1 00:00:00,000 --> 00:00:05,397 and innovators like Alex himself who were at the frontier of developing this 2 00:00:05,480 --> 00:00:08,717 technology and I thought they needed to be in the 3 00:00:08,800 --> 00:00:12,837 room not least because it's businesses like yours that are have the resources 4 00:00:12,920 --> 00:00:14,437 to invest in this just to give you a bit 5 00:00:14,520 --> 00:00:17,237 of context this year you know the the 6 00:00:17,320 --> 00:00:20,757 large AI companies are going to spend 20 7 00:00:20,840 --> 00:00:24,717 times more on developing this technology than the 8 00:00:24,800 --> 00:00:28,917 US did for the Manhattan Project. Can you think about seminal moments where 9 00:00:29,000 --> 00:00:33,997 the government mobilized to do something 20 times more just this year alone. So I 10 00:00:34,080 --> 00:00:37,637 thought look it's vital that we do this in a different way not like the G7 and 11 00:00:37,720 --> 00:00:40,917 G20 we bring industry innovators and and 12 00:00:41,000 --> 00:00:44,917 government together and that's that's what we did at Bletchley Park a few 13 00:00:45,000 --> 00:00:46,877 years ago. The summit series went to 14 00:00:46,960 --> 00:00:49,357 Paris, South Korea and now brilliantly 15 00:00:49,440 --> 00:00:52,557 in India a country with huge digital 16 00:00:52,640 --> 00:00:56,397 ambitions and indeed capacity. 17 00:00:56,480 --> 00:00:58,757 But I think over those years and Alex 18 00:00:58,840 --> 00:01:03,437 let's start with this. I think actually the the the debate on AI has shifted 19 00:01:03,520 --> 00:01:07,837 from kind of technology to strategy. 20 00:01:07,920 --> 00:01:12,157 You know from what these tools can do to what governments are choosing to do with 21 00:01:12,240 --> 00:01:15,557 them. And I wanted to maybe just start there. 22 00:01:15,640 --> 00:01:19,037 Is if you think about the last few years you are someone who is at the frontier 23 00:01:19,120 --> 00:01:22,357 of developing this technology. You know that the journey that we've been on in 24 00:01:22,440 --> 00:01:24,557 the last few years you know help us 25 00:01:24,640 --> 00:01:27,717 understand that where have we come 26 00:01:27,800 --> 00:01:30,037 what is it that is exciting at this this 27 00:01:30,120 --> 00:01:35,157 moment and just ground us in in where we are so that we can get get on to some of 28 00:01:35,240 --> 00:01:38,037 these questions about what we do with it. 29 00:01:38,120 --> 00:01:39,837 Yeah I think um 30 00:01:39,920 --> 00:01:42,637 I mean we are sitting here right now at 31 00:01:42,720 --> 00:01:45,677 such an incredibly exciting moment for 32 00:01:45,760 --> 00:01:50,437 the technology. I think we are um at the beginning of 33 00:01:50,520 --> 00:01:55,277 uh you know of a true acceleration in the technology and you know in many ways 34 00:01:55,360 --> 00:01:56,917 it's felt like this very clear 35 00:01:57,000 --> 00:01:59,677 acceleration even until now, but I think 36 00:01:59,760 --> 00:02:02,717 we're going to see sort of 2026 is an 37 00:02:02,800 --> 00:02:04,717 inflection point in many ways. I think 38 00:02:04,800 --> 00:02:09,877 to contextualize this over the past few years, I think from 39 00:02:09,960 --> 00:02:11,557 2018 40 00:02:11,640 --> 00:02:14,677 through 2024 41 00:02:14,760 --> 00:02:17,957 really, which is a good 6-year period, 42 00:02:18,040 --> 00:02:20,477 we were writing we were in the era of 43 00:02:20,560 --> 00:02:25,437 pre-training. So we were writing one very large exponential curve towards 44 00:02:25,520 --> 00:02:27,197 improving the performance of the models. 45 00:02:27,280 --> 00:02:30,797 And that curve was very predictable and it 46 00:02:30,880 --> 00:02:34,637 sort of had clear characteristics where the more resources you put in, the more 47 00:02:34,720 --> 00:02:38,117 results you would get out and there was a lot of questions at the time of, you 48 00:02:38,200 --> 00:02:41,277 know, whether or not this curve would continue returns and I think this was 49 00:02:41,360 --> 00:02:43,757 sort of one of the 50 00:02:43,840 --> 00:02:46,357 one of the more recent eras of AI 51 00:02:46,440 --> 00:02:50,397 skepticism. And then in 2024, 52 00:02:50,480 --> 00:02:55,237 like towards the end of 2024, began the era of reinforcement learning. 53 00:02:55,320 --> 00:02:57,317 So this was sort of the the next major 54 00:02:57,400 --> 00:02:59,757 wave that stacked on top of the previous 55 00:02:59,840 --> 00:03:02,397 wave and this is when we saw the models 56 00:03:02,480 --> 00:03:07,157 begin to learn to reason. And you know, there's always this meme of 57 00:03:07,240 --> 00:03:09,277 what did Ilya see? Well, Ilya saw 58 00:03:09,360 --> 00:03:11,597 reasoning from the models and it was 59 00:03:11,680 --> 00:03:14,997 very clearly this this next mega wave of 60 00:03:15,080 --> 00:03:20,317 of the technology and even reinforcement learning had, you know, clearly some 61 00:03:20,400 --> 00:03:24,117 limitations and there were all these questions around when that was going to, 62 00:03:24,200 --> 00:03:25,757 you know, how long that era was going to 63 00:03:25,840 --> 00:03:27,677 last and maybe there were limitations in 64 00:03:27,760 --> 00:03:31,157 that era. And then at the end of 2025, 65 00:03:31,240 --> 00:03:32,917 so just a few months ago, I think we've 66 00:03:33,000 --> 00:03:35,077 really begun 67 00:03:35,160 --> 00:03:38,677 an entirely new paradigm or era of the 68 00:03:38,760 --> 00:03:40,997 technology, which is 69 00:03:41,080 --> 00:03:45,757 really beginning the era of recursive self-improvement. And 70 00:03:45,840 --> 00:03:49,917 I think we're really starting to see this in the development of our models. I 71 00:03:50,000 --> 00:03:52,597 I you know, most of the major labs are seeing this in the development of their 72 00:03:52,680 --> 00:03:54,397 models, which is that the models 73 00:03:54,480 --> 00:03:57,957 themselves have now become instrumental 74 00:03:58,040 --> 00:04:02,877 in accelerating the process of producing the next AIs. And so, you know, the the 75 00:04:02,960 --> 00:04:06,477 sort of from the outside what you see is maybe an acceleration of overall 76 00:04:06,560 --> 00:04:10,677 development velocity, an acceleration in at which the speed at which the the new 77 00:04:10,760 --> 00:04:14,117 models are being released. Um internally, what we see is just dramatic 78 00:04:14,200 --> 00:04:16,717 speed ups. I think the sort of um 79 00:04:16,800 --> 00:04:18,877 the productivity of an individual 80 00:04:18,960 --> 00:04:21,757 researcher has grown dramatically. Um 81 00:04:21,840 --> 00:04:24,677 and we don't expect that to stop. In 82 00:04:24,760 --> 00:04:29,637 fact, we expect it to kind of smoothly continue accelerating um where the 83 00:04:29,720 --> 00:04:32,957 output of an individual researcher, an individual engineer, well, I think 84 00:04:33,040 --> 00:04:36,357 that itself will continue growing exponentially as the technology 85 00:04:36,440 --> 00:04:38,557 improves. And um 86 00:04:38,640 --> 00:04:43,637 the other mega wave that is somewhat coincident with this new paradigm within 87 00:04:43,720 --> 00:04:46,637 the sort of development of the technology of uh 88 00:04:46,720 --> 00:04:50,117 recursive self-improvement is really the 89 00:04:50,200 --> 00:04:54,277 arrival of agents in earnest. And um 90 00:04:54,360 --> 00:04:56,597 this is something that has been talked 91 00:04:56,680 --> 00:04:59,957 about since I honestly want to say 2023 92 00:05:00,040 --> 00:05:02,517 with a lot of fanfare. And for a long 93 00:05:02,600 --> 00:05:05,477 long time, it was sort of um one of 94 00:05:05,560 --> 00:05:09,677 these hype uh sort of uh buzzwords that 95 00:05:09,760 --> 00:05:13,637 you know, never actually lived up to the expectations. And I think also at the 96 00:05:13,720 --> 00:05:16,677 end of 2025, we and toward you know, 97 00:05:16,760 --> 00:05:19,517 middle to end of 2025, we started to see 98 00:05:19,600 --> 00:05:21,917 agents actually work. And this is you 99 00:05:22,000 --> 00:05:26,557 know, these are where the models can actually start taking um actions um and 100 00:05:26,640 --> 00:05:28,637 start automating entire workflows and 101 00:05:28,720 --> 00:05:32,037 being um sort of dramatically more uh 102 00:05:32,120 --> 00:05:35,157 productive as actual sort of entities 103 00:05:35,240 --> 00:05:39,477 unto themselves versus assistants. And um 104 00:05:39,560 --> 00:05:42,997 and this era is you know, it started with coding agents. We're starting to 105 00:05:43,080 --> 00:05:46,317 see it um really percolate through personal agents. This is a big part of 106 00:05:46,400 --> 00:05:49,237 what uh we believe in at Meta. 107 00:05:49,320 --> 00:05:52,277 And I think over the course of 2026, we 108 00:05:52,360 --> 00:05:56,077 will see large-scale agent deployments 109 00:05:56,160 --> 00:05:58,717 in many areas of the economy, many areas 110 00:05:58,800 --> 00:06:01,797 of the world. And 111 00:06:01,880 --> 00:06:07,597 the sort of, you know, if the the GDP of AI, so to speak, is going to grow 112 00:06:07,680 --> 00:06:11,077 exponentially. >> Well, that that is very welcome to to 113 00:06:11,160 --> 00:06:15,917 hear. I think this you hit on something that is important because 114 00:06:16,000 --> 00:06:19,277 I remember when I first started doing this in the job, the conversations were 115 00:06:19,360 --> 00:06:22,117 all around these scaling laws and would they continue? And you mentioned the 116 00:06:22,200 --> 00:06:24,157 skepticism. I mean, not only have they 117 00:06:24,240 --> 00:06:26,877 continued and continued to deliver this 118 00:06:26,960 --> 00:06:31,437 exponential growth. It feels very much like the last few months have seen an 119 00:06:31,520 --> 00:06:35,677 acceleration. That's what you touched on. It's what Dario and and Demis have 120 00:06:35,760 --> 00:06:40,077 also said to me and and publicly, driven by this recursive self-improvement that 121 00:06:40,160 --> 00:06:44,397 the models themselves are now able to help us develop the new models quicker. 122 00:06:44,480 --> 00:06:48,277 So, you know, we're at this quite exciting but also a moment of some 123 00:06:48,360 --> 00:06:51,277 anxiety because of that accelerating 124 00:06:51,360 --> 00:06:55,637 development time scale. So, I wanted to just then talk about trust. 125 00:06:55,720 --> 00:06:58,077 Because I I tell you give you a story. 126 00:06:58,160 --> 00:07:02,717 My I have two, you know, young girls and you know, as they're using their 127 00:07:02,800 --> 00:07:06,517 chatbots and things and they're always like, "Please, you know, so-and-so and 128 00:07:06,600 --> 00:07:10,197 so-and-so." And "Thank you." when they finish writing their request or they get 129 00:07:10,280 --> 00:07:14,157 the answer back. There's lots of pleases and and thank yous everywhere. And I and 130 00:07:14,240 --> 00:07:16,637 I I thought that was you know, interesting. I said to them, I 131 00:07:16,720 --> 00:07:20,317 said, "Look, this, you know, you don't need to do that. It's not not a person. 132 00:07:20,400 --> 00:07:24,357 It doesn't It doesn't matter. And And by the way, you know, it costs a fortune 133 00:07:24,440 --> 00:07:26,477 when we have to do the compute for that." 134 00:07:26,560 --> 00:07:30,357 And And they they said to me, "You know what, Daddy?" They said, "If If AI takes 135 00:07:30,440 --> 00:07:34,037 over the world, we want to have been kind to the AI." 136 00:07:34,120 --> 00:07:37,717 And I thought it was good good insurance policy. But it kind of you know, it 137 00:07:37,800 --> 00:07:39,877 touches on this anxiety. >> Very wise. 138 00:07:39,960 --> 00:07:43,397 >> Yeah. >> [laughter] >> In that maybe. 139 00:07:43,480 --> 00:07:46,637 You know, across the world, we're seeing these different attitudes 140 00:07:46,720 --> 00:07:51,117 uh towards AI. In countries like India, where we are, there's enormous optimism 141 00:07:51,200 --> 00:07:54,637 and and trust. And then actually, in Western countries, you know, we're 142 00:07:54,720 --> 00:07:56,997 seeing that anxiety is still the kind of 143 00:07:57,080 --> 00:07:59,597 dominant feeling towards AI. Now, I 144 00:07:59,680 --> 00:08:03,237 think closing that confidence gap is as 145 00:08:03,320 --> 00:08:07,477 much a policy task as it is a a technical one, but I I'd love to just 146 00:08:07,560 --> 00:08:11,077 get your thoughts. I mean, obviously, you are at a you know, a B2C company 147 00:08:11,160 --> 00:08:13,917 dealing with, you know, billions of consumers every 148 00:08:14,000 --> 00:08:17,717 day. You know, how do you think about this trust question? You know, how do we 149 00:08:17,800 --> 00:08:23,037 get trust up in places where it is not for something that, you know, you believe, I 150 00:08:23,120 --> 00:08:27,197 believe, and everyone in this room probably believes can do untold good. 151 00:08:27,280 --> 00:08:30,957 Yeah, this is this is one of the most important questions. And actually, 152 00:08:31,040 --> 00:08:34,437 perhaps even more so than the technology and the development of the technology, 153 00:08:34,520 --> 00:08:38,157 um, maybe will govern how how 154 00:08:38,240 --> 00:08:40,637 uh successful the technology diffuses, 155 00:08:40,720 --> 00:08:44,157 um, more so than anything. And, um, 156 00:08:44,240 --> 00:08:46,877 you know, we we think deeply about this 157 00:08:46,960 --> 00:08:49,637 cuz our vision for the technology is 158 00:08:49,720 --> 00:08:51,957 that it is um we can deploy personal 159 00:08:52,040 --> 00:08:56,277 agents that uh know you better than most 160 00:08:56,360 --> 00:09:00,037 people know you, know about your health, know about your goals, know about your 161 00:09:00,120 --> 00:09:04,077 relationships, know about your family and your friends, and um can really help 162 00:09:04,160 --> 00:09:08,517 you live your life more effectively, you know, can help you be healthier, can 163 00:09:08,600 --> 00:09:12,437 help you accomplish projects that you would never would have had time for, can 164 00:09:12,520 --> 00:09:13,597 help you build the business you always 165 00:09:13,680 --> 00:09:16,157 wanted, um, can help you explore your 166 00:09:16,240 --> 00:09:19,757 interests. And to properly build that 167 00:09:19,840 --> 00:09:22,757 requires an immense amount of trust from 168 00:09:22,840 --> 00:09:26,357 consumers, from governments, from um, 169 00:09:26,440 --> 00:09:30,677 from effectively everyone, from all organizations. And so, you know, this is 170 00:09:30,760 --> 00:09:34,837 one of these interesting moments where, um, 171 00:09:34,920 --> 00:09:37,637 I I because the the sort of 172 00:09:37,720 --> 00:09:39,917 the form that the of the technology that 173 00:09:40,000 --> 00:09:42,877 we wanted to take is so intimate and is 174 00:09:42,960 --> 00:09:46,357 so deeply tied with what we think is 175 00:09:46,440 --> 00:09:48,757 really the the future of, you know, the 176 00:09:48,840 --> 00:09:51,877 consumer life and the consumer, um, you 177 00:09:51,960 --> 00:09:53,557 know, is at the core of the relationship 178 00:09:53,640 --> 00:09:56,197 with technology. Um, 179 00:09:56,280 --> 00:09:59,237 the figuring out ways to develop the the 180 00:09:59,320 --> 00:10:03,317 technology in partnership with governing bodies and in partnership with, um, 181 00:10:03,400 --> 00:10:07,717 nonprofits and other organizations to garner that trust is very important. 182 00:10:07,800 --> 00:10:11,877 This is something we've seen at Meta, actually, in, um, with WhatsApp. You 183 00:10:11,960 --> 00:10:15,957 know, one of the we were talking about this actually backstage, but, um, 184 00:10:16,040 --> 00:10:18,197 WhatsApp has become, uh, incredibly 185 00:10:18,280 --> 00:10:20,517 ubiquitous, um, and a real, I think, 186 00:10:20,600 --> 00:10:23,077 utility for much of the world, um, in 187 00:10:23,160 --> 00:10:28,517 large part due to, uh, its commitment to privacy and the the sort of trust that 188 00:10:28,600 --> 00:10:32,437 we've built around WhatsApp as an organization. Um, oftentimes at the, uh, 189 00:10:32,520 --> 00:10:35,837 sort of, uh, you know, not necessarily for the 190 00:10:35,920 --> 00:10:39,957 optimization of our business objectives, but for the optimization of trust in 191 00:10:40,040 --> 00:10:42,157 WhatsApp as a platform. I think a 192 00:10:42,240 --> 00:10:45,317 similar journey will be necessary for 193 00:10:45,400 --> 00:10:49,197 the AI products of the modern age, and we've seen, um, 194 00:10:49,280 --> 00:10:53,197 I mean, we've already seen various uh, kerfuffles or sort of jabs back and 195 00:10:53,280 --> 00:10:57,357 forth between the existing AI, you know, products and labs around, you know, 196 00:10:57,440 --> 00:11:00,677 whether their actions are truly garnering, you know, truly building 197 00:11:00,760 --> 00:11:04,877 this, uh, these levels of trust. But, um, and then I think the, you know, the 198 00:11:04,960 --> 00:11:08,557 topic that you really kicked off at Bletchley 199 00:11:08,640 --> 00:11:13,557 around, um, in a sort of like global way around safety, I think, is more 200 00:11:13,640 --> 00:11:14,717 important than ever. I think we're 201 00:11:14,800 --> 00:11:17,237 already seeing that, um, 202 00:11:17,320 --> 00:11:20,877 the models are, uh, 203 00:11:20,960 --> 00:11:23,637 are are achieving levels of capability 204 00:11:23,720 --> 00:11:26,237 which cause some pause around some of 205 00:11:26,320 --> 00:11:29,797 the, sort of like, risks that, uh, we 206 00:11:29,880 --> 00:11:32,197 um, monitor for these models as it comes 207 00:11:32,280 --> 00:11:34,997 to various national security risks or 208 00:11:35,080 --> 00:11:38,357 various cybersecurity risks. And so 209 00:11:38,440 --> 00:11:40,957 the the importance of properly testing 210 00:11:41,040 --> 00:11:45,877 and then building the right systems to monitor and deploy these models safely 211 00:11:45,960 --> 00:11:47,197 is is of paramount importance. 212 00:11:47,280 --> 00:11:49,957 >> And that you and I have been talking about the same topic for some years. And 213 00:11:50,040 --> 00:11:52,277 that was key to us at 214 00:11:52,360 --> 00:11:57,077 Bletchley was establishing the AI security institute in the UK to do 215 00:11:57,160 --> 00:12:00,357 exactly that work. And the And the thought was there was a gap where the 216 00:12:00,440 --> 00:12:04,437 technical capability didn't exist amongst governments to evaluate some of 217 00:12:04,520 --> 00:12:08,717 these risks. So how could we in the UK hopefully take a lead, but in sense 218 00:12:08,800 --> 00:12:13,397 provide a global public good where we work in partnership with 219 00:12:13,480 --> 00:12:16,997 companies like yours and the other frontier labs to do pre-deployment 220 00:12:17,080 --> 00:12:21,397 testing on these models. And my thought was always that by doing that, and 221 00:12:21,480 --> 00:12:24,677 that's not regulation, but it gives citizens confidence that the technology 222 00:12:24,760 --> 00:12:28,357 is safe cuz ultimately people don't adopt a technology that they don't 223 00:12:28,440 --> 00:12:32,317 trust. And I So what I my sense hearing you is that there's a role for the 224 00:12:32,400 --> 00:12:34,557 companies. I thought the the privacy 225 00:12:34,640 --> 00:12:36,237 example with WhatsApp is a good one is 226 00:12:36,320 --> 00:12:39,997 how do they design AI products that will 227 00:12:40,080 --> 00:12:43,797 will engender trust. And then there's a role, it sounds like you agree, for 228 00:12:43,880 --> 00:12:48,277 governments to do some of that technical evaluation. And is your sense that we 229 00:12:48,360 --> 00:12:51,637 need, you know, more cooperation between governments on that, more interaction 230 00:12:51,720 --> 00:12:52,997 with the labs? Is that working as well 231 00:12:53,080 --> 00:12:56,397 as it should at the moment? Um 232 00:12:56,480 --> 00:13:01,237 I think the interaction is working well, but I think this is one area where 233 00:13:01,320 --> 00:13:04,797 you know, uh we always can be more thoughtful. And 234 00:13:04,880 --> 00:13:09,917 especially as we enter this phase of dramatically increased AI progress, 235 00:13:10,000 --> 00:13:13,157 I think, you know, we constantly need to be thinking about how we can do better. 236 00:13:13,240 --> 00:13:15,877 I I think one of the other areas where 237 00:13:15,960 --> 00:13:20,397 this debate on trust will be won or lost is in the public sector. I think it's 238 00:13:20,480 --> 00:13:24,397 citizens that you experience that health care, more 239 00:13:24,480 --> 00:13:27,597 efficient government services, a state that is 240 00:13:27,680 --> 00:13:32,277 quicker at dealing with you. I think this debate goes from abstract to real 241 00:13:32,360 --> 00:13:36,437 when you have a real way to demonstrate benefit. And do you have a a sense from 242 00:13:36,520 --> 00:13:38,037 where you sit about where the most 243 00:13:38,120 --> 00:13:41,157 obvious use cases or deployment cases 244 00:13:41,240 --> 00:13:44,637 are in the public sector for AI that we 245 00:13:44,720 --> 00:13:49,077 should be excited about and that governments should be thinking about? 246 00:13:49,160 --> 00:13:51,357 I do. I think that um 247 00:13:51,440 --> 00:13:53,717 one of the first 248 00:13:53,800 --> 00:13:57,077 you know, if you think governments as um 249 00:13:57,160 --> 00:13:59,157 service providers in a sense to their 250 00:13:59,240 --> 00:14:01,797 citizens, I think there's there's a huge 251 00:14:01,880 --> 00:14:06,397 opportunity for all governments to be able to deliver their services more 252 00:14:06,480 --> 00:14:10,437 effectively. We're already seeing that actually in India 253 00:14:10,520 --> 00:14:13,157 on top of WhatsApp funnily enough where 254 00:14:13,240 --> 00:14:18,037 there are in certain states they actually deliver fast majority of the 255 00:14:18,120 --> 00:14:19,717 government services directly through 256 00:14:19,800 --> 00:14:22,877 WhatsApp. And then the for transit a 257 00:14:22,960 --> 00:14:28,437 nation actually able to a true what's up in India. 258 00:14:28,520 --> 00:14:32,237 last year in some 259 00:14:32,320 --> 00:14:36,077 what's up That's not AI 260 00:14:36,160 --> 00:14:41,277 the future opportunity there that is quite exciting is actually deployment of 261 00:14:41,360 --> 00:14:44,237 uh uh of AI to 262 00:14:44,320 --> 00:14:47,037 greater increase the ease and speed at 263 00:14:47,120 --> 00:14:48,357 which these government services are 264 00:14:48,440 --> 00:14:51,597 deployed and provided to citizens. 265 00:14:51,680 --> 00:14:54,477 I think that you know there's one 266 00:14:54,560 --> 00:14:56,877 term that I've always I've used and 267 00:14:56,960 --> 00:14:59,437 and I like this concept of the magic 268 00:14:59,520 --> 00:15:02,077 quadrant, right? And it really is you 269 00:15:02,160 --> 00:15:06,557 know if you think about how should governments be 270 00:15:06,640 --> 00:15:08,997 um how should the state be reimagined? 271 00:15:09,080 --> 00:15:12,437 Um you know I think we need to move away from 272 00:15:12,520 --> 00:15:16,517 large-scale bureaucratic services and um 273 00:15:16,600 --> 00:15:21,077 and these organizations and think about how can the government move as quickly 274 00:15:21,160 --> 00:15:25,837 as possible to deliver using a combination of technology and humans. verify 275 00:15:25,920 --> 00:15:29,077 one of those agents the fastest possible public public 276 00:15:29,160 --> 00:15:31,877 services for citizens in a 277 00:15:31,960 --> 00:15:36,877 country or a state to develop um and I think this cuts through civil 278 00:15:36,960 --> 00:15:41,197 services, I think it cuts through health care you mentioned and certainly has a 279 00:15:41,280 --> 00:15:45,917 has a large role to play in security and in 280 00:15:46,000 --> 00:15:51,077 in government this government So 281 00:15:51,160 --> 00:15:54,557 I think it's quite a problem here. Yeah, I think you were 282 00:15:54,640 --> 00:15:59,560 you were contacted by the Chinese government 283 00:16:01,120 --> 00:16:05,557 I need one more one more mic. I wanted to suggest I pick up on 284 00:16:05,640 --> 00:16:10,317 something you talked about one of the benefits of of leaving office 285 00:16:10,400 --> 00:16:13,237 is you have Thank you. A lot of benefits of leaving 286 00:16:13,320 --> 00:16:17,637 office is you have more more time at your desk and when I left someone gave 287 00:16:17,720 --> 00:16:20,957 me a great book by a guy called Jeffrey Professor Ding and it's called 288 00:16:21,040 --> 00:16:23,117 Technology and and Great Powers I'm sure 289 00:16:23,200 --> 00:16:25,397 you you know the book and and in it he 290 00:16:25,480 --> 00:16:30,317 makes a really compelling argument about technology diffusion essentially looking 291 00:16:30,400 --> 00:16:33,677 at the history of general purpose technologies going all the way back to 292 00:16:33,760 --> 00:16:37,477 the printing press and making the argument that you don't need to be the 293 00:16:37,560 --> 00:16:41,557 country that invents the technology to be the country that gets the most 294 00:16:41,640 --> 00:16:44,397 benefit from it and I think you know you 295 00:16:44,480 --> 00:16:47,717 touched on India India has recognized 296 00:16:47,800 --> 00:16:50,357 that technology leadership exactly does 297 00:16:50,440 --> 00:16:52,397 not depend on just inventing the 298 00:16:52,480 --> 00:16:57,477 technology it's about how effectively you deploy it and they here are focused 299 00:16:57,560 --> 00:17:00,917 on mass broad adoption and obviously 300 00:17:01,000 --> 00:17:03,637 supported by a few critical attributes. 301 00:17:03,720 --> 00:17:06,077 You have a very AI 302 00:17:06,160 --> 00:17:11,437 talent deep talent pool here. You talked about the rails of distribution 303 00:17:11,520 --> 00:17:16,317 obviously the digital public infrastructure here are there the ID UPI 304 00:17:16,400 --> 00:17:20,757 the payments infrastructure and then most recently Ayushman Bharat health 305 00:17:20,840 --> 00:17:25,837 accounts provide these infrastructure rails to distribute apps to over a 306 00:17:25,920 --> 00:17:29,477 billion people. And they also have a population that supportive of that. 307 00:17:29,560 --> 00:17:34,317 And I think focusing on that has allowed India to position itself as a leader 308 00:17:34,400 --> 00:17:39,477 by by pursuing the deployment and adoption strategy. But 309 00:17:39,560 --> 00:17:44,477 but thinking about that, you know, what are those critical attributes that 310 00:17:44,560 --> 00:17:48,757 government has to get right if we want to turn your aspiration into a reality 311 00:17:48,840 --> 00:17:52,397 about a gentle government, about delivering for our citizens. You know, I 312 00:17:52,480 --> 00:17:54,997 think government does need to think about things differently and needs to 313 00:17:55,080 --> 00:17:58,397 build the the architecture around 314 00:17:58,480 --> 00:18:02,077 deployment and diffusion, whether it's skills, whether it's compute, whether 315 00:18:02,160 --> 00:18:06,077 it's regulation. But crucially probably also data, 316 00:18:06,160 --> 00:18:09,717 which is something that you know better than most. So maybe if we start there 317 00:18:09,800 --> 00:18:13,797 and then broaden it out, if I'm, you know, sitting as a government leader as 318 00:18:13,880 --> 00:18:16,917 many people in the audience are, I'm trying to figure out what do I need 319 00:18:17,000 --> 00:18:21,597 to get right? What are the building blocks to deliver this thing properly? 320 00:18:21,680 --> 00:18:24,597 Let's start with data and what would your advice be there and then we'll 321 00:18:24,680 --> 00:18:26,437 we'll broaden it out a little bit. 322 00:18:26,520 --> 00:18:29,877 Yeah, there there is uh 323 00:18:29,960 --> 00:18:31,077 data 324 00:18:31,160 --> 00:18:33,677 is is so important and I think over the 325 00:18:33,760 --> 00:18:37,277 past few years many people have I think 326 00:18:37,360 --> 00:18:41,077 you know realized this as we sort of look at the power of these models and we 327 00:18:41,160 --> 00:18:44,837 look behind the curtains and you realize that all the powers and beauty of these 328 00:18:44,920 --> 00:18:49,717 models are actually imbued by data. And if you think about 329 00:18:49,800 --> 00:18:53,117 So if you think about data is like in some ways this sort of new oil, this 330 00:18:53,200 --> 00:18:57,437 sort of like base material that is incredibly important for the future 331 00:18:57,520 --> 00:18:59,237 of of AI and the development of these 332 00:18:59,320 --> 00:19:04,717 models. Um then I think as like governments should think about what are 333 00:19:04,800 --> 00:19:07,957 their, you know, what are their reserves? What are their data reserves? 334 00:19:08,040 --> 00:19:12,277 What are their data assets? And Um, and to 335 00:19:12,360 --> 00:19:15,277 in many ways build strategies where they have 336 00:19:15,360 --> 00:19:18,957 sovereignty over some set of data or or 337 00:19:19,040 --> 00:19:23,397 they're able to build certain certain pockets of data sets that enable the the 338 00:19:23,480 --> 00:19:27,237 country to perform the best. I think one of the areas that is the two areas that 339 00:19:27,320 --> 00:19:30,957 is clear that are clearest for this are one is health care. 340 00:19:31,040 --> 00:19:33,877 I think many countries um 341 00:19:33,960 --> 00:19:36,277 uh already are and other countries need 342 00:19:36,360 --> 00:19:38,397 to be thinking I think about um 343 00:19:38,480 --> 00:19:41,237 very effective mechanisms for 344 00:19:41,320 --> 00:19:44,157 uh uh for health care data within the 345 00:19:44,240 --> 00:19:46,957 country to enable ultimately over time 346 00:19:47,040 --> 00:19:51,357 there to be AI models and AI agents that are built on top of that to deliver 347 00:19:51,440 --> 00:19:54,597 better uh health services to their citizens. And then the second is 348 00:19:54,680 --> 00:19:57,717 national security. And I think that is um you know, in many ways pretty 349 00:19:57,800 --> 00:20:02,037 self-explanatory, but I think you know, countries need to be thinking about that 350 00:20:02,120 --> 00:20:06,917 um uh quite deeply as to what are the sort of like core data assets. Then I think 351 00:20:07,000 --> 00:20:10,877 if you if you sort of build around data and you look at the 352 00:20:10,960 --> 00:20:13,277 next few layers, um the next one is is 353 00:20:13,360 --> 00:20:15,517 infrastructure. Um, so you know, we 354 00:20:15,600 --> 00:20:18,677 think about the power, the um 355 00:20:18,760 --> 00:20:23,437 the computational infrastructure, um and and what does that story look like? I 356 00:20:23,520 --> 00:20:27,397 don't think every country needs to build large-scale AI data centers in their 357 00:20:27,480 --> 00:20:31,637 country, but they do need to have a strategy of over time how are they going 358 00:20:31,720 --> 00:20:33,557 to have access to that infrastructure. 359 00:20:33,640 --> 00:20:35,717 And so whether this is in the form of 360 00:20:35,800 --> 00:20:39,557 alliances of various formats or um or uh 361 00:20:39,640 --> 00:20:43,957 partnerships even with with uh corporate entities, the large cloud providers, I 362 00:20:44,040 --> 00:20:45,557 think that's an important element to 363 00:20:45,640 --> 00:20:47,237 think through. Um, 364 00:20:47,320 --> 00:20:51,637 then comes I think the sort of uh the um 365 00:20:51,720 --> 00:20:53,597 the innovation ecosystem. I do think 366 00:20:53,680 --> 00:20:55,357 that, you know, we're in a moment of 367 00:20:55,440 --> 00:20:59,837 momentous technological change and um it 368 00:20:59,920 --> 00:21:03,917 is I think incredibly important for I think there's 369 00:21:04,000 --> 00:21:05,917 near boundless uh 370 00:21:06,000 --> 00:21:11,317 entrepreneurial and economic opportunity Um, and I think it is the responsibility 371 00:21:11,400 --> 00:21:15,997 of of governments and countries to think through how they can build ecosystems 372 00:21:16,080 --> 00:21:19,717 that support that. I think India is actually in many ways a great, 373 00:21:19,800 --> 00:21:23,557 very positive case study in large part due to the due to the talent pool, but 374 00:21:23,640 --> 00:21:28,357 you know, I was at a dinner with a number of Indian founders and venture 375 00:21:28,440 --> 00:21:30,277 capitalist last night and and the stat 376 00:21:30,360 --> 00:21:32,717 was I think there are more 377 00:21:32,800 --> 00:21:35,837 consumer AI companies in India consumer 378 00:21:35,920 --> 00:21:39,957 AI startups in India than in the United States. And so, there really are some of 379 00:21:40,040 --> 00:21:41,877 these like, you know, shining examples 380 00:21:41,960 --> 00:21:47,237 of of incredible development of these ecosystems. And then, 381 00:21:47,320 --> 00:21:49,557 and then lastly, I think are the government services which we talked 382 00:21:49,640 --> 00:21:53,037 about previously. So, we've got I think the elements of almost a a playbook 383 00:21:53,120 --> 00:21:57,197 emerging, right? You need to have the right data and make sure it's it's used 384 00:21:57,280 --> 00:22:00,117 effectively. You need some degree of compute. 385 00:22:00,200 --> 00:22:03,997 You've got appropriate regulation as we've talked about to build to build 386 00:22:04,080 --> 00:22:09,077 trust. And then we've got some public sector use cases as well and an innovation 387 00:22:09,160 --> 00:22:12,677 ecosystem. So, these are the kind of ingredients where you can start to turn 388 00:22:12,760 --> 00:22:15,877 these ambitions into reality and and 389 00:22:15,960 --> 00:22:19,317 actual impact on people. I I just 390 00:22:19,400 --> 00:22:24,157 staying on that theme, you know, you you work at an incredible company. You work 391 00:22:24,240 --> 00:22:28,517 with other enterprises. You know, I talked to a lot of CEOs and 392 00:22:28,600 --> 00:22:33,597 and I'm struck by how they're thinking about deploying AI in their businesses 393 00:22:33,680 --> 00:22:37,597 and thinking about the parallel between that and and what governments should be 394 00:22:37,680 --> 00:22:41,157 doing. And you know, I think there is a kind of clear parallel. I'm I'm 395 00:22:41,240 --> 00:22:43,717 intrigued. I think CEOs are you know, 396 00:22:43,800 --> 00:22:48,317 kind of constantly thinking about AI. I think almost any large company CEO is 397 00:22:48,400 --> 00:22:51,877 spending their time figuring out, am I going to get disrupted, disintermediated 398 00:22:51,960 --> 00:22:55,997 by AI, how do I use it to stay ahead, where do I use it to to get more 399 00:22:56,080 --> 00:23:01,917 efficiency and more growth. And they are driving projects from the top down. And 400 00:23:02,000 --> 00:23:06,437 they are saying, "Look, this is something that is, you know, not 401 00:23:06,520 --> 00:23:10,277 a topic that could just stay with the IT department, right? This is something 402 00:23:10,360 --> 00:23:12,757 that has to be driven by the CEO, where 403 00:23:12,840 --> 00:23:17,397 we pick the use cases, we have accountability, and we start piloting 404 00:23:17,480 --> 00:23:19,957 these and scaling them up quite quickly." 405 00:23:20,040 --> 00:23:22,597 I you know, I my sense is we haven't 406 00:23:22,680 --> 00:23:27,397 quite seen that that same transition in governments yet, but I thought it's a 407 00:23:27,480 --> 00:23:30,397 good analogy in that look, this has to be driven by the person at the top. At 408 00:23:30,480 --> 00:23:33,637 the end of the day, if you're a prime minister, and I'm sure Tony would say 409 00:23:33,720 --> 00:23:37,717 the same thing, and indeed has, you can only do a few things that you drive 410 00:23:37,800 --> 00:23:41,277 personally from the top, and this really has to be one of them. You know, what 411 00:23:41,360 --> 00:23:43,757 are your kind of thoughts or advice for 412 00:23:43,840 --> 00:23:46,117 I get both CEOs and and governments 413 00:23:46,200 --> 00:23:48,957 about how to kind of drive the machine. 414 00:23:49,040 --> 00:23:53,637 What does it take to get execution on this? I mean, you've had to do that at 415 00:23:53,720 --> 00:23:56,557 Meta yourself, right? So, you'll have a sense from that. 416 00:23:56,640 --> 00:24:01,957 Yeah, I think it's a mix of of two things. I think first, um at 417 00:24:02,040 --> 00:24:05,597 uh at the highest level, you need to make 418 00:24:05,680 --> 00:24:09,237 it very clear that the organization will be an AI-first organization. And what 419 00:24:09,320 --> 00:24:10,757 that means for every organization is 420 00:24:10,840 --> 00:24:14,317 slightly different, but it it it um like 421 00:24:14,400 --> 00:24:18,517 the way that truly I think about it is that uh the success or failure of the 422 00:24:18,600 --> 00:24:21,077 organization actually entirely rests on 423 00:24:21,160 --> 00:24:26,157 its ability to properly embrace AI. And so, that's sort of the tops-down um 424 00:24:26,240 --> 00:24:29,637 component of it. And you know, there's sort of a strategy that must be designed 425 00:24:29,720 --> 00:24:32,917 more thoughtfully. And actually, honestly, you can probably ask AI uh to 426 00:24:33,000 --> 00:24:36,397 help you design that strategy now, and it'll probably do a pretty good job. And 427 00:24:36,480 --> 00:24:39,357 then you need um a a 428 00:24:39,440 --> 00:24:42,957 bed of bottoms-up adoption and 429 00:24:43,040 --> 00:24:46,837 bottoms-up um success stories. And for 430 00:24:46,920 --> 00:24:50,997 uh for us at Meta, like the example of this are, you know, we have these 431 00:24:51,080 --> 00:24:52,717 engineers throughout the company who are 432 00:24:52,800 --> 00:24:56,117 able to use AI to uh be you know, 10 to 433 00:24:56,200 --> 00:24:59,597 100 times more productive. And um those 434 00:24:59,680 --> 00:25:05,357 are just such incredible examples that where all of a sudden everyone else in 435 00:25:05,440 --> 00:25:08,597 the organization looks around and realizes, wow, this is really something 436 00:25:08,680 --> 00:25:12,717 that must be taken very seriously. It's not just something that, you know, um 437 00:25:12,800 --> 00:25:16,797 the CEO or the C-level individuals are talking about and, you know, that 438 00:25:16,880 --> 00:25:20,797 they're so detached from reality. It's like, no, it's very real on the ground 439 00:25:20,880 --> 00:25:24,717 um in a very big way. And so, I think you need this sort of mix of what are 440 00:25:24,800 --> 00:25:28,757 the success stories that cause everyone to realize how big of a shift it 441 00:25:28,840 --> 00:25:33,277 actually is, and then also ensure that there's a continual um there's a very 442 00:25:33,360 --> 00:25:36,957 clear top-down direction that this transition is is mandatory. 443 00:25:37,040 --> 00:25:38,717 >> It's mandatory. Very helpful advice. 444 00:25:38,800 --> 00:25:41,237 We're almost out of time. So, maybe the 445 00:25:41,320 --> 00:25:44,477 last final point uh to close on is, you 446 00:25:44,560 --> 00:25:49,397 know, you're looking ahead, you know, what are you most excited about uh in 447 00:25:49,480 --> 00:25:52,317 the development of this of this technology? You've touched on personal 448 00:25:52,400 --> 00:25:55,157 agents, other scientific breakthroughs. 449 00:25:55,240 --> 00:25:59,237 You know, what where is this going, and what should governments be doing now, 450 00:25:59,320 --> 00:26:00,877 you know, so that they are prepared and 451 00:26:00,960 --> 00:26:03,797 and not playing catch-up? 452 00:26:03,880 --> 00:26:08,597 I think the things that um you can depend on, I think the the piece 453 00:26:08,680 --> 00:26:10,197 of technology development will continue 454 00:26:10,280 --> 00:26:16,117 accelerating. Um I think this this era of recursive self-improvement will 455 00:26:16,200 --> 00:26:21,157 uh feel like everything is speeding up, and I think that um the technology will 456 00:26:21,240 --> 00:26:22,997 continue developing very quickly. 457 00:26:23,080 --> 00:26:25,477 Um I also think that the the diffusion 458 00:26:25,560 --> 00:26:30,117 of the technology is is going to accelerate. I think there's been this 459 00:26:30,200 --> 00:26:33,717 question for many for the for the past many years, which is everyone's talking 460 00:26:33,800 --> 00:26:35,757 about AI, but where is AI in my daily 461 00:26:35,840 --> 00:26:38,517 life? I think that um that really is 462 00:26:38,600 --> 00:26:42,837 going to accelerate. Maybe not um you know, it's not going to go be 100 463 00:26:42,920 --> 00:26:46,837 times faster, but maybe it'll be, you know, two or three times faster every 464 00:26:46,920 --> 00:26:48,637 year for the you know, for the next many 465 00:26:48,720 --> 00:26:50,357 years. Um 466 00:26:50,440 --> 00:26:54,757 and and I think the thing that uh 467 00:26:54,840 --> 00:26:57,277 where where I really am excited about 468 00:26:57,360 --> 00:26:59,757 this going is you know, we talk about 469 00:26:59,840 --> 00:27:04,597 this idea of personal super intelligence within meta which is, you know, how does 470 00:27:04,680 --> 00:27:08,437 you know, how does each person have an agent that helps them accomplish the 471 00:27:08,520 --> 00:27:12,197 things that they're you know, ex- extremely excited about. 472 00:27:12,280 --> 00:27:15,477 How can that help them, you know, explore their passions, build a 473 00:27:15,560 --> 00:27:19,917 business, make a discovery, invent a thing, you know, 474 00:27:20,000 --> 00:27:21,757 you know, achieve the most maximal 475 00:27:21,840 --> 00:27:25,157 version of their own lives and I think 476 00:27:25,240 --> 00:27:28,357 where this is going even beyond then is 477 00:27:28,440 --> 00:27:32,397 sort of this concept of interpersonal super intelligence which is 478 00:27:32,480 --> 00:27:36,237 I think we already see in human society sometimes the thing that is necessary 479 00:27:36,320 --> 00:27:40,677 for big things to happen are just for the right set of people to be organized 480 00:27:40,760 --> 00:27:43,917 in the right way. You see this with companies, you see this with nonprofits, 481 00:27:44,000 --> 00:27:46,317 you see this with large movements, you 482 00:27:46,400 --> 00:27:48,997 see this even with with governments when 483 00:27:49,080 --> 00:27:52,117 they when they reorganize and I think 484 00:27:52,200 --> 00:27:53,597 there's an incredible opportunity for 485 00:27:53,680 --> 00:27:57,597 the technology of AI to enable humans to 486 00:27:57,680 --> 00:28:00,797 organize in ways that are even better 487 00:28:00,880 --> 00:28:04,917 than we do today and all of a sudden maybe we'll be able to organize as 488 00:28:05,000 --> 00:28:08,797 communities or as groups or as new companies or who knows what it'll look 489 00:28:08,880 --> 00:28:12,637 like to to solve many of our greatest challenges and so 490 00:28:12,720 --> 00:28:16,357 you know, it sounds a bit high falutin but but it's something I'm genuinely quite 491 00:28:16,440 --> 00:28:18,557 excited about. >> Yeah, I can see that and it's a very a 492 00:28:18,640 --> 00:28:21,877 very compelling vision. Look, this has been fascinating conversation. It's 493 00:28:21,960 --> 00:28:25,957 clear that the technology is is not just keeping pace that it's accelerating. 494 00:28:26,040 --> 00:28:29,317 It's clear that the debate is moving from, you know, just talking about the 495 00:28:29,400 --> 00:28:33,237 technology to as you said, how is it making a a difference in people's lives 496 00:28:33,320 --> 00:28:37,157 and being deployed. And that one of one of my concerns and maybe we'll finish on 497 00:28:37,240 --> 00:28:39,797 this is that I think some not all but 498 00:28:39,880 --> 00:28:44,517 some political leaders think that AI is is going to be tomorrow's issue when I 499 00:28:44,600 --> 00:28:49,477 think they need to recognize that it's it's an action this day issue. And this 500 00:28:49,560 --> 00:28:51,237 needs to go from, you know, AI needs to 501 00:28:51,320 --> 00:28:53,437 go from being a kind of specialized 502 00:28:53,520 --> 00:28:57,517 topic to a centralized responsibility of 503 00:28:57,600 --> 00:29:01,877 government. And it needs to have that same urgency and and action attached to 504 00:29:01,960 --> 00:29:05,197 it so we can realize all the benefits that Alex is talking about. But this has 505 00:29:05,280 --> 00:29:08,317 been a great discussion. Thanks for all your advice and tips everyone in the 506 00:29:08,400 --> 00:29:10,917 audience and giving us an insight into where things are going. Thanks very 507 00:29:11,000 --> 00:29:15,470 much. Thank you so much. >> [applause] 508 00:29:19,240 --> 00:29:21,317 >> Thank you so much to our speakers. 509 00:29:21,400 --> 00:29:24,677 Earlier, can we have another round of applause, please, for Rishi Sunak and 510 00:29:24,760 --> 00:29:28,837 Alexander Wang? >> [applause] >> Thank you. 511 00:29:28,920 --> 00:29:33,357 >> When I became Prime Minister, it's been an exact AI obsessed because of my time 512 00:29:33,440 --> 00:29:37,117 in in California. You you get the job in Downing Street and one of the earliest 513 00:29:37,200 --> 00:29:41,677 meetings you have is with the the Foreign Affairs team. And they sit down 514 00:29:41,760 --> 00:29:45,597 with you. I can I can see someone who will remember this in the audience. And 515 00:29:45,680 --> 00:29:48,117 they go over your international calendar 516 00:29:48,200 --> 00:29:53,397 for the next 12 months. And they they want to kind of figure out, you know, 517 00:29:53,480 --> 00:29:56,557 what you should be doing. And it turns out there's an international summit for 518 00:29:56,640 --> 00:30:00,637 basically every single thing that you can imagine. Turns out there were about 519 00:30:00,720 --> 00:30:05,157 150. And the British Foreign Office being the British Foreign Office, they wanted me 520 00:30:05,240 --> 00:30:08,637 to go to all of them. And although I did I did say I'm going 521 00:30:08,720 --> 00:30:12,237 to as a British Prime Minister, I think I can draw the line at the United 522 00:30:12,320 --> 00:30:17,197 Nations high-level meeting on sandstorms. That's where we agreed to disagree. 523 00:30:17,280 --> 00:30:20,597 But I noticed that there was no gathering to bring together people to 524 00:30:20,680 --> 00:30:24,357 talk about what I thought was the most transformative technology of our time. 525 00:30:24,440 --> 00:30:28,437 And I think we're all fortunate to be living at a time where AI is a 526 00:30:28,520 --> 00:30:32,037 general-purpose technology. These don't come along all that often 527 00:30:32,120 --> 00:30:35,837 and when they do, they had the opportunity in the past and will in the 528 00:30:35,920 --> 00:30:38,277 future to change every aspect of our 529 00:30:38,360 --> 00:30:42,957 economies, of our societies, of our lives. And that's why I thought it was 530 00:30:43,040 --> 00:30:48,237 necessary that leaders came together to discuss this at a regular basis. But, I 531 00:30:48,320 --> 00:30:50,957 also thought it needed to be done slightly differently. 532 00:30:51,040 --> 00:30:52,557 In contrast to previous technology 533 00:30:52,640 --> 00:30:56,077 cycles, it's companies like Alex's and 534 00:30:56,160 --> 00:31:00,317 innovators like Alex himself who were at the frontier of developing this 535 00:31:00,400 --> 00:31:03,637 technology. And I thought they needed to be in the 536 00:31:03,720 --> 00:31:07,757 room. Not least because it's businesses like yours that are have the resources 537 00:31:07,840 --> 00:31:12,037 to invest in this. Just to give you a bit of context. This year, you know, the 538 00:31:12,120 --> 00:31:14,157 the large AI companies are going to 539 00:31:14,240 --> 00:31:17,597 spend 20 times more 540 00:31:17,680 --> 00:31:22,397 on developing this technology than the US did for the Manhattan Project. If you 541 00:31:22,480 --> 00:31:26,477 think about seminal moments where the government mobilized to do something, 20 542 00:31:26,560 --> 00:31:30,677 times more just this year alone. So, I thought, "Look, it's vital that we do 543 00:31:30,760 --> 00:31:32,477 this in a different way, not like the G7 544 00:31:32,560 --> 00:31:35,517 and G20. We bring industry, innovators, 545 00:31:35,600 --> 00:31:39,677 and and government together." And that's that's what we did at Bletchley Park a 546 00:31:39,760 --> 00:31:41,837 few years ago. The summit series went to 547 00:31:41,920 --> 00:31:44,317 Paris, South Korea, and now brilliantly 548 00:31:44,400 --> 00:31:47,477 in India, a country with huge digital 549 00:31:47,560 --> 00:31:51,357 ambitions and indeed capacity. 550 00:31:51,440 --> 00:31:53,717 But, I think over those years, and Alex 551 00:31:53,800 --> 00:31:55,557 will start with this, I think actually 552 00:31:55,640 --> 00:31:59,917 the debate on AI has shifted from 553 00:32:00,000 --> 00:32:02,757 kind of technology to strategy. 554 00:32:02,840 --> 00:32:06,957 You know, from what these tools can do to what governments are choosing to do 555 00:32:07,040 --> 00:32:10,517 with them. And I wanted to maybe just start there. 556 00:32:10,600 --> 00:32:13,397 Is if you think about the last few years, you are someone who is at the 557 00:32:13,480 --> 00:32:15,837 frontier of developing this technology. 558 00:32:15,920 --> 00:32:19,117 You know, the the journey that we've been on in the last few years, you know, 559 00:32:19,200 --> 00:32:22,677 help us understand that. Where have we come? 560 00:32:22,760 --> 00:32:24,997 What is it that is exciting at this this 561 00:32:25,080 --> 00:32:30,077 moment? And just ground us in in where we are so that we can get get on to some 562 00:32:30,160 --> 00:32:32,997 of these questions about what we do with it. 563 00:32:33,080 --> 00:32:34,797 Yeah, I think um 564 00:32:34,880 --> 00:32:37,637 I I we are sitting here right now at 565 00:32:37,720 --> 00:32:40,637 such an incredibly exciting moment for 566 00:32:40,720 --> 00:32:45,437 the technology. I think we are um at the beginning of 567 00:32:45,520 --> 00:32:50,277 uh you know, of a true acceleration in the technology. And, you know, in many ways 568 00:32:50,360 --> 00:32:51,837 it's felt like this very clear 569 00:32:51,920 --> 00:32:54,597 acceleration even until now, but I think 570 00:32:54,680 --> 00:32:57,637 we're going to see sort of 2026 is uh an 571 00:32:57,720 --> 00:33:02,117 inflection point in many ways. I think to contextualize this over the past few 572 00:33:02,200 --> 00:33:04,877 years, I think from um 573 00:33:04,960 --> 00:33:06,477 2018 574 00:33:06,560 --> 00:33:10,237 through 2024 575 00:33:10,320 --> 00:33:12,877 really, which is a good 6-year period, 576 00:33:12,960 --> 00:33:15,397 we were writing we were in the era of 577 00:33:15,480 --> 00:33:20,357 pre-training. So, we were writing um one very large exponential curve towards 578 00:33:20,440 --> 00:33:22,117 improving the performance of the models. 579 00:33:22,200 --> 00:33:25,717 And um that curve was very predictable and it 580 00:33:25,800 --> 00:33:29,557 sort of had clear characteristics where the more resources you put in, the more 581 00:33:29,640 --> 00:33:33,077 results you would get out. And there was a lot of questions at the time of, you 582 00:33:33,160 --> 00:33:36,597 know, whether or not this curve would continue. And even to the degree it did 583 00:33:36,680 --> 00:33:40,157 continue, were there diminishing marginal returns? And I think this was sort of um 584 00:33:40,240 --> 00:33:41,277 one of the 585 00:33:41,360 --> 00:33:43,317 uh one of the more recent sort of 586 00:33:43,400 --> 00:33:47,597 eras of AI skepticism. And then in 2024, 587 00:33:47,680 --> 00:33:50,437 um like towards the end of 2024, uh 588 00:33:50,520 --> 00:33:52,837 began the era of reinforcement learning. 589 00:33:52,920 --> 00:33:54,957 So, this was sort of the the next major 590 00:33:55,040 --> 00:33:57,397 wave that stacked on top of the previous 591 00:33:57,480 --> 00:34:00,037 wave. And this is when we saw the models 592 00:34:00,120 --> 00:34:04,877 begin to learn to reason. And um you know, there's always this meme of 593 00:34:04,960 --> 00:34:06,917 what did Ilya see? Uh well, Ilya saw 594 00:34:07,000 --> 00:34:09,237 reasoning uh from the models. And it was 595 00:34:09,320 --> 00:34:12,637 very clearly this this next mega wave of 596 00:34:12,720 --> 00:34:17,357 of the technology. And even reinforcement learning had, you know, 597 00:34:17,440 --> 00:34:20,757 clearly some limitations. And there were all these questions around when that was 598 00:34:20,840 --> 00:34:24,677 going to, you know, how long that era was going to last and maybe there were 599 00:34:24,760 --> 00:34:27,517 limitations in that era. And then at the 600 00:34:27,600 --> 00:34:30,157 end of 2025, so just a few months ago, I 601 00:34:30,240 --> 00:34:34,597 think we really begun um an entirely new 602 00:34:34,680 --> 00:34:38,677 paradigm or era of the technology, which is 603 00:34:38,760 --> 00:34:41,237 really beginning the era of recursive 604 00:34:41,320 --> 00:34:46,317 self-improvement. And I think we're really starting to see this in the 605 00:34:46,400 --> 00:34:49,477 development of our models. I think you know, most of the major labs are seeing 606 00:34:49,560 --> 00:34:53,277 this in the development of their models, which is that the models themselves have 607 00:34:53,360 --> 00:34:56,437 now become instrumental in accelerating 608 00:34:56,520 --> 00:34:58,717 the process of producing the next AIs. 609 00:34:58,800 --> 00:35:02,877 And so, you know, the the sort of from the outside what you see is maybe an 610 00:35:02,960 --> 00:35:06,917 acceleration of overall development velocity, an acceleration in which the 611 00:35:07,000 --> 00:35:10,557 speed at which the the new models are being released. Internally, what we see 612 00:35:10,640 --> 00:35:14,317 is just dramatic speedups. I think the sort of 613 00:35:14,400 --> 00:35:16,437 the productivity of an individual 614 00:35:16,520 --> 00:35:19,317 researcher has grown dramatically. 615 00:35:19,400 --> 00:35:22,277 And we don't expect that to stop. In 616 00:35:22,360 --> 00:35:27,637 fact, we expect it to kind of smoothly continue accelerating where the output 617 00:35:27,720 --> 00:35:31,197 of an individual researcher, an individual engineer, well, I think that 618 00:35:31,280 --> 00:35:36,157 itself will continue growing exponentially as the technology improves. And 619 00:35:36,240 --> 00:35:41,237 the other mega wave that is somewhat coincident with this new paradigm within 620 00:35:41,320 --> 00:35:44,237 the sort of development of the technology of 621 00:35:44,320 --> 00:35:47,237 a recursive self-improvement is really 622 00:35:47,320 --> 00:35:51,877 the arrival of agents in earnest. And 623 00:35:51,960 --> 00:35:54,197 this is something that has been talked 624 00:35:54,280 --> 00:35:57,597 about since I honestly want to say 2023 625 00:35:57,680 --> 00:36:00,117 with a lot of fanfare. And for a lot 626 00:36:00,200 --> 00:36:02,837 long time, it was sort of 627 00:36:02,920 --> 00:36:06,637 one of these hype sort of buzzwords 628 00:36:06,720 --> 00:36:08,557 that, you know, never actually lived up 629 00:36:08,640 --> 00:36:11,037 to the expectations. And I think also at 630 00:36:11,120 --> 00:36:14,317 the end of 2025, we and toward you know, 631 00:36:14,400 --> 00:36:17,117 middle to end of 2025, we started to see 632 00:36:17,200 --> 00:36:19,557 agents actually work. And this is, you 633 00:36:19,640 --> 00:36:21,437 know, these are where the models can 634 00:36:21,520 --> 00:36:24,797 actually start taking actions and start 635 00:36:24,880 --> 00:36:27,157 automating entire workflows and being 636 00:36:27,240 --> 00:36:29,717 sort of dramatically more 637 00:36:29,800 --> 00:36:32,797 productive as actual sort of entities 638 00:36:32,880 --> 00:36:37,117 onto themselves versus assistants. And 639 00:36:37,200 --> 00:36:41,557 in this era is you know it started with coding agents. We're starting to see it 640 00:36:41,640 --> 00:36:44,837 really percolate through personal agents. This is a big part of what we 641 00:36:44,920 --> 00:36:46,877 believe in at Meta. 642 00:36:46,960 --> 00:36:49,957 And I think over the course of 2026 we 643 00:36:50,040 --> 00:36:53,757 will see large-scale agent deployments 644 00:36:53,840 --> 00:36:56,357 in many areas of the economy, many areas 645 00:36:56,440 --> 00:36:59,437 of the world. And 646 00:36:59,520 --> 00:37:05,237 the sort of you know if the the GDP of AI so to speak is going to grow 647 00:37:05,320 --> 00:37:08,757 exponentially. >> Well that that is very welcome to to 648 00:37:08,840 --> 00:37:13,557 hear. I I think this you you hit on something that is important because 649 00:37:13,640 --> 00:37:16,957 I remember when I first started doing this in the job the conversations were 650 00:37:17,040 --> 00:37:19,797 all around these scaling laws and would they continue. And you mentioned the 651 00:37:19,880 --> 00:37:21,837 skepticism. I mean not only have they 652 00:37:21,920 --> 00:37:24,557 continued and continued to deliver this 653 00:37:24,640 --> 00:37:29,117 exponential growth it feels very much like the last few months have seen an 654 00:37:29,200 --> 00:37:31,357 acceleration. It's what you touched on. 655 00:37:31,440 --> 00:37:36,357 It's what Dario and Demis have have also said to me in in publicly driven by this 656 00:37:36,440 --> 00:37:39,717 recursive self-improvement that the models themselves are now able to help 657 00:37:39,800 --> 00:37:42,597 us develop the new models quicker. So 658 00:37:42,680 --> 00:37:46,797 you know we're at this quite exciting but also a moment of some anxiety 659 00:37:46,880 --> 00:37:50,797 because of that accelerating development time scale. So I wanted to 660 00:37:50,880 --> 00:37:53,317 just then talk about trust 661 00:37:53,400 --> 00:37:55,437 because I tell you 662 00:37:55,520 --> 00:37:58,557 so I might I have two young girls and 663 00:37:58,640 --> 00:38:02,557 you know as they're using their chatbots and things and they're always like 664 00:38:02,640 --> 00:38:06,757 please you know so and so and so and so and thank you when they finish writing 665 00:38:06,840 --> 00:38:10,037 their request or they get the answer back. There's lots of pleases and and 666 00:38:10,120 --> 00:38:13,837 thank yous everywhere. And I and I I thought that was you know interesting. I 667 00:38:13,920 --> 00:38:16,717 said to them I said look there's you know you don't need to do that. There's 668 00:38:16,800 --> 00:38:21,237 not not a person. It doesn't it doesn't matter. And and by the way you know it 669 00:38:21,320 --> 00:38:25,197 it a fortune when we have to do the compute for that. Um and and they they 670 00:38:25,280 --> 00:38:29,277 said to me, "You know what, Daddy?" They said, "If if AI takes over the world, we 671 00:38:29,360 --> 00:38:31,717 want to have been kind to the AI." 672 00:38:31,800 --> 00:38:35,037 Uh and I thought that was good good insurance policy. But it it kind of you 673 00:38:35,120 --> 00:38:37,077 know, it touches on this anxiety. 674 00:38:37,160 --> 00:38:41,037 >> wise. >> Yeah. >> [laughter] >> Um in that maybe. 675 00:38:41,120 --> 00:38:44,237 You know, across the world, we're seeing these different attitudes 676 00:38:44,320 --> 00:38:48,757 uh towards AI. In countries like India, where we are, there's enormous optimism 677 00:38:48,840 --> 00:38:52,277 and and trust. And then actually in Western countries, you know, we're 678 00:38:52,360 --> 00:38:54,597 seeing that anxiety is still the kind of 679 00:38:54,680 --> 00:38:57,197 dominant feeling towards AI. Now, I 680 00:38:57,280 --> 00:39:00,677 think closing that confidence gap is uh 681 00:39:00,760 --> 00:39:05,117 as much a policy task as it is a a technical one. But I I'd love to just 682 00:39:05,200 --> 00:39:08,637 get your thoughts. I mean, obviously, you are at a you know, B2C company 683 00:39:08,720 --> 00:39:11,557 dealing with you know, billions of consumers every 684 00:39:11,640 --> 00:39:15,357 day. You know, how do you think about this trust question? You know, how do we 685 00:39:15,440 --> 00:39:20,717 get trust up in places where it is not for something that you know, you believe, I 686 00:39:20,800 --> 00:39:24,837 believe, and everyone in this room probably believes can do untold good. 687 00:39:24,920 --> 00:39:28,597 Yeah, this is this is one of the most important questions. And actually, 688 00:39:28,680 --> 00:39:30,277 perhaps even more so than the technology 689 00:39:30,360 --> 00:39:32,797 and development of the technology, um 690 00:39:32,880 --> 00:39:35,797 maybe will govern how how 691 00:39:35,880 --> 00:39:40,757 uh successfully the technology diffuses um more so than anything. And 692 00:39:40,840 --> 00:39:44,517 um you know, we we think deeply about this 693 00:39:44,600 --> 00:39:47,277 cuz our vision for the technology is 694 00:39:47,360 --> 00:39:49,597 that it is um we can deploy personal 695 00:39:49,680 --> 00:39:53,917 agents that uh know you better than most 696 00:39:54,000 --> 00:39:57,677 people know you, know about your health, know about your goals, know about your 697 00:39:57,760 --> 00:40:01,717 relationships, know about your family and your friends, and um can really help 698 00:40:01,800 --> 00:40:06,157 you live your life more effectively, you know, can help you be healthier, can 699 00:40:06,240 --> 00:40:10,077 help you accomplish projects that you would never would have had time for, can 700 00:40:10,160 --> 00:40:11,237 help you build the business you always 701 00:40:11,320 --> 00:40:13,797 wanted, um can help you explore your 702 00:40:13,880 --> 00:40:17,397 interests. And to properly build that 703 00:40:17,480 --> 00:40:20,437 requires an immense amount of trust from 704 00:40:20,520 --> 00:40:23,997 consumers, from governments, from 705 00:40:24,080 --> 00:40:28,277 from effectively everyone, from all organizations. And so it you know, this 706 00:40:28,360 --> 00:40:32,517 is one of these interesting moments where 707 00:40:32,600 --> 00:40:35,277 I think because the the sort of 708 00:40:35,360 --> 00:40:37,717 the form that of the technology that we 709 00:40:37,800 --> 00:40:40,517 wanted it to take is so intimate and is 710 00:40:40,600 --> 00:40:43,997 so deeply tied with what we think is 711 00:40:44,080 --> 00:40:49,477 really the the future of you know, the consumer life and the consumer 712 00:40:49,560 --> 00:40:52,757 you know, is at the core of the relationship with technology. 713 00:40:52,840 --> 00:40:56,877 Um The figuring out ways to develop the 714 00:40:56,960 --> 00:41:00,957 technology in partnership with governing bodies and in partnership with 715 00:41:01,040 --> 00:41:05,397 nonprofits and other organizations to garner that trust is very important. 716 00:41:05,480 --> 00:41:08,517 This is something we've seen at Meta actually in 717 00:41:08,600 --> 00:41:12,237 with WhatsApp. You know, one of the we were talking about this actually 718 00:41:12,320 --> 00:41:15,197 backstage, but WhatsApp has become 719 00:41:15,280 --> 00:41:18,117 incredibly ubiquitous and a real I think 720 00:41:18,200 --> 00:41:20,957 utility for much of the world in large 721 00:41:21,040 --> 00:41:25,477 part due to its commitment to privacy and the the 722 00:41:25,560 --> 00:41:28,997 sort of trust that we've built around WhatsApp as an organization. 723 00:41:29,080 --> 00:41:33,477 Often times at the sort of you know, not necessarily for the 724 00:41:33,560 --> 00:41:37,637 optimization of our business objectives, but for the optimization of trust in 725 00:41:37,720 --> 00:41:39,837 WhatsApp as a platform. I think a 726 00:41:39,920 --> 00:41:45,317 similar journey will be necessary for the AI products of the modern age and 727 00:41:45,400 --> 00:41:48,837 we've seen I mean we've already seen various 728 00:41:48,920 --> 00:41:53,197 kerfuffles or sort of jabs back and forth between the existing AI you know, 729 00:41:53,280 --> 00:41:56,277 products and labs around you know, whether their actions are truly 730 00:41:56,360 --> 00:42:01,197 garnering you know, truly building this these levels of trust. But 731 00:42:01,280 --> 00:42:04,117 and then I think the you know, the topic that 732 00:42:04,200 --> 00:42:06,157 you really kicked off at Bletchley 733 00:42:06,240 --> 00:42:08,277 around in a sort of like global way 734 00:42:08,360 --> 00:42:13,357 around safety, I think is more important than ever. I think we're already seeing 735 00:42:13,440 --> 00:42:17,317 that um the models are 736 00:42:17,400 --> 00:42:21,317 uh are are achieving levels of capability 737 00:42:21,400 --> 00:42:23,877 which cause some pause around some of 738 00:42:23,960 --> 00:42:27,717 the sort of like risks that uh we 739 00:42:27,800 --> 00:42:30,357 monitor for these models as it comes to 740 00:42:30,440 --> 00:42:32,637 various national security risks or uh 741 00:42:32,720 --> 00:42:35,997 various cybersecurity risks. And so um 742 00:42:36,080 --> 00:42:38,597 the the importance of properly testing 743 00:42:38,680 --> 00:42:43,077 and then building the right systems to uh monitor and deploy these models 744 00:42:43,160 --> 00:42:44,837 safely is is a paramount importance. 745 00:42:44,920 --> 00:42:47,557 >> And that you and I have been talking about the same topic for some years. And 746 00:42:47,640 --> 00:42:51,437 that was key to us at uh Bletchley was 747 00:42:51,520 --> 00:42:55,837 establishing the AI Security Institute in the UK to do exactly that work. And 748 00:42:55,920 --> 00:42:59,357 the and the thought was there was a gap where the technical capability didn't 749 00:42:59,440 --> 00:43:03,917 exist amongst governments to evaluate some of these risks. So how could we in 750 00:43:04,000 --> 00:43:06,077 the UK hopefully take a lead, but in 751 00:43:06,160 --> 00:43:11,037 sense provide a global public good uh where we're working partnership with 752 00:43:11,120 --> 00:43:14,637 companies like yours and the other frontier labs to do pre-deployment 753 00:43:14,720 --> 00:43:19,037 testing on these models. And my thought was always that by doing that, and 754 00:43:19,120 --> 00:43:22,317 that's not regulation, but it gives citizens confidence that the technology 755 00:43:22,400 --> 00:43:25,997 is safe cuz ultimately people don't adopt a technology that they don't 756 00:43:26,080 --> 00:43:29,997 trust. And it's what I my sense hearing you is that there's a role for the 757 00:43:30,080 --> 00:43:33,877 companies, I thought the the privacy example with WhatsApp is a good one, is 758 00:43:33,960 --> 00:43:37,637 how do they design AI products that will um 759 00:43:37,720 --> 00:43:41,437 will engender trust. And then there's a role, it sounds like you agree, for 760 00:43:41,520 --> 00:43:45,917 governments to do some of that technical evaluation. And is your sense that we 761 00:43:46,000 --> 00:43:49,277 need, you know, more cooperation between governments on that, more interaction 762 00:43:49,360 --> 00:43:50,597 with the labs? Is that working as well 763 00:43:50,680 --> 00:43:54,037 as it should at the moment? Um 764 00:43:54,120 --> 00:43:58,877 I think the interaction is working well, um but I think this is one area where 765 00:43:58,960 --> 00:44:02,437 you know uh we always can be more thoughtful, and 766 00:44:02,520 --> 00:44:07,637 especially as we enter this phase of dramatically increased AI progress. Um I 767 00:44:07,720 --> 00:44:10,837 think, you know, we constantly need to be thinking about how we can do better. 768 00:44:10,920 --> 00:44:13,517 I I think one of the other areas where 769 00:44:13,600 --> 00:44:18,117 this debate on trust will be won or lost is in the public sector. I think it's 770 00:44:18,200 --> 00:44:22,077 this is the If you experience that health care, more 771 00:44:22,160 --> 00:44:25,277 efficient government services, a state that is 772 00:44:25,360 --> 00:44:29,957 quicker at dealing with you, I think this debate goes from abstract to 773 00:44:30,040 --> 00:44:32,597 you have a real way to demonstrate benefit. 774 00:44:32,680 --> 00:44:34,637 Do you have a a sense from where you sit 775 00:44:34,720 --> 00:44:37,517 about where the most obvious use cases 776 00:44:37,600 --> 00:44:40,917 or deployment cases are in the public 777 00:44:41,000 --> 00:44:44,837 for AI that we should be excited about and that governments should be thinking 778 00:44:44,920 --> 00:44:48,997 about? I I do. I think that um 779 00:44:49,080 --> 00:44:53,757 one of the first you know, if you think of governments as 780 00:44:53,840 --> 00:44:56,797 um uh service providers in a sense to their 781 00:44:56,880 --> 00:44:59,477 citizens, I think there's there's a huge 782 00:44:59,560 --> 00:45:04,077 opportunity for all governments to um be able to deliver their services more 783 00:45:04,160 --> 00:45:08,717 effectively. Um we're already seeing that uh actually in India um on top of 784 00:45:08,800 --> 00:45:12,997 WhatsApp, funnily enough, where uh there are in certain states they actually 785 00:45:13,080 --> 00:45:16,557 deliver uh fast majority of the government services 786 00:45:16,640 --> 00:45:19,197 directly through WhatsApp. And then the 787 00:45:19,280 --> 00:45:24,917 uh for transit a nation actually able to uh through what in India. 788 00:45:25,000 --> 00:45:29,157 Uh we last year 100 million sub 789 00:45:29,240 --> 00:45:33,757 uh in the What? And that's not AI per se, but the 790 00:45:33,840 --> 00:45:37,477 the future opportunity that is quite exciting is actually the 791 00:45:37,560 --> 00:45:39,717 deployment of uh 792 00:45:39,800 --> 00:45:44,597 uh of AI to greatly increase the ease and and speed 793 00:45:44,680 --> 00:45:46,077 at which these government services are 794 00:45:46,160 --> 00:45:49,237 deployed and provided assistance. Um 795 00:45:49,320 --> 00:45:52,117 I think that you know, there's one uh 796 00:45:52,200 --> 00:45:54,397 term that I've always I've used and and 797 00:45:54,480 --> 00:45:56,837 I like this concept of proactive 798 00:45:56,920 --> 00:45:59,717 government, right? And it really is, you 799 00:45:59,800 --> 00:46:02,517 know, if you think about how should my 800 00:46:02,600 --> 00:46:07,237 governments be how should the state be imagined? 801 00:46:07,320 --> 00:46:10,117 You know, I think we need to move away from 802 00:46:10,200 --> 00:46:14,237 large-scale bureaucratic services 803 00:46:14,320 --> 00:46:18,557 and support organizations and think about how can the government deliver 804 00:46:18,640 --> 00:46:23,477 quickly and promptly deliver using computer people and make it easy to verify 805 00:46:23,560 --> 00:46:27,677 one of those agencies as fast as possible government services 806 00:46:27,760 --> 00:46:32,357 so that citizens can steer the state to develop solutions. 807 00:46:32,440 --> 00:46:35,917 Um and I think this cuts through civil services I think it cuts through health 808 00:46:36,000 --> 00:46:42,600 care as you mentioned and certainly has a has a large role to play in security 809 00:46:42,960 --> 00:46:47,677 and in the government with the government 810 00:46:47,760 --> 00:46:50,517 so I think it's quite a problem. 811 00:46:50,600 --> 00:46:57,080 I think you were you were talking about the concept of jet technology 812 00:46:57,400 --> 00:46:58,677 and that sort 813 00:46:58,760 --> 00:47:04,840 I need one more like one more like Uh I want to suggest something 814 00:47:05,440 --> 00:47:09,117 One of the benefits of leaving office is you have 815 00:47:09,200 --> 00:47:13,597 I think A lot of benefits leaving office you have a lot more time as I said 816 00:47:13,680 --> 00:47:17,637 and when I left someone gave me a great book by a guy called Jeffrey Ding, 817 00:47:17,720 --> 00:47:21,477 Professor Ding and technology and and great powers I'm sure you you know the 818 00:47:21,560 --> 00:47:26,237 book and and in it he makes a really compelling argument about technology 819 00:47:26,320 --> 00:47:30,477 diffusion essentially looking at the history of general purpose these going 820 00:47:30,560 --> 00:47:34,077 all the way back to the printing press and making the argument that you don't 821 00:47:34,160 --> 00:47:36,317 need to be the country that invents the 822 00:47:36,400 --> 00:47:38,397 technology to be the country that gets 823 00:47:38,480 --> 00:47:41,557 the most benefit from it and I think you 824 00:47:41,640 --> 00:47:44,237 know you you touched on India and India 825 00:47:44,320 --> 00:47:46,357 has recognized that technology 826 00:47:46,440 --> 00:47:49,277 leadership exactly does not depend on 827 00:47:49,360 --> 00:47:54,317 just inventing the technology it's about how effectively you deploy it and they 828 00:47:54,400 --> 00:47:58,597 here have focused on mass broad adoption and obviously 829 00:47:58,680 --> 00:48:01,317 supported by a few critical attributes. 830 00:48:01,400 --> 00:48:05,997 You have a very deep talent pool 831 00:48:06,080 --> 00:48:09,957 You talked about the rails of distribution. Obviously the digital 832 00:48:10,040 --> 00:48:12,397 public infrastructure here. 833 00:48:12,480 --> 00:48:15,277 The ID, UPI, the payments infrastructure 834 00:48:15,360 --> 00:48:18,797 and then most recently health accounts 835 00:48:18,880 --> 00:48:23,757 provide these infrastructure rails to distribute apps to over a billion 836 00:48:23,840 --> 00:48:27,197 people. And they also have a population that's supportive of that. 837 00:48:27,280 --> 00:48:31,917 And I think focusing on that has allowed India to position itself as a leader 838 00:48:32,000 --> 00:48:37,117 by by pursuing the deployment and adoption strategy. 839 00:48:37,200 --> 00:48:42,077 But thinking about that, you know, what are those critical attributes that 840 00:48:42,160 --> 00:48:46,397 government has to get right? If we want to turn your aspiration into a reality 841 00:48:46,480 --> 00:48:49,997 about agentic government, about delivering for our citizens. You know, I 842 00:48:50,080 --> 00:48:52,637 think government does need to think about things differently. It needs to 843 00:48:52,720 --> 00:48:56,597 build the architecture around deployment 844 00:48:56,680 --> 00:48:59,957 and diffusion, whether it's skills, whether it's compute, whether it's 845 00:49:00,040 --> 00:49:03,717 regulation. But crucially probably also data, 846 00:49:03,800 --> 00:49:07,357 which is something that you know better than most. So maybe if we start there 847 00:49:07,440 --> 00:49:11,437 and then broaden it out. If I'm you know, sitting as a government leader as 848 00:49:11,520 --> 00:49:14,677 many people in the audience are and trying to figure out what do I need to 849 00:49:14,760 --> 00:49:19,477 get right? What are the building blocks to deliver this thing properly? Let's 850 00:49:19,560 --> 00:49:22,397 start with data and what would your advice be there and then we'll we'll 851 00:49:22,480 --> 00:49:26,837 broaden it out a little bit. Yeah, there there is 852 00:49:26,920 --> 00:49:28,757 uh data 853 00:49:28,840 --> 00:49:31,277 is is so important. I think over the 854 00:49:31,360 --> 00:49:34,877 past few years many people have I think 855 00:49:34,960 --> 00:49:38,677 you know, realized this as we sort of look at the power of these models and we 856 00:49:38,760 --> 00:49:42,477 look behind the curtains and you realize that all the powers and beauty of these 857 00:49:42,560 --> 00:49:47,357 models are actually imbued by data. And if you think about 858 00:49:47,440 --> 00:49:50,917 So if you think about data is like in some ways the sort of new oil, the sort 859 00:49:51,000 --> 00:49:55,117 of like base material that is incredibly important for the future 860 00:49:55,200 --> 00:49:58,437 of of AI and the development of these models. 861 00:49:58,520 --> 00:50:03,437 Then I think as like governments should think about what are their, you know, 862 00:50:03,520 --> 00:50:06,197 what are their reserves? What are their data reserves? What are their data 863 00:50:06,280 --> 00:50:11,597 assets? And and to in many ways 864 00:50:11,680 --> 00:50:16,557 build strategies where they have sovereignty over some set of data or or 865 00:50:16,640 --> 00:50:21,037 they're able to build certain certain pockets of data sets that enable the the 866 00:50:21,120 --> 00:50:24,877 country to perform the best. I think one of the areas that is the two areas that 867 00:50:24,960 --> 00:50:27,077 is clear that are clearest for this are 868 00:50:27,160 --> 00:50:31,957 one is health care. I think many countries 869 00:50:32,040 --> 00:50:35,997 already are and other countries need to be thinking I think about 870 00:50:36,080 --> 00:50:39,437 very effective mechanisms for uh 871 00:50:39,520 --> 00:50:42,397 for health care data within the country 872 00:50:42,480 --> 00:50:44,837 to enable ultimately over time there to 873 00:50:44,920 --> 00:50:49,637 be AI models and AI agents that are built on top of that to deliver better 874 00:50:49,720 --> 00:50:53,157 health services to their citizens. And then the second is national security. 875 00:50:53,240 --> 00:50:56,797 And I think that is you know, in many ways pretty self-explanatory, but I 876 00:50:56,880 --> 00:51:00,677 think you know, countries need to be thinking about that 877 00:51:00,760 --> 00:51:04,557 quite deeply as to what are their sort of like core data assets. Then I think 878 00:51:04,640 --> 00:51:08,557 if you if you sort of build around data and you look at the 879 00:51:08,640 --> 00:51:10,957 next few layers, the next one is is 880 00:51:11,040 --> 00:51:13,357 infrastructure. So, you know, we think 881 00:51:13,440 --> 00:51:16,317 about the power, the 882 00:51:16,400 --> 00:51:21,037 the computational infrastructure and and what does that story look like? 883 00:51:21,120 --> 00:51:24,957 I don't think every country needs to build large-scale AI data centers in 884 00:51:25,040 --> 00:51:29,157 their country, but they do need to have a strategy of over time how are they 885 00:51:29,240 --> 00:51:32,797 going to have access to that infrastructure. And so whether this is 886 00:51:32,880 --> 00:51:36,197 in the form of alliances of various formats or 887 00:51:36,280 --> 00:51:41,597 or partnerships even with with corporate entities, the large cloud providers, I 888 00:51:41,680 --> 00:51:44,877 think that's an important element to think through. 889 00:51:44,960 --> 00:51:48,357 Then comes I think the sort of the 890 00:51:48,440 --> 00:51:51,277 the innovation ecosystem. I do think 891 00:51:51,360 --> 00:51:52,997 that, you know, we're in a moment of 892 00:51:53,080 --> 00:51:57,357 momentous technological change, and 893 00:51:57,440 --> 00:52:01,597 it is, I think, incredibly important for I think there is 894 00:52:01,680 --> 00:52:04,917 near boundless entrepreneurial and economic 895 00:52:05,000 --> 00:52:10,477 opportunity, and I think it is the responsibility of 896 00:52:10,560 --> 00:52:13,597 governments and countries to think through how they can build ecosystems 897 00:52:13,680 --> 00:52:17,277 that support that. I think India is actually, in many ways, a great 898 00:52:17,360 --> 00:52:21,117 very positive case study, in large part due to the due to the talent pool, but 899 00:52:21,200 --> 00:52:25,917 you know, I was at a dinner with a number of Indian founders and venture 900 00:52:26,000 --> 00:52:27,837 capitalists last night, and and the stat 901 00:52:27,920 --> 00:52:30,277 was, I think, there are more 902 00:52:30,360 --> 00:52:33,437 consumer AI companies in India consumer 903 00:52:33,520 --> 00:52:37,557 AI startups in India than in the United States. And so, there really are some of 904 00:52:37,640 --> 00:52:39,477 these like, you know, shining examples 905 00:52:39,560 --> 00:52:45,197 of incredible development of these ecosystems. And then, um And then, 906 00:52:45,280 --> 00:52:47,397 lastly, I think are the government services, which we talked about 907 00:52:47,480 --> 00:52:50,677 previously. So, we've got, I think, the elements of almost a a playbook 908 00:52:50,760 --> 00:52:54,837 emerging, right? You need to have the right data and make sure it's it's used 909 00:52:54,920 --> 00:52:57,757 effectively. You need some degree of compute. 910 00:52:57,840 --> 00:53:01,637 You've got appropriate regulation, as we've talked about, to build to build 911 00:53:01,720 --> 00:53:04,557 trust. And then, we've got some public sector 912 00:53:04,640 --> 00:53:08,477 use cases, as well, and an innovation ecosystem. So, these are the kind of 913 00:53:08,560 --> 00:53:10,317 ingredients where you can start to turn 914 00:53:10,400 --> 00:53:13,517 these ambitions into reality and and 915 00:53:13,600 --> 00:53:16,957 actual impact on people. I I just 916 00:53:17,040 --> 00:53:21,757 staying on that theme, you know, you you work at an incredible company. You work 917 00:53:21,840 --> 00:53:26,117 with other enterprises. You know, I talked to a lot of CEOs, and 918 00:53:26,200 --> 00:53:31,197 and I'm struck by how they're thinking about deploying AI in their businesses, 919 00:53:31,280 --> 00:53:35,237 and thinking about the parallel between that and and what governments should be 920 00:53:35,320 --> 00:53:39,437 doing. And you know, I think there is a kind of clear parallel. I'm intrigued. I 921 00:53:39,520 --> 00:53:43,637 think CEOs are, don't, know, kind of constantly thinking about AI. I think 922 00:53:43,720 --> 00:53:47,957 almost any large company CEO is spending their time figuring out, am I going to 923 00:53:48,040 --> 00:53:52,237 get disrupted, disintermediated by AI, how do I use it to stay ahead, where do 924 00:53:52,320 --> 00:53:54,317 I use it to to get more efficiency and 925 00:53:54,400 --> 00:53:57,037 more growth. And they are driving 926 00:53:57,120 --> 00:54:01,997 projects from the top down. And they are saying, look, this is something that is, 927 00:54:02,080 --> 00:54:06,517 you know, not not a topic that can just stay with the IT 928 00:54:06,600 --> 00:54:10,397 department, right? This is something that has to be driven by the CEO, where 929 00:54:10,480 --> 00:54:13,957 we pick the use cases, we have accountability, 930 00:54:14,040 --> 00:54:17,597 and we start piloting these and scaling them up quite quickly. 931 00:54:17,680 --> 00:54:20,237 I, you know, I my sense is we haven't 932 00:54:20,320 --> 00:54:25,037 quite seen that that same transition in governments yet, but I thought it's a 933 00:54:25,120 --> 00:54:28,037 good analogy in that look, this has to be driven by the person at the top. At 934 00:54:28,120 --> 00:54:31,277 the end of the day, if you're a prime minister, and I'm sure Tony would say 935 00:54:31,360 --> 00:54:35,357 the same thing, and indeed has, you can only do a few things that you drive 936 00:54:35,440 --> 00:54:38,917 personally from the top, and this really has to be one of them. You know, what 937 00:54:39,000 --> 00:54:41,357 are your kind of thoughts or advice for, 938 00:54:41,440 --> 00:54:43,757 I guess, both CEOs and and governments 939 00:54:43,840 --> 00:54:46,637 about how to kind of drive the machine. 940 00:54:46,720 --> 00:54:51,197 What does it take to get execution on this? And I mean, you've had to do that 941 00:54:51,280 --> 00:54:54,197 at Meta yourself, right? So, you'll have a sense from that. 942 00:54:54,280 --> 00:54:59,517 Yeah, I think it's a mix of of two things. I think first, um at 943 00:54:59,600 --> 00:55:03,197 uh at the highest level, you need to make 944 00:55:03,280 --> 00:55:06,877 it very clear that the organization will be an AI-first organization. And what 945 00:55:06,960 --> 00:55:08,357 that means for every organization is 946 00:55:08,440 --> 00:55:11,957 slightly different, but it it it um like 947 00:55:12,040 --> 00:55:16,117 the way that truly I think about it is that uh the success or failure of the 948 00:55:16,200 --> 00:55:18,677 organization actually entirely rests on 949 00:55:18,760 --> 00:55:23,757 its ability to properly embrace AI. And so, that's sort of the tops-down um 950 00:55:23,840 --> 00:55:27,277 component of it. And you know, there's sort of a strategy that must be designed 951 00:55:27,360 --> 00:55:30,557 more thoughtfully. And actually, honestly, you can probably ask AI uh to 952 00:55:30,640 --> 00:55:33,997 help you design that strategy now, and it'll probably do a pretty good job. And 953 00:55:34,080 --> 00:55:37,157 then you need um a a 954 00:55:37,240 --> 00:55:40,637 bed of bottoms-up adoption and 955 00:55:40,720 --> 00:55:43,357 bottoms-up, um, success stories. And 956 00:55:43,440 --> 00:55:48,477 for, uh, for us at Meta, like the example of this are, you know, we have 957 00:55:48,560 --> 00:55:50,237 these engineers throughout the company 958 00:55:50,320 --> 00:55:53,157 who are able to use AI to, uh, be, you 959 00:55:53,240 --> 00:55:55,597 know, 10 to 100 times more productive. 960 00:55:55,680 --> 00:55:59,517 And, um, those are just such incredible 961 00:55:59,600 --> 00:56:03,637 examples that where all of a sudden everyone else in the organization looks 962 00:56:03,720 --> 00:56:07,037 around and realizes, "Wow, this is really something that must be taken very 963 00:56:07,120 --> 00:56:11,797 seriously. It's not just something that, you know, um, the CEO or the C-level 964 00:56:11,880 --> 00:56:15,117 individuals are talking about and, you know, that they're so detached from 965 00:56:15,200 --> 00:56:19,477 reality." It's like, "No, it's very real on the ground, um, in a very big way." 966 00:56:19,560 --> 00:56:23,397 And so, I think you need this sort of mix of what are the success stories that 967 00:56:23,480 --> 00:56:27,997 cause everyone to realize how big of a shift it actually is. And then also 968 00:56:28,080 --> 00:56:31,997 ensure that there's a continual, um, there's a very clear top-down direction 969 00:56:32,080 --> 00:56:34,717 that this transition is, is mandatory. 970 00:56:34,800 --> 00:56:37,237 >> very helpful advice. Look, we're almost 971 00:56:37,320 --> 00:56:39,877 out of time. So, maybe a last final 972 00:56:39,960 --> 00:56:42,517 point, uh, to close on is, you know, 973 00:56:42,600 --> 00:56:47,117 you're looking ahead, you know, what are you most excited about, uh, in the 974 00:56:47,200 --> 00:56:48,917 development of this, of this technology? 975 00:56:49,000 --> 00:56:52,917 You've touched on personal agents, other scientific breakthroughs. You know, 976 00:56:53,000 --> 00:56:56,957 what, where is this going and what should governments be doing now, you 977 00:56:57,040 --> 00:56:58,677 know, so that they are prepared and and 978 00:56:58,760 --> 00:57:01,317 not playing catch-up? 979 00:57:01,400 --> 00:57:06,357 I think the things that, um, you can depend on, I think the, the pace of 980 00:57:06,440 --> 00:57:07,797 technology development will continue 981 00:57:07,880 --> 00:57:13,757 accelerating. Um, I think this, this era of recursive self-improvement will, 982 00:57:13,840 --> 00:57:16,957 uh, feel like everything is speeding up and 983 00:57:17,040 --> 00:57:21,357 I think that, um, the technology will continue developing very quickly. Um, I 984 00:57:21,440 --> 00:57:25,997 also think that the, the diffusion of the technology is, is going to 985 00:57:26,080 --> 00:57:29,717 accelerate. I think there's been this question for many, for the, for the past 986 00:57:29,800 --> 00:57:33,437 many years, which is, everyone's talking about AI, but where is AI in my daily 987 00:57:33,520 --> 00:57:36,237 life? I think that um that really is 988 00:57:36,320 --> 00:57:40,757 going to accelerate. Maybe not um you know, it's not going to go be 100 times 989 00:57:40,840 --> 00:57:42,797 faster, but maybe it'll be you know, two 990 00:57:42,880 --> 00:57:45,517 or three times faster every year for the 991 00:57:45,600 --> 00:57:47,997 you know, for the next many years. Um 992 00:57:48,080 --> 00:57:53,117 and and I think the thing that uh where 993 00:57:53,200 --> 00:57:57,557 where I really am excited about this going is um you know, we talk about this 994 00:57:57,640 --> 00:58:02,237 idea of personal superintelligence within Meta, which is you know, how does 995 00:58:02,320 --> 00:58:06,077 you know, how does each person have an agent that helps them accomplish the 996 00:58:06,160 --> 00:58:09,837 things that they're you know, ex- extremely excited about. 997 00:58:09,920 --> 00:58:13,117 How can they help them, you know, explore their passions, build a 998 00:58:13,200 --> 00:58:15,437 business, make a discovery, invent a 999 00:58:15,520 --> 00:58:18,557 thing, um you know, um you know, achieve 1000 00:58:18,640 --> 00:58:20,957 the most maximal version of their own 1001 00:58:21,040 --> 00:58:24,517 lives. And I think where um this is 1002 00:58:24,600 --> 00:58:28,197 going even beyond then is sort of this concept of interpersonal 1003 00:58:28,280 --> 00:58:31,877 superintelligence, which is um I think we already see in human society 1004 00:58:31,960 --> 00:58:36,597 sometimes the thing that is necessary for big things to happen are just for 1005 00:58:36,680 --> 00:58:39,797 the right set of people to be organized in the right way. You see this with 1006 00:58:39,880 --> 00:58:43,837 companies, you see this with nonprofits, you see this with large movements, um 1007 00:58:43,920 --> 00:58:47,757 you see this even with with um governments when they uh when they 1008 00:58:47,840 --> 00:58:50,037 reorganize. And I think there's an 1009 00:58:50,120 --> 00:58:51,357 incredible opportunity for the 1010 00:58:51,440 --> 00:58:55,237 technology of AI to enable humans to 1011 00:58:55,320 --> 00:58:58,437 organize in ways that are even better um 1012 00:58:58,520 --> 00:59:02,557 than we do today. And all of a sudden maybe we'll be able to organize as 1013 00:59:02,640 --> 00:59:06,437 communities or as groups or as new companies or who knows what it'll look 1014 00:59:06,520 --> 00:59:10,517 like to uh to solve many of our greatest challenges. And so um you know, it 1015 00:59:10,600 --> 00:59:13,997 sounds a bit highfalutin, but um but it's something I'm genuinely quite 1016 00:59:14,080 --> 00:59:16,477 excited about. >> No, I can see that. It's a very a very 1017 00:59:16,560 --> 00:59:19,757 compelling vision. But this has been fascinating conversation. It's clear 1018 00:59:19,840 --> 00:59:23,557 that the technology is is not just keeping pace, that it's accelerating. 1019 00:59:23,640 --> 00:59:27,037 It's clear that the debate is moving from you know, just talking about the 1020 00:59:27,120 --> 00:59:30,917 technology to as you said, how is it making a a difference in people's lives 1021 00:59:31,000 --> 00:59:34,757 and being deployed. And that one of one of my concerns, and maybe we'll finish 1022 00:59:34,840 --> 00:59:38,717 on this, is that, you know, I think some, not all, some political leaders 1023 00:59:38,800 --> 00:59:42,637 think that AI is is going to be tomorrow's issue, when I think they need 1024 00:59:42,720 --> 00:59:47,717 to recognize that it's it's an action this day issue. And this needs to go 1025 00:59:47,800 --> 00:59:49,317 from, you know, AI needs to go from 1026 00:59:49,400 --> 00:59:52,477 being a kind of specialized topic to a 1027 00:59:52,560 --> 00:59:57,117 centralized responsibility of government. Uh and it needs to have that 1028 00:59:57,200 --> 01:00:00,877 same urgency and and action attached to it so we can realize all the benefits 1029 01:00:00,960 --> 01:00:04,557 that Alex is talking about. But this has been a great discussion. Thanks for all 1030 01:00:04,640 --> 01:00:07,397 your advice and tips for everyone in the audience and giving us an insight into 1031 01:00:07,480 --> 01:00:09,197 how things are going. Thanks very much. 1032 01:00:09,280 --> 01:00:13,080 Thank you so much. >> [applause] 1033 01:00:16,880 --> 01:00:18,997 >> Thank you so much to our speakers. 1034 01:00:19,080 --> 01:00:22,317 Earlier, can we have another round of applause, please, for Rishi Sunak and 1035 01:00:22,400 --> 01:00:26,357 Alexander Wa When I became Prime Minister, I'm an AI obsessed, AI 1036 01:00:26,440 --> 01:00:29,837 obsessed because of my time in in California. You you get the job in 1037 01:00:29,920 --> 01:00:33,037 Downing Street and one of the earliest meetings you have is with the the 1038 01:00:33,120 --> 01:00:37,877 Foreign Affairs team. And they sit down with you. I can I can see someone who 1039 01:00:37,960 --> 01:00:39,757 will remember this in the audience. 1040 01:00:39,840 --> 01:00:41,917 And they go over your international 1041 01:00:42,000 --> 01:00:47,517 calendar for the next 12 months. And they they want to kind of figure out, 1042 01:00:47,600 --> 01:00:50,357 you know, what you should be doing. And it turns out there's an international 1043 01:00:50,440 --> 01:00:54,677 summit for basically every single thing that you can imagine. Uh turns out there 1044 01:00:54,760 --> 01:00:58,277 were about 150. Uh and the British Foreign Office being 1045 01:00:58,360 --> 01:01:01,037 the British Foreign Office, they wanted me to go to all of them. 1046 01:01:01,120 --> 01:01:04,157 Uh and although I did I did say I'm going to as a British Prime Minister, I 1047 01:01:04,240 --> 01:01:07,877 think I can draw the line at the United Nations high-level meeting on 1048 01:01:07,960 --> 01:01:12,357 sandstorms. That's where we agreed to disagree. Um but I noticed that there 1049 01:01:12,440 --> 01:01:16,157 was no gathering to bring together people to talk about what I thought was 1050 01:01:16,240 --> 01:01:19,317 the most transformative technology of our time. And I think we're all 1051 01:01:19,400 --> 01:01:22,317 fortunate to be living at a time where 1052 01:01:22,400 --> 01:01:24,717 AI is a general purpose technology. 1053 01:01:24,800 --> 01:01:28,597 These don't come along all that often and when they do, they had the 1054 01:01:28,680 --> 01:01:32,597 opportunity in the past and will in the future to change every aspect of our 1055 01:01:32,680 --> 01:01:37,197 economies, of our societies, uh of our lives. And that's why I thought it was 1056 01:01:37,280 --> 01:01:42,397 necessary that leaders came together to discuss this at a regular basis. But, uh 1057 01:01:42,480 --> 01:01:45,957 I also thought it needed to be done slightly differently. Um in contrast to 1058 01:01:46,040 --> 01:01:47,517 previous technology cycles, it's 1059 01:01:47,600 --> 01:01:50,397 companies like Alex's and 1060 01:01:50,480 --> 01:01:53,117 innovators like Alex himself who were at 1061 01:01:53,200 --> 01:01:57,477 the frontier of developing this technology. Uh and I thought they needed 1062 01:01:57,560 --> 01:02:01,397 to be in the room. Not least because it's businesses like yours that are have 1063 01:02:01,480 --> 01:02:05,877 the resources to invest in this. Just to give you a bit of context, this year, 1064 01:02:05,960 --> 01:02:08,157 you know, the the large AI companies are 1065 01:02:08,240 --> 01:02:11,917 going to spend 20 times more 1066 01:02:12,000 --> 01:02:16,717 on developing this technology than the US did for the Manhattan Project. And if 1067 01:02:16,800 --> 01:02:19,597 you think about seminal moments where the government mobilized to do 1068 01:02:19,680 --> 01:02:23,717 something, 20 times more just this year alone. Um so, I thought, look, it's 1069 01:02:23,800 --> 01:02:25,637 vital that we do this in a different 1070 01:02:25,720 --> 01:02:28,477 way, not like the G7 and G20. We bring 1071 01:02:28,560 --> 01:02:32,837 industry, innovators, and and government uh together and that's uh that's what we 1072 01:02:32,920 --> 01:02:35,117 did at Bletchley Park a few years ago. 1073 01:02:35,200 --> 01:02:36,917 The summit series went to Paris, South 1074 01:02:37,000 --> 01:02:39,197 Korea, and now, brilliantly, in India, a 1075 01:02:39,280 --> 01:02:43,317 country with huge digital ambitions and 1076 01:02:43,400 --> 01:02:46,357 uh indeed capacity. Um but I think over 1077 01:02:46,440 --> 01:02:48,557 those years, and Alex, let's start with 1078 01:02:48,640 --> 01:02:50,677 this, I think actually the the the 1079 01:02:50,760 --> 01:02:54,237 debate on AI has shifted from 1080 01:02:54,320 --> 01:02:57,157 kind of technology to strategy. Uh you 1081 01:02:57,240 --> 01:03:01,437 know, from what these tools can do to what governments are choosing to do with 1082 01:03:01,520 --> 01:03:04,797 them. And I wanted to maybe just start there. 1083 01:03:04,880 --> 01:03:07,717 Is if you think about the last few years, you are someone who is at the 1084 01:03:07,800 --> 01:03:10,157 frontier of developing this technology. 1085 01:03:10,240 --> 01:03:13,397 You know, the the journey that we've been on in the last few years, you know, 1086 01:03:13,480 --> 01:03:16,997 help us understand that. Where have we come? 1087 01:03:17,080 --> 01:03:19,357 What is it that is exciting at this this 1088 01:03:19,440 --> 01:03:24,517 moment and just ground us in in where we are so that we can get get on to some of 1089 01:03:24,600 --> 01:03:27,237 these questions about what we do with it. 1090 01:03:27,320 --> 01:03:29,117 Yeah, I think 1091 01:03:29,200 --> 01:03:31,957 I mean we are sitting here right now at 1092 01:03:32,040 --> 01:03:34,957 such an incredibly exciting moment for 1093 01:03:35,040 --> 01:03:37,637 the technology. I think we are 1094 01:03:37,720 --> 01:03:42,677 at the beginning of uh you know, of a true acceleration in 1095 01:03:42,760 --> 01:03:46,197 the technology and you know, in many ways it's felt like this very clear 1096 01:03:46,280 --> 01:03:48,917 acceleration even until now, but I think 1097 01:03:49,000 --> 01:03:53,757 we're going to see sort of 2026 is an inflection point in many ways. I 1098 01:03:53,840 --> 01:03:55,517 think to contextualize this over the 1099 01:03:55,600 --> 01:03:59,117 past few years, I think from 1100 01:03:59,200 --> 01:04:00,797 2018 1101 01:04:00,880 --> 01:04:04,597 through 2024 1102 01:04:04,680 --> 01:04:07,197 really, which is a good 6-year period, 1103 01:04:07,280 --> 01:04:09,717 we were writing we were in the era of 1104 01:04:09,800 --> 01:04:14,637 pre-training. So, we were writing one very large exponential curve towards 1105 01:04:14,720 --> 01:04:16,477 improving the performance of the models. 1106 01:04:16,560 --> 01:04:20,037 And that curve was very predictable and it 1107 01:04:20,120 --> 01:04:23,877 sort of had clear characteristics where the more resources you put in, the more 1108 01:04:23,960 --> 01:04:27,397 results you would get out and there was a lot of questions at the time of, you 1109 01:04:27,480 --> 01:04:30,917 know, whether or not this curve would continue and even to the degree it did 1110 01:04:31,000 --> 01:04:34,517 continue, were there diminishing marginal returns? And I think this was sort of 1111 01:04:34,600 --> 01:04:35,677 one of the 1112 01:04:35,760 --> 01:04:37,637 one of the more recent sort of 1113 01:04:37,720 --> 01:04:42,357 eras of AI skepticism. And then in 2024, 1114 01:04:42,440 --> 01:04:47,157 like towards the end of 2024, began the era of reinforcement learning. 1115 01:04:47,240 --> 01:04:49,277 So, this was sort of the the next major 1116 01:04:49,360 --> 01:04:51,677 wave that stacked on top of the previous 1117 01:04:51,760 --> 01:04:54,357 wave and this is when we saw the models 1118 01:04:54,440 --> 01:04:59,117 begin to learn to reason. And you know, there's always this meme of 1119 01:04:59,200 --> 01:05:01,237 what did Ilya see? Well, Ilya saw 1120 01:05:01,320 --> 01:05:03,557 reasoning from the models. And it was 1121 01:05:03,640 --> 01:05:06,957 very clearly this this next mega wave of 1122 01:05:07,040 --> 01:05:12,277 of the technology and even reinforcement learning had, you know, clearly some 1123 01:05:12,360 --> 01:05:16,117 limitations and there were all these questions around when that was going to, 1124 01:05:16,200 --> 01:05:17,757 you know, how long that era was going to 1125 01:05:17,840 --> 01:05:19,677 last and maybe there were limitations in 1126 01:05:19,760 --> 01:05:23,157 that era. And then at the end of 2025, 1127 01:05:23,240 --> 01:05:24,877 so just a few months ago, I think we 1128 01:05:24,960 --> 01:05:27,077 really began 1129 01:05:27,160 --> 01:05:30,677 an entirely new paradigm or era of the 1130 01:05:30,760 --> 01:05:32,997 technology, which is 1131 01:05:33,080 --> 01:05:35,557 really beginning the era of recursive 1132 01:05:35,640 --> 01:05:40,677 self-improvement. And I think we're really starting to see this in the 1133 01:05:40,760 --> 01:05:43,797 development of our models. I think, you know, most of the major labs are seeing 1134 01:05:43,880 --> 01:05:47,557 this in the development of their models, which is that the models themselves have 1135 01:05:47,640 --> 01:05:50,717 now become instrumental in accelerating 1136 01:05:50,800 --> 01:05:53,037 the process of producing the next AIs. 1137 01:05:53,120 --> 01:05:57,237 And so, you know, the the sort of from the outside what you see is maybe an 1138 01:05:57,320 --> 01:06:01,117 acceleration of overall development velocity, an acceleration in at which 1139 01:06:01,200 --> 01:06:04,797 the speed at which the the new models are being released. Internally, what we 1140 01:06:04,880 --> 01:06:08,637 see is just dramatic speed-ups. I think the sort of 1141 01:06:08,720 --> 01:06:10,797 the productivity of an individual 1142 01:06:10,880 --> 01:06:13,677 researcher has grown dramatically, 1143 01:06:13,760 --> 01:06:16,637 and we don't expect that to stop. In 1144 01:06:16,720 --> 01:06:21,957 fact, we expect it to kind of smoothly continue accelerating where the output 1145 01:06:22,040 --> 01:06:25,117 of an individual researcher, an individual engineer, well, I think 1146 01:06:25,200 --> 01:06:28,317 that itself will continue growing exponentially as the technology 1147 01:06:28,400 --> 01:06:30,517 improves. And 1148 01:06:30,600 --> 01:06:35,597 the other mega wave that is somewhat coincident with this new paradigm within 1149 01:06:35,680 --> 01:06:38,597 the sort of development of the technology of 1150 01:06:38,680 --> 01:06:41,597 a recursive self-improvement is really 1151 01:06:41,680 --> 01:06:46,237 the arrival of agents in earnest. And 1152 01:06:46,320 --> 01:06:48,557 this is something that has been talked 1153 01:06:48,640 --> 01:06:51,917 about since I honestly want to say 2023 1154 01:06:52,000 --> 01:06:54,437 with a lot of fanfare, and for a long 1155 01:06:54,520 --> 01:06:57,157 long time it was sort of 1156 01:06:57,240 --> 01:07:02,197 one of these hype sort of buzzwords that, you know, never 1157 01:07:02,280 --> 01:07:04,117 actually lived up to the expectations. 1158 01:07:04,200 --> 01:07:07,797 And I think also at the end of 2025, we 1159 01:07:07,880 --> 01:07:12,357 and toward you know, middle to end of 2025, we started to see agents actually 1160 01:07:12,440 --> 01:07:16,357 work. And this is, you know, these are where the models can actually start 1161 01:07:16,440 --> 01:07:19,197 taking um actions um and start 1162 01:07:19,280 --> 01:07:21,517 automating entire workflows and being um 1163 01:07:21,600 --> 01:07:23,837 sort of dramatically more 1164 01:07:23,920 --> 01:07:27,157 uh productive as actual sort of entities 1165 01:07:27,240 --> 01:07:31,437 onto themselves versus assistants. And um 1166 01:07:31,520 --> 01:07:34,957 And this era is, you know, it started with coding agents. We're starting to 1167 01:07:35,040 --> 01:07:36,797 see it um really percolate through 1168 01:07:36,880 --> 01:07:38,277 personal agent. This is a big part of 1169 01:07:38,360 --> 01:07:42,077 what uh we believe in at Meta. Um and I 1170 01:07:42,160 --> 01:07:44,397 think over the course of 2026, we will 1171 01:07:44,480 --> 01:07:48,797 see large-scale agent deployments um in 1172 01:07:48,880 --> 01:07:50,957 many areas of the economy, many areas of 1173 01:07:51,040 --> 01:07:54,957 of the world, and um the sort of, you 1174 01:07:55,040 --> 01:07:58,677 know, if the the GDP of AI, so to speak, 1175 01:07:58,760 --> 01:08:00,277 uh is going to grow exponentially. 1176 01:08:00,360 --> 01:08:03,037 >> Well, that that is uh very welcome to to 1177 01:08:03,120 --> 01:08:07,797 hear. I I think this you you hit on something that is important because 1178 01:08:07,880 --> 01:08:11,237 I remember when I first started doing this in the job, the conversations were 1179 01:08:11,320 --> 01:08:14,077 all around these scaling laws and would they continue? And you mentioned the 1180 01:08:14,160 --> 01:08:16,117 skepticism. I mean, not only have they 1181 01:08:16,200 --> 01:08:18,837 continued and continue to deliver this 1182 01:08:18,920 --> 01:08:23,397 exponential growth, it feels very much like the last few months have seen an 1183 01:08:23,480 --> 01:08:25,637 acceleration. It's what you touched on. 1184 01:08:25,720 --> 01:08:30,317 It's what Dario and and Demis have have also said to me and and publicly, driven 1185 01:08:30,400 --> 01:08:33,757 by this recursive self-improvement, that the models themselves are now able to 1186 01:08:33,840 --> 01:08:36,357 help us develop the new models quicker. 1187 01:08:36,440 --> 01:08:40,197 So, you know, we're at this quite exciting but also a moment of some 1188 01:08:40,280 --> 01:08:43,237 anxiety because of that accelerating uh 1189 01:08:43,320 --> 01:08:47,637 development time scale. So, I wanted to just then talk about trust 1190 01:08:47,720 --> 01:08:50,037 because I I tell you give you a story. 1191 01:08:50,120 --> 01:08:54,677 My I have two, you know, young girls and you know, as they're using their 1192 01:08:54,760 --> 01:08:58,397 chatbots and things and they're always like, "Please, you know, if so-and-so 1193 01:08:58,480 --> 01:09:02,237 and so-and-so." And thank you when they finish writing their request or they get 1194 01:09:02,320 --> 01:09:06,157 the answer back. There's lots of pleases and and thank yous everywhere. And I and 1195 01:09:06,240 --> 01:09:08,797 I I thought that was, you know, interesting. But I said to them, I said, 1196 01:09:08,880 --> 01:09:12,677 "Look, there's, you know, you don't need to do that. There's not not a person it 1197 01:09:12,760 --> 01:09:16,357 doesn't it doesn't matter. And and by the way, you know, it costs a fortune 1198 01:09:16,440 --> 01:09:18,277 when we have to do the compute for that. 1199 01:09:18,360 --> 01:09:21,997 Um and and they they said to me, "You know what, Daddy?" They said, "If if AI 1200 01:09:22,080 --> 01:09:25,997 takes over the world, we want to have been kind to the AI." 1201 01:09:26,080 --> 01:09:29,237 Uh and I thought that was good good insurance policy. But it it it kind of 1202 01:09:29,320 --> 01:09:32,317 it, you know, it touches on this anxiety. Very wise. 1203 01:09:32,400 --> 01:09:35,357 >> [laughter] >> Um in that, maybe. 1204 01:09:35,440 --> 01:09:38,557 You know, across the world, we're seeing these different attitudes 1205 01:09:38,640 --> 01:09:43,077 uh towards AI. In countries like India, where we are, there's enormous optimism 1206 01:09:43,160 --> 01:09:46,557 and and trust. And then actually in Western countries, you know, we're 1207 01:09:46,640 --> 01:09:48,917 seeing that anxiety is still the kind of 1208 01:09:49,000 --> 01:09:51,557 dominant feeling towards AI. Now, I 1209 01:09:51,640 --> 01:09:54,997 think closing that confidence gap is uh 1210 01:09:55,080 --> 01:09:59,437 as much a policy task as it is a a technical one. But I I'd love to just 1211 01:09:59,520 --> 01:10:02,957 get your thoughts. And then obviously you are at a, you know, B2C company 1212 01:10:03,040 --> 01:10:05,877 dealing with, you know, billions of consumers every 1213 01:10:05,960 --> 01:10:09,677 day. You know, how do you think about this trust question? You know, how do we 1214 01:10:09,760 --> 01:10:13,517 get trust up in places where it is not? For 1215 01:10:13,600 --> 01:10:16,037 something that, you know, you believe, I believe, and everyone in this room 1216 01:10:16,120 --> 01:10:19,197 probably believes can do untold good. 1217 01:10:19,280 --> 01:10:22,917 Yeah, this is this is one of the most important questions. And actually, 1218 01:10:23,000 --> 01:10:26,397 perhaps even more so than the technology and the development of the technology, 1219 01:10:26,480 --> 01:10:30,197 um maybe will govern how how 1220 01:10:30,280 --> 01:10:35,117 uh successfully the technology diffuses um more so than anything. And 1221 01:10:35,200 --> 01:10:38,837 um you know, we we think deeply about this 1222 01:10:38,920 --> 01:10:41,637 cuz our vision for the technology is 1223 01:10:41,720 --> 01:10:43,917 that it is um we can deploy personal 1224 01:10:44,000 --> 01:10:48,237 agents that uh know you better than most 1225 01:10:48,320 --> 01:10:51,997 people know you, know about your health, know about your goals, know about your 1226 01:10:52,080 --> 01:10:56,037 relationships, know about your family and your friends, and um can really help 1227 01:10:56,120 --> 01:11:00,517 you live your life more effectively, you know, can help you be healthier, can 1228 01:11:00,600 --> 01:11:04,517 help you accomplish projects that you never would have had time for, can help 1229 01:11:04,600 --> 01:11:05,597 you build the business you always 1230 01:11:05,680 --> 01:11:08,117 wanted, um can help you explore your 1231 01:11:08,200 --> 01:11:11,717 interests. And to properly build that 1232 01:11:11,800 --> 01:11:14,717 requires an immense amount of trust from 1233 01:11:14,800 --> 01:11:18,037 consumers, from governments, from 1234 01:11:18,120 --> 01:11:20,357 um from effectively everyone, from all 1235 01:11:20,440 --> 01:11:25,117 organizations. And so it you know, this is one of these interesting moments 1236 01:11:25,200 --> 01:11:29,597 where um I think because the the sort of 1237 01:11:29,680 --> 01:11:31,877 the form that the of the technology that 1238 01:11:31,960 --> 01:11:34,837 we wanted to take is so intimate and is 1239 01:11:34,920 --> 01:11:38,317 so deeply tied with what we think is 1240 01:11:38,400 --> 01:11:40,717 really the the future of, you know, the 1241 01:11:40,800 --> 01:11:45,517 consumer life and the consumer um you know, is at the core of the relationship 1242 01:11:45,600 --> 01:11:48,037 with technology. Um 1243 01:11:48,120 --> 01:11:51,197 The figuring out ways to develop the the 1244 01:11:51,280 --> 01:11:55,317 technology in partnership with governing bodies and in partnership with um 1245 01:11:55,400 --> 01:11:59,717 nonprofits and other organizations to garner that trust is very important. 1246 01:11:59,800 --> 01:12:04,197 This is something we've seen at Meta actually in um with WhatsApp. You know, 1247 01:12:04,280 --> 01:12:06,557 one of the we were talking about this actually 1248 01:12:06,640 --> 01:12:09,637 backstage, but um WhatsApp has become 1249 01:12:09,720 --> 01:12:12,277 uh incredibly ubiquitous um and a real I 1250 01:12:12,360 --> 01:12:14,877 think utility for much of the world um 1251 01:12:14,960 --> 01:12:20,397 in large part due to uh its commitment to privacy and the the sort of trust 1252 01:12:20,480 --> 01:12:21,677 that we've built around WhatsApp as an 1253 01:12:21,760 --> 01:12:24,557 organization. Um oftentimes at the sort 1254 01:12:24,640 --> 01:12:27,797 of uh you know, not necessarily for the 1255 01:12:27,880 --> 01:12:31,957 optimization of our business objectives, but for the optimization of trust in 1256 01:12:32,040 --> 01:12:34,157 WhatsApp as a platform. I think a 1257 01:12:34,240 --> 01:12:37,277 similar journey will be necessary for 1258 01:12:37,360 --> 01:12:39,597 the AI products of the modern age and 1259 01:12:39,680 --> 01:12:42,037 we've seen um I mean we've already seen 1260 01:12:42,120 --> 01:12:44,877 various uh kerfuffles or sort of jabs 1261 01:12:44,960 --> 01:12:49,157 back and forth between the existing AI, you know, products and labs around, you 1262 01:12:49,240 --> 01:12:53,237 know, whether their actions are truly garnering, you know, truly building this 1263 01:12:53,320 --> 01:12:57,277 uh these levels of trust. But and then I think the you know, the topic 1264 01:12:57,360 --> 01:13:00,557 that you really kicked off at Bletchley 1265 01:13:00,640 --> 01:13:02,637 around in a sort of like global way 1266 01:13:02,720 --> 01:13:07,677 around safety. I think is more important than ever. I think we're already seeing 1267 01:13:07,760 --> 01:13:11,677 that the models are 1268 01:13:11,760 --> 01:13:15,637 uh are are achieving levels of capability 1269 01:13:15,720 --> 01:13:18,197 which cause some pause around some of 1270 01:13:18,280 --> 01:13:22,757 the sort of like risks that we monitor 1271 01:13:22,840 --> 01:13:24,917 for these models as it comes to various 1272 01:13:25,000 --> 01:13:27,677 national security risks or various cyber 1273 01:13:27,760 --> 01:13:30,277 security risks. And so 1274 01:13:30,360 --> 01:13:32,877 the the importance of properly testing 1275 01:13:32,960 --> 01:13:37,797 and then building the right systems to monitor and deploy these models safely 1276 01:13:37,880 --> 01:13:39,317 is is of paramount importance. 1277 01:13:39,400 --> 01:13:42,637 >> you and I have been talking about the same topic for some years. And that was 1278 01:13:42,720 --> 01:13:47,037 key to us at Bletchley was establishing the AI 1279 01:13:47,120 --> 01:13:50,517 security institute in the UK to do exactly that work. And the And the 1280 01:13:50,600 --> 01:13:54,517 thought was there was a gap where the technical capability didn't exist 1281 01:13:54,600 --> 01:13:58,637 amongst governments to evaluate some of these risks. So how could we in the UK 1282 01:13:58,720 --> 01:14:03,317 hopefully take a lead but in sense provide a global public good 1283 01:14:03,400 --> 01:14:08,800 where we work in partnership with companies like yours and the other