The Hardest Problem AI Ever Solved, with Google DeepMind CEO

Something's obviously not quite right about the definition of intelligence. If we play this out, what's the limit here? The best use case of AI was to improve human health. It was the moment I've been waiting for that could achieve something no other system could. I want to use AI as a tool to help us understand the nature of reality around it. Governments are going to use AI. What would

you hope that they use it for? There's two things to worry about. One is...

That's Demis Hassabis, the CEO of Google DeepMind, Nobel Prize winner. He is one of the most important people alive on what is quickly becoming the biggest technological leap in our lifetime. Because the biggest way that AI is going to impact our lives isn't something that we can see. It's not a chatbot. It's not an image

generator. It's tools that are invisible to us in drug design and natural disaster detection and

generator. It's tools that are invisible to us in drug design and natural disaster detection and nuclear fusion and quantum computing. Tools that he and his team are building. Here he is winning the Nobel Prize for just one of those tools. So, who he is and what he chooses to build matters a

lot for you and me. And he's fascinating. He's a childhood chess prodigy who at 17 turned down a reportedly million-dollar job offer from a gaming company to go to college instead and then got a PhD in cognitive neuroscience. He founded his company DeepMind with a mission to solve

intelligence starting with beating video games. He then sold that company to Google specifically because they promised to let DeepMind focus on scientific research. But as this has turned into the most intense technological battle in recent history, Demis is now in charge of much,

much more. He's now behind basically everything Google does in AI. He's making decisions that

much more. He's now behind basically everything Google does in AI. He's making decisions that affect your life and millions of other lives every single day. So, what is he planning to do with all of that power? My goal is to show you the future that Demis Hassabis wants to

build so that you can decide for yourself what you think of it. Welcome to HUGE* Conversations.

Thanks so much for doing this. It's great to be here. Really appreciate it. You already know that HUGE* Conversations is a different kind of interview. I'm not going to ask you about financials. I'm not going to ask you about your management style. I all well covered elsewhere.

financials. I'm not going to ask you about your management style. I all well covered elsewhere.

What I'm hoping to do in this conversation is think about it more like an explainer that we're making live together. And I have some props. This is not actually meant to be a Jenga game. We're

going to play Jenga. Um each block represents a project or a model and I want to talk about them and how they fit together. And so they were meant to be visual aids but as we were setting up we started playing Jenga with them and it turned out to be way more fun than anything I had planned. Also I know that you like games. Yes, I love games. So this is great first in an interview

planned. Also I know that you like games. Yes, I love games. So this is great first in an interview anyway. So yeah. So my hope in this conversation is to make this explainer together and to help

anyway. So yeah. So my hope in this conversation is to make this explainer together and to help people see what's happening right now in AI really and what is the future that you see coming. What

are you hoping to do in this conversation? A lot of the reasons that I got into AI 30 plus years ago now is to um advance science and medicine and I've always thought of AI as potentially the ultimate tool to do that. So, I'm hoping we're going to talk about that today. And really, that's been my passion for what to apply AI to, although of course it can be applied to many things. Oh,

this is going to be a lot of fun. Yeah. So, in this Jenga game that we have, a lot of these are blocks that people will have heard of, right? These are, you know, this one is Gemini, right? But I would argue that the ways in which AI is meaningfully shaping people's lives most

right? But I would argue that the ways in which AI is meaningfully shaping people's lives most are the things that are invisible to them most of the time. So I want to start by talking about the project that you won the Nobel Prize for AlphaFold. Yeah, good Jenga playing. I want to tell the story of AlphaFold with all of its drama because some people might not have heard it,

but then I want to get really quickly to the cutting edge of this sort of category of science.

Why did you decide to tackle this problem out of all of the many? Mhm. Well, I came across it actually uh as an undergrad in Cambridge. So I had a lot of biologist friends and one of them specifically was obsessed with what's called the protein folding problem. So proteins are what everything in your body relies on. They make biology possible and living possible. And what's

important about them is their 3D structure. So in the body they fold up into kind of 3D structures and those structures determine what function they have or partially determine what function they have. And so the protein folding problem is really about can you predict this 3D structure just from

have. And so the protein folding problem is really about can you predict this 3D structure just from the one-dimensional amino acid sequence. So that's the kind of 50-year grand challenge of protein folding. So I love challenges. I love puzzles. So I couldn't resist it from a scientific point

folding. So I love challenges. I love puzzles. So I couldn't resist it from a scientific point of view as this um probably you know is described to me as the equivalent of Fermat's last theorem but for biology. So who couldn't be interested in that? But also when I first heard it, I I thought um the kind of problem it was would be suitable for AI one day. Even though we of course this is

in the late '9s, we didn't have any kind of AI that would be possible to work on this. But I

thought one day that would be possible. And then the final thing was just the impact it would make if you cracked it because it would open up all these downstream possibilities for research and especially in things like drug discovery and understanding disease. So, which I think is, you know, the most important thing to apply AI to is improving human health. And the reason that

this would be huge for human health is that up until now, in order to develop new medicines, we'd had to spend hundreds of thousands of dollars and years of human effort to find out the structure of a single protein by shooting X-rays at it. So, we had figured out some protein structures, but it was slow and expensive. So I'm skipping over an enormous amount of hard work here by

you and your team, but I think by the way that I'm asking the questions, it is very obvious to people that you solved it. Yeah. So there's this moment where you realize that it is genuinely useful and you have solved what had been called one of the most important unsolved problems in modern medicine. Mhm. And it's 2021. You're in a meeting. I am so glad that there was a camera in this

medicine. Mhm. And it's 2021. You're in a meeting. I am so glad that there was a camera in this meeting. It is one of the most incredible moments I have ever seen. Can we use AlphaFold to to solve

meeting. It is one of the most incredible moments I have ever seen. Can we use AlphaFold to to solve it? I think you're talking with your team about setting up a system where scientists could send

it? I think you're talking with your team about setting up a system where scientists could send in a request for a specific protein like a website and then get the protein folded. Yes. And then

someone else has a very different idea. Yes. Can you walk me through what happens in that meeting?

And then you your reaction is incredible. Uh and I really want to know what you were thinking. Yeah,

sure. Well, look, we we it's it was funny that the cameras were there happened to be in that in that particular meeting. It was crazy that it was that day, you know, they very rarely followed us, but it was for that meeting. And um normally what happens for these sorts of uh prediction models is you the traditional thing is you kind of set up a server and then other scientists send

you their protein sequences and they say, "Oh, I'm interested in this protein. Can you send me back the the predictor structure?" So you know uh and that's how it been done in the whole field for the last 40 plus years. Um and the reason is is because most of the prediction algorithms are quite slow. So maybe it would take a few days and then you'd get back the you know you'd email your

quite slow. So maybe it would take a few days and then you'd get back the you know you'd email your you email back the structure um and then you'd ask for you know you'd ask for the next one. But

once I realized sort of in that meeting actually that how quickly uh not only how accurately we could fold the proteins but how quickly you know in a matter of seconds and then I was just sort of doing the back of envelope calculation like how many proteins are there known to science known in nature 200 million and then how many computers do we have and uh how many would we need and then

you know per and then if we folded one every 10 seconds and I sort of realized sort of in the middle of that meeting I was fiddling on my phone that it would be possible in a year. And so why go to all the effort of building the servers and the databases and and you know the emails uh uh um client and all of that when we could just actually fold everything ourselves, everything anyone could

ever request and ever want and then put it on a database somewhere for free for all the scientists in the world to use. So it just suddenly hit me. We should just do that. Now why don't we just do that? Well, so that's one of the options like we uh you know there's this we should just we should

that? Well, so that's one of the options like we uh you know there's this we should just we should that's a that's a great idea. We should just run every protein in existence and then release that.

Suddenly all these things must have been going on the back of my mind and I suddenly realized that that would be the obvious thing to do and it would be actually probably less effort than setting up the server. So actually it would save us time. And you in that meeting your reaction is something like why don't we just do that? That would be way better. We should clearly do that and then you do.

All of a sudden, this crucial process that had been so hard is suddenly fast and easy and it's being used by scientists all over the world. This huge unsolved problem now solved. Is it correct to say that we have now predicted the structure of almost all proteins known to science? Yes.

And we keep updating it. So every time, you know, somebody scoops a pale out of the ocean somewhere and then, you know, there's loads of different types of organisms in that bucket of water, sea water, and then they sequence them all. And so the sequencing technology has obviously uh improved many orders of magnitude since the human genome was sequenced. So now the problem

was the structural biology, the finding these 3D structures was taking was far lacking far behind the genetic sequencing. So now with these computational resources like AlphaFold 2, we can actually keep up with oh here's a new million uh genetic sequences from some new strange organisms

we found. Uh oh here are the structures and so uh we have a kind of a small team uh at the at the

we found. Uh oh here are the structures and so uh we have a kind of a small team uh at the at the European Birmatics Institute that keeps updating every year you know all the new sequences that have been found that year. So, we're we're now, you know, always at the cutting edge. Like, we

know what all of these different uh uh uh protein structures mostly uh look like. That's so awesome.

It is pretty amazing. It's especially amazing actually for the people, researchers that work on um slightly more obscure, you know, organisms or animals and things like uh for example, wheat. I found out a lot of plants have way more uh uh genomic data than mammals and humans,

wheat. I found out a lot of plants have way more uh uh genomic data than mammals and humans, which is very strange. they seem to have multiple copies of of of of their genome and things and it's uh it's it's it's it's a kind of strange and bizarre world I think the plant world but my plant scientist friends of mine is like you know they they they don't have the resources um like with

you know human genome as a lot of work's been done on that but some of these more obscure organisms um that still really important for humanity you know like crops and things like that uh now we're able to um immediately jump to the science around what they want to do with the proteins. Um maybe,

you know, help them be more resilient to to climate change, things like that. And uh and and they can jump straight to the problem they're actually interested in rather than getting bogged down with trying to crystallize the proteins that they're interested in. Another boon is for um researchers who work on neglected diseases uh that affect primarily the more developing parts of the

world, things like malaria or chagus disease or leechmanasis, these they affect you know hundreds of millions of people around the world and um but there's not uh a lot of money in that if if big pharma um try to research that and find cures because they're in the more poorer parts of the world so they tend to be neglected the research that goes into that. So there's these amazing

uh nonprofit organizations that do the research on that uh um but they don't have a lot of money or resources. So um giving them the structures of the proteins that are involved in say malaria virus

or resources. So um giving them the structures of the proteins that are involved in say malaria virus uh is a huge boon for them too because they can go straight to the drug discovery phase. That was

one of the hardest things to figure out as I was doing this research because there's this moment where scientists all around the world have access to AlphaFold. You can see like the map lights up.

You can see that people are using it. But I wasn't easily able to figure out what and a great example would be to talk to you about of a scientist using AlphaFold and then that speeding up a drug process that results in a drug that I could now take. Yeah. What is your favorite example of a scientist using AlphaFold for something the audience might understand or have seen. So over

3 million uh scientists are now using AlphaFold. We think it's pretty much every biologist in the world at this point. Uh and one actually scientist at a at a pharma company said to me uh that uh you know almost every drug developed from now on will have probably used AlphaFold in its uh in its

process which is sort of you know mind-blowing really and amazing that that's that that that impact it's having. So um but it still takes time with drug discovery. So we're still mostly in the fundamental biology stage of understanding the disease. What is the protein we're targeting?

Is that the right biological mechanism? And then uh as I understand it, some of these uh drugs are now in kind of the the the clinical trials phase and then uh hopefully we'll see in a few years time you know whole dozens of drugs that were partially helped by at least uh AlphaFold and um in terms of my favorite breakthrough is more that so far has happened with the help of

AlphaFold is there's this protein in called the nuclear pore complex and it's one of the biggest proteins in the body. It's huge for protein and what it does very important job. It's basically

the gateway that opens up and closes to let nutrients come in and out of the cell nucleus. So,

it's it's basically the gate it's like a big donut ring that opens and closes. And um but we didn't know until very recently what the structure of this was cuz it's so big and complicated.

Uh it's pretty hard to crystallize and actually see. And so uh almost I think it was pretty much 6 months or a year after we put AlphaFold out uh some teams used it along with experimental data to finally work out what this beautiful shape was of this uh this gateway protein and uh that was amazing to me. Is, you know, one of the biggest proteins in the body. That's um and

and AlphaFold was was was very useful in helping determining that structure. And so perhaps we can design drugs or treatments that better that that use that somehow that that better access the Yes, potentially. I think that was more for fundamental biology understanding but but obviously there is

potentially. I think that was more for fundamental biology understanding but but obviously there is uh I mean we ourselves we've spun out a new company Isomorphic labs that uh actually uh tries to build on AlphaFold and uses it to indeed in this in this block here uses it to uh uh as as

one of the pieces of the puzzle to um massively speed up drug discovery. So on average, you know, it takes like 10 years to uh uh to to kind of develop a drug. It's crazy long time, unbelievable amount of hard work, very expensive and huge failure rates. You know, only about 10% of drugs actually get through all the clinical stages. So we need to vastly improve that if we want

to improve human health, I think. And I think the way to do that is by using uh incilicone methods, AlphaFold 2 being uh one of those components. Um but knowing the structure of a protein is only one small part of the drug discovery process. You need a lot of chemistry like what compound should you design to bind to it all of these things. So we're trying to isomeorphic um build these you can think

of them as uh adjacent systems that work with AlphaFold more advanced AlphaFold AlphaFold3 AlphaFold 4 you could call it and um and then uh end to end basically create these drugs that have you know very minimal kind of side effects and uh and incredibly effective at addressing

uh uh the type of disease we're we're trying to help with and we're working on you know I think at this point are like 18 19 different drug programs um across the gamut of things from cardiovascular heart disease to cancer to iminology. So um I think eventually these these types of technologies should be able to help um across almost every therapeutic area. In prep for this I did a

background interview with your fellow Nobel Prize winner John Jumper. He really stressed that it's one part of a larger problem of drug discovery. And so that brings us to the cutting edge today.

I've taken some of the some of the examples that I want to talk about. Um what is the cutting edge now? Sure. So we we're we're building many different components that can kind of go

now? Sure. So we we're we're building many different components that can kind of go together. So AlphaFold is one of the lynch pins. So that's the structure of the protein. But if you

together. So AlphaFold is one of the lynch pins. So that's the structure of the protein. But if you think about it, let's say you understand what the shape of the protein is. Okay? And then now you know which bit of the protein is the important part that does its function. So now if you think about drug discovery, so say you want to block the effect of that protein or enhance it in some way,

you know, you need which part of the protein surface do you have to bind to. So now you have to discover a chemical compound that will that will attach to the right place on the protein, right? And and you want to know how strong will it attach and will it and then on top of that,

right? And and you want to know how strong will it attach and will it and then on top of that, even more important is not just will it attach to the thing you're interested in, make sure it doesn't attach to other things because if it does, that would be toxic. toxicity. So you don't want it to have these side effects or we call it side effects with drugs. So you want to minimize

those. So but because now we have all of these amazing um algorithmic tools, we can sort of do a

those. So but because now we have all of these amazing um algorithmic tools, we can sort of do a virtual screen of like oh here's a compound one of our AI systems is designed. It binds really this is our prediction of how strong it binds to the protein surface. And then we can check that very

quickly like in a matter of hours that particular compound how does it attach to any of the other 20,000 proteins in the human body. And so we can just do it like that you know within a few a few minutes and then um modifi keep modifying the compound so that it has less and less side effects ideally none on any of the other proteins but um increasingly strong effect on the one that

you want. So you can see I've just outlined a self-improvement process or self modification

you want. So you can see I've just outlined a self-improvement process or self modification process and this is extremely fast and efficient if you can do it in silicone and then um you know on on on computers and then at the last stage um only at the final stage do you check it in the wet lab. So you still have to validate it. you can't you make all these predictions. You do all your

lab. So you still have to validate it. you can't you make all these predictions. You do all your search in silicon but then at the final stage you check your final uh uh proposed compounds in the wet lab um and then check it really does what the predictions say. But that you can imagine would save you can search thousands of times more compounds or maybe even millions at some point

more quickly and efficiently that way and then just at the end check that they're correct. That's

so much more efficient than doing the search in in the wet lab, which is what effectively is done today. Oh, one of my favorites also is Alpha Genome. Yes. So, I reached out to yet another Nobel Prize winner. Good grief. Um, Dr. Jennifer Doudna who I've had on the show. Fantastic.

And she sent a question for you. So, I'm going to read this question from Dr. Doudna. Okay. So she

says crisper, the gene editing technology that she pioneered, can now target nearly any DNA sequence, but for most genetic diseases, we still don't fully understand which changes in the DNA are actually driving the problem, especially in the 98% of the genome that doesn't code for proteins.

With tools like Alpha Genome starting to decode that 98%, how close do you think we are to the moment where AI can reliably point to the exact genetic change causing a patient's disease so that technologies like crispr can fix it? Yeah, what awesome question. So, um, you know, I've discussed this with her actually in the past and and it is really exciting. I think with alpha

genome which is exactly that kind of technology. It takes the big long genetic sequences and then it tries to predict you know if you made a mutation to this particular single letter singles position in the genetic sequence will that be a harmful you know mutation that might cause disease or is it benign and it won't do anything. And Alpha Genome which we just released is the

best uh system in the world for predicting that. So that's exactly what you then want if it got um it's still not probably good enough yet, but you can imagine a future version of Alpha Genomes that are accurate enough to sort of really know like oh that particular mutation in combination with this other one. That's the hard part is what if they're multigenic diseases where there's cascades

of mutations caused the problem. Those are even harder to detect but actually perfect for sort of AI to try and help with. Then um you could go in with something like crispr maybe one day and go in and fix that mutation um and then fix the problem. So that would be so a kind of combination of things like Alpha Genome and crispr could be incredibly powerful and hopefully one day we'll

be you know collaborating with with with the likes of Jennifer on that. Last year you said something to the Guardian that I found really interesting. You said that if I'd had my way I would have left AI in the lab for longer. Oh, sure. And the quote is done more things like AlphaFold, maybe cured cancer or something like that. From the outside, it looks like the story goes, you found Deep

Mind with the mission to solve intelligence and use it to solve everything else. Yes. And then

you sell to Google specifically because they will allow the freedom to explore science in this way. Yes. And for a long time that's your exclusive focus. Yeah. And then ChatGPT comes

this way. Yes. And for a long time that's your exclusive focus. Yeah. And then ChatGPT comes out. Google goes code red and you become the head of all Google AI, including the consumer products

out. Google goes code red and you become the head of all Google AI, including the consumer products that you weren't spending as much time on before. And it feels to me like watching that from afar, it mirrors somewhat the larger experience of AI, which is just this incredible change. Yes. Um,

in the last couple years. What was gained and what was lost in that change? Yeah, I think um that's exactly right, what you describe is is sort of what how it felt from the inside too. And

um for me uh as I mentioned earlier was that that AI uh the the best use case of AI was to improve uh human health and accelerate scientific discovery. In fact for me I I I've got I got into AI in the first place because I was interested in all the big questions in the world. The nature of reality, nature of consciousness, these kinds of things. And um I felt we needed a tool to help us

even the best scientists to help us make sense of the amount of data and information out there and find insights in that and that's happening which is amazing and obviously Alpha Fold was the our first and you know so far best expression of that let's say uh and I always had that on my mind and many other and other problems like that. Um so it would have been great I think to and and

given how important AGI is and how transformative a technology is maybe the most transformative one in human history, um then uh I thought it would be best to approach these kinds of uh the the sort of latter stages of building it which we're in now in we're using the scientific method very carefully very precisely very thoughtfully um and rigorously with all the best scientists

kind of in in my ideal world collaborating on um in kind of CERN like way effort uh on making sure each step we understood each step each as we got to the final goal of of building AGI um seems to me like that would make the most sense with a technology like this. Um and then and of course

you don't have to wait. So that might take a lot longer, maybe a decade, even a two decades longer, but I think that would make sense given the enormity of of what we're dealing with. And and

then my other idea was but we don't have to wait till AGI rise to get start getting the benefits of AI. We could use uh more specialized systems that maybe make use of the general technologies,

of AI. We could use uh more specialized systems that maybe make use of the general technologies, the general algorithms we're developing for AGI, but are not in themselves general intelligences.

They're narrow AIs if you if you want to call them like AlphaFold, which does a specific purpose and and only that purpose. And we could we could have we could and we still are I'm still doing this uh create you know many types of Alpha Folds and isomorphics uh while we're building AGI in this careful scientific way and then benefit the humanity could benefit from the from the proceeds

of that like cures for cancer uh or maybe new energy sources or new materials. And so I felt that that would be, you know, maybe looking at this from 20, 30 years ago when I started out on all of this, that would have been the ideal way for it to play out um in my opinion. Now,

it didn't happen like that because technology is unpredictable. And in fact, it turns out that things like language were a lot easier than we were all expecting. Even those of us who were obviously optimists about the whole technology and eventually we'll crack language. But it

seems funny to think of it now, but language and concepts and abstractions, things that the current models, foundation models like Gemini do incredibly well. We thought that maybe there would be one or two or three more breakthroughs needed before we could get there. But it turned out transformers, which my Google colleagues invented and some reinforcement learning as well on top was

enough to crack things like language and and uh we were sort of playing around with that.

So were the other leading labs. But um of course with ChatGPT and fair play to Open AI they scaled it and then they put it out there and I think even they say it was a sort of science it was kind of a research experiment they didn't realize it would go so viral and I think none of us did and we had sort of fairly equivalent systems at the time because I think when you're building

that technology uh you are so close to it you um you're very aware of the things it can't do the flaws it has and you don't realize that actually people out there would find use even though it was hallucinating and doing other things that we're obviously all still trying to improve on now, still not completely fixed. Um, but there's still interesting use cases like summarizing things or,

you know, um, brainstorming, things like that that people use, you know, everyone uses chat bots for today. Now, the downside of it is is that, um, we're in this sort of ferocious commercial pressure race that that that everyone's sort of locked into currently. And then on top of that there's geopolitical issues like the US China race and so on. So there's sort of

multiple levels of rate of of of of pressure to sort of move fast. So the benefit of that of course you get faster progress obviously. So you know the progress is just like at lightning speed these days. Um so that's good for all the good use cases. The second benefit is that everybody all of the viewers out there everyone you're all getting to use the most cutting edge

AI technology perhaps only 3 to 6 months behind what is actually in the labs. So that's kind of mind-blowing. It's also great because I think it gives everyone a feeling for it's democratizing

mind-blowing. It's also great because I think it gives everyone a feeling for it's democratizing AI. It's giving everyone a feeling for what it's like uh to interact with cutting edge

AI. It's giving everyone a feeling for what it's like uh to interact with cutting edge AI and what it can do and what it can't do. And I think that's good for society to start um getting normalizing itself to what is going to be an enormous change with this technology coming.

So it's probably better that we get to sample that in incremental steps rather than it's just a shock to the system. Here's a you know there's no AGI and then here's AGI one day. Probably that

that that's not good. Although I think there could have been many ways it could have rolled out. Um,

and then the final thing that's actually on the benefit side is that um, uh, uh, you you can you can't really fully understand your systems until they're stress tested by millions of people. So

it doesn't matter how good your testing is and you know your in-house testing obviously millions of smart people trying out things and then you seeing what bubbles to the top or the feedback you get um, is really important for building more robust systems and um, better systems. So I think there's positives about uh and negatives about how the way it's gone. Um it's not the way

I dreamed about years ago where we would be sort of contemplating this philosophically and and and and sort of um carefully considering each next step. Um we're not in that world and I'm I mean although I'm a scientist first and foremost, I'm also a pragmatic engineer. So

um we we you know we have to deal with the world as we find it and make the best of that. And we

try to do that by advancing the frontier but also trying to be as responsible as we can with doing that as we deploy these you know very powerful technologies um like Gemini and AlphaFold. There's

another story happening at the same time as this and I want to get back to your concerns and how you weight those concerns and the cost. Um, in order to understand that, I think we need to tell a story about AI being very creative, unexpectedly creative. And that story begins, let me find my

Jenga block. That story begins here. So, let's go back to March 10, 2016. Yeah. There's a very

Jenga block. That story begins here. So, let's go back to March 10, 2016. Yeah. There's a very famous Go player that sits down to play against a system that you designed. Mhm. And at this point, computers have beat humans at all kinds of games, but Go is really interesting because

there are more potential moves in Go than atoms in the universe. They're they go back and forth.

They're playing and then your system makes a move that is so surprising because it is incredibly unlikely that a human would figure out a move like that. Move 37. Yes. And you see Lisa Doll sitting there. He's just got this shock on his face. He's got his head in his hands like this.

sitting there. He's just got this shock on his face. He's got his head in his hands like this.

I think people like yourself saw ahead to the creativity that we would find in AI systems that are very different than the systems that we've talked about so far. So there's a category where you're giving a huge amount of data and you're asking to make new predictions. And I understand this is much more complicated than this oversimplification. But then there's a category where you're not

giving data. You're giving rules like with math or physics or games like Go. and it has this

giving data. You're giving rules like with math or physics or games like Go. and it has this incredible opportunity for creativity. Yeah. Where were you when that moment happened and what future did you see ahead? Yeah, it was an incredible moment that you're describing and it's actually almost exactly 10 years ago now, which is feels like a century ago actually. But

I think in many ways it was the dawn of the modern AI uh era because until that point there were many AI programs that could beat world champions at games, things like chess, but they were done with what's called expert systems. So they were systems where the a team of smart programmers with a team of smart in that case chess grandmasters came together tried to distill the knowledge the

chess grand masters have into a set of rules and huristics and then the programmers would build a system kind of a brute force system that would use a lot of compute like on a supercomputer like IBM did with Deep Blue uh to be Gary Kasparov and they would in sort of encapsulate the rules they were given by the chess experts and then the the system would sort of dumbly execute those those rules

of heristics uh and do a millions and millions of searching of of moves and then uh try and work out against those heristics which is the best one to do. Now the thing with that is um for me that was not satisfactory when I saw that in the '90s. Um I was doing my undergrad at the time. I didn't feel

like that was proper AI because that system let's take D blue okay it's it's it's world champion level at at at chess but it can't do anything else. Not only can't it do, you know, language and robotics or any of those kind of things, it it can't even play strictly simpler game like tic-tac-toe, right? So, something's obviously not quite right about the definition of intelligence,

tic-tac-toe, right? So, something's obviously not quite right about the definition of intelligence, right? In the sense of like no human, you could imagine a human grandmaster um not being able to

right? In the sense of like no human, you could imagine a human grandmaster um not being able to learn how to play tic-tac-toe. It would make no sense because it's strictly simpler. So,

so there's something sort of wrong about um its generalization capability and the fact that it didn't learn. It was just given the answer, right? So where if you could ask for something like Deep

didn't learn. It was just given the answer, right? So where if you could ask for something like Deep Blue, where did the intelligence reside of the system? Well, it wasn't in the system. It was in the minds of the chess grandmasters and the and the and the programmers. They solved the problem of chess and then implemented the solution. The the program just dumbly executed the solution.

Now Go, as you mentioned, is um the sort of final frontier for games. It's it's the most complex game humans have ever invented. It's also the oldest game. So it's just amazing in many ways and it's also very beautiful. So in Asia where they play it in China and Japan, Korea instead of you know it takes all of they play instead of chess basically occupies that intellectual echelon but

it's a much more intuitive game sort of artistic game almost. So you you play patterns that look beautiful and they turn out to be you know really strong which is why the game has a little bit of a mystical element to it like almost encapsulate the top go players would say to you encapsulates the mysteries of the universe in the game. I think that's how the ancient uh uh uh Chinese thought

about it. And so um and and also just its raw complexity as as you mentioned has more possible

about it. And so um and and also just its raw complexity as as you mentioned has more possible ball positions 10 to the^ 170 than there are atoms in the universe. So what that means is there's no way you can brute force it in the way that we did with chess. Um, and furthermore, because

the game's so intuitive and so esoteric, um, there aren't really these rules that you can encapsulate easily for a machine to follow. So when you talk to a go master, unlike a chess master, they'll tell you things like, "Oh, why did you play there?" They'll say, "It felt right." Okay,

but as a chess player will never say that. They would say like, "I did it because I'm calculating this, this, and then they'll tell you the calculation." So that intuitive feeling is obviously very hard to encapsulate in a system. You can't really program that directly. So it's

the perfect um proving ground I would say for these new techniques that we were pioneering in the early days of DeepMind of deep reinforcement learning. Can you use build systems that learn from themselves directly from experience? So in this in the in the case of Alpha Go um Alpha Go started by looking at all the games on the internet that humans have played and learning

the types of moves humans would do but then we overlaid it with a Monte Carlo tree search that allowed it to sort of discover new branches of the tree of knowledge if you like in go starting with what humans knew and then going beyond that and that's what we hoped was going to happen. So,

so the amazing thing about that match which was ended up being watched by 200 million people around the world was that um not only did we win the match 4-1 that was the main objective but in game two specifically it played this famous move 37 that you talk about this creative move that was

um it was on the fifth line of the board and early in the game and it's it's sort of a big no no to do that in Go right like Go a Go if you were being taught by a Go master they would slap your wrist playing on that because it's just regarded as a bad move and and and but not only was that

um a great move, it ended up winning the game for Alpha Go, like a 100 moves, 200 moves later, it was in the right place as if it sort of presignly put the stone there. So, it was the critical, not only was a surprising move, it was the critical move for later for it to be exactly in the right place

to decide the game. So, obviously, it's changed the way all Go players play Go. But for me it was um uh the moment I'd been waiting for uh in terms of building a system that we'd already spent six years by then building these types of learning systems that could achieve something no other system could you know this sort of Mount Everest of games AI you know the final frontier if you

like of can you beat the Go world champion um but also not only did it win the match but it was how it won and with these creative new ideas like move 37 and that for me was the signal that we were ready to turn it to scientific problems like AlphaFold. To say this back to you, the reason why it's important that this audience that wants to understand the future understand what happened

with move 37 and go is because the implication is if DeepMind can build a system that can do that, it can also perhaps build a system that can play any game. Yes. It can also perhaps build systems that can um figure out in real world problems what is the best solution in quantum computing or in

nuclear fusion or in matrix multiplication or what else do I have chip design so many projects or etc etc could you tell me about the cutting edge here? yes. Pick one of these systems what is the move 37 of these the surprising creative ative elements going on. I think um Alpha Zero is

very interesting to talk about which was the evolution of Alpha Go. So after we we we won um you know got to the pinnacle of Go and showed that it could come up with new ideas at least in Go move 37 and actually many other ideas that it came up with which has revolutionized how people professionals play Go. Now uh we then generalized it further to a system called Alpha Zero which I

think is going to turn out to be a very important system uh for today as well where with Alph Go uh we started with all the human games that that we could find on the internet. Um and also there were a few other couple of things that were specific about Go that we were built into the Alpha Go system like the symmetry of the board and things like that. So, we wanted to get rid of all

of those assumptions completely and actually start from scratch um as if the the the the program and the algorithm didn't know anything about what it was trying to do to start off with. And that's why this is what the zero refers to in Alpha Zero is it's sort of like Alpha Go, but now removing any knowledge human crafted knowledge both in the data and in the uh any of the kind of huristics

that we've given the system. So, Alpha Zero starts like tabular browser almost. Obviously, it's a has a learning system. It's a it's it's got a neural network. Um, we we set up the parameters, but we didn't give it any domain specific knowledge about Go or any other game. And then what we tested

Alpha Zero on was first of all, could it learn Go from scratch and then be alphaged to do that. So,

uh it takes 17 evolutions of the program. So you can imagine what happens is Alpha Zero starts off random uh to begin with. It it just it only has the rules of the game. Plays randomly. Obviously

it's terrible at playing. Uh it creates its own data set by playing a 100,000 games against itself, right? And then it can see what which moves won or lost. Even though it's playing more

itself, right? And then it can see what which moves won or lost. Even though it's playing more or less randomly to begin with, there'll be some moves that are slightly better than other moves.

Okay. So now it takes the 100,000 it it we train a new version of itself on uh version two now of Alpha Zero with that new data that version two is slightly better than version one. So now it's not random anymore but it's not great. It's not good but it's playing like okay moves and then those okay moves end up um being better. And so then a version two gets trained, a version three, a

version four. And so uh each time that new system gets played against the old system and sees is it

version four. And so uh each time that new system gets played against the old system and sees is it um significantly better or not. And it turns out that at least in Go and chess and things like that around 16 17 generations of that is enough to go from random to better than world champion. And at

least in the case of chess which I actually once watched live happen because I was fascinated by obviously playing chess myself is you know it starts in the morning random then you know by lunchtime I could still just about compete with it myself and then by tea time it's better than all grand masters and then by dinner time it's better than the world champion and you've just seen the

entire evolution of that from scratch and also it's playing interesting new chess that that even chess computers like Stockfish um you know with the more the kind of expert and brute force ones uh haven't discovered those types of new types of moves. So Alpha Zero was the full generalization of the Alpha Go ideas. And interestingly, I think we need these types of ideas back here now with

um our foundation models, the new, you know, Gemini and these kinds of things which you can think of are generalized models of everything, language, the world around us, not just a game obviously like Go. Um but we still need the this ability to search and think and reason on top of those models. And uh sometimes we call those world models. And um I think that

still hasn't fully been cracked yet how to do that. Bring back bringing back some of these Alpha Go ideas. But now instead of just a narrow game applying it to that but to the whole world and maybe interestingly parts of science uh too like material design um and things like chip design and uh quantum computers all these cool projects that you know so many just when I see all these bricks

I can't believe we're actually working on all these things. But it's true is and this is sort of the dream is like I get to I love all so much of I mean I love every branch of science and I get to um indulge myself in all these different areas of science because AI is such a general tool it can really uh make a huge difference to all these areas. So maybe one example I give

is just designing new materials. you know, if we want a material with a special type of property, can we go beyond uh what is currently known uh uh in material science? And I think Alpha Go like processes could be very useful there. And the equivalent of a move 37 would be like alpha tensor finding

a new algorithm that makes you know matrix multiplication faster. Exactly. Exactly. So

you can apply it in algorithmic space which is quite exciting because then the algorithm itself gets faster. So there's some circular circular sort of improvement there. And yes,

alpha tensor just making the matrix multiplication which is the the basis of all neural networks. It

turns out everything's matrix multiplication. uh you know if you just make that 5% faster that's a hu you know the tens of billions being spent on training that's a huge costsaving and uh and so these are good examples of of of um ideas in and I think we're still early you know also like things like the design of chips on a on a on a uh on a on a die you know making it as

efficient as possible the routting you know it's a kind of uh NP hard problem you know in terms of like the the traveling salesman like what's the shortest distance you can wire up all of these things and alpha chip and programs like that are really good, better in some cases than human chip designers at dealing with that. So I think we're just scratching the surface I would say of what's

going to be possible in the next few years with um today's kind of more general systems combined with these types of ideas from from Alpha Go and and Alpha Zero I think are going to come back.

These two categories, the story that starts with Alpha Fold, the story that starts with Alpha Go, these are the kinds of AI that make me feel really optimistic. I also think that being really optimistic, and you do this a lot in public, which I appreciate, is fully thinking through the ways in which something can go wrong and what we can do to prevent that. Yeah. So, I want to insert

one other in here. Do this on purpose. Okay. This one. Yes. And the reason why I bring up this game is this is a real time war game. Yeah. And in the videos where this system is absolutely crushing

humans, you can see the engineers cheering for the victory of their system. But of course, as someone who didn't build the system, I'm thinking to myself, what if that's real? And we're speaking

right now during a time when the debate about militaries and governments using AI is a huge topic of conversation. I want this conversation to last for 10 years. I want it to be useful for that long. So I don't want to talk about specific companies, specific terms of service.

I also think people are in some way missing the forest for the trees here because bigger picture governments are going to use AI. And so what I want to know from you as someone building these systems is if you could wave your magic wand, what would you hope that they use it for? Well, look,

I think governments uh and governments should be using uh um AI and you know, we want to support all sort of democratically elected governments and I think um the things I would love to see them use it for and what we're trying to build our systems to be good for is uh things like improving public health, uh education. I mean, all of these things need to be rethought. The efficiency gains and the

amount of good we can uh uh do with it governments could do with it for their citizens could be incredible. And I think some countries are doing it like Singapore and UAE I think are leaning into

incredible. And I think some countries are doing it like Singapore and UAE I think are leaning into uh uh these types of use cases. I would love to see it being used for uh things like energy like optimizing energy grids. Um we did that with our data centers and save 30% of the you know energy used for the cooling systems. I think there's enormous societal gain from applying AI at scale

to these types of areas. So that's what we you know um I've always thought about and and and and um hope that um governments will pick up and use for and we we you know want to support all of that. Um, of course, you know, ge geopolitics of the of of the world is very complicated right

of that. Um, of course, you know, ge geopolitics of the of of the world is very complicated right now and these are dualpurpose technologies and um, you know, I worry about a couple of use case things that can go wrong with AI that you know in the bigger picture as you say I think sometimes the is the the the kind of details are uh get people get bogged down in the details but

actually there's big and big picture there's two things to worry about one is bad actors whether that's individuals or all the up to nation states using uh repurposing these technologies that we're trying to build for good like curing diseases and advancing material science and energy and so on

um for harmful ends, right? And whether that's uh inadvertently or intentionally. Uh and then the second branch of things I worry about uh is the AI itself uh going rogue um or going off the rails. If as they get more powerful that's not today's systems but maybe in the next 2 3

rails. If as they get more powerful that's not today's systems but maybe in the next 2 3 four years uh especially as we go towards more the agentic era which we're entering now and by agents I mean systems that are capable of completing entire tasks on their own. So you can of course we want those because they'll be very useful like as an assistant or something like that but also

that means they'll be increasingly capable and autonomous and so um how do we make sure as as one of the frontier labs and the frontier labs all have to think about this is the guardrails are put in place that they and that we can ensure that they do exactly what they've been told to do or the goals they've been given and they've been specified clearly enough and there's no way of

them circumventing that or accidentally uh uh breaching those guardrails And that's going to get that's an incredibly hard technical challenge if you think about how powerful and how smart and capable these systems eventually going to get. So I tend to worry about th those are that you could call them medium-term now even though three four years is not really medium-term but those are

the things I think people are perhaps not paying enough attention to at the moment and I think will um be the biggest uh issues that we're going to have to contend with if we're going to get through the the AGI moment in a in a in a way that's beneficial for for humanity. Yeah. One

of the biggest questions I came in for you with, you know, if I get an hour with you in my life was next time I read a headline, how do I weight the concerns that we're all going to have over the next 30 years? Yes. You know, like what are the things that people are worrying too much about and what are the things that they are not worrying enough about? Yeah. So I think the

two things I just mentioned are the things that maybe the average person is not worrying enough about. But even I think some of the experts and the scientists in the field I feel like

about. But even I think some of the experts and the scientists in the field I feel like those are the key things that are more societal affecting um that if we if we there are other things that we need to worry about too like deep fakes and and we're and we try to help those are immediate term worries right misinformation deep fakes these kinds of things and you know we work

on this system called synth ID which is you know a watermarking system actually an AI watermark probably somewhere one of these bricks yeah one of them and and and uh we need AI to sort through them and and and it it's it's it's uses AI to actually digitally watermark any generated image.

So all the things all the Google technologies VO and everything else and Nana Banana uh they all they all they all have this uh uh um uh watermarking technology. So we can detect and flag to the user or or government or whoever that these are fake. Um and I think actually

I would advocate all uh uh companies working on generative uh AI uh should build in something uh some kind of technology like that so at least uh it can be detected or they can detect which things have been built with their uh technologies and I think that's going to be increasingly important but I think that still pales into into you know as a small issue compared to some of these bigger

issues around um AGI itself um uh becoming very capable and how do we put make sure that you know guardrails are put in place that we understand uh what that those types of systems are capable of as we get towards AGI and you know I think a lot more research a lot more effort needs to go into that from from everyone and actually I would love to see international cooperation

and cooperation around amongst the you know leading labs around these safety issues and um and including you know with with with places like the AI safety institutes and and also academia um to help kind of work out how we navigate that next step because it's unprecedented to create

technology like that. If we play this out, what's the limit here? What are the things that you think AI cannot do that humans can do? You've called this the central question of your life. Yes,

it is. And it's very related to um you know some some scientific thinking of some of my all-time heroes like Alan Turing. you know, he described cheuring machines which were these theoretical constructs that actually all modern computers are basically chewing machines that are able to um compute anything that's computable. Um so anything that can be described as an algorithm

uh that this this type of machine can compute and I think that the systems we're building are approximate chewing machines and potentially a lot of neuroscientists including me think that maybe the brain a good model for the brain is an approximate cheuring machine. So the question is and but there are others like um friends of mine like Roger Penrose uh and uh who you

know believes there might be some quantum effect in the brain right and I'm sure you've probably done videos about that that and he you know we've had some very uh good natured debates about this but so far neuroscience uh hasn't found any um quantum effects in the brain. Um doesn't mean they won't be found but so far people have looked quite carefully and they haven't we haven't found

any. So it looks like most of what's going on in the brain is kind of classical computation

any. So it looks like most of what's going on in the brain is kind of classical computation and so therefore um it's not clear what the limit would be in terms of eventually what uh an AI system could do and could mimic. But um you know I think that's an empirical question. I

think that's one of the um you know the questions around consciousness. I mean I don't think it's very well defined what it is but we all intuit it what it is. And um I think this journey we're on of building an intelligent artifact I think we'll have almost like a controlled study comparison uh to the human mind and then I think we'll see uh in this journey like what are the differences

and what's unique about the mind and I'm very open-minded about that. I think there could be uh unique things and um certainly unique connections between humans that will never be replicated by you know these AI systems. But I think a lot of things that um we currently are not in reach like long-term planning and reasoning and maybe some forms of creativity I think eventually

AI systems will be able to do. I want to be honest about what's happening in my mind right now and it is that I am doing exactly the thing that humans have done throughout history. I am

trying to find the reason why we are special. Yes, it is that we have to be at the center of the universe. Oh wait, we're not. We have to be the ones that are emotionally attuned. Oh,

the universe. Oh wait, we're not. We have to be the ones that are emotionally attuned. Oh,

wait. Elephants have funerals. Oh, we must be the ones that can be creative and create art. Oh,

wait. Gemini can do that. Or like what? Um, oh, we must be special. Do you find yourself doing that as well? That's my reaction as you're describing this future of AI. Yeah. No, I think I think we

as well? That's my reaction as you're describing this future of AI. Yeah. No, I think I think we are special and I think there is some there's a lot of deep mysteries about how the universe works and including a lot of things that that are in our minds, but also things out there in physics.

You know, I think that's why I got I think I decided from a very young age to do AI is because I was obsessed when I was a kid uh at school with with with the big questions. And normally when you you know, physics was my favorite subject at school because that is the subject you're supposed to study when you're interested in all the big questions. And um but the thing was I

just realized uh uh I guess as a young teenager reading all these science books and biographies on the best scientists. Richard Feynman is one of you know my all-time heroes. Uh that they actually although they we discovered a lot and we know a lot about the world there's so much we don't know.

Like there's just incredible like we don't know what time is. I mean this is this is insane to me.

Like we you know we don't we can't even describe something as that. It's just we're swimming in it.

But what is it? We you know of course there's you know entropy and things like that but it's nothing it's nothing satisfactory about what it really is and um you know we don't understand a lot of quantum effects and gravity properly and and consciousness all most of the things we we care about and and but we just sort of I feel like most people we just distract ourselves all day with you

know TV shows and games and things and don't worry too much about it but I'm I've never been like that. I' I it's just it it's sort of um it's it's it's these deep mysteries kind of play on my mind

that. I' I it's just it it's sort of um it's it's it's these deep mysteries kind of play on my mind all the time and I think um I'm quite open-minded about what the answers might be eventually about what's going on here, the nature of reality. I think that's ultimately what I'm after and I want to use AI as a tool to help us understand the nature of reality around it. And I'm quite sanguin

about whatever the answer might be. I'm not, you know, I guess I'm a true scientist in that sense of like I don't actually I don't really have any predescribed notion of what the answer should be.

I just want to know the answer. Me too. One way to describe what you're trying to do is effectively this, which is to say to create a system that wouldn't be especially good at one thing or another thing, but rather to create, as you've been saying, AGI, artificial general intelligence,

that would be good at it all. Yes. I know you're a fan of sci-fi. I am, too. Um, could you play out for me the plot of the sci-fi movie in your head? Yeah. that is the future where you actually do this. Yeah, I can I I think um I love sci-fi too and probably I read too much of it when I was a

this. Yeah, I can I I think um I love sci-fi too and probably I read too much of it when I was a when I was a kid may explain a few things but one of my favorite serieses was the culture series um by Ian Banks. Um I think it just paints a really interesting actually post AGI world. He didn't

call it AGI but that's what it was describing like a thousand years in the future but I think even 50 years some of this could happen where we've we've got through the AGI moment safely.

It's built. It's it's it's um helpful for society and it's it's and and you know it's it's here and maybe we all have it in our pockets even and um we've used it to crack some of these what I call root node problems in science. Alpha Fold was one of those, right? So these are problems if you think of the tree of all knowledge. These are kind of root node problems which if you cracked it,

it would unlock a whole branch of new research or new applications. And I think there are other things like fusion we briefly mentioned uh or better maybe room temperature superconductors at atmospheric pressure that you could then combine with optimal batteries and things like that. I think though there will be a solution to the energy problem. So free pretty much free

that. I think though there will be a solution to the energy problem. So free pretty much free renewable clean energy one way or another fusion or you know better solar uh and then that will unlock uh us to really travel the stars because the main cost of you know Elon does amazing work with SpaceX and those things but the main cost is still the rocket fuel right the energy cost so if

that's sort of zero somehow um because we can just make infinite rocket fuel out of seawater because we have we've cracked fusion so we can have you know catalyst plants and desalination everywhere um then you know that unlocks the really unlocks space and then we'll um be able to get a lot more resources because we can mine asteroids. All of these things the purview of science fiction become

I think very plausible in the next 50 years. Dyson spheres around the sun. Uh Mercury's sort of conveniently in the right place actually made with the right material which is kind of amazing if you think about what's going on in the universe. And um and and then that should hopefully lead to you know maximum human flourishing and we help um cure all these terrible diseases so we live much

longer healthier lives and traveling to the stars bringing consciousness to the rest of the galaxy.

That would be I think an amazing outcome and I think could happen within the next 50 years. I

believe you say these things and I like when you're saying them I believe you. That's the

that's what I'm trying to do at least. Yeah. So this is my last question. Mhm. If I were a fly on the wall at my own funeral after they said she loved her husband and her family and her friends, I would hope that they would say that she spent her life trying to help people see optimistic futures so that they can be part of making them happen that they can make them

happen more quickly or better for more people or whatever it is that people decide to do with the vision that they see. And so my last question for you is what do you hope that they say about you?

I would hope that they would say that, you know, my life was of benefit and service to humanity. That's I think what I'm trying to do. So that's maybe would be the best thing. Thank you so

humanity. That's I think what I'm trying to do. So that's maybe would be the best thing. Thank you so much for your time. Thank you. Really appreciate it. Really fun. Thanks. Awesome. If you want to play Jenga anytime. Exactly. You seem pretty good version of Jenga. You did that very well. So yeah,

this is actually awesome. I can't believe how many projects we've done. Silly crazy. When I saw the bricks, so they all got our Yeah, they have all got our projects on them. Did you memorize where everything was? Okay. Of course. So, the uh the game is you pull it out and we were playing this.

everything was? Okay. Of course. So, the uh the game is you pull it out and we were playing this.

It It's unfair to play with you, but it would be you have to say what that project was and you don't get the point if you get it wrong. Oh my god. So, for example, it would be this is material science. Yeah. It's a little bit unfair on you. I mean, I would hope I would win this game, but

science. Yeah. It's a little bit unfair on you. I mean, I would hope I would win this game, but um although you're probably way better at Jenga than me. Yeah. Let's see. Let's do this one. There

you go. Okay. Alpha code. Cleo Jenga. Yeah, that one that one's clearer, right? Code code forces.

Yeah. Sense. This is uh genetics, but the 2% that codes for proteins. Yes, we have to do this now.

I've got time. I can push back my name. Great. What? Wait, I have I have one more question.

You know Alpha? Alpha Evolve is coding. Yeah, we can be used for coding. Programming. It's

it's it's combining genetic algorithms with um with Gemini. So this is this is our this is one attempt at doing like Alpha Go stuff beyond what is known. So I wouldn't get the point for that one. No. Half a point. Half a point. Okay. One more question for you then while I have I'm just

one. No. Half a point. Half a point. Okay. One more question for you then while I have I'm just going to keep going while I have you cuz why not? Still we're still rolling. Uh obviously

um Okay. What did I not ask you that you think is important for people to know? Um what did I not ask me? I think we covered a lot actually. Um Gencast this is weather prediction. Yes. Oh yeah,

we didn't cover that. Navia Stokes. completely forgot about solving solving that whole bunch of things. I completely forgot about that thing I did solving. So that was one interesting thing

of things. I completely forgot about that thing I did solving. So that was one interesting thing is simulations. We didn't talk much about that or genie which is um the the role of simulations to

is simulations. We didn't talk much about that or genie which is um the the role of simulations to um DQN of course started it all off the Atari stuff um simulations to help you uh understand some area of science that um or even social science like economics that you can't are very

hard to run either expensive to run experiments or you can't run controlled experiments in. So

I've always loved simulation. Oh yeah, ISO. There you go. Um, we're both very competitive, I think, so this is going to this is going to be quite serious actually. Are you in Jenga? Are you is the rules if you touch it, you have to move it, do you or not? We are playing a loose the looser the easier version. Also, because we uh we were doing a creative thing where you're allowed to like

easier version. Also, because we uh we were doing a creative thing where you're allowed to like push them together. You can use two hands also. Okay. You're not allowed to normally do that, right? Right. Okay. I'll just going to take this one. I'm going to cheat with alpha code.

right? Right. Okay. I'll just going to take this one. I'm going to cheat with alpha code.

One of the um questions I think people will have for you is if they're watching this and they, you know, are very optimistic, Gemini, everybody. Yes. They're very optimistic about the futures that you've described. They're they have all of your same concerns. They generally have gotten to

the end of this conversation and they're thinking, I believe in this future and I want to be part of it. How would you code mener I think that finds bugs in code. Yes. Very good. How would you

it. How would you code mener I think that finds bugs in code. Yes. Very good. How would you advise them to participate if this is all about helping people participate in the future? I would

um when I do sort of talks at at universities and schools um I would say they you've got to just go with the flow of the direction. I would immerse myself in every tool available and just become almost like superpowered. Fantastic. um superpowered with uh those tools and those um

uh uh uh those capabilities cuz I think my impression is even at the frontier labs we are um there's so much work has to go into just making the next versions of these frontier models and then all the adjacent models. So for us like VIO and Nano Banana and Gemini um that we even we can

only explore a fraction of what the the applied things you could do with it the applications you could you could make with it. So and I think that gap's getting bigger and bigger in terms of like the overhang of the capabilities all the cool stuff on the latest models and that that time the the release schedules are getting faster and faster on that. So I think the opportunity

space is getting huge if for people who are really expert and um at using those tools and then apply it to some new domain. So um I think a a kid these days could probably start a multi-billion dollar business in some ways using these tools in some new way that no one had thought about. Um and I

think things like um Open Claw is a good example of that. Yeah. Yeah. Maybe we should call it a draw because I think uh I think I don't think I don't think either of us could bear to lose that. Right. So, it's your move. It's your move. Is it my move? Yeah, it's your move. We can end

that. Right. So, it's your move. It's your move. Is it my move? Yeah, it's your move. We can end on your move. I'll try my my end of my move. Go on then. You're going to make me I knew it was going to get me another move. In 2016, you had a sticky note on your board that said, "Solve protein folding." Smiley face. Yeah. What is What is on the board now in your proverbial sticky? Oh

protein folding." Smiley face. Yeah. What is What is on the board now in your proverbial sticky? Oh

my gosh. Uh to I've got a pile of about a hundred sticky notes on my desk. So what's what's on it?

Alpha chip. This is what's on it. What's in it? I I can't actually remember. It will be a list of um about 30 things that need to be done by like this evening. So I better probably get to them.

But look, great. Should we do you want you want to actually I'm going to keep going until you stop. So you can stop. Yeah, we're about to What time is it? Okay, I'll I'll do one more

stop. So you can stop. Yeah, we're about to What time is it? Okay, I'll I'll do one more move. But now we're now we're kind of cheating. We're we're using our the pieces that already

move. But now we're now we're kind of cheating. We're we're using our the pieces that already Oh, you're going to you're going to try I'm going to go ambitious in our last Oh, come on. Come on.

Come on. Come on. If I get this one, I get another question. Yeah. Okay, that seems fair. God,

how is that going to balance? Surely not. No. Yes. All right. Thank you so

much. That was awesome. Thanks. That was a great great idea to have that.

much. That was awesome. Thanks. That was a great great idea to have that.