1 00:00:00,000 --> 00:00:02,357 To discuss the technological trajectory 2 00:00:02,440 --> 00:00:05,317 and strategic implications of AI, the 3 00:00:05,400 --> 00:00:07,837 future of America's innovation power, 4 00:00:07,920 --> 00:00:10,357 and changes in warfare. Please welcome 5 00:00:10,440 --> 00:00:14,317 Dr. Eric Schmidt, chair of SCSP, and CEO 6 00:00:14,400 --> 00:00:16,957 of Relativity Space, in conversation 7 00:00:17,040 --> 00:00:19,077 with Tom Shanker, director for the 8 00:00:19,160 --> 00:00:22,997 project of media and national security. 9 00:00:23,080 --> 00:00:26,557 Uh thank you so much and good afternoon everyone. 10 00:00:26,640 --> 00:00:31,317 Um It's my great honor to moderate this discussion with Dr. Eric Schmidt. There 11 00:00:31,400 --> 00:00:33,117 are few people who've shaped the 12 00:00:33,200 --> 00:00:35,437 technology landscape of the last half 13 00:00:35,520 --> 00:00:38,357 century century and are still shaping 14 00:00:38,440 --> 00:00:40,517 for the future as our speaker Dr. 15 00:00:40,600 --> 00:00:45,037 Schmidt. He led Google from a promising startup to one of the most consequential 16 00:00:45,120 --> 00:00:49,237 companies in history. He then chaired the Defense Innovation Board and the 17 00:00:49,320 --> 00:00:53,797 National Commission on Artificial Intelligence. In 2021, 18 00:00:53,880 --> 00:00:56,157 he co-founded and endowed the Special 19 00:00:56,240 --> 00:01:00,917 Competitive Studies Project, our host today for this important panel. 20 00:01:01,000 --> 00:01:05,157 Uh in his spare time, he's written extensively a number of books 21 00:01:05,240 --> 00:01:07,557 uh on all sorts of things, most recently 22 00:01:07,640 --> 00:01:11,277 Agenda AI. And in March 2025, he became 23 00:01:11,360 --> 00:01:14,517 CEO of Relativity Space, bringing the 24 00:01:14,600 --> 00:01:16,917 same energy to the race for space that 25 00:01:17,000 --> 00:01:18,917 he brought to the race for the internet 26 00:01:19,000 --> 00:01:22,077 and AI. And guys my age only have one um 27 00:01:22,160 --> 00:01:24,317 audio visual aid. I wore this of the 28 00:01:24,400 --> 00:01:29,437 cosmos, Dr. Schmidt, just to honor your work in space. So, thank you. 29 00:01:29,520 --> 00:01:33,237 Um thanks for all you've done here for your time today. There's so much to talk 30 00:01:33,320 --> 00:01:37,677 about, but I wanted to start with the headlines, which is the drone war, 31 00:01:37,760 --> 00:01:41,757 Strait of Hormuz, Ukraine. And I know you're just back 32 00:01:41,840 --> 00:01:44,717 from Ukraine making another of your valuable trips. 33 00:01:44,800 --> 00:01:46,717 On the topic of AI here today, Dr. 34 00:01:46,800 --> 00:01:49,397 Schmidt, at the tactical level, what do 35 00:01:49,480 --> 00:01:51,677 you see as the future of the marriage of 36 00:01:51,760 --> 00:01:54,957 AI and drones? And then strategically, 37 00:01:55,040 --> 00:01:57,877 how is AI going to reshape the face of 38 00:01:57,960 --> 00:02:02,077 combat? Um so first thing, Tom, thank you for it for doing this. I want to 39 00:02:02,160 --> 00:02:06,637 thank you all for being here. This was an experiment that Illy came up with, 40 00:02:06,720 --> 00:02:09,637 and I said, "Well, you know, it'll probably work." And it's worked 41 00:02:09,720 --> 00:02:13,277 enormously well, and I want to thank Illy and the entire team putting this 42 00:02:13,360 --> 00:02:17,997 event on. Uh we're committed to national security. 43 00:02:18,080 --> 00:02:21,037 We're committed to working with the government. We're committed to working 44 00:02:21,120 --> 00:02:23,197 with you who work with the government. 45 00:02:23,280 --> 00:02:26,197 Uh we we want to make America safe and 46 00:02:26,280 --> 00:02:29,197 powerful and keep it that way for many, 47 00:02:29,280 --> 00:02:33,397 many decades. To answer your question, uh I spent lots of time in Ukraine. I 48 00:02:33,480 --> 00:02:35,757 have a company there. Um and I spent 49 00:02:35,840 --> 00:02:37,637 lots of time on the front lines, uh 50 00:02:37,720 --> 00:02:40,757 which is not for the faint of heart. 51 00:02:40,840 --> 00:02:44,117 And I think now we're seeing there So, 52 00:02:44,200 --> 00:02:48,197 it it's important to understand that Ukraine is the laboratory for future 53 00:02:48,280 --> 00:02:50,877 war. Uh I'm not in any way endorsing 54 00:02:50,960 --> 00:02:53,077 this war. It's a horrible war. I've seen 55 00:02:53,160 --> 00:02:56,237 plenty people die. It's just horrible. 56 00:02:56,320 --> 00:02:59,877 Horrible, horrible, horrible. Uh having said that, it's important to 57 00:02:59,960 --> 00:03:03,717 understand what's going on. Uh today, the flood, as it's known, is 58 00:03:03,800 --> 00:03:06,357 quite wide. That's the actual line 59 00:03:06,440 --> 00:03:09,557 demarcating the two sides. Um and the 60 00:03:09,640 --> 00:03:12,357 people who are on that area are in 61 00:03:12,440 --> 00:03:15,477 foxholes. The minimum number of days 62 00:03:15,560 --> 00:03:19,637 they spend in the foxholes is 50, 5 0. 63 00:03:19,720 --> 00:03:22,437 But in many cases, it's up to 3 to 4 64 00:03:22,520 --> 00:03:25,557 months. How do they survive? Drones 65 00:03:25,640 --> 00:03:29,917 supply them with water and food. If they stick their head out of the ground, 66 00:03:30,000 --> 00:03:33,677 they're killed. That's what a transparent battlefield 67 00:03:33,760 --> 00:03:37,877 looks like. And I don't think we Americans understand this. I think 68 00:03:37,960 --> 00:03:39,637 almost no country understands that 69 00:03:39,720 --> 00:03:41,637 that's what the future of drone war 70 00:03:41,720 --> 00:03:44,997 means. Um the result of this is that it 71 00:03:45,080 --> 00:03:47,917 is the the front is largely frozen. It's 72 00:03:48,000 --> 00:03:50,557 very hard for either side to go against 73 00:03:50,640 --> 00:03:55,357 the other. And people take enormous risks running along the fields, hiding 74 00:03:55,440 --> 00:03:59,717 under trees, and so forth, while the drone hunters are hunting them. And it's 75 00:03:59,800 --> 00:04:03,357 just horrible to watch. Uh in Ukraine, they've recently 76 00:04:03,440 --> 00:04:07,237 announced that they've hit their fourth month of killing their quota of Russian 77 00:04:07,320 --> 00:04:11,677 targets, which they say is 35,000. To give you an an 78 00:04:11,760 --> 00:04:15,957 an idea of the scale of this war, it's hard for us to understand. Think about 79 00:04:16,040 --> 00:04:19,597 the number of people who we lost in Vietnam in the context of of what's 80 00:04:19,680 --> 00:04:21,677 going on in Ukraine. The Ukraine numbers 81 00:04:21,760 --> 00:04:23,597 are not released, uh but it's 82 00:04:23,680 --> 00:04:27,277 substantially lower than 35,000. 83 00:04:27,360 --> 00:04:30,157 And the consequence of this this this 84 00:04:30,240 --> 00:04:32,637 thing is anything that moves is 85 00:04:32,720 --> 00:04:35,517 destroyed. So, get rid of your tanks, 86 00:04:35,600 --> 00:04:37,637 armored personnel carriers, 87 00:04:37,720 --> 00:04:40,237 uh artillery, flamethrowers, all the fun 88 00:04:40,320 --> 00:04:42,717 stuff that you see in movies, right? 89 00:04:42,800 --> 00:04:45,677 It's sort of over. Um replaced by these 90 00:04:45,760 --> 00:04:48,357 powerful robotic systems. 91 00:04:48,440 --> 00:04:50,517 Technically, the way they work is they 92 00:04:50,600 --> 00:04:55,477 uh can work without GPS, they can communicate through electronic warfare. 93 00:04:55,560 --> 00:04:59,797 That technology is getting stronger on both sides. So, this is the future. 94 00:04:59,880 --> 00:05:02,157 The battle now has moved to the 95 00:05:02,240 --> 00:05:05,357 mid-strike area, which is roughly 40 to 96 00:05:05,440 --> 00:05:08,637 100 km. And that's where the supply line 97 00:05:08,720 --> 00:05:12,437 on the other on the enemy side is, supplying these people who are 98 00:05:12,520 --> 00:05:16,157 unfortunately in the foxholes. And that's where the bombing is, and that's 99 00:05:16,240 --> 00:05:17,517 where the war is largely being fought 100 00:05:17,600 --> 00:05:21,477 right now. Um and Ukraine is slowly uh 101 00:05:21,560 --> 00:05:25,437 is as pretty much stopped Russia's advance, which is extraordinary, and is 102 00:05:25,520 --> 00:05:26,997 slowly beginning to respond 103 00:05:27,080 --> 00:05:30,757 successfully. You asked about AI. 104 00:05:30,840 --> 00:05:33,997 Um if you take the the principle that I 105 00:05:34,080 --> 00:05:37,677 described, which is that basically uh 106 00:05:37,760 --> 00:05:40,477 air, land, and sea drones are going to 107 00:05:40,560 --> 00:05:43,357 be the way that this that is 108 00:05:43,440 --> 00:05:46,557 um code for robots and automation and 109 00:05:46,640 --> 00:05:50,117 AI. Um they will all be interconnected. 110 00:05:50,200 --> 00:05:53,677 They will all operate in the equivalent of swarms and they'll all operate in 111 00:05:53,760 --> 00:05:57,917 what are called coordinated attacks. So, a typical example is you'll have a 112 00:05:58,000 --> 00:06:00,317 target and you'll surround the target by 113 00:06:00,400 --> 00:06:02,317 a number of drones and you hit it 114 00:06:02,400 --> 00:06:05,397 simultaneously and most targets do not 115 00:06:05,480 --> 00:06:07,677 have the ability to simultaneously in a 116 00:06:07,760 --> 00:06:10,877 360 azimuth defend themselves. So, 117 00:06:10,960 --> 00:06:14,757 that's roughly where we are. Thanks. That's on the battlefield there. 118 00:06:14,840 --> 00:06:18,597 If you think about that as a Russia-Ukraine competition, add in Dr. 119 00:06:18,680 --> 00:06:21,677 Schmidt, the US and China. How will this 120 00:06:21,760 --> 00:06:27,077 influence how we should both prepare and deter war and fight if required? And I 121 00:06:27,160 --> 00:06:31,677 have to ask the former journalist question, who's ahead? 122 00:06:31,760 --> 00:06:35,677 So, it's a a slightly different subject 123 00:06:35,760 --> 00:06:40,917 would be on AI. They're all interrelated, of course. 124 00:06:41,000 --> 00:06:45,437 A week ago, China released Deep Seek V4. 125 00:06:45,520 --> 00:06:47,437 And when I was working on the National 126 00:06:47,520 --> 00:06:49,757 Security Commission for AI, we worked 127 00:06:49,840 --> 00:06:53,637 really hard with Trump 1 and Biden to 128 00:06:53,720 --> 00:06:57,037 implement hardware controls for chips. 129 00:06:57,120 --> 00:06:58,957 We were very successful collectively, 130 00:06:59,040 --> 00:07:01,917 all of us, to limit hardware access to 131 00:07:02,000 --> 00:07:04,077 China. And I was strongly in favor of 132 00:07:04,160 --> 00:07:06,757 this policy as were many people. 133 00:07:06,840 --> 00:07:09,437 It looks like the policy has worked, but 134 00:07:09,520 --> 00:07:12,997 it's largely beginning to not work. 135 00:07:13,080 --> 00:07:15,717 The Chinese are clever, they're smart. 136 00:07:15,800 --> 00:07:18,477 Um and they have now built systems that 137 00:07:18,560 --> 00:07:23,517 are within striking range of the top models in America, 138 00:07:23,600 --> 00:07:27,837 but with much, much less powerful hardware. The The technical summary is 139 00:07:27,920 --> 00:07:32,757 that they use a chip called the Ascend chip. It uses a slower process and 140 00:07:32,840 --> 00:07:36,477 they've invented new software ways to get around the various latency and 141 00:07:36,560 --> 00:07:40,837 architectural problems. Um what I like about this is that we 142 00:07:40,920 --> 00:07:42,837 have real competitors. What I don't like 143 00:07:42,920 --> 00:07:46,557 about it is China is very focused in 144 00:07:46,640 --> 00:07:48,237 broad diffusion of this technology 145 00:07:48,320 --> 00:07:51,077 globally and it's all open source, which 146 00:07:51,160 --> 00:07:53,757 means it's largely uncontrolled and not 147 00:07:53,840 --> 00:07:58,397 controlled in any way by us. Um so I think a fair statement is a year 148 00:07:58,480 --> 00:08:01,877 ago I said I thought that they were 1 to 2 years 149 00:08:01,960 --> 00:08:06,117 behind. It looks like they've caught up enough that the most recent analysis is 150 00:08:06,200 --> 00:08:08,717 China's within 6 months, 151 00:08:08,800 --> 00:08:11,037 which is a nano second, right, in our 152 00:08:11,120 --> 00:08:13,357 world. So it gives you a sense of how 153 00:08:13,440 --> 00:08:16,037 committed China is to the AI leadership 154 00:08:16,120 --> 00:08:19,197 position and they're not going to stop. 155 00:08:19,280 --> 00:08:21,557 Um if it makes you feel any better, in 156 00:08:21,640 --> 00:08:23,437 order to do this, you have to have a 157 00:08:23,520 --> 00:08:26,557 whole country of, you know, engineers, 158 00:08:26,640 --> 00:08:31,117 scientists, nerds, money, hardware, and so forth. There are not going to be many 159 00:08:31,200 --> 00:08:33,477 company countries that can do this on 160 00:08:33,560 --> 00:08:38,237 their own, but China's clearly one, America's another one with our allies, 161 00:08:38,320 --> 00:08:41,317 of course. Maybe there'll be a third or fourth. And 162 00:08:41,400 --> 00:08:44,597 as we look not just in national security, but to how this country's 163 00:08:44,680 --> 00:08:47,557 dealing with AI from A to Z, 164 00:08:47,640 --> 00:08:52,037 how would you tell the American leadership to deal with AI? What's the 165 00:08:52,120 --> 00:08:54,237 left and right guardrail? What's the 166 00:08:54,320 --> 00:08:56,037 role of private sector and how do we 167 00:08:56,120 --> 00:08:59,357 build consensus? So so so 168 00:08:59,440 --> 00:09:01,517 let me say some some uncomfortable 169 00:09:01,600 --> 00:09:04,557 facts, which I think are true and if you 170 00:09:04,640 --> 00:09:08,837 don't believe me, please write what I say down and then check me on a year in 171 00:09:08,920 --> 00:09:10,117 a year. 172 00:09:10,200 --> 00:09:13,397 Um we build an enormous number of tanks, 173 00:09:13,480 --> 00:09:17,557 ships, missiles, uh human-powered jets, 174 00:09:17,640 --> 00:09:19,397 driven jets, etc. 175 00:09:19,480 --> 00:09:22,877 Um they're all now vulnerable. 176 00:09:22,960 --> 00:09:25,917 Anything that's exquisite, big, slow, 177 00:09:26,000 --> 00:09:29,997 and emits hit heat, it's a problem. 178 00:09:30,080 --> 00:09:32,397 So the conclusion you come to if you sit 179 00:09:32,480 --> 00:09:34,357 in my position is we should be focusing 180 00:09:34,440 --> 00:09:37,837 our national security on automation of 181 00:09:37,920 --> 00:09:40,477 land, sea, and you know, air and so 182 00:09:40,560 --> 00:09:45,477 forth. Uh we have fantastic military, very, very smart people. We should be 183 00:09:45,560 --> 00:09:50,077 able to make this transition. It takes a while. Um and by the way, it doesn't 184 00:09:50,160 --> 00:09:54,157 produce less jobs, it just produces different ones. And by the way, this 185 00:09:54,240 --> 00:09:56,877 doesn't mean the budgets go down, they 186 00:09:56,960 --> 00:09:59,157 probably go up. But they go up with 187 00:09:59,240 --> 00:10:01,357 different things. So, for example, why 188 00:10:01,440 --> 00:10:04,477 would you build artillery for 155 189 00:10:04,560 --> 00:10:08,757 piece, these big 155 things, which are completely untargeted and only go about 190 00:10:08,840 --> 00:10:11,757 15 km? Makes no sense. You should be building 191 00:10:11,840 --> 00:10:15,677 the equivalent thing for a drone, which is far more uh 192 00:10:15,760 --> 00:10:19,317 it doesn't cost any more, in fact, costs probably less. You can get a lot more 193 00:10:19,400 --> 00:10:21,117 for the same amount of budget. Plus, you 194 00:10:21,200 --> 00:10:23,917 can swarm them and so forth and so on. 195 00:10:24,000 --> 00:10:26,917 So, so as a matter of military doctrine, 196 00:10:27,000 --> 00:10:31,837 I think all that stuff that we learned watching the you know, the courage of 197 00:10:31,920 --> 00:10:33,397 the Marines in Iwo Jima, you know, all 198 00:10:33,480 --> 00:10:36,037 of that stuff is all true, and it's also 199 00:10:36,120 --> 00:10:39,277 over. Right? That the future combat will 200 00:10:39,360 --> 00:10:41,837 be largely robotic. Um it will be 201 00:10:41,920 --> 00:10:44,317 automated, it will be controlled by uh 202 00:10:44,400 --> 00:10:47,037 the laws of war. America has a very 203 00:10:47,120 --> 00:10:51,877 clear model of of human control, which I strongly support. I helped write it. 204 00:10:51,960 --> 00:10:55,357 Um and I feel very, very strongly that this is the direction that the nation 205 00:10:55,440 --> 00:10:56,877 should take. 206 00:10:56,960 --> 00:11:00,197 Um with respect to AI in general, 207 00:11:00,280 --> 00:11:02,717 uh I'll just confess what's really going 208 00:11:02,800 --> 00:11:05,277 on in my mind. I'm in mourning. 209 00:11:05,360 --> 00:11:07,997 And I'm in mourning because I started as 210 00:11:08,080 --> 00:11:10,637 a programmer when I was basically 13 or 211 00:11:10,720 --> 00:11:15,637 14. And that is essentially over. 212 00:11:15,720 --> 00:11:19,517 Right? How in How can your entire identity and life's career as a 213 00:11:19,600 --> 00:11:22,797 programmer, computer scientist be over in one lifetime? It's supposed to be 214 00:11:22,880 --> 00:11:25,357 like your children or your grandchildren. 215 00:11:25,440 --> 00:11:28,797 And what's happened I'll I'll give you an example. Uh 216 00:11:28,880 --> 00:11:32,317 So, the way programming is done at the state of the art now 217 00:11:32,400 --> 00:11:36,477 Is a programmer wakes up, goes to the office uh cuz they're social and they 218 00:11:36,560 --> 00:11:38,117 sit there, but they don't have that many 219 00:11:38,200 --> 00:11:40,197 friends. So, they sit in their office 220 00:11:40,280 --> 00:11:43,557 and they collect 10 Claude friends 221 00:11:43,640 --> 00:11:47,597 or 10 Gemini friends. And they set them up with objective 222 00:11:47,680 --> 00:11:51,397 functions and they watch what they wrote they the code that they write. 223 00:11:51,480 --> 00:11:55,837 Then they go out to lunch and they make sure that they have long enough tasks 224 00:11:55,920 --> 00:11:58,957 that they continue working while they're having lunch. 225 00:11:59,040 --> 00:12:01,477 So, then they go back to the office and they do this and then it's time to go 226 00:12:01,560 --> 00:12:05,997 home to see the family and so forth and they set up an objective function 227 00:12:06,080 --> 00:12:09,237 of this is what I want you to do and here's the verification and it's a long 228 00:12:09,320 --> 00:12:11,877 enough project that it takes them all night. 229 00:12:11,960 --> 00:12:15,917 So, they've gone from writing code which is what I did and I thought I was the 230 00:12:16,000 --> 00:12:19,997 hottest thing ever when I was like 20 and I'm sure that wasn't true, but 231 00:12:20,080 --> 00:12:24,237 welcome to being 20 to now having this kind of power. And 232 00:12:24,320 --> 00:12:26,557 I've watched what these systems can do 233 00:12:26,640 --> 00:12:29,957 and it blows me away, right? So, we have 234 00:12:30,040 --> 00:12:32,637 a major major change happening. This 235 00:12:32,720 --> 00:12:34,997 happened this started about October of 236 00:12:35,080 --> 00:12:38,077 last year. It's new for this conference. 237 00:12:38,160 --> 00:12:39,957 And if you're writing code in any 238 00:12:40,040 --> 00:12:45,037 traditional way, stop. Right? It's over. 239 00:12:45,120 --> 00:12:49,477 And go and if you manage a company and you have software programmers say, "Why 240 00:12:49,560 --> 00:12:53,397 are you still writing code code the way you did it 6 months ago?" 241 00:12:53,480 --> 00:12:56,437 This has a lot of implications globally 242 00:12:56,520 --> 00:13:00,877 because it means that the productivity of software, which has 243 00:13:00,960 --> 00:13:05,997 always been an issue, is going to become far far greater. It also means that 244 00:13:06,080 --> 00:13:07,677 individuals will be able to build 245 00:13:07,760 --> 00:13:10,277 incredibly powerful applications and so 246 00:13:10,360 --> 00:13:13,117 forth. You saw for example this huge 247 00:13:13,200 --> 00:13:17,597 sell-off in software as a service stocks, essentially software 248 00:13:17,680 --> 00:13:21,917 traditional software stocks. That's because the market decided that those 249 00:13:22,000 --> 00:13:25,317 products would literally go away, which of course is not true. They're probably 250 00:13:25,400 --> 00:13:30,157 oversold, but the they're directionally in trouble and they themselves will have 251 00:13:30,240 --> 00:13:34,077 to adapt. Well, as a moderator, as a father, I have to 252 00:13:34,160 --> 00:13:37,277 follow up on something that's not in my notes. You talked about these young 253 00:13:37,360 --> 00:13:40,037 people should stop coding now. 254 00:13:40,120 --> 00:13:44,757 For the young people here, what should they do? Study plumbing or 255 00:13:44,840 --> 00:13:48,277 what? So, what what it says is that the 256 00:13:48,360 --> 00:13:53,877 traditional writing code, you know, like if then else, the stuff that I used to do, 257 00:13:53,960 --> 00:13:55,117 you're not going to do that anymore. 258 00:13:55,200 --> 00:13:58,197 What you're going to have to learn is to write down what you want in a way that 259 00:13:58,280 --> 00:14:02,917 the computer can understand it and then monitor it producing it. 260 00:14:03,000 --> 00:14:05,477 Do you remember two or three years ago there was this whole thing about 261 00:14:05,560 --> 00:14:08,757 hallucinations and we couldn't trust these systems and they were off, you 262 00:14:08,840 --> 00:14:12,757 know, falling in love with the the therapist and things like that. That's 263 00:14:12,840 --> 00:14:17,397 largely under control. Uh I can sit in a meeting and somebody will say, "Well, we 264 00:14:17,480 --> 00:14:20,477 just don't have the right user interface." And I can basically say, 265 00:14:20,560 --> 00:14:24,837 while the meeting's going on, I can specify within these tools, "Build me a 266 00:14:24,920 --> 00:14:26,437 UI with these features." And it 267 00:14:26,520 --> 00:14:29,117 produces. That doesn't work yet. 268 00:14:29,200 --> 00:14:34,517 But that's the change. So, you learn the new programming, if you will, is 269 00:14:34,600 --> 00:14:38,277 learning how to manage the programming tools. You've gone from building the 270 00:14:38,360 --> 00:14:43,157 house to becoming the architect and the builder is under your control. 271 00:14:43,240 --> 00:14:44,717 So, there is some reason for optimism. 272 00:14:44,800 --> 00:14:48,397 I'm glad to hear that. And I should say, by the way, that the people who are it's 273 00:14:48,480 --> 00:14:51,197 always been true, sorry to go on about this, 274 00:14:51,280 --> 00:14:53,517 it's always been true in software that 275 00:14:53,600 --> 00:14:56,917 the very tippy-top programmer was worth 276 00:14:57,000 --> 00:14:59,557 10 times what the next level was. 277 00:14:59,640 --> 00:15:02,437 Um it's similar in if you think about 278 00:15:02,520 --> 00:15:06,757 football salaries or NBA salaries, their productivity was so great. 279 00:15:06,840 --> 00:15:08,957 So, my guess is that there will be a 280 00:15:09,040 --> 00:15:13,957 smaller number of software people in aggregate, but the very, very top ones, 281 00:15:14,040 --> 00:15:17,877 the top ones that can manage this productivity, will have enormous 282 00:15:17,960 --> 00:15:21,157 economic return to themselves, their corporations, things like that. That's 283 00:15:21,240 --> 00:15:24,437 so interesting. Thank you. I want to return to national security. You talked 284 00:15:24,520 --> 00:15:28,277 about missiles and bombers and all of that. I'm old enough to remember the 285 00:15:28,360 --> 00:15:30,757 missile gap with the Soviets, the bomber 286 00:15:30,840 --> 00:15:33,437 gap. Is there any kind of an AI 287 00:15:33,520 --> 00:15:35,877 predictability gap because you can't 288 00:15:35,960 --> 00:15:39,157 really count AI? You can't track it from 289 00:15:39,240 --> 00:15:41,357 a satellite. So, if you can't if you 290 00:15:41,440 --> 00:15:45,477 can't count or map an enemy's AI, an 291 00:15:45,560 --> 00:15:48,997 adversary can't map or count yours. 292 00:15:49,080 --> 00:15:52,877 What does that do to deterrence and stability? 293 00:15:52,960 --> 00:15:56,397 So, there's a Again, let me give you a bit of the 294 00:15:56,480 --> 00:16:00,797 background. Um AI went from language to language to 295 00:16:00,880 --> 00:16:04,557 language to action. So, when you go and look at the robots 296 00:16:04,640 --> 00:16:09,997 that we have here in the demo, um that you can communicate with them 297 00:16:10,080 --> 00:16:13,597 Uh they they look at something, you can communicate with them, and they take an 298 00:16:13,680 --> 00:16:16,117 action. Those are called VLA models. 299 00:16:16,200 --> 00:16:19,317 That technology is emerging very, very fast. 300 00:16:19,400 --> 00:16:23,037 Um those require an enormous amount of training data. 301 00:16:23,120 --> 00:16:26,077 The reason language to language works so well is cuz there was so much training 302 00:16:26,160 --> 00:16:29,157 data. And all And we sucked in all the world's language and built all these 303 00:16:29,240 --> 00:16:33,637 incredibly powerful models. And one of the emergent behaviors of 304 00:16:33,720 --> 00:16:36,517 these things is they could begin to do planning. 305 00:16:36,600 --> 00:16:39,997 So, somehow they learned how to do planning. 306 00:16:40,080 --> 00:16:43,357 So, you can say uh again, it's called chain of thought 307 00:16:43,440 --> 00:16:47,877 reasoning. Um you can say, "Show me the steps 308 00:16:47,960 --> 00:16:51,797 to do something." So, all of that is pretty well assembled. 309 00:16:51,880 --> 00:16:56,197 The next thing is prediction. And these models are very good at 310 00:16:56,280 --> 00:17:00,517 predicting things. So, in war, for example, you do predictive battlefield 311 00:17:00,600 --> 00:17:04,197 analysis. I want to know what happens if I do this, what happens? Very 312 00:17:04,280 --> 00:17:07,677 reasonable. The same is true in a company or government or whatever. It's 313 00:17:07,760 --> 00:17:11,917 not unique to the military. The next one is called reinforcement 314 00:17:12,000 --> 00:17:15,637 learning, and that's a simulation task. And that's 315 00:17:15,720 --> 00:17:17,757 how we would, example won the the game 316 00:17:17,840 --> 00:17:22,837 of Go at Google, which was thought to be incomputable. That's how 317 00:17:22,920 --> 00:17:27,037 something called AlphaFold was done at Google, which is a 318 00:17:27,120 --> 00:17:29,717 Everyone won Nobel Prizes for that. 319 00:17:29,800 --> 00:17:31,677 The ability to understand how proteins 320 00:17:31,760 --> 00:17:36,877 fold is a very big deal. This technology is generally available now if you have 321 00:17:36,960 --> 00:17:39,757 the data. So, if you want to do the kind of stuff 322 00:17:39,840 --> 00:17:43,477 you're talking about for a national security reasons, you have to have a 323 00:17:43,560 --> 00:17:45,877 great deal of training data. The only 324 00:17:45,960 --> 00:17:48,157 place I have found that has enough 325 00:17:48,240 --> 00:17:51,277 training data is inside of Ukraine. And 326 00:17:51,360 --> 00:17:53,917 shockingly, Ukrainians will not release 327 00:17:54,000 --> 00:17:58,797 it for obvious reasons. I'm joking. It's highly classified. You can't get to it 328 00:17:58,880 --> 00:18:00,597 for all sorts of very good national 329 00:18:00,680 --> 00:18:04,077 security reasons. So, for America to win 330 00:18:04,160 --> 00:18:06,277 in this particular area, we're going to 331 00:18:06,360 --> 00:18:08,197 have to have lots of training data. One 332 00:18:08,280 --> 00:18:11,357 way to get that is by doing things like 333 00:18:11,440 --> 00:18:15,757 we have in the cage outside, where you basically have drone competitions. 334 00:18:15,840 --> 00:18:20,477 Um, and I think my guess is that our military ranges will ultimately have 335 00:18:20,560 --> 00:18:23,997 drone-on-drone competitions with live fire, and that's how the training will 336 00:18:24,080 --> 00:18:26,157 occur. I know you've been thinking a lot 337 00:18:26,240 --> 00:18:28,517 about one of the critical enablers of 338 00:18:28,600 --> 00:18:31,837 AI, which is energy. The huge amounts of 339 00:18:31,920 --> 00:18:33,837 electricity that will be needed. Do you 340 00:18:33,920 --> 00:18:37,237 see the need for what? More wind, solar, 341 00:18:37,320 --> 00:18:40,397 small modular reactors? How do we power 342 00:18:40,480 --> 00:18:43,157 the AI future that is undeniably coming 343 00:18:43,240 --> 00:18:47,757 our way? So, um just one more thing on energy for the 344 00:18:47,840 --> 00:18:51,837 national security. So, our military 345 00:18:51,920 --> 00:18:56,157 has now, which is great, has now figured out they need real classified data 346 00:18:56,240 --> 00:19:00,557 centers. And so, they've started looking at how to partner with the commercial 347 00:19:00,640 --> 00:19:02,157 sector and so forth in this way, and I 348 00:19:02,240 --> 00:19:04,557 think this is wonderful. We're not going 349 00:19:04,640 --> 00:19:10,037 to be safe without our government having its own very, very powerful data center, 350 00:19:10,120 --> 00:19:13,557 something which they've never really had. Um, and when I was working for the 351 00:19:13,640 --> 00:19:17,677 Secretary of Defense and we're on the Ash Carter stage, so I'll always owe Ash 352 00:19:17,760 --> 00:19:20,117 a lot for getting me involved with this. 353 00:19:20,200 --> 00:19:23,517 There was essentially none. So the military now has a plan to do this. This 354 00:19:23,600 --> 00:19:26,117 is very welcome. 355 00:19:26,200 --> 00:19:30,877 In general, you're going to read Let's go through this sort of what 356 00:19:30,960 --> 00:19:32,837 you'll read in the press. 357 00:19:32,920 --> 00:19:35,197 The AI boom has contributed between 1 358 00:19:35,280 --> 00:19:40,357 and 2% of the GDP of America. That's a very big number. 359 00:19:40,440 --> 00:19:44,237 For most of you, you're going to say, "My God, these people are idiots." 360 00:19:44,320 --> 00:19:48,477 I mean, how could you possibly waste all that money? That's not my view. I think 361 00:19:48,560 --> 00:19:50,557 that the AI revolution is under hyped, 362 00:19:50,640 --> 00:19:53,477 not over hyped. This is my opinion. 363 00:19:53,560 --> 00:19:57,677 And I started one of a number of 364 00:19:57,760 --> 00:20:01,277 data center businesses to study this and to build data centers. 365 00:20:01,360 --> 00:20:05,037 And I can tell you roughly what you conclude. First, 366 00:20:05,120 --> 00:20:08,157 the interconnection grid is not going to 367 00:20:08,240 --> 00:20:12,357 survive all of this new power. There's not enough switches, there's not enough 368 00:20:12,440 --> 00:20:14,597 wires, there's not enough management. 369 00:20:14,680 --> 00:20:18,797 The interconnection queues, as they're called, are are very long one. 370 00:20:18,880 --> 00:20:22,557 So what people are now doing is they're building data centers in remote places 371 00:20:22,640 --> 00:20:26,757 with their own power plants. The technical term for that is behind behind 372 00:20:26,840 --> 00:20:29,877 behind the meter. What they do is they build essentially 373 00:20:29,960 --> 00:20:33,997 air-cooled data centers of enormous scale. You've seen the pictures. They're 374 00:20:34,080 --> 00:20:36,837 essentially air air-cooled systems. They 375 00:20:36,920 --> 00:20:38,517 don't use as much water as you think. 376 00:20:38,600 --> 00:20:41,717 The only water they use is internal for 377 00:20:41,800 --> 00:20:45,797 internal transfer. Very little water escapes. But they use a lot of 378 00:20:45,880 --> 00:20:47,757 electricity. And they're measured in 379 00:20:47,840 --> 00:20:50,957 megawatts. The largest data centers are 380 00:20:51,040 --> 00:20:53,677 in the 1 and 2 gigawatt stage, and there 381 00:20:53,760 --> 00:20:56,237 are people talking about A gigawatt data 382 00:20:56,320 --> 00:20:58,277 center costs something on the order of 383 00:20:58,360 --> 00:21:01,517 50 billion with a B number. 384 00:21:01,600 --> 00:21:06,237 How could these people in my industry be so crazy to raise all that money and 385 00:21:06,320 --> 00:21:09,797 build all the centers and the answer is they see the demand. 386 00:21:09,880 --> 00:21:13,877 And if you the my industry has changed so that if you want to make more money, 387 00:21:13,960 --> 00:21:17,317 you need more servers cuz the servers are how you make the money cuz that's 388 00:21:17,400 --> 00:21:20,597 the reasoning. So absent an AI 389 00:21:20,680 --> 00:21:24,477 breakthrough algorithmically, which is a change to what is called the transformer 390 00:21:24,560 --> 00:21:28,877 architecture which is invented at Google, thank you, in 2017, 391 00:21:28,960 --> 00:21:31,797 um we're sort of stuck with this cost structure. 392 00:21:31,880 --> 00:21:36,597 So my own opinion is you're going to see a lot of remote America 393 00:21:36,680 --> 00:21:39,997 build these behind the meter power plants that takes forever to get the 394 00:21:40,080 --> 00:21:44,557 turbines and all of that. And I think the real limit to AI is not energy, it's 395 00:21:44,640 --> 00:21:48,237 actually cash. Because when you add up the cost of 396 00:21:48,320 --> 00:21:52,597 these, if you take 50 I'm just round numbers, 50 billion a gigawatt, so 10 397 00:21:52,680 --> 00:21:54,957 gigawatts is half a trillion dollars. 398 00:21:55,040 --> 00:21:57,597 How many companies, countries, and so 399 00:21:57,680 --> 00:22:00,197 forth can hand an industry a trillion 400 00:22:00,280 --> 00:22:04,877 dollars of capital? Very, very few. The Chinese could certainly do it. I don't 401 00:22:04,960 --> 00:22:08,077 know if they're doing it. I'm going to try to find out. In the America, there 402 00:22:08,160 --> 00:22:11,437 are people who hope that's going to happen. It's interesting that you can 403 00:22:11,520 --> 00:22:16,077 finance these things. Because the the the the brilliance of 404 00:22:16,160 --> 00:22:20,117 the American capital market allows us to borrow that kind of money. So for 405 00:22:20,200 --> 00:22:24,597 example, the Europeans can't do this, right, which they're sort of sore about. 406 00:22:24,680 --> 00:22:28,757 But this is good for America. Mhm. So interesting. Of your many very valuable 407 00:22:28,840 --> 00:22:32,397 books, my favorite is the one you co-authored with Henry Kissinger. I 408 00:22:32,480 --> 00:22:36,037 recommend it to everyone. Kissinger, of course, made a career of looking at the 409 00:22:36,120 --> 00:22:38,397 world and deciding if it was organized 410 00:22:38,480 --> 00:22:41,917 along values, geography, military 411 00:22:42,000 --> 00:22:44,437 necessity, trade. As you look to the 412 00:22:44,520 --> 00:22:46,837 future, Dr. Kissinger's not here to ask, 413 00:22:46,920 --> 00:22:49,357 do you see the world, Dr. Schmidt, 414 00:22:49,440 --> 00:22:53,517 redrawing the map along AI alignments? 415 00:22:53,600 --> 00:22:57,197 The Chinese model, our model, energy 416 00:22:57,280 --> 00:23:00,917 providers in in the Gulf, will AI redraw 417 00:23:01,000 --> 00:23:04,437 the map of the world? Well, you know, Henry was my closest 418 00:23:04,520 --> 00:23:08,757 friend. And so I miss him, you know, every day. And I don't know how to bring 419 00:23:08,840 --> 00:23:12,157 him back. Uh The empty chair is for his spirit. 420 00:23:12,240 --> 00:23:16,597 >> Yes, he would be He would be very helpful now in the Iran crisis, for 421 00:23:16,680 --> 00:23:19,437 example. He understood all that very well. 422 00:23:19,520 --> 00:23:20,837 And um 423 00:23:20,920 --> 00:23:23,917 Henry was very concerned about 424 00:23:24,000 --> 00:23:27,957 competition between America and China in AI. 425 00:23:28,040 --> 00:23:32,077 That it would somehow unleash an unstable situation. 426 00:23:32,160 --> 00:23:36,157 And he felt very strongly and started a so-called track two dialogue, which 427 00:23:36,240 --> 00:23:39,477 continues uh with the Chinese. 428 00:23:39,560 --> 00:23:44,157 And the the concerns have I'll give you an example. If you look at Methuselah, 429 00:23:44,240 --> 00:23:47,277 which is the Anthropic model that's so powerful, 430 00:23:47,360 --> 00:23:50,157 uh we collectively believe that 431 00:23:50,240 --> 00:23:54,357 Methuselah is just the first of a set of very powerful models that have the 432 00:23:54,440 --> 00:23:57,517 ability to, for example, do cyber attacks. 433 00:23:57,600 --> 00:24:01,797 And this is not a desirable thing. It's desirable if you're in offensive cyber 434 00:24:01,880 --> 00:24:03,517 attack mode, but it's certainly not 435 00:24:03,600 --> 00:24:06,117 desirable uh if you're on a company or 436 00:24:06,200 --> 00:24:09,277 or in the military and so forth. 437 00:24:09,360 --> 00:24:11,877 And so you have to to think of it in the 438 00:24:11,960 --> 00:24:17,037 following way. You have these enormously powerful models uh coming out. 439 00:24:17,120 --> 00:24:20,437 How many will there be? Well, there's four or five in the US. 440 00:24:20,520 --> 00:24:23,797 There's at least three powerful ones in China. There'll be a few others. Let's 441 00:24:23,880 --> 00:24:27,477 say there's 10. How do you want to organize the world? 442 00:24:27,560 --> 00:24:29,557 Can you have meetings where every all 443 00:24:29,640 --> 00:24:33,717 the players agree to limitations? 444 00:24:33,800 --> 00:24:36,797 And the concern goes something like this. 445 00:24:36,880 --> 00:24:39,077 Um you and I are on opposite sides, 446 00:24:39,160 --> 00:24:42,077 which we would never be, and we agree. 447 00:24:42,160 --> 00:24:47,197 But then I build something which you know I'm doing, and it's really good. 448 00:24:47,280 --> 00:24:51,557 And I'm going to get a significant advantage by undercutting you, by by 449 00:24:51,640 --> 00:24:54,677 running ahead of you, by violating our agreement. 450 00:24:54,760 --> 00:24:56,797 Uh it sounds a lot like OPEC, 451 00:24:56,880 --> 00:24:59,797 Right? So, we've not We don't have 452 00:24:59,880 --> 00:25:04,277 language among the countries yet on how to handle this. The 453 00:25:04,360 --> 00:25:08,957 Trump administration has figured this out and wants to start working on this, 454 00:25:09,040 --> 00:25:10,877 which I think is very welcome. 455 00:25:10,960 --> 00:25:15,597 And we'll see. The general issue of global instability 456 00:25:15,680 --> 00:25:19,397 is actually more even more serious than that. Mythos is just a good example that 457 00:25:19,480 --> 00:25:24,037 everybody's familiar with. But the Chinese models are open-source 458 00:25:24,120 --> 00:25:26,157 open weights. 459 00:25:26,240 --> 00:25:28,677 So, for example, this means that if you 460 00:25:28,760 --> 00:25:31,277 want to take deep seek V4 and you want 461 00:25:31,360 --> 00:25:35,717 to add powerful capabilities and you're evil, which none of you are, 462 00:25:35,800 --> 00:25:38,877 uh and I'm not suggesting the Chinese would do this, but somebody could do 463 00:25:38,960 --> 00:25:42,877 this. Some evil person exists in the world. How would we know that? How would 464 00:25:42,960 --> 00:25:45,477 we police it? How would we see it? 465 00:25:45,560 --> 00:25:49,877 At least with the uh American models, the big ones are under corporate 466 00:25:49,960 --> 00:25:52,317 control, proprietary, we know who to 467 00:25:52,400 --> 00:25:57,357 call, the military can call, the police can call, and so forth. But if you 468 00:25:57,440 --> 00:26:00,917 release this stuff open-source, it's dangerous enough 469 00:26:01,000 --> 00:26:03,677 that a a lone actor, a new kind of 470 00:26:03,760 --> 00:26:08,317 terrorist could emerge. Obviously, we want to avoid that. And we want to And 471 00:26:08,400 --> 00:26:11,837 we want to acknowledge it's possible and we want to get ahead of it. And it 472 00:26:11,920 --> 00:26:14,157 requires multi-stakeholder agreement. 473 00:26:14,240 --> 00:26:17,997 Hm. Well, for all of us insomniacs, you've given us more reason to lose 474 00:26:18,080 --> 00:26:21,477 sleep at night. Um I have enough questions to fill the day, but we always 475 00:26:21,560 --> 00:26:25,117 love to hear what's on the mind of of the audience. Um if you will raise your 476 00:26:25,200 --> 00:26:26,917 hand and I'll uh we have about 10 477 00:26:27,000 --> 00:26:30,637 minutes to for questions. Uh anyone? 478 00:26:30,720 --> 00:26:33,637 Uh yes, please. Standing here. 479 00:26:33,720 --> 00:26:35,077 Hang on. There there there's a microphone coming. 480 00:26:35,160 --> 00:26:39,040 >> Yeah, we'll have to get the mic so everybody can hear. 481 00:26:41,200 --> 00:26:43,077 Oh, you followed Okay. Um I'm curious to 482 00:26:43,160 --> 00:26:45,957 hear uh Dr. Eric Schmidt's opinion on 483 00:26:46,040 --> 00:26:50,797 things like UBI, universal basic income, and like Elon Musk likes to call it 484 00:26:50,880 --> 00:26:54,557 universal high income. How do you think the future of that will look if we ever 485 00:26:54,640 --> 00:26:57,557 get to that stage with AI? So, the tech 486 00:26:57,640 --> 00:27:01,877 industry is full of people who pontificate on this stuff, and they're 487 00:27:01,960 --> 00:27:05,037 not trained in economics, they're not trained in social sciences cuz they 488 00:27:05,120 --> 00:27:06,597 didn't have time to take those classes. 489 00:27:06,680 --> 00:27:08,837 They were too busy programming. 490 00:27:08,920 --> 00:27:13,157 Um so, first place, maybe we should ask people who have actually studied this. 491 00:27:13,240 --> 00:27:17,317 So, my in talking to economists who have 492 00:27:17,400 --> 00:27:23,037 studied labor economics and so forth, the argument goes something like this. 493 00:27:23,120 --> 00:27:26,557 Um the power of AI in terms of efficiency 494 00:27:26,640 --> 00:27:30,717 is so great that companies will adopt it, 495 00:27:30,800 --> 00:27:34,597 and the companies themselves become more profitable and bigger. 496 00:27:34,680 --> 00:27:38,877 And although there's plenty of internal disruption, so it's painful, 497 00:27:38,960 --> 00:27:41,917 the fact of the matter is the company gets bigger and the economy grows 498 00:27:42,000 --> 00:27:47,677 overall. That has been true basically for 200 years, and it's been true forever. 499 00:27:47,760 --> 00:27:52,797 So, the first question is, do you think that that will continue? And I do. 500 00:27:52,880 --> 00:27:57,357 And I think that what will happen is we'll have a shortage of job of people 501 00:27:57,440 --> 00:28:00,557 for jobs because the economy will grow so quickly 502 00:28:00,640 --> 00:28:03,877 because of the productivity. That's the optimistic view. 503 00:28:03,960 --> 00:28:07,877 The accelerationist view is a stronger version of that, 504 00:28:07,960 --> 00:28:11,637 which is that things become so inexpensive that we can all sit around 505 00:28:11,720 --> 00:28:16,117 on the beach, uh and things like that. I think my own view, 506 00:28:16,200 --> 00:28:18,637 and again, this is conjecture, is that's 507 00:28:18,720 --> 00:28:22,237 probably unlikely. And the reason is, 508 00:28:22,320 --> 00:28:26,797 in the at least in the American system, uh we love lawyers fighting lawyers, we 509 00:28:26,880 --> 00:28:30,317 love watching sports teams, we love We're not going to be sitting around uh 510 00:28:30,400 --> 00:28:34,357 drinking mai tais and so forth on the beach. We'll just engage in a higher 511 00:28:34,440 --> 00:28:37,917 level of competition. Um and I think that that will provide 512 00:28:38,000 --> 00:28:40,717 plenty of employment. Uh there's lots of 513 00:28:40,800 --> 00:28:43,757 evidence. So, many, many people blame AI 514 00:28:43,840 --> 00:28:45,837 for stuff. When you You at the actual 515 00:28:45,920 --> 00:28:48,717 job losses, there's two categories. The 516 00:28:48,800 --> 00:28:53,877 first is mine, right? There's no question that if you are a young 517 00:28:53,960 --> 00:28:57,957 software person entering the software field, it's harder to get a job for the 518 00:28:58,040 --> 00:29:02,277 reasons that I stated. The other one are low-end or low-paid customer service 519 00:29:02,360 --> 00:29:05,477 workers. If you think about it in 10 years, 520 00:29:05,560 --> 00:29:07,797 they'll probably be a lot more. 521 00:29:07,880 --> 00:29:12,037 Right? But it's it's much slower than people think. I don't spend my time 522 00:29:12,120 --> 00:29:15,917 worrying about this question. That's very helpful, thanks. Yes, right 523 00:29:16,000 --> 00:29:22,800 here in front. This is Yes, ma'am. Here it comes. The microphone is coming this way. 524 00:29:24,440 --> 00:29:27,837 Oh, hello. I'm Dr. IG, private sector. 525 00:29:27,920 --> 00:29:31,877 Could you please put it up? I have a question. One of the things 526 00:29:31,960 --> 00:29:33,877 that I hear 527 00:29:33,960 --> 00:29:36,117 within the field of AI with the people 528 00:29:36,200 --> 00:29:39,877 working on AI is that everybody talks 529 00:29:39,960 --> 00:29:42,077 about a human-centered 530 00:29:42,160 --> 00:29:45,437 AI. Yet, when we go at the technical 531 00:29:45,520 --> 00:29:48,437 level, that falls or is not at all. 532 00:29:48,520 --> 00:29:50,277 So, and I believe that one of the 533 00:29:50,360 --> 00:29:52,757 reasons is because most of the people 534 00:29:52,840 --> 00:29:58,117 that are building AI are engineers, coders, programmers, and so 535 00:29:58,200 --> 00:30:01,597 on. And now think people are really thinking 536 00:30:01,680 --> 00:30:04,837 that we need to bring all the humanistic 537 00:30:04,920 --> 00:30:07,277 humanic aspects, all the brain science, 538 00:30:07,360 --> 00:30:13,437 cog science, psychology into the AI 539 00:30:13,520 --> 00:30:18,717 during the entire cycle of building AI, training, and so on, and creating the 540 00:30:18,800 --> 00:30:21,597 algorithms. So, my 541 00:30:21,680 --> 00:30:24,677 I have noticed that big companies like 542 00:30:24,760 --> 00:30:25,997 Google, 543 00:30:26,080 --> 00:30:29,797 DeepMind, correct me if I'm wrong, now 544 00:30:29,880 --> 00:30:33,077 they have jobs for psychologists, cog 545 00:30:33,160 --> 00:30:37,237 scientists. Is this is becoming a reality? I would like to hear your 546 00:30:37,320 --> 00:30:39,477 opinion on that. Thank you. So, you're 547 00:30:39,560 --> 00:30:41,557 exactly right, and thank you for making 548 00:30:41,640 --> 00:30:44,437 this point. So, in our books, Dr. 549 00:30:44,520 --> 00:30:47,717 Kissinger and I basically said it's AI 550 00:30:47,800 --> 00:30:51,837 is too important to leave it to people like me, right? 551 00:30:51,920 --> 00:30:54,277 Who have a technical background. We need 552 00:30:54,360 --> 00:30:59,077 the great philosophers, um the the the sort of humanists, if as 553 00:30:59,160 --> 00:31:02,837 you will. Um they are beginning to show up. 554 00:31:02,920 --> 00:31:07,077 And the reason is the human the humanists produce graduate students. 555 00:31:07,160 --> 00:31:10,357 And this is the most interesting thing for a graduate student in humanities to 556 00:31:10,440 --> 00:31:12,877 work on is the impact on their field in 557 00:31:12,960 --> 00:31:17,557 psychology or or government or political science or whatever of these powerful 558 00:31:17,640 --> 00:31:21,237 set of tools. So, I think we're in we're getting in better shape. 559 00:31:21,320 --> 00:31:23,517 The companies use the word human 560 00:31:23,600 --> 00:31:26,837 alignment to refer to both values and 561 00:31:26,920 --> 00:31:32,157 safety. The first one is they have a set of tests. So, for example, if you say to 562 00:31:32,240 --> 00:31:33,677 any of these models, how do I kill 563 00:31:33,760 --> 00:31:36,277 myself? It will say, "Don't do it. Call 564 00:31:36,360 --> 00:31:39,797 a suicide hotline." That's added It by 565 00:31:39,880 --> 00:31:43,557 the way, it learned how to kill yourself, but it's suppressing that 566 00:31:43,640 --> 00:31:48,117 answer. That's a good thing. The second question is more subtle, 567 00:31:48,200 --> 00:31:51,557 which has to do when you're talking to it, what's its style, what's its 568 00:31:51,640 --> 00:31:55,357 communication, what's its value, and that's trained. 569 00:31:55,440 --> 00:31:58,157 Um the rhetorical question is, do we all 570 00:31:58,240 --> 00:32:01,077 agree on what human values are? 571 00:32:01,160 --> 00:32:02,677 I suspect with this audience, we 572 00:32:02,760 --> 00:32:05,277 probably are in favor of Western liberal 573 00:32:05,360 --> 00:32:08,637 values, which includes um 574 00:32:08,720 --> 00:32:11,717 uh rights of women and minorities and uh 575 00:32:11,800 --> 00:32:15,717 broadly speech, the ability to express yourself, and so forth. Well, what 576 00:32:15,800 --> 00:32:20,597 happens if the Chinese models are trained in a different way and they take over? 577 00:32:20,680 --> 00:32:24,317 And because of open source, it's reasonable that the majority of the 578 00:32:24,400 --> 00:32:27,997 models in the world, because it's open source is just 579 00:32:28,080 --> 00:32:30,317 cheaper, will be Chinese resident, not 580 00:32:30,400 --> 00:32:35,597 American resident. Now, there are American values ones being built. Um 581 00:32:35,680 --> 00:32:39,837 there is a debate about which ones are coming. I'll give an example today, um, 582 00:32:39,920 --> 00:32:44,237 there's a something called Gemma 4 from Google, which is obviously have American 583 00:32:44,320 --> 00:32:48,877 values. There's something called Nemo Tron from Nvidia, which has a similar 584 00:32:48,960 --> 00:32:52,397 values. There's a couple of startups coming up with better ones. I'm very 585 00:32:52,480 --> 00:32:57,197 excited about those. But, when you say human values 586 00:32:57,280 --> 00:32:59,397 and alignment, you have to decide tell 587 00:32:59,480 --> 00:33:03,317 me which ones. I like the US ones. 588 00:33:03,400 --> 00:33:07,197 It's marvelous. Thank you. Uh, yes, a question almost to the back, the 589 00:33:07,280 --> 00:33:09,637 gentleman raising his right hand here. 590 00:33:09,720 --> 00:33:13,637 Yes. I'm coming. A microphone's coming to you, sir. 591 00:33:13,720 --> 00:33:15,317 Thank you. 592 00:33:15,400 --> 00:33:17,317 Um, my name's Kevin. 593 00:33:17,400 --> 00:33:21,037 How do we prevent another Twitter files, 594 00:33:21,120 --> 00:33:23,797 um, and I want to 595 00:33:23,880 --> 00:33:28,677 get trust back in science, and 596 00:33:28,760 --> 00:33:30,357 the, um, 597 00:33:30,440 --> 00:33:36,037 COVID lab origin, we, you know, with Peter Daszak, 598 00:33:36,120 --> 00:33:40,037 and Ralph Baric, and she's in the Why can't 599 00:33:40,120 --> 00:33:41,637 AI 600 00:33:41,720 --> 00:33:45,117 say yes, Peter Daszak was a bad actor, 601 00:33:45,200 --> 00:33:50,837 and we're going to correct, uh, rogue scientist, 602 00:33:50,920 --> 00:33:54,317 rogue, kind of dangerous gain of function 603 00:33:54,400 --> 00:33:55,917 experiments, 604 00:33:56,000 --> 00:33:58,917 and we'll have better 605 00:33:59,000 --> 00:34:03,437 confidence next pandemic. 606 00:34:03,520 --> 00:34:05,637 Um, I only could hear about half of your 607 00:34:05,720 --> 00:34:08,717 words. I I'm sorry. So, so Francis Lee 608 00:34:08,800 --> 00:34:11,437 wrote a book called In COVID's Wake. She 609 00:34:11,520 --> 00:34:15,037 said, "People don't trust science or 610 00:34:15,120 --> 00:34:19,877 news because we got gaslit in the COVID 611 00:34:19,960 --> 00:34:24,797 I understand. Um, let's start with some basic facts, 612 00:34:24,880 --> 00:34:29,077 and it's Washington, so you have to start with actual facts. 613 00:34:29,160 --> 00:34:30,237 Um, so 614 00:34:30,320 --> 00:34:33,437 >> Thank you for that. So, science 615 00:34:33,520 --> 00:34:36,637 and technology innovation is the cause 616 00:34:36,720 --> 00:34:40,917 of American greatness over hundreds of years. 617 00:34:41,000 --> 00:34:42,837 The innovation 618 00:34:42,920 --> 00:34:45,517 that America has led brought us to this 619 00:34:45,600 --> 00:34:48,197 extraordinary power around the world. 620 00:34:48,280 --> 00:34:50,317 And it's really important that science 621 00:34:50,400 --> 00:34:53,117 remain non-partisan, 622 00:34:53,200 --> 00:34:56,717 focused, well-regulated, and funded. Um 623 00:34:56,800 --> 00:35:00,357 I've committed my own I've made lots of money from Google thank thank goodness 624 00:35:00,440 --> 00:35:04,317 and all that money will eventually go into science in one form or another. Our 625 00:35:04,400 --> 00:35:06,837 military security depends on science. 626 00:35:06,920 --> 00:35:09,477 This AI future depends on science. 627 00:35:09,560 --> 00:35:14,877 So I start with a science statement. With respect to the specific questions that 628 00:35:14,960 --> 00:35:17,797 you are describing which have to do with misinformation, 629 00:35:17,880 --> 00:35:22,677 that's not a solved problem because if you think about it, if people 630 00:35:22,760 --> 00:35:27,037 are willing to pay more for things that scare them than things which are long 631 00:35:27,120 --> 00:35:29,357 and reasonable, then we have an economic 632 00:35:29,440 --> 00:35:31,797 misalignment. In other words, the the 633 00:35:31,880 --> 00:35:35,357 CEO of that vendor is 634 00:35:35,440 --> 00:35:37,797 has an incentive to not be completely 635 00:35:37,880 --> 00:35:42,797 truthful. And that's the that conundrum exists in every democracy. But I think 636 00:35:42,880 --> 00:35:46,237 the most important thing for this audience is to say regardless of your 637 00:35:46,320 --> 00:35:50,797 political views and so forth, if you want our country to be great, which we 638 00:35:50,880 --> 00:35:54,717 all do, and you want us to grow, you have to invest in the kind of stuff 639 00:35:54,800 --> 00:35:57,077 we're doing. I'll give you an example. 640 00:35:57,160 --> 00:35:59,157 The gains that we're going to see in 641 00:35:59,240 --> 00:36:02,317 America from the application of AI into 642 00:36:02,400 --> 00:36:07,437 science and medicine in the next 5 to 10 years, typically done by graduate 643 00:36:07,520 --> 00:36:10,677 students and postdocs, the faculty supervised, but it's really that sort of 644 00:36:10,760 --> 00:36:16,477 young cohort is phenomenal. The breakthroughs that we're about to see in 645 00:36:16,560 --> 00:36:17,637 energy, 646 00:36:17,720 --> 00:36:20,677 uh quantum uh various quantum effects, 647 00:36:20,760 --> 00:36:23,637 um in in literally chemistry and how 648 00:36:23,720 --> 00:36:27,917 bonds work together. If I told you that as a result of these discoveries, 649 00:36:28,000 --> 00:36:31,317 everything fell going back to the earlier abundance question, everything 650 00:36:31,400 --> 00:36:35,437 you purchased was twice as powerful, half the price, and so forth, you'd be a 651 00:36:35,520 --> 00:36:39,397 pretty happy citizen. That's all within our reach, and I'm not even talking 652 00:36:39,480 --> 00:36:43,117 about healthcare. Um and the gains in terms of drugs, 653 00:36:43,200 --> 00:36:46,917 uh Demis Hassabis and I were talking. He started a 654 00:36:47,000 --> 00:36:49,797 company, and he their goal is to solve 655 00:36:49,880 --> 00:36:54,517 all disease within 20 or 30 years. Wow, that's a pretty good goal. Sign me up 656 00:36:54,600 --> 00:36:56,757 for that. And and what do you have to 657 00:36:56,840 --> 00:36:59,637 do? You have to invest in science. 658 00:36:59,720 --> 00:37:03,197 Thank you. I think we have time for probably one 659 00:37:03,280 --> 00:37:07,037 more very fast question. Yes, the gentleman right right here. 660 00:37:07,120 --> 00:37:11,640 And sir, I'm going to ask you to be really brief if it's okay. 661 00:37:13,520 --> 00:37:16,277 Uh as a country, what do we need to do 662 00:37:16,360 --> 00:37:19,197 prepare our university, high school, 663 00:37:19,280 --> 00:37:23,317 elementary students to participate next 5 10 years? What a 664 00:37:23,400 --> 00:37:26,437 perfect ending question. >> so um 665 00:37:26,520 --> 00:37:30,717 let's think about us an a 17 or 18 year old. I think people here look like you 666 00:37:30,800 --> 00:37:33,157 have kids, or maybe grandkids in some 667 00:37:33,240 --> 00:37:34,557 cases. 668 00:37:34,640 --> 00:37:37,877 What's their life going to be like? 669 00:37:37,960 --> 00:37:42,197 Their entire lives will be mediated by this new form of artificial 670 00:37:42,280 --> 00:37:45,717 intelligence. Um they'll be their friend, they'll be 671 00:37:45,800 --> 00:37:49,277 their psychologist, they'll be their advisor, they'll be their programmer as 672 00:37:49,360 --> 00:37:52,157 we've already discussed. My career is over. 673 00:37:52,240 --> 00:37:54,717 Um what's the most important thing to teach 674 00:37:54,800 --> 00:37:57,557 them? Is to use those tools. 675 00:37:57,640 --> 00:38:00,477 So, my proposal, to be very specific, is 676 00:38:00,560 --> 00:38:04,637 that every university in the nation have a mandatory class 677 00:38:04,720 --> 00:38:09,797 that's in freshman year, which is how to use these tools. And I'll design one. It 678 00:38:09,880 --> 00:38:13,317 starts off by learning how to use the power of them, and it ends by building a 679 00:38:13,400 --> 00:38:17,677 system that grades your your friends in a way that promotes you. Yeah, or pick 680 00:38:17,760 --> 00:38:19,917 some that makes sense to an 18-year-old 681 00:38:20,000 --> 00:38:24,837 student. And you sit there and you so what a conservative proposal. You should 682 00:38:24,920 --> 00:38:29,117 do this in high school. Yes, let's do it in college first to catch all the people 683 00:38:29,200 --> 00:38:32,677 who didn't get it in high school and then move it into the high schools. 684 00:38:32,760 --> 00:38:36,517 But uh I'll give you an example. Um in my 685 00:38:36,600 --> 00:38:38,557 military work, I'm working with a 686 00:38:38,640 --> 00:38:42,117 20-year-old at Stanford 687 00:38:42,200 --> 00:38:46,277 who is not graduated yet. And I hired him and I told him 688 00:38:46,360 --> 00:38:50,797 that the only rule he has is he has to send me a picture of him graduating from 689 00:38:50,880 --> 00:38:53,837 his university with his parents. 690 00:38:53,920 --> 00:38:58,197 Right? That's the deal. Cuz he's so good. He He skipped all 691 00:38:58,280 --> 00:39:01,117 those other steps and he's now doing essentially reinforcement learning 692 00:39:01,200 --> 00:39:05,237 around swarming. Now, this is the future. I want to 693 00:39:05,320 --> 00:39:08,757 capture those people and I want to get them I want to get them to be early 694 00:39:08,840 --> 00:39:14,317 successful. And I'm sure that the power of these tools is so great 695 00:39:14,400 --> 00:39:17,717 that we can address a lot of the complaints that young people have today 696 00:39:17,800 --> 00:39:21,837 about their lives, about their economic opportunities, their reach, and so 697 00:39:21,920 --> 00:39:26,437 forth. And plus, it's fun. Right? The reason I got into this when I was so 698 00:39:26,520 --> 00:39:30,077 young, and I grew up by the way in Virginia, and my father got me a 699 00:39:30,160 --> 00:39:34,397 teletype, and off I was going, is I As a young person, I like to make things. 700 00:39:34,480 --> 00:39:37,637 These are the most powerful tools you could imagine to make things cuz you can 701 00:39:37,720 --> 00:39:41,637 design almost anything. Right? Including bad things, which hopefully you'll teach 702 00:39:41,720 --> 00:39:44,237 people not to do. So a very So and 703 00:39:44,320 --> 00:39:46,677 universities are resistant to this cuz 704 00:39:46,760 --> 00:39:49,717 they still want to teach in the old way. 705 00:39:49,800 --> 00:39:52,357 But that's going away. And the new model 706 00:39:52,440 --> 00:39:54,797 is going to be having professors teach 707 00:39:54,880 --> 00:39:58,397 students how to ask a creative question, 708 00:39:58,480 --> 00:40:02,517 not a rote learning question. When I was in Virginia, by the way, when I was in 709 00:40:02,600 --> 00:40:06,357 seventh grade, I was required to memorize the names of all of the 710 00:40:06,440 --> 00:40:10,877 counties of Virginia, which were like 50. And I managed to do it. 711 00:40:10,960 --> 00:40:15,117 Why in the world is that kind of thinking still around? 712 00:40:15,200 --> 00:40:18,917 Right? When these systems are always available, they're so powerful and they 713 00:40:19,000 --> 00:40:22,637 can be used for this extraordinary analytical capability. They need 714 00:40:22,720 --> 00:40:25,197 creative juices, they need oversight. 715 00:40:25,280 --> 00:40:28,117 That's what we should be teaching for anything. 716 00:40:28,200 --> 00:40:32,077 Thank you, Tom. No, thank you for Thank you for ending on such an optimistic 717 00:40:32,160 --> 00:40:37,157 note. Thank you all. Thanks to SCSP for hosting us. And Dr. Schmidt, thank you 718 00:40:37,240 --> 00:40:41,117 not just for all you've done for our country, but for explaining it so 719 00:40:41,200 --> 00:40:44,477 clearly. >> you. You're very kind. Thank you all. 720 00:40:44,560 --> 00:40:48,760 >> Thank you. Thank you. It's great to see you.