1 00:00:00,720 --> 00:00:12,360 David Gan is a university and business leader with extensive international experience in innovation strategy and technology management, 2 00:00:12,360 --> 00:00:16,260 is professor of Innovation and Technology Management, Imperial College Business School, 3 00:00:16,260 --> 00:00:20,880 and now is a local guy because he's the chairman of the UK Atomic Energy Authority. 4 00:00:20,880 --> 00:00:23,130 So he's just down the road in Oxfordshire. 5 00:00:23,130 --> 00:00:30,300 His research explores why and how innovation happens and the way conciliation transforms the world and how it can be managed. 6 00:00:30,300 --> 00:00:36,540 And so we've asked him to come and talk today about the business of science and the science of business. 7 00:00:36,540 --> 00:00:50,200 David's straight over to. Thank you, Phil and Tony, for inviting me, and good afternoon, ladies and gentlemen. 8 00:00:50,200 --> 00:00:56,530 There's a title in the programme, which is a draught title when I try to put it on the slide, it was too long, 9 00:00:56,530 --> 00:01:04,090 so I've shortened it to the business of science and the science of business because I think that's what we're here to talk about. 10 00:01:04,090 --> 00:01:11,110 And I want to flesh back a little way in history and say, Where have we come from? 11 00:01:11,110 --> 00:01:19,240 Take some insights and is rather partial, and we can just take a few little nuggets out and say, Where are we going to? 12 00:01:19,240 --> 00:01:26,680 And perhaps the previous discussions already prefaced some of some of the issues that are drawn. 13 00:01:26,680 --> 00:01:31,240 You'll notice a picture here that I felt I had to use, 14 00:01:31,240 --> 00:01:37,150 given it's the 50th anniversary and in the title in the programme, it says, I think back 30 years, 15 00:01:37,150 --> 00:01:44,110 but allow me to to celebrate with you that science and business did come together to an extraordinary effect, 16 00:01:44,110 --> 00:01:49,690 actually, and we still use the word moonshot to mean an audacious project. 17 00:01:49,690 --> 00:02:00,580 And with that in mind, let let me start off and I feel you kindly gave my academic credentials and leadership a column for those of you don't know me, 18 00:02:00,580 --> 00:02:09,940 I also work in business a lot. I've had five start-ups, one relatively successful one, and I'm on a number of boards and have been for a while. 19 00:02:09,940 --> 00:02:23,020 So you'll excuse me if I start off with business the first insight and I'm taking you back one hundred and forty years to Thomas Edison, 20 00:02:23,020 --> 00:02:24,640 who was extraordinary in many ways. 21 00:02:24,640 --> 00:02:32,230 So one thousand ninety three patents is a lot of patents to have your name on, and that number is only exceeded in two thousand and three. 22 00:02:32,230 --> 00:02:38,590 So you held the world record for the best part of one hundred years in terms of the number of patents, 23 00:02:38,590 --> 00:02:43,960 thus productivity in spewing out new ideas and logging them in the world. 24 00:02:43,960 --> 00:02:47,980 And of course, we've all benefited from the electric light bulb. 25 00:02:47,980 --> 00:02:58,900 The Phonogram, whatever it was called at the time, he and the reason I put him here is not for those patents, it's for the mechanisms he put in place. 26 00:02:58,900 --> 00:03:11,620 So arguably some of you know this. Edison created the first research laboratory that provided ideas on contract in third party form to others. 27 00:03:11,620 --> 00:03:16,720 And this was the invention laboratory at Menlo Park, and this is Menlo Park in New Jersey. 28 00:03:16,720 --> 00:03:21,190 So don't get your geographies too confused at this point in history. 29 00:03:21,190 --> 00:03:25,900 First Industrial Research Laboratory A small thing every 10 days and the big thing every six months. 30 00:03:25,900 --> 00:03:28,180 That was the mantra that was running through this, 31 00:03:28,180 --> 00:03:35,320 and it worked because he selected technologists and scientists and universities that have started to 32 00:03:35,320 --> 00:03:41,620 educate people with the right skills to go and work in line and solve problems for other people. 33 00:03:41,620 --> 00:03:46,330 And within a short seven year period, it produced 400 patents. 34 00:03:46,330 --> 00:03:54,310 And if you will, that's the tipping point between hero individuals who invented things and sometimes change the world. 35 00:03:54,310 --> 00:04:01,150 And a systematic approach to deploying science and technology to solve problems for commercial gain. 36 00:04:01,150 --> 00:04:06,100 And we're talking about technology transfer and all of that gubbins, the relationship stuff. 37 00:04:06,100 --> 00:04:13,000 So we go back one hundred and forty years to see the person who arguably started it all. 38 00:04:13,000 --> 00:04:22,480 And so that's the first insight. Where did it come from? And then I think, you know, we connect into how do universities get funded? 39 00:04:22,480 --> 00:04:28,310 What do governments do about all of this? So the second thing I wanted to mention was the emergence of science policy. 40 00:04:28,310 --> 00:04:34,210 Now you have to go back to about nineteen hundred, you could probably go back further if you really want to. 41 00:04:34,210 --> 00:04:44,410 But I've taken all dean just as a little inflexion point here because the whole principle which I have, you know, 42 00:04:44,410 --> 00:04:52,300 was that researchers are better at determining what research should be funded than politicians, right? 43 00:04:52,300 --> 00:05:00,400 And that gave us in this country in the UK at least the arm's length relationship between the funding agencies and the political process. 44 00:05:00,400 --> 00:05:04,360 And I think in the US and many other countries, we have similar formations, 45 00:05:04,360 --> 00:05:14,680 which means that the rhythm of how we fund the flywheel, the science base is determined by peer review and so on. 46 00:05:14,680 --> 00:05:17,110 And of course, that was very good, 47 00:05:17,110 --> 00:05:26,170 except if US dotty scientists go off in a direction and you can't see much impact for a while and coming into the The Second World War, 48 00:05:26,170 --> 00:05:33,010 I've chosen J.D. Burnell for several reasons. But, you know, a great historian of science, by the way. 49 00:05:33,010 --> 00:05:41,080 Huge number of books and articles, he wrote, as well as being an Irish X-ray crystallography molecular. 50 00:05:41,080 --> 00:05:48,880 Biologist and a communist in the Second World War, he was in the British government and he said, 51 00:05:48,880 --> 00:05:53,140 Actually, come on, scientists, we've got to think about social purpose. 52 00:05:53,140 --> 00:06:00,040 We need to think about some direction here, putting our great minds and infrastructure together. 53 00:06:00,040 --> 00:06:06,280 He was one of the people who masterminded Overlord, by the way, at the end of the Second World War. 54 00:06:06,280 --> 00:06:14,410 Systems Engineer. So here we have the freedom of the academic and here a wake up call from government saying, hang on a minute. 55 00:06:14,410 --> 00:06:21,190 What are you going to do with this stuff? Is it going to be useful? And of course, that time around, about that time in the US, 56 00:06:21,190 --> 00:06:31,150 we had the most fabulous piece of work by Vannevar Bush and Science, The Endless Frontier and for all you scientists, 57 00:06:31,150 --> 00:06:35,740 and you're just going to love this if you haven't read it before opening basic research on a massive scale, 58 00:06:35,740 --> 00:06:41,080 that's what he advocated science, the seed corn of technological advance research. 59 00:06:41,080 --> 00:06:45,700 The pacemaker of technological progress, chiefly in academic institutions. 60 00:06:45,700 --> 00:06:52,940 Scientists may work in an atmosphere relatively free from adverse pressure of convention prejudice or commercial necessity. 61 00:06:52,940 --> 00:06:59,740 Yeah, give us the money. Scientists need maximum autonomy to pursue research free from the influence of pressure groups, 62 00:06:59,740 --> 00:07:04,360 free from the necessity of producing immediate results free from any central board. 63 00:07:04,360 --> 00:07:06,040 Now he got away with that. 64 00:07:06,040 --> 00:07:16,210 And not only that, he really lifted the budget for science in the US and, you know, by implication, for other countries too. 65 00:07:16,210 --> 00:07:25,360 And one thing you must note, and Matt Wolpert made a special mention of engagement in his opening remarks. 66 00:07:25,360 --> 00:07:29,920 I'm not sure that any piece of science policy is ever had so much engagement with this space. 67 00:07:29,920 --> 00:07:36,910 This became a bestseller. This document people were walking around Times Square reading it in Fortune magazine. 68 00:07:36,910 --> 00:07:41,320 So here we get science policy. Big, audacious. 69 00:07:41,320 --> 00:07:49,210 Give us the money. And this really unlocked a [INAUDIBLE] of a lot of new activities coming out of the Second World War, 70 00:07:49,210 --> 00:07:56,530 the military industrial complex and so on in the US and soon after. 71 00:07:56,530 --> 00:08:01,430 In many of our countries, we started thinking, how do we connect this to industrial strategy? 72 00:08:01,430 --> 00:08:06,190 And so we put the T word with science, policy, science and technology policy. 73 00:08:06,190 --> 00:08:13,390 And Solly Zuckerman actually came out with a very clear statement saying, 74 00:08:13,390 --> 00:08:22,210 Let's not to get too hung up between applied and basic research because these things had gone off in different directions. 75 00:08:22,210 --> 00:08:29,020 And you can see many, many policy documents, too many to put up on the screen since that time. 76 00:08:29,020 --> 00:08:32,860 But there's one that was influential for me particularly. 77 00:08:32,860 --> 00:08:39,040 And I think for many of us in the UK system in nineteen ninety three realising our potential. 78 00:08:39,040 --> 00:08:51,520 William Aldergrove. And in that document, it really makes a link to industrial strategy and it's a strategy for science, engineering and technology. 79 00:08:51,520 --> 00:08:59,680 But an interesting to hear the discussion about manufacturing just now because this was the beginning of what we had in the UK, 80 00:08:59,680 --> 00:09:01,900 the innovative manufacturing initiative. 81 00:09:01,900 --> 00:09:12,910 And it was a 1996 that I won the Royal Academy of Engineering in Innovative Manufacturing Initiative Chair and worked in this area. 82 00:09:12,910 --> 00:09:19,960 So I thank William for the funding. But these were the insights, really and how arguments morphed. 83 00:09:19,960 --> 00:09:26,110 And actually, if we went back to nineteen ninety three or nineteen seventy one roomful of people like this from all over the world, 84 00:09:26,110 --> 00:09:36,070 we might be having quite similar discussions. So one of the reasons saying this is not a lot new under the Sun when it comes to this, 85 00:09:36,070 --> 00:09:43,390 and maybe that's my reflection on the past, and I'll just tilt in a little bit into the future. 86 00:09:43,390 --> 00:09:48,130 And there are many ways of cutting up history. 87 00:09:48,130 --> 00:09:55,240 But if you take this sort of large 50 year chunks, you come out of the 19th century with Edison here. 88 00:09:55,240 --> 00:10:04,870 The great entrepreneur turned systematise of science and technology gave us the first research lab, which of course grew in a 50 year period. 89 00:10:04,870 --> 00:10:14,230 There's Bell Labs, but it could be any one of the huge labs from the big corporates into the corporate research and development machine. 90 00:10:14,230 --> 00:10:24,010 And our universities, of course, were educating the talent that would feed these huge audacious machines and great companies like in this country. 91 00:10:24,010 --> 00:10:30,730 GSK and others invest heavily in research and employ a lot of scientists with a bet on the future. 92 00:10:30,730 --> 00:10:33,940 You know, that's money that could go out as a dividend at the end of the year, 93 00:10:33,940 --> 00:10:41,040 but it has to be thought through with a business case for its research investment and go across the world and you. 94 00:10:41,040 --> 00:10:48,570 You come in to companies contentious these days like Huawei. Seventy thousand people working in R&D. 95 00:10:48,570 --> 00:10:55,830 I've been into every one of their major labs and seen some extraordinary things that they do. 96 00:10:55,830 --> 00:10:59,010 Of course, that really was a fairly settled system, 97 00:10:59,010 --> 00:11:06,600 and that industrial mechanism reflected back in on how we funded science and how the universities responded to them. 98 00:11:06,600 --> 00:11:11,220 And we had corporate partnerships and so on, and we kind of knew that entrepreneurship was important, 99 00:11:11,220 --> 00:11:20,850 but we thought it was the 19th century sort of thing that Thomas Edison and people did until around 15, 20 years ago. 100 00:11:20,850 --> 00:11:34,620 The World Wide Web and internet became really useful. And I worked with Procter and Gamble and Eli Lilly on some studies that resulted in over part of 101 00:11:34,620 --> 00:11:41,370 the creation of what became a sort of eBay on the web where you could trade intellectual property, 102 00:11:41,370 --> 00:11:47,640 you could trade licences. And that really opened up this whole discussion about what is open innovation. 103 00:11:47,640 --> 00:11:54,690 How can we reach out for new ideas? And since then, we ask companies, what's your strategy for search? 104 00:11:54,690 --> 00:12:00,570 And they say, Well, what are you talking about? Well, if the ideas can be everywhere, you better have a strategy for searching. 105 00:12:00,570 --> 00:12:05,670 So it's opened up a whole lot of new ways of thinking about how you manage the innovation process. 106 00:12:05,670 --> 00:12:09,030 And of course, the corporate lab hasn't gone away and entrepreneurs haven't come away. 107 00:12:09,030 --> 00:12:14,970 In many senses they've come back in, and I'll end on that point in just a minute. 108 00:12:14,970 --> 00:12:23,790 So there are a few new things and some of this, not all of it, but some of it is driven by technology. 109 00:12:23,790 --> 00:12:31,510 I hope Simon Sciences of the artificial, you know, artificial intelligence and artificiality, 110 00:12:31,510 --> 00:12:37,140 how science and technology creates the modern world go back a very long way. 111 00:12:37,140 --> 00:12:45,990 This is nineteen sixty nine publication that links to some work. 112 00:12:45,990 --> 00:12:56,710 I'm very much in favour of Thomas Crump, and many others have written good histories on the evolution of scientific instruments. 113 00:12:56,710 --> 00:13:02,970 Right. So one thing we often don't talk about enough is what the science contribute in terms 114 00:13:02,970 --> 00:13:07,830 of ways of seeing the world that allow new industries to emerge and then flourish, 115 00:13:07,830 --> 00:13:12,420 the electron tunnelling microscope and nanotechnology, for example. 116 00:13:12,420 --> 00:13:24,720 And so the sciences that create the instrumentation that allow us to do new things and speed up increased productivity are incredibly important. 117 00:13:24,720 --> 00:13:26,700 And they just happen to be getting very good. 118 00:13:26,700 --> 00:13:34,650 So this AI stuff that's been around in the research lab for 50 years is now coming out and British government, other governments are publishing on it. 119 00:13:34,650 --> 00:13:46,170 I wrote a book fifteen years ago about this think play do with colleagues looking at how digitisation in the research lab was speeding things up. 120 00:13:46,170 --> 00:13:58,800 And the main ingredients the intensification of science and innovation come from our ability to model and simulate the real world in a virtual world. 121 00:13:58,800 --> 00:14:06,360 Now, engineers have done that forever in mathematical models, but the power has a force multiplier on it. 122 00:14:06,360 --> 00:14:08,730 When you can use a digital toolkits that we've got, 123 00:14:08,730 --> 00:14:18,990 we know that we're talking here about machine learning where we put the ability to capture and harness data, keep it somewhere and analyse it. 124 00:14:18,990 --> 00:14:25,570 Robotics to do it automatically. Yeah, some curious new things coming in like blockchain security of that data, 125 00:14:25,570 --> 00:14:31,860 the parcels and the image I really like is the one of the car crash test dummies. 126 00:14:31,860 --> 00:14:36,570 You know, they were ubiquitous in every car company 25 years ago. 127 00:14:36,570 --> 00:14:42,810 And what we call time to market, the lead time it takes to create a new product and sell it to us was partly conditioned 128 00:14:42,810 --> 00:14:48,900 by the time it took to prove cabin safety by crashing these poor little mannequins. 129 00:14:48,900 --> 00:14:58,260 And, you know, as human beings, different shapes and sizes, it takes a bit of time to work out whether we've got a safe design or not. 130 00:14:58,260 --> 00:15:08,100 I've got time to get into the history of the software, except it came from US military designs of trying to understand explosions at the tip of a 131 00:15:08,100 --> 00:15:14,100 nuclear warhead when that came out of licence and was developed for looking at impact assessment. 132 00:15:14,100 --> 00:15:20,430 Eventually, we managed to have a working tool for modelling car crash tests. 133 00:15:20,430 --> 00:15:27,330 And of course, you can do thousands of these iterations a day. There's not a car company in the world that relies upon mannequins. 134 00:15:27,330 --> 00:15:33,270 You go out of business straightaway, you have to do it in silica. You still have a few mannequins to test your model. 135 00:15:33,270 --> 00:15:40,610 So the message from this is it's a lot cheaper to fail in bits rather. 136 00:15:40,610 --> 00:15:45,910 Is in that and the other lesson, by the way. What is it? 137 00:15:45,910 --> 00:15:47,560 Cars have got a lot safer. 138 00:15:47,560 --> 00:15:55,210 The learning curve is gone up almost exponentially, so every time an iteration happens, there's a learning curve in the computer. 139 00:15:55,210 --> 00:16:00,370 And so we have much safer vehicles than we would have had. It was incremental before. We've had a step change in the last 20 years. 140 00:16:00,370 --> 00:16:05,170 That's happened everywhere. You can go online to a body map and design your own fashion. 141 00:16:05,170 --> 00:16:13,090 It's the same sort of tool kit. It's really accelerating the innovation process and it's transforming the way we do science. 142 00:16:13,090 --> 00:16:18,400 So true, and let me just walk through a few questions. We still love discovery. 143 00:16:18,400 --> 00:16:22,510 Science. OK, curiosity. We've heard a bit about that today. 144 00:16:22,510 --> 00:16:29,140 We still need to have people who want to detect solar flares or cosmic microwave. 145 00:16:29,140 --> 00:16:32,590 Some of these things come back an incredibly important, you know, 146 00:16:32,590 --> 00:16:39,220 our communications systems would fall apart if there's a cosmic flare of a certain size. 147 00:16:39,220 --> 00:16:47,140 We need to know about this linking curiosity with practicality, as we did in the UK. 148 00:16:47,140 --> 00:17:01,480 In the cosmic microwave detector that was made here, sponsored by UK, our RFI Initiative C has resulted in a new way of doing security detection. 149 00:17:01,480 --> 00:17:09,580 And the first implication application of that was in Los Angeles rail system in 2018. 150 00:17:09,580 --> 00:17:19,150 So discovery. Yes. Let's see how we can continue our curiosity and having people doing extraordinary things. 151 00:17:19,150 --> 00:17:29,770 I mean, CERN is the obvious example, isn't it, because trying to understand the Higgs boson or the nature of the universe is a big discovery quest. 152 00:17:29,770 --> 00:17:38,770 But the fact that on the left, our friend Tim Berners-Lee could produce a paper and you can just about see the date March nineteen ninety nine. 153 00:17:38,770 --> 00:17:46,330 I reckon that's about 30 years and a few months ago. This is really not ambitiously titled information management a proposal. 154 00:17:46,330 --> 00:17:50,020 OK? And his boss said vague but exciting. 155 00:17:50,020 --> 00:17:55,000 That was the only note that was written on it. Well, that's the paper that set out the World Wide Web. 156 00:17:55,000 --> 00:17:58,090 And look what it's done for science. 157 00:17:58,090 --> 00:18:05,920 The things you may not know, but you would guess certainly is that if you're detecting hadrons, you can create pet scans. 158 00:18:05,920 --> 00:18:13,300 And if you've got to build a great big tunnel 27 seven kilometres with the sort of accuracy to put those beams in, 159 00:18:13,300 --> 00:18:17,110 you better have a tunnel boring machine with some new sensors on it, 160 00:18:17,110 --> 00:18:23,800 which has led to much better tunnel boring machines for civil engineering, the ones we used in Crossrail. 161 00:18:23,800 --> 00:18:31,480 So some of these areas of discovery have extraordinary power later, and perhaps we can harness those better. 162 00:18:31,480 --> 00:18:36,760 The other way around, of course, is mission oriented, and most of our language these days is about mission. 163 00:18:36,760 --> 00:18:40,870 And here is the existential threat, one of them that we've got to get real about. 164 00:18:40,870 --> 00:18:46,270 And it takes a 16 year old at the World Economic Forum this year to wake the world up yet again. 165 00:18:46,270 --> 00:18:48,610 And the clock is ticking. 166 00:18:48,610 --> 00:18:57,820 And there's a lot of science that needs to be done here to understand the ramifications and the mechanisms of global warming and what to do about it. 167 00:18:57,820 --> 00:19:00,880 And there are a lot of scientists working in this field, 168 00:19:00,880 --> 00:19:10,000 and I am chairman of the Atomic Energy Authority and our mission in the research field is to develop fusion energy. 169 00:19:10,000 --> 00:19:17,140 And all the way from that mission, we've had to look at the exhaust of very hot plasma just down the road. 170 00:19:17,140 --> 00:19:23,530 We've created a plasma at 350 million degrees that's nearly 20 times hotter than the centre of the Sun. 171 00:19:23,530 --> 00:19:30,040 That's pretty incredible. From an engineering point of view, trying to exhaust it led us into something which is equally incredible. 172 00:19:30,040 --> 00:19:39,160 And a company called Reaction Engines is on the campus, and it has a mechanism that we think will produce a hypersonic jet engine and give us flight 173 00:19:39,160 --> 00:19:45,190 times from London to New York or wherever you want to go in the matter of narrative. 174 00:19:45,190 --> 00:19:53,080 So I'm just helping you understand the things you already know the world's largest scientific collaboration. 175 00:19:53,080 --> 00:19:57,010 This is the E2 fusion reactor in the south of France. 176 00:19:57,010 --> 00:20:04,270 One of the other benefits we don't talk about enough is the engineering supply chain that is required. 177 00:20:04,270 --> 00:20:09,430 This is a 20 billion behemoth dollar, euro pound. 178 00:20:09,430 --> 00:20:16,300 They're all equal now. He's created a new industrial supply chain. 179 00:20:16,300 --> 00:20:22,330 I was down there the other week and the stainless stainless steel containment for this vessel, 180 00:20:22,330 --> 00:20:24,580 which weighs twenty three thousand tons, it's going to sit in. 181 00:20:24,580 --> 00:20:34,390 The middle is thirty mm thick and there's one company in the world that reckons it can weld that using robotic welding, it's a German company. 182 00:20:34,390 --> 00:20:42,580 And the amount of lessons that are coming out of that because it's a problem solving approach in engineering will spill over into many, 183 00:20:42,580 --> 00:20:47,330 many other areas. I'm sure. Of course, medicine. 184 00:20:47,330 --> 00:20:53,150 And we've already heard a bit from Mark and others. Antibiotic resistance, another existential threat. 185 00:20:53,150 --> 00:20:59,180 And the Francis Crick Institute, which I think in this country actually melts together nicely. 186 00:20:59,180 --> 00:21:06,440 Discovery mission. You know, Paul Nurse will talk about wanting to discover the meaning of life or what life is. 187 00:21:06,440 --> 00:21:16,700 The mission is improving human health. And so these are the big, big questions that we are looking at and wanting industrial collaboration. 188 00:21:16,700 --> 00:21:19,610 But they're also throwing up new dilemmas. 189 00:21:19,610 --> 00:21:29,510 In fact, that digital world is really creating all sorts of questions that scientists on their own ill equipped to answer. 190 00:21:29,510 --> 00:21:36,830 I don't want to pick on anyone, particularly but Cambridge University and Cambridge Analytica, 191 00:21:36,830 --> 00:21:45,050 Airbnb and residents who can't find any way to live in the centre of New York because everyone's renting out the French love of protest. 192 00:21:45,050 --> 00:21:50,870 This is Uber and taxi drivers in France, you know, going for it with a bit of a right. 193 00:21:50,870 --> 00:22:02,550 And here we have a Chinese bicycle rental online company business that's gone bankrupt and everyone just there. 194 00:22:02,550 --> 00:22:10,640 The manifestation of a digital bankruptcy has physical reality in the streets of Shanghai with these mountains of bicycles. 195 00:22:10,640 --> 00:22:18,020 And so it goes on. And so the lesson from from this looking to the future is and we heard it a little bit steam art. 196 00:22:18,020 --> 00:22:19,950 We need all the social sciences here. 197 00:22:19,950 --> 00:22:28,880 A huge opportunity, of course, with Conte, because social media is quantifying the social world in one dimension, at least. 198 00:22:28,880 --> 00:22:30,530 But also, we need to build theory. 199 00:22:30,530 --> 00:22:39,470 So we do need people with cool heads to sit in places like Oxford or York over if they sit and think and philosophise about what's happening. 200 00:22:39,470 --> 00:22:47,450 And if we don't do that, we might be riddled with all sorts of questions that we can't understand. 201 00:22:47,450 --> 00:22:50,450 So just to summarise, and this is a, I hope, 202 00:22:50,450 --> 00:23:01,130 a slightly provocative challenge given the discussion just now about place is that quite obviously innovation isn't evenly distributed. 203 00:23:01,130 --> 00:23:04,880 I happen to think it never will be evenly distributed. 204 00:23:04,880 --> 00:23:13,280 And that's partly the nature of the way in which ideas are developed, and it's partly the economic systems that we live in and play with, you know, 205 00:23:13,280 --> 00:23:23,750 the return on investment, which is a very important side to think about from some of our science, the funding and the mechanisms of where people are. 206 00:23:23,750 --> 00:23:28,700 So his five thoughts for you specialism? 207 00:23:28,700 --> 00:23:34,340 Well, I think the knowledge economy, whatever that is, really took off from about 1980. 208 00:23:34,340 --> 00:23:40,220 And if you were an academic in the knowledge economy, you better specialise because you've got to publish. 209 00:23:40,220 --> 00:23:43,670 Otherwise, you're not going to get visibility for promotion. 210 00:23:43,670 --> 00:23:49,550 And if you're very specialised, you have limitations in terms of how much impact you can have on your own. 211 00:23:49,550 --> 00:23:52,190 You need to buddy up with other specialists. 212 00:23:52,190 --> 00:23:58,760 And what we've seen, I think in the last 40 years is a concentration of specialists working with other specialists. 213 00:23:58,760 --> 00:24:03,990 So an agglomeration scale effect. Indeed, I've shown you big science today. 214 00:24:03,990 --> 00:24:09,530 I'll show you some very big equipment, $20 billion worth of equipment just to do one set of experiments. 215 00:24:09,530 --> 00:24:18,200 No country can afford that, typically. So we're starting to see scale and agglomeration, which counts against some of the spread out. 216 00:24:18,200 --> 00:24:23,270 And there's a big question mark. How far are we happy with that? 217 00:24:23,270 --> 00:24:27,020 What does it really look like? What can it look like in the future? Who's going to pay for it? 218 00:24:27,020 --> 00:24:33,980 What talent going to do? Instrumentation? Who's creating it? 219 00:24:33,980 --> 00:24:37,510 Mark talked about improbable. What about DeepMind? 220 00:24:37,510 --> 00:24:49,380 Know the private sector now, perhaps controlling even more the infrastructure that science needs to be unfettered in Vancouver, Bush's words with? 221 00:24:49,380 --> 00:24:54,170 And how do we get into that data and analytics collaboration? 222 00:24:54,170 --> 00:24:59,900 This does pretty well too big for any of the mighty players to do on their own, so we know we need to collaborate. 223 00:24:59,900 --> 00:25:10,520 But what does that mean? And we need supply chains. We need engineering and technology companies that can produce and feed our labs and our systems. 224 00:25:10,520 --> 00:25:11,780 It's good money in that. 225 00:25:11,780 --> 00:25:19,670 I mean, in the last three years, the Atomic Energy Authority, which leads for the U.K. in the technical supply chain to build beta, 226 00:25:19,670 --> 00:25:26,510 has already won half a billion pounds worth of contracts from that international community back into the U.K. 227 00:25:26,510 --> 00:25:30,980 So this business there? But how? How do we do it? How do we understand timescales? 228 00:25:30,980 --> 00:25:35,090 So of course, the investor wants the return pretty well now. 229 00:25:35,090 --> 00:25:44,400 We need patient money for some of this stuff and some of it. Serendipitously, we can't even see the outcome, perhaps in half a lifetime. 230 00:25:44,400 --> 00:25:52,160 So. Somehow, we've got to manage expectations. And lastly, we've got to think about how do new areas emerge? 231 00:25:52,160 --> 00:25:55,870 And it's something I'm particularly concerned about because I spent six years 232 00:25:55,870 --> 00:26:00,730 on the leadership of Imperial College and we invested in some good new things, 233 00:26:00,730 --> 00:26:05,590 some of them that I hardly heard of before the last six years, things like synthetic biology. 234 00:26:05,590 --> 00:26:15,980 But we don't really have a plan for the next things that will keep the flywheel of science and business working, 235 00:26:15,980 --> 00:26:24,100 and I don't think we're very strategic about that. And who's going to pay for it and how do we and fitter ourselves in that regard? 236 00:26:24,100 --> 00:26:33,100 So going back to business business and there are some of you here represent mighty companies that have done very, very well with science. 237 00:26:33,100 --> 00:26:40,000 Increasingly, I think is looking for new ideas. And at Imperial, we held a conference recently. 238 00:26:40,000 --> 00:26:43,090 One of the first on corporate accelerators, two companies. 239 00:26:43,090 --> 00:26:49,090 They're trying to learn How do we get outside of our R&D box and see what's happening in the street? 240 00:26:49,090 --> 00:26:55,930 And all of them we're talking about how do we learn from entrepreneurs, particularly deep tech entrepreneurs, these people who take risks? 241 00:26:55,930 --> 00:27:02,950 And I was writing, sorry, I'm plugging to my post this one, a recent one, the playful entrepreneur writing this book. 242 00:27:02,950 --> 00:27:13,420 And I was talking to Lord Skidelsky, who, if you note, wrote the best biography of Keynes three volumes massive task about entrepreneurs. 243 00:27:13,420 --> 00:27:22,690 And he said the more unstable the parameters in the world you find yourself in, the more the insights or intuition of the entrepreneur matter. 244 00:27:22,690 --> 00:27:27,130 And I'll just leave you with that thought because I think I'm bringing back Thomas 245 00:27:27,130 --> 00:27:32,680 Edison and that spirit of entrepreneurship into that science business mix, 246 00:27:32,680 --> 00:27:38,474 and I think we need to find the right conditions for that. Thank you very much.