Can Google’s New AI Solve Everything?  | Titans and Disruptors

In 10, 15, years time, we'll be in a kind of new golden era of discovery. That's why, I hope a kind of new renaissance.

discovery. That's why, I hope a kind of new renaissance.

We're in the thick of the AI revolution, but we might look back on January 2014 as one of the most pivotal moments in business history. That was the month that Demis Hassabis sold

business history. That was the month that Demis Hassabis sold his AI company, Deep Mind to Google. He rebuffed a higher offer from metas Mark Zuckerberg, and the acquisition scared Elon Musk so much that he decided to launch a Rimmel company with Sam Altman, now called Open AI. Fast forward to today, and Demis is still the one to beat. He runs all of Google's AI initiatives, including Gemini, which is

quickly eating away at open AI's user base in his spare time, Demis won a Nobel Prize, and he runs a startup called isomorphic that wants to solve all disease with AI. I sat down with Demis at the World Economic Forum in Davos to learn where he thinks the future is heading. I'm Allison Chantal, and this is fortune 500 Titans and disruptors of industry. We'll be

back with Demis after a quick message from our sponsor.

This is on the topic of every CXO conversation I'm a part of, and I think the thought process has to be looking for high impact areas that may not be necessarily the most glamorous or high profile functional areas, but are ripe for automation and use of this technology to create efficiencies as well as innovation. And over time, AI

agents will be also in customer facing and growth oriented domains. In our case Deloitte, we're using it within our

domains. In our case Deloitte, we're using it within our financial organization, looking at very mundane processes like expense management and working capital management. We're seeing

other organizations using it in call centers and with software development that can be automated and comes down to intentionality. And so I think that intentionality in going functional area by functional area, in concert with business and IT leadership and enterprise, it needs to be a

mainstream business planning effort that's budgeted, that's KPIs are developed, and there's real accountability for actual business outcomes and impact because of agentic capabilities.

Demas, we're here at Davos. Thank you for making the time to do this. Great to be here. You've had a huge 2025 it sounds

do this. Great to be here. You've had a huge 2025 it sounds like you're gearing up for great 2026 but before we get into both of those things, I want to just take a step back so people can get to know you a little bit better. One of the things you love is chess. Yes, you're chess master. You also love astronomy, yes. And I'm curious how both of those things took you into AI or

yes. And I'm curious how both of those things took you into AI or shape. How you think about AI? Yeah.

shape. How you think about AI? Yeah.

Well, I've always been interested in things like astronomy, cosmology, physics as a kid, because I've always been interested in the big questions. So you know what's actually happening here in the universe, consciousness, nature of consciousness, all of these types of things. So you get sort of drawn to physics if you're interested in the big questions.

And then for me, for chess, I also love games. Love strategy.

Ended up training my own mind by playing chess as a kid, very seriously, and then that got me thinking about thinking and how does the brain work? And then I've combined all that together.

That sort of led me to AI and computers and AI being a way to understand our own minds, but also a perfect tool for science and understand the universe out there, equipped with a degree in computer science and a PhD in cognitive neuroscience, Hassabis co founded Deep Mind in 2010 the company launched with an ambitious goal to solve intelligence under Hassabis leadership, the Deep Mind team

made significant strides in its artificial intelligence models, and just four years later, Google purchased the research startup for hundreds of millions of dollars. You first started Deep Mind. You co founded it a number of years ago, and about

Deep Mind. You co founded it a number of years ago, and about 2014 you sold it to Google for about $500 million at the time, it was a hot deal. I know meta wanted it too, and from my perspective, I think we're going to look back on that moment as one of the most transformative moments in business history, you've given Google the foundation of which to build an

incredible AI machine and really take it into the future. When

you look back on that, how do you feel about that moment? How

did you make that decision? Did you know it was gonna be such a big moment?

Yeah, we did. Actually, those of us, you know, were involved in the science. So it's interesting. We started DeepMind

the science. So it's interesting. We started DeepMind in 2010 which was, you know, 15 years plus ago now. And nobody

was talking about AI, but we knew, and we set out with the mission of solving intelligence and then using it to solve everything else. So we wanted to be the first company to build

everything else. So we wanted to be the first company to build artificial general intelligence. And the main thing we wanted to apply it to was, was solving scientific problems. So when Google came a. Long in 2014 and it was actually driven by Larry at the time. Larry Page, who was the CEO. We knew that in some ways, we were sort of underselling, but on the other

hand, what mattered to me was not the money. It was being able to it was the mission and be able to accelerate our progress towards artificial general intelligence and answering these scientific questions that that we were, we were trying to solve, and I felt that teaming up with Google would accelerate that, mostly because they had obviously enormous compute power, and we see today that how important that is for developing

intelligence. So at the time, I did mention to Larry, and also

intelligence. So at the time, I did mention to Larry, and also the head of search at the time, who was driving the deal that this would turn out to be, or they didn't look like it. Now it

now it might turn out to be the most important acquisition Google has ever done, which is saying something, because they acquire YouTube and their history and Android, they've got a good history of buying important things.

And now, if you go back and you look at the origins of open AI, if Elon and Sam got together because they were afraid that Google might now have a monopoly in AI space with the Deep Mind acquisition. So really, that also kind of created a mega

acquisition. So really, that also kind of created a mega competitor at the time.

Yeah, I guess there's all these sort of butterfly effects that happen. And I think partly also was the success of things like

happen. And I think partly also was the success of things like AlphaGo, first program to be the world champion at the game of Go using these kinds of learning systems that we're familiar with today. You know, reinforcement learning, deep learning, at the

today. You know, reinforcement learning, deep learning, at the heart of it, I think that was a big watershed moment as well. In

2016 is actually like the 10 year anniversary of that breakthrough this year. And I think that really started the starting gun for the modern AI era, including things like, you know, open AI and the founders of that watched that match and wanted a piece of that action.

Following Google's DeepMind acquisition, the company repeatedly made headlines for its accomplishments in AI in 2015 the company's AI model AlphaGo became the first computer to defeat a champion Go player, and later on, it defeated top players in chess, Stratego and StarCraft two, which is a popular Real Time Strategy computer game in 2020 Google's Deep Mind Alpha Fold two solved the protein folding

problem. For decades, scientists had struggled to predict how

problem. For decades, scientists had struggled to predict how protein sequence forms in its final structure. The model

accomplished this with remarkable accuracy. The team

has since scaled its process to predict over 200 million structures, all of which are now available in an online database.

Pasabas received the Nobel Prize in Chemistry and a knighthood in 2024 in part for this achievement under Google at alphabet, you've been able to have a lot of moon shots, take risks, try things that haven't necessarily led to money immediately, but have profound breakthroughs, and one of them, you won a Nobel Prize. So I was wondering, congratulations, it's incredible. I was wondering if you could just tell me a little

incredible. I was wondering if you could just tell me a little bit more about Alpha fold and why that's such a big deal in terms of how we could be looking at solving diseases moving ahead?

Yeah, I think this is one of the benefits of being as part of Google and alphabet, was having the resources and the time to really go after these sort of deep scientific problems. And alphafold, I think, is the best example of that. It's basically

a solution to a 50 year old grand challenge in biology of can you determine the 3d structure of a protein just from its amino acid sequence, basically from its genetic sequence? And this is incredibly important, because proteins

sequence? And this is incredibly important, because proteins basically do everything in your body, from muscles to neurons firing. Everything depends on proteins. And if you know the 3d

firing. Everything depends on proteins. And if you know the 3d structure of a protein, what it looks like in your body, then you kind of partially know what the function it does, what it supports. Obviously, it's important also for disease,

supports. Obviously, it's important also for disease, because things can go wrong with proteins. They can fold in the wrong way, like in something like Alzheimer's, and then that can create a disease. So really important for drug discovery, as well as fundamental biology and alpha fold. It was a solution to this problem that was posed 50 years ago by another Nobel Prize winner, actually Christian and Vincent, that this should be

possible, and to go directly from a kind of one dimensional string of amino acid sequence to this 3d structure, this kind of, how does it scrunch up into a ball? And alpha vault was a solution, and it's so efficient. Not only is it accurate, we folded all 200 million proteins known to science, and then we put that on a huge database with the European Bioinformatics Institute and for free into the world for everyone to use. So

now over 3 million researchers around the world make use of alpha vault every day.

Wow. And it's turned into you're using some of it, I believe, for isomorphic, which is a startup that you have, I want to say, on the side. So you're doing two huge jobs at once. Here you've

the side. So you're doing two huge jobs at once. Here you've

raised hundreds of millions of dollars in Google, of course, as a backer, yeah, for isomorphic. Can you just explain the mission there? And you have some lofty goals, like you say that we're

there? And you have some lofty goals, like you say that we're going to solve all disease, yes. You don't say cure, yes. You say

solve, yes. And also walk me through how hard it is to get a drug to trial, because that's historically been very difficult.

That was always the idea behind Alpha fold. So obviously there's a lot of fundamental science that can be done if you understand the structures of proteins, including designing new proteins that do new things. So you can sort of use alpha fold in reverse to sort of go, Okay, I want this particular shape. How do I get it from a genetic sequence? But to do drug

shape. How do I get it from a genetic sequence? But to do drug discovery, knowing the structure of protein is only. One small

part of that whole process. And usually it takes, like, on average, you know, 10 years to to go from understanding a target, you know, for a disease, to all the way to a drug that's ready for in the market. So it's an enormous amount of time and cost, you know, billions of dollars a decade or more. And

most drugs, you know, fail along the way. Is only like a kind of 10% success rate. So it's just incredibly inefficient, because biology is so complicated. So what I've always dreamed about doing, and it was the first thing you know, I wanted to apply AI to, was human health, improving human health. What

could be more important use of AI? And alpha fold was the proof point of that this could be possible. And then isomorphic, we spun that out after alpha fold was done so three, four years ago, to develop additional Alpha fold level breakthroughs surrounding Alpha fold so you can think more in the chemistry space. So if you now know the structure of a protein, you need

space. So if you now know the structure of a protein, you need to know where the chemical compound you're designing the drug, basically where it's going to bind to the protein, and what is it going to do. And so you need to build other AI systems that can predict all of that. So that's what we've been doing in isomorphic it's going incredibly well. We have great partners

with Eli Lilly and Novartis, the best farmers in the world on we have like, 17 drug programs active already, and we plan to go to, you know, eventually be hundreds. And I think this is the way to make real step change progress in human health, is you basically do your search and your hypothesis searching in silico. And that's, you know, hundreds, 1000 times more

silico. And that's, you know, hundreds, 1000 times more efficient than doing it in a wet lab. And you save the wet lab part just for the validation step. Of course, eventually you have to test it in, you know, trials, human trials, and all those types of things to make sure everything's safe. But you

can do all of your search and design, or almost all of it in silico, that's the plan. And so 2026 you mentioned, is going to be a big year, I imagine, for both Google and for isomorphic. Do you anticipate in early 2026 this could be the moment that you get the first drug to trial, and might it be in cancer, yes.

So we're working on, actually, several spaces, cancer, cardiovascular, immunology, and then eventually we'd like to branch out to all therapeutic areas. We're building a, you know, a general drug discovery engine platform you can think, and we are already in pre clinical trials, very early stage, for some cancer drugs. And then, you know, hopefully by the end of the year, if those are successful, we'll start going towards clinical trials.

How do you manage yourself and your time and your teams?

Because you're achieving really, really difficult things, whether it's the launch of Gemini three, which was very successful and well received, or it's getting drugs to trial, these are two sounding like very different things, two different teams to run. You can't be in two places at once. How are you doing this?

run. You can't be in two places at once. How are you doing this?

How are you running two companies?

One of my skills is bringing together amazing, world class, interdisciplinary teams. I've loved managing those teams. I love composing those management teams together. And I've got incredible teams, both at Google, DeepMind and isomorphic.

And if we take isomorphic, for example, we've blended top biologists and chemists along with top machine learning and engineering. And I think there's a lot of magic happens when you

engineering. And I think there's a lot of magic happens when you have these kind of interdisciplinary groups. And

then if we think about on the Google DeepMind side there, we've tried to blend together the best of the startup worlds, like what we were doing at DeepMind originally, and then at scale, you know, in a kind of multinational scale, with all the advantages of having these amazing product surfaces that we can immediately deploy, you know, technologies like Gemini, three, two, and immediately get great feedback from users, and

also, you know, help in everyday lives of billions of users. So

it's amazingly exciting and motivating, actually. And in

terms of the way I manage my time is, you know, I don't sleep very much, but, like, a couple hours, yeah, well, if you know a bit more than that, that would be bad for the brain. Yeah, I do try and get six, but I have unusual, you know, sleeping habits I sort of manage during the day. And do try and pack my day in the office with as many meetings as possible, back to

back, almost no time, no break between. Then I get home, spend a little bit of time with family, have dinner, and then I sort of start a second day of work about 10pm and go to 4am where I do my thinking and kind of more creative work and research work. And it's worked out, you know, I've done that

research work. And it's worked out, you know, I've done that for about a decade now, and it works well.

I can't imagine being creative at four in the morning. But,

yeah, I come alive at about 1am in 2023 Google was facing increasing competition from other rapidly growing search engines and from the launch of chat GPT. In the same year, Google pivoted to merge two of

chat GPT. In the same year, Google pivoted to merge two of its AI teams, Deep Mind and Brain under Hassan. This is

leadership, and the goal was clear, jump start the next generation of AI. You're clearly good at motivating teams to do hard things. I know in 2023 a decision was made at Google to

hard things. I know in 2023 a decision was made at Google to put two different AI teams under you. How did you work out management kinks there and get the team shipping again? Because

there was this feeling that Google was a little bit asleep at the wheel for AI. And I'm curious if. You think that's true, and how you got them to wake up?

Yeah, well, we had two world class groups in original Deep Mind and Google Brain. And actually, I think often, as a collector, we don't get enough credit for the fact that, you know, I think about 90% of the modern AI industry is built on technology or discoveries made by one of those two groups, from transformers to AlphaGo and deep reinforcement learning. So we

have, when we still have, I think, the deepest and broadest research bench. So we have incredible talent, I think,

research bench. So we have incredible talent, I think, better than anywhere else in the world, by a long way. But it was getting complicated having two groups, especially given the amount of compute needed in this scaling era. So that was really why we had to put the two groups together, so we could pull all of the talents together working on a single project in Gemini.

But also, even someone like Google didn't have enough compute to have two frontier projects under one house, so we needed to combine all of our resources together. I'm very

collaborative person. I'm very open minded about different ways of working and try to I'm always looking to improve as well. Like

one of my watch words I live by is this Japanese word Kaizen that I love, which is sort of striving for continual self improvement, and that's what I always try and do. I'm always in learning mode. Maybe, perhaps that's why I like building

learning mode. Maybe, perhaps that's why I like building learning machines, because I like learning, and there's always something you can learn, no matter how expert you are at what you do. And bringing the two groups together and trying to combine the best of both cultures has been great, and I think we're reaping the rewards of that now and now Google Deep Mind is really we, the way we think about is like the engine

room of Google. So we're sort of powering it's like the nuclear power plant that's plugged into the rest of this amazing company in Google. And I think one of the things we did is, one of the

in Google. And I think one of the things we did is, one of the things I'm very proud of, is getting the shipping culture going and sort of rediscovering, I guess, the golden era of Google back 1015, years ago, and taking risk, calculated risk, shipping things fast, and being innovative. And I think that's all working out really well now, whilst at the same time being thoughtful and scientific about and rigorous about what we put

out in the world, whether that's engineering or scientifically.

And I think, and I hope, you know, we're getting that balance right.

Yeah, and you mentioned kind of going back to the golden era of Google, so much so that the founders, at least Sergey seems like he's back involved. How is it like working with him on AI and Google? It's been great, and Larry is too, in different ways.

and Google? It's been great, and Larry is too, in different ways.

Larry, more strategically. Sergey has been in the in the weeds, programming away, you know, and things like Gemini, and it's been fantastic seeing them engage, putting him to work. Are you like Sir Gandhi was code? Right? No, it's more

work. Are you like Sir Gandhi was code? Right? No, it's more like he chooses what to work on, but it's, it's, it's, it's great see, you know, seeing him in the office and pushing things in certain directions. And it's easier if the founders are kind

certain directions. And it's easier if the founders are kind of heavily involved, and I still act as well like as a co founder of Google Deep Mind, of of like as a kind of founder or two, right, in terms of like, what we've got to do, and strategically, what we pick to do. And that's something I think I've learned to do well over the last. You know, 1015, years is

when you have some ambitious goal, like solve all disease or build AGI. What are the intermediate goals that are also

build AGI. What are the intermediate goals that are also very ambitious, but there are kind of way points. What are the right ones to pick? And I think we've done that historically pretty, pretty well with most of the Alpha projects, Alpha Go, alpha fold and so on. And then now Gemini, and I think that's really critical, actually, for any very ambitious scientific and engineering project, is breaking it down into manageable

steps so that you can see you're on the right direction. And I

think that we're very clearly are, I think, with the technology that we're building, and it's been an incredible couple of years for us, and I think we're getting into our groove, I would say, and I think other people and the external world are starting to feel that, you know, including things like Wall Street and the share price in 2005 any questions about whether Google was facing an

innovator's dilemma when it came to AI in search were answered after the first quarter, its shares skyrocketed, driven in part by advancements it made in AI development, including the launch of Google's viral image generation model nano banana and Gemini three alphabet. Shares rose about 65% by the end of the year, marking its best performance since 2009 and making it the top performing stock among the Magnificent

Seven. It definitely seems like there must have been some sort

Seven. It definitely seems like there must have been some sort of KPI measurement charge ahead unifying moment, because, I mean the launch of Gemini three, much fanfare, among other launches that caused open AI to go to a code red, which they claim happens all the time, like, Okay, sure. And then you have this huge monster deal with apple that is monumental, I think, for the industry. So I'm curious what happened

internally, behind the scenes. How did you set those KPIs for the team? And then how are you setting them to keep the

the team? And then how are you setting them to keep the momentum in 2026 Well, look, I think for me, it always starts with the research, like having the best models in this case, and obviously fundamental research feeding into that, and I always believe you then need to reflect that obviously as quickly as possible in your products, and then you've got to get your marketing and distribution right. But none of it matters if your models aren't

distribution right. But none of it matters if your models aren't best in class, in state of the art. And so that's what we focused on, first with the Gemini models, but also our other models, things like nano banana, our image. Model, which

went super viral, and that was a big part of their success last year, our video model, VO, our world model. So there's more than just large language models, and we're kind of, you know, state of the art on all of those. And then it was about sorting things out internally, almost rebuilding the infrastructure in some way of Google, so that you could reflect very quickly the power of the latest models into the

lighthouse products, including Search, YouTube and chrome, all these amazing surfaces that we have as well, of course, as the Gemini app, it was new for everyone in industry. And I

think, you know, takes a little while to kind of re architect things around that. And very much, you know, again, this idea of of Google DeepMind being the engine room, providing the engine for the rest of the organization to use. And I think that took a year, 18 months, to get right, but I think we're seeing the results of that now, and I think there's still more

to go, by the way, and we can have even faster velocity. And I

think the other thing is just also instilling this culture of intensity and pace and focus and really focusing only on the things that matter, and kind of cutting out distractions. And

then maybe the final thing I would say is, I think there's a lot to say, especially in today's very noisy world, to just consistently deliver good decisions, good rational decisions, and over time, minimal drama. And then I think it's just amazing how much that compounds over time. And you

know, I think we're building a lot of momentum now, and I think hopefully you will, we'll see that even more this year, sort of like we mentioned before, the decision to sell DeepMind to Google is a monumental moment, transformational moment in business, if you're successful now, I think that will be perhaps the biggest transformation in business. How does that weigh on you to make sure that you as a leader, are driving this in a direction

that's good for society, good for the workforce, good for Google, because it is a little bit of an innovator's dilemma where this is the search King, huge business model based on ads if you're successful.

Yeah, I don't know Sure. Well, look, I mean, it is a classic innovator's dilemma. I think we've navigated it pretty well

innovator's dilemma. I think we've navigated it pretty well so far. And search is more, you know, is more successful than

so far. And search is more, you know, is more successful than ever. But also there's this aspect of like, if we don't

ever. But also there's this aspect of like, if we don't disrupt ourselves, someone else will. So we, you know, you're, you're better off sort of being ahead of that, I think, and kind of doing it on your terms. And so I think that's what we found in terms of responsibility. I feel that, you know, I felt that way since, not just at Google, but before that deep mind, and

before that, even in my academic career. Because if we, you know, myself and Shane, especially, our chief scientists, you know, when we started Deep Mind, we it seemed like a fanciful idea, but we really believed that it would be possible to create artificial general intelligence, and we understood, what I think more and more people understanding now is how transformative to the world that would be, but also, of course, amazing for things

like science and human health and maybe helping with energy and so on. But also, there are risks. It's a dual purpose technology. You know, harmful actors. You know bad actors.

technology. You know, harmful actors. You know bad actors.

Could use it for harmful ends, and eventually, as AGI becomes AI, but technology becomes more autonomous, more genetic, and we get towards AGI, there's technological risk too. And so I worry a lot about all of those things. And I also, of course, you know, we have to make sure that the the engine and the economic engine works as well, so we have enough money to fund

our research and fund things like alpha fold and give it to the world for free. You know, that's not easy. It costs a lot of money to create something like and hire the researchers to create something like alpha fold, but we do a lot of things like that, and I want to do more things like that for the world, but that requires us to be successful also on the

commercial side. So I think, you know, there's a balance to be

commercial side. So I think, you know, there's a balance to be had there, but the responsibility, I think part comes as well as, and I feel like we can do this at Google as well is we have the platform to show how AI can be deployed in a responsible way and a beneficial way for all of society. And, you

know, all of us who are frontier labs producing AI, we have choices about, what should we use AI for? Are we going to use it for things like medicine and for alleviating, you know, administration and helping with things like poverty, or we're going to use it for exploitative things, and I think that we're going to try and be a role model for all the good things that can

come with AI doesn't mean we won't make any mistakes. We will

do because it's such a nascent and complex technology, but we'll try and be as thoughtful as possible as we can with it, and we'll try and be as scientific about it as possible, too. The scientific rigor we bring to our work, and always

too. The scientific rigor we bring to our work, and always have, I think, is going to matter here a lot. I mean, it's a scientific endeavor in the end. And then, you know, I hope that what we you know, the kind of reliability and security and safety that we like to work on, will come through in our products. And then I think the market will reward that. Because

products. And then I think the market will reward that. Because

if you think about enterprises that use these technologies, as they get more sophisticated, they're going to want to know, you know, if you're a big bank or or, you know, insurance company, whatever health, health company, that medical company that you you have some guarantees about what your AI systems that you're bringing in are going to do. And. And so I think that could be a good aspect of AI becoming very

commercial, is that there'll be sort of commercial incentives to be robust and reliable and secure, and all the things that you'd want actually in preparation for AGI coming into the world.

So when you look at the year ahead, what do you think the story of AI will be? What will we achieve?

Well, I think this year, I mean, every I say this, every year that, you know, every year is pretty pivotal in AI, and it feels like, at least for those of us in science, working at the at the coalface, you know, like 10 years, almost happens every year. And I think this year will be no different. It's very

year. And I think this year will be no different. It's very

intense. But you've also got to, kind of, every now and again, look up at the strategic picture. I think that at least for us, with Gemini three, we cross the watershed moment, in my opinion. And hopefully, those of you who've used it will feel

my opinion. And hopefully, those of you who've used it will feel that in that it's very capable now, and I'm certainly using it in my everyday life to help me with my research and summarizing things and doing some coding. So I think that these systems are now ready to maybe build agents. We've talked the whole industry's talked a lot about agents and more autonomous systems and delegating whole tasks to them. But I think maybe

by the end of this year, we'll really start seeing that. I'm

very excited about assistance coming into the real world with you, maybe on glasses. We have a big project on smart glasses. I

think that the AI technology is only just about there to make that actually viable. And I think that could be a kind of killer app for force for glasses, you know, I think that part of bringing that into the world, also robotics, I think is gonna, I still think there's more research to be done on robotics, but I think over the next 18 months or so, I think we're gonna see kind of breakthrough moment in robotics

too. So all of these areas we're pushing very hard on as well as,

too. So all of these areas we're pushing very hard on as well as, of course, improving Gemini itself.

Those are the glasses, right? No, they're not. I would buy those if they were. They look yeah. I was going to ask you about the future form of how computers were not built for AI, and all the things that AI can do. What do you think is the future for sounds like glasses? And do you think would just be one of the solution? I have this side notion of, we

talk about this notion internally of a universal assistant, and what we mean by that is an assistant that's super helpful in everyday life, recommending new things, enriching your life. And, you know, dealing with admin, all of these types of things, but it goes across all the surfaces. So

it's this on your computer, on your browser, on your phone. And

then I think there'll be new devices too, like glasses, and it will be the same assistant that kind of understands your context across the different conversations you've had, whether that's in your car or in your office. And if you want it to, you know that can all be integrated together. And I think help you, you know, improve your life across all those different aspects of your life, maybe for Christmas next year, the holidays next

year, we can all get our Google Glasses. That's, that's the idea you guys were just way too early, I think before when they I think, you know, and like a lot of things we've done at Google, we maybe were, you know, we pioneered all these spaces, perhaps a little bit too early, in hindsight, with glasses, both the technology, the technology of making them not too chunky

and things, but also, I think it was missing the killer app. And

I think an AI digital system could be that.

Yeah, amazing, yeah. Well, I just one last question for you.

I want to ask your biggest, boldest prediction for how AI will transform the world when you look ahead. I know you said 10 years is one year now it's true, yes, but when you're looking ahead, are you like the abundance world where AI can solve all of our problems, like, what does it look like?

I think done right? We will be an incredible, you know, in 1015, years time, we'll be in a kind of new golden era of discovery. That's why I hope a kind of new renaissance. And I

discovery. That's why I hope a kind of new renaissance. And I

think human health will be revolutionized. It won't

medicine won't look like it does today. I think that personalized medicine, for example, will be a real reality, and I think we'll have solved, used these AI technologies to solve many big problems in science, and things like new materials, maybe help with fusion or solar or optimal batteries, some way of solving the energy crisis. And then I think we'll be in a in a world

of radical abundance, where we can use those energy sources to, you know, travel the stars and explore, you know, the galaxy.

That's, that's what I think our destiny is going to be amazing. Well, thank you. I hope that that's what you build. And thank you for all your efforts on it. Great to

build. And thank you for all your efforts on it. Great to

talk to you. Likewise.