1 00:00:02,240 --> 00:00:06,800 Speaker 1: This is Master's in Business with Verry Ridholts on Bloomberg Radio. 2 00:00:07,480 --> 00:00:10,360 Speaker 1: This week on the podcast, I have an extra special guest. 3 00:00:10,520 --> 00:00:13,039 Speaker 1: His name is Safi Pacall and he is the author 4 00:00:13,080 --> 00:00:17,159 Speaker 1: of a fascinating book called moon Shots. Not only was 5 00:00:17,239 --> 00:00:21,800 Speaker 1: he on President Obama's counsel on the Future of Technology, 6 00:00:21,840 --> 00:00:25,599 Speaker 1: but he has consulted for the CIA, the Navy, all 7 00:00:25,680 --> 00:00:30,240 Speaker 1: sorts of organizations, teaching them how to nurture innovation in 8 00:00:30,320 --> 00:00:34,160 Speaker 1: institutions that normally are not very good at that sort 9 00:00:34,200 --> 00:00:37,760 Speaker 1: of thing. He's really a fascinating guy background in physics, 10 00:00:37,880 --> 00:00:41,320 Speaker 1: ran a biotech company and along the way picked up 11 00:00:41,440 --> 00:00:46,559 Speaker 1: lots of insights into what leads to successful moon shots 12 00:00:46,600 --> 00:00:50,479 Speaker 1: as projects and why so much innovation is really just 13 00:00:51,040 --> 00:00:54,560 Speaker 1: smothered in the lab, smothered in the cradle, when it 14 00:00:54,600 --> 00:00:59,600 Speaker 1: has potential to literally change the war. The radar is 15 00:00:59,720 --> 00:01:03,080 Speaker 1: argued with the technology that won the war for the Allies. 16 00:01:03,320 --> 00:01:07,640 Speaker 1: It had been sitting in the Navy for eighteen years unused, 17 00:01:07,959 --> 00:01:11,680 Speaker 1: and had it not been discovered and brought forward by 18 00:01:11,920 --> 00:01:15,080 Speaker 1: van Ouer Bush and other people, there's a very real 19 00:01:15,160 --> 00:01:18,560 Speaker 1: chance the global map would look totally different. So I 20 00:01:18,600 --> 00:01:22,000 Speaker 1: think you'll find this conversation to be absolutely fascinating. My 21 00:01:22,200 --> 00:01:28,600 Speaker 1: conversation with Safie Picoll. This is Masters in Business with 22 00:01:28,680 --> 00:01:33,319 Speaker 1: Barry Ridholts on Bloomberg Radio. My extra special guest this 23 00:01:33,360 --> 00:01:37,640 Speaker 1: week is Safie Picall. He is the former CEO and 24 00:01:37,800 --> 00:01:41,600 Speaker 1: co founder of Sense of Pharmaceuticals. He served on President 25 00:01:41,680 --> 00:01:47,480 Speaker 1: Obama's Council of Science Advisors, and he is the author 26 00:01:47,840 --> 00:01:51,560 Speaker 1: of loon Shots, How to Nurture the crazy ideas that 27 00:01:51,640 --> 00:01:57,840 Speaker 1: wind wars cured diseases and transformed industries. Sofie Picall, Welcome 28 00:01:57,840 --> 00:02:00,680 Speaker 1: to Bloomberg. Thanks very glad to be here. So you 29 00:02:00,720 --> 00:02:04,400 Speaker 1: have a really fascinating background. Both your parents are physicists 30 00:02:04,480 --> 00:02:08,360 Speaker 1: or astrophysicists. You eventually go to Harvard, get a degree 31 00:02:08,400 --> 00:02:12,160 Speaker 1: in theoretical physics, go to Stanford for your PhD in physics. 32 00:02:12,520 --> 00:02:14,960 Speaker 1: How on earth does that lead to a career in 33 00:02:14,960 --> 00:02:17,600 Speaker 1: in biotech. I was kind of a little bit of 34 00:02:17,639 --> 00:02:20,120 Speaker 1: a random walk, but I I spent probably the first 35 00:02:20,840 --> 00:02:23,559 Speaker 1: thirty years of my life kind of on a university. 36 00:02:23,639 --> 00:02:26,280 Speaker 1: And because my parents were physicists, we grew up in Princeton, 37 00:02:26,360 --> 00:02:28,840 Speaker 1: and then I was college and then grad school and 38 00:02:28,840 --> 00:02:31,000 Speaker 1: then post doc and going through the faculty stuff. And 39 00:02:31,480 --> 00:02:33,919 Speaker 1: at some point I realized, and this might be a 40 00:02:33,919 --> 00:02:38,600 Speaker 1: little shocking, that more than that people are not in 41 00:02:38,639 --> 00:02:41,560 Speaker 1: the world, are not theoretical physicist or mathematicians. They do 42 00:02:41,639 --> 00:02:45,200 Speaker 1: stuff other than write papers and go down for inces. 43 00:02:45,240 --> 00:02:48,360 Speaker 1: And I just got curious, So you started looking around. 44 00:02:48,400 --> 00:02:52,240 Speaker 1: How do you go from theoretical physics to stumble into 45 00:02:52,440 --> 00:02:55,760 Speaker 1: via random walk biotech. I was just curious about what 46 00:02:55,880 --> 00:02:57,680 Speaker 1: is it that makes the world go round? And what 47 00:02:57,760 --> 00:03:00,280 Speaker 1: do other people do when they're not writing pay person 48 00:03:00,280 --> 00:03:02,840 Speaker 1: about mathematics or physics. And I actually remember I was 49 00:03:02,919 --> 00:03:06,320 Speaker 1: dating a woman at the time and she had she 50 00:03:06,400 --> 00:03:08,440 Speaker 1: was working as a paralegal, which was a real job, 51 00:03:08,480 --> 00:03:10,320 Speaker 1: and I'd never been to an office, and I remember 52 00:03:10,360 --> 00:03:13,280 Speaker 1: asking her, could you take me in? I was in 53 00:03:13,360 --> 00:03:15,280 Speaker 1: my mid twenty Could you take me into this thing 54 00:03:15,320 --> 00:03:17,880 Speaker 1: you call an office? Did it horrify you? Well, I 55 00:03:17,880 --> 00:03:19,400 Speaker 1: remember she said, well, listen, I can take you in 56 00:03:19,480 --> 00:03:22,040 Speaker 1: Friday happy hour. That's when people sort of stand around 57 00:03:22,120 --> 00:03:23,880 Speaker 1: you could talk. And so I came in Friday happy 58 00:03:24,000 --> 00:03:26,000 Speaker 1: and I said, I said, well, what should I ask? 59 00:03:26,000 --> 00:03:27,600 Speaker 1: And I said, well, look, are you happy doing what 60 00:03:27,639 --> 00:03:29,480 Speaker 1: you're doing? And I went around. It was actually a 61 00:03:29,480 --> 00:03:31,680 Speaker 1: well known law firm in Silicon Valley, and I said, 62 00:03:31,919 --> 00:03:34,360 Speaker 1: are you happy doing what you're doing? I would say, 63 00:03:34,639 --> 00:03:37,560 Speaker 1: of the twenty five people I asked, twenty five said no. 64 00:03:38,560 --> 00:03:40,560 Speaker 1: And I was like, well, that probably set me back 65 00:03:40,560 --> 00:03:43,360 Speaker 1: another five years in the academic world. But I just 66 00:03:43,400 --> 00:03:46,000 Speaker 1: got very curious. And so I heard from another physicist 67 00:03:46,040 --> 00:03:49,200 Speaker 1: friend who had gone into a consulting world, and they 68 00:03:49,240 --> 00:03:51,640 Speaker 1: had sort of they help you transition, they kind of 69 00:03:51,960 --> 00:03:54,760 Speaker 1: from academia to business. Yes, so I went to a 70 00:03:54,800 --> 00:03:58,600 Speaker 1: consulting firm in New York called McKenzie and Joint Company, 71 00:03:58,640 --> 00:04:01,520 Speaker 1: always in the news, US in the news a lot lately, 72 00:04:02,120 --> 00:04:04,720 Speaker 1: and it's kind of like a halfway house for academics. 73 00:04:04,720 --> 00:04:06,440 Speaker 1: It's sort of like they don't want to let you 74 00:04:06,480 --> 00:04:08,880 Speaker 1: loose on the real world, and they sort of coach 75 00:04:08,960 --> 00:04:10,840 Speaker 1: you and the skills you need to survive, how to 76 00:04:10,880 --> 00:04:12,920 Speaker 1: speak to people, how to work on teams, how to 77 00:04:12,960 --> 00:04:15,320 Speaker 1: solve real problems. And so that's how I got a 78 00:04:15,360 --> 00:04:18,640 Speaker 1: sense of kind of how the real world works. And 79 00:04:18,640 --> 00:04:21,920 Speaker 1: then I got a taste for wanting to do something, 80 00:04:21,960 --> 00:04:25,560 Speaker 1: build something, and also bigger than myself, bigger than you know, 81 00:04:25,640 --> 00:04:28,040 Speaker 1: your your own career or your own papers. Or your 82 00:04:28,040 --> 00:04:32,599 Speaker 1: own ideas and bigger than just advising companies. Uh. You know, 83 00:04:32,640 --> 00:04:35,599 Speaker 1: I had friends whose parents were getting sick. I was 84 00:04:35,640 --> 00:04:39,040 Speaker 1: going through some illness in my own family, and I thought, 85 00:04:39,080 --> 00:04:42,360 Speaker 1: how motivating would it be if when I woke up 86 00:04:42,360 --> 00:04:44,400 Speaker 1: in the morning, I was working in something that could 87 00:04:44,440 --> 00:04:47,400 Speaker 1: give people on Earth more time with their loved ones. 88 00:04:47,760 --> 00:04:51,000 Speaker 1: It's just an incredibly exciting and fun way to work. So, 89 00:04:51,120 --> 00:04:52,400 Speaker 1: you know, some part of that is you want to 90 00:04:52,440 --> 00:04:54,160 Speaker 1: do some good, but some part of that is selfish. 91 00:04:54,520 --> 00:04:56,840 Speaker 1: It's a great way to motivate yourself and others. So 92 00:04:56,880 --> 00:05:00,560 Speaker 1: that's really fascinating. So you decide you want to participate 93 00:05:00,640 --> 00:05:05,400 Speaker 1: in something more than just a narrow, little research niche 94 00:05:05,600 --> 00:05:10,800 Speaker 1: that has bigger ramifications for friends, family, society, the whole world. 95 00:05:11,240 --> 00:05:15,039 Speaker 1: So from nine you're in Mackenzie. By two thousand and 96 00:05:15,080 --> 00:05:19,040 Speaker 1: eight your name the Ernstein Young New England Biotech Entrepreneur 97 00:05:19,040 --> 00:05:22,360 Speaker 1: of the Year. What took place in that decade between 98 00:05:22,440 --> 00:05:26,960 Speaker 1: Mackenzie and winning that award, Well, a lot of learning. 99 00:05:27,000 --> 00:05:29,120 Speaker 1: So when I went to start a company after a while, 100 00:05:29,160 --> 00:05:32,400 Speaker 1: I really wanted to start something of my own, and 101 00:05:32,520 --> 00:05:35,080 Speaker 1: I found that I was There was a niche which 102 00:05:35,120 --> 00:05:37,719 Speaker 1: is where scientist I would talk to in the lab, 103 00:05:37,960 --> 00:05:41,520 Speaker 1: or biologists or chemists really enjoyed talking to me because 104 00:05:41,520 --> 00:05:45,479 Speaker 1: I wasn't actually one of some enemy camp for a biologist, 105 00:05:45,520 --> 00:05:47,960 Speaker 1: I wasn't a chemist. For chemist, I wasn't a biologist. 106 00:05:47,960 --> 00:05:51,239 Speaker 1: As a physicist, you're sort of like Switzerland, your neutral, 107 00:05:51,440 --> 00:05:53,760 Speaker 1: and everybody is sort of intrigued and talking to you, 108 00:05:53,800 --> 00:05:56,800 Speaker 1: and you can ask everybody interesting questions and learn and 109 00:05:56,800 --> 00:05:58,960 Speaker 1: it's kind of fun. And I found that I was, 110 00:05:59,040 --> 00:06:02,240 Speaker 1: on the one hand, good at having those discussions and 111 00:06:02,279 --> 00:06:05,320 Speaker 1: getting those people fired up about a big goal. On 112 00:06:05,360 --> 00:06:08,080 Speaker 1: the other hand, I was also good at talking to 113 00:06:08,160 --> 00:06:11,960 Speaker 1: people in the finance community. My mother's Israeli. I lived 114 00:06:11,960 --> 00:06:13,760 Speaker 1: in Tel Aviv and we grew up a little bit 115 00:06:13,760 --> 00:06:17,160 Speaker 1: in Israel, and so talking to traders and investors is 116 00:06:17,279 --> 00:06:20,040 Speaker 1: not much different than walking around a shook in Jerusalem 117 00:06:20,120 --> 00:06:23,159 Speaker 1: or Tel Aviv. People are very focused on the immediate, 118 00:06:23,200 --> 00:06:26,080 Speaker 1: what's the bottom line, How is this gonna help me? 119 00:06:27,080 --> 00:06:30,040 Speaker 1: And I found I could bridge those two worlds pretty well, 120 00:06:30,080 --> 00:06:32,760 Speaker 1: and that was actually pretty interesting. And there were all 121 00:06:32,800 --> 00:06:35,279 Speaker 1: these ideas that seemed to be trapped in the lab. 122 00:06:36,000 --> 00:06:38,560 Speaker 1: They could help people, and they were stuck because there 123 00:06:38,600 --> 00:06:41,080 Speaker 1: was no bridge between those two words you mentioned, trapped 124 00:06:41,120 --> 00:06:44,200 Speaker 1: in the lab. There's a chapter and loon shot that 125 00:06:44,400 --> 00:06:49,599 Speaker 1: I love this heading, Engineers of Serendipity. How do people 126 00:06:49,800 --> 00:06:53,640 Speaker 1: in that line of work R and D research labs? 127 00:06:54,160 --> 00:06:58,240 Speaker 1: Why are so many great discoveries, so much fascinating innovation 128 00:06:58,520 --> 00:07:02,160 Speaker 1: and discovery? Why does that stay trapped in a lab? Well, 129 00:07:02,200 --> 00:07:04,120 Speaker 1: you see this all over the place, and we're talking 130 00:07:04,120 --> 00:07:07,719 Speaker 1: a little bit about biology and biotech or research companies, 131 00:07:07,760 --> 00:07:10,880 Speaker 1: but the same thing is true in tech companies and 132 00:07:10,960 --> 00:07:14,680 Speaker 1: engineering companies and product design companies. And the last few months, 133 00:07:14,720 --> 00:07:16,720 Speaker 1: I've actually spent a lot of time with the Department 134 00:07:16,760 --> 00:07:20,480 Speaker 1: of Defense and national security organizations, all of whom have 135 00:07:20,600 --> 00:07:24,480 Speaker 1: the exact same question. Why do good teams keep killing 136 00:07:24,520 --> 00:07:27,720 Speaker 1: great ideas? Why are all these promising things stuck in 137 00:07:27,720 --> 00:07:31,400 Speaker 1: the lab? And part of that is there isn't this bridge. 138 00:07:31,440 --> 00:07:33,840 Speaker 1: There isn't the bridge between those who are focused on 139 00:07:33,880 --> 00:07:37,960 Speaker 1: the core on how do I improve my operations and 140 00:07:38,040 --> 00:07:40,760 Speaker 1: my execution and those who are working on the new 141 00:07:41,120 --> 00:07:43,800 Speaker 1: and that because of two people. There the two types 142 00:07:43,840 --> 00:07:46,520 Speaker 1: of people who work on those things speak different languages, 143 00:07:47,080 --> 00:07:49,280 Speaker 1: They don't understand each other, and they often don't like 144 00:07:49,440 --> 00:07:51,560 Speaker 1: each other. I'll give you an example. So I was 145 00:07:51,920 --> 00:07:55,360 Speaker 1: meeting with the admiral who's responsible for transforming the Navy 146 00:07:55,400 --> 00:07:57,640 Speaker 1: for the twenty first century, and so I was standing 147 00:07:57,640 --> 00:08:00,000 Speaker 1: on a nuclear submarine about thirty ft from the nuclear 148 00:08:00,080 --> 00:08:03,120 Speaker 1: or engine. And this is exactly what's on their mind, 149 00:08:03,160 --> 00:08:05,600 Speaker 1: what was on his mind and his team's mind. If 150 00:08:05,680 --> 00:08:08,760 Speaker 1: you are a hundred miles from shore, deep underwater, you 151 00:08:08,800 --> 00:08:12,200 Speaker 1: don't want to start hearing clanking noises from your nuclear engine. 152 00:08:12,480 --> 00:08:15,520 Speaker 1: That's the core. But at the same time, you don't 153 00:08:15,560 --> 00:08:17,760 Speaker 1: want to be surprised by a new kind of torpedo. 154 00:08:18,520 --> 00:08:22,160 Speaker 1: That's the new. So for companies, for organizations, for teams, 155 00:08:22,560 --> 00:08:24,920 Speaker 1: it's a matter of p and L. You don't want 156 00:08:24,920 --> 00:08:27,680 Speaker 1: to be surprised by your competitor. But for national security 157 00:08:27,760 --> 00:08:31,120 Speaker 1: organizations it's a matter of life and death. But it 158 00:08:31,160 --> 00:08:34,040 Speaker 1: all comes back to that same question, how do we 159 00:08:34,080 --> 00:08:36,360 Speaker 1: balance the core and the new Why do we keep 160 00:08:36,400 --> 00:08:39,200 Speaker 1: having these battles between here these promising ideas and here 161 00:08:39,120 --> 00:08:42,160 Speaker 1: are these people who are shooting them down? And what 162 00:08:42,200 --> 00:08:44,160 Speaker 1: can we do about it? And so that's behind some 163 00:08:44,200 --> 00:08:45,920 Speaker 1: of the research that led to the loon shots and 164 00:08:46,000 --> 00:08:49,679 Speaker 1: loon shots is all about the dichonomy between the franchisees 165 00:08:50,320 --> 00:08:54,680 Speaker 1: and the innovators. Will continue discussing that shortly. So let's 166 00:08:54,720 --> 00:08:57,960 Speaker 1: talk about the early two thousand's when you were launching 167 00:08:58,520 --> 00:09:01,600 Speaker 1: Center UH and up a little bit. How did you 168 00:09:01,640 --> 00:09:05,600 Speaker 1: meet your co founder and partner? Well around the time 169 00:09:05,640 --> 00:09:08,240 Speaker 1: that I was getting kind of antsy, as after a 170 00:09:08,240 --> 00:09:10,240 Speaker 1: few years as a consultant, I wanted to do something. 171 00:09:10,280 --> 00:09:13,160 Speaker 1: I knew that I wanted to work on helping get 172 00:09:13,200 --> 00:09:15,440 Speaker 1: those promising ideas out of the labs and into the 173 00:09:15,440 --> 00:09:18,080 Speaker 1: real world. And so I spent maybe six months, maybe 174 00:09:18,080 --> 00:09:22,240 Speaker 1: a year, going around universities academic labs where I had 175 00:09:22,280 --> 00:09:25,320 Speaker 1: some connections and talking to promising scientists, and one thing 176 00:09:25,360 --> 00:09:29,040 Speaker 1: led to another. Encountered this guy at Harvard at Dana 177 00:09:29,120 --> 00:09:33,440 Speaker 1: Farber Cancer Institute who just was sitting on like this giant, 178 00:09:34,160 --> 00:09:38,640 Speaker 1: you know, untapped mind, really interesting, really promising ideas, And 179 00:09:38,679 --> 00:09:40,920 Speaker 1: I thought, this is a guy who would be fun 180 00:09:40,960 --> 00:09:43,520 Speaker 1: to partner with. And a lot of people, you know, 181 00:09:43,640 --> 00:09:46,640 Speaker 1: listened to him but just missed all this stuff that 182 00:09:46,640 --> 00:09:49,199 Speaker 1: that he was sitting on, not only him, but people 183 00:09:49,240 --> 00:09:51,840 Speaker 1: he collaborated with. So I decided to partner with him 184 00:09:51,840 --> 00:09:55,959 Speaker 1: and start a new company. And this is landbou chan So. 185 00:09:56,240 --> 00:10:00,400 Speaker 1: In the book, there's a fascinating discussion about the broad 186 00:10:00,440 --> 00:10:05,560 Speaker 1: approaches to looking for a drug to either pure disease 187 00:10:05,720 --> 00:10:10,880 Speaker 1: or manage a disease. And one approach is to work backwards. 188 00:10:10,960 --> 00:10:13,360 Speaker 1: We try and figure out what the mechanism of the 189 00:10:13,400 --> 00:10:18,080 Speaker 1: disease is and find something that halts that mechanism. But 190 00:10:18,120 --> 00:10:22,679 Speaker 1: there was another approach, which was people have invented thousands 191 00:10:22,720 --> 00:10:26,600 Speaker 1: upon thousands of different molecules. Let's just run these molecules 192 00:10:26,640 --> 00:10:29,319 Speaker 1: against all these diseases and see what they do. That 193 00:10:29,360 --> 00:10:32,040 Speaker 1: seems to be his approach, Am I am I getting 194 00:10:32,040 --> 00:10:35,480 Speaker 1: that correct? Yeah? He was. I mean, there's a combination 195 00:10:35,559 --> 00:10:37,920 Speaker 1: of two and if you actually just pick one or 196 00:10:37,960 --> 00:10:40,640 Speaker 1: the other, you will do less well. So if you 197 00:10:40,679 --> 00:10:43,920 Speaker 1: start with, well, here's how the protein works, here's what 198 00:10:44,000 --> 00:10:46,400 Speaker 1: it does in the cell, and here's what we need 199 00:10:46,440 --> 00:10:49,280 Speaker 1: to get done. Let's put that on a computer. It 200 00:10:49,400 --> 00:10:53,040 Speaker 1: sounds very logical. It tends to go very badly, really, 201 00:10:53,120 --> 00:10:58,959 Speaker 1: because people don't realize how important serendipity is in drug discovery. 202 00:10:59,000 --> 00:11:03,080 Speaker 1: Almost all of the major drugs and the vast majority 203 00:11:03,080 --> 00:11:07,600 Speaker 1: of the major breakthroughs that we have in science, but 204 00:11:07,600 --> 00:11:11,040 Speaker 1: but especially in drug discovery, there's always some element of 205 00:11:11,080 --> 00:11:14,720 Speaker 1: serendipity because the body is so much more complex. What 206 00:11:14,880 --> 00:11:17,199 Speaker 1: works in a cell dish or what works in a 207 00:11:17,200 --> 00:11:20,920 Speaker 1: academic paper that you published and submit to prestigious journal 208 00:11:21,559 --> 00:11:26,240 Speaker 1: very often is completely different, sometimes exactly the opposite of 209 00:11:26,280 --> 00:11:29,360 Speaker 1: what works in a giant, complex system in a very 210 00:11:29,400 --> 00:11:33,040 Speaker 1: difficult environment. And so I think what I liked about 211 00:11:33,080 --> 00:11:35,440 Speaker 1: his approach and what resonated and the people that he 212 00:11:35,480 --> 00:11:37,600 Speaker 1: worked with in our approach was that we did both. 213 00:11:37,960 --> 00:11:41,800 Speaker 1: We looked on the one hand for the underlying science 214 00:11:41,840 --> 00:11:43,959 Speaker 1: and the rigor, but on the other hand, we balanced 215 00:11:44,000 --> 00:11:47,440 Speaker 1: that with an understanding of how important serendipity is and 216 00:11:47,480 --> 00:11:50,920 Speaker 1: how important surprises are. And one of the things I 217 00:11:50,960 --> 00:11:55,080 Speaker 1: found really fascinating was the approach, and this was in 218 00:11:55,120 --> 00:11:59,839 Speaker 1: particular with cancer, of not just trying one thing that 219 00:12:00,280 --> 00:12:03,680 Speaker 1: might work, put the disease into remission, and then the disease, 220 00:12:04,520 --> 00:12:07,920 Speaker 1: I don't know if it's evolution or just adaptation, eventually 221 00:12:07,960 --> 00:12:11,240 Speaker 1: comes back because it's figured out how to overcome whatever 222 00:12:11,240 --> 00:12:15,160 Speaker 1: that obstacle is. You write in the book about how 223 00:12:15,240 --> 00:12:19,080 Speaker 1: a one to three four approach left hook, right jab, 224 00:12:19,280 --> 00:12:23,600 Speaker 1: an operacot throwing enough stuff at a particular disease not 225 00:12:23,640 --> 00:12:26,240 Speaker 1: only knocks it out, but it prevents it from adapting 226 00:12:26,320 --> 00:12:30,439 Speaker 1: to whatever that therapy is discussed that a little bit. 227 00:12:30,640 --> 00:12:32,920 Speaker 1: I mean, you can imagine it by example with a 228 00:12:32,960 --> 00:12:34,960 Speaker 1: military and sort of a terrorist. Let's you have a 229 00:12:35,000 --> 00:12:37,800 Speaker 1: city that seems to be taken over by terrorists. If 230 00:12:37,880 --> 00:12:40,319 Speaker 1: you have just one approach for how you're going to 231 00:12:40,400 --> 00:12:42,760 Speaker 1: go fight those terrorists, Let's say it's a sniper, or 232 00:12:42,800 --> 00:12:45,240 Speaker 1: it's an air attack or it's a drone attack, they 233 00:12:45,280 --> 00:12:47,400 Speaker 1: will adapt. They will figure out a way to hide, 234 00:12:47,480 --> 00:12:50,640 Speaker 1: they will figure out how to evade that particular angle. 235 00:12:50,920 --> 00:12:53,240 Speaker 1: And so if you want to succeed, you need a 236 00:12:53,240 --> 00:12:55,319 Speaker 1: shock cannot You need to use the army. You need 237 00:12:55,360 --> 00:12:57,240 Speaker 1: to use the air force, you need to use the navy. 238 00:12:57,280 --> 00:12:59,679 Speaker 1: You need to use drones, you need to use snipers, 239 00:12:59,679 --> 00:13:03,240 Speaker 1: you need to use cyber You have all of them 240 00:13:03,400 --> 00:13:05,960 Speaker 1: at once in order to completely wipe it out. And 241 00:13:05,960 --> 00:13:08,440 Speaker 1: it's the same thing with cancer. Cancer is a rogue 242 00:13:08,440 --> 00:13:11,480 Speaker 1: set of cells operating in your body. They're just growing 243 00:13:11,480 --> 00:13:14,520 Speaker 1: out of control. There is an accelerator pedal that's stuck 244 00:13:14,520 --> 00:13:17,040 Speaker 1: and so it just broke, and there's a brake pedal 245 00:13:17,200 --> 00:13:20,160 Speaker 1: that halts the growth that also broke. That's why cancer 246 00:13:20,160 --> 00:13:22,720 Speaker 1: takes decades to develop. You have to have all these 247 00:13:22,720 --> 00:13:25,920 Speaker 1: systems just break down, whether it's with time or because 248 00:13:25,960 --> 00:13:28,839 Speaker 1: you smoke and you're you're you're messing up the cell 249 00:13:28,920 --> 00:13:33,080 Speaker 1: with the carcinogens and smoke. It takes time to accumulate 250 00:13:33,120 --> 00:13:36,120 Speaker 1: these failures. And after the first break system fails and 251 00:13:36,160 --> 00:13:38,640 Speaker 1: the second break system and the backup system fails, and 252 00:13:38,720 --> 00:13:41,920 Speaker 1: this cancer cell starts growing and doubling and growing and doubling. 253 00:13:42,760 --> 00:13:45,160 Speaker 1: And you can't just attack it with just the army 254 00:13:45,559 --> 00:13:47,840 Speaker 1: or just the navy or just the air force. You 255 00:13:47,920 --> 00:13:50,720 Speaker 1: need to come at it from all directions at the 256 00:13:50,760 --> 00:13:53,000 Speaker 1: same time, and then you have a chance. The few 257 00:13:53,040 --> 00:13:55,440 Speaker 1: cancers that we've cured, and there's a small handful of 258 00:13:55,440 --> 00:13:58,800 Speaker 1: cancels that we've cured, that's what we've done. It's almost 259 00:13:58,840 --> 00:14:03,080 Speaker 1: always some combination and shock attack, and it's the right 260 00:14:03,120 --> 00:14:06,360 Speaker 1: combination at the right time that can actually overwhelm the 261 00:14:06,400 --> 00:14:09,240 Speaker 1: cancer and wipe it out. So how far along is 262 00:14:09,360 --> 00:14:14,440 Speaker 1: humanity in during quote unquote cancer, which we know is 263 00:14:14,720 --> 00:14:19,040 Speaker 1: three hundred or so separate diseases to be fair, right, 264 00:14:19,080 --> 00:14:22,800 Speaker 1: And so the reason people in in UH medicine or 265 00:14:22,840 --> 00:14:25,040 Speaker 1: science on talk so much about curing cancer is because 266 00:14:25,040 --> 00:14:28,160 Speaker 1: it is three hundred different diseases. You don't your diabetes 267 00:14:28,240 --> 00:14:30,200 Speaker 1: at the same time as you cure heart attacks at 268 00:14:30,200 --> 00:14:33,600 Speaker 1: the same time as you cure Alzheimer's, because they're really 269 00:14:33,640 --> 00:14:36,360 Speaker 1: different diseases. And cancer is sort of like that. But 270 00:14:36,440 --> 00:14:39,280 Speaker 1: let's say if you call it roughly three hundred different 271 00:14:39,280 --> 00:14:42,320 Speaker 1: types of cancer, I mean it's you know, less than 272 00:14:42,400 --> 00:14:46,200 Speaker 1: ten that we've made such astounding progress on. You know, 273 00:14:46,240 --> 00:14:50,920 Speaker 1: it's childhood leukemias is one example where it used to 274 00:14:50,960 --> 00:14:55,640 Speaker 1: be mortality rate within six months or a year, and 275 00:14:55,720 --> 00:14:58,400 Speaker 1: now it's more like eight or go on to live 276 00:14:58,480 --> 00:15:02,600 Speaker 1: long lives. That's a stounding and you can see we're 277 00:15:02,640 --> 00:15:05,440 Speaker 1: not there yet with many other cancers. But for example, 278 00:15:05,480 --> 00:15:09,520 Speaker 1: when I started almost twenty years ago, melanoma was a 279 00:15:09,560 --> 00:15:12,400 Speaker 1: death sentence. Every time you ran a melanoma trial for 280 00:15:12,480 --> 00:15:16,680 Speaker 1: probably thirty years clinical trial, the placebo arm or the 281 00:15:16,720 --> 00:15:20,720 Speaker 1: standard care arm and the treatment arm would be identical. 282 00:15:21,200 --> 00:15:23,680 Speaker 1: He'd spent two or three or four years. Everybody would 283 00:15:23,720 --> 00:15:26,240 Speaker 1: put in all this time, and it was always the 284 00:15:26,320 --> 00:15:31,560 Speaker 1: same disappointing result. Now almost so many trials are positive. 285 00:15:31,960 --> 00:15:34,880 Speaker 1: Something turned the corner why is that. Why are we 286 00:15:34,920 --> 00:15:39,200 Speaker 1: now more successful with that form of cancer versus others. Well, 287 00:15:39,240 --> 00:15:42,200 Speaker 1: melanoma turned out to be a type of cancer that's 288 00:15:42,320 --> 00:15:44,960 Speaker 1: very sensitive to the immune system, and we still don't 289 00:15:44,960 --> 00:15:47,800 Speaker 1: know why. There's a lot of theories, but it happens 290 00:15:47,840 --> 00:15:51,120 Speaker 1: to be one of not many, but a small number 291 00:15:51,160 --> 00:15:53,560 Speaker 1: of cancer types. And it may be because it's near 292 00:15:53,600 --> 00:15:55,800 Speaker 1: the skin and the skin is so sensitive to the 293 00:15:55,800 --> 00:15:57,840 Speaker 1: immune system. There are a lot of immune cells there. 294 00:15:58,000 --> 00:16:01,160 Speaker 1: Because when something lands on your skin, you or your cut, 295 00:16:01,560 --> 00:16:03,880 Speaker 1: you have a cut, the immune system rushes over. So 296 00:16:03,920 --> 00:16:07,560 Speaker 1: it may be that melanoma is a special case. But 297 00:16:07,840 --> 00:16:11,680 Speaker 1: in it what was fantastic is when maybe seven eight, 298 00:16:11,800 --> 00:16:16,000 Speaker 1: nine years ago the first trials started showing dramatic effect. 299 00:16:16,400 --> 00:16:19,640 Speaker 1: It's not a cure, but we are so much better 300 00:16:20,160 --> 00:16:24,040 Speaker 1: in that disease. There was a lot of serendipity there. 301 00:16:24,640 --> 00:16:27,520 Speaker 1: Like even the work on the immune system, that's a 302 00:16:27,600 --> 00:16:31,280 Speaker 1: hundred year old idea. People notice that a long time 303 00:16:31,320 --> 00:16:34,840 Speaker 1: ago that sometimes when the immune system is activated, cancers 304 00:16:34,880 --> 00:16:37,920 Speaker 1: go away. You you sneeze on people if you just 305 00:16:38,160 --> 00:16:40,080 Speaker 1: if your treatment is, hey, let's just sneeze on this 306 00:16:40,120 --> 00:16:43,240 Speaker 1: cancer patient. Right, Five out of a hundred or ten 307 00:16:43,280 --> 00:16:45,400 Speaker 1: out of a hundred might have complete remissions of their 308 00:16:45,400 --> 00:16:48,760 Speaker 1: two People knew that. People knew that, they knew that. Actually, 309 00:16:48,880 --> 00:16:53,360 Speaker 1: fifty sixty years ago from the clinics for tuberculosis, we 310 00:16:53,400 --> 00:16:55,800 Speaker 1: don't have those that anymore. These clinics people who had 311 00:16:55,800 --> 00:16:59,440 Speaker 1: tuberculosis used to get sent to, but people who had melanoma, 312 00:16:59,680 --> 00:17:02,880 Speaker 1: and we go to these tuberculous as clinics, a surprisingly 313 00:17:02,960 --> 00:17:06,520 Speaker 1: high percentage of them their cancers would disappear. And that's 314 00:17:06,560 --> 00:17:09,160 Speaker 1: because they were like fighting this infection and the immune 315 00:17:09,160 --> 00:17:12,160 Speaker 1: system was activated. So we still don't understand the signs 316 00:17:12,200 --> 00:17:14,280 Speaker 1: of that. But you said, how far are we We're 317 00:17:14,320 --> 00:17:17,000 Speaker 1: at the beginning of the exponential curve. What's happened is 318 00:17:17,240 --> 00:17:20,320 Speaker 1: about fifteen or twenty years ago we got the tools, 319 00:17:20,400 --> 00:17:23,919 Speaker 1: we sequenced the genome and the biotech knowledgy kind of 320 00:17:24,119 --> 00:17:27,840 Speaker 1: revolution of years ago that allowed us to grow proteins 321 00:17:27,880 --> 00:17:32,080 Speaker 1: in the lab and make those a commercial scale, create 322 00:17:32,119 --> 00:17:35,600 Speaker 1: a new set of tools, and we're just starting. It's 323 00:17:35,640 --> 00:17:39,800 Speaker 1: really just the beginning of using those amazing tools to 324 00:17:39,920 --> 00:17:43,119 Speaker 1: do things like one of those things is getting the 325 00:17:43,119 --> 00:17:46,360 Speaker 1: immune system to fight fight tums. And like I said, 326 00:17:46,400 --> 00:17:48,480 Speaker 1: there was about a hundred years of trying to do that, 327 00:17:48,600 --> 00:17:51,639 Speaker 1: and none of the standard approaches work, and then no 328 00:17:51,720 --> 00:17:55,399 Speaker 1: one exactly knows why someone tried a slightly different angle. 329 00:17:55,480 --> 00:17:58,960 Speaker 1: Let's target this protein, not that immune protein. Boom. It worked, 330 00:17:59,680 --> 00:18:02,199 Speaker 1: and and it wasn't even the original protein that the 331 00:18:02,280 --> 00:18:05,439 Speaker 1: scientists had been thinking about would be the key to 332 00:18:05,520 --> 00:18:07,840 Speaker 1: it all for many many years. Even the scientist who 333 00:18:07,840 --> 00:18:09,359 Speaker 1: win the Nobel Prize, they thought it would be this 334 00:18:09,440 --> 00:18:12,080 Speaker 1: other thing that would be the key. It turns out 335 00:18:12,080 --> 00:18:15,040 Speaker 1: it was this thing, and nobody knew. So there's so much. 336 00:18:16,359 --> 00:18:21,040 Speaker 1: It's really the combination of strong science and an appreciation 337 00:18:21,080 --> 00:18:23,880 Speaker 1: of serendipity. When you can put those two things together, 338 00:18:24,359 --> 00:18:27,280 Speaker 1: the magic can happen. So taking where we are in 339 00:18:27,320 --> 00:18:30,119 Speaker 1: that J curve, starting that that ramp up of the 340 00:18:30,200 --> 00:18:34,119 Speaker 1: hockey stick. UH, is it a fair question to ask, 341 00:18:34,680 --> 00:18:39,520 Speaker 1: will we see cancer the Emperor of maladies? To borrow 342 00:18:39,560 --> 00:18:42,600 Speaker 1: the book title, UH, cured in our lifetime? I think 343 00:18:42,640 --> 00:18:45,560 Speaker 1: we will see certain cancers we've seen three or four 344 00:18:45,640 --> 00:18:50,479 Speaker 1: or five go, you know, well beyond of people who 345 00:18:50,560 --> 00:18:53,640 Speaker 1: are diagnosed with it have more than five or sometimes 346 00:18:53,680 --> 00:18:57,679 Speaker 1: more than ten years survival. So by any reasonable definition, 347 00:18:57,720 --> 00:19:00,960 Speaker 1: you could call that a cure. And slowly, we're getting 348 00:19:01,000 --> 00:19:03,600 Speaker 1: better and better. The survival times in melanoma I have 349 00:19:03,680 --> 00:19:07,920 Speaker 1: gotten longer and longer multiple mileoma. You know, twenty years 350 00:19:07,960 --> 00:19:11,880 Speaker 1: ago that was a death sentence. There was nothing. And then, 351 00:19:11,880 --> 00:19:15,560 Speaker 1: in an almost serendipitous event, there was a woman here 352 00:19:15,600 --> 00:19:18,840 Speaker 1: in New York who was thirty one who called a 353 00:19:18,960 --> 00:19:20,960 Speaker 1: doctor that I actually I used to work with in 354 00:19:21,160 --> 00:19:24,240 Speaker 1: Boston named Judah Folkman, who had this kind of wild, 355 00:19:24,359 --> 00:19:28,800 Speaker 1: crazy idea, Hey, let's fight tumors by blocking blood vessels 356 00:19:28,800 --> 00:19:31,800 Speaker 1: to the tumors. And everybody thought that was the dumbest 357 00:19:31,840 --> 00:19:36,359 Speaker 1: idea they've ever heard, really anti angiogenesis exactly. At the time, 358 00:19:36,880 --> 00:19:40,760 Speaker 1: everybody knew that the only way to fight tumors was 359 00:19:40,840 --> 00:19:44,080 Speaker 1: to bomb them, would keep poison them with chemotherapy like 360 00:19:44,119 --> 00:19:47,440 Speaker 1: a giant bomb, or burn them with radiation. The idea 361 00:19:47,480 --> 00:19:50,639 Speaker 1: that there were these subtle signals that tumors were like 362 00:19:50,760 --> 00:19:53,200 Speaker 1: homes and they were laying down pipes, and if you 363 00:19:53,200 --> 00:19:55,359 Speaker 1: could block those pipes, you could slow the growth of 364 00:19:55,400 --> 00:19:58,879 Speaker 1: those homes. That was considered radical. Judah was asked to 365 00:19:59,359 --> 00:20:01,520 Speaker 1: There was a spe shall committee at his hospital that 366 00:20:01,640 --> 00:20:03,800 Speaker 1: was asked to evaluate his work because it was so 367 00:20:03,880 --> 00:20:06,920 Speaker 1: controversial and it said, well, we don't think there's any 368 00:20:07,000 --> 00:20:10,280 Speaker 1: value here. You need to resign, really stop what you're doing. 369 00:20:10,440 --> 00:20:14,680 Speaker 1: That's astonishing NY. And how did that progress from there? Well, 370 00:20:14,720 --> 00:20:17,240 Speaker 1: he was I remember he was talking to some post 371 00:20:17,320 --> 00:20:19,440 Speaker 1: docs and student who said who was like, oh, I'm 372 00:20:19,440 --> 00:20:22,119 Speaker 1: really depressed. You know, my paper was rejected. And he 373 00:20:22,160 --> 00:20:25,280 Speaker 1: said to her said, well, if you want to understand rejection, 374 00:20:25,320 --> 00:20:27,440 Speaker 1: come to my office. I will show you pink slips 375 00:20:27,640 --> 00:20:31,919 Speaker 1: with the word clown on them. Right, thirty two years 376 00:20:31,960 --> 00:20:36,200 Speaker 1: after he first suggested his idea, on a stage in Chicago, 377 00:20:36,720 --> 00:20:41,600 Speaker 1: a guy got up, a scientist got up, packed auditorium two, 378 00:20:42,840 --> 00:20:46,119 Speaker 1: present the data from the largest study ever run to 379 00:20:46,200 --> 00:20:50,080 Speaker 1: that date, and patients with advanced calling cancer pressed a button, 380 00:20:50,480 --> 00:20:53,840 Speaker 1: flipped the slide. Patients who had received the drug based 381 00:20:53,840 --> 00:20:58,200 Speaker 1: on Judah's ideas lived longer on average, and anyone had 382 00:20:58,320 --> 00:21:01,000 Speaker 1: ever lived with advance It's calling cancer. It was like 383 00:21:01,000 --> 00:21:04,439 Speaker 1: a standing ovation. This was thirty two years after Judah 384 00:21:04,440 --> 00:21:07,359 Speaker 1: first suggested his ideas. It was a standing ovation. I 385 00:21:07,359 --> 00:21:11,280 Speaker 1: remember a speaker said something like, if only Dr Folkman 386 00:21:11,359 --> 00:21:15,960 Speaker 1: were alive to see this, And Judah was actually sitting 387 00:21:15,960 --> 00:21:18,080 Speaker 1: in the back, you know, and turned to his neighbor 388 00:21:18,119 --> 00:21:21,399 Speaker 1: and smiled, and and he didn't raise his hand and 389 00:21:21,480 --> 00:21:23,960 Speaker 1: say still here, I'm still here. No, but it was 390 00:21:24,040 --> 00:21:27,000 Speaker 1: just and you love telling that story, but it's uh 391 00:21:27,040 --> 00:21:32,160 Speaker 1: an example of all these these ideas that are dismissed 392 00:21:32,200 --> 00:21:34,720 Speaker 1: as crazy and so many things that we take for 393 00:21:34,800 --> 00:21:38,240 Speaker 1: granted today, even Facebook. If you remember when Zuckerberg was 394 00:21:38,240 --> 00:21:40,240 Speaker 1: going around to raise money for it in two thousand 395 00:21:40,240 --> 00:21:44,000 Speaker 1: and four, every social network had failed. My Space was 396 00:21:44,080 --> 00:21:47,200 Speaker 1: just failing. Friends to well, my Space was friends to 397 00:21:47,359 --> 00:21:51,800 Speaker 1: was just failing. And every every investment, almost all the 398 00:21:51,840 --> 00:21:55,080 Speaker 1: intelligent investors, said well, there's no money. Social networks can't 399 00:21:55,119 --> 00:21:57,840 Speaker 1: make any money because they're just fats. Everybody goes to 400 00:21:57,880 --> 00:22:02,200 Speaker 1: the next one. Well cool, well they were Well one 401 00:22:02,240 --> 00:22:04,360 Speaker 1: investor took a look at that and he said, well 402 00:22:04,440 --> 00:22:06,240 Speaker 1: is that really true? And you know that was Peter 403 00:22:06,320 --> 00:22:09,080 Speaker 1: till and a five thousand dollar check and sold it 404 00:22:09,119 --> 00:22:11,280 Speaker 1: eight years later for a billion dollars. Not not a 405 00:22:11,280 --> 00:22:13,600 Speaker 1: bad return. But it was the same thing with so 406 00:22:13,640 --> 00:22:16,399 Speaker 1: many of these things. Search everybody said, oh, you're crazy, 407 00:22:16,400 --> 00:22:21,480 Speaker 1: you can't make it. Was Google and everybody said, you 408 00:22:21,480 --> 00:22:23,560 Speaker 1: can't make any money. It's just like a Yellow Pages. 409 00:22:23,640 --> 00:22:26,040 Speaker 1: There's no money in yellow pages. Come on, give us 410 00:22:26,080 --> 00:22:28,919 Speaker 1: a break. One of my favorite books is William Goldman's 411 00:22:28,960 --> 00:22:34,840 Speaker 1: Adventures UH in Screenwriting, and he talks about nobody knows 412 00:22:34,920 --> 00:22:38,280 Speaker 1: anything in Hollywood and the list of of movies that 413 00:22:38,359 --> 00:22:41,920 Speaker 1: were passed over by every studio from E T two 414 00:22:41,920 --> 00:22:45,360 Speaker 1: Star Wars, two Raiders of the Law, like one amazing 415 00:22:45,400 --> 00:22:48,239 Speaker 1: movie after another nobody wanted anything to do with. One 416 00:22:48,240 --> 00:22:50,679 Speaker 1: of my favorite is the James Bond story. Ian Fleming 417 00:22:50,680 --> 00:22:53,320 Speaker 1: when he wrote this, you know, I wrote this book 418 00:22:53,359 --> 00:22:56,159 Speaker 1: that became reasonably popular but not a huge success. He 419 00:22:56,320 --> 00:22:58,359 Speaker 1: really wanted that kind of life that, you know, the 420 00:22:58,440 --> 00:23:02,960 Speaker 1: James Bond, and every studio nine years just said, are 421 00:23:02,960 --> 00:23:07,520 Speaker 1: you kidding a metrosexual British spy who saves the world. 422 00:23:07,560 --> 00:23:09,800 Speaker 1: No American audience is going to go for that. And 423 00:23:09,880 --> 00:23:11,680 Speaker 1: he finally kind of gave up, gave it to these 424 00:23:11,680 --> 00:23:15,560 Speaker 1: two sort of not very reputable producers, and they the 425 00:23:16,119 --> 00:23:19,920 Speaker 1: studio that finally took it, had so little confidence. They said, 426 00:23:19,920 --> 00:23:22,119 Speaker 1: who is this actor in here? Used to drive a 427 00:23:22,160 --> 00:23:24,200 Speaker 1: milk truck. He's been in like two movies. His name 428 00:23:24,200 --> 00:23:27,320 Speaker 1: is Sean what Connery? What? No way, No one's gonna 429 00:23:27,440 --> 00:23:29,960 Speaker 1: They opened it in to drive in studios in Texas 430 00:23:29,960 --> 00:23:32,960 Speaker 1: and Oklahoma, and of course it became the most successful, 431 00:23:33,040 --> 00:23:36,520 Speaker 1: longest running film franchise in history. So most of these 432 00:23:36,520 --> 00:23:39,520 Speaker 1: things that we take for granted today began life as 433 00:23:39,520 --> 00:23:42,080 Speaker 1: a loon shot. And one of the things, one of 434 00:23:42,119 --> 00:23:44,520 Speaker 1: the lessons that you learn when you work on these 435 00:23:44,520 --> 00:23:47,760 Speaker 1: things is you need to expect the three deaths, the 436 00:23:47,840 --> 00:23:52,800 Speaker 1: deaths that they will die several times. Quite fascinating. Let's 437 00:23:52,880 --> 00:23:57,080 Speaker 1: talk a little bit about life at a pharmaceutical company 438 00:23:57,200 --> 00:24:02,439 Speaker 1: or a biotech company you mentioned, and how often things 439 00:24:02,560 --> 00:24:08,159 Speaker 1: are overlooked, How how many times drugs and ideas and 440 00:24:08,240 --> 00:24:12,160 Speaker 1: concepts have to fail. How frustrating is it for researchers 441 00:24:12,240 --> 00:24:16,800 Speaker 1: to constantly be dealing with failure. It's worse than being 442 00:24:17,080 --> 00:24:20,640 Speaker 1: a hitter in baseball if you hit three hundred and pharmaceuticals, 443 00:24:21,040 --> 00:24:24,399 Speaker 1: that's crazy. It's like a nincent failure rate, isn't it. 444 00:24:24,480 --> 00:24:28,159 Speaker 1: That's right, It's very you know, it's a combination of 445 00:24:28,240 --> 00:24:32,000 Speaker 1: two things. On the one hand, there's this enormous motivation 446 00:24:32,280 --> 00:24:36,280 Speaker 1: because everybody has someone who is touched by a severe disease, 447 00:24:36,359 --> 00:24:41,080 Speaker 1: and the idea that you're working every day to make 448 00:24:41,119 --> 00:24:45,080 Speaker 1: a difference, that idea if something you do contributes to people. 449 00:24:45,400 --> 00:24:48,000 Speaker 1: Anybody who's lost a family member or a loved one, 450 00:24:48,560 --> 00:24:52,199 Speaker 1: how much do they wish they had more time with them? Right? 451 00:24:52,280 --> 00:24:57,119 Speaker 1: And that's pretty much everybody. And that's what we can do, 452 00:24:57,200 --> 00:24:59,280 Speaker 1: and that's what we're on the cusp of doing with 453 00:24:59,359 --> 00:25:03,320 Speaker 1: science and medicine. So being part of that is incredibly exciting. 454 00:25:03,440 --> 00:25:07,520 Speaker 1: It's the advantage of it. It's unlike almost any other 455 00:25:08,400 --> 00:25:11,639 Speaker 1: industry or field because you feel like you could do 456 00:25:11,720 --> 00:25:16,040 Speaker 1: something that just affects millions if you do it well. 457 00:25:16,760 --> 00:25:18,720 Speaker 1: The flip side of that is, as you say, the 458 00:25:18,760 --> 00:25:23,440 Speaker 1: failure rate is astounding. You know, cent of drugs that 459 00:25:23,560 --> 00:25:26,280 Speaker 1: start clinical trials won't make it to the end, and 460 00:25:26,320 --> 00:25:28,800 Speaker 1: so you just have to accept that there's the benefits 461 00:25:28,800 --> 00:25:31,000 Speaker 1: and there's the cost, is that there's a very high 462 00:25:31,400 --> 00:25:34,440 Speaker 1: failure rate. Was it a ciro endo who said it's 463 00:25:34,480 --> 00:25:36,800 Speaker 1: not a good drug unless it's been killed three times? 464 00:25:36,880 --> 00:25:38,879 Speaker 1: Is that right? And I was actually, Uh. We had 465 00:25:38,920 --> 00:25:42,480 Speaker 1: an advisor, a guy named Jim Black, Sir James Black, 466 00:25:42,520 --> 00:25:45,760 Speaker 1: who won the Nobel Prize for developing two of the 467 00:25:45,800 --> 00:25:50,440 Speaker 1: most important drugs drug categories in the twentieth century, of 468 00:25:50,480 --> 00:25:53,719 Speaker 1: beta blockers, and histamine antagonists. Anyways, he was in his 469 00:25:53,840 --> 00:25:56,639 Speaker 1: eighties when I got introduced, and he loved what we 470 00:25:56,640 --> 00:25:59,080 Speaker 1: were doing at this little biotech company. Reminded him, I 471 00:25:59,080 --> 00:26:02,040 Speaker 1: guess of how he went his approach and philosophy to 472 00:26:02,560 --> 00:26:07,159 Speaker 1: discovering new drugs. And he would fly over from Scotland 473 00:26:07,800 --> 00:26:09,720 Speaker 1: every now and then and meet with our team and 474 00:26:09,760 --> 00:26:12,399 Speaker 1: advise us. And I remember one night he came, it 475 00:26:12,480 --> 00:26:14,760 Speaker 1: was like eight am, it was like seven pm. We 476 00:26:14,800 --> 00:26:18,000 Speaker 1: sent everybody home for dinner, and we went to dinner, 477 00:26:18,000 --> 00:26:19,920 Speaker 1: and then it was like nine or ten pm. Everybody 478 00:26:19,960 --> 00:26:23,119 Speaker 1: went and I was like exhausted, and he was like, Safi, 479 00:26:23,200 --> 00:26:26,520 Speaker 1: Saffie stayed with me, and have another drink, have some whiskey. 480 00:26:26,560 --> 00:26:28,359 Speaker 1: And I was like, how does this eighty two year 481 00:26:28,400 --> 00:26:31,920 Speaker 1: old guy five three thou miles talk all day long? 482 00:26:32,119 --> 00:26:34,080 Speaker 1: So I we started chatting and I tell him on 483 00:26:34,240 --> 00:26:37,240 Speaker 1: I'm feeling kind of down because this very promising project 484 00:26:37,720 --> 00:26:39,639 Speaker 1: we had in the lab I was really excited about 485 00:26:39,680 --> 00:26:42,879 Speaker 1: and it just like didn't work negative data. And he 486 00:26:42,960 --> 00:26:46,200 Speaker 1: leans over to me Pat's money and says, Safie, it's 487 00:26:46,240 --> 00:26:48,680 Speaker 1: not a good drug unless it's been killed three times. 488 00:26:49,600 --> 00:26:51,200 Speaker 1: And that stayed with me and I mentioned so to 489 00:26:51,240 --> 00:26:53,840 Speaker 1: earlier expect the three deaths. And that's what you see 490 00:26:54,440 --> 00:26:58,879 Speaker 1: so often with really promising ideas, that you need to 491 00:26:59,000 --> 00:27:02,280 Speaker 1: expect the fact that you're going to get punches in 492 00:27:02,320 --> 00:27:05,840 Speaker 1: the stomach many times. Maybe it's three times, maybe it's 493 00:27:05,840 --> 00:27:08,760 Speaker 1: four times, maybe it's ten times. If it's a really 494 00:27:08,800 --> 00:27:13,000 Speaker 1: important idea, it will have those big punches in the stomach. 495 00:27:13,359 --> 00:27:16,720 Speaker 1: So let's talk about something that was punched repeatedly, a 496 00:27:16,840 --> 00:27:20,720 Speaker 1: less glamle. That's right, So tell us about that drug. 497 00:27:21,080 --> 00:27:25,320 Speaker 1: How was that molecule uncovered and how did it actually 498 00:27:25,359 --> 00:27:30,000 Speaker 1: work to arrest certain cancers? Well, I wish it had 499 00:27:30,040 --> 00:27:33,040 Speaker 1: a better ending. So it had some very promising early 500 00:27:33,119 --> 00:27:35,760 Speaker 1: data like a lot of drugs, and then in later 501 00:27:35,840 --> 00:27:39,240 Speaker 1: stage trials it didn't work like a lot of drugs, 502 00:27:39,520 --> 00:27:43,639 Speaker 1: and so it sounds like a great story. What was 503 00:27:43,800 --> 00:27:46,359 Speaker 1: exciting about it is that it did uncover a totally 504 00:27:46,400 --> 00:27:50,119 Speaker 1: new mechanism, which was what So it targets the way 505 00:27:50,560 --> 00:27:54,840 Speaker 1: cancer cells breathe, So it targets and fire. When you 506 00:27:54,880 --> 00:27:59,600 Speaker 1: say breathe, you mean consume oxygensume oxygen, so the mitochondria 507 00:28:00,000 --> 00:28:04,040 Speaker 1: and so it specifically went into the mitochondria and specifically 508 00:28:04,080 --> 00:28:07,760 Speaker 1: targeted a part of the mitochondria in a very unusual way, 509 00:28:08,840 --> 00:28:12,800 Speaker 1: right exactly, And it did so in a way that 510 00:28:13,320 --> 00:28:16,280 Speaker 1: nobody had ever thought about doing before. And we found 511 00:28:16,320 --> 00:28:18,840 Speaker 1: it because it was a screen. Meaning you take this 512 00:28:19,000 --> 00:28:23,840 Speaker 1: library of chemicals and you don't the really unusual structures, 513 00:28:23,920 --> 00:28:28,439 Speaker 1: and we happened to, through all sorts of long back stories, 514 00:28:28,680 --> 00:28:31,680 Speaker 1: have a collection of chemical structures that nobody else had. 515 00:28:31,800 --> 00:28:35,000 Speaker 1: Your partner was funded by Michael Milken, anyone out and 516 00:28:35,040 --> 00:28:39,080 Speaker 1: bought thousands of drugs from all around the world, including 517 00:28:39,520 --> 00:28:44,320 Speaker 1: Soviet Russia exactly. And so in fact, that's a fascinating story. 518 00:28:44,520 --> 00:28:47,920 Speaker 1: Milkin gets colon cancer, writes a four million dollar check 519 00:28:48,440 --> 00:28:51,400 Speaker 1: to your partner, who says, I know there are all 520 00:28:51,400 --> 00:28:54,360 Speaker 1: these chemicals and labs that nobody's doing with, send me 521 00:28:54,400 --> 00:28:57,640 Speaker 1: some vials from the Soviet Union. That's exactly what happened. 522 00:28:57,640 --> 00:29:00,840 Speaker 1: This was actually a Russian military lab, were just collecting 523 00:29:00,880 --> 00:29:04,960 Speaker 1: soil samples and they at the time, well thirty years ago, 524 00:29:05,120 --> 00:29:08,360 Speaker 1: people didn't really think about, oh, what's the value of 525 00:29:08,400 --> 00:29:13,800 Speaker 1: having these unknown, uncategorized chemical structures. Today we understand that 526 00:29:14,360 --> 00:29:19,520 Speaker 1: just the pure science logical, deductive reductionist reasoning of here's 527 00:29:19,560 --> 00:29:22,160 Speaker 1: the protein, and here's what it looks like, and let's 528 00:29:22,200 --> 00:29:25,800 Speaker 1: target it on the computer. Or something just misses a ton, 529 00:29:26,280 --> 00:29:30,080 Speaker 1: and sometimes having a random soil sample that somebody picked 530 00:29:30,160 --> 00:29:33,840 Speaker 1: up can contain something that you never expected. I think 531 00:29:33,880 --> 00:29:40,080 Speaker 1: your partner described that chemical as something some engineer, some 532 00:29:40,160 --> 00:29:42,920 Speaker 1: chemists made in a lab for fun and then put 533 00:29:42,960 --> 00:29:45,360 Speaker 1: away and never thought of. Again, that's right, and so 534 00:29:45,400 --> 00:29:48,840 Speaker 1: many of these discoveries kind of begin like that. And 535 00:29:48,920 --> 00:29:52,320 Speaker 1: so then this is where you allow room for serendipity. 536 00:29:52,640 --> 00:29:54,520 Speaker 1: You say, let's just take what we have and see 537 00:29:54,560 --> 00:29:57,440 Speaker 1: what happens. And then we saw this signal from this molecule, 538 00:29:57,520 --> 00:30:01,120 Speaker 1: which was really surprising. Nobody expected to see this strong 539 00:30:01,240 --> 00:30:04,320 Speaker 1: signal in the lab and a cell cultured dish that 540 00:30:04,440 --> 00:30:09,720 Speaker 1: this molecule acted in this unusual way, and we said, well, 541 00:30:09,720 --> 00:30:12,240 Speaker 1: why is it doing that. We actually couldn't figure it out. 542 00:30:12,280 --> 00:30:16,280 Speaker 1: It took almost twenty years to understand how it was working. 543 00:30:16,640 --> 00:30:20,120 Speaker 1: And while that might sound sort of surprising today, because 544 00:30:20,120 --> 00:30:22,280 Speaker 1: today there's this idea that you should be able to 545 00:30:22,360 --> 00:30:26,240 Speaker 1: understand everything right away, and that's actually the reverse. That 546 00:30:26,280 --> 00:30:29,120 Speaker 1: actually is a very bad idea that can really hurt 547 00:30:29,440 --> 00:30:32,040 Speaker 1: innovation and create the solution for us figure out why 548 00:30:32,040 --> 00:30:35,120 Speaker 1: it works later. Almost so many of the drugs we 549 00:30:35,240 --> 00:30:37,959 Speaker 1: used to, like even the anty, almost everything people use 550 00:30:38,040 --> 00:30:41,880 Speaker 1: in the brain, whether it's antidepressants or anesthetics, they were 551 00:30:41,960 --> 00:30:44,720 Speaker 1: discovered by accident and it took sometimes a hundred years. 552 00:30:44,920 --> 00:30:47,720 Speaker 1: With an aspirint, nobody knew how that worked, probably for 553 00:30:47,760 --> 00:30:51,960 Speaker 1: close to a hundred years. So, you know, anesthesi was 554 00:30:52,000 --> 00:30:54,280 Speaker 1: discovered because there was some you know, young guy in 555 00:30:54,320 --> 00:30:56,240 Speaker 1: the lab who had a chemical and he kind of 556 00:30:56,240 --> 00:30:58,080 Speaker 1: put it on his finger and he put it on 557 00:30:58,120 --> 00:31:01,160 Speaker 1: his tongue, which you don't do any more, and then 558 00:31:01,160 --> 00:31:03,400 Speaker 1: his tongue went nomin He was like, that's weird, and 559 00:31:03,440 --> 00:31:05,600 Speaker 1: then he kind of explored that, and that became like 560 00:31:05,640 --> 00:31:08,560 Speaker 1: one of the major classes of anesthetics. Wait, we don't 561 00:31:08,600 --> 00:31:10,480 Speaker 1: just you know, stick, we don't we don't just lick 562 00:31:10,560 --> 00:31:15,280 Speaker 1: new chemicals to see what the results. Generally, generally you 563 00:31:15,320 --> 00:31:18,920 Speaker 1: don't lick the cultured edition the lab probably probably a 564 00:31:19,000 --> 00:31:23,440 Speaker 1: smart smart approach um that that that's quite fascinating. And 565 00:31:23,560 --> 00:31:29,120 Speaker 1: to wrap up a less glamal, somehow the copper gets 566 00:31:29,120 --> 00:31:33,680 Speaker 1: into the nucleus of the cancer cell and essentially burns it. 567 00:31:33,840 --> 00:31:38,040 Speaker 1: It blocks the mitochondria from firing, so it basically messes 568 00:31:38,160 --> 00:31:41,640 Speaker 1: up the battery of which is sort of the breathing 569 00:31:41,640 --> 00:31:44,840 Speaker 1: engine or the battery the mitochondria of a cancer cell. 570 00:31:44,920 --> 00:31:47,360 Speaker 1: It took twenty years to understand how that really works. 571 00:31:47,400 --> 00:31:52,400 Speaker 1: So one would think that might be effective at fighting cancer. Yeah, 572 00:31:52,440 --> 00:31:55,520 Speaker 1: and so we ran a couple of clinical trials and 573 00:31:55,680 --> 00:31:58,360 Speaker 1: some worked and unfortunately the large one that we did 574 00:31:58,360 --> 00:32:01,840 Speaker 1: in metastatic melanoma didn't work, so we had to halt 575 00:32:01,880 --> 00:32:05,160 Speaker 1: development on that drug. Now I understand that this is 576 00:32:05,200 --> 00:32:08,440 Speaker 1: now ten or fifteen years later, there are still people studying, 577 00:32:08,560 --> 00:32:11,120 Speaker 1: in fact their papers coming out on it. So maybe 578 00:32:11,480 --> 00:32:13,640 Speaker 1: someone will pick it up in the future and use 579 00:32:13,720 --> 00:32:16,040 Speaker 1: either the understanding of the science this is a new 580 00:32:16,080 --> 00:32:19,800 Speaker 1: way to target cancer drugs or that kind of molecular 581 00:32:19,840 --> 00:32:22,640 Speaker 1: related molecules in the future. But what we did was 582 00:32:22,680 --> 00:32:26,360 Speaker 1: a first step. We had uncovered a new way to 583 00:32:26,440 --> 00:32:28,680 Speaker 1: kind of block the battery of the Kansas cell, which 584 00:32:28,720 --> 00:32:31,280 Speaker 1: really hadn't been talked about much before. And to wrap 585 00:32:31,360 --> 00:32:34,880 Speaker 1: up with Senta, you you left Center after thirteen years 586 00:32:35,680 --> 00:32:38,360 Speaker 1: the company had gone I p O. What is the 587 00:32:38,400 --> 00:32:41,840 Speaker 1: state of Center today? Sent emerged with another company. It's 588 00:32:41,880 --> 00:32:44,800 Speaker 1: publicly traded. There's a different management team and board, and 589 00:32:45,520 --> 00:32:49,160 Speaker 1: so it's going strong move. It moved to Nash, a 590 00:32:49,200 --> 00:32:53,240 Speaker 1: different disease, a different therapeutic careers. Quite fascinating. I found 591 00:32:53,240 --> 00:32:55,360 Speaker 1: the book to be very fascinating. It was a really 592 00:32:55,360 --> 00:32:59,440 Speaker 1: interesting read. But one of the themes that keeps recurring 593 00:32:59,720 --> 00:33:05,280 Speaker 1: is the ongoing battle in institutions between the franchise ers 594 00:33:05,320 --> 00:33:08,479 Speaker 1: and the innovators. Explain that, sure, you can think of 595 00:33:08,640 --> 00:33:12,920 Speaker 1: whenever you organize people into a group with a mission 596 00:33:12,920 --> 00:33:15,440 Speaker 1: and a reward system tied to that mission, you have 597 00:33:15,840 --> 00:33:18,160 Speaker 1: just created two different kind of what you can think 598 00:33:18,160 --> 00:33:21,880 Speaker 1: of as phases of human organization, and it's the same 599 00:33:21,920 --> 00:33:24,560 Speaker 1: thing as whenever you Then this will sound crazy, but 600 00:33:24,600 --> 00:33:27,000 Speaker 1: like this glass of water that's sitting in front of me, 601 00:33:27,280 --> 00:33:31,000 Speaker 1: whenever you put molecules together, they can exist in two phases. 602 00:33:31,040 --> 00:33:33,480 Speaker 1: One is liquid where they're all slashing around, and one 603 00:33:33,560 --> 00:33:36,840 Speaker 1: is solid where they're totally rigid. But it's exactly the 604 00:33:36,920 --> 00:33:40,160 Speaker 1: same molecules. The behaviors are totally different, and the same 605 00:33:40,160 --> 00:33:42,480 Speaker 1: thing is true with teams and companies. You can have 606 00:33:42,560 --> 00:33:46,760 Speaker 1: the same people organized into a group, whether mission and 607 00:33:46,840 --> 00:33:49,280 Speaker 1: reward system tied to that mission, and you will find 608 00:33:49,280 --> 00:33:51,920 Speaker 1: there two different phases. There's one let's say a small 609 00:33:52,000 --> 00:33:56,880 Speaker 1: company ten people. In that phase, everybody's steak and outcome 610 00:33:56,960 --> 00:34:00,960 Speaker 1: is huge, and they will unite around the crazy ideas, 611 00:34:00,960 --> 00:34:03,840 Speaker 1: and when they stumble, they'll go and rescue them. The 612 00:34:03,960 --> 00:34:06,080 Speaker 1: perks of rank, which is sort of the other force 613 00:34:06,160 --> 00:34:08,359 Speaker 1: that that's pulling out them, is tiny compared to that 614 00:34:08,800 --> 00:34:11,840 Speaker 1: incredible steak and outcome. If their drug or their movie 615 00:34:11,840 --> 00:34:14,560 Speaker 1: project works at a ten percent company, they're gonna be 616 00:34:14,560 --> 00:34:19,480 Speaker 1: heroes and millionaires forever. But doesn't they're unemployed. Now, imagine 617 00:34:19,520 --> 00:34:25,360 Speaker 1: the same people inside a much larger company ten thousand, 618 00:34:24,560 --> 00:34:27,600 Speaker 1: fifty people. Well, their steak in the outcome of what 619 00:34:27,640 --> 00:34:30,680 Speaker 1: they're working is much less, but the perks of rank 620 00:34:30,719 --> 00:34:34,480 Speaker 1: are much more. So these two forces have shifted. Just 621 00:34:34,520 --> 00:34:36,680 Speaker 1: like in a glass of water, there're two forces, one 622 00:34:36,719 --> 00:34:39,120 Speaker 1: that wants to make molecules run around and one that 623 00:34:39,160 --> 00:34:41,400 Speaker 1: wants to lock them rigidly in place. And in the 624 00:34:41,400 --> 00:34:43,839 Speaker 1: case of a glass of water, as you rotate temperature, 625 00:34:44,320 --> 00:34:47,560 Speaker 1: you change between these two things. And in companies, as 626 00:34:47,600 --> 00:34:51,520 Speaker 1: you increase team size, increase group size, increased company size, 627 00:34:51,600 --> 00:34:55,799 Speaker 1: you change between these behaviors of nurturing crazy ideas and 628 00:34:55,840 --> 00:34:58,640 Speaker 1: rejecting them and the focus on execution and what works 629 00:34:58,680 --> 00:35:01,839 Speaker 1: well in committees, And so many entrepreneurs who have these, 630 00:35:01,880 --> 00:35:05,840 Speaker 1: you know, five person companies that go to fifties thousand, 631 00:35:05,840 --> 00:35:08,280 Speaker 1: and they come to me like, that's exactly what happened. 632 00:35:08,280 --> 00:35:11,640 Speaker 1: Conversation in the hallway five years ago, when we were 633 00:35:11,719 --> 00:35:16,040 Speaker 1: much smaller, was all about the project. Today conversations in 634 00:35:16,040 --> 00:35:19,800 Speaker 1: the hallway are about politics. And there's this sudden shift. 635 00:35:20,160 --> 00:35:22,800 Speaker 1: The glass of water has gone from liquid to solid. 636 00:35:23,560 --> 00:35:25,799 Speaker 1: And while that sounds like an analogy, what makes it 637 00:35:25,840 --> 00:35:29,239 Speaker 1: genuinely interesting is that you can write that down from 638 00:35:29,280 --> 00:35:32,680 Speaker 1: first principles. You could look at people's incentives. You can 639 00:35:32,719 --> 00:35:35,680 Speaker 1: write down an equation for their incentives, and you can 640 00:35:35,719 --> 00:35:39,520 Speaker 1: see when that shift will happen. And once you understand that, 641 00:35:40,040 --> 00:35:41,719 Speaker 1: just like with a glass of water, you can begin 642 00:35:41,800 --> 00:35:45,359 Speaker 1: to manage it. So let's talk about how to manage that. 643 00:35:45,960 --> 00:35:49,800 Speaker 1: And the solution you have written about is to take 644 00:35:49,960 --> 00:35:54,600 Speaker 1: the wild ideas, the loon shots, and put them in 645 00:35:54,640 --> 00:36:01,240 Speaker 1: a separate organization apart from the franchises. So my favorite 646 00:36:01,239 --> 00:36:04,919 Speaker 1: example in the book is radar technology that I think 647 00:36:04,960 --> 00:36:08,719 Speaker 1: the Navy had sometime in the after World War one 648 00:36:08,800 --> 00:36:12,040 Speaker 1: and it just sat there for a decade or longer. 649 00:36:12,640 --> 00:36:14,480 Speaker 1: Tell us about that. Yeah, that's funny. And you know, 650 00:36:14,640 --> 00:36:16,680 Speaker 1: just incidentally, as an anti I was telling you, I 651 00:36:16,760 --> 00:36:19,200 Speaker 1: was on a nuclear submarine talking to this senior admiral 652 00:36:19,200 --> 00:36:21,480 Speaker 1: in the Navy about how do we help the Navy 653 00:36:21,480 --> 00:36:24,520 Speaker 1: innovate faster and better? And he had my book and 654 00:36:24,520 --> 00:36:26,480 Speaker 1: we were sitting down, and I was a little nervous 655 00:36:26,520 --> 00:36:29,680 Speaker 1: what was he going to say? Because I talked about 656 00:36:29,680 --> 00:36:32,960 Speaker 1: how radar, which is a technology that ended up turning 657 00:36:32,960 --> 00:36:34,759 Speaker 1: the course of the Second World War and helping the 658 00:36:34,800 --> 00:36:38,560 Speaker 1: Allies catch up to Nazi Germany eventually defeat Nazi Germany, 659 00:36:38,880 --> 00:36:41,680 Speaker 1: was buried in the Navy for eighteen years and we 660 00:36:41,760 --> 00:36:44,120 Speaker 1: lost lives as a result of that. I lost it 661 00:36:44,200 --> 00:36:47,160 Speaker 1: wasn't ready in time for Pearl Harbor. And I was 662 00:36:47,160 --> 00:36:50,400 Speaker 1: pretty nervous that, oh man, because I spend you know, 663 00:36:50,760 --> 00:36:52,560 Speaker 1: a little bit of this chapter talking about how the 664 00:36:52,640 --> 00:36:55,960 Speaker 1: Navy buried this really important technology. And I said, I 665 00:36:56,000 --> 00:36:58,839 Speaker 1: asked him kind of gingerly, well how did you feel 666 00:36:58,840 --> 00:37:01,480 Speaker 1: about that story? And he said, that was my favorite 667 00:37:01,560 --> 00:37:06,080 Speaker 1: story in the book. So you've got to give military people, 668 00:37:06,360 --> 00:37:10,680 Speaker 1: at least senior ranks credit. They are all historians, and 669 00:37:10,880 --> 00:37:14,440 Speaker 1: they don't look at the Navy from as their navy. 670 00:37:14,600 --> 00:37:17,600 Speaker 1: It's just a previous generation and if there's something to 671 00:37:17,640 --> 00:37:19,839 Speaker 1: be learned from it, they seem to be pretty good 672 00:37:19,840 --> 00:37:23,560 Speaker 1: about adapting as they go forward. Maybe a little too slowly, 673 00:37:23,960 --> 00:37:29,080 Speaker 1: but the better military people are tacticians and historians. With 674 00:37:29,200 --> 00:37:32,640 Speaker 1: many of the senior leaders in the national intelligence agencies 675 00:37:32,719 --> 00:37:37,360 Speaker 1: or the Department of Defense, the best leaders are thinking 676 00:37:38,200 --> 00:37:40,799 Speaker 1: over the course of decades and sometimes century. I think 677 00:37:40,800 --> 00:37:44,279 Speaker 1: you had Ash Carter on your show is not only 678 00:37:44,320 --> 00:37:47,560 Speaker 1: a physicist, but a student of medieval history, and those 679 00:37:47,560 --> 00:37:50,240 Speaker 1: tend to be the most interesting thinkers. And so, for example, 680 00:37:50,280 --> 00:37:52,400 Speaker 1: one of the reasons that I'm spending a lot of 681 00:37:52,440 --> 00:37:55,600 Speaker 1: time kind of pro bono there is that what's worked 682 00:37:55,640 --> 00:37:58,360 Speaker 1: for the last hundred years is not going to work anymore. 683 00:37:58,440 --> 00:38:00,719 Speaker 1: And that's pretty much always true. If you're fighting the 684 00:38:00,800 --> 00:38:03,400 Speaker 1: last war, you're gonna lose that. Well, there's a famous 685 00:38:03,400 --> 00:38:07,040 Speaker 1: sarying the generals tend to fight the current war with 686 00:38:07,080 --> 00:38:10,960 Speaker 1: the technology that won the previous war. But what's happening now, 687 00:38:11,520 --> 00:38:14,000 Speaker 1: and we started most of if you look at military history, 688 00:38:14,040 --> 00:38:16,000 Speaker 1: we started most of the wars in the past hundred 689 00:38:16,080 --> 00:38:20,960 Speaker 1: years behind in some way in technology or in strategy 690 00:38:21,000 --> 00:38:23,400 Speaker 1: compared to our enemies, but because of some advantages that 691 00:38:23,480 --> 00:38:26,399 Speaker 1: we have as a country, we were able to catch up. 692 00:38:26,840 --> 00:38:30,080 Speaker 1: What's happening now is that the source of battlefield advantage 693 00:38:30,280 --> 00:38:34,759 Speaker 1: is shifting from hardware to software, and our organizations are 694 00:38:34,800 --> 00:38:38,759 Speaker 1: designed for hardware. They're not designed for the pace of 695 00:38:38,800 --> 00:38:41,880 Speaker 1: innovation of software, which is much faster. So they're going 696 00:38:41,920 --> 00:38:44,919 Speaker 1: to break and we may not be so lucky next time. 697 00:38:45,160 --> 00:38:48,600 Speaker 1: The next war, the next battle fielded where the last 698 00:38:48,600 --> 00:38:50,319 Speaker 1: thing any of us wants to see is we put 699 00:38:50,320 --> 00:38:53,440 Speaker 1: our soldiers on a battlefield surrounded by machine learning robots 700 00:38:53,520 --> 00:38:56,120 Speaker 1: and slaughtered. That's one of the reasons I'm having all 701 00:38:56,160 --> 00:38:58,920 Speaker 1: these discussions, and many of the senior military leaders have 702 00:38:59,040 --> 00:39:02,680 Speaker 1: realized that what we just talked about just separating is 703 00:39:02,719 --> 00:39:06,200 Speaker 1: not enough. That's like innovation theater. You create a box, 704 00:39:06,239 --> 00:39:09,319 Speaker 1: put the artist, the crazy people working on the crazy ideas. Here, 705 00:39:09,560 --> 00:39:13,120 Speaker 1: there's a widespread perception in the military that innovation labs 706 00:39:13,120 --> 00:39:16,080 Speaker 1: have maybe a three or four year lifespan and then 707 00:39:16,120 --> 00:39:19,680 Speaker 1: they die. So what's missing is the second part of that, 708 00:39:19,719 --> 00:39:22,680 Speaker 1: which is you need to separate these groups because the 709 00:39:22,719 --> 00:39:24,920 Speaker 1: people working on the crazy ideas have a very different 710 00:39:25,000 --> 00:39:28,040 Speaker 1: job than the soldiers who are working on on time, 711 00:39:28,040 --> 00:39:31,479 Speaker 1: on budget, on spec. So what's the go between these 712 00:39:31,520 --> 00:39:35,560 Speaker 1: two radically groups. That's what's been missing people. You know, 713 00:39:35,640 --> 00:39:37,840 Speaker 1: Let's take the Navy has a seven thousand people at 714 00:39:37,880 --> 00:39:41,480 Speaker 1: Johns Hopkins Applied Physics Lab, incredible scientists, and they have 715 00:39:41,600 --> 00:39:46,360 Speaker 1: incredible training and career path and development for the soldiers 716 00:39:46,400 --> 00:39:49,240 Speaker 1: who are running ships on time, on budget, on spec. 717 00:39:50,000 --> 00:39:52,360 Speaker 1: But the person going between those two groups is like 718 00:39:52,400 --> 00:39:54,840 Speaker 1: a high school kid with the clipboard. Why So in 719 00:39:54,880 --> 00:39:57,880 Speaker 1: the book you talk about Van over Bush, who is 720 00:39:57,920 --> 00:40:01,040 Speaker 1: the person who has both the background and m I 721 00:40:01,080 --> 00:40:04,600 Speaker 1: T and is respected by the academics, but comes from 722 00:40:04,600 --> 00:40:07,640 Speaker 1: a Navy family. And I believe was he a physicist 723 00:40:07,719 --> 00:40:11,320 Speaker 1: also he was an engineer, engineer, and he was really 724 00:40:11,560 --> 00:40:15,320 Speaker 1: not only able to bring radar forward, bring a number 725 00:40:15,320 --> 00:40:19,400 Speaker 1: of really amazing technological innovations that helped win the war, 726 00:40:19,760 --> 00:40:24,600 Speaker 1: the radar proximity fuse, the proximity fuse right, that really 727 00:40:24,880 --> 00:40:27,920 Speaker 1: changed the course of the war. And as I was 728 00:40:27,960 --> 00:40:30,960 Speaker 1: reading your description of the war, very much made me 729 00:40:31,000 --> 00:40:34,520 Speaker 1: feel like, oh, we really could have very easily lost 730 00:40:34,560 --> 00:40:37,840 Speaker 1: World War two. Yeah, people don't realize by the single 731 00:40:38,200 --> 00:40:40,759 Speaker 1: greatest set of the war, as Churchill and Roosevelt knew, 732 00:40:41,400 --> 00:40:45,479 Speaker 1: was the U boats Hitler had. When we started, Hitler 733 00:40:45,520 --> 00:40:47,719 Speaker 1: had a Nazi Germany had a lot of advantages. Their 734 00:40:47,800 --> 00:40:51,400 Speaker 1: U boats were something the Allies had no answer for 735 00:40:51,480 --> 00:40:53,719 Speaker 1: and looked ready to strangle the Atlantic, which they did. 736 00:40:54,160 --> 00:40:56,560 Speaker 1: Their planes in the looft Off, the German Air Force 737 00:40:56,600 --> 00:40:59,240 Speaker 1: outclassed anything the Allies had and looked ready to bomb 738 00:40:59,239 --> 00:41:02,080 Speaker 1: West in Europe into submission, which they did exactly a 739 00:41:02,160 --> 00:41:04,640 Speaker 1: year later, within a matter of weeks. And you have 740 00:41:04,719 --> 00:41:07,520 Speaker 1: these two German scientists who discovered something called nuclear fission, 741 00:41:07,640 --> 00:41:10,399 Speaker 1: which put Hitler within reach of the greatest weapon ever, 742 00:41:10,480 --> 00:41:12,680 Speaker 1: most destructive weapon ever created by man. And so we 743 00:41:12,719 --> 00:41:15,799 Speaker 1: started behind, and you know, we managed to catch up 744 00:41:15,800 --> 00:41:17,920 Speaker 1: in some things, but not in the U boats, and 745 00:41:18,160 --> 00:41:21,720 Speaker 1: Hitler was unable to eat defeated most of Western Europe 746 00:41:22,000 --> 00:41:25,040 Speaker 1: except for Britain and I. In fact, it was a 747 00:41:25,160 --> 00:41:28,840 Speaker 1: radar that helped save Britain in the Battle of Britain. 748 00:41:29,120 --> 00:41:33,200 Speaker 1: But Hitler decided and correctly eventually understood that he could 749 00:41:33,239 --> 00:41:35,520 Speaker 1: win the war with the U boats. That those U 750 00:41:35,560 --> 00:41:38,759 Speaker 1: boats were sinking down Allied ships every single month for 751 00:41:38,800 --> 00:41:41,319 Speaker 1: the first four years of the war, faster than the 752 00:41:41,360 --> 00:41:44,760 Speaker 1: Allies could build them. And in spring of nine Britain 753 00:41:44,840 --> 00:41:49,600 Speaker 1: was down to three months of oil, three months of planes, tanks, 754 00:41:49,960 --> 00:41:54,120 Speaker 1: trucks all need oil, and we were three months away 755 00:41:54,200 --> 00:41:58,200 Speaker 1: from a united Nazi Western Europe. The world map would 756 00:41:58,200 --> 00:42:02,320 Speaker 1: have been completely different. And that's when the first by 757 00:42:02,600 --> 00:42:05,799 Speaker 1: Liberator bombers, with a couple of technologies from Van over 758 00:42:05,920 --> 00:42:08,920 Speaker 1: Bush and his group of loons sailed out over the Atlantic, 759 00:42:08,960 --> 00:42:13,120 Speaker 1: concluding and most importantly Mike Grave radar that allowed the 760 00:42:13,120 --> 00:42:17,560 Speaker 1: pilots to see just a periscope, they could exactly and 761 00:42:17,640 --> 00:42:20,720 Speaker 1: began within a matter of weeks shooting down U boats 762 00:42:20,760 --> 00:42:23,600 Speaker 1: like shooting fish in a barrel. And Hitler lost one 763 00:42:23,680 --> 00:42:26,080 Speaker 1: third of his entire U boat fleet in that four 764 00:42:26,120 --> 00:42:28,319 Speaker 1: week period, more than in any other year of the war. 765 00:42:28,880 --> 00:42:32,319 Speaker 1: One month, he lost more than any previous year. That's 766 00:42:32,320 --> 00:42:35,399 Speaker 1: exactly right. And within six weeks later and they were 767 00:42:35,560 --> 00:42:39,719 Speaker 1: this close to winning Western Europe. Six weeks later, in 768 00:42:39,800 --> 00:42:44,160 Speaker 1: the end of May, Admiral Dunnetts, who is the head 769 00:42:44,160 --> 00:42:47,640 Speaker 1: of the German Navy, sends a radio blast across the Atlantic. 770 00:42:47,960 --> 00:42:51,000 Speaker 1: All remaining U boats withdraw the Battle of the Atlantic 771 00:42:51,040 --> 00:42:53,680 Speaker 1: has been lost, and then the lanes were cleared to 772 00:42:53,719 --> 00:42:56,480 Speaker 1: resupply Britain, and the lanes were cleared for an Allied 773 00:42:56,520 --> 00:42:59,799 Speaker 1: invasion of Europe. And a lot of people aren't quite 774 00:42:59,800 --> 00:43:03,560 Speaker 1: aware of that history. It's left out of many popular histories. 775 00:43:03,840 --> 00:43:06,680 Speaker 1: But that was the decisive turning point, firstly in the 776 00:43:06,680 --> 00:43:09,000 Speaker 1: Battle of the Atlantic, but that Battle of the Intic 777 00:43:09,120 --> 00:43:12,319 Speaker 1: was a decisive turning point in the entire war. Quite 778 00:43:12,320 --> 00:43:13,840 Speaker 1: fastening and can you stick around a little bit, I 779 00:43:13,840 --> 00:43:16,360 Speaker 1: have lot more questions for you. We have been speaking 780 00:43:16,400 --> 00:43:19,200 Speaker 1: with Sofie Bicol. He is the author of loon Shots, 781 00:43:19,200 --> 00:43:22,400 Speaker 1: How to Nurture the crazy ideas that wind wars, cure 782 00:43:22,440 --> 00:43:26,920 Speaker 1: diseases and transform industries. If you enjoy this conversation, be 783 00:43:26,960 --> 00:43:29,120 Speaker 1: sure and check out the podcast extras, where we keep 784 00:43:29,120 --> 00:43:33,800 Speaker 1: the tape rolling and continue discussing all things innovation related. 785 00:43:34,160 --> 00:43:38,840 Speaker 1: You can find that at Apple iTunes, Google podcast, Stitcher, Spotify, 786 00:43:38,920 --> 00:43:42,600 Speaker 1: wherever your finer podcasts are sold. We love your comments, 787 00:43:42,640 --> 00:43:46,719 Speaker 1: feedback and suggestions right to us at m ib podcast 788 00:43:46,760 --> 00:43:48,800 Speaker 1: at Bloomberg dot net. Be sure and give us a 789 00:43:48,840 --> 00:43:52,120 Speaker 1: review on Apple iTunes. Check out my weekly column on 790 00:43:52,160 --> 00:43:55,200 Speaker 1: Bloomberg dot com. You can follow me on Twitter at Rippolts. 791 00:43:55,400 --> 00:43:58,520 Speaker 1: I'm Barry Dults. You're listening to Masters and Business on 792 00:43:58,640 --> 00:44:06,080 Speaker 1: Bloomberg Radio. Welcome to the podcast, Sophie. Thank you so 793 00:44:06,160 --> 00:44:09,120 Speaker 1: much for doing this. I mentioned earlier, I start a 794 00:44:09,120 --> 00:44:11,080 Speaker 1: lot of books, I don't finish a lot of books. 795 00:44:11,400 --> 00:44:13,480 Speaker 1: I plowed straight through this. I found loon Shots to 796 00:44:13,520 --> 00:44:17,400 Speaker 1: be really interesting and intriguing and filled with lots of 797 00:44:18,400 --> 00:44:22,000 Speaker 1: of fascinating ideas. You have to be really proud of 798 00:44:22,000 --> 00:44:24,399 Speaker 1: what you accomplished for this. This was your first book, 799 00:44:24,440 --> 00:44:27,240 Speaker 1: is that right? It was? It's funny. It was about 800 00:44:27,239 --> 00:44:29,680 Speaker 1: a year ago. I submitted like the final edits, and 801 00:44:29,719 --> 00:44:31,360 Speaker 1: I can tell you I knew one thing for sure, 802 00:44:31,880 --> 00:44:34,480 Speaker 1: my mother would read it, right. I had no idea 803 00:44:34,560 --> 00:44:36,360 Speaker 1: it was you know, it's a way. It's got physics 804 00:44:36,360 --> 00:44:39,280 Speaker 1: and business and history, but it's very accessible. I didn't 805 00:44:39,280 --> 00:44:42,960 Speaker 1: find it remotely like, oh, this was written for phdl 806 00:44:43,160 --> 00:44:46,080 Speaker 1: D level. This is this is a straightforward business book. 807 00:44:46,160 --> 00:44:49,640 Speaker 1: Yeah now, and uh, you know, and I tell stories. 808 00:44:49,760 --> 00:44:51,680 Speaker 1: For me, it was a lot of fun learning how 809 00:44:51,719 --> 00:44:54,239 Speaker 1: to tell stories and how to structure those stories, whether 810 00:44:54,239 --> 00:44:57,400 Speaker 1: it's Panama or James Bond or Pixar or toy story 811 00:44:57,480 --> 00:44:59,960 Speaker 1: and why the world speaks English and how they're all 812 00:45:00,000 --> 00:45:02,600 Speaker 1: connected by one idea. And so I was having fun 813 00:45:02,600 --> 00:45:04,480 Speaker 1: and my wife would talk about, Sofie, what are you doing. 814 00:45:04,520 --> 00:45:08,120 Speaker 1: I'm just laughing, like I'm telling thinking something funny and 815 00:45:08,120 --> 00:45:10,120 Speaker 1: writing it down. But a lot of times you think 816 00:45:10,120 --> 00:45:12,480 Speaker 1: stuff is funny and then you discover well, other people 817 00:45:12,520 --> 00:45:15,040 Speaker 1: don't quite think so. So I had no idea. But 818 00:45:15,080 --> 00:45:19,520 Speaker 1: then I remember when, um it was not long ago. Uh, 819 00:45:20,080 --> 00:45:22,520 Speaker 1: someone was interviewing Bill Gates for the Wall Street Journal 820 00:45:22,600 --> 00:45:25,400 Speaker 1: and they asked him, like what his three favorite recent 821 00:45:25,400 --> 00:45:28,080 Speaker 1: books were or something, and he mentioned, you know, there 822 00:45:28,160 --> 00:45:31,480 Speaker 1: was a social justice book because he does Gillapoor the 823 00:45:32,000 --> 00:45:35,520 Speaker 1: history of pistory. And then he mentioned loon Shots. And 824 00:45:35,560 --> 00:45:38,239 Speaker 1: then like I get this. I found out about it 825 00:45:38,280 --> 00:45:42,080 Speaker 1: because like my publisher editor, like fifteen people start calling me, 826 00:45:42,440 --> 00:45:43,880 Speaker 1: and I was like, oh, that's nice. I didn't know 827 00:45:43,920 --> 00:45:46,200 Speaker 1: that that was, but apparently that's a really big deal. 828 00:45:46,400 --> 00:45:48,440 Speaker 1: He does a booklist every year, and and it is 829 00:45:48,480 --> 00:45:51,800 Speaker 1: a big deal. If you're a geek or a business 830 00:45:51,840 --> 00:45:56,400 Speaker 1: person or a software person. You you see bills list 831 00:45:56,440 --> 00:45:59,759 Speaker 1: every year. Yeah, that was cool. And then when, um, again, 832 00:45:59,800 --> 00:46:01,480 Speaker 1: I know if anybody would like it. And then when 833 00:46:01,640 --> 00:46:04,560 Speaker 1: Donnie Kinneman, who wrote Thinking Fast and Slow and you 834 00:46:04,560 --> 00:46:09,719 Speaker 1: know as a really interesting, intelligent, thoughtful reader and writer. Um, 835 00:46:10,440 --> 00:46:14,680 Speaker 1: and when he very early on supported came out very 836 00:46:14,760 --> 00:46:17,880 Speaker 1: nicely in support of it, I was like, God, I got, 837 00:46:17,920 --> 00:46:20,239 Speaker 1: I got my mother and Danny k like, dude, that's 838 00:46:20,280 --> 00:46:22,279 Speaker 1: two people, all right, we have a win. And he 839 00:46:22,400 --> 00:46:25,399 Speaker 1: is the most charming person he is ever want to meet, 840 00:46:25,560 --> 00:46:29,840 Speaker 1: so thoughtful and it just likes to think about problems 841 00:46:29,840 --> 00:46:32,160 Speaker 1: in a deep and big way. Remind me later, I 842 00:46:32,200 --> 00:46:35,239 Speaker 1: have a hilarious Donny Danny Kneman story to tell. I'll 843 00:46:35,239 --> 00:46:38,600 Speaker 1: tell you afterwards. So, um, there's a few things I 844 00:46:38,640 --> 00:46:41,640 Speaker 1: didn't get to that I have to ask because they're 845 00:46:41,680 --> 00:46:44,320 Speaker 1: really quite fascinating. But I want to stay with the book, 846 00:46:45,080 --> 00:46:48,280 Speaker 1: um for for a bit and go over some stuff. 847 00:46:48,320 --> 00:46:52,120 Speaker 1: The Moses trap. What is the Moses trap? The Moses 848 00:46:52,160 --> 00:46:55,480 Speaker 1: trap is this idea, is this myth of great leadership 849 00:46:55,520 --> 00:46:58,400 Speaker 1: that if you ever read, you know, these business journals 850 00:46:58,440 --> 00:47:01,479 Speaker 1: with these you know color cove or covers where there's 851 00:47:01,480 --> 00:47:04,319 Speaker 1: some great leader who's the Moses, who stands on top 852 00:47:04,320 --> 00:47:06,600 Speaker 1: of a mountain, and they say the reason this leader 853 00:47:06,680 --> 00:47:09,200 Speaker 1: is so great is they raised their staff and they 854 00:47:09,200 --> 00:47:13,440 Speaker 1: anointed the chosen project, the whole, the iPod, the Holy 855 00:47:13,480 --> 00:47:16,799 Speaker 1: loon shot, whatever it is. And that's a complete myth. 856 00:47:17,400 --> 00:47:20,040 Speaker 1: What happens in those kind of companies when leaders lead 857 00:47:20,080 --> 00:47:22,239 Speaker 1: like that is you get into the cycle if you 858 00:47:22,320 --> 00:47:24,759 Speaker 1: think you're great at choosing product, and so you just 859 00:47:24,840 --> 00:47:27,360 Speaker 1: keep going bigger, faster better, bigger faster better, and you 860 00:47:27,600 --> 00:47:30,400 Speaker 1: keep raising your staff and picking that project, and eventually 861 00:47:30,880 --> 00:47:33,680 Speaker 1: that cycle, that wheel turns one too many times and 862 00:47:33,719 --> 00:47:35,759 Speaker 1: it's a disaster. That happened to Steve Jobs in his 863 00:47:35,880 --> 00:47:38,800 Speaker 1: first few companies. It happened to edwin Land and Polaroid. 864 00:47:38,840 --> 00:47:42,320 Speaker 1: It happened to one trip at PanAm. I mean Kodak 865 00:47:42,440 --> 00:47:47,120 Speaker 1: very famously invented the digital camera and then decided, oh no, 866 00:47:47,200 --> 00:47:50,920 Speaker 1: this will cut film sales. Yeah. So there's uh by 867 00:47:50,920 --> 00:47:53,759 Speaker 1: the Moses trap, I mean that there's a takeaway there 868 00:47:54,320 --> 00:47:57,520 Speaker 1: for business leaders, which is, or even anyone who's managing 869 00:47:57,520 --> 00:48:00,320 Speaker 1: a team or an organization, which is, you want to 870 00:48:00,400 --> 00:48:03,200 Speaker 1: lead much more like a gardener rather than a Moses. 871 00:48:03,239 --> 00:48:05,600 Speaker 1: In other words, you want to have these two groups, 872 00:48:05,680 --> 00:48:08,040 Speaker 1: the artists working on the crazy stuff. Their job is 873 00:48:08,080 --> 00:48:11,560 Speaker 1: to take as much risk. You're asking them to take risks. 874 00:48:12,239 --> 00:48:14,799 Speaker 1: And then the soldiers. Let's say you have some some 875 00:48:15,719 --> 00:48:18,040 Speaker 1: guy whose job it is to man, you know, make planes. 876 00:48:18,120 --> 00:48:20,320 Speaker 1: You don't want to tell You don't want him to 877 00:48:20,360 --> 00:48:22,759 Speaker 1: be saying, well, I have an idea. Let's put ten 878 00:48:22,800 --> 00:48:25,840 Speaker 1: planes in the sky, see which nine fall down. Okay, 879 00:48:26,719 --> 00:48:30,200 Speaker 1: it's completely opposite jobs. And the failure point in most 880 00:48:30,239 --> 00:48:33,200 Speaker 1: innovation is never in the supply of new ideas, or 881 00:48:33,320 --> 00:48:34,839 Speaker 1: you put ten people in a room with a stack 882 00:48:34,840 --> 00:48:37,480 Speaker 1: of posts. For now we're gonnat a thousand ideas. The 883 00:48:37,600 --> 00:48:40,600 Speaker 1: failure point in most innovation in large companies with a 884 00:48:40,680 --> 00:48:44,719 Speaker 1: public sector a private sector is in that transfer. The 885 00:48:44,840 --> 00:48:47,080 Speaker 1: truly great leaders, whether it was Fan of a Bush 886 00:48:47,160 --> 00:48:49,480 Speaker 1: or even Steve Jobs his second time around it Apple 887 00:48:49,719 --> 00:48:53,520 Speaker 1: had understood this is they manage like a gardener. They 888 00:48:53,560 --> 00:48:56,759 Speaker 1: take those baby stage ideas out and they bring it 889 00:48:56,800 --> 00:48:59,600 Speaker 1: into the field where they will always be resistance. And 890 00:48:59,600 --> 00:49:02,520 Speaker 1: that's normal, and you want that. You want that conflict. 891 00:49:02,640 --> 00:49:05,239 Speaker 1: The group that's making the money rarely likes the group 892 00:49:05,239 --> 00:49:08,160 Speaker 1: that's spending the money, and vice versa. And you want 893 00:49:08,200 --> 00:49:10,160 Speaker 1: that tens and you want one group taking as much 894 00:49:10,280 --> 00:49:12,960 Speaker 1: risk as possible. You want them trying ten things night 895 00:49:13,000 --> 00:49:16,799 Speaker 1: of which will fail because if they don't, then your 896 00:49:16,840 --> 00:49:19,879 Speaker 1: competitor is and your competitor will discover that ten thing 897 00:49:19,880 --> 00:49:22,200 Speaker 1: and you will see it too late when it's a 898 00:49:22,239 --> 00:49:25,359 Speaker 1: bullet coming to your head. You mentioned Steve Jobs had 899 00:49:25,520 --> 00:49:29,799 Speaker 1: Johnny I've as the dreamer and Tim Cook as the 900 00:49:29,840 --> 00:49:32,800 Speaker 1: soldier and turned out to be a really powerful combination. 901 00:49:32,880 --> 00:49:34,640 Speaker 1: Right now, this myth that he was the Moses on 902 00:49:34,680 --> 00:49:37,880 Speaker 1: the mountain raising his staff and anointing the chosen project 903 00:49:38,120 --> 00:49:39,680 Speaker 1: is really a myth. He did of one of the 904 00:49:39,680 --> 00:49:41,799 Speaker 1: first things he did when he got into Apple. The 905 00:49:41,800 --> 00:49:44,200 Speaker 1: second time was promoted Johnny ive is one of the 906 00:49:44,200 --> 00:49:46,920 Speaker 1: great product designers. Then the second thing he one of 907 00:49:46,920 --> 00:49:49,000 Speaker 1: the next things he did is he recruited a guy 908 00:49:49,080 --> 00:49:52,440 Speaker 1: named Tim Cook, with his previous job was known as 909 00:49:52,480 --> 00:49:54,520 Speaker 1: the Attila, the hunt of inventory. So if there's a 910 00:49:54,560 --> 00:49:56,680 Speaker 1: better name for a soldier inside a company, I don't 911 00:49:56,680 --> 00:49:59,960 Speaker 1: know it. And he led by bringing them together, man 912 00:50:00,040 --> 00:50:03,680 Speaker 1: eaging the transfer between those two. The iPad when it 913 00:50:03,760 --> 00:50:06,880 Speaker 1: was introduced was a beautiful design and original product, but 914 00:50:06,920 --> 00:50:08,880 Speaker 1: if it had cost six thousand dollars, there'd be no 915 00:50:08,920 --> 00:50:11,239 Speaker 1: Apple today. It was Tim Cook that got it from 916 00:50:11,280 --> 00:50:14,640 Speaker 1: six thousand to six hundred. So what people miss is 917 00:50:14,680 --> 00:50:16,960 Speaker 1: they focused on, oh, you know, he picked this product. 918 00:50:17,080 --> 00:50:20,920 Speaker 1: Let's they focus. They get to they manage the technology 919 00:50:21,040 --> 00:50:23,360 Speaker 1: rather than than transfer. What you want to do is 920 00:50:23,400 --> 00:50:27,520 Speaker 1: manage the transfer, not the technology. So something like Lucent 921 00:50:27,800 --> 00:50:30,800 Speaker 1: or a te Bell Labs as it was known as previously, 922 00:50:30,920 --> 00:50:33,920 Speaker 1: that was around for I don't know, almost a century. 923 00:50:34,520 --> 00:50:41,240 Speaker 1: How often can these labs of innovation execute on that 924 00:50:41,719 --> 00:50:44,719 Speaker 1: transference of ideas? And you look at Lucin. A ton 925 00:50:44,760 --> 00:50:47,080 Speaker 1: of great stuff came out of Bell Labs that got 926 00:50:47,200 --> 00:50:50,919 Speaker 1: used um and a lot of it was post any 927 00:50:51,000 --> 00:50:54,640 Speaker 1: trust breakup, so it was offered out as patents more 928 00:50:54,719 --> 00:50:58,520 Speaker 1: or less for free. In some ways, we owe Van 929 00:50:58,600 --> 00:51:01,640 Speaker 1: ever Bush, who created this system inside the federal government 930 00:51:01,640 --> 00:51:04,440 Speaker 1: that allowed the US military to catch up rapidly to 931 00:51:04,520 --> 00:51:09,040 Speaker 1: Nazi Germany eventually exceed in the technologies that ended up 932 00:51:09,080 --> 00:51:12,719 Speaker 1: being decisive in the Second World War, actually got his 933 00:51:12,760 --> 00:51:15,640 Speaker 1: ideas from Bell Labs. So the guy who was president 934 00:51:15,640 --> 00:51:18,279 Speaker 1: of Bell Labs at the time, Frank Jewett, was a 935 00:51:18,320 --> 00:51:22,239 Speaker 1: mentor of his who actually helped bring vanever Bush into Washington. 936 00:51:22,600 --> 00:51:24,919 Speaker 1: So in some ways vanever Bushes, you know, we owe 937 00:51:24,960 --> 00:51:27,520 Speaker 1: a lot to bell Ab more than we think because 938 00:51:27,560 --> 00:51:31,719 Speaker 1: it inspired Van ever Bush and vanever Bush's system. The 939 00:51:31,880 --> 00:51:34,680 Speaker 1: organization that he created called the O s r D. 940 00:51:34,760 --> 00:51:38,080 Speaker 1: Office of Science Research and Development, ended up becoming the 941 00:51:38,160 --> 00:51:41,440 Speaker 1: National Science Foundation, the National Institutes of Health, and was 942 00:51:41,520 --> 00:51:45,640 Speaker 1: the backbone, the inspiration for all of the national research 943 00:51:45,640 --> 00:51:48,320 Speaker 1: infrastructure of the US. And when we look at the 944 00:51:48,360 --> 00:51:52,759 Speaker 1: post War two era, the Technology Development Group that was 945 00:51:52,800 --> 00:51:56,399 Speaker 1: put together really was a giant impetus for the next 946 00:51:56,440 --> 00:51:59,719 Speaker 1: fifty years of economic growth as well. Well. It was 947 00:51:59,760 --> 00:52:02,480 Speaker 1: a rate source of competitive advantage and it's a source 948 00:52:02,520 --> 00:52:04,880 Speaker 1: of concern today. And here's what I mean by that. 949 00:52:05,440 --> 00:52:09,719 Speaker 1: It is that national research infrastructure, which we understood and 950 00:52:09,800 --> 00:52:12,960 Speaker 1: many other countries didn't. That investment that gave us the 951 00:52:12,960 --> 00:52:18,040 Speaker 1: biotechnology industry, GPS, Internet, even the transistor was supported by 952 00:52:18,080 --> 00:52:22,359 Speaker 1: that integrated circuit. So much of our progress as an 953 00:52:22,360 --> 00:52:26,360 Speaker 1: economy and as a nation comes from that investment. That 954 00:52:26,520 --> 00:52:29,839 Speaker 1: essentially a market failure. No one, no one company could 955 00:52:29,840 --> 00:52:33,279 Speaker 1: have invested to create the biotech industry decades long. You 956 00:52:33,360 --> 00:52:35,640 Speaker 1: can't you can't have that. It was It's like a 957 00:52:35,719 --> 00:52:38,719 Speaker 1: game theory market theory problem, which is it's not good 958 00:52:38,719 --> 00:52:42,400 Speaker 1: for anyone participant but it's good for everyone as a whole. 959 00:52:42,560 --> 00:52:46,319 Speaker 1: And that's where the national research labs came in, and 960 00:52:46,440 --> 00:52:50,000 Speaker 1: that's what drove so much success for the United States. 961 00:52:50,400 --> 00:52:53,200 Speaker 1: Roughly half of the many trillions of dollars of GDP 962 00:52:53,320 --> 00:52:57,040 Speaker 1: growth is attributed to those kinds of technologies since World 963 00:52:57,120 --> 00:53:00,360 Speaker 1: War Two. The concern is that while we've figured that 964 00:53:00,400 --> 00:53:04,440 Speaker 1: out seventy years ago and milk that for the last 965 00:53:05,040 --> 00:53:09,200 Speaker 1: fifty years, China and Russia have understood that now and 966 00:53:09,280 --> 00:53:12,200 Speaker 1: that's what they're doing now. Have we fallen behind in 967 00:53:12,360 --> 00:53:16,719 Speaker 1: making those sorts of long term investments in basic science? 968 00:53:17,040 --> 00:53:19,880 Speaker 1: Are we lagging China were lagging other cos we're starting 969 00:53:19,880 --> 00:53:23,239 Speaker 1: to We're declining and they're increasing and that's not a 970 00:53:23,280 --> 00:53:26,560 Speaker 1: good look. That's not a good trend. Where it's especially 971 00:53:26,680 --> 00:53:31,000 Speaker 1: dangerous is in the shift from hardware to software as 972 00:53:31,040 --> 00:53:35,720 Speaker 1: the source of advantage of national security. China and Russia 973 00:53:35,760 --> 00:53:41,600 Speaker 1: today are weaponizing AI and machine learning. They have an 974 00:53:41,680 --> 00:53:46,920 Speaker 1: unfortunate advantage, which is they have no ethical constraints and 975 00:53:46,960 --> 00:53:49,600 Speaker 1: how they use those weapons, and they're using them on 976 00:53:49,640 --> 00:53:52,800 Speaker 1: the battlefield in pretty horrific ways. For example, in Ukraine, 977 00:53:53,040 --> 00:53:57,360 Speaker 1: you had a Russian cybergroup send text messages to families 978 00:53:57,360 --> 00:54:00,160 Speaker 1: saying your son has been killed on the battlefield, which 979 00:54:00,160 --> 00:54:04,040 Speaker 1: immediately lead to a lot of text messages are you okay? 980 00:54:04,200 --> 00:54:05,719 Speaker 1: They looked at that on the grid and then began 981 00:54:05,760 --> 00:54:09,840 Speaker 1: bombing those targets, and that actually in a two minute attack, 982 00:54:10,640 --> 00:54:14,560 Speaker 1: turned back so much progress in the UK, so China, 983 00:54:14,760 --> 00:54:17,120 Speaker 1: and China is using that to all these kinds of 984 00:54:17,160 --> 00:54:21,879 Speaker 1: technologies to suppress dissident minorities. So my I'm optimistic about 985 00:54:21,920 --> 00:54:24,600 Speaker 1: what we can do about many technologies, but I'm very 986 00:54:24,680 --> 00:54:30,360 Speaker 1: concerned about how totalitarian regimes can use those technologies to 987 00:54:30,440 --> 00:54:35,360 Speaker 1: advance their interests. So there is now in uh Washington, 988 00:54:35,440 --> 00:54:38,799 Speaker 1: I met with them a few times a national by 989 00:54:38,840 --> 00:54:42,200 Speaker 1: part as a National Security Commission on AI, which will 990 00:54:42,239 --> 00:54:47,520 Speaker 1: hopefully chart a path that we can compete. Quite quite interesting. 991 00:54:47,680 --> 00:54:50,879 Speaker 1: And in the book, one of the last chapters is 992 00:54:51,000 --> 00:54:54,080 Speaker 1: why the World Speaks English? And I love the description 993 00:54:54,520 --> 00:54:57,719 Speaker 1: you have in it tell the story as to why 994 00:54:57,840 --> 00:55:00,440 Speaker 1: so much of the world is English speaking. Didn't have 995 00:55:00,480 --> 00:55:02,279 Speaker 1: to go that way, did it? A thousand years of 996 00:55:02,320 --> 00:55:05,719 Speaker 1: history and thirty seconds ago, let's go, let's do it. Well, 997 00:55:05,760 --> 00:55:08,359 Speaker 1: it's it's actually taking this idea of why is it 998 00:55:08,400 --> 00:55:11,200 Speaker 1: that small companies seem to be better at embracing new 999 00:55:11,239 --> 00:55:14,960 Speaker 1: ideas and large companies reject them, and that as you 1000 00:55:15,000 --> 00:55:16,799 Speaker 1: read in the book, it's about this idea of a 1001 00:55:16,800 --> 00:55:19,080 Speaker 1: phase transition and then what you can do about it 1002 00:55:19,120 --> 00:55:21,680 Speaker 1: once you understand it. But let's take that idea of 1003 00:55:21,719 --> 00:55:25,960 Speaker 1: metal level. So if you look for about a millennium, 1004 00:55:26,000 --> 00:55:29,160 Speaker 1: not one generation or two generations, but about a thousand years, 1005 00:55:29,600 --> 00:55:34,000 Speaker 1: China and India dominated the world in the early science 1006 00:55:34,000 --> 00:55:36,880 Speaker 1: and technology, paper and printing was invented in China. You 1007 00:55:36,960 --> 00:55:40,719 Speaker 1: had the compass and the gunpowder, you had the currency systems, 1008 00:55:40,760 --> 00:55:45,400 Speaker 1: you had advanced mining, advanced drilling, advanced irrigation. There was 1009 00:55:45,680 --> 00:55:49,719 Speaker 1: no competition. I mean, China, India was fifty more than 1010 00:55:49,719 --> 00:55:53,000 Speaker 1: fifty percent of the world GDP. Britain for example, was 1011 00:55:53,080 --> 00:55:57,160 Speaker 1: less than half a percent. And we were talking between 1012 00:55:57,160 --> 00:56:00,239 Speaker 1: the middle of the yeah, about five to about between 1013 00:56:00,320 --> 00:56:04,960 Speaker 1: hundred thousand years at the time. You know, early science 1014 00:56:04,960 --> 00:56:08,120 Speaker 1: and astronomy in the Islamic Empire far exceed between the 1015 00:56:08,200 --> 00:56:12,520 Speaker 1: ninth and thirteenth century, far exceeded anything in Western Europe. 1016 00:56:12,640 --> 00:56:17,359 Speaker 1: The most widely used medical textbook in Western Europe for 1017 00:56:17,520 --> 00:56:22,480 Speaker 1: seven hundred years was by Ibn Sina an Islamic medical scholar, 1018 00:56:22,640 --> 00:56:26,000 Speaker 1: a scientist and medical scholar. And imagine today using a 1019 00:56:26,040 --> 00:56:29,200 Speaker 1: book for seven years it's already or seventy years. There's 1020 00:56:29,200 --> 00:56:33,080 Speaker 1: no way you would use a textbook seven hundred years 1021 00:56:33,440 --> 00:56:38,000 Speaker 1: Islamic science and astronomy and technology. The mathematics that Copernicus used, 1022 00:56:38,200 --> 00:56:40,799 Speaker 1: much of the ideas that he based what he did 1023 00:56:41,040 --> 00:56:46,399 Speaker 1: on came from India, from Islamic Empire, from China. So 1024 00:56:46,480 --> 00:56:51,680 Speaker 1: why English, these tiny little countries in Western Europe or 1025 00:56:51,719 --> 00:56:55,400 Speaker 1: like the backwater China and India were dominating the world 1026 00:56:55,440 --> 00:56:59,640 Speaker 1: and everything that matters. So why Well, what I talked 1027 00:56:59,640 --> 00:57:02,799 Speaker 1: about there is why China, Islam and India were like 1028 00:57:02,840 --> 00:57:05,319 Speaker 1: the Mark Fiser Novartis of their day. They were the 1029 00:57:05,520 --> 00:57:09,680 Speaker 1: dominant global empires. They were great like Laurel. Large companies 1030 00:57:09,880 --> 00:57:14,640 Speaker 1: tend to be at franchises, building the Great Wall, building 1031 00:57:14,640 --> 00:57:19,400 Speaker 1: the Great Canal system, irrigating hundreds of millions of square miles, 1032 00:57:19,680 --> 00:57:23,800 Speaker 1: building the taj Mahal, giant franchises, but they were not 1033 00:57:23,840 --> 00:57:28,120 Speaker 1: good at nurturing loon shots. They were not good. They 1034 00:57:28,280 --> 00:57:33,160 Speaker 1: About six centuries before Copernicus, san Teco, Brachi and Newton, 1035 00:57:33,640 --> 00:57:37,680 Speaker 1: there was a couple of scientists in Northern Sung dynasty 1036 00:57:37,800 --> 00:57:42,320 Speaker 1: in China. They came up with brilliant ideas about astronomy, 1037 00:57:42,360 --> 00:57:44,960 Speaker 1: and they were really headed in that direction, and they 1038 00:57:45,000 --> 00:57:47,320 Speaker 1: were right on the cusp. You could argue of the 1039 00:57:47,400 --> 00:57:52,200 Speaker 1: scientific revolution happening in China five d six hundred years 1040 00:57:52,240 --> 00:57:56,480 Speaker 1: before seventeenth century Western Europe. But there was an emperor 1041 00:57:57,640 --> 00:58:00,280 Speaker 1: and they and he quashed that idea after a few years. 1042 00:58:00,280 --> 00:58:02,400 Speaker 1: He said he didn't like it. He was the Moses 1043 00:58:02,840 --> 00:58:05,680 Speaker 1: with his staff, saying, not that idea, this whole thing 1044 00:58:05,720 --> 00:58:08,240 Speaker 1: about how the planets move. Let's move on to something 1045 00:58:08,280 --> 00:58:12,400 Speaker 1: that's boring. Whereas in Western Europe, Western Europe was like 1046 00:58:12,440 --> 00:58:15,880 Speaker 1: the tiny teeming market of biotechs. If China is Slam 1047 00:58:15,920 --> 00:58:18,520 Speaker 1: in India were the Mark Fiser Novartis. These were the 1048 00:58:18,640 --> 00:58:22,840 Speaker 1: hundreds of little biotechs in Boston or San Francisco, each 1049 00:58:22,880 --> 00:58:24,960 Speaker 1: of whom has some crazy idea and most of them fail. 1050 00:58:25,720 --> 00:58:28,720 Speaker 1: Nine out of ten fail. But here's a crazy idea. 1051 00:58:28,920 --> 00:58:32,200 Speaker 1: The Earth goes around the Sun. When anybody on the 1052 00:58:32,200 --> 00:58:34,560 Speaker 1: planet for two thousand years could see obviously the Sun 1053 00:58:34,640 --> 00:58:37,040 Speaker 1: is going around us, the planets are going around us, 1054 00:58:37,400 --> 00:58:40,400 Speaker 1: the stars are going around us. And Copernic has kind 1055 00:58:40,400 --> 00:58:42,720 Speaker 1: of switched it around, and he wasn't the first that 1056 00:58:42,880 --> 00:58:45,640 Speaker 1: that idea had been floated around for two thousand years. 1057 00:58:45,680 --> 00:58:48,320 Speaker 1: He made it a little more mathematical, borrowing some science 1058 00:58:48,360 --> 00:58:54,120 Speaker 1: from Islamic astronomers, and that idea was actually quashed by 1059 00:58:54,440 --> 00:58:56,560 Speaker 1: for about seven year eighty years, everybody said, well, that's 1060 00:58:56,560 --> 00:58:59,200 Speaker 1: obviously a deiotic If the Earth is spinning on its 1061 00:58:59,240 --> 00:59:03,560 Speaker 1: axis so fast, why aren't birds flung from their nests. 1062 00:59:03,920 --> 00:59:06,840 Speaker 1: This is stupid. It's a crazy idea, just like Facebook 1063 00:59:06,880 --> 00:59:08,920 Speaker 1: was a crazy idea. Google was a crazy idea was 1064 00:59:08,920 --> 00:59:12,920 Speaker 1: at But what happened is because you had hundreds hundreds 1065 00:59:12,960 --> 00:59:16,240 Speaker 1: of these tiny little nation states. When Tycho Brahi about 1066 00:59:16,280 --> 00:59:18,720 Speaker 1: fifty sixty years later, said let me measure this thing. 1067 00:59:18,880 --> 00:59:21,440 Speaker 1: Let me see if I could. Obviously Copernicus is an idiot, 1068 00:59:21,440 --> 00:59:24,160 Speaker 1: because we can all tell that the sun doesn't go, 1069 00:59:24,240 --> 00:59:26,120 Speaker 1: but I have this other theory, and let me measure it. 1070 00:59:26,160 --> 00:59:28,520 Speaker 1: And he started trying to measure it, and he was 1071 00:59:28,520 --> 00:59:31,560 Speaker 1: working on it, and then he got fired. He got 1072 00:59:31,560 --> 00:59:33,640 Speaker 1: fired because he was kind of a big, obnoxious ass, 1073 00:59:33,680 --> 00:59:35,720 Speaker 1: and he had a little The King of Denmark had 1074 00:59:35,720 --> 00:59:39,640 Speaker 1: given him this little little island and he was the 1075 00:59:39,680 --> 00:59:41,520 Speaker 1: old king died. The young king took over and he said, 1076 00:59:41,560 --> 00:59:43,160 Speaker 1: you come visit me. I'm the big shot, and the 1077 00:59:43,200 --> 00:59:45,760 Speaker 1: young king is like, I'm writing your checks, you come 1078 00:59:45,840 --> 00:59:48,840 Speaker 1: visit me. They got into this like pissing battle, and 1079 00:59:48,880 --> 00:59:52,360 Speaker 1: he was fired. But that's just like a little company 1080 00:59:52,360 --> 00:59:54,640 Speaker 1: where has the CEO has a battle with his board 1081 00:59:54,680 --> 00:59:57,560 Speaker 1: of directors. So then he went and he took his 1082 00:59:57,600 --> 00:59:59,800 Speaker 1: ideas and marched around all the little kings and looking 1083 01:00:00,240 --> 01:00:04,520 Speaker 1: if Europe, and found one in Prague, Rudolph the second. 1084 01:00:04,560 --> 01:00:06,400 Speaker 1: He said, yeah, I can knock yourself out. Go go 1085 01:00:06,520 --> 01:00:10,920 Speaker 1: build another another, another observatory. He brought the young Kepler 1086 01:00:11,000 --> 01:00:14,560 Speaker 1: there and that's where they discovered that what we've been 1087 01:00:14,600 --> 01:00:17,640 Speaker 1: saying for two thousand years was wrong. The planets don't 1088 01:00:17,640 --> 01:00:20,680 Speaker 1: move in circles. They move in this very different way. 1089 01:00:20,800 --> 01:00:23,600 Speaker 1: And that was, in some ways the spark when you 1090 01:00:23,680 --> 01:00:26,600 Speaker 1: throw away what everybody know to be true for two 1091 01:00:26,640 --> 01:00:30,840 Speaker 1: thousand years, that heavenly bodies always move in circles. They 1092 01:00:30,840 --> 01:00:34,640 Speaker 1: move in these things called ellipses. And nobody saw that. 1093 01:00:35,480 --> 01:00:38,560 Speaker 1: And Kepler and many others began saying, well, if we've 1094 01:00:38,600 --> 01:00:41,840 Speaker 1: been wrong about that, maybe there's something else maybe there's 1095 01:00:41,880 --> 01:00:45,200 Speaker 1: this thing called laws of nature and they work, not 1096 01:00:45,360 --> 01:00:49,240 Speaker 1: what religious authorities tell us or divine rulers tell us, 1097 01:00:49,800 --> 01:00:52,720 Speaker 1: but maybe there are these fundamental things called laws of nature, 1098 01:00:52,720 --> 01:00:54,600 Speaker 1: and maybe we need to just look at nature, and 1099 01:00:54,640 --> 01:00:59,400 Speaker 1: maybe everybody could figure out. Anybody who can measure well 1100 01:01:00,040 --> 01:01:03,320 Speaker 1: and can do experiments can figure out truth. It was 1101 01:01:03,360 --> 01:01:08,600 Speaker 1: the beginning of the democratization of truth and that transformed 1102 01:01:08,600 --> 01:01:11,800 Speaker 1: our species and the Enlightenment and and so forth. I'm 1103 01:01:11,880 --> 01:01:18,320 Speaker 1: fascinated by um the idea that what Westerners think of 1104 01:01:18,440 --> 01:01:21,560 Speaker 1: as the Dark Age, the Dark Ages from I don't know, 1105 01:01:21,680 --> 01:01:26,760 Speaker 1: three hundred a d. Tod and something was really not 1106 01:01:26,960 --> 01:01:29,600 Speaker 1: the Dark Ages for most of the world. Rest of 1107 01:01:29,640 --> 01:01:32,480 Speaker 1: the world. It's just the Western perspective. Oh, this was 1108 01:01:32,520 --> 01:01:35,200 Speaker 1: the Dark Ages, not in India, not in China now, 1109 01:01:35,240 --> 01:01:38,320 Speaker 1: and not only that, we used so much of what 1110 01:01:38,360 --> 01:01:42,640 Speaker 1: we used in advance was just the final icing on 1111 01:01:42,680 --> 01:01:45,560 Speaker 1: the cake that had been built by China Islam in India. 1112 01:01:46,400 --> 01:01:49,480 Speaker 1: That that's quite quite intriguing. Do do you miss physics 1113 01:01:49,520 --> 01:01:51,360 Speaker 1: at all? You have your I think one of your 1114 01:01:51,360 --> 01:01:55,600 Speaker 1: parents was an astrophysicist. Both were. Oh so, so let's 1115 01:01:55,640 --> 01:01:58,360 Speaker 1: talk a little bit about astrophysics. Do you do you like? 1116 01:01:58,440 --> 01:02:02,360 Speaker 1: What is Thanksgiving dinner commerce station at your home? Like? Uh? 1117 01:02:02,760 --> 01:02:08,000 Speaker 1: Is that a regular um discussion of quasars and black 1118 01:02:08,040 --> 01:02:15,360 Speaker 1: holes and the universal galactic constant? Or what? What's the conversation? Like? 1119 01:02:16,480 --> 01:02:18,760 Speaker 1: It was about you know, I think what was different 1120 01:02:18,760 --> 01:02:20,720 Speaker 1: and what we got, me and my brother and sister 1121 01:02:20,760 --> 01:02:26,600 Speaker 1: got out of having parents who are in science is curiosity. 1122 01:02:26,640 --> 01:02:33,360 Speaker 1: Because what drives scientists is curiosity about how the world 1123 01:02:33,440 --> 01:02:37,680 Speaker 1: works and asking questions. And so that's what our kind 1124 01:02:37,680 --> 01:02:40,320 Speaker 1: of home life was like. Was about asking good questions. 1125 01:02:40,320 --> 01:02:42,360 Speaker 1: And it wasn't so much about what do you know? 1126 01:02:42,520 --> 01:02:44,920 Speaker 1: What did you learn? But it was about did you 1127 01:02:44,960 --> 01:02:47,840 Speaker 1: ask any good questions? And that actually turns out to 1128 01:02:47,880 --> 01:02:53,120 Speaker 1: be an incredibly important and valuable lesson that I took 1129 01:02:53,160 --> 01:02:54,680 Speaker 1: for many years. I didn't you know that all this 1130 01:02:54,720 --> 01:02:58,280 Speaker 1: stuff about follow your passion, I never really understand. I 1131 01:02:58,320 --> 01:03:02,000 Speaker 1: love banana pudding, but I don't fall not going for 1132 01:03:02,040 --> 01:03:05,040 Speaker 1: a career in banana. No, that's it's a passion for me. 1133 01:03:05,080 --> 01:03:08,640 Speaker 1: But I don't follow banana pudding. But I do follow 1134 01:03:08,640 --> 01:03:13,120 Speaker 1: my curiosity. So when I sort of plateaued in physics 1135 01:03:13,120 --> 01:03:15,320 Speaker 1: that my curiosity wasn't there the way it was when 1136 01:03:15,320 --> 01:03:17,480 Speaker 1: I was first started. Then I made a switch and 1137 01:03:17,480 --> 01:03:20,400 Speaker 1: when my curiosity plateau about how big businesses work and 1138 01:03:20,400 --> 01:03:23,120 Speaker 1: are structured from the consulting world, and I went and 1139 01:03:23,120 --> 01:03:26,760 Speaker 1: started a company. So I kept following my curiosity. You know, 1140 01:03:26,800 --> 01:03:28,160 Speaker 1: how do you write a book? How do you tell 1141 01:03:28,240 --> 01:03:31,040 Speaker 1: stories that bridge together PanAm and James Bond and why 1142 01:03:31,040 --> 01:03:34,240 Speaker 1: the world speaks English? With this one theme that seems 1143 01:03:34,280 --> 01:03:36,600 Speaker 1: really difficult? But how do other you know, how do 1144 01:03:36,640 --> 01:03:40,040 Speaker 1: you do that? I'm really curious, and so I just 1145 01:03:40,120 --> 01:03:43,000 Speaker 1: kept following my curiosity. So what you get from that 1146 01:03:43,160 --> 01:03:44,920 Speaker 1: is wonderful and I hope to pass on to my 1147 01:03:45,000 --> 01:03:50,200 Speaker 1: children some day. Is a passion for asking good questions. 1148 01:03:50,720 --> 01:03:54,480 Speaker 1: And I wish more people would ask good questions. Speaking 1149 01:03:54,600 --> 01:03:58,080 Speaker 1: of good questions, before I get to my favorite ones, 1150 01:03:58,160 --> 01:03:59,760 Speaker 1: I don't I don't have you all day. But there 1151 01:03:59,800 --> 01:04:04,640 Speaker 1: were one or two questions I missed um during the 1152 01:04:04,680 --> 01:04:07,400 Speaker 1: broadcast portion, and let me see what I want to 1153 01:04:07,400 --> 01:04:12,160 Speaker 1: get to, oh um, President Obama's Council of Science Advisors 1154 01:04:12,640 --> 01:04:16,080 Speaker 1: on the National Future of National Research. You served on that. 1155 01:04:16,480 --> 01:04:20,600 Speaker 1: What was that experience like? Well, that actually was the 1156 01:04:20,640 --> 01:04:23,640 Speaker 1: spark for the research. It became this book because when 1157 01:04:23,680 --> 01:04:26,360 Speaker 1: I was called President Obama. I think like to have 1158 01:04:26,400 --> 01:04:28,160 Speaker 1: a lot of academic There were a lot of very 1159 01:04:28,320 --> 01:04:32,120 Speaker 1: august academics. They are very impressive backgrounds, and I think 1160 01:04:32,160 --> 01:04:35,160 Speaker 1: at some point they realized they didn't have someone in 1161 01:04:35,160 --> 01:04:38,920 Speaker 1: the business world, especially in the startup innovation biomedical. And 1162 01:04:39,280 --> 01:04:41,800 Speaker 1: one of the people there had you know, knew me 1163 01:04:41,880 --> 01:04:43,920 Speaker 1: from when I was a student, and so called me 1164 01:04:44,000 --> 01:04:45,760 Speaker 1: up and said, I hear you do something in this 1165 01:04:45,840 --> 01:04:48,440 Speaker 1: bio world, whatever that is. Could you come on you 1166 01:04:48,480 --> 01:04:50,880 Speaker 1: have any interest. I was like, sures, I'm But when 1167 01:04:50,920 --> 01:04:55,320 Speaker 1: I got there, the chairman said, there was a small 1168 01:04:55,360 --> 01:04:57,640 Speaker 1: group of us that were selected to work on this 1169 01:04:57,680 --> 01:05:00,400 Speaker 1: project on the future of national research. We've had these 1170 01:05:00,480 --> 01:05:03,680 Speaker 1: national research labs for seventy years, which the next seven 1171 01:05:03,880 --> 01:05:07,000 Speaker 1: years look like. And he stood up and he said, 1172 01:05:07,080 --> 01:05:10,320 Speaker 1: your job is to write the next generation of the 1173 01:05:10,360 --> 01:05:13,680 Speaker 1: van ever Bush Report. What should the future look like? 1174 01:05:14,640 --> 01:05:18,440 Speaker 1: And I remember thinking I have no idea who van 1175 01:05:18,480 --> 01:05:20,880 Speaker 1: ever bushes or what his report was. You know, I 1176 01:05:20,880 --> 01:05:22,440 Speaker 1: was trying to run a public company I didn't have 1177 01:05:22,440 --> 01:05:24,640 Speaker 1: I wasn't doing a lot of science policy reading or 1178 01:05:24,760 --> 01:05:28,160 Speaker 1: in history. And I remember my first reaction was probably 1179 01:05:28,200 --> 01:05:31,480 Speaker 1: like a lot of people's m how do I get 1180 01:05:31,480 --> 01:05:34,680 Speaker 1: out of this assignment, because like, really, I'm really not 1181 01:05:34,760 --> 01:05:36,480 Speaker 1: the right guy for this job because I have no 1182 01:05:36,560 --> 01:05:38,880 Speaker 1: idea what this guy is talking about, and kind of 1183 01:05:38,880 --> 01:05:41,800 Speaker 1: maybe I can go back to the real world and uh. 1184 01:05:42,120 --> 01:05:44,240 Speaker 1: But then the more I started reading about it, I 1185 01:05:44,320 --> 01:05:46,360 Speaker 1: was fascinating. He took people talk about, oh, it's hard 1186 01:05:46,360 --> 01:05:49,520 Speaker 1: to innovate when you're large. Well, van ever, Bush was 1187 01:05:49,520 --> 01:05:52,840 Speaker 1: faced with a two million person organization and he created 1188 01:05:52,880 --> 01:05:58,440 Speaker 1: this astonishing system for innovating incredibly fast that played an 1189 01:05:58,480 --> 01:06:02,200 Speaker 1: incredibly important role in Second World War and the future 1190 01:06:02,240 --> 01:06:05,680 Speaker 1: of the United States. How did he do it? Why? 1191 01:06:05,680 --> 01:06:08,280 Speaker 1: Why did it work? What did he understand? And I 1192 01:06:08,360 --> 01:06:10,320 Speaker 1: just got more and more curious about coming back to 1193 01:06:10,360 --> 01:06:13,400 Speaker 1: asking questions, why did it work so well? How did 1194 01:06:13,400 --> 01:06:15,240 Speaker 1: he do it? What insights did he have? And I've 1195 01:06:15,280 --> 01:06:17,320 Speaker 1: had The more I read about him, the more I 1196 01:06:17,320 --> 01:06:19,560 Speaker 1: found out that there were all these lessons that he 1197 01:06:19,600 --> 01:06:22,360 Speaker 1: had picked up over the years that were really applicable 1198 01:06:22,400 --> 01:06:24,840 Speaker 1: to the business world and private sector, not just the 1199 01:06:24,840 --> 01:06:28,480 Speaker 1: public sector, and nobody had really gone there. And you 1200 01:06:28,560 --> 01:06:34,040 Speaker 1: give some credit to FDR for basically recognizing that Bush 1201 01:06:34,160 --> 01:06:37,960 Speaker 1: had correctly diagnosed the problem with the Pentagon. I don't 1202 01:06:37,960 --> 01:06:40,720 Speaker 1: even know what was closed the War Department then uh 1203 01:06:40,800 --> 01:06:44,919 Speaker 1: and had some solutions to bridge that gap. I don't 1204 01:06:44,920 --> 01:06:47,120 Speaker 1: know a lot of presidents that would have been as 1205 01:06:47,200 --> 01:06:51,840 Speaker 1: insightful as that pre war time president. It was a 1206 01:06:51,880 --> 01:06:53,760 Speaker 1: ten minute meeting and a Bush was at the time 1207 01:06:53,880 --> 01:06:55,840 Speaker 1: dean of engineering at m i T. Was a provost, 1208 01:06:55,840 --> 01:06:57,080 Speaker 1: He was a number two at m i T to 1209 01:06:57,080 --> 01:06:58,600 Speaker 1: help turn m I T. Was it kind of an 1210 01:06:58,680 --> 01:07:01,600 Speaker 1: organizational genius. He had turn m i T from a 1211 01:07:01,600 --> 01:07:04,840 Speaker 1: average university into the leading technology university in the country 1212 01:07:04,840 --> 01:07:07,480 Speaker 1: and then eventually the world. But he saw the writing 1213 01:07:07,480 --> 01:07:09,480 Speaker 1: on the wall. He saw from all the fleeing Jewish 1214 01:07:09,560 --> 01:07:12,720 Speaker 1: refugees from Nazi Germany that they were far ahead of 1215 01:07:12,800 --> 01:07:17,080 Speaker 1: us in critical technologies. In yeah. Well, he and several 1216 01:07:17,080 --> 01:07:23,400 Speaker 1: others were hearing this from the Jewish refugees, and he understood. 1217 01:07:23,400 --> 01:07:26,000 Speaker 1: He had worked with the military, he understood that we 1218 01:07:26,000 --> 01:07:28,720 Speaker 1: were far behind, and so he quit his job as 1219 01:07:28,760 --> 01:07:32,720 Speaker 1: a tenured professor, moved to Washington, talked his way into 1220 01:07:32,720 --> 01:07:35,040 Speaker 1: a ten minute meeting with FDR, and he told FDR 1221 01:07:36,360 --> 01:07:38,440 Speaker 1: there's a war coming and we're gonna lose because the 1222 01:07:38,560 --> 01:07:40,919 Speaker 1: army and the Navy will never catch up in time. 1223 01:07:41,560 --> 01:07:43,960 Speaker 1: And he gave him a proposal, three short bullets. I 1224 01:07:43,960 --> 01:07:47,160 Speaker 1: want you to authorize a new group inside the federal 1225 01:07:47,200 --> 01:07:49,120 Speaker 1: government that will report only to me, and I will 1226 01:07:49,160 --> 01:07:52,080 Speaker 1: report only to you, and I will mobilize the nation's 1227 01:07:52,080 --> 01:07:54,760 Speaker 1: scientists for war. It was a ten minute meeting. If 1228 01:07:54,840 --> 01:07:57,080 Speaker 1: DR read it, looked him up and down, signed it. Okay, 1229 01:07:57,120 --> 01:08:00,920 Speaker 1: FDR turned around, got to work. That's astonishing, just astonishing. 1230 01:08:00,960 --> 01:08:03,960 Speaker 1: People have no idea how close we really came to 1231 01:08:04,040 --> 01:08:06,480 Speaker 1: losing World War two, and how close we came to 1232 01:08:06,600 --> 01:08:10,080 Speaker 1: have completely different world map. And and if you take 1233 01:08:10,120 --> 01:08:12,760 Speaker 1: twenty popular histories of the Second World War, you might 1234 01:08:12,760 --> 01:08:14,840 Speaker 1: find van for Bush's name in one or two of them. 1235 01:08:14,880 --> 01:08:17,680 Speaker 1: That's amazing. So I don't have you all day. I 1236 01:08:17,720 --> 01:08:20,599 Speaker 1: want to get to some of my favorite questions I 1237 01:08:20,680 --> 01:08:24,080 Speaker 1: ask all of my guests. I find these are revealing 1238 01:08:24,280 --> 01:08:28,560 Speaker 1: of who they are and what we don't know about them. Um, 1239 01:08:28,600 --> 01:08:31,800 Speaker 1: what are you streaming, watching listening to? Tell us about 1240 01:08:31,880 --> 01:08:34,759 Speaker 1: podcasts or Netflix? What do you what are you enjoying 1241 01:08:34,800 --> 01:08:38,560 Speaker 1: these days? Well, my wife and I actually really loved Homeland, 1242 01:08:38,920 --> 01:08:42,320 Speaker 1: Homeland and the Americans, and I think if I have 1243 01:08:42,320 --> 01:08:45,559 Speaker 1: a guilty pleasure probably be that new Sherlock Holmes one 1244 01:08:45,600 --> 01:08:47,880 Speaker 1: with Benedict cover Batch. It's a lot of fun. I 1245 01:08:47,880 --> 01:08:50,880 Speaker 1: haven't gotten to that. If you like um Homeland, do 1246 01:08:50,920 --> 01:08:56,880 Speaker 1: you ever watched Fouda so it's an Israeli security group. 1247 01:08:57,520 --> 01:08:59,559 Speaker 1: Do not watch it before you go to bed because 1248 01:08:59,640 --> 01:09:03,360 Speaker 1: it is so gripping f A U d A so 1249 01:09:03,439 --> 01:09:06,800 Speaker 1: gripping and so exciting, Like when you're done, you're like, 1250 01:09:08,240 --> 01:09:12,360 Speaker 1: So it's very much a challenging thing to watch. And 1251 01:09:12,400 --> 01:09:15,920 Speaker 1: then Jack Ryan is is really worth checking out. It's 1252 01:09:15,920 --> 01:09:18,320 Speaker 1: so funny because I spent we were talking before I 1253 01:09:18,360 --> 01:09:21,120 Speaker 1: spent a while at the CIA, and I remember asking them, 1254 01:09:21,520 --> 01:09:23,320 Speaker 1: I said, to go, like his Tom Crui is gonna 1255 01:09:23,320 --> 01:09:25,720 Speaker 1: come jumping down here from somewhere. Yeah, it's really fun. 1256 01:09:26,240 --> 01:09:29,280 Speaker 1: But I remember asking them about do you guys watch 1257 01:09:29,320 --> 01:09:31,800 Speaker 1: any of these shows? And they were big fans of 1258 01:09:32,120 --> 01:09:34,479 Speaker 1: The people I spoke to were pretty big fans of Homeland, 1259 01:09:34,560 --> 01:09:38,240 Speaker 1: but the Jack Ryan They're like, yeah, it's just not well, 1260 01:09:38,479 --> 01:09:41,000 Speaker 1: you know, any of the Tom Clancy stuff, the Cold 1261 01:09:41,120 --> 01:09:44,360 Speaker 1: War things that was his forte. Everybody has kind of 1262 01:09:44,439 --> 01:09:46,439 Speaker 1: moved on and it's a little bit of a Cold 1263 01:09:46,520 --> 01:09:50,040 Speaker 1: War story in the modern era. So it it may 1264 01:09:50,080 --> 01:09:52,200 Speaker 1: not be their cup of tea, but everybody in it 1265 01:09:52,360 --> 01:09:55,599 Speaker 1: is really interesting. Tell us some tell us the most 1266 01:09:55,680 --> 01:10:01,000 Speaker 1: important thing people don't know about Sofia bicol. Wow, I 1267 01:10:01,080 --> 01:10:03,240 Speaker 1: don't even know where to I guess you know. When 1268 01:10:03,240 --> 01:10:06,519 Speaker 1: I was in college, I had this dream. A buddy 1269 01:10:06,520 --> 01:10:08,360 Speaker 1: of mine and I had this dream that we would 1270 01:10:08,439 --> 01:10:11,800 Speaker 1: ride motorcycles across the country. And then when um, when 1271 01:10:11,840 --> 01:10:13,879 Speaker 1: I got out of call, of course I was never happening. 1272 01:10:14,000 --> 01:10:16,599 Speaker 1: Do you ride at all? So yeah, when as soon 1273 01:10:16,600 --> 01:10:19,559 Speaker 1: as I graduated and moved to the Bay Area, one 1274 01:10:19,560 --> 01:10:21,560 Speaker 1: of the first things I did was bought a motorcycle, 1275 01:10:21,720 --> 01:10:23,800 Speaker 1: and I spent much of that ten years at the 1276 01:10:23,880 --> 01:10:25,840 Speaker 1: time that I was in San Francisco Bay Area on 1277 01:10:25,880 --> 01:10:28,800 Speaker 1: the bike. So I love to ride. You you can't 1278 01:10:28,880 --> 01:10:33,160 Speaker 1: ride around here. When I moved to New York uh 1279 01:10:34,680 --> 01:10:36,519 Speaker 1: years ago, I sold the bike. At that when I 1280 01:10:36,600 --> 01:10:39,800 Speaker 1: first started, I was an idiot, like any of course something. 1281 01:10:40,120 --> 01:10:43,400 Speaker 1: I remember that there was no helmet law back then, 1282 01:10:44,120 --> 01:10:47,840 Speaker 1: But that's important because there's a shortage of organs and 1283 01:10:48,120 --> 01:10:51,320 Speaker 1: the lack of helmet law. States with no helmets don't 1284 01:10:51,360 --> 01:10:53,360 Speaker 1: have a backup. If you want to do good for 1285 01:10:53,439 --> 01:10:55,880 Speaker 1: the world and don't at your organs, don't wear a helmet. 1286 01:10:56,160 --> 01:11:00,880 Speaker 1: It's because you basically have a nicely preserved body. That's right, 1287 01:11:02,360 --> 01:11:07,280 Speaker 1: it's k or it just goes straight to the hospital, 1288 01:11:07,320 --> 01:11:09,599 Speaker 1: skip the motorcycle and say cut cut off my head. 1289 01:11:10,520 --> 01:11:12,360 Speaker 1: But I actually I remember one day going out. I 1290 01:11:12,479 --> 01:11:14,640 Speaker 1: was living in Palo Alto and East Palo Alto at 1291 01:11:14,680 --> 01:11:16,720 Speaker 1: the time. People I don't know this, but it was 1292 01:11:16,760 --> 01:11:19,160 Speaker 1: like the murder capital of the United have like the 1293 01:11:19,240 --> 01:11:21,400 Speaker 1: wealthiest group and then you have like the murder capital. 1294 01:11:21,840 --> 01:11:23,320 Speaker 1: And I remember I had to buy a helmet. I 1295 01:11:23,360 --> 01:11:25,960 Speaker 1: remember going, you know, looked into classified I don't even 1296 01:11:26,000 --> 01:11:27,840 Speaker 1: know if there was a Craigslist back then, and I 1297 01:11:27,960 --> 01:11:30,880 Speaker 1: found this guy in East Palo Alto and I remember 1298 01:11:31,000 --> 01:11:33,120 Speaker 1: driving over there and said, oh yeah. It was like 1299 01:11:33,240 --> 01:11:35,720 Speaker 1: this Hell's Angel guy with a giant gud and the 1300 01:11:36,040 --> 01:11:39,200 Speaker 1: huge beard and the tattoos. And I was like, yeah, man, 1301 01:11:39,280 --> 01:11:41,120 Speaker 1: this sucks. I have to buy a helmet, isn't it 1302 01:11:41,160 --> 01:11:43,960 Speaker 1: really doesn't really suck? And he just looked at me 1303 01:11:44,000 --> 01:11:48,160 Speaker 1: and said, you're an idiot. Really, he said, you're an idiot. 1304 01:11:48,200 --> 01:11:50,120 Speaker 1: Do you know how many friends I've lost because they 1305 01:11:50,160 --> 01:11:53,280 Speaker 1: didn't wear helmets? And you could. We could survive a 1306 01:11:53,360 --> 01:11:57,360 Speaker 1: fall with a helmet pretty sometimes yeah, assuming it's you know, 1307 01:11:57,479 --> 01:11:59,320 Speaker 1: you're not going a hundred miles, but it was. I 1308 01:11:59,439 --> 01:12:02,280 Speaker 1: was an idiot it and anybody who like you say, 1309 01:12:02,560 --> 01:12:05,280 Speaker 1: when the Hell's angels say you're taking too much risk, 1310 01:12:05,360 --> 01:12:07,439 Speaker 1: you need to you need to head yourself a little 1311 01:12:07,439 --> 01:12:10,200 Speaker 1: bit with a helmet. You gotta pay attention. Um, So, 1312 01:12:10,280 --> 01:12:12,680 Speaker 1: who are your early mentors who affected the way you 1313 01:12:12,880 --> 01:12:20,280 Speaker 1: look at the world of innovation and franchise and managing institutions. Well, 1314 01:12:20,280 --> 01:12:21,760 Speaker 1: I think when I was really honest with my father, 1315 01:12:21,920 --> 01:12:25,760 Speaker 1: he was just not only an impressive scientist and a 1316 01:12:26,200 --> 01:12:28,920 Speaker 1: real believer in a bigger purpose which is kind of 1317 01:12:28,920 --> 01:12:30,840 Speaker 1: the search for truth and asking questions, but it was 1318 01:12:30,880 --> 01:12:35,080 Speaker 1: also incredibly impressive and motivating people. I think when I 1319 01:12:35,120 --> 01:12:39,040 Speaker 1: got to Stanford there was a scientist named Lenny Suskin 1320 01:12:39,080 --> 01:12:41,280 Speaker 1: who was like a disciple of Richard Feynman. He had 1321 01:12:41,320 --> 01:12:45,400 Speaker 1: this awesome style of really figure everything out for yourself. 1322 01:12:45,520 --> 01:12:47,559 Speaker 1: Like he said to me, Sophia, never want to see 1323 01:12:47,600 --> 01:12:51,200 Speaker 1: you in a library, really, And that's what Dick Feynman 1324 01:12:51,280 --> 01:12:54,160 Speaker 1: had told him. And it's when you have a problem 1325 01:12:54,240 --> 01:12:56,519 Speaker 1: that's sort of like underlying this book I came into 1326 01:12:56,920 --> 01:12:58,800 Speaker 1: you know, there's two hundred years of people thinking and 1327 01:12:58,840 --> 01:13:02,240 Speaker 1: writing about organization, and I just came from a completely 1328 01:13:02,320 --> 01:13:05,760 Speaker 1: different angle. And in part that's due to just think 1329 01:13:05,800 --> 01:13:08,400 Speaker 1: about things for yourself. Try to come up. Don't read. 1330 01:13:08,920 --> 01:13:12,439 Speaker 1: Actually the opposite, don't read and absorb what everybody tells 1331 01:13:12,479 --> 01:13:14,920 Speaker 1: you is to try to think of things from first 1332 01:13:14,960 --> 01:13:19,200 Speaker 1: principles in the business world, probably in your world. I 1333 01:13:19,640 --> 01:13:22,840 Speaker 1: one of my early board members was a guy named 1334 01:13:22,880 --> 01:13:27,679 Speaker 1: Bruce Kavner, I know the name, ran a very successful 1335 01:13:27,760 --> 01:13:30,679 Speaker 1: hedge fund called Caxton and he was a very deep, 1336 01:13:31,120 --> 01:13:36,320 Speaker 1: thoughtful thinker, and he, you know, he said little, but 1337 01:13:36,479 --> 01:13:39,320 Speaker 1: the little that he said was very thoughtful that would 1338 01:13:39,920 --> 01:13:44,519 Speaker 1: help you rethink the strategy, how players are acting in 1339 01:13:44,560 --> 01:13:48,400 Speaker 1: the world around you, strategy, competition, and so I got 1340 01:13:48,400 --> 01:13:50,599 Speaker 1: a lot of useful stuff from him as well, quite 1341 01:13:50,680 --> 01:13:53,439 Speaker 1: quite interesting. Let's talk about books. What are some of 1342 01:13:53,479 --> 01:13:55,439 Speaker 1: your favorite books? What are you reading? What do you 1343 01:13:55,600 --> 01:13:59,200 Speaker 1: recommend to people? Oh? My god, there's so many. By 1344 01:13:59,240 --> 01:14:01,720 Speaker 1: the way, this is the favorite question. I get more 1345 01:14:01,760 --> 01:14:07,000 Speaker 1: emails about this question than anything else. Many got it. Well, Firstly, 1346 01:14:07,040 --> 01:14:10,040 Speaker 1: I love biographies and I read so many biographies. But 1347 01:14:10,120 --> 01:14:13,960 Speaker 1: there's there's one. When I was writing that book, I 1348 01:14:14,120 --> 01:14:15,479 Speaker 1: was sort of three years in a cave and I 1349 01:14:15,600 --> 01:14:17,760 Speaker 1: was just there's so many histories I had to learn, 1350 01:14:17,800 --> 01:14:20,760 Speaker 1: and so many industries. So I was really focused on 1351 01:14:22,000 --> 01:14:25,080 Speaker 1: reading for that. There was only one author who made 1352 01:14:25,120 --> 01:14:27,200 Speaker 1: me stop and like read for fun that had nothing 1353 01:14:27,280 --> 01:14:30,640 Speaker 1: to do. His name is Scott Burke. He's a He 1354 01:14:31,200 --> 01:14:34,760 Speaker 1: wrote this awesome book on Charles Lindberg, the best book 1355 01:14:34,760 --> 01:14:37,280 Speaker 1: on Lindbergh, and it is such an incredible story because 1356 01:14:37,320 --> 01:14:40,720 Speaker 1: Lindburgh there really hasn't been a hero, an American hero 1357 01:14:40,920 --> 01:14:44,280 Speaker 1: like that in a century. People don't realize what a hero. 1358 01:14:44,600 --> 01:14:47,679 Speaker 1: When he completed that voyage over the Atlantic. At the time, 1359 01:14:47,760 --> 01:14:50,840 Speaker 1: flight was this crazy thing. People wouldn't weren't even taking 1360 01:14:50,920 --> 01:14:53,519 Speaker 1: odds on his life because it was so bad, so 1361 01:14:53,680 --> 01:14:56,479 Speaker 1: unlikely that this young kid would make it flying solo 1362 01:14:56,520 --> 01:14:59,720 Speaker 1: across the Atlantic. People across the country were praying for him. 1363 01:14:59,720 --> 01:15:02,080 Speaker 1: And when he came back, it's like a quarter million 1364 01:15:02,240 --> 01:15:04,200 Speaker 1: stories about him. He went on a tour of the 1365 01:15:04,320 --> 01:15:08,000 Speaker 1: United States, literally a third of the population not to 1366 01:15:08,080 --> 01:15:12,439 Speaker 1: see him, and that's and it's this incredible arc because 1367 01:15:12,479 --> 01:15:15,840 Speaker 1: he was more popular than FDR. He was this unbelievably 1368 01:15:15,960 --> 01:15:23,320 Speaker 1: popular guy, and f DR felt threatened, and FDR orchestrated 1369 01:15:23,360 --> 01:15:27,320 Speaker 1: this campaign against him that painted him as this Nazi sympathizer, 1370 01:15:27,479 --> 01:15:31,640 Speaker 1: appeasement sympathizer, anti Semite even and it just was not 1371 01:15:31,960 --> 01:15:34,680 Speaker 1: the case. But he was a very reserved guy and 1372 01:15:34,720 --> 01:15:36,600 Speaker 1: he didn't fight back, and so he got kind of 1373 01:15:36,680 --> 01:15:40,880 Speaker 1: tainted by a pretty orchestrated campaign against him. And just 1374 01:15:41,120 --> 01:15:44,439 Speaker 1: and he went from the most famous guy in the country, 1375 01:15:45,240 --> 01:15:48,720 Speaker 1: two people like you know, burning his books. It was 1376 01:15:48,880 --> 01:15:50,920 Speaker 1: just an unbelievable up and down. And then he redeemed 1377 01:15:50,960 --> 01:15:53,599 Speaker 1: himself in the Second World War. And just the life 1378 01:15:53,680 --> 01:15:56,840 Speaker 1: story is an incredible story, and anything Scott and that 1379 01:15:56,920 --> 01:16:01,000 Speaker 1: biographer is a beautiful writer and combines great storytelling with 1380 01:16:01,720 --> 01:16:05,120 Speaker 1: very accurate history. So before I ask you about more 1381 01:16:05,200 --> 01:16:09,160 Speaker 1: books you like biographies and obviously flying, did you read 1382 01:16:09,200 --> 01:16:12,040 Speaker 1: the McCullough book on the Right Brothers. It's on my list, 1383 01:16:12,120 --> 01:16:14,360 Speaker 1: but I haven't. It's it's I can't wait through, you can't, 1384 01:16:14,479 --> 01:16:17,200 Speaker 1: can't can't recommend enough. What else are you reading? What 1385 01:16:17,560 --> 01:16:20,600 Speaker 1: you know? Biographies? Um, I feel like I used to 1386 01:16:20,640 --> 01:16:24,160 Speaker 1: play competitive tennis in the juniors. Yeah, I am very 1387 01:16:24,320 --> 01:16:26,080 Speaker 1: late to the game of tennis. I picked it up 1388 01:16:26,080 --> 01:16:29,200 Speaker 1: in my fifties and I find it a fascinating and 1389 01:16:29,439 --> 01:16:32,520 Speaker 1: frustrating sports. I really enjoy it. Well. That an anecdote 1390 01:16:32,560 --> 01:16:33,760 Speaker 1: there is there was a guy I don't know if 1391 01:16:33,960 --> 01:16:36,720 Speaker 1: you know this name, but you know, when you're young 1392 01:16:36,760 --> 01:16:38,880 Speaker 1: and the juniors, you often go around to tournaments with 1393 01:16:39,200 --> 01:16:41,439 Speaker 1: there's somebody local and you're one of your dad's drives you. 1394 01:16:41,560 --> 01:16:43,200 Speaker 1: So the guy would go around to tournaments with him 1395 01:16:43,200 --> 01:16:45,320 Speaker 1: play all the time. Was with a guy named Lyle Menendez, 1396 01:16:46,120 --> 01:16:51,200 Speaker 1: and he became famous later unfortunately for murdering his parents. Yeah, 1397 01:16:51,280 --> 01:16:56,720 Speaker 1: so I began my life in crime with Eric. But 1398 01:16:57,000 --> 01:16:59,479 Speaker 1: were you? Were you any good? I was ranked in 1399 01:16:59,520 --> 01:17:02,680 Speaker 1: middle state really so, I mean Lyle was better, but 1400 01:17:02,840 --> 01:17:06,840 Speaker 1: I was pretty good. Um, but I grew up in 1401 01:17:06,880 --> 01:17:10,639 Speaker 1: sort of that. Andre Agassi Pete Sampras, the Andrea Agassi 1402 01:17:10,720 --> 01:17:14,360 Speaker 1: biography open. It's like one of the best sports biographies. 1403 01:17:14,920 --> 01:17:17,680 Speaker 1: It's I would never in a million years. Actually, you 1404 01:17:17,720 --> 01:17:19,519 Speaker 1: know what it's funny is that Pete Sampras wrote a 1405 01:17:19,520 --> 01:17:22,120 Speaker 1: biography first, and it was very good, especially if you 1406 01:17:22,200 --> 01:17:24,720 Speaker 1: like tennis, because you know it's it's good. And then 1407 01:17:25,200 --> 01:17:28,800 Speaker 1: Andrea Agassi comes out with his biography. Second, and he 1408 01:17:28,920 --> 01:17:32,880 Speaker 1: wins like that hand he beats Pete Finally, finally, he 1409 01:17:33,000 --> 01:17:36,400 Speaker 1: beats Pete off. It's a it's a it's not only 1410 01:17:36,439 --> 01:17:38,479 Speaker 1: a sports story, and but it's also called all this 1411 01:17:38,600 --> 01:17:40,960 Speaker 1: drama because he had this huge hatred of the sport 1412 01:17:41,000 --> 01:17:42,680 Speaker 1: and a huge love of the sport that came from 1413 01:17:42,720 --> 01:17:45,479 Speaker 1: his father. And then how he meets stephie Graff. It's 1414 01:17:45,479 --> 01:17:50,080 Speaker 1: an incredible love story, the love story and sports story. 1415 01:17:50,400 --> 01:17:53,840 Speaker 1: Give us one more book, one more book, one more book. 1416 01:17:54,439 --> 01:17:58,519 Speaker 1: Let's see. There is a book that I like by 1417 01:17:58,600 --> 01:18:02,479 Speaker 1: Gary caspar Off called about How Life Imitates Chess. It's 1418 01:18:02,520 --> 01:18:06,000 Speaker 1: a small book, but cass profits fascinating guy. I mean, 1419 01:18:06,040 --> 01:18:09,080 Speaker 1: really the longest reigning chess champion in history. And there 1420 01:18:09,120 --> 01:18:12,639 Speaker 1: he kind of breaks down what it was about how 1421 01:18:12,800 --> 01:18:16,240 Speaker 1: he approached the game that helped him become this, you know, 1422 01:18:16,320 --> 01:18:20,040 Speaker 1: the longest reigning chess champion in history, and it was 1423 01:18:20,120 --> 01:18:24,360 Speaker 1: some very interesting insights that actually I still apply to 1424 01:18:24,520 --> 01:18:26,920 Speaker 1: this day, which is that he when he makes a 1425 01:18:27,040 --> 01:18:29,840 Speaker 1: mistake or or loses a game, or even he wins 1426 01:18:29,880 --> 01:18:32,479 Speaker 1: a game, he doesn't ask what was wrong with that move? 1427 01:18:32,840 --> 01:18:35,200 Speaker 1: Why did you know? You know? Night to Bishop three 1428 01:18:36,080 --> 01:18:38,280 Speaker 1: was good or bad? He has what was in my 1429 01:18:38,479 --> 01:18:42,400 Speaker 1: mind that when I made that decision, What was my 1430 01:18:42,560 --> 01:18:46,280 Speaker 1: decision making process by which I arrived at that conclusion, 1431 01:18:46,320 --> 01:18:48,760 Speaker 1: and how should I improve my process. It's a little 1432 01:18:48,800 --> 01:18:51,559 Speaker 1: like an investor, I think. You don't say you lose 1433 01:18:51,600 --> 01:18:53,720 Speaker 1: money on an investment. You don't say, well, you know, 1434 01:18:53,800 --> 01:18:56,120 Speaker 1: the balance sheet was this, or the p was that, 1435 01:18:56,400 --> 01:18:57,800 Speaker 1: or the team was at and I should look at 1436 01:18:58,080 --> 01:19:00,639 Speaker 1: you say how did I decide? What was I thinking 1437 01:19:01,040 --> 01:19:05,080 Speaker 1: during that day? What are my mental checklists? And then 1438 01:19:05,120 --> 01:19:08,160 Speaker 1: you go back and try to change your mental checklists, 1439 01:19:08,600 --> 01:19:11,640 Speaker 1: so you gain much more than just the one understanding 1440 01:19:11,680 --> 01:19:14,400 Speaker 1: of the one move. Basically comes down to keep asking 1441 01:19:14,520 --> 01:19:18,599 Speaker 1: why why did you make that move? Always process versus results. 1442 01:19:18,720 --> 01:19:21,559 Speaker 1: If you focus too much on resulting, as any Duke 1443 01:19:21,800 --> 01:19:24,720 Speaker 1: called it and thinking and bets, you're drawing the wrong 1444 01:19:24,800 --> 01:19:27,719 Speaker 1: conclusion as opposed to what process led to that result. 1445 01:19:28,200 --> 01:19:32,400 Speaker 1: And in a very interesting and strategic way. Speaking of failure, 1446 01:19:32,520 --> 01:19:34,519 Speaker 1: tell us about a time you failed and what you 1447 01:19:34,680 --> 01:19:38,160 Speaker 1: learned from the experience. Oh, there was so many. I 1448 01:19:38,200 --> 01:19:39,679 Speaker 1: mean I don't even know where to start. They could 1449 01:19:39,680 --> 01:19:43,559 Speaker 1: be another hour. Um. In fact, when I first started 1450 01:19:43,600 --> 01:19:46,800 Speaker 1: a company, My favorite question was going around to all 1451 01:19:46,840 --> 01:19:50,120 Speaker 1: the successful people, any successful people that would take five 1452 01:19:50,160 --> 01:19:51,640 Speaker 1: minutes to talk to me, and ask what do you 1453 01:19:51,720 --> 01:19:56,080 Speaker 1: wish you'd done differently? And by the way, I asked 1454 01:19:56,120 --> 01:19:59,360 Speaker 1: that question to everybody who we talked to about renovating 1455 01:19:59,400 --> 01:20:02,479 Speaker 1: their kitchen, because we're about to start a kitchen renovation. 1456 01:20:02,800 --> 01:20:06,160 Speaker 1: What what did you like about the process? What what 1457 01:20:06,280 --> 01:20:08,960 Speaker 1: would you do differently? What mistakes did you make? And 1458 01:20:09,080 --> 01:20:12,480 Speaker 1: I have found that that question is great for everything. 1459 01:20:12,760 --> 01:20:15,200 Speaker 1: It's the best question. And I learned so much. And 1460 01:20:15,800 --> 01:20:18,120 Speaker 1: one of my mistakes was not listening enough to what 1461 01:20:18,200 --> 01:20:21,640 Speaker 1: I heard and uh, good advice ignored, you know, I mean, 1462 01:20:21,680 --> 01:20:24,600 Speaker 1: there's there's there's two kinds of failures where you do 1463 01:20:24,680 --> 01:20:26,920 Speaker 1: the right stuff but things don't work out. Like you 1464 01:20:27,600 --> 01:20:30,160 Speaker 1: do all the right stuff about developing a drug, but 1465 01:20:30,320 --> 01:20:34,000 Speaker 1: it just doesn't work and there's something fundamental about the technology. 1466 01:20:34,120 --> 01:20:36,599 Speaker 1: That's a good failure. That's a failure, but it's very 1467 01:20:36,640 --> 01:20:39,160 Speaker 1: depressing if you've spent years of your life or the 1468 01:20:39,240 --> 01:20:40,920 Speaker 1: team and you had all these high hopes and these 1469 01:20:41,120 --> 01:20:44,120 Speaker 1: it's it's it happens all the time, but still when 1470 01:20:44,160 --> 01:20:46,479 Speaker 1: you go through that and it's you know, people are 1471 01:20:46,560 --> 01:20:50,040 Speaker 1: crying their emotional because of all the promise that we 1472 01:20:50,120 --> 01:20:52,439 Speaker 1: thought we could do for the world and then it 1473 01:20:52,520 --> 01:20:55,920 Speaker 1: doesn't work. It's really and when you go through those 1474 01:20:55,960 --> 01:20:59,000 Speaker 1: hard times. I had a terrific hr guy who was 1475 01:20:59,040 --> 01:21:01,320 Speaker 1: a great advice are older than me, quite a bit older, 1476 01:21:01,360 --> 01:21:04,439 Speaker 1: and he was a former actually professional football player. He 1477 01:21:04,479 --> 01:21:07,519 Speaker 1: played for the Patriots in the NFL defensive back. So 1478 01:21:07,600 --> 01:21:09,519 Speaker 1: he had this great way about I mean, was you know, 1479 01:21:09,680 --> 01:21:11,880 Speaker 1: just tough, che feely enough to do the job well, 1480 01:21:12,120 --> 01:21:14,600 Speaker 1: but not so much that you're like, come on, so 1481 01:21:15,240 --> 01:21:17,960 Speaker 1: And one of the things in difficult times like that 1482 01:21:19,080 --> 01:21:21,680 Speaker 1: and failures like that, that you know he coached me 1483 01:21:21,760 --> 01:21:24,080 Speaker 1: on and coached all of us on, was how to 1484 01:21:24,160 --> 01:21:27,680 Speaker 1: make sure you treat people with dignity and fairness and 1485 01:21:27,800 --> 01:21:31,479 Speaker 1: how do you help them because relationships last longer than 1486 01:21:31,760 --> 01:21:34,840 Speaker 1: any company or any project, and so the importance of 1487 01:21:34,880 --> 01:21:40,040 Speaker 1: relationships and leading by relationships was incredibly powerful and the 1488 01:21:40,479 --> 01:21:43,280 Speaker 1: lesson that I really should have understood better when I 1489 01:21:43,439 --> 01:21:45,680 Speaker 1: was a young guy and asking people, you know all 1490 01:21:45,720 --> 01:21:50,680 Speaker 1: these you know, successful people, Almost invariably the number one 1491 01:21:50,720 --> 01:21:53,400 Speaker 1: answer I got about what do you wish you'd done differently? 1492 01:21:53,439 --> 01:21:57,080 Speaker 1: When I talked to successful CEOs off off the record 1493 01:21:58,400 --> 01:22:06,400 Speaker 1: was get rid of people faster, real hiring mistakes. I 1494 01:22:06,560 --> 01:22:12,400 Speaker 1: was too slow to make a change anyone who's gott 1495 01:22:12,400 --> 01:22:14,840 Speaker 1: into a large organization. As you you lead through your team, 1496 01:22:14,920 --> 01:22:17,640 Speaker 1: you don't really so much. And if you're trying to 1497 01:22:17,720 --> 01:22:20,120 Speaker 1: make individual decisions, you know it's going to be a 1498 01:22:20,160 --> 01:22:22,800 Speaker 1: big problem. But you lead through the team that you assembled. 1499 01:22:23,720 --> 01:22:26,960 Speaker 1: And almost everyone I talked to he said, I wish 1500 01:22:27,000 --> 01:22:31,840 Speaker 1: I'd move more quickly on some and some bad members. 1501 01:22:32,280 --> 01:22:35,960 Speaker 1: And uh, you know, ten or fifteen years later, I 1502 01:22:36,120 --> 01:22:40,080 Speaker 1: was saying the same thing because my mistake was me 1503 01:22:40,280 --> 01:22:42,320 Speaker 1: and some of the folks I worked. I was thinking like, oh, 1504 01:22:42,360 --> 01:22:44,479 Speaker 1: we have this bad problem. We think we can fix it. 1505 01:22:45,280 --> 01:22:47,960 Speaker 1: Sometimes you can't fix a problem. Sometimes you just need 1506 01:22:48,040 --> 01:22:50,280 Speaker 1: to make a change. So what do you do when 1507 01:22:50,479 --> 01:22:52,200 Speaker 1: for fun, when you're when you're not down in the 1508 01:22:52,280 --> 01:22:54,479 Speaker 1: c I a telling them how to save the world. 1509 01:22:54,920 --> 01:22:59,120 Speaker 1: What do you do for um to relax and kick back? Well? 1510 01:22:59,200 --> 01:23:01,880 Speaker 1: I have Uh my wife is pretty cool. So we 1511 01:23:02,000 --> 01:23:03,960 Speaker 1: got from long walks and we have two young kids. 1512 01:23:04,000 --> 01:23:06,040 Speaker 1: We just I could just sit and watch them and 1513 01:23:06,120 --> 01:23:08,800 Speaker 1: that's pretty cool. So now I do running. I don't 1514 01:23:08,840 --> 01:23:12,640 Speaker 1: have so much time for you know, tennis or or 1515 01:23:12,720 --> 01:23:16,080 Speaker 1: other endurance sports. But I go running and endurance sports. 1516 01:23:16,760 --> 01:23:19,600 Speaker 1: Uh yeah, I did tryathlon for quite a while, and 1517 01:23:20,280 --> 01:23:25,000 Speaker 1: uh you've replaced triathlon just with the running portion. Triathlon 1518 01:23:25,120 --> 01:23:27,719 Speaker 1: is another job. If you do kind of long distance 1519 01:23:27,840 --> 01:23:29,760 Speaker 1: like half fire and or iron. I did half firing 1520 01:23:29,800 --> 01:23:32,479 Speaker 1: it into Iron. I mean it's ten to fifteen hours 1521 01:23:32,520 --> 01:23:35,760 Speaker 1: a week of training, and it's a brutal race and 1522 01:23:35,920 --> 01:23:40,720 Speaker 1: of itself. I think everybody took the wrong lesson from marathons. 1523 01:23:40,760 --> 01:23:44,360 Speaker 1: Remember the first guy who arrived at Sparta, he dropped dead. 1524 01:23:44,439 --> 01:23:46,719 Speaker 1: That's right. That should have been a cautionary tale. Don't 1525 01:23:46,760 --> 01:23:48,720 Speaker 1: don't do that. But he didn't have good shoes. Well 1526 01:23:48,760 --> 01:23:52,200 Speaker 1: that's true. Um so in the world of and I 1527 01:23:52,240 --> 01:23:54,560 Speaker 1: don't even want to limit it to pharmaceuticals, in the 1528 01:23:54,680 --> 01:23:59,280 Speaker 1: world of large important institutions. What are you optimistic about 1529 01:23:59,360 --> 01:24:03,680 Speaker 1: and what do you pessimistic about looking forward? Well, I'm 1530 01:24:03,720 --> 01:24:06,800 Speaker 1: optimistic about the progress of medicine. I mean, we're just 1531 01:24:07,720 --> 01:24:11,080 Speaker 1: beginnings of growing organs in the lab. And speaking of 1532 01:24:11,520 --> 01:24:14,240 Speaker 1: we were talking about organ replacement if you're ride a motorcycle. 1533 01:24:14,320 --> 01:24:17,360 Speaker 1: But it's unbelievable. We just just recently just a few 1534 01:24:17,400 --> 01:24:21,639 Speaker 1: weeks ago, they are three D printing blood vessels onto 1535 01:24:22,000 --> 01:24:24,760 Speaker 1: early proto organs in the lab. And that's just just 1536 01:24:24,920 --> 01:24:26,920 Speaker 1: in the last few weeks. Imagine where will be five 1537 01:24:27,000 --> 01:24:28,720 Speaker 1: or ten years from now, and we're kind of on 1538 01:24:28,800 --> 01:24:31,880 Speaker 1: the verge of helping blind people see by rewiring the 1539 01:24:31,960 --> 01:24:34,960 Speaker 1: inputs to the brain if their eyes are fried, or 1540 01:24:35,040 --> 01:24:39,479 Speaker 1: helping deaf people here. It's just incredible the pace of 1541 01:24:39,920 --> 01:24:42,280 Speaker 1: science and medicine and where we might be ten twenty 1542 01:24:42,800 --> 01:24:46,599 Speaker 1: fifty years from now. What I'm concerned about is where 1543 01:24:46,760 --> 01:24:52,560 Speaker 1: that technology gets used for bad purposes, Specifically what we 1544 01:24:52,640 --> 01:24:57,360 Speaker 1: were talking about earlier in totalitarian regimes taking those some 1545 01:24:57,479 --> 01:25:01,280 Speaker 1: of those technologies, whether it's AI or machine or bioengineering, 1546 01:25:02,439 --> 01:25:07,479 Speaker 1: and what totalitarian regimes can do when they weaponize them, 1547 01:25:08,920 --> 01:25:12,760 Speaker 1: not only against others, but against their own people. It 1548 01:25:12,880 --> 01:25:17,280 Speaker 1: helps keep them in power. So I'm concerned about something 1549 01:25:17,360 --> 01:25:19,400 Speaker 1: that hasn't happened in the history of our species, which 1550 01:25:19,439 --> 01:25:23,599 Speaker 1: we are creating these new technologies which can actually help 1551 01:25:24,040 --> 01:25:28,599 Speaker 1: keep totalitarian regimes in power longer. And our final two questions, 1552 01:25:29,080 --> 01:25:31,599 Speaker 1: what sort of advice would you give to a recent 1553 01:25:31,840 --> 01:25:36,000 Speaker 1: college graduate who was thinking about a career either in 1554 01:25:36,640 --> 01:25:42,160 Speaker 1: pharmaceuticals or entrepreneurship. What would you advise them? Well, first 1555 01:25:42,160 --> 01:25:45,280 Speaker 1: of all, go around an experiment, because that's the only way. 1556 01:25:45,400 --> 01:25:49,000 Speaker 1: No amount of reading or listening to people tell you 1557 01:25:49,160 --> 01:25:51,679 Speaker 1: what you should or shouldn't do is going to replace 1558 01:25:51,800 --> 01:25:55,799 Speaker 1: trying something and just getting your hands wet with something. 1559 01:25:56,760 --> 01:26:01,200 Speaker 1: And the second thing is curiosity is ask people. Don't 1560 01:26:01,200 --> 01:26:03,280 Speaker 1: ask people, Go around and ask as many people as 1561 01:26:03,360 --> 01:26:07,439 Speaker 1: you cannot, how did you succeed? Because often people succeed 1562 01:26:07,479 --> 01:26:09,040 Speaker 1: because they got lucky. They were in the right place 1563 01:26:09,080 --> 01:26:10,400 Speaker 1: at the right time. And they're not going to tell 1564 01:26:10,400 --> 01:26:13,040 Speaker 1: you that, But ask them what we just talked about, 1565 01:26:13,080 --> 01:26:15,599 Speaker 1: which is what do you wish you'd done differently? Because 1566 01:26:15,680 --> 01:26:18,959 Speaker 1: that they've probably thought about a lot. If they're intelligent, 1567 01:26:19,000 --> 01:26:21,840 Speaker 1: thoughtful people, and they can have a pretty good answer 1568 01:26:22,120 --> 01:26:24,800 Speaker 1: because they've there's no I was in the right place 1569 01:26:24,880 --> 01:26:27,840 Speaker 1: at the wrong time. There's some people, if they're not 1570 01:26:28,040 --> 01:26:30,000 Speaker 1: very good at this, will say oh, I was unlucky. 1571 01:26:30,360 --> 01:26:32,479 Speaker 1: But the people who are much better and take lessons, 1572 01:26:33,160 --> 01:26:34,720 Speaker 1: we'll say, well, I should have done X, and I 1573 01:26:34,720 --> 01:26:36,880 Speaker 1: should have done why. That's a great thing to learn. 1574 01:26:37,360 --> 01:26:39,800 Speaker 1: And and my final question, what do you know about 1575 01:26:39,880 --> 01:26:44,200 Speaker 1: the world of organizations and decision making an innovation today 1576 01:26:44,640 --> 01:26:47,280 Speaker 1: that you wish you knew twenty plus years ago when 1577 01:26:47,320 --> 01:26:52,080 Speaker 1: you were first getting started. I would say it's important 1578 01:26:52,120 --> 01:26:55,559 Speaker 1: if you're running something of having people around you, whether 1579 01:26:55,600 --> 01:27:00,680 Speaker 1: they're inside your organization or oftentimes even better outside, who 1580 01:27:00,760 --> 01:27:02,559 Speaker 1: you can count on to tell you when you're being 1581 01:27:02,560 --> 01:27:06,840 Speaker 1: an idiot, to tell you the truth, because, especially if 1582 01:27:06,880 --> 01:27:10,519 Speaker 1: you're in some kind of position of authority, people don't 1583 01:27:10,560 --> 01:27:12,799 Speaker 1: want to tell you that if you have some influence 1584 01:27:12,880 --> 01:27:15,559 Speaker 1: over their lives. They have other things that they care 1585 01:27:15,560 --> 01:27:17,880 Speaker 1: about other than telling you the truth. So it's and 1586 01:27:18,280 --> 01:27:21,200 Speaker 1: sometimes it's important to get out of your organization, listen 1587 01:27:21,240 --> 01:27:25,000 Speaker 1: to people who are really out there and get different perspectives. 1588 01:27:25,000 --> 01:27:28,000 Speaker 1: So I wish I had done more of that. Quite fascinating. 1589 01:27:28,360 --> 01:27:31,200 Speaker 1: Thank you Sophia Picole for being so generous with your time. 1590 01:27:31,640 --> 01:27:34,679 Speaker 1: We have been speaking with the author of loon Shots 1591 01:27:35,479 --> 01:27:38,040 Speaker 1: how to nurture the crazy ideas that wind War's cure 1592 01:27:38,120 --> 01:27:42,639 Speaker 1: diseases and transform industries. If you enjoy this conversation, well, 1593 01:27:42,760 --> 01:27:44,160 Speaker 1: be sure to look up an inch or down an 1594 01:27:44,200 --> 01:27:47,080 Speaker 1: inch on Apple iTunes and you could see any of 1595 01:27:47,240 --> 01:27:51,200 Speaker 1: the previous three hundred or so such conversations we've had 1596 01:27:51,280 --> 01:27:54,599 Speaker 1: over the past five years. We love your comments, feedback 1597 01:27:54,640 --> 01:27:58,160 Speaker 1: and suggestions. Write to us at m IB podcast at 1598 01:27:58,200 --> 01:28:01,679 Speaker 1: Bloomberg dot net. If you're still here after three hours, 1599 01:28:02,000 --> 01:28:04,120 Speaker 1: well then go to Apple iTunes and give us a 1600 01:28:04,240 --> 01:28:07,960 Speaker 1: delightful review. You can check out my daily reads on 1601 01:28:08,160 --> 01:28:10,360 Speaker 1: rit Halts dot com. You can follow me on Twitter 1602 01:28:10,880 --> 01:28:13,519 Speaker 1: at rit Halts. I would be remiss if I did 1603 01:28:13,560 --> 01:28:16,559 Speaker 1: not thank the crack staff that helps put together these 1604 01:28:16,640 --> 01:28:22,479 Speaker 1: conversations each week. Carolyn O'Brien is my audio engineer on 1605 01:28:22,640 --> 01:28:27,400 Speaker 1: My head of research is Michael Batnick. Tracy Walsh is 1606 01:28:27,479 --> 01:28:31,280 Speaker 1: my project manager. I'm Barry rit Halts. You've been listening 1607 01:28:31,360 --> 01:28:33,920 Speaker 1: to Masters in Business on Bloomberg Radio