The chatbot becomes the teacher

24:37But it turns out there's just all these places where you've got all your company documents and you want to create an FAQ for new employees. Like, no one is going to win a Nobel Prize for literature for creating that document. And if you can get a first draft of that document in 45 seconds instead of in four hours, like, that's a win, right? That is good news. And so there are all these different workflows that are out there where there's massive information needs to be kind of, like, filtered in some way and turned into something else. All right. We've got to take a break, and then we will be back with more from Stephen Johnson.

25:16We'll be right back.

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26:20Find your next great hire on LinkedIn. Post your job for free at linkedin.com slash track. That's linkedin.com slash track to post your job for free. Terms and conditions apply. All right, we're back. One of the things I've been tracking about Notebook for a while is how it approaches things like hallucinations and citations. because this isn't like a silly chatbot, you know? It's a research tool. It's not silly or acceptable or just a signal that it's early if it's wrong. It's a problem if it's wrong. It's useless, frankly, if it's wrong. So I asked even how they're trying to solve that very real problem in AI.

27:01So before we get too far away from it, I do want to talk about the accuracy and the citations bit of it because you signed yourself up for a pretty high bar on both of those things. both by virtue of like the people who are going to use your product and just what the product is, right? Like you don't really get to have the warning at the bottom of everything that says, this thing makes some information up. Like its entire purpose is not to make information up. How do you, what have you guys done? I know Notebook was an early experiment in RAG, which is a way of basically winnowing down some of these systems.

27:36But like what have you guys done differently at Notebook to try and solve that? And I'm curious both on the underlying tech side and on the user experience side. Yeah, well, we tried. I kept calling it source grounding because I think that is a better name than RAG. I don't know, man. We all know GPT now, so I've given up. We're all doing the acronyms now. So, you know, I think part of it was the fact that we were doing it from the beginning, like that it started with that. It wasn't like, oh, let's build a chat model and, oh, shoot, we need to be able to ground it in other documents. It was from the beginning we were doing that.

28:13And so we just had a lot of time to iterate and explore. The Gemini models are really good at source grounding. There's a lot of training sets. We contributed a bunch of them to just, given this document, answer questions factually based on the information in that document. So we inherited like a great tool that, you know, we did very little, you know, kudos to the Gemini team for building a model that is much more faithful to source material you give it. But we built it also, you know, this is one of these things where it's like it's underlying model plus the UI has always been like our mantra from the beginning.

28:55And, you know, both things are required. And so one of the key things that we've had pretty much from the beginning is that you can always read your sources in the app. And then with the release in June, we switched it over so that you now have inline citations to everything. So anything the model says has a little link. You can read the original passage if you hover over it that shows you the source material for that. And you can click on it and you can go read the source in the app. Do you think that's enough? we've talked about so so one of the things you can do right now actually with notebook that we want to actually turn into a proper feature but you can do right now is you can upload a bunch of you know kind of source material factual source material and then you can upload the article you're writing for instance and you can say fact check this article based on these sources and suggest improvements oh that's clever that's a good idea and yeah it's it's amazing and it will go through and be like, well, this is correct.

29:53This is potentially wrong. It will suggest, and it'll have links to everything. And so to me, if I felt that the model were just hallucinating wildly in its responses, then I do not believe that, you know, just providing citations and the ability to kind of fact check manually and go back and see the original passage would be enough. But we've, you know, we've been sitting there banging away at like quality reviews like constantly for the last year and a half. Like we have a poll, you know, sheets and sheets and sheets of sample questions and sample documents. And we can just see that accuracy rate going up, you know, dramatically, particularly with these latest models.

30:34And so right now, I feel like we're in a pretty good place. I rarely, I honestly, I rarely see Notebook.LM just wildly hallucinate something. I mean, one thing that's really important, people may not know this, I take this for granted because I've been living with this product. If you load in a bunch of sources about the history of NASA and then ask a question about Taylor Swift, in general, Notebook LM will say, I'm sorry, your sources don't discuss Taylor Swift, so I can't answer this question. And obviously, the model knows a lot about Taylor Swift. It's probably a pretty big fan of Taylor Swift.

31:05But it's been specifically, you know, instructed to not answer questions that are outside the source material. I mean, to a fault, I think sometimes, you know, one would like to bring in some outside knowledge, you know, and trust but verify with that. But we've erred on the side of, like, stick to the facts in these documents. And so with the increase in accuracy and with the UI of citations, there's more things we could do. We could make that fact-checking feature, you know, double-check this kind of as something. But I feel pretty good about where we are in terms of the quality side of that.

31:36where we are kind of state-of-the-art is upload many, many documents, ask a complex question that involves multiple variables drawn across multiple documents, get a deep, long answer with citations, follow those citations to read in the original text. I think that Notebook LM kind of does that flow as well as anybody right now. I feel like you just described like a personal Wikipedia. And I mean that as a compliment, Like the thing that Wikipedia is best at is just being like a starting point to go learn about something on the Internet, right? Like you open a Wikipedia page, you go click on all the references at the bottom, and then you go read those references and you're off and running.

32:19And I feel like what you just described is like I can shortcut that with any process of any single thing that I want to learn more about. I just dump it in and I'm like, what's going on here? And it'll just be like, here's some stuff. I have so many things to say to that, David. You're going to have to give me 20 minutes. Okay, so one thing is I'm in the process of thinking about what I'm going to write the next book on. And so I created the – this is just kind of second nature workflow for me now, but, you know, wouldn't have occurred to me a year ago. So I created a new notebook called The Next Book.

32:49And whenever there's an idea that kind of comes to my mind or an article I read or something like that, I dump it into that notebook. It's the new Spark file. It's the new Spark file. Wow. But it's focused on this project of, like, what should the next book be? And so the other day, I, you know, late night, I have this kind of thing I do late night because I have no life. My children have all gone to college and I have nothing to do with sit around and think about it. You just talk to Bart. I talk to Bart. So I was like, I wonder, you know, has there been a good book written about the anti-nuclear movements, anti-nuclear power movements of the 60s and 70s?

33:20Because that's an interesting case where we like stopped a technology partially in its tracks and maybe made a mistake. And, you know, how do we interpret that? and so i just went and grabbed like two wikipedia pages in the past i would have like you know started by reading through the wikipedia pages but i brought it into notebook and i was like i'm stephen johnson i'm writing thinking about writing a book you know in the mode of my other books like infernal machine and ghost map potentially about the anti-nuclear powers take a look at these like what do you think of the are there any interesting storylines that there would be good starting places like what you know what would be good there and it just does it.

33:55Like it, you know, goes through and it's like, well, you can focus on this period. This figure is kind of interesting, whatever. And then I went and read all the, you know, I'm going to read the material, but as a, as a first glance, like inroads the material, it's amazing for that kind of exploration. The experience of navigating through a book, through a conversational interface is really interesting. And it's one of these things where like, you know, until now, if you wanted to explore the ideas in an author's work through a conversation, you could only do that by finding the author in person or finding a scholar or a tutor who's an expert in the author's ideas.

34:35And like, that was it. That was not, there was no other way to do it. But now you can load, you know, a book in and you can start with like, I'm interested in this, tell me about that. and then slowly read the book in a nonlinear way through a conversational interface, kind of dipping in and out of the kind of original passages, which is terrible if it's a novel or terrible if it's a straight linear kind of history book, like I sometimes write. But if it's an advice book or a book of ideas, there are lots of books that I think could be explored in that way that just weren't possible before. Does it feel like cheating to do that?

35:09Like for you as an author, you have worked through these books, you have done the hard work, You have stayed up late at night. I'm sure you've like woken up in the middle of the night with a book idea. Does sitting down and asking a computer what your next book should be feel like cheating? I think if I were literally like, hey, what should my next book be? One, I don't think it would. I mean, right now, in a way, it's easy to answer this question right now because it wouldn't be good enough. Like it wouldn't generate like what? Tell me what my book should be and now start writing it like it just wouldn't be able to do it.

35:36OK. Current trend lines continue. I was going to say, give it a minute. Come back to me in two years. We'll see. But what I have to do is like, I'm not using it that way. I'm using it like these very like kind of targeted queries. Like, you know, I was, I actually was using the NASA notebook because I was interested in like, you know, maybe there's something to be done about the Apollo 13 fire. And so then it was kind of like, okay, look, I need to know what I need to read because it's a million pages, a million words of transcripts. I actually don't need to read the whole thing. What I want to read are the sections that are relevant to the Apollo 13 fire.

36:11And so I just went to notebook and said, tell me what I should read, you know, and it was like, you should start here, you should read there. And I can click immediately in there and read through that. And so to me, I'm using it as a way of accelerating the process of discovery that would just have been painful to do before. But I'm making just as many unplanned serendipitous discoveries along the way. I mean, I think the criticism is like, well, if the model serves it up to you so quickly, even if you end up writing the book, you've missed the surprising thing that you would have never found.

36:45You would have only found it by reading through it in an incredibly linear way. And I think that that's just not true. It's constantly surfacing things that I hadn't thought of and making connections that I hadn't thought of. And so it's helping me understand the material more deeply. where it gets complicated, and you and I have talked about this before. The way I think about it is it's a tool that helps you understand things. If you are genuinely interested in understanding things and having that understanding be in your brain, it's a huge net positive. 100%. Like if you go into it with good intentions, it is an amazing tool for thought.

37:24It helps you have richer, more complex ideas, understand material better. If you are not interested in understanding things, but rather interested in creating the illusion that you understand things and just want to bluff your way through life without ever actually understanding anything, but creating outputs that make it look like you understand things. It potentially would help you do that as well. And the question is, the question is, like, how often is that useful as a strategy in the world? And to me, like, if you go into work and you're like, I've got this great hack. I never read any of the emails from my boss.

37:57I just, like, put it into the model and then I output it and think. Eventually, your boss will, like, have a conversation with you and he'll say, Stephen, you don't understand anything. You have not learned anything and you'll get fired, right? It just doesn't – it's not a good long-term strategy. The one place where it's tricky is school, where there is potentially a strange incentive to bluff your way through something. We think that, you know, we're seeing a lot of adoption of Notebook Elements, 18 plus currently. So, it's not usable by high schoolers, but we're seeing a lot of college and higher ed adoption of it.

38:29it's probably our biggest community so far. We are very excited about that use case, but we're also like, you know, we've done a lot of things to, you know, ensure that, you know, it's hard to use it to bypass understanding. Like it's constantly steering you back towards the original text. You're always kind of being pointed back towards that source material. It's always there in the UI with you. So, and there are more things we can do on that front. But that's the line that I think is like, you know, if you go into this with good intentions, I don't feel concerned about using the tool in this way.

39:02Okay, I buy that. And it feels like part of the line there exists in kind of what you let people make with what is coming out of it. And I wonder, like, we've talked a lot about kind of the inputs and the processing and the understanding, and it's not, you're sort of one, you know, publish this answer on Amazon as a self-published ebook with your name on it, away from this being a very different conversation. But my sense is you're deliberately or not not investing a ton in the like write me a research paper from these six documents kind of use case. Yeah. If you look at the notebook guides, like one of them is called study guide, right?

39:40It creates a study guide. It doesn't replace your work. It helps you actually like, you know, it gives you a set of review questions. It creates a little multiple choice quiz. It creates like essay suggest questions. It creates a glossary of key terms. so you know that's what we're trying to do inside of the product we're steering everybody towards that kind of like help me understand mode with the idea that you know the people have different ways of understanding there are different ways that people have like information sticks with them through different forms and you know our job is to kind of do that and that's it you know it turns out as writing about this a little bit this week that you know translation and summarization is something that the models have always done quite well, like from the beginning, like kind of like the first deep learning breakthrough that really made a difference to consumers was really like translate, Google Translate and others.

40:28It was basically like take this set of tokens that are in this language and turn it into the set of tokens in this language. And they're really good at summarizing in all of its different forms. And so we've kind of like embraced that because embrace the things that the models do well It's generally a good strategy. All right. We have to take one more break and then we will be back with the rest of my conversation with Steven Johnson. We'll be right back.

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42:11We're back. Okay, we have waited long enough to talk about audio overviews, those wild AI-generated podcasts Notebook has been working on. So that is what I asked Stephen about next. So, and speaking of that, actually, this is a good time to talk about audio overviews in the sense of different ways that people learn. Would never in a million years have guessed this was going to be the next big feature to come out of Notebook.lm. So rewind the history a little bit here and tell me, where did this come from? Yeah, it's a really interesting story. So let's just describe it briefly um it has been all over uh the twitter sphere and other places i still call it twitter sorry it's okay i do too since we this is just uh a few days ago that it launched so basically it's an extension of the notebook guide kind of panel and instead of taking your sources that you've uploaded and turning them into a uh a briefing doc a text-based briefing doc or an FAQ, it turns them into a roughly 10-minute long podcast conversation between two AI hosts who discuss the material, tell stories, share interesting facts, banter in a playful way with remarkable human-like intonation and all this stuff.

43:23And so it's basically the idea is like some people like to learn through reading a briefing doc. Some people like to learn and remember better when they hear information conveyed to an interesting, stimulating, engaging conversation between two people. And you couldn't create that artificially before, now you can. So we've added it to the suite of tools. That feels like it causes a thousand new interesting things. Like on the one hand, it's not the thing that you were describing earlier in this conversation, which is something that is not full of personality. It doesn't say I a lot. It's not, this is not a tool anymore.

44:01This is two slightly weird people hanging out in my ears for 10 minutes. And they are like, whatever dial you have that says like, make them say puns and try and be kind of cringely. It's like that dial is at 14 out of 10 on most of the ones that I've listened to. And I kind of get why, right? Like if you take the it should have no personality, it should just be very straightforward approach that makes sense on showing me a bunch of text and apply it to a podcast, that's a bad podcast. But it does feel kind of incongruous with a lot of the stuff you've been saying is like core to what you've been trying to do well.

44:38I mean, it's such a great question. So let's just play. I want you to play this in case people haven't heard it. Okay, so get this. Picture a world where the hottest tech isn't microchips or the Internet, but dynamite. Yeah, dynamite. And it's not just blowing stuff up. Think skyscrapers, railroads, massive engineering projects. But also, you guessed it, assassination attempts, terrorist plots, all that volatile stuff. It's a wild period, right? Wild is an understatement. And that's where our deep dive today comes in. We're cracking open Stephen Johnson's The Infernal Machine, a book that digs into this explosive era where dynamite, anarchists, and the birth of, believe it or not, modern data collection all collided.

45:19Yeah. Yeah, get ready to have your mind blown because the connections he makes are mind-blowing. Absolutely. It's just everything after, yeah, dynamite just kills me. I love it. You know, one of the things is they are enthusiastic. And so people have been uploading their CVs. And it's just like if they were feeling down about yourself, they're like, oh, John Smith. They just make you a promo podcast. I love that. Um, so I think when we first talked about what was then project tailwind, um, for the verge, like, you know, rise and I were talking about how, you know, we, we don't, the model doesn't have a, a, a persona really.

46:03Like it doesn't use the subjective eye, uh, in general. It's not trying to be your friend. Uh, it's just trying to get you the information you need. And that was, that was kind of the house style that we felt was appropriate for what we were trying to build. But if you want to have a conversational audio format, there is no way to do that without their having a sense of like human personas. It just won't work. No one wants to listen to two series talk to each other, right? That is not, I'm sure. There's a lot of that on the internet and it's not good. The new series is better, but I'm sure, but the old series, right?

46:36You know, you want to listen to two robots talking to each other. And initially it was a, another team inside of Labs that had built this prototype that was like, you know, create a document. There are actually a couple of projects like this at Google. There's another wonderful one called Illuminate that has a slightly more like kind of scholarly tone to it. But it was just one of these things where like it became possible to do this. And so, you know, some folks were exploring this and it was a very good demo. Like we've seen from the last few days with audio overviews, like people are impressed.

47:08People want to share it. But it didn't kind of have a place to live. And so kind of right before IO, actually, there was this idea of like, well, maybe we could actually put it into, you know, would it make sense inside of Notebook? And, you know, we were just rolling out the Notebook guides. and when I first heard it that the the podcast stuff was like actually even bigger like the host had names and things like that and okay and so you know but I like it definitely made you know I think Verizon I immediately saw that it was a continuation of the philosophy of the guides that you know we'll take whatever you give us and we will turn it into the format that makes it easiest for you to understand and we know we know from you know the success of podcasts in general that people do like to learn that way.

47:56I also like to travel with audio and drive and walk around the city listening to it. So we knew there were a lot of reasons why audio conversations would make sense. Are you starting to have similar conversations about things like video? I can just imagine like starting to think about what Notebook LM looks like when you're trying to be like TikTok native and YouTube native and Instagram Reels native. It's like, it's just what a wild kind of road to end up heading down in terms of like, how do we communicate to people where they are, but sort of in this house style? This is one of the reasons why it's good that I am like 56-year-old gray-haired Steven is not actually driving things.

48:33Because I would be terrible at helping with that. But yes, I think that could be part of our future. Pretty fun. One of the things I think is really interesting about it in the response, it takes like four or five minutes to sometimes three to five minutes to generate one of these. And that's because there is a really complex series of kind of, you know, compute and inference that's going on. Basically, you can think about it kind of in an editorial way, right? It's kind of drafting a version of it, and then it's filling in the details of the script, and then it's revising the script based on the overall goals.

49:06Like, there are multiple cycles of basically an edit cycle. And that takes time. And then crucially, there's a stage where, my favorite new word is where disfluencies are added, right? So that it deliberately makes it kind of noisier and more like the way that people talk when they're in conversation, where they overlap and they have partial phrases, they complete each other's thoughts and stuff like that. All the things that make a traditional transcript hard to read if it's not been cleaned up. If you don't have those things in, it's on the wrong side of the uncanny valley, or it's right in the middle of the uncanny valley, I guess.

49:39you can debate and obviously like we're gonna you can imagine we would introduce abilities to steer in different directions and dial up dial up down things or focus on different topics but the basic structure of kind of you know doing this long edit cycle and then humanizing yeah the thing i called wrong on this one uh was that i thought audio overviews would be one of those things that people were like, oh, well, this is silly, right? It's like every time a new image generator comes out, a bunch of people make wild stuff with it for 48 hours and then kind of move on. And we don't talk about it anymore until some new image generator comes out.

50:18This one doesn't seem to have gone like that. And I think it somewhere in between the like, is it remarkable that it's possible versus is this actually useful? I think it is more in the this is actually useful camp for people already than I expected it to be. Like, it's definitely remarkable that it is possible. And you showed a sort of sincerely new thing you can do with AI. And whenever that happens, people get really excited on Twitter, right? Like that's just one way to get people really riled up on Twitter for 12 hours. But I think there was, you overlapped the, this actually does something for people side of things more than I expected when I first heard, you can make an AI podcast out of your notes.

50:57Well, I want to thank you, one, for changing your mind. But two, you remember how Jobs used to have that kind of apples, the intersection of liberal arts and technology or whatever? Like what you just described, the intersection of what is newly possible and what is genuinely useful, like that's what we're trying to do at Notebook. And that's kind of what Labs is trying to do, right? Like, it's just like, what can we do? And what would actually be like, you know, what was unthinkable six months ago? And then what would it be? And, you know, part of it is, you know, I think in some cases people are using it like, I want to study and this is a better way for me to, you know, take my documents and, you know, just learn or learn on the go.

51:36The other thing is like creating things that are effectively podcasts where no podcast would possibly ever exist in the real world, right? You know, so people are like, here's this very obscure niche topic that no one, like the economics of the podcast business will never support a podcast. I want the podcast on my D &D game that I play with my friends. Yeah, yeah, yeah. No, totally. And, and, or it's like, you know, we've seen people doing like, again, thinking about notebook element in a kind of a team context at work, like, okay, like the weekend review, like give it all your documents. I always say to people that one of the best ways to get to know Notebook is to, if you're a Google Docs and Slides Drive user, just grab, create a new Notebook and grab the last, like, 20 Docs that you've created.

52:22And just create a Notebook with that. Oh, that's funny. And then just ask it questions. And it's the ability to kind of grasp, like, what you're working on and the issues and things like that. Do that and create an audio overview. And it's like, this is what Stephen was working on for the past week. Like, you know, here's two happy and enthusiastic people to discuss, like, the things you've been working on. And sharing that with your team, like, it's pretty nice. Like, that's a great way to, like, look back on the week and think about what you've been working on. And, you know, you can imagine other ways to explore that as well.

52:53All right. A couple more questions. I know I've kept you a long time, but I'll leave you on here in a minute. I can talk to you about this forever. Yeah, you're even better than talking to Bard. This is great. Anytime. I'm here for you. I don't like skiing either. Do you think there's a big mainstream use case for something like Notebook LM? Obviously, there are people and industries and jobs and schools. I can imagine a bunch of people for whom this is useful. Do you think there is an everyday, everybody use case for something like this? Everybody, everyday? I don't know. Let me put this in a slightly different context.

53:29Google is a company that is famous for making things that a billion people use. Are there a billion notebook LM users out there? I think if you define it as there is an AI that is an expert in the information that is really relevant to you, that you've curated, and that you can engage with in different forms, whether it's chat or listening to an audio overview, and you can basically cultivate your own personal AI with a lot of information. Maybe it's all your journals. Maybe it's, you know, your company's history, whatever it is. And just by, like, dragging and dropping files in there, like, it develops this kind of knowledge of everything that has happened to you or your organization and is able to kind of deliver advice or help you make decisions or just recall a fact that you're trying to remember and create these new documents.

54:24Is that a big, like, will maybe a billion people, like, be doing some variation of that in five years? I think that's pretty plausible because there's so many different things you could do in that kind of context. You know, it could be like a writer working on a book, but it could be an executive trying to make a complicated decision. Or it could just be somebody trying to remember things that happened in their lives. And it's like just keeping a journal only you've got an AI that's helping you do it. So I think there's a big market for it. Okay. You know what I've been thinking about as you were saying that there's this app called MyMind.

54:57Have you ever seen it? I've seen it a little bit, yeah. It's a great app. You should play with it. I think you of all people would enjoy some of the AI stuff they're doing. But I've started using that app and I've gotten in the habit of basically every time I encounter something I like, I just put it there. Like if I love podcasts or a video or a thing I read or a photo I take or a quote that I see, I'm just pouring it all in there with no organization, no nothing. And there's nothing particularly special about that app. It's just like, it's pretty to look at. So I like using it. And already just the thing where you open it up and it has a bunch of modes where you can just type in like movies and it'll show you all the movies and movie adjacent stuff that you've been saving.

55:35And it's like there's something really powerful about like here is just a compendium of stuff that I find interesting. And there's just enough manual work in that that I think it's it's there's something to solve in the like, how do you collect that data from people in a way that is both sort of easy and frictionless, but also not like gross and privacy invading. That is like the eternal Google question. I have high hopes that somebody will figure it out eventually. That is a bias that I've always had, probably to a fault that, you know, like we don't, we really don't have, we don't even have kind of folders for your sources or your notes or things like that, like basic kind of low level things that we will no doubt add, but we've been focused on these kind of cooler features.

56:17But part of me is like the beauty, like the complex systems of organization where you're tagging things and putting them into the right folder and stuff like that are not as necessary now because the AI does that and makes the connections for you and finds the thing that you're looking for. So just have one place, make it easy to grab as much stuff from as many different places and just dump it into a notebook. And then we'll do the magic in figuring out the connections or finding the information you want after that. again is you said it correctly before like the closest thing to this before was wikipedia like i start here in this wikipedia page about elephants and then i follow it following these interesting paths but a dialogue is just an even better way to do that um and if it's trustworthy and so the mode that i really love is and and again this is only possible with conversation history in long context is to go on one of those like exploratory conversations through an idea and then at the end you're like okay this has been great will you format that all as a single document that just has kind of key takeaways and insights from it so i can just capture it for later because i don't necessarily want to read the whole conversation again but i want to i want to get the like great pieces from it and boom it does it and you save that and that's your kind of record of what you just did.

57:37Like that, I mean, like that's a beautiful way to walk through information space and full of surprise and unexpected turns and discovery. So that feels like a keeper. What's the next step in getting better at that? Like one thing that it seems to me that we need pretty badly in that way of thinking, which I love, like I love the idea of just like, I'm going to sit here and spend three hours learning about something. And then at the end, you're going to deliver me like a handy summary of everything that I've learned. Amazing. That's the dream. It does trick me that one thing we desperately need all of these tools to get better at to pull that off is multimedia, right?

58:12Like, I want to know at the end of it, like, go listen to these three podcasts and this YouTuber, you're going to love him. Go check it out. And this person on Instagram is doing that. There's like, the internet is such a messy place in the best way now that it feels like that's one thing. And I know a lot of folks are working on that. Are there other things that you look at and, you know, you're working on this stuff too. Is there a next kind of turn coming that's going to make all that stuff even better? Right now, Notebook's ability to help you discover information to put in your notebook is exactly zero.

58:45It does nothing in that. You are on your own. You need to supply the sources. We will not help you discover anything across the internet in any form. It turns out, just as a coincidence that Google is really good at that stuff. Like I did not know. I learned when I got there, they have this search thing that is apparently a lot of people use. So it's obviously like a place where, you know, I would love it. I want it to be source grounded, but I would love you to stay in Notebook to be able to find things. And, you know, we would be really interested in exploring that. I think you'll see that in 2025 for sure.

59:24All right, that is it for the Vergecast today. Thanks again to Steven for being on the show. And thank you as always for listening. There's lots more on everything we talked about at theverge.com. I'll put some links in the show notes to some of the stuff Steven has written about this too. His new book is really great. Go to theverge.com. Lots of show notes, lots of notebook coverage, all kinds of good stuff. As always, if you have thoughts, questions, feelings, or other ideas for AI-generated podcasts, you can always email us at vergecast at theverge.com or call the hotline 866-VERGE11. We love hearing from you.

59:55want to hear all your thoughts. Send them all. Can't wait to hear it. This show is produced by Liam James, Will Poore, and Eric Gomez. VergeCast is a Verge production and part of the Vox Media Podcast Network. We'll be back with our regularly scheduled programming on Tuesday and Friday this week. We have a lot of headsets in particular to talk about this week. So get ready. We'll see you then. Rock and roll.

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