The AI Daily Brief: Artificial Intelligence News and Analysis - A Conversation with NotebookLM's Founding Engineer

Episode Date: January 30, 2025

Google's NotebookLM has become one of the most compelling AI tools for working with text. In this conversation, Adam Bignell, the project's founding engineer, shares insights into its development, unexpected use cases, and the future of AI-assisted knowledge management. The discussion highlights how NotebookLM shapes people's interaction with information, from its early prototypes to its expansion into enterprise applications. Learn more about Adam: https://www.adambignell.com/ Brought to you by: KPMG – Go to ⁠⁠⁠⁠⁠⁠⁠⁠www.kpmg.us/ai⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about how KPMG can help you drive value with our AI solutions. Vanta - Simplify compliance - ⁠⁠⁠⁠⁠⁠⁠https://vanta.com/nlw The Agent Readiness Audit from Superintelligent - Go to https://besuper.ai/ to request your company's agent readiness score. The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614 Subscribe to the newsletter: https://aidailybrief.beehiiv.com/ Join our Discord: https://bit.ly/aibreakdown

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 conversation with Adam Bignell. Adam, as you'll learn, is a little bit of a little bit of a founding engineer of Google's Notebook L.M, but he's also a writer, a musician, and a lot of our

conversation is about the particular set of quirkiness that made Notebook L.M what it is. Now, for those of you regular listeners, you will know that I call Notebook L.M the most important Gen A.I 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 L.M. I think before we do, though, I love just a little bit of background and context 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 important to know for context for this particular story. 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.

Like, I, yeah, I 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. I didn't do any of it in high school or anything. And then after graduating, I, like, had worked at bookstores for, like, a really long time. And I encountered Borges, like, Library of Battleable. So that book, like, really kind of, I don't know what a story, I guess, but that one really got me interested in how to, 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 this infinite library spend their whole lives, like, reading books and trying to find ones that are meaningful and stuff. So the reason I mentioned that is I think I've approached computer science like 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, like, latency analysis tools. I worked on like a geospatial kind of software. But then, yeah, 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 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. 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 called Talk to Small Corpus. I think Stephen Johnson has mentioned that before. And this was like summer 2020. 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 co-lab notebooks and on our laptops. And we were just, you know, seeing it turned into like what is, you know, rag now what everyone calls. But we were 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 then, I guess, concurrent with that, there was this little reading group, I guess, or like a think tank-y kind of thing in labs that was meeting every week to discuss tools for thought. And that's where I met Stephen Johnson and Razum Martin. And so Stephen 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. 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 like Tools for thought product 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, you know, staff up a team. And then we had like this sprint to get to Google I.O. This was 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 engineery 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, 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, 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? Was that, like, on the roadmap from early on? And it just was. ready yet or did that come, you know, in a random flash of inspiration? Like, how did that piece come about? Yeah, it's, uh, it's definitely more like random flash of inspiration. Um, it definitely was not this like, like, long running thing that we were like cooking. Um, 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. Like, they had this prototype in labs. Um, and they, they were just, they, 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. And then it seemed like it was a natural pairing with notebook because, you know, you naturally needed some content to make the podcast about. And, you know, it was like, well, we have this product where people are curating content. And, you know, I bet they want to listen to an AI podcast about it. So, yeah. So it was really like, 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 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 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. I think like the shift that happened for me was like I had a lot of. conviction that notebook was useful. Like I used 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, 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 also just seeing all the, you know, the big names, like, mention it. Like, I think, like, Chenson 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 choices, this always happens with products. But, like, maybe that team was hyper aware. And they knew they wanted the two hosts. 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 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. It's the actual interaction is so, it's like a, it's like now a full modality of communication

that people have that, you know, 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 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, you know, documents, but also it being a broad enough for 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, I think validating of that point is 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 fair to say that there was sort of like, yes, you're solving a problem, but almost even more you were kind of thinking about just new possibilities and what was possible now with these technologies? Yeah, certainly, certainly. 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 want to see what people will do. And I think like,

you know, an old manager I had would really like hammer on this point. Like, you'd be in a meeting and people would say, oh, you know, users want like XYZ. And, you know, users want like XYZ. And, like, you would never let us get away with that. Like, you'd say, like, you don't know that. Like, you have, like, no reason to believe that except your own conviction. And when you put it in front of people, they, like, almost always do something completely different. And so that was, like, totally the case. You know, we would, you know, think that the product, like, did something really obvious.

And then we'd bring it to, like, a UX research session and people would just do, like, you know, 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 i think we always wanted it to be um flexible enough to let people like find the cool interesting things um you know the the worst thing would be if you made it so locked down that you like didn't let people do those things and then they couldn't even communicate to you that they wanted to um and and then you know there's no opportunity to do like the the interesting stuff so certainly we we always wanted to be like at least a little bit open-ended just because as soon as you're dealing with the space of

like people upload things it's like 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. And so, yeah, we always, I think we have a very healthy spirit of experimentation. People like around our desks have sticky notes that say let people do weird stuff. So I think that that's like 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

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 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 you don't necessarily usually see big breakout consumer products from inside big companies. Yeah, that's probably changing a bit. Like, I think that people are realizing that like AI moves so fast and like that it's actually like really in our interest to like launch a lot of things quickly. And that like the appetite for for just trying these things is huge. So yeah, I'm grateful that that that, that, we have the space to do that. And I also think it's getting like a little bit better. What are some of the 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 I, it's like sort of like a pure utility, but I, I wouldn't have immediately thought to do this. And somebody on our team just actually did do this. They were like buying a house and they had this like super long disclosure. And they just put it in and said, like, 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. And it worked, and, like, I think they did get a discount. And so, like, that was, like, just really cool. 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. you know people have used like the customization to do interesting stuff like they'll use like the customization for the notebook for like the responses to make it answer I'll do this as well like I'll put in my favorite books and I'll have it answer as like 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 like now it's like 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.

And 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 to see people persuade. When it comes to the enterprise, so notebook L.M. is now in a workspace. It happened. I mean, it's been available for business. Obviously, you could just sign up and kind of use it that way. But now it's more explicit. Have you seen any sort of shifting patterns and 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, 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, enthusiasts 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 obvious thing. Today's episode is brought to you by Vanta.

Trust isn't just earned, it's demanded. Whether you're a startup founder navigating your first audit or a seasoned security professional scaling your GRC program, proving your commitment to security has never been more critical or more complex. That's where Vanta comes in. businesses use Vanta to establish trust by automating compliance needs across over 35 frameworks like SOC2 and ISO-2-2-1 centralized security workflows, complete questionnaires up to 5X faster, and proactively manage vendor risk. Vanta can help you start or scale up your security program

by connecting you with auditors and experts to conduct your audit and set up your security program quickly. Plus, with automation and AI throughout the platform, Vanta gives you time back, so you can focus on building your company. Join over 9,000 global companies like Atlassian, Quora, and Factory who use Vanta to manage risk and prove security in real time. For a limited time, this audience gets $1,000 off Vanta at vanta.com slash NLW. That's VANTA.com slash NLW for $1,000 off. If there is one thing that's clear about AI in 2025, it's that the agents are coming. vertical agents by industry, horizontal agent platforms, agents per function.

If you are running a large enterprise, you will be experimenting with agents next year. And given how new this is, all of us are going to be back in pilot mode. That's why Super Intelligence is offering a new product for the beginning of this year. It's an agent readiness and opportunity audit. Over the course of a couple quick weeks, we dig in with your team to understand what type of agents make sense for you to test, what type of infrastructure support you, you need to be ready, and to ultimately come away with a set of actionable recommendations that get you prepared to figure out how agents can transform your business. If you are interested in the

agent readiness and opportunity audit, reach out directly to me, NLW at B-Supert.A.I. Put the word agent in the subject line so I know what you're talking about, and let's have you be a leader in the most dynamic part of the AI market. Hello, AI Daily Brief listeners. Taking a quick break to share some very interesting findings from KPMG's latest AI quarterly Pulse survey. You know that 67% of business leaders expect AI to fundamentally transform their businesses within the next two years? And yet, it's not all smooth sailing. The biggest challenges that they face include things like data quality, risk management, and employee adoption. KPMG is at the forefront of helping organizations navigate these hurdles. They're not just talking about AI. They're

leading the charge with practical solutions and real-world applications. For instance, over half of the organizations surveyed are exploring AI agents to handle tasks like administrative duties and call center operations. So if you're looking to stay ahead in the AI game, keep an eye on KPMG. They're not just a part of the conversation. They're helping shape it. Learn more about how KPMG is driving AI innovation

at KPMG.com slash US. 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 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, 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, 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're already talking about, like, what other kind of output types 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, you know, somebody making a meme on Reddit. And so, like, we want to support across 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 you're there 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. It's 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? going to be all generated, you know, audio in the future. I was like, well, hold that aside. You know, that's, that's always 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 the core, you know, business type of 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, we're recording on the day that everyone's freaking out about deep seek and R1 and actually like understand, you know, floating points. 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 sciop 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 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. 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. And so I think like the nice thing is like you can do this back and forth with with questions and like and get this handle.

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 right 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, 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 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 at 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 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 a human cognition standpoint, being able to start with something to your point, that's not the like 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 and the last couple pages and hopefully that you get enough from that, right? Like, that was our old version of notebook LM, 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 did. 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 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 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 new way to interact with things

like 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 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 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 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 face, 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 Romantasy trend with, you know, like Akatar and the fourth wing series, which just had their, you know, 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 like along those lines, like the, the D&D Dungeon Master use case is pretty great, where you can just like feed in, you know, your whole campaign and you can say, like, you know, 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 much more about sort of 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&D themed, like how to use this for different things for D&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 they made an argument that they think that Notebook L.M 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 is 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 I 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 does, how do you think notebook LM plays in agent space, right? Is everyone kind of turns their attention to, uh, the agentified version of things, which obviously means a million different things to a million different people.

Do you guys think about like what the implications of that are for, for notebook as a product? Yeah. Um, 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 four loop. I think of it as like, you know, maybe it can decide to use some tools. It can, you know, make some of going calls to other APIs, like whatever. and yeah, I think that if that, I think that that's going to be useful. Like, audio overviews are by some characterization, like, already an agent, right? Like, it's generating this AI with multiple calls to an LLM.

Like, I think that's arguably an agent. 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 No Book 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 definitely do that. But yeah, I think that agents are probably going to be

useful as the calls become cheaper, as as people get a better proficiency at designing agentic 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 like 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 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 that 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 like outputs and these like produced things and 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 think it's a, I think I thought that that was a really smart or clever interface. Just from it, like 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 fun product. I think 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 such a breakout is that it's just like, it's also capital of fun to use this stuff. You know, like first time you use mid-jurney, you feel like a wizard, right?

And I think that notebook LM gave some of that feeling back to people that they had an experience for, you know, endless numbers of years in AI time, which is like, you know, six or seven months. Yeah, I, I'm really, you know, grateful you said that. 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? 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, that's what 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've. that you really want to see someone like 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 a, you know, a novel, a book, like really strongly with notebook in the loop. Like, even just like critiquing the outputs, It's, 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, you know, suppose that we have all the output types I would like to have. Like, you could do kind of the full, like, multimedia thing with this. You know, 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 of creating something that's really like important to 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, Wattpat, I think was this, you know, big startup that was trying to like, you know, write your book like a little out of time and see where stories come and get interaction. But it feels like. like no one's taking 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 this different format, you know. I think like that is like a lot of us like so Dale and I at the beginning were, we're actively you're like working on novels. 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. 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 L.M., I promise. Love it. Love it. Awesome. Well, Adam, thank you so much for joining the 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.