WEBVTT 1 00:00:00.150 --> 00:00:03.210 It's my pleasure to be in Boston today with you, David. David, 2 00:00:03.210 --> 00:00:07.380 you're one of the managing directors and partners of BCG X. 3 00:00:07.740 --> 00:00:10.260 You started your career, if I may say, 4 00:00:10.260 --> 00:00:12.780 with a PhD in satellites. 5 00:00:13.140 --> 00:00:17.250 Why on earth does one start a career with a PhD in satellites? 6 00:00:17.940 --> 00:00:22.890 That's a super question. I get that question a lot, Silvain. 7 00:00:23.010 --> 00:00:27.240 And for me as a little boy, I fell in love with space. 8 00:00:27.690 --> 00:00:31.590 The space shuttle, the space program was in its heyday when I was a child, 9 00:00:31.590 --> 00:00:36.210 and so I had a dream of becoming an astronaut from my earliest memory. 10 00:00:36.750 --> 00:00:40.410 And I also loved maps. I grew up all over the world. 11 00:00:40.470 --> 00:00:42.810 My family traveled because my father was a pilot, 12 00:00:43.320 --> 00:00:47.670 and so I saw a lot of the world and used maps to figure out where I was and what 13 00:00:47.670 --> 00:00:48.570 was going on around me. 14 00:00:48.900 --> 00:00:52.800 So that seed was planted early and I started my career in the Navy. 15 00:00:53.010 --> 00:00:55.110 And if anything, that just made me more curious. 16 00:00:55.110 --> 00:00:59.340 I was learning about the world at sea using maps and charts and imagery. 17 00:00:59.700 --> 00:01:01.110 And so when I got out of the Navy, 18 00:01:01.110 --> 00:01:05.130 the first thing I did is sign up for a grad program to understand what is going 19 00:01:05.130 --> 00:01:09.720 on under the hood that makes our computer maps and imagery work. 20 00:01:10.110 --> 00:01:14.970 And a couple years in, a lot of us that master's became a PhD. 21 00:01:15.090 --> 00:01:18.420 And I was really obsessed, I would say for about four or five years there. 22 00:01:18.510 --> 00:01:23.430 My master's thesis before I did the PhD was around the Appalachian Trail and my 23 00:01:23.430 --> 00:01:26.940 thesis, I corralled a bunch of other grad students in hindsight, 24 00:01:26.940 --> 00:01:28.560 a little bit like an XO, 25 00:01:28.770 --> 00:01:33.630 and we all teamed together on proving the threat to the Appalachian trail around 26 00:01:33.630 --> 00:01:35.850 deforestation using satellites. 27 00:01:35.940 --> 00:01:39.600 And we presented our paper at all the right conferences and talked to the park 28 00:01:39.600 --> 00:01:42.630 service. And I think I just thought, well, things will change. 29 00:01:43.470 --> 00:01:46.530 You put the information, you do the analysis, you do the work, 30 00:01:46.740 --> 00:01:50.340 like a lot of scientists think, I think, and then the world will. Of course, 31 00:01:50.490 --> 00:01:53.880 nothing happened. And I think that stuck with me. 32 00:01:54.210 --> 00:01:58.440 It was probably part of the reason why I realized that academia wasn't going to 33 00:01:58.440 --> 00:02:00.450 be the perfect right fit for me. 34 00:02:00.570 --> 00:02:04.650 But what's really exciting about X is that the value proposition is 35 00:02:05.400 --> 00:02:09.000 you can't and shouldn't let go of the scientist tether. 36 00:02:09.060 --> 00:02:13.860 You really do have to step into an athlete role and help clients 37 00:02:13.860 --> 00:02:17.250 understand where the value is. And a lot of times, 38 00:02:17.250 --> 00:02:21.270 you have to actually be thinking pretty orthogonal to what a scientist would 39 00:02:21.270 --> 00:02:24.330 think around practicality, effectiveness, and speed. 40 00:02:24.630 --> 00:02:28.950 Fascinating. This is all about innovation. Innovation and science altogether. 41 00:02:28.950 --> 00:02:31.320 That's an important part of your job. David, 42 00:02:31.350 --> 00:02:35.160 how do you see that sort of intersect between science and business evolving? 43 00:02:36.060 --> 00:02:40.650 I think the thing I'm most excited about is this intersection between generative 44 00:02:40.650 --> 00:02:43.800 AI technologies and satellite and geospatial. 45 00:02:44.130 --> 00:02:48.600 And we wouldn't have generative AI without a strong commercial engine driving 46 00:02:48.600 --> 00:02:49.470 that movement. 47 00:02:50.700 --> 00:02:54.450 And we're at this really amazing point where for the first time, 48 00:02:54.450 --> 00:02:58.560 the space-born perspective is the velocity, the volume, 49 00:02:58.590 --> 00:03:02.500 the quality and variety of our perspective from overhead is like it's never been 50 00:03:02.500 --> 00:03:03.333 before. 51 00:03:03.520 --> 00:03:07.240 And what we've been missing is a brain to sort of make sense of all of that. 52 00:03:07.330 --> 00:03:11.020 But I think what's coming next is a new wave of generative AI that's trained not 53 00:03:11.020 --> 00:03:16.000 only on human language and text of the internet, but the imagery of the planet. 54 00:03:16.000 --> 00:03:18.580 And I think the potential of that is pretty limitless. 55 00:03:18.880 --> 00:03:23.710 What I learned is that some of the most important data assets 56 00:03:23.770 --> 00:03:26.770 and client relationships are sitting with some of the biggest, 57 00:03:26.770 --> 00:03:30.910 most important companies in the world. And this way we have of helping them 58 00:03:30.970 --> 00:03:34.930 accelerate is something that I'm very passionate about. And, 59 00:03:35.560 --> 00:03:36.130 as you know, 60 00:03:36.130 --> 00:03:39.700 I think there's two things in the adventure that are happening right now. 61 00:03:39.790 --> 00:03:42.460 One is this amazing partnership we have with NASA, 62 00:03:42.910 --> 00:03:47.740 really right at the heart of how NASA makes sense of weather 63 00:03:47.740 --> 00:03:48.880 and satellite imagery. 64 00:03:49.090 --> 00:03:53.920 And working on real foundation models there with NASA aims has been a 65 00:03:53.920 --> 00:03:54.940 highlight of my career. 66 00:03:55.090 --> 00:03:58.990 The second thing that's going on right now is helping one of the most iconic 67 00:03:59.710 --> 00:04:03.880 agriculture brands in the world really learn the language of farming in their 68 00:04:03.880 --> 00:04:08.410 generative AI adventure and what that might do for the way we make food. 69 00:04:08.830 --> 00:04:11.770 And in general, if you ask about a mission, I mean, for me, 70 00:04:12.070 --> 00:04:16.780 it's so obvious that we have so much to do in terms of climate change 71 00:04:16.870 --> 00:04:19.750 and changing the way that we operate out in the world. 72 00:04:20.050 --> 00:04:24.730 And this idea of doing the things that we're used to doing in industrial goods 73 00:04:24.730 --> 00:04:27.310 inside of factories, robots, and automation, 74 00:04:27.550 --> 00:04:31.120 bringing that outside into the wild and the way that we make food, 75 00:04:31.120 --> 00:04:33.580 the way that we make timber, the way that we mine. 76 00:04:33.970 --> 00:04:37.510 That's what I'm really passionate about. And that's where satellites matter, 77 00:04:37.510 --> 00:04:39.250 not just because the imagery is beautiful, 78 00:04:39.970 --> 00:04:43.300 but because that perspective helps us just in time, 79 00:04:43.300 --> 00:04:46.090 maybe given what we need to do in terms of transition, 80 00:04:46.330 --> 00:04:47.740 helps us start to get that right. 81 00:04:49.070 --> 00:04:49.250 Thank you, David.