Transcription
We really see this as a tool for understanding things; right, like you have word processors help you create a document, and Photoshop helps you, you know, adjust pixels in an image. This is a tool that helps you understand things.
Well, Casey, we talk a lot about AI tools and products on this show, and I have one that I'm really excited to talk about today.
Yeah, I'm excited to talk about this too. This is a new product called Notebook LM. It is a tool from Google, and uh, you can think of it like a kind of personal research assistant. It's a piece of software that allows you to upload documents, PDFs, Word files, even audio files, websites—whatever you want—into these things called notebooks and then use AI Gemini, Google's AI model, to basically chat with the documents, to sort of have a conversation, to ask questions, to create study guides or summaries, um, and you can even use it to create a podcast about the material that you've uploaded.
Yeah, and you know, this was a product that was announced at Google I/O; they called it Project Tailwind back then. And from the moment I saw them talking about it on stage, I thought, I have got to get my hands on this thing. And while it is true that you can use other tools to chat with documents, that is not unique to Notebook LM. They have really focused on making sure that you can cram as much material as possible into their system so that you can have conversations with not only one very long document but many very long documents, and that has really been the difference for them.
But, as you know, they recently came out with something that is maybe even more impressive.
Yeah, the AI audio overviews feature, uh, is really what has been sort of lighting up the internet over the past week or so. Like, I'll just be honest, uh, we see a lot of AI products; we get a lot of Early Access and demos of things, and many times they show some potential, but if you actually start digging in, uh, they are not all that useful, or they hallucinate, or they're just not reliable enough to be useful for people like you and me.
Yeah, Notebook LM is, I would say, one of my favorite AI products that I have used this year because it is, uh, it is not trying to do everything for everyone. It is a tool for research, for writing, um, it is, uh, really, really capable at what it does, and the audio feature in particular is just pretty stunning. So today I want to talk about Notebook LM.
So today we're going to bring in one of the key people who helped conceive of, build Notebook LM, uh, Steven Johnson. Steph's path to working at Google is pretty unusual. His main career, the thing that he's most known for, is a writer. He has written, uh, for many years for the New York Times and New York Times magazine and other places. He's the author of more than a dozen books, including his latest, *The Infernal Machine: A True Story of Dynamite, Terror, and the Rise of the Modern Detective*, and he's been one of my favorite writers about tech and the future. And a few years ago, uh, Google approached him and basically said, "Hey, want to help us make a tool for writers?"
Yeah.
Yeah, and you know, I got to know Stephen a little bit, uh, as they were launching Notebook LM, and we met, and he told me all about his note-taking process and how he wanted to use AI to, to sort of improve his writing, and we, we truly just became fast friends because we have the exact same view of this stuff, which is: give me the most technology to make my job as easy as it possibly can be. And, um, unlike me, he's now actually working inside this company trying to make something that does just that.
Yeah. And why haven't you written 14 books for...?
Well, you know, I've been busy lately, Kevin, but I'm going to get around to it one of these days. Let's bring in Stephen. Stephen Johnson, welcome to Hardfork, guys.
It's great to be here.
Hey Stephen.
So Stephen, I remember reading a piece you wrote for the New York Times magazine back in April of 2022. It was about six months before ChatGPT came out, and you had this big, great piece about how AI was starting to get really good at language through these new things called large language models, and your sort of predictions about how that would have all kinds of profound effects on society. But I remember that piece so vividly because it captured this feeling that I, uh, was having at the time, which Casey now starts has started calling AI vertigo—basically this sort of head-spinning sensation when things are just moving so quickly. I'm curious, like, what got you, as a writer, interested in AI to the point that you decided to sort of build AI products at Google?
You know, I, I just have spent all of my career as a writer, always dabbling with tools to help me do the writing, with, with, with all the latest software. Like Casey and I have this kind of shared obsession with note-taking software. One of those—I know you know—don't even get started about Scrivener and all the different things that we can talk about. I always saw the computer and software as a kind of companion and a kind of brainstorming partner, like, and I was always pushing the technology to do that in my own work. And so the idea that I could just kind of say, "Hey, let's think out loud about this particular topic," and it would understand, on some level, and respond with coherent sentences—you know, obviously there were hallucinations and there were all the things that we know are problems, which I also wrote about in that piece—but it was clear that some new set of doors of possibility had just opened up for the first time, and, and I just got really interested in walking through those doors.
So how does Google first approach you? And is the idea, "Hey, we want to make a tool for writers like you," or was it something else?
Yeah, it was a little bit like that. So Google had just spun up, right around this time, a, a new division called Google Labs. There was an old Google Labs; this is kind of a new iteration of it. And there was a guy running it named Clay Bavor, who's since left, um, but, uh, and now Josh Woodward is running it. And Clay and Josh had this idea that Google Labs could be a space where you could do kind of product-focused experimentation with new emerging technologies. And they also had this idea that there would be co-creation would be built into the kind of ethos of Labs. And so if you were making a music product, you would have a musician in the room, like, as you were building it. So it wasn't just about, "You know, we're going to go out and do some user research with musicians;" we're actually going to have somebody through the life of the product there. And so they were just kind of cooking up these ideas, and they had both read my books over the years; they'd read that Times piece; they read my Substack. And all that together caused them to think, "Wonder if Steven Johnson could be the first kind of guinea pig for this."
So, um, Stephen, you and I have had a chance to chat before, and, um, I truly aspire to be the note-taker that you are because I have talked a big game on this podcast about how I'm trying to write down sort of every interesting, like, quote or idea that I come across and link those together. And I made some strides there, but, like, you showed me your system at one point, and it is the real deal. Like, you truly have been keeping track of every idea that you've come across for, for seemingly quite some time. What was the moment that you said, "Oh, like, this intersects with AI in a way that maybe this Notebook LM can realize in a product?"
I want you to remember, Kevin, that every time you think that Casey is such a super nerd with his note-taking, like, there are even more dous people in the world. So you are the alpha nerd of the note-taking community. So, uh, yeah, I can tell you exactly what it was. So, so I have been collecting quotations from books that I've that I've read, initially by typing them up in the late 90s, um, and then once, you know, ebooks came out, I, you could save quotes and things like that. It's an amazing program that I think you use called Readwise that lets you organize all your quotes from if you read on the Kindle or any other ebook. And so I have something like 8,000 quotes from books that date back to the late 90s, um, that I've collected, and that is really the history of all the ideas that really shaped who I am, right? Like my mental model of the world is shaped by the other ideas that that I've read from other people. And so Notebook now lets you have, um, up to 25 million words in a single notebook. Put that in terms of pages; how many pages is that? What would that be?
That would be like 40 books.
Yeah, right, something in that order. And do your 8,000 quotes fit in one in one of these 25 million word, uh, documents?
Yeah, yeah, they're only about three million words, so I've loaded them all in as, you know, a bunch of documents. Thirty years of collecting quotes fits easily into one of these things. And what I'm slowly adding to that notebook is all the stuff that I've written too. So it's kind of everything I've read that's important, and literally every word I've published is eventually going to be in that one notebook. I just haven't got—Kevin can't do that because it would poison the data set, but I think it's good—it works for you. The safety flags would be going off; it'd be terrible.
So, so when I was able to do that, which was really, I don't know, about a year ago for the first time, where I can get all that stuff in there, um, I call that notebook my "everything notebook," and then I could sit down and just be like, "I'm thinking about, you know, writing a piece about X, like, here, or here's a paragraph from the piece that I've just wrote, just written, um, what am I missing? What am I forgetting? Um, give me, give me an overview of all the stuff that I've read that is related to this particular topic," and it would return—particularly once we switched to Gemini—like Gemini was the big kind of paradigm shift here—um, I get this like incredibly nuanced response that is constantly reminding me of things that I've forgotten. And now, as of like three months ago, we have inline citations in, in all the comments from the model, and you can click on each citation, and it takes you directly to the original quote.
Yes, I love this feature. I've been playing around with, with Notebook LM, and it is truly one of the best features about it is that, you know, it'll show you something—you're talking to it about something you've read or something you've written—and it just has that little sort of like citation; you click on it, it takes you right to the source material, so you can see, see for yourself, like, this actually is an accurate representation of what was in the PDF I uploaded. And you know, that, that's one of the things that's like, what an incredibly interesting, like, learning mode that is, right? Like, up until now, if you wanted to have a conversation with the material in a book, like, you had to find the author or you had to find a tutor and an expert who knew the material really well, and those people are in scarce supply. But now you can actually, like, load in the book and navigate it through conversation and dialogue, which is a, a form that people really obviously like to use. So that, that is amazing and I think will probably be the primary way that people access it. But because of the fact that you have taken these notes for 30 years, you're able to use this in this different way, which is essentially, like, "Take me through my own intellectual history, and let me talk to the entire, like, course of learning that I've had over three decades. Remind me of things, right, or make new connections for me." And that feels like the kind of augmentation that I truly have always wanted AI to give us, right? Like, that is the good stuff, right?
I'm glad you, you said that; that, that means a lot coming from you. Um, I, I think though, I think there was an early tension in, um, in, in creating Notebook LM, which was the question of, like, "How normal am I?" And you know, we have this amazing colleague, Risa Martin, who's the product manager, who's been incredible, was on, on it from the beginning. And I think, like, you know, she, she was kind of like, "This has to work for people who don't collect 8,000 quotations over 30 years of their lives, right? If it doesn't, you know, work for them, and so..." But I think one of the things that we've learned is you actually, you know, particularly in a digital age, like, you have, you know, you can import Docs and slides. And so if you're a Google Drive user, the history of all the Docs you have in there is actually a history of the last four or five years of things that you've been interested in and that you've been working on, whatever your job is. And so one of the things I often tell people with Notebook LM is just, "First thing to do is create a notebook. If you are a Drive user, grab the last 20 Docs in there, and just don't even think about organizing them; just dump them all in, even if they're for different projects, and just start having a conversation." And the, the sense of, "Oh, this AI actually knows what I'm doing and understands what I've been working on and can piece together kind of, you know, insights from that," is pretty, is pretty amazing. And by the way, I should say, you know, when we do this, we're not training the model. That was one of my questions is like, I think a lot of people would say, "Okay, well, if I upload a, a copy of my book or some, some documents that are personal to me, is Google going to then be able to sort of see...?" And so what we are doing is not training the model; that would take a long time to train a model on your data, um, and it actually wouldn't, for complicated reasons, work as well as, as the way that we're doing it. Um, we're just taking the information you have and putting it in the model's context window, which is kind of like the short-term memory of the model; it's the easiest way to understand it. And, um, the beauty of that is, one, the model is much more accurate with information in its context, um, and, and so the hallucination rates drop down dramatically; you can do things like citations that you wouldn't be able to do otherwise, um, but it also means that the second you close your session, that information goes away. And so there's no way for—we're not learning from it; we're not making the model smarter in the long run—and there's no way for that information to kind of leak out into other users. And that has been a, a fundamental principle of the product from the very beginning.
Yeah, I hope you take this next thing that I'm about to say the right way because I do mean it as a compliment, um, but Notebook LM strikes me as an extremely UN-Googly product, right? It is, it is probably not the kind of thing that's going to get a billion users, which is how Google has historically decided what to build. Um, it didn't have like a big, splashy launch with ads running on the Olympics; uh, it's not sort of promoted on a bunch of other Google products as far as I can tell. It has a Discord server, uh, and, and the design of the actual tool just feels different than a lot of what Google has built in AI. Um, it sort of feels to me like it might be this kind of isolated, kind of semi-autonomous region within Google, uh, that doesn't have that much contact with the rest of the company. Is that right?
Well, that's an interesting question. Um, yeah, some of that is right, um, and some of that is a reflection of what Labs set out to do, right? Which is to, like, let, let's create a space where we can, um, be, be more comfortable with being experimental. And that enabled us to do some things like, um, experiment with different types of interfaces that would not necessarily have the polish that you would expect from other Google official products. Um, the Discord is a great example; that was one of Risa Martin's ideas. You know, we just wanted to, like, build a community around it. And I remember Risa coming to me and saying, "You know, I want to build a Discord for this product," and I said, "What is a Discord?" I had no—my kids were, were Discord users, so I rang a bell, but I'd never been on Discord before. And now I'm in there all the time, and we have, we have like 45,000 people who are members of this community now. And so kind of—and we just discover so many things from them; like, it's like Notebook is taking off with, um, D&D players, like Dungeons & Dragons, like Dungeon Masters, because they have—it's a very, like, literary genre of game, right?—and you have these long campaigns, rich lore...
Yeah.
Yeah, same thing with, like, fantasy novelists and sci-fi novelists, where they have a backstory that's enormous, and they can't keep track of it at all. Like, if George R.R. Martin would adopt our product, like, we would, we would have winter.
Yes, maybe we could actually get a new book.
I spent some time in the, in the Notebook LM Discord; it's a very fun place because you get to sort of see how people are using it and sort of get ideas. I also love that there's one person in there who's just constantly posting about how they're using Notebook LM to analyze a huge database of Sonic the Hedgehog fan fiction.
[Laughter]
Talking about 100%—they've been for a while. It's really interesting; they, they found a lot of use cases for Sonic-related, uh, you know, work, and they're going to be on the show next week. But you know what, Kevin, I, I want to disagree with you about whether this is Googly or not because I think this is like old-school Google, and this is the Google I like. Like, do you remember back in the day when the Googlers could just do anything they wanted in their 20% time? They give them a day a week to be like, "Hack around on something," right? Do something interesting. To me, this is the sort of thing that would come out of 20% time, where it's like, "Let's find some of the, you know, the biggest—everyone at Google is a nerd—but let's, let's find people who are nerdy about something really in particular that could be massively useful to maybe a narrow group of people, but maybe we find something in there that, who knows, does scale up to a billion people eventually." Yeah, but maybe we should talk about the audio, Kevin.
Yes, we have to talk about the audio feature because this is what really has put this, uh, this tool on the map for a lot of people. Um, this is an amazing feature. The first time I saw it, I did have a moment of AI vertigo, and I think I emailed you and was like, "Oh my God, what is this thing? How is this feature so good? How does it work? Uh, what is it trained on? How did it learn how to do podcast banter?" Just tell us about this feature.
There's another great Labs, um, case study. Um, it was another team inside of Labs that had basically developed a, a tool that would take any source material you wanted, um, and generate an audio conversation that would sound like two engaged, entertaining people having a conversation. You may be familiar with this genre, uh, about the whatever material you gave it. And the, the kind of two use cases that we were talking about in, in the early days was, um, M, kind of source material that no, no one would ever build a real podcast for—so arcane City Council meetings that no one—there's no economics—and turning that into a podcast, or personalized learning, where you're, like, you're an auditorial learner, learner, and you want to, you know, do a review of the week's, you know, uh, assignment, and you'd rather digest it in the form—or you'd like to augment it with—listening to a conversation, um, because people remember better with conversations, and they can do it on the go. And so they had this incredible demo, and the thing about it, like, behind the scenes is that a lot of the breakthrough is actually the, the edit cycle. So behind the scenes, it's basically running through, you know, stuff that we all do professionally all the time, which is: it generates an outline; it, it kind of revises that outline; it generates a detailed version of, of the script; and then it has a kind of critique phase; and, um, and then it modifies it based on the critique; and, and so it takes about four or five minutes to generate, and it's because it's going through all these different passes. And you know, you can call that, like, Chain of Thought reasoning, or you know, you could do... But when I saw it, I was like, "No, no, no, that's an edit cycle. Like, that's what you—you did a draft, and then you revised it, and then you got, got better over time."
Are you taking notes? You could do this for your book. You help—go ahead, Stephen.
So, and, and then at the end of it, the, there's a stage where it adds my favorite new word, which is "disfluencies." So it takes a kind of sterile script and turns—adds all the, the banter and the pauses and the, the "like" words. And that turns out to be crucial because you cannot listen to two robots talking to each other; no one—it would be just painful to listen to. Um, it'd be like, I don't know, the Lex Friedman podcast is pretty popular.
[Sten:] Oh, boy.
So not even going to bring that up. Uh, follow up on that. So, so that was crucial. And then on top of that, all there is a, um, um, there's some new voice technology, which I'm, I'm not qualified to kind of explain, but that adds a really, an incredible layer, which is, like, figuring out, without any coding in the script, figuring out that this is a point that they are trying to emphasize, and so they're speaking more slowly, or they're trying to imply that they're hesitating a little bit, and so they're raising their intonation a little bit. All that stuff it does. And having two people talking to each other like that, it just, it is one of those moments when I first heard it, I was like, "This is incredible." And we were already—we were already—we rolled out these Notebook guides that take your, all your documents and turn them into a briefing doc or an FAQ or a timeline, which is incredibly useful for, you know, kind of writers, um, and so this was just like, "Oh, we can now do it in another form," like, if maybe you want to take your sources and listen to a conversation about them. And so it just was a beautiful fit inside of Notebook LM, and so we just have been scrambling all summer to get this out, and, uh, we're—it's, it's been really cool to see the really awesome—I made a podcast about, uh, my, my new, uh, vacuum cleaner that I got by feeding it the, the user's manual PDF, and out pops this eight-minute explanation of all the features of my new vacuum. It's really cool.
Okay, so I have a confession, which, which is: I am obsessed with this stuff. And Stephen was very kind and, and gave me early access to this, but at the time I was, you know, getting ready for Meta Connect and some other things, and I just did not have the, the, the time and attention to focus on it. But then when I found out when you were coming on the show, I thought, "I am not going to listen to any of these until we're all in the room together."
Oh my gosh.
And here, and here's why: because I've learned from YouTube that the most popular thing that you can do on YouTube is to hear something for the first time. So, you know, I don't know if you've seen this, Kevin, but it's like, if, if you listen to Metallica for the first time on YouTube, you got a million views. But I already listened to Metallica in high school. So I thought, "I'll do the next best thing and listen to the Notebook LM audio right here on Hardfork." So let's listen to a few examples of this audio feature. Uh, I have been playing around with this for a couple of days now, having a lot of fun with it. Uh, so a lot of times in our work as journalists, we have to sort of make sense of a bunch of different documents, whether they're legal filings or, or what have you. And, um, so I was doing some research about Waymo and their self-driving cars, and there have been a few studies that have come out recently about, uh, the safety data of these cars—of human drivers, sort of how, how safe are Waymos compared to human drivers—and it's been a really hot topic; we've talked about it on the show.
Totally.
Very controversial, but it's a, it's a little hard to understand; the data is a little mangled, and, and they're just—these papers are quite long. And so, uh, this morning I was going, uh, into the studio, and I thought, "I'm just going to dump a whole bunch of these PDFs of these studies into a Notebook LM and generate a podcast that I can listen to on the way to the office and maybe get a sort of high-level overview of what this, these studies have shown." So I want to play for you the, the first sort of 30 or so seconds of my Waymo data podcast.
All right.
Ever see one of those Waymo cars just cruising around with, like, no one behind the wheel?
Yeah.
I always wonder, is that thing safe? I mean, no offense to robots or anything, but handing over the keys to a machine—it just, it just feels different.
Yeah, it really does make you think about, like, trusting technology with our lives.
Exactly. Especially when it's something as important as, like, driving, you know.
Totally.
So that's what we're diving into today—the safety of those driverless Waymos. We've got a bunch of research lined up, including some really recent data, to get past the headlines and figure out what's really going on.
So that's, that's amazing. That's one clip. Um, that's amazing; it's really cool. Okay, and it, it does actually continue on for, like, minutes after that and sort of break down the data in these papers quite well, from what I can tell. I also put in your, uh, latest Platformer newsletter, uh, into this and had it generate a podcast about that. So here's a clip from the Platformer newsletter AI podcast.
Wow.
Speaking of risks, Newton's decision to leave Substack—that was a risk, but it sounds like he's thinking long term.
He is. He really is. And I think that's key—building a sustainable media business in this day and age, it's not easy. It takes more than just great content; you need smart business decisions, too. It's a balancing act, right? You got to stay true to your vision, but also make sure you can, you know, keep the lights on. And Newton's been very open about Platformer's finances, about the challenges that come with going independent. Transparency builds trust, and these days, trust is invaluable. And let's be real—leaving a platform like Substack, even one with its problems, it's going to come with some financial growing pains for sure. It's like jumping off a cliff and hoping you can build your wings on the way down.
I like these people; they're smart people. What they're talking about...
Yeah, they're big fans of you.
The last clip I want to play—I, I was just sort of thinking, like, "How, how esoteric can I get here? Like, what can I make a podcast about using Notebook LM?" So I uploaded my most recent credit card statement to Notebook LM and had it try to make an AI podcast about some of the things that I've been spending my money on. And, uh, so this is the, the, the most recent, uh, credit card statement that I got in podcast form.
I love it.
Okay, let's see. I'm noticing a pattern here—quite a few Uber rides between August 8th and September 9th.
Yeah.
And that's something to consider, right? Especially if you live in an area with, you know, readily available public transportation or bike-friendly routes. Most ride shares can really add up.
For sure, they do. I mean, for example, let's say an average Uber ride costs you $20, and you're taking four of those a week—well, that's $320 a month. Think about it; that's money that could be going to other things, other financial goals.
Yeah, absolutely. Small changes can make a big difference.
Yeah, it really told me to get my ass on the bus. It's, it's like a financial advisor in your pocket.
Truly.
So this, uh, blew my mind. Does—what do you—what's your reaction?
It really is extraordinary. And, um, you know, again, I knew we were going to do this today; I wanted to wait until this moment to hear it. But in the meantime, I was seeing so many folks on social media saying, "You have to listen to this; like, it is so eerily good." And you know, my mind is already alive with, you know, a problem that I have, which is: because we do a podcast, we often talk to the authors of books, and often we decide we want to talk to them, you know, a week before, and then I get a PDF in, you know, in my inbox, and, and now I have seven days to read it, and it's incredibly difficult. If I could listen to a podcast about it, I would love it. Now I realize how painful that's going to be to hear for every author who exists, but, um, you know, if it—information more accessible—I want to try it.
Yeah. And, and probably people will make podcasts about a lot of the books that we talk on this—talk about on this show. But no one's going to make a podcast about my credit card statement or, uh, you know, the policies at my kids' daycare or something like that. So I—it's a really interesting way to, to sort of transform these sort of more, uh, esoteric, uh, documents. I mean, once I heard about your prof at Uber spending, I do kind of want to do a podcast about your spending habits, but go on.
Well, I, I think actually that one was really interesting because, one, its ability to figure out a way to rationalize a podcast is amazing. We would give it kind of internal Notebook LM documents, and they would be like, "Well, it's really exciting; we've got our hands on some internal documents from Google, and you know, they just—they want to turn it into something that's a show," um, but, but what you could hear there is an interesting thing: they are generally, um, instructed to be enthusiastic and engaged. So one thing people are doing is putting their CVs and resumes in there, and they're like, "John Smith—I mean, what an amazing career he's had! I mean, assistant vice president at the bank—that's impressive!" You know? But what happened there, apparently, because your Uber riding is so excessive, was an interesting suggested mode, which is "critique," yes, right? So you can imagine a future version, which is like, "I actually, I want some tough love here. Like, here, here's the thing I'm working on, like, talk me through, like, what the problems are. I, I want to hear that." And that could be something that—I didn't give it any prompt; I didn't say, "Criticize my spending on Uber." It was literally just, "Make a podcast out of my credit card." One click, upload the PDF; I pushed a button; I waited a couple of minutes; I, I listened to this podcast, and it had pulled out some details and spending patterns for my credit card statement. That's just incredible.
Are we, are we seeing the seeds of our demise, podcasters, like, in this segment?
Well, that was the second thing I was going to say, which is that they're, they're instructed to—and my mind is, is kind of changing on this as users are experimenting with it more—but, um, they're instructed to be fun and engaging. And I listen to dozens and dozens—into hundreds—of these, you know, as it was in development over the summer, and, um, they were always fun and engaging; there's banter and all that stuff. I never once heard them be funny. I never once laughed. And I thought about—I'm, you know, since you've been saying so many nice things about me—I thought about Hardfork, which I laugh at out loud all the time when I listen to you guys. And it occurred to me, like, the, the one thing, interestingly, that the models—they're so good at so many things, so almost, you know, superhuman in some of their abilities—but they can't yet be funny in this way. And the kind of the banter back and forth between them is never actually funny; it's fun, but not funny, right? And then somebody on the internet—there's a, there's a fake scholarly PDF, the "chicken PDF"—have you ever seen this?
Know this one?
Yeah, it's just—it looks exactly like an academic PDF; it's got charts and graphs and things like that, but every single word is "chicken" in the entire document. I don't know why this was made, but they put it into as the source, and the host just kind of went insane, but were hilarious. And there's a whole riff where they're like, "This document is making us insane; it's just chicken, chicken, chicken, chicken, chicken, chicken, chicken," and they do all these things with their intonation. And at some point, the host says—one of the, the guy says something like, "It's, it's like we've been taken hostage by chicken, and the ransom is our sanity." I literally laughed out loud. So I—it is possible to elicit stuff that is genuinely funny from them, um, so you know, who knows? Obviously, being able to, to push it in directions and give it some guidance is something we're getting a lot of requests for, um, so I would expect to—you could also imagine being able to sort of pick, like, "Do you want this to be a two-person, sort of hosted podcast, um, you know, or do you want it to be just one
Right, like you have word processors help you create a document, and Photoshop helps you, you know, adjust pixels in an image. This is a tool that helps you understand things, and like if you are trying in good faith to understand the material as a student, or as a knowledge worker, as a writer, or whatever, it is like this is a tool that should really help you do that better. Yeah.
Um, what's next for you personally? Are you going to stick around at Google and build some more stuff? Uh, are you are you are you yearning for the solitary life of a writer again? I am not yearning. I am really enjoying this, and there's so much to build, um, and to be in the middle of, you know, the most important technological change of my life, life, um, working with really interesting people on this thing that I've always wanted. Like I would kind of be an idiot to stop now. Yeah. Also, the snacks are way better at Google. Unbelievable cafeteria.
All right, Stephen Johnson, thanks so much for coming on.
Thanks guys.
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