1 00:00:02,400 --> 00:00:06,760 Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. 2 00:00:07,040 --> 00:00:09,120 Speaker 2: Look, we've got to talk about the impact of generative 3 00:00:09,119 --> 00:00:12,879 Speaker 2: AI in real life across industries, Biofarmer being one of them, 4 00:00:13,119 --> 00:00:14,920 Speaker 2: and I'm priessed to welcome with the show Paul Hudson, 5 00:00:15,040 --> 00:00:18,840 Speaker 2: CEO of Sanofi, discuss how your enormous French her Farmer 6 00:00:18,880 --> 00:00:22,680 Speaker 2: company has been leveraging the new technology in its operations. 7 00:00:23,000 --> 00:00:26,200 Speaker 2: And yes, there's drug discovery, but also what's so interesting, Paul, 8 00:00:26,280 --> 00:00:29,080 Speaker 2: is you're integrating it within the functionality of your business 9 00:00:29,080 --> 00:00:30,680 Speaker 2: on a day to day basis. 10 00:00:31,280 --> 00:00:33,560 Speaker 1: Yeah, thank you, and it's a pleasure to be here. 11 00:00:33,600 --> 00:00:34,440 Speaker 1: We're really trying. 12 00:00:34,280 --> 00:00:36,960 Speaker 3: To do incredible things for patients and discovering new medicines 13 00:00:37,000 --> 00:00:40,320 Speaker 3: and you know, with AI being the great disruptor and 14 00:00:40,400 --> 00:00:43,360 Speaker 3: all of the excitement last year, the wow of large 15 00:00:43,400 --> 00:00:45,680 Speaker 3: anguige models, and this year for us, it's all about 16 00:00:45,680 --> 00:00:48,400 Speaker 3: the how and how we can get our you know, 17 00:00:48,520 --> 00:00:52,760 Speaker 3: the large amounts of our organization focused whether you're in 18 00:00:53,159 --> 00:00:56,840 Speaker 3: a lab doing expert AI, whether using generative AI in 19 00:00:57,480 --> 00:01:02,480 Speaker 3: regulatory or text driven of our business and importantly snackable 20 00:01:02,480 --> 00:01:05,360 Speaker 3: AI when we're looking at really trying to give better 21 00:01:05,400 --> 00:01:09,360 Speaker 3: decision intelligence for almost everybody in the organization with the 22 00:01:09,480 --> 00:01:12,759 Speaker 3: sole intent of becoming much more efficient effective in delivering 23 00:01:12,800 --> 00:01:13,160 Speaker 3: new meds. 24 00:01:13,200 --> 00:01:13,920 Speaker 1: And it's amazing. 25 00:01:13,959 --> 00:01:16,880 Speaker 2: Really, I love this snackable AI you're using play it's 26 00:01:16,920 --> 00:01:19,320 Speaker 2: a proprietary app. But I've got to ask sort of, 27 00:01:19,560 --> 00:01:22,600 Speaker 2: how have you thought about which underlying large language model 28 00:01:22,600 --> 00:01:24,840 Speaker 2: you should look to. How you integrate this do you 29 00:01:24,880 --> 00:01:27,560 Speaker 2: go to outsourced do you build it from within. 30 00:01:27,400 --> 00:01:32,520 Speaker 3: Pol Well, we're drug discoverers and developers, we're not coders 31 00:01:32,600 --> 00:01:35,720 Speaker 3: at our heart. So we have this first of its 32 00:01:35,800 --> 00:01:39,880 Speaker 3: kind relationship now with open AI, partnership with Formation bio 33 00:01:39,959 --> 00:01:42,680 Speaker 3: translating that for is to make sure that we can 34 00:01:42,720 --> 00:01:46,520 Speaker 3: be customizing large language models to look at drug discovery, 35 00:01:46,880 --> 00:01:50,880 Speaker 3: to try and drug undruggable diseases. To this point, and 36 00:01:50,920 --> 00:01:53,720 Speaker 3: we're doing things that have really never been done before. 37 00:01:53,760 --> 00:01:57,559 Speaker 3: It's early days, but it's extremely promising. And remember there's 38 00:01:57,600 --> 00:02:00,400 Speaker 3: such a large amount of diseases that don't have medsons 39 00:02:00,400 --> 00:02:02,640 Speaker 3: available for them because we simply have not had the 40 00:02:02,680 --> 00:02:06,760 Speaker 3: computing pairer of the opportunity to deploy ourselves. And now 41 00:02:07,040 --> 00:02:10,440 Speaker 3: just now we're at this next great revolution in science. 42 00:02:10,520 --> 00:02:12,920 Speaker 3: It's such a privilege actually to lead a company at 43 00:02:12,919 --> 00:02:17,519 Speaker 3: this point. As for play, our sort of snackable approach, 44 00:02:18,040 --> 00:02:22,000 Speaker 3: we partnered with a small startup out of Germany, a Ley. 45 00:02:22,360 --> 00:02:25,200 Speaker 3: They've been working withthers, They've grown weathers and that's, by 46 00:02:25,200 --> 00:02:26,480 Speaker 3: the way, open for everybody. 47 00:02:26,560 --> 00:02:28,560 Speaker 1: We don't own it, were having to share it. 48 00:02:28,600 --> 00:02:31,239 Speaker 3: We want all boats to rise win healthcare after all, 49 00:02:31,560 --> 00:02:34,440 Speaker 3: and so the more users, the more fun, the more accuracy, 50 00:02:34,680 --> 00:02:36,320 Speaker 3: the more chance to discovering new medicines. 51 00:02:36,480 --> 00:02:39,359 Speaker 2: So have your talent reacted because there's so much do 52 00:02:39,600 --> 00:02:41,760 Speaker 2: mongering that AI is going to take all our jobs. 53 00:02:43,520 --> 00:02:45,880 Speaker 3: Well, I think our approach has been how to weaponize 54 00:02:45,880 --> 00:02:47,080 Speaker 3: our people to do better. 55 00:02:47,160 --> 00:02:49,240 Speaker 1: Like you know, I don't know whether you're familiar with. 56 00:02:49,160 --> 00:02:51,840 Speaker 3: Ways, the driving app, but I see it very much 57 00:02:51,960 --> 00:02:53,720 Speaker 3: like that. I got to get in the car. I'm 58 00:02:53,720 --> 00:02:55,360 Speaker 3: going to start the engine and drive to the end 59 00:02:55,400 --> 00:02:58,520 Speaker 3: of the street. And if Ways says I'm statistically more 60 00:02:58,639 --> 00:03:01,320 Speaker 3: likely to have an easier journey turning left rather than 61 00:03:01,400 --> 00:03:05,080 Speaker 3: turning right, I want that advice as crowdsource large data, 62 00:03:05,120 --> 00:03:07,720 Speaker 3: real time. I want our people to feel they are 63 00:03:08,120 --> 00:03:10,120 Speaker 3: amplified or multiplied in that event. 64 00:03:10,600 --> 00:03:14,120 Speaker 1: In that event too, The goal is not about less people. 65 00:03:14,160 --> 00:03:17,079 Speaker 3: The goal is about putting our people to be more 66 00:03:17,120 --> 00:03:20,680 Speaker 3: effective and to make and to have better decision intelligence 67 00:03:20,720 --> 00:03:23,720 Speaker 3: that in itself gives us a great opportunity to do 68 00:03:23,800 --> 00:03:28,480 Speaker 3: something incredible. And we've only seen huge Appetite's why on 69 00:03:28,560 --> 00:03:32,360 Speaker 3: any given day we have eighteen thousand people in our 70 00:03:32,480 --> 00:03:35,320 Speaker 3: organization using the EI we make available and I think 71 00:03:35,360 --> 00:03:36,440 Speaker 3: that's pretty fantastic. 72 00:03:36,880 --> 00:03:40,160 Speaker 2: Let's talk about doing the amazing the incredible, because we 73 00:03:40,240 --> 00:03:42,760 Speaker 2: have just seen a push up in your share price 74 00:03:42,800 --> 00:03:45,680 Speaker 2: on the day and you've been reorientating this business of 75 00:03:45,760 --> 00:03:48,760 Speaker 2: Sonofye and thinking about more R and D, about drug 76 00:03:48,760 --> 00:03:52,440 Speaker 2: discovery and applicate and look some costly cases in the 77 00:03:52,480 --> 00:03:54,800 Speaker 2: way that you have to experiment do clinical trials, but 78 00:03:54,840 --> 00:03:56,880 Speaker 2: they've paid off today and we think about the drug 79 00:03:56,880 --> 00:04:00,480 Speaker 2: discovery when it comes to a key lung chronic lung disorder. 80 00:04:00,520 --> 00:04:01,760 Speaker 2: Can you just tell us about what that means to 81 00:04:01,800 --> 00:04:02,240 Speaker 2: the business. 82 00:04:03,000 --> 00:04:06,680 Speaker 3: Well, in Europe, just yesterday today we got approval for 83 00:04:06,840 --> 00:04:10,320 Speaker 3: chronic constructive coon me disease. COPD picks a huge number 84 00:04:10,320 --> 00:04:13,119 Speaker 3: of people third biggest killer. We're ready to go now 85 00:04:13,160 --> 00:04:15,840 Speaker 3: in Europe to be able to provide an advanced therapy 86 00:04:15,840 --> 00:04:17,920 Speaker 3: that will reduce their chance to go to hospital by 87 00:04:18,160 --> 00:04:20,680 Speaker 3: a third, and that's a breakthrough. 88 00:04:20,200 --> 00:04:21,479 Speaker 1: And there's been so little innovation. 89 00:04:22,120 --> 00:04:24,599 Speaker 3: We aspire to be the world's leading immnology company, and 90 00:04:24,640 --> 00:04:28,200 Speaker 3: that's partly driven by moments like that that we live 91 00:04:28,279 --> 00:04:31,520 Speaker 3: for in our industry to do things that transform or 92 00:04:31,560 --> 00:04:34,159 Speaker 3: protect patients lives, will prevent disease. 93 00:04:34,720 --> 00:04:34,960 Speaker 1: You know. 94 00:04:35,200 --> 00:04:39,480 Speaker 3: The reality is we're a very proud, huge, big company, 95 00:04:39,560 --> 00:04:42,440 Speaker 3: but we wanted to be at the forefront of science 96 00:04:42,440 --> 00:04:45,279 Speaker 3: again and we've made some big bets, but appropriately so 97 00:04:45,520 --> 00:04:47,719 Speaker 3: to try and make sure our science is some of 98 00:04:47,839 --> 00:04:50,760 Speaker 3: the most exciting and it's first in class or best 99 00:04:50,800 --> 00:04:53,880 Speaker 3: in class. And to couple it with our conversation of earlier, 100 00:04:53,880 --> 00:04:56,360 Speaker 3: the more we add AI, the more we improve our 101 00:04:56,400 --> 00:05:00,240 Speaker 3: decision intelligence, the more opportunity we have to do more 102 00:05:00,240 --> 00:05:02,719 Speaker 3: in that space. And I think that's what's catching the 103 00:05:02,760 --> 00:05:07,000 Speaker 3: excitement in our organization, and we believe we're quite far ahead. 104 00:05:07,040 --> 00:05:09,640 Speaker 3: I mean, of course, we're very responsible about how we 105 00:05:10,000 --> 00:05:12,360 Speaker 3: use AI, but others are still hesitating. 106 00:05:12,400 --> 00:05:15,120 Speaker 1: We're taking our moment, but sometimes you need. 107 00:05:15,040 --> 00:05:18,359 Speaker 2: Good data to feed in to these large language models. Paul, 108 00:05:18,680 --> 00:05:21,280 Speaker 2: what about the data on viruses such as well bird 109 00:05:21,320 --> 00:05:24,800 Speaker 2: flu At the moment here in the UK and the US. Indeed, Look, 110 00:05:24,839 --> 00:05:26,680 Speaker 2: there is a worry that the data hasn't been good 111 00:05:26,800 --> 00:05:28,720 Speaker 2: enough in the United States. Is that something that worries 112 00:05:28,839 --> 00:05:30,880 Speaker 2: you and you need to be developing a new vaccine 113 00:05:30,880 --> 00:05:31,080 Speaker 2: for that. 114 00:05:32,279 --> 00:05:34,760 Speaker 3: Look, I mean we've been through one pandemic. I think 115 00:05:34,760 --> 00:05:36,680 Speaker 3: we've learned a great deal about trying to have the 116 00:05:36,720 --> 00:05:39,680 Speaker 3: best data, and I think we position ourselves to take 117 00:05:39,680 --> 00:05:42,600 Speaker 3: advantage of those opportunities on behalf of society. Frankly, we 118 00:05:42,640 --> 00:05:46,119 Speaker 3: played a part small part in COVID, but we stand 119 00:05:46,200 --> 00:05:49,159 Speaker 3: ready should we be asked the data and it's legitimacy, 120 00:05:49,279 --> 00:05:52,040 Speaker 3: you know. We often that data comes in through government channels, 121 00:05:52,040 --> 00:05:54,679 Speaker 3: through health systems, you know, and we work at real 122 00:05:54,960 --> 00:05:58,120 Speaker 3: time speed to make sure that we have the very 123 00:05:58,240 --> 00:06:01,799 Speaker 3: best data available in terms of large language models and 124 00:06:01,960 --> 00:06:04,080 Speaker 3: the rest of the data sets. 125 00:06:04,400 --> 00:06:06,320 Speaker 1: You know, we collaborate far and wide. 126 00:06:06,360 --> 00:06:10,159 Speaker 3: We have punishes with oak in with ADOM, wise with excyensia. 127 00:06:10,320 --> 00:06:12,960 Speaker 3: We really are trying to make sure that we have 128 00:06:13,480 --> 00:06:15,720 Speaker 3: at our fingertips huge data sets. 129 00:06:15,760 --> 00:06:17,680 Speaker 1: To mean, our accuracy. 130 00:06:17,320 --> 00:06:20,240 Speaker 3: And our predictive capability is far ahead of the competition. 131 00:06:20,480 --> 00:06:23,919 Speaker 3: We won't get it right everywhere, but the data integrity 132 00:06:24,120 --> 00:06:27,839 Speaker 3: of the absolute fundamental start in place. Our relationship with 133 00:06:27,920 --> 00:06:30,600 Speaker 3: open AI and with Formation by It, we're putting our 134 00:06:30,640 --> 00:06:34,080 Speaker 3: own data at play to make sure that they can 135 00:06:34,160 --> 00:06:35,000 Speaker 3: learn from it too. 136 00:06:35,080 --> 00:06:37,560 Speaker 1: Again, all boats your rights. We're in healthcare to do good. 137 00:06:38,200 --> 00:06:40,599 Speaker 2: You're also selling off bits of healthcare because you're focusing 138 00:06:40,680 --> 00:06:42,479 Speaker 2: in on the R and D, the drug side, and 139 00:06:42,520 --> 00:06:45,640 Speaker 2: maybe the consumer business goes for a hefty price tag. 140 00:06:45,720 --> 00:06:48,520 Speaker 2: You've been thinking about IPO and you've been thinking about sale. 141 00:06:49,000 --> 00:06:50,320 Speaker 2: How is that process going poor? 142 00:06:51,200 --> 00:06:53,320 Speaker 3: Well, we announced that October that we were going to 143 00:06:53,320 --> 00:06:55,120 Speaker 3: become a pure play by a Farmer. You know, we 144 00:06:55,160 --> 00:06:57,320 Speaker 3: want to double down on the things we just talked about, 145 00:06:57,320 --> 00:07:02,719 Speaker 3: and we have an incredibly successful and energize consumer business. 146 00:07:03,400 --> 00:07:05,800 Speaker 3: We feel that they feel in fact that they can 147 00:07:05,839 --> 00:07:08,040 Speaker 3: stand better on their own two feet and be independent, 148 00:07:08,120 --> 00:07:10,720 Speaker 3: raise their own capital, take on their own projects, and 149 00:07:10,960 --> 00:07:12,920 Speaker 3: we want to support them in that it's the right 150 00:07:12,960 --> 00:07:16,440 Speaker 3: thing to do. Our capital should be allocated towards discovering 151 00:07:16,440 --> 00:07:19,239 Speaker 3: new medicines, and there should be about growing their business 152 00:07:19,240 --> 00:07:21,400 Speaker 3: fast in the market, which by the way, they're doing 153 00:07:21,480 --> 00:07:24,760 Speaker 3: very successfully. We've declared that buy as early as Q 154 00:07:24,840 --> 00:07:28,440 Speaker 3: four twenty four will be ready to go to separate 155 00:07:28,480 --> 00:07:31,200 Speaker 3: that business. We haven't shared how we will do it, 156 00:07:31,240 --> 00:07:32,680 Speaker 3: but I think it's quite. 157 00:07:32,480 --> 00:07:33,240 Speaker 1: Well known that. 158 00:07:33,880 --> 00:07:37,880 Speaker 3: You know, we're talking to interesting parties, certainly Pe, and 159 00:07:37,920 --> 00:07:41,960 Speaker 3: we have the capital markets vehicles available to us too, 160 00:07:42,080 --> 00:07:45,520 Speaker 3: So no, we're excited for them. There's a huge amount 161 00:07:45,560 --> 00:07:48,240 Speaker 3: of interest. It's a great business and we hope to 162 00:07:48,240 --> 00:07:51,520 Speaker 3: create value for patients, for stakeholders, and indeed for the 163 00:07:51,560 --> 00:07:52,440 Speaker 3: people in the business. 164 00:07:52,720 --> 00:07:57,760 Speaker 2: Q four twenty four is pretty notable given that we're 165 00:07:57,800 --> 00:08:01,200 Speaker 2: in quite a politically uncertain time. Look, we two brips 166 00:08:01,200 --> 00:08:03,000 Speaker 2: sit here looking that there's going to be an election 167 00:08:03,320 --> 00:08:05,920 Speaker 2: and people voting in the UK tomorrow and then of 168 00:08:05,960 --> 00:08:08,720 Speaker 2: course this weekend is all important for France. How as 169 00:08:08,720 --> 00:08:11,040 Speaker 2: a leader of such a French giant, are you feeling 170 00:08:11,080 --> 00:08:13,920 Speaker 2: about the political risk of doing a deal in this market? 171 00:08:15,560 --> 00:08:18,320 Speaker 3: Well, look, you know, in terms of healthcare and being 172 00:08:18,320 --> 00:08:21,160 Speaker 3: a leader of an incredibly important French company, you know, 173 00:08:21,200 --> 00:08:24,480 Speaker 3: our first priority is to ensure that whoever governs, they 174 00:08:24,520 --> 00:08:28,040 Speaker 3: provide a predictable, stable environment. Because research is a ten 175 00:08:28,080 --> 00:08:31,520 Speaker 3: to fifteen year process, so you know, we need to 176 00:08:31,560 --> 00:08:34,040 Speaker 3: think long term. We hope that whoever leads the country 177 00:08:34,280 --> 00:08:36,560 Speaker 3: will think long term. We know the current government has 178 00:08:36,600 --> 00:08:40,160 Speaker 3: been incredibly supportive of innovation and creating that ecosystem. We 179 00:08:40,240 --> 00:08:42,120 Speaker 3: hope that that would continue. 180 00:08:42,320 --> 00:08:42,880 Speaker 1: Of course. 181 00:08:43,160 --> 00:08:45,600 Speaker 3: You know, there are people trying to understand what it 182 00:08:45,679 --> 00:08:48,880 Speaker 3: means for the economy, much like for the change in 183 00:08:48,960 --> 00:08:51,880 Speaker 3: government if it's to come about in the UK, and 184 00:08:52,120 --> 00:08:55,040 Speaker 3: people want that predictability in the markets too. You know, 185 00:08:55,200 --> 00:08:57,880 Speaker 3: we're long term player, trying to do the right things 186 00:08:57,880 --> 00:09:00,680 Speaker 3: for patients, trying to drug the undruggable, and we hope 187 00:09:00,760 --> 00:09:03,880 Speaker 3: to partner with whoever's in power, of course, because we 188 00:09:04,040 --> 00:09:06,240 Speaker 3: have to be ready to do our part for society 189 00:09:06,280 --> 00:09:10,120 Speaker 3: and and you know, so far we believe that can continue. 190 00:09:10,440 --> 00:09:13,600 Speaker 2: Paul, it's been great talking to you the intersection of 191 00:09:13,679 --> 00:09:17,400 Speaker 2: drug discovery of artificial intelligence slackable AI as you call it. 192 00:09:17,440 --> 00:09:20,119 Speaker 2: Paul Hudson, great to have some time. CEO of Sanofie 193 00:09:20,400 --> 00:09:20,840 Speaker 2: say well,