 00:15:00,838 --> 00:15:02,880 What portion of their money comes from production 296 00:15:02,880 --> 00:15:04,503 versus services, right. 297 00:15:04,503 --> 00:15:07,170 And these are the companies that are delivering for the economy, 298 00:15:07,170 --> 00:15:08,100 frankly. 299 00:15:08,100 --> 00:15:11,070 These are the big companies that tend to dominate 300 00:15:11,070 --> 00:15:11,970 a lot of employment. 301 00:15:11,970 --> 00:15:13,930 AUDIENCE: So why is there this big discrepancy 302 00:15:13,930 --> 00:15:16,084 and why isn't anyone [INAUDIBLE] I mean, 303 00:15:16,084 --> 00:15:18,042 if it's only above 10% of the poorer economy? 304 00:15:18,042 --> 00:15:19,750 WILLIAM BONVILLIAN: Because manufacturing 305 00:15:19,750 --> 00:15:24,790 is pulling above its weight in what it delivers. 306 00:15:24,790 --> 00:15:27,070 So the currency of international trade, 307 00:15:27,070 --> 00:15:30,760 really, is about complex high value goods. 308 00:15:30,760 --> 00:15:34,360 It's not about services. 309 00:15:34,360 --> 00:15:36,130 That's what this story is. 310 00:15:36,130 --> 00:15:38,680 That's what this graph on services-- 311 00:15:38,680 --> 00:15:42,280 trade surplus versus goods deficit-- 312 00:15:42,280 --> 00:15:43,540 is all about. 313 00:15:43,540 --> 00:15:46,840 The returns are really coming, predominantly worldwide, 314 00:15:46,840 --> 00:15:48,995 from complex high value goods. 315 00:15:48,995 --> 00:15:51,370 So if you start to reduce your capability-- and remember, 316 00:15:51,370 --> 00:15:52,990 the United States is still, by far, 317 00:15:52,990 --> 00:15:56,890 the second largest manufacturing entity in the world-- 318 00:15:56,890 --> 00:15:59,200 there's still a lot going on here. 319 00:15:59,200 --> 00:16:04,780 80% of US exports are in this high value, goods area. 320 00:16:04,780 --> 00:16:10,270 Yet, we're running, in 2012, a $700 billion deficit in goods. 321 00:16:10,270 --> 00:16:12,520 We talked about this a bit. 322 00:16:12,520 --> 00:16:15,190 At the end of-- or, began to talk about it. 323 00:16:15,190 --> 00:16:19,540 At the end of World War II, when the US dominated the world 324 00:16:19,540 --> 00:16:23,440 production system, frankly, we were 325 00:16:23,440 --> 00:16:26,750 able to get a full spectrum of gains. 326 00:16:26,750 --> 00:16:29,920 So we innovated here, and we produced here, 327 00:16:29,920 --> 00:16:31,600 and we got the full benefit of gains 328 00:16:31,600 --> 00:16:34,120 across the entire spectrum. 329 00:16:34,120 --> 00:16:38,380 And then, as Suzanne Burger's book showed us last week, 330 00:16:38,380 --> 00:16:41,130 we figured out how to distribute production. 331 00:16:41,130 --> 00:16:43,270 How to separate production and design-- 332 00:16:43,270 --> 00:16:47,830 largely, IT enabled and driven by the financial services 333 00:16:47,830 --> 00:16:48,880 models-- 334 00:16:48,880 --> 00:16:52,600 we figured out how to separate those two. 335 00:16:52,600 --> 00:16:56,870 And we increasingly began to try to innovate here and produce 336 00:16:56,870 --> 00:16:57,370 there. 337 00:17:00,040 --> 00:17:02,470 That means that you're losing a good part 338 00:17:02,470 --> 00:17:06,290 of the full spectrum of gains. 339 00:17:06,290 --> 00:17:12,470 Now, the risk here is that if innovation is, in fact, 340 00:17:12,470 --> 00:17:14,839 related to production-- particularly, 341 00:17:14,839 --> 00:17:16,859 initial production of a new technology-- 342 00:17:16,859 --> 00:17:19,160 If that's a very creative, innovative 343 00:17:19,160 --> 00:17:23,540 stage, and you're shifting that-- 344 00:17:23,540 --> 00:17:26,599 then producing there is going to mean that you're 345 00:17:26,599 --> 00:17:28,670 going to innovate there. 346 00:17:28,670 --> 00:17:30,080 Right? 347 00:17:30,080 --> 00:17:32,420 Because the production has got to be located 348 00:17:32,420 --> 00:17:35,570 close to the innovation-- 349 00:17:35,570 --> 00:17:36,800 for lots of kinds of goods. 350 00:17:36,800 --> 00:17:39,560 Not all goods, but for lots of kinds of goods. 351 00:17:39,560 --> 00:17:42,860 So the risk we're running, and that we're 352 00:17:42,860 --> 00:17:47,570 starting to see signs of, is that producing there 353 00:17:47,570 --> 00:17:50,240 may lead to innovating there as well. 354 00:17:50,240 --> 00:17:52,970 So, Matthew, in the example you were giving before about 355 00:17:52,970 --> 00:17:55,400 your-- was it you who raised the hard technology startup? 356 00:17:55,400 --> 00:17:55,750 AUDIENCE: Yeah. 357 00:17:55,750 --> 00:17:56,940 WILLIAM BONVILLIAN: Yeah. 358 00:17:56,940 --> 00:17:59,420 Your friends moving to Shenzhen to do the rapid scale up, 359 00:17:59,420 --> 00:18:01,580 which Shenzhen is extremely good at-- 360 00:18:01,580 --> 00:18:03,110 logical move. 361 00:18:03,110 --> 00:18:04,910 But they're locating in a significant part 362 00:18:04,910 --> 00:18:07,400 of their innovation capability there, now. 363 00:18:07,400 --> 00:18:09,467 And my guess is that when it comes time for them 364 00:18:09,467 --> 00:18:11,300 to do the next round of incremental advances 365 00:18:11,300 --> 00:18:15,140 on their good, they're going to innovate there. 366 00:18:15,140 --> 00:18:19,880 So in a nutshell, that's what's going on now on a larger scale. 367 00:18:19,880 --> 00:18:22,737 So that affects the spectrum of gain 368 00:18:22,737 --> 00:18:24,320 that gets distributed in this country. 369 00:18:27,420 --> 00:18:33,710 So let's suppose that the US wanted to go back 370 00:18:33,710 --> 00:18:36,980 to production leadership. 371 00:18:36,980 --> 00:18:38,900 It's not going to have production leadership 372 00:18:38,900 --> 00:18:41,268 in everything, by any means. 373 00:18:41,268 --> 00:18:43,310 But let's say there were some areas that we could 374 00:18:43,310 --> 00:18:46,460 have production leadership on. 375 00:18:46,460 --> 00:18:49,710 What would we need to think about? 376 00:18:49,710 --> 00:18:52,920 What will we need to understand? 377 00:18:52,920 --> 00:18:58,550 So the essential argument that the Production 378 00:18:58,550 --> 00:19:02,150 in the Innovation Economy study and that the Advanced 379 00:19:02,150 --> 00:19:06,230 Manufacturing Partnership study came to 380 00:19:06,230 --> 00:19:12,500 was that, historically, shifts in manufacturing advantage 381 00:19:12,500 --> 00:19:14,750 have stemmed from the introduction of a combination 382 00:19:14,750 --> 00:19:18,680 of technology advances, accompanying process 383 00:19:18,680 --> 00:19:24,180 advances, and new business and organizational models. 384 00:19:24,180 --> 00:19:28,700 If you can combine these, you can get a production advance. 385 00:19:28,700 --> 00:19:33,560 So we were talking last week about the remarkable quality 386 00:19:33,560 --> 00:19:38,630 production model that Japan launched in the 70s and 80s-- 387 00:19:38,630 --> 00:19:41,300 on the world. 388 00:19:41,300 --> 00:19:42,710 That's what they did. 389 00:19:42,710 --> 00:19:45,020 They introduced new technology advances, process, 390 00:19:45,020 --> 00:19:48,080 and new business models in combination. 391 00:19:48,080 --> 00:19:49,770 It was a remarkable story. 392 00:19:49,770 --> 00:19:53,570 And they captured a significant portion of world production 393 00:19:53,570 --> 00:19:56,510 in areas like, autos and consumer electronics. 394 00:19:56,510 --> 00:19:59,870 When the US was developing its mass production model, 395 00:19:59,870 --> 00:20:03,500 we did the same, and, exactly, the same thing 396 00:20:03,500 --> 00:20:08,600 occurred-- technology process business model. 397 00:20:08,600 --> 00:20:14,810 So are there new technology advances 398 00:20:14,810 --> 00:20:20,690 that scientists and engineers tell us may be at hand-- 399 00:20:20,690 --> 00:20:28,580 that we could use to develop production innovation? 400 00:20:28,580 --> 00:20:31,760 So how come the US has such trouble 401 00:20:31,760 --> 00:20:33,530 competing in manufacturing? 402 00:20:33,530 --> 00:20:34,910 There's a bunch of macro factors, 403 00:20:34,910 --> 00:20:38,300 and I do not want to underestimate those. 404 00:20:38,300 --> 00:20:42,650 So the US tends to have a very high valued dollar. 405 00:20:42,650 --> 00:20:45,290 Countries like Japan and China work 406 00:20:45,290 --> 00:20:48,080 on maintaining a lower currency valuation. 407 00:20:48,080 --> 00:20:51,350 So they get a competitive advantage every time they 408 00:20:51,350 --> 00:20:52,570 sell a good. 409 00:20:52,570 --> 00:20:53,600 Right? 410 00:20:53,600 --> 00:20:57,110 That's not to be underestimated. 411 00:20:57,110 --> 00:21:03,290 The tax system in the US tends to actually, somewhat, favor 412 00:21:03,290 --> 00:21:05,810 components that are imported rather than components produced 413 00:21:05,810 --> 00:21:07,220 here. 414 00:21:07,220 --> 00:21:09,200 So part of what the president is attempting 415 00:21:09,200 --> 00:21:12,290 to address in this whole debate over border adjustability, 416 00:21:12,290 --> 00:21:15,200 is that tax problem. 417 00:21:15,200 --> 00:21:18,980 We also have a tax system that favors debt 418 00:21:18,980 --> 00:21:22,370 over equity, which tends to make our companies much more 419 00:21:22,370 --> 00:21:23,390 fragile. 420 00:21:23,390 --> 00:21:27,410 Equity is much more assured in longer term. 421 00:21:27,410 --> 00:21:29,270 Debt has to be accounted for and managed 422 00:21:29,270 --> 00:21:31,310 and can hit you in the short term, very quickly. 423 00:21:31,310 --> 00:21:34,130 It makes our companies more fragile. 424 00:21:34,130 --> 00:21:37,610 So there's lots of macro factors here. 425 00:21:37,610 --> 00:21:40,160 But what's new in this story is an attempt 426 00:21:40,160 --> 00:21:43,250 to put an innovation story on the table. 427 00:21:43,250 --> 00:21:49,640 Could we bring our still-strong innovation system to bear, 428 00:21:49,640 --> 00:21:52,100 really, for the first time, frankly-- 429 00:21:52,100 --> 00:21:54,020 on some of these production challenges? 430 00:21:54,020 --> 00:21:57,620 Now, we did have an episode, we talked about briefly, 431 00:21:57,620 --> 00:22:03,620 with Sumatech, in the 1980s and early 90s. 432 00:22:03,620 --> 00:22:06,050 A challenge to semiconductor production 433 00:22:06,050 --> 00:22:09,890 led by Canada and Nissan in-- 434 00:22:09,890 --> 00:22:13,550 Canada Nikon in Japan. 435 00:22:13,550 --> 00:22:17,630 And the US organized what was, in effect, a manufacturing 436 00:22:17,630 --> 00:22:21,890 institute and got its production process down-- 437 00:22:21,890 --> 00:22:24,680 and went to creating much higher quality 438 00:22:24,680 --> 00:22:29,100 goods that could meet and match their global competitors. 439 00:22:29,100 --> 00:22:31,830 So that's the one kind of episode 440 00:22:31,830 --> 00:22:34,070 where we've tried to do something in the past. 441 00:22:34,070 --> 00:22:38,510 But there's been nothing like that since the early 90s. 442 00:22:38,510 --> 00:22:41,600 But the point that the engineers and scientists 443 00:22:41,600 --> 00:22:44,330 seem to be telling us is that there 444 00:22:44,330 --> 00:22:47,600 looks like there's a bunch of technology advances, around 445 00:22:47,600 --> 00:22:52,130 which, we could construct new production paradigms. 446 00:22:52,130 --> 00:22:55,220 Like, what Japan did on quality. 447 00:22:55,220 --> 00:22:58,610 Like, what the US did on mass production. 448 00:22:58,610 --> 00:23:00,997 And there's lots of lists of these, 449 00:23:00,997 --> 00:23:03,330 and we'll talk some more in a minute about some of them. 450 00:23:03,330 --> 00:23:07,550 But maybe, we could do network centric production. 451 00:23:07,550 --> 00:23:10,880 You know, a mix of advanced IT RFID sensors, 452 00:23:10,880 --> 00:23:13,555 make every stage of the production process smart. 453 00:23:13,555 --> 00:23:15,680 From the origin of the resource, through the entire 454 00:23:15,680 --> 00:23:17,360 lifecycle of the good. 455 00:23:17,360 --> 00:23:20,120 And it talks to you and informs you about what's happening 456 00:23:20,120 --> 00:23:21,830 and what's going wrong and how to fix it. 457 00:23:25,070 --> 00:23:28,220 And bring in the mix of advanced robotics, which is really 458 00:23:28,220 --> 00:23:29,570 co-botics, at this point. 459 00:23:29,570 --> 00:23:33,140 Supercomputing modeling simulation-- all that stuff. 460 00:23:33,140 --> 00:23:36,800 Could that bear on a whole new way of doing, 461 00:23:36,800 --> 00:23:39,370 kind of, smart manufacturing-- we could call it. 462 00:23:39,370 --> 00:23:42,710 There are lots of advances in materials-- 463 00:23:42,710 --> 00:23:44,240 it's breathtaking. 464 00:23:44,240 --> 00:23:47,060 We're thinking about a materials genome, 465 00:23:47,060 --> 00:23:49,970 including, with a lot of leadership here at MIT-- 466 00:23:49,970 --> 00:23:52,580 where we would be able to, precisely, 467 00:23:52,580 --> 00:23:57,740 design a material to fit the exact need of a product-- 468 00:23:57,740 --> 00:24:00,320 from a molecular structural point of view. 469 00:24:00,320 --> 00:24:03,170 That's absolutely breathtaking. 470 00:24:03,170 --> 00:24:07,970 Nano fabrication-- all kinds of possibilities emerging here. 471 00:24:07,970 --> 00:24:09,980 Something called mass customization-- 472 00:24:09,980 --> 00:24:11,930 we may be approaching the ability 473 00:24:11,930 --> 00:24:19,280 to produce small lots of goods at the same price 474 00:24:19,280 --> 00:24:22,640 as high volume production. 475 00:24:22,640 --> 00:24:25,402 So that changes everything, right? 476 00:24:25,402 --> 00:24:27,110 That means, you can do local production-- 477 00:24:27,110 --> 00:24:29,030 like you do local food? 478 00:24:29,030 --> 00:24:30,710 You can do highly localized production 479 00:24:30,710 --> 00:24:34,010 for highly customized designs. 480 00:24:34,010 --> 00:24:38,450 We would get to participate in the design process 481 00:24:38,450 --> 00:24:43,760 rather than being a customer at arm's length. 482 00:24:43,760 --> 00:24:46,850 The history of manufacturing has been ever more relentless, 483 00:24:46,850 --> 00:24:48,920 to scale up. 484 00:24:48,920 --> 00:24:51,530 This could completely change the story to scale down-- 485 00:24:51,530 --> 00:24:54,320 and much more, in effect, personalized production 486 00:24:54,320 --> 00:24:55,310 technologies. 487 00:24:55,310 --> 00:25:01,580 So things like 3D printing and computer driven technologies 488 00:25:01,580 --> 00:25:05,420 and equipment, combined with a series of other steps, 489 00:25:05,420 --> 00:25:09,950 might enable production of small lots at the same cost 490 00:25:09,950 --> 00:25:11,750 as production of large lots. 491 00:25:11,750 --> 00:25:15,475 That would be remarkable. 492 00:25:15,475 --> 00:25:16,850 Just-- all kinds of gains to come 493 00:25:16,850 --> 00:25:19,070 from distribution efficiencies. 494 00:25:19,070 --> 00:25:21,840 All kinds of gains to come from energy efficiency. 495 00:25:21,840 --> 00:25:25,640 So there's a series of these potential new paradigms 496 00:25:25,640 --> 00:25:30,290 that could be there, that could give the US an innovation 497 00:25:30,290 --> 00:25:32,780 advantage and better be able to compete. 498 00:25:35,360 --> 00:25:37,280 In effect, we'd be competing on innovation 499 00:25:37,280 --> 00:25:42,560 in historic strength, rather than competing in areas 500 00:25:42,560 --> 00:25:45,090 that are much more outside our control. 501 00:25:45,090 --> 00:25:48,020 So what would you do? 502 00:25:48,020 --> 00:25:50,330 Manufacturing is sectoral. 503 00:25:50,330 --> 00:25:53,810 Aerospace is really different than making cars-- 504 00:25:53,810 --> 00:25:58,100 which is really different than electronics. 505 00:25:58,100 --> 00:26:00,380 But could you launch technology paradigms 506 00:26:00,380 --> 00:26:02,810 that create benefits across a series 507 00:26:02,810 --> 00:26:04,850 of these historic sectors? 508 00:26:04,850 --> 00:26:07,070 Well, let's run it. 509 00:26:07,070 --> 00:26:14,870 So across the top are a matrix of potential new paradigms-- 510 00:26:14,870 --> 00:26:16,100 or, series of sectors-- 511 00:26:16,100 --> 00:26:18,320 and on the left are new production paradigms. 512 00:26:18,320 --> 00:26:19,160 How do they match? 513 00:26:19,160 --> 00:26:21,410 Do they serve a lot of sectors? 514 00:26:21,410 --> 00:26:24,620 It looks like they do. 515 00:26:24,620 --> 00:26:28,190 So then you're getting a multiplier out 516 00:26:28,190 --> 00:26:32,000 of your investment, in a new production paradigm. 517 00:26:32,000 --> 00:26:35,600 Step 3-- as we talked earlier, it's no longer 518 00:26:35,600 --> 00:26:37,780 manufacturing or services. 519 00:26:37,780 --> 00:26:39,470 The 21st century firm is probably 520 00:26:39,470 --> 00:26:43,040 going to combine the two to create tradable goods 521 00:26:43,040 --> 00:26:45,770 with tradable services. 522 00:26:45,770 --> 00:26:49,350 Step 4, we need to understand what our competitor nations are 523 00:26:49,350 --> 00:26:49,850 doing. 524 00:26:49,850 --> 00:26:51,090 We just talked about China. 525 00:26:51,090 --> 00:26:53,090 It's really important to understand the advances 526 00:26:53,090 --> 00:26:54,140 they've come up with-- 527 00:26:54,140 --> 00:26:56,600 production-- there's a lot to learn. 528 00:26:56,600 --> 00:26:58,430 There's deep workforce issues, and there's 529 00:26:58,430 --> 00:27:00,740 some lessons from the way Germany does things. 530 00:27:00,740 --> 00:27:02,780 But that's not going to fit here too well. 531 00:27:02,780 --> 00:27:06,040 Maybe, there are other models we can utilize. 532 00:27:06,040 --> 00:27:09,680 There is this deep financing problem. 533 00:27:09,680 --> 00:27:11,990 The financial services sector has 534 00:27:11,990 --> 00:27:14,990 gone to international modeling, and the end 535 00:27:14,990 --> 00:27:16,520 of face to face banking has really 536 00:27:16,520 --> 00:27:18,410 affected small and midsize manufacturers-- 537 00:27:18,410 --> 00:27:20,930 as we talked about earlier. 538 00:27:20,930 --> 00:27:25,940 The focus on core competency, and driving firms 539 00:27:25,940 --> 00:27:28,790 to go asset light, has had a profound effect 540 00:27:28,790 --> 00:27:32,090 on industrial strength, overall. 541 00:27:32,090 --> 00:27:33,862 These are big challenges. 542 00:27:33,862 --> 00:27:35,570 A lot of what's happened in manufacturing 543 00:27:35,570 --> 00:27:37,850 has been driven by financial services models. 544 00:27:37,850 --> 00:27:39,710 There's no getting around that. 545 00:27:39,710 --> 00:27:40,820 It's hard to change these. 546 00:27:40,820 --> 00:27:44,510 But, maybe, we could think about ways 547 00:27:44,510 --> 00:27:48,080 of substituting on the venture capital problem we're 548 00:27:48,080 --> 00:27:50,360 having now-- which is, that it's, largely, not 549 00:27:50,360 --> 00:27:53,960 available to hard technologies. 550 00:27:53,960 --> 00:27:58,250 So that's kind of the story on these two readings. 551 00:27:58,250 --> 00:28:00,470 I'll just close with an image for you 552 00:28:00,470 --> 00:28:05,730 on how to understand employment in manufacturing. 553 00:28:05,730 --> 00:28:08,130 So think about an hourglass. 554 00:28:08,130 --> 00:28:13,500 And at the top of the hourglass, here's the production moment. 555 00:28:13,500 --> 00:28:17,360 And there's about 12 million workers there. 556 00:28:17,360 --> 00:28:20,480 Flowing into that production moment 557 00:28:20,480 --> 00:28:26,570 are all the resources, suppliers, component makers, 558 00:28:26,570 --> 00:28:30,980 and, as we talked about earlier, a large part of the R&D system. 559 00:28:30,980 --> 00:28:33,282 That's flowing in to that production moment. 560 00:28:33,282 --> 00:28:35,240 And then flowing out of the production moment-- 561 00:28:39,540 --> 00:28:44,060 whole distribution system, lots of services, lots of sales, 562 00:28:44,060 --> 00:28:46,280 lifecycle of the product, repair-- 563 00:28:46,280 --> 00:28:46,935 huge sectors. 564 00:28:46,935 --> 00:28:48,560 The top and the bottom up the hourglass 565 00:28:48,560 --> 00:28:51,050 are much bigger than that production moment. 566 00:28:51,050 --> 00:28:55,370 And, through the hourglass, our value chains of firms-- 567 00:28:55,370 --> 00:28:57,680 firms that are linked to each other. 568 00:28:57,680 --> 00:29:04,960 And when you snap the value chain, by ending production, 569 00:29:04,960 --> 00:29:08,620 you're messing up those value chains. 570 00:29:08,620 --> 00:29:11,890 That's what we did in 2007 and 2008. 571 00:29:11,890 --> 00:29:13,390 We did it for the entire decade but, 572 00:29:13,390 --> 00:29:15,610 particularly at that time period. 573 00:29:15,610 --> 00:29:20,890 And it's very hard to reestablish these value chains. 574 00:29:20,890 --> 00:29:25,040 So the effects of manufacturing employment are not simply here, 575 00:29:25,040 --> 00:29:27,790 they're throughout the whole system. 576 00:29:27,790 --> 00:29:30,800 And look, on the other side of the coin, 577 00:29:30,800 --> 00:29:33,580 the jobs in manufacturing are not necessarily 578 00:29:33,580 --> 00:29:34,870 going to be here. 579 00:29:34,870 --> 00:29:37,450 They're going to be in the system 580 00:29:37,450 --> 00:29:39,370 because the system is so interdependent. 581 00:29:39,370 --> 00:29:43,090 So manufacturing is well understood to be the strongest 582 00:29:43,090 --> 00:29:44,055 jobs multiplier. 583 00:29:44,055 --> 00:29:45,640 In other words, a manufacturing job 584 00:29:45,640 --> 00:29:48,670 leads to more jobs because of these interrelated value 585 00:29:48,670 --> 00:29:51,790 chains, throughout the whole system. 586 00:29:51,790 --> 00:29:56,650 Service is a much, much lower job multiplier. 587 00:29:56,650 --> 00:29:58,840 So if you affect manufacturing, you're 588 00:29:58,840 --> 00:30:02,800 affecting job multiplication throughout your whole economy. 589 00:30:02,800 --> 00:30:05,260 And the reality is that the jobs in manufacturing 590 00:30:05,260 --> 00:30:08,380 need to be seen as part of this system, 591 00:30:08,380 --> 00:30:11,500 not, simply, at the production environment. 592 00:30:11,500 --> 00:30:13,690 And we've been affecting the whole system. 593 00:30:13,690 --> 00:30:16,840 Now if you substitute an imported good 594 00:30:16,840 --> 00:30:18,550 for a US produced good, then you can 595 00:30:18,550 --> 00:30:22,870 re-establish a good part of the distribution system repair 596 00:30:22,870 --> 00:30:23,650 and so forth. 597 00:30:23,650 --> 00:30:27,370 But you don't re-establish the top end. 598 00:30:27,370 --> 00:30:31,270 And you often have a different value chain arranged for that. 599 00:30:31,270 --> 00:30:35,467 So I tried to come up with an image that, kind of, explained 600 00:30:35,467 --> 00:30:36,550 what's been going on here. 601 00:30:36,550 --> 00:30:38,050 But I think that's probably the best 602 00:30:38,050 --> 00:30:39,830 one I've been able to cook up, in terms 603 00:30:39,830 --> 00:30:41,080 of what the effects have been. 604 00:30:41,080 --> 00:30:43,540 And we've been living the effects 605 00:30:43,540 --> 00:30:47,830 of snapping those value chains and firms 606 00:30:47,830 --> 00:30:49,827 by snapping production. 607 00:30:49,827 --> 00:30:51,577 AUDIENCE: So the general idea I'm getting, 608 00:30:51,577 --> 00:30:53,077 is that we basically need to develop 609 00:30:53,077 --> 00:30:57,005 new tech that no one else is prone to be 610 00:30:57,005 --> 00:30:57,838 able to manufacture. 611 00:30:57,838 --> 00:31:00,800 And we need to be able to manufacture it before they can. 612 00:31:00,800 --> 00:31:03,217 WILLIAM BONVILLIAN: And that's what we're going to debate. 613 00:31:03,217 --> 00:31:04,910 That's where we're going to go to next. 614 00:31:04,910 --> 00:31:08,240 So Steph do you want to lead us off in some Q&A? 615 00:31:08,240 --> 00:31:09,230 It's all yours. 616 00:31:09,230 --> 00:31:11,665 I know you're ready for this. 617 00:31:11,665 --> 00:31:13,040 You've been gunning for this one. 618 00:31:13,040 --> 00:31:14,707 AUDIENCE: I think we've all been gunning 619 00:31:14,707 --> 00:31:16,370 for this one, for some time. 620 00:31:16,370 --> 00:31:17,990 WILLIAM BONVILLIAN: And you can come gunning after me, too, 621 00:31:17,990 --> 00:31:19,115 if you want to-- it's fine. 622 00:31:21,526 --> 00:31:23,193 AUDIENCE: So first of all, I just wanted 623 00:31:23,193 --> 00:31:25,878 say that it is a nice privilege to be in this room, 624 00:31:25,878 --> 00:31:27,560 to hear all of your resolve. 625 00:31:27,560 --> 00:31:29,600 You're incredibly intelligent. 626 00:31:29,600 --> 00:31:31,690 And as someone who comes from a low income 627 00:31:31,690 --> 00:31:34,190 immigrant background, to be a part of the conversation-- 628 00:31:34,190 --> 00:31:36,296 to sit at the table is an immense privilege 629 00:31:36,296 --> 00:31:38,404 and an incredible rarity. 630 00:31:38,404 --> 00:31:42,850 And so we've talked about those really difficult cliches-- 631 00:31:42,850 --> 00:31:45,620 I hope that we remember our experiences influence 632 00:31:45,620 --> 00:31:46,530 who we are-- 633 00:31:46,530 --> 00:31:50,520 and how the family that we belong to and peers that we 634 00:31:50,520 --> 00:31:53,220 have and how technology [INAUDIBLE] shift our lives-- 635 00:31:53,220 --> 00:31:55,890 in addition to the ways in which education, our nation, 636 00:31:55,890 --> 00:31:58,440 and the conventional wisdoms of our culture 637 00:31:58,440 --> 00:32:00,870 have really shaped our understandings of power 638 00:32:00,870 --> 00:32:04,160 and influence of English clarity-- 639 00:32:04,160 --> 00:32:08,370 with relation to how difficult the next decade is going 640 00:32:08,370 --> 00:32:10,400 to be for Americans and for people 641 00:32:10,400 --> 00:32:13,680 around the world who are going to be affected by the election. 642 00:32:13,680 --> 00:32:17,720 But, remember also, that while these factors are influencing 643 00:32:17,720 --> 00:32:21,150 us, we have have the ability to influence these factors back. 644 00:32:21,150 --> 00:32:24,655 I just wanted to take a second to remember that. 645 00:32:24,655 --> 00:32:26,780 The second thing that I wanted to ensure to address 646 00:32:26,780 --> 00:32:29,835 was something that I spoke with Bill about last week-- 647 00:32:29,835 --> 00:32:31,770 it was in the context of Japan-- 648 00:32:31,770 --> 00:32:34,560 and that's, the role of mental health in the workforce. 649 00:32:34,560 --> 00:32:37,540 And Japan has one of the highest rates of suicide 650 00:32:37,540 --> 00:32:40,262 amongst labor workforce. 651 00:32:40,262 --> 00:32:43,250 And that has only been increasing over the last two 652 00:32:43,250 --> 00:32:44,080 decades. 653 00:32:44,080 --> 00:32:46,190 And in particular, because of the strenuous hours 654 00:32:46,190 --> 00:32:48,553 that workers are forced to attain-- 655 00:32:48,553 --> 00:32:50,095 in addition to the enormous pressures 656 00:32:50,095 --> 00:32:52,640 to work for very influential firms. 657 00:32:52,640 --> 00:32:55,220 In addition to the fact that most of them say, 658 00:32:55,220 --> 00:32:56,780 I want a firm, their whole life-- 659 00:32:56,780 --> 00:32:58,855 only switching if they're immensely uncomfortable 660 00:32:58,855 --> 00:33:00,540 and get a better position. 661 00:33:00,540 --> 00:33:03,260 Which, is obviously a rarity in our society. 662 00:33:03,260 --> 00:33:05,660 And to that point, the flip side of the story 663 00:33:05,660 --> 00:33:07,430 is that in the United States, there's 664 00:33:07,430 --> 00:33:09,380 also been a sharp increase in suicide 665 00:33:09,380 --> 00:33:10,460 amongst white population. 666 00:33:10,460 --> 00:33:12,980 In particular, white rural peoples. 667 00:33:12,980 --> 00:33:16,850 And there is a study that came out last year in The Washington 668 00:33:16,850 --> 00:33:21,000 Post, where in February 2016-- 669 00:33:21,000 --> 00:33:23,080 only a couple of months before the election-- 670 00:33:23,080 --> 00:33:25,580 they made a very good point about talking about how 671 00:33:25,580 --> 00:33:29,810 rural women had an increase in suicide rates from 47% 672 00:33:29,810 --> 00:33:32,590 in the last 10 years-- more than urban women. 673 00:33:32,590 --> 00:33:35,690 So rural women are disproportionately 674 00:33:35,690 --> 00:33:38,740 experiencing the impacts of the loss of manufacturing 675 00:33:38,740 --> 00:33:43,030 capabilities and the loss of [INAUDIBLE] dynamism 676 00:33:43,030 --> 00:33:45,470 in production because they are the ones who have to, 677 00:33:45,470 --> 00:33:49,103 ultimately, bear the brunt of the home economics. 678 00:33:49,103 --> 00:33:51,020 And then, in addition to that-- white suicide, 679 00:33:51,020 --> 00:33:54,860 generally, has only decreased 1% since 1995. 680 00:33:54,860 --> 00:33:57,720 Whereas, suicide rates amongst Hispanics and African-Americans 681 00:33:57,720 --> 00:34:00,350 were two of the groups surveyed and have decreased 682 00:34:00,350 --> 00:34:03,530 over 47% in the last 20 years. 683 00:34:03,530 --> 00:34:07,310 So the fact is, that in addition to that-- in the last decade, 684 00:34:07,310 --> 00:34:09,590 there's actually been an increase in white suicide 685 00:34:09,590 --> 00:34:10,090 as well. 686 00:34:10,090 --> 00:34:12,770 In particular, after 2007, amongst white men. 687 00:34:12,770 --> 00:34:16,652 And so white men are now independent of people 688 00:34:16,652 --> 00:34:21,590 being targeted for homophobia, transphobia, et cetera. 689 00:34:21,590 --> 00:34:24,360 White men, and especially middle aged white men, 690 00:34:24,360 --> 00:34:27,060 are the ones who are most at risk of committing suicide-- 691 00:34:27,060 --> 00:34:30,639 as a result of the downturn in the American economy. 692 00:34:30,639 --> 00:34:33,030 So the 2007 crisis is a strong correlation 693 00:34:33,030 --> 00:34:35,500 with the increase of suicide amongst white males. 694 00:34:35,500 --> 00:34:39,340 So consider, these are very real issues that we're dealing with. 695 00:34:39,340 --> 00:34:41,220 And I think I was sharing with Bill 696 00:34:41,220 --> 00:34:44,290 that one of the difficulties of having this conversation-- 697 00:34:44,290 --> 00:34:47,090 I think, in this room, and more across our homelives 698 00:34:47,090 --> 00:34:49,500 and our areas of influence, is that I think 699 00:34:49,500 --> 00:34:51,030 it can be very hard to put ourselves 700 00:34:51,030 --> 00:34:53,010 in the shoes of the American manufacturer who 701 00:34:53,010 --> 00:34:54,679 has lost their job. 702 00:34:54,679 --> 00:34:58,080 It can be hard to understand the implications of what it means 703 00:34:58,080 --> 00:35:03,240 to be a person who is now facing a job loss or the extinction 704 00:35:03,240 --> 00:35:05,220 of their job opportunities. 705 00:35:05,220 --> 00:35:07,350 And, now, here we are talking about the potential 706 00:35:07,350 --> 00:35:10,080 for retraining programs and what does that look like. 707 00:35:10,080 --> 00:35:12,480 And what opportunity will they have in the next decade 708 00:35:12,480 --> 00:35:14,290 and the next generation? 709 00:35:14,290 --> 00:35:18,490 And for myself, a moment where I felt this very intimately was-- 710 00:35:18,490 --> 00:35:23,310 maybe, two weeks ago when I was studying for my CS111 p-set 711 00:35:23,310 --> 00:35:26,775 that's undertaking learning to code in Python. 712 00:35:26,775 --> 00:35:28,650 And I'm doing it because I want to learn more 713 00:35:28,650 --> 00:35:30,880 about technology because that's my concentration. 714 00:35:30,880 --> 00:35:32,850 And here I am having a panic attack 715 00:35:32,850 --> 00:35:35,045 at 10:00 PM on a Saturday night because I'm trying 716 00:35:35,045 --> 00:35:36,128 to figure out how to code. 717 00:35:36,128 --> 00:35:40,686 And I realize that this is not my livelihood on the line. 718 00:35:40,686 --> 00:35:42,670 It's just me trying to learn something new just 719 00:35:42,670 --> 00:35:44,158 because I enjoy it. 720 00:35:44,158 --> 00:35:47,630 So I hope that we take the spirit of understanding 721 00:35:47,630 --> 00:35:50,915 and compassion in trying to figure out how easily we can 722 00:35:50,915 --> 00:35:53,582 negotiate between protecting the communities that matter to us-- 723 00:35:53,582 --> 00:35:56,062 as well as maintaining American competitiveness 724 00:35:56,062 --> 00:35:57,750 and collaboration on a global level. 725 00:35:57,750 --> 00:35:59,617 And, at the same time, remembering 726 00:35:59,617 --> 00:36:01,742 the immense difficulties that people in our country 727 00:36:01,742 --> 00:36:02,740 are facing. 728 00:36:02,740 --> 00:36:05,235 And, at the same time, how difficult 729 00:36:05,235 --> 00:36:06,980 it is for the United States because we're 730 00:36:06,980 --> 00:36:10,097 in a compromised position-- because so much of our economy 731 00:36:10,097 --> 00:36:11,722 has been built on the backs of laborers 732 00:36:11,722 --> 00:36:13,219 in developing countries. 733 00:36:13,219 --> 00:36:15,215 So human rights exploitation is not something 734 00:36:15,215 --> 00:36:17,211 that we directly talk about in this course. 735 00:36:17,211 --> 00:36:19,207 But it's certainly something that is relevant. 736 00:36:19,207 --> 00:36:21,158 So I hope that as we enter this discussion, 737 00:36:21,158 --> 00:36:22,700 we keep all of those factors in mind. 738 00:36:22,700 --> 00:36:25,942 Again, approaching this with an open heart and open mind 739 00:36:25,942 --> 00:36:29,187 and being to critically assess these very difficult questions 740 00:36:29,187 --> 00:36:31,183 that we will have to deal with as emerging 741 00:36:31,183 --> 00:36:32,181 leaders in the field. 742 00:36:32,181 --> 00:36:33,442 And with that-- 743 00:36:33,442 --> 00:36:35,400 WILLIAM BONVILLIAN: Steph, let me add something 744 00:36:35,400 --> 00:36:37,890 to your powerful points. 745 00:36:40,440 --> 00:36:47,880 We've talked in significant part of the effect of manufacturing 746 00:36:47,880 --> 00:36:52,320 decline on white males. 747 00:36:52,320 --> 00:36:55,440 Those were the data points that I brought to you. 748 00:36:55,440 --> 00:36:59,070 But we need to remember here, something 749 00:36:59,070 --> 00:37:02,910 that's been going on that's every bit as powerful. 750 00:37:02,910 --> 00:37:05,370 In that post-World War II era-- 751 00:37:05,370 --> 00:37:11,250 during World War II and that post-World War II era 752 00:37:11,250 --> 00:37:13,260 the African-American community in the south 753 00:37:13,260 --> 00:37:16,680 goes to great diaspora and moves north 754 00:37:16,680 --> 00:37:20,730 to seek an opportunity to break in the middle class 755 00:37:20,730 --> 00:37:24,160 with these industrial jobs. 756 00:37:24,160 --> 00:37:25,780 And it's a moment of incredible hope. 757 00:37:25,780 --> 00:37:29,520 There's actually a path forward with these well-paid industrial 758 00:37:29,520 --> 00:37:30,720 jobs. 759 00:37:30,720 --> 00:37:34,440 And that community enters areas like the auto-sector 760 00:37:34,440 --> 00:37:37,020 in significant numbers. 761 00:37:37,020 --> 00:37:39,220 And that's going to be the path ahead. 762 00:37:39,220 --> 00:37:42,270 And those hopes, in significant part, 763 00:37:42,270 --> 00:37:45,780 have now been totally wrecked. 764 00:37:45,780 --> 00:37:48,667 And we've got all over the country these shell cities. 765 00:37:48,667 --> 00:37:49,500 Think about Detroit. 766 00:37:49,500 --> 00:37:51,990 Think what's happened in Detroit. 767 00:37:51,990 --> 00:37:57,060 This is not simply a white, working class problem; 768 00:37:57,060 --> 00:37:59,400 this is a much deeper problem in our society 769 00:37:59,400 --> 00:38:04,660 that we're now just starting to think about and confront. 770 00:38:04,660 --> 00:38:07,668 So I just wanted to add that on top of your picture. 771 00:38:07,668 --> 00:38:09,960 AUDIENCE: [INAUDIBLE] I think there's a lot to be said, 772 00:38:09,960 --> 00:38:15,190 I think, for the differentiation or the impact. 773 00:38:15,190 --> 00:38:16,880 The other point I would add to that 774 00:38:16,880 --> 00:38:18,630 would also be the role of the exploitation 775 00:38:18,630 --> 00:38:21,160 of immigrant communities over all areas, 776 00:38:21,160 --> 00:38:24,732 in particular that benefit off of the manufacturing sector. 777 00:38:24,732 --> 00:38:27,190 For example, so if my family members worked at meat packing 778 00:38:27,190 --> 00:38:30,520 plants in Nebraska, others will get production plants 779 00:38:30,520 --> 00:38:32,260 in other parts of Nebraska. 780 00:38:32,260 --> 00:38:35,010 And my life looks very different than theirs 781 00:38:35,010 --> 00:38:36,750 just because I got really lucky and got 782 00:38:36,750 --> 00:38:38,540 to go to school in Texas. 783 00:38:38,540 --> 00:38:40,836 So I invested in me, got to go to Wellesley 784 00:38:40,836 --> 00:38:45,520 and my life is different from theirs. 785 00:38:45,520 --> 00:38:49,150 So as we think about the nuances of the implications 786 00:38:49,150 --> 00:38:52,270 on this election, the first question I wanted to start off 787 00:38:52,270 --> 00:38:58,600 was actually Luyao was talking about earlier, specifically 788 00:38:58,600 --> 00:39:01,420 on this question of the America First framework. 789 00:39:01,420 --> 00:39:03,730 You mentioned how far can we see firms 790 00:39:03,730 --> 00:39:06,910 as customers who use their investment decisions as votes 791 00:39:06,910 --> 00:39:08,890 for different economies and markets. 792 00:39:08,890 --> 00:39:12,160 Outsourcing US firms can be seen as a reduced marginal utility 793 00:39:12,160 --> 00:39:14,060 of manufacturing investment. 794 00:39:14,060 --> 00:39:18,500 So in a world in which we understand America's firms 795 00:39:18,500 --> 00:39:20,080 as in investing in other countries, 796 00:39:20,080 --> 00:39:23,102 what does this say about their values as in your opinion? 797 00:39:25,488 --> 00:39:27,530 WILLIAM BONVILLIAN: Now, that was a big question. 798 00:39:27,530 --> 00:39:28,080 That was big. 799 00:39:28,080 --> 00:39:29,310 AUDIENCE: [LAUGHS] 800 00:39:29,310 --> 00:39:31,960 WILLIAM BONVILLIAN: In fact the word huge comes to mind. 801 00:39:31,960 --> 00:39:32,370 AUDIENCE: [LAUGHS] 802 00:39:32,370 --> 00:39:32,780 WILLIAM BONVILLIAN: [LAUGHS] 803 00:39:32,780 --> 00:39:34,697 AUDIENCE: What does it say about our companies 804 00:39:34,697 --> 00:39:37,860 because they value profits? 805 00:39:37,860 --> 00:39:40,090 AUDIENCE: I think it's still a question of what 806 00:39:40,090 --> 00:39:42,662 to do to make the firms-- to attract the firms back 807 00:39:42,662 --> 00:39:43,400 to the US. 808 00:39:47,336 --> 00:39:48,812 I think I'm not-- 809 00:39:48,812 --> 00:39:55,208 I've only started to follow US politics after I came here. 810 00:39:55,208 --> 00:40:01,604 [INAUDIBLE] Trump would propose how to do this, 811 00:40:01,604 --> 00:40:09,588 telling them not to [INAUDIBLE] 812 00:40:09,588 --> 00:40:11,880 WILLIAM BONVILLIAN: Let me just add a statistic, Luyao, 813 00:40:11,880 --> 00:40:14,040 to what you're saying. 814 00:40:14,040 --> 00:40:17,460 Like weekly job churn in the United States, 815 00:40:17,460 --> 00:40:21,970 the number of jobs lost, and acquired, 75,000. 816 00:40:21,970 --> 00:40:22,710 Right? 817 00:40:22,710 --> 00:40:23,910 Staggering. 818 00:40:23,910 --> 00:40:26,520 So if you've got the president on the phone 819 00:40:26,520 --> 00:40:31,470 twice a month saving 200 jobs, you haven't done anything. 820 00:40:31,470 --> 00:40:35,410 These are systems issues, deeply etched into the system 821 00:40:35,410 --> 00:40:38,130 we've got, and those are arguably the issues 822 00:40:38,130 --> 00:40:39,000 we need to tackle. 823 00:40:44,323 --> 00:40:46,740 AUDIENCE: I don't know how much you can blame corporations 824 00:40:46,740 --> 00:40:50,490 for these decisions in general, at least for larger ones. 825 00:40:50,490 --> 00:40:54,247 They're subject to their shareholders, which 826 00:40:54,247 --> 00:40:56,690 is, their shareholders will want returns, 827 00:40:56,690 --> 00:40:59,370 but if you could get a large group of shareholders that 828 00:40:59,370 --> 00:41:04,123 said, we want you to make less money and build in America 829 00:41:04,123 --> 00:41:06,290 or we're all going to sell and your company is going 830 00:41:06,290 --> 00:41:10,112 to lose all its value, then they would actually 831 00:41:10,112 --> 00:41:10,820 change something. 832 00:41:10,820 --> 00:41:14,400 But I feel like it's kind of like a grassroots movement that 833 00:41:14,400 --> 00:41:15,990 would be necessary, like the idea 834 00:41:15,990 --> 00:41:19,200 behind the divestment movement is to kind of encourage that. 835 00:41:19,200 --> 00:41:24,920 But even those groups, while they are growing inside 836 00:41:24,920 --> 00:41:27,680 are too small to really make a big difference right now. 837 00:41:27,680 --> 00:41:29,371 So we'd have to come from like-- 838 00:41:29,371 --> 00:41:30,746 AUDIENCE: Another thing too, even 839 00:41:30,746 --> 00:41:32,860 if you make all the choices, like the CEO of the company, 840 00:41:32,860 --> 00:41:34,800 and you get sued if your stock drops too much. 841 00:41:34,800 --> 00:41:37,305 So it's like, yeah, [INAUDIBLE] thinks that he 842 00:41:37,305 --> 00:41:40,055 would ultimately [INAUDIBLE]. 843 00:41:40,055 --> 00:41:41,430 WILLIAM BONVILLIAN: So thank you. 844 00:41:41,430 --> 00:41:44,970 Appealing to corporate goodwill to reduce profits 845 00:41:44,970 --> 00:41:48,330 is probably completely unworkable, right? 846 00:41:48,330 --> 00:41:50,590 So what are the other-- 847 00:41:50,590 --> 00:41:52,376 what other territory can we work in? 848 00:41:52,376 --> 00:41:54,084 AUDIENCE: The other things that you could 849 00:41:54,084 --> 00:41:55,557 do-- you give them incentives. 850 00:41:55,557 --> 00:41:58,503 So either, for example, manufacturing in China-- 851 00:41:58,503 --> 00:41:59,976 make it less attractive. 852 00:41:59,976 --> 00:42:02,431 Or make manufacturing more attractive. 853 00:42:02,431 --> 00:42:04,886 So in order to make it less attractive, 854 00:42:04,886 --> 00:42:07,341 manufacturing in China, you could [INAUDIBLE] 855 00:42:07,341 --> 00:42:09,305 like trade tariffs. 856 00:42:09,305 --> 00:42:12,463 We don't have any tariffs with China, do we? 857 00:42:12,463 --> 00:42:14,130 WILLIAM BONVILLIAN: Well, both countries 858 00:42:14,130 --> 00:42:15,910 are part of the World Trade agreement. 859 00:42:15,910 --> 00:42:18,110 So there are modest tariffs remaining. 860 00:42:18,110 --> 00:42:21,780 But it turns out to be a much more complicated global system 861 00:42:21,780 --> 00:42:23,453 than simply the way tariffs operate. 862 00:42:23,453 --> 00:42:25,620 AUDIENCE: Like one thing is never going to fix this. 863 00:42:25,620 --> 00:42:26,703 WILLIAM BONVILLIAN: Right. 864 00:42:26,703 --> 00:42:28,170 And look, the multinationals have 865 00:42:28,170 --> 00:42:29,205 to be in all the big markets. 866 00:42:29,205 --> 00:42:29,910 AUDIENCE: Yeah. 867 00:42:29,910 --> 00:42:30,600 WILLIAM BONVILLIAN: Right? 868 00:42:30,600 --> 00:42:32,790 If you're running a multinational that's only, 869 00:42:32,790 --> 00:42:35,370 you know, selling into one continent 870 00:42:35,370 --> 00:42:39,720 rather than four or five, you've got a heck of a problem, right? 871 00:42:39,720 --> 00:42:43,680 So you're going to need to be where your markets are. 872 00:42:43,680 --> 00:42:46,460 So what else can we do? 873 00:42:49,360 --> 00:42:51,990 AUDIENCE: I think going off that idea of getting incentives, 874 00:42:51,990 --> 00:42:54,980 I think kind of relaxing maybe tax 875 00:42:54,980 --> 00:42:59,060 rates on a lot of corporations, like overseas funds, 876 00:42:59,060 --> 00:43:01,470 could be an interesting way to approach it. 877 00:43:01,470 --> 00:43:04,070 Like right now, I'm taking a tax class from Michelle Hanlon, 878 00:43:04,070 --> 00:43:10,340 and she's really behind the idea that the overseas, the 40% tax 879 00:43:10,340 --> 00:43:13,340 rate that would be implied, or sorry, 880 00:43:13,340 --> 00:43:15,320 levied on anything that comes back 881 00:43:15,320 --> 00:43:17,540 into the US, which could be feeding 882 00:43:17,540 --> 00:43:20,780 economic growth, creation of jobs, 883 00:43:20,780 --> 00:43:24,420 perhaps fuel more pursuits here in America-- 884 00:43:24,420 --> 00:43:26,900 those are just staying stagnant pretty much overseas, 885 00:43:26,900 --> 00:43:29,840 because those are tax havens. 886 00:43:29,840 --> 00:43:32,990 And that's how they can best maximize 887 00:43:32,990 --> 00:43:35,450 the game of not paying so many taxes 888 00:43:35,450 --> 00:43:39,260 and still having a lot of reserves for their company 889 00:43:39,260 --> 00:43:42,590 to show the profits. 890 00:43:42,590 --> 00:43:45,320 But obviously, controversial, and tax policy 891 00:43:45,320 --> 00:43:47,737 takes a long time to change. 892 00:43:47,737 --> 00:43:50,070 AUDIENCE: Martin, do you have a comment about tax policy 893 00:43:50,070 --> 00:43:51,225 [INAUDIBLE]? 894 00:43:51,225 --> 00:43:52,100 AUDIENCE: [INAUDIBLE] 895 00:43:52,100 --> 00:43:53,456 AUDIENCE: Slightly [INAUDIBLE]. 896 00:43:53,456 --> 00:43:55,170 AUDIENCE: Yeah. 897 00:43:55,170 --> 00:43:57,110 I was just going to say, it's something that-- 898 00:43:57,110 --> 00:44:00,365 OK, as a business person, I feel like it's definitely 899 00:44:00,365 --> 00:44:02,240 a political spectrum, the political side that 900 00:44:02,240 --> 00:44:03,510 needs to deal with this issue. 901 00:44:03,510 --> 00:44:05,880 But also, as somebody who knows history well, 902 00:44:05,880 --> 00:44:08,010 I'm very doubtful of politicians that 903 00:44:08,010 --> 00:44:12,010 have to get funded every four years or two years by somebody. 904 00:44:12,010 --> 00:44:14,640 And I just don't think it's a great system that way, 905 00:44:14,640 --> 00:44:17,160 because people have this short term thinking, 906 00:44:17,160 --> 00:44:18,810 and then most likely will be somebody 907 00:44:18,810 --> 00:44:20,602 with corporate interests or corporate ties, 908 00:44:20,602 --> 00:44:23,390 and lobbying will be a effect. 909 00:44:23,390 --> 00:44:30,197 And so that's-- that's the thing that I think is a big issue. 910 00:44:30,197 --> 00:44:32,280 AUDIENCE: But just maybe to clarify a little bit-- 911 00:44:32,280 --> 00:44:34,190 so I understood [INAUDIBLE]. 912 00:44:34,190 --> 00:44:38,910 Instead of these offshore tax havens, [INAUDIBLE] 913 00:44:38,910 --> 00:44:42,090 taxes levied for leaving interest offshore, 914 00:44:42,090 --> 00:44:44,190 and you want to bring those assets, 915 00:44:44,190 --> 00:44:47,700 and kind of make it more attractive to keep your money 916 00:44:47,700 --> 00:44:50,580 and let it be taxed here by relaxing these rates? 917 00:44:50,580 --> 00:44:51,540 AUDIENCE: Mmhm. 918 00:44:51,540 --> 00:44:52,500 AUDIENCE: Interesting. 919 00:44:55,666 --> 00:44:58,083 AUDIENCE: Anybody have any more comments about tax havens? 920 00:44:58,083 --> 00:44:58,770 AUDIENCE: Oh. 921 00:44:58,770 --> 00:45:01,350 You could take the other route, and rather 922 00:45:01,350 --> 00:45:03,990 than just get rid of the taxes, which like you said, 923 00:45:03,990 --> 00:45:06,060 could create a lot of jobs and a lot of wealth, 924 00:45:06,060 --> 00:45:08,100 rather than just reduce them, why not 925 00:45:08,100 --> 00:45:11,480 just make it so that these tax havens are impossible to use? 926 00:45:11,480 --> 00:45:13,744 Which should be illegal, but-- 927 00:45:16,666 --> 00:45:19,101 AUDIENCE: Is that politically possible, though? 928 00:45:19,101 --> 00:45:20,248 AUDIENCE: Probably not. 929 00:45:20,248 --> 00:45:21,290 WILLIAM BONVILLIAN: Look. 930 00:45:21,290 --> 00:45:24,140 I mean, the new administration is, 931 00:45:24,140 --> 00:45:26,510 as I mentioned earlier, pushing hard 932 00:45:26,510 --> 00:45:28,220 on border adjustability, which frankly, 933 00:45:28,220 --> 00:45:31,130 is the system virtually every other country has. 934 00:45:31,130 --> 00:45:34,340 It's going to be really hard politically to get that done. 935 00:45:34,340 --> 00:45:38,780 And it's also pushing hard on a new way of treating debt versus 936 00:45:38,780 --> 00:45:40,640 equity in the tax system. 937 00:45:40,640 --> 00:45:44,540 Those two pieces alone could be actually fairly significant 938 00:45:44,540 --> 00:45:47,165 if they're able to do them. 939 00:45:47,165 --> 00:45:48,630 You know, we'll see. 940 00:45:48,630 --> 00:45:50,750 But Steph, go back to some of the questions 941 00:45:50,750 --> 00:45:54,140 that you've got ready-- 942 00:45:54,140 --> 00:45:56,210 the arsenal that you've got waiting for us. 943 00:45:56,210 --> 00:45:57,990 AUDIENCE: She's like, all right, let's go. 944 00:45:57,990 --> 00:45:58,490 [LAUGHTER] 945 00:45:58,490 --> 00:46:02,490 AUDIENCE: [INAUDIBLE] actually [INAUDIBLE].. 946 00:46:02,490 --> 00:46:05,774 So I thought that this was a-- this one's from Beth. 947 00:46:05,774 --> 00:46:08,107 And I thought that this was a really important question, 948 00:46:08,107 --> 00:46:09,941 in particular because of the group of people 949 00:46:09,941 --> 00:46:11,131 that's assembled here. 950 00:46:11,131 --> 00:46:13,079 She asks, manufacturing is no longer 951 00:46:13,079 --> 00:46:15,514 seen as a cool or innovative job by many students, 952 00:46:15,514 --> 00:46:17,462 especially at MIT. 953 00:46:17,462 --> 00:46:20,384 As startups in Silicon Valley attract talent, 954 00:46:20,384 --> 00:46:22,558 how can these perceptions be altered? 955 00:46:22,558 --> 00:46:23,850 AUDIENCE: This is really funny. 956 00:46:23,850 --> 00:46:25,910 I was just talking with a friend of mine 957 00:46:25,910 --> 00:46:27,202 from another school about this. 958 00:46:27,202 --> 00:46:29,645 We were watching Saturday Night Live, I think, 959 00:46:29,645 --> 00:46:31,720 and a GE commercial came on. 960 00:46:31,720 --> 00:46:35,843 And we commented how literally, from the last few years, 961 00:46:35,843 --> 00:46:37,260 all of the GE commercials have not 962 00:46:37,260 --> 00:46:38,427 been about, buy our product. 963 00:46:38,427 --> 00:46:40,020 They've been, please come work for us. 964 00:46:40,020 --> 00:46:42,673 So they had this slew of them that were like-- 965 00:46:42,673 --> 00:46:44,340 I don't know if you guys have seen them, 966 00:46:44,340 --> 00:46:46,715 but there was like, the young kid who goes to his parents 967 00:46:46,715 --> 00:46:48,670 and says, I got a job at GE, or to his friend, 968 00:46:48,670 --> 00:46:49,420 I got a job at GE. 969 00:46:49,420 --> 00:46:50,120 I'm gonna be writing code. 970 00:46:50,120 --> 00:46:51,220 The jig is the world. 971 00:46:51,220 --> 00:46:53,600 And his parents are struggling to see that 972 00:46:53,600 --> 00:46:55,600 as an interesting thing, because in their minds, 973 00:46:55,600 --> 00:46:57,100 GE is the big hammer. 974 00:46:57,100 --> 00:47:00,180 They build the big machinery, and it's very mechanical, 975 00:47:00,180 --> 00:47:02,340 old manufacturing style to them. 976 00:47:02,340 --> 00:47:05,070 And then there was a GE commercial that just came out 977 00:47:05,070 --> 00:47:07,950 recently, the [INAUDIBLE] one, that 978 00:47:07,950 --> 00:47:11,100 was talking about how they have this new initiative 979 00:47:11,100 --> 00:47:13,170 to encourage, to get a higher number of women 980 00:47:13,170 --> 00:47:16,640 in technical jobs by 2020, I think. 981 00:47:16,640 --> 00:47:18,570 But just-- yeah, I think it's definitely 982 00:47:18,570 --> 00:47:19,870 a very pervasive attitude. 983 00:47:19,870 --> 00:47:21,960 And maybe some of these older giants 984 00:47:21,960 --> 00:47:23,370 are starting to take note of that 985 00:47:23,370 --> 00:47:26,379 as they're looking at their workforce age dwindling. 986 00:47:26,379 --> 00:47:28,004 AUDIENCE: If I can ask you a followup-- 987 00:47:28,004 --> 00:47:31,220 I know in the reading, it talked about the importance 988 00:47:31,220 --> 00:47:34,140 of working in the manufacturing plant for engineers to really 989 00:47:34,140 --> 00:47:37,650 get a sense innovation capacity in production. 990 00:47:37,650 --> 00:47:39,400 Could you talk a little bit about how 991 00:47:39,400 --> 00:47:42,732 you might feel if you had to go to a production plant 992 00:47:42,732 --> 00:47:44,030 to start off your career? 993 00:47:44,030 --> 00:47:44,655 AUDIENCE: Yeah. 994 00:47:44,655 --> 00:47:46,678 No, I agree with previous sentiments 995 00:47:46,678 --> 00:47:50,093 that other people have expressed regarding that. 996 00:47:50,093 --> 00:47:52,260 I think it's very important for engineers, or people 997 00:47:52,260 --> 00:47:54,930 who work strictly at design or maybe, 998 00:47:54,930 --> 00:47:56,820 you know, with a CAD program for the majority 999 00:47:56,820 --> 00:47:59,790 of their career to see how things 1000 00:47:59,790 --> 00:48:01,770 work on the ground floor. 1001 00:48:01,770 --> 00:48:04,410 I had an internship last summer where-- 1002 00:48:04,410 --> 00:48:05,930 I wasn't working on this project. 1003 00:48:05,930 --> 00:48:07,472 But the person who was paired with me 1004 00:48:07,472 --> 00:48:10,620 on the same project, her entire summer 1005 00:48:10,620 --> 00:48:12,804 was spent designing a wheel. 1006 00:48:12,804 --> 00:48:14,860 Not just one wheel-- it was a filter wheel. 1007 00:48:14,860 --> 00:48:17,940 It was a very complicated piece of machinery. 1008 00:48:17,940 --> 00:48:19,500 But it was all very computer-based 1009 00:48:19,500 --> 00:48:20,610 and very design-based. 1010 00:48:20,610 --> 00:48:24,390 But she spent half the summer shadowing 1011 00:48:24,390 --> 00:48:26,223 the guy in the machine shop who was actually 1012 00:48:26,223 --> 00:48:27,432 going to be making the wheel. 1013 00:48:27,432 --> 00:48:29,050 And this ended up motivating so many 1014 00:48:29,050 --> 00:48:30,974 of her design requirements and decisions. 1015 00:48:30,974 --> 00:48:33,224 And I think, at least from an engineering perspective, 1016 00:48:33,224 --> 00:48:34,599 it's very important to understand 1017 00:48:34,599 --> 00:48:37,617 how that works so that you can not forge ahead 1018 00:48:37,617 --> 00:48:39,075 beyond the capabilities that exist, 1019 00:48:39,075 --> 00:48:42,725 and take advantage of the innovation that's 1020 00:48:42,725 --> 00:48:44,017 happening on the factory floor. 1021 00:48:44,017 --> 00:48:45,100 WILLIAM BONVILLIAN: Right. 1022 00:48:45,100 --> 00:48:46,770 And as we talked about last week, 1023 00:48:46,770 --> 00:48:48,780 one of the things that Japan figured out 1024 00:48:48,780 --> 00:48:50,670 in its highly innovative production process 1025 00:48:50,670 --> 00:48:54,290 towards quality was not separating the engineering 1026 00:48:54,290 --> 00:48:57,330 workforce from the factory floor workforce, 1027 00:48:57,330 --> 00:48:59,520 but really integrating them, right? 1028 00:48:59,520 --> 00:49:01,710 And the engineering community would come on, 1029 00:49:01,710 --> 00:49:04,890 and its first round of jobs was to understand 1030 00:49:04,890 --> 00:49:06,930 how the factory floor operated. 1031 00:49:06,930 --> 00:49:09,240 So Germany is famous for this. 1032 00:49:09,240 --> 00:49:13,860 But when there's a problem, when there's a production problem, 1033 00:49:13,860 --> 00:49:17,840 a swarm forms in these Mittelstand firms. 1034 00:49:17,840 --> 00:49:20,880 The engineers and the highly educated workforce 1035 00:49:20,880 --> 00:49:24,180 team up and are working the thing out together, right? 1036 00:49:24,180 --> 00:49:25,830 That happens less than the US system. 1037 00:49:25,830 --> 00:49:29,220 So this kind of integration of the design 1038 00:49:29,220 --> 00:49:31,020 team and the production team is something 1039 00:49:31,020 --> 00:49:34,590 that has been organized in countries like Japan and-- 1040 00:49:34,590 --> 00:49:37,782 AUDIENCE: [INAUDIBLE] consulting firms just sort of build 1041 00:49:37,782 --> 00:49:38,790 that gap. 1042 00:49:38,790 --> 00:49:39,380 WILLIAM BONVILLIAN: Oh, yeah. 1043 00:49:39,380 --> 00:49:39,880 Right. 1044 00:49:39,880 --> 00:49:41,063 I'm sure they do. 1045 00:49:41,063 --> 00:49:43,230 McKenzie is right there on the factory floor, right? 1046 00:49:43,230 --> 00:49:44,022 AUDIENCE: Oh, yeah. 1047 00:49:44,022 --> 00:49:46,374 AUDIENCE: Well, and what's fascinating is-- at MIT-- 1048 00:49:46,374 --> 00:49:48,800 have any of you guys taken classes at D-Lab? 1049 00:49:48,800 --> 00:49:49,385 AUDIENCE: No. 1050 00:49:49,385 --> 00:49:51,090 But they've helped me build some stuff. 1051 00:49:51,090 --> 00:49:51,370 AUDIENCE: OK. 1052 00:49:51,370 --> 00:49:51,870 Great. 1053 00:49:51,870 --> 00:49:54,757 So they're really big advocates of creative capacity building 1054 00:49:54,757 --> 00:49:57,920 and co-creation, so building products 1055 00:49:57,920 --> 00:50:01,010 for communities in developing countries 1056 00:50:01,010 --> 00:50:02,930 with those communities. 1057 00:50:02,930 --> 00:50:04,815 And I think it's been fascinating 1058 00:50:04,815 --> 00:50:07,398 that MIT is at the forefront of that for developing countries. 1059 00:50:07,398 --> 00:50:08,757 And yet-- 1060 00:50:08,757 --> 00:50:11,340 WILLIAM BONVILLIAN: We've got a developing country right here. 1061 00:50:11,340 --> 00:50:12,140 AUDIENCE: Right. 1062 00:50:12,140 --> 00:50:13,230 WILLIAM BONVILLIAN: Right? 1063 00:50:13,230 --> 00:50:17,430 AUDIENCE: I think companies, particularly 1064 00:50:17,430 --> 00:50:19,901 like the German style sort of rotational programs 1065 00:50:19,901 --> 00:50:22,276 for these engineers, making sure that they get experience 1066 00:50:22,276 --> 00:50:23,193 on the factory floor-- 1067 00:50:23,193 --> 00:50:26,027 I think, I want to say chemical production plants-- so 1068 00:50:26,027 --> 00:50:27,902 people who graduate with chemical engineering 1069 00:50:27,902 --> 00:50:29,473 degrees [INAUDIBLE] oil pipelines, 1070 00:50:29,473 --> 00:50:30,515 they do this pretty well. 1071 00:50:30,515 --> 00:50:32,970 So they go and they rotate and they see the oil pipelines 1072 00:50:32,970 --> 00:50:34,470 and stuff like that, so if they have 1073 00:50:34,470 --> 00:50:36,800 to build these complex parts in front of the computer, 1074 00:50:36,800 --> 00:50:39,210 they go to the site and go and see what's going on. 1075 00:50:39,210 --> 00:50:41,420 I feel like they have those rotational programs 1076 00:50:41,420 --> 00:50:42,295 for the established-- 1077 00:50:42,295 --> 00:50:43,870 I'm going to use chemical engineering 1078 00:50:43,870 --> 00:50:45,870 firms, because that's where I've heard the most. 1079 00:50:45,870 --> 00:50:51,682 But there is this sentiment that maybe touring these factory 1080 00:50:51,682 --> 00:50:53,640 floors and actually seeing what you're building 1081 00:50:53,640 --> 00:50:55,300 is going to be really important. 1082 00:50:55,300 --> 00:50:57,517 But I think the only other rotational programs 1083 00:50:57,517 --> 00:50:59,850 that I hear about, when people do internships like this, 1084 00:50:59,850 --> 00:51:02,410 is in I want to say finance. 1085 00:51:02,410 --> 00:51:03,450 They do this very well. 1086 00:51:03,450 --> 00:51:08,130 They rotate you in different sectors. 1087 00:51:08,130 --> 00:51:12,090 I think Goldman-- all the big banks have rotational programs, 1088 00:51:12,090 --> 00:51:14,736 where you have your first two or three years. 1089 00:51:14,736 --> 00:51:15,830 And Visa does it as well. 1090 00:51:15,830 --> 00:51:16,580 Everybody does it. 1091 00:51:16,580 --> 00:51:19,920 But you basically spend a year or two 1092 00:51:19,920 --> 00:51:22,770 working with a particular team with a particular focus. 1093 00:51:22,770 --> 00:51:26,020 And then you tour all up and down the pipeline. 1094 00:51:26,020 --> 00:51:28,170 So if it's asset management, you go top to bottom. 1095 00:51:28,170 --> 00:51:29,650 AUDIENCE: [INAUDIBLE] 1096 00:51:29,650 --> 00:51:31,150 AUDIENCE: [INAUDIBLE]. 1097 00:51:31,150 --> 00:51:31,650 Excellent. 1098 00:51:31,650 --> 00:51:35,010 So if you transition and make sure 1099 00:51:35,010 --> 00:51:37,260 that these internships that people are getting sort of 1100 00:51:37,260 --> 00:51:40,550 do more of that, and kind of mimic those models, 1101 00:51:40,550 --> 00:51:43,093 where you rotate around and see what's available, 1102 00:51:43,093 --> 00:51:44,760 integrating those pipelines, then you'll 1103 00:51:44,760 --> 00:51:49,200 have a better idea of what you're actually engineering for 1104 00:51:49,200 --> 00:51:51,200 and what these design processes are actually 1105 00:51:51,200 --> 00:51:53,262 supposed to look like within manufacturing. 1106 00:51:53,262 --> 00:51:55,530 AUDIENCE: I want to call on Max. 1107 00:51:55,530 --> 00:51:57,880 AUDIENCE: So I've been thinking a couple things. 1108 00:51:57,880 --> 00:52:02,310 One would be that that approach of forcing engineers 1109 00:52:02,310 --> 00:52:05,670 to actually to basically get their hands dirty-- 1110 00:52:05,670 --> 00:52:08,105 I feel like it would make them happier, because-- 1111 00:52:08,105 --> 00:52:11,120 AUDIENCE: Well, a lot of the people [INAUDIBLE].. 1112 00:52:11,120 --> 00:52:13,343 [INTERPOSING VOICES] 1113 00:52:13,343 --> 00:52:14,760 AUDIENCE: A lot of the people that 1114 00:52:14,760 --> 00:52:17,560 go into engineering, they're the kids who played with LEGOs. 1115 00:52:17,560 --> 00:52:18,810 And they thought, oh, this is what the engineering's 1116 00:52:18,810 --> 00:52:19,890 going to be like. 1117 00:52:19,890 --> 00:52:22,050 And then they find out it's a bunch of CAD drawings and Excel 1118 00:52:22,050 --> 00:52:22,592 spreadsheets. 1119 00:52:22,592 --> 00:52:27,425 And they're just like, wow, this is isn't exactly satisfying. 1120 00:52:27,425 --> 00:52:29,050 I can't speak to everyone's experience. 1121 00:52:29,050 --> 00:52:30,660 I can speak to mine. 1122 00:52:30,660 --> 00:52:33,550 And I know that was one of my reactions. 1123 00:52:33,550 --> 00:52:35,857 It's still like, yeah, you're designing something 1124 00:52:35,857 --> 00:52:36,940 that's changing the world. 1125 00:52:36,940 --> 00:52:39,800 But when it comes down to it, it's still all on a computer. 1126 00:52:39,800 --> 00:52:43,510 You're staring at a computer for eight hours a day. 1127 00:52:43,510 --> 00:52:46,050 And as other people were saying, yeah, 1128 00:52:46,050 --> 00:52:47,920 it definitely gives people exposure 1129 00:52:47,920 --> 00:52:50,260 to concepts like machine tolerance 1130 00:52:50,260 --> 00:52:55,870 and the ability for, I don't know-- you realize that, hey, 1131 00:52:55,870 --> 00:53:00,055 I can't design something that has design specifications 1132 00:53:00,055 --> 00:53:01,810 within a femtometer. 1133 00:53:01,810 --> 00:53:03,825 That's 10 to the negative 15 meters. 1134 00:53:03,825 --> 00:53:04,450 It's 15, right? 1135 00:53:04,450 --> 00:53:05,406 I think it's 15. 1136 00:53:05,406 --> 00:53:07,740 AUDIENCE: It's very small. 1137 00:53:07,740 --> 00:53:10,570 AUDIENCE: It's small. 1138 00:53:10,570 --> 00:53:14,800 So yeah, I generally agree with the general sentiment. 1139 00:53:14,800 --> 00:53:19,450 WILLIAM BONVILLIAN: So Max, how pervasive is the maker movement 1140 00:53:19,450 --> 00:53:20,860 now at MIT? 1141 00:53:20,860 --> 00:53:23,290 I mean, I think we have some 40 maker spaces. 1142 00:53:23,290 --> 00:53:26,470 And Professor Marty Culpepper's or makers 1143 00:53:26,470 --> 00:53:28,300 are obviously a very talented guy. 1144 00:53:28,300 --> 00:53:31,090 AUDIENCE: I do know that a couple of professors 1145 00:53:31,090 --> 00:53:34,647 in the nuclear department are trying to design a maker space. 1146 00:53:34,647 --> 00:53:36,480 They're trying to get some support together, 1147 00:53:36,480 --> 00:53:37,397 some funding together. 1148 00:53:37,397 --> 00:53:38,833 AUDIENCE: [INAUDIBLE] 1149 00:53:38,833 --> 00:53:39,708 AUDIENCE: [INAUDIBLE] 1150 00:53:39,708 --> 00:53:40,585 [INTERPOSING VOICES] 1151 00:53:40,585 --> 00:53:41,460 AUDIENCE: Mike Short. 1152 00:53:41,460 --> 00:53:42,335 AUDIENCE: Mike Short? 1153 00:53:42,335 --> 00:53:43,986 How do you know Mike Short? 1154 00:53:43,986 --> 00:53:47,103 AUDIENCE: I study nuclear. 1155 00:53:47,103 --> 00:53:49,520 AUDIENCE: Beth, I know that you wanted to respond to that. 1156 00:53:49,520 --> 00:53:49,760 AUDIENCE: Yeah. 1157 00:53:49,760 --> 00:53:50,145 AUDIENCE: Oh, yeah. 1158 00:53:50,145 --> 00:53:51,330 AUDIENCE: Could you respond to [INAUDIBLE]?? 1159 00:53:51,330 --> 00:53:53,390 AUDIENCE: I probably said something horrible. 1160 00:53:53,390 --> 00:53:54,098 AUDIENCE: No, no. 1161 00:53:54,098 --> 00:53:59,600 I just think-- my view of a lot of MIT students 1162 00:53:59,600 --> 00:54:03,570 is that we've been prepared is like white collar engineers. 1163 00:54:03,570 --> 00:54:07,890 We're people who want to go straight to the office. 1164 00:54:07,890 --> 00:54:09,140 And I think part of it's good. 1165 00:54:09,140 --> 00:54:10,057 We want to be leaders. 1166 00:54:10,057 --> 00:54:12,150 We want to be tackling big projects. 1167 00:54:12,150 --> 00:54:13,880 But we also view ourselves as not 1168 00:54:13,880 --> 00:54:18,570 wanting to get into the nitty-gritty, out in the FRCs 1169 00:54:18,570 --> 00:54:21,860 in the field, doing the hardcore engineering-- that we've 1170 00:54:21,860 --> 00:54:23,980 kind of surpassed that and should 1171 00:54:23,980 --> 00:54:28,130 be doing higher and better things than that. 1172 00:54:28,130 --> 00:54:30,100 And then kind of unrelated to that-- 1173 00:54:30,100 --> 00:54:34,550 well, somewhat related, is demographic change, 1174 00:54:34,550 --> 00:54:39,380 but changes in people's preferences. 1175 00:54:39,380 --> 00:54:41,810 I know, personally, I was offered 1176 00:54:41,810 --> 00:54:45,290 a job in somewhat manufacturing in kind 1177 00:54:45,290 --> 00:54:46,810 of the middle of nowhere. 1178 00:54:46,810 --> 00:54:49,010 And so I have a strong preference to live in a city. 1179 00:54:49,010 --> 00:54:51,320 And so that has made me less likely to [INAUDIBLE] 1180 00:54:51,320 --> 00:54:52,780 manufacturing outside. 1181 00:54:52,780 --> 00:54:54,830 And there's not really the opportunities to-- 1182 00:54:54,830 --> 00:54:56,330 I mean, there are some opportunities 1183 00:54:56,330 --> 00:54:58,997 to have like the urban lifestyle and also work in manufacturing. 1184 00:54:58,997 --> 00:55:02,295 But that's becoming less and less possible. 1185 00:55:02,295 --> 00:55:05,000 AUDIENCE: I think something I've noticed amongst our responses-- 1186 00:55:05,000 --> 00:55:06,710 and well, we aren't talking particularly 1187 00:55:06,710 --> 00:55:08,182 about the skilled workforce. 1188 00:55:08,182 --> 00:55:10,640 So I was wondering if you could elaborate a little bit more 1189 00:55:10,640 --> 00:55:15,530 about what training programs or what this experience might look 1190 00:55:15,530 --> 00:55:17,860 time for the, quote unquote, unskilled workforce, 1191 00:55:17,860 --> 00:55:21,550 or the non college-educated crowd, 1192 00:55:21,550 --> 00:55:23,353 as we are going to be transitioning 1193 00:55:23,353 --> 00:55:25,618 to a question about that momentarily. 1194 00:55:25,618 --> 00:55:26,530 AUDIENCE: Yeah. 1195 00:55:26,530 --> 00:55:30,362 So in my hometown, we're just by a really large shipyard. 1196 00:55:30,362 --> 00:55:32,570 And so there's a pretty strong apprenticeship program 1197 00:55:32,570 --> 00:55:34,403 that a bunch of kids are going to school. 1198 00:55:34,403 --> 00:55:36,070 WILLIAM BONVILLIAN: Where is that, Beth? 1199 00:55:36,070 --> 00:55:36,870 AUDIENCE: It's the Newport News Shipyard. 1200 00:55:36,870 --> 00:55:37,953 WILLIAM BONVILLIAN: Right. 1201 00:55:37,953 --> 00:55:39,032 That's what I thought. 1202 00:55:39,032 --> 00:55:40,990 AUDIENCE: And so I've seen that a lot of people 1203 00:55:40,990 --> 00:55:42,790 that I know from high school who have 1204 00:55:42,790 --> 00:55:45,390 had very successful points in their careers now. 1205 00:55:45,390 --> 00:55:47,230 They finished their apprenticeship program 1206 00:55:47,230 --> 00:55:49,900 and are now full-time employees of the shipyard. 1207 00:55:49,900 --> 00:55:53,200 And that does seem to be an exception rather than a rule. 1208 00:55:53,200 --> 00:55:56,420 And it seems-- it is very much like the shipyard runs 1209 00:55:56,420 --> 00:55:56,980 it itself. 1210 00:55:56,980 --> 00:56:00,250 It's not like wider movement. 1211 00:56:00,250 --> 00:56:02,920 And it's been really, I think, successful for them as well 1212 00:56:02,920 --> 00:56:05,830 in having a continuous skilled workforce, 1213 00:56:05,830 --> 00:56:07,880 and repeating that elsewhere seems 1214 00:56:07,880 --> 00:56:12,840 like it could be a good idea. 1215 00:56:12,840 --> 00:56:14,266 AUDIENCE: So this is-- 1216 00:56:14,266 --> 00:56:15,790 [INTERPOSING VOICES] 1217 00:56:15,790 --> 00:56:16,320 AUDIENCE: I want to hear from Kevin. 1218 00:56:16,320 --> 00:56:17,880 He actually wants to go into manufacturing. 1219 00:56:17,880 --> 00:56:19,005 AUDIENCE: Yeah, yeah, yeah. 1220 00:56:19,005 --> 00:56:20,837 [INTERPOSING VOICES] 1221 00:56:20,837 --> 00:56:23,170 AUDIENCE: First of all, I don't know about my last year. 1222 00:56:23,170 --> 00:56:26,950 So before this semester, I was actually taking time off. 1223 00:56:26,950 --> 00:56:30,030 And for about a year, I worked as a manufacturing operations 1224 00:56:30,030 --> 00:56:33,270 manager at a digital printing company. 1225 00:56:33,270 --> 00:56:36,660 It was a small startup, kind of new. 1226 00:56:36,660 --> 00:56:38,970 But for the first two or three weeks, 1227 00:56:38,970 --> 00:56:44,020 they had me learn every bit of the process, 1228 00:56:44,020 --> 00:56:47,650 from picking out access material by hand, 1229 00:56:47,650 --> 00:56:49,930 to learning all their new software, 1230 00:56:49,930 --> 00:56:54,160 to understanding how the big wide format printers work, 1231 00:56:54,160 --> 00:56:58,000 setting up the conveyor oven with the owners-- 1232 00:56:58,000 --> 00:57:01,430 every part of the process. 1233 00:57:01,430 --> 00:57:03,490 And I can definitely say that helped a lot 1234 00:57:03,490 --> 00:57:05,977 in my understanding of being able to manage 1235 00:57:05,977 --> 00:57:07,810 the employees in the different departments-- 1236 00:57:07,810 --> 00:57:09,470 the shipping, the art department, all 1237 00:57:09,470 --> 00:57:11,908 that-- because I knew it worked. 1238 00:57:11,908 --> 00:57:12,950 I was in touch with them. 1239 00:57:12,950 --> 00:57:14,100 I was in touch with the employees. 1240 00:57:14,100 --> 00:57:16,350 At the meetings we had, it was just the owners and me. 1241 00:57:16,350 --> 00:57:17,140 It was the owners. 1242 00:57:17,140 --> 00:57:18,030 There was someone would be there from shipping. 1243 00:57:18,030 --> 00:57:19,620 Someone some would be there from art. 1244 00:57:19,620 --> 00:57:22,280 And everybody would say, look, this is great, 1245 00:57:22,280 --> 00:57:24,090 we would love to get orders out this fast, 1246 00:57:24,090 --> 00:57:26,382 but shipping can't do it because of these capabilities. 1247 00:57:26,382 --> 00:57:28,770 And next week, we'd make a change 1248 00:57:28,770 --> 00:57:31,170 that would make that possible. 1249 00:57:31,170 --> 00:57:34,020 Going back to Beth's point about manufacturing not being cool-- 1250 00:57:36,610 --> 00:57:38,240 this is just my opinion, but I really 1251 00:57:38,240 --> 00:57:42,670 don't want to kiss up to someone for funding for an idea. 1252 00:57:42,670 --> 00:57:43,170 Right? 1253 00:57:43,170 --> 00:57:47,799 I am very happy learning every bit of process 1254 00:57:47,799 --> 00:57:49,466 in the manufacturing operation, and then 1255 00:57:49,466 --> 00:57:50,940 being able to lead that. 1256 00:57:50,940 --> 00:57:53,580 And I definitely enjoyed that the last year, which 1257 00:57:53,580 --> 00:57:57,150 is why it just kind of reaffirms my decision 1258 00:57:57,150 --> 00:57:59,460 to go into manufacturing after I graduate. 1259 00:57:59,460 --> 00:58:02,750 WILLIAM BONVILLIAN: Kevin, you're going to save us all. 1260 00:58:02,750 --> 00:58:04,080 AUDIENCE: That's the plan. 1261 00:58:04,080 --> 00:58:05,163 WILLIAM BONVILLIAN: Right. 1262 00:58:05,163 --> 00:58:07,055 All right. 1263 00:58:07,055 --> 00:58:08,430 You get one more question, Steph, 1264 00:58:08,430 --> 00:58:10,040 because then I've got to get to the last reading, 1265 00:58:10,040 --> 00:58:11,430 because the clock is upon us. 1266 00:58:11,430 --> 00:58:13,260 AUDIENCE: Oh, this is so stressful. 1267 00:58:13,260 --> 00:58:15,620 So I'll say, I think-- 1268 00:58:15,620 --> 00:58:18,143 there it a question that I'll mention that we will not 1269 00:58:18,143 --> 00:58:20,101 get to debate, but I hope that everyone sort of 1270 00:58:20,101 --> 00:58:22,980 reflects on this point, which is a point that Chris raised. 1271 00:58:22,980 --> 00:58:25,950 She says, how do we expect domestic policy, job creation, 1272 00:58:25,950 --> 00:58:28,770 and manufacturing to change under Trump's administration? 1273 00:58:28,770 --> 00:58:32,125 Obviously, that's something that we'll sort of witness, 1274 00:58:32,125 --> 00:58:33,300 unfortunately, or-- 1275 00:58:35,900 --> 00:58:38,080 no value judgments here. 1276 00:58:38,080 --> 00:58:40,080 But I do think it was important to raise that is 1277 00:58:40,080 --> 00:58:42,810 a reality, as we all know. 1278 00:58:42,810 --> 00:58:46,050 The question that I really want us to flush out is from Lily. 1279 00:58:46,050 --> 00:58:49,260 And she says, would this not be an ideal time 1280 00:58:49,260 --> 00:58:51,360 for a very effective national advertising 1281 00:58:51,360 --> 00:58:54,360 campaign promoting technical schools 1282 00:58:54,360 --> 00:58:56,170 and starting to promote technical skills? 1283 00:58:56,170 --> 00:58:59,070 If the administration intends to create increased manufacturing 1284 00:58:59,070 --> 00:59:02,228 jobs, won't it need a ready workforce in a few years? 1285 00:59:06,008 --> 00:59:07,300 AUDIENCE: I'll jump in on that. 1286 00:59:07,300 --> 00:59:09,092 I think, yeah, it's definitely a good time. 1287 00:59:09,092 --> 00:59:11,360 I think we're kind of in a pseudo college bubble-- 1288 00:59:11,360 --> 00:59:12,620 not to say that college isn't valuable. 1289 00:59:12,620 --> 00:59:14,880 But it's not proportionate to the amount of people we 1290 00:59:14,880 --> 00:59:16,460 need in the economy for jobs. 1291 00:59:16,460 --> 00:59:18,190 So you need blue collar people that 1292 00:59:18,190 --> 00:59:22,400 would be educated at a higher university and grad students. 1293 00:59:22,400 --> 00:59:24,560 And so there is a huge divide in that people just 1294 00:59:24,560 --> 00:59:25,690 don't go with the blue collar jobs 1295 00:59:25,690 --> 00:59:27,065 because they don't seem valuable. 1296 00:59:27,065 --> 00:59:29,930 So a lot of it is based on is this a valuable profession 1297 00:59:29,930 --> 00:59:31,756 for me to go into, especially because there 1298 00:59:31,756 --> 00:59:32,810 aer some people who, if they want 1299 00:59:32,810 --> 00:59:35,480 to get very practical, street smart, manufacturing skills, 1300 00:59:35,480 --> 00:59:38,090 they might want to go work in manufacturing 1301 00:59:38,090 --> 00:59:41,070 versus actually studying a mechanical engineering degree. 1302 00:59:41,070 --> 00:59:43,070 And so it's how do you make those jobs valuable, 1303 00:59:43,070 --> 00:59:46,642 but also a good use of people's time. 1304 00:59:46,642 --> 00:59:47,325 Yeah. 1305 00:59:47,325 --> 00:59:47,950 AUDIENCE: Yeah. 1306 00:59:47,950 --> 00:59:51,490 Thinking about that, [INAUDIBLE] case study in 1307 00:59:51,490 --> 00:59:54,965 education [INAUDIBLE] high school. 1308 00:59:54,965 --> 00:59:58,480 If you look at Sweden's education system versus the US, 1309 00:59:58,480 --> 01:00:01,960 and in Sweden, any high school teacher 1310 01:00:01,960 --> 01:00:03,730 needs to have a master's degree. 1311 01:00:03,730 --> 01:00:07,930 And becoming a teacher, a high school teacher, is like the job 1312 01:00:07,930 --> 01:00:10,940 that people want to enter. 1313 01:00:10,940 --> 01:00:13,420 And it just makes me think about and consider 1314 01:00:13,420 --> 01:00:16,360 how much of our perception of a job 1315 01:00:16,360 --> 01:00:22,070 and how much demand there is for certain jobs is flexible. 1316 01:00:22,070 --> 01:00:25,830 And it's not necessarily-- 1317 01:00:25,830 --> 01:00:28,745 well, the value you add is as perceived by people 1318 01:00:28,745 --> 01:00:29,890 in the country, right? 1319 01:00:29,890 --> 01:00:36,460 So would it help even just to have a PR campaign 1320 01:00:36,460 --> 01:00:37,770 for manufacturing jobs? 1321 01:00:37,770 --> 01:00:40,890 And would that even be enough to shift 1322 01:00:40,890 --> 01:00:42,320 people's behavior towards it? 1323 01:00:42,320 --> 01:00:44,770 AUDIENCE: But I mean, that's a PR [INAUDIBLE].. 1324 01:00:44,770 --> 01:00:46,390 And I had dinner with this guy who's 1325 01:00:46,390 --> 01:00:48,830 the president of an energy company in Australia. 1326 01:00:48,830 --> 01:00:49,760 And he talked a lot about credibility, 1327 01:00:49,760 --> 01:00:50,740 not just being credible. 1328 01:00:50,740 --> 01:00:53,157 So the GE commercial, you could hear it and see, oh, yeah, 1329 01:00:53,157 --> 01:00:55,070 well, I don't really trust it. 1330 01:00:55,070 --> 01:00:56,750 I don't believe it, because I know you're marketing to me. 1331 01:00:56,750 --> 01:00:57,000 Right? 1332 01:00:57,000 --> 01:00:59,125 And this is a big thing business, especially today, 1333 01:00:59,125 --> 01:01:01,140 because people can sense bullshit, right? 1334 01:01:01,140 --> 01:01:02,967 It's just like, yeah, it's not real, 1335 01:01:02,967 --> 01:01:04,550 because you used to be like, oh, yeah, 1336 01:01:04,550 --> 01:01:05,842 safety's a really big priority. 1337 01:01:05,842 --> 01:01:06,570 And he would go-- 1338 01:01:06,570 --> 01:01:09,220 he was part of this mining slash energy company with oil. 1339 01:01:09,220 --> 01:01:10,080 And he would go in and he wouldn't 1340 01:01:10,080 --> 01:01:11,163 wear his safety equipment. 1341 01:01:11,163 --> 01:01:12,970 And he would go on the job and in the field 1342 01:01:12,970 --> 01:01:14,470 and everyone would see him not wearing safety equipment. 1343 01:01:14,470 --> 01:01:16,220 And then he'd go and push for-- 1344 01:01:16,220 --> 01:01:17,420 oh, yeah, let's do-- 1345 01:01:17,420 --> 01:01:18,670 we need to be very safe, guys. 1346 01:01:18,670 --> 01:01:20,020 And they're like, well, you're the boss, 1347 01:01:20,020 --> 01:01:21,228 and you never do any of this. 1348 01:01:21,228 --> 01:01:22,570 Why should I listen to you? 1349 01:01:22,570 --> 01:01:24,130 So it's like, how do you do it in a very credible way 1350 01:01:24,130 --> 01:01:26,890 that people can actually see people that are successful? 1351 01:01:26,890 --> 01:01:29,182 Because right now, who do you see as successful, right? 1352 01:01:29,182 --> 01:01:30,968 It's mostly some college educated-- 1353 01:01:30,968 --> 01:01:33,510 I don't want to say white male, but it's definitely not women 1354 01:01:33,510 --> 01:01:36,615 and minorities, really. 1355 01:01:36,615 --> 01:01:37,115 Yeah. 1356 01:01:37,115 --> 01:01:39,590 Good point. 1357 01:01:39,590 --> 01:01:42,820 AUDIENCE: Well, I had a lot of considerations 1358 01:01:42,820 --> 01:01:45,200 going into this question. 1359 01:01:45,200 --> 01:01:48,200 One is, is wistfulness. or hopefulness. 1360 01:01:48,200 --> 01:01:51,920 I just wish that, with the energy that 1361 01:01:51,920 --> 01:01:57,025 was generated during the election and the promise 1362 01:01:57,025 --> 01:02:00,020 of jobs, and you saw a lot of people-- 1363 01:02:00,020 --> 01:02:03,048 I think there's anger, but also hopefulness. 1364 01:02:03,048 --> 01:02:05,340 Like yes, we're going to bring back manufacturing jobs. 1365 01:02:05,340 --> 01:02:06,770 So then build on that. 1366 01:02:06,770 --> 01:02:11,540 Identify-- it would be excellent if the administration could 1367 01:02:11,540 --> 01:02:13,520 pinpoint what sorts of sectors they 1368 01:02:13,520 --> 01:02:16,730 want to promote and incentivize, and then 1369 01:02:16,730 --> 01:02:18,350 create an advertising campaign. 1370 01:02:18,350 --> 01:02:22,660 I know when I was younger, I used to see the-- 1371 01:02:22,660 --> 01:02:25,223 I think the military divisions have really excellent 1372 01:02:25,223 --> 01:02:26,140 advertising campaigns. 1373 01:02:26,140 --> 01:02:29,000 I saw it, the Air Force, and be like, oh, man, I 1374 01:02:29,000 --> 01:02:30,250 want to go into the Air Force. 1375 01:02:30,250 --> 01:02:31,140 [INTERPOSING VOICES] 1376 01:02:31,140 --> 01:02:31,740 AUDIENCE: Have you seen [INAUDIBLE] Marine commercial? 1377 01:02:31,740 --> 01:02:33,688 Have you been targeted on YouTube yet? 1378 01:02:33,688 --> 01:02:35,149 It's so good. 1379 01:02:35,149 --> 01:02:37,430 AUDIENCE: I don't watch enough TV these days. 1380 01:02:37,430 --> 01:02:42,200 But I think that this would be an opportune time to do that, 1381 01:02:42,200 --> 01:02:44,720 so that if you generate manufacturing jobs 1382 01:02:44,720 --> 01:02:48,520 over the next couple of years, as they promised or intended-- 1383 01:02:48,520 --> 01:02:51,560 and more manufacturing jobs have come back 1384 01:02:51,560 --> 01:02:53,520 over the last few years, as we've seen. 1385 01:02:53,520 --> 01:02:55,760 So then have that ready workforce. 1386 01:02:55,760 --> 01:03:02,300 Also, I'm from a rural area, a farming area in Missouri, 1387 01:03:02,300 --> 01:03:07,713 where a lot of jobs have been lost over the last decade. 1388 01:03:07,713 --> 01:03:09,380 Most of the people I went to school with 1389 01:03:09,380 --> 01:03:12,320 don't have the money to go to a four-year university, 1390 01:03:12,320 --> 01:03:15,242 and they don't have the money to not make money for four years. 1391 01:03:15,242 --> 01:03:17,450 So something that's more short term, like a one-year, 1392 01:03:17,450 --> 01:03:20,690 a two-year technical program is much more in line 1393 01:03:20,690 --> 01:03:22,840 with what they're able to do. 1394 01:03:22,840 --> 01:03:25,695 Those were the considerations. 1395 01:03:25,695 --> 01:03:26,320 AUDIENCE: Yeah. 1396 01:03:26,320 --> 01:03:28,487 And then I do think what else needs to be considered 1397 01:03:28,487 --> 01:03:32,132 is what exactly would we recommend to train people 1398 01:03:32,132 --> 01:03:32,840 in at this point? 1399 01:03:32,840 --> 01:03:35,180 What are the-- because you don't want 1400 01:03:35,180 --> 01:03:36,950 to train a bunch of people in something 1401 01:03:36,950 --> 01:03:40,167 that is no longer useful in five or 10 years. 1402 01:03:40,167 --> 01:03:42,000 So should we be teaching people how to code? 1403 01:03:42,000 --> 01:03:47,233 Should there be programs in using complex robots? 1404 01:03:47,233 --> 01:03:48,400 I don't know what to expect. 1405 01:03:48,400 --> 01:03:49,692 And I think that's a challenge. 1406 01:03:49,692 --> 01:03:51,215 AUDIENCE: No, it should be targeted. 1407 01:03:51,215 --> 01:03:52,233 Yeah. 1408 01:03:52,233 --> 01:03:54,650 WILLIAM BONVILLIAN: So I spent some time with Sanjay Sarma 1409 01:03:54,650 --> 01:03:55,970 earlier this afternoon. 1410 01:03:55,970 --> 01:03:57,520 And he's a-- 1411 01:03:57,520 --> 01:03:59,840 AUDIENCE: [INAUDIBLE] what does he do? 1412 01:03:59,840 --> 01:04:01,340 WILLIAM BONVILLIAN: He's a professor 1413 01:04:01,340 --> 01:04:02,900 of mechanical engineering, and has 1414 01:04:02,900 --> 01:04:07,520 spent a lot of time creating a lot of the RFID capability, 1415 01:04:07,520 --> 01:04:12,140 and did a lot of work in manufacturing. 1416 01:04:12,140 --> 01:04:15,020 But he's leading MIT's online education efforts. 1417 01:04:15,020 --> 01:04:21,200 So he leads MITx and the Office of Digital Learning ODL. 1418 01:04:21,200 --> 01:04:24,740 So that was his question. 1419 01:04:24,740 --> 01:04:27,950 We can take these new platforms we've built, 1420 01:04:27,950 --> 01:04:29,710 and they could be valuable. 1421 01:04:29,710 --> 01:04:31,570 They could be new assets. 1422 01:04:31,570 --> 01:04:34,340 And we could create blended learning models 1423 01:04:34,340 --> 01:04:36,870 with community colleges, for example. 1424 01:04:36,870 --> 01:04:41,070 But what is the content of the education? 1425 01:04:41,070 --> 01:04:42,180 How do we figure that out? 1426 01:04:42,180 --> 01:04:42,680 Right? 1427 01:04:42,680 --> 01:04:44,533 And it's really a big, important question. 1428 01:04:44,533 --> 01:04:46,450 And that's his central question at this point. 1429 01:04:46,450 --> 01:04:47,380 He's ready to do it. 1430 01:04:47,380 --> 01:04:49,030 But he's got to figure out a process 1431 01:04:49,030 --> 01:04:51,820 by which to get to the answers of what are the most 1432 01:04:51,820 --> 01:04:53,530 critical skills to educate for? 1433 01:04:53,530 --> 01:04:56,650 Obviously, they're going to vary to some extent by sector. 1434 01:04:56,650 --> 01:05:00,680 But that's a really big question. 1435 01:05:00,680 --> 01:05:03,040 So Steph, give us a closing comment. 1436 01:05:03,040 --> 01:05:04,480 AUDIENCE: I am prepared for this. 1437 01:05:04,480 --> 01:05:06,210 WILLIAM BONVILLIAN: I know you are. 1438 01:05:06,210 --> 01:05:07,704 [LAUGHTER] 1439 01:05:09,198 --> 01:05:12,184 AUDIENCE: The haircut-- yeah, this is what I want to study, 1440 01:05:12,184 --> 01:05:12,684 right? 1441 01:05:12,684 --> 01:05:13,680 This is what I love. 1442 01:05:13,680 --> 01:05:16,535 And I think in particular [INAUDIBLE] 1443 01:05:16,535 --> 01:05:18,660 I was doing research that was funded by [INAUDIBLE] 1444 01:05:18,660 --> 01:05:22,146 here at MIT D Lab, Wellesley, and some organizations 1445 01:05:22,146 --> 01:05:24,636 in Thailand to research precisely how you do 1446 01:05:24,636 --> 01:05:28,122 creative capacity building and increase civic participation 1447 01:05:28,122 --> 01:05:31,110 and innovation in ways that really support 1448 01:05:31,110 --> 01:05:35,094 collaboration and are responsive and understanding 1449 01:05:35,094 --> 01:05:38,578 of the political and economic contexts. 1450 01:05:38,578 --> 01:05:39,078 Right? 1451 01:05:39,078 --> 01:05:41,568 And we did this research in two villages in Thailand. 1452 01:05:41,568 --> 01:05:44,722 And I think preliminarily, the most important outcomes 1453 01:05:44,722 --> 01:05:48,540 of this study were realizing that a lot of technologies 1454 01:05:48,540 --> 01:05:50,818 fade rather quickly, right? 1455 01:05:50,818 --> 01:05:52,315 They're no longer useful. 1456 01:05:52,315 --> 01:05:53,812 They become outdated. 1457 01:05:53,812 --> 01:05:54,810 They fail to run. 1458 01:05:54,810 --> 01:05:56,639 And I think we do a really great job 1459 01:05:56,639 --> 01:05:58,306 of studying this in the developing world 1460 01:05:58,306 --> 01:06:00,798 when we ship tools to other places 1461 01:06:00,798 --> 01:06:02,794 about how they're no longer modular. 1462 01:06:02,794 --> 01:06:05,866 And I think we could apply that sort of same line of reasoning 1463 01:06:05,866 --> 01:06:08,283 here to the United States about the ways in which we treat 1464 01:06:08,283 --> 01:06:11,027 critical thinking education and ways in which we make 1465 01:06:11,027 --> 01:06:13,772 our education systems modular and understandable 1466 01:06:13,772 --> 01:06:16,267 for all at every point, rather than teaching 1467 01:06:16,267 --> 01:06:19,261 particular skills that may be outdated, because then they'll 1468 01:06:19,261 --> 01:06:22,504 end up in crises like I had two Saturdays ago-- crying 1469 01:06:22,504 --> 01:06:25,249 in my room, saying, why can't I get this problem? 1470 01:06:25,249 --> 01:06:28,243 And I don't know that the kind of support system that's 1471 01:06:28,243 --> 01:06:31,736 necessary for an initiative such as blended learning 1472 01:06:31,736 --> 01:06:33,233 is [INAUDIBLE] currently. 1473 01:06:33,233 --> 01:06:36,726 And that's why I think it's so important to really assess 1474 01:06:36,726 --> 01:06:39,720 cultural factors, and also questions of support 1475 01:06:39,720 --> 01:06:43,712 as we move forward with how to improve the innovation 1476 01:06:43,712 --> 01:06:45,710 and manufacturing harmony. 1477 01:06:45,710 --> 01:06:46,960 WILLIAM BONVILLIAN: All right. 1478 01:06:46,960 --> 01:06:47,870 Thank you. 1479 01:06:47,870 --> 01:06:48,370 All right. 1480 01:06:48,370 --> 01:06:51,400 So I'm going to just summarize this closing reading. 1481 01:06:51,400 --> 01:06:53,380 We've talked a lot about the problems. 1482 01:06:53,380 --> 01:06:56,470 This has been one attempt at a fix. 1483 01:06:56,470 --> 01:07:00,430 So these two reports came out in 2012, 2014. 1484 01:07:00,430 --> 01:07:03,520 They were prepared by a collaboration 1485 01:07:03,520 --> 01:07:08,110 between major industry CEOs and a whole range of sectors, 1486 01:07:08,110 --> 01:07:11,620 and a group of university presidents 1487 01:07:11,620 --> 01:07:14,910 who had particularly strong engineering links, including 1488 01:07:14,910 --> 01:07:16,870 MIT's president. 1489 01:07:16,870 --> 01:07:20,170 So both Susan Hockfield at MIT and Rafael Reif at MIT were 1490 01:07:20,170 --> 01:07:23,307 both co-chairs, university co-chairs, 1491 01:07:23,307 --> 01:07:24,640 of these two different reports-- 1492 01:07:24,640 --> 01:07:28,720 Susan for the 2012 one, Rafael for the 2014 one. 1493 01:07:28,720 --> 01:07:32,110 So there's been a very major focus on these issues at MIT. 1494 01:07:32,110 --> 01:07:35,500 MIT did the production and the innovation economy. 1495 01:07:35,500 --> 01:07:38,920 It was, frankly, educating the administration 1496 01:07:38,920 --> 01:07:40,390 of what the issues were. 1497 01:07:40,390 --> 01:07:43,810 And then, surprise, the Obama administration 1498 01:07:43,810 --> 01:07:47,020 came back and said, all right, put your money 1499 01:07:47,020 --> 01:07:50,870 where your mouth is, and help us pull these reports together. 1500 01:07:50,870 --> 01:07:53,320 So I had the privilege of being part 1501 01:07:53,320 --> 01:07:57,700 of a delegation of MIT folks to help in preparing these. 1502 01:07:57,700 --> 01:08:00,700 And it was a fascinating experience. 1503 01:08:00,700 --> 01:08:03,670 So there were essentially four recommendations 1504 01:08:03,670 --> 01:08:05,170 that came out of these reports. 1505 01:08:05,170 --> 01:08:07,960 There are transformative technologies. 1506 01:08:07,960 --> 01:08:11,290 And could we develop, to achieve these transformative 1507 01:08:11,290 --> 01:08:15,010 technologies and production, technology strategies that 1508 01:08:15,010 --> 01:08:19,390 were linked to the R&D system? 1509 01:08:19,390 --> 01:08:22,090 Could we implement manufacturing institutes 1510 01:08:22,090 --> 01:08:24,729 and network the manufacturing institutes 1511 01:08:24,729 --> 01:08:28,210 organized around these transformative technologies? 1512 01:08:28,210 --> 01:08:31,540 Could we have, as part of the role of these manufacturing 1513 01:08:31,540 --> 01:08:35,500 institutes, they would play a role in demand-driven workforce 1514 01:08:35,500 --> 01:08:36,529 solutions? 1515 01:08:36,529 --> 01:08:40,060 And then finally, could we develop a technology scale-up 1516 01:08:40,060 --> 01:08:43,880 policy to deal with this scale-up problem? 1517 01:08:43,880 --> 01:08:48,069 So these reports really focused on this stuff. 1518 01:08:48,069 --> 01:08:50,830 They argued that we need innovation-based efficiency 1519 01:08:50,830 --> 01:08:53,830 gains to compete with lower cost, lower wage nations. 1520 01:08:53,830 --> 01:08:55,300 There's no substitute for that. 1521 01:08:55,300 --> 01:08:56,979 The macro factors don't necessarily 1522 01:08:56,979 --> 01:08:58,960 give this, those solutions. 1523 01:08:58,960 --> 01:09:01,920 We've got to put the innovation system on this problem. 1524 01:09:01,920 --> 01:09:02,500 Right? 1525 01:09:02,500 --> 01:09:04,359 There's no getting around it. 1526 01:09:04,359 --> 01:09:06,399 It's time. 1527 01:09:06,399 --> 01:09:08,859 So there's 14 advanced manufacturing institutes now. 1528 01:09:08,859 --> 01:09:10,779 They've been set up all over the country. 1529 01:09:10,779 --> 01:09:11,930 They are collaborative. 1530 01:09:11,930 --> 01:09:13,960 They are industry, university, government. 1531 01:09:13,960 --> 01:09:16,300 They are federal government seed funding, 1532 01:09:16,300 --> 01:09:20,890 but often overmatched by industry with seed funding 1533 01:09:20,890 --> 01:09:24,760 as well from state and sometimes regional governments. 1534 01:09:24,760 --> 01:09:26,290 They have a big test bed roll. 1535 01:09:26,290 --> 01:09:29,200 So they bring in the small and mid-sized manufacturers 1536 01:09:29,200 --> 01:09:32,220 and put them in as part of the innovation system. 1537 01:09:32,220 --> 01:09:32,720 Right? 1538 01:09:32,720 --> 01:09:35,950 They're connecting the small and large manufacturers, just as 1539 01:09:35,950 --> 01:09:38,760 in the German fraunhofer model. 1540 01:09:38,760 --> 01:09:41,490 They have a big test bed role in testing technologies out so 1541 01:09:41,490 --> 01:09:44,490 that a small manufacturer would know how to play with them, 1542 01:09:44,490 --> 01:09:46,600 know how to work with them. 1543 01:09:46,600 --> 01:09:49,319 And then they're organized around these potential new 1544 01:09:49,319 --> 01:09:51,267 production paradigms. 1545 01:09:51,267 --> 01:09:53,850 They're cost-shared, as I said, between the federal government 1546 01:09:53,850 --> 01:09:55,267 industry and the state government. 1547 01:09:55,267 --> 01:09:57,150 So they're very collaborative. 1548 01:09:57,150 --> 01:10:01,410 An example-- what does advanced manufacturing technology 1549 01:10:01,410 --> 01:10:02,380 look like? 1550 01:10:02,380 --> 01:10:04,310 Here's a printed car. 1551 01:10:04,310 --> 01:10:05,160 Right? 1552 01:10:05,160 --> 01:10:08,730 Concept to print, six weeks, 500 parts-- 1553 01:10:08,730 --> 01:10:10,680 took 24 hours to print this. 1554 01:10:10,680 --> 01:10:11,910 That's a Shelby Cobra. 1555 01:10:11,910 --> 01:10:12,720 It's electric. 1556 01:10:12,720 --> 01:10:14,660 It's battery-powered. 1557 01:10:14,660 --> 01:10:17,677 Pretty interesting, right? 1558 01:10:17,677 --> 01:10:20,010 It gives us some idea of what some of these capabilities 1559 01:10:20,010 --> 01:10:21,610 are going to look like. 1560 01:10:21,610 --> 01:10:23,460 What's the stage that the institutes 1561 01:10:23,460 --> 01:10:25,120 are supposed to address? 1562 01:10:25,120 --> 01:10:28,590 And we've seen designs like this earlier in the class. 1563 01:10:28,590 --> 01:10:32,250 But we've got basic R&D, government support 1564 01:10:32,250 --> 01:10:36,870 of universities, a gap here, and then a private sector role. 1565 01:10:36,870 --> 01:10:39,890 And the gap occurs-- 1566 01:10:39,890 --> 01:10:42,830 basic research, proof of concept, 1567 01:10:42,830 --> 01:10:46,940 then actual undertaking, kind of the initial production 1568 01:10:46,940 --> 01:10:49,640 stage at a lab kind of level, capacity 1569 01:10:49,640 --> 01:10:54,770 to produce the advanced prototype, and the capability, 1570 01:10:54,770 --> 01:10:58,610 really about here, to operate in a production environment, 1571 01:10:58,610 --> 01:11:00,260 bring the technology to production. 1572 01:11:00,260 --> 01:11:01,400 That's where the gap is. 1573 01:11:01,400 --> 01:11:04,490 It's in these stages here. 1574 01:11:04,490 --> 01:11:06,050 Right? 1575 01:11:06,050 --> 01:11:08,050 There's a missing link in the innovation system. 1576 01:11:08,050 --> 01:11:09,592 Remember, we talked about doing a gap 1577 01:11:09,592 --> 01:11:12,740 analysis of the innovation system and filling the gaps in? 1578 01:11:12,740 --> 01:11:14,240 That's exactly what these institutes 1579 01:11:14,240 --> 01:11:17,810 are attempting to undertake. 1580 01:11:17,810 --> 01:11:19,650 And here's the organization of them. 1581 01:11:19,650 --> 01:11:21,280 This is a very complex organization. 1582 01:11:21,280 --> 01:11:21,780 Right? 1583 01:11:21,780 --> 01:11:26,060 So you've got an institute, which has prototype labs. 1584 01:11:26,060 --> 01:11:26,840 It has shops. 1585 01:11:26,840 --> 01:11:28,040 It has research facilities. 1586 01:11:28,040 --> 01:11:31,400 It's got a computerized lab for simulation and modeling. 1587 01:11:31,400 --> 01:11:34,130 These are shared use facilities, where small and large firms 1588 01:11:34,130 --> 01:11:37,280 can collaborate together. 1589 01:11:37,280 --> 01:11:42,525 They're bringing in universities for engineering capability 1590 01:11:42,525 --> 01:11:44,900 from the university level to help in the innovation stage 1591 01:11:44,900 --> 01:11:46,310 and technology development. 1592 01:11:46,310 --> 01:11:48,602 They're bringing in community colleges on the workforce 1593 01:11:48,602 --> 01:11:50,030 development side. 1594 01:11:50,030 --> 01:11:52,430 Over here, they're bringing in large manufacturing 1595 01:11:52,430 --> 01:11:53,815 firms, small and mid-sized. 1596 01:11:53,815 --> 01:11:55,190 And a number of them are starting 1597 01:11:55,190 --> 01:11:56,870 to think now about startups. 1598 01:11:56,870 --> 01:11:59,330 How do we get them into this mix? 1599 01:11:59,330 --> 01:12:01,310 And then the support system-- federal, state, 1600 01:12:01,310 --> 01:12:03,590 and local-- and economic development organizations 1601 01:12:03,590 --> 01:12:05,090 are all feeding in here. 1602 01:12:05,090 --> 01:12:08,450 And then above this, we have 14 of these institutes now. 1603 01:12:08,450 --> 01:12:10,760 Could we create a network of them 1604 01:12:10,760 --> 01:12:14,540 so that we get learning across the system? 1605 01:12:14,540 --> 01:12:17,270 So that's essentially the organizational design. 1606 01:12:17,270 --> 01:12:20,600 It's a very complex model. 1607 01:12:20,600 --> 01:12:22,630 What does an R&D agency typically do? 1608 01:12:22,630 --> 01:12:30,140 It gives an award of $300,000 to a principal investigator. 1609 01:12:30,140 --> 01:12:32,630 As a Defense Department official told me, 1610 01:12:32,630 --> 01:12:34,910 this is like standing up a country. 1611 01:12:34,910 --> 01:12:37,910 You've got literally more than 100 actors 1612 01:12:37,910 --> 01:12:39,995 involved in these manufacturing institutes. 1613 01:12:39,995 --> 01:12:41,120 How do you coordinate them? 1614 01:12:41,120 --> 01:12:42,230 How do you pull these things together? 1615 01:12:42,230 --> 01:12:44,188 We've never tried to do anything as complicated 1616 01:12:44,188 --> 01:12:45,530 as this in the R&D system. 1617 01:12:45,530 --> 01:12:48,570 It's a real challenge. 1618 01:12:48,570 --> 01:12:52,070 So this is the first group of manufacturing institutes. 1619 01:12:52,070 --> 01:12:56,000 You can see that they're pretty distributed across the country. 1620 01:12:56,000 --> 01:12:57,680 And then we've had five more institutes 1621 01:12:57,680 --> 01:13:00,162 since these were stood up. 1622 01:13:00,162 --> 01:13:01,370 Here's what we're working on. 1623 01:13:01,370 --> 01:13:05,060 These are the territories of advanced manufacturing. 1624 01:13:05,060 --> 01:13:08,300 So we've got 3D, printing digital manufacturing 1625 01:13:08,300 --> 01:13:13,130 and design, lightweight and modern metals, next generation 1626 01:13:13,130 --> 01:13:16,820 power electronics, which is largely wide band gap 1627 01:13:16,820 --> 01:13:18,560 semiconductor-- 1628 01:13:18,560 --> 01:13:20,330 completely changes power electronics, 1629 01:13:20,330 --> 01:13:22,280 but a lot of other things too-- 1630 01:13:22,280 --> 01:13:28,020 advanced composites, photonics, flexible hybrid electronics. 1631 01:13:28,020 --> 01:13:33,320 MIT is leading the advanced fiber institute, which 1632 01:13:33,320 --> 01:13:35,450 is completely revolutionary. 1633 01:13:35,450 --> 01:13:38,990 The US lost the textile sector ages ago. 1634 01:13:38,990 --> 01:13:42,770 So this is an idea, bringing entirely new functionality 1635 01:13:42,770 --> 01:13:45,350 into fiber and textiles. 1636 01:13:45,350 --> 01:13:47,150 So the idea here-- 1637 01:13:50,370 --> 01:13:53,880 my shirt is my cell phone. 1638 01:13:53,880 --> 01:13:56,010 My coat is my laptop. 1639 01:13:56,010 --> 01:13:58,770 It's just full of communication fiber, right? 1640 01:13:58,770 --> 01:14:02,820 But why can't you put a whole new level of functionality 1641 01:14:02,820 --> 01:14:05,670 into fibers and textiles, a whole kind of new utility, 1642 01:14:05,670 --> 01:14:08,130 and a completely invented sector? 1643 01:14:08,130 --> 01:14:12,270 As Yoel Fink, who's head of that institute, 1644 01:14:12,270 --> 01:14:15,120 reminded me on many occasions, and his colleagues as well, 1645 01:14:15,120 --> 01:14:19,280 said, Egypt came up with cotton about 4,000 years ago. 1646 01:14:19,280 --> 01:14:21,557 And that's about the last big thing that happened. 1647 01:14:21,557 --> 01:14:22,347 [LAUGHTER] 1648 01:14:22,347 --> 01:14:23,430 WILLIAM BONVILLIAN: Right? 1649 01:14:23,430 --> 01:14:26,383 So this crowd is completely trying to rethink-- 1650 01:14:26,383 --> 01:14:27,800 and they've got a fascinating mix. 1651 01:14:27,800 --> 01:14:29,580 They've got Apple in there. 1652 01:14:29,580 --> 01:14:31,830 And then they've got Nike and Adidas. 1653 01:14:31,830 --> 01:14:33,450 And then they've got Under Armor. 1654 01:14:33,450 --> 01:14:35,430 And then they've got a bunch of textile firms 1655 01:14:35,430 --> 01:14:36,820 that still remain in the US. 1656 01:14:36,820 --> 01:14:39,600 And then they've got fashion design shops. 1657 01:14:39,600 --> 01:14:42,210 And it's a really interesting mix 1658 01:14:42,210 --> 01:14:46,990 as they try to rethink an entire sector. 1659 01:14:46,990 --> 01:14:48,180 Smart men-- I'm sorry. 1660 01:14:48,180 --> 01:14:48,780 Go ahead, Max. 1661 01:14:48,780 --> 01:14:52,830 AUDIENCE: What is next gen power electronics? 1662 01:14:52,830 --> 01:14:55,530 WILLIAM BONVILLIAN: If you did wide band gap semiconductor, 1663 01:14:55,530 --> 01:14:57,270 you lose-- 1664 01:14:57,270 --> 01:15:00,090 you have much more efficiency in the power connections. 1665 01:15:00,090 --> 01:15:03,600 And you have much less power loss 1666 01:15:03,600 --> 01:15:06,600 in a switching process or a connection process. 1667 01:15:06,600 --> 01:15:07,410 Right? 1668 01:15:07,410 --> 01:15:12,120 So it requires a new kind of semiconductor 1669 01:15:12,120 --> 01:15:13,240 to undertake this. 1670 01:15:13,240 --> 01:15:16,680 But it could be in everything, and very perfect 1671 01:15:16,680 --> 01:15:20,280 pervasive, huge power savings, much more efficiency 1672 01:15:20,280 --> 01:15:24,630 in production as a result. 1673 01:15:24,630 --> 01:15:26,130 There's five more institutes that 1674 01:15:26,130 --> 01:15:30,900 were named at the very end of 2016, beginning of 2017-- 1675 01:15:30,900 --> 01:15:35,970 Bioengineering for Regenerative Medicine, Assistive Robotics, 1676 01:15:35,970 --> 01:15:38,190 Modular Chemical Process Intensification-- 1677 01:15:38,190 --> 01:15:40,770 in other words, rethinking the whole chemical engineering 1678 01:15:40,770 --> 01:15:44,400 process system to radically reduce the number of stages-- 1679 01:15:44,400 --> 01:15:47,580 Sustainable Remanufacturing Recycling. 1680 01:15:47,580 --> 01:15:49,200 And then the Department of Commerce 1681 01:15:49,200 --> 01:15:51,000 had an open competition, and they came up 1682 01:15:51,000 --> 01:15:53,940 with biopharma manufacturing. 1683 01:15:53,940 --> 01:15:57,180 There are major new advances in biopharma manufacturing 1684 01:15:57,180 --> 01:15:58,980 for continuous production. 1685 01:15:58,980 --> 01:16:01,740 So it's a very interesting list. 1686 01:16:01,740 --> 01:16:02,250 Right? 1687 01:16:02,250 --> 01:16:04,500 What's interesting about the US list 1688 01:16:04,500 --> 01:16:06,888 is that it covers a wide range of stuff, 1689 01:16:06,888 --> 01:16:08,430 where there are potential advances it 1690 01:16:08,430 --> 01:16:09,862 could be quite powerful. 1691 01:16:09,862 --> 01:16:10,820 Some are going to work. 1692 01:16:10,820 --> 01:16:12,420 Some are going to fail. 1693 01:16:12,420 --> 01:16:17,180 But overall, it's a pretty interesting model. 1694 01:16:17,180 --> 01:16:19,980 If we could get these technologies stood up 1695 01:16:19,980 --> 01:16:23,010 and a workforce that's ready to use them, 1696 01:16:23,010 --> 01:16:25,650 then you really might have something pretty significant. 1697 01:16:25,650 --> 01:16:29,320 And as you remember from that matrix chart I had before, 1698 01:16:29,320 --> 01:16:32,490 these are designed to stretch across a whole series 1699 01:16:32,490 --> 01:16:35,490 of industries, not just a single industry. 1700 01:16:35,490 --> 01:16:35,990 Beth? 1701 01:16:35,990 --> 01:16:38,470 AUDIENCE: Are these vulnerable to being cut? 1702 01:16:38,470 --> 01:16:40,950 Or right now, they have secure funding? 1703 01:16:40,950 --> 01:16:41,820 WILLIAM BONVILLIAN: I'm definitely worried. 1704 01:16:41,820 --> 01:16:42,360 We'll see. 1705 01:16:42,360 --> 01:16:43,344 [LAUGHTER] 1706 01:16:44,362 --> 01:16:46,570 WILLIAM BONVILLIAN: The funding's by no means secure. 1707 01:16:46,570 --> 01:16:48,360 But Congress has passed legislation 1708 01:16:48,360 --> 01:16:50,530 supporting this on a completely bipartisan basis. 1709 01:16:50,530 --> 01:16:53,760 There are strong advocates in both parties for these. 1710 01:16:53,760 --> 01:16:56,100 Senator Blunt from Missouri is a very strong advocate 1711 01:16:56,100 --> 01:16:58,710 for example, has been a real champion of this stuff. 1712 01:16:58,710 --> 01:17:00,570 So there's a lot of Congressional support 1713 01:17:00,570 --> 01:17:02,418 here, mainly because members of Congress 1714 01:17:02,418 --> 01:17:04,710 are seeing what's been happening in their Congressional 1715 01:17:04,710 --> 01:17:05,970 districts. 1716 01:17:05,970 --> 01:17:10,740 So I think this is a more survivable model. 1717 01:17:10,740 --> 01:17:12,960 And what's wrong with this? 1718 01:17:12,960 --> 01:17:15,630 I mean, this is a total public-private partnership 1719 01:17:15,630 --> 01:17:19,140 with the private sector dominating and leading 1720 01:17:19,140 --> 01:17:22,640 and making by far the largest contribution to the funding. 1721 01:17:22,640 --> 01:17:23,217 Right? 1722 01:17:23,217 --> 01:17:25,050 So this is not some federal subsidy program. 1723 01:17:25,050 --> 01:17:27,100 This is very cost-shared. 1724 01:17:27,100 --> 01:17:28,620 So I think it may be survivable. 1725 01:17:28,620 --> 01:17:29,970 We'll see. 1726 01:17:29,970 --> 01:17:32,520 MIT is in nine of these 14 manufacturing institutes 1727 01:17:32,520 --> 01:17:33,520 in one way or the other. 1728 01:17:33,520 --> 01:17:36,930 We're very active in about four and leading on one. 1729 01:17:36,930 --> 01:17:43,260 So there's now a whole community at MIT of leading researchers 1730 01:17:43,260 --> 01:17:45,300 that's into this stuff. 1731 01:17:45,300 --> 01:17:48,540 And MIT had not been in this area for a long time. 1732 01:17:48,540 --> 01:17:51,980 It just wasn't-- you didn't get R&D funding for manufacturing. 1733 01:17:51,980 --> 01:17:52,480 Right? 1734 01:17:52,480 --> 01:17:54,750 This is a whole new kind of territory. 1735 01:17:54,750 --> 01:17:56,400 So we'll see. 1736 01:17:56,400 --> 01:17:58,410 Hopefully, it'll work for you, Kevin. 1737 01:17:58,410 --> 01:17:59,470 Maybe some others. 1738 01:17:59,470 --> 01:18:01,262 AUDIENCE: We just answered our last debate. 1739 01:18:01,262 --> 01:18:03,610 This is what we designed the technical training programs 1740 01:18:03,610 --> 01:18:04,110 around. 1741 01:18:04,110 --> 01:18:05,660 WILLIAM BONVILLIAN: Yes.