A Conversation with NotebookLM's Founding Engineer

Today on the AI Daily Brief, a.

Conversation with Adam Bignell, the founding engineer.

Of Google's Notebook lm.

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord.

Link in our show. Notes.

Hello, friends. Welcome back to the AI Daily Brief. I thought since we've had two heavy.

Days of deep seek and contemporary news.

We might all want a little bit of a breather. And for that, I give you something a little bit different than we normally do. What I'm about to share is a convers with Adam Bignell. Adam, as you'll learn, is a founding engineer of Google's Notebook lm, but he's also a writer, a musician, and a lot of our conversation is about the particular set of quirkiness that made NotebookLM what it is. Now, for those of you regular listeners, you will know that I called NotebookLM the most important gen AI product of last year. I think it sits at that perfect intersection of not only doing old things better, but also opening up totally new opportunities. In the conversation we learn about how Notebook came to be, some of the most interesting use cases that Adam has found and, and get some hints around where it might be going in the future.

All right, Adam, welcome to the AI Daily Brief. Excited to have you here.

Thank you. Excited to be here. Yeah.

So, so we're talking today about like I was just telling you what, what I called my number one Gen AI product for last year, which, you know, I've talked extensively about on the show. But we're, we're here to talk Notebook lm. I think before we do though, I love just a little bit of background and context on, on you and you know, it doesn't have to just be how you came to work on this particular thing, but you know, whatever is, whatever's important to know for, for context for.

Yeah, cool. So yeah, my name is Adam Bignell. I got actually to computer science in a little bit of a roundabout way. So I made a lot of music, I published a ton of music and I went to a one year technical music program before I ever did computer science. And I also just love books and have been kind of a lifelong reader and I actually really didn't understand what computer science was. I didn't do any of it in high school or anything. And then after graduating I had worked at bookstores for a really long time and I encountered Borges Library of Battle. So that book really kind of, I don't know or story, I guess, but that one really got me interested in how to think about navigating knowledge, I guess. So it's a story that's set in an infinite library and there's every possible 410 page book on the shelves. And the characters in this infinite library spend their whole lives reading books and trying to find ones that are meaningful and stuff. So the reason I mention that is I think I've approached computer science sort of through that lens for a long time and I really wanted to work on stuff that was about language. So I've done other jobs in computer science. I had worked on latency analysis tools, I worked on geospatial kind of software. But then, yeah, I had. The next thing was, okay, I got to, got to get back to where my heart is, which is like language and understanding things and, and yeah, so then that's, that's how I arrived at, at Notebook.

So what were the, what was the.

Early genesis of Notebook? Like, how did it come together? What were the first steps? And you know, sort of, yeah, let's just start there, I guess.

Yeah. So when I had joined Google Labs, there was this new group that was all about language. And it was like in its total infancy. There wasn't a real team per se, or at least not projects. So I was hired and I was this free agent and I was paired with this woman, Dale Markowitz. And we had been given kind of the vague task of using AI to talk to a book. It was called Talk to Small Corpus. I think Steven Johnson has mentioned that before. And this was like summer 2022. So it was like not. People didn't really take that for granted yet. And so Dale and I were just cranking on a bunch of prototypes. We had stuff running in colab notebooks and on our laptops, and we were just seeing it turned into what is rag now, what everyone calls. But we were just trying lots of different things and seeing how can you find the right context in a book and talk to that context. And then I guess concurrent with that, there was this little reading group, I guess, or like a think tanky kind of thing in labs that was meeting every week to discuss tools for thought. And that's where I met Steven Johnson and Reza Martin. And so Steven Johnson is an accomplished writer and he's sort of like the ultimate power user. He's like always thinking about. He has been thinking about tools for thought for decades. And it seemed like we had these models internally that could help do those things. And so just in talking to that group, we realized these prototypes that we've been building that can let you talk to books, but any document are kind of like the foundational software that could be turned into this tools for thought product that, that Stephen and Riza were thinking about. And so yeah, it's just we found each other and we started building ever more serious prototypes. And as we presented them around Google Labs and then Google more broadly, people started to kind of take note of them and then we had to staff up a team and then we had this sprint to get to Google I o. This is when it was still Project Tailwind. And so we had that green and blue ui. I don't know if you can picture it, but yeah, and then it really just progressed as like a kind of typical product. But I will say that the people who have been involved on the project have been, a lot of them have just been artists. Like a lot of people just write and think about writing a lot and do this in their spare time. So that's helped a lot. I think it's helped to steer us away from being too kind of like pure engineer Y or like, you know, too Silicon Valley centric. The other thing is that we're just like very scrappy. Like the team just really, really loves like launching stuff. And so it means that like we're, we're really motivated to kind of like build things. Right, but build things fast and try like weird things and yeah, get stuff in front of people.

That's awesome. So I remember actually I was covering, I mean I've been covering, you know, everyday news for a couple of years now in AI and I remember when it was first announced, but it didn't have the audio overviews FE feature when it, when it first came out where. So how early did you guys start thinking about audio overviews? Was that like on the roadmap from early on and it just wasn't ready yet or did that come, you know, in a random flash of inspiration? Like how did that piece come about?

Yeah, it's, it's definitely more like random flash of inspiration. It definitely was not this like long running thing that we were like cooking. It was like also like a very self organized thing. Like the people who built it actually weren't even on the Core Notebook LM team. They had this prototype in labs and they had access to voice models and they thought we could probably make pretty good podcasts with this thing. And so they built these prototypes and they started sending them around to people. And when we got them, we were just blown away and thought they were really incredible. And then it Seemed like it was a natural pairing with Notebook because you naturally needed some content to make the podcasts about. And it was like, well, we have this product where people are curating content and I bet they want to listen to an AI podcast about it. So it was really totally just self organized. Someone came to us and said, hey, check this out.

Yeah, it was interesting. A couple of the folks on our team at Superintelligent have been using it to organize kind of lessons in learning about AI in advance of audio overviews. And then when, when the overviews hit and everyone started talking about it on, you know, Twitter, SLX and whatever, the rest of the team kind of came to it. But, you know, do you remember when you guys knew you had sort of a hit on your hands and this is like, you know, kind of popping off in a way that was maybe unanticipated?

Yeah, I think there was like two moments for me. So one of them for me was just when we realized that it was a product at all. When you have these prototypes, on average, prototypes don't really go anywhere. You'll show them to people, but you might have to beg people to use it so you can get some usage statistics. And just the fact that we had these demos and other people within Google were asking us to use it and they understood what it was for and what you could do with it, and they could kind of imagine the future that we were imagining. I think that was good and gave us a lot of conviction that, yeah, this is a product and using an AI to interact with your documents is something that people would want to do and that you don't need to really explain to them that much. And then of course, the audio overviews was when it went totally ballistic. I think the shift that happened for me was like, I had a lot of conviction that Notebook was useful. Like, I use it all the time. But I think like, seeing audio overviews and how much people took to it really made me understand that we were building a product that was like really kind of the vanguard of like AI products right now and that, you know, we were doing something new. We had sort of the space to like, be a little bit weird and yeah, just seeing like, I don't think we expected how creative people were going to get with it. People putting in their resumes and putting in the chicken research paper and making them self aware, or at least making them act self, aware, all that was just super exciting. And I think that that made us realize, okay, people actually want to experiment with this stuff. People don't just want this pure, make my normal work routine go a little faster. It's like they want to be part of the experimentation of AI. So yeah, that really excited me. And also just seeing all the big names mention it. I think Jensen Wong mentioned it and Robert Downey Jr. Mentioned it. It's totally bizarre. Every time we got one of these, I'm like, okay, this is mind boggling.

Yeah, I mean I think that it certainly hit the zeitgeist too. Podcasts had quite a moment last year. Regardless of what you think about the election results, obviously they played a role in the elections in a way that wasn't there. And I like some of the, some of the choices. This always happens with products. But like maybe that team was hyper aware and they knew they wanted the to host convention and they knew, you know, they wanted some specific kind of artifacts of that. But either that or they just completely just nailed what, you know, what, what people expected out of that. Because I think even it's not just the audio overview. A ton of companies have now come out and done sort of similar like create a podcast with one click type things. It's the actual interaction is so it's like a, it's like now a full modality of communication that, that people have that, you know, like even the things that are annoying about it, like are familiar about it and you like and respond to them, which is just fascinating to see. So going back, I mean almost to the sort of the, the, the way that you guys were thinking about this as a product, it feels to me, and correct me if I'm, if I'm sort of misstating this, that it was like you guys had the, a pretty interesting combination of solving a problem. Yes. In the sense of people are probably going to want to talk to, you know, documents, but also it being a broad enough or flexible enough problem that you could mostly sort of play around in like new opportunities and not be overly prescriptive. Right. This wasn't like enterprises are desperately begging us for this and here's a set of specs and things that they want to solve. You know, it's like, you sure there's, there's definitely going to be usage for this. I mean to, I think validating of that point is still half of the enterprises that we talk to one of their core AI applications that they built internally is some custom built, you know, chat with your documents type application. But is that, is that fair to say that there was sort of like, yes, you were solving a problem, but, but almost even more you were kind of thinking about just new possibilities and what was possible now with these technologies.

Yeah, certainly, certainly. Like I, you know, I think like a core thing that Google Labs is supposed to do is experiment. And like experimentation means you don't, you don't know the outcome to begin with. Right. Like you, you want to see what people will do. And I think an old manager I had would really hammer on this point. You'd be in a meeting and people would say, oh, users want xyz. And he would never let us get away with that. He'd say, you don't know that. You have no reason to believe that except your own conviction. And when you put it in front of people, they almost always do something completely different. That was totally the case. We would think that the product did something really obvious and then we'd bring it to a UX research session and people would just do bizarre things. And so some of them are kind of no brainers. It's like if you need to do a little report or something, of course you're going to do that. But I think we always wanted it to be flexible enough to let people find the cool, interesting things. The worst thing would be if you made it so locked down that you didn't let people do those things and then they couldn't even communicate to you that they wanted to. And then there's no opportunity to do the interesting stuff. So certainly we always wanted it to be at least a little bit open ended. Just because as soon as you're dealing with the space of people upload things, it's easy to assume, okay, well people are going to use this for business. But loads of people are just uploading their resumes and nobody thought, oh, this is clearly a resume tool. We never designed it as a resume tool, but yeah, it works on resumes or works on a draft for a short story, it works on whatever your credit card statement. And so yeah, I think we have a very healthy spirit of experimentation. People around our desks have sticky notes that say let people do weird stuff. So I think that that's kind of a core tenant for us.

Yeah, I mean, I think that that's one of the reasons that it's so hard if you're ever in startups to like think about you wanting to go off and do like a consumer startup is you basically have to hope you get something in the ballpark of interesting or useful enough that people then tell you what it was actually supposed to be in the first place. Right. Like, like I can't actually think I mean, maybe Facebook, like original Facebook, but it's hard to think of a, of a consumer product that got big that did exactly imagined it was going to do. I mean even Facebook, you could argue like it was, you know, just a very different kind of more limited focus of, you know, rating girls or whatever that he wanted to do. And so it was different. But, but that's, that's a very hard magic to capture and it's extra hard from within a big company, obviously. It's why sort of you don't necessarily usually see big breakout consumer products from, from inside big companies.

Yeah, I, that's probably changing a bit. Like I think that people are realizing that like I move so fast and like that it's actually really in our interest to launch a lot of things quickly and that the appetite for just trying these things is huge. So yeah, I'm grateful that we have the space to do that. And I also think it's getting a little bit better.

What are some of the weird or interesting or off kilter unexpected use cases that you've seen that have gotten you or your team most excited or interested?

Yeah, one that it's sort of like a pure utility. But I wouldn't have immediately thought to do this. And somebody on our team just actually did do this. They were buying a house and they had this super long disclosure and they just put it in and said, this is the asking price and here's the disclosure for the house. Tell me everything wrong with the house and give me an estimate of how much it would cost and give me a counter price that I should ask for. And it worked. And I think they did get a discount. That was just really cool. I don't think that's that weird, but it was just pleasing to see it actually help somebody in that way. People have used the customization to do interesting stuff. They'll use the customization for the notebook for the responses to make it answer. I'll do this as well. I'll put in my favorite books and I'll have it answer as the characters in the book, which is fun because then you're not doing this kind of like Notebook is a de facto narrator or it has its own voice now. It's informed by the book itself. And it's like a medium is the message kind of thing. And then also just all the ways people have customized the audio overviews. We've seen everything from people saying, be less personable, just give me the hard facts. Or we've seen people be like, you should swear more and be like really crass and like, okay, sure. Like, you know, that's really delightful. Like whenever we see those pop up on Reddit, like we're all sending them around and we're just like, like to see people participate.

When it comes to the enterprise notebook is now in workspace that happened. I mean, it's been available for business. Obviously you could just sign up and kind of use it in that way, but now it's more explicit. Have you seen any sort of shifting patterns in how people are using it inside, inside an enterprise use case?

Or.

Or is it sort of just following some of the same patterns that you're seeing with consumers?

I think it's still really early for that and we want to try to pursue what people are doing and figure out all the different ways that people want to use it. I would say probably one difference is the scale of usage. I think individuals, they won't have just the scale of documents, both in the detail of the documents and the number of documents. Maybe that's one kind of obvious difference, but I think a lot of the usage across students or just enthusiast, consumers or business are all actually kind of in the same realm anyways. People want to produce certain things. They need help just understanding stuff. Things are really dense. I don't think there's been one real oh, okay, this is the obvious thing.

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Yeah, I think one of the things that I think about a lot, you know, as we're dealing with enterprise or we're talking with them about how to use different tools is because because of the sort of the it produces podcasts, I think some people's first instinct is to think about it as an external facing tool when a lot of the more interesting use cases that we've seen, at least initially, are internal facing. Right. Like knowledge shar across the company tell, you know, storytelling around successes and things like that. Obviously there's like, you know, just data processing huge numbers of documents is sort of like the obvious, like kind of Layer one thing, but we're seeing an interesting combination of sort of basically like, you know, data plus storytelling, you know, largely for, for internal purposes.

Yeah, definitely. Like I. So one thing I'll say is that, like, I don't think we should think about Notebook as like a podcast platform. I think the great thing that podcasts have done is show that people understand that you can transform documents to make them more understandable in a different format. And podcasts are one flavor of that. But we're already talking about what other kind of output types can we support. And that's going to be very different if you're a law firm compared to if you're somebody making a meme on Reddit. And so we want to support across that whole spectrum. And yeah, I think the more kind of faces of your documents that you have, I think the easier they are to understand them. And being able to massage them in multiple different ways, I think actually will help you just understand it more. You know, it's like learning multiple languages is just good for understanding broadly. And I think that that's kind of what these are, is like podcasts are one, but we'll have more things soon and those will, I think, help in more use cases.

Yeah, it was interesting. So when, when, when the audio overviews first started to pop off, obviously as a podcaster, like 75% of the articles I feel like were like, turn anything into a podcast or some, some variation on that flavor for, for a, for a title. And so I got the question a ton of times, like, you know, is this coming for podcasting? Is it going to be all generated, you know, audio in the future? I was like, well, hold that aside. You know, that's, that's obviously a separate question, but in terms of this tool specifically, it seemed pretty clear to me that again, like the first thing that people would try is kind of just replacing one to one the stuff that they were already doing.

But what, what.

There was so clearly different capabilities that had not been capabilities before.

Right.

So for me, like dealing with, you know, I'm speaking largely to a non developer audience, right? Not non technical per se, but certainly not developers, at least as, as the core, you know, business type audience translating AI. And a lot of times, from the standpoint of trying to actually let people know what's going on, it is important to go dense, right, to understand what's being said in some research paper because that actually in this sector that, that impacts significantly what's going on. Right. Like we're recording on the day that, that everyone's freaking out about deep seq and R1 and actually like, understand, you know, floating points is like, is useful for understanding what's going on and whether it's actually plausible that they did this or whether it's just a ccp, psyop or whatever.

Right.

And for me, like Notebook lm, one of the most default use cases is translate, you know, dense, thick, kind of, you know, technical, you know, resources into something first that I can understand, then potentially that my audience can understand. And I feel like there's a million use cases like that where it's like, I couldn't have done that in any way before. So it's fundamentally new.

Yeah, totally. It's like you kind of want this funnel of understanding where you don't want exactly just a one line sentence explaining it, but you also don't want the whole thing. It's like you want something in between. And I think we find that the best way to get a handle on these things is to, to engage with it. And so I think the nice thing is you can do this back and forth with questions and get this handle. And I also think that the podcasts are a nice thing as sort of a fun little. They entice you to use the product. It's a hook. Yeah, exactly. And it's like I found that hook even working on me. I work on the product, I don't need to be told to use it, but I found that I will just whatever, like I will hear about like Ozempic or something. And it's like, this was an interesting one where it's like, I've heard about it in the news, I know that it's this like new drug. I've also seen news that says it's like, has these like real like health benefits. And I'm like, okay, like, how real is all that? And where previously I might have just sort of wondered idly or like read, you know, from a news source I trusted or something, now I'm like, okay, no, I'm going to go get the research paper and I'm probably not going to read it in full, but like, I will interact with it and I'll ask some questions and I'll click the citation and I'll read that part in full. And it's like, I find it's good because it actually lowers the barrier of entry for me interacting with it, period. It's like now I have this thing in my head where when somebody sends me something dense, I think, oh, I can like understand this a little bit. Where Previously, I would have understood it like zero percent.

Yeah, no, I think. I think that that is a profound use case. I mean, one of the first things that I thought when I started digging in was like, I literally cannot foresee a world where pretty much every, like, college level learning process doesn't start with like the Notebook LM summary of whatever it is you're trying to learn. Not to the exclusion of digging deep in the material and actually understanding the source, but like just from. From a. From a human cognition standpoint, being able to start with something to your point that's not the, like the. The three sentence abstract. But also isn't. The whole thing is. You know, I mean, the way that we used to do this when we were in college is like, you read the first couple pages in the last couple pages and hopefully you get enough from that. Right. That like, that was our old version of Notebook, you know?

Yeah, absolutely. Yeah. I think about, like when I was doing research, like, I. Yeah, it's exactly that. It's like I had this the very first research semester I did. I was like, actually the only one. I was an undergrad. And like, you know, I had this sort of fear where I was like, if I do like this lit review at the density that, that I think I'm supposed to. It's like my grant's going to run out. It's like I can't possibly read these hundreds and hundreds of relevant papers. That's like, I need to have some heuristic for navigating these and finding the most relevant and so on. So, yeah, I think that that's a really good use case. I also think it's just kind of a new way to read and a new way to interact with things. I've been revisiting some of my favorite books and it's like there are so many of these great little details that I've forgotten about the books that I'll say, give me an especially beautiful paragraph and it'll give me one and it'll explain it and I'll go click back and I'll read it. And I'm like, I read the book already. I read the whole thing, but I'm now interacting with it again. And I'm getting this thing again that I don't typically reopen a book to a random page and check if a paragraph is beautiful. But. But now it's like I have a way to do that efficiently. So I think it's also just a way to. It's kind of like making my reading like, nonlinear or something. It's like a new just way to interact with text.

Yeah, there's this like secretly or I mean it's not so secret, but a not so subtle reading renaissance happening right now thanks to like things like booktok and like, you know, these, these Facebook. Like some of the most active Facebook groups now are, you know, people who get together to read books as like basically online book clubs and also have this. There are certain trends that are intersecting with this. Like the romantasy trend with you know, like Acotar and the fourth Wing series which just had their, you know, their, their like third and most recent story came out on Tuesday. And I haven't seen like the fan theory usage for this yet, but I feel like it's, it's only a matter of time before people start feeding in these books to ask like what, what, you know, what's going to happen predictions, you know.

Definitely.

Yeah.

I think along those lines, the D and D Dungeon Master use case is pretty great where you can just feed in your whole campaign and you can say what are some interesting twists or whatever. Totally.

So super Intelligent has evolved from where it was. It was originally tutorials. It's now much more about agent readiness and AI enablement. But when we were doing tutorials at the very beginning of the company, a shocking number of them were D and D themed, like how to use this for different things for D and D. So I buy it. Here's a question for you. I was asking a friend of mine, one of my colleagues about, you know, this discussion and, and they, they, they made an argument that they think that Notebook LM is the first mainstream product of RAG for ordinary people. Basically the idea of sort of normalizing, like uploading and connecting relevant background. And does that mean that in the future everything is retrieval augmented, like, like, you know, instead of these sort of elaborate strategies to build reference libraries. It's just every day you're going to feed your brain so that it's ready to use when, when you need it. I don't know, I thought it was an interesting way of looking at it.

Yeah, I, I think I kind of buy that. Like, I think that the, you know, like a lot of AI products like support, I mean presumably support RAG under the hood, like when they let you upload a file or whatever. But I think something that's been really great about Notebook is that it's sort of exposed people to that more explicitly. When you see it on the side and you see the citations, you kind of understand maybe not you don't need to know anything about embeddings, you just kind of understand that it's looking at chunks and it understands your documents and you can add and delete those things and it's like a little database. And. Yeah. So I think that it definitely has helped to sort of seed that perception for people. I imagine, large swaths of the world just like, I've never heard of RAG and don't know what that is at all. Right. People are just getting used to AI chatbots. And so, yeah, I think that that's a pretty reasonable way to look at it. I like the framing.

Yeah. So here's a question which is honestly, admittedly super annoying, but I got to ask it. How do you think NotebookLM plays in agent space? Right. Everyone kind of turns their attention to the agentified version of things, which obviously means a million different things to a million different people. Do you guys think about what the implications of that are for a notebook as a product?

Yeah. So I'll say something that Notebook in general benefits from a lot is that we kind of ride the rising tide of things. Gemini has gotten better, and as a result, Notebook has gotten better. And so that's been great. And I really see agents as an extension of this. It's such an overloaded term. My definition of an agent is pretty permissive. It's like LLM calls in a for loop. I think of it as, like, maybe it can decide to use some tools, it can make some outgoing calls to other APIs, like whatever. And yeah, I think that that's going to be useful. Audio overviews are, by some characterization, already an agent. It's generating this AI with multiple calls to an LLM. I think that's arguably an agent. And then, yeah, certainly as agentic capabilities become more powerful and cheaper and more commodified, I think that they'll just be all over the place, probably Notebook column included. But it's like, I think a mistake that some people make is because the word agent is hot, they try to jam something that they can call it agentic somewhere. And you don't want to do that. Right. It's like, don't use a for loop if you don't need one. If you can get away with just a raw model call, then definitely do that. But yeah, I think that agents are probably going to be useful as the calls become cheaper, as people get a better proficiency at designing agented capabilities. I think they're just going to be all over the place. And like I said, they're already there in the form of audio overview.

Yeah, I think that's actually a correct or a good way at least to think about audio overviews. Because one of the places that seems sort of fairly obvious to me is Notebook LM becoming Brain. And you know, basically it automating a set of different pipelines to output different things from said brain.

Right.

It's like, you know, like right now you can output this podcast thing, but like, theoretically, why, why couldn't you? I mean, it could literally start a, a video, you know, production process. It could start a storyboard process. I mean, it could like there's sort of endless possibilities for if it's a, you know, aggregate a set of documents and information, like take essential, organize essential information about it and let you interact with it.

Output.

It's just like, you know, you've just teased the sort of the very beginning of what an output could be.

Definitely. Yeah. Yeah. Like the way you just characterized it is like why we have like the new UI that we have. It's like we really wanted to make it clear that it's like, you know, left panel you are like curating stuff and in the center panel you are interacting with it and you're doing stuff like kind of online in a way. And then in the far right panel you have these outputs and these produced things. And I think a lot of people have thought of it that way and so we're trying to make it visually represent that intuition.

Yeah, I thought that that was a really smart or clever interface just from it. It does make that logical sense, although I guess you have to reverse it in the Middle east for it to make sense in the same way. Awesome, man. Well, listen, it's such a, such a fun product. I think it's, it's, it is genuinely fun, which is why, you know, one of the things that's hard because AI is so powerful and so exciting in terms of what it can do and the enterprise implications and stuff. I think people sometimes forget that part of why it's been so such a breakout is that it's just like, it's also capital F fun to use this stuff. You know, like, first time you use Mid Journey, you feel like a wizard. Right. And I think that Notebook LM gave some of that feeling back to people that they hadn't experienced for, you know, endless numbers of years in a time which is like, you know, six or seven months.

Yeah, I'm really, you know, grateful you said that. I like, I think that's something that I really find like essential just in, in a lot of tools is that like if things are fun, then you don't need to convince people to do them, you know, and it's like, if we can make like, I find reading extremely dense, long books fun, but like, lots of people don't. And like, how can we make that fun? If. If that was fun, then people might do it more. I have some conviction that that would be a good thing. But like, in general, just like, I think if ever you take these, like, productivity tasks or like, whatever, like these things that are strongly associated with work, like if they're fun, like, I mean, having. Having fun on the job is like, so everyone wants to do right? So like, I, yeah, I'm grateful you think it's fun and hopefully we can continue to make it funner.

All right, last question. Is there one use case that you've expected or think would be awesome that you haven't seen anyone do yet that you really want to see someone really take all the way?

Let me think. It's a good question. Okay. This is just because it's close to home for me is I would love to see somebody write a really long form thing, like a novel, a book, really strongly with notebook in the loop, even just critiquing the outputs. Maybe it's helping set the stage or giving these little aha. Moments of, oh, it could have been this thing and it was this other thing, and then you could imagine. Suppose that we have all the output types I would like to have. You could do kind of the full multimedia thing with this. You could publish the podcast alongside it and whatever else. So, yeah, I would love to see it. Just. I want to see somebody really use it as kind of like a creative prosthetic. This thing was essential to the act of creating something that's really important to. To that creator and hopefully other people as well.

Yeah, I. I will totally co sign this. I feel like, you know, we've started to see some amount of like, serialization with books and experiments with that. There were startups like, you know, a decade ago that were trying out, you know, wattpad, I think was this, you know, big startup that was trying to like, you know, write your book, like a little at a time and see where stories come and get interaction. But it feels like no one's taken that to that sort of full extreme of like, let creation happen in this totally different way, have it be output in this totally different way. I think maybe because, like, people who love books, like, want to write a book, you know, they want to write it as a book. Like, they love the format of it. So you need someone who's, like, diabolical enough to, like, sort of, you know, want to write a book, but you'd be totally into this different format.

You know, I think, like, that is, like, a lot of us, like, so Dale and I at the beginning were actively, like, working on novels. And Steven Johnson just is a writer. He was writing books while we were working on stuff, and so he takes it to the extreme. You should see Steven Johnson's Notebooks. What I've described is almost what he's doing, but maybe I just want somebody to do it publicly. Yeah.

Listen, if we end up selling Super Intelligent someday and I can finally go write ridiculous, like, old school religious conspiracy thrillers that have an element of eldritch horror, I'll do it with Notebook LLM. I promise.

Love it. Love it.

Awesome. Well, Adam, thank you so much for joining the AI Daily Brief. Really appreciate you here. Keep up the good work. Excited to see what you guys do next.

Yeah. Thank you so much for having me. It's been a pleasure. Yeah. Thank you.