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 00:00:21,541 --> 00:00:25,690 SANAM: --the founding of Genentech. 9 00:00:25,690 --> 00:00:29,780 Yeah, so a little bit about these two people, first. 10 00:00:29,780 --> 00:00:33,150 We have Herbert Boyer, who was born in 1936. 11 00:00:33,150 --> 00:00:36,210 He was a biochemist from Pennsylvania, 12 00:00:36,210 --> 00:00:37,710 an orchard in Pennsylvania, and then 13 00:00:37,710 --> 00:00:42,130 worked at the University of California in San Francisco. 14 00:00:42,130 --> 00:00:43,890 And he's, sort of, considered one 15 00:00:43,890 --> 00:00:48,660 of the first pioneers in the field of molecular genetics. 16 00:00:48,660 --> 00:00:50,430 And we also have Robert Swanson, who 17 00:00:50,430 --> 00:00:54,360 was born 1947 and died in 1999. 18 00:00:54,360 --> 00:00:56,220 He was from Brooklyn, went to MIT, 19 00:00:56,220 --> 00:00:59,250 and then eventually got into venture capital. 20 00:00:59,250 --> 00:01:04,080 And he was instrumental in finding Genentech 21 00:01:04,080 --> 00:01:08,130 and, kind of, developing the financing structure for that. 22 00:01:08,130 --> 00:01:12,480 So here's a list of some other players that were involved. 23 00:01:12,480 --> 00:01:15,720 They kind of came in throughout the process of various towns 24 00:01:15,720 --> 00:01:20,370 that were recruited, both, nationally and internationally. 25 00:01:20,370 --> 00:01:24,330 Boyer worked with Arthur Riggs and Keiichi Itakura 26 00:01:24,330 --> 00:01:26,280 from the Beckman Research Institute, 27 00:01:26,280 --> 00:01:29,460 and the group had become the first to successfully express 28 00:01:29,460 --> 00:01:35,740 a human gene in bacteria when they produced somatostatin 29 00:01:35,740 --> 00:01:37,700 in 1977. 30 00:01:37,700 --> 00:01:40,730 And then David Goeddel and Dennis Kleid, as well, 31 00:01:40,730 --> 00:01:45,780 were brought on later on, in 1978, I believe. 32 00:01:45,780 --> 00:01:47,880 Yeah, and they went on to be instrumental figures 33 00:01:47,880 --> 00:01:50,830 in the company as well. 34 00:01:50,830 --> 00:01:54,370 So I'll go into a little bit about what they accomplished-- 35 00:01:54,370 --> 00:01:58,270 just, sort of, the early history of the company. 36 00:01:58,270 --> 00:01:59,950 They really were the first, kind of, 37 00:01:59,950 --> 00:02:02,340 kickstarted the American biotech industry. 38 00:02:02,340 --> 00:02:06,840 And it all started when the two of them kind of met-- 39 00:02:06,840 --> 00:02:10,690 Boyer and Swanson met over beer and lay down $500 40 00:02:10,690 --> 00:02:13,570 to start this pharmaceutical company that 41 00:02:13,570 --> 00:02:15,523 would explore proteins that bacteria 42 00:02:15,523 --> 00:02:16,690 could be engineered to make. 43 00:02:16,690 --> 00:02:22,270 And they first targeted human insulin 44 00:02:22,270 --> 00:02:26,720 because that was, kind of, a competitive area at the time. 45 00:02:26,720 --> 00:02:28,745 And there was a lot of demand for treatments, 46 00:02:28,745 --> 00:02:30,370 but there was a worry that there wasn't 47 00:02:30,370 --> 00:02:34,390 enough supply of animal insulin to meet this rising demand. 48 00:02:34,390 --> 00:02:36,820 So they kind of decided on working 49 00:02:36,820 --> 00:02:39,100 to make synthetic insulin up to scratch 50 00:02:39,100 --> 00:02:42,460 and be competitive with what was previously used. 51 00:02:42,460 --> 00:02:44,530 And they targeted meeting the requirements 52 00:02:44,530 --> 00:02:51,250 of Eli Lilly, who they, kind of, wanted to get his business to-- 53 00:02:51,250 --> 00:02:52,750 they targeted his requirements, they 54 00:02:52,750 --> 00:02:54,125 could get his business eventually 55 00:02:54,125 --> 00:02:55,840 and get the company going. 56 00:02:55,840 --> 00:02:59,330 So getting money was kind of a tough process 57 00:02:59,330 --> 00:03:01,120 throughout the entire time. 58 00:03:01,120 --> 00:03:04,960 They both really staked a lot of their career on this endeavor. 59 00:03:04,960 --> 00:03:07,720 And Swanson, at the time, was very young 60 00:03:07,720 --> 00:03:09,820 and went through many periods of unemployment 61 00:03:09,820 --> 00:03:13,030 as he was trying to get the money for financing this. 62 00:03:13,030 --> 00:03:16,860 And Boyer continued his day job teaching. 63 00:03:16,860 --> 00:03:24,070 So eventually-- so they ran into many obstacles financially, 64 00:03:24,070 --> 00:03:26,020 but they also, at the time, when they 65 00:03:26,020 --> 00:03:28,870 decided to target humans insulin in the first place, 66 00:03:28,870 --> 00:03:33,082 that kind of kickstarted a nationwide race to do this. 67 00:03:33,082 --> 00:03:34,540 And so there was a group at Harvard 68 00:03:34,540 --> 00:03:36,700 and another group at some-- 69 00:03:36,700 --> 00:03:38,705 another university in California. 70 00:03:38,705 --> 00:03:39,900 I'm blanking on the name. 71 00:03:39,900 --> 00:03:43,780 But, yeah, they were particularly formidable rivals, 72 00:03:43,780 --> 00:03:45,310 and they had those other two groups 73 00:03:45,310 --> 00:03:47,420 who were, kind of, entrenched in academia. 74 00:03:47,420 --> 00:03:50,290 And they had all the resources that that brought them, 75 00:03:50,290 --> 00:03:54,460 whereas Boyer and Swanson were relatively resource-poor. 76 00:03:54,460 --> 00:03:57,400 But they had one advantage-- 77 00:03:57,400 --> 00:03:59,320 Boyer was working with synthetic DNA, which 78 00:03:59,320 --> 00:04:02,410 would allow him to get around regulations that the NIH put 79 00:04:02,410 --> 00:04:06,230 on natural DNA during this process. 80 00:04:06,230 --> 00:04:09,130 So they faced a lot of other obstacles, 81 00:04:09,130 --> 00:04:11,290 but they still had advantages-- other teams still 82 00:04:11,290 --> 00:04:14,800 had advantages of scale and sales ability. 83 00:04:14,800 --> 00:04:16,540 Swanson's goal was to make Genentech 84 00:04:16,540 --> 00:04:18,820 a fully integrated pharmaceutical company, 85 00:04:18,820 --> 00:04:20,380 and they wanted-- the eventual goal 86 00:04:20,380 --> 00:04:24,520 was to be able to produce and sell a wide range of drugs. 87 00:04:24,520 --> 00:04:26,380 And they believed that if they were 88 00:04:26,380 --> 00:04:29,020 the first to make the insulin, that 89 00:04:29,020 --> 00:04:32,680 would give them the lift up in the industry that they needed. 90 00:04:32,680 --> 00:04:35,400 So Swanson went on a hiring spree and recruited talent. 91 00:04:35,400 --> 00:04:38,890 We were able to open up their own lab, eventually. 92 00:04:38,890 --> 00:04:41,800 He was able to get 100k from various sources 93 00:04:41,800 --> 00:04:43,870 for that initial push. 94 00:04:43,870 --> 00:04:47,560 So eventually, by August of 1978, 95 00:04:47,560 --> 00:04:49,240 they were able to make their first 20 96 00:04:49,240 --> 00:04:50,980 nanograms of the insulin. 97 00:04:50,980 --> 00:04:53,410 And they immediately contacted Eli Lilly, 98 00:04:53,410 --> 00:04:55,720 and Swanson kind of went against convention 99 00:04:55,720 --> 00:04:58,300 and had this huge televised press conference, where 100 00:04:58,300 --> 00:05:00,733 he announced that they had achieved 101 00:05:00,733 --> 00:05:01,900 what they wanted to achieve. 102 00:05:01,900 --> 00:05:03,820 And that kind of did what he intended, 103 00:05:03,820 --> 00:05:06,460 and put Genentech-- really put it on the map 104 00:05:06,460 --> 00:05:08,840 in the public eye. 105 00:05:08,840 --> 00:05:11,830 And eventually, Eli Lilly's company 106 00:05:11,830 --> 00:05:14,620 came forward with $10 million for Genentech, 107 00:05:14,620 --> 00:05:17,950 and they were eventually able to fast-track it 108 00:05:17,950 --> 00:05:20,980 for industrial production. 109 00:05:20,980 --> 00:05:23,200 And in 1980, they had their IPO, and it 110 00:05:23,200 --> 00:05:27,700 was one of the most spectacular IPOs seen from this industry. 111 00:05:27,700 --> 00:05:31,070 And they were eventually able to-- 112 00:05:31,070 --> 00:05:34,220 in 1985, they were able to release a second drug. 113 00:05:34,220 --> 00:05:37,602 And these are some of the early products of-- 114 00:05:37,602 --> 00:05:38,564 thank you. 115 00:05:42,420 --> 00:05:47,840 Yeah, so going on to the types of qualities of great groups 116 00:05:47,840 --> 00:05:49,560 that we've been talking about. 117 00:05:49,560 --> 00:05:51,080 So I think one of the main things 118 00:05:51,080 --> 00:05:52,880 was Swanson's leadership. 119 00:05:52,880 --> 00:05:56,520 So the article mentioned that he had a very down-to-earth style 120 00:05:56,520 --> 00:05:58,670 and was well-connected with everyone 121 00:05:58,670 --> 00:06:01,640 who was involved, as well as a great generosity 122 00:06:01,640 --> 00:06:03,550 towards everybody. 123 00:06:03,550 --> 00:06:05,420 And so he was-- he offered, basically, 124 00:06:05,420 --> 00:06:08,570 everyone, from the top scientists to the custodians, 125 00:06:08,570 --> 00:06:11,030 a share of the company at one point. 126 00:06:11,030 --> 00:06:14,090 And he had a really great-- 127 00:06:14,090 --> 00:06:15,650 effective recruitment of talent. 128 00:06:15,650 --> 00:06:18,890 He was able to see where they needed people 129 00:06:18,890 --> 00:06:22,500 and how they could fill those positions. 130 00:06:22,500 --> 00:06:26,010 Another really important element is that there 131 00:06:26,010 --> 00:06:27,590 was a real conviction of cause. 132 00:06:27,590 --> 00:06:31,220 So Boyer initially wanted to get into commercialization 133 00:06:31,220 --> 00:06:35,525 because he felt that university research 134 00:06:35,525 --> 00:06:37,400 and government-funded research wouldn't allow 135 00:06:37,400 --> 00:06:40,580 him to see the real benefits-- practical benefits-- 136 00:06:40,580 --> 00:06:41,450 of his work. 137 00:06:41,450 --> 00:06:45,570 So it's kind of a really altruistic goal of his. 138 00:06:45,570 --> 00:06:50,420 And that mission kind of carried them throughout this process-- 139 00:06:50,420 --> 00:06:51,830 that they were going to be making 140 00:06:51,830 --> 00:06:55,018 something that would better people's lives, save lives. 141 00:06:55,018 --> 00:06:56,810 Another important element is that they were 142 00:06:56,810 --> 00:06:58,670 working against a common enemy. 143 00:06:58,670 --> 00:07:00,790 So it wasn't exactly an enemy, but they were-- 144 00:07:00,790 --> 00:07:03,290 there were those other teams that they were working against, 145 00:07:03,290 --> 00:07:05,600 and that competition really fueled them. 146 00:07:05,600 --> 00:07:07,250 And going along with that, they were 147 00:07:07,250 --> 00:07:09,890 kind of like the underdogs and the young company 148 00:07:09,890 --> 00:07:12,620 that didn't have the resources and didn't have the funding, 149 00:07:12,620 --> 00:07:16,730 so that really was another motivating factor as well. 150 00:07:16,730 --> 00:07:20,990 Another one is optimism and a degree of naivety. 151 00:07:20,990 --> 00:07:25,040 So that kind of came into play when they first made insulin, 152 00:07:25,040 --> 00:07:27,070 but Eli Lilly said that they needed-- 153 00:07:27,070 --> 00:07:30,140 had put really strict timeline on them 154 00:07:30,140 --> 00:07:33,020 for developing it for industrial production. 155 00:07:33,020 --> 00:07:35,465 And most people would have said that that was impossible, 156 00:07:35,465 --> 00:07:37,340 and that they couldn't do that in that frame. 157 00:07:37,340 --> 00:07:41,370 But they were none the wiser and kind of went for it anyways 158 00:07:41,370 --> 00:07:44,640 and it ended up working out for them. 159 00:07:44,640 --> 00:07:47,450 Another thing is that the people were not interchangeable. 160 00:07:47,450 --> 00:07:49,407 So like I said before, they were-- 161 00:07:49,407 --> 00:07:50,990 that list of players that I showed you 162 00:07:50,990 --> 00:07:53,960 were very instrumental in their very specific expertise 163 00:07:53,960 --> 00:07:56,570 that they brought to the project. 164 00:07:56,570 --> 00:07:58,400 Also that there was relative freedom 165 00:07:58,400 --> 00:08:00,800 for a lot of the scientists involved in Genentech. 166 00:08:00,800 --> 00:08:02,300 So one of the things that Swanson 167 00:08:02,300 --> 00:08:06,620 did to get people on board, especially David Goeddel, 168 00:08:06,620 --> 00:08:08,708 he said that they could still have the freedom 169 00:08:08,708 --> 00:08:10,750 to publish under their own name, even though they 170 00:08:10,750 --> 00:08:13,700 were contracted to Genentech, so that was a real incentive 171 00:08:13,700 --> 00:08:15,540 for people to join them. 172 00:08:15,540 --> 00:08:17,990 And lastly, there was a really casual environment 173 00:08:17,990 --> 00:08:19,060 in the office-- 174 00:08:19,060 --> 00:08:24,770 T-shirt attire, and there wasn't any strict rules or a sense 175 00:08:24,770 --> 00:08:29,610 of top-down management. 176 00:08:29,610 --> 00:08:36,405 So some other dynamics that I think are important-- 177 00:08:36,405 --> 00:08:38,280 this was an interesting case of a great group 178 00:08:38,280 --> 00:08:39,780 because it came at a time when there 179 00:08:39,780 --> 00:08:42,539 was kind of a changing relationship between science, 180 00:08:42,539 --> 00:08:46,260 and business, and academia, and the commercial sector. 181 00:08:46,260 --> 00:08:48,490 And Boyer's journey was pretty interesting. 182 00:08:48,490 --> 00:08:50,200 He started out firmly in academia, 183 00:08:50,200 --> 00:08:52,230 but he chose to look beyond that. 184 00:08:52,230 --> 00:08:55,410 His words, like I said-- his words at the initial meeting 185 00:08:55,410 --> 00:08:56,910 with Swanson-- 186 00:08:56,910 --> 00:08:59,940 about wanting to see the real practical benefits of his work 187 00:08:59,940 --> 00:09:04,260 kind of carried the whole team throughout. 188 00:09:04,260 --> 00:09:07,710 But the flipside of that was that in the early history 189 00:09:07,710 --> 00:09:10,950 of Genentech, he was a target for academic scientists, who 190 00:09:10,950 --> 00:09:13,338 dismissed the idea that corporate science would ever 191 00:09:13,338 --> 00:09:14,880 be able to achieve the kind of things 192 00:09:14,880 --> 00:09:17,130 that they would be able to achieve. 193 00:09:17,130 --> 00:09:19,620 And his move to commercialization 194 00:09:19,620 --> 00:09:21,728 really drew the animosity of academia 195 00:09:21,728 --> 00:09:23,770 because he was one of the first of many scientist 196 00:09:23,770 --> 00:09:27,180 at the frontier of molecular biology, who 197 00:09:27,180 --> 00:09:32,190 sought to capitalize on the commercial opportunities. 198 00:09:32,190 --> 00:09:35,220 And one of Swanson's, and Boyer's, to some extent, 199 00:09:35,220 --> 00:09:37,950 strength was their ability to recognize 200 00:09:37,950 --> 00:09:41,760 the exact moment in basic research 201 00:09:41,760 --> 00:09:47,580 in molecular biotechnology that was most beneficial to open it 202 00:09:47,580 --> 00:09:48,910 up to a commercial endeavor. 203 00:09:48,910 --> 00:09:51,660 So they were able to combine an extensive knowledge 204 00:09:51,660 --> 00:09:53,460 of the science that they're trying to do 205 00:09:53,460 --> 00:09:57,330 and also the business acumen needed to move it forward. 206 00:09:57,330 --> 00:09:59,940 And this really set them apart from other big drug companies 207 00:09:59,940 --> 00:10:04,290 at the time, and, I think, ultimately contributed 208 00:10:04,290 --> 00:10:06,120 to their great success as well. 209 00:10:09,928 --> 00:10:13,700 So this is a quote about Swanson, who was really, 210 00:10:13,700 --> 00:10:15,650 kind of, one of the first figures 211 00:10:15,650 --> 00:10:19,370 who was able to really bridge that partnership 212 00:10:19,370 --> 00:10:22,350 between science and business. 213 00:10:22,350 --> 00:10:24,290 So in a way, you can kind of think of him 214 00:10:24,290 --> 00:10:26,630 as like an advocate for the scientists that he 215 00:10:26,630 --> 00:10:29,490 was representing and the people at Genentech who 216 00:10:29,490 --> 00:10:32,390 were looking to really advance in this field that 217 00:10:32,390 --> 00:10:33,380 was quite new. 218 00:10:33,380 --> 00:10:36,820 So I guess one of-- 219 00:10:36,820 --> 00:10:39,410 going on to, sort of, discretion questions, 220 00:10:39,410 --> 00:10:42,020 one of my main questions was-- 221 00:10:42,020 --> 00:10:45,890 did the relative newness of this field and this industry 222 00:10:45,890 --> 00:10:47,190 really help them? 223 00:10:47,190 --> 00:10:49,100 I mean, did that offer opportunities 224 00:10:49,100 --> 00:10:52,190 to Swanson and Boyer that maybe more established industry 225 00:10:52,190 --> 00:10:53,633 or field would not have? 226 00:10:53,633 --> 00:10:54,300 Did that allow-- 227 00:10:54,300 --> 00:10:55,925 WILLIAM BONVILLIAN: So I'm going to put 228 00:10:55,925 --> 00:10:57,960 that on hold for just a second and just throw 229 00:10:57,960 --> 00:11:00,490 a couple of framing points out. 230 00:11:00,490 --> 00:11:01,980 But I think that's a key question, 231 00:11:01,980 --> 00:11:05,190 and I'll lead up to that, Sanam. 232 00:11:05,190 --> 00:11:07,440 This is the first biotech, right. 233 00:11:07,440 --> 00:11:09,130 This creates the model. 234 00:11:09,130 --> 00:11:13,590 And as you point out, this combination 235 00:11:13,590 --> 00:11:17,430 of Boyer and Swanson is a fascinating combination, 236 00:11:17,430 --> 00:11:20,550 because they are, as you put it, able to kind of bridge 237 00:11:20,550 --> 00:11:22,710 this divide between business and science, 238 00:11:22,710 --> 00:11:27,390 and that is the inherent brilliance 239 00:11:27,390 --> 00:11:28,830 of the biotech model. 240 00:11:28,830 --> 00:11:30,990 And these folks really figure out how to do it, 241 00:11:30,990 --> 00:11:33,570 and Genentech, to this day, is an extremely successful 242 00:11:33,570 --> 00:11:34,500 company. 243 00:11:34,500 --> 00:11:38,370 So it has been able to keep on that innovation pathway, which 244 00:11:38,370 --> 00:11:42,510 means that the culture that they set up here 245 00:11:42,510 --> 00:11:47,070 has been able to keep innovation occurring 246 00:11:47,070 --> 00:11:50,515 on a kind of ongoing basis. 247 00:11:50,515 --> 00:11:52,890 And some of the points you made, Sanam, just to emphasize 248 00:11:52,890 --> 00:11:54,098 some of the points you made-- 249 00:11:57,040 --> 00:12:02,350 The fact that Boyer and Swanson allowed their researchers 250 00:12:02,350 --> 00:12:05,020 to do their own publishing, right, 251 00:12:05,020 --> 00:12:07,540 that they didn't treat what they were working on 252 00:12:07,540 --> 00:12:09,700 as a complete trade secret-- 253 00:12:09,700 --> 00:12:13,330 that was a huge enabler in enabling the academic community 254 00:12:13,330 --> 00:12:15,680 to kind of enter into these biotechs. 255 00:12:15,680 --> 00:12:19,120 In other words, their academic role was going to be respected. 256 00:12:19,120 --> 00:12:21,100 Their role as researchers and scientists 257 00:12:21,100 --> 00:12:23,110 who contributed in general knowledge 258 00:12:23,110 --> 00:12:24,910 was going to respect it here. 259 00:12:24,910 --> 00:12:27,810 And that created a rule in this biotech that 260 00:12:27,810 --> 00:12:33,310 prevails to this day, and it very much 261 00:12:33,310 --> 00:12:35,600 influenced that kind of model. 262 00:12:35,600 --> 00:12:37,180 But the ability-- 263 00:12:37,180 --> 00:12:45,390 So in the biotech model, and Swanson and Boyer pioneer this, 264 00:12:45,390 --> 00:12:50,480 they create this revolving door capability. 265 00:12:50,480 --> 00:12:53,990 So in other words, scientists at universities now in the United 266 00:12:53,990 --> 00:12:58,910 States that work in life science areas and related areas 267 00:12:58,910 --> 00:13:02,450 are able to have careers, whether in the Academy-- 268 00:13:02,450 --> 00:13:06,140 they move to a biotech as a scientist for a period of time. 269 00:13:06,140 --> 00:13:08,060 They can move back to the Academy, 270 00:13:08,060 --> 00:13:10,800 right, taking their advances with them. 271 00:13:10,800 --> 00:13:13,370 In other words, they can move between these sectors, 272 00:13:13,370 --> 00:13:15,800 so there was, in European science, 273 00:13:15,800 --> 00:13:19,420 an historical upstairs-downstairs treatment, 274 00:13:19,420 --> 00:13:20,270 right? 275 00:13:20,270 --> 00:13:22,100 The academic scientists thought they 276 00:13:22,100 --> 00:13:24,860 were in curiosity-driven basic research 277 00:13:24,860 --> 00:13:26,930 and should have nothing to do with the, kind of, 278 00:13:26,930 --> 00:13:30,290 ugliness and, kind of, downstairs 279 00:13:30,290 --> 00:13:32,800 of commercialization of products. 280 00:13:32,800 --> 00:13:34,940 And that these worlds ought to be kept separate. 281 00:13:34,940 --> 00:13:38,480 And biotech is the first really serious attempt 282 00:13:38,480 --> 00:13:41,540 in the Academy in America to kind of bridge 283 00:13:41,540 --> 00:13:42,740 these worlds effectively. 284 00:13:42,740 --> 00:13:44,450 And they do, brilliantly. 285 00:13:44,450 --> 00:13:47,990 And part of it is, as you point out, 286 00:13:47,990 --> 00:13:50,180 Swanson is a chemistry major at MIT. 287 00:13:50,180 --> 00:13:53,120 So he is not afraid of science, just as you suggested. 288 00:13:53,120 --> 00:13:54,770 He understands what it is. 289 00:13:54,770 --> 00:13:57,230 He has the ability to be in the room 290 00:13:57,230 --> 00:14:00,960 with these academic scientists and be in the game. 291 00:14:00,960 --> 00:14:04,520 But he also comes to this with early venture capital 292 00:14:04,520 --> 00:14:05,120 experience. 293 00:14:05,120 --> 00:14:13,790 So he first goes to work on Sandhill Road in Silicon 294 00:14:13,790 --> 00:14:15,350 Valley, outside Stanford. 295 00:14:15,350 --> 00:14:20,830 And they're only interested in doing IT. 296 00:14:20,830 --> 00:14:23,800 They have no idea about this biotech stuff. 297 00:14:23,800 --> 00:14:28,000 So he has to leave this major event-- early venture capital 298 00:14:28,000 --> 00:14:31,360 firm that he's with and essentially starve, eat 299 00:14:31,360 --> 00:14:34,060 hot dogs for months at a time as he 300 00:14:34,060 --> 00:14:35,740 tries to figure out how is he going 301 00:14:35,740 --> 00:14:39,520 to work in this biotech area? 302 00:14:39,520 --> 00:14:43,330 And famously, he, as Sanam suggested-- famously, 303 00:14:43,330 --> 00:14:44,800 he starts cold calling. 304 00:14:44,800 --> 00:14:49,840 He has this idea that bioengineering 305 00:14:49,840 --> 00:14:53,140 is going to be this incredibly creative new field. 306 00:14:53,140 --> 00:14:55,840 He's aware of it from his own training. 307 00:14:55,840 --> 00:14:58,180 So he starts cold calling scientists 308 00:14:58,180 --> 00:15:00,100 that are working in this bioengineering field. 309 00:15:00,100 --> 00:15:03,790 He only has to get to the bees, right, to Boyer. 310 00:15:03,790 --> 00:15:06,653 And then they have this famous meeting at Churchill's Pub, 311 00:15:06,653 --> 00:15:08,320 where they actually so much hit it off-- 312 00:15:08,320 --> 00:15:10,480 exactly as Sanam described-- 313 00:15:10,480 --> 00:15:13,120 Boyer is concerned that if he just stays in the Academy, 314 00:15:13,120 --> 00:15:16,330 nothing he works on is ever going to get out, right? 315 00:15:16,330 --> 00:15:17,920 And he wants to get his stuff out. 316 00:15:17,920 --> 00:15:21,640 He truly believes this. 317 00:15:21,640 --> 00:15:23,320 He is not out to get rich here. 318 00:15:23,320 --> 00:15:25,390 He is really out to get-- 319 00:15:25,390 --> 00:15:28,910 to save people and get the technology out the door. 320 00:15:28,910 --> 00:15:32,530 So there's a statue in front of Genentech, 321 00:15:32,530 --> 00:15:38,200 to this day, that pictures Boyer and Swanson sitting at the bar, 322 00:15:38,200 --> 00:15:40,300 each putting the $100 down on the table 323 00:15:40,300 --> 00:15:43,690 as they toast each other with beers. 324 00:15:43,690 --> 00:15:46,090 That's what you see when you drive in and see the company 325 00:15:46,090 --> 00:15:47,960 headquarters at Genentech. 326 00:15:47,960 --> 00:15:51,070 And that's symbolic, I think, of what's happening here. 327 00:15:51,070 --> 00:15:54,460 These folks figure out how to make a really workable marriage 328 00:15:54,460 --> 00:15:58,570 between business and science that's 329 00:15:58,570 --> 00:16:01,600 remarkably productive on both sides of the world. 330 00:16:01,600 --> 00:16:06,370 Boyer is totally ostracized for doing this, right? 331 00:16:06,370 --> 00:16:08,890 He faces tremendous recrimination 332 00:16:08,890 --> 00:16:13,390 at UCSF for having gone this commercial route. 333 00:16:13,390 --> 00:16:16,180 And it's only when they come up with these stunning successes 334 00:16:16,180 --> 00:16:17,680 that the scientific community really 335 00:16:17,680 --> 00:16:22,120 has to rethink the kind of way that it went after him. 336 00:16:22,120 --> 00:16:24,610 So this was not easy for either of these players, 337 00:16:24,610 --> 00:16:25,660 but it's a really-- 338 00:16:25,660 --> 00:16:29,000 it's a fascinating moment, in a way, 339 00:16:29,000 --> 00:16:31,250 creates a culture that, to this day, 340 00:16:31,250 --> 00:16:34,010 kind of still dominates that whole sector. 341 00:16:34,010 --> 00:16:36,080 That's a fair summary. 342 00:16:36,080 --> 00:16:38,240 Now, let's come back to your great question. 343 00:16:38,240 --> 00:16:40,150 Why don't you just pose it again, quickly, Sanam, and then 344 00:16:40,150 --> 00:16:40,798 we'll do it. 345 00:16:40,798 --> 00:16:41,298 SANAM: Yeah. 346 00:16:41,298 --> 00:16:42,890 Do you want to say something? 347 00:16:42,890 --> 00:16:45,380 AUDIENCE: Actually, I had a curiosity question. 348 00:16:45,380 --> 00:16:48,020 I was wondering, well-- 349 00:16:48,020 --> 00:16:52,813 You had mentioned the ability to publish under their own name. 350 00:16:52,813 --> 00:16:54,230 Was there anything else that might 351 00:16:54,230 --> 00:16:59,030 have spurred the success of this marriage, 352 00:16:59,030 --> 00:17:01,190 as you put it, between academia and business? 353 00:17:01,190 --> 00:17:04,490 WILLIAM BONVILLIAN: Well that's [INAUDIBLE] recruitment tool. 354 00:17:04,490 --> 00:17:08,630 In other words, if you leave academic research-- 355 00:17:08,630 --> 00:17:11,569 university labs-- and come to work for Genentech, 356 00:17:11,569 --> 00:17:14,359 you don't have to abandon your academic career. 357 00:17:14,359 --> 00:17:16,670 You can still keep doing your research work 358 00:17:16,670 --> 00:17:19,339 and building your research reputation while you're 359 00:17:19,339 --> 00:17:20,300 working at a company. 360 00:17:20,300 --> 00:17:21,540 And that's what that enabled. 361 00:17:21,540 --> 00:17:25,339 So it enabled academic researchers 362 00:17:25,339 --> 00:17:28,119 to feel that they weren't leaving the universe that they 363 00:17:28,119 --> 00:17:29,450 had-- that had nurtured them. 364 00:17:29,450 --> 00:17:33,500 They could continue doing terrific academic work, as well 365 00:17:33,500 --> 00:17:37,130 as practical work in this kind of company setting. 366 00:17:37,130 --> 00:17:41,330 And that was very reassuring, and it was a great-- 367 00:17:41,330 --> 00:17:43,670 it broke down a whole divide between the worlds, 368 00:17:43,670 --> 00:17:46,970 because previously, in pharmaceutical companies, 369 00:17:46,970 --> 00:17:48,680 everything was secret, right? 370 00:17:48,680 --> 00:17:51,050 Everything was treated as a trade. 371 00:17:51,050 --> 00:17:52,640 So there was no outside discussion 372 00:17:52,640 --> 00:17:55,790 allowed until patents occurred. 373 00:17:55,790 --> 00:17:57,860 This really changed that culture. 374 00:17:57,860 --> 00:17:59,468 But Sanam, restate your question, 375 00:17:59,468 --> 00:18:00,760 because it was a very good one. 376 00:18:00,760 --> 00:18:01,690 Let's go back to it. 377 00:18:01,690 --> 00:18:04,130 SANAM: Yeah, so my question was, do 378 00:18:04,130 --> 00:18:09,920 you think that the fact that this field was so new and so-- 379 00:18:09,920 --> 00:18:12,545 starting very few years before that 380 00:18:12,545 --> 00:18:14,920 really helped them or afforded them certain opportunities 381 00:18:14,920 --> 00:18:16,670 that they might not have been able to have 382 00:18:16,670 --> 00:18:19,260 in a more established, entrenched field or industry? 383 00:18:22,494 --> 00:18:24,800 AUDIENCE: I'd say absolutely-- because there 384 00:18:24,800 --> 00:18:26,240 was no previous infrastructure. 385 00:18:26,240 --> 00:18:28,280 There was no establishment to fight. 386 00:18:28,280 --> 00:18:30,650 There was just the scientific challenge 387 00:18:30,650 --> 00:18:32,278 and then trying to commercialize it. 388 00:18:32,278 --> 00:18:34,070 Of course, I mean, there were other-- there 389 00:18:34,070 --> 00:18:36,200 were other companies that were trying to do these things, 390 00:18:36,200 --> 00:18:38,450 but because it wasn't as established as, I don't know, 391 00:18:38,450 --> 00:18:42,030 coal, or the railroad industry, or something like that. 392 00:18:44,990 --> 00:18:47,600 I think it certainly made things a lot easier. 393 00:18:47,600 --> 00:18:51,920 The market was a lot more fluid, and they had a lot more areas 394 00:18:51,920 --> 00:18:53,822 to expand into. 395 00:18:53,822 --> 00:18:55,530 AUDIENCE: I think not only that-- sorry-- 396 00:18:55,530 --> 00:18:58,500 there is also a lot more room for error, 397 00:18:58,500 --> 00:19:00,500 especially as they're getting started initially. 398 00:19:00,500 --> 00:19:02,900 They're able to experiment. 399 00:19:02,900 --> 00:19:06,050 They have more leeway in the way they can market and present 400 00:19:06,050 --> 00:19:10,617 their product, because there is no established market. 401 00:19:10,617 --> 00:19:12,950 AUDIENCE: I was wondering if someone could share, maybe, 402 00:19:12,950 --> 00:19:15,450 what they thought the regulatory challenges that they didn't 403 00:19:15,450 --> 00:19:16,040 face were? 404 00:19:19,246 --> 00:19:22,050 AUDIENCE: I mean, I think it was mentioned that they pretty much 405 00:19:22,050 --> 00:19:24,810 used the ability to use synthetic DNA in order to get 406 00:19:24,810 --> 00:19:26,790 over a lot of regulations. 407 00:19:26,790 --> 00:19:28,230 Also, it's relatively early. 408 00:19:28,230 --> 00:19:29,880 Nowadays it's a lot harder. 409 00:19:29,880 --> 00:19:34,638 I think the price point to make a drug is over a billion. 410 00:19:34,638 --> 00:19:36,430 Back then, I really don't know what it was. 411 00:19:36,430 --> 00:19:38,280 So really it was a great opportunity. 412 00:19:38,280 --> 00:19:41,040 Also their investors were Kleiner Perkins. 413 00:19:41,040 --> 00:19:43,027 Also one of them was-- 414 00:19:43,027 --> 00:19:43,860 [INTERPOSING VOICES] 415 00:19:43,860 --> 00:19:45,600 WILLIAM BONVILLIAN: --Swanson had originally worked for, 416 00:19:45,600 --> 00:19:48,183 and then had to leave, because they didn't want to do biotech. 417 00:19:48,183 --> 00:19:51,270 But he went back to them with this advance, 418 00:19:51,270 --> 00:19:52,923 and they gave him some money. 419 00:19:52,923 --> 00:19:54,340 AUDIENCE: And I'm pretty sure they 420 00:19:54,340 --> 00:19:56,790 were in different spaces that are difficult. 421 00:19:56,790 --> 00:19:57,820 They would have helped. 422 00:19:57,820 --> 00:19:59,775 But the investors were adequate in that space. 423 00:19:59,775 --> 00:20:00,900 AUDIENCE: What about Chris? 424 00:20:00,900 --> 00:20:03,770 Do you have any thoughts on the regulatory challenge? 425 00:20:03,770 --> 00:20:08,280 AUDIENCE: Yes, so definitely using synthetic, kind of, 426 00:20:08,280 --> 00:20:09,930 molecules in an approach definitely 427 00:20:09,930 --> 00:20:13,620 helps, because you can surpass that whole clinical challenge, 428 00:20:13,620 --> 00:20:16,920 which not only is hard to pass, but also takes a long time. 429 00:20:16,920 --> 00:20:17,420 right? 430 00:20:17,420 --> 00:20:19,470 And especially, they were mentioning 431 00:20:19,470 --> 00:20:21,000 there was a real time crunch. 432 00:20:21,000 --> 00:20:25,650 A lot of groups were doing this kind of approach and research 433 00:20:25,650 --> 00:20:26,613 in this field. 434 00:20:26,613 --> 00:20:28,530 They really had to be the first ones out there 435 00:20:28,530 --> 00:20:30,510 so they could establish themselves 436 00:20:30,510 --> 00:20:33,510 as the leader and the real innovators in, kind of, 437 00:20:33,510 --> 00:20:35,610 this new biotech field, so. 438 00:20:35,610 --> 00:20:37,257 I think their approach was smart. 439 00:20:37,257 --> 00:20:38,340 WILLIAM BONVILLIAN: Right. 440 00:20:38,340 --> 00:20:44,580 I mean, this is a moment where there's a deep concern that 441 00:20:44,580 --> 00:20:47,340 comes up from the public, but also in the academic community 442 00:20:47,340 --> 00:20:52,410 itself, about the implications of this genetic engineering, 443 00:20:52,410 --> 00:20:52,980 right? 444 00:20:52,980 --> 00:20:54,438 I mean that's what Genentech stands 445 00:20:54,438 --> 00:20:55,920 for-- genetic engineering, right? 446 00:20:55,920 --> 00:20:57,930 That's what they're all about. 447 00:20:57,930 --> 00:21:01,770 And there's a major effort to put this whole movement 448 00:21:01,770 --> 00:21:03,690 on pause, right. 449 00:21:03,690 --> 00:21:08,450 And there is an outcry in the city council in Cambridge, 450 00:21:08,450 --> 00:21:09,660 here-- 451 00:21:09,660 --> 00:21:12,330 a wild hearing by the city council 452 00:21:12,330 --> 00:21:17,045 trying to shut down related research at Harvard and MIT, 453 00:21:17,045 --> 00:21:19,170 because they're very worried about the implications 454 00:21:19,170 --> 00:21:20,712 of what's going to be happening here. 455 00:21:20,712 --> 00:21:23,580 So then there's an effort by the scientific community-- 456 00:21:23,580 --> 00:21:27,030 this famous conference called Asimolar out in California, 457 00:21:27,030 --> 00:21:30,090 where the whole community comes together and kind of really 458 00:21:30,090 --> 00:21:31,890 begins to work through the ethics. 459 00:21:31,890 --> 00:21:35,910 But their principal competitor, Wally Gilbert at Harvard, 460 00:21:35,910 --> 00:21:39,960 is forced, because of this limit on genetic engineering 461 00:21:39,960 --> 00:21:42,540 and the ability to use genetic DNA-- 462 00:21:42,540 --> 00:21:44,473 he has to go to England, where they're still 463 00:21:44,473 --> 00:21:45,890 allowing that research, and enlist 464 00:21:45,890 --> 00:21:48,780 a whole group of British scientists 465 00:21:48,780 --> 00:21:50,130 to undertake his research. 466 00:21:50,130 --> 00:21:53,790 But Swanson and Boyer get around that by using synthetic DNA, 467 00:21:53,790 --> 00:21:55,290 and they avoid the whole outcry. 468 00:21:55,290 --> 00:21:57,990 So it's a very interesting development. 469 00:21:57,990 --> 00:22:01,620 It slowed down their competitors pretty considerably. 470 00:22:01,620 --> 00:22:03,880 Wally Gilbert was a great talent to be up against-- 471 00:22:03,880 --> 00:22:09,443 I mean, a famous scientific leader and researcher. 472 00:22:09,443 --> 00:22:10,860 AUDIENCE: I didn't do this reading 473 00:22:10,860 --> 00:22:15,420 or know that much about the genetic backgrounds at all, 474 00:22:15,420 --> 00:22:16,978 but were the pharmaceutical companies 475 00:22:16,978 --> 00:22:18,020 at all their competitors? 476 00:22:18,020 --> 00:22:23,250 Or are they very much so focused on just chemicals? 477 00:22:23,250 --> 00:22:24,900 AUDIENCE: I think Boyer really came-- 478 00:22:24,900 --> 00:22:28,980 you saw that drawing with the pseudo-- 479 00:22:28,980 --> 00:22:30,840 that's a bacterial plastic DNA. 480 00:22:30,840 --> 00:22:32,100 WILLIAM BONVILLIAN: Let's go back to that, Sanam-- 481 00:22:32,100 --> 00:22:33,808 that original picture of the two of them. 482 00:22:33,808 --> 00:22:36,047 AUDIENCE: And you insert your synthetic gene into it. 483 00:22:36,047 --> 00:22:37,380 WILLIAM BONVILLIAN: There it is. 484 00:22:37,380 --> 00:22:37,660 AUDIENCE: That-- 485 00:22:37,660 --> 00:22:38,285 AUDIENCE: Yeah. 486 00:22:38,285 --> 00:22:39,270 AUDIENCE: --rectangle. 487 00:22:39,270 --> 00:22:42,270 He basically came up with-- that wasn't in existence before, 488 00:22:42,270 --> 00:22:45,760 and now it's used every day, in almost every molecular biology 489 00:22:45,760 --> 00:22:46,260 lab. 490 00:22:46,260 --> 00:22:50,220 Yeah, so they don't think that the pharmaceuticals 491 00:22:50,220 --> 00:22:51,330 were onto this. 492 00:22:51,330 --> 00:22:53,080 WILLIAM BONVILLIAN: Yeah, looking at that, 493 00:22:53,080 --> 00:22:56,087 you can try and guess which one went to the Sloan School, 494 00:22:56,087 --> 00:22:56,587 right? 495 00:22:56,587 --> 00:23:02,697 [LAUGHTER] 496 00:23:02,697 --> 00:23:03,405 AUDIENCE: I know. 497 00:23:03,405 --> 00:23:05,280 They're wearing the same kind of tie, though. 498 00:23:05,280 --> 00:23:05,940 [LAUGHTER] 499 00:23:05,940 --> 00:23:07,830 WILLIAM BONVILLIAN: Yeah, Boyer almost never 500 00:23:07,830 --> 00:23:10,008 looked that good, let me tell you. 501 00:23:10,008 --> 00:23:10,800 AUDIENCE: Oh, yeah. 502 00:23:10,800 --> 00:23:11,688 [INAUDIBLE] 503 00:23:11,688 --> 00:23:13,020 They look pretty similar. 504 00:23:13,020 --> 00:23:16,650 I mean, also, Swanson was chemistry every day 505 00:23:16,650 --> 00:23:18,327 at MIT and then Sloan masters. 506 00:23:18,327 --> 00:23:19,410 WILLIAM BONVILLIAN: Right. 507 00:23:19,410 --> 00:23:21,172 AUDIENCE: He was like pseudo-Sloan [INAUDIBLE].. 508 00:23:21,172 --> 00:23:22,255 WILLIAM BONVILLIAN: Right. 509 00:23:25,030 --> 00:23:27,940 WILLIAM BONVILLIAN: How about another good question, Sanam? 510 00:23:27,940 --> 00:23:30,850 SANAM: So something that some of you who had read this kind of 511 00:23:30,850 --> 00:23:33,655 raised, and I was curious about as well, was-- 512 00:23:33,655 --> 00:23:36,838 so the initial years, because it was so new 513 00:23:36,838 --> 00:23:38,380 and because they were doing something 514 00:23:38,380 --> 00:23:39,838 that hadn't been done before, there 515 00:23:39,838 --> 00:23:42,830 was an increased amount of risk-taking that was needed, 516 00:23:42,830 --> 00:23:45,670 and they both really staked their careers on it, 517 00:23:45,670 --> 00:23:47,590 especially Swanson, who ended up having 518 00:23:47,590 --> 00:23:51,190 to check himself into hospital when they had setback. 519 00:23:51,190 --> 00:23:53,620 So I wonder if there's, kind of, a way 520 00:23:53,620 --> 00:23:58,480 to encourage more sustainable risk-taking in people who 521 00:23:58,480 --> 00:24:04,900 have the visions, like Swanson, and people who are starting up 522 00:24:04,900 --> 00:24:06,300 endeavors like this? 523 00:24:06,300 --> 00:24:10,808 So there is a way to make it that they are incentivized 524 00:24:10,808 --> 00:24:12,850 to take these risks that are necessary to further 525 00:24:12,850 --> 00:24:16,512 their careers, but also, maybe not to the point 526 00:24:16,512 --> 00:24:17,220 that Swanson did. 527 00:24:20,699 --> 00:24:22,120 AUDIENCE: At this point, I've led 528 00:24:22,120 --> 00:24:26,680 three innovation groups, where I teach, sort of, people-- 529 00:24:26,680 --> 00:24:29,170 not just students, but people-- 530 00:24:29,170 --> 00:24:30,880 the engineering design cycle. 531 00:24:30,880 --> 00:24:33,100 And I think one of my big takeaways-- 532 00:24:33,100 --> 00:24:36,050 having taught students as young as five years old 533 00:24:36,050 --> 00:24:40,317 to individuals as old as in their 50s, maybe 60s-- 534 00:24:40,317 --> 00:24:42,400 is that there really needs to be a sense of safety 535 00:24:42,400 --> 00:24:43,930 and security-- which was something that was brought up, 536 00:24:43,930 --> 00:24:45,608 I think, in the Biederman reading-- 537 00:24:45,608 --> 00:24:47,650 which is, I think, one of the reasons why there's 538 00:24:47,650 --> 00:24:50,020 that, sort of, big paradigm of founding a company out 539 00:24:50,020 --> 00:24:50,930 of your dorm room. 540 00:24:50,930 --> 00:24:52,930 Because there's something that's implied there-- 541 00:24:52,930 --> 00:24:54,710 which is that you have a place to live. 542 00:24:54,710 --> 00:24:57,040 And that if you attend an institution where 543 00:24:57,040 --> 00:24:59,198 you have a dorm, you also have access to food. 544 00:24:59,198 --> 00:25:01,240 And you're also not super concerned about getting 545 00:25:01,240 --> 00:25:02,300 a paycheck. 546 00:25:02,300 --> 00:25:05,110 And so, I think that sense of security, 547 00:25:05,110 --> 00:25:08,470 sort of both mentally, to sort of take a step back and explore 548 00:25:08,470 --> 00:25:12,160 your dreams, is really important into the sense of stability. 549 00:25:12,160 --> 00:25:14,570 Like, literally, access to shelter, access to food, 550 00:25:14,570 --> 00:25:17,890 access to health care becomes really important. 551 00:25:17,890 --> 00:25:22,390 So in those circumstances, I feel like universities-- 552 00:25:22,390 --> 00:25:26,140 and not just, maybe, in their undergraduate programs, 553 00:25:26,140 --> 00:25:28,570 but maybe universities generally-- provide 554 00:25:28,570 --> 00:25:30,520 really interesting opportunities to innovate 555 00:25:30,520 --> 00:25:33,100 for these kinds of very disruptive innovations, 556 00:25:33,100 --> 00:25:35,830 because they do provide this real holistic support 557 00:25:35,830 --> 00:25:37,150 to an individual. 558 00:25:37,150 --> 00:25:39,270 But that's still a theory that I'm working under 559 00:25:39,270 --> 00:25:41,560 and sort of hope to continue exploring over, 560 00:25:41,560 --> 00:25:44,880 maybe, a master's thesis or a PhD. 561 00:25:44,880 --> 00:25:49,282 But I think the other really big point about mental health 562 00:25:49,282 --> 00:25:51,490 is something that was also addressed in the Biederman 563 00:25:51,490 --> 00:25:53,950 reading, and it goes back to that sense of safety 564 00:25:53,950 --> 00:25:58,660 and security that, a lot of times, individuals 565 00:25:58,660 --> 00:26:02,380 who don't feel comfortable in their, 566 00:26:02,380 --> 00:26:07,180 sort of, organic or natural ecosystem feel more stressed, 567 00:26:07,180 --> 00:26:12,180 and thus, that prevents them from being productive. 568 00:26:12,180 --> 00:26:14,520 And so, I think about that in the context 569 00:26:14,520 --> 00:26:16,275 of the current political situation 570 00:26:16,275 --> 00:26:18,150 and the ways in which, maybe, some people who 571 00:26:18,150 --> 00:26:20,100 might be interested in innovating or pursuing 572 00:26:20,100 --> 00:26:22,410 innovative projects, may not be able to do 573 00:26:22,410 --> 00:26:25,530 so effectively because of the ways in which 574 00:26:25,530 --> 00:26:30,120 the political situation is literally motivating stress 575 00:26:30,120 --> 00:26:33,930 in their lives and is preventing them from innovating. 576 00:26:33,930 --> 00:26:35,190 So those are some thoughts. 577 00:26:35,190 --> 00:26:36,210 I have a lot of them. 578 00:26:36,210 --> 00:26:37,767 [INAUDIBLE] share those too. 579 00:26:37,767 --> 00:26:39,600 AUDIENCE: I mean, on the other side of that, 580 00:26:39,600 --> 00:26:42,060 I think this-- the aspect that having 581 00:26:42,060 --> 00:26:45,390 stress, a necessary stress, is important. 582 00:26:45,390 --> 00:26:48,390 I think with the Manhattan Project-- 583 00:26:48,390 --> 00:26:50,280 like there's no safety and security 584 00:26:50,280 --> 00:26:53,130 in thinking the Germans are going to get the bomb first. 585 00:26:53,130 --> 00:26:55,230 I mean, I couldn't think of a better impetus 586 00:26:55,230 --> 00:26:59,370 than this has to work, or I don't have a house to live in, 587 00:26:59,370 --> 00:27:02,320 or food to eat. 588 00:27:02,320 --> 00:27:05,302 AUDIENCE: Well, actually, that's actually not what 589 00:27:05,302 --> 00:27:06,510 they had to deal with at all. 590 00:27:06,510 --> 00:27:08,970 I mean, granted, they would say, yeah, oh, we 591 00:27:08,970 --> 00:27:10,790 don't have any lives, but-- 592 00:27:10,790 --> 00:27:13,450 so I kind of agree, but-- 593 00:27:13,450 --> 00:27:15,607 I mean, Oppenheimer did mention in that paper 594 00:27:15,607 --> 00:27:17,190 he went six months, and he didn't even 595 00:27:17,190 --> 00:27:20,640 realize he had a paycheck-- he hadn't received one. 596 00:27:20,640 --> 00:27:22,410 And I saw that, and I just thought 597 00:27:22,410 --> 00:27:24,610 that was such privilege. 598 00:27:24,610 --> 00:27:30,640 So the fact that someone could have that sense of, OK, 599 00:27:30,640 --> 00:27:32,280 well if this-- 600 00:27:32,280 --> 00:27:33,960 ignoring the implications of, obviously, 601 00:27:33,960 --> 00:27:37,470 failing on this project, outside of the Manhattan Project, 602 00:27:37,470 --> 00:27:40,200 any other technological pursuit, I 603 00:27:40,200 --> 00:27:42,420 think that definitely helps someone 604 00:27:42,420 --> 00:27:45,780 think more creatively when you think about, oh, 605 00:27:45,780 --> 00:27:47,190 how am I going to pay for my car? 606 00:27:47,190 --> 00:27:48,990 Or how am I going to get to work? 607 00:27:48,990 --> 00:27:49,997 Or whatever. 608 00:27:49,997 --> 00:27:52,080 WILLIAM BONVILLIAN: So one of the important rules, 609 00:27:52,080 --> 00:27:54,330 I think here, is that the group needs 610 00:27:54,330 --> 00:27:55,737 to be on a protected island. 611 00:27:55,737 --> 00:27:57,570 And the point you're adding, Steph, I think, 612 00:27:57,570 --> 00:27:59,580 is a significant one. 613 00:27:59,580 --> 00:28:01,898 It needs to feel secure in itself, right? 614 00:28:01,898 --> 00:28:03,690 People have to be comfortable in this group 615 00:28:03,690 --> 00:28:05,970 to be willing to be creative together and contribute 616 00:28:05,970 --> 00:28:07,090 to each other. 617 00:28:07,090 --> 00:28:09,720 I think that's an important perception here 618 00:28:09,720 --> 00:28:12,160 that probably applies to a lot of these groups. 619 00:28:12,160 --> 00:28:12,660 But-- 620 00:28:12,660 --> 00:28:14,880 AUDIENCE: --has to do with that scarcity-- 621 00:28:14,880 --> 00:28:17,160 why having too little means so much. 622 00:28:17,160 --> 00:28:18,660 You don't have your full bandwidth 623 00:28:18,660 --> 00:28:21,742 if you're worried about what you don't have 624 00:28:21,742 --> 00:28:22,950 and what you need to work on. 625 00:28:22,950 --> 00:28:25,320 So that's part of creativity. 626 00:28:25,320 --> 00:28:27,270 WILLIAM BONVILLIAN: Sanam, did this group 627 00:28:27,270 --> 00:28:30,430 have that sense of security? 628 00:28:30,430 --> 00:28:33,020 SANAM: I think from what I read here, they definitely did. 629 00:28:33,020 --> 00:28:34,875 I mean, a lot of them were-- 630 00:28:34,875 --> 00:28:36,250 WILLIAM BONVILLIAN: You don't see 631 00:28:36,250 --> 00:28:38,410 in this picture the whole team, but there's a whole team here. 632 00:28:38,410 --> 00:28:38,770 SANAM: Yeah, there's a whole-- 633 00:28:38,770 --> 00:28:41,502 WILLIAM BONVILLIAN: It's very collaborative with this duo. 634 00:28:41,502 --> 00:28:43,960 SANAM: Yeah, and I think that that was an interesting point 635 00:28:43,960 --> 00:28:45,080 about-- 636 00:28:45,080 --> 00:28:47,620 I did kind of get a sense that class 637 00:28:47,620 --> 00:28:52,265 came into this in a way that really benefited-- 638 00:28:52,265 --> 00:28:53,890 he went into commercializing because he 639 00:28:53,890 --> 00:28:56,770 had an altruistic vision, which is interesting. 640 00:28:56,770 --> 00:29:00,520 But he had that ability to not have 641 00:29:00,520 --> 00:29:02,580 to worry about making money and things like that. 642 00:29:02,580 --> 00:29:04,330 So I think that's a very interesting point 643 00:29:04,330 --> 00:29:06,130 that you all have raised. 644 00:29:06,130 --> 00:29:07,790 And yeah, what you're saying about how 645 00:29:07,790 --> 00:29:10,150 people who feel like they are actively under threat, 646 00:29:10,150 --> 00:29:15,220 or they actively don't have the basic necessities taken 647 00:29:15,220 --> 00:29:17,890 care of, would not be able to function, maybe, 648 00:29:17,890 --> 00:29:20,610 in the way that make those innovations that-- 649 00:29:20,610 --> 00:29:20,830 WILLIAM BONVILLIAN: Right. 650 00:29:20,830 --> 00:29:21,680 They're on an island. 651 00:29:21,680 --> 00:29:22,910 They're not starving on the island. 652 00:29:22,910 --> 00:29:23,530 SANAM: Yeah, exactly. 653 00:29:23,530 --> 00:29:25,405 AUDIENCE: I think an addendum that would be-- 654 00:29:25,405 --> 00:29:27,540 have any of you read When Breath Becomes Air. 655 00:29:27,540 --> 00:29:29,770 It's a book by Paul Kalanithi. 656 00:29:29,770 --> 00:29:32,590 He was a neuroscientist, who was a resident, 657 00:29:32,590 --> 00:29:34,330 I believe, at Stanford. 658 00:29:34,330 --> 00:29:39,670 [INAUDIBLE] neurosurgeon, who is a medical resident at Stanford, 659 00:29:39,670 --> 00:29:42,280 who's just about to complete his neuroscience residency when 660 00:29:42,280 --> 00:29:44,480 he was diagnosed with brain cancer 661 00:29:44,480 --> 00:29:47,350 and ended up passing away, just shy 662 00:29:47,350 --> 00:29:49,530 of completing his residency. 663 00:29:49,530 --> 00:29:51,790 But there's a really incredible portion 664 00:29:51,790 --> 00:29:53,890 in his memoir, where he talks about what 665 00:29:53,890 --> 00:29:56,740 he had to do in order to be able to achieve what he did, 666 00:29:56,740 --> 00:29:59,200 which is, essentially, after going to Stanford undergrad, 667 00:29:59,200 --> 00:30:02,590 he spent a year, essentially, being homeless, 668 00:30:02,590 --> 00:30:06,340 living in an abandoned home and taking classes at Stanford 669 00:30:06,340 --> 00:30:08,150 while studying for the MCAT. 670 00:30:08,150 --> 00:30:09,523 And I think there's a really-- 671 00:30:09,523 --> 00:30:11,440 the reason I recommend everyone read this book 672 00:30:11,440 --> 00:30:12,857 if they're interested in, sort of, 673 00:30:12,857 --> 00:30:15,160 becoming innovators, or entrepreneurs, or people 674 00:30:15,160 --> 00:30:18,790 who change the world is that he elicits 675 00:30:18,790 --> 00:30:23,980 a really great point about not necessarily 676 00:30:23,980 --> 00:30:26,270 just quote unquote male privilege, but just privilege, 677 00:30:26,270 --> 00:30:28,090 generally, because he was able to, sort of, 678 00:30:28,090 --> 00:30:30,667 opt out of being alive and utilize 679 00:30:30,667 --> 00:30:32,500 whatever money his parents were sending him, 680 00:30:32,500 --> 00:30:34,570 or whatever money he had saved up, in order 681 00:30:34,570 --> 00:30:37,150 to take these classes, in order to study for the kind, 682 00:30:37,150 --> 00:30:40,480 and then go on to do incredible work in medical school 683 00:30:40,480 --> 00:30:42,020 and then in residency. 684 00:30:42,020 --> 00:30:44,470 And I feel like that's, sort of, close as you get 685 00:30:44,470 --> 00:30:47,320 and the clearest, sort of, articulation 686 00:30:47,320 --> 00:30:51,010 of the process of giving up your whole life in order to pursue 687 00:30:51,010 --> 00:30:52,325 this big dream that you have. 688 00:30:52,325 --> 00:30:53,950 And so, Paul Kalanithi's book, I think, 689 00:30:53,950 --> 00:30:56,408 is a really, really great study, not just of a great group, 690 00:30:56,408 --> 00:30:58,630 but of a great individual, and a person who's 691 00:30:58,630 --> 00:31:01,180 sort of a risk taker, and willing to give up their life 692 00:31:01,180 --> 00:31:02,980 for their work, and then ultimately, 693 00:31:02,980 --> 00:31:05,660 obviously the conclusion of it is quite beautiful, 694 00:31:05,660 --> 00:31:07,920 and the narrative arc is there, but-- 695 00:31:07,920 --> 00:31:11,200 WILLIAM BONVILLIAN: And these folks are giving up medicine 696 00:31:11,200 --> 00:31:12,865 to pursue the dream. 697 00:31:12,865 --> 00:31:15,210 AUDIENCE: We're also looking at the winners, though. 698 00:31:15,210 --> 00:31:17,752 WILLIAM BONVILLIAN: We're only looking at the winners, right? 699 00:31:19,577 --> 00:31:21,160 AUDIENCE: I would say that pretty much 700 00:31:21,160 --> 00:31:25,360 every biotech entrepreneur ends up with ulcers and high blood 701 00:31:25,360 --> 00:31:25,930 pressure. 702 00:31:25,930 --> 00:31:27,420 That's just-- they do. 703 00:31:27,420 --> 00:31:27,920 And-- 704 00:31:27,920 --> 00:31:30,270 AUDIENCE: Or is it every entrepreneur? 705 00:31:30,270 --> 00:31:31,062 AUDIENCE: Probably. 706 00:31:31,062 --> 00:31:32,812 WILLIAM BONVILLIAN: She only said biotech. 707 00:31:32,812 --> 00:31:33,580 You're safe. 708 00:31:33,580 --> 00:31:36,550 AUDIENCE: I know I'm more familiar with the biotech side. 709 00:31:36,550 --> 00:31:40,780 But I think that because these-- 710 00:31:40,780 --> 00:31:42,940 I think that's also a characteristic 711 00:31:42,940 --> 00:31:44,350 of the great group leaders-- 712 00:31:44,350 --> 00:31:48,480 that they're able to take risks and get ulcers 713 00:31:48,480 --> 00:31:52,090 so that the rest of their team can feel the insulation 714 00:31:52,090 --> 00:31:56,680 and as though they have the opportunity for success, 715 00:31:56,680 --> 00:31:58,820 resources, and security. 716 00:31:58,820 --> 00:32:03,035 So I that's an aspect of this. 717 00:32:03,035 --> 00:32:04,410 SANAM: Yeah, I agree. 718 00:32:04,410 --> 00:32:08,410 There was a quote in the paper that the scientist at Genentech 719 00:32:08,410 --> 00:32:11,920 had no boss other than the Swanson's nervous vigilance, 720 00:32:11,920 --> 00:32:14,490 so that really carried them through [INAUDIBLE].. 721 00:32:14,490 --> 00:32:16,448 WILLIAM BONVILLIAN: That's a great line, Sanam. 722 00:32:16,448 --> 00:32:17,680 That's good. 723 00:32:17,680 --> 00:32:20,390 All right, let's do Venter, Lily. 724 00:32:20,390 --> 00:32:22,230 LILY: OK. 725 00:32:22,230 --> 00:32:26,550 So I'm going over the Craig Venter great group today. 726 00:32:26,550 --> 00:32:28,530 A few of you have heard of him. 727 00:32:28,530 --> 00:32:30,330 Most have not. 728 00:32:30,330 --> 00:32:33,980 The leader of the great group, of course, was Craig Venter. 729 00:32:33,980 --> 00:32:37,140 And just going through some of the Bennis and Biederman 730 00:32:37,140 --> 00:32:40,920 necessities of great groupness, or greatness in a group. 731 00:32:40,920 --> 00:32:43,740 His mission from God was to sequence and decode 732 00:32:43,740 --> 00:32:47,430 the human genome-- so check, we have a mission from God. 733 00:32:47,430 --> 00:32:51,390 The island-- in this particular case study, 734 00:32:51,390 --> 00:32:53,550 while they were decoding sequencing and decoding 735 00:32:53,550 --> 00:32:56,160 the human genome, their island was Celera, 736 00:32:56,160 --> 00:32:59,180 which was a biotech that was-- 737 00:32:59,180 --> 00:32:59,908 it's complicated. 738 00:32:59,908 --> 00:33:01,950 I'm going to try to keep it as simple as possible 739 00:33:01,950 --> 00:33:04,410 for the purposes of this presentation. 740 00:33:04,410 --> 00:33:10,470 Just know that Craig Venter had many, many for-profits 741 00:33:10,470 --> 00:33:14,390 and non-for-profits institutions all running at the same time. 742 00:33:14,390 --> 00:33:17,140 He now has now more than he even used to, 743 00:33:17,140 --> 00:33:20,970 so I'm just really going to talk about Celera for the purposes 744 00:33:20,970 --> 00:33:23,680 of this presentation. 745 00:33:23,680 --> 00:33:28,090 And then the mainland-- he has connections with the public. 746 00:33:28,090 --> 00:33:31,630 There's a lot of publicity going on at this time over the Human 747 00:33:31,630 --> 00:33:32,480 Genome Project. 748 00:33:32,480 --> 00:33:38,730 So that's sort of their highway to the public, or the mainland. 749 00:33:38,730 --> 00:33:40,470 And he has contacts, still, at the NIH 750 00:33:40,470 --> 00:33:42,990 and adversaries at the NIH as well. 751 00:33:42,990 --> 00:33:45,540 And they are, indeed, the underdog. 752 00:33:45,540 --> 00:33:49,380 Craig Venter's great group defect from the NIH, 753 00:33:49,380 --> 00:33:52,680 and they're up against funding from the US government 754 00:33:52,680 --> 00:33:55,480 and, actually, international collaboration, 755 00:33:55,480 --> 00:33:58,775 especially with the British government. 756 00:33:58,775 --> 00:33:59,400 And the enemy-- 757 00:33:59,400 --> 00:34:01,230 WILLIAM BONVILLIAN: Sorry-- I think it's actually Watson 758 00:34:01,230 --> 00:34:02,700 rather than Crick, right? 759 00:34:02,700 --> 00:34:03,540 LILY: Oh. 760 00:34:03,540 --> 00:34:04,070 Yeah. 761 00:34:04,070 --> 00:34:04,680 Yeah, yeah, yeah. 762 00:34:04,680 --> 00:34:05,040 Sorry. 763 00:34:05,040 --> 00:34:05,820 WILLIAM BONVILLIAN: Right. 764 00:34:05,820 --> 00:34:06,320 OK. 765 00:34:06,320 --> 00:34:07,890 LILY: It is Watson. 766 00:34:07,890 --> 00:34:09,333 I put the wrong one in there. 767 00:34:09,333 --> 00:34:11,250 No, Francis Crick had nothing to do with this. 768 00:34:11,250 --> 00:34:12,758 It was Watson. 769 00:34:12,758 --> 00:34:15,300 WILLIAM BONVILLIAN: And Watson was heading the Genome Project 770 00:34:15,300 --> 00:34:16,610 at NIH at the time. 771 00:34:16,610 --> 00:34:17,110 LILY: Yeah. 772 00:34:17,110 --> 00:34:17,400 Yeah. 773 00:34:17,400 --> 00:34:19,380 WILLIAM BONVILLIAN: And he drives Venter out. 774 00:34:19,380 --> 00:34:26,969 LILY: And Venter and Watson begin as a very powerful duo. 775 00:34:26,969 --> 00:34:30,389 And Venter thinks that the genome needs 776 00:34:30,389 --> 00:34:33,179 to be sequenced in a slightly different way, 777 00:34:33,179 --> 00:34:37,440 and Watson is not OK with that, because so much money 778 00:34:37,440 --> 00:34:41,260 and so much effort has been put into it-- 779 00:34:41,260 --> 00:34:43,500 the sort of outdated way. 780 00:34:43,500 --> 00:34:46,110 And he completely undermines Venter in front 781 00:34:46,110 --> 00:34:47,429 of US Congress, et cetera. 782 00:34:47,429 --> 00:34:48,971 And I'll get into that a little more. 783 00:34:48,971 --> 00:34:50,820 But yeah, basically, the enemy is 784 00:34:50,820 --> 00:34:53,219 anyone who pisses off Venter, which 785 00:34:53,219 --> 00:34:57,330 turns out to be quite a lot of people over the years. 786 00:34:57,330 --> 00:35:01,830 Venter-- Craig Venter, I think, is one of the more-- 787 00:35:01,830 --> 00:35:04,410 one of the most controversial scientists 788 00:35:04,410 --> 00:35:06,450 in biotech in our time. 789 00:35:06,450 --> 00:35:13,650 He's a very-- a little bit of a hothead, and he-- 790 00:35:13,650 --> 00:35:16,350 people in science either love him or hate him. 791 00:35:18,960 --> 00:35:20,430 So a little bit of background. 792 00:35:20,430 --> 00:35:23,570 His early life-- he's sort of-- 793 00:35:23,570 --> 00:35:27,780 he is reminiscent, for me, of Thomas Edison. 794 00:35:27,780 --> 00:35:31,980 He has some severe learning issues, 795 00:35:31,980 --> 00:35:35,490 almost flunks out of high school, does not go to college. 796 00:35:35,490 --> 00:35:39,000 He ends up a surfer bum in Southern California 797 00:35:39,000 --> 00:35:40,370 on Redondo Beach. 798 00:35:40,370 --> 00:35:42,360 WILLIAM BONVILLIAN: Now I want to add one fact. 799 00:35:42,360 --> 00:35:45,120 Not just one parent, but both his parents 800 00:35:45,120 --> 00:35:46,625 were Marines sergeants. 801 00:35:46,625 --> 00:35:47,250 LILY: Yep, so-- 802 00:35:47,250 --> 00:35:49,030 WILLIAM BONVILLIAN: You can only imagine what that does to you. 803 00:35:49,030 --> 00:35:50,960 LILY: From this militant household 804 00:35:50,960 --> 00:35:52,740 and decides that as soon as he is-- 805 00:35:52,740 --> 00:35:55,290 I think he's 17 when he graduates from high school. 806 00:35:55,290 --> 00:35:57,330 He decides, I'm getting out of here. 807 00:35:57,330 --> 00:36:00,660 I'm going to live in Redondo Beach, in a surf shack, 808 00:36:00,660 --> 00:36:02,860 and grow my hair out long, and wear cutoffs. 809 00:36:02,860 --> 00:36:06,675 So that was his, I think, rebellion against his parents. 810 00:36:09,240 --> 00:36:12,630 This is during the time of Vietnam. 811 00:36:12,630 --> 00:36:16,752 Venter realizes-- he comes from a military family, first 812 00:36:16,752 --> 00:36:18,960 of all, so I think he has a somewhat a sense of duty. 813 00:36:18,960 --> 00:36:21,570 But he also realizes that he's probably going to get drafted, 814 00:36:21,570 --> 00:36:24,840 and it's better for him if he joins the Navy voluntarily. 815 00:36:24,840 --> 00:36:28,980 He's actually an extremely good competitive swimmer, 816 00:36:28,980 --> 00:36:30,673 possibly Olympic quality. 817 00:36:30,673 --> 00:36:32,340 So he joins the Navy, thinking that he's 818 00:36:32,340 --> 00:36:38,190 going to be on the swim team and not actually see active duty. 819 00:36:38,190 --> 00:36:41,022 And this turns out to be-- 820 00:36:41,022 --> 00:36:42,480 couldn't be farther from the truth. 821 00:36:45,450 --> 00:36:51,420 Vietnam ramps up, and he is trained as an EMT 822 00:36:51,420 --> 00:36:52,920 and deployed to Da Nang. 823 00:36:52,920 --> 00:36:55,755 And he actually has two active deployments in Vietnam-- 824 00:36:58,618 --> 00:37:00,910 sees some things that I think changed his life forever. 825 00:37:00,910 --> 00:37:03,020 In the interview, he talks about how 826 00:37:03,020 --> 00:37:08,590 he is the person he is because of his time in Vietnam. 827 00:37:08,590 --> 00:37:11,890 He survives and attends community college 828 00:37:11,890 --> 00:37:13,570 in California. 829 00:37:13,570 --> 00:37:16,990 Transfers to UCSD, which is University of California San 830 00:37:16,990 --> 00:37:23,240 Diego, gets a BS and then a PhD there. 831 00:37:23,240 --> 00:37:25,435 I think he completed his PhD in three years. 832 00:37:25,435 --> 00:37:26,800 It was the fastest-- 833 00:37:26,800 --> 00:37:31,780 at that time, it was the fastest biochem PhD in UCSD history. 834 00:37:31,780 --> 00:37:34,420 He goes immediately to a faculty position at Buffalo, 835 00:37:34,420 --> 00:37:36,610 in New York. 836 00:37:36,610 --> 00:37:39,860 Interesting transition for him, yeah. 837 00:37:39,860 --> 00:37:41,740 That's part of the interview. 838 00:37:41,740 --> 00:37:45,400 He talks about how he's this Californian with long hair 839 00:37:45,400 --> 00:37:49,600 and then goes to goes to Buffalo, where he begins 840 00:37:49,600 --> 00:37:51,080 to make people angry, actually. 841 00:37:51,080 --> 00:37:54,400 So he starts young on his path. 842 00:37:54,400 --> 00:37:57,070 So he marries his PhD student, Claire Fraser. 843 00:37:57,070 --> 00:37:59,930 He's very clear that they get married after she graduates, 844 00:37:59,930 --> 00:38:02,200 so there's nothing wrong with that at all. 845 00:38:02,200 --> 00:38:06,880 And in 1984, he leaves Buffalo, pretty much so 846 00:38:06,880 --> 00:38:09,790 that she can start her-- 847 00:38:09,790 --> 00:38:12,820 prove herself as her own scientist at the NIH. 848 00:38:12,820 --> 00:38:16,210 And I think this is where the great group really starts 849 00:38:16,210 --> 00:38:19,360 to coalesce and starts to form. 850 00:38:22,780 --> 00:38:25,780 Some sequence of events. 851 00:38:25,780 --> 00:38:30,820 So while at the NIH, Venter is traditionally 852 00:38:30,820 --> 00:38:34,030 trained as a neuroscientist working in receptors. 853 00:38:34,030 --> 00:38:36,340 So is Claire Fraser. 854 00:38:36,340 --> 00:38:38,830 They have separate lab at the NIH, 855 00:38:38,830 --> 00:38:40,960 but something that Venter gets really interested in 856 00:38:40,960 --> 00:38:42,220 is molecular biology. 857 00:38:42,220 --> 00:38:44,530 He sees it as this field that's going to explode, 858 00:38:44,530 --> 00:38:47,410 is extremely important, and is, basically, 859 00:38:47,410 --> 00:38:49,750 our path to future medicine. 860 00:38:49,750 --> 00:38:55,900 So he starts to try to get funding to do molecular biology 861 00:38:55,900 --> 00:38:57,760 experiments and get into this field, 862 00:38:57,760 --> 00:38:58,933 buy the machines necessary. 863 00:38:58,933 --> 00:39:00,600 And he's totally stonewalled at the NIH. 864 00:39:00,600 --> 00:39:02,642 He's basically told, no, you're a neuroscientist. 865 00:39:02,642 --> 00:39:05,650 You can't work in this other stovepipe at the NIH. 866 00:39:05,650 --> 00:39:07,640 That's just not done. 867 00:39:07,640 --> 00:39:08,680 It won't work. 868 00:39:08,680 --> 00:39:10,510 So he becomes increasingly and increasingly 869 00:39:10,510 --> 00:39:13,695 frustrated with the bureaucracy, basically. 870 00:39:13,695 --> 00:39:15,070 And he says that he looks around, 871 00:39:15,070 --> 00:39:18,580 and he sees people who are lifers at the NIH, who just 872 00:39:18,580 --> 00:39:20,860 go through the motions of being a scientist, who 873 00:39:20,860 --> 00:39:26,230 couldn't really compete in the academic, or the outside, 874 00:39:26,230 --> 00:39:28,990 world. 875 00:39:28,990 --> 00:39:32,980 So he does something a little maverick. 876 00:39:32,980 --> 00:39:38,313 He creates this-- he's reading about and hearing 877 00:39:38,313 --> 00:39:40,105 about the Human Genome Project because it's 878 00:39:40,105 --> 00:39:42,160 a big initiative at NIH. 879 00:39:42,160 --> 00:39:44,140 And he decides, well, if they're not 880 00:39:44,140 --> 00:39:46,390 going to let me get into molecular about biology 881 00:39:46,390 --> 00:39:47,950 and sequencing the human genome, then 882 00:39:47,950 --> 00:39:52,600 I'm going to sneak in by, basically, 883 00:39:52,600 --> 00:39:58,930 sequencing a gene involved in neurobiology. 884 00:39:58,930 --> 00:40:02,533 So he does that, and he invents this thing called EST. 885 00:40:02,533 --> 00:40:03,700 And I'm not getting into it. 886 00:40:03,700 --> 00:40:05,950 Honestly, it's such an outdated thing 887 00:40:05,950 --> 00:40:10,780 that I didn't even learn about it during my studies. 888 00:40:10,780 --> 00:40:16,417 And then Watson and Venter have met. 889 00:40:16,417 --> 00:40:19,000 WILLIAM BONVILLIAN: So Watson is running the huge Human Genome 890 00:40:19,000 --> 00:40:20,470 Project at NIH. 891 00:40:20,470 --> 00:40:21,970 LILY: Right. 892 00:40:21,970 --> 00:40:26,150 Venter comes up-- he has the sequence of this gene. 893 00:40:26,150 --> 00:40:28,360 He's made some pretty cool breakthroughs and goes in 894 00:40:28,360 --> 00:40:29,380 and meets with Watson. 895 00:40:29,380 --> 00:40:31,900 And Watson gets excited about it at first. 896 00:40:31,900 --> 00:40:37,930 But then, Craig Venter goes off and, kind of, procures 897 00:40:37,930 --> 00:40:40,135 a machine, sort of behind Watson's back-- 898 00:40:40,135 --> 00:40:41,830 it's what Watson thinks-- 899 00:40:41,830 --> 00:40:43,690 and makes Watson really mad. 900 00:40:43,690 --> 00:40:47,050 So he totally undermines and vilifies 901 00:40:47,050 --> 00:40:49,720 Venter in front of Congress and the US public, 902 00:40:49,720 --> 00:40:51,570 because this is a huge-- 903 00:40:51,570 --> 00:40:54,760 this is a huge initiative. 904 00:40:54,760 --> 00:40:56,760 People are talking about it at the dinner table. 905 00:40:56,760 --> 00:41:01,480 It's a big deal back in the mid-1990s, I would say. 906 00:41:01,480 --> 00:41:07,700 So Watson basically doesn't like the way Craig's doing things. 907 00:41:07,700 --> 00:41:08,200 So-- 908 00:41:08,200 --> 00:41:08,650 WILLIAM BONVILLIAN: So let me just 909 00:41:08,650 --> 00:41:10,000 add one quick detail, Lily. 910 00:41:10,000 --> 00:41:13,570 So there is an issue here on what 911 00:41:13,570 --> 00:41:17,410 gets patented on a project like the human genome. 912 00:41:17,410 --> 00:41:20,080 Can you patent the genome, right? 913 00:41:20,080 --> 00:41:22,840 So this whole issue is coming up. 914 00:41:22,840 --> 00:41:29,050 The general counsel at NIH advises Venter 915 00:41:29,050 --> 00:41:31,840 that, yes, we are going to be able to patent 916 00:41:31,840 --> 00:41:36,250 a lot of this emerging genetics field, so let's protect-- 917 00:41:36,250 --> 00:41:38,390 he's a researcher for NIH. 918 00:41:38,390 --> 00:41:41,170 So he's intramural researcher at NIH. 919 00:41:41,170 --> 00:41:45,940 Let's protect NIH by filing a lot of the patents ourselves. 920 00:41:45,940 --> 00:41:48,430 And the confrontation with Congress 921 00:41:48,430 --> 00:41:55,330 is where Watson attacks Venter for patenting genome 922 00:41:55,330 --> 00:41:56,740 technologies. 923 00:41:56,740 --> 00:41:58,240 In other words, it's not going to be 924 00:41:58,240 --> 00:41:59,967 available to the scientific world. 925 00:41:59,967 --> 00:42:01,300 It's not going to be open based. 926 00:42:01,300 --> 00:42:03,190 It's not going to be in the commons, 927 00:42:03,190 --> 00:42:05,730 even though Venter had received-- 928 00:42:05,730 --> 00:42:09,250 had been told by the general counsel's office at NIH 929 00:42:09,250 --> 00:42:10,510 that that's what he had to do. 930 00:42:10,510 --> 00:42:15,080 So that leads to this explosion of anger by Venter. 931 00:42:15,080 --> 00:42:16,270 LILY: Yeah, so-- 932 00:42:16,270 --> 00:42:17,530 WILLIAM BONVILLIAN: Is that a fair summary? 933 00:42:17,530 --> 00:42:17,820 LILY: Definitely. 934 00:42:17,820 --> 00:42:18,110 WILLIAM BONVILLIAN: OK. 935 00:42:18,110 --> 00:42:20,180 LILY: Yeah, from Venter's side of the story, 936 00:42:20,180 --> 00:42:25,330 he says, I didn't necessarily want to patent these genes. 937 00:42:25,330 --> 00:42:27,160 The NIH advised me to. 938 00:42:27,160 --> 00:42:28,960 It was their initiative. 939 00:42:28,960 --> 00:42:32,050 And Watson used it as a way to undermine me 940 00:42:32,050 --> 00:42:34,750 in front of the American public because he 941 00:42:34,750 --> 00:42:37,210 didn't want me to use my technology that I had come up 942 00:42:37,210 --> 00:42:38,380 with. 943 00:42:38,380 --> 00:42:41,650 So that's the-- sort of the backstory. 944 00:42:41,650 --> 00:42:47,020 Venter is insanely angry, and he and his wife 945 00:42:47,020 --> 00:42:50,500 defect from the NIH and form Celera 946 00:42:50,500 --> 00:42:52,420 with the help of venture capitalists, who 947 00:42:52,420 --> 00:42:55,870 later also anger Venter, and that explodes. 948 00:42:55,870 --> 00:42:59,650 And they take with them 12 men of their lab members 949 00:42:59,650 --> 00:43:02,450 from the NIH. 950 00:43:02,450 --> 00:43:03,350 So the team. 951 00:43:03,350 --> 00:43:05,380 And I think that Craig Venter really exemplifies 952 00:43:05,380 --> 00:43:10,120 a great group leader in that he seems able to identify talent 953 00:43:10,120 --> 00:43:12,130 in others. 954 00:43:12,130 --> 00:43:14,800 So they start out with the 12 from NIH. 955 00:43:14,800 --> 00:43:17,710 They have their island of Celera. 956 00:43:17,710 --> 00:43:20,170 And the team that they start to form-- 957 00:43:22,790 --> 00:43:25,270 this is not including the board of directors and such, 958 00:43:25,270 --> 00:43:27,880 which also seemed to be pretty amazing people. 959 00:43:27,880 --> 00:43:31,800 But they recruit Hamilton-- actually I think-- 960 00:43:31,800 --> 00:43:33,550 Well, Hamilton Smith comes onto the scene, 961 00:43:33,550 --> 00:43:36,237 and he's their premier molecular biologist. 962 00:43:36,237 --> 00:43:38,320 WILLIAM BONVILLIAN: And he's a Nobel Prize winner. 963 00:43:38,320 --> 00:43:38,710 LILY: Did he? 964 00:43:38,710 --> 00:43:39,106 WILLIAM BONVILLIAN: Yeah. 965 00:43:39,106 --> 00:43:39,502 LILY: OK. 966 00:43:39,502 --> 00:43:39,900 At-- 967 00:43:39,900 --> 00:43:41,340 AUDIENCE: Was that during the time period or after? 968 00:43:41,340 --> 00:43:41,760 WILLIAM BONVILLIAN: Prior. 969 00:43:41,760 --> 00:43:42,180 [INAUDIBLE] 970 00:43:42,180 --> 00:43:42,650 LILY: Yeah. 971 00:43:42,650 --> 00:43:43,150 Yeah. 972 00:43:43,150 --> 00:43:45,953 So I don't think any of-- 973 00:43:45,953 --> 00:43:48,370 this is actually a question I'm going to pose to the class 974 00:43:48,370 --> 00:43:49,810 at the end of my presentation. 975 00:43:49,810 --> 00:43:52,450 I don't think anyone in this great group 976 00:43:52,450 --> 00:43:55,840 has won a Nobel Prize, except-- 977 00:43:55,840 --> 00:43:56,950 like for this. 978 00:43:56,950 --> 00:43:57,917 Previously Ham Smith. 979 00:43:57,917 --> 00:43:59,000 WILLIAM BONVILLIAN: Right. 980 00:43:59,000 --> 00:44:02,260 LILY: So Marshall Peterson is this computer geek 981 00:44:02,260 --> 00:44:05,800 because not only is Hamilton Smith literally coming up 982 00:44:05,800 --> 00:44:09,830 with the sequencing technology to do this-- 983 00:44:09,830 --> 00:44:12,310 this is 30 billion base pairs of DNA. 984 00:44:12,310 --> 00:44:15,610 The most anyone has sequenced at this point is like C. elegans, 985 00:44:15,610 --> 00:44:17,320 or like a worm-- 986 00:44:17,320 --> 00:44:18,430 tiny, tiny genome. 987 00:44:18,430 --> 00:44:20,560 So this is a huge deal. 988 00:44:20,560 --> 00:44:23,320 They have to come up with new sequencing technologies, 989 00:44:23,320 --> 00:44:28,750 new ways to decode the sequence, and new computing. 990 00:44:28,750 --> 00:44:32,440 So they literally build the third largest computer 991 00:44:32,440 --> 00:44:35,350 on the planet, I think. 992 00:44:35,350 --> 00:44:38,470 The DOE has a larger computer, and there's someone 993 00:44:38,470 --> 00:44:39,760 somewhere else in the world. 994 00:44:39,760 --> 00:44:43,720 And they build this massive computing facility 995 00:44:43,720 --> 00:44:47,530 and then bring on Gene Myers as this coder who's 996 00:44:47,530 --> 00:44:50,680 coming up with the algorithms to try to piece together 997 00:44:50,680 --> 00:44:54,400 all these little pieces of DNA and build a 30 billion 998 00:44:54,400 --> 00:44:57,440 base sequence. 999 00:44:57,440 --> 00:45:05,320 So in the meantime, the public sector-- 1000 00:45:05,320 --> 00:45:09,970 academia, the NIH-- are becoming increasingly angry 1001 00:45:09,970 --> 00:45:12,370 with Craig Venter, saying that he's 1002 00:45:12,370 --> 00:45:14,590 defecting to the private sector, and he's 1003 00:45:14,590 --> 00:45:17,650 going after the human genome for profit. 1004 00:45:17,650 --> 00:45:19,690 Craig is saying, I don't-- 1005 00:45:19,690 --> 00:45:21,010 I'm not in this for profit. 1006 00:45:21,010 --> 00:45:23,050 I'm in this because we need a human genome 1007 00:45:23,050 --> 00:45:26,950 so that we can promote human molecular, biology-based 1008 00:45:26,950 --> 00:45:28,330 medicine. 1009 00:45:28,330 --> 00:45:32,020 In the meantime, he sequences the drosophilid genome 1010 00:45:32,020 --> 00:45:33,160 and gives it-- 1011 00:45:33,160 --> 00:45:35,240 publishes it in science. 1012 00:45:35,240 --> 00:45:35,740 Gives it-- 1013 00:45:35,740 --> 00:45:37,170 WILLIAM BONVILLIAN: Fruit fly. 1014 00:45:37,170 --> 00:45:39,220 LILY: Yeah, the fruit fly genome, which people 1015 00:45:39,220 --> 00:45:43,720 have been trying to figure out what genes in fruit fly 1016 00:45:43,720 --> 00:45:45,530 do for decades and decades and decades. 1017 00:45:45,530 --> 00:45:47,110 And so, there's this really cool part 1018 00:45:47,110 --> 00:45:51,460 in the interview where he talks about this community of fruit 1019 00:45:51,460 --> 00:45:54,180 fly researchers, who, I think, are kind of like the physics 1020 00:45:54,180 --> 00:45:54,680 community. 1021 00:45:54,680 --> 00:45:56,080 And they're pretty close-knit. 1022 00:45:56,080 --> 00:45:57,850 They have their conferences, and they 1023 00:45:57,850 --> 00:46:02,590 try to get together, and decode the fruit fly, which 1024 00:46:02,590 --> 00:46:05,620 they don't have the basis or the knowledge to do that yet. 1025 00:46:05,620 --> 00:46:10,060 So they're doing it gene by gene by gene over many years. 1026 00:46:10,060 --> 00:46:14,870 And Craig decodes the fruit fly genome and says, 1027 00:46:14,870 --> 00:46:16,780 come to Celera. 1028 00:46:16,780 --> 00:46:19,270 I have all of the information that you've been looking 1029 00:46:19,270 --> 00:46:20,780 for the past few decades. 1030 00:46:20,780 --> 00:46:22,180 And you can have it. 1031 00:46:22,180 --> 00:46:23,120 It's here. 1032 00:46:23,120 --> 00:46:27,490 So he says, maybe on the order of 100 or so fruit fly 1033 00:46:27,490 --> 00:46:28,297 researchers come. 1034 00:46:28,297 --> 00:46:30,130 And they are just like kids in a candy shop. 1035 00:46:30,130 --> 00:46:31,920 They stay up all hours of the night 1036 00:46:31,920 --> 00:46:34,840 and making these breakthroughs that they've 1037 00:46:34,840 --> 00:46:37,360 been looking for for decades. 1038 00:46:37,360 --> 00:46:39,970 So that's pretty fun, pretty cool. 1039 00:46:39,970 --> 00:46:47,020 So fruit fly sequenced, NIH. 1040 00:46:47,020 --> 00:46:51,190 You would probably know more about the interplay 1041 00:46:51,190 --> 00:46:53,730 between Francis Collins and Venter, because that-- 1042 00:46:53,730 --> 00:46:56,645 I don't-- I'm not-- it's a little fuzzy for me. 1043 00:46:56,645 --> 00:46:58,020 I can never figure out if they're 1044 00:46:58,020 --> 00:46:59,970 working together or against each other, 1045 00:46:59,970 --> 00:47:04,500 but eventually, what happens in 2001-- 1046 00:47:04,500 --> 00:47:08,100 the NIH and the public-- 1047 00:47:08,100 --> 00:47:11,040 like academia, so people from different universities-- 1048 00:47:11,040 --> 00:47:12,960 come together and publish the human genome 1049 00:47:12,960 --> 00:47:15,030 in the journal Nature. 1050 00:47:15,030 --> 00:47:18,192 And two days later, Celera announces the-- 1051 00:47:18,192 --> 00:47:19,400 WILLIAM BONVILLIAN: Same day. 1052 00:47:19,400 --> 00:47:21,120 LILY: Oh it is the same day? 1053 00:47:21,120 --> 00:47:23,100 It comes out in Science. 1054 00:47:23,100 --> 00:47:24,695 So this is-- 1055 00:47:24,695 --> 00:47:25,930 AUDIENCE: Was that planned? 1056 00:47:25,930 --> 00:47:26,220 WILLIAM BONVILLIAN: Yeah. 1057 00:47:26,220 --> 00:47:26,720 LILY: Yeah. 1058 00:47:26,720 --> 00:47:29,070 WILLIAM BONVILLIAN: So there had to be-- 1059 00:47:29,070 --> 00:47:31,380 I mean it's a fascinating story, right? 1060 00:47:31,380 --> 00:47:33,990 And there's rich MIT history, which 1061 00:47:33,990 --> 00:47:38,840 MIT was on the side of NIH, just so you know. 1062 00:47:38,840 --> 00:47:47,000 So Venter, through a research model 1063 00:47:47,000 --> 00:47:49,160 that was extremely focused on getting 1064 00:47:49,160 --> 00:47:51,230 this project done, right? 1065 00:47:51,230 --> 00:47:53,690 Everything was to be organized for the project. 1066 00:47:53,690 --> 00:47:56,270 NIH is on a research model that could 1067 00:47:56,270 --> 00:47:59,780 be described as, let's let 1,000 flowers bloom. 1068 00:47:59,780 --> 00:48:03,680 We'll have a lot of RL1 researchers out there. 1069 00:48:03,680 --> 00:48:07,310 Eventually, that will turn into the Human Genome Project. 1070 00:48:07,310 --> 00:48:10,220 Someone made the analogy to that research model 1071 00:48:10,220 --> 00:48:13,910 as, OK, if you put a whole lot of monkeys into a room 1072 00:48:13,910 --> 00:48:15,860 and give them typewriters, eventually 1073 00:48:15,860 --> 00:48:18,590 we'll get Shakespeare, right? 1074 00:48:18,590 --> 00:48:20,870 It's obviously exaggeration here, 1075 00:48:20,870 --> 00:48:23,390 but Venter, with his very focused research 1076 00:48:23,390 --> 00:48:26,030 project, and these computer scientists, and Venter himself, 1077 00:48:26,030 --> 00:48:28,400 becomes a master of the computers 1078 00:48:28,400 --> 00:48:31,550 that he's working on building, and EST technology 1079 00:48:31,550 --> 00:48:34,480 is from that-- 1080 00:48:34,480 --> 00:48:38,110 versus a much more decentralized research operation. 1081 00:48:38,110 --> 00:48:43,550 So slowly NIH realized, it's going to lose the race, 1082 00:48:43,550 --> 00:48:45,280 unless it gets its act together. 1083 00:48:45,280 --> 00:48:53,340 So they begin to focus on getting rid 1084 00:48:53,340 --> 00:48:54,460 of the 1,000 flowers. 1085 00:48:54,460 --> 00:48:58,470 Let's get down to a small number of focused research centers. 1086 00:48:58,470 --> 00:49:07,480 And it's a race, really, between-- 1087 00:49:07,480 --> 00:49:09,640 who's that Broad Institute head? 1088 00:49:09,640 --> 00:49:12,390 What's his name? 1089 00:49:12,390 --> 00:49:15,100 Oh, how can I forget? 1090 00:49:15,100 --> 00:49:15,613 Eric Lander. 1091 00:49:15,613 --> 00:49:16,280 Yeah, of course. 1092 00:49:16,280 --> 00:49:17,530 Sorry, excuse me. 1093 00:49:17,530 --> 00:49:19,930 So Eric's a mathematician. 1094 00:49:19,930 --> 00:49:22,720 He's not a life scientist, right? 1095 00:49:22,720 --> 00:49:31,040 And he becomes the leader of the Human Genome Project for NIH. 1096 00:49:31,040 --> 00:49:32,870 Collins is heading it, but Eric is 1097 00:49:32,870 --> 00:49:35,500 the one who's carrying out the computational parts of it, 1098 00:49:35,500 --> 00:49:36,230 right? 1099 00:49:36,230 --> 00:49:39,590 And they do it in cooperation with this Department of Energy 1100 00:49:39,590 --> 00:49:42,230 supercomputer labs, because they need their own supercomputing 1101 00:49:42,230 --> 00:49:42,860 capability. 1102 00:49:42,860 --> 00:49:45,380 Venter's built his. 1103 00:49:45,380 --> 00:49:46,940 NIH uses DOE. 1104 00:49:46,940 --> 00:49:49,730 So this race is ongoing. 1105 00:49:49,730 --> 00:49:52,640 The NIH crowd is attacking Venter 1106 00:49:52,640 --> 00:49:56,030 constantly for, essentially, trying to profit 1107 00:49:56,030 --> 00:49:58,213 from patenting the genome. 1108 00:49:58,213 --> 00:49:59,630 That's their complaint-- that he's 1109 00:49:59,630 --> 00:50:03,170 going to take this critical scientific advance, 1110 00:50:03,170 --> 00:50:05,870 critical to the future of medicine, 1111 00:50:05,870 --> 00:50:08,630 and take it out of circulation and access to science, 1112 00:50:08,630 --> 00:50:10,730 and patent it, and prevent anybody from using it. 1113 00:50:10,730 --> 00:50:14,650 That's the case being made against him. 1114 00:50:14,650 --> 00:50:18,440 It's pretty far, in reality, from what the truth is, 1115 00:50:18,440 --> 00:50:20,030 but that's the case being made. 1116 00:50:20,030 --> 00:50:26,910 And Eric and the NIH team are gradually-- 1117 00:50:26,910 --> 00:50:29,100 they are using some of Venter's technology. 1118 00:50:29,100 --> 00:50:31,960 They're originating, certainly, many of their own. 1119 00:50:31,960 --> 00:50:33,390 They're within range. 1120 00:50:33,390 --> 00:50:36,090 So at that point, a negotiated truce 1121 00:50:36,090 --> 00:50:39,300 is arranged so that nobody gets embarrassed here, right? 1122 00:50:39,300 --> 00:50:42,390 And the real worry was that NIH would lose the race, right? 1123 00:50:42,390 --> 00:50:46,160 So eventually, Venter is prevailed on to declare a tie, 1124 00:50:46,160 --> 00:50:47,880 and each side will publish, in the two 1125 00:50:47,880 --> 00:50:52,630 major scientific publications, their version of the genome. 1126 00:50:52,630 --> 00:50:54,480 And that's, in fact, what occurs. 1127 00:50:54,480 --> 00:50:56,677 It's-- Go ahead. 1128 00:50:56,677 --> 00:50:58,010 LILY: Two points I want to make. 1129 00:50:58,010 --> 00:51:00,340 One is that in this interview, Venter 1130 00:51:00,340 --> 00:51:06,250 says that he finds out that Francis Collins, the head 1131 00:51:06,250 --> 00:51:10,060 of the NIH, has budgeted less than half 1132 00:51:10,060 --> 00:51:12,310 of what it's going to take at their current rate 1133 00:51:12,310 --> 00:51:13,810 to complete the human genome. 1134 00:51:13,810 --> 00:51:16,360 So in his mind, Collins has no intention 1135 00:51:16,360 --> 00:51:18,640 of really completing it. 1136 00:51:18,640 --> 00:51:22,720 And they can't-- as the way things stood. 1137 00:51:22,720 --> 00:51:26,710 The other point I wanted to make is that the NIH-- 1138 00:51:26,710 --> 00:51:29,463 Venter's enemies and adversaries keep bombarding him 1139 00:51:29,463 --> 00:51:30,880 in the public by saying he's going 1140 00:51:30,880 --> 00:51:32,240 to profit off the human genome. 1141 00:51:32,240 --> 00:51:35,140 And there is a little-- there's actually 1142 00:51:35,140 --> 00:51:38,560 a lot of friction between Venter and his venture capitalists, 1143 00:51:38,560 --> 00:51:40,360 because he wants to publish. 1144 00:51:40,360 --> 00:51:42,173 Publish, publish, publish the findings 1145 00:51:42,173 --> 00:51:44,590 that they're making so that other people can utilize them. 1146 00:51:44,590 --> 00:51:47,358 And the venture capitalists keep saying, no, we 1147 00:51:47,358 --> 00:51:48,650 don't want-- you can't do that. 1148 00:51:48,650 --> 00:51:49,400 You can't do that. 1149 00:51:49,400 --> 00:51:50,440 It's private. 1150 00:51:50,440 --> 00:51:52,180 We want to patent, et cetera. 1151 00:51:52,180 --> 00:51:54,490 So there's that friction going on. 1152 00:51:54,490 --> 00:51:59,050 So yes, eventually published at the same time. 1153 00:51:59,050 --> 00:52:04,000 Celera actually-- Venter is-- 1154 00:52:04,000 --> 00:52:08,022 both quits and is fired from Celera at the same time. 1155 00:52:08,022 --> 00:52:09,290 And-- 1156 00:52:09,290 --> 00:52:10,940 AUDIENCE: Which one came first? 1157 00:52:10,940 --> 00:52:14,680 LILY: In Venter's mind, he was about to quit anyway, 1158 00:52:14,680 --> 00:52:17,620 but yeah, he was basically asked to leave on a Monday morning. 1159 00:52:17,620 --> 00:52:19,580 AUDIENCE: He's truly the Steve Jobs of biology. 1160 00:52:19,580 --> 00:52:20,080 LILY: Yeah. 1161 00:52:20,080 --> 00:52:20,770 Yeah. 1162 00:52:20,770 --> 00:52:22,285 Yeah, there are a lot of parallels. 1163 00:52:22,285 --> 00:52:25,350 AUDIENCE: Even the hippie phase. 1164 00:52:25,350 --> 00:52:29,140 WILLIAM BONVILLIAN: But Martine, he's different, right? 1165 00:52:29,140 --> 00:52:32,170 He creates this group, originally, at his NIH lab. 1166 00:52:32,170 --> 00:52:34,090 It's basically still with him, right? 1167 00:52:34,090 --> 00:52:34,740 LILY: Yeah, they want to follow him. 1168 00:52:34,740 --> 00:52:35,823 WILLIAM BONVILLIAN: Right. 1169 00:52:35,823 --> 00:52:39,340 This is not Jobs's abrasiveness, right, and screaming. 1170 00:52:39,340 --> 00:52:42,360 This is somebody who is really-- 1171 00:52:42,360 --> 00:52:43,810 he's a pretty charismatic figure. 1172 00:52:43,810 --> 00:52:46,090 I've spent a little time with him. 1173 00:52:46,090 --> 00:52:49,600 And he is able to keep an absolutely remarkable team 1174 00:52:49,600 --> 00:52:53,810 together for a remarkably long period of time. 1175 00:52:53,810 --> 00:52:57,130 So it's a different personal atmosphere, a different kind 1176 00:52:57,130 --> 00:52:58,780 of leadership style. 1177 00:52:58,780 --> 00:53:01,530 But remember what it must have been like. 1178 00:53:01,530 --> 00:53:04,540 I mean, he comes back from the Vietnam War 1179 00:53:04,540 --> 00:53:08,290 full of Navy and Marine Corps tattoos, right? 1180 00:53:08,290 --> 00:53:13,968 He's a veteran of this ghastly war, and he's been-- 1181 00:53:13,968 --> 00:53:15,510 he's seen some of the worst outcomes, 1182 00:53:15,510 --> 00:53:18,420 running a whole hospital wing, virtually on his own, 1183 00:53:18,420 --> 00:53:22,500 in Da Nang, and sees death and loss of life, 1184 00:53:22,500 --> 00:53:26,280 and trying to save life, firsthand, 1185 00:53:26,280 --> 00:53:28,380 in a very personal kind of way. 1186 00:53:28,380 --> 00:53:32,640 He's completely different than the kind of culture of NIH. 1187 00:53:32,640 --> 00:53:35,280 He's up from a working class, military family, 1188 00:53:35,280 --> 00:53:38,910 and it's just a different world that he's been exposed to. 1189 00:53:38,910 --> 00:53:40,830 And he doesn't get along with this kind 1190 00:53:40,830 --> 00:53:45,310 of established liberal community of scientists at NIH. 1191 00:53:45,310 --> 00:53:47,790 So that's part of what's going on here, I think, really. 1192 00:53:47,790 --> 00:53:48,540 LILY: Yeah, I think what's-- 1193 00:53:48,540 --> 00:53:51,210 WILLIAM BONVILLIAN: He's just a really different character. 1194 00:53:51,210 --> 00:53:54,080 LILY: What's always pushing him at the NIH is 1195 00:53:54,080 --> 00:53:56,610 and throughout the Human Genome Project is, 1196 00:53:56,610 --> 00:54:01,860 decode the human genome so we don't see the situation, 1197 00:54:01,860 --> 00:54:04,850 or the things that he saw in the hospital at Da Nang. 1198 00:54:04,850 --> 00:54:07,500 He doesn't-- he thinks that modern medicine should 1199 00:54:07,500 --> 00:54:12,570 eradicate those sorts of injuries. 1200 00:54:12,570 --> 00:54:14,400 His vision is synthetic biology. 1201 00:54:14,400 --> 00:54:14,957 Regrow. 1202 00:54:14,957 --> 00:54:16,290 Regrow everything synthetically. 1203 00:54:16,290 --> 00:54:18,130 Regrow human limbs. 1204 00:54:18,130 --> 00:54:20,220 He's very forward thinking as far 1205 00:54:20,220 --> 00:54:24,060 as the medical uses of knowing genetics. 1206 00:54:24,060 --> 00:54:25,310 AUDIENCE: I had this question. 1207 00:54:25,310 --> 00:54:29,600 So what exactly were they trying to patent on either side 1208 00:54:29,600 --> 00:54:31,650 that we see? 1209 00:54:31,650 --> 00:54:34,960 LILY: Lots of things. 1210 00:54:34,960 --> 00:54:39,730 So I think that one worry was that his invention-- 1211 00:54:39,730 --> 00:54:47,170 Craig's method of EST would be patented itself. 1212 00:54:47,170 --> 00:54:50,350 And it's an extremely-- it was for a-- well some areas 1213 00:54:50,350 --> 00:54:51,430 of biology still use it. 1214 00:54:51,430 --> 00:54:53,680 Not my particular field, but a lot of areas of biology 1215 00:54:53,680 --> 00:54:56,230 still use it. 1216 00:54:56,230 --> 00:54:59,880 And if it were patented, they wouldn't be able to, obviously. 1217 00:54:59,880 --> 00:55:01,790 So it's widely applicable. 1218 00:55:01,790 --> 00:55:03,200 It's technique. 1219 00:55:03,200 --> 00:55:06,345 And there was worry that he would try to patent EST, 1220 00:55:06,345 --> 00:55:07,220 or there might have-- 1221 00:55:07,220 --> 00:55:09,160 I think there was a patent in, and the NIH 1222 00:55:09,160 --> 00:55:10,577 pulled it, or something like that, 1223 00:55:10,577 --> 00:55:13,330 with so much negative press. 1224 00:55:13,330 --> 00:55:15,730 There were a couple of other things up for patent, maybe, 1225 00:55:15,730 --> 00:55:17,838 but I don't know the specifics. 1226 00:55:17,838 --> 00:55:20,130 WILLIAM BONVILLIAN: Why don't we get in some questions. 1227 00:55:20,130 --> 00:55:20,830 LILY: Yeah, OK. 1228 00:55:20,830 --> 00:55:21,070 WILLIAM BONVILLIAN: Do you have any more slides? 1229 00:55:21,070 --> 00:55:22,480 LILY: But I wanted to let you know-- 1230 00:55:22,480 --> 00:55:22,580 WILLIAM BONVILLIAN: Oh. 1231 00:55:22,580 --> 00:55:22,630 Oh yes. 1232 00:55:22,630 --> 00:55:23,020 OK. 1233 00:55:23,020 --> 00:55:24,010 LILY: What does he do? 1234 00:55:24,010 --> 00:55:24,510 So Celera-- 1235 00:55:24,510 --> 00:55:26,510 WILLIAM BONVILLIAN: This is what he's doing now. 1236 00:55:26,510 --> 00:55:29,540 LILY: Celera goes boom with the dot come bubble. 1237 00:55:29,540 --> 00:55:33,880 It's worth $15 billion, and the stock goes from 500 bucks 1238 00:55:33,880 --> 00:55:34,700 to $6. 1239 00:55:37,420 --> 00:55:39,430 At that point, Craig had already left. 1240 00:55:39,430 --> 00:55:41,770 One of the reasons that the stock dropped so drastically 1241 00:55:41,770 --> 00:55:44,780 was because people found out that Craig left. 1242 00:55:44,780 --> 00:55:47,800 So now-- well not now, but then, he decides, 1243 00:55:47,800 --> 00:55:49,660 well I'm going to do my own science, 1244 00:55:49,660 --> 00:55:51,848 and I'm going to sequence the entire ocean. 1245 00:55:51,848 --> 00:55:53,140 I'm done with the human genome. 1246 00:55:53,140 --> 00:55:54,680 Now I'm going to go to the ocean. 1247 00:55:54,680 --> 00:55:57,550 So he gets on this 100 foot yacht-- the Sorcerer II. 1248 00:55:57,550 --> 00:56:00,520 And a couple of people-- 1249 00:56:00,520 --> 00:56:03,940 I taught with a woman who's a professor at USC, 1250 00:56:03,940 --> 00:56:07,450 who was on some of the Sorcerer legs of the journey. 1251 00:56:07,450 --> 00:56:09,760 And then her husband, John Heidelberg 1252 00:56:09,760 --> 00:56:11,400 is on-- he was on my thesis committees, 1253 00:56:11,400 --> 00:56:13,900 so he's the third author on this paper. 1254 00:56:13,900 --> 00:56:18,243 And they have lots of fun Craig stories as well. 1255 00:56:18,243 --> 00:56:18,910 And one of the-- 1256 00:56:18,910 --> 00:56:21,130 I think one of the themes throughout the great groups 1257 00:56:21,130 --> 00:56:23,410 is that, there are a lot of partiers. 1258 00:56:23,410 --> 00:56:26,070 If they're not doing really crazy, good science, 1259 00:56:26,070 --> 00:56:27,454 they're partying. 1260 00:56:27,454 --> 00:56:30,580 So they dock, and they just have the big huge party. 1261 00:56:30,580 --> 00:56:32,710 So then he finishes sailing around the world 1262 00:56:32,710 --> 00:56:36,040 a couple of times and sequencing all the bacteria in the ocean 1263 00:56:36,040 --> 00:56:40,360 and sets up the JCVI, which is located in La Jolla, 1264 00:56:40,360 --> 00:56:42,810 right next to his alma mater, UCSD. 1265 00:56:42,810 --> 00:56:46,400 So that's a picture of Craig from a few years ago. 1266 00:56:46,400 --> 00:56:49,498 So to conclude, a few choice quotes from Craig. 1267 00:56:49,498 --> 00:56:51,040 "I've gotten some pretty nice awards. 1268 00:56:51,040 --> 00:56:54,460 And I'm having trouble finding places to put them all." 1269 00:56:54,460 --> 00:56:56,830 And then one that's more pertinent to this class 1270 00:56:56,830 --> 00:57:00,740 is, "The environment has fallen to the wayside in politics." 1271 00:57:00,740 --> 00:57:04,120 And lastly-- oh, I wanted to go over Venter's thoughts 1272 00:57:04,120 --> 00:57:07,240 on innovation, but that's not-- we're getting close on time, 1273 00:57:07,240 --> 00:57:08,540 so that's not really necessary. 1274 00:57:08,540 --> 00:57:10,540 These are better. 1275 00:57:10,540 --> 00:57:13,240 WILLIAM BONVILLIAN: How about some questions? 1276 00:57:13,240 --> 00:57:16,510 LILY: OK, so show of-- 1277 00:57:16,510 --> 00:57:19,670 I wanted to see, in the class, by a show of hands. 1278 00:57:19,670 --> 00:57:22,570 Who thinks that Venter, or maybe a couple of people 1279 00:57:22,570 --> 00:57:23,380 within his group-- 1280 00:57:23,380 --> 00:57:27,970 I mean, they decoded the human genome, which was-- 1281 00:57:27,970 --> 00:57:30,490 just the amount of new technologies that they came up 1282 00:57:30,490 --> 00:57:33,070 with, both in computing and molecular biology, 1283 00:57:33,070 --> 00:57:35,090 in order to do that are astounding. 1284 00:57:35,090 --> 00:57:38,560 So who in here would say he should be a Nobel Prize winner? 1285 00:57:38,560 --> 00:57:39,518 Or people in his group? 1286 00:57:39,518 --> 00:57:41,018 AUDIENCE: I'm sure there's got to be 1287 00:57:41,018 --> 00:57:43,680 some other equivalent, biology-centered award, right? 1288 00:57:43,680 --> 00:57:45,660 AUDIENCE: I mean, he could just make his own award and then. 1289 00:57:45,660 --> 00:57:46,285 AUDIENCE: Yeah. 1290 00:57:46,285 --> 00:57:48,595 LILY: Well, he's got a lot of them [INAUDIBLE].. 1291 00:57:48,595 --> 00:57:49,970 AUDIENCE: I mean, but [INAUDIBLE] 1292 00:57:49,970 --> 00:57:50,980 give out your own award. 1293 00:57:50,980 --> 00:57:52,480 AUDIENCE: If there's a Watson and Crick award, 1294 00:57:52,480 --> 00:57:54,729 and he wins it, imagine how hilarious that would be. 1295 00:57:54,729 --> 00:57:56,312 WILLIAM BONVILLIAN: That won't happen. 1296 00:57:56,312 --> 00:57:58,600 AUDIENCE: --it was self-critical and then was like-- 1297 00:57:58,600 --> 00:57:59,260 WILLIAM BONVILLIAN: Watson. 1298 00:57:59,260 --> 00:58:00,100 AUDIENCE: Oh, Watson. 1299 00:58:00,100 --> 00:58:00,460 LILY: Yeah, sorry. 1300 00:58:00,460 --> 00:58:02,502 AUDIENCE: When they started, a lot of their stuff 1301 00:58:02,502 --> 00:58:04,560 was based off of Schrodinger's What is Life? 1302 00:58:04,560 --> 00:58:07,510 That's how they come up with the DNA, because it was mentioned. 1303 00:58:07,510 --> 00:58:10,520 And they were seen as nobodys up to that point. 1304 00:58:10,520 --> 00:58:11,520 It's really interesting. 1305 00:58:11,520 --> 00:58:13,228 AUDIENCE: Lily, can I ask a really simple 1306 00:58:13,228 --> 00:58:14,240 semantic question? 1307 00:58:14,240 --> 00:58:16,540 Why do they call it decoding, and not encoding, 1308 00:58:16,540 --> 00:58:18,640 the human genome? 1309 00:58:18,640 --> 00:58:21,180 LILY: I think it would be called decoding because-- 1310 00:58:21,180 --> 00:58:22,555 AUDIENCE: You're only reading it. 1311 00:58:22,555 --> 00:58:25,510 LILY: What this looks like when you actually read 1312 00:58:25,510 --> 00:58:28,390 the bases is fluorescent dye. 1313 00:58:28,390 --> 00:58:29,920 So you have to read-- 1314 00:58:29,920 --> 00:58:33,190 you have to decode the fluorescent signature in order 1315 00:58:33,190 --> 00:58:34,460 to get the base pairs. 1316 00:58:34,460 --> 00:58:36,130 AUDIENCE: So it's not about-- 1317 00:58:36,130 --> 00:58:40,540 it's about, more so, evaluating the results than it is about, 1318 00:58:40,540 --> 00:58:43,620 sort of, expressing the result? 1319 00:58:43,620 --> 00:58:44,370 LILY: Yeah. 1320 00:58:44,370 --> 00:58:45,720 Yeah, I would [INAUDIBLE]. 1321 00:58:45,720 --> 00:58:47,595 AUDIENCE: They also did something cool that's 1322 00:58:47,595 --> 00:58:49,210 you inject DNA into like a bacteria 1323 00:58:49,210 --> 00:58:51,400 and it changed the way it was. 1324 00:58:51,400 --> 00:58:53,530 [INAUDIBLE] changed the lifeform. 1325 00:58:53,530 --> 00:58:54,220 I was-- 1326 00:58:54,220 --> 00:58:57,680 WILLIAM BONVILLIAN: One thing about this effort here-- 1327 00:58:57,680 --> 00:58:59,680 you have to get a sense for how transformational 1328 00:58:59,680 --> 00:59:01,840 this seemed in the 1990s, because it was really 1329 00:59:01,840 --> 00:59:03,490 quite amazing. 1330 00:59:03,490 --> 00:59:06,550 But this was one of the great scientific competitions 1331 00:59:06,550 --> 00:59:07,720 of all time, right? 1332 00:59:07,720 --> 00:59:10,870 This incredible race to do the-- 1333 00:59:10,870 --> 00:59:12,250 to decode the genome. 1334 00:59:12,250 --> 00:59:15,410 And this is being-- 1335 00:59:15,410 --> 00:59:17,500 this a major news story, right? 1336 00:59:17,500 --> 00:59:20,860 And Francis Collins has a Harley motorcycle, 1337 00:59:20,860 --> 00:59:23,290 and sings in a blues band, and he 1338 00:59:23,290 --> 00:59:26,480 has his own fast-- he still rides his motorcycle into work 1339 00:59:26,480 --> 00:59:28,300 at NIH. 1340 00:59:28,300 --> 00:59:34,180 Venter is a yachtsman, who buys these wild, incredibly 1341 00:59:34,180 --> 00:59:40,180 dangerous sailboats and loves that kind of wild adventure 1342 00:59:40,180 --> 00:59:44,800 of being in a tempestuous sea, close to the wind, 1343 00:59:44,800 --> 00:59:49,410 and near capsize danger, with these monster sailboats. 1344 00:59:49,410 --> 00:59:52,310 So both of them have their, kind of, wild side here. 1345 00:59:52,310 --> 00:59:54,580 There's no question about it. 1346 00:59:54,580 --> 00:59:58,890 But this competition captures the public imagination. 1347 00:59:58,890 --> 01:00:01,530 And it's a very important race, because the race 1348 01:00:01,530 --> 01:00:05,280 forces incredible speed in the project. 1349 01:00:05,280 --> 01:00:07,830 So what had probably been originally viewed 1350 01:00:07,830 --> 01:00:10,710 as a 40-year project that gets knocked down 1351 01:00:10,710 --> 01:00:13,050 to well less than a decade by the time 1352 01:00:13,050 --> 01:00:15,450 these characters kind of complete the race 1353 01:00:15,450 --> 01:00:19,710 and agree to a truce by publishing on the same day. 1354 01:00:19,710 --> 01:00:23,100 And in turn, I was working in the Senate 1355 01:00:23,100 --> 01:00:24,520 while this is all going on. 1356 01:00:24,520 --> 01:00:27,090 And the public was getting a sense, gee, 1357 01:00:27,090 --> 01:00:28,560 we really are getting fairly close 1358 01:00:28,560 --> 01:00:32,040 to some absolutely fundamental answers, here. 1359 01:00:32,040 --> 01:00:35,580 And when two US senators-- 1360 01:00:35,580 --> 01:00:37,350 Senator Specter and Senator Harkin 1361 01:00:37,350 --> 01:00:39,660 from Pennsylvania and Iowa, respectively-- 1362 01:00:39,660 --> 01:00:43,230 were chair and ranking on the appropriations subcommittee 1363 01:00:43,230 --> 01:00:45,000 that handles NIH. 1364 01:00:45,000 --> 01:00:46,530 They actually-- there's was-- 1365 01:00:46,530 --> 01:00:48,990 in the Clinton years-- because of the IT revolution is 1366 01:00:48,990 --> 01:00:51,750 creating a lot of extra tax revenue-- 1367 01:00:51,750 --> 01:00:52,920 were balancing the budget. 1368 01:00:52,920 --> 01:00:55,480 So there's revenue, on one of those rare moments, 1369 01:00:55,480 --> 01:00:59,530 there's revenue in the federal government Specter and Harkin 1370 01:00:59,530 --> 01:01:01,600 go down and take the revenue. 1371 01:01:01,600 --> 01:01:04,600 And that's what doubles NIH. 1372 01:01:04,600 --> 01:01:07,870 And nobody says they're wrong because of the excitement 1373 01:01:07,870 --> 01:01:10,150 that this race to get to the genome has created 1374 01:01:10,150 --> 01:01:13,090 and what it's scientific possibilities are. 1375 01:01:13,090 --> 01:01:16,750 So sure, there's a whole political effort 1376 01:01:16,750 --> 01:01:19,000 to radically increase NIH funding, 1377 01:01:19,000 --> 01:01:21,610 but the enabler here is the public excitement 1378 01:01:21,610 --> 01:01:23,950 around the genome race and what its possibilities might 1379 01:01:23,950 --> 01:01:25,480 be to accomplish. 1380 01:01:25,480 --> 01:01:28,810 So this has enduring, kind of, lasting effects 1381 01:01:28,810 --> 01:01:30,790 that spill over in many kind of ways 1382 01:01:30,790 --> 01:01:34,500 into the life science arena. 1383 01:01:34,500 --> 01:01:35,430 Next question. 1384 01:01:35,430 --> 01:01:38,730 How about-- you want to pose a couple of questions, 1385 01:01:38,730 --> 01:01:41,407 and then we'll wrap things up. 1386 01:01:41,407 --> 01:01:43,740 LILY: Yeah, one of the things I was thinking about while 1387 01:01:43,740 --> 01:01:45,740 reading this and the other readings in the class 1388 01:01:45,740 --> 01:01:51,120 is that Venter seems to form and dissolve and reform 1389 01:01:51,120 --> 01:01:53,580 multiple great groups throughout his lifetime-- 1390 01:01:53,580 --> 01:01:56,100 from the 80s to present day. 1391 01:01:56,100 --> 01:01:57,955 And in thinking about other great-- 1392 01:01:57,955 --> 01:01:59,580 WILLIAM BONVILLIAN: But Lily, he's only 1393 01:01:59,580 --> 01:02:01,110 changing the formal structure. 1394 01:02:01,110 --> 01:02:04,360 The team really follows him from one organizational model 1395 01:02:04,360 --> 01:02:04,860 to another-- 1396 01:02:04,860 --> 01:02:05,190 WILLIAM BONVILLIAN: That's true. 1397 01:02:05,190 --> 01:02:06,760 WILLIAM BONVILLIAN: In many way. 1398 01:02:06,760 --> 01:02:09,150 LILY: So do we think that most great group 1399 01:02:09,150 --> 01:02:11,520 leaders have a great-- 1400 01:02:11,520 --> 01:02:15,390 one great group, and that's their opus. 1401 01:02:15,390 --> 01:02:18,000 They don't go on and-- 1402 01:02:18,000 --> 01:02:18,970 they have one mission. 1403 01:02:18,970 --> 01:02:22,070 AUDIENCE: When you say group, do you mean a following? 1404 01:02:22,070 --> 01:02:23,670 Or a team? 1405 01:02:23,670 --> 01:02:24,502 LILY: One team. 1406 01:02:24,502 --> 01:02:26,210 AUDIENCE: Yeah, I mean, I would disagree. 1407 01:02:26,210 --> 01:02:28,620 Well just a counterexample I'll give is Apple. 1408 01:02:28,620 --> 01:02:30,870 Because there is the Mac team-- those are very unique. 1409 01:02:30,870 --> 01:02:32,470 So I'll take certain products. 1410 01:02:32,470 --> 01:02:35,400 Or if it's a one particular problem, you have a group. 1411 01:02:35,400 --> 01:02:38,768 And them you can choose to decay the group after. 1412 01:02:38,768 --> 01:02:41,310 WILLIAM BONVILLIAN: Yeah, Apple is remarkable for its ability 1413 01:02:41,310 --> 01:02:45,570 to go from one major technology launch to another in sequence. 1414 01:02:45,570 --> 01:02:46,290 It's really-- 1415 01:02:46,290 --> 01:02:47,660 AUDIENCE: It's hard, yeah. 1416 01:02:47,660 --> 01:02:49,785 WILLIAM BONVILLIAN: --very hard and very difficult. 1417 01:02:49,785 --> 01:02:53,487 And interestingly, it hasn't happened since Jobs died. 1418 01:02:53,487 --> 01:02:54,570 AUDIENCE: That we know of. 1419 01:02:54,570 --> 01:02:56,320 WILLIAM BONVILLIAN: That we know of, yeah. 1420 01:02:58,935 --> 01:03:01,270 AUDIENCE: Who else is a [INAUDIBLE] 1421 01:03:01,270 --> 01:03:02,770 AUDIENCE: I feel like they'd release 1422 01:03:02,770 --> 01:03:04,308 some information about that, seeing 1423 01:03:04,308 --> 01:03:07,692 as how the hype around Apple's kind of starting to die down. 1424 01:03:07,692 --> 01:03:09,650 WILLIAM BONVILLIAN: There's a model in history. 1425 01:03:09,650 --> 01:03:11,520 I mean, how does a big company innovate? 1426 01:03:11,520 --> 01:03:13,670 It's a terribly difficult problem, right? 1427 01:03:13,670 --> 01:03:14,420 They're the suits. 1428 01:03:14,420 --> 01:03:16,190 They're the bureaucracy. 1429 01:03:16,190 --> 01:03:19,730 Lockheed is kind of noted as the experimenter here. 1430 01:03:19,730 --> 01:03:23,360 They set up something called the Skunk Works 1431 01:03:23,360 --> 01:03:26,180 as a completely side, separate organization 1432 01:03:26,180 --> 01:03:27,770 on a protected island, with a bridge 1433 01:03:27,770 --> 01:03:29,930 back to the Lockheed management. 1434 01:03:29,930 --> 01:03:36,660 And they do remarkable advances in aeronautics and aerospace. 1435 01:03:36,660 --> 01:03:39,380 So they do the U-2. 1436 01:03:39,380 --> 01:03:42,440 They do the SR-71 Blackbird, which many people to this day 1437 01:03:42,440 --> 01:03:45,060 think is the most remarkable plane ever built. 1438 01:03:45,060 --> 01:03:48,950 But then a whole sequence of other aircraft. 1439 01:03:48,950 --> 01:03:51,260 And they do stealth, right? 1440 01:03:51,260 --> 01:03:54,950 They're the implementers of a lot of the stealth technology. 1441 01:03:54,950 --> 01:03:57,860 So that's an example of how a corporation is 1442 01:03:57,860 --> 01:04:01,040 able to create an entity that can 1443 01:04:01,040 --> 01:04:04,310 move through a series of great groups and keep innovating. 1444 01:04:04,310 --> 01:04:07,070 That's kind of held up as a kind of iconic model. 1445 01:04:07,070 --> 01:04:08,120 So it is possible. 1446 01:04:08,120 --> 01:04:10,910 So when your-- the small companies that you all set up 1447 01:04:10,910 --> 01:04:14,870 become major corporations, read about Skunk Works, 1448 01:04:14,870 --> 01:04:18,150 because that'll be your clue to continued survival. 1449 01:04:18,150 --> 01:04:19,850 AUDIENCE: Ray Stata did something 1450 01:04:19,850 --> 01:04:22,657 interesting like that, where his company, they wouldn't fund-- 1451 01:04:22,657 --> 01:04:24,740 WILLIAM BONVILLIAN: That's Analog Devices, just up 1452 01:04:24,740 --> 01:04:25,430 the street. 1453 01:04:25,430 --> 01:04:27,097 AUDIENCE: They wouldn't fund [INAUDIBLE] 1454 01:04:27,097 --> 01:04:28,097 to digital technologies. 1455 01:04:28,097 --> 01:04:29,847 And so what he had to do is, he personally 1456 01:04:29,847 --> 01:04:32,050 putting in his own money to make a new company 1457 01:04:32,050 --> 01:04:34,370 that the old company could not buy back. 1458 01:04:34,370 --> 01:04:36,640 So he had to get over it that way. 1459 01:04:36,640 --> 01:04:38,913 WILLIAM BONVILLIAN: That's Interesting. 1460 01:04:38,913 --> 01:04:40,580 Lily, how about a closing thought for us 1461 01:04:40,580 --> 01:04:41,930 about this amazing crowd? 1462 01:04:44,840 --> 01:04:46,370 LILY: Let's see. 1463 01:04:46,370 --> 01:04:52,508 I think that Craig is right when he says you have to take risks. 1464 01:04:52,508 --> 01:04:54,050 You have to take risks or else you're 1465 01:04:54,050 --> 01:04:58,030 not going to do anything worth writing about. 1466 01:04:58,030 --> 01:05:00,910 WILLIAM BONVILLIAN: And that's a great way to close.