1 00:00:04,400 --> 00:00:07,560 Speaker 1: On this episode of news World, I'm pleased to welcome 2 00:00:07,640 --> 00:00:12,320 Speaker 1: Mark Mills to the podcast. His new book, The Cloud Revolution, 3 00:00:12,800 --> 00:00:16,599 Speaker 1: How the convergence of new technologies will unleash the next 4 00:00:16,640 --> 00:00:20,239 Speaker 1: economic boom and a Roaring twenty twenties is out now. 5 00:00:20,880 --> 00:00:24,200 Speaker 1: He makes the case that a roaring twenty twenties is arriving, 6 00:00:24,520 --> 00:00:27,760 Speaker 1: and it won't come from any singular invention, but from 7 00:00:27,760 --> 00:00:35,760 Speaker 1: the convergence of radical advances in three primary technology domains microprocessors, materials, 8 00:00:35,800 --> 00:00:41,560 Speaker 1: and machines. Accelerating and enabling this technological revolution, according to Mark, 9 00:00:42,000 --> 00:00:47,000 Speaker 1: is the cloud history's biggest infrastructure, which is itself made 10 00:00:47,040 --> 00:00:51,760 Speaker 1: possible by the revolutions in those three technological domains. We've 11 00:00:51,800 --> 00:00:55,600 Speaker 1: seen this pattern before. The technology revolution that drove the 12 00:00:55,640 --> 00:00:59,800 Speaker 1: great economic expansion of the twentieth century can be traced 13 00:00:59,800 --> 00:01:03,160 Speaker 1: to similar confluence one that was first visible in the 14 00:01:03,240 --> 00:01:07,919 Speaker 1: nineteen twenties. A new information infrastructure back then telephone and radio, 15 00:01:08,400 --> 00:01:12,839 Speaker 1: new machines, cars and power, plants, and new materials plastics 16 00:01:12,840 --> 00:01:18,320 Speaker 1: and pharmaceuticals. Single inventions don't drive great, long cycle booms. 17 00:01:18,800 --> 00:01:26,480 Speaker 1: It always takes convergent revolutions in technologies. Three core spheres information, materials, 18 00:01:26,520 --> 00:01:30,920 Speaker 1: and machines. Over history, that convergence has only happened a 19 00:01:30,920 --> 00:01:34,560 Speaker 1: few times, and every time it has had an extraordinary impact. 20 00:01:35,360 --> 00:01:38,640 Speaker 1: Mark makes the case that a great convergence is now 21 00:01:38,720 --> 00:01:42,000 Speaker 1: under way that will ignite the twenty twenties, and this time, 22 00:01:42,520 --> 00:01:47,520 Speaker 1: unlike any previous historical epoch, we have the cloud amplifying everything. 23 00:01:47,960 --> 00:02:05,240 Speaker 1: I'm really pleased to welcome Mark Mills. Thanks for having me. 24 00:02:05,560 --> 00:02:08,120 Speaker 1: I'm delighted to talk with you about all this stuff. Well, 25 00:02:08,160 --> 00:02:10,920 Speaker 1: before we get into the book itself, I wonder if 26 00:02:10,919 --> 00:02:13,320 Speaker 1: you share a little bit of your background. I understand 27 00:02:13,320 --> 00:02:17,000 Speaker 1: that you're a physicist by training. I confess, although one 28 00:02:17,040 --> 00:02:20,400 Speaker 1: doesn't usually confess that in the Washington circles that I 29 00:02:20,440 --> 00:02:25,440 Speaker 1: live in in cocktail parties. It's a conversation killer. Well, 30 00:02:25,480 --> 00:02:27,440 Speaker 1: but it does give you a unique background to be 31 00:02:28,200 --> 00:02:30,840 Speaker 1: thinking about these things. I actually did real work at 32 00:02:30,840 --> 00:02:33,000 Speaker 1: one time in my life, invented things, have patents, and 33 00:02:33,480 --> 00:02:37,040 Speaker 1: worked in missile guidance, fiber optics and microprocessors of my 34 00:02:37,080 --> 00:02:40,839 Speaker 1: early career. So it has informed how I look at 35 00:02:41,520 --> 00:02:45,160 Speaker 1: forecasting and policy because, like a lot of people, what 36 00:02:45,280 --> 00:02:47,079 Speaker 1: you do early in your life sort of imprints how 37 00:02:47,120 --> 00:02:51,000 Speaker 1: you think well and you also, I understand it. We're 38 00:02:51,040 --> 00:02:56,440 Speaker 1: at the convergence of technology and science and politics and government. 39 00:02:56,440 --> 00:02:59,359 Speaker 1: When he served in the Reagan White House Science Office, Yeah, 40 00:02:59,400 --> 00:03:02,520 Speaker 1: that was profoundly more valuable experience than I realized at 41 00:03:02,520 --> 00:03:04,760 Speaker 1: the time. You know, I was a young man and 42 00:03:04,880 --> 00:03:08,920 Speaker 1: I enjoyed it. It was incredibly fascinating. But as you know, 43 00:03:09,040 --> 00:03:11,360 Speaker 1: from the Reagan White House, which was run by adults, 44 00:03:11,440 --> 00:03:14,160 Speaker 1: the kids like me didn't meet with the president. They 45 00:03:14,240 --> 00:03:16,960 Speaker 1: kept us in cubicles, gave us our job, and the 46 00:03:17,000 --> 00:03:19,720 Speaker 1: Science Office is sort of a backwater always has been, 47 00:03:20,200 --> 00:03:23,160 Speaker 1: which is I learned a profound advantage. You could sort 48 00:03:23,200 --> 00:03:26,519 Speaker 1: of maneuver and do things, learn things, go places a 49 00:03:26,639 --> 00:03:28,960 Speaker 1: low profile, but it was great experience. I learned a 50 00:03:28,960 --> 00:03:32,480 Speaker 1: lot about how policy works, which I had honestly minimal 51 00:03:32,520 --> 00:03:35,200 Speaker 1: interests in earlier in my life, in my career, and 52 00:03:35,280 --> 00:03:38,760 Speaker 1: it really animated my longstanding interest in policy, which is 53 00:03:38,760 --> 00:03:41,840 Speaker 1: why I'm at the Manhattan Instant now. Frankly, policy matters. 54 00:03:41,840 --> 00:03:44,200 Speaker 1: As I write in my book, I mean, I'm excited 55 00:03:44,240 --> 00:03:47,840 Speaker 1: about the future offers, but I have a predicate my introduction. 56 00:03:48,240 --> 00:03:51,400 Speaker 1: You've got to get the politics right, because one can 57 00:03:52,000 --> 00:03:56,280 Speaker 1: sovietizing economy we've seen it happen. My books not about that, 58 00:03:56,360 --> 00:03:59,520 Speaker 1: but I have to start with the preface that we 59 00:03:59,640 --> 00:04:02,600 Speaker 1: do have to get the politics right to unleash this boom. Well, 60 00:04:02,640 --> 00:04:05,680 Speaker 1: in addition to your policy studies at the Manhattan in Stude, 61 00:04:05,880 --> 00:04:08,920 Speaker 1: you also are a faculty fellow at the Northwestern Universities 62 00:04:09,520 --> 00:04:13,280 Speaker 1: McCormick School of Engineering and Applied Science, and you codirect 63 00:04:13,320 --> 00:04:17,440 Speaker 1: the Institute on Manufacturing Science and Innovation, which strikes me 64 00:04:17,480 --> 00:04:21,640 Speaker 1: as a pretty useful place to be thinking about the 65 00:04:21,720 --> 00:04:25,000 Speaker 1: kind of changes that you focus on. It is. It's 66 00:04:25,160 --> 00:04:27,359 Speaker 1: kind of an honorific to be a faculty fellow, you know, 67 00:04:27,440 --> 00:04:30,320 Speaker 1: a bfellow is a different thing than having to be 68 00:04:30,360 --> 00:04:32,440 Speaker 1: a professor. I get a lecture a year kind of thing. 69 00:04:32,480 --> 00:04:36,479 Speaker 1: But what it gives me access to and I get 70 00:04:36,680 --> 00:04:40,120 Speaker 1: the luxury of talking to the people, and you know 71 00:04:40,200 --> 00:04:42,920 Speaker 1: the phrase the people who invent the future. These are 72 00:04:42,960 --> 00:04:47,880 Speaker 1: the kinds of engineers and scientists incredibly created bright not 73 00:04:47,920 --> 00:04:49,480 Speaker 1: many people hear about. I mean, once in a while 74 00:04:49,640 --> 00:04:53,040 Speaker 1: names pop up in the news because something gets invented 75 00:04:53,160 --> 00:04:56,960 Speaker 1: or reported, But that's where the revolutions are happening. And 76 00:04:57,000 --> 00:04:59,240 Speaker 1: in fact, you know the reason I wrote the book 77 00:04:59,480 --> 00:05:02,120 Speaker 1: in large measure is that you see the future in 78 00:05:02,160 --> 00:05:07,960 Speaker 1: the present. I mean, what businesses will manufacture, what services 79 00:05:07,960 --> 00:05:11,720 Speaker 1: will have. Aren't ideas that are invented tomorrow. There are 80 00:05:11,720 --> 00:05:14,200 Speaker 1: things that were invented a little while ago. They're not 81 00:05:14,279 --> 00:05:18,240 Speaker 1: quite commercial, just sort of proto commercial. You see that 82 00:05:18,440 --> 00:05:21,600 Speaker 1: in that environment, and it's exciting. I mean almost every 83 00:05:21,600 --> 00:05:25,960 Speaker 1: domain one touches on when you talk to these innovators, boy, 84 00:05:26,000 --> 00:05:29,400 Speaker 1: it's profoundly exciting. It's a luxury, it really is. Well 85 00:05:29,440 --> 00:05:31,440 Speaker 1: and then you get to test out your theories in 86 00:05:31,480 --> 00:05:34,599 Speaker 1: the real world as a partner in Montrose Lane, which 87 00:05:34,680 --> 00:05:37,239 Speaker 1: is an energy tech venture funds. So at one level 88 00:05:37,640 --> 00:05:40,479 Speaker 1: you're literally putting your money where your mouth is. Yes, 89 00:05:40,600 --> 00:05:42,920 Speaker 1: literally am doing that. I have a dog in the race, 90 00:05:42,960 --> 00:05:45,840 Speaker 1: as they say. The other domain I've spent time on 91 00:05:46,040 --> 00:05:49,240 Speaker 1: is this venture world, investment world. And you'll appreciate this. 92 00:05:49,279 --> 00:05:52,320 Speaker 1: When I first got involved in venture in a fund 93 00:05:52,360 --> 00:05:54,960 Speaker 1: in New York City, I live in washing DC area, 94 00:05:55,000 --> 00:05:56,280 Speaker 1: but doing a lot of policy and I used to 95 00:05:56,320 --> 00:05:59,920 Speaker 1: tell my friends, oh, you know, I'm leaving spin Alley, 96 00:06:00,200 --> 00:06:02,719 Speaker 1: going up to do some work in finance where you know, 97 00:06:02,760 --> 00:06:06,040 Speaker 1: the facts matter. And what I learned was guess what 98 00:06:06,640 --> 00:06:11,320 Speaker 1: it's about spin there too. Now, before you got to 99 00:06:11,440 --> 00:06:15,400 Speaker 1: writing your book on the cloud, you wrote a book 100 00:06:15,480 --> 00:06:17,560 Speaker 1: on Work in the Age of Robots and a book 101 00:06:17,560 --> 00:06:21,080 Speaker 1: on digital cathedrals. Can you just very briefly sort of 102 00:06:21,560 --> 00:06:24,159 Speaker 1: tell us what your thesis was. Sure, and I weave 103 00:06:24,200 --> 00:06:26,600 Speaker 1: a lot of those ideas into my longer book. Those 104 00:06:26,600 --> 00:06:30,159 Speaker 1: are both shorter books for those who have short attention spans, 105 00:06:30,200 --> 00:06:32,640 Speaker 1: which could be a lot of us. So Work in 106 00:06:32,680 --> 00:06:35,520 Speaker 1: the Age of Robots was a brief exploration of a 107 00:06:35,640 --> 00:06:38,719 Speaker 1: very simple idea. We are on the cusp of the 108 00:06:38,720 --> 00:06:41,279 Speaker 1: age of robots, almost in the way science fiction imagined it. 109 00:06:41,360 --> 00:06:44,799 Speaker 1: You know, people call automatic washing machines robots. That's true, 110 00:06:44,920 --> 00:06:47,880 Speaker 1: they're autonomous, but everybody knows what they mean by robot 111 00:06:47,920 --> 00:06:50,279 Speaker 1: when you say that word. We're on the cusp of 112 00:06:50,400 --> 00:06:54,680 Speaker 1: profound automation advances, which is very positive for job creation 113 00:06:55,080 --> 00:06:59,120 Speaker 1: as opposed to the thesis of job destruction. In my 114 00:06:59,200 --> 00:07:02,400 Speaker 1: book on digital cathedrals is more focused on the cloud itself, 115 00:07:02,440 --> 00:07:04,880 Speaker 1: the data centers that sit at the center of the cloud. 116 00:07:05,560 --> 00:07:09,279 Speaker 1: Google engineers created a word some years ago called them 117 00:07:09,440 --> 00:07:13,360 Speaker 1: warehouse scale computers, because these buildings are the size of 118 00:07:13,360 --> 00:07:16,520 Speaker 1: a Walmart and you know, for science fiction fans, I've 119 00:07:16,600 --> 00:07:19,040 Speaker 1: never been in one. It's like going into the borg ship. 120 00:07:19,160 --> 00:07:23,120 Speaker 1: It's just it's massive building full of blinking lights of computers, 121 00:07:23,160 --> 00:07:27,400 Speaker 1: no people. So the warehouse scale computers, the digital cathedrals 122 00:07:27,400 --> 00:07:30,360 Speaker 1: of our era are those are the skyscrapers of our era, 123 00:07:30,760 --> 00:07:35,280 Speaker 1: but they're invisible to everyday sort of life in terms 124 00:07:35,320 --> 00:07:38,200 Speaker 1: of physical visibility. But they are at the heart of 125 00:07:38,200 --> 00:07:42,160 Speaker 1: the cloud. So I wrote about that infrastructure, which is incredible. 126 00:07:42,200 --> 00:07:44,560 Speaker 1: I mean, I think most people in general commerce and 127 00:07:44,640 --> 00:07:47,720 Speaker 1: they hear the word cloud or data center doesn't mean much. Well, 128 00:07:47,720 --> 00:07:51,280 Speaker 1: it's a big building full of computers, hence warehouse scale computer. 129 00:07:51,880 --> 00:07:56,880 Speaker 1: But maybe context on how big this infrastructure is. These 130 00:07:57,000 --> 00:08:00,640 Speaker 1: capital spending dollars spend on hardware building out the cloud 131 00:08:00,720 --> 00:08:04,360 Speaker 1: globally is now greater than the total amount of money 132 00:08:04,400 --> 00:08:07,920 Speaker 1: being spent by all the world's electric utilities building out 133 00:08:07,920 --> 00:08:13,360 Speaker 1: hardware to make electricity. It's a stunning physical infrastructure. And 134 00:08:13,480 --> 00:08:15,000 Speaker 1: that's one of the things that I've always struck with 135 00:08:15,560 --> 00:08:19,080 Speaker 1: as I looked at the whole rise of modern information flow, 136 00:08:19,160 --> 00:08:22,880 Speaker 1: that there's just these massive systems all across the planet. 137 00:08:23,560 --> 00:08:27,440 Speaker 1: It's staggering at scales. You know the expression which has 138 00:08:27,440 --> 00:08:31,440 Speaker 1: became very famous, the information super highway, which of course 139 00:08:31,800 --> 00:08:35,600 Speaker 1: Vice President then Senator Gore got credit for coining, but 140 00:08:35,640 --> 00:08:38,960 Speaker 1: he borrowed it from an engineer who actually coined it, 141 00:08:39,640 --> 00:08:42,080 Speaker 1: and it was made popular by an artist nine Pack 142 00:08:42,480 --> 00:08:45,640 Speaker 1: back in the saventies. But the information super highway is 143 00:08:45,640 --> 00:08:47,719 Speaker 1: actually a good way of thinking about it, because it's 144 00:08:47,760 --> 00:08:50,000 Speaker 1: a highway right and moving information on it. Of course, 145 00:08:50,000 --> 00:08:53,040 Speaker 1: the information highways have a feature that physical highways don't have. 146 00:08:53,520 --> 00:08:57,400 Speaker 1: You can do mathematical tricks on an information highway. They're 147 00:08:57,440 --> 00:09:00,720 Speaker 1: the equivalent of stacking up I don't know, five thousand 148 00:09:00,760 --> 00:09:04,640 Speaker 1: layers of roads on the same roadway. You can't do 149 00:09:04,679 --> 00:09:07,600 Speaker 1: that in the physical world. In the virtual world you 150 00:09:07,640 --> 00:09:10,760 Speaker 1: can do that. And the highways have a length, I mean, 151 00:09:10,800 --> 00:09:13,200 Speaker 1: you can measure them the way we measure physical highways. Well, 152 00:09:14,000 --> 00:09:17,720 Speaker 1: the physical highways of the worlds, we measure their collective 153 00:09:17,800 --> 00:09:19,880 Speaker 1: lengths in the sort of tens and hundreds of thousands 154 00:09:19,880 --> 00:09:23,120 Speaker 1: of miles. The information highways we now measure them in 155 00:09:23,240 --> 00:09:26,880 Speaker 1: lengths of billions of miles. So now we have not 156 00:09:27,000 --> 00:09:31,080 Speaker 1: only billions of miles, as you say, they're ubiquitous. We 157 00:09:31,240 --> 00:09:35,080 Speaker 1: connect with in an invisible highway system on the order 158 00:09:35,080 --> 00:09:38,000 Speaker 1: of three billion humans. There's billions more to connect, and 159 00:09:38,080 --> 00:09:40,520 Speaker 1: we're going to connect them all. But this is just 160 00:09:40,679 --> 00:09:46,120 Speaker 1: unprecedented in history to have direct connectivity to billions of 161 00:09:46,160 --> 00:09:49,160 Speaker 1: people in real time. It was a big deal to 162 00:09:49,200 --> 00:09:53,160 Speaker 1: go to the telegraph, right when we connected communities instantaneously, 163 00:09:53,559 --> 00:09:56,760 Speaker 1: so you have one connection per community. Then we went 164 00:09:56,800 --> 00:10:01,160 Speaker 1: to the telephone with one connection per household. Old the 165 00:10:01,240 --> 00:10:03,520 Speaker 1: cell phone got us one connection per person. But the 166 00:10:03,600 --> 00:10:07,760 Speaker 1: cloud gets us connections to people and machines and our cars. 167 00:10:08,360 --> 00:10:13,320 Speaker 1: The scale of this is so hyperbolic that even hyperboly 168 00:10:13,360 --> 00:10:16,840 Speaker 1: sort of fails to capture. Amazing it is. Well, can 169 00:10:16,920 --> 00:10:19,120 Speaker 1: you explain something to me which I want to confess 170 00:10:19,240 --> 00:10:23,200 Speaker 1: up front. I am totally ignorant of what is a 171 00:10:23,320 --> 00:10:27,400 Speaker 1: metaverse and why is that becoming a new conversation. Well, 172 00:10:27,480 --> 00:10:30,160 Speaker 1: the metaverse is a funny word. It's kind of hard 173 00:10:30,320 --> 00:10:33,920 Speaker 1: to find myself agreeing with Zuckerberg. I mean, he makes 174 00:10:33,960 --> 00:10:36,360 Speaker 1: himself not necessarily someone everyone wants to agree with, but 175 00:10:36,440 --> 00:10:39,960 Speaker 1: he's onto something with this idea of metaverse. What they're 176 00:10:39,960 --> 00:10:44,960 Speaker 1: really talking about is the importance of going into computing 177 00:10:44,960 --> 00:10:48,520 Speaker 1: systems that allow you to engage in virtual or augmented reality. 178 00:10:49,280 --> 00:10:52,280 Speaker 1: We know what that means. If I say, people who 179 00:10:52,320 --> 00:10:56,920 Speaker 1: are video gamers, and you can wear these goofy clunky goggles, 180 00:10:56,960 --> 00:10:59,120 Speaker 1: I mean they still are goofy and clunky, as magical 181 00:10:59,160 --> 00:11:02,080 Speaker 1: as they are, and you can see a virtual universe 182 00:11:02,160 --> 00:11:05,040 Speaker 1: right a virtual space, and you can play in it, 183 00:11:05,440 --> 00:11:08,360 Speaker 1: you can shop in it. But what the idea that 184 00:11:08,600 --> 00:11:11,920 Speaker 1: is captured by this is probably not the right word. 185 00:11:11,920 --> 00:11:14,000 Speaker 1: I don't know how sticky it's going to be. What 186 00:11:14,040 --> 00:11:17,840 Speaker 1: we're doing now is allowing the expansion of the Internet 187 00:11:17,840 --> 00:11:19,920 Speaker 1: in the cloud to allow you not just to see 188 00:11:19,960 --> 00:11:23,640 Speaker 1: planar images, not just to see pictures of things, but 189 00:11:23,720 --> 00:11:30,000 Speaker 1: to overlay information or images onto reality. That's augmented reality. 190 00:11:30,600 --> 00:11:33,560 Speaker 1: A good example would be if you're looking at something 191 00:11:33,559 --> 00:11:35,520 Speaker 1: with your cell phone. Is you know some people use 192 00:11:35,600 --> 00:11:39,080 Speaker 1: this function. I can use the camera to overlay and 193 00:11:39,160 --> 00:11:41,720 Speaker 1: have information about the thing I'm looking at. Instead of 194 00:11:41,960 --> 00:11:44,400 Speaker 1: taking a picture of a building, you can have your 195 00:11:44,400 --> 00:11:46,679 Speaker 1: camera live and while you're looking at the building through 196 00:11:46,760 --> 00:11:50,320 Speaker 1: your smartphone, it will say what that building is and 197 00:11:50,320 --> 00:11:54,080 Speaker 1: what its address is. That's augmented reality. Virtual reality would 198 00:11:54,120 --> 00:11:58,040 Speaker 1: be I'm playing a game and the building looks real, 199 00:11:58,120 --> 00:12:00,920 Speaker 1: but actually I can move it around like it's a toy. 200 00:12:01,559 --> 00:12:05,360 Speaker 1: Those things are kind of fine for games, but they're 201 00:12:05,360 --> 00:12:10,319 Speaker 1: extraordinarily important for things like education and training, or for healthcare. 202 00:12:10,679 --> 00:12:15,079 Speaker 1: Because already this is no longer a theoretical idea, there 203 00:12:15,080 --> 00:12:20,520 Speaker 1: are several businesses offering physicians the means to use virtual 204 00:12:20,559 --> 00:12:24,360 Speaker 1: and augmented reality to take high resolution images of your 205 00:12:24,400 --> 00:12:27,880 Speaker 1: body and practice surgery before they performance surgery on you, 206 00:12:28,600 --> 00:12:32,760 Speaker 1: including overlaying images virtual reality on you as surgery is 207 00:12:32,800 --> 00:12:36,760 Speaker 1: going on. This is really consequential as they extend that 208 00:12:36,840 --> 00:12:41,479 Speaker 1: into all manners of the physical world, manufacturing and construction. 209 00:12:42,080 --> 00:12:47,280 Speaker 1: It really amplifies our ability to do things safely, more precisely, faster. 210 00:12:48,280 --> 00:12:51,360 Speaker 1: Same un training, I mean virtual and augmented reality the 211 00:12:51,400 --> 00:12:57,360 Speaker 1: metaverse will allow us to enhance the skilled training domains 212 00:12:57,400 --> 00:13:00,520 Speaker 1: that are now lagging. If you want to learn a skill, 213 00:13:01,120 --> 00:13:04,360 Speaker 1: you have to do the thing that's always been true, 214 00:13:04,520 --> 00:13:09,120 Speaker 1: but one hundred years ago, when Link created the flight simulator, 215 00:13:09,480 --> 00:13:12,480 Speaker 1: he profoundly improved the speed of which we could train pilots. 216 00:13:12,520 --> 00:13:16,680 Speaker 1: At World War two's fatality rate and training new pilots collapsed, 217 00:13:17,559 --> 00:13:19,920 Speaker 1: it got far safer by using the simulator first and 218 00:13:19,920 --> 00:13:22,400 Speaker 1: then you fly a real plane. In practice, that kind 219 00:13:22,440 --> 00:13:26,320 Speaker 1: of simulation is extremely difficult to do, but now becoming 220 00:13:26,360 --> 00:13:29,160 Speaker 1: feasible to do across all kinds of domains because of 221 00:13:29,200 --> 00:13:31,880 Speaker 1: what we'll loosely calling the metaverse. But what's made possible 222 00:13:31,880 --> 00:13:37,160 Speaker 1: by our better vision systems, better computing, better imagers. You know, 223 00:13:37,200 --> 00:13:40,440 Speaker 1: all the things that are the hardware side combined with 224 00:13:40,480 --> 00:13:45,880 Speaker 1: more powerful computing allow us to do this realism, virtual reality, 225 00:13:46,040 --> 00:13:49,320 Speaker 1: augmented reality for those kinds of spaces. I think while 226 00:13:49,520 --> 00:13:52,400 Speaker 1: Zuckerberg and the metaverse and renaming the company or focus 227 00:13:52,480 --> 00:13:56,320 Speaker 1: clearly on social stuff, I'm sure they know what their 228 00:13:56,320 --> 00:13:59,200 Speaker 1: goal is. Their goal is to frankly get into things 229 00:13:59,200 --> 00:14:03,520 Speaker 1: like education and healthcare. So just to be clear for 230 00:14:03,520 --> 00:14:07,480 Speaker 1: a second, So the early flight simulators, for example, were 231 00:14:07,520 --> 00:14:12,000 Speaker 1: in a sense primitive examples of quertium metaverse. Yeah, I 232 00:14:12,080 --> 00:14:14,360 Speaker 1: think you live in the metaverse when you're a flight simulator. 233 00:14:14,400 --> 00:14:18,120 Speaker 1: The new flight simulators are profoundly realistic, if you've ever 234 00:14:18,440 --> 00:14:20,520 Speaker 1: had the luxury of being in one. I crashed a 235 00:14:20,640 --> 00:14:24,680 Speaker 1: triple seven at Delta and a simulator. They reassured me 236 00:14:24,760 --> 00:14:26,720 Speaker 1: I was exactly lined up with the runway, but it 237 00:14:26,760 --> 00:14:29,320 Speaker 1: was about a half mile short, but that the wreckage 238 00:14:29,360 --> 00:14:32,360 Speaker 1: would have actually gone straight down the runways. I thought 239 00:14:32,360 --> 00:14:36,240 Speaker 1: I had some dignity getting out of the plane. We'll see. 240 00:14:36,560 --> 00:14:38,880 Speaker 1: That's exactly why we have simulators, and of course the 241 00:14:38,960 --> 00:14:42,280 Speaker 1: big equipment makers already have simulators for excavators. These are 242 00:14:42,360 --> 00:14:45,480 Speaker 1: big pieces of expensive equipment. Better that you learn on 243 00:14:45,480 --> 00:14:49,000 Speaker 1: the simulator first before you practice early on in the 244 00:14:49,120 --> 00:14:52,360 Speaker 1: real equipment. And as that technology gets cheaper and inevitably 245 00:14:52,400 --> 00:14:54,680 Speaker 1: gets cheaper, it moves into sort of the kinds of 246 00:14:54,720 --> 00:15:12,360 Speaker 1: things we do in daily life. One of the key 247 00:15:12,400 --> 00:15:16,400 Speaker 1: points you make, which I agree with totally, is that 248 00:15:16,720 --> 00:15:20,320 Speaker 1: these kind of patterns tend to occur as a complex 249 00:15:20,360 --> 00:15:24,120 Speaker 1: interactive wave in which you're getting breakthroughs in three or 250 00:15:24,120 --> 00:15:28,880 Speaker 1: four different areas in parallel, and that it's the synergistic 251 00:15:28,920 --> 00:15:32,600 Speaker 1: effect of each one working with the other to create 252 00:15:32,640 --> 00:15:37,000 Speaker 1: opportunities that literally did not exist. That's the essence of 253 00:15:37,040 --> 00:15:39,320 Speaker 1: what innovation is. As you know, I mean, when you 254 00:15:39,360 --> 00:15:43,800 Speaker 1: look at the brilliant innovators, inventors, the captains of industries 255 00:15:43,800 --> 00:15:46,240 Speaker 1: of the past and the present, the tech titans today, 256 00:15:46,880 --> 00:15:50,480 Speaker 1: most of them are building on and they acknowledgist what 257 00:15:50,520 --> 00:15:52,680 Speaker 1: others have done. What they do is to see a 258 00:15:52,720 --> 00:15:56,320 Speaker 1: combination that others missed. I mean, the most obvious iconic 259 00:15:56,360 --> 00:15:59,840 Speaker 1: example would be the smartphone itself. Everybody wanted to make 260 00:15:59,840 --> 00:16:03,200 Speaker 1: smaller computers and better cell phones, but Steve Jobs with 261 00:16:03,320 --> 00:16:07,240 Speaker 1: the one that took three things his company had nothing 262 00:16:07,240 --> 00:16:11,000 Speaker 1: to do with inventing the collapsing, size and power of 263 00:16:11,040 --> 00:16:14,120 Speaker 1: the microprocessor of the computer chip. They didn't invent it, 264 00:16:14,160 --> 00:16:16,400 Speaker 1: but it finally got powerful enough and small enough that 265 00:16:16,400 --> 00:16:20,920 Speaker 1: that combined with a TV screen that's flattened tiny, which 266 00:16:20,960 --> 00:16:23,800 Speaker 1: he didn't invent. It was then an LCD screen looked 267 00:16:23,800 --> 00:16:28,720 Speaker 1: with crystal display and combined with the lithium battery, because 268 00:16:28,840 --> 00:16:30,960 Speaker 1: you can't power the thing with the lead acid battery, 269 00:16:30,960 --> 00:16:34,200 Speaker 1: otherwise you'd be carrying around battery like your car starter battery. 270 00:16:34,360 --> 00:16:38,360 Speaker 1: It's hardly a practical handheld device. He didn't invent that either. 271 00:16:38,440 --> 00:16:40,920 Speaker 1: It was invented by an Exon chemist in the seventies 272 00:16:41,280 --> 00:16:43,800 Speaker 1: of all things, for which he got a Nobel Prize, 273 00:16:44,080 --> 00:16:48,400 Speaker 1: but the three things maturing in parallel independent of Apple 274 00:16:49,000 --> 00:16:54,000 Speaker 1: all available. He recognized it and his team did this 275 00:16:54,120 --> 00:16:58,840 Speaker 1: delicious design of a machine that revolutionized the world and 276 00:16:58,960 --> 00:17:02,360 Speaker 1: everybody imitated. That's how the car was invented. Henry Ford 277 00:17:02,400 --> 00:17:05,600 Speaker 1: didn't invent the assembly line, or the internal combustion engine 278 00:17:05,680 --> 00:17:08,199 Speaker 1: or high strain steel, all three of which had to 279 00:17:08,240 --> 00:17:14,000 Speaker 1: mature contemporaneously to allow the invention of the model T. 280 00:17:15,240 --> 00:17:18,960 Speaker 1: So when you look at this kind of synergistic effective, 281 00:17:19,320 --> 00:17:22,040 Speaker 1: I've read one time that if you actually look at 282 00:17:22,080 --> 00:17:25,960 Speaker 1: the evolution of certain things, the really big breakthroughs often 283 00:17:26,040 --> 00:17:29,879 Speaker 1: take a full generation to work into the system, to 284 00:17:30,040 --> 00:17:34,480 Speaker 1: reshape the factory, to reshape the products. It doesn't happen overnight, 285 00:17:34,520 --> 00:17:37,560 Speaker 1: but that you have this steady migration as people learn 286 00:17:37,640 --> 00:17:40,119 Speaker 1: how to do it, and as they gradually improve the 287 00:17:40,160 --> 00:17:43,880 Speaker 1: whole system. Looking at the economy, how do you see 288 00:17:43,920 --> 00:17:47,080 Speaker 1: the next ten to twenty years? Your point is exactly right. 289 00:17:47,480 --> 00:17:50,119 Speaker 1: If you talk to an entrepreneur who has an incredibly 290 00:17:50,119 --> 00:17:54,080 Speaker 1: successful company and ask him how long did it take 291 00:17:54,080 --> 00:17:57,440 Speaker 1: you to get to where you are? The overnight success 292 00:17:57,560 --> 00:18:00,480 Speaker 1: is always a decade, or it's the long slog, sometimes 293 00:18:00,480 --> 00:18:03,480 Speaker 1: twenty years. Andy Grove wrote about this before he died, 294 00:18:04,160 --> 00:18:08,040 Speaker 1: about the inflection point where companies just take off the 295 00:18:08,119 --> 00:18:11,639 Speaker 1: overnight successes. You know, I dabbled in history throughout my 296 00:18:11,680 --> 00:18:15,160 Speaker 1: book because the history matters, right, it tells you something 297 00:18:15,200 --> 00:18:18,520 Speaker 1: about the future in terms of patterns, and they're predictive 298 00:18:19,000 --> 00:18:21,919 Speaker 1: in ways that are sort of, i think somewhat obvious 299 00:18:22,119 --> 00:18:25,280 Speaker 1: to your point. All of the technologies that profoundly affected 300 00:18:25,280 --> 00:18:27,520 Speaker 1: the nineteen twenties, so let's go back to that period. 301 00:18:28,040 --> 00:18:30,600 Speaker 1: All of them were invented to ten to thirty years earlier. 302 00:18:31,080 --> 00:18:34,320 Speaker 1: What happened was they were all becoming useful by that time. 303 00:18:34,359 --> 00:18:36,679 Speaker 1: The car was invented in the late nineteenth century. It 304 00:18:36,720 --> 00:18:40,359 Speaker 1: was eighteen nineties, but to make a useful car took 305 00:18:40,440 --> 00:18:43,359 Speaker 1: almost twenty five years, and that was when the first 306 00:18:43,359 --> 00:18:45,200 Speaker 1: Model A showed up, and then the Model T was 307 00:18:45,240 --> 00:18:48,080 Speaker 1: really useful and took off. That's true for the radio, 308 00:18:48,160 --> 00:18:51,919 Speaker 1: it's true for the television. It's true for computers. By 309 00:18:51,960 --> 00:18:54,399 Speaker 1: the way, the first computer was actually before the ENIAC. 310 00:18:54,520 --> 00:18:57,520 Speaker 1: It was in Illinois in nineteen thirty six, and we'd 311 00:18:57,560 --> 00:18:59,960 Speaker 1: go back to mechanical computers, as you know, to Babbage 312 00:19:00,080 --> 00:19:04,480 Speaker 1: far earlier. The pattern is interesting. It's typically ten to 313 00:19:04,520 --> 00:19:08,600 Speaker 1: twenty years from an idea to the first realization as 314 00:19:08,640 --> 00:19:11,399 Speaker 1: a product, and then it's another ten to twenty years 315 00:19:11,400 --> 00:19:16,240 Speaker 1: before that product is commercially viable, and it's after that 316 00:19:16,240 --> 00:19:18,720 Speaker 1: that you get businesses creating things. When you get to 317 00:19:18,800 --> 00:19:21,920 Speaker 1: quote inflection point. What I looked at on my book 318 00:19:22,160 --> 00:19:24,719 Speaker 1: is I'm really more interested not in what is invented 319 00:19:24,760 --> 00:19:28,200 Speaker 1: today that might make something possible in near twenty fifty. 320 00:19:28,240 --> 00:19:31,720 Speaker 1: I was interested in exactly the same pattern, what's been 321 00:19:31,760 --> 00:19:35,119 Speaker 1: invented but not really commercial. But it's ten years in 322 00:19:35,760 --> 00:19:38,680 Speaker 1: and now we might expect it to be commercially viable, 323 00:19:39,440 --> 00:19:42,320 Speaker 1: and maybe fifteen years in, but it's not really a 324 00:19:42,320 --> 00:19:45,439 Speaker 1: significant market yet. So what do I expect in the 325 00:19:45,520 --> 00:19:49,560 Speaker 1: twenty twenties. Well, robots are now possible. We've been talking 326 00:19:49,600 --> 00:19:53,400 Speaker 1: about anthropomorphic or untethered or rolling robots for a very 327 00:19:53,440 --> 00:19:56,440 Speaker 1: long time in modern history. I mean the word robot 328 00:19:56,480 --> 00:19:58,959 Speaker 1: was created by is you know cap check back one 329 00:19:59,000 --> 00:20:02,800 Speaker 1: hundred years ago last year in a play about machines 330 00:20:02,840 --> 00:20:05,760 Speaker 1: that would relieve the drudgery of horrible work, the hard work. 331 00:20:06,200 --> 00:20:08,480 Speaker 1: What's still what we want to do is they're damned 332 00:20:08,520 --> 00:20:11,600 Speaker 1: hard to make. And it took advances sort of in 333 00:20:11,640 --> 00:20:14,320 Speaker 1: three key areas. Not in computing so much computing matters. 334 00:20:14,320 --> 00:20:17,639 Speaker 1: The cloud infuses everything, microprocess there's are sort of the 335 00:20:17,680 --> 00:20:21,639 Speaker 1: predicate and all the revolutions of the next century. But 336 00:20:21,760 --> 00:20:25,359 Speaker 1: it took advances in material science to make the kinds 337 00:20:25,359 --> 00:20:28,879 Speaker 1: of actuators that are possible to have the power to 338 00:20:29,040 --> 00:20:32,720 Speaker 1: sort of be synthetic muscles. And it took vision systems 339 00:20:32,800 --> 00:20:35,840 Speaker 1: to allow the robot, whether it's weeld or walking, to 340 00:20:35,920 --> 00:20:38,240 Speaker 1: be able to do what we do, be aware of 341 00:20:38,280 --> 00:20:41,040 Speaker 1: its environment. All those, by the way, were invented by 342 00:20:42,000 --> 00:20:45,159 Speaker 1: companies to make cameras better, for example, in smartphones, or 343 00:20:45,200 --> 00:20:48,320 Speaker 1: to make cruise controls better in cars, not roboticists. And 344 00:20:48,440 --> 00:20:50,400 Speaker 1: of course it took a power source. It took really 345 00:20:50,480 --> 00:20:53,320 Speaker 1: cheap powerful batteries lithium battery, because you've got to have 346 00:20:53,320 --> 00:20:55,639 Speaker 1: an untethered or it's not really useful. Robot can't be 347 00:20:55,680 --> 00:20:58,360 Speaker 1: dragging a corner around behind it everywhere it goes. So 348 00:20:58,720 --> 00:21:01,240 Speaker 1: robots are coming and we're seen them first in warehouses, 349 00:21:01,280 --> 00:21:03,720 Speaker 1: which is kind of interesting. Physical warehouse is one of 350 00:21:03,760 --> 00:21:06,680 Speaker 1: the oldest pieces of infrastructure and civilization. They date back 351 00:21:06,760 --> 00:21:09,639 Speaker 1: to the Greek times and we've been have warehouses forever 352 00:21:09,880 --> 00:21:12,840 Speaker 1: and they haven't changed much except now they're now being 353 00:21:12,880 --> 00:21:17,719 Speaker 1: invaded by untethered robots that are relieving the problem that 354 00:21:18,200 --> 00:21:21,640 Speaker 1: e commerce created in the cloud. Everybody has one click. 355 00:21:22,200 --> 00:21:26,320 Speaker 1: By the way, I've just watched a BBC special on 356 00:21:26,359 --> 00:21:31,440 Speaker 1: a grocery store warehouse which was totally robotic and they 357 00:21:31,480 --> 00:21:34,639 Speaker 1: fill up your bag with your order. It's amazing. We 358 00:21:34,760 --> 00:21:37,840 Speaker 1: haven't got a nomenclature for those yet, the two favored 359 00:21:37,960 --> 00:21:43,040 Speaker 1: terms or cloud to kitchens or dark kitchens, and so 360 00:21:43,080 --> 00:21:46,240 Speaker 1: the food, not just the grocery store, but the restaurant 361 00:21:46,359 --> 00:21:50,240 Speaker 1: right is almost entirely automated, in some cases fully automated, 362 00:21:50,800 --> 00:21:53,400 Speaker 1: and the human beings are on the periphery, so to speak, 363 00:21:53,400 --> 00:21:58,159 Speaker 1: physically or operationally. These things have been possible, Engineers have 364 00:21:58,200 --> 00:22:01,040 Speaker 1: imagined them, they've made them, but they've been too expensive, 365 00:22:01,080 --> 00:22:05,480 Speaker 1: too unreliable, too difficult to control. And they're maturing now. 366 00:22:05,520 --> 00:22:08,879 Speaker 1: And to your point, the BBC special you saw illuminates 367 00:22:09,280 --> 00:22:12,640 Speaker 1: this revolution and doesn't matter, but sure is. You well 368 00:22:12,720 --> 00:22:15,800 Speaker 1: know what we have going on now, and the demography 369 00:22:15,800 --> 00:22:19,280 Speaker 1: of the marketplace is the great resignation. We have more 370 00:22:19,359 --> 00:22:21,199 Speaker 1: jobs that we have people who can do them. We 371 00:22:21,240 --> 00:22:23,399 Speaker 1: have more skilled jobs and people who have skills that 372 00:22:23,440 --> 00:22:26,639 Speaker 1: do them. The boomers have decided to retire. Remember before 373 00:22:26,680 --> 00:22:30,439 Speaker 1: COVID and the Lockdown's Pure research published the poll pointing 374 00:22:30,440 --> 00:22:32,280 Speaker 1: out that boomers are going to work into their eighties. 375 00:22:32,320 --> 00:22:35,520 Speaker 1: They just didn't want to retire. They're gonna be around forever. Well, 376 00:22:35,560 --> 00:22:38,159 Speaker 1: guess what, they've had enough. I'm out of here. So 377 00:22:38,680 --> 00:22:41,280 Speaker 1: it just totally flipped. And of course the birth rates 378 00:22:41,320 --> 00:22:43,800 Speaker 1: have collapsed. I think that's a short term collapse, but 379 00:22:43,840 --> 00:22:47,199 Speaker 1: it's slowing down. If we didn't have the robots coming along, 380 00:22:47,680 --> 00:22:52,159 Speaker 1: I think we would be facing long term, profound inflationary period. 381 00:22:52,200 --> 00:22:54,479 Speaker 1: I think what the robots and automation will do, including 382 00:22:54,560 --> 00:22:58,920 Speaker 1: virtual robots AI, is take the pressure off inflation far 383 00:22:59,000 --> 00:23:03,320 Speaker 1: faster the future then could have happened anytime in recent history. 384 00:23:03,480 --> 00:23:06,160 Speaker 1: We're going to see inflation because this has been induced 385 00:23:06,160 --> 00:23:09,800 Speaker 1: by our policy makers, but the engineers are going to 386 00:23:09,840 --> 00:23:13,840 Speaker 1: take the pressure off it. The public policy is unbelievably inflationary, 387 00:23:13,880 --> 00:23:18,760 Speaker 1: but the rate of technological change is extraordinarily deflationary. It is, 388 00:23:18,920 --> 00:23:20,679 Speaker 1: and it's going to save their bacon, but it's not 389 00:23:20,680 --> 00:23:22,560 Speaker 1: going to save it overnight and won't save it in 390 00:23:22,600 --> 00:23:25,280 Speaker 1: a year. But I think we're going to see deflationary 391 00:23:25,320 --> 00:23:28,840 Speaker 1: pressure from technology within a few years, which is very 392 00:23:28,920 --> 00:23:31,439 Speaker 1: very fast. We're already seeing some of it in the 393 00:23:31,440 --> 00:23:35,440 Speaker 1: warehouse space. The growth of automation and warehouses is just 394 00:23:35,600 --> 00:23:39,639 Speaker 1: at a turret pace. The installation of robots and warehouses 395 00:23:39,640 --> 00:23:42,760 Speaker 1: to move things, whether it's dark kitchens or just moving 396 00:23:42,800 --> 00:23:46,800 Speaker 1: books or toilet paper, it's growing it. It's one to 397 00:23:46,800 --> 00:23:48,800 Speaker 1: two hundred percent a year. Well, and if you look 398 00:23:48,840 --> 00:23:51,680 Speaker 1: at a system like Amazon, a large part of its 399 00:23:51,680 --> 00:23:56,200 Speaker 1: reach is the combination of information flow and robotics. Absolutely. 400 00:23:56,200 --> 00:23:59,760 Speaker 1: As I point out my book, they epitomize the new modality, 401 00:23:59,760 --> 00:24:03,560 Speaker 1: which is what engineers called cyberphysical systems, the emerging of 402 00:24:03,560 --> 00:24:07,199 Speaker 1: the two. That's how we commas works. When you hit by. Now, 403 00:24:07,720 --> 00:24:10,119 Speaker 1: what you're doing is you're not only lighting up computers 404 00:24:10,200 --> 00:24:13,959 Speaker 1: communications systems all over the country and planet, you're actually 405 00:24:14,000 --> 00:24:18,480 Speaker 1: animating robots in a warehouse to move the goods almost instantaneously. 406 00:24:19,200 --> 00:24:21,200 Speaker 1: So tell me from it. How do you think all 407 00:24:21,200 --> 00:24:24,200 Speaker 1: of this applies to health, which, as you know, went 408 00:24:24,200 --> 00:24:27,159 Speaker 1: through a huge revolution in the nineteen twenties, and I 409 00:24:27,200 --> 00:24:31,120 Speaker 1: think the average lifespan of Americans grew very dramatically. As 410 00:24:31,200 --> 00:24:34,199 Speaker 1: late as nineteen hundred, the average American lived to be 411 00:24:34,280 --> 00:24:37,480 Speaker 1: forty six, and now, of course we live in the 412 00:24:37,560 --> 00:24:41,880 Speaker 1: late seventies, although between drug addictions, suicide and COVID. That's 413 00:24:41,920 --> 00:24:43,720 Speaker 1: marginally come down by I think a year and a 414 00:24:43,760 --> 00:24:46,760 Speaker 1: half or so, but I think that's probably a temporary problem. 415 00:24:47,040 --> 00:24:52,760 Speaker 1: But how do you see this whole synergistic revolution affecting healthcare? Yeah, 416 00:24:52,760 --> 00:24:56,560 Speaker 1: I think the two hardest areas to improve productivity have 417 00:24:56,680 --> 00:25:00,520 Speaker 1: been healthcare, as you know, and the other services industries 418 00:25:00,520 --> 00:25:03,800 Speaker 1: which relate to we'll call entertainment and travel. They've just 419 00:25:03,840 --> 00:25:07,440 Speaker 1: been very tough. But healthcare in particular, it's suffered incredible 420 00:25:07,480 --> 00:25:11,200 Speaker 1: cost increases. And you get more healthcare by spending more 421 00:25:11,200 --> 00:25:14,919 Speaker 1: money with more people, which is the profound opposite of productivity. 422 00:25:14,960 --> 00:25:17,920 Speaker 1: As you all know, we want to have less money, 423 00:25:18,000 --> 00:25:22,119 Speaker 1: fewer people, more robots, more AI and healthcare with better outcomes. 424 00:25:22,359 --> 00:25:26,040 Speaker 1: That's the economist definition of productivity. Well, i'll give three 425 00:25:26,119 --> 00:25:29,000 Speaker 1: quick examples of what we know is already happening. And 426 00:25:29,040 --> 00:25:31,240 Speaker 1: again in the book that I try to focus on, 427 00:25:31,520 --> 00:25:34,440 Speaker 1: this was the Peter Drucker line. He said he predicted 428 00:25:34,480 --> 00:25:37,040 Speaker 1: the future based on what had already happened. People laughed 429 00:25:37,080 --> 00:25:39,240 Speaker 1: when he said it, but he was being serious, said 430 00:25:39,280 --> 00:25:40,880 Speaker 1: I don't want to guess what might happen. I'm gonna 431 00:25:40,880 --> 00:25:43,040 Speaker 1: look at what's going on, and you look at the trend, 432 00:25:43,080 --> 00:25:45,600 Speaker 1: so we already know. As hard as it is to 433 00:25:45,720 --> 00:25:49,560 Speaker 1: use the cloud effectively, it's getting much much easier. So 434 00:25:50,000 --> 00:25:53,719 Speaker 1: we'll talk about telemedicine in terms of telehealth and zooming 435 00:25:53,720 --> 00:25:56,080 Speaker 1: with your doctor, which was the illegal you know, doctor 436 00:25:56,080 --> 00:25:58,680 Speaker 1: couldn't provide a prescription pre COVID on a zoom call. 437 00:25:59,040 --> 00:26:02,320 Speaker 1: Now you can release that restriction because of COVID. I 438 00:26:02,320 --> 00:26:05,879 Speaker 1: think it will stay permanently gone. That's profoundly more productive 439 00:26:06,119 --> 00:26:09,200 Speaker 1: than having to drag yourself to the doctor's office. That's 440 00:26:09,240 --> 00:26:12,560 Speaker 1: not nothing. The aspiration that IBM had with Watson trying 441 00:26:12,560 --> 00:26:15,560 Speaker 1: to bring AI to doctors, they sort of got over 442 00:26:15,600 --> 00:26:17,120 Speaker 1: their skis a bit, but they were on the right 443 00:26:17,119 --> 00:26:20,080 Speaker 1: track and it's already happening, which is to say, you 444 00:26:20,160 --> 00:26:23,240 Speaker 1: want artificial intelligence, which is quote machine learning. You want 445 00:26:23,240 --> 00:26:27,160 Speaker 1: computers to provide assistance to physicians on the front line, 446 00:26:27,200 --> 00:26:30,240 Speaker 1: not to make the diagnosis, but when you have a 447 00:26:30,240 --> 00:26:36,440 Speaker 1: computing system that can scan in milliseconds, all the healthcare literature, 448 00:26:36,520 --> 00:26:39,399 Speaker 1: all the technical literature about the symptoms that the doctor 449 00:26:39,520 --> 00:26:42,880 Speaker 1: seeing and audibly say like Sirie voice or Alexa. Had 450 00:26:42,920 --> 00:26:46,119 Speaker 1: you considered did you think about X ray. Same. It's 451 00:26:46,160 --> 00:26:49,159 Speaker 1: true on pathology reports you still need a pathologist. But 452 00:26:49,280 --> 00:26:51,560 Speaker 1: having a computer with computer vision that can look at 453 00:26:51,600 --> 00:26:54,320 Speaker 1: the same images and say did you look at this 454 00:26:54,400 --> 00:26:57,359 Speaker 1: as well? That kind of amplifier is already in play, 455 00:26:57,440 --> 00:27:00,480 Speaker 1: getting better very rapidly. And then let just look at 456 00:27:00,520 --> 00:27:03,879 Speaker 1: a drug discovery. Drug discovery now is going on in 457 00:27:04,000 --> 00:27:07,840 Speaker 1: selico instead of in humans in situ. It's not perfect 458 00:27:07,840 --> 00:27:11,840 Speaker 1: any more than the simulator for the airplane is exactly 459 00:27:11,880 --> 00:27:13,880 Speaker 1: the same as flying it like an airplane. But it's 460 00:27:13,920 --> 00:27:17,119 Speaker 1: awfully good. And so now we are already seeing and 461 00:27:17,119 --> 00:27:20,159 Speaker 1: this is not theoretical. It's what happened with the vaccines 462 00:27:20,680 --> 00:27:24,879 Speaker 1: for the coronavirus. We accelerate not only the discovery of 463 00:27:24,960 --> 00:27:30,440 Speaker 1: possible therapeutics by having artificial intelligence based supercomputers that can 464 00:27:30,520 --> 00:27:34,720 Speaker 1: simulate diseases and therapies. We can combine them with physical 465 00:27:35,800 --> 00:27:37,879 Speaker 1: organs on a chip. It sounds crazy to say, but 466 00:27:37,960 --> 00:27:40,840 Speaker 1: the capacities with the modern machines as part of my 467 00:27:41,119 --> 00:27:44,480 Speaker 1: machine thesis and three D printers as one can actually 468 00:27:44,560 --> 00:27:49,760 Speaker 1: print living tissue, the lung tissue, heart tissue, and do 469 00:27:49,960 --> 00:27:54,680 Speaker 1: simulations of therapeutics inside of real living tissue to begin 470 00:27:54,760 --> 00:27:58,520 Speaker 1: to look at the early testing of therapeutics in the 471 00:27:58,560 --> 00:28:01,040 Speaker 1: real tissue before you do it people. So you do 472 00:28:01,080 --> 00:28:03,040 Speaker 1: it in the computers, you do it in a physical 473 00:28:03,160 --> 00:28:05,480 Speaker 1: organ on a chip, and then when you get to 474 00:28:05,520 --> 00:28:08,720 Speaker 1: the person, it's safer and faster. Lastly, let's just talk 475 00:28:08,760 --> 00:28:11,560 Speaker 1: about physical hospitals. You're always going to need hospitals. You 476 00:28:11,640 --> 00:28:14,840 Speaker 1: need the physical buildings. One of the most annoying and 477 00:28:14,920 --> 00:28:17,840 Speaker 1: difficult tasks for nurses and it leads to a lot 478 00:28:17,880 --> 00:28:21,760 Speaker 1: of occupational challenges. That's picking up lifting patients. It's a 479 00:28:21,880 --> 00:28:26,560 Speaker 1: very simple example of moving human beings around gently takes humans. 480 00:28:27,160 --> 00:28:29,240 Speaker 1: We know we can build robots to help on that. 481 00:28:29,359 --> 00:28:32,000 Speaker 1: In fact, robots like that are already in service in 482 00:28:32,040 --> 00:28:35,480 Speaker 1: some Japanese hospitals. What that means is that instead of 483 00:28:35,960 --> 00:28:38,680 Speaker 1: two nurses to move a patient that needs to be moved, 484 00:28:38,880 --> 00:28:41,440 Speaker 1: you have one nurse in a robot that has soft 485 00:28:41,480 --> 00:28:45,360 Speaker 1: actuators that can't hurt you. The nurse now is part 486 00:28:45,400 --> 00:28:47,640 Speaker 1: of the process, but I don't have two nurses in 487 00:28:47,680 --> 00:28:50,440 Speaker 1: the room. I have one that's obviously productive. I am 488 00:28:50,480 --> 00:28:53,520 Speaker 1: also relieving the possibility of having back injury for the 489 00:28:53,640 --> 00:28:57,040 Speaker 1: nurse moving a heavy patient. These are no longer science 490 00:28:57,040 --> 00:29:01,280 Speaker 1: fiction ideas. These are really happening. These are deeply productive 491 00:29:01,600 --> 00:29:04,240 Speaker 1: as well as providing for better outcome, productive in the 492 00:29:04,280 --> 00:29:10,320 Speaker 1: sense fuer labor, more machines, more AI, better outcomes. I 493 00:29:10,360 --> 00:29:12,440 Speaker 1: think the one server lining we're going to get from 494 00:29:12,440 --> 00:29:15,920 Speaker 1: the huge missteps and how we handled the therapeutic part 495 00:29:15,920 --> 00:29:20,040 Speaker 1: of this. Once we finish with the finger pointing, which 496 00:29:20,080 --> 00:29:24,320 Speaker 1: is always appropriate, if not necessary, we will, I think, 497 00:29:24,360 --> 00:29:26,800 Speaker 1: get to true blue ribbon panels and begin to look 498 00:29:26,840 --> 00:29:29,160 Speaker 1: at what do we do wrong? What can we do 499 00:29:29,280 --> 00:29:31,000 Speaker 1: right with the technology we have today? And I think 500 00:29:31,000 --> 00:29:33,600 Speaker 1: when we begin to ask those questions now, with the 501 00:29:33,680 --> 00:29:36,760 Speaker 1: tool kit that we have now, I think we'll see 502 00:29:36,800 --> 00:29:40,400 Speaker 1: a step function change in the productivity of healthcare, including 503 00:29:40,440 --> 00:29:42,800 Speaker 1: self diagnostics. By the way, you know, we have these 504 00:29:42,800 --> 00:29:46,040 Speaker 1: home test kits for COVID. They're kind of clunky, but 505 00:29:46,560 --> 00:29:49,760 Speaker 1: we now know it's possible to make test kits that 506 00:29:49,800 --> 00:29:53,200 Speaker 1: would be far more sophisticated but not commercialized yet, that 507 00:29:53,280 --> 00:29:56,800 Speaker 1: would employ the computing in your smartphone to do us 508 00:29:56,800 --> 00:29:59,560 Speaker 1: alive a test for example, and a little slide that 509 00:29:59,600 --> 00:30:02,200 Speaker 1: you plu into your smartphone. It'll do the analytics and 510 00:30:02,240 --> 00:30:04,600 Speaker 1: then talk to your doctor or nurse, you can keep 511 00:30:04,600 --> 00:30:07,800 Speaker 1: the information secure to yourself. All those kinds of things 512 00:30:08,480 --> 00:30:14,080 Speaker 1: are now emerging at protocommercial phases. To come back to 513 00:30:14,160 --> 00:30:16,080 Speaker 1: your basic question that what doesn't mean for healthcare, It 514 00:30:16,080 --> 00:30:19,640 Speaker 1: means healthcare gets more productive with better outcomes, and pretty quickly, 515 00:30:19,680 --> 00:30:22,600 Speaker 1: certainly in the decade coming. I once co chaired to 516 00:30:22,720 --> 00:30:26,280 Speaker 1: three year study on Alzheimer's and one of the things 517 00:30:26,360 --> 00:30:29,200 Speaker 1: we need is precisely the kind of robotics you're describing, 518 00:30:29,640 --> 00:30:33,000 Speaker 1: so that the home care person, let's say your husband 519 00:30:33,080 --> 00:30:35,560 Speaker 1: or your wife. You want to keep them at home, 520 00:30:36,080 --> 00:30:38,760 Speaker 1: but today there are challenges of trying to get them 521 00:30:38,760 --> 00:30:40,960 Speaker 1: in and out of bed and so forth. We're right 522 00:30:41,000 --> 00:30:44,800 Speaker 1: at the edge of being able to decentralize robotics so 523 00:30:44,920 --> 00:30:49,200 Speaker 1: that you personally will have an assistant and effect helping you, 524 00:30:49,280 --> 00:30:52,080 Speaker 1: which will be a huge breakthrough. It'd be fabulous, And 525 00:30:52,160 --> 00:30:53,640 Speaker 1: who doesn't want to be at home instead of in 526 00:30:53,680 --> 00:30:56,360 Speaker 1: a hospital to the longest extent you can possibly do that. 527 00:31:15,000 --> 00:31:17,120 Speaker 1: One of the greatest challenges of the next ten to 528 00:31:17,200 --> 00:31:20,440 Speaker 1: twenty years is going to be our competition with China. 529 00:31:20,560 --> 00:31:24,160 Speaker 1: How do you see this revolution playing out in terms 530 00:31:24,160 --> 00:31:27,160 Speaker 1: of the US and China. I'm fully in the camp 531 00:31:27,200 --> 00:31:29,640 Speaker 1: that it's good to trade with people, it's great to 532 00:31:29,680 --> 00:31:34,160 Speaker 1: exchange ideas, of course, but I'm deeply concerned about the 533 00:31:34,200 --> 00:31:40,800 Speaker 1: Communist Party and their aspirations geopolitically economically. On the technology side, 534 00:31:41,360 --> 00:31:44,800 Speaker 1: we have an enormous advantage over China, one you're familiar with. 535 00:31:44,880 --> 00:31:47,600 Speaker 1: I've been to maybe fifteen or twenty cities in China. 536 00:31:47,720 --> 00:31:51,840 Speaker 1: I like Chinese engineers. Their system, though, is profoundly anathetical 537 00:31:52,240 --> 00:31:54,680 Speaker 1: to the kind of innovation that goes on in America. 538 00:31:54,960 --> 00:31:58,080 Speaker 1: They're at a deep disadvantage on that, but their system 539 00:31:58,120 --> 00:32:00,680 Speaker 1: has an advantage in terms of deploying technologies that have 540 00:32:00,720 --> 00:32:03,480 Speaker 1: already been invented or are moving because they tilt the 541 00:32:03,520 --> 00:32:06,120 Speaker 1: playing field. They subsidize the hell out of stuff and 542 00:32:06,240 --> 00:32:11,040 Speaker 1: disadvantage the competition by making tariffs are invisible or visible, 543 00:32:11,400 --> 00:32:14,040 Speaker 1: and just make it impossible to compete in their markets 544 00:32:14,040 --> 00:32:16,560 Speaker 1: where they have lots of people. So we have the 545 00:32:16,600 --> 00:32:20,360 Speaker 1: advantage and innovation. We've shot ourselves in the foot on 546 00:32:20,520 --> 00:32:24,040 Speaker 1: building the things we innovate by you know, we talk 547 00:32:24,080 --> 00:32:28,800 Speaker 1: about manufacturing the computer chips. Manufacturing got chased out of 548 00:32:28,800 --> 00:32:32,160 Speaker 1: our country by regulators. Innovators would rather build their factories 549 00:32:32,200 --> 00:32:33,400 Speaker 1: here and talk to any of the Of course they'd 550 00:32:33,480 --> 00:32:35,920 Speaker 1: rather build them here if you've made it as easy 551 00:32:35,960 --> 00:32:37,680 Speaker 1: to build a factory here as it is they build 552 00:32:37,680 --> 00:32:40,560 Speaker 1: a factory in China, they build them here. It's not complicated. 553 00:32:41,720 --> 00:32:44,360 Speaker 1: But I think we'll probably politically get to that point. 554 00:32:44,440 --> 00:32:47,120 Speaker 1: We're gitting to that point pretty quickly on both parties, 555 00:32:47,440 --> 00:32:50,920 Speaker 1: is my observation. Not university, but we're getting there. And 556 00:32:51,080 --> 00:32:55,360 Speaker 1: the new stuff, the new classes of computing, biocompatible computing, 557 00:32:55,520 --> 00:32:58,120 Speaker 1: the new kinds of materials that will make healthcare possible, 558 00:32:58,120 --> 00:33:00,720 Speaker 1: they're all being invented here. Will manufacture them here. Gives 559 00:33:00,760 --> 00:33:03,640 Speaker 1: us an advantage. Not everything will be done here, but 560 00:33:03,720 --> 00:33:05,440 Speaker 1: we're the epicenter. We still have a majority of the 561 00:33:05,440 --> 00:33:08,280 Speaker 1: world's leading universities. By whatever measure you want for a 562 00:33:08,360 --> 00:33:13,320 Speaker 1: leading university, we have the most flexible innovative capital system 563 00:33:13,360 --> 00:33:17,400 Speaker 1: for vanture capital on the planet. Still, we also have 564 00:33:17,440 --> 00:33:20,360 Speaker 1: a real advantage in demographics China last year, not because 565 00:33:20,360 --> 00:33:23,480 Speaker 1: of COVID. When at the negative population growth rate, that's 566 00:33:23,520 --> 00:33:27,080 Speaker 1: a deep disadvantage because what that means is over the 567 00:33:27,160 --> 00:33:31,920 Speaker 1: coming half century, the age of their workforce is getting 568 00:33:31,920 --> 00:33:35,080 Speaker 1: older much faster than our workforce We're getting older too, 569 00:33:35,200 --> 00:33:38,800 Speaker 1: but a much slower rate. So the average age of 570 00:33:38,840 --> 00:33:42,520 Speaker 1: the productive workforce in America is going to be much 571 00:33:42,560 --> 00:33:46,360 Speaker 1: lower than China very soon. That hugely disadvantages them. These 572 00:33:46,400 --> 00:33:48,440 Speaker 1: are all good things for the economy, they're not great 573 00:33:48,440 --> 00:33:51,640 Speaker 1: things to you politically, because it's going to stress China 574 00:33:51,680 --> 00:33:53,880 Speaker 1: and it's going to create incentives on their part to 575 00:33:53,920 --> 00:33:58,440 Speaker 1: do things that are frankly politically worrisome. So I think 576 00:33:58,440 --> 00:34:02,920 Speaker 1: we have inherited advantage as culture, technologically and demographically. If 577 00:34:02,960 --> 00:34:06,680 Speaker 1: we can take advantage of those without being idiots, which 578 00:34:06,760 --> 00:34:09,920 Speaker 1: is not guaranteed, I mean, I understand that, but if 579 00:34:09,920 --> 00:34:13,799 Speaker 1: we can, I think we can exercise the kind of 580 00:34:13,840 --> 00:34:18,880 Speaker 1: a soft power that's extraordinarily important and powerful over history. 581 00:34:19,200 --> 00:34:22,880 Speaker 1: I worked in defense weapons systems. I think we have 582 00:34:22,960 --> 00:34:27,279 Speaker 1: to continue to invest in our military so that it 583 00:34:27,320 --> 00:34:30,640 Speaker 1: becomes untenable for anybody to want to compete with us militarily. 584 00:34:31,239 --> 00:34:34,239 Speaker 1: But that should just be in the background, right What 585 00:34:34,280 --> 00:34:37,480 Speaker 1: should be in the foreground is an incredibly powerful soft power, 586 00:34:38,440 --> 00:34:40,479 Speaker 1: both in technology and, as I write in my book, 587 00:34:40,520 --> 00:34:43,920 Speaker 1: also an energy. I mean, the energy advantages in America 588 00:34:43,960 --> 00:34:47,520 Speaker 1: has as a technological advantage that we're in the process 589 00:34:47,520 --> 00:34:49,759 Speaker 1: of trying to squander But I don't think we'll squander it. 590 00:34:50,040 --> 00:34:53,680 Speaker 1: John Moody wrote a fascinating novel called of Course They Knew, 591 00:34:54,320 --> 00:34:57,799 Speaker 1: which is about Wuhan and COVID. But the thing I 592 00:34:57,840 --> 00:35:01,680 Speaker 1: find most fascinating about it is his portrait. And he 593 00:35:01,719 --> 00:35:03,360 Speaker 1: wrote it deliberately as the novel because he said he 594 00:35:03,360 --> 00:35:07,440 Speaker 1: couldn't prove all this, but his portrait of the Chinese 595 00:35:07,600 --> 00:35:11,680 Speaker 1: thinking about artificial intelligence and particularly the use of it 596 00:35:11,719 --> 00:35:15,239 Speaker 1: to manipulate public opinion, particularly in our country, because we're 597 00:35:15,280 --> 00:35:17,920 Speaker 1: so open that you get so much stuff in the Internet, 598 00:35:17,920 --> 00:35:19,640 Speaker 1: you have no idea whether it's true or not, and 599 00:35:19,680 --> 00:35:23,960 Speaker 1: it sounds true. How do you see artificial intelligence playing 600 00:35:24,000 --> 00:35:27,959 Speaker 1: into the revolution you're describing? As I write in my book, 601 00:35:27,960 --> 00:35:32,400 Speaker 1: it's a central feature of how computing has changed. But 602 00:35:32,520 --> 00:35:35,440 Speaker 1: it's important to know what we really mean by that phrase. 603 00:35:35,960 --> 00:35:39,400 Speaker 1: It was created in the mid fifties by computer scientist 604 00:35:39,480 --> 00:35:41,719 Speaker 1: McCarthy at Dartmouth, who's still alive and with us. A 605 00:35:41,800 --> 00:35:47,000 Speaker 1: brilliant guy. He may regret treading the phrase because artificial 606 00:35:47,040 --> 00:35:52,440 Speaker 1: intelligence isn't It isn't intelligence. It's like calling a car 607 00:35:52,480 --> 00:35:56,760 Speaker 1: an artificial horse, or an airplane an artificial bird. Obviously 608 00:35:56,800 --> 00:36:02,680 Speaker 1: there's some functional overlap, but they're very different. When computers 609 00:36:02,680 --> 00:36:06,719 Speaker 1: engage at inference, it's opposed to calculation, which is what 610 00:36:06,760 --> 00:36:10,520 Speaker 1: AI really means. That's a very different thing. And this 611 00:36:10,680 --> 00:36:12,440 Speaker 1: is an important point that I think is lost in 612 00:36:12,560 --> 00:36:14,160 Speaker 1: a lot of people. Go and then once you understand 613 00:36:14,160 --> 00:36:17,560 Speaker 1: what AI really is, you know why. It's a powerful 614 00:36:18,280 --> 00:36:21,640 Speaker 1: adjunct to amplifier to humans. Just the way that we 615 00:36:21,680 --> 00:36:26,160 Speaker 1: amplify our muscles with machines, we amplify our cognition with 616 00:36:26,280 --> 00:36:30,120 Speaker 1: artificial intelligence. We don't replace it. One plus one equals two. 617 00:36:30,280 --> 00:36:33,000 Speaker 1: Let's what computers do, and they compute. There's only one answer, 618 00:36:33,040 --> 00:36:35,799 Speaker 1: and it's a right answer. That's not what inference is. 619 00:36:36,239 --> 00:36:38,480 Speaker 1: I want a computer not to say the answer is 620 00:36:38,560 --> 00:36:41,400 Speaker 1: go this way. It would be this is probably a 621 00:36:41,400 --> 00:36:43,440 Speaker 1: better way to go, based on what I know now, 622 00:36:43,640 --> 00:36:46,120 Speaker 1: because use traffic is an example, based on the traffic 623 00:36:46,120 --> 00:36:49,160 Speaker 1: information I have now and what I think your behaviors are, 624 00:36:49,360 --> 00:36:51,719 Speaker 1: and you want to avoid tolls, so probably this is 625 00:36:51,719 --> 00:36:53,600 Speaker 1: the way you're going to go. And as we all 626 00:36:53,600 --> 00:36:56,359 Speaker 1: know from using mapping programs, nine times out of ten 627 00:36:56,400 --> 00:36:58,960 Speaker 1: it's a pretty good map, right, But one time out 628 00:36:58,960 --> 00:37:01,719 Speaker 1: of ten got it wrong. I mean, they didn't see 629 00:37:01,760 --> 00:37:04,040 Speaker 1: some traffic or it's not how I want to behave. 630 00:37:04,760 --> 00:37:07,520 Speaker 1: But that's not a calculation, that's inference. AI does that 631 00:37:07,600 --> 00:37:12,000 Speaker 1: stuff very useful. It's getting the answer about right. But 632 00:37:12,120 --> 00:37:16,960 Speaker 1: that requires an intervention of human behavior as well. So 633 00:37:17,040 --> 00:37:20,279 Speaker 1: they compliment each other as supposed to replace each other. Now, 634 00:37:21,200 --> 00:37:26,520 Speaker 1: would somebody with an AI enabled earbud that's pick whatever 635 00:37:26,640 --> 00:37:28,840 Speaker 1: job it is, whether it's your desk clerk at a hotel, 636 00:37:29,360 --> 00:37:32,760 Speaker 1: or whether you're manufacturing something or you're a plumber, it'd 637 00:37:32,760 --> 00:37:36,320 Speaker 1: be great to have your work have a computer remotely 638 00:37:36,600 --> 00:37:38,480 Speaker 1: when you run into a problem and you say, you know, 639 00:37:38,640 --> 00:37:41,759 Speaker 1: I want to look at the drawing for an X 640 00:37:41,840 --> 00:37:44,440 Speaker 1: to know whether I don't recognize this machine or this 641 00:37:44,800 --> 00:37:48,200 Speaker 1: body part or this animal or whatever it is, And 642 00:37:48,520 --> 00:37:50,200 Speaker 1: what do you think I should do? I mean, you'd 643 00:37:50,200 --> 00:37:52,680 Speaker 1: say to the computer. It might give you a recommendation 644 00:37:52,719 --> 00:37:54,800 Speaker 1: that you say I hadn't thought of. Great, But that's AI. 645 00:37:55,080 --> 00:37:59,040 Speaker 1: This is really really powerful stuff. Does it replace humans? Sure? 646 00:37:59,120 --> 00:38:02,040 Speaker 1: In the same way that the Lodites were right, they're automated. 647 00:38:02,080 --> 00:38:05,120 Speaker 1: LOOM eliminated their jobs, but it didn't eliminate all jobs, 648 00:38:05,120 --> 00:38:08,280 Speaker 1: and it didn't eliminate the work of humans in clothing. 649 00:38:08,320 --> 00:38:11,040 Speaker 1: It just changed the work they did. So let me ask. 650 00:38:11,120 --> 00:38:13,920 Speaker 1: I can't help myself. You're so interesting and you have 651 00:38:13,960 --> 00:38:21,440 Speaker 1: such a broad range. What are you working on now, well, 652 00:38:21,440 --> 00:38:24,160 Speaker 1: other than our venture fund where we're looking for software 653 00:38:24,200 --> 00:38:26,560 Speaker 1: and energy, because it's exciting to think about how you 654 00:38:26,640 --> 00:38:29,920 Speaker 1: bring the digitalization. If you like of the energy business, 655 00:38:29,960 --> 00:38:33,359 Speaker 1: it's really hard to do well, I could confess to you. 656 00:38:33,960 --> 00:38:35,920 Speaker 1: I guess I'm going to be confessing this to more 657 00:38:35,920 --> 00:38:39,879 Speaker 1: people than just you. Have a big audience like you. 658 00:38:40,160 --> 00:38:42,880 Speaker 1: I like fiction as well, and I have written a 659 00:38:42,880 --> 00:38:46,160 Speaker 1: science fiction book which I now have to turn back 660 00:38:46,200 --> 00:38:49,320 Speaker 1: to and clean up because fiction writing is very different. 661 00:38:49,440 --> 00:38:52,320 Speaker 1: Writing is a lonely business, but it's a very different 662 00:38:52,360 --> 00:38:54,400 Speaker 1: You have to get your brain at a different mode, 663 00:38:55,080 --> 00:38:57,480 Speaker 1: and so I have to get out of prediction mode 664 00:38:57,880 --> 00:39:01,240 Speaker 1: that's supposed to be realistic, which is what my books about, 665 00:39:01,600 --> 00:39:04,799 Speaker 1: and get into prediction mode that's kind of fun and 666 00:39:05,360 --> 00:39:09,799 Speaker 1: not so realistic. So I find fiction writing harder than nonfiction. 667 00:39:10,120 --> 00:39:12,360 Speaker 1: It is a different skill, but it's important to stretch 668 00:39:12,360 --> 00:39:15,040 Speaker 1: your brain. There Manhattan Institute, which is my home for 669 00:39:15,160 --> 00:39:18,520 Speaker 1: policy meddling, I'm still working on energy areas. I'm going 670 00:39:18,560 --> 00:39:21,440 Speaker 1: to be doing a study. My nominal title for it 671 00:39:21,520 --> 00:39:23,439 Speaker 1: is the end of the Age of oil question mark. 672 00:39:24,120 --> 00:39:27,239 Speaker 1: The answer is no never. But that's the study which 673 00:39:27,239 --> 00:39:31,080 Speaker 1: i'll put out in a policy's kind of framework. People 674 00:39:31,080 --> 00:39:32,640 Speaker 1: are set up in their heads that we're going to 675 00:39:33,120 --> 00:39:35,160 Speaker 1: eliminate the use of oil and cars and all kinds 676 00:39:35,160 --> 00:39:39,759 Speaker 1: of places overnight, you know, maybe in a century. I'm 677 00:39:39,800 --> 00:39:41,440 Speaker 1: working on that. And the other thing I'm working on, 678 00:39:41,520 --> 00:39:44,080 Speaker 1: of course, is one of my favorite subjects is to 679 00:39:44,160 --> 00:39:48,360 Speaker 1: understand what science is, and specifically what it means to 680 00:39:48,440 --> 00:39:51,600 Speaker 1: fund science if you're in the government, Because we're about 681 00:39:51,640 --> 00:39:55,440 Speaker 1: to embark again, as you know, on this massive effort 682 00:39:55,480 --> 00:39:57,600 Speaker 1: that Schumer started with and a lot of Republicans gone 683 00:39:57,600 --> 00:40:00,400 Speaker 1: on board with, you know, the Endless Frontiers Act, to 684 00:40:00,760 --> 00:40:03,759 Speaker 1: throw huge amounts of money at science, and there's a 685 00:40:03,800 --> 00:40:05,839 Speaker 1: lot of support for that. But I think we're throwing 686 00:40:05,840 --> 00:40:08,520 Speaker 1: the money at the wrong stuff, and we don't understand 687 00:40:08,520 --> 00:40:10,160 Speaker 1: what science is. So I'm going to do a policy 688 00:40:10,200 --> 00:40:13,440 Speaker 1: paper on what it is that we got wrong and 689 00:40:13,480 --> 00:40:15,760 Speaker 1: why it's important to get it right. I look forward 690 00:40:15,800 --> 00:40:17,920 Speaker 1: to your paper, and I think it's a very important topic. 691 00:40:18,120 --> 00:40:20,080 Speaker 1: I hope I'm glad you're working on it. Mark, What 692 00:40:20,160 --> 00:40:22,439 Speaker 1: do you think the next ten years looked like if 693 00:40:22,440 --> 00:40:28,399 Speaker 1: we don't sovietize our economy through political malfeasance. I think 694 00:40:28,440 --> 00:40:31,880 Speaker 1: are going to see economic growth outstrip anything that's happened 695 00:40:31,880 --> 00:40:34,879 Speaker 1: in the last twenty years and return to the kind 696 00:40:34,920 --> 00:40:38,560 Speaker 1: of raw growth that happened because of the Reagan boom 697 00:40:38,640 --> 00:40:41,080 Speaker 1: this time and for the similar reasons, because of a 698 00:40:41,200 --> 00:40:45,319 Speaker 1: technology efflorescence. So we need to unleash it. You know 699 00:40:45,360 --> 00:40:48,959 Speaker 1: what that means in terms of markets and light handed regulation. Mark. 700 00:40:49,000 --> 00:40:51,480 Speaker 1: Do you think that a big technology could be on 701 00:40:51,520 --> 00:40:55,440 Speaker 1: the edge of emerging that would change society as much as, 702 00:40:55,480 --> 00:40:58,840 Speaker 1: for example, the Internet or the smartphone did in the 703 00:40:58,920 --> 00:41:02,920 Speaker 1: last generation. Yeah, I do it. It's already visible. It's 704 00:41:02,960 --> 00:41:05,799 Speaker 1: called the cloud. That's the type of my book. The 705 00:41:05,840 --> 00:41:08,280 Speaker 1: cloud is very new, it's not even a decade old. 706 00:41:08,320 --> 00:41:10,480 Speaker 1: And the framing of what we would call the cloud, 707 00:41:11,280 --> 00:41:14,160 Speaker 1: and a decade into the Internet, it was still not 708 00:41:14,239 --> 00:41:16,879 Speaker 1: a big deal. People were barely doing You got mail 709 00:41:17,000 --> 00:41:20,800 Speaker 1: and AOL, And as the Internet took off, it changed 710 00:41:20,840 --> 00:41:23,200 Speaker 1: the world. We are just at the beginning of the 711 00:41:23,200 --> 00:41:26,960 Speaker 1: cloud expanding and changing the world. We will see as 712 00:41:27,000 --> 00:41:30,440 Speaker 1: the web telescope continues to evolve, just how much we 713 00:41:30,480 --> 00:41:33,439 Speaker 1: really can learn at the margins. It'll be fun. Mark. 714 00:41:33,480 --> 00:41:35,920 Speaker 1: I want to thank you for joining me today. I 715 00:41:35,960 --> 00:41:39,319 Speaker 1: think the future you're describing is exciting, and I think 716 00:41:39,320 --> 00:41:42,400 Speaker 1: it's historically probably right. And I want to recommend to 717 00:41:42,400 --> 00:41:45,719 Speaker 1: our listeners that they read your book The Cloud Revolution, 718 00:41:46,120 --> 00:41:49,919 Speaker 1: How the Convergence of New Technologies will unleash the next 719 00:41:49,960 --> 00:41:52,799 Speaker 1: economic Boom and a roaring twenty twenties, And we'll have 720 00:41:52,800 --> 00:41:55,960 Speaker 1: a link to your book on our show page. Thank you, 721 00:41:56,160 --> 00:41:59,440 Speaker 1: that's very generous. Enjoy talking to you, and thank you 722 00:41:59,480 --> 00:42:01,520 Speaker 1: for your sir, as you've been an inspiration to so 723 00:42:01,600 --> 00:42:08,440 Speaker 1: many of us. Thank you to my guest, Mark Mills. 724 00:42:08,560 --> 00:42:10,840 Speaker 1: You can get a link to buy his book The 725 00:42:10,960 --> 00:42:15,760 Speaker 1: Cloud Revolution, How the Convergence of New Technologies will unleash 726 00:42:15,800 --> 00:42:19,160 Speaker 1: the next economic boom and are Roaring twenty twenties on 727 00:42:19,160 --> 00:42:23,040 Speaker 1: our show page at newtsworld dot com. Newts World is 728 00:42:23,040 --> 00:42:27,800 Speaker 1: produced by Gingwich three sixty and iHeartMedia. Our executive producer 729 00:42:28,200 --> 00:42:32,239 Speaker 1: is Garnsey slam Our producer is Rebecca Howe, and our 730 00:42:32,280 --> 00:42:36,320 Speaker 1: researcher is Rachel Peterson. The artwork for the show was 731 00:42:36,400 --> 00:42:40,120 Speaker 1: created by Steve Penley, Special thanks to the team at 732 00:42:40,120 --> 00:42:43,840 Speaker 1: gingwidge three sixty. If you've been enjoying Newtsworld, I hope 733 00:42:43,840 --> 00:42:46,640 Speaker 1: you'll go to Apple Podcast and both rate us with 734 00:42:46,719 --> 00:42:50,279 Speaker 1: five stars and give us a review so others can 735 00:42:50,400 --> 00:42:53,759 Speaker 1: learn what it's all about. Right now, listeners of newts 736 00:42:53,800 --> 00:42:57,360 Speaker 1: World can sign up from my three free weekly columns 737 00:42:57,640 --> 00:43:02,640 Speaker 1: at gingwidge three sixty dot com slash newsletter I'm new Gingrich. 738 00:43:03,200 --> 00:43:04,240 Speaker 1: This is news World.