A Conversation with NotebookLM's Founding Engineer
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Thank you.

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 conversation with Adam Bignall. 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 Notebook LM what it is.

Now, for those of you regular listeners, you will know that I call Notebook LM 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 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 we're talking today about, like I was just telling you, what I called my number one Gen AI product for last year, which I've talked extensively about on this show, but we're here to talk Notebook LM. I think before we do, though, I'd love just a little bit of background and context on you. And it doesn't have to just be how you came to work on this particular thing, but whatever is important to know for context for this particular story.

Yeah, cool. So yeah, my name is Adam Bignall. I got actually to computer science in a little bit of a roundabout way. So I made a lot of music. Like I, yeah, I've published a ton of music. And I went to like a one year technical music program before I ever did computer science. And I also just like love books and have been kind of like a lifelong reader. And I actually like really didn't understand what computer science was. Like I didn't do any of it in high school or anything. And I was like,

And then after graduating, I had worked at bookstores for a really long time. And I encountered Borges' Library of Babel. So that book really kind of... I don't know what story, I guess, but that one really got me interested in how to...

uh like think about navigating knowledge I guess so you know 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 the in this infinite library spend their whole lives like reading books and trying to find ones that are that are meaningful and stuff um so the reason I mentioned that is I think like I've

approached computer science like sort of through that lens for a long time and I really wanted to work on on stuff that was about language. So I've done other jobs in computer science. I had worked on like latency analysis tools. I worked on like a geo spatial kind of software. But then yeah, I had

The next thing was, okay, I got to get back to where my heart is, which is like language and understanding things. And yeah, so then that's how I arrived at Notebook. So what was the early genesis of Notebook? How did it come together? What were the first steps? And 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, like there wasn't a real team per se, or at least not projects. So I was hired and I was like this free agent. And I was paired with this woman, Dale Markowitz. And we had been given kind of the vague task of like, use an AI to talk to a book. It was 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 like take that for granted yet.

And so Dale and I were just cranking on a bunch of prototypes. We had like stuff running in Colab notebooks and on our laptops. And we were just, you know, seeing it turned into like what is, you know, rag now, whatever it calls. But we're just trying lots of different things and seeing like, how can you find the right context in a book and talk to that context? And yeah.

And then I guess concurrent with that, there was this little reading group, I guess, or like a think tank kind of thing in labs that was meeting every week to discuss tools for thought and

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, you know, it seems like we have these models internally that could like help do those things. And so just in talking to that group, we realized like these prototypes that we've been building that can let you, you know, talk to books, but any document,

are kind of like the foundational software that could be turned into this like tools for thought product that Stephen and Ryza 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, you know, staff up a team. And then we had like this sprint to get to Google I.O. This is when it was still Project Tailwind.

And so we had like 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 like typical product. But I will say that like 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 like in their spare time. So that's helped a lot. I think it's helped to steer us away from like being too kind of

like pure engineer-y or like, you know, too Silicon Valley centric. And 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 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 feature when it when it first came out. Where? So how early did you guys start thinking about audio overviews? Is 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 definitely more like random flash of inspiration. It definitely was not this long-running thing that we were cooking. It was also a very self-organized thing. The people who built it actually weren't even on the core Notebook LM team. They had this prototype in labs,

And they were just, you know, they had access to voice models and they thought, you know, we could probably make pretty good podcasts with this thing. And so they, yeah, they built these prototypes and they started sending them around to people. And like when we got them, we were just blown away and thought they were like really incredible.

Um, and then it seemed like it was a natural pairing with notebook because, you know, you naturally needed some content to make the podcasts about. Um, and, you know, it was like, well, we have this product where people are, are curating content. Um, and you know, I, I bet they want to listen to an AI podcast about it. So, yeah. Um, so it was really like, uh,

totally like just self-organized. Someone came to us and said like, Hey, check this out. Yeah. It was interesting. A couple of the folks on our team at super intelligent had been using it to organize kind of lessons and 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 slash X 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? So 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. Like, you know, when you have these prototypes, like on average, like prototypes don't really go anywhere. You know, you'll show them to people, but you might have to like kind of beg people to use it so you can get some like usage statistics. And just the fact that we had like a little

these demos and like other people within Google were like asking us to use it. And they kind of like understood like 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 like, yeah, this is a product. And like, you know, using an AI to interact with your documents is like something that people would want to do and that you don't need to like really explain to them that much. And then of course, like the audio overviews was like when it

went like totally ballistic. Like, I think like 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 and yeah, just seeing like I don't think we expected how creative people were going to get with it. Like people, you know, putting in their resumes and putting in like the chicken research paper and making them making them like self-aware, you know, or at least making them act self-aware. All that was just super exciting. And I think that that.

made us realize like, okay, like people actually want to experiment with this stuff. Like people don't just want this like pure, you know, make my normal work routine go a little faster. It's like they want to be part of like the experimentation of AI. So yeah, that really excited me. And, and also just seeing all the, you know, the big names like mentioned it, like, I think like

Jensen Wong mentioned it and like Robert Downey Jr. mentioned it. It's like totally bizarre. Like every time we got one of these, I'm like, okay, like this is mind boggling. Yeah. I mean, I think that it certainly hit the zeitgeist too. Like podcasts had quite a moment last year, you know, regardless of what you think about the election results, obviously they,

played a role in the elections in a way that like wasn't there. And I think even 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 two 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 because a ton of companies have now come out and done sort of similar, like create a podcast with one click type things, but

It's the actual interaction is so... It's like now a full modality of communication that people have. Even the things that are annoying about it are familiar about it, and you like and respond to them, which is just fascinating to see. So going back almost to sort of the...

the way that you guys were thinking about this as a product, it feels to me, and correct me if I'm sort of misstating this, that it was like you guys had a pretty interesting combination of solving a problem, yes, in the sense of people are probably going to want to talk to 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, sure, there's definitely going to be usage for this. I mean, to,

I think validating of that point, I still half of the enterprises that we talked 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're solving a problem, but but almost even more, you're kind of thinking about just new possibilities and what was possible now with 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 experimentation means you don't know the outcome to begin with. 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 like 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. And so 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...

Like some of them are kind of, you know, like no brainers. Like it's like if you need to, you know, do a little report or something like, 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, you know, loads of people are just uploading their resumes. And like, nobody thought like, oh, this is clearly like a resume tool, right? Like, we never designed it as a resume tool. But yeah, it works on resumes, or works on like a draft for a short story. It works on your, whatever your credit card statement like. And so, yeah, we always, I think we have a very healthy spirit of experimentation. And

People around our desks have sticky notes that say, let people do weird stuff. So I think that that's kind of a core tenet 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 think about you wanting to go off and do a consumer startup is you basically have to hope that

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, I can't actually think...

I mean, maybe Facebook, like original Facebook, but it's hard to think of a consumer product that got big, that did exactly what people 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 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 inside big companies. Yeah.

Yeah, that's probably changing a bit. I think that people are realizing that AI moves so fast and 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 or unexpected use cases that you've seen that have gotten you or your team most excited or interested?

Yeah, one that I... 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 like tell me everything wrong with the house and give me an estimate of how much it would cost and give me like a you know a counter price that i should ask for um and it worked and like i think they did get a discount and so like uh that was like just really cool um i i don't think that's like that weird but it was like just pleasing to see like it actually you know help somebody in that way um

People have used the customization to do interesting stuff. I...

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 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. Like we've seen everything from people saying like, be less personable, like just give me like 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 persuade.

When it comes to the enterprise, so Notebook LLM is now in Workspace. It 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 an enterprise use case? 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, like,

probably one difference is the scale of usage. Like, I think individuals, they won't have like just the scale of documents, both in like, you know, the detail of the documents and the number of documents. So maybe that's one kind of like obvious difference. But I think a lot of the like, the usage across students or just like enthusiast consumers or business like are all actually like

kind of in the same realm anyways, like people want to produce certain things, they need help just understanding stuff, things are really dense. Yeah, so I don't think there's been like one real like, oh, okay, this is like the the obvious thing.

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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 sharing across the company, uh, 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. Um, but we're seeing an interesting combination of sort of, uh,

basically like, you know, data plus storytelling, you know, largely 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, you know, like I think like the great thing that podcasts have done is show that like people understand that you can transform documents to make them more understandable in a different format. And podcasts are one flavor of that.

But, you know, we were already talking about like what other kind of output types can we can we support. Right. And that's going to be very different if you're, you know, like a law firm compared to if you're somebody making a meme on Reddit. And so, like, we want to support across that that whole spectrum. And, yeah, it's like I think the more that.

the more kind of like faces of your documents that you have, I think the easier they are to understand them and like 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 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 variation on that flavor 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 always a separate question. But in terms of this tool specifically, it seemed pretty clear to me that,

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, uh,

non-developer audience, right? Not non-technical per se, but certainly not developers, at least 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 impacts significantly what's going on, right? Like,

uh, we're recording on the day that, um, that everyone's freaking out about deep seek and, 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, uh, you know, technical, uh,

you know, resources into something first that I can understand them 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, I, you kind of want this, like, you know,

funnel of like understanding where uh you don't want like a exactly just a one-line sentence explaining it um but you also don't want the whole thing it's like you want something in between and um and you know i think we find that like the best way to to get a handle on these things is to to like engage with it um and so i think like the the nice thing is like you can do this back and forth with with questions and like and get this handle um and i also think that like

The podcasts are a nice thing as like sort of a fun little, you know, they like entice you to use the product. It's a hook. Yeah, exactly. And it's like I found that hook even working on me. It's like, you know, I work on the product. I don't need to be told to use it. But I found that like 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, OK, 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, OK, 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 like 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 0%. Yeah. No, 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...

Um, 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, 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 was like, you read the first

couple pages in the last couple pages and hopefully you get enough from that. That was our old version of Notebook LM. Yeah, absolutely. Yeah, I think about when I was doing research, 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 did, I was an undergrad and 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 I my grants gonna 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 um so yeah I think it's I think that that's like a really good use case I also think it's just like kind of a new way to to read and a

I've been like revisiting some of my favorite books and it's like there are so many of these great little details that like I've forgotten about the books that I'll say like, you know, what give me an especially beautiful paragraph. Right. And it'll give me one and they'll explain it and I'll go click back and I'll read it. And I'm like,

I read the book already. Like, I read the whole thing. But I'm now, like, interacting with it again. And I'm getting, like, this thing again that I... Like, I don't typically reopen a book to a random page and check if a paragraph is beautiful, right? 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, it's... 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 book talk and like, you know, these Facebook, like some of the most active Facebook groups now are, you know, people who get together to read books. It's like basically online book clubs.

And you also have this, there are certain trends that are intersecting with this, like the romanticist 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&D Dungeon Master use case is pretty great, where you can just feed in your whole campaign and you can say what

what are some interesting twists or like whatever. Totally. We, um, so super intelligent has evolved from where it was. It was originally tutorials. It's now, uh, much more about sort of agent readiness and, and, and AI enablement. But, um, 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, for D and D. So I buy it. Um, uh, here's a question for you. I was asking a friend of mine, one of my colleagues, uh,

about this discussion. And 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, 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 you need it. I don't know. I thought it was an interesting way of looking at it. Yeah, 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 like 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, like, when you see it on the side, and you see the citations, like you kind of understand, maybe not, you don't need to know anything about embeddings, right? Like you just kind of understand that it's looking at chunks, and it understands like 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. Like 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. Like people are just getting used to like AI chatbots. And so, yeah, I think that that's like a pretty like reasonable way to look at it. I like the framing. Yeah. Yeah. So here's a question, which is like, honestly, like admittedly super annoying, but I got to ask it.

How do you think Notebook LM 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 Notebook as a product? Yeah. Yeah.

So I'll say like something that notebook in general benefits from a lot is that we kind of ride the rising tide of things, right? Like Gemini has gotten better. And as a result, notebook has gotten better. And so that's been great. And I really see agents as like an extension of this. Like, yeah, it's such like an overloaded term. You know, my definition of an agent is like,

pretty permissive it's like you know llm calls in a for loop like it's i i think of it as like you know maybe it can decide to use some tools it can you know make some outgoing calls to other apis like whatever um and uh yeah i think like if that i think that that's going to be useful um like audio overviews are by some characterization like already uh an agent right like it's generating this ai with multiple calls to an llm like i i think that's arguably an agent um

And then, yeah, certainly, like, as agentic capabilities, like, become more powerful and cheaper and more kind of, like, commodified, like, I think that they'll just be all over the place. Probably notebook LM included. But it's, like, I think a mistake that some people make is, like, because the word agent is hot, they, like, try to jam, like...

something that they can call it agentic somewhere and it's like that's 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 like a raw model call then uh then definitely do that um but yeah i i think that agents are probably going to be useful um as the calls become cheaper as people get a better proficiency at designing agentic capabilities i think they're just going to be all over the place and and like i said like

they're already there in the form of audio overview. So, yeah, I think, 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 like fairly obvious to me is, um,

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 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

We really wanted to make it clear that it's like, you know, left panel you are curating stuff and in the center panel you are interacting with it and you're doing stuff online in a way. And then in the far right panel you have these outputs and these produced things.

um i think like a lot of people have have thought of it that way and so we're trying to like make it visually represent that intuition yeah i i think it's uh i think i i thought that that was a really um smart or clever interface uh just from it like it does make that logical sense although i guess you have to reverse it in the middle east um for it to make sense in the same in the same way um awesome man well listen i it's such a such a fun product i think it's it's uh

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, uh, notebook LM gave some of that feeling back to people that they hadn't experienced for, you know, endless numbers of years in AI time, which is like, you know, six or seven months. Yeah.

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 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? Um, if, if that was fun and people might do it more, I have some conviction that that would be a good thing. Um, but like in general, just like, I think if ever you take these like, uh, 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, that's what everyone wants to do. Right. So like I, uh,

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.

That's a good question. Okay, I would... This is just because it's, like, close to home for me. Is I would love to see somebody write, like, a really long-form thing. Like, you know, a novel, a book. Like, really strongly with notebook in the loop. Like...

Even just like critiquing the outputs, you know, maybe it's like helping set the stage or giving these little aha moments of, oh, like it could have been this thing and it was this other thing. And then you could imagine like

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. Like this thing like was essential to the act of creating something that's really like important to that creator and hopefully other people as well. Yeah, 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 we're 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 the 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 but it'd be totally into uh this different format you know i think like that is like um a lot of us like so dale and i at the beginning were were actively like working on novels um and like stephen johnson just is a writer like he's he was writing like

books while we were working on stuff and so uh he he takes it to like the extreme like you should see stephen johnson's notebooks um what i've described is like almost what he's doing but maybe i just want somebody to do it publicly yeah listen if i uh if we end up selling super intelligent someday and uh and i can finally go write um you know ridiculous uh like

old school religious conspiracy thrillers that have an element of eldritch horror i'll do it with notebook lm i promise love it love it awesome well adam thank you so much for uh 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 uh been a pleasure so yeah thank you