this year google talked about ai very differently this time they want you to sit up they want you to lean in they want you to pay them 250 and they want you to get to work i've been working every hour there is for the last 20 years because i felt the how important and momentous this this technology would be whether it's five years or ten years or two years that they're all actually quite short timelines when you're talking discussing what were the enormity of the transformation of this technology you know this technology is going to bring when you see when i see a van goff you know hair's gone back my spine because of i remember what they went through and um the struggle to produce that right in every brush stroke of van goff's brush strokes even if the ai mimicked that and you were told that it was like so what now there's a very large what looks like a circus tent over there what do you think's going on in there that is from the amphitheater oh that's the amphitheater under that tent yes i thought that was just some carnival that they were setting up for employees okay my mistake i thought ringling brothers had entered into a partnership with a revival tent they're bringing christianity back i'm kevin russe a tech columnist at the new york times i'm casey newton from platformer and this is hard fork this week our field trip to google will tell you all about everything the company announced at its biggest show of the year then google deep mind ceo demis hasabas returns to the show to discuss the road to agi the future of education and what life could look like in 2030 kevin being very old for starters somebody did ask me text me to ask why i freaking yell the name of the show every episode did you say it's because i started yelling my name i said it's because of the cold brew well casey our decor is a little different this week it's i'll say it it looks better yes we are not in our normal studio in san francisco we are down in mountain view california where we are inside google's headquarters i'm just thrilled to be sitting here surrounded by so much training data that's what they call books here at google so we are here because this week is google's annual developer conference google io there were many many announcements from a parade of google executives about all the ai stuff that they have coming and uh we are going to talk in a little bit with uh demis hasabas who is the ceo of google deep mind essentially their ai division who's been driving a lot of these ai projects forward but first let's just sort of set the scene for people uh because i don't think we've ever been together at an io before so what is it like so google io has a bit of a festival atmosphere it takes place at the shoreline amphitheater which is a concert venue uh but once a year it gets transformed into a sort of nerd concert where instead of seeing musicians perform you see google employees vibe coding on stage yes there's a vibe coding demo um there were many other things i i did actually see uh as i was leaving the uh google acapella group google pella was like sort of doing their warm-ups in anticipation of doing some concert so you've got some like old school google vibes here but uh also a lot of excitement around all the ai stuff so now i didn't see google pella perform where was this performance i didn't see them perform either i just saw them warming up they were sort of doing their scales they sounded great you know what i bet it was a classic acapella situation where they warmed up and someone came up to them and they said please don't perform all right kevin well before we get into it shall we say our disclosures yes i work for the new york times which is suing open ai and microsoft uh over copyright violations related to training of ai systems and my boyfriend works in anthropic a google investment oh that's right yeah so let's talk about some of what was announced this week there was so so much we can't get to all of it but uh what were the highlights from your perspective well so look i wrote a column about this kevin i felt a little bit like like i was in a fever dream at this conference you know i think often it is the case at a developer conference where they'll sort of try to break it out into one two three big bullet points this one felt a little bit like a fire hose of stuff and so by the end i'm looking at my notes saying okay so email's gonna start writing in my voice and i can turn my pdfs into video ted talks sure why not um so i had a a little bit of fever of dream mentality what was your feeling yeah i told someone uh yesterday that i thought the name of the event should have been everything everywhere all at once like that didn't actually feel like what they were saying is like every google product that you use is going to have more ai that ai is going to be better and it is all going to make your life better in various ways uh but it was a lot to keep track of yeah i mean look if we were going to try to to pull out one very obvious theme from everything that we saw it was ai is coming to all of the things and it's probably worth drilling down a little bit into what some of those things are yeah so the thing that got my attention and i was sitting right next to you uh the one time when i really noticed you perking up was when they started talking about this new ai mode in google search their core search product so talk about ai mode and what they announced yesterday so kevin this gets a little confusing because there are now three different kinds of major google searches i would say there is the normal google search which is now augmented in many cases by what they call ai overviews which is sort of ai answer at the top yeah that's the little thing that will tell you like what the meaning of phrases like you can't like a badger twice is right that's right and if you don't know the meaning of that google it um so that's sort of the thing one thing two is the gemini app which is kind of like a one-for-one like chat gpt competitor that's in its own you know standalone app standalone website and then the big thing that they announced this week was ai mode which has been in testing for a little while and i think this sort of lands in between the first two things right it is a tab now within search and this is rolling out to everybody in the united states and a few other countries and you sort of tap over there and now you can have the sort of longer you know multi-step questions that you might have with a gemini or a chat gpt but you can do it right from the google search interface yeah and i've been playing with this feature for a few weeks now it was in their labs section so you could try it out if you were enrolled in that um and it's it's really nice like it's a very clean thing there's no ads yet uh they will probably appear soon it does this thing called the fan out which is is very funny to me like you ask it a question and it kind of dispatches like a bunch of different google searches to like crawl a bunch of different web pages and like bring you back the answer and it actually tells you like how many searches it is doing and how many different websites it's doing so i asked it for example like how much does a costco membership cost it's there 72 websites for the answer to that question so ai mode is very very eager to answer your question even if it does verge on overkill sometimes yeah well so uh you know you and i had a chance to meet with robbie stein who is uh one of the people who is leading ai mode and i was surprised by how enthusiastic about it you were like you said that you've really actually found this quite useful in a way that i think i have not so far so what are you noticing about this i mean the main thing is it's just such a clean experience like on a regular google search results page you and i have talked about this like it has just gotten very cluttered there's a lot of stuff there there's ads there's carousels of images there's sometimes a shopping module there's sometimes a maps module like it's just it's hard to actually like find the blue links sometime uh and i imagine that ai mode will become more cluttered as they try to make more money off of it but right now if you go to it it's like a much simpler experience it's much easier to find what you're looking for yeah and at the same time they're also trying to do some really interestingly complex stuff like one of the things that they showed off during the keynote was somebody asked a question about baseball statistics that required finding you know three or four different kind of you know tricky to locate stats and then combining them all together in an interactive chart that was just a demo we don't have access to that yet but that is one of the those things where it's like well if that works that could be a meaningful improvement to search yeah it could be meaningful improvement to search and we should also say like it's a big unknown how all of this will affect uh the main google search product right this is for now it's a tab um they have not sort of merged it into the main core google search uh in part because it's not monetized yet it costs a lot more to serve those results than a traditional google search but i imagine over time these things will kind of merge which will have lots of implications for publishers people who make things on the internet the whole sort of economic model of the internet but before we get dragged down that rabbit hole um let's just talk about a few other things that they uh said on stage at google io so i was really struck by the usage numbers that they trotted out for their products um gemini according to them um the app now has 400 million monthly users um that is a lot that is not quite as many as chat gpt but it is a lot more than a products like claude and other uh ai chat bots they said that their tokens that are being output by gemini has increased 50 times since last year um and is just like way like so people are using this stuff in other words this is not just like some feature that google is shoving into these products that people are trying to sort of navigate around like people are really using gemini i think that that's right and i think it's the gemini number in particular is the one that struck me like 400 million is a lot of people and i don't see that many obvious ways that google could be like faking that stat uh you know in contrast to for example they said one and a half billion people see ai overviews every month it's like well yeah you just put them in google search results like that's an entirely passive phenomenon but like gemini you got to go to the website you got to download the app so that tells me that people actually are finding real utility there so that's gemini but they also released a bunch of other stuff like new image and video models do you want to talk about those yeah so um you know like like the other companies they're working on text to image text to video and while open ai's models have gotten most of the attention in this regard google's really are quite good i think the the marquee feature uh for this year's io is that the video generating model vo3 can also generate sound so it showed us a demo for example of an owl flapping its wings you hear the wings flap it comes down to the ground there's this sort of nervous badger character and they exchanged some dialogue which was basically incomprehensible just pure slop but they were able to generate that from scratch and i guess that's something they left behind a a ball today it bounced higher than i can jump what manner of magic is that yep um they also announced a new ultra subscription to google's ai products now if you want to be on the bleeding edge of google's ai offerings you can pay 250 a month for gemini ultra and casey i thought to myself no one is gonna do this who is gonna pay 250 a month that's a fortune for access to google's leading ai products and then i look over to my right and there's casey newton in the middle of the keynote pulling out his credit card from his wallet and entering it into buy a subscription to this extremely expensive ai product so you might have been the first customer of this product why well and i hope that they don't forget that uh when it comes time to feed me into the large language model um look i want to be able to have the the latest models and you know one i think clever thing that these ai companies are doing is they're saying we will give you the latest and greatest before everyone else but you have to pay us a ridiculous amount of money and you know if you're a reporter and you're reporting about this stuff every day i do think you sort of want to be in that camp now is it true that i now spend more on monthly ai subscriptions than i paid for my apartment in phoenix in the year 2010 yes and i don't feel great about it but i'm trying to be a good journalist kevin please your family is dying um another thing i that made me perk up was uh they talked a lot about personalization right this is something we've been talking about for years basically google has all of you know billions of people's email their search histories their calendars all their personal information and we've been sort of waiting for them to start weaving that stuff in so that you can use gemini to do things in those products um that has been slow um but they are sort of taking baby steps and they did show off a few things including this new personalized smart replies feature that is going to be uh available for uh subscribers later this year in gmail so that instead of just getting the kind of formulaic suggested replies at the bottom of of an email it'll actually kind of learn from how you write and maybe it can access some things in your calendar or your documents and really like suggest a better reply you'll still have to like hit send but it'll like sort of pre-populate a message for you yeah you know i have to say i'm i'm somewhat bearish on this one kevin only because i i think that if this were easy like it would just sort of be here already right like when you think about how formulaic so much email is it doesn't seem to me like it should be that hard to figure out like what kind of email are you like i'm basically a two-sentence email or you know that doesn't seem like that that's hard to mimic um so that's just kind of an area where i've been a little bit surprised and disappointed we also know large language models do not have large memories so one thing that i would love for gmail to do but it cannot is just sort of understand all of my email and use that to inform the tone of my voice but it can't do that it can only take a much more limited subset is that going to make it sort of difficult to accurately mimic my tone i know so what i'm trying to say here is i think there's a lot of problems here and my expectations are like pretty low on this one yeah that was the part where i was like i i will believe that this exists and is good when i can use this but as with other companies like like apple uh which demoed a bunch of ai features at its developer conference last year and then never launched half of them um i have become like a little bit skeptical until i can actually use the thing myself yeah it really is amazing how looking back last year's wwdc was just like a movie about what a competent ai company might have done in an alternate future it had very little bearing on our reality but it was admittedly an interesting set of proposals okay so that is the the software ai portion of io there was also a a demo of a new hardware product that google is working on which are these uh android xr glasses basically their version of what meta has been showing off it's orion glasses where you have a pair of glasses they have like sort of chunky black frames they've got like sort of a hologram lens in them and you can actually like see a little like thing overlaid on your vision uh telling you you know what the weather is or what time it is or that you have a new message or they have this uh integration with google maps that they showed off where you can like it'll like show you you know the little miniature google map right there inside your glasses and it'll sort of turn as you turn and tell you where to go um they did say this is a prototype but um what did you make of this well i think a lot of it looked really cool like probably my favorite part of the demo was uh when the person who was demonstrating looked down at her feet because she was getting ready to to walk to a coffee shop and the google map was actually projected at her feet and so she know okay go to the left go to the right if you've ever been walking around a sort of foreign city and and desperately wanted this feature i think you would see that and be pretty excited yeah what did you think um yeah i i thought to myself google glass is back it was away for so long in the wilderness and now it's back and it might actually work this time absolutely i did get to try the the glasses there was a very long line for the demo but um let me guess you said i'm kevin roost let me to the front of the line no they made me wait for two hours i mean i didn't literally wait for two hours i went and did some stuff and then came back but i got my demo it was like five minutes long um and it was uh you know it was it was pretty basic but it is cool like you can look now look around and you can say hey what's this plant and it'll sort of gem and i will kind of like look at what you're seeing and tell you what the plant is totally i i did a demo um a few months back and also like really enjoyed it um so i think there's something here and i think more importantly kevin consumers now when they look at google and meta they finally have a choice whose advertising monopoly do i want to feed with my personal data and you have consumer choice now and i think that's beautiful and that's what capitalism is all about so okay those are some of the announcements but what did you make of the sort of overall tenor of the event what stuck out to you as far as the vibe so the thing that stuck out to me the most was just contrasting it with last year's event because last year they had this phrase that they kept repeating let google do the googling for you which to me put me in the mind of somebody sort of leaning back into your like floating chair from the wall-e movie and just sort of letting the ai like run roughshod over your life this year google talked about ai very differently this time they want you to sit up they want you to lean in they want you to pay them 250 and they want you to get to work you know ai is your your superpower it's your bionic arm and you're going to use it to get sort of further and farther than ever before but even while presenting that vision kevin they were also very much like but it's it's going to be normal it's going to be chill it's going to be kind of like your life is now you're still going to be in the backyard with your kids doing science experiments you're still going to be planning a girls weekend in nashville right there was not really a lot of science fiction here there was just a little bit of like oh we uh we put a little bit of ai in this so that was interesting to me yeah so i had a slightly different take which is that i think google is being agi pilled um you know for years now google has sort of distanced itself from the conversation about agi you know it had deep mind which was sort of its its agi division but they were over in london and they were sort of a separate thing um and people at google would sort of not laugh exactly but kind of chuckle when you asked them about agi it just didn't seem real to them or it was so remote that it wasn't worth considering they would say what does this have to do with search advertising exactly so now you know it's still the case that this is a company that wants you to think about it as a product company a search company they're not like going all in on agi but once you start looking for it you do see that that the the sort of culture of uh ai and how the people at google talk about ai has really been shifting it is it is starting to seep into conversation here in a way that i think is unusual and maybe indicative that the technology is just getting better um faster than even a lot of people at google were thinking it would so i don't totally agree with you kevin because while i'm sure that they're having more conversations about agi here than they were a year ago when you look at what they're building it doesn't seem like there's been a lot of rip it up and start again it seems a lot like how do we plug ai systems into google shape holes and maybe that will eventually ladder up to something like agi but i don't think we've seen it quite yet the other observation i would make is that i think the google of 2025 has a lot more swagger and confidence when it comes to ai than the google of 2024 or 2023 i mean two years ago um google was still trying to make bard a thing and i think they were feeling very insecure that that open ai had beaten them to a consumer chatbot that had found some mass adoption and so they were just playing catch up and i don't think anyone would have said that google was in the lead when it came to generative ai just a few years ago but now they they feel like there is a race and that they are in a good position to win it they were talking about how gemini stacks up well against all these other models it's at the top of this leaderboard lm arena for all these different categories i don't love the way that ai is sometimes covered as if it were like sports you know who's up who's down who's winning who's losing but i do feel like google has the confidence now when it comes to ai of a team that like knows it's going to be in the playoffs at least and that was evident oh yeah i mean well when you look at the competition just what's happened over the past year you have apple doing a bunch of essentially fictional demos at wwdc and you have meta cheating to win at lm arena making 27 different versions of a model just to come up with one that would be good at one thing right so i think if you're google you're looking at that and you're thinking i could be those guys so that is um that is what it felt like inside google io um what was the reaction from outside i noticed that for example the company's stock actually fell like not not by a lot but like you know to a degree that suggested that wall street was kind of meh on a lot of what was announced but what was the reaction like outside of google i think the external reaction that i saw was just struggling a little bit to connect the dots right like that is the issue with announcing so many things during a two-hour period is sometimes people don't have that one thing that they're taking away saying i can't wait to try that and when you're just looking at a bunch of google products that you're already using i think if you're an investor it's probably hard to understand well i don't understand why this is unlocking so much more value at google now maybe millions of people are going to spend 250 a month on gemini ultra but unless that happens i can understand why some people feel like hmm this feels a little like the status quo yeah i see that i also think there are like many unanswered questions about how all of this will be monetized and you know it's google has built one of the most profitable products in the history of capitalism in the google search engine and the advertising business that supports it um it is not clear to me that whatever ai mode becomes or whatever ai features it can jam into search if search as a category is just declining across the board if people are not going to google.com to look things up in the way they were a few years ago um i think it's an open question like what the next thing is and whether google can can seize on it as effectively as they did with search well i think that they gave us one vision of what that might be and that is shopping a significant portion of the keynote was devoted to one executive talking about a new shopping experience inside of google where you can take a picture of yourself upload it and then sort of virtually try things on and it will sort of use ai to understand your proportions and you know accurately map a garment onto you and there was a lot of stuff in there that would just sort of let google take a cut right obviously you can advertise the individual thing to buy maybe you're taking some sort of like cut of the payment there's an there's an affiliate fee that is in there somewhere so one of the things i'm trying to do is i cover google going forward is understanding that yes search is the core but there but gemini could be a springboard to build a lot of other really valuable businesses an important question i know that i always ask you when i go to these things how was the food let's see i think the food was really nice so here's the thing last year it was a purely savory experience at breakfast and i am shamefully an american who likes a little sweet treat when i woke up this year they had both bagels and an apple cinnamon coffee cake and so when i was heading into that keynote i was in a pretty good mood i had some of that they have like little bottles of cold brew and i i'm like a huge caffeine addict so i took two of them um and boy i was on rocket fuel all day i was just humming around i was like bouncing off the walls i was like doing parkour i was like i was feeling great i thought i saw you warming up with the acapella team now it all makes sense when we come back we'll talk with demis asabas ceo of google deep mind about his vision of the ai future demis asabas welcome back to hard fork thanks for having me again a lot has happened since the last time you were on the show um most notably you won a nobel prize congrats on that thank you um ours must be still in the mail can you put in a good word for next year with the committee i will do i will do i imagine it's very exciting to win a nobel prize i know that had been a goal for a long time of yours um i imagine it also leads to like a lot of people giving you crap like during everyday activities like if you're you know struggling to work the printer and people are just like oh mr nobel lorry like does that happen um a little bit i mean look i tried to say look i can't you know that maybe it's a good excuse to like not have to fix uh those kinds of things right so it's more shield um so you just had google io and it was really the gemini show i mean i think gemini's name was mentioned something like 95 times in the keynote of all the stuff that was announced what do you think will be the biggest deal for the average user wow well i mean we did announce a lot of things i think for for the average user i think it's the new powerful models and i hope uh this astro type technology coming into gemini live i think it's really magical actually when people use it for the first time and they realize that actually ai is capable already today of doing much more than what they thought uh and then i guess vo3 was the big uh uh the biggest announcement of the show probably and seems to be going viral now and that's pretty exciting as well i think yeah one thing that struck me about io this year compared to previous years um is that it seems like google is sort of getting agi pilled as they say um i remember interviewing people researchers at google even a couple years ago and um there was a little taboo about talking about agi they would sort of be like oh that's like demis and his deep mind people in london that's sort of like their crazy thing uh that they're excited about but here we're doing like you know real research um but now you've got like senior google executives uh talking openly about it what explains that shift i think the sort of ai part of the of the equation becoming more and more central like i sometimes describe uh google deep mind now as the engine room of google and i think you saw that probably in the keynote yesterday really if you take a step back um and then it's very clear uh i think you could sort of say agi pilled is maybe the right word that we're quite close to this uh human level general intelligence uh maybe closer than people thought even a couple of years ago and it's going to have broad cross-cutting impact and i think there's another thing that you saw at the keynote it's sort of literally popping up everywhere because it's this horizontal layer that's going to underpin everything and i think everyone is starting to understand that and um maybe a bit of the deep mind ethos is bleeding into the into the general google which is which is great you mentioned um that project astra is powering some things that maybe people don't even realize that ai can yet do i think this speaks to a real challenge in the ai business right now which is that the models have these pretty amazing capabilities but either the products aren't selling them or the users just sort of haven't figured them out yet so how are you thinking about that challenge and how much do you bring yourself to the product question as opposed to the research question yeah it's great great question i mean i think um one of the challenges i think of this space is obviously the underlying tech is moving unbelievably fast and i think that's quite different even from the other big revolutionary techs internet and mobile at some point you get some sort of stabilization of the tech stack so that then the you know the focus can be on product right or exploiting that tech stack and what we've got here which i think is very unusual but also quite exciting from a researcher perspective is that the tech stack itself is evolving incredibly fast as you guys know so i think that makes it uniquely challenging actually on the product side um not just for us at google and deep mind but for startups for for anyone really any any any uh company small and large is where do you what do you bet on right now when that could be a hundred percent better uh in a year as we've seen and and so you've got you've got this interesting thing where you need kind of fairly um deeply technical sort of product people product designers and managers i think to in order to sort of intercept where the technology may be in a year so so there's things it can't do today and you want to design a product that's going to come out in a year so you've got to kind of put you've got a pretty deep understanding of the tech and where it might go to to sort of work out what features you can rely on and so it's it's it's an interesting one i think that's what you're seeing so many different things being tried out and then if something works we've got to really double down quickly on that yeah during your keynote you talked about gemini as powering both uh sort of productivity assistant style stuff and also fundamental uh science and and research challenges and i wonder in your mind is that the same problem that sort of like one great model can solve or are those sort of very different problems that just require different approaches i think you know when you look at it looks like an incredible breadth of things which is true and how are these things related uh other than the fact i'm interested in all of them but is that uh that was always the idea with building general intelligence you know truly generally and and and and this in this way that we're doing it should be applicable to almost anything right that being productivity which is very exciting help billions of people in their everyday lives to cracking some of the biggest problems in science um 90 i would say of it is is the underlying core general models uh you know in our case gemini especially 2.5 and they're in most of these areas you still need additional applied research or some a little bit of um special casing from the domain maybe it's special data or whatever um to tackle that problem and you know maybe we work with domain experts in in the scientific uh areas uh but underlying it you all the when you crack one of those areas you can also put those learnings back into the general model and then the general model gets better and better so it's a kind of very interesting flywheel and um it's great fun for someone like me who's very interested in many things you get to use this technology and sort of um uh go into almost any field that you find interesting i think that a lot of ai companies are wrestling with right now is how many resources to devote to sort of the core ai push on the foundation models making the the models better at the basic level versus how much time and energy and money do you spend trying to spin out parts of that and commercialize it and turn it into products and i i imagine this is both like a resources challenge but also like a a personnel challenge because say you join deepmind as an engineer and you want to like build a gi and then someone from google comes to you and says like we actually want your help like building the shopping thing that's gonna like let people try on clothes yeah is that a challenging conversation to have with people who joined for one reason and may be asked to work on something else yeah well we we don't you know it's sort of self-selecting internally we don't have to we're that's one advantage of being quite large there are enough engineers on the product teams and the product areas you know that can deal with the the product development prod eng and the researchers if they want to stay in core research that they're absolutely that's fine and we need that um but actually you'll find a lot of researchers are quite um motivated by real world impact be that in medicine obviously and and things like isomorphic but also um uh to to have billions of people use their research it's actually really motivating and so there's plenty of people that like to do both so um yeah we don't there's no need for us to sort of have to pivot people to certain things um you did a panel yesterday with uh sergey brin google's co-founder yeah um who has been working on this stuff back in the office and uh interestingly he has shorter agi timelines than you um he thought agi would arrive before 2030 and you said just after he actually accused you of sandbagging basically like artificially pushing out your estimates so that you could like under promise and over deliver um but i'm curious about that because you will often hear people at different ai companies arguing about when the timelines are but presumably you and sergey have access to all the same information and the same roadmaps and you you understand what's possible um and what's not so what is he seeing that you're not or vice versa that leads you to different conclusions about when agi is going to arrive uh okay well first of all there isn't that much difference in our timelines if he's just before 2030 and i'm just after also our time my timeline has been pretty consistent since the start of deep mind in 2010 so we thought it was roughly a 20-year mission and amazingly we're on track so it's it's somewhere around then i would think and i and i feel like between i actually have obviously a probability distribution of you know where the most mass of that is between five and ten years from now and i think partly it's to do with predicting anything precisely five to ten years out is very difficult so there's uncertainty bars around that and then also um there's uncertainty about how many more breakthroughs are required right and also about the definition of agi i have quite a high bar which i've always had which is it's it should be able to do all of the things that the human brain can do right even theoretically and so that's that's a higher bar than say what the typical individual human could do which is obviously very economically important that would be a big milestone but not in my view enough to call it agi um and and we talked on stage a little bit about what there's missing from today's systems sort of true out of the box invention and thinking um sort of inventing a conjecture rather than just solving a math conjecture solving one's pretty good but actually inventing like the reaman hypothesis or something as significant as that the mathematicians agree is really important is very is much harder um and uh also consistency so the consistency is a requirement of generality really and you should it should be very very difficult for even top experts to find uh flaws especially trivial flaws in the systems which we can easily find today and you know the average person can do that so there's a sort of capabilities gap and there's a consistency gap before we get to what i would consider agi and when you think about closing that gap do you think it arrives via incremental two five percent improvements in each successive model just kind of stacked up over a long period of time or do you think it's more likely that we'll hit some sort of technological breakthrough and then all of a sudden there's liftoff and we hit some sort of intelligence explosion i i think it's i think it could be both and and and i and i think for sure both is going to be useful which is why we push unbelievably hard on the scaling and the you know what you would call incremental although actually there's a lot of innovation even in that to keep moving that forward in pre-training post-training infants time compute all of that stack so there's actually lots of exciting research and we showed some of that that diffusion model um the deep think model um so we're innovating it all parts of that the traditional stacks we call it and then on top of that we're doing uh more greenfield things more blue sky things like alpha evolve maybe you could you could include in that which um is there a difference between a greenfield thing and a blue sky thing i'm not sure maybe they're maybe they're pretty similar so uh some new area let's call it and uh and uh and then that could come back into the main branch right and we've i've all i mean as you both know i've been fundamental believer in sort of foundational research we've always had the broadest deepest research bench i think of any lab out there um and that's what allowed us to do past big breakthroughs obviously transformers but alpha go alpha zero all of these things distillation um and if to the extent any of those things are needed again another big breakthrough of that level um i would back us to do that and uh we're you know pursuing lots of very exciting avenues that could bring that sort of step change uh as well as the incremental and then they of course also interact um because the better you have your your your base models the more you things you can try on top of it um again like alpha evolve you know add in evolutionary programming in that case on top of the the llms um we recently talked to karen howe who's a journalist um just wrote a book about ai um and she was making an argument essentially against scale um that you don't need these big general models that are incredibly energy intensive and compute intensive and require billions of dollars and new data centers and and all kinds of uh of resources to make happen that instead of doing that kind of thing you could build smaller models you could build narrower models you could have a model like alpha fold that is just designed to uh predict the 3d structures of proteins you don't need a huge behemoth of a model to accomplish that what's your response to that well i think you need those big models we're you know we love big and small models so you need the big models often to train the smaller models so uh we're very proud of our kind of flash models which are the most you know we call them our workhorse models really efficient some of the most popular models we use a ton of those types of size models internally but you can't build those kind of models without distilling um from the larger teacher models and um and even things like alpha fold which obviously i i'm a huge advocate of more of those types of models that can tackle right now we don't have to wait to agi we can tackle now really important problems in science and medicine uh today and uh that will require taking the general techniques but then potentially specializing it you know in that case around protein structure prediction and i think there's huge potential for doing more of those things um and we are largely in our science work ai for science work um and i think you know we're producing something pretty cool on that pretty much every month these days and um i think there should be a lot more exploration on that probably a lot of startups could be built uh combining some kind of general model that exists today with some domain specificity and um but if you're interested in agi you've got to push the the again both sides of that it's it's not an either or in my mind i'm i'm an and right like let's scale let's let's look at specialized techniques combining a and hybrid systems sometimes they're called and let's look at um new uh blue sky research that could deliver the next transformers um you know we're betting on all of those things you mentioned alpha evolve something that kevin and i were both really fascinated by tell us what of all this well a high level it's basically taking our um latest gemini models actually two different ones uh uh to generate sort of ideas hypotheses about programs and other uh mathematical functions and then it goes they go into sort of evolutionary programming process to decide which ones of those are most promising and then that gets sort of ported into the next step and tell us a little bit about what evolutionary programming it sounds very exciting yeah so it's it's basically a way for uh systems to kind of uh explore new space right so like what things should we you know in genetics like mutate to uh to give you a kind of new organism so you can think about the same way in programming or mathematics you know you change the program in some way and then uh you compare it to some answer you're trying to get and then the ones that fit best according to a sort of evaluation function you put back into the next set of generating new ideas uh we have our most efficient model sort of flash model generating uh possibilities and then we have the pro model uh critiquing that right and deciding which one of those is most promising for the to be selected for the next uh next round of evolution it's sort of like an autonomous ai research organization almost where you have some ais coming up with hypotheses other ais testing them and supervising them and the goal as i understand it is to have an ai that can kind of improve itself or over time or suggest improvements to existing problems yes so it's the beginning of i think that's why people are so excited about and we are excited about it's the beginning of a kind of automated process it's still not fully automated and also it's still relatively narrow we've applied it to many things like chip design uh scheduling uh ai tasks on our on our data centers more efficiently um even improving matrix multiplication one of the most fundamental units of training uh training algorithms uh so it's it's actually amazingly useful already but um it's still constrained to domains that are kind of provably correct right which uh obviously maths and coding are but we need to sort of fully generalize that but it's interesting because i think for a lot of people the knock they have on llms in general is well all you can really give me is the statistical median of your training data but what you're saying is we now have a way of going beyond that to potentially generate novel ideas that are actually useful in advancing the state of the art that's right and and but we we already had these this is another approach alpha evolve using evolutionary methods but but we already had evidence of that even way back in alpha go days so you know it's alpha go came up with new go strategies most famously move 37 in game two of our big lisa doll world championship match and okay it was limited to a game but it was a genuinely new strategy that had never been seen before even though we play go for hundreds of years so that's when i kicked off our sort of alpha fold projects and science projects because i was waiting for to see evidence of that kind of spark of um creativity you could call it right or originality uh at least in the within the domain of what we know but there's still a lot further that has to you know so we we know that these kinds of models paired with things like monte carlo tree search or reinforcement learning planning techniques uh can get you to new regions of space to explore and evolutionary methods is another way of going beyond what the current model knows to explore force it into a new regime where it's not seen it before i've been looking for a good monte carlo tree for so long now so if you could help me find one it would honestly be a huge one of these things could help yeah okay great um so i read the alpha of all paper or to be more precise i fed it into notebook lm and had it make a podcast that i could then listen to that would explain it to me at a slightly more elementary level and one fascinating thing that stuck out to me um is a detail about how you were able to make alpha evolve more creative and one of the ways that you did it was by essentially forcing the model to hallucinate i mean so many people right now are obsessed with eliminating hallucinations but it seemed to me like one way to read that paper is that there's there is actually a scenario in which you want models to hallucinate or be creative whatever you want to call it yes well i think that's right i think you you know hallucination in when you want factual things obviously is if you don't want um but in creative situations where you know you can think of it as a little bit like lateral thinking in an mba course or something right uh is just just create some crazy ideas most of them don't make sense um but the odd one or two may get you to a region of the search space that is actually quite valuable it turns out once you evaluate it afterwards uh and so um you can substitute the word hallucination maybe for imagination at that point right uh there's they're obviously two sides of the of the same coin yeah i did talk to one uh ai safety person who was a little bit worried about alpha evolve not not because of the actual technology and the experiments which this person said you know they're fascinating but because of the way it was rolled out so uh deep google deep mind created alpha evolve and then used it to optimize some systems inside google and kept it sort of hidden for a number of months and only then sort of released it to the public and this person was saying well if we really are getting to the point where these ai systems are starting to become recursively self-improving and they can sort of build a better ai doesn't that imply that when google if google deep mind does build agi or even super intelligence that it's going to keep it to itself for a while rather than doing the responsible thing and informing the public well i think it's a bit of both actually you need to for first of all alpha evolve is a very nascent self-improvement thing right and still got human in the loop and it's um and it's only shaving off you know albeit important percentage of points off of already existing tasks you know that's valuable but it's not some it's not creating any kind of step changes uh and there's a there's a trade-off between you know carefully evaluating things internally before you release it to the public out into the world um and then also getting the extra critique back which is also very useful from the academic community and so on and also we we have a lot of trusted tester type of programs that we talk about where people get early access to these things um and um and then give us feedback and and stress test them uh including sometimes the the safety institutes as well but my understanding was you weren't just like red teaming this internally within google you were actually like using it to make the data centers more efficient using it to make the kernels that train the ai models more efficient so i guess what this person is saying is like it's just we we want to start getting good habits around these things now before they become something like agi and uh they were just a little worried that maybe this is going to be something that stays hidden for longer than it needs to so i don't like you you i would love to hear your response to that yeah well look i i mean i think that that system is not uh uh anything really that i would say you know has any risk on the agi type of front i think as we get and i think today's system still are not although very impressive are not that powerful um from a you know any kind of agi risk standpoint that maybe this person was talking about um and i think you need to have both you need to have incredibly rigorous internal tests of these things and then we need to also get collaborative inputs from external so i think it's a bit of both i actually don't know the details of uh of of the alpha evolve uh uh process for the last few you know the first few months it was just function search before and then it become more general so it's it's sort of evolved it's evolved itself over the last year in terms of becoming this general purpose tool um and it still has a lot of um way to go before we can actually use it in our main branch which is that at that point i think then becomes more serious like with gemini it's sort of separate from from that currently let's talk about ai safety a little bit more broadly uh it's been my observation that it seemed like if the further back in time you go and the less powerful ai systems you have the more everyone seemed to talk about the safety risk and it seems like now as the models improve we we hear about it less and less including you know at the keynote yesterday so i'm curious what you make of this moment in ai safety uh if you feel like you're paying enough attention to the risk that could be created by the systems that you have and if you are as committed to it as you were say three or four years ago a lot of these outcomes seem less likely yeah well we're we're just as committed as we've ever been i mean we we've we've from the beginning of deep mind we plan for success so success meant something looking like this is what we kind of imagined i mean it's sort of unbelievable still that it's actually happened but it's it is sort of in the in the overton window of what we thought was going to happen if if these technologies really did develop the way we thought they were going to um and the risk and attending to mitigating those risks was was part of that and so we do a huge amount of work on our systems i think we have very robust red teaming uh uh uh processes both pre and post launches um and we've learned a lot uh and i think that's what's the difference now between having these systems have albeit early systems contact with the real world i think that's actually been i'm sort of persuaded now that that has been a useful thing overall and i wasn't sure um i you know i think five years ago ten years ago i may have thought maybe maybe it's better staying in a research lab and and you know kind of collaborating with academia and that but actually there's a lot of things you don't get to see or understand unless millions of people try it so it's this weird trade-off again between um you you can only do it when there's there's millions of smart people uh uh try your uh technology and then you find all these edge cases so you know however big your your testing team is it's only going to be you know 100 people or a thousand people or something so it's not comparable to tens of millions of people using your your systems but on the other hand you want to know as much as possible uh ahead of time so you can mitigate the risks before they happen so and this is so this is interesting and it's good learning i think what's happened in the industry in the last two three years has been great because we've been learning when the systems are not that powerful or risky as you were saying earlier right i think things are going to get very serious in two three years time when these agent systems start becoming really capable we're only seeing the beginnings of the agent era let's call it but you can imagine and i think hopefully you understood from the keynote what the ingredients are what it's going to come together with and then i think we really need a step change in research on analysis and understanding controllability but the other key thing is it's got to be international you know that's pretty difficult and i've been very consistent on that because it's an inter it's gonna it's a technology gonna fit everyone in the world it's being built by different countries and different companies in different countries so you've got to get some international kind of norm i think around uh what we want to use these systems for and and what are the kinds of benchmarks that we want to test safety and reliability on um but there's plenty of work to get on with now like we don't have those benchmarks we should we and the industry and academia should be agreeing to consensus of what those are what role do you want to see export controls play in doing what you just said well export controls is a very complicated issue and and obviously geopolitics today is extremely complicated um and there you know i can i see both sides of the arguments on that you know there's proliferation uncontrolled proliferation of these technologies uh do you want different places to have frontier modeling uh training capability uh i'm not sure that's a good idea but on the other hand um you want western technology to be to be uh the thing that's adopted uh around the world so it's a complicated trade-off like if there was an easy answer i think we'd all you know i would be you know shouting from the rooftops but i think there's it's it's nuanced like most real world problems are do you think we're heading into a bipolar conflict with china over ai if we aren't in one already i mean just recently we saw the trump administration making a big push to uh make the middle east uh countries in the gulf like saudi arabia and the uae into ai powerhouses have them you know use american chips to to train models that will not be sort of accessible to to china and its ai powers do you see that becoming sort of the foundations of a new global conflict well i hope not but i i think uh short term you know i feel like ai is getting caught up in the in the bigger geopolitical shifts that are going on so i think it's just part of that and it happens to be one of the most uh topical new things that's appearing but on the other hand what i'm hoping is as people as these technologies get more and more powerful the world will realize we're all in this together because we are and so uh you know and the the the the last few steps towards agi um hopefully we're on the longer timelines actually right um the more the timelines i'm thinking about then we get time to sort of get the the the the collaboration we need at least on a scientific level um before before then would be good do you feel like you're in sort of the the final home stretch to agi i mean sergey brin google's co-founder had a memo that was reported on by my my colleague at the new york times earlier this year that went out to google employees and said you know we're in the sort of the home stretch and everyone needs to get back to the office and be working all the time uh because this this is when it really matters do you have that sense of like of of finality or sort of entering a new phase or an end game i think we are past the middle game that's for sure but i've been working every hour there is for the last 20 years because i've felt the how important and momentous this this technology would be and we thought it was possible for 20 years and i think it's coming into view now i agree with that and um whether it's five years or 10 years or two years that they're all actually quite short timelines when you're talking discussing what were the enormity of the transformation of this technology you know this technology is going to bring uh that none of those timelines are very long when we come back more from demis isabes about the strange futures that lie ahead we're going to switch to some more general questions about the ai future sure a lot of people now are starting to at least in conversations that i'm involved in think about what the world might look like after agi um the context in which i actually hear the most about this is from parents who want to know um what their kids should be doing studying will they go to college yeah um you have kids they're older than than my kid um how are you thinking about that so i think that the when it comes to the kids and i get asked this quite a lot is is uh university students um i think first of all i wouldn't dramatically uh change some of the basic advice on stem uh getting good at even the things like coding i would still recommend because i think whatever happens with these ai tools you'll be better off understanding how they work and how they function and you know what you can do with them um i would also say immerse yourself now that's what i would be doing as a teenager today and in trying to become a sort of ninja at using the the the latest tools i think you can almost be sort of superhuman in some ways if you got really good at using uh all the latest uh coolest ai tools um but don't neglect the basics too because you need the fundamentals and then i think uh teach sort of meta skills really of um like learning to learn and the only thing we know for sure is there's going to be a lot of change over the next 10 years right so how does one get ready for that what kind of skills are useful for that creativity skills um adaptability resilience i think all of these sort of you know meta skills is what will be important uh for the next generation um and i think it'll be very interesting to see what they do because they're going to grow up ai native just like the last generation grew up mobile and and ipad and you know sort of that that kind of you know tablet native and then previously internet and computers which was my era and um you know they always i think the kids of that era always seem to adapt to uh make use of the latest coolest tools and i think there's more we can do on the ai side to make the tools actually um if people are going to use them for school and education let's make them really good for that and sort of provably good and i'm very excited about bringing it to education in a big way and also to you know if you had an ai tutor uh uh to bring it to poorer parts of the world that don't have good educational systems um so i think there's a lot of upside there too another thing that kids are doing with ai is chatting a lot with digital companions um google deep mind doesn't make any of these companions yet um some of what i've seen so far seems pretty worrying it seems pretty easy to create a chat bot that just does nothing but tell you how wonderful you are and that can sort of like lead into some dark and weird places so i'm curious what observations you've had as you like look at this uh market for ai companions and whether you think i i might want to build this someday or i'm gonna leave that to other people yeah i think we've got to be very careful as we as we start entering that domain and and that's why we we haven't yet and we've been very thoughtful about that my view on this is um more through the lens of uh the universal assistant that we talked about yesterday which is something that's incredibly useful for your everyday productivity you know gets rid of the boring mundane tasks that we all hate doing to give you more time to do the things that you love doing i also really um hope that they're going to enrich your lives by giving you incredible recommendations for example on all sorts of amazing things that um you didn't realize you would enjoy you know sort of the delight you with surprising things um so i think these are the the ways i'm hoping that uh these systems will go and actually on the positive side i feel like um we if this assistant becomes really useful and knows you well you could sort of program it with you obviously with natural language to protect your attention so you could almost think of it as a system that works for you you know as an individual it's yours and um it protects your attention from being assaulted by other algorithms that want your attention which is actually nothing to do with ai most most social media sites that's what they're doing effectively their algorithms are trying to gain your attention and i think that's actually the worst thing and it'd be great to to protect that so we can be more in you know creative flow whatever it is that you once you want to do that's how i would want these systems to be useful to people if you could build a system like that i think people would be so incredibly happy i think right now people feel assailed by the algorithms in their life and they don't know what to do about it well the reason is is because you have to use your you've got one brain and you have to let's say whatever it is a social media stream you have to dip into that torrent to then get the piece of information you want but then you've already but you're doing it with the same brain so you've already affected your mind and your mood and other things by dipping into that torrent and you know to find the valuable you know the piece of information that you wanted but if if an assistant digital assistant did that for you you would you know you'd only get the useful nugget and you wouldn't need to um break your you know your your mood or what is it you're doing the day or your concentration with your family whatever it is um i think that would be wonderful yeah casey loves that idea you love that idea i love this idea of an ai agent that protects your attention from all the forces trying to assault it i'm not sure how the the ads team at google is gonna feel about this um but we can ask them when the time comes um some people are starting to look at the job market especially for recent college graduates and uh worry that there were already starting to see signs of ai power job loss um anecdotally i talked to young people who uh you know a couple years ago might have been interested in going into fields like tech or consulting or finance or law who are just saying like i don't know that these jobs are going to be around much longer um a recent article in the atlantic wondered if we're starting to see ai competing with college graduates for these entry-level positions do you have a view on that i haven't looked at that you know i i don't know i haven't seen the studies on that but um you know maybe it's starting to appear now i i don't think there's any hard numbers on that yet at least i haven't seen it um i think for now i mostly see these as tools that augmenting what you can do and what you can achieve um i think like with most i think the next era i mean maybe after agi things will be different again but over the next five to ten years i think we're going to find uh what normally happens with with big sort of new technology shifts which is that some jobs get disrupted but then new um you know more valuable usually more interesting jobs get created so i do think that's what's going to happen in the in the nearer term um so you know today's graduates and the next you know next five years let's say i think it's very difficult to predict after that um that's part of this sort of more societal change that we need to get ready for i mean i think the the tension there is that you're right these tools do give people so much more leverage um but they also like reduce the need for big teams of people doing certain things i was talking to someone recently who said you know they had been at a data science uh company in their previous job that had 75 people working on some kind of data science tasks and now they're at a startup that has one person doing the work that used to require 75 people and so i guess the question i'd be curious to get your view on is what are the other 74 people supposed to do well look i think um uh these tools are going to unlock uh the ability to create things much more quickly so you know that i think there'll be more people that will do startup things i mean there's a lot more surface area one could attack and try with these tools um that was possible before so let's take programming for example um you know so obviously these these systems are getting better at coding but the best coders i think are getting differential value out of it because they still understand how to pose the question and architect the whole code base and and check what the coding does but simultaneously at the hobbyist end it's allowing designers and maybe non-technical people to vibe code some things you know whether that's prototyping games or or websites or uh movie ideas so in theory it should be those other 70 people whatever should could be creating new startup ideas maybe it's going to be less of these bigger teams and more smaller teams are very empowered by ai tools um but it but that goes back to the education thing then which skills are now important it might be different skills like creativity sort of vision and uh design sensibility um you know could become increasingly important do you think you'll hire as many engineers next year as you hire this year i think so yeah that's that's the i mean there's no plan to to hire less but you know we again you have we have to see how fast the the coding uh agents improve um today they they they're they're not you know they can't do things on their own they need to they need uh they're just helpful for for the best you know for the best human coders last time we talked to you we asked you about some of the more pessimistic views about ai in the public and one of the things you said to us was that the field needed to demonstrate concrete use cases that were just clearly beneficial to people to kind of shift yes my observation is that i think there are even more people now who are like actively antagonistic toward ai and i think maybe one reason is they hear folks at the big labs saying pretty loudly eventually this is going to replace your job and most people just think well i don't want that you know so i'm curious like looking on from that past conversation if you feel like we have seen some use cases enough use cases to start to shift public opinion or if not what some of those things might be that actually changed views here well i think we're we're we're working on those things they take time to develop um i think the a kind of universal assistant would be one of those things if it was uh kind of really yours and working for you effectively so technology that works for you um i think that this is what economists and other experts should be working on is do you have uh does everyone have manager uh a a suite of of you know fleet of agents that are doing things for you and you know including potentially earning you money or building you things um you know does that become part of the normal job process i could imagine that in the next four or five years i also think that as we get closer to agi and we make breakthroughs and we probably talked about last time material sciences energy fusion these sorts of things help by ai um uh we should have we should start getting to a position in society where we're getting towards what i would call radical abundance where there's a lot of resources uh to go around and then again it's more of a political question of how would you distribute that in a fair way right so i've heard this term like universal high income well something like that uh i think it's gonna probably be you know good and necessary but obviously there's a lot of uh complications that they need to be thought through um so and and then in between there's this transition period you know between now and whenever we we have a that sort of situation where what what do we do about the change in in the in the interim and depends on how long that is too what part of the economy do you think agi will transform last well i mean i think that parts of the economy where you know involves human to human interaction and emotion um and those things i think uh you know will probably be the hardest things for for ai to do so um you know are people already you do an ai therapy and talking with chat bots for things that they might have paid someone you know a hundred dollars an hour for well therapies are very narrow domain and i'm not sure exactly there's a lot of you know hype about those things i'm not actually sure how many uh of those things are really going on in terms of actually affecting the real economy and rather than just sort of more toy things um and i don't think the ai systems are like capable of doing that properly yet um but just the kind of emotional connection uh and uh that we get from talking to each other and um doing things in nature in the real world uh i don't think that ai can really replicate all of those things so if you lead hikes would be a good job yeah yeah i'm gonna climb everest yeah my intuition on this is that it's going to be some heavily regulated industry where there will just be like a massive pushback on the use of ai to displace labor or or take people's jobs like health care or or education or something like that um but you think it's going to be an easier lift in those heavily regulated industries well i don't know i mean it might be but then we have to weigh that up as society whether we want all the all the all the positives of that for example you know curing all diseases or or um you know i think there's a lot of uh finding new energy sources so i think these things would be clearly very beneficial for society and i think we need um to for our other big challenges it's not like there's no challenges in society other than uh ai but i think ai can be a solution to a lot of those uh other challenges be that energy resource constraints uh aging disease um you know you name it and water access etc a ton of problems facing us today climate um i think ai can potentially help with all of those and i agree with you society will need to decide what um it wants to use this these technologies for and um but then you know what's also changing is what we discussed earlier with products is the technology is going to continue advancing um and that will open up new possibilities like uh the kind of radical abundance space travel these things um which are a little bit out of scope today unless you read a lot of sci-fi but i think rapidly becoming uh real during the industrial revolution there were lots of people who embraced new technologies moved from farms to cities to work in the new factories uh were sort of early adopters on that curve um but that was also when the transcendentalists started retreating into nature and rejecting technology that's when thoreau went to walden pond and there was a big movement of americans who just saw the new technology and said i don't think so not for me do you think there will be a similar movement around rejection of ai and if so how how big do you think it'll be um i don't know if it'll be i mean there could be a get back to nature and i mean i think a lot of people will want to do that and and i think this potentially will give them the room and space to do it right if you're in a world of radical abundance i fully expect that's what what a lot of us will want to do is use it to you know i think again i'm thinking about it sort of space faring and and and and more you know kind of um maximum human flourishing but uh i think that will be that will be exactly some of the things that a lot of us will choose to do and but i have time and the space and the the resources to do it are there parts of your life where you say i'm not going to use ai for that even though it might be pretty good at it for some sort of reason wanting to protect your creativity or your thought process or something else um i don't think ai is good enough yet to impinged on any of those sorts of errors where i would you know it's mostly i'm using it for you know things like you did with notebook lm which i feel find great like breaking uh the ice on a new topic scientific topic and then deciding if i want to get more deep into it that's one of my main use cases summarization those things i think those are all just helpful um but you know we'll see i haven't got any examples of what of what you suggested yet but maybe as ai gets more powerful there will be when we talked to dario amade of anthropic recently he talked about this feeling of excitement mixed with a kind of melancholy about the progress that ai was making in domains where he had spent a lot of time trying to be very good yes coding yes where it was like you see a new coding system that comes out it's better than you you think that's amazing and then your second thought is like oh that stings a little bit yeah have you had any experiences of course so i maybe maybe one reason doesn't sting me so much is i've had that experience when i was very young with chess so you know um chess was going to be my first career and you know i was playing pretty professionally when i was a kid for the england junior teams and then deep blue came along right and clearly uh the computers were going to be much more powerful than the world champion forever after that and so but yeah i still enjoy playing chess um people still do it's different you know but it's a bit like i can you know usain bolt we celebrate him for for running the hundred meters incredibly fast but we've got cars but we don't care about that right like it's we're interested in other humans doing it and um i think that'll be the same with robotic football and all of these other things so um and that maybe goes back to what we discussed earlier about what i think in the end we're interested in in other human beings that's why even like a novel maybe it maybe ai could write one day a novel that's sort of technically good but i don't think it would have the same soul or connection to the reader that um uh if you knew it was was written by an ai at least as far as i can see for now you mentioned robotic football is that a real thing we're not sports fans so i just want to make sure i haven't missed something i was meaning soccer yeah no yeah no no uh i don't know i i think there are there are um robo cup uh sort of uh soccer type little robots trying to kick balls and things uh i'm not sure how serious it is but there is a there is a field of robotic football you mentioned the you know sometimes a novel written by a robot might not feel like it have a soul i have to say for as incredible as the technology is in vo or imagine i sort of feel that way with it where it's like it's beautiful to look at but i don't know what to do with it right you know what i mean exactly and that's that's what i was you know that's why we work with great artists like darren aronofsky and shanker on the music um is i i totally agree i think these are tools and they can come up with technically good things and i mean vo3 is unbelievable like when i look at the you know i don't know if you've seen some of the things that are going viral being posted the moment with the voices actually i didn't realize how big a difference audio is going to make to the video i think it just really brings it to life but it's still not as darren would say yesterday when we were discussing on an interview it it doesn't he brings the storytelling it's not got deep storytelling like a master filmmaker will do or master novelist you know the top of their game and um it might never do right it's just always going to feel something's missing it's a sort of a soul for a better word of the piece you know the real humanity the magic if you like the the great pieces of art you know art too when you when i see a van gogh or a rothko or you know why does that touch your you know i spill you know um sort of you know hair's gone back my my spine because of i remember you know and you know about what what they went through and um the struggle to produce that right in every brushstroke of van gogh's brushstrokes his his sort of uh torture and i'm not sure what that would mean even if the ai mimicked that and you were told that it was like so what right and and and so i think that is the piece that at least as far as i can see out to five ten years um the the the top human creators will always be bringing and that's why we've done all of our tools vo lyria in income in collaboration um with top creative artists the new pope pope leo yes is reportedly interested in agi i don't know if he's agi pilled or not but uh that's something that he's spoken about before um do you think we will have a religious revival or a renaissance of interest in faith and spirituality in a world where agi is forcing us to think about what gives our lives meaning i think that potentially could be the case and um i actually did speak to the last pope about that and and the vatican's been interested but even prior to this pope haven't spoken to him yet but on these these matters how does ai and religion uh and uh technology in general and religion uh interact and and what's interesting about the catholic churches and i'm a member of the pontifical academy of sciences is they've always had uh which is strange for a religious body a scientific arm you know which they like to always say galileo was the founder of and uh although it's interesting so so but then and and it's actually really separate and i always thought that was quite interesting and people like stephen hawking and and you know avowed atheists were part of the academy and and that's partly why i agreed to join it is because it's a fully scientific body and uh it's very interesting and i was fascinated they've been interested in this for 10 plus years so they they were on on this early in terms of like how interesting or how from a physical philosophical point i think um uh this this this technology will be and i and i i actually think we need more of that type of thinking and work from from philosophers and theologians uh actually would be really really good so i hope the new pope is genuinely interested um we'll close on a question that uh i recently heard tyler cowen ask jack clark from anthropic that i thought was so good and decided to just steal it whole cloth in the ongoing ai revolution what is the worst age to be oh wow uh well i i don't i mean you know um gosh i haven't thought about that but i mean i think any age uh uh where you can live to see it is a good age because i think we are going to make some great strides uh with things like you know medicine and so um i think it's going to be incredible journal none of us know you know exactly how it's going to transpire it's very difficult to say but it's going to be very interesting to find out try to be young if you can yes young is always better yeah i mean in general young is always better all right demis asabes thanks so much thank you very much you