The Exchanges

Q What's the initial product, just text to speech, or were you also doing kind of like a synthesizing of the content, refining it, or were you just helping people read through it?

A Before we did the IO announcement in 23, we'd already done a lot of studies. And one of the first things that I realized was the first thing anybody ever typed was summarize the thing, right? Summarize the document. And it was like half like a test and half just like, oh, I know the content. I want to see how well it does this. So as part of the first thing that we launched, um, it was called Project Tailwind back then. It was just Q and A. So you can chat with the doc just through text and it would automatically generate a summary as well. I'm not sure if we had it back then. I think we did. It would also generate the key topics in your document and it could support up to like 10 documents. So it wasn't just like a single doc.

AI assessment note: “It was just Q and A. So you can chat with the doc”

Q And then what was the discussion from there to where we are today? Is there any maybe intermediate step of the product that people missed, uh, between this was launch or?

A It was interesting because every step of the way, I think we had, we hit like some pretty critical milestones. So I think from the initial demo, I think there was so much excitement of like, wow, what is this thing that Google is launching? And so we capitalized on that. We built the wait list. That's actually when we also launched the Discord server, which has been huge for us because for us in particular, one of the things that I really wanted to do was to be able to launch features and get feedback ASAP. Like the moment somebody tries it, like I want to hear what they think right now. And I want to ask follow-up questions and the discord has just been so great for that. But then we basically took the feedback from IO. We continued to refine the product. So we added more features. We added sort of like the ability to save notes, write notes. We generate follow-up questions. So there's a bunch of stuff in the product that shows like a lot of that research, but it was really the rolling out of things. Like we removed the waitlist. So rolled out to all of the United States. We rolled out to, uh, over 200 countries and territories. We started supporting more languages, both in the UI and, like, the actual source stuff. We experienced, like, in terms of milestones, there was, like, an explosion of, like, users in Japan. This was super interesting in terms of just, like, unexpected…

AI assessment note: “every step of the way, I think we had, we hit like some pretty critical milestones.”

Q So did he just commit to using Notebook LM for everything? Or did you just model his existing workflow?

A Both, right? Like in the beginning, there was no product for him to use. And so he just kept describing the thing that he wanted. And then eventually, like we started building the thing and then I would start watching him use it. One of the things that I love about Steven, Is he uses the product in ways where it kind of does it, but doesn't quite like he's always using it at like the absolute max limit of this thing. But the way that he describes it is so full of promise where he's like, I can see it going here. And all I have to do is sort of like meet him there and sort of pressure test whether or not, you know, everyday people want it and we just have to build it.

AI assessment note: “Both, right? Like in the beginning, there was no product for him to use.”

Q there. How should people think about that? Like, is this, and also like the future of the product as far as monetization too, you know, like, is it going to be The voice thing going to be a core to it? Is it just going to be one output modality and like you're still looking to build like a broader kind of like a interface with data and documents platform?

A I mean, that's such a, that's such a good question that I think the answer it's, I'm waiting to get more data. I think because we are still in the period where everyone's really excited about it. Everyone's trying it. I think I'm getting a lot of sort of like positive feedback on the audio. We have some early signal that says it's a really good hook. But people stay for the other features. So that's really good too. I was making a joke yesterday. I was like, it'd be really nice, you know, if it was just the audio, because then I could just like simplify the train, right? I don't have to think about all this other functionality. But I think the reality is that the framework kind of like what we were talking about earlier that we had laid out, which is like, you bring your own sources, there's something you do in the middle, and then there's an output is a really extensible one. And it's a really interesting one. And I think like, Particularly when we think about what a big business looks like, especially when we think about commercialization, audio is just one such modality, but the editor itself, like the space in which you're able to do these things is like, that's the business, right? Like maybe the audio by itself, not so much, but like in this big package, like, oh, I can see that. I can see that being like a, a really big business.

AI assessment note: “audio is just one such modality, but the editor itself... that's the business”

Q Are you adding like real-time chat on the output? Like, you know, there's kind of like the deep dive show and then there's like the listeners call in and say, Hey.

A Yeah. We're actively, that's one of the things we're actively prioritizing. Actually, one of the interesting things is now we're like, why would anyone want to do that? Right? Like, what are the actual, like kind of going back to sort of having a strong POV about the experience. It's like, what Is better? Like what is fundamentally better about doing that? That's not just like being able to Q&A or, you know, how is that different from like a conversation? Is it just the fact that like there was a show and you want to tweak the show? Is it because you want to participate? So I think there's a lot there that like we can continue to unpack, but yes, that's coming.

AI assessment note: “Yeah. We're actively, that's one of the things we're actively prioritizing.”

Q And obviously the answer for most people is going to be a spectrum in between those two, like big model, small model. When do you decide that?

A I think it depends on the task. It also depends on, well, it depends on the task, but ultimately depends on what is your desired outcome? Like, what am I engineering for here? And I think there's like several potential outputs and there's sort of like general categories. Am I trying to delight somebody? Am I trying to just like meet whatever the person is trying to do? Am I trying to sort of simplify a workflow? At what layer am I implementing this? Am I trying to implement this as part of the stack? To reduce like friction, you know, particularly for like engineers or something, or am I trying to engineer it so that I deliver like a super high quality thing? I think that the question of like, which of those two, I think you're right, it is a spectrum, but I think fundamentally it comes down to like, it's a craft, like it's still a craft as much as it is a science. And I think the reality is like, you have to have a really strong POV about like what you want to get out of it. And to be able to make that decision. Because I think if you don't have that strong POV, like you're going to get lost in sort of the detail of like capability. And capability is sort of the last thing that matters because it's like models will catch up, right? Like models will be able to do, you know, whatever in the next five years, it's going to be insane. So I think this is like a race to like value. A…

AI assessment note: “I think it depends on the task. It also depends on... what is your desired outcome?”