# LIVE: Governing in the Age of AI | fireside chat with former UK PM Rishi Sunak, Meta’s Alexandr Wang

*ANI News* · https://www.youtube.com/watch?v=tHrJnEWbYrM

## Summary

In this fireside chat, former UK Prime Minister Rishi Sunak speaks with Meta's Alexandr Wang about governing in the age of AI, covering the technology's accelerating development, public trust, government deployment, and national AI strategy. Wang describes 2026 as an inflection point driven by recursive self-improvement and working agents, and argues trust will determine diffusion more than the technology itself. Sunak closes by urging political leaders to treat AI as an immediate, centralized responsibility of government rather than tomorrow's issue.

## Key points

- Wang traces three eras of AI: pre-training (2018–2024), reinforcement learning and reasoning (from late 2024), and recursive self-improvement beginning at the end of 2025.
- He predicts 2026 will be an inflection point with large-scale agent deployments across many areas of the economy.
- Sunak notes large AI companies will spend 20 times more on the technology this year than the US spent on the Manhattan Project.
- Wang says trust, more than the technology itself, will govern how successfully AI diffuses, citing WhatsApp's privacy commitment as a model.
- Sunak highlights the UK's AI Security Institute, created after Bletchley Park, to do pre-deployment testing of frontier models in partnership with labs.
- Wang's playbook for governments includes data sovereignty (especially healthcare and national security data), compute access, innovation ecosystems, and public sector use cases.
- Wang advises organizations to become AI-first with top-down direction plus bottom-up success stories, noting some Meta engineers are 10 to 100 times more productive with AI.
- Sunak argues AI must move from a specialized topic to a centralized responsibility of government with urgent action.

## Notes

### Context and Origins
- Rishi Sunak explains why he convened the AI summit at Bletchley Park: there was no regular gathering of leaders on the most transformative technology of the time, and industry needed to be in the room alongside government.
- He notes large AI companies will spend 20 times more on developing the technology this year than the US spent on the Manhattan Project.
- The summit series has since gone to Paris, South Korea, and now India, a country with huge digital ambitions and capacity.

### The Technology Journey
- Alexandr Wang describes three eras: pre-training (2018–2024) with a predictable exponential scaling curve; reinforcement learning from late 2024, when models began to reason; and recursive self-improvement beginning at the end of 2025, where models accelerate the production of the next AIs.
- He calls 2026 an inflection point and predicts large-scale agent deployments across many areas of the economy, with the "GDP of AI" growing exponentially.
- Individual researcher productivity has grown dramatically and is expected to keep accelerating.

### Trust
- Sunak observes optimism and trust toward AI in India versus persistent anxiety in Western countries, framing the confidence gap as both a policy and technical task.
- Wang says trust may matter more than the technology itself for diffusion, citing WhatsApp's privacy commitment as the model for AI products.
- Sunak describes the UK's AI Security Institute, established after Bletchley, doing pre-deployment testing with frontier labs as a global public good.

### Government Playbook
- Wang urges governments to treat data as the "new oil," building sovereignty over data assets, especially in healthcare and national security.
- Other layers: compute/infrastructure access via alliances or cloud partnerships, innovation ecosystems (India has more consumer AI startups than the US), and government services.
- Sunak references Professor Jeffrey Ding's book "Technology and Great Powers": you don't need to invent a general-purpose technology to benefit most from it — India's deployment-focused strategy with UPI, ID, and digital public infrastructure proves the point.

### Execution and Vision
- Wang advises becoming an AI-first organization: top-down mandatory direction plus bottom-up success stories, with some Meta engineers 10 to 100 times more productive using AI.
- His long-term vision is personal superintelligence — an agent helping each person live their maximal life — and interpersonal superintelligence, where AI helps humans organize to solve great challenges.
- Sunak closes: AI is an "action this day" issue that must move from a specialized topic to a centralized responsibility of government.

## Transcript

[00:00] and innovators like Alex himself who were at the frontier of developing this technology and I thought they needed to be in the room not least because it's businesses like yours that are have the resources to invest in this just to give you a bit of context this year you know the the large AI companies are going to spend 20 times more on developing this technology than the US did for the Manhattan Project. Can you think about seminal moments where the government mobilized to do something 20 times more just this year alone. So I thought look it's vital that we do this in a different way not like the G7 and G20 we bring industry innovators and and government together and that's that's what we did at Bletchley Park a few

[00:45] years ago. The summit series went to Paris, South Korea and now brilliantly in India a country with huge digital ambitions and indeed capacity. But I think over those years and Alex let's start with this. I think actually the the the debate on AI has shifted from kind of technology to strategy. You know from what these tools can do to what governments are choosing to do with them. And I wanted to maybe just start there. Is if you think about the last few years you are someone who is at the frontier of developing this technology. You know that the journey that we've been on in the last few years you know help us understand that where have we come what is it that is exciting at this this moment and just ground us in in where we

[01:32] are so that we can get get on to some of these questions about what we do with it. Yeah I think um I mean we are sitting here right now at such an incredibly exciting moment for the technology. I think we are um at the beginning of uh you know of a true acceleration in the technology and you know in many ways it's felt like this very clear acceleration even until now, but I think we're going to see sort of 2026 is an inflection point in many ways. I think to contextualize this over the past few years, I think from 2018 through 2024 really, which is a good 6-year period, we were writing we were in the era of pre-training. So we were writing one very large exponential curve towards improving the performance of the models.

[02:27] And that curve was very predictable and it sort of had clear characteristics where the more resources you put in, the more results you would get out and there was a lot of questions at the time of, you know, whether or not this curve would continue returns and I think this was sort of one of the one of the more recent eras of AI skepticism. And then in 2024, like towards the end of 2024, began the era of reinforcement learning. So this was sort of the the next major wave that stacked on top of the previous wave and this is when we saw the models begin to learn to reason. And you know, there's always this meme of what did Ilya see? Well, Ilya saw reasoning from the models and it was very clearly this this next mega wave of

[03:15] of the technology and even reinforcement learning had, you know, clearly some limitations and there were all these questions around when that was going to, you know, how long that era was going to last and maybe there were limitations in that era. And then at the end of 2025, so just a few months ago, I think we've really begun an entirely new paradigm or era of the technology, which is really beginning the era of recursive self-improvement. And I think we're really starting to see this in the development of our models. I I you know, most of the major labs are seeing this in the development of their models, which is that the models themselves have now become instrumental in accelerating the process of producing

[04:00] the next AIs. And so, you know, the the sort of from the outside what you see is maybe an acceleration of overall development velocity, an acceleration in at which the speed at which the the new models are being released. Um internally, what we see is just dramatic speed ups. I think the sort of um the productivity of an individual researcher has grown dramatically. Um and we don't expect that to stop. In fact, we expect it to kind of smoothly continue accelerating um where the output of an individual researcher, an individual engineer, well, I think that itself will continue growing exponentially as the technology improves. And um the other mega wave that is somewhat coincident with this new paradigm within

[04:43] the sort of development of the technology of uh recursive self-improvement is really the arrival of agents in earnest. And um this is something that has been talked about since I honestly want to say 2023 with a lot of fanfare. And for a long long time, it was sort of um one of these hype uh sort of uh buzzwords that you know, never actually lived up to the expectations. And I think also at the end of 2025, we and toward you know, middle to end of 2025, we started to see agents actually work. And this is you know, these are where the models can actually start taking um actions um and start automating entire workflows and being um sort of dramatically more uh productive as actual sort of entities

[05:35] unto themselves versus assistants. And um and this era is you know, it started with coding agents. We're starting to see it um really percolate through personal agents. This is a big part of what uh we believe in at Meta. And I think over the course of 2026, we will see large-scale agent deployments in many areas of the economy, many areas of the world. And the sort of, you know, if the the GDP of AI, so to speak, is going to grow exponentially. >> Well, that that is very welcome to to hear. I think this you hit on something that is important because I remember when I first started doing this in the job, the conversations were all around these scaling laws and would they continue? And you mentioned the

[06:22] skepticism. I mean, not only have they continued and continued to deliver this exponential growth. It feels very much like the last few months have seen an acceleration. That's what you touched on. It's what Dario and and Demis have also said to me and and publicly, driven by this recursive self-improvement that the models themselves are now able to help us develop the new models quicker. So, you know, we're at this quite exciting but also a moment of some anxiety because of that accelerating development time scale. So, I wanted to just then talk about trust. Because I I tell you give you a story. My I have two, you know, young girls and you know, as they're using their chatbots and things and they're always

[07:04] like, "Please, you know, so-and-so and so-and-so." And "Thank you." when they finish writing their request or they get the answer back. There's lots of pleases and and thank yous everywhere. And I and I I thought that was you know, interesting. I said to them, I said, "Look, this, you know, you don't need to do that. It's not not a person. It doesn't It doesn't matter. And And by the way, you know, it costs a fortune when we have to do the compute for that." And And they they said to me, "You know what, Daddy?" They said, "If If AI takes over the world, we want to have been kind to the AI." And I thought it was good good insurance policy. But it kind of you know, it touches on this anxiety. >> Very wise.

[07:39] >> Yeah. >> [laughter] >> In that maybe. You know, across the world, we're seeing these different attitudes uh towards AI. In countries like India, where we are, there's enormous optimism and and trust. And then actually, in Western countries, you know, we're seeing that anxiety is still the kind of dominant feeling towards AI. Now, I think closing that confidence gap is as much a policy task as it is a a technical one, but I I'd love to just get your thoughts. I mean, obviously, you are at a you know, a B2C company dealing with, you know, billions of consumers every day. You know, how do you think about this trust question? You know, how do we get trust up in places where it is not for something that, you know, you believe, I

[08:23] believe, and everyone in this room probably believes can do untold good. Yeah, this is this is one of the most important questions. And actually, perhaps even more so than the technology and the development of the technology, um, maybe will govern how how uh successful the technology diffuses, um, more so than anything. And, um, you know, we we think deeply about this cuz our vision for the technology is that it is um we can deploy personal agents that uh know you better than most people know you, know about your health, know about your goals, know about your relationships, know about your family and your friends, and um can really help you live your life more effectively, you know, can help you be healthier, can

[09:08] help you accomplish projects that you would never would have had time for, can help you build the business you always wanted, um, can help you explore your interests. And to properly build that requires an immense amount of trust from consumers, from governments, from um, from effectively everyone, from all organizations. And so, you know, this is one of these interesting moments where, um, I I because the the sort of the form that the of the technology that we wanted to take is so intimate and is so deeply tied with what we think is really the the future of, you know, the consumer life and the consumer, um, you know, is at the core of the relationship with technology. Um, the figuring out ways to develop the the

[09:59] technology in partnership with governing bodies and in partnership with, um, nonprofits and other organizations to garner that trust is very important. This is something we've seen at Meta, actually, in, um, with WhatsApp. You know, one of the we were talking about this actually backstage, but, um, WhatsApp has become, uh, incredibly ubiquitous, um, and a real, I think, utility for much of the world, um, in large part due to, uh, its commitment to privacy and the the sort of trust that we've built around WhatsApp as an organization. Um, oftentimes at the, uh, sort of, uh, you know, not necessarily for the optimization of our business objectives, but for the optimization of trust in WhatsApp as a platform. I think a

[10:42] similar journey will be necessary for the AI products of the modern age, and we've seen, um, I mean, we've already seen various uh, kerfuffles or sort of jabs back and forth between the existing AI, you know, products and labs around, you know, whether their actions are truly garnering, you know, truly building this, uh, these levels of trust. But, um, and then I think the, you know, the topic that you really kicked off at Bletchley around, um, in a sort of like global way around safety, I think, is more important than ever. I think we're already seeing that, um, the models are, uh, are are achieving levels of capability which cause some pause around some of the, sort of like, risks that, uh, we

[11:29] um, monitor for these models as it comes to various national security risks or various cybersecurity risks. And so the the importance of properly testing and then building the right systems to monitor and deploy these models safely is is of paramount importance. >> And that you and I have been talking about the same topic for some years. And that was key to us at Bletchley was establishing the AI security institute in the UK to do exactly that work. And the And the thought was there was a gap where the technical capability didn't exist amongst governments to evaluate some of these risks. So how could we in the UK hopefully take a lead, but in sense provide a global public good where we work in partnership with

[12:13] companies like yours and the other frontier labs to do pre-deployment testing on these models. And my thought was always that by doing that, and that's not regulation, but it gives citizens confidence that the technology is safe cuz ultimately people don't adopt a technology that they don't trust. And I So what I my sense hearing you is that there's a role for the companies. I thought the the privacy example with WhatsApp is a good one is how do they design AI products that will will engender trust. And then there's a role, it sounds like you agree, for governments to do some of that technical evaluation. And is your sense that we need, you know, more cooperation between governments on that, more interaction

[12:51] with the labs? Is that working as well as it should at the moment? Um I think the interaction is working well, but I think this is one area where you know, uh we always can be more thoughtful. And especially as we enter this phase of dramatically increased AI progress, I think, you know, we constantly need to be thinking about how we can do better. I I think one of the other areas where this debate on trust will be won or lost is in the public sector. I think it's citizens that you experience that health care, more efficient government services, a state that is quicker at dealing with you. I think this debate goes from abstract to real when you have a real way to demonstrate benefit. And do you have a a sense from

[13:36] where you sit about where the most obvious use cases or deployment cases are in the public sector for AI that we should be excited about and that governments should be thinking about? I do. I think that um one of the first you know, if you think governments as um service providers in a sense to their citizens, I think there's there's a huge opportunity for all governments to be able to deliver their services more effectively. We're already seeing that actually in India on top of WhatsApp funnily enough where there are in certain states they actually deliver fast majority of the government services directly through WhatsApp. And then the for transit a nation actually able to a true what's up in India.

[14:28] last year in some what's up That's not AI the future opportunity there that is quite exciting is actually deployment of uh uh of AI to greater increase the ease and speed at which these government services are deployed and provided to citizens. I think that you know there's one term that I've always I've used and and I like this concept of the magic quadrant, right? And it really is you know if you think about how should governments be um how should the state be reimagined? Um you know I think we need to move away from large-scale bureaucratic services and um and these organizations and think about how can the government move as quickly as possible to deliver using a combination of technology and humans. verify

[15:25] one of those agents the fastest possible public public services for citizens in a country or a state to develop um and I think this cuts through civil services, I think it cuts through health care you mentioned and certainly has a has a large role to play in security and in in government this government So I think it's quite a problem here. Yeah, I think you were you were contacted by the Chinese government I need one more one more mic. I wanted to suggest I pick up on something you talked about one of the benefits of of leaving office is you have Thank you. A lot of benefits of leaving office is you have more more time at your desk and when I left someone gave me a great book by a guy called Jeffrey

[16:20] Professor Ding and it's called Technology and and Great Powers I'm sure you you know the book and and in it he makes a really compelling argument about technology diffusion essentially looking at the history of general purpose technologies going all the way back to the printing press and making the argument that you don't need to be the country that invents the technology to be the country that gets the most benefit from it and I think you know you touched on India India has recognized that technology leadership exactly does not depend on just inventing the technology it's about how effectively you deploy it and they here are focused on mass broad adoption and obviously supported by a few critical attributes.

[17:03] You have a very AI talent deep talent pool here. You talked about the rails of distribution obviously the digital public infrastructure here are there the ID UPI the payments infrastructure and then most recently Ayushman Bharat health accounts provide these infrastructure rails to distribute apps to over a billion people. And they also have a population that supportive of that. And I think focusing on that has allowed India to position itself as a leader by by pursuing the deployment and adoption strategy. But but thinking about that, you know, what are those critical attributes that government has to get right if we want to turn your aspiration into a reality about a gentle government, about

[17:50] delivering for our citizens. You know, I think government does need to think about things differently and needs to build the the architecture around deployment and diffusion, whether it's skills, whether it's compute, whether it's regulation. But crucially probably also data, which is something that you know better than most. So maybe if we start there and then broaden it out, if I'm, you know, sitting as a government leader as many people in the audience are, I'm trying to figure out what do I need to get right? What are the building blocks to deliver this thing properly? Let's start with data and what would your advice be there and then we'll we'll broaden it out a little bit. Yeah, there there is

[18:29] uh data is is so important and I think over the past few years many people have I think you know realized this as we sort of look at the power of these models and we look behind the curtains and you realize that all the powers and beauty of these models are actually imbued by data. And if you think about So if you think about data is like in some ways this sort of new oil, this sort of like base material that is incredibly important for the future of of AI and the development of these models. Um then I think as like governments should think about what are their, you know, what are their reserves? What are their data reserves? What are their data assets? And Um, and to in many ways build strategies where they have

[19:15] sovereignty over some set of data or or they're able to build certain certain pockets of data sets that enable the the country to perform the best. I think one of the areas that is the two areas that is clear that are clearest for this are one is health care. I think many countries um uh already are and other countries need to be thinking I think about um very effective mechanisms for uh uh for health care data within the country to enable ultimately over time there to be AI models and AI agents that are built on top of that to deliver better uh health services to their citizens. And then the second is national security. And I think that is um you know, in many ways pretty self-explanatory, but I think you know,

[20:00] countries need to be thinking about that um uh quite deeply as to what are the sort of like core data assets. Then I think if you if you sort of build around data and you look at the next few layers, um the next one is is infrastructure. Um, so you know, we think about the power, the um the computational infrastructure, um and and what does that story look like? I don't think every country needs to build large-scale AI data centers in their country, but they do need to have a strategy of over time how are they going to have access to that infrastructure. And so whether this is in the form of alliances of various formats or um or uh partnerships even with with uh corporate entities, the large cloud providers, I

[20:44] think that's an important element to think through. Um, then comes I think the sort of uh the um the innovation ecosystem. I do think that, you know, we're in a moment of momentous technological change and um it is I think incredibly important for I think there's near boundless uh entrepreneurial and economic opportunity Um, and I think it is the responsibility of of governments and countries to think through how they can build ecosystems that support that. I think India is actually in many ways a great, very positive case study in large part due to the due to the talent pool, but you know, I was at a dinner with a number of Indian founders and venture capitalist last night and and the stat was I think there are more

[21:32] consumer AI companies in India consumer AI startups in India than in the United States. And so, there really are some of these like, you know, shining examples of of incredible development of these ecosystems. And then, and then lastly, I think are the government services which we talked about previously. So, we've got I think the elements of almost a a playbook emerging, right? You need to have the right data and make sure it's it's used effectively. You need some degree of compute. You've got appropriate regulation as we've talked about to build to build trust. And then we've got some public sector use cases as well and an innovation ecosystem. So, these are the kind of ingredients where you can start to turn

[22:12] these ambitions into reality and and actual impact on people. I I just staying on that theme, you know, you you work at an incredible company. You work with other enterprises. You know, I talked to a lot of CEOs and and I'm struck by how they're thinking about deploying AI in their businesses and thinking about the parallel between that and and what governments should be doing. And you know, I think there is a kind of clear parallel. I'm I'm intrigued. I think CEOs are you know, kind of constantly thinking about AI. I think almost any large company CEO is spending their time figuring out, am I going to get disrupted, disintermediated by AI, how do I use it to stay ahead, where do I use it to to get more

[22:56] efficiency and more growth. And they are driving projects from the top down. And they are saying, "Look, this is something that is, you know, not a topic that could just stay with the IT department, right? This is something that has to be driven by the CEO, where we pick the use cases, we have accountability, and we start piloting these and scaling them up quite quickly." I you know, I my sense is we haven't quite seen that that same transition in governments yet, but I thought it's a good analogy in that look, this has to be driven by the person at the top. At the end of the day, if you're a prime minister, and I'm sure Tony would say the same thing, and indeed has, you can only do a few things that you drive

[23:37] personally from the top, and this really has to be one of them. You know, what are your kind of thoughts or advice for I get both CEOs and and governments about how to kind of drive the machine. What does it take to get execution on this? I mean, you've had to do that at Meta yourself, right? So, you'll have a sense from that. Yeah, I think it's a mix of of two things. I think first, um at uh at the highest level, you need to make it very clear that the organization will be an AI-first organization. And what that means for every organization is slightly different, but it it it um like the way that truly I think about it is that uh the success or failure of the organization actually entirely rests on

[24:21] its ability to properly embrace AI. And so, that's sort of the tops-down um component of it. And you know, there's sort of a strategy that must be designed more thoughtfully. And actually, honestly, you can probably ask AI uh to help you design that strategy now, and it'll probably do a pretty good job. And then you need um a a bed of bottoms-up adoption and bottoms-up um success stories. And for uh for us at Meta, like the example of this are, you know, we have these engineers throughout the company who are able to use AI to uh be you know, 10 to 100 times more productive. And um those are just such incredible examples that where all of a sudden everyone else in the organization looks around and

[25:06] realizes, wow, this is really something that must be taken very seriously. It's not just something that, you know, um the CEO or the C-level individuals are talking about and, you know, that they're so detached from reality. It's like, no, it's very real on the ground um in a very big way. And so, I think you need this sort of mix of what are the success stories that cause everyone to realize how big of a shift it actually is, and then also ensure that there's a continual um there's a very clear top-down direction that this transition is is mandatory. >> It's mandatory. Very helpful advice. We're almost out of time. So, maybe the last final point uh to close on is, you know, you're looking ahead, you know,

[25:46] what are you most excited about uh in the development of this of this technology? You've touched on personal agents, other scientific breakthroughs. You know, what where is this going, and what should governments be doing now, you know, so that they are prepared and and not playing catch-up? I think the things that um you can depend on, I think the the piece of technology development will continue accelerating. Um I think this this era of recursive self-improvement will uh feel like everything is speeding up, and I think that um the technology will continue developing very quickly. Um I also think that the the diffusion of the technology is is going to accelerate. I think there's been this question for many for the for the past

[26:32] many years, which is everyone's talking about AI, but where is AI in my daily life? I think that um that really is going to accelerate. Maybe not um you know, it's not going to go be 100 times faster, but maybe it'll be, you know, two or three times faster every year for the you know, for the next many years. Um and and I think the thing that uh where where I really am excited about this going is you know, we talk about this idea of personal super intelligence within meta which is, you know, how does you know, how does each person have an agent that helps them accomplish the things that they're you know, ex- extremely excited about. How can that help them, you know, explore their passions, build a

[27:15] business, make a discovery, invent a thing, you know, you know, achieve the most maximal version of their own lives and I think where this is going even beyond then is sort of this concept of interpersonal super intelligence which is I think we already see in human society sometimes the thing that is necessary for big things to happen are just for the right set of people to be organized in the right way. You see this with companies, you see this with nonprofits, you see this with large movements, you see this even with with governments when they when they reorganize and I think there's an incredible opportunity for the technology of AI to enable humans to organize in ways that are even better

[28:00] than we do today and all of a sudden maybe we'll be able to organize as communities or as groups or as new companies or who knows what it'll look like to to solve many of our greatest challenges and so you know, it sounds a bit high falutin but but it's something I'm genuinely quite excited about. >> Yeah, I can see that and it's a very a very compelling vision. Look, this has been fascinating conversation. It's clear that the technology is is not just keeping pace that it's accelerating. It's clear that the debate is moving from, you know, just talking about the technology to as you said, how is it making a a difference in people's lives and being deployed. And that one of one of my concerns and maybe we'll finish on

[28:37] this is that I think some not all but some political leaders think that AI is is going to be tomorrow's issue when I think they need to recognize that it's it's an action this day issue. And this needs to go from, you know, AI needs to go from being a kind of specialized topic to a centralized responsibility of government. And it needs to have that same urgency and and action attached to it so we can realize all the benefits that Alex is talking about. But this has been a great discussion. Thanks for all your advice and tips everyone in the audience and giving us an insight into where things are going. Thanks very much. Thank you so much. >> [applause]

[29:19] >> Thank you so much to our speakers. Earlier, can we have another round of applause, please, for Rishi Sunak and Alexander Wang? >> [applause] >> Thank you. >> When I became Prime Minister, it's been an exact AI obsessed because of my time in in California. You you get the job in Downing Street and one of the earliest meetings you have is with the the Foreign Affairs team. And they sit down with you. I can I can see someone who will remember this in the audience. And they go over your international calendar for the next 12 months. And they they want to kind of figure out, you know, what you should be doing. And it turns out there's an international summit for basically every single thing that you

[29:58] can imagine. Turns out there were about 150. And the British Foreign Office being the British Foreign Office, they wanted me to go to all of them. And although I did I did say I'm going to as a British Prime Minister, I think I can draw the line at the United Nations high-level meeting on sandstorms. That's where we agreed to disagree. But I noticed that there was no gathering to bring together people to talk about what I thought was the most transformative technology of our time. And I think we're all fortunate to be living at a time where AI is a general-purpose technology. These don't come along all that often and when they do, they had the opportunity in the past and will in the future to change every aspect of our

[30:38] economies, of our societies, of our lives. And that's why I thought it was necessary that leaders came together to discuss this at a regular basis. But, I also thought it needed to be done slightly differently. In contrast to previous technology cycles, it's companies like Alex's and innovators like Alex himself who were at the frontier of developing this technology. And I thought they needed to be in the room. Not least because it's businesses like yours that are have the resources to invest in this. Just to give you a bit of context. This year, you know, the the large AI companies are going to spend 20 times more on developing this technology than the US did for the Manhattan Project. If you

[31:22] think about seminal moments where the government mobilized to do something, 20 times more just this year alone. So, I thought, "Look, it's vital that we do this in a different way, not like the G7 and G20. We bring industry, innovators, and and government together." And that's that's what we did at Bletchley Park a few years ago. The summit series went to Paris, South Korea, and now brilliantly in India, a country with huge digital ambitions and indeed capacity. But, I think over those years, and Alex will start with this, I think actually the debate on AI has shifted from kind of technology to strategy. You know, from what these tools can do to what governments are choosing to do with them. And I wanted to maybe just start there.

[32:10] Is if you think about the last few years, you are someone who is at the frontier of developing this technology. You know, the the journey that we've been on in the last few years, you know, help us understand that. Where have we come? What is it that is exciting at this this moment? And just ground us in in where we are so that we can get get on to some of these questions about what we do with it. Yeah, I think um I I we are sitting here right now at such an incredibly exciting moment for the technology. I think we are um at the beginning of uh you know, of a true acceleration in the technology. And, you know, in many ways it's felt like this very clear acceleration even until now, but I think

[32:54] we're going to see sort of 2026 is uh an inflection point in many ways. I think to contextualize this over the past few years, I think from um 2018 through 2024 really, which is a good 6-year period, we were writing we were in the era of pre-training. So, we were writing um one very large exponential curve towards improving the performance of the models. And um that curve was very predictable and it sort of had clear characteristics where the more resources you put in, the more results you would get out. And there was a lot of questions at the time of, you know, whether or not this curve would continue. And even to the degree it did continue, were there diminishing marginal returns? And I think this was

[33:38] sort of um one of the uh one of the more recent sort of eras of AI skepticism. And then in 2024, um like towards the end of 2024, uh began the era of reinforcement learning. So, this was sort of the the next major wave that stacked on top of the previous wave. And this is when we saw the models begin to learn to reason. And um you know, there's always this meme of what did Ilya see? Uh well, Ilya saw reasoning uh from the models. And it was very clearly this this next mega wave of of the technology. And even reinforcement learning had, you know, clearly some limitations. And there were all these questions around when that was going to, you know, how long that era was going to last and maybe there were

[34:24] limitations in that era. And then at the end of 2025, so just a few months ago, I think we really begun um an entirely new paradigm or era of the technology, which is really beginning the era of recursive self-improvement. And I think we're really starting to see this in the development of our models. I think you know, most of the major labs are seeing this in the development of their models, which is that the models themselves have now become instrumental in accelerating the process of producing the next AIs. And so, you know, the the sort of from the outside what you see is maybe an acceleration of overall development velocity, an acceleration in which the speed at which the the new models are

[35:08] being released. Internally, what we see is just dramatic speedups. I think the sort of the productivity of an individual researcher has grown dramatically. And we don't expect that to stop. In fact, we expect it to kind of smoothly continue accelerating where the output of an individual researcher, an individual engineer, well, I think that itself will continue growing exponentially as the technology improves. And the other mega wave that is somewhat coincident with this new paradigm within the sort of development of the technology of a recursive self-improvement is really the arrival of agents in earnest. And this is something that has been talked about since I honestly want to say 2023 with a lot of fanfare. And for a lot

[36:00] long time, it was sort of one of these hype sort of buzzwords that, you know, never actually lived up to the expectations. And I think also at the end of 2025, we and toward you know, middle to end of 2025, we started to see agents actually work. And this is, you know, these are where the models can actually start taking actions and start automating entire workflows and being sort of dramatically more productive as actual sort of entities onto themselves versus assistants. And in this era is you know it started with coding agents. We're starting to see it really percolate through personal agents. This is a big part of what we believe in at Meta. And I think over the course of 2026 we will see large-scale agent deployments

[36:53] in many areas of the economy, many areas of the world. And the sort of you know if the the GDP of AI so to speak is going to grow exponentially. >> Well that that is very welcome to to hear. I I think this you you hit on something that is important because I remember when I first started doing this in the job the conversations were all around these scaling laws and would they continue. And you mentioned the skepticism. I mean not only have they continued and continued to deliver this exponential growth it feels very much like the last few months have seen an acceleration. It's what you touched on. It's what Dario and Demis have have also said to me in in publicly driven by this recursive self-improvement that the

[37:37] models themselves are now able to help us develop the new models quicker. So you know we're at this quite exciting but also a moment of some anxiety because of that accelerating development time scale. So I wanted to just then talk about trust because I tell you so I might I have two young girls and you know as they're using their chatbots and things and they're always like please you know so and so and so and so and thank you when they finish writing their request or they get the answer back. There's lots of pleases and and thank yous everywhere. And I and I I thought that was you know interesting. I said to them I said look there's you know you don't need to do that. There's not not a person. It doesn't it doesn't

[38:19] matter. And and by the way you know it it a fortune when we have to do the compute for that. Um and and they they said to me, "You know what, Daddy?" They said, "If if AI takes over the world, we want to have been kind to the AI." Uh and I thought that was good good insurance policy. But it it kind of you know, it touches on this anxiety. >> wise. >> Yeah. >> [laughter] >> Um in that maybe. You know, across the world, we're seeing these different attitudes uh towards AI. In countries like India, where we are, there's enormous optimism and and trust. And then actually in Western countries, you know, we're seeing that anxiety is still the kind of dominant feeling towards AI. Now, I think closing that confidence gap is uh

[39:00] as much a policy task as it is a a technical one. But I I'd love to just get your thoughts. I mean, obviously, you are at a you know, B2C company dealing with you know, billions of consumers every day. You know, how do you think about this trust question? You know, how do we get trust up in places where it is not for something that you know, you believe, I believe, and everyone in this room probably believes can do untold good. Yeah, this is this is one of the most important questions. And actually, perhaps even more so than the technology and development of the technology, um maybe will govern how how uh successfully the technology diffuses um more so than anything. And um you know, we we think deeply about this

[39:44] cuz our vision for the technology is that it is um we can deploy personal agents that uh know you better than most people know you, know about your health, know about your goals, know about your relationships, know about your family and your friends, and um can really help you live your life more effectively, you know, can help you be healthier, can help you accomplish projects that you would never would have had time for, can help you build the business you always wanted, um can help you explore your interests. And to properly build that requires an immense amount of trust from consumers, from governments, from from effectively everyone, from all organizations. And so it you know, this is one of these interesting

[40:30] moments where I think because the the sort of the form that of the technology that we wanted it to take is so intimate and is so deeply tied with what we think is really the the future of you know, the consumer life and the consumer you know, is at the core of the relationship with technology. Um The figuring out ways to develop the technology in partnership with governing bodies and in partnership with nonprofits and other organizations to garner that trust is very important. This is something we've seen at Meta actually in with WhatsApp. You know, one of the we were talking about this actually backstage, but WhatsApp has become incredibly ubiquitous and a real I think utility for much of the world in large

[41:21] part due to its commitment to privacy and the the sort of trust that we've built around WhatsApp as an organization. Often times at the sort of you know, not necessarily for the optimization of our business objectives, but for the optimization of trust in WhatsApp as a platform. I think a similar journey will be necessary for the AI products of the modern age and we've seen I mean we've already seen various kerfuffles or sort of jabs back and forth between the existing AI you know, products and labs around you know, whether their actions are truly garnering you know, truly building this these levels of trust. But and then I think the you know, the topic that you really kicked off at Bletchley

[42:06] around in a sort of like global way around safety, I think is more important than ever. I think we're already seeing that um the models are uh are are achieving levels of capability which cause some pause around some of the sort of like risks that uh we monitor for these models as it comes to various national security risks or uh various cybersecurity risks. And so um the the importance of properly testing and then building the right systems to uh monitor and deploy these models safely is is a paramount importance. >> And that you and I have been talking about the same topic for some years. And that was key to us at uh Bletchley was establishing the AI Security Institute in the UK to do exactly that work. And

[42:55] the and the thought was there was a gap where the technical capability didn't exist amongst governments to evaluate some of these risks. So how could we in the UK hopefully take a lead, but in sense provide a global public good uh where we're working partnership with companies like yours and the other frontier labs to do pre-deployment testing on these models. And my thought was always that by doing that, and that's not regulation, but it gives citizens confidence that the technology is safe cuz ultimately people don't adopt a technology that they don't trust. And it's what I my sense hearing you is that there's a role for the companies, I thought the the privacy example with WhatsApp is a good one, is

[43:33] how do they design AI products that will um will engender trust. And then there's a role, it sounds like you agree, for governments to do some of that technical evaluation. And is your sense that we need, you know, more cooperation between governments on that, more interaction with the labs? Is that working as well as it should at the moment? Um I think the interaction is working well, um but I think this is one area where you know uh we always can be more thoughtful, and especially as we enter this phase of dramatically increased AI progress. Um I think, you know, we constantly need to be thinking about how we can do better. I I think one of the other areas where this debate on trust will be won or lost

[44:15] is in the public sector. I think it's this is the If you experience that health care, more efficient government services, a state that is quicker at dealing with you, I think this debate goes from abstract to you have a real way to demonstrate benefit. Do you have a a sense from where you sit about where the most obvious use cases or deployment cases are in the public for AI that we should be excited about and that governments should be thinking about? I I do. I think that um one of the first you know, if you think of governments as um uh service providers in a sense to their citizens, I think there's there's a huge opportunity for all governments to um be able to deliver their services more effectively. Um we're already seeing

[45:05] that uh actually in India um on top of WhatsApp, funnily enough, where uh there are in certain states they actually deliver uh fast majority of the government services directly through WhatsApp. And then the uh for transit a nation actually able to uh through what in India. Uh we last year 100 million sub uh in the What? And that's not AI per se, but the the future opportunity that is quite exciting is actually the deployment of uh uh of AI to greatly increase the ease and and speed at which these government services are deployed and provided assistance. Um I think that you know, there's one uh term that I've always I've used and and I like this concept of proactive government, right? And it really is, you

[45:59] know, if you think about how should my governments be how should the state be imagined? You know, I think we need to move away from large-scale bureaucratic services and support organizations and think about how can the government deliver quickly and promptly deliver using computer people and make it easy to verify one of those agencies as fast as possible government services so that citizens can steer the state to develop solutions. Um and I think this cuts through civil services I think it cuts through health care as you mentioned and certainly has a has a large role to play in security and in the government with the government so I think it's quite a problem. I think you were you were talking about the concept

[46:54] of jet technology and that sort I need one more like one more like Uh I want to suggest something One of the benefits of leaving office is you have I think A lot of benefits leaving office you have a lot more time as I said and when I left someone gave me a great book by a guy called Jeffrey Ding, Professor Ding and technology and and great powers I'm sure you you know the book and and in it he makes a really compelling argument about technology diffusion essentially looking at the history of general purpose these going all the way back to the printing press and making the argument that you don't need to be the country that invents the technology to be the country that gets the most benefit from it and I think you

[47:41] know you you touched on India and India has recognized that technology leadership exactly does not depend on just inventing the technology it's about how effectively you deploy it and they here have focused on mass broad adoption and obviously supported by a few critical attributes. You have a very deep talent pool You talked about the rails of distribution. Obviously the digital public infrastructure here. The ID, UPI, the payments infrastructure and then most recently health accounts provide these infrastructure rails to distribute apps to over a billion people. And they also have a population that's supportive of that. And I think focusing on that has allowed India to position itself as a leader

[48:32] by by pursuing the deployment and adoption strategy. But thinking about that, you know, what are those critical attributes that government has to get right? If we want to turn your aspiration into a reality about agentic government, about delivering for our citizens. You know, I think government does need to think about things differently. It needs to build the architecture around deployment and diffusion, whether it's skills, whether it's compute, whether it's regulation. But crucially probably also data, which is something that you know better than most. So maybe if we start there and then broaden it out. If I'm you know, sitting as a government leader as many people in the audience are and

[49:13] trying to figure out what do I need to get right? What are the building blocks to deliver this thing properly? Let's start with data and what would your advice be there and then we'll we'll broaden it out a little bit. Yeah, there there is uh data is is so important. I think over the past few years many people have I think you know, realized this as we sort of look at the power of these models and we look behind the curtains and you realize that all the powers and beauty of these models are actually imbued by data. And if you think about So if you think about data is like in some ways the sort of new oil, the sort of like base material that is incredibly important for the future of of AI and the development of these

[49:57] models. Then I think as like governments should think about what are their, you know, what are their reserves? What are their data reserves? What are their data assets? And and to in many ways build strategies where they have sovereignty over some set of data or or they're able to build certain certain pockets of data sets that enable the the country to perform the best. I think one of the areas that is the two areas that is clear that are clearest for this are one is health care. I think many countries already are and other countries need to be thinking I think about very effective mechanisms for uh for health care data within the country to enable ultimately over time there to be AI models and AI agents that are

[50:47] built on top of that to deliver better health services to their citizens. And then the second is national security. And I think that is you know, in many ways pretty self-explanatory, but I think you know, countries need to be thinking about that quite deeply as to what are their sort of like core data assets. Then I think if you if you sort of build around data and you look at the next few layers, the next one is is infrastructure. So, you know, we think about the power, the the computational infrastructure and and what does that story look like? I don't think every country needs to build large-scale AI data centers in their country, but they do need to have a strategy of over time how are they

[51:29] going to have access to that infrastructure. And so whether this is in the form of alliances of various formats or or partnerships even with with corporate entities, the large cloud providers, I think that's an important element to think through. Then comes I think the sort of the the innovation ecosystem. I do think that, you know, we're in a moment of momentous technological change, and it is, I think, incredibly important for I think there is near boundless entrepreneurial and economic opportunity, and I think it is the responsibility of governments and countries to think through how they can build ecosystems that support that. I think India is actually, in many ways, a great very positive case study, in large part

[52:19] due to the due to the talent pool, but you know, I was at a dinner with a number of Indian founders and venture capitalists last night, and and the stat was, I think, there are more consumer AI companies in India consumer AI startups in India than in the United States. And so, there really are some of these like, you know, shining examples of incredible development of these ecosystems. And then, um And then, lastly, I think are the government services, which we talked about previously. So, we've got, I think, the elements of almost a a playbook emerging, right? You need to have the right data and make sure it's it's used effectively. You need some degree of compute. You've got appropriate regulation, as

[52:59] we've talked about, to build to build trust. And then, we've got some public sector use cases, as well, and an innovation ecosystem. So, these are the kind of ingredients where you can start to turn these ambitions into reality and and actual impact on people. I I just staying on that theme, you know, you you work at an incredible company. You work with other enterprises. You know, I talked to a lot of CEOs, and and I'm struck by how they're thinking about deploying AI in their businesses, and thinking about the parallel between that and and what governments should be doing. And you know, I think there is a kind of clear parallel. I'm intrigued. I think CEOs are, don't, know, kind of constantly thinking about AI. I think

[53:43] almost any large company CEO is spending their time figuring out, am I going to get disrupted, disintermediated by AI, how do I use it to stay ahead, where do I use it to to get more efficiency and more growth. And they are driving projects from the top down. And they are saying, look, this is something that is, you know, not not a topic that can just stay with the IT department, right? This is something that has to be driven by the CEO, where we pick the use cases, we have accountability, and we start piloting these and scaling them up quite quickly. I, you know, I my sense is we haven't quite seen that that same transition in governments yet, but I thought it's a good analogy in that look, this has to

[54:26] be driven by the person at the top. At the end of the day, if you're a prime minister, and I'm sure Tony would say the same thing, and indeed has, you can only do a few things that you drive personally from the top, and this really has to be one of them. You know, what are your kind of thoughts or advice for, I guess, both CEOs and and governments about how to kind of drive the machine. What does it take to get execution on this? And I mean, you've had to do that at Meta yourself, right? So, you'll have a sense from that. Yeah, I think it's a mix of of two things. I think first, um at uh at the highest level, you need to make it very clear that the organization will be an AI-first organization. And what

[55:06] that means for every organization is slightly different, but it it it um like the way that truly I think about it is that uh the success or failure of the organization actually entirely rests on its ability to properly embrace AI. And so, that's sort of the tops-down um component of it. And you know, there's sort of a strategy that must be designed more thoughtfully. And actually, honestly, you can probably ask AI uh to help you design that strategy now, and it'll probably do a pretty good job. And then you need um a a bed of bottoms-up adoption and bottoms-up, um, success stories. And for, uh, for us at Meta, like the example of this are, you know, we have these engineers throughout the company

[55:50] who are able to use AI to, uh, be, you know, 10 to 100 times more productive. And, um, those are just such incredible examples that where all of a sudden everyone else in the organization looks around and realizes, "Wow, this is really something that must be taken very seriously. It's not just something that, you know, um, the CEO or the C-level individuals are talking about and, you know, that they're so detached from reality." It's like, "No, it's very real on the ground, um, in a very big way." And so, I think you need this sort of mix of what are the success stories that cause everyone to realize how big of a shift it actually is. And then also ensure that there's a continual, um, there's a very clear top-down direction

[56:32] that this transition is, is mandatory. >> very helpful advice. Look, we're almost out of time. So, maybe a last final point, uh, to close on is, you know, you're looking ahead, you know, what are you most excited about, uh, in the development of this, of this technology? You've touched on personal agents, other scientific breakthroughs. You know, what, where is this going and what should governments be doing now, you know, so that they are prepared and and not playing catch-up? I think the things that, um, you can depend on, I think the, the pace of technology development will continue accelerating. Um, I think this, this era of recursive self-improvement will, uh, feel like everything is speeding up and

[57:17] I think that, um, the technology will continue developing very quickly. Um, I also think that the, the diffusion of the technology is, is going to accelerate. I think there's been this question for many, for the, for the past many years, which is, everyone's talking about AI, but where is AI in my daily life? I think that um that really is going to accelerate. Maybe not um you know, it's not going to go be 100 times faster, but maybe it'll be you know, two or three times faster every year for the you know, for the next many years. Um and and I think the thing that uh where where I really am excited about this going is um you know, we talk about this idea of personal superintelligence within Meta, which is you know, how does

[58:02] you know, how does each person have an agent that helps them accomplish the things that they're you know, ex- extremely excited about. How can they help them, you know, explore their passions, build a business, make a discovery, invent a thing, um you know, um you know, achieve the most maximal version of their own lives. And I think where um this is going even beyond then is sort of this concept of interpersonal superintelligence, which is um I think we already see in human society sometimes the thing that is necessary for big things to happen are just for the right set of people to be organized in the right way. You see this with companies, you see this with nonprofits, you see this with large movements, um

[58:43] you see this even with with um governments when they uh when they reorganize. And I think there's an incredible opportunity for the technology of AI to enable humans to organize in ways that are even better um than we do today. And all of a sudden maybe we'll be able to organize as communities or as groups or as new companies or who knows what it'll look like to uh to solve many of our greatest challenges. And so um you know, it sounds a bit highfalutin, but um but it's something I'm genuinely quite excited about. >> No, I can see that. It's a very a very compelling vision. But this has been fascinating conversation. It's clear that the technology is is not just keeping pace, that it's accelerating.

[59:23] It's clear that the debate is moving from you know, just talking about the technology to as you said, how is it making a a difference in people's lives and being deployed. And that one of one of my concerns, and maybe we'll finish on this, is that, you know, I think some, not all, some political leaders think that AI is is going to be tomorrow's issue, when I think they need to recognize that it's it's an action this day issue. And this needs to go from, you know, AI needs to go from being a kind of specialized topic to a centralized responsibility of government. Uh and it needs to have that same urgency and and action attached to it so we can realize all the benefits that Alex is talking about. But this has

[1:00:02] been a great discussion. Thanks for all your advice and tips for everyone in the audience and giving us an insight into how things are going. Thanks very much. Thank you so much. >> [applause]

[1:00:16] >> Thank you so much to our speakers. Earlier, can we have another round of applause, please, for Rishi Sunak and Alexander Wa When I became Prime Minister, I'm an AI obsessed, AI obsessed because of my time in in California. You you get the job in Downing Street and one of the earliest meetings you have is with the the Foreign Affairs team. And they sit down with you. I can I can see someone who will remember this in the audience. And they go over your international calendar for the next 12 months. And they they want to kind of figure out, you know, what you should be doing. And it turns out there's an international summit for basically every single thing that you can imagine. Uh turns out there

[1:00:54] were about 150. Uh and the British Foreign Office being the British Foreign Office, they wanted me to go to all of them. Uh and although I did I did say I'm going to as a British Prime Minister, I think I can draw the line at the United Nations high-level meeting on sandstorms. That's where we agreed to disagree. Um but I noticed that there was no gathering to bring together people to talk about what I thought was the most transformative technology of our time. And I think we're all fortunate to be living at a time where AI is a general purpose technology. These don't come along all that often and when they do, they had the opportunity in the past and will in the future to change every aspect of our

[1:01:32] economies, of our societies, uh of our lives. And that's why I thought it was necessary that leaders came together to discuss this at a regular basis. But, uh I also thought it needed to be done slightly differently. Um in contrast to previous technology cycles, it's companies like Alex's and innovators like Alex himself who were at the frontier of developing this technology. Uh and I thought they needed to be in the room. Not least because it's businesses like yours that are have the resources to invest in this. Just to give you a bit of context, this year, you know, the the large AI companies are going to spend 20 times more on developing this technology than the US did for the Manhattan Project. And if

[1:02:16] you think about seminal moments where the government mobilized to do something, 20 times more just this year alone. Um so, I thought, look, it's vital that we do this in a different way, not like the G7 and G20. We bring industry, innovators, and and government uh together and that's uh that's what we did at Bletchley Park a few years ago. The summit series went to Paris, South Korea, and now, brilliantly, in India, a country with huge digital ambitions and uh indeed capacity. Um but I think over those years, and Alex, let's start with this, I think actually the the the debate on AI has shifted from kind of technology to strategy. Uh you know, from what these tools can do to what governments are choosing to do with

[1:03:01] them. And I wanted to maybe just start there. Is if you think about the last few years, you are someone who is at the frontier of developing this technology. You know, the the journey that we've been on in the last few years, you know, help us understand that. Where have we come? What is it that is exciting at this this moment and just ground us in in where we are so that we can get get on to some of these questions about what we do with it. Yeah, I think I mean we are sitting here right now at such an incredibly exciting moment for the technology. I think we are at the beginning of uh you know, of a true acceleration in the technology and you know, in many ways it's felt like this very clear

[1:03:46] acceleration even until now, but I think we're going to see sort of 2026 is an inflection point in many ways. I think to contextualize this over the past few years, I think from 2018 through 2024 really, which is a good 6-year period, we were writing we were in the era of pre-training. So, we were writing one very large exponential curve towards improving the performance of the models. And that curve was very predictable and it sort of had clear characteristics where the more resources you put in, the more results you would get out and there was a lot of questions at the time of, you know, whether or not this curve would continue and even to the degree it did continue, were there diminishing marginal returns? And I think this was

[1:04:33] sort of one of the one of the more recent sort of eras of AI skepticism. And then in 2024, like towards the end of 2024, began the era of reinforcement learning. So, this was sort of the the next major wave that stacked on top of the previous wave and this is when we saw the models begin to learn to reason. And you know, there's always this meme of what did Ilya see? Well, Ilya saw reasoning from the models. And it was very clearly this this next mega wave of of the technology and even reinforcement learning had, you know, clearly some limitations and there were all these questions around when that was going to, you know, how long that era was going to last and maybe there were limitations in

[1:05:19] that era. And then at the end of 2025, so just a few months ago, I think we really began an entirely new paradigm or era of the technology, which is really beginning the era of recursive self-improvement. And I think we're really starting to see this in the development of our models. I think, you know, most of the major labs are seeing this in the development of their models, which is that the models themselves have now become instrumental in accelerating the process of producing the next AIs. And so, you know, the the sort of from the outside what you see is maybe an acceleration of overall development velocity, an acceleration in at which the speed at which the the new models are being released. Internally, what we

[1:06:04] see is just dramatic speed-ups. I think the sort of the productivity of an individual researcher has grown dramatically, and we don't expect that to stop. In fact, we expect it to kind of smoothly continue accelerating where the output of an individual researcher, an individual engineer, well, I think that itself will continue growing exponentially as the technology improves. And the other mega wave that is somewhat coincident with this new paradigm within the sort of development of the technology of a recursive self-improvement is really the arrival of agents in earnest. And this is something that has been talked about since I honestly want to say 2023 with a lot of fanfare, and for a long long time it was sort of

[1:06:57] one of these hype sort of buzzwords that, you know, never actually lived up to the expectations. And I think also at the end of 2025, we and toward you know, middle to end of 2025, we started to see agents actually work. And this is, you know, these are where the models can actually start taking um actions um and start automating entire workflows and being um sort of dramatically more uh productive as actual sort of entities onto themselves versus assistants. And um And this era is, you know, it started with coding agents. We're starting to see it um really percolate through personal agent. This is a big part of what uh we believe in at Meta. Um and I think over the course of 2026, we will see large-scale agent deployments um in

[1:07:48] many areas of the economy, many areas of of the world, and um the sort of, you know, if the the GDP of AI, so to speak, uh is going to grow exponentially. >> Well, that that is uh very welcome to to hear. I I think this you you hit on something that is important because I remember when I first started doing this in the job, the conversations were all around these scaling laws and would they continue? And you mentioned the skepticism. I mean, not only have they continued and continue to deliver this exponential growth, it feels very much like the last few months have seen an acceleration. It's what you touched on. It's what Dario and and Demis have have also said to me and and publicly, driven

[1:08:30] by this recursive self-improvement, that the models themselves are now able to help us develop the new models quicker. So, you know, we're at this quite exciting but also a moment of some anxiety because of that accelerating uh development time scale. So, I wanted to just then talk about trust because I I tell you give you a story. My I have two, you know, young girls and you know, as they're using their chatbots and things and they're always like, "Please, you know, if so-and-so and so-and-so." And thank you when they finish writing their request or they get the answer back. There's lots of pleases and and thank yous everywhere. And I and I I thought that was, you know, interesting. But I said to them, I said,

[1:09:08] "Look, there's, you know, you don't need to do that. There's not not a person it doesn't it doesn't matter. And and by the way, you know, it costs a fortune when we have to do the compute for that. Um and and they they said to me, "You know what, Daddy?" They said, "If if AI takes over the world, we want to have been kind to the AI." Uh and I thought that was good good insurance policy. But it it it kind of it, you know, it touches on this anxiety. Very wise. >> [laughter] >> Um in that, maybe. You know, across the world, we're seeing these different attitudes uh towards AI. In countries like India, where we are, there's enormous optimism and and trust. And then actually in Western countries, you know, we're

[1:09:46] seeing that anxiety is still the kind of dominant feeling towards AI. Now, I think closing that confidence gap is uh as much a policy task as it is a a technical one. But I I'd love to just get your thoughts. And then obviously you are at a, you know, B2C company dealing with, you know, billions of consumers every day. You know, how do you think about this trust question? You know, how do we get trust up in places where it is not? For something that, you know, you believe, I believe, and everyone in this room probably believes can do untold good. Yeah, this is this is one of the most important questions. And actually, perhaps even more so than the technology and the development of the technology,

[1:10:26] um maybe will govern how how uh successfully the technology diffuses um more so than anything. And um you know, we we think deeply about this cuz our vision for the technology is that it is um we can deploy personal agents that uh know you better than most people know you, know about your health, know about your goals, know about your relationships, know about your family and your friends, and um can really help you live your life more effectively, you know, can help you be healthier, can help you accomplish projects that you never would have had time for, can help you build the business you always wanted, um can help you explore your interests. And to properly build that requires an immense amount of trust from

[1:11:14] consumers, from governments, from um from effectively everyone, from all organizations. And so it you know, this is one of these interesting moments where um I think because the the sort of the form that the of the technology that we wanted to take is so intimate and is so deeply tied with what we think is really the the future of, you know, the consumer life and the consumer um you know, is at the core of the relationship with technology. Um The figuring out ways to develop the the technology in partnership with governing bodies and in partnership with um nonprofits and other organizations to garner that trust is very important. This is something we've seen at Meta actually in um with WhatsApp. You know,

[1:12:04] one of the we were talking about this actually backstage, but um WhatsApp has become uh incredibly ubiquitous um and a real I think utility for much of the world um in large part due to uh its commitment to privacy and the the sort of trust that we've built around WhatsApp as an organization. Um oftentimes at the sort of uh you know, not necessarily for the optimization of our business objectives, but for the optimization of trust in WhatsApp as a platform. I think a similar journey will be necessary for the AI products of the modern age and we've seen um I mean we've already seen various uh kerfuffles or sort of jabs back and forth between the existing AI, you know, products and labs around, you

[1:12:49] know, whether their actions are truly garnering, you know, truly building this uh these levels of trust. But and then I think the you know, the topic that you really kicked off at Bletchley around in a sort of like global way around safety. I think is more important than ever. I think we're already seeing that the models are uh are are achieving levels of capability which cause some pause around some of the sort of like risks that we monitor for these models as it comes to various national security risks or various cyber security risks. And so the the importance of properly testing and then building the right systems to monitor and deploy these models safely is is of paramount importance. >> you and I have been talking about the

[1:13:40] same topic for some years. And that was key to us at Bletchley was establishing the AI security institute in the UK to do exactly that work. And the And the thought was there was a gap where the technical capability didn't exist amongst governments to evaluate some of these risks. So how could we in the UK hopefully take a lead but in sense provide a global public good where we work in partnership with companies like yours and the other