Google DeepMind C.E.O. Demis Hassabis on Living in an A.I. Future

“The only thing we know for sure is there is going to be a lot of change over the next 10 years.”

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Support for this podcast comes from Dell Technologies. It's time to put AI to work. Is your business ready? The challenge isn't getting started with AI. It's scaling beyond pilots while maintaining control of your data, governance, infrastructure, and costs. Dell AI Factory provides the integrated foundation you need to scale AI securely and efficiently with one trusted partner, Dell, AI on your terms. From idea to impact. Learn more at dell.com/yourwaytoAI. That's dell.com/yourwaytoAI.

• 0:35Kevin Roose?

  Now, there's a very large, what looks like a circus tent over there. What do you think's going on in there?

• 0:39Casey Newton?

  That is your main amphitheater.

• 0:40Kevin Roose?

  Oh, that's the amphitheater?

• 0:41Casey Newton?

  Yes, you were under that tent yesterday.

• 0:42Kevin Roose?

  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 the Google Corporation.

• 0:50Casey Newton?

  No, it's a revival tent. They're bringing Christianity back.

• 0:58Kevin Roose?

  I'm Kevin Roos, a tech columnist at The New York Times.

• 1:00Casey Newton?

  I'm Casey Newen from Platformer.

• 1:02Kevin Roose?

  And this is Hard Fork.

• 1:03Casey Newton?

  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 DeepMind CEO Demis Hassabis returns to the show to discuss the road to AGI, the future of education, and what life could look like in 2030.

• 1:19Kevin Roose?

  Kevin being very old for starters. Don't be that old.

• 1:28Kevin Roose?

  Well, Casey, our decor is a little different this week.

• 1:31Casey Newton?

  Mm-hmm. I'll say it, it looks better.

• 1:33Kevin Roose?

  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.

• 1:41Casey Newton?

  I'm just thrilled to be sitting here surrounded by so much training data. That's what they call books here at Google.

• 1:48Kevin Roose?

  So we are here because this week is Google's annual developer conference, Google I/O. There were many, many announcements from a parade of Google executives about all the AI stuff that they have coming. And we are going to talk in a little bit with Demis Hassabis, who is the CEO of Google DeepMind, 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 because I don't think we have ever been together at an I/O before. So what is it like?

• 2:16Casey Newton?

  So Google I/O has a bit of a festival atmosphere. It takes place at the Shoreline Amphitheater, which is a concert venue. 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.

• 2:36Kevin Roose?

  Yes, there was a vibe coding demo. There were many other things. I did actually see, as I was leaving, the Google acapella group, Google Pella, was like sort of doing their warmups in anticipation of doing some concert. So you've got some like old school Google vibes here, but also a lot of excitement around all the AI stuff.

• 2:55Casey Newton?

  Wait, now, I didn't see Google Pella perform. Where was his performance?

• 2:58Kevin Roose?

  I didn't see them perform either. I just saw them warming up. They were sort of doing their scales. They sounded great.

• 3:02Casey Newton?

  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.

• 3:08Casey Newton?

  All right, Kevin. Well, before we get into it, shall we say our disclosures?

• 3:10Kevin Roose?

  Yes, I work for the New York Times, which is suing OpenAI and Microsoft over copyright violations related to training of AI systems.

• 3:16Casey Newton?

  And my boyfriend works at Anthropic, a Google investment.

• 3:20Kevin Roose?

  Oh, that's right.

• 3:21Casey Newton?

  Yeah.

• 3:22Kevin Roose?

  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 what were the highlights from your perspective?

• 3:30Casey Newton?

  Well, so, look, I wrote a column about this, Kevin. I felt a little bit 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 going to start writing in my voice and I can turn my PDFs into video TED Talks? Sure, why not? So I had a little bit of fever of dream mentality. What was your feeling?

• 4:02Kevin Roose?

  Yeah, I told someone 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, but it was a lot to keep track of.

• 4:21Casey Newton?

  Yeah, I mean, look, if we were going to try 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.

• 4:33Kevin Roose?

  Yeah, so the thing that got my attention, and then I was sitting right next to you, 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.

• 4:47Casey Newton?

  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.

• 5:03Kevin Roose?

  Yeah, that's the little thing that will tell you, like, what the meaning of phrases like "you can't lick a badger twice" is, right?

• 5:08Casey Newton?

  That's right. And if you don't know the meaning of that, Google it. So, that's sort of the thing one. Thing two is the Gemini app, which is kind of like a one-for-one, like ChatGPT competitor. That's in its own 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 ChatGPT, but you can do it right from the Google Search interface.

• 5:48Kevin Roose?

  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. And it's really nice. Like, it's a very clean thing. There's no ads yet. They will probably appear soon. It does this thing called the fan-out, which 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 webpages 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.

• 6:33Casey Newton?

  Yeah, well, so you and I had a chance to meet with Robbie Stein, who is one of the people who is leading AI Mode. And I was surprised by how enthusiastic about 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?

• 6:49Kevin Roose?

  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. 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.

• 7:17Casey Newton?

  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 those things where it's like, well, if that works, that could be a meaningful improvement to search.

• 7:41Kevin Roose?

  Yeah, it could be a meaningful improvement to search. And we should also say, like, it's a big unknown how all of this will affect the main Google search product, right? This is, for now, it's a tab. They have not sort of merged it into the main core Google search, in part because it's not monetized yet, and 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, let's just talk about a few other things that they said on stage at Google I/O. So I was really struck by the usage numbers that they

• 8:22Kevin Roose?

  Trotted out for their products. Gemini, according to them, the app now has 400 million monthly users. That is a lot. That is not quite as many as ChatGPT, but it is a lot more than products like Claude and other AI chatbots. They said that their tokens that are being output by Gemini has increased 50 times since last year. 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.

• 8:56Casey Newton?

  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. 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.

• 9:24Kevin Roose?

  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?

• 9:29Casey Newton?

  Yeah. So, um, you know, like, like the other companies, they're working on text to image, text to video. And while OpenAI's models have gotten most of the attention in this regard, Google's really are quite good. I think the the marquee feature for this year's I O is that the video generating model V O three 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.

• 10:07Kevin Roose?

  Yep. 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?

• 10:29Kevin Roose?

  $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?

• 10:49Casey Newton?

  Well, and I hope that they don't forget that when it comes time to feed me into the large language model. Look, I want to be able to have 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.

• 11:22Kevin Roose?

  Please, your family is dying. Another thing that made me perk up was they talked a lot about personalization, right? This is something we've been talking about for years. Basically, Google has, 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. That has been slow, 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 available for subscribers later this year in Gmail so that instead of just getting the kind of formulaic suggested replies at the bottom of an email, it'll actually kind

• 12:06Kevin Roose?

  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.

• 12:17Casey Newton?

  Yeah, you know, I have to say, I'm somewhat bearish on this one, Kevin, only because 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 emailer, you know? That doesn't seem like that that's hard to mimic. 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 don't 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.

• 13:01Kevin Roose?

  Yeah, that was the part where I was like, I will believe that this exists and is good when I can use this. But as with other companies like Apple, which demoed a bunch of AI features at its developer conference last year and then never launched half of them. I have become like a little bit skeptical until I can actually use the thing myself.

• 13:18Casey Newton?

  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.

• 13:31Kevin Roose?

  So that is the the software AI portion of I/O. There was also a demo of a new hardware product that Google is working on, which are these 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 thing overlaid on your vision, telling you, you know, what the weather is or what time it is or that you have a new message. Or they have this 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. They did say this is a prototype, but what did you make of this?

• 14:16Casey Newton?

  Well, I think a lot of it looked really cool. Like probably my favorite part of the demo was when the person who was demonstrating looked down at her feet because she was getting ready 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 desperately wanted this feature, I think you would see that and be pretty excited. What did you think?

• 14:40Kevin Roose?

  Yeah, I thought to myself, Google Glass is back.

• 14:45Kevin Roose?

  It was away for so long in the wilderness and now it's back and it might actually work this time.

• 14:49Casey Newton?

  Absolutely.

• 14:50Kevin Roose?

  I did get to try the glasses. There was a very long line for the demo, but I-

• 14:54Casey Newton?

  And let me guess, you said, "I'm Kevin Roos, let me in the front of the line."

• 14:57Kevin Roose?

  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 and it was, 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.

• 15:15Casey Newton?

  Totally. I did a demo a few months back and also like really enjoyed it. 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.

• 15:33Kevin Roose?

  And that's what capitalism's all about.

• 15:35Casey Newton?

  Exactly.

• 15:35Kevin Roose?

  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?

• 15:43Casey Newton?

  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 Wally 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

• 16:27Casey Newton?

  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, we put a little bit of AI in this." So that was interesting to me.

• 16:40Kevin Roose?

  Yeah. So I had a slightly different take, which is that I think Google is being AGI pilled. You know, for years now, Google has sort of distanced itself from the conversation about AGI. You know, it had DeepMind, which was sort of its AGI division, but they were over in London. And they were sort of a separate thing. And people at Google would sort of not laugh exactly, but kind of chuckle when you ask them about AGI. It just didn't seem real to them, or it was so remote that it wasn't worth considering.

• 17:07Casey Newton?

  They would say, what does this have to do with search advertising?

• 17:10Kevin Roose?

  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 the sort of culture of AI and how the people at Google talk about AI has really been shifting. 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, faster than even a lot of people at Google were thinking it would.

• 17:41Casey Newton?

  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-shaped holes? And maybe that will eventually ladder up to something like AGI, but I don't think we've seen it quite yet.

• 18:04Kevin Roose?

  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, Google was still trying to make Bard a thing. And I think they were feeling very insecure that that OpenAI 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 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, L M Arena for all these different categories.

• 18:50Kevin Roose?

  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.

• 19:08Casey Newton?

  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.

• 19:26Kevin Roose?

  Right. So that is what it felt like inside Google I/O. 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?

• 19:44Casey Newton?

  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.

• 20:19Kevin Roose?

  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. 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, I think it's an open question, like what the next thing is and whether Google can seize on it as effectively as they did with search.

• 21:00Casey Newton?

  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 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 affiliate fee that is in there somewhere. So one of the things I'm trying to do, as I cover Google going forward, is understanding that, yes, search is the core, but Gemini could be a springboard

• 21:45Casey Newton?

  to build a lot of other really valuable businesses.

• 21:47Kevin Roose?

  Yeah. An important question I know that I always ask you when I go to these things. How was the food?

• 21:52Casey Newton?

  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.

• 22:10Kevin Roose?

  I had some of that. They have like little bottles of cold brew, and I'm like a huge caffeine addict, so I took two of them. 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.

• 22:25Casey Newton?

  I thought I saw you warming up with the a cappella team. Now it all makes sense.

• 22:35Kevin Roose?

  When we come back, we'll talk with Demis Hassabis, CEO of Google DeepMind, about his vision of the AI future.

• 22:59Speaker 00

  Support for this podcast comes from Dell Technologies. It's time to put AI to work. Is your business ready? The challenge isn't getting started with AI. It's scaling beyond pilots while maintaining control of your data, governance, infrastructure, and costs. Dell AI Factory provides the integrated foundation you need to scale AI securely and efficiently with one trusted partner. Dell, AI on your terms. From idea to impact. Learn more at dell.com/yourwaytoAI. That's dell.com/yourwaytoAI.

• 23:33Speaker 03

  Your sales team is at risk of missing quota. Don't panic. Just ask Rippling AI. Built on your real time people and business data, Rippling AI can pull metrics from Rippling and your CRM into a meeting ready dashboard showing quota attainment, headcount trajectory, and revenue to quota by region. In seconds, you'll see what's behind your quota risk and fix it before it's missed. Head to rippling.ai/hardfork to get the only AI built to give you full visibility and take complex actions across your whole business. That's R-I-P-P-L-I-N-G dot AI slash hard fork. Sign up today.

• 24:07Speaker 04

  Today's AI builders need infrastructure that scales with their ambitions. Iren is a vertically integrated AI cloud platform, delivering data centers, compute, and software for AI training and inference, underpinned by its expansive portfolio of land and grid connected power in renewable rich regions globally. Iren's AI cloud gives direct access to Nvidia GPUs, scalable from hundreds to thousands. For builders, that means direct access to AI compute, rapid scaling, and predictable pricing. Iren, AI cloud for the next big thing.

• 24:42Kevin Roose?

  Well, Casey, I guess we behaved ourselves at I O because Google has made Demis Hassabis, the CEO of Google DeepMind, available for us to interview today. We talked to him last February, but of course, a lot has happened since then, starting with his Nobel Prize, but continuing on through a slew of announcements that he just made on stage. What kind of Nobel Prize would you want to win? Probably just for being handsome. Yeah, mine, peace for me.

• 25:06Kevin Roose?

  Let's bring him in.

• 25:13Kevin Roose?

  Demis Asabas, welcome back to Hard Fork.

• 25:14Speaker 05

  Thanks for having me again.

• 25:15Kevin Roose?

  A lot has happened since the last time you were on the show. Most notably, you won a Nobel Prize. Congrats on that.

• 25:22Speaker 05

  Thank you.

• 25:24Kevin Roose?

  Ours must be still in the mail. Can you put in a good word for next year with the committee?

• 25:28Speaker 05

  I will do. I will do.

• 25:30Kevin Roose?

  I imagine it's very exciting to win a Nobel Prize. I know that had been a goal for a long time of yours. 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 laureate. Like, does that happen?

• 25:49Speaker 05

  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 those kinds of things, right? So it's more shield.

• 26:00Kevin Roose?

  So you just had Google I/O 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?

• 26:16Speaker 05

  Wow. Well, I mean, we did announce a lot of things. I think for the average user, I think it's the new powerful models and I hope this Astra-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. And then I guess VO3 was the biggest announcement of the show probably and seems to be going viral now and that's pretty exciting as well, I think.

• 26:42Kevin Roose?

  Yeah. One thing that struck me about I/O this year compared to previous years is that it seems like Google is sort of getting AGI-pilled, as they say. I remember interviewing people, researchers at Google even a couple of years ago, and there was a little taboo about talking about AGI. They would sort of be like, oh, that's like Demis and his DeepMind people in London. That's sort of like their crazy thing that they're excited about. But here, we're doing like, you know, real research. But now you've got like senior Google executives talking openly about it. What explains that shift?

• 27:18Speaker 05

  I think the sort of AI part of the of the equation becoming more and more central. Like I sometimes describe Google DeepMind 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. And then it's very clear, I think you could sort of say AGI-pilled is maybe the right word, that we're quite close to this human-level general intelligence, maybe closer than people thought even a couple of years ago. And it's going to have broad cross-cutting impact. And I think that'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 maybe a bit of the DeepMind ethos is bleeding into the into the general Google, which is which is great.

• 28:01Casey Newton?

  You mentioned 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?

• 28:27Speaker 05

  Yeah, it's a great great question. I mean, I think 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 texts, internet and mobile. At some point, you get some sort of stabilization of the tech stack so that then, 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, not just for us at Google and DeepMind, but for startups, for for anyone, really, any any any company,

• 29:09Speaker 05

  small and large, is what do you bet on right now when that could be 100% better 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 deeply technical sort of product people, product designers and managers, I think, in order to sort of intercept. Where the technology may be in a year. 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 a pretty deep understanding of the tech and where it might go to sort of work out what features you can rely on. And so 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.

• 29:50Casey Newton?

  Yeah, during your keynote, you talked about Gemini as powering both sort of productivity assistant style stuff and also fundamental science 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?

• 30:15Speaker 05

  I think when you look at it, it looks like incredible breadth of things, which is true. And how are these things related? Other than the fact I'm interested in all of them. But that was always the idea with building general intelligence, you know, truly generally and 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. Ninety percent, I would say of it, is the underlying core general models. You know, in our case, Gemini, especially 2.5. And in most of these areas, you still need additional applied research or a little bit

• 30:55Speaker 05

  of special casings from the domain, maybe it's special data or whatever, to tackle that problem. And, you know, maybe we work with domain experts in the scientific areas. But underlying it, 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 it's great fun for someone like me who's very interested in many things. You get to use this technology and sort of go into almost any field that you find interesting.

• 31:26Kevin Roose?

  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 imagine this is both like a resources challenge, but also like a personnel challenge, because say you join DeepMind as an engineer and you want to like build AGI 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.

• 32:00Speaker 05

  Yeah.

• 32:00Kevin Roose?

  Is that a challenging conversation to have with people who joined for one reason and maybe asked to work on something else?

• 32:06Speaker 05

  Yeah, well, we don't, you know, it's sort of self-selecting internally. We don't have to, 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, prodeng, and the researchers, if they want to stay in core research, that they're absolutely, that's fine, and we need that. But actually, you'll find a lot of researchers are quite motivated by real world impact, be that in medicine, obviously, and and things like isomorphic, but also 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, yeah, we don't, there's no need for us to sort of have to pivot people to certain things.

• 32:50Kevin Roose?

  You did a panel yesterday with Sergey Brin, Google's co-founder.

• 32:54Speaker 05

  Yeah.

• 32:55Kevin Roose?

  Who has been working on this stuff back in the office. And interestingly, he has shorter AGI timelines than you. 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. 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 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.

• 33:37Speaker 05

  Uh, like, well, first of all, there wasn't that much difference in our timelines if he's just before twenty thirty and I'm just after. Also, our time, my timeline's been pretty consistent since the start of DeepMind in twenty ten. So we thought it was roughly a twenty year mission. And amazingly, we're on track. So it's it's somewhere around then, I would think. And I, and I, 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

• 34:19Speaker 05

  do all of the things that the human brain can do, right? E- 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. 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 maths conjecture. Solving one's pretty good, but actually inventing like the Riemann hypothesis or something as significant as that, that mathematicians agree is really important is very, is much harder. And also consistency. So the consistency is a requirement of generality, really.

• 35:02Speaker 05

  And you should, it should be very, very difficult for even top experts to find 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.

• 35:19Casey Newton?

  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?

• 35:40Speaker 05

  I think it's, I think it could be both. 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. Pre-training, post-training, inference 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 at all parts of that, uh, the traditional stack, should 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.

• 36:19Kevin Roose?

  Is there a difference between a greenfield thing and a blue sky thing?

• 36:22Speaker 05

  I'm not sure. Maybe they're, maybe they're pretty similar. So, uh, some new area, let's call it. 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. And that's what allowed us to do past big breakthroughs, obviously transformers, but AlphaGo, AlphaZero, all of these things, distillation. And if to the extent any of those things are needed again, another big breakthrough of that level, 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 because

• 37:07Speaker 05

  the better you have your base models, the more you, things you can try on top of it. Um, again, like AlphaEvolve, you know, add in evolutionary programming in that case on top of the, the L L Ms.

• 37:20Kevin Roose?

  We recently talked to Karen How, who's a journalist, um, just wrote a book about AI. Um, and she was making an argument essentially against scale, 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 AlphaFold 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?

• 37:56Speaker 05

  Well, I think you need those big models. We, you know, we love big and small models. So you need the big models often to train the smaller models. So 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 from the larger teacher models. And, and even things like AlphaFold, 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 today. And that will require taking the general techniques, but then potentially specializing it.

• 38:41Speaker 05

  You know, in that case around protein structure prediction. And I think there's huge potential for doing more of those things. And we are largely in our science work, AI for science work. And I think, you know, we're producing something pretty cool on that pretty much every month these days. And I think there should be a lot more exploration on that. Probably a lot of startups could be built. Combining some kind of general model that exists today with some domain specificity. But if you're interested in AGI, you've got to push 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 AI and hybrid systems, sometimes they're called. And let's look at new blue sky research that could deliver the next transformers.

• 39:26Speaker 05

  We're betting on all of those things.

• 39:28Casey Newton?

  You mentioned AlphaEvolve, something that Kevin and I were both really fascinated by. Tell us what AlphaEvolve is.

• 39:35Speaker 05

  Well, at a high level, it's basically taking our latest Gemini models, actually two different ones, to generate sort of ideas, hypotheses about programs and other 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.

• 39:55Casey Newton?

  And tell us a little bit about what evolutionary programming is. It sounds very exciting.

• 39:59Speaker 05

  Yeah, so it's, it's basically a way for systems to kind of explore new space, right? So Like, you know, what things should we, you know, in genetics like mutate to 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 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. And we have our most efficient model, sort of flash model, generating possibilities. And then we have the pro model critiquing that, right? And deciding which one of those is most promising for the to be selected for the next round of evolution.

• 40:42Kevin Roose?

  So 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 over time or suggest improvements to existing problems.

• 41:01Speaker 05

  Yes. So it's the beginning of, I think that's what 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, scheduling AI tasks on our data centers more efficiently, even improving matrix multiplication, one of the most fundamental units of training algorithms. So it's actually amazingly useful already, but it's still constrained to domains that are kind of provably correct, right? Which obviously maths and coding are, but we need to sort of fully generalize that.

• 41:33Casey Newton?

  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 we are 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.

• 41:51Speaker 05

  That's right. And but 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 AlphaGo days. So, you know, it's AlphaGo came up with new Go strategies, most famously Move37 in game two of our big Lee Sedol 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 played Go. For hundreds of years. So that's when I kicked off our sort of AlphaFold projects and science projects because I was waiting to see evidence of that kind of spark of creativity, you could call it, right? Or originality, at least within the domain of what we know. But there's still a lot further that has to, you know, so we know that these kinds of

• 42:35Speaker 05

  models paired with things like Monte Carlo tree search or reinforcement learning planning techniques 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.

• 42:52Casey Newton?

  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 help.

• 42:57Speaker 05

  One of these things could help.

• 42:58Casey Newton?

  Yeah, okay, great.

• 43:00Kevin Roose?

  So I read the AlphaEvolve 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 is a detail about how you were able to make AlphaEvolve 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 actually a scenario in which you want models to hallucinate or be creative, whatever you want to call it.

• 43:37Speaker 05

  Yes. Well, I think that's right. I think, you know, hallucination in when you want factual things, obviously, is if you don't want. 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? Is just create some crazy ideas. Most of them don't make sense. 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. And so you can substitute the word hallucination maybe for imagination at that point, right? There's obviously two sides of the same coin.

• 44:12Kevin Roose?

  Yeah. I did talk to one AI safety person who was a little bit worried about AlphaEvolve, 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 Google DeepMind created AlphaEvolve 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 DeepMind does build AGI or even superintelligence, that it's going to keep it to itself for a while,

• 44:57Kevin Roose?

  rather than doing the responsible thing and informing the public?

• 45:00Speaker 05

  Well, I think it's a bit of both, actually. You need to, first of all, AlphaEvolve is a very nascent self-improvement thing, right? And it's still got human in the loop and it's only shaving off, albeit important percentage of points off of already existing tasks, that's valuable, but it's not creating any kind of step changes. And there's a trade-off between carefully evaluating things internally before you release it to the public out into the world, and then also getting the extra critique back, which is also very useful from the academic community and so on. And also, we have a lot of trust-to-tester type of programs that we talk about, where people get early access to these things and then give us feedback and stress-test them, including sometimes the safety institutes as well.

• 45:45Kevin Roose?

  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 want to start getting good habits around these things now before they become something like AGI. And 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 would love to hear your response to that.

• 46:13Speaker 05

  Yeah, well, look, I think that that system is not anything really that I would say has any risk on the AGI type of front. I think as we get, and I think today's systems still are not, although very impressive, are not that powerful from any kind of AGI risk standpoint that maybe this person was talking about. 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 the Alpha evolve 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.

• 46:57Speaker 05

  And it does a lot of 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.

• 47:08Casey Newton?

  Let's talk about AI safety a little bit more broadly. 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, 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.

• 47:41Speaker 05

  Yeah, well, we're just as committed as we've ever been. I mean, we've we've from the beginning of DeepMind, 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. 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 processes, both pre and post launches. And we've learned a lot. 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

• 48:26Speaker 05

  persuaded now that that has been a useful thing overall. And I wasn't sure, you know, I think five years ago, 10 years ago, I may have thought, maybe it's better staying in a research lab 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. The trade off again between you you can only do it when there's there's millions of smart people. Try your 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, a hundred people or a thousand people or something. So it's not comparable to tens of millions of people using your your your systems. But on the other hand, you want to know as much as possible ahead of time. So you can mitigate the risks before they happen.

• 49:11Speaker 05

  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 it's a technology for everyone in the world.

• 49:53Speaker 05

  It's been built by different countries and different companies in different countries. So you've got to get some international kind of norm, I think, around 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? 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.

• 50:18Casey Newton?

  What role do you want to see export controls play in doing what you just said?

• 50:22Speaker 05

  Well, export controls is a very complicated issue. And and obviously geopolitics today is extremely complicated. 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. Do you want different places to have frontier modeling, training capability? I'm not sure that's a good idea, but on the other hand, you want Western technology to be the thing that's adopted 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.

• 50:59Kevin Roose?

  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 make the Middle East, countries in the Gulf like Saudi Arabia and the UAE into AI powerhouses, have them, you know, use American ships 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?

• 51:25Speaker 05

  Well, I hope not, but I I think 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 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, you know, and the the the last few steps towards AGI, hopefully, we're on the longer timelines, actually, right? The more the timelines I'm thinking about, then we get time to sort of get the collaboration we need, at least on a scientific level, before before then would be good.

• 52:08Kevin Roose?

  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 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?

• 52:33Speaker 05

  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 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 whether it's five years or 10 years or two years, that they're all actually quite short timelines. When you're discussing what would the enormity of the transformation of this technology, you know, this technology is going to bring, that none of those timelines are very long.

• 53:07Kevin Roose?

  When we come back, more from Demis Hassabis about the strange futures that lie ahead.

• 53:42Speaker 00

  Support for this podcast comes from Dell Technologies. It's time to put AI to work. Is your business ready? The challenge isn't getting started with AI. It's scaling beyond pilots while maintaining control of your data, governance, infrastructure, and costs. Dell AI Factory provides the integrated foundation you need to scale AI securely and efficiently with one trusted partner, Dell, AI on your terms. From idea to impact. Learn more at dell.com/yourwaytoAI. That's dell.com/yourwaytoAI.

• 54:16Speaker 03

  Your sales team is at risk of missing quota. Don't panic. Just ask Rippling AI. Built on your real-time people and business data, Rippling AI can pull metrics from Rippling and your CRM into a meeting-ready dashboard showing quota attainment, headcount trajectory, and revenue to quota by region. In seconds, you'll see what's behind your quota risk and fix it before it's missed. Head to rippling.ai/hardfork to get the only AI built to give you full visibility and take complex actions across your whole business. That's R-I-P-P-L-I-N-G dot AI slash hardfork. Sign up today.

• 54:50Speaker 06

  Building and scaling AI today can feel pretty chaotic, mostly because teams are juggling too many fragmented tools at once. That's where OutSystems comes in. OutSystems gives you one unified agentic systems platform to manage your entire portfolio of agents, apps, and workflows. You get to innovate at the speed of AI, but with the security, governance, and control your enterprise actually needs. And the results are already there. A major manufacturer used OutSystems to build a multi-agent system that analyzes blueprints, and a global bank cut their development time by 60%. Own your agentic future today at outsystems.com/nyt.

• 55:28Kevin Roose?

  We're going to switch to some more general questions about the AI future.

• 55:31Speaker 05

  Sure.

• 55:32Kevin Roose?

  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. The context in which I actually hear the most about this is from parents who want to know what their kids should be doing, studying, will they go to college? You have kids, they're older than than my kid. How are you thinking about that?

• 55:54Speaker 05

  So I think that the, when it comes to kids, and I get asked this quite a lot, is university students, I think first of all, I wouldn't dramatically change some of the basic advice on STEM, 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. I would also say immerse yourself now, that's what I would be doing as a teenager today, in trying to become a sort of ninja at using the latest tools. I think you could almost be sort of superhuman in some ways if you got really good at using all the latest coolest AI tools. But don't neglect the basics too, because you need the fundamentals.

• 56:39Speaker 05

  And then I think teach sort of meta skills really of 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, adaptability, resilience. I think all of these sort of, you know, meta skills is what will be important for the next generation. 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 iPad and, you know, sort of that kind of tablet native, and then previously internet and computers, which was my era. And, you know, they always, I think the kids of that era always seem to adapt to make use of the latest coolest tools.

• 57:25Speaker 05

  And I think there's more we can do on the AI side to make the tools actually, 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 To education in a big way and also to, you know, if you had an AI tutor

• 57:43Speaker 05

  to bring it to poorer parts of the world that don't have good educational systems. So I think there's a lot of upside there, too.

• 57:48Casey Newton?

  Another thing that kids are doing with AI is chatting a lot with digital companions. Google DeepMind doesn't make any of these companions yet. Some of what I've seen so far seems pretty worrying. It seems pretty easy to create a chatbot 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 market for AI companions and whether you think I might want to build this someday or I'm gonna leave that to other people.

• 58:18Speaker 05

  Yeah, I think we've got to be very careful as we as we start entering that domain and that's why we we haven't yet and we've been very thoughtful about that. My view on this is more through the lens of 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 hope that they're going to enrich your lives by giving you incredible recommendations, for example, on all sorts of amazing things that you didn't realize you would enjoy, you know, sort of delight you with surprising things. So I think these are the ways I'm hoping that these systems will go.

• 59:01Speaker 05

  And actually, on the positive side, I feel like 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 assistant that works for you, you know, as an individual, it's yours, and it protects your attention from being assaulted by other algorithms that want your attention, which is actually nothing to do with AI. 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 want you want to do. That's how I would want these systems to be useful to people.

• 59:45Casey Newton?

  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.

• 59:53Speaker 05

  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 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. I think that would be wonderful.

• 1:00:29Kevin Roose?

  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 going to feel about this, but we can ask them when the time comes. Some people are starting to look at the job market, especially for recent college graduates and worry that there were already starting to see signs of AI powered job loss. Anecdotally, I talked to young people who, you know, a couple of 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.

• 1:01:10Kevin Roose?

  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?

• 1:01:18Speaker 05

  I haven't looked at that. You know, I don't know. I haven't seen the studies on that, but you know, maybe it's starting to appear now. I didn't think there's any hard numbers on that yet. At least I haven't seen it. I think for now, I mostly see these as tools that are augmenting what you can do and what you can achieve. I think the next era, I mean, maybe after AGI, things will be different again, but over the next five to 10 years. I think we're going to find what normally happens with with big sort of new technology shifts, which is that some jobs get disrupted. But then new, 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. 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.

• 1:02:01Speaker 05

  That's part of this sort of more societal change that we need to get ready for.

• 1:02:05Kevin Roose?

  I mean, I think the the tension there is that you're right, these tools do give people so much more leverage. 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 company in their previous job that had seventy five 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 seventy five people. And so I guess the question, I'd be curious to get your view on is what are the other seventy four people supposed to do?

• 1:02:37Speaker 05

  Well, look, I think these tools are going to unlock the ability to create things much more quickly. So, you know, 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 that was possible before. So let's take programming, for example. 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 movie ideas.

• 1:03:22Speaker 05

  So i- in theory, it should be those other seventy 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. 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 design sensibility, you know, could become increasingly important.

• 1:03:47Casey Newton?

  Do you think you'll hire as many engineers next year as you hire this year?

• 1:03:51Speaker 05

  I think so, yeah, that's, that's, I mean, there's no plan to, to hire less, but, you know, we, again, we have to see how fast the, the coding agents improve. Today, they, they're not, you know, they can't do things on their own. They're just helpful for, for the best, you know, for the best human coders.

• 1:04:08Casey Newton?

  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 things. 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?

• 1:04:53Speaker 05

  Well, I think we're, we're, we're working on those things. They take time to develop. I think the, a kind of universal assistant would be one of those things, if it was kind of really yours and you're working for you effectively, so technology that works for you. I think that this is what economists and other experts should be working on, is, does everyone have, manage a, 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? You know, does that become part of the normal job process? I could imagine that in the next four, 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 helped

• 1:05:37Speaker 05

  by AI, 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 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, something like that, I think is going to probably be, you know, good and necessary, but obviously there's a lot of complications that they need to be thought through. And then in between, there's this transition period, you know, between now and whenever we have 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?

• 1:06:20Speaker 05

  Well, I mean, I think the parts of the economy where, you know, involves human to human interaction and emotion and those things, I think, uh, you know, will probably be the hardest things for AI to do. So, you know,

• 1:06:34Kevin Roose?

  But aren't people already doing AI therapy and talking with chatbots for things that they might have paid someone, you know, a hundred dollars an hour for?

• 1:06:42Speaker 05

  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 of those things are really going on in terms of actually affecting the real economy and rather than just sort of more toy things. And I don't think the AI systems are like capable of doing that properly yet. But just the kind of emotional connection that we get from talking to each other and doing things in nature in the real world. I don't think that AI can really replicate all of those things. So if you lead hikes, it'd be a good job. Yeah, yeah. Want to climb Everest.

• 1:07:15Kevin Roose?

  Yeah, my intuition on this is that it's going to be some heavily regulated industry where there'll just be like a massive pushback on the use of AI to displace labor or or take people's jobs like healthcare or or education or something like that. But you think it's going to be an easier lift in those heavily regulated industries?

• 1:07:33Speaker 05

  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 the positives of that, for example, you know, curing all diseases or or, you know, I think there's a lot of finding new energy sources. So I think these things would be clearly very beneficial for society and I think we need to for our other big challenges. It's not like there's no challenges in society other than AI, but I think AI can be a solution to a lot of those other challenges, be that energy resource constraints, aging, disease, you know, you name it, and water access, etc. A ton of problems facing us today, climate. I think AI can potentially help with all of those. And I agree with you, society will need to decide what it wants to use

• 1:08:16Speaker 05

  these these technologies for. But then, you know, what's also changing is that what we discussed earlier with products is the technology is going to continue advancing and that will open up new possibilities like a kind of radical abundance, space travel, these things, which are a little bit out of scope today unless you read a lot of sci fi, but I think rapidly becoming real.

• 1:08:36Kevin Roose?

  During the Industrial Revolution, there were lots of people who embraced new technologies, moved from farms to cities to work in the new factories, were sort of early adopters on that curve. 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 big do you think it'll be?

• 1:09:04Speaker 05

  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 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'd fully expect that's 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 spacefaring and more, you know, kind of maximum human flourishing. But I think that will be, that will be exactly some of the things that a lot of us will choose to do. And but have time and the space and the resources to do it.

• 1:09:36Casey Newton?

  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?

• 1:09:48Speaker 05

  I don't think AI is good enough yet to impinge on any of those sorts of areas 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 fine great, like breaking 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. But, you know, we'll see. I haven't got any examples of what you suggested yet, but maybe as AI gets more powerful, there will be.

• 1:10:16Kevin Roose?

  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, like coding.

• 1:10:31Speaker 05

  Yes.

• 1:10:32Kevin Roose?

  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, ooh, that stings a little bit. Have you had any experiences like that?

• 1:10:41Speaker 05

  Of course. So maybe, maybe one reason it doesn't sting me so much is I've had that experience when I was very young with chess. So you know chess was gonna 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 the computers were gonna be much more powerful than the world champion forever after that. And so, but yeah, I still enjoy playing chess. 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 I think that'll be the same with robotic football and all of these other things.

• 1:11:22Speaker 05

  So, 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 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 if you knew it was written by an AI, at least as far as I can see for now.

• 1:11:42Casey Newton?

  You mentioned robotic football. Is that a real thing? We're not sports fans, so I just wanna make sure I haven't missed something.

• 1:11:48Speaker 05

  I was meaning soccer. Yeah, no, yeah, no, no, I don't know. I think there are there are RoboCup sort of soccer type little robots trying to kick balls and things. I'm not sure how serious it is, but there is a there is a field of robotic football.

• 1:12:01Casey Newton?

  You you mentioned the, you know, sometimes a novel written by a robot might not feel like it'd 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. You know what I mean?

• 1:12:18Speaker 05

  Exactly. And that's that's what I was, you know, that's why we work with great artists like Darren Aronofsky and Shankar on the music, is 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 at the moment with the voices. Actually, I didn't realize how big a difference audio is gonna 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 doesn't, he brings the storytelling. It's not got deep storytelling like a master filmmaker will do or a master novelist, you know, the top of their game. And it might never do, right?

• 1:13:01Speaker 05

  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 great pieces of art, you know, art too. When I see a Van Gogh or a Rothko or, you know, why does that touch your, you know, I was spilled, you know, sort of, you know, hairs going on the back of my spine because of, I remember, you know, and you know about what they went through and the struggle to produce that, right? And every brush stroke of Van Gogh's brushstrokes, his sort of 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 so I think that is the piece that, at least as far as I can see, out to five,

• 1:13:46Speaker 05

  10 years, the top human creators will always be bringing. And that's why we've done all of our tools, VO, Lyria, in collaboration with top creative artists.

• 1:13:57Kevin Roose?

  The new Pope, Pope Leo, is reportedly interested in AGI. I don't know if he's AGI-pilled or not, but that's something that he's spoken about before. 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?

• 1:14:17Speaker 05

  I think that potentially could be the case. And I actually did speak to the last Pope about that and the Vatican's been interested, but even prior to this Pope, haven't spoken to him yet. But on these matters, how does AI and religion and technology in general and religion interact? And what's interesting about the Catholic Church is, and I'm a member of the Pontifical Academy of Sciences, is they've always had, which is strange for a religious body, a scientific arm, you know, which they like to always say Galileo was the founder of. And, and, although it's interesting. Didn't go great for him. Turned out great. So, but then, but then, but then. And they really 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.

• 1:15:02Speaker 05

  And, and that's partly why I agreed to join it is because it's a fully scientific body. And it's very interesting that, and I was fascinated, they've been interested in this for 10 plus years. So they, they were on, you know, on this early in terms of like how from a philosophical point, I think, this technology will be. And I, and I actually think we need more of that type of thinking and work from, from philosophers and theologians. Actually would be really, really good. So I hope the new pope is genuinely interested.

• 1:15:32Kevin Roose?

  We'll close on a question that 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?

• 1:15:44Speaker 05

  Oh, wow.

• 1:15:47Speaker 05

  Well, I don't, I mean, you know,

• 1:15:53Speaker 05

  gosh, I haven't thought about that, but I mean, I think any age where you can live to see it is a good age. Because I think we are going to make some great strides with things like, you know, medicine. And so I think it's going to be incredible journey. 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, if young is always better. Yeah. I mean, in general, young is always better.

• 1:16:20Kevin Roose?

  All right, Demis Sabas, thanks so much for coming.

• 1:16:23Speaker 05

  Thank you very much.

• 1:16:43Speaker 00

  Support for this podcast comes from Dell Technologies. It's time to put AI to work. Is your business ready? The challenge isn't getting started with AI. It's scaling beyond pilots while maintaining control of your data, governance, infrastructure and costs. Dell AI Factory provides the integrated foundation you need to scale AI securely and efficiently with one trusted partner. Dell, AI on your terms. From idea to impact. Learn more at dell.com/yourwaytoAI. That's dell.com/yourwaytoAI.

• 1:17:17Speaker 03

  Your sales team is at risk of missing quota. Don't panic, just ask Rippling AI. Built on your real time people and business data, Rippling AI can pull metrics from Rippling and your CRM into a meeting-ready dashboard showing quota attainment, headcount trajectory, and revenue to quota by region. In seconds, you'll see what's behind your quota risk and fix it before it's missed. Head to rippling.ai/hardfork to get the only AI built to give you full visibility and take complex actions across your whole business. That's R-I-P-P-L-I-N-G dot AI slash hard fork. Sign up today.

• 1:17:51Speaker 06

  It's time to innovate at the speed of AI without compromising control. That's OutSystems. Take a look at what happened with one global retailer during a period of massive expansion. They used OutSystems to build AI agents to automate invoicing and translation processes, shifting 95% of their daily store operations to a new digital app, saving thousands of hours of manual work. OutSystems gives you the flexibility to deploy how you want and the governance to do it safely. Own your agentic future. Learn more at outsystems.com/nyt. That's outsystems.com/nyt.

• 1:18:28Casey Newton?

  Hard Fork is produced by Whitney Jones and Rachel Coen. We're edited this week by Matt Collette. We're fact-checked by Anna Alvarado. Today's show was engineered by Alyssa Moxley. Original music by Pat McCusker, Rowan Nemisto, and Dan Powell. Our executive producer is Jen Poyant. Video production by Chris Schott, Sawyer Roque, Pat Gunther, and Anne Roth. You can watch this whole episode on YouTube at youtube.com/hardfork. Special thanks to Paula Schuman, Puywing Tam, Zaya Hadad, and Jeffrey Miranda. You can email us at [email protected]. What would you tell the Pope about AI?

• 1:19:24Speaker 06

  Building and scaling AI today can feel pretty chaotic, mostly because teams are juggling too many fragmented tools at once. That's where OutSystems comes in. OutSystems gives you one unified agentic systems platform to manage your entire portfolio of agents, apps, and workflows. You get to innovate at the speed of AI, but with the security, governance, and control your enterprise actually needs. And the results are already there. A major manufacturer used OutSystems to build a multi-agent system that analyzes blueprints, and a global bank cut their development time by 60%. Own your agentic future today at outsystems.com/nyt.