1 00:00:06,790 --> 00:00:08,610 Welcome to this episode of Oxford+. 2 00:00:09,510 --> 00:00:13,720 This episode was recorded in front of a live studio audience at Modern Art Oxford. 3 00:00:14,110 --> 00:00:15,620 My guest is Cici Muldoon. 4 00:00:16,200 --> 00:00:19,939 Cici holds a degree in physics and finance from Princeton University and 5 00:00:19,939 --> 00:00:23,860 a doctorate in experimental atomic and laser physics from Oxford University. 6 00:00:24,570 --> 00:00:28,849 Cici is the founder and CEO of Verity Group, an Oxford based technology 7 00:00:28,849 --> 00:00:33,689 company specialising in the spectroscopic analysis of highly complex dilute liquids. 8 00:00:33,689 --> 00:00:38,080 Verity Group is currently creating a low cost, easy to use point of care 9 00:00:38,169 --> 00:00:42,435 solution that uses a simple urine sample to identify cancer and other 10 00:00:42,435 --> 00:00:44,144 illnesses as early as possible. 11 00:00:44,905 --> 00:00:48,864 A qualified Oenophile, Cici has completed the bachelor level WSET 12 00:00:48,894 --> 00:00:50,455 Diploma in Wines and Spirits. 13 00:00:50,805 --> 00:00:54,435 She's also an amateur ballerina and a classic car judge focusing 14 00:00:54,435 --> 00:00:58,065 on the Ferrari mark and judging at numerous international events. 15 00:00:58,495 --> 00:01:02,295 Cici speaks five languages and is originally from Guadalajara, Mexico. 16 00:01:02,295 --> 00:01:12,615 Cici, thank you for joining us this evening. 17 00:01:13,095 --> 00:01:18,615 So I've described your company Verity, but you've actually founded two companies 18 00:01:18,870 --> 00:01:20,960 with this particular technology. 19 00:01:21,210 --> 00:01:22,780 Can you tell us a little bit about the first one? 20 00:01:22,780 --> 00:01:24,280 Because I feel like it's interesting. 21 00:01:24,280 --> 00:01:25,100 Yes, absolutely. 22 00:01:25,150 --> 00:01:29,250 So basically I have gone in life a circuitous route via quantum 23 00:01:29,250 --> 00:01:33,940 computing in my PhD to analysing wine, to analysing urine. 24 00:01:35,370 --> 00:01:39,460 Basically, the reason for that was I specialised in the interaction 25 00:01:39,460 --> 00:01:43,380 of matter and light in my PhD, and it's still a huge passion for me. 26 00:01:43,430 --> 00:01:47,589 It's fascinating what happens when light interacts with molecules, with atoms. 27 00:01:48,249 --> 00:01:53,149 I ended up working in spectroscopy post PhD briefly, and one day on the 28 00:01:53,150 --> 00:01:57,040 way to work, I had this question pop into my head, which was, why don't we 29 00:01:57,040 --> 00:02:01,414 use spectroscopy, which is essentially the study of matter with light to 30 00:02:01,455 --> 00:02:05,605 look for cork taint in unopened bottles of wine and the small niggling 31 00:02:05,625 --> 00:02:07,475 thing became an obsession of mine. 32 00:02:07,485 --> 00:02:10,555 So, I used to run the wine club at Oxford when I did my PhD. 33 00:02:10,595 --> 00:02:14,985 I was captain of the blind tasting team, so queen of the nerds, and I have a 34 00:02:14,985 --> 00:02:17,045 particularly sensitive nose to cork taint. 35 00:02:17,085 --> 00:02:20,194 Which is actually a very small innocuous little molecule called 36 00:02:20,194 --> 00:02:24,525 2,4,6-trichloroanisole, which actually blocks a neural pathway in your brain 37 00:02:24,545 --> 00:02:29,174 and makes you smell wet dog basement and disgustingness in wine and it ruins many 38 00:02:29,175 --> 00:02:33,225 bottles and You don't know when you're going to get it and I thought to myself, 39 00:02:33,225 --> 00:02:37,395 well, it would be fantastic, why can't we test it without taking the cork out? 40 00:02:37,885 --> 00:02:43,884 So me being a little bit crazy, I ended up actually starting a company. 41 00:02:44,265 --> 00:02:48,265 Some of the people in this room, knew me back then and I was the crazy wine lady 42 00:02:48,265 --> 00:02:52,655 that went around Oxford and I figured out at that point, I'd done the research, 43 00:02:52,655 --> 00:02:56,954 that Raman spectroscopy was the way to go, this technique, which, Actually, it's 44 00:02:56,954 --> 00:03:01,605 quite old, so it's a technique that was developed in 1923, discovered by a man 45 00:03:01,605 --> 00:03:06,375 called Chandrasekhara Raman, who was the first Asian Nobel Prize in Physics, 1928. 46 00:03:06,544 --> 00:03:07,894 So that's how old we're talking, right? 47 00:03:07,894 --> 00:03:10,385 This stuff has been used since the 80s and 90s. 48 00:03:10,385 --> 00:03:15,365 It's a workhorse in pharma, in airport security, in defense, it's how they 49 00:03:15,635 --> 00:03:19,985 identify stuff and I'd figured out that Raman spectroscopy was a great way to 50 00:03:19,985 --> 00:03:25,225 look at aqueous complex mixtures and so off I went, basically to cut short so 51 00:03:25,225 --> 00:03:28,064 that you can ask me another question, off I went and I tried to get this 52 00:03:28,065 --> 00:03:33,144 company off the ground and it was really difficult because, what I was proposing 53 00:03:33,144 --> 00:03:37,095 to do was do R&D in industry, right? 54 00:03:37,235 --> 00:03:41,515 And so a lot of the answer I got was go do a postdoc, because basically it's not 55 00:03:41,515 --> 00:03:44,474 the spectroscopy that was the problem, it was the fact that there's a dark, 56 00:03:44,575 --> 00:03:49,185 curved, tinted piece of glass in between what you want to see and analyse and 57 00:03:49,394 --> 00:03:54,655 your spectrometer and basically I ended up doing something that is, for many 58 00:03:54,655 --> 00:03:57,975 years I was highly embarrassed about, but it's very unconventional, which 59 00:03:57,975 --> 00:04:03,334 is that I ended up funding it myself and the reason for that was because I 60 00:04:03,345 --> 00:04:07,834 had applied for an Innovate UK grant with my then PhD supervisor, who didn't 61 00:04:07,834 --> 00:04:10,574 think I was crazy, and we won the award. 62 00:04:10,865 --> 00:04:14,244 But, as for many cases, we didn't have the cash flow. 63 00:04:14,795 --> 00:04:18,424 So the project start date was ticking forward, and I found 64 00:04:18,454 --> 00:04:19,964 myself saying, well, now what? 65 00:04:20,075 --> 00:04:23,825 I was speaking to a lot of people, but all the VCs said, too much technical risk, 66 00:04:24,125 --> 00:04:30,000 you know, too early doors, all this, that, the other and finally, we had a Christmas 67 00:04:30,000 --> 00:04:33,500 dinner with my PhD supervisor and my family was around and my dad just turned 68 00:04:33,500 --> 00:04:37,669 around and said, for God's sake, let's just fund it ourselves and so we did. 69 00:04:38,029 --> 00:04:42,079 So I'm a little bit of a black sheep and an exception, I realise in this startup 70 00:04:42,079 --> 00:04:44,785 world, but it is a very difficult journey. 71 00:04:44,785 --> 00:04:47,525 I know this is not the question you asked me, but it is a really 72 00:04:47,595 --> 00:04:52,964 difficult thing to get something that is brave out there and prove it. 73 00:04:53,565 --> 00:04:57,175 We did do that, five years on Verovin worked. 74 00:04:57,445 --> 00:05:01,215 We were analysing bottles of wine through the glass and correctly classifying 75 00:05:01,215 --> 00:05:05,325 them using machine learning and we could distinguish down to different chateaus, 76 00:05:05,365 --> 00:05:07,955 vintages, but we had three problems. 77 00:05:08,145 --> 00:05:11,094 Problem one, the wine industry is a very heterogeneous one. 78 00:05:11,225 --> 00:05:14,464 So you've got people with big pockets at the bottom that wanted to do thousands 79 00:05:14,465 --> 00:05:17,625 of bottles per minute on a bottling line, the producers, we couldn't do that. 80 00:05:17,645 --> 00:05:18,995 The machine just doesn't work that way. 81 00:05:19,345 --> 00:05:23,185 Top end, we had people that wanted a really cheap, portable machine that you 82 00:05:23,185 --> 00:05:27,729 could use one on one, the merchants and we couldn't service everybody easily. 83 00:05:27,729 --> 00:05:30,940 It was a very complicated business model for a very heterogeneous industry. 84 00:05:31,419 --> 00:05:35,010 Two, the fine wine industry, which is where our business case really was, 85 00:05:35,340 --> 00:05:37,789 actually doesn't want to know that there's a problem, because our main 86 00:05:37,789 --> 00:05:41,589 business case was we can weed out fake and faulty, particularly counterfeit wine. 87 00:05:41,589 --> 00:05:45,900 So we would train the algorithm and we would say, here's a bottle of Lafite 85, 88 00:05:45,900 --> 00:05:49,729 52 percent Cabernet Sauvignon, 48 percent Merlot, metadata, metadata, metadata. 89 00:05:50,050 --> 00:05:51,539 Here's a hundred mystery bottles. 90 00:05:51,590 --> 00:05:52,570 Do they all cluster together? 91 00:05:52,570 --> 00:05:53,099 Do they not? 92 00:05:53,390 --> 00:05:57,409 And sometimes they wouldn't, and the fine wine industry was our main goal 93 00:05:57,409 --> 00:06:00,350 because we thought, well, they want to know that they've got lots of fake wine. 94 00:06:00,390 --> 00:06:01,819 Well, it turned out they didn't. 95 00:06:02,299 --> 00:06:07,430 So we built a fantastic technology solution platform consisting of hardware, 96 00:06:07,440 --> 00:06:11,570 software, machine learning, much overused term, but it is a real thing and it's not 97 00:06:11,570 --> 00:06:13,559 magic, for a customer that didn't want it. 98 00:06:14,290 --> 00:06:16,270 And the third problem we had was a physics problem. 99 00:06:16,740 --> 00:06:21,400 It was that we could only see through 70 percent of the bottles that we tested. 100 00:06:21,669 --> 00:06:25,900 30 percent of the time, and it wasn't predictable which 30%, the 101 00:06:25,900 --> 00:06:27,460 spectrometry would just get nothing out. 102 00:06:28,409 --> 00:06:31,560 So we turned around and said, you know what? 103 00:06:31,790 --> 00:06:36,175 In this journey, we have tested all manner of highly complex dilute liquids. 104 00:06:36,175 --> 00:06:42,514 We tested olive oil, injectables, manuka honey, perfume and our chairman, Steve 105 00:06:42,515 --> 00:06:45,445 Davies, turned around to me and said, you've never wanted to look at blood 106 00:06:45,445 --> 00:06:50,490 and I said, no, quite frankly, no, because I don't want to be Liz Holmes. 107 00:06:50,709 --> 00:06:56,309 I mean, I already get the Theranos comparison all the time, but it planted 108 00:06:56,309 --> 00:07:01,279 the seed and off we went and we started looking into blood and soon thereafter, 109 00:07:01,329 --> 00:07:05,329 we sort of tripped upon a bunch of literature where people were using a 110 00:07:05,329 --> 00:07:08,850 variant of Raman Spectroscopy called Surface Enhanced Raman Spectroscopy, which 111 00:07:08,850 --> 00:07:13,319 is a little bit more complicated, but same idea and machine learning, emboldened by 112 00:07:13,319 --> 00:07:16,749 machine learning, because this is what's happening to these, you know, old tools 113 00:07:16,749 --> 00:07:20,260 in physics that are being emboldened by machine learning and they were correctly 114 00:07:20,270 --> 00:07:26,389 classifying urine as disease, non disease and there are papers all over the world, 115 00:07:26,389 --> 00:07:30,479 academic institutions, mainly, most of them in the US, China, South Korea, 116 00:07:30,479 --> 00:07:34,830 but everywhere and they're looking at pancreatic cancer versus prostate 117 00:07:34,830 --> 00:07:40,070 cancer, they're looking at hematuria, they're looking at UTIs, they're looking 118 00:07:40,100 --> 00:07:44,240 at gestational diabetes, you name it, people are using this technique. 119 00:07:44,520 --> 00:07:45,690 But it's all in academia. 120 00:07:46,300 --> 00:07:49,349 Pre-eclampsia is another one, which is a project that we're quite passionate about 121 00:07:49,969 --> 00:07:54,230 and we turned around and said, well, we've got this platform that we built for wine. 122 00:07:54,810 --> 00:07:59,840 Wine, 85 percent water, 12-14 percent ethanol, 1%, 1, 000 different organic 123 00:07:59,840 --> 00:08:01,870 molecules, which kind of all look alike. 124 00:08:02,919 --> 00:08:03,489 Urine? 125 00:08:04,140 --> 00:08:07,799 You know, 90 percent water and then lots of little organic molecules that 126 00:08:07,799 --> 00:08:10,810 all look alike, and we were like, yeah, they're actually not that different 127 00:08:10,940 --> 00:08:15,755 and at the end of the day, that's the real beauty of the technique, 128 00:08:15,755 --> 00:08:20,375 is that it doesn't really care what liquid it's looking at, what biofluid, 129 00:08:20,375 --> 00:08:22,325 it can be plasma, it can be saliva. 130 00:08:22,795 --> 00:08:25,315 The machine itself doesn't care, right? 131 00:08:25,315 --> 00:08:27,565 And the algorithm doesn't care what metadata it's getting. 132 00:08:27,570 --> 00:08:32,175 It doesn't care if it's looking for cancer of the left nostril because all you're 133 00:08:32,175 --> 00:08:36,225 doing is you're telling the machine, this is the metadata, learn on this. 134 00:08:36,380 --> 00:08:40,479 It's really interesting, thank you, and the people it does matter to, and I know 135 00:08:40,479 --> 00:08:43,150 this cause I've looked on your website, we've spoken about it, we've spoken 136 00:08:43,150 --> 00:08:44,909 about the values within your company. 137 00:08:45,599 --> 00:08:49,049 Those are hugely different applications and one of them has a sort of an impact 138 00:08:49,079 --> 00:08:52,459 and a legacy, which I know is something you're really passionate about and 139 00:08:52,459 --> 00:08:56,530 the other, quite frankly, it's very important to my own palate, but as you 140 00:08:56,530 --> 00:09:00,180 have pointed out, and indeed the industry pointed out, perhaps less required by 141 00:09:00,180 --> 00:09:01,870 society, if I were to put it that way. 142 00:09:02,170 --> 00:09:05,269 How do you feel it's different now that you're setting up a company 143 00:09:05,269 --> 00:09:09,160 that has those different qualities in terms of its application and 144 00:09:09,160 --> 00:09:10,390 their importance in society? 145 00:09:10,390 --> 00:09:13,720 I mean, it is for me, it has been a transformational two years. 146 00:09:13,730 --> 00:09:18,130 It's been about two years since we decided to pivot and I know it sounds 147 00:09:18,130 --> 00:09:21,200 like a bit grandiose and exaggeration, but I feel like I found my calling 148 00:09:21,200 --> 00:09:24,700 in life, because my passion is for taking science out of the lab, like 149 00:09:24,700 --> 00:09:26,189 I want to take this great tool. 150 00:09:26,389 --> 00:09:28,950 I see, like, these two worlds waiting to collide. 151 00:09:28,950 --> 00:09:33,050 There's sort of the world of spectroscopy over here, with all the tools, and 152 00:09:33,099 --> 00:09:37,450 there's, like, the biotech, biomed world and they're just waiting, I think. 153 00:09:37,640 --> 00:09:42,050 So our goal is we want to displace wet chemistry with advanced photonics and 154 00:09:42,060 --> 00:09:45,720 machine learning, because most things nowadays, when you get a diagnosis, 155 00:09:45,780 --> 00:09:49,550 most of us in this room will have been touched by IVD and vitro diagnosis 156 00:09:49,609 --> 00:09:52,670 at some point in our lives, we have something diagnosed and it'll be 157 00:09:52,670 --> 00:09:56,539 either imaging or most likely a blood test, which means full venous blood 158 00:09:56,539 --> 00:10:01,420 sample, which hurts, requires a trained phlebotomist, is expensive, and then 159 00:10:01,420 --> 00:10:05,530 moreover that sample has to be transported to a lab to be tested in a lab. 160 00:10:05,975 --> 00:10:07,925 So all of that process is expensive. 161 00:10:07,925 --> 00:10:10,055 It's a weight on the healthcare system where there is one. 162 00:10:10,325 --> 00:10:14,095 It's costly in different ways for patient, payer and the physician. 163 00:10:14,535 --> 00:10:18,365 So we see that these two worlds coming together are a huge 164 00:10:18,365 --> 00:10:21,805 opportunity and for me, it's a personal passion to take science out. 165 00:10:21,975 --> 00:10:27,730 However, that said, obviously, you know, getting behind saving lives versus saving 166 00:10:27,730 --> 00:10:30,920 fake wine, you know, saving someone from drinking fake wine, absolutely, 167 00:10:30,960 --> 00:10:35,150 but with it comes huge responsibility and that's where the values come in 168 00:10:35,150 --> 00:10:39,369 because me telling you that your wine is plonk, whatever, but if I tell you 169 00:10:39,370 --> 00:10:43,680 that, you know, if I'm going to enable, have a technology that's going to enable 170 00:10:43,680 --> 00:10:48,329 a doctor to give an earlier diagnosis of whatever disease state it is that 171 00:10:48,339 --> 00:10:52,415 we might be looking at, and we do have focus areas by the way, but it's 172 00:10:52,415 --> 00:10:57,605 something that I feel ethically very responsible for and if anything, I think 173 00:10:57,605 --> 00:11:01,534 the experience of Elizabeth Holmes, whom I kind of, you know, through suppliers 174 00:11:01,535 --> 00:11:05,694 and through sort of personal connections, I sort of knew closely about, it just 175 00:11:05,704 --> 00:11:10,155 puts a huge shining light on the fact that science needs to be vetted, right? 176 00:11:10,495 --> 00:11:12,905 So one of our values is integrity. 177 00:11:13,265 --> 00:11:15,525 So, complete intellectual honesty. 178 00:11:15,765 --> 00:11:18,825 The science doesn't lie, and you don't lie about what the science says. 179 00:11:18,885 --> 00:11:21,045 I mean, everyone at work knows this and says it. 180 00:11:21,275 --> 00:11:24,125 Because when I did my PhD, I did an experiment where we 181 00:11:24,125 --> 00:11:25,275 were trapping single atoms. 182 00:11:25,275 --> 00:11:28,134 We were meant to trap single atoms, move them around to implement a quantum 183 00:11:28,135 --> 00:11:31,754 bit and I got to a point where I had built this experiment from scratch 184 00:11:31,775 --> 00:11:34,785 myself, and I trapped 1.5 atoms. 185 00:11:34,785 --> 00:11:39,920 I did all the calculations back and I got to 1.5, and I went to Axel, my supervisor, 186 00:11:39,920 --> 00:11:42,300 and I said, can I round down to one? 187 00:11:42,300 --> 00:11:45,110 And he said, Cici, what does 1.5 round to? 188 00:11:45,350 --> 00:11:45,630 Two. 189 00:11:46,130 --> 00:11:47,219 And that's what we published. 190 00:11:47,500 --> 00:11:51,090 But for all intents and purposes, had I gone in that paper and written one 191 00:11:51,100 --> 00:11:55,599 atom, who's gonna come and recreate that experiment, quantum optics, with all of 192 00:11:55,599 --> 00:11:59,400 the vacuum chamber and the optics and the electronics, and tell me that I was wrong? 193 00:11:59,810 --> 00:12:03,920 So science, scientists have a huge responsibility to be ethical and to 194 00:12:03,920 --> 00:12:05,530 be transparent and to communicate. 195 00:12:05,530 --> 00:12:08,450 I, you know, I try to explain to people when they ask me, I 196 00:12:08,450 --> 00:12:09,560 say, do you have five minutes? 197 00:12:09,580 --> 00:12:13,070 Because I'll explain Raman Spectroscopy, I feel more comfortable if you do. 198 00:12:13,219 --> 00:12:14,019 So that's one of them. 199 00:12:14,030 --> 00:12:18,479 The other one we have is legacy, you know, I want to make a difference with this. 200 00:12:18,480 --> 00:12:21,530 I do believe it can actually really help in healthcare and I haven't 201 00:12:21,530 --> 00:12:24,760 drunk the Kool Aid, I just believe in, you know, it's profit driven 202 00:12:24,760 --> 00:12:26,130 philanthropy, right, in some way. 203 00:12:26,130 --> 00:12:29,480 It's doing some good, and then commitment, because it requires 204 00:12:29,480 --> 00:12:30,730 a huge amount of commitment. 205 00:12:30,790 --> 00:12:33,620 We're now a team of 16 people who I'm immensely proud of. 206 00:12:33,820 --> 00:12:35,130 One of them's in the audience. 207 00:12:35,440 --> 00:12:38,919 They are all really talented and actually much more accomplished than me. 208 00:12:38,920 --> 00:12:43,270 You know, there's a guy that used to run Point of Care at John Radcliffe, you 209 00:12:43,270 --> 00:12:46,694 know, somebody who is CFO of Siemens who's sitting in the audience somewhere, you 210 00:12:46,694 --> 00:12:51,944 know, very talented people and I feel that they need to all be committed to make this 211 00:12:51,944 --> 00:12:56,465 happen together to get it somewhere and then the last value, courage, because it 212 00:12:56,465 --> 00:13:00,734 takes a huge amount of guts to go up there and say, there is no pattern recognition 213 00:13:00,734 --> 00:13:02,564 based medical device on the market yet. 214 00:13:02,895 --> 00:13:07,555 Going to regulation, 510k, CAC mark, is a massive journey. 215 00:13:07,645 --> 00:13:11,245 Just getting samples, you know, that's, we've got a fully functional 216 00:13:11,565 --> 00:13:16,105 lab now that can handle, store, thaw, freeze, and test with our tech, 217 00:13:16,445 --> 00:13:18,365 human disease, human urine samples. 218 00:13:18,545 --> 00:13:21,135 But sourcing those samples is massively difficult. 219 00:13:21,395 --> 00:13:26,004 So, you know, the whole undertaking is so titanic that without courage and 220 00:13:26,004 --> 00:13:27,394 commitment, we wouldn't be able to do it. 221 00:13:27,699 --> 00:13:30,939 We've spoken a bit about this, but you've just referenced a few of them through 222 00:13:30,939 --> 00:13:35,115 the conversation already, you know, the lab space that you've got, it wasn't just 223 00:13:35,115 --> 00:13:38,385 there, ready, waiting, and I'm sure lots of people will be familiar with this, but 224 00:13:38,555 --> 00:13:41,765 it had to be built, it had to be built bespoke, it had to be made, you wrote 225 00:13:41,765 --> 00:13:45,574 your own patent, you had the experience of people not kind of supporting the 226 00:13:45,574 --> 00:13:47,264 first application of your technology. 227 00:13:47,964 --> 00:13:50,704 Every time I speak to you, I feel like there's another reason that lesser 228 00:13:50,704 --> 00:13:54,635 people probably would have given up and on the one hand, that's kudos 229 00:13:54,635 --> 00:13:58,255 to you, on the other hand, if we're looking at the ecosystem, there is 230 00:13:58,255 --> 00:14:01,714 that question mark of, oh God, how do we make sure that more people can sort 231 00:14:01,714 --> 00:14:03,395 of slip through the proverbial net? 232 00:14:03,885 --> 00:14:07,795 And I'd be really interested to hear your perspectives on perhaps some of the 233 00:14:07,805 --> 00:14:11,645 things that you found difficult that you just don't think needed to be, and that 234 00:14:11,645 --> 00:14:16,384 actually the university or the ecosystem could have been there either kind of 235 00:14:16,384 --> 00:14:20,905 supporting physically or with kind of lab space or with money, and I don't 236 00:14:20,905 --> 00:14:22,104 know if there's anything obvious to you? 237 00:14:22,104 --> 00:14:25,564 I mean, the thing is I had a generation, I think, the current 238 00:14:25,735 --> 00:14:27,345 generation has a lot more support. 239 00:14:27,355 --> 00:14:29,044 I think there's a lot more going on from... 240 00:14:29,255 --> 00:14:29,935 That's reassuring. 241 00:14:30,010 --> 00:14:33,550 So, no, no, I mean, 'cause when I first tried to set VeriVin up, 242 00:14:34,090 --> 00:14:39,730 we were talking around 2017 was when I was going around trying... 243 00:14:39,744 --> 00:14:40,724 Not that long ago. 244 00:14:40,810 --> 00:14:43,449 Not that long ago, but the idea came much earlier. 245 00:14:43,454 --> 00:14:46,180 I then went off and did the WSET diploma 'cause I basically could, 246 00:14:46,185 --> 00:14:47,230 I didn't know how to start. 247 00:14:47,560 --> 00:14:49,810 I was like, you know, what do I do first? 248 00:14:49,810 --> 00:14:50,380 And how do you know? 249 00:14:50,380 --> 00:14:51,069 And how do I...? 250 00:14:51,240 --> 00:14:53,540 That in itself is something I keep hearing. 251 00:14:53,540 --> 00:14:55,799 It's like people almost don't, you know, obviously we've got incubators, 252 00:14:55,800 --> 00:15:00,710 accelerators, but there needs to be more of a road map almost, like, where are you? 253 00:15:00,750 --> 00:15:01,870 Are you at idea stage? 254 00:15:01,870 --> 00:15:03,319 Are you at needing funding stage? 255 00:15:03,319 --> 00:15:04,220 Are you needing this? 256 00:15:04,600 --> 00:15:07,339 And just helping people with great ideas. 257 00:15:07,339 --> 00:15:11,455 I mean with entrepreneurship, and I've only, this is my second time doing 258 00:15:11,455 --> 00:15:15,105 something, obviously, so it's not like I speak from great experience, but we 259 00:15:15,105 --> 00:15:18,865 have a saying in Spanish that says, "Andando se hace camino", which means, 260 00:15:18,884 --> 00:15:23,255 walking you make the way and I think in some ways you just gotta go through it 261 00:15:23,255 --> 00:15:26,835 and learn from it, you know, there isn't a perfect magic, you know, equation. 262 00:15:27,055 --> 00:15:30,914 I do think, you know, we could improve probably the 263 00:15:30,915 --> 00:15:33,185 funding in the ecosystem a bit. 264 00:15:33,345 --> 00:15:37,104 When I was trying to get VeriVin off the ground, I did go eventually to the 265 00:15:37,104 --> 00:15:42,035 incubator, to OUI and OUI suggested some really useful things, which was to apply 266 00:15:42,035 --> 00:15:45,485 for Innovate UK grant money and then they suggested some that weren't quite 267 00:15:45,485 --> 00:15:50,365 right, like go to VCs, for whom this was too much of a technical risk and we 268 00:15:50,365 --> 00:15:56,505 had one particular VC who reviewed the tech and they misunderstood a conceptual 269 00:15:56,525 --> 00:15:59,574 thing about how we look at spectra. 270 00:15:59,585 --> 00:16:03,685 So basically when we get our spectra from via wine, urine, whatever. 271 00:16:03,875 --> 00:16:07,325 We get the combined Raman Spectrum of all those thousand molecules kind of 272 00:16:07,335 --> 00:16:11,264 multiplied together and it's not like you can un multiply them and unpick them, 273 00:16:11,264 --> 00:16:13,875 because essentially what's happening is you're shining all this light in, 274 00:16:14,085 --> 00:16:18,435 all these molecules are, for a period of time, bouncing their photons off 275 00:16:18,474 --> 00:16:21,354 in their own way, each of them will give a chemical signature of their own 276 00:16:21,595 --> 00:16:24,745 and all those photons are traveling together to the detector, right? 277 00:16:25,235 --> 00:16:28,245 So it's not like you can unpick this one that came from there or there. 278 00:16:28,465 --> 00:16:31,915 You separate them by frequency, which is something called a diffraction grading, 279 00:16:31,915 --> 00:16:36,035 they land on the detector, and you can't un-multiply where they came from, right? 280 00:16:36,315 --> 00:16:39,894 So what we do is, instead of trying to unpick them and look at single biomarkers 281 00:16:39,894 --> 00:16:43,565 or single molecules, we look at an ensemble of them, and we use machine 282 00:16:43,565 --> 00:16:47,915 learning to basically do a mathematical expression for this group of things. 283 00:16:48,165 --> 00:16:52,750 Point being that this person had understood that the photon from 284 00:16:52,750 --> 00:16:55,310 one molecule would basically affect the next molecule. 285 00:16:55,319 --> 00:16:58,780 He thought it was like a linear addition and when they sent me their sort of review 286 00:16:58,790 --> 00:17:02,429 of the points, it was like, green, green, green, and then it was red on the tech. 287 00:17:02,439 --> 00:17:06,560 But it was red on the tech for reasons that were like, he misunderstood physics 288 00:17:07,190 --> 00:17:11,190 and the problem I had is that then, I have heard that person then spoke 289 00:17:11,190 --> 00:17:14,660 to another VC, that we all, and then spoke to another one and then I heard 290 00:17:14,660 --> 00:17:20,035 it from OUI, that, you know, so it was okay, like, all these conversations have 291 00:17:20,035 --> 00:17:22,055 gone on and nobody, like, came to me. 292 00:17:22,055 --> 00:17:22,765 Validated it. 293 00:17:22,835 --> 00:17:25,035 Yeah, but look, it's really difficult with these things. 294 00:17:25,035 --> 00:17:27,835 I understand them because they're being asked to invest in stuff that is 295 00:17:27,835 --> 00:17:29,865 so out there, like quantum computing. 296 00:17:30,245 --> 00:17:32,634 If you asked me if I would invest in a quantum computing company, 297 00:17:32,634 --> 00:17:37,210 I'd be like, ciao baby, no way, no, like, and I did a PhD in it, right? 298 00:17:37,210 --> 00:17:39,180 And I think people just don't understand it at all. 299 00:17:39,190 --> 00:17:42,750 I mean, people use these terms like AI or machine learning or, you know, 300 00:17:42,780 --> 00:17:45,590 quantum computing, and they have not the first clue what a photon is. 301 00:17:45,600 --> 00:17:47,840 How can you possibly understand what quantum bit is if you don't... 302 00:17:47,840 --> 00:17:52,040 So then taking that, you know, something that one has seen for instance, 303 00:17:52,190 --> 00:17:56,879 in Cambridge with R and Capital is that they have real brilliant 304 00:17:56,879 --> 00:17:58,839 scientists, you know, Nobel laureates. 305 00:17:58,879 --> 00:18:01,799 I was joking on a previous podcast and we were saying, you know, 306 00:18:01,850 --> 00:18:03,409 casually have a few Nobel laureates. 307 00:18:03,409 --> 00:18:07,940 But I have heard in various interviews I've done about this thing of, you 308 00:18:07,940 --> 00:18:10,840 know, you do need the experts in the room, you do need people that 309 00:18:10,840 --> 00:18:11,944 understand what they're looking at. 310 00:18:12,565 --> 00:18:15,665 It's a tricky dynamic because you can't have the depth in everything. 311 00:18:15,665 --> 00:18:19,245 I've also heard people say, well, I've got a PhD in physics, but you know, actually 312 00:18:19,485 --> 00:18:23,165 I look over here and it's not in my wheelhouse and so nobody's ever going to 313 00:18:23,185 --> 00:18:27,724 be an expert in everything, but I think your point is really valid and how can 314 00:18:27,725 --> 00:18:31,365 we supplement what's there to improve it? 315 00:18:31,365 --> 00:18:35,895 Scientists be better communicators, genuinely, I think, I do, because I think, 316 00:18:36,565 --> 00:18:40,054 I mean, we did this, we practiced pitch to somebody in London yesterday and of 317 00:18:40,054 --> 00:18:43,054 course it's a really valid point that all the people putting money in care 318 00:18:43,054 --> 00:18:45,455 about at the end of the day is what's the sensitivity and specificity of the 319 00:18:45,455 --> 00:18:48,435 test and does it exceed the predicate test that you're going up against. 320 00:18:48,925 --> 00:18:51,774 Yes, of course, proof needs to be in the pudding and of course they care 321 00:18:51,774 --> 00:18:55,125 that the machine learning is correct and the spectrometer works, yes. 322 00:18:55,335 --> 00:18:56,475 But they care about the end result. 323 00:18:56,675 --> 00:19:00,784 However, I would personally feel uncomfortable investing in something that 324 00:19:00,784 --> 00:19:02,835 I didn't at least kind of understand. 325 00:19:03,385 --> 00:19:07,424 So, I think scientists often just go like this and they go, it's really difficult, 326 00:19:07,424 --> 00:19:11,165 you're not gonna, it's arrogance, you won't be able to understand and actually, 327 00:19:11,634 --> 00:19:16,284 if you as a scientist got out of your way to explain it in layman's terms, you 328 00:19:16,284 --> 00:19:20,674 understand it better, and that person then probably feels more confidence. 329 00:19:20,835 --> 00:19:24,425 I mean, you described something else, which, you know, I think is a danger in 330 00:19:24,435 --> 00:19:27,835 any group of investors, and I've seen it coming from a slightly different 331 00:19:27,895 --> 00:19:31,795 background myself, but, which is just groupthink to a degree, where people 332 00:19:31,795 --> 00:19:36,955 sort of are relying upon one another to corroborate things and create, 333 00:19:37,395 --> 00:19:40,705 hopefully, kind of rolling balls gathering moss on the positive, but it 334 00:19:40,705 --> 00:19:43,495 has this danger that you've identified on the other side, which is something 335 00:19:43,495 --> 00:19:47,165 can be written off, and then written off by more people, because it's... 336 00:19:47,185 --> 00:19:50,795 Yeah, I mean, to be fair, I do ask, I have tried to put myself in the shoes of 337 00:19:50,795 --> 00:19:53,465 somebody running a fund and thinking... 338 00:19:53,495 --> 00:19:54,405 Limited time? 339 00:19:54,825 --> 00:19:58,660 Well, you know, it's like it's on you and like, yes, it's, you know, it's 340 00:19:58,660 --> 00:20:01,989 somebody else's money, first there's a responsibility there and you don't 341 00:20:01,990 --> 00:20:04,809 want to be the first and I totally get why they don't because it's like, 342 00:20:04,810 --> 00:20:07,230 well, okay, but somebody else has gone and then it's kind of derailed. 343 00:20:07,240 --> 00:20:08,550 It's gambling, right? 344 00:20:09,020 --> 00:20:11,959 It's, we're all in a casino gambling on whether something's going to work 345 00:20:11,959 --> 00:20:15,170 or not and I'm not ignorant to the fact that one in 10 startups fail. 346 00:20:15,180 --> 00:20:19,130 So, yeah, of course there's groupthink and I think it's, in 347 00:20:19,130 --> 00:20:21,799 some ways it's unavoidable that if you're in an ecosystem, you will 348 00:20:21,800 --> 00:20:23,450 have groupthink to a certain degree. 349 00:20:23,845 --> 00:20:28,264 But it's important to reach out and actually, like, open 350 00:20:28,285 --> 00:20:29,825 your eyes to the wide world. 351 00:20:29,965 --> 00:20:30,295 Yeah. 352 00:20:30,965 --> 00:20:35,004 At some point when VeriVin was flailing and I got very distracted doing car 353 00:20:35,004 --> 00:20:40,410 stuff, I also started a small angel syndicate, and I wanted to expose the 354 00:20:40,410 --> 00:20:42,610 Oxford ecosystem to foreign investors. 355 00:20:42,610 --> 00:20:46,190 I have a lot of contacts in, I'm Mexican by the way, I'm not American, but in the 356 00:20:46,190 --> 00:20:49,979 States I've got a lot of contacts, and I thought there should be some brilliant 357 00:20:50,000 --> 00:20:52,750 people that would be very interested in the Oxford ecosystem, back when there 358 00:20:52,750 --> 00:20:53,879 was lots of money floating around. 359 00:20:54,449 --> 00:20:58,210 And, you know, I didn't have the time to do it in the end, but I do 360 00:20:58,230 --> 00:21:03,469 think that we would benefit in Oxford from perhaps a bit more openness... 361 00:21:03,469 --> 00:21:04,120 Diversity. 362 00:21:04,159 --> 00:21:04,989 Yeah, diversity. 363 00:21:05,244 --> 00:21:08,025 It's really interesting to hear you say that and funny enough, you know, I was 364 00:21:08,025 --> 00:21:11,090 just circling something down here, which I don't know if you would agree with this, 365 00:21:11,130 --> 00:21:15,090 but, you know, you were saying how, much of a moonshot so many of these things are, 366 00:21:15,139 --> 00:21:18,700 and yet, we all agree that they're in the public interest for money to be put into 367 00:21:18,700 --> 00:21:22,620 them, and it does seem, and in fairness, you know, there are government grants and 368 00:21:22,620 --> 00:21:26,969 funds for these things, but perhaps not at the scale that, certainly not at the scale 369 00:21:26,969 --> 00:21:30,195 that the U.S has, where you know, everyone seems to have forgotten that Silicon 370 00:21:30,195 --> 00:21:34,075 Valley is a major defense project that's ongoing, you know, they pump millions, and 371 00:21:34,085 --> 00:21:38,175 by the way, you know, the IRA that they've announced is 365 billion that they're 372 00:21:38,175 --> 00:21:40,065 just gonna pump in because of inflation. 373 00:21:40,525 --> 00:21:43,674 But, you know, there's so much money going in from the state, and yet, in 374 00:21:43,675 --> 00:21:48,195 my own experience here, these are sort of public goods and yet the government 375 00:21:48,215 --> 00:21:50,275 is like, it's a private sector issue. 376 00:21:50,545 --> 00:21:55,185 We don't want to put money into this, it's not ours to fix and it does seem 377 00:21:55,185 --> 00:21:59,900 to me that there's a call for money that isn't just chasing that return and that 378 00:21:59,910 --> 00:22:04,280 is a bit more kind of community focused on things like what you're doing now. 379 00:22:04,280 --> 00:22:05,975 I completely agree because there is... 380 00:22:06,754 --> 00:22:12,705 So there's a fundamental mismatch in incentives between the people who fund, 381 00:22:13,395 --> 00:22:16,295 you know, and it's obvious, right? 382 00:22:16,295 --> 00:22:20,184 I mean, because it's all big Ponzi scheme because you're waiting until, you 383 00:22:20,184 --> 00:22:23,644 know, when you can flip it so that your portfolio value goes up and you make your 384 00:22:23,715 --> 00:22:26,014 commission and then it goes to somebody else and it gets sold on to somebody else. 385 00:22:26,185 --> 00:22:29,054 Nobody actually cares whether the damn thing works at the end of the day, right? 386 00:22:29,054 --> 00:22:30,814 They care about whether they make their money out and 387 00:22:30,814 --> 00:22:32,064 that's totally understandable. 388 00:22:32,244 --> 00:22:35,804 There are some people that need to do that and, you know, in this world, but if 389 00:22:35,804 --> 00:22:38,615 you want to do like proper philanthropy? 390 00:22:38,615 --> 00:22:41,905 Well, yes, then it has to be government, I suppose, there'd be no other way, 391 00:22:42,060 --> 00:22:42,450 Yeah. 392 00:22:42,950 --> 00:22:46,600 So in terms of your investors that you have now, again, for the size and the 393 00:22:46,650 --> 00:22:50,540 scale that you're at already and the business, you've actually got quite 394 00:22:50,540 --> 00:22:52,250 an unusual cap table now, because... 395 00:22:52,250 --> 00:22:52,370 yes. 396 00:22:52,370 --> 00:22:52,770 Tell us more. 397 00:22:52,770 --> 00:22:53,584 Cici's laughing by the way for anyone listening. 398 00:22:57,954 --> 00:23:01,114 Well, no, I'm laughing because I've basically, the gist of it is we've 399 00:23:01,114 --> 00:23:03,934 got, our cap table is very small. 400 00:23:04,195 --> 00:23:07,125 I mean, without having to divulge a bit, but we've got, our chairman, 401 00:23:07,135 --> 00:23:10,730 who's invested personally, another private American investor 402 00:23:10,740 --> 00:23:14,580 who's invested personally, OUI strong there, and my family. 403 00:23:14,730 --> 00:23:19,109 So, I've put my money where my mouth is and the way I like to put it to people 404 00:23:19,109 --> 00:23:23,659 is, so basically, getting the tech up to where it is to date, and I include 405 00:23:23,659 --> 00:23:28,910 VeriVin and its pivot or failure, see how you will, we did it with 3.5 million 406 00:23:29,380 --> 00:23:34,400 all in so far, which is a shoestring, and it's because when we were doing all 407 00:23:34,400 --> 00:23:38,280 the R&D for VeriVin, if we had, like, a 3D printer that broke, it would go 408 00:23:38,280 --> 00:23:41,590 back to Amazon, and I'd be like, get a return, because it was my money. 409 00:23:41,699 --> 00:23:44,180 So it's kind of, it's a very different feeling. 410 00:23:44,840 --> 00:23:47,199 Because I'm sitting there going, like, let's be honest, 411 00:23:47,199 --> 00:23:48,579 I'm spending my inheritance. 412 00:23:48,580 --> 00:23:50,160 Yeah, so it's just very different. 413 00:23:50,265 --> 00:23:52,655 It's really interesting because, you know, I asked earlier and, 414 00:23:53,075 --> 00:23:55,805 tragically, you didn't have an answer for me to this question, but, you 415 00:23:55,805 --> 00:23:58,344 know, what could be improved to let more people slip through the net? 416 00:23:58,344 --> 00:24:02,045 But again, we're kind of talking about that attitude that you have that embeds 417 00:24:02,085 --> 00:24:05,485 through the company, that it's your money and that you care about it and 418 00:24:05,485 --> 00:24:08,775 that you have this value, this integrity, this commitment to everything you're 419 00:24:08,775 --> 00:24:12,345 doing and you want to create a legacy, which arguably is your inheritance. 420 00:24:12,974 --> 00:24:18,225 But, and just that whole kind of owner mindset, not just founder 421 00:24:18,264 --> 00:24:19,964 mindset, but owner founder mindset. 422 00:24:20,564 --> 00:24:23,195 I've spoken to quite a lot of people that have said, you know, there's a real 423 00:24:23,195 --> 00:24:26,334 risk with taking on external capital, that people, the first thing they want 424 00:24:26,335 --> 00:24:30,720 to do is sort of pull out the exec team, you know, and they change a lot of that 425 00:24:30,760 --> 00:24:34,850 and on the flip side, I've heard a kind of, you know, opinions obviously vary 426 00:24:34,850 --> 00:24:40,020 on this, but people saying that often is because people are looking for the 427 00:24:40,070 --> 00:24:42,570 playbook, they want to flip it, they want to get it out of the portfolio 428 00:24:42,610 --> 00:24:47,189 because that's what it's gone into and that there's less ambition attached 429 00:24:47,200 --> 00:24:51,610 to that whole model because they're not trying to build something larger. 430 00:24:51,610 --> 00:24:55,180 So in terms taking outside external funding, right? 431 00:24:55,565 --> 00:24:59,065 It gives you external validation, which is really important and then from a personal 432 00:24:59,085 --> 00:25:01,095 perspective, it de-risks the thing. 433 00:25:01,475 --> 00:25:05,165 But your point about the team being artificially inserted, 434 00:25:05,235 --> 00:25:07,064 that's an Oxford problem I see. 435 00:25:07,645 --> 00:25:10,805 This is just my personal opinion, but I think what happens often, which is 436 00:25:10,805 --> 00:25:15,275 the disconnect and where things go wrong, is you have brilliant academic, 437 00:25:15,870 --> 00:25:22,200 fantastic idea, go to OUI, sort out, go to OSC, set everything up, external team 438 00:25:22,200 --> 00:25:28,640 gets put in and that CEO will not have necessarily the depth of knowledge about 439 00:25:28,699 --> 00:25:34,190 the tech or the passion to drive it and so there's a little bit of a disconnect 440 00:25:34,190 --> 00:25:37,720 there and then I've seen often that can lead to, for example, the text starts 441 00:25:37,720 --> 00:25:42,040 not working and it doesn't percolate through and then by the time that like 442 00:25:42,080 --> 00:25:45,509 the person that comes in that goes, what the hell this thing is like, you 443 00:25:45,509 --> 00:25:47,530 know, it's makes it more disconnected. 444 00:25:47,530 --> 00:25:51,110 So I don't know if there could be any way to bridge that gap more. 445 00:25:51,110 --> 00:25:51,550 I don't know. 446 00:25:51,660 --> 00:25:54,530 I agree with that, by the way, I think that's valid and I think that from 447 00:25:54,530 --> 00:25:58,589 your perspective, I wouldn't have that perspective, but something else that 448 00:25:58,729 --> 00:26:02,329 one thinks if you're thinking bigger and you're thinking kind of UK PLC 449 00:26:02,330 --> 00:26:07,170 Oxford ecosystem, it's just bad for the ecosystem if things aren't being 450 00:26:07,180 --> 00:26:12,339 grown by the people that want to make them larger or as large as they can be, 451 00:26:12,339 --> 00:26:16,910 that have that ambition beyond a sort of five year exit and when I was doing 452 00:26:16,920 --> 00:26:20,990 Boost, which was alluded to earlier, we did a lot of interviews as part of the 453 00:26:20,990 --> 00:26:27,670 kind of consultation and this ambition gap between the US and the UK, but also 454 00:26:27,670 --> 00:26:29,840 just full stop really came through. 455 00:26:30,449 --> 00:26:35,350 and I think that for Oxford itself, it's something that we could be more ambitious 456 00:26:35,510 --> 00:26:39,770 and I don't know, I mean, in terms of Oxford itself, how was your experience? 457 00:26:39,790 --> 00:26:42,910 Because, you know, you've got some areas that you clearly weren't 458 00:26:42,930 --> 00:26:46,780 hugely helpful, and others that you're hugely positive about. 459 00:26:46,810 --> 00:26:50,355 I mean, I personally, I, look, I live here as a choice. 460 00:26:50,355 --> 00:26:52,835 I live in Oxfordshire now, in Coombe. 461 00:26:53,285 --> 00:26:55,615 But, I love my alma mater. 462 00:26:55,955 --> 00:26:58,345 If I'm honest, I don't have any passion for Princeton. 463 00:26:58,345 --> 00:27:01,734 Princeton was a great education, undergraduate education, but I'm 464 00:27:01,734 --> 00:27:05,385 not really, like, brouhaha, but, whereas Oxford, I really genuinely 465 00:27:05,385 --> 00:27:09,685 feel like it made me who I am, my PhD, and I love the university with 466 00:27:09,685 --> 00:27:13,605 all its quirks and its bureaucracies and its administrative delays. 467 00:27:13,605 --> 00:27:16,075 I actually really, I really love Oxford. 468 00:27:16,075 --> 00:27:20,874 I think, you know, and I think, you know, what was good, I did get support 469 00:27:20,905 --> 00:27:22,445 and I think it's a tough one, right? 470 00:27:22,445 --> 00:27:23,434 We're not the US. 471 00:27:23,654 --> 00:27:26,965 We're a small little island, like we don't have the funds and all our 472 00:27:26,965 --> 00:27:28,665 companies go list in the States. 473 00:27:29,440 --> 00:27:32,680 You know, I mean, like, all our IP flows that way, so it's almost like I can 474 00:27:32,680 --> 00:27:37,100 understand why the university's kind of like, no, because, it just all goes. 475 00:27:37,510 --> 00:27:39,779 Yeah, I don't, I really don't know, I don't have the 476 00:27:39,779 --> 00:27:41,320 answer to what you could do. 477 00:27:41,320 --> 00:27:43,390 Get more people to stay here. 478 00:27:43,440 --> 00:27:43,500 Yeah. 479 00:27:43,690 --> 00:27:44,360 Keep them here. 480 00:27:44,420 --> 00:27:46,120 Cici, thank you so much. 481 00:27:46,120 --> 00:27:47,640 This has been a wonderful conversation. 482 00:27:47,659 --> 00:27:50,020 I am now going to throw it out to the crowd and see if there 483 00:27:50,020 --> 00:27:52,789 are any questions that anyone particularly wanted to ask Cici. 484 00:27:54,440 --> 00:27:55,400 Sorry, I'm Adam. 485 00:27:55,400 --> 00:27:56,620 I was there with Cici at the beginning. 486 00:27:57,109 --> 00:28:02,620 Building on your point, yes, a lot of our, alma maters, alumni go off to the States. 487 00:28:03,050 --> 00:28:07,780 We're not particularly good at keeping our successful entrepreneurs in Oxford. 488 00:28:07,780 --> 00:28:10,900 So what could we be doing differently to encourage them to come back? 489 00:28:11,579 --> 00:28:14,000 Go for a second or third or fourth time like you see in America. 490 00:28:14,129 --> 00:28:17,289 I don't know, because I've never made it and gone to the States yet. 491 00:28:17,800 --> 00:28:19,270 Ask me when I make it, Adam! 492 00:28:19,610 --> 00:28:21,800 Speak to the, AIM market and improve it. 493 00:28:21,809 --> 00:28:23,380 Make it cost less to list here. 494 00:28:23,450 --> 00:28:25,020 I think she's answered your question. 495 00:28:25,600 --> 00:28:27,580 I could give you a longer list if you'd like? 496 00:28:28,430 --> 00:28:32,110 No, I think we really need a recycling of talent and you talked about it earlier 497 00:28:32,140 --> 00:28:34,925 because you were saying that you weren't speaking to somebody that understood the 498 00:28:34,925 --> 00:28:39,295 tech and one of the virtues of the US system is that so often entrepreneurs are 499 00:28:39,295 --> 00:28:43,354 the ones that are reinvesting and it's such a positive loop and so the answer 500 00:28:43,355 --> 00:28:47,205 is there's lots of things that we should be doing to encourage people to stay 501 00:28:47,205 --> 00:28:52,455 here, to come back here perhaps, we need really good HQs in Oxford that we get 502 00:28:52,465 --> 00:28:56,975 tax subsidies to encourage them to put them here, we need pension fund money, 503 00:28:57,225 --> 00:29:02,085 that's my big drum that I bang all the time, there is movement, it's just... 504 00:29:02,785 --> 00:29:04,085 Can I speak? 505 00:29:04,100 --> 00:29:08,010 So I'm not, this is obviously I'm not a finance person when I say the 506 00:29:08,010 --> 00:29:10,669 finance bit I did at Princeton was like financial modeling, like Black 507 00:29:10,669 --> 00:29:12,150 Scholes Equation stuff, not real. 508 00:29:12,510 --> 00:29:16,160 But what I always think is, how in the world is it that there's so many ultra 509 00:29:16,160 --> 00:29:20,130 high net worth out there with like, who look for, like, I know a guy in London 510 00:29:20,140 --> 00:29:25,190 who's sole job it is to find super billionaires ways to spend what they no 511 00:29:25,190 --> 00:29:28,930 longer know how to, they like, they've got everything and you think with all 512 00:29:28,930 --> 00:29:30,969 that money, like, why can we not redirect? 513 00:29:30,970 --> 00:29:33,699 This is such a naive physicist thing to say, but I'm like, why can't we 514 00:29:33,699 --> 00:29:35,310 redirect some of this stuff, you know? 515 00:29:35,310 --> 00:29:38,939 And yeah, you could say all sorts of things about taxes and I don't 516 00:29:38,939 --> 00:29:40,369 know, perhaps, okay, that's one. 517 00:29:40,779 --> 00:29:44,779 Maybe there should be more kind of family office or super 518 00:29:44,779 --> 00:29:46,419 angel investment looked at. 519 00:29:46,490 --> 00:29:50,480 Yeah, and I think Oxford could do a lot to welcome in family offices, sovereign 520 00:29:50,480 --> 00:29:54,910 wealth funds, to really create a pipe that comes in here and says, you know, 521 00:29:54,910 --> 00:29:57,820 you're so welcome, we're going to lay out the red carpet for you, we're so 522 00:29:57,820 --> 00:30:01,439 excited to have you here and somebody said something wonderful to me today, that 523 00:30:01,440 --> 00:30:05,660 apparently Irene Tracy describes Oxford as a federation, and she's not CEO of 524 00:30:05,670 --> 00:30:10,625 a single corporation and I think that's such a good point because it is lots of 525 00:30:10,625 --> 00:30:14,805 little parts that make up the whole and therefore it is unwieldy but, you know, 526 00:30:14,835 --> 00:30:18,955 part of the aim of this podcast, and not to sound too grandiose, is to have these 527 00:30:18,955 --> 00:30:22,305 conversations in the public domain because most of them aren't insurmountable. 528 00:30:22,325 --> 00:30:27,585 It's just an attitude shift that we can do something differently and that's what kind 529 00:30:27,585 --> 00:30:30,575 of, you know, not quite gets me out of bed in the morning but I think it's exciting. 530 00:30:30,575 --> 00:30:33,305 We can move the needle on this and particularly, you know, as you 531 00:30:33,305 --> 00:30:36,294 start smaller and then build from there, it's something we can do. 532 00:30:36,294 --> 00:30:40,200 But I mean, and also let's not be totally negative because the UK is, you know, 533 00:30:40,520 --> 00:30:42,130 they have championed quantum tech. 534 00:30:42,270 --> 00:30:44,250 They are championing machine learning. 535 00:30:44,430 --> 00:30:47,410 Innovate UK is a great tool, it really is. 536 00:30:47,410 --> 00:30:51,709 I mean, it like, and there's a lot of grant funding out there and there's 537 00:30:51,709 --> 00:30:55,760 a lot you can do and R&D tax credits are really good, I mean, they've 538 00:30:55,760 --> 00:30:57,980 helped us, so it's not all bad. 539 00:30:58,160 --> 00:30:59,080 SEIs, exactly. 540 00:30:59,230 --> 00:30:59,930 not all bad. 541 00:30:59,930 --> 00:31:00,480 It's not all bad. 542 00:31:00,490 --> 00:31:01,825 Okay, well listen, thank you so much. 543 00:31:01,825 --> 00:31:03,245 You've been very patient with us. 544 00:31:03,355 --> 00:31:03,605 Yeah. 545 00:31:07,865 --> 00:31:10,605 Thanks for listening to this episode of Oxford+, presented 546 00:31:10,625 --> 00:31:12,025 by me, Susannah de Jager. 547 00:31:12,495 --> 00:31:15,905 If you want to stay up to date with all things Oxford+, please visit our website, 548 00:31:15,955 --> 00:31:20,775 OxfordPlus.co.uk and sign up to our newsletter so you never miss an update. 549 00:31:21,365 --> 00:31:24,525 Oxford+ was made in partnership with Mishcon de Reya and is produced 550 00:31:24,525 --> 00:31:31,275 and edited by Story Ninety-Four.