Calm AI for Crazy Days: Inside Granola's Design Philosophy, with co-founder Sam Stephenson

Sam Stephenson, co-founder of Granola, explains how a deliberately minimalist design philosophy helped turn the AI note-taking app into one of the fastest-growing products in the market. He shares why Granola focuses on doing one job exceptionally well, how note sharing drives growth, and what they’ve learned from surprising use cases, recipes, and constant user research. The conversation also covers privacy and consent, transcription and cost choices, team collaboration, and Sam’s hopes for AI products that create less screen time and more space for reflection. Google: Try Gemini’s image creation model, Nano Banana, to create original art in seconds with easy iterative prompting via Google AI Studio or the Gemini app. Sponsors: Roboflow: Roboflow gives robotics and embodied AI teams the visual AI infrastructure to turn messy, real-world perception into precise, reliable action. Learn how leading robotics companies solve bin picking and more at https://roboflow.com VCX: VCX, by Fundrise, is the public ticker for private tech, giving everyday investors access to high-growth private companies in AI, space, defense tech, and more. Learn how to invest at https://getvcx.com Claude: Claude is the AI collaborator that understands your entire workflow, from drafting and research to coding and complex problem-solving. Start tackling bigger problems with Claude and unlock Claude Pro’s full capabilities at https://claude.ai/tcr Tasklet: Build your own Cognitive Revolution monitoring agent in one click. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai CHAPTERS: (00:00) About the Episode (03:52) Special Sponsor (05:52) Granola growth and users (17:14) System2 goals and context (Part 1) (17:19) Sponsors: Roboflow | VCX (20:15) System2 goals and context (Part 2) (33:09) Costs, pricing, and transcription (Part 1) (33:22) Sponsors: Claude | Tasklet (37:12) Costs, pricing, and transcription (Part 2) (47:38) Meeting privacy and consent (54:13) Agents, memory, and simplicity (01:03:49) Recipes, use cases, and growth (01:11:52) AI product design culture (01:28:08) Future risks and vision (01:33:33) Episode Outro (01:36:59) Outro PRODUCED BY: https://aipodcast.ing

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Nathan Labenz?

  Hello and welcome back to The Cognitive Revolution. Today, my guest is Sam Stevenson, co-founder and designer at Granola, the breakout AI note-taking app that recently raised $125 million at a $1.5 billion valuation. As a user of the app myself, I've been struck by how streamlined, even minimalist, the Granola product experience is. And considering how easy it is to code up new features these days, I just knew that this had to be a very deliberate design choice on Sam's part. And so I wanted to use this conversation to hear what he's learned about designing AI products for mass-market adoption. We begin with Granola's product design philosophy, which Sam playfully calls "surprisingly unambitious",

• 0:44Nathan Labenz?

  at least in terms of the number of jobs that they aim to do for users. Taking inspiration from the kitchen tool company OXO, which designs products that work for people with disabilities and end up delighting everyone else, Granola aims to provide a calm product experience for people with crazy workdays. In practice, as you'll hear, the keys to Sam's success are simple, timeless design disciplines: spend lots of time with lots of users, understand their challenges and needs deeply, and do one thing extremely well before adding other features. All very familiar advice to any product leader, but increasingly counter to the trend in the vibe-coding era. As always, we get into a lot of additional detail along the way. Sam says that Granola's rapid growth

• 1:28Nathan Labenz?

  has been driven overwhelmingly by the single core mechanism of users sharing call notes with teammates and partners. He also highlights a number of popular and surprising use cases and explains how they're using recipes, such as the blind spot finder that I created when Granola sponsored a number of episodes earlier this year, both for marketing purposes and, more importantly, to inspire users to use the product to its fullest potential. He tells us what partners they're using for transcription, why Granola has no limits or concept of credits, and how they're thinking about managing inference costs as they add new features over time. He explains their decision to make granola work at the operating system audio level, rather than joining calls as a participant like most other note-taking

• 2:10Nathan Labenz?

  apps do, and how this relates to their thoughts on privacy and consent, their decision to store call transcripts but not raw audio, and the idea that we might want to engineer at least some AI systems to forget certain details over time, just as we humans do. He also unpacks how granola is thinking about their new team collaboration features, the tricky balance between the immense upside of better information sharing across organizations, and the risks of oversharing sensitive information, and also how much we can trust AIs to decide what should be shared with whom. We discuss how granola's team works today, including why he thinks that Figma remains valuable but should be nervous, and more importantly, how they are using a combination of internal demo days and intensive dog fooding to inform what are ultimately mostly

• 2:55Nathan Labenz?

  vibes-based decisions about what to launch. Toward the end, we get Sam's greatest hopes and fears for granola, namely that it will help people live something closer to their best lives, with less screen time and more space for reflection and strategic thinking, and on the other hand, the possibility that a few leading AI hyperscalers could end up dominating the market and capturing most of the value that AI creates. Clearly, there are multiple ways to build a successful AI product in today's world, but for me, as someone who's constantly obsessing over frontier capabilities, this conversation was a very useful way to calibrate myself on how mainstream users understand and relate to AI, and also a great reminder that timeless design discipline continues to work.

• 3:40Nathan Labenz?

  With that, I hope you enjoy this inside look at the design thinking powering one of the fastest-growing AI products in the market today, with Sam Stevenson, co-founder of Granola. The Cognitive Revolution is brought to you in part by Google, makers of the Gemini family of models, and much more. Here's a fun fact about me. The first thing I knew I wasn't going to be when I grew up, going all the way back to first grade, was an artist. I could vaguely imagine what I wanted to create, but I couldn't come anywhere close to creating what I'd imagined. And so I concluded that art wasn't for me. Recently, though, I've been using the Gemini image creation model, which you may know as Nano Banana, to create art for every new episode of the show.

• 4:25Nathan Labenz?

  The workflow is honestly shockingly simple. I started by identifying a source of inspiration. In my case, it's a set of massive frescoes at the Detroit Institute of Arts that depict Henry Ford's iconic assembly lines and evoke the transformative impact, for better and for worse, that the Industrial Revolution had on human workers and society as a whole. I then use a three-part prompt, which consists of a style guide, combining colors, textures, and details I remember from the paintings with my own ideas about cyberpunk futurism, a few notes on the episode title and themes I want to feature, and a few reference photos of the episode guest. Mere seconds later, I'm opening original art. And while it does usually take a few rounds of iteration to land on something that I'm proud of, that process is also pretty easy and quite fun.

• 5:10Nathan Labenz?

  I just give my favorite generated images and guest photos back to the model with some additional natural language instructions and wait to see what it makes. I never thought I'd be able to create a consistent visual identity for the show. But with Nano Banana, I can have fun creating things that I enjoy, that make my guests look good, that perform well on YouTube, and that pay homage to a uniquely memorable piece about the promise and peril of technological change, all in just a few minutes. It truly is an incredible time to be alive. So try Nano Banana for yourself via Google's AI Studio or the Gemini app. Thank you to Google for supporting the cognitive revolution. And now, on with the show.

• 5:52Nathan Labenz?

  Sam Stevenson, co-founder and designer at Granola. Welcome to the Cognitive Revolution.

• 5:58Speaker 02

  Hey, thank you very much for having me on. It's fun to be here.

• 6:01Nathan Labenz?

  I'm excited for the conversation. I have been using the product a bit over the last few months. And then scrolling Twitter, as I am constantly doing, something popped up not too long ago that really caught my attention, which was a report from Ramp, which said that, I believe it was in the month of January, that Granola was the number two company out of every company tracked by Ramp, which I assume is pretty much everybody, in terms of the number of new customers added, trailing only Anthropic in that month. Which I thought was a credible feather in your cap. And so I wanted to start there and just say like, how are you doing it? That's a lot of new customers to be adding. And clearly something is really clicking.

• 6:45Nathan Labenz?

  So what would you say is really clicking and driving that growth for Granola?

• 6:50Speaker 02

  Yeah, I was taken aback when I saw that too. Like, I guess we know, we know what's happening to us internally, but like, yeah, to see yourself among that company is like, it's a great feeling and it's very validating. So, yeah, I mean, like, I feel like Granola is growing fast and it's like almost entirely through word of mouth, people recommending it to each other or sharing notes with each other. And I guess we kind of, you know, when we were starting that we could like, build a thing good enough that people would talk about it with their friends and that hopefully had some inbuilt product virality. And it's, I mean, like, it took us a while to unlock some of those viral loops and to get that to start happening.

• 7:33Speaker 02

  But I feel like the thing's really starting to snowball at the moment. And we're growing a lot, a lot month to month, over month.

• 7:41Nathan Labenz?

  I definitely want to get back to the viral loops and get into more detail on that. Maybe just start with like, who are your customers? How do you understand them? How do you go out to meet them? How do you think about how they understand AI? How they relate to it? What they want from it? What's the sort of character sketch of your ICP?

• 7:59Speaker 02

  Yeah, I think, I think this is interesting because like, we, I think the character that we hold in our heads is probably a more extreme than the average actual Granola user. When we were set up to build Granola, we, I think, were most interested actually in not in like meeting notes. We were really just interested in, we wanted to be playing in the space of inventing what are, what are going to be the UIs and the interfaces that let ordinary people who are like, using computers to do their work, but see the computer as a means to an end, not as like this thing to geek out on and to spend all this time fiddling with. How do we help those people like access the power of this new technology? And notes were, I think, our way of getting our foot in the door into their lives and starting to build a product that could be habitual and could let us do more over time.

• 8:42Speaker 02

  I think we spoke to a lot of people in the early days in a very open-ended, very explorative way with no real agenda, just trying to learn about their work lives. And I think the like archetypal person that emerged as like a good target user for us over time was like, Somebody, basically, somebody who's in back-to-back meetings all day. And that could be like many kinds of jobs. That could be like a salesperson, a account manager, a recruiter. Could be an investor. It could be a founder of a company. It could be anyone in client services is like is doing calls all day long, talking to clients and things like that. And I think they appealed because, not because we want to build a product only for them, but there's a great analogy.

• 9:24Speaker 02

  My co-founder Chris kind of brought and I really like, which was, um, there's this kitchen utensils brand. I'm gonna fuck it up. I think it was, I think it was OXO, but I might be wrong. And they had this thing where they would deliberately design products for handicap people. People who was like had struggle like holding like gripping utensils or only have one arm or they like they have some kind of disability. And and they deliberately designed products for those people, not because they just wanted them to be used by those people, but because if you succeeded at building, like designing a product for the most extreme kind of user like that, then you're in a creating a very kind of friendly, easy to use product for the rest of us too.

• 10:07Speaker 02

  And, uh, yeah, this is like, um, I think if you work in like accessibility and UI design, this is a common, common thing people talk about too. But I think our version of that was like somebody who is running from back-to-back meeting all day long, frazzled by the context switching, barely have time to go to the bathroom between meetings. Their calendar is just like a constant block of stuff all the way through the day. And these people like exist in real life, but they also are like just the most extreme portrayal of reality that all of us have to deal with pretty often in our lives. And so we felt if we set our sights on building a great product for a person like that, then hopefully we achieve it. But hopefully we also just create a really simple, easy to use thing for the rest of us who, who still have those moments of frantically jumping between things.

• 10:52Nathan Labenz?

  Yeah, I like that. I've had a little bit of experience in my day as a product designer. Usually I've been so frantic and hustling to try to get to some level of product market fit that I'm not yet thinking of the extreme long tail user. And maybe that's a mistake. Maybe I should be reversing the order in which I'm thinking about it. But it's an interesting kind of flip from the way I usually have approached things, which is hit the center of the distribution first and then work out from there. What, could you give me a little bit more, and I've seen some comments from you in other places around system one, system two thinking, but like, how could you maybe describe a little bit more? Like, what is the, obviously, people are busy, right? And everybody's short on time.

• 11:36Nathan Labenz?

  But what does that translate to in terms of what they are unable to do that you are specifically trying to design around?

• 11:46Speaker 02

  Yeah, yeah, yeah. Yeah, I think, I think as to build is myself included. I think, I think we fall into this trap way too often of like, assuming that the user of your software is like, coming to your software in this like calm state of mind where they're going to give it their full attention. And they're going to think through their actions and they're going to be capable of like, stringing together quite complicated sequences of maneuvers in the software to do what they want to do.

• 12:18Speaker 02

  Yeah, you know, and therefore, and because of that, you know, we feel like we can get away with like, pretty sophisticated, complicated bits of software that people have to learn and develop mastery over time. And I think like, sometimes that's the case, but I think it's probably way less than we would like to think. And I just think the reality of like work for most people who work on a computer is, is way more reactive and way more like chaotic. And so much of the time, people are not operating with their like rational, methodical parts of their brain. They're in like, They're in like a reactive, like the the system one kind of kind of brain, you know, if you're familiar with that, where you're, you know, where where really like you you

• 13:03Speaker 02

  wake up in the morning, you check your inbox, you're like, ah, fuck, there's three things that that that are like burning fires and I gotta deal with it right now. But I also have a meeting starting in 20 minutes and like, oh, shit, how am I gonna, how am I gonna juggle this all this? And then, and then they're just kind of in a constant state of like feeling behind, feeling overwhelmed, never quite getting on top of things. And I think, I think more, you know, I think just that's the reality for a lot of people that work. And as software designers, we need to kind of assume that reality and design for our software to fit in that reality.

• 13:39Nathan Labenz?

  Yeah, that immediately calls into question the way that most people do user testing, right? Where you're like, it's been a while, again, it's been a little while since I've done this. But when you sit down with somebody and you're like, I would just like to watch you use my software and I'm not going to interfere at all. But you are immediately putting them in a state of full focus on this because they don't want to look stupid to you if nothing else. So that I think is immediately insightful. Is it, do you have a tip for like, how to user test in a way that kind of captures that real world frazzled state of mind as opposed to the kind of best self that you don't actually expect to get from new users most of the time?

• 14:20Speaker 02

  Yeah, it's a great point. Yeah, I feel like I'm, I I fall into the same, I feel like I'm guilty of the same thing all the time, too, you know, like you're, you want to watch someone use your thing and get validated that they can, they understand it. And yeah, I think, well, it helps. I think one, one thing we found incredibly helpful is whenever possible, like when we're talking to a user, I'll just try and get as grounded as possible in like, what's the reality of your working day and how has Granola fit into it? And this is hard with like watching them use the products live. But for example, when we were

• 15:03Speaker 02

  When we were thinking about introducing folders in granola, or like some kind of organizational principle, the way we started interviewing people about it was we would ask them to share their screen and bring up granola. And then we just go like, go to their granola home screen where they have a list of all the meetings they've done. And we go meeting by meeting and I click on one and ask them, you know, like, what was this meeting about? Who was there? Who should, if you could share these notes with someone, who should see them? What would you think about organizing this in your head? And it's, they're still, they're in an interview, they're in like a rational state of mind, but they also can't lie about the facts of what's on the screen in front of them and what notes they took and what meeting actually happened. Like, I think

• 15:47Speaker 02

  the trap which I'm at least constantly trying to fight when I'm talking with a user is as soon as you get into abstraction, like layers of abstraction and them talking about theoretically or generally what they do, then you can't trust any of that. Like it's, you're in like a, yeah, you're getting the like person's imagined view of themselves, which is often so different from how they actually behave in the real world. We did a similar thing with people's calendars, getting people to pull up their calendars and using that as like a forcing function to talk about the reality of their day and what's happened in their day. Really helpful for the same reason, yeah. And then I guess that's helpful for like discovery, like talking to people and learning about their work and how granola might fit into it. In terms of evaluating stuff we've built, I think we're very dependent on

• 16:32Speaker 02

  how it feels for us to use it ourselves. And now the team's a little bigger, like how we can observe people in the team using it like in the wild. And I find that's maybe more useful and telling than getting somebody to talk about their experience. Because you can literally, like I can sit in on a sales call and quietly watch somebody on our team using granola on the call. And I get a much less filtered version of what they're actually doing with the product.

• 16:58Nathan Labenz?

  That's interesting and helpful. I'm imagining even a more perhaps comical version of sit down with somebody immediately, like a fake fire alarm goes off in the background and then you like spill your coffee and then you might have to get through the university review board before you can deploy something like that.

• 17:13Speaker 02

  Yeah, yeah.

• 17:14Nathan Labenz?

  Hey, we'll continue our interview in a moment after a word from our sponsors.

• 17:19Nathan Labenz?

  How about, I want to get into also the, how this is all translating into actual form factor, design decisions of the product. But before going to that, just on a philosophy level, one of the, a big tension between, tension might be a little bit of an overstatement, but we hear a lot in the AI space about how AI is going to do the routine work. It's going to do the stuff we don't want to do. It's going to take care of the drudgery. And then the upside of that is going to be we get to be our better, more system two, more strategic, higher level of thinking, best selves. Okay, that's great. But then the other reality of product design, which I think you're embracing is like meet people

• 18:03Nathan Labenz?

  where they are. And so I'm wondering if you are, do you think that like the, in the fullness of time or in the, the most successful form of the product, are you still meeting people with a jam packed calendar who are stuck in system one and helping them manage that reality? Or do you think you are ultimately like helping them get into that system to state which they're not in today? And then that would, if so, that would maybe imply a different regime of product design in the future. Do you come down on one side of that debate? Or do you manage to find a balance between those two competing kinds of thought?

• 18:40Speaker 02

  It's a good question. It's a good question. I like, I think both. Like I feel like we'll be able to make progress in some areas faster and other areas not there. Like, I think, for example, there are so many tools out there trying to be like a work assistant, you know, executive assistant for you for work, or like a, you know, agent helper that understands what's going on and helps you do your knowledge work. And I've tried a lot of them. And, and like, I think the thing that they struggle with every time is like, Knowledge work is just so messy and nuanced.

• 19:21Speaker 02

  And I just think like, we're still a long way off. I think like, AI is having access to all of the information about a person's life and understanding all of the social nuance of like, what's my relationship with this person? And how should I write when I write to that person? And how should I rank that? Like, the importance of what they're asking for versus the importance of the other things that are being asked for. And therefore, the, I don't know, the to-do lists that these things create for you, or the drafted emails that they go and write for you. You can look at a screenshot of it and be like, oh yeah, that looks like it's gonna really help me. But then when you're actually engaged with the content, so often they just, if they miss the mark even slightly, then they're useless.

• 20:04Speaker 02

  They're like, ah, it's almost there, but I'm just not quite comfortable using that. And they fail. And I think, I think we'll get there. I think models will get better and it'll get easier to connect all the tools you need to connect. But I think it might take a surprisingly long time before we get there. And so I think the, we wanna be playing in that space too. And so the game for us is like, how do we? I think a principle that I think we found helpful often with this stuff is like, to almost be like surprisingly unambitious in the kind of tasks we promise to help you with. I think for, maybe, I think it'll be a while before Granola can help you with the kind of thorny, top of mind, most intense burning fires going on in your work life at any time. But I could definitely see Granola helping you with the menial stuff that you keep putting off,

• 20:48Speaker 02

  because it's not that important and also, and there's a lot of it and it's worrying. And I think just being very deliberate about where we focus our attention there will mean we can help and be more effective in helping you more quickly. Yeah, so I think like, I definitely see, in terms of helping people get into system two mode, I think that's the lens we have to take and that's how we have to approach it. I think there'll be surfaces in the product where, that are designed for you to spend more time in them and to do more kind of thinking work. So like, the full-screen chat interface we have in Granola now that has access to all of your meetings and all your company's meetings is designed to be sat with and used in long-form conversation. And I'll use that to write posts or like job descriptions,

• 21:30Speaker 02

  or I'll use it to analyze what's going on in a certain part of the company. And it's a very different mode to the like, meeting notepad, back-to-back meeting moment. And means we can do different things, design-wise.

• 21:41Nathan Labenz?

  Yeah, I like the multi-meeting feature a lot. It was one of the biggest things that I found to be particularly cool about using the product. Let's talk about the actual kind of design then a little bit. Actually, let me do one more beat on kind of what do you think the barriers are to to that kind of deep context? I would say personally, I've made progress and I'm obviously an early adopter and willing to put in the work. But and regular listeners have heard me talk about this a little bit, so I'll keep it brief. But basically, over the last couple of months, I've been really investing in making sure that the whatever agent I'm using has enough context where it's not for lack

• 22:24Nathan Labenz?

  of context that it fails. And that has involved a pretty tedious process of exporting everything from the systems where they live. All my emails from the last five years, all my Slack messages from the last five years, DMs across all these different channels, actually all calls that I've transcribed over time, even all the podcasts with all the speaker recognition direization, all that now lives in a single database on my computer and the agent can query it. And so it's not for lack of access at this point to my general circumstance that it fails. And I'd say it definitely has moved the needle in terms of how much I or how often I can actually say, hey, can you write an intro to this person, look up my history

• 23:07Nathan Labenz?

  with each one and my kind of pattern of intros and actually get something out that I am, if not immediately ready to send, at least yes, that definitely worked. And with a little tweak, it'll get there. So I feel like I'm starting to see the promised land. If I wouldn't necessarily say I've entered it, I can see over the hill at least a bit. What do you think is going to make that slow for most people? Is it going to be like trust? Or I guess we could also imagine that the systems themselves, I think we see a little bit of this from some systems are going to try to hoard their data and prevent people from exporting it all and doing that. Like, expertise from Slack is not a ton of fun. Pick on one particular platform. What do you see as the kind of fundamental barriers there that

• 23:52Nathan Labenz?

  that keep that from happening, say, in the next quarter or two?

• 23:57Speaker 02

  Yeah, I mean,

• 24:00Speaker 02

  I mean, everything you've said makes me, I feel like it's the things that you've done, that you're doing like connecting, like the machine is only as good as the context you've given it, as you're saying, like

• 24:12Speaker 02

  I think I mean, a lot's possible nowadays, like on a personal level, getting all your, you know, getting all your stuff into one place, like, I think

• 24:22Speaker 02

  it's much tougher if you work at a company and a lot of the context that needs to happen is like, you know, inside a company with all of the complicated permissions and and, you know, you've got to get, get security to sign off on connecting, like, you know, all the tools together and things like that. I think things are way more fragmented there still for most people.

• 24:42Speaker 02

  It's, it's, there are definitely like, a lot of granola customers are like, surprisingly progressive, I think, in, you know, how, how much they're willing to let employees like explore with this stuff and figure out good workflows. I think everyone kind of realizes that we're in a moment where if you don't, then then you're going to get like left behind really quickly. Yeah, but I think, I think context is a big, big one and then, and then just like the level of personalization and memory, like that's necessary, necessary to like, for the agent to do a good job, I think, is maybe underestimated by a lot of software, like

• 25:26Speaker 02

  I think the agent just has to know like a lot about you, right? A lot about you, about the people you work with, the projects you're working on,

• 25:35Speaker 02

  the priority level of things and, and I think, I mean, it's possible, you know, with like OpenClaw or any of the general purpose agents to like, teach them, teach them those things if you're extremely proactive today, but average, average computer user is not extremely proactive, like, you know, in, in setting these things up.

• 25:58Speaker 02

  And I think the job for us is to kind of make as much of that happen in the background as possible so that, you know, you don't have to think and you just get useful stuff put in front of you every time you turn on Granola.

• 26:10Nathan Labenz?

  The point about permissions is a, it's funny how simple that point is and how in theory easy to resolve it would be, but also how fundamental it really might be for a while yet. Notably, I am an admin on all of the systems that I was using. And I do think the ability, at least for me, to get that unified view that crosses personal and professional and just puts all of me into one database has been huge. And I just wouldn't have it if I weren't the admin on all of the systems. And I think it probably would be pretty hard to talk the admins into allowing you to do it if I wasn't myself the admin. So I definitely see that as a real challenge.

• 26:54Nathan Labenz?

  Presumably, you could get around it in the not too distant future with like computer use. You don't necessarily need the Slack API if your computer use agent can just go troll through. But still, you're going to be probably told explicitly, you're not allowed to do that. So that'll be another barriers, just outright rules. Yeah, that's really interesting. It makes me think of a context as a service startup as an interesting opportunity.

• 27:18Speaker 02

  Yeah, yeah, I think that's just like, I feel like innovation in this area would like unlock so much for so many companies and all of us at the moment, you know, like, how do you make the agents understand what's okay to be shared versus not okay to be shared? And who should know what kinds of information and things like that, you know, like we like granola, granola defaults to private for everything. So like every, every node you start is only visible to you unless you take an action to put it in a shared space. And and it's because like, conversations are like potentially really personal, right? And like, you only have to say one personal or dodgy thing to like, kind of pollute a transcript

• 28:03Speaker 02

  and make it not okay to share with with the wider organization. But that means that gives us like a really tricky design problem in that like, you've got to, you know, so much of the value of like transcripts in granola is them being in a shared space where the company can collectively access it. But how do you help those end up in the right places?

• 28:25Speaker 02

  You know, while also not? Putting information that shouldn't be shared into places into public places. It's a it's a tricky one and we haven't nailed it yet, you know, like we're trying stuff and it's getting better, but we could we could do a lot better still.

• 28:39Nathan Labenz?

  Yeah, that's interesting. How yeah, this is an obviously AI-pilled take on the question, but my first instinct would be to run the transcripts through and actually do this sometimes with the podcast, right? Like occasionally, especially if it's we get into politics or whatever, I'll sometimes run the transcript through the AI and say, is there anything in this episode of this podcast that the guest might regret having said publicly or something like that? And I would say they're pretty good at coming back with sensitive things. Those are these are meant to be public conversations, so it's probably a little bit more obvious in general, like what is going on and what would be sensitive or not in the context of private company internal conversations. You've got a lot that the AI doesn't have background knowledge on, but is that kind

• 29:24Nathan Labenz?

  of the direction that you're headed? Try to use the AI to help surface these potentially sensitive things for people?

• 29:30Speaker 02

  No, we're tinkering with this a bunch at the moment. Yeah, yeah, it's I feel like there probably is a world where we could do, we could like automatically put stuff in the right places 99 and 98% of the time. And the jury's out as to whether that's enough for people, like if there's even a tiny risk that like something sensitive goes into a public place, then that's not okay for a lot of people. But definitely like we have, I guess we have a basic version of this at the moment where you finish a note, Grinella will auto suggest the folder it should go into and that's an LLM on the backend, like it's got a bunch of it's like looking at the call, looking at the list of folders you have, what and the characteristics of things that usually end up in that folder and suggesting the one that it should go in. And I think it's not accurate enough for us to turn that on automatically at the moment, but there's a world where it could be with more work and with better models.

• 30:12Nathan Labenz?

  Yeah, I hear better models are coming soon, so that won't that trend doesn't seem to be stopping anytime in the immediate future. Hey, we'll continue our interview in a moment after a word from our sponsors.

• 30:24Nathan Labenz?

  Today's episode is brought to you by Anthropic, makers of Claude and Claude Code. Over the last few months, Claude has helped me build and refine a personal deep context database that now contains all of my emails, Slack messages, tweets, DMs across platforms, video calls and podcast transcripts going back a full five years. On top of that, we've now layered summary articles describing my relationship with hundreds of contacts, organizations, and ideas. And now that this exists, there's almost nothing that Claude can't help with. For my angel investing, Claude can now draft investment memos in exactly the form that my venture fund requires, based on the calls I've had

• 31:06Nathan Labenz?

  and the emails I've exchanged with the founders. And when someone needs a favor, Claude can often do it as well as I can. Recently, a friend reached out to ask if I know anyone who might be a fit for a role that he is currently hiring for. Initially, nobody came to mind, but then I thought to ask Claude, and sure enough, it identified two great leads. Claude is the AI for minds that don't stop at good enough. It's the collaborator that actually understands your entire workflow and thinks with you. So for problems worth solving, get started with Claude at claude.ai/tcr. That's claude.ai/tcr. And check out Claude

• 31:47Nathan Labenz?

  Pro, which includes all of the features mentioned in today's episode. That's claude.ai/tcr.

• 31:54Nathan Labenz?

  Okay, I do want to, maybe, let's just talk about inference budget, because some of the things you're saying there are really interesting, very much feels like the future, and also, I'm not sure how you're going to make it work at the current price point. So I'm wondering how you're thinking about that. Like, I, it's my full-time job to be a student of AI, and it's a very justifiable business expense to me to pay whatever my inference bill ends up being, and it's just me. When you are a company, you've got to be a little more disciplined about that. And when you are offering a product at a fixed monthly per seat price point, that is another level of that problem. Maybe I've missed

• 32:38Nathan Labenz?

  it, or maybe I haven't hit certain limits or warnings or whatever, but one thing that jumped out at me as I was kind of reflecting on my use of the product is I've never seen a You have this many credits this month, or you're like approaching that limit, or this action is going to use this many things. As far as I have experienced, it's always been just use it and it feels like it's unlimited. So maybe tell me if I haven't hit certain limits or I'm missing things. But that's an interesting design choice. And it also has me thinking like, are you guys in the Uber phase of who cares what it costs? Let's just make it great and we'll worry about our margins later. Or are there tricks, or maybe it's maybe usage patterns are such that it actually is working economically? And how much, especially when you want to go into these really deep contexts, right?

• 33:21Nathan Labenz?

  And you're getting into easily now, it can be a dollar plus for a single API call. If you throw 10 different meeting transcripts into Sonnet alone, you're getting to a dollar. So if you're going to then say, okay, I'm going to try to organize your folders with this really full contextual awareness, you're starting to spend real money, right? You're starting to see why Anthropic's revenue is going the way it is. So I guess a lot of directions to go there, but broadly like, how are you thinking about inference budget? And how it relates to driving the futuristic value that you're describing?

• 33:54Speaker 02

  Yeah, yeah, I think, I think, I mean, I think where we're at today is like really a function of where we're at as a company and like, you know, the how we've prioritized things over the last couple of years. Like when we were tiny and like, you know, pre-launch and just and like, you know, around launch, I think we explicitly had, we explicitly told ourselves like, like there is no budget. Like whatever, whatever makes us create the best product, we should work on that. And just creating a good product is going to be hard enough. Like when the user numbers are small enough, you know, like it's a big waste of time to focus on optimizing cost. And so, yeah, Granola was, I mean, Granola was actually really expensive, like when

• 34:37Speaker 02

  we launched, because so much of the cost was in transcription APIs. And that's the cost of that has gone down a lot as we've scaled and as the technology's got more commoditized and cheaper. So, yeah, and I think that was kind of the other days. I think today where we're at is like, Most usage of the product is like is the notes and that's a pretty predictable predictable cost, you know, like the the number of meetings someone does has like physical limits to it and and it averages out over the cost of a month and you can, you know, figure out what margins you're comfortable with there. I do think like as granola, like, you know,

• 35:18Speaker 02

  sophisticated users who have lots of meetings and want to be able to chat and do stuff with, you know, do stuff with large amounts of meetings, do rack up pretty big bills which we which we swallow at the moment. And and yeah, I think over time as granola kind of transitions to kind of more directly doing work for you as like rather than just being a kind of companion or aide or whatever, like, I think I think we'll have to look at the pricing plans and how do we, I think, usage based pricing or like a cursor or Claude model makes a lot of sense, like, you know, but I think there is, there's something

• 35:59Speaker 02

  very nice and transparent about like paying a price and getting a getting a thing and especially especially what people think of us as like a meeting notes app where you bring it to your meetings and you get meeting notes out and that's a that's the core of it. People really like the simple seat based thing.

• 36:17Nathan Labenz?

  Yeah, that echoes a lot of my kind of experience and general advice I've given over time, although you've taken it to quite a bit larger scale than I ever have, that's for sure. But I do think people in general shouldn't optimize too soon on cost. And then the question just becomes, can you get all the way to a unicorn valuation before you really have to get serious about that? And I guess the answer is yes, you can. So.

• 36:36Speaker 02

  Yeah, I can on that. The line of our burn rate, we plotted, we have a line that we keep up to date of if user growth keeps going at the rate it's going and how much the product costs. There was a point like a year ago or something, like six months after we launched where if you, from where we were then with what granola cost, if you plotted it out like a year from now, it got terrifyingly expensive. It's like, this is like a kind of distressing to look at, but the transcription costs have gone down a lot for us and that was. I think there was a time where half of our burn rate as a company was going on transcription. That's a lot better and more under control now. Yeah, so like, I think we will probably, we might, the pendulum might actually swing back again as we do more L O M stuff and

• 37:19Speaker 02

  work assistant type stuff over the coming months, probably get more expensive for us. And I think at some point, like, that's going to trigger a, we should think about usage based pricing. But yeah, for now, we're comfortable with where we're at.

• 37:31Nathan Labenz?

  Yeah, that's, and there could be kind of railing forces there too, in the same way there has been with transcription. Obviously, a lot of downward price trend in model costs is obviously dramatically down for a given level of capability anyway.

• 37:46Speaker 02

  We all like to tell ourselves that the things are going to get cheaper, but also the new like good models are always so enticing that.

• 37:51Nathan Labenz?

  Yeah, so far at least, I do think at some point, I think of this as being an Ahmad Mostak idea from, you know, he's the CEO of Stability and now has the Intelligent Internet Project. And he's a huge believer in satisficing. He's like, you know, yes, so far, every new generation of model, everybody's kind of found new utility in it. But how much longer is that really going to go before you'll get to a point where something really will be good enough for like a large majority of general knowledge work cases and the best models will really be kind of doing the genius in a data center thing. And do you really need a genius to like turn your meeting notes into a, you know, follow up email? Or is there a point where you can kind of

• 38:34Nathan Labenz?

  let, you know, let the cost come down again? I suspect that there probably is. On transcription, I don't know if this is something you can share or want to share, but how does transcription work today? Is it, it's pretty fast, I noticed that in using it. And it was so fast, I was like, is this running locally? I think there are small models that maybe could run locally, but I wasn't sure. So what can you tell us about how transcription works? Or what have you learned about transcription that you think maybe is underappreciated?

• 39:02Speaker 02

  Yeah, yeah, the way it works is pretty simple. Like, we record audio from the microphone and the system audio of your computer, and we pipe those to real-time transcription APIs from third-party providers. We work with a couple of those. And so it's all like cloud-based APIs. You transcribe in real-time for a bunch of reasons. It's very comforting to see the transcript come in real-time. You trust that the thing is working. It means that we can generate notes as soon as the meeting ends. We don't have to wait for the notes to arrive. And it also means we can discard the audio. So we deliberately don't hold on to any audio from the conversation. I just think it's far less creepy to only be keeping a transcript and not the full-on audio recording. Yeah, and there's a lot of trade-offs there.

• 39:44Speaker 02

  Like, the real-time transcription is a lot worse than if we were to send all the audio in one go and wait a minute for the transcript to come back. The quality is a bit worse. The speaker separation is a lot worse. There's a lot of trade-offs. But overall, I think it's the right move for us. Yeah, we're kind of like constantly looking at transcription because it's maybe like the most fundamental of the technologies in granola. And if you improve that by 10%, then there are a lot of knock-on effects to the rest of the product. I think it probably makes sense for us to move to one-device models at some point for speed and cost. Although, like, speaker separation is something that if we can make that better in the product, we'll just make everything else 10 times better.

• 40:28Speaker 02

  And so I can also see us just chasing whatever provider or solution is going to get us to that. So, yeah, I don't know. We have to stay nimble. We've architected the product so we can swap out transcription models fairly quickly and easily. And so we're constantly trying new ones and trying to figure out what the best strategy is there.

• 40:44Nathan Labenz?

  I don't know if you want to name names, but I'll tell you, on my end, I'm a pretty happy DeepGram customer. And I don't do real-time because I don't really need to. But I have a whole skill of skills. After we record, I'll get the audio out of Riverside and send it over to DeepGram and transcribe. And then downstream of that, as much other things, clip ideation and clip creation, and so on and so forth. These days, I'm even making songs out of Suno that all come, including key phrases, fun phrases, out of the transcript. So I'd say DeepGram has worked pretty well for me. Is there anything else that you would suggest that people try? That, again, you think is like underappreciated? Or anybody would want to shout out as being particularly good?

• 41:25Speaker 02

  Deepgram and Assembly are the two main providers we work with. Both are great. We've used Deepgram. I think we settled on them before launch and they're just, every time we evaluate, the models are good. And we can't find a better one. So Assembly, I think. Assembly, we've been using for a while now too. I think we actually split traffic in the desktop app between the both of them because they're comparable. And it's helpful to have redundancy with it because it's such a core infrastructure thing. Yeah, no, I think we're really, we're already happy with that. It's just that we keep our eyes open for like, how do we make this faster? Or how do we make this, have better speaker separation and better quality all the time?

• 42:02Nathan Labenz?

  Yeah, totally. Makes sense. Even just for API, we're definitely entering a world where up times ain't what they used to be. And I haven't had any problems particularly with Deepgram on the API uptime front, to be clear. But 98 point something is, you want to have a fallback. If for no other reason than you don't want to go down just because one of your API providers goes down.

• 42:21Speaker 02

  Yeah, people really squeal. When another goes down, it's, you know, it's very quickly and it's a very painful thing. Yeah, the redundancy is important.

• 42:28Nathan Labenz?

  Okay, you mentioned this idea of not storing audio. So that was definitely something I also wanted to dig in on from a kind of design perspective. I think there's multiple different.

• 42:42Nathan Labenz?

  Give me all the layers, but like the layers that I'm noticing immediately, as you said, is kind of, it's less creepy if it keeps the audio. I would be really interested to hear your thoughts on the way people are thinking about AI and being surveilled. We're entering this realm where certain powerful entities are indicating their interest in processing large amounts of data on individual citizens. There's that out there. And then there's this kind of, we're doing it to ourselves, which we obviously have that right to do. But then we're also doing it to everybody around us as we're doing it to ourselves. And this also relates, I think, to the decision to have the product work at the operating system audio layer as opposed to like joining the call.

• 43:25Nathan Labenz?

  Everybody has seen, and I actually use both in today's world. I use both a call joiner, note taker. And then I also have Grindr on my computer and they both do their thing. And again, this is something I do because I'm weird and try to use all the AI products. I don't think most people need to have multiple note takers on every call. But it stands out, right, as the sort of, it works on your computer, it works at the audio level. This does mean that it can happen without people on the other end of the call knowing that it's happening. You do have guidelines and some features for like, how you should talk about it and putting, there's one feature that allows you to automatically put a notice on the calendar invite that we'll be using granola for this. I don't know how many people actually do that. What are your thoughts on privacy, surveillance,

• 44:10Nathan Labenz?

  disclosure, consent? What really matters in this case? Is it really that big of a difference if it's audio versus the transcript? I don't have a great intuition for that, but it doesn't, it's not immediately obvious that I should be like that much more comfortable just because it's a transcript versus the original audio. So I'm sure you've come at this from every angle. Tell me everything.

• 44:30Speaker 02

  This has been like one of the, this is a sticky question, like I think we ended with a grapple with at the very beginning, making the decisions about being a desktop app and whatever. And it like continues to be a thing we have to keep looking at and improving today. Like now we have much bigger enterprise customers using us and they have much harder requirements than a average person spending a lot of time on Twitter who finds us and downloads us. Yeah, so I guess at the beginning, there are a few things we wanted to optimize for. I guess we wanted people to never have to, like we wanted granola to work everywhere. You shouldn't have to think about, oh, can I use granola for this meeting? Or should I use granola for this meeting? It should just work like wherever you're having a conversation.

• 45:12Speaker 02

  We're interested in like, at the beginning, actually, we were really, we thought notes is, we just need to get notes, that's all. And we toyed a lot with, should we even show the transcript? Or should we, should the transcript self-destruct after a little while? Because really just like, notes is the value we're giving to people. We like, I think we learned pretty quickly that notes are good for humans, but like the transcript is amazing for LLMs to do stuff with. And we keep the transcript.

• 45:38Speaker 02

  I think we're not, we're not interested in having a word for word league, like, record of the conversation that would hold up in court as like, he said this or she said this. We get so little, like, all Grunella needs is the, it needs the notes of what happened and it needs the kind of enough detail to inform like the, to give the LLM the knowledge to go and do the work it needs to do. But we don't need the word for word. So I think, yeah, I think because of that, like, audio doesn't really matter. Like, we initially, I think this lasted a week. We were like, when we first started giving the product to people, we stored the audio in a S3 bucket and they were like, we're not gonna use it now, but at some point in the future,

• 46:21Speaker 02

  there will probably be like, we can probably train a model on all of this audio and it's gonna make everything way better. And so, let's just keep it, just, just in case. And we lasted like a week and then we're like, no, this is too creepy. It feels, it makes the product feel heavy and it's serious, like a thing that you're using against people. And none of that felt good. And so we, we turned that off. Yeah. And so that's kind of how we got to just getting the transcript. I think the, on the question of like consent, letting people know how to use it, we, Grunella is like a, it is a tool that you can use on your computer and if you want it to be, if you want it to be done quietly where nobody knows about it, then you can. Like our stance is it's a tool, like, like voice memos on your phone or like a notepad where you can record what's happening.

• 47:04Speaker 02

  And so it's on you to follow the laws of wherever you are and to disclose it to people, disclose to people that you're using it. I think there's no reason why, for us, why Grunella should be hidden. Like, it's in our interest for Grunella to be out in the open and for everybody to be on the same page that it's being used. It's great for us for growth and for people discovering us and learning about it. We handicap ourselves by not being a bot that joins the meeting, being like this huge glowing orb with a logo on it that people can discover. We have, we built these, some ways to help users like disclose it and we're like, working on a bunch more to make it more transparent that you're using it. Cause it's in everybody's interest for that to be out in the open. Uh, yeah. And I think that's where we're at today.

• 47:45Speaker 02

  I do think Grunella is a tool for work primarily. People use it in their personal lives, but it's primarily for work. And I think. I do believe that over the coming years, it's just going to become more and more normal that people transcribe conversations at work. There's so much upside to it. And I think we and others will learn, will figure out how to help mitigate the downsides of it, that it will become a default. Like, we used to, we used to get a lot more questions about privacy and consent when we started two years ago, and we get much less now. Like, people are much, much more comfortable with it already. I only see that continuing. I do think it's very different talking about a work context and a personal context.

• 48:24Speaker 02

  Like, I, I think the always unwearable thing in my jigs, like, that they have a real, they're gonna have a real hard time navigating the privacy question in people's personal lives. We have a much easier, meetings are like a set piece where you sit down and you all agree that you're gonna do work together. And it's a nice social moment where you can, you can have a social contract around, around recording this conversation that doesn't exist out in the rest of your life. Yeah, that's a, that's roughly how I think about it today. But we're still, still a lot to figure out there.

• 48:57Nathan Labenz?

  It's interesting how much you have seen a shift already. And I do feel like you're probably right, that there's just gonna be more and more shift, because the value is, is really hard to pass up. You were commenting about like, you know, potentially, you've got kind of three layers, right, of raw audio, which you don't even keep, transcripts, which you sort of thought, maybe we don't need to keep, but it is really valuable. And then also, I do see in the products today, there's like, grounding of answers and notes to, you know, specific moments in the transcript, which I think is huge for confidence building, if nothing else, and, you know, ability to dive in and sanity check. And then the most

• 49:39Nathan Labenz?

  kind of superficial, in theory, you know, should have full value, but sometimes doesn't, is the notes itself. I wonder how you think about that in the context of, you know, just the ever more AI-ified future. Like, one thing I'm doing right now is, you know, many people, I've got my main personal computer, and then I've got my new Mac Mini that's kind of riding shotgun with me. And on my main computer, that's where the database, you know, of all my contacts that I mentioned previously, lives. And that's where I've got a Claude code is kind of my go-to, because I do trust it the most, certainly relative to god knows what Open Claw model with who knows what model in there. You know, with this kind of, and also on this computer,

• 50:22Nathan Labenz?

  I'm logged into everything, too. So there's another consideration. But I basically run my, my agent on my computer as kind of an extension of myself, where I'm giving it real-time instructions. It's kind of a copilot model. It's agentic in the sense that it can do stuff, but it's copilot in the sense that I don't have it, like, doing super-long-running loops, or like, waking up when I'm not around. You know, it's taking explicit direction from me as kind of the main mode. And because of that, and because I generally trust the provider of the model, I'm pretty comfortable with it having such deep access. A question I'm thinking about a lot right now is, okay, for this agent on the other computer, what sort of access should it have? What should the model be for how it gets information

• 51:04Nathan Labenz?

  about me? One of the things I'm experimenting with is basically creating summaries. Right now, going through this full five years and kind of creating a monthly summary, and then I plan to create like, an annual summary, and then I kind of have a few other views that I'm thinking about, like, project level, you know, what, overall time, like, what were the projects that kind of, you know, were discrete things, and which of those are active? And then relationships, you know, across the last five years, like, what is the sort of survey-level view of all the relationships that matter, and the deep context on those? And I'm thinking, maybe that's what I provide to the agent, you know, that I'm thinking of more as the autonomous thing. Like, this is the one that I do plan to let go off and do work without me, and it will wake up and check its, you know, inbox. And it's going to have its own email, and it's going to have the ability to kind of send

• 51:48Nathan Labenz?

  email perhaps without my review. So maybe that, a more abstracted... Understanding of of me is the right thing. Or maybe there's the other option I'm kind of toying with is like, maybe that agent should call the personal agent and they can kind of talk to each other and sort of the one like represents me as like, what is the least information that you really need to do a good job? And maybe I'll give that to you. But I'm still not quite sure I trust that dynamic either, right? Because certainly, models are all very foolable. They're not they're not adversarially robust to to attacks on on information extraction. I don't know. Any tips for me on how you think I should be thinking about this in a more sophisticated way?

• 52:31Speaker 02

  No, I don't know. You I think you're already more sophisticated than me. I like the I like thinking about it like humans that I would be interacting with like really helps me think about it, I think. There are. So we have a we have an executive assistant on our team who works with Chris and me on a with a bunch of stuff. And you work with an executive assistant like they can they're only as good as the context you give them and as how up to date they are. But I wouldn't want them with me every moment of the day and not every moment that Chris and me are together. There's it's there's it's I like being able to talk with just Chris in the room and to be like be able to speak freely and not worry about the outside of the relationship we have. And and that comes at a cost of the of our executive system not having the full picture

• 53:16Speaker 02

  of everything we talked about. But but we have like touch points that give them enough to do their work and go away and do things. And yeah, I feel like I'm not sure anyone's really figured this out yet, but I feel like we need to figure out those kind of ways of agents interacting with each other. I think forgetfulness is a thing that we don't. We don't think about very much with these things, too, but it's a it's baked into all of us as humans for the fidelity of our memories to drop off pretty quickly. I feel like for there could be a lot of cases where that's a feature, not a bug for agents, too. It's an elegant way of diffusing the dangerous stuff from from the transcript or from your emails or from anything if stuff gets fuzzy pretty quickly in the agent's memory.

• 53:56Speaker 02

  Yeah.

• 53:57Nathan Labenz?

  Yeah, I like that a lot, actually. I've been toying with this idea of agents that, and there's a lot of open questions here, many unanswered questions on this idea, but the idea of agents that would get smaller over time, literally in terms of like the weights of the model kind of being gradually pruned down so that the model, in theory, the agent sort of settles into its niche and can be like very good in its niche. But yeah, but like over time, you sort of, as it gets smaller and smaller, you get efficiency out of that, and you also get the assurance that like, it really can't do anything else at some point. And I do think there's, there's a lot of design space.

• 54:40Nathan Labenz?

  I feel like in general with AI, we're doing this depth first search on this one particular architecture with this one kind of particular vision of like the everything AI. And I really wish we were doing a lot more breadth of search before going so deep on, on one particular thing because it just seems like, you know, we're, we're baking in a lot of decisions on trade offs for just having kind of one main thing that everybody's chasing. And there are all these different scenarios and contexts and, and just different, different people probably would want to make decisions very differently that I, such that I do wish we had a more breadth, wider view, you know, before we get to AGI with like the, you know,

• 55:24Nathan Labenz?

  don't, whatever, I won't make silly analogies, but it's odd that we're taking the first AI that worked straight to AGI as opposed to exploring like a bit of a wider sampling of possible AI design space. Coming back to Earth, features. So I would say of all the AI products I have used, granola really stands out for maybe being the most disciplined about. Feature bloat. And that discipline, I assume, is got to be something that you are holding the line on incredibly purposefully, because obviously, in today's world, like, we can vibe code our way to new features 10 times before lunch, right?

• 56:07Nathan Labenz?

  In a world where you could code anything, you guys have made the very, again, disciplined choice to like, keep the number of features to a relative minimum. How do you think about that? Why are there not more features? I, obviously, this has something to do with people being like overwhelmed as they're encountering the software. But then how do you think about what features actually ought to carry enough weight that they actually get promoted and put into the product?

• 56:34Speaker 02

  Yeah, it's a good question. I think you're right, Philip. What you said about us being cognizant of keeping the product feeling stress-free for people who are operating in quite a stressful environment is a lot of it, honestly. I think, especially in the, yeah, I think we, we have different approaches for different parts of the product. Definitely for the core flow of, it pops up before the meeting, you click the button, you have the notepad with you during the meeting, and then that turns into generated notes afterwards. We are very, at least I'm almost like paranoid of adding stuff to that view. Like, it's got to, it's got to pass a really high bar, and I think it's got to be really useful to justify the expense of it making the notepad feel like less of a calm place, or less of a, less of a place that you want to spend time in. Which is

• 57:17Speaker 02

  tricky. It means that, it either means that we like bury features that are actually really useful. The templates thing that lets you like structure the notes in any way you want. A lot of users haven't even discovered that. Like, it's way too hidden. But it's hidden because most users shouldn't have to think about it, I think. It's for people who want to go deep and like really optimize their workflow. And yeah, there's a lot more features like that where we, we are, we're paying a price in like making them small and tucked away, but I think it's worth it in the bigger picture to have a very calm, nice feeling app. We, I think we're more lenient in other parts of the app. If you're, if you're looking at a folder, or if you're chatting with a lot of meetings, kind of like I was talking about earlier, like, I think you're usually in a different frame of mind. You're

• 57:59Speaker 02

  I think when you're in a meeting, we can expect to have 2% of your attention and the other 98% is on the person you're talking to. Whereas when you're chatting with Granola, I think we can have more like 80% of your attention and therefore we can afford to throw more stuff at you. And so we're a bit faster and looser in the stuff that we try and the stuff that we put in there. Yeah, yeah. And I think honestly, I would like us to be more, I think we can afford to be more experimental, especially in those areas of the app. We do, we are on the side of keeping things very calm and zen, but sometimes at the price of like getting good things out quickly.

• 58:32Nathan Labenz?

  One thing you had mentioned at the top was viral hooks working, or I don't know if you used the word viral exactly, but features that are driving growth. And then you also mentioned that the most obvious one, which is having the bot join the call in a visible way, is not one that you're using. What I have used is the recipes feature, which I created the blind spot finder, because my personal mission for my AI study in general is have no major blind spots in the AI landscape, an increasingly impossible mission, but nevertheless, I try. And so I love the fact that it can go back and grab, and this is something, as we've discussed, like this is something I can mention here on my own, that obviously the vast majority of people can't.

• 59:13Nathan Labenz?

  I love the fact that it can go back into deep context and pull all that in and then try to take this sort of higher level view that that does feel like very consistent with the, help me with the system two side of my life. Maybe, so I guess the two questions are like, what are some of your favorite other recipes, other use cases that you've seen people create that are cool? And is that like a big driver of peer-to-peer growth? Or what, if not that, like what are the other things that are really helping you propagate the product through users?

• 59:48Speaker 02

  Yeah, yeah. Recipes have been really interesting and yeah, I think with like a lot of use cases, like the one you described, have come out the woodwork and weren't really things that we were anticipating when we built the thing. The, I think the motivation for it was like, was pretty simple. It was like power users, power users of Granola have figured out that you can chat with the body of meetings and learn some pretty profound things or get it to do some very, some really interesting things. But we just wanted a way to make it repeatable and very easy to trigger that. And so we built recipes, which is create a prompt essentially that you can run at the click of a button. I think the use cases that have surprised me or seem interesting is like, there are a bunch of

• 1:00:29Speaker 02

  There are a bunch of cases where it's hard to sit down and write something, but it's very easy to just get in a room with people and shoot the shit, talk about it. And then with Granola's help, you can like corral that messy transcript into something useful. Um, so like our CX team who are like fielding bug reports and customer issues all day long, will pretty regularly like do a, do a meeting where they just bring up the main issues that people have talked about or the things people are getting confused by. And then after the meeting, one of them will go and hit the recipe that converts that transcript into a bunch of suggested updates for our documentation example. And it can take the transcript, go look at our documentation, figure out where the holes are, and then suggest a bunch of edits.

• 1:01:13Speaker 02

  We do, we do similar things with like our job descriptions on the website. If we're looking to hire for a new role, it's easier often to just go talk about it with the people involved in that role. And then we have enough examples on our website that you can usually point Granola at those examples, plus the transcripts of the conversations. And it'll do a pretty great first draft of a job description. Yeah, those are, I think in terms of like frequency of use, at least for us internally, those are the biggest ones and the most time-saving. The other one that really caught me off guard was like, I like personal coaching type stuff, where Granola can look across the history of conversations you've had and pick out patterns about how you are as a person and how, how you could be better. Has, I've like never worked on anything, I think, that's had such a strong emotional

• 1:01:58Speaker 02

  reaction as those things. When we were doing user interviews for the recipes feature, we'd use the Coach Me Matt recipe, which is, it's like, uh, it's written by Matt Mockery, who's a famous CEO coach. And we'd like ask someone to, to get in a call with us and then go to their recipes page and click this recipe and then we'd watch them react to the result of it. And I don't think anyone quite got moved to tears, but there were like real, like profound emotional reactions to this thing, which was incredibly like validating and satisfying as a product builder to have that. And yeah, I think people get real deep value in that. Granola really understands a surprising amount about you just by being there, being with you in all the conversations. And I'm continually surprised by the depth of its understanding there.

• 1:02:40Nathan Labenz?

  That reminds me, another one that I thought was really cool, which was from Dan Shipper, who created the, I forget exactly what he called it, but it was basically the implicit company culture recipe that just looks at calls across your business and tries to say, like, what is the culture of this business in practice, as opposed to, you know, what you may have put on your website or your handbook or whatever. I suspect for a lot of leaders, that would be a really incredible source of reality check in some cases and ideas and, you know, who knows what else. One quick double click on how you're using it. Do you guys, are you an in-person company? And when you talk about getting people together,

• 1:03:24Nathan Labenz?

  are you like putting a laptop in a, like on the table in a conference room and just like hitting record and then everybody's in the same space along with the recording? Or are you like, you're nodding, I guess that is it?

• 1:03:37Speaker 02

  Yeah, pretty much. Yeah, yeah. We're like, yeah, we're 100% in person, which is ironic for a meeting. Like, we built a thing that's designed to be used on video calls mostly, but now we have the mobile app. The mobile app is like the main thing we use in person. And yeah, someone will turn Grenada on, put the phone on the table, and then we talk about it for half an hour. And yeah, and you don't have to be super structured. People can just air out their thoughts, debate things, leave it up to the robots to figure out the structure and like how to format that information.

• 1:04:02Nathan Labenz?

  Yeah, I love that for a job description in particular. The idea that you could go to the team and just kind of get random, unstructured thoughts and then make sure that is really represented. I think that could take the level of job descriptions that most companies put out up quite significantly.

• 1:04:21Speaker 02

  Our like design and product team is growing at the moment, and I'm going through a process of trying to write down some of our design product principles for everyone internally. For the next designer that joins, how do they ramp up on how we think about product quickly? And it's been super helpful for that because it's a very, you kind of want to, I want those principles to be written in the language that our team understands and like to feel like it's going to come from us as a collective. And just being able to get in the room and talk about them and then as a group, but also one-on-one with each other and stuff. And then amalgamate all that together has been super helpful. And you get, you can get the AI to come up with pretty punchy principles that are like worded in a more interesting, different way to how than me trying to like capture the average of everything everyone was saying.

• 1:05:05Nathan Labenz?

  So are these recipes the thing that is driving the growth? Or are there other viral, true design principles you could share?

• 1:05:12Speaker 02

  I think, I mean, like, recipes do well on like, social media and public sharing. Because people create a recipe, they want to show it off to the world and talk about it with people. And so that's, that's been pretty good for us. Most of our virality is like, either just people recommending it to each other, or people sharing notes with each other. We try and make the sharing notes as quick and seamless as possible. And yeah, so often I'll be talking to a user and I'll ask how they found about, found out about Granola. And they'll say, I was in this meeting. And then, and then 30 seconds after the meeting, these beautiful notes just popped up in Slack. And I was like, how did you do that? There's no way you could have written those in that amount of time. And how did they end up in Slack so quickly? And, and then they dig and they find out that it's Granola and they want to try it for themselves.

• 1:05:57Speaker 02

  Yeah, that's honestly, those are the main ones. We, I think as we've worked on building more team functionality into Granola, like we're starting to unlock, like there is now, there's a benefit to having all of your team using Granola, not just each individual making themselves more productive. You can pull stuff together and use that shared context. So we are like, we, we have a bunch of teams now, like actively encouraging each other to use the thing. And all like procurement teams buying a license for everyone in the company so that everyone's on it. But most of it is pretty basic. People like the product and they tell each other about it.

• 1:06:35Nathan Labenz?

  You only need one viral growth mechanism if it really works. That's one of the profound lesson of growth in general. It usually doesn't come from that many different hooks. It's like one hook really works and that drives it. Okay, let's talk about design. You said your team is growing. It strikes me that There's a lot of good candidates, but I would say product design and product building and the sort of relationship between design and building is a pretty good candidate for one of the jobs that has changed the most over the last year or so. So what does that look like for you? Like, how big is the team? Do you still have traditional design and/or product manager and separate engineering

• 1:07:20Nathan Labenz?

  roles? How much of those blurred together? How helpful are AIs when it comes to going from an idea to a candidate design in your design system? Like, how well do those things work for you? What have you learned about actually making the AIs useful as a sort of designer assistant? In short, like, what is, how has AI transformed your product creation practice at Granola?

• 1:07:45Speaker 02

  It feels like a slowish change on the day-to-day, but then if I compare what we're doing today to what we were doing last year, it's like completely different. Yeah. So I guess as a team, like, as a company, we're like 60 people now, I think. And I think if I break that down, that's 25 to 30 engineers, and then three product people, three designers, three designers, three designers plus me, a couple of like design engineers, and then everyone not on the product engineering design side. And yeah, I think we, as I've been hiring designers especially, like, I guess I'm closest to design and lead the hiring for that. And I, it's a prerequisite or like a necessary thing that you're at least curious and playing,

• 1:08:30Speaker 02

  actively playing with quote code, or you're like using AI to help you build stuff. I think that's important in some areas more than others. Granola's core interfaces, like the notepad, I find Figma mocks like really hard. It's really hard to evaluate whether a thing is good, like looking at a mock-up in Figma of the Granola notepad. I think just because so much of what matters is like, how does it feel with your real content in it? And how does it feel like? Economically to have it open on a meeting and be like glancing at it out of the side of your eye while you're also trying to talk to somebody. And there's no substitute to just building a prototype version of a feature in the real app and using it for real in the meeting.

• 1:09:15Speaker 02

  And so they're like, they're really biased towards using AI coding tools to like hack stuff together into the real app. And all of our designers are doing that at this point. I think there are other parts where like, it's, I think it's like more traditional, like mocking up a flow and Figma and evaluating it that way still makes sense. The like, the stuff around our paywall, like, how do you like sign up to granola and what's the sequence of screens you, we show you and what information and what like things do you need to understand as you do that? It's just really useful being able to see the whole thing in its entirely entirety. And the design problems are all around copyrighting and, and are we putting the right ideas in your head at the right moments? And yeah, like, that's just a much easier thing to evaluate and move quickly on, on

• 1:10:00Speaker 02

  a canvas where you're not having to everything into the product. So, yeah, it's a mixed bag, but the lines are, the lines are really blurry these days. Like, designers are submitting code, building prototypes and code, engineers are doing design as you would have thought of it like a few years ago. Yeah, it's a real, like, it's a real hotspodge, in a good way. I'm, I like having more fun, I think, than I ever have as a designer builder person.

• 1:10:22Nathan Labenz?

  Yeah, I'm right with you on that. It's unbelievably empowering and unbelievably fun. How would you say it has impact? I don't know if you have metrics on this sort of thing, or you could just give me a finger to the wind, but what's the before and after when it comes to idea to ship? And whatever, maybe intermediate milestones you think are most important between those starting and ending points?

• 1:10:47Speaker 02

  Yeah, I think, what's it improved? I think it's been immensely helpful in evaluating whether a thing is worth pursuing or not, or worth building. What's an example? The, so the granola, the chat floats at the bottom of the app, basically everywhere in the app now, right? And in the same way. And that wasn't always the case. I think we introduced that in September last year and before that, it was like only in some parts of the app and mostly in like a sidebar view. And I think. Debated, should we change this chat, make it bigger, make it more prominent, make it more globally accessible, blah, blah, blah, for a long time. And I think I'd been mocking up versions of it for a while, like in different places, in different parts of the app. And the trigger point for being like, oh yeah, shit, this is obvious, we should do this, was

• 1:11:33Speaker 02

  I was able to just like prototype, putting it in like vibe code our way from getting a sidebar chat into a floating chat in the app that we could all live with internally. And within a day of everybody having it internally, it was obvious that it was just way better and we should move to it. So it's like really sped up the time from like idea to a thing that we can start using and feeling in the app for ourselves. Like, I think the

• 1:11:58Speaker 02

  still lots of pieces of UI and lots of flows need a lot of thinking through all of the states and edge cases and things like that. And there, I think still some of the old ways are still very useful. Figma's great for seeing everything out and being able to see all the options and states and stuff. But yeah, idea to like evaluation, so much faster.

• 1:12:19Nathan Labenz?

  So you're not on the Figma, the death of Figma is perhaps exaggerated in your mind?

• 1:12:26Speaker 02

  I think they've got their work cut out. At least for me, Figma's just become a more specialist tool. It's like, I use Figma now for ideating. And whereas Figma used to be like the start, middle and end of my design process. It's now like a thing I pick up and use every now and again. I'll often start

• 1:12:43Speaker 02

  just building the first thing that's in my head in the product. And then I'll get it working, but then I'm like, ah, this bit doesn't feel good. And it's faster to jump into Figma and try 10 different versions of it side by side and figure out which one of those feels good. And then jump back into code in it and implement that and then rinse and repeat, or have something feeling good overall. So it's, yeah, I think the days of having full app schematics of everything, all of the screens and all of the flows laid out in Figma are like totally gone for us. And it's way more of an ad hoc thing that you pick up and put down.

• 1:13:17Nathan Labenz?

  Okay, I'm not sure where that bottom line's at for me in terms of the future of Figma, but

• 1:13:22Speaker 02

  Yeah, I don't know.

• 1:13:22Nathan Labenz?

  It does, if I'm Figma, it makes me a little nervous.

• 1:13:26Speaker 02

  Yeah, I'd be nervous too. Yeah, I like, yeah, yeah. The question is, can they?

• 1:13:32Speaker 02

  I feel like I don't think that need is going to go away. If they can hold on to that and cement themselves as the best place for that kind of work for ideating, then great, but other people are gonna, I presume Claude Code is gonna run at it from their direction and other people are gonna come at it.

• 1:13:45Nathan Labenz?

  Yeah, there's a lot of candidates to try to be the everything app these days. So it's, yeah, it's gonna be very interesting to watch those dynamics. In terms of like the team dynamic that you have, I was sort of guessing coming in that it would be a challenge to manage kind of product discipline and also to just manage people's feelings about their product ideas because the ratio of like random ideas that people have and like vibe code to things that actually get launched has gotta be, I mean, to correct me if I'm wrong, but it feels like it's gotta be like a hundred to one, which in a sense is maybe good, like that, you know, I certainly can see the argument

• 1:14:29Nathan Labenz?

  that that's what users, you know, ultimately benefit from and deserve. From the individual engineer's standpoint or the individual, you know, person who came up with the idea standpoint, I could also kind of imagine it being frustrating. And I guess I'm wondering like how, but then you also said when something really hits, if you can get into the internal version of the app quickly, then it can become maybe obvious if it's like working or not. And so I think maybe what would create frustration in a lot of cases is I have this idea, we're not even trying it, whereas if I had this idea and we tried it and nobody really cared, then that's at least like closure. So how is the team dynamic there in terms of, and I guess the other question you've got to keep in mind too, is like, how do I make sure people keep actually trying, right?

• 1:15:13Nathan Labenz?

  If their ideas are only getting through to product at sub 1%, how do I avoid people just getting discouraged and feeling why even bother that it's probably not going to work? So I see a bunch of kind of competing challenges there. I'm wondering how you're dealing with that from a team, culture and management perspective.

• 1:15:32Speaker 02

  Yeah, yeah, it's definitely a thing. I think, one, I think we're very mindful of

• 1:15:40Speaker 02

  Like Granola is, as a company, is

• 1:15:44Speaker 02

  we're a company of people who like to build great product experiences. And I think we're not like exceptionally technically deep company. Or we're not, we're not exceptionally sales driven. Like building good experiences is the thing that we like to focus on. And we look for people who feel like they fit that mold when we're hiring. And so like humility and open mindedness are like two huge characteristics that we really look for in people that join, which means like you should be curious. And you should like, and we want people who are gonna think freely and then and come up with good ideas. But also have the humility to assume that nine times out of the 10, your idea is not gonna work, like when it hits the, hits the ground and is put in front of real people.

• 1:16:29Speaker 02

  That's, and that's not, I think.

• 1:16:32Speaker 02

  It wasn't, it's not because Chris or I says it won't work, but because it, because it just doesn't land with the end users.

• 1:16:39Speaker 02

  Yeah, so we, we try and get the right people in the door, basically, that think and feel like us and approach building software like us. And then I think, and then I think the next thing is just like,

• 1:16:51Speaker 02

  people, if you have smart people, I feel like a lot of the time they'll make good decisions if they have good information about what's happening, what's the state of the product, what's, and the thing that he's working on. So every engineer is involved in user, like talking to users. And as, as much as any product or designer there, there's not like, uh, yeah, there's no kind of like waterfall effect or anything going on there.

• 1:17:15Speaker 02

  And so I think it doesn't always happen, but like a lot of the time that leads to engineers being the ones that have the ideas that end up being the right, the thing that goes into production and solving the user's problem. Yeah, so people, giving them good information and like actually being close to the users. And then, yeah, we, I think we just try and encourage this like nature all the time, like everyone should be just, if you have an idea, you should try it. And you should put it out in the company and see what people think. We do demos every Friday, which is like one of the highlights of my week always like, uh, and it's, sometimes it's stuff that's on the roadmap and people are working towards. But sometimes it's like a completely left field idea. What if Granola was, what if we just got rid of the Granola sidebar and just had it be like the one interface?

• 1:17:59Speaker 02

  It's like this, or what if, what if transcription worked in this radically different way? And people are trying stuff like that all the time. And I think.

• 1:18:07Speaker 02

  We try and cultivate this idea that the trying is good and it's okay to build a thing, see if it works, show everybody, you know, so that they can learn from it and then put it to rest and like, and take what you learn from that and move on. And I try and be, I try and, I guess I try and exhibit those qualities too. If I have an idea about how the app should be, I will try and make time to flesh it out and share it on Slack. And usually it doesn't go anywhere. Like usually my ideas are terrible too, and that's okay. And hopefully like collectively, we all inspire each other and move our thinking forward. And that ends up putting us in a good place.

• 1:18:44Nathan Labenz?

  As we're talking, I just went back to the labs tab in granola and joined the beta. And I wonder what you could tell me about what I should expect as a beta user. And this seems like something that might, especially if you have any real scale, right? If you have, if you can get to the point where you've got some critical mass of people who want to be those beta users that could really grease a lot of the friction away in terms of kind of everything we've just been talking about, right? Like, how do you get real responses? How do people feel like their ideas got a fair shot, et cetera, et cetera? How are you using the the beta program?

• 1:19:24Speaker 02

  Yeah, not enough, honestly. It's probably a bit disappointing. It's big. Like, I think we have 10,000-ish people on it. And I think we, we make product decisions almost exclusively qualitatively. Like, we, we do some measurement and some, especially like in the growth stuff, we'll measure things in A B tests. But a lot of the core product stuff is purely decided based on gut feel and, and like observing users using the thing. And so all of those decisions about what we're building and is a core feature, right? Tend to happen.

• 1:19:57Speaker 02

  Either from us using it and talking about it internally, or us working with very specific people. Like, I think when we were like pre-launch, we deliberately didn't try and go out and get users. We just iterated and iterated with a handful of people and tried to just be as close to them as we could and observe them using it in the wild. And higher fidelity feedback you get from being really close to someone, I think outweighs the quantity of talking with a lot of people really quickly. And we still try and follow that mandate today. So any features, most features, we're like working very directly with a small number of people to iterate on. And so by the time things go to bed, they're actually pretty well baked already usually. And it's like bug catching, stability testing, just sense checking. We didn't, you know, fuck up some obvious thing. Yeah.

• 1:20:43Nathan Labenz?

  That's interesting. I do wonder if we're going to soon get to the point where AIs will be able to make enough sense out of the kind of usage logs where you could potentially accelerate that process and potentially even raise the quality of the decisions you could make by just like launching a ton of stuff to a small number of users and kind of seeing what happens. And then you get like a real, you know, you get a real ground truth measurement there, right? I mean, I think everything you're saying there sounds enlightened in the sense that it's often, and I feel like I've encountered this

• 1:21:23Nathan Labenz?

  many times with successful product builders where it's like, it's not really that complicated conceptually what you need to do, but it is still something that a lot of people just don't want to do, or don't find intuitive to do, or don't have the, whatever the disposition to do. And so just being really focused on who the users are, what they want, listening to them, spending a lot of time with them. Those themes just come up over and over again. And can AI do it? Hmm, maybe soon. I don't know. Maybe it'll be interesting to see if we can cross that threshold in the not too distant future. Any thoughts on, are you holding your breath for that? Or maybe dreading that day if it comes?

• 1:22:01Speaker 02

  Definitely. That would be the moment it takes my job. Yeah. But yeah, I don't know. I feel like if humans like. We'll see, but if humans are the ones making the decision, still I feel like humans are gonna wanna be the ones, you're gonna want like pretty raw input to feel good about making that decision, I think. Who knows, I might be, that might be very outdated thinking very quickly, but I think that's where I'm at today, yeah. I could see features where like the usage is like really disparate, people use it for many different things, like chat, for example, like open in chat. I think that's one where we go to beta earlier, because you learn from having a volume of people using it in different ways and you learn what the patterns are and stuff like that.

• 1:22:41Speaker 02

  And I could see those, that feels like an area where if an AI was understanding the themes of what people were asking and could therefore bias the interface towards being good at those things, that does seem interesting.

• 1:22:51Nathan Labenz?

  What scares you the most in terms of granola's future? I'll give you just kind of one candidate, which is my friend Andrew Cricht, coined the term the big tech singularity, which in his mind is just this future state where because a few companies have such powerful AIs, they can turn their focus to any particular niche or even industry. He's thinking like pharma and material science, he's thinking at the macro scale. But big and small, right? If they have just such a dominant AI advantage, then potentially they can turn their focus to any particular area and steal what you've learned,

• 1:23:32Nathan Labenz?

  clone what you've built and even potentially offer it at subsidized prices or whatever. This is not, that's not a granola specific risk, but I do worry about that a lot myself. Do you worry about that and what else keeps you up at night as you think about granola's future?

• 1:23:48Speaker 02

  That's the main one, I think, yeah. I feel like I've always worried, like I've worried about this since day one. The moment ChatGPT came out, you could like plot some like hand wavy line into the future where like the just this one tool does everything and no, no startup or specialized service ever needs to exist again. Yeah, it's, I don't know, it's a depressing version of the future and definitely, I don't know, it feels like a version of the future that could pan out, but I certainly hope it's not. And I don't have, I don't have great answers as to why. I do what I do because I enjoy it and I would rather, even if we are marching towards a big tech singularity, I would, I'll be much happier building granola in the meantime until that singularity.

• 1:24:33Speaker 02

  But I also think, I don't know, I feel like we Especially in tech, we like love to assume that the next big thing that comes along is gonna swallow everything and gonna be the thing that takes over. And I feel like, I'm really feeling this about Alexa and I think, I think we like Facebook Messenger had some chatbot, like, I don't know, twenty thirteen or back in the day. And I remember thinking, damn, like, interfaces, they're cooked. Like, we're just gonna be like chatting with these bots backwards and forwards. And maybe that's a case where that just, that technology was too early and it really is coming true now. But I just, I don't know, I feel like we over-index on one solution being the thing that's gonna be the be all and end all. And in reality, like, life is messy and complicated. And there is still a lot of merit in having specialized tools for different things. And

• 1:25:19Speaker 02

  yeah, I think all we can do at Granola is be really good at the stuff we're really good at, I think. And then

• 1:25:25Speaker 02

  architects, the product, like, the product and everything in a way where we get to stand on the shoulders of the giants building the big models. If Granola doesn't get better when, you know, when the next version of Claude or GPT comes out, then I think over, over time, we're cooked. Like, we have to go with the rising tide and just be a little bit better than them in our particular area. Trust that people will pay for that, which I think back-to-back meetings are painful enough for enough people that I think people will pay for that.

• 1:25:50Nathan Labenz?

  That plus maybe a little bit more vigorous antitrust enforcement and you'll have a place in the future. How about just your general positive vision for the AI future? One of the, my common refrains is the scariest resource is a positive vision for the future. And so I'd love to hear yours.

• 1:26:08Speaker 02

  Yeah, I think when you like sit down and study somebody's day, like work life, if you're like a laptop, email, knowledge worker, whatever, so much of it is you being like a slave to your computer, like a kind of, you're like reduced to a machine that needs to go move the mouse around and click buttons and move organization from one, move information from one place to the next. And I just, I think, I think we are going to a world where we're gonna have help and so much of that, so much of that, like, kind of mealy or mindless work is gonna be taken care of. Or like, we're gonna have, we're all just gonna have help with it. And I, yeah, I think I just.

• 1:26:47Speaker 02

  I think that sounds like it can move work towards being a much more interesting place for a lot of people. And I think like in meetings, the number one piece of positive feedback we get from Granola is about Granola is that it helps me feel more present in the meeting and more engaged and I'm able to, my brain's able to be firing on all cylinders because I'm not caught up frantically trying to write note down everything that I need to be remembering. And I think that's, I don't know, I feel really proud to have created that for some people. And I think that analogy played out across a bunch of other parts of our work life, I think is quite an exciting feature.

• 1:27:23Nathan Labenz?

  I just asked Granola how I should bring this conversation to a close. And it said, to thank you for the specifics. And I do, I think that's a great suggestion. You've definitely shared a lot of really interesting details. So thank you for that. And then second point was call back the positive vision. And on that point, I'm definitely with you in terms of if we can be less slaves to our computer, I'm trying to measure myself by, am I getting outside more this year? And if I'm not, is AI really serving me? So I wouldn't say I've moved the needle on that yet, but I do love the vision of untethering ourselves from our computers. So thank you. This has been an excellent conversation. I really appreciate it. And it's been a great window into the thinking behind how

• 1:28:04Nathan Labenz?

  to make a viral hit AI product that everyone can use and enjoy. Sam Stevenson, co-founder and designer at Granola, thank you for being part of the cognitive revolution.

• 1:28:15Speaker 02

  Thanks so much, Nathan.

• 1:28:43Speaker 03

  Talking fast, but I can't see.

• 1:28:49Speaker 03

  Scribbling hard, falling behind.

• 1:28:58Speaker 03

  Half the words just slip my mind.

• 1:29:06Speaker 03

  Two percent, that's all I need. Two percent and I take the lead. A calm place in a crazy day. Let the notes fall. Where they may.

• 1:29:25Speaker 03

  Oh, not the flash, not the grand design.

• 1:29:34Speaker 03

  Just the quiet, kept right on time.

• 1:29:41Speaker 03

  Simple things. Simple things. Done beautifully.

• 1:29:50Speaker 03

  That's the secret no one sees. Oh, two percent, that's all I need. Two percent and I take the lead. A calm place in a crazy day. Let the notes fall. Where they may. Oh, word got out down the morning hall. How'd you get those notes so fast? Snowball rolling, friend to friend. Something real that won't pretend.

• 1:30:35Speaker 03

  Hey, help me look them in the eye.

• 1:30:44Speaker 03

  Let the weight fall. Let me fly. A little more present. A little more free. Oh, a little more free. That's all I ever wanna be. Oh, two percent. That's all I need. Two percent. And I'll take the lead. A calm place in a crazy day. Let the notes fall. Where they may. Two percent.

• 1:31:27Speaker 03

  That's all I need. Two percent. Oh, let it breathe.

• 1:31:43Nathan Labenz?

  If you're finding value in the show, we'd appreciate it if you'd take a moment to share with friends, post online, write a review on Apple Podcasts or Spotify, or just leave us a comment on YouTube. Of course, we always welcome your feedback, guest and topic suggestions, and sponsorship inquiries, either via our website, cognitiverevolution.ai, or by DMing me on your favorite social network. The Cognitive Revolution is part of the Turpentine Network, a network of podcasts, which is now part of A16Z, where experts talk technology, business, economics, geopolitics, culture, and more. We're produced by AI Podcasting. If you're looking for podcast production help for everything from the moment you stop recording to the moment your audience starts listening, check them out and see my endorsement at ai podcast dot I N G.

• 1:32:27Nathan Labenz?

  And thank you to everyone who listens for being part of the Cognitive Revolution.