WEBVTT 1 00:00:02.150 --> 00:00:04.110 What role did artificial intelligence have 2 00:00:04.110 --> 00:00:07.180 in helping combat the coronavirus pandemic? 3 00:00:07.180 --> 00:00:09.083 Find out today when we talk with an innovative company 4 00:00:09.083 --> 00:00:11.060 that used artificial intelligence 5 00:00:11.060 --> 00:00:12.870 to help solve the critical problems 6 00:00:12.870 --> 00:00:14.623 society faced in the last year. 7 00:00:15.970 --> 00:00:18.040 Welcome to "Me, Myself, and AI," 8 00:00:18.040 --> 00:00:21.220 a podcast on artificial intelligence in business. 9 00:00:21.220 --> 00:00:22.180 Each episode, 10 00:00:22.180 --> 00:00:25.080 we introduce you to someone innovating with AI. 11 00:00:25.080 --> 00:00:28.010 I'm Sam Ransbotham, Professor of Information Systems 12 00:00:28.010 --> 00:00:29.630 at Boston College. 13 00:00:29.630 --> 00:00:31.050 I'm also the guest editor 14 00:00:31.050 --> 00:00:33.910 for the AI and Business Strategy Big Idea program 15 00:00:33.910 --> 00:00:36.710 at MIT Sloan Management Review 16 00:00:36.710 --> 00:00:38.830 And I'm Shervin Khodabandeh 17 00:00:38.830 --> 00:00:40.600 Senior Partner with BCG, 18 00:00:40.600 --> 00:00:44.400 and I co-lead BCG's AI practice in North America. 19 00:00:44.400 --> 00:00:48.480 And together MIT SMR and BCG have been researching AI 20 00:00:48.480 --> 00:00:49.920 for five years, 21 00:00:49.920 --> 00:00:52.080 interviewing hundreds of practitioners 22 00:00:52.080 --> 00:00:54.110 and surveying thousands of companies 23 00:00:54.110 --> 00:00:56.030 on what it takes to build 24 00:00:56.030 --> 00:00:58.740 and to deploy and scale AI capabilities 25 00:00:58.740 --> 00:01:00.100 across the organization, 26 00:01:00.100 --> 00:01:03.347 and really transform the way organizations operate. 27 00:01:03.347 --> 00:01:05.870 (upbeat music) 28 00:01:05.870 --> 00:01:07.620 Today we're talking with Dave Johnson, 29 00:01:07.620 --> 00:01:11.390 Chief Data and Artificial Intelligence Officer at Moderna. 30 00:01:11.390 --> 00:01:12.350 Dave, thanks for joining us. 31 00:01:12.350 --> 00:01:13.210 Welcome. 32 00:01:13.210 --> 00:01:15.010 Thanks guys for having me. 33 00:01:15.010 --> 00:01:18.150 Can you describe your current role at Moderna? 34 00:01:18.150 --> 00:01:20.920 I'm Chief Data and AI Officer at Moderna. 35 00:01:20.920 --> 00:01:21.753 In my role, 36 00:01:21.753 --> 00:01:24.190 I'm responsible for all of our enterprise data functions 37 00:01:24.190 --> 00:01:27.150 from data engineering to data science integration. 38 00:01:27.150 --> 00:01:29.660 And I also manage a software engineering team building, 39 00:01:29.660 --> 00:01:30.493 you know, 40 00:01:30.493 --> 00:01:31.420 unique custom applications 41 00:01:31.420 --> 00:01:33.680 to curate and create new data sets, 42 00:01:33.680 --> 00:01:36.290 but also to then take those AI models that are created 43 00:01:36.290 --> 00:01:38.370 and build them into processes. 44 00:01:38.370 --> 00:01:39.840 So it's kind of end-to-end 45 00:01:39.840 --> 00:01:41.970 everything to actually deploy an AI model 46 00:01:41.970 --> 00:01:45.150 to build, deploy, and put an AI model into production. 47 00:01:45.150 --> 00:01:46.170 How did you end up in that role? 48 00:01:46.170 --> 00:01:48.355 I know you have physics in your background. 49 00:01:48.355 --> 00:01:50.870 That's not... I didn't hear any physics 50 00:01:50.870 --> 00:01:52.500 in what you just said. 51 00:01:52.500 --> 00:01:53.890 Yeah, no, it's a good point. 52 00:01:53.890 --> 00:01:57.140 So I have my PhD in what's called Information Physics, 53 00:01:57.140 --> 00:01:59.652 which is a field closely related to data science actually. 54 00:01:59.652 --> 00:02:03.310 It's about the foundations of Bayesian statistics 55 00:02:03.310 --> 00:02:04.700 and information theory, 56 00:02:04.700 --> 00:02:07.460 a lot of what is involved in data science. 57 00:02:07.460 --> 00:02:10.160 My particular research was in applying that 58 00:02:10.160 --> 00:02:13.050 to a framework that derives quantum mechanics 59 00:02:13.050 --> 00:02:15.370 from the rules of information theory. 60 00:02:15.370 --> 00:02:17.670 So that part, you're right is not particularly relevant 61 00:02:17.670 --> 00:02:19.110 to my day-to-day job, 62 00:02:19.110 --> 00:02:21.650 but the information theory part and the Bayesian stats is 63 00:02:21.650 --> 00:02:24.110 completely on target for what I do. 64 00:02:24.110 --> 00:02:24.943 In addition to that, 65 00:02:24.943 --> 00:02:27.230 I spent many years doing independent consulting 66 00:02:27.230 --> 00:02:30.210 in a kind of a software engineering data science capacity. 67 00:02:30.210 --> 00:02:31.830 And when I finished my PhD, you know, 68 00:02:31.830 --> 00:02:34.120 I realized academia wasn't really for me, 69 00:02:34.120 --> 00:02:36.250 I wanted to do applications. 70 00:02:36.250 --> 00:02:37.770 And I ended up with a consulting firm 71 00:02:37.770 --> 00:02:40.090 doing work for large pharmaceutical companies. 72 00:02:40.090 --> 00:02:42.350 So I spent a number of years doing that 73 00:02:42.350 --> 00:02:43.900 and it turned out to be a real great marriage 74 00:02:43.900 --> 00:02:45.040 of my skill sets, you know, 75 00:02:45.040 --> 00:02:46.980 understanding of science, understanding of data, 76 00:02:46.980 --> 00:02:49.400 understanding of, you know, software engineering. 77 00:02:49.400 --> 00:02:52.210 And so I did one project in particular for a number of years 78 00:02:52.210 --> 00:02:54.970 in research at a pharmaceutical company 79 00:02:54.970 --> 00:02:57.110 around capturing data, you know, 80 00:02:57.110 --> 00:03:00.660 structured, useful way in the preclinical space 81 00:03:00.660 --> 00:03:03.010 in order to feed into kind of advanced data 82 00:03:03.010 --> 00:03:04.460 and advanced models. 83 00:03:04.460 --> 00:03:06.670 So very much what I'm doing today. 84 00:03:06.670 --> 00:03:09.640 And about seven years ago, I moved over to Moderna. 85 00:03:09.640 --> 00:03:10.830 And at that time we were, you know, 86 00:03:10.830 --> 00:03:12.280 a preclinical stage company. 87 00:03:12.280 --> 00:03:14.727 And the big challenge we had was producing 88 00:03:14.727 --> 00:03:18.420 enough small-scale mRNA to run our experiments. 89 00:03:18.420 --> 00:03:19.970 And what we're really trying to do 90 00:03:19.970 --> 00:03:22.000 is accelerate the pace of research, 91 00:03:22.000 --> 00:03:24.100 so that we can get as many drugs in the clinic 92 00:03:24.100 --> 00:03:25.850 as quickly as possible. 93 00:03:25.850 --> 00:03:27.833 And one of the big bottlenecks was having 94 00:03:27.833 --> 00:03:30.940 this mRNA for the scientists to run tests in. 95 00:03:30.940 --> 00:03:33.050 And so what we did is we put in place a ton 96 00:03:33.050 --> 00:03:34.780 of robotic automation, 97 00:03:34.780 --> 00:03:36.530 put in place a lot of digital systems 98 00:03:36.530 --> 00:03:39.780 and process automation and AI algorithms as well. 99 00:03:39.780 --> 00:03:42.370 And what went from maybe, you know, 100 00:03:42.370 --> 00:03:45.640 about 30 mRNAs manually produced in a given month, 101 00:03:45.640 --> 00:03:48.430 to a capacity of about a thousand in a month period. 102 00:03:48.430 --> 00:03:49.320 So without 103 00:03:49.320 --> 00:03:50.153 you know, 104 00:03:50.153 --> 00:03:51.560 significantly more resources 105 00:03:51.560 --> 00:03:54.400 and much better consistency in quality and so on. 106 00:03:54.400 --> 00:03:56.470 And so then I just kind of from there grew with the company 107 00:03:56.470 --> 00:03:58.320 and grew into this role that we have now 108 00:03:58.320 --> 00:04:00.250 where I'm applying those same ideas 109 00:04:00.250 --> 00:04:02.310 to the broader enterprise. 110 00:04:02.310 --> 00:04:03.560 That's great Dave, 111 00:04:03.560 --> 00:04:08.070 can you comment a bit on the spectrum of use cases 112 00:04:08.070 --> 00:04:10.610 that AI is being applied to here 113 00:04:10.610 --> 00:04:13.240 and is it really making a difference? 114 00:04:13.240 --> 00:04:14.620 For us what we've seen 115 00:04:14.620 --> 00:04:16.360 a lot of is in the research space 116 00:04:16.360 --> 00:04:17.790 and particularly in Moderna, 117 00:04:17.790 --> 00:04:20.700 that's been because that's where we digitized early. 118 00:04:20.700 --> 00:04:23.950 We see that putting in digital systems and processes 119 00:04:23.950 --> 00:04:26.990 to actually capture, you know, homogenous good data 120 00:04:26.990 --> 00:04:28.500 that can feed into that 121 00:04:28.500 --> 00:04:30.690 is obviously a really important first step, 122 00:04:30.690 --> 00:04:33.070 but it also lays the foundation of processes 123 00:04:33.070 --> 00:04:35.120 that are then amenable to these greater degrees 124 00:04:35.120 --> 00:04:36.310 of automation. 125 00:04:36.310 --> 00:04:39.490 So that's where we've seen a lot of that value is, you know, 126 00:04:39.490 --> 00:04:40.750 in this preclinical production, 127 00:04:40.750 --> 00:04:41.950 we have kind of high throughput. 128 00:04:41.950 --> 00:04:42.980 We have lots of data. 129 00:04:42.980 --> 00:04:45.250 We're able to start automating those steps 130 00:04:45.250 --> 00:04:48.180 and judgments that were previously done by humans. 131 00:04:48.180 --> 00:04:51.800 So one example is our mRNA sequence design. 132 00:04:51.800 --> 00:04:53.090 We're coding for some protein, 133 00:04:53.090 --> 00:04:54.970 which is an amino acid sequence, 134 00:04:54.970 --> 00:04:56.440 but there's a huge degeneracy 135 00:04:56.440 --> 00:04:59.500 of potential nucleotide sequences that could code for that. 136 00:04:59.500 --> 00:05:01.510 And so starting from an amino acid sequence, 137 00:05:01.510 --> 00:05:04.230 you have to figure out what's the ideal way to get there, 138 00:05:04.230 --> 00:05:05.170 right? 139 00:05:05.170 --> 00:05:06.470 And so what we have is algorithms 140 00:05:06.470 --> 00:05:07.970 that can do that translation 141 00:05:07.970 --> 00:05:09.350 in an optimal way. 142 00:05:09.350 --> 00:05:10.910 And then we have algorithms that can take one 143 00:05:10.910 --> 00:05:12.370 and then optimize it even further 144 00:05:12.370 --> 00:05:13.850 to make it better for production 145 00:05:13.850 --> 00:05:15.390 or to avoid things that we know 146 00:05:15.390 --> 00:05:19.450 are bad for this mRNA in production or for expression. 147 00:05:19.450 --> 00:05:21.880 And so we can integrate those into these live systems 148 00:05:21.880 --> 00:05:22.713 that we have 149 00:05:22.713 --> 00:05:24.130 so that scientists just press a button 150 00:05:24.130 --> 00:05:25.420 and the work is done for them, 151 00:05:25.420 --> 00:05:27.080 and they don't know what's going on behind the scenes, 152 00:05:27.080 --> 00:05:27.913 but then poof, 153 00:05:27.913 --> 00:05:30.471 the outcome is this better sequence for them. 154 00:05:30.471 --> 00:05:33.130 And then we've seen it with quality control steps as well. 155 00:05:33.130 --> 00:05:34.920 We're also doing some work right now 156 00:05:34.920 --> 00:05:38.180 with our clinical partners in the clinical operations space, 157 00:05:38.180 --> 00:05:40.810 in terms of like optimal trial planning. 158 00:05:40.810 --> 00:05:42.700 We're doing some work right now 159 00:05:42.700 --> 00:05:44.840 around our call center planning 160 00:05:44.840 --> 00:05:46.840 now that we're rolling our vaccine out 161 00:05:46.840 --> 00:05:48.250 across the whole world. 162 00:05:48.250 --> 00:05:49.660 More and more phone calls are coming in. 163 00:05:49.660 --> 00:05:51.300 And as we look to launching in new countries, 164 00:05:51.300 --> 00:05:53.410 we have to start planning our resources for that. 165 00:05:53.410 --> 00:05:54.950 So we're looking at machine learning models 166 00:05:54.950 --> 00:05:58.040 to help predict the forecast of these calls, 167 00:05:58.040 --> 00:06:00.110 so we can then staff up appropriately. 168 00:06:00.110 --> 00:06:01.720 So we just see it across, you know, 169 00:06:01.720 --> 00:06:03.670 a variety of different areas. 170 00:06:03.670 --> 00:06:05.100 You mentioned pressing the button, 171 00:06:05.100 --> 00:06:08.140 your scientists press the button and some trials happen. 172 00:06:08.140 --> 00:06:09.670 So what are these scientists thinking? 173 00:06:09.670 --> 00:06:12.240 I mean, you've suddenly taken away something that used to be 174 00:06:12.240 --> 00:06:13.520 something that they did 175 00:06:13.520 --> 00:06:16.270 and you're having AI do it. 176 00:06:16.270 --> 00:06:18.680 What's the reaction? Do they, are they thrilled? 177 00:06:18.680 --> 00:06:22.490 Are they despondent? You know, or somewhere in between? 178 00:06:22.490 --> 00:06:25.130 I would say closer to the thrilled side, 179 00:06:25.130 --> 00:06:26.200 usually how it works. 180 00:06:26.200 --> 00:06:27.180 You know we're a company 181 00:06:27.180 --> 00:06:30.320 that believes in giving people a lot of responsibility 182 00:06:30.320 --> 00:06:32.360 and, you know, people work really hard 183 00:06:32.360 --> 00:06:33.210 and what that leads to 184 00:06:33.210 --> 00:06:35.140 is people doing a lot of work, right? 185 00:06:35.140 --> 00:06:38.280 And so what often happens is folks will come to us and say, 186 00:06:38.280 --> 00:06:40.240 "Look, I'm doing this activity over and over. 187 00:06:40.240 --> 00:06:43.840 I would really love some help to automate this process." 188 00:06:43.840 --> 00:06:45.400 And so in that case they're thrilled, right? 189 00:06:45.400 --> 00:06:46.290 They don't want to be, 190 00:06:46.290 --> 00:06:48.360 you know, looking at some screen of data 191 00:06:48.360 --> 00:06:50.050 over and over and over again. 192 00:06:50.050 --> 00:06:53.140 They want to be doing something insightful and creative. 193 00:06:53.140 --> 00:06:54.900 And so that's where we really partner with them 194 00:06:54.900 --> 00:06:57.100 and take off that component of what they do. 195 00:06:58.050 --> 00:06:59.550 Dave, I want to build on that, 196 00:06:59.550 --> 00:07:01.150 because I think you're putting your finger 197 00:07:01.150 --> 00:07:02.360 on something quite interesting. 198 00:07:02.360 --> 00:07:05.332 And in addition to the financial impact 199 00:07:05.332 --> 00:07:07.820 that many get from AI 200 00:07:07.820 --> 00:07:10.520 productivity efficiency and all of that, 201 00:07:10.520 --> 00:07:12.550 you talked about some of those, Dave. 202 00:07:12.550 --> 00:07:16.080 There is an impact in overall organizational culture 203 00:07:16.080 --> 00:07:19.970 and you know teams being more collaborative, 204 00:07:19.970 --> 00:07:22.050 higher morale, happier, 205 00:07:22.050 --> 00:07:24.480 more confident, et cetera. 206 00:07:24.480 --> 00:07:25.910 Are those some of the things 207 00:07:25.910 --> 00:07:27.650 that you guys are seeing as well? 208 00:07:27.650 --> 00:07:29.190 Yeah, for sure. 209 00:07:29.190 --> 00:07:30.460 I think one of the surest signs of that 210 00:07:30.460 --> 00:07:32.650 is we get a lot of repeat customers. 211 00:07:32.650 --> 00:07:35.100 You know, if we do some particular algorithm for somebody, 212 00:07:35.100 --> 00:07:37.400 that person comes back with the next one or, 213 00:07:37.400 --> 00:07:40.120 you know, that their team comes back time and time again, 214 00:07:40.120 --> 00:07:43.610 we don't think about AI in the context of replacing humans. 215 00:07:43.610 --> 00:07:45.850 Like we always think about it in terms of 216 00:07:45.850 --> 00:07:48.190 this human-machine collaboration, 217 00:07:48.190 --> 00:07:49.700 because they're good at different things. 218 00:07:49.700 --> 00:07:50.533 You know, 219 00:07:50.533 --> 00:07:52.810 humans are really good at creativity and flexibility 220 00:07:52.810 --> 00:07:53.900 and insight, 221 00:07:53.900 --> 00:07:55.860 whereas machines are really good at, you know, 222 00:07:55.860 --> 00:07:58.223 precision and giving you the exact same result 223 00:07:58.223 --> 00:08:01.400 every single time and doing it at scale and speed. 224 00:08:01.400 --> 00:08:03.350 What we find the most successful projects 225 00:08:03.350 --> 00:08:05.440 are where we kind of put the two together, 226 00:08:05.440 --> 00:08:07.990 have the machine do the parts of the job that it's good at. 227 00:08:07.990 --> 00:08:10.520 Let the humans take over for the rest of that. 228 00:08:10.520 --> 00:08:13.300 So with this freedom, what have people done? 229 00:08:13.300 --> 00:08:15.275 You know you've opened up this time, 230 00:08:15.275 --> 00:08:16.108 (Shervin chuckles) 231 00:08:16.108 --> 00:08:18.524 What kind of new, you know... 232 00:08:18.524 --> 00:08:20.149 I got two shots of that. 233 00:08:20.149 --> 00:08:21.212 (all laugh) 234 00:08:21.212 --> 00:08:22.045 What people have done... 235 00:08:22.045 --> 00:08:23.200 Yeah actually there's at least one product 236 00:08:23.200 --> 00:08:24.524 that's in the market now isn't there? 237 00:08:24.524 --> 00:08:25.630 I think I've heard something 238 00:08:25.630 --> 00:08:26.463 in the news. 239 00:08:26.463 --> 00:08:28.610 Just there's one. Yeah. You know, 240 00:08:28.610 --> 00:08:30.810 I always like to joke that work is like a gas 241 00:08:30.810 --> 00:08:33.140 that always expands to fill the container. 242 00:08:33.140 --> 00:08:34.850 So if you take something off somebody's plate, 243 00:08:34.850 --> 00:08:36.360 there's all this mountain of work 244 00:08:36.360 --> 00:08:38.700 that they didn't even realize just wasn't being done. 245 00:08:38.700 --> 00:08:41.850 And so people are always relieved to then go on and find, 246 00:08:41.850 --> 00:08:43.100 you know, the next mountain to climb 247 00:08:43.100 --> 00:08:44.400 and the next thing to do. 248 00:08:44.400 --> 00:08:45.630 But what are these kinds of things 249 00:08:45.630 --> 00:08:49.040 like, you know how are people choosing how to, you know, 250 00:08:49.040 --> 00:08:51.110 expand to fill that space? 251 00:08:51.110 --> 00:08:52.970 Well, if you think of the examples, you know, 252 00:08:52.970 --> 00:08:55.080 like the preclinical quality control steps 253 00:08:55.080 --> 00:08:56.290 that we've automated, 254 00:08:56.290 --> 00:08:59.170 the reality is, you know, 255 00:08:59.170 --> 00:09:01.690 one operator stretched over a huge amount of work is, 256 00:09:01.690 --> 00:09:02.540 it's really hard for them 257 00:09:02.540 --> 00:09:05.620 to really do really in-depth inspection of these samples. 258 00:09:05.620 --> 00:09:07.100 And so by taking off, you know, 259 00:09:07.100 --> 00:09:07.933 a bulk of that work, 260 00:09:07.933 --> 00:09:10.250 80-90% for the algorithm to do that, 261 00:09:10.250 --> 00:09:12.940 we're able to do just do a better more thorough job 262 00:09:12.940 --> 00:09:15.410 of inspecting the samples that are left. 263 00:09:15.410 --> 00:09:16.400 It also means that we're not hiring 264 00:09:16.400 --> 00:09:17.320 a whole bunch of other people 265 00:09:17.320 --> 00:09:19.870 just to go look at, you know, screens of data. 266 00:09:19.870 --> 00:09:22.300 It's a bit of an immediate gain for the people who are there 267 00:09:22.300 --> 00:09:23.870 and then kind of this longer-term gain 268 00:09:23.870 --> 00:09:25.590 on our headcount plans. 269 00:09:25.590 --> 00:09:27.320 Some folks, you know, talk about AI 270 00:09:27.320 --> 00:09:29.020 in the pharma space being like, 271 00:09:29.020 --> 00:09:31.360 "I just want an algorithm that can predict from 272 00:09:31.360 --> 00:09:33.320 the you know, the structure of a small molecule, 273 00:09:33.320 --> 00:09:34.631 the efficacy in humans" right? 274 00:09:34.631 --> 00:09:37.600 Like that's the entire drug discovery process. 275 00:09:37.600 --> 00:09:38.740 That's just not going to happen. 276 00:09:38.740 --> 00:09:40.630 That's completely unrealistic. 277 00:09:40.630 --> 00:09:43.080 So, you know, we just think about the fact that, you know, 278 00:09:43.080 --> 00:09:44.350 there are countless processes, it's a 279 00:09:44.350 --> 00:09:47.220 very complicated process to bring something to market. 280 00:09:47.220 --> 00:09:50.300 And there are just numerous opportunities along the way. 281 00:09:50.300 --> 00:09:51.920 Even within a specific use case, 282 00:09:51.920 --> 00:09:54.160 you're rarely using one AI algorithm. 283 00:09:54.160 --> 00:09:55.980 And so often, you know, for this part of the problem, 284 00:09:55.980 --> 00:09:57.010 I need to use this algorithm, 285 00:09:57.010 --> 00:09:59.030 for this I need to use another. 286 00:09:59.030 --> 00:10:00.870 Dave I want to ask you something about 287 00:10:00.870 --> 00:10:03.070 the talent base and people. 288 00:10:03.070 --> 00:10:06.570 You commented that Moderna is a kind of company 289 00:10:06.570 --> 00:10:08.920 that likes to give people a lot of freedom, 290 00:10:08.920 --> 00:10:11.270 you know, highly motivated, 291 00:10:11.270 --> 00:10:16.200 smart, ambitious team working to do the best they can. 292 00:10:16.200 --> 00:10:18.930 How do you bring in and cultivate that talent? 293 00:10:18.930 --> 00:10:23.710 And what are you finding to be some of the lessons learned 294 00:10:23.710 --> 00:10:27.470 in terms of how to build a high performing team? 295 00:10:27.470 --> 00:10:28.540 It's a good question. 296 00:10:28.540 --> 00:10:29.810 I don't know that, you know, 297 00:10:29.810 --> 00:10:31.610 if we look across the company as a whole, 298 00:10:31.610 --> 00:10:34.780 there is one particular place where we hire people. 299 00:10:34.780 --> 00:10:37.820 We get people from biotechs, you know, 300 00:10:37.820 --> 00:10:41.030 five people, to pharmas of a hundred thousand people, 301 00:10:41.030 --> 00:10:41.890 and everywhere in between 302 00:10:41.890 --> 00:10:44.150 inside the industry and outside the industry. 303 00:10:44.150 --> 00:10:45.750 I think for us it's always about finding 304 00:10:45.750 --> 00:10:47.110 the right person for the job 305 00:10:47.110 --> 00:10:50.320 regardless of where they come from and their background. 306 00:10:50.320 --> 00:10:51.620 I think the important thing for us 307 00:10:51.620 --> 00:10:54.920 is to make sure that we set expectations appropriately 308 00:10:54.920 --> 00:10:55.910 as we bring them in. 309 00:10:55.910 --> 00:10:58.320 And we say, look this is a digital company. 310 00:10:58.320 --> 00:11:00.450 We're really bold. We're really ambitious. 311 00:11:00.450 --> 00:11:03.080 We have really high quality standards. 312 00:11:03.080 --> 00:11:05.280 And if we set those expectations really high, 313 00:11:05.280 --> 00:11:07.450 you know, it does start to self-select a lot of the people 314 00:11:07.450 --> 00:11:10.090 who want to come through that process. 315 00:11:10.090 --> 00:11:11.700 Do you want to flip over and talk you know, 316 00:11:11.700 --> 00:11:13.400 you mentioned some of 317 00:11:13.400 --> 00:11:16.680 the infrastructure I would call it that you put in place 318 00:11:16.680 --> 00:11:20.250 that suddenly the world benefited from a few months ago. 319 00:11:20.250 --> 00:11:22.250 How do people know to get those things set up 320 00:11:22.250 --> 00:11:23.083 in the first place? 321 00:11:23.083 --> 00:11:24.510 You mentioned being able to scale from, 322 00:11:24.510 --> 00:11:27.650 I think you know, 30 to a thousand difference. 323 00:11:27.650 --> 00:11:30.010 How did you know that was the direction 324 00:11:30.010 --> 00:11:33.800 or that was the vision to get those things set up? 325 00:11:33.800 --> 00:11:35.140 Yeah, that's a great point. 326 00:11:35.140 --> 00:11:37.140 The whole COVID vaccine development, 327 00:11:37.140 --> 00:11:38.820 you know, we're immensely proud of the work 328 00:11:38.820 --> 00:11:39.680 that we've done there. 329 00:11:39.680 --> 00:11:41.840 And we're immensely proud of the superhuman effort 330 00:11:41.840 --> 00:11:43.240 that our people went through 331 00:11:43.240 --> 00:11:45.670 to bring it to market so quickly. 332 00:11:45.670 --> 00:11:47.700 But a lot of it was built on just what you said, 333 00:11:47.700 --> 00:11:50.120 this infrastructure that we had put in place 334 00:11:50.120 --> 00:11:53.130 where we didn't build algorithms specifically for COVID. 335 00:11:53.130 --> 00:11:55.420 We just put them through the same pipeline of activity 336 00:11:55.420 --> 00:11:56.253 that we've been doing. 337 00:11:56.253 --> 00:11:58.440 We just turned it as fast as we could. 338 00:11:58.440 --> 00:12:01.410 When we think about everything we do at Moderna, 339 00:12:01.410 --> 00:12:04.170 we think about this platform capability. 340 00:12:04.170 --> 00:12:05.003 You know, 341 00:12:05.003 --> 00:12:06.090 we were never going to make one drug, 342 00:12:06.090 --> 00:12:07.140 that was never the plan. 343 00:12:07.140 --> 00:12:10.480 The plan was always to make a whole platform around mRNA. 344 00:12:10.480 --> 00:12:12.640 Because since it's an information-based product, 345 00:12:12.640 --> 00:12:15.250 all you do is change the information encoded in the molecule 346 00:12:15.250 --> 00:12:17.410 and you have a completely different drug. 347 00:12:17.410 --> 00:12:19.000 We knew that if you get one in the market, 348 00:12:19.000 --> 00:12:21.025 you can get any number of them to the market. 349 00:12:21.025 --> 00:12:23.560 And so all the decisions we made around 350 00:12:23.560 --> 00:12:25.050 how we designed the company 351 00:12:25.050 --> 00:12:27.380 and how we designed the digital infrastructure 352 00:12:27.380 --> 00:12:29.440 was all around this platform notion 353 00:12:29.440 --> 00:12:31.380 that we're not going to build this for one thing, 354 00:12:31.380 --> 00:12:32.670 we're going to build a solution 355 00:12:32.670 --> 00:12:34.630 that services this whole platform. 356 00:12:34.630 --> 00:12:36.470 And so that's exactly why we built, you know, 357 00:12:36.470 --> 00:12:37.910 this early preclinical stuff 358 00:12:37.910 --> 00:12:39.620 where, you know, we can just crank 359 00:12:39.620 --> 00:12:40.560 through quite a few of these. 360 00:12:40.560 --> 00:12:43.340 That's why we built these algorithms to automate activities 361 00:12:43.340 --> 00:12:45.930 anytime where we see something where we know that, 362 00:12:45.930 --> 00:12:46.763 you know, 363 00:12:46.763 --> 00:12:48.527 scale and making it parallel was going to improve things, 364 00:12:48.527 --> 00:12:51.300 we put in place this process. 365 00:12:51.300 --> 00:12:52.840 The proof is certainly in the pudding. 366 00:12:52.840 --> 00:12:55.490 One thing that I'm kind of finding fascinating is how 367 00:12:56.420 --> 00:12:57.570 like normal this all... 368 00:12:57.570 --> 00:12:59.620 like I guess I'm just surprised 369 00:12:59.620 --> 00:13:01.720 at how much that seems to be part of your, 370 00:13:02.740 --> 00:13:04.775 can I use the word DNA here in this, 371 00:13:04.775 --> 00:13:06.540 (Sam chuckles) 372 00:13:06.540 --> 00:13:08.899 all right RNA, Is part of mRNA 373 00:13:11.470 --> 00:13:12.303 Yeah, no, it's true. 374 00:13:12.303 --> 00:13:13.657 You know, 375 00:13:13.657 --> 00:13:15.010 we were founded as a digital biotech 376 00:13:15.010 --> 00:13:17.010 and like a lot of companies say things 377 00:13:17.010 --> 00:13:17.843 and put taglines on stuff, 378 00:13:17.843 --> 00:13:19.410 but we really meant it. 379 00:13:19.410 --> 00:13:21.190 And we have pushed on this for many years 380 00:13:21.190 --> 00:13:23.210 and we've built out this for many years. 381 00:13:23.210 --> 00:13:24.630 Yeah it's the platform we built 382 00:13:24.630 --> 00:13:26.280 and now, now he's running. 383 00:13:26.280 --> 00:13:27.280 Yeah. 384 00:13:27.280 --> 00:13:28.990 And it's the platform approach we take to our data science 385 00:13:28.990 --> 00:13:30.810 and AI projects as well. 386 00:13:30.810 --> 00:13:33.110 I hear a lot of struggles from folks around 387 00:13:33.110 --> 00:13:36.090 like, great I built a model in a Jupyter Notebook 388 00:13:36.090 --> 00:13:37.960 now what do I do with it? Right? 389 00:13:37.960 --> 00:13:41.020 Because there's all this data cleansing and data curation 390 00:13:41.020 --> 00:13:43.600 to even get it to be in a useful state. 391 00:13:43.600 --> 00:13:46.230 And then they don't know where to go from it to deploy it, 392 00:13:46.230 --> 00:13:47.063 right? 393 00:13:47.063 --> 00:13:49.370 And we took the same platform approach 394 00:13:49.370 --> 00:13:50.590 to our data science activities. 395 00:13:50.590 --> 00:13:53.270 We spent a lot of time on the data curation, 396 00:13:53.270 --> 00:13:54.840 the data ingestion to make sure the data 397 00:13:54.840 --> 00:13:56.990 is good to be used right away. 398 00:13:56.990 --> 00:13:59.540 And then we put a lot of tooling and infrastructure in place 399 00:13:59.540 --> 00:14:02.120 to get those models into production and integrated. 400 00:14:02.120 --> 00:14:03.280 This platform mentality 401 00:14:03.280 --> 00:14:05.130 is just so ingrained in how we think. 402 00:14:06.130 --> 00:14:07.990 Take us back to, you know, 403 00:14:07.990 --> 00:14:11.138 early in the COVID race for a vaccine, 404 00:14:11.138 --> 00:14:11.971 you know, 405 00:14:11.971 --> 00:14:14.150 what was it like being part of that team 406 00:14:14.150 --> 00:14:15.260 and a part of that process? 407 00:14:15.260 --> 00:14:17.930 I mean, what were the emotions like when the algorithms 408 00:14:17.930 --> 00:14:20.840 when the people find something that seems to work 409 00:14:20.840 --> 00:14:22.270 or it seems promising. 410 00:14:22.270 --> 00:14:24.390 Does that lead to a massive appetite 411 00:14:24.390 --> 00:14:27.410 for more artificial intelligence and more algorithms? 412 00:14:27.410 --> 00:14:30.420 I guess to tell us a little bit about that story. 413 00:14:30.420 --> 00:14:32.870 I think if you look at how people felt 414 00:14:32.870 --> 00:14:33.930 in general at the time, 415 00:14:33.930 --> 00:14:37.707 I mean it was a real sense of honor and pride right? 416 00:14:37.707 --> 00:14:40.330 We felt very uniquely positioned. 417 00:14:40.330 --> 00:14:43.210 We even spent a decade getting to this point 418 00:14:43.210 --> 00:14:45.210 and putting all of this infrastructure in place 419 00:14:45.210 --> 00:14:47.210 and putting things in the clinic before this, 420 00:14:47.210 --> 00:14:48.210 to get to this moment. 421 00:14:48.210 --> 00:14:51.510 And so we just felt truly honored to be in that position. 422 00:14:51.510 --> 00:14:53.500 And for those of us on the digital side, 423 00:14:53.500 --> 00:14:55.870 who have kind of contributed to this and built it. 424 00:14:55.870 --> 00:14:57.020 You know, this is why we did it. 425 00:14:57.020 --> 00:14:58.010 This is why we're here 426 00:14:58.010 --> 00:15:00.380 is to help bring as many patients 427 00:15:00.380 --> 00:15:02.930 as fast lanes as quickly and safely as possible 428 00:15:02.930 --> 00:15:04.100 to the world. 429 00:15:04.100 --> 00:15:05.250 But there was always the question of 430 00:15:05.250 --> 00:15:07.320 would this thing work in the real world? 431 00:15:07.320 --> 00:15:09.530 You know, and that's where the proof came 432 00:15:09.530 --> 00:15:10.560 in the clinical data 433 00:15:10.560 --> 00:15:12.090 and we're all anxiously waiting 434 00:15:12.090 --> 00:15:14.710 like everybody else to see that readout. 435 00:15:14.710 --> 00:15:19.640 Was AI always front and center at Moderna 436 00:15:19.640 --> 00:15:23.130 or has it become more critical 437 00:15:23.130 --> 00:15:26.313 as a pillar of growth and innovation over time? 438 00:15:27.240 --> 00:15:29.450 Yeah I think it's always been there 439 00:15:29.450 --> 00:15:31.960 that we probably didn't call it that in the early days 440 00:15:31.960 --> 00:15:34.070 it's become obviously much more of, you know, 441 00:15:34.070 --> 00:15:36.380 a hot marketing term than it used to be. 442 00:15:36.380 --> 00:15:39.700 But the notion of algorithms taking over decision making, 443 00:15:39.700 --> 00:15:41.300 and you know, data science capability 444 00:15:41.300 --> 00:15:42.960 was absolutely always there. 445 00:15:42.960 --> 00:15:45.230 We were very thoughtful about how we 446 00:15:45.230 --> 00:15:46.610 built this digital landscape, 447 00:15:46.610 --> 00:15:48.720 such that we're collecting structured data 448 00:15:48.720 --> 00:15:50.190 across all these steps, 449 00:15:50.190 --> 00:15:51.630 knowing full well that what we want to do 450 00:15:51.630 --> 00:15:54.010 is then turn those into algorithms to do things. 451 00:15:54.010 --> 00:15:56.170 So it was very purposeful for that. 452 00:15:56.170 --> 00:15:58.990 But I do think it's also come into a greater focus, right? 453 00:15:58.990 --> 00:16:00.690 Because we've seen the power of it 454 00:16:00.690 --> 00:16:02.250 very recently obviously we've seen 455 00:16:02.250 --> 00:16:03.627 how this digital infrastructure 456 00:16:03.627 --> 00:16:04.780 and how these algorithms 457 00:16:04.780 --> 00:16:06.760 can really help push things forward. 458 00:16:06.760 --> 00:16:09.100 And so it's got that kind of a renewed focus 459 00:16:09.100 --> 00:16:10.870 and importance in the company. 460 00:16:10.870 --> 00:16:12.760 We tend not to be a company of half measures. 461 00:16:12.760 --> 00:16:14.210 So when we decided we're going to do something, 462 00:16:14.210 --> 00:16:15.051 we're going to do it. 463 00:16:15.051 --> 00:16:17.970 And so it's been a very strong message 464 00:16:17.970 --> 00:16:19.349 from our senior leadership 465 00:16:19.349 --> 00:16:21.730 about this is the future of the company 466 00:16:21.730 --> 00:16:24.500 is in injecting digital and AI into everything we do. 467 00:16:24.500 --> 00:16:25.880 And under no uncertain terms, 468 00:16:25.880 --> 00:16:28.580 this is happening to the point that, you know, 469 00:16:28.580 --> 00:16:29.610 as we think about the fact that 470 00:16:29.610 --> 00:16:31.047 we are growing really fast as a company, 471 00:16:31.047 --> 00:16:32.650 you know, we just doubled, 472 00:16:32.650 --> 00:16:34.410 we're probably going to double again. 473 00:16:34.410 --> 00:16:35.900 We're bringing in a lot of new folks 474 00:16:35.900 --> 00:16:37.670 from outside the company to grow, 475 00:16:37.670 --> 00:16:39.920 who are not necessarily familiar with this digital culture 476 00:16:39.920 --> 00:16:41.100 that we've had. 477 00:16:41.100 --> 00:16:43.060 And so what we're working on right now 478 00:16:43.060 --> 00:16:46.060 is actually developing what we're calling an AI academy, 479 00:16:46.060 --> 00:16:49.960 which we intend to be a very thorough in-depth training 480 00:16:49.960 --> 00:16:51.160 for our company. 481 00:16:51.160 --> 00:16:54.410 So from people who would use and interact with AI models 482 00:16:54.410 --> 00:16:55.890 in their daily basis 483 00:16:55.890 --> 00:16:57.500 to senior leaders who would be responsible, 484 00:16:57.500 --> 00:17:00.760 kind of a portfolio of potential projects in their areas. 485 00:17:00.760 --> 00:17:02.490 And that just shows the level of serious commitment 486 00:17:02.490 --> 00:17:04.020 we have about this. 487 00:17:04.020 --> 00:17:07.860 We were built in this concept of having a smaller company 488 00:17:07.860 --> 00:17:10.300 that's very agile and can move fast. 489 00:17:10.300 --> 00:17:12.820 So we see digital as a key enabler for that 490 00:17:12.820 --> 00:17:14.750 and AI as a key enabler for that. 491 00:17:14.750 --> 00:17:16.760 And so the hope is that that helps us to compete 492 00:17:16.760 --> 00:17:18.700 in ways that other companies can't. 493 00:17:18.700 --> 00:17:21.130 And that is certainly the intention here. 494 00:17:21.130 --> 00:17:23.110 Well Dave thanks so much for talking with us today. 495 00:17:23.110 --> 00:17:23.943 We really enjoyed, 496 00:17:23.943 --> 00:17:26.760 I mean, you mentioned Moderna hires smart people 497 00:17:26.760 --> 00:17:29.000 and we know that from the sample size of one 498 00:17:29.000 --> 00:17:30.190 that's clearly true. 499 00:17:30.190 --> 00:17:32.500 Thanks for taking the time to talk with us today. 500 00:17:32.500 --> 00:17:33.460 Thank you so much. 501 00:17:33.460 --> 00:17:35.917 Absolutely guys, I really appreciate it. 502 00:17:35.917 --> 00:17:38.500 (upbeat music) 503 00:17:41.150 --> 00:17:43.300 Sam that was an awesome conversation with Dave. 504 00:17:43.300 --> 00:17:44.470 What do you think? 505 00:17:44.470 --> 00:17:45.310 Yep. Impressive. 506 00:17:45.310 --> 00:17:46.170 I'm glad he's around. 507 00:17:46.170 --> 00:17:48.320 I'm glad Moderna is around. 508 00:17:48.320 --> 00:17:49.153 That's right. 509 00:17:49.153 --> 00:17:51.000 He's not... 510 00:17:51.000 --> 00:17:54.580 lip service to buzzwords and this that 511 00:17:54.580 --> 00:17:58.230 the other he's just like, yeah, we started this way. 512 00:17:58.230 --> 00:17:59.520 That's why we're doing it. 513 00:17:59.520 --> 00:18:02.790 We would not have existed without digital 514 00:18:02.790 --> 00:18:04.900 and AI and data and analytics. 515 00:18:04.900 --> 00:18:05.733 Of course, it's real. 516 00:18:05.733 --> 00:18:06.670 Like, that's what we are. 517 00:18:06.670 --> 00:18:08.844 I mean, he said Moderna is a digital company, 518 00:18:08.844 --> 00:18:09.730 that's what he said. 519 00:18:09.730 --> 00:18:10.820 It's just part of their process. 520 00:18:10.820 --> 00:18:11.820 I mean, some of the questions, 521 00:18:11.820 --> 00:18:13.270 it didn't even occur to him 522 00:18:13.270 --> 00:18:14.560 that it was artificial intelligence 523 00:18:14.560 --> 00:18:16.880 and that's just the way they do things. 524 00:18:16.880 --> 00:18:20.640 I wonder if that's like, the new industry after industry, 525 00:18:20.640 --> 00:18:22.450 we're going to see the Moderna-type approach 526 00:18:22.450 --> 00:18:23.610 has come into industries 527 00:18:23.610 --> 00:18:27.160 and just be the dominant, you know, 528 00:18:27.160 --> 00:18:29.060 the vestiges of historical 529 00:18:29.060 --> 00:18:30.490 we've been around for a hundred years 530 00:18:30.490 --> 00:18:33.570 are almost a liability versus a plus. 531 00:18:33.570 --> 00:18:35.880 I think this sort of contrast, you know, Sam, 532 00:18:35.880 --> 00:18:39.270 that you were sort of trying to get at, which is like, 533 00:18:39.270 --> 00:18:41.050 how come it's so easy for you guys? 534 00:18:41.050 --> 00:18:44.390 And what about the pre-POST and the transformation? 535 00:18:44.390 --> 00:18:46.830 I mean, these guys, you know, he's like, well, 536 00:18:46.830 --> 00:18:48.370 we actually started this way. 537 00:18:48.370 --> 00:18:49.573 You know, we said, we want to be... 538 00:18:49.573 --> 00:18:50.620 They started POST. 539 00:18:50.620 --> 00:18:52.240 Yeah we started POST. 540 00:18:52.240 --> 00:18:53.886 We wanted to be agile. 541 00:18:53.886 --> 00:18:55.380 We wanted to be small. 542 00:18:55.380 --> 00:19:00.380 We wanted to do a lot more with everything that we had. 543 00:19:00.670 --> 00:19:03.840 And so that had to be platform centric, data centric, 544 00:19:03.840 --> 00:19:05.490 AI centric, and that's how we built. 545 00:19:05.490 --> 00:19:07.120 And so AI is everywhere. 546 00:19:07.120 --> 00:19:08.640 Why are you surprised Sam? 547 00:19:08.640 --> 00:19:10.290 That AI is everywhere? Of course, it's everywhere. 548 00:19:10.290 --> 00:19:14.630 We do it for planning and trials and sequencing 549 00:19:14.630 --> 00:19:17.130 and it's quite energizing 550 00:19:17.130 --> 00:19:18.870 and intriguing how it's just like 551 00:19:18.870 --> 00:19:21.360 a very different mind towards AI. 552 00:19:21.360 --> 00:19:22.760 Right and I know that 553 00:19:22.760 --> 00:19:24.140 we don't want to make everything AI. 554 00:19:24.140 --> 00:19:25.480 There's a lot that is going on there. 555 00:19:25.480 --> 00:19:27.130 That's not artificial intelligence you know, 556 00:19:27.130 --> 00:19:29.030 I don't want to paint it as entirely, 557 00:19:29.030 --> 00:19:31.380 but that certainly was a big chunk of this, 558 00:19:31.380 --> 00:19:32.660 of the speed story here. 559 00:19:32.660 --> 00:19:34.844 And it's pretty fascinating. 560 00:19:34.844 --> 00:19:36.180 (upbeat music) 561 00:19:36.180 --> 00:19:37.210 Thanks for joining us 562 00:19:37.210 --> 00:19:40.030 for this bonus episode of "Me, Myself, and AI." 563 00:19:40.030 --> 00:19:40.960 We'll be back in the Fall 564 00:19:40.960 --> 00:19:43.290 with new episodes for season three. 565 00:19:43.290 --> 00:19:45.595 In the meantime, stay in touch with us on LinkedIn. 566 00:19:45.595 --> 00:19:48.480 We've created a group called AI for Leaders, 567 00:19:48.480 --> 00:19:50.840 specifically for audience members like you. 568 00:19:50.840 --> 00:19:53.140 You can catch up on back episodes of the show, 569 00:19:53.140 --> 00:19:54.900 meet show creators and hosts, 570 00:19:54.900 --> 00:19:57.020 tell us what you want to hear about in season three, 571 00:19:57.020 --> 00:19:59.360 and discuss key issues about AI implementation 572 00:19:59.360 --> 00:20:00.675 with other like-minded people. 573 00:20:00.675 --> 00:20:05.675 Find the group at www.mitsmr.com/AIforLeaders 574 00:20:06.330 --> 00:20:07.810 which will redirect you to LinkedIn, 575 00:20:07.810 --> 00:20:09.590 where you can request to join. 576 00:20:09.590 --> 00:20:11.167 We'll put that link in the show notes as well, 577 00:20:11.167 --> 00:20:13.006 and we hope to see you there. 578 00:20:13.006 --> 00:20:15.589 (upbeat music)