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

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, 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 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 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 dogfooding to inform what are ultimately mostly 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. 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. 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 3-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. 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.

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

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

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 2 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 an incredible 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. So what would you say is really clicking and driving that growth for Granola?

Yeah, I was taken aback when I saw that too. Like, like, I guess we know, we know what's happening to us internally, but like, uh, yeah, to see us, to see yourself among that company is like a, It's a great feeling and it's very validating. So yeah, I mean, like, I feel like Granola's 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, that 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, took us a while to unlock some of those viral loops and to get that to start happening. But I feel like the thing's really starting to snowball at the moment. And we're growing a lot, a lot month on month over month.

I definitely wanna 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?

Yeah, I think, I think this is interesting because like we, I think the character that we hold in our heads is probably more extreme than the average actual Granola user. When we set out to build Granola, we, I think, were most interested actually in not in like meeting notes. We were really just interested in, uh, we wanted to be playing in the space of inventing what are the, what are gonna 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 loads of 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. 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 archetypal person that emerged as a good target user for us over time was somebody, basically somebody who's in back-to-back meetings all day. And that could be many kinds of jobs. That could be a salesperson, an account manager, a recruiter, could be an investor, it could be a founder of a company, it could be anyone in client services is doing calls all day long talking with 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 my co-founder Chris kind of brought and I really like, which was there's this kitchen utensils brand. I'm going to fuck it up. I think it was OXO, but I might be wrong. And they had this thing where they would deliberately design products for handicapped people, people who had struggle holding on, gripping utensils or only have one arm or they have some kind of disability and they deliberately design products for those people, not because they just wanted them to be used by those people, but because if you succeeded at building, designing a product for the most extreme kind of user like that, then you end up creating a very kind of friendly, easy-to-use product for the rest of us too. And yeah, this is like, I think if you work in accessibility in UI design, this is a common thing people talk about too. But I think our version of that was 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 a constant block of stuff all the way through the day. And these people exist in real life, but they also are 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.

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, uh, you know, hustling to try to get to some 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 centre of the distribution first and then work out from there. Could you give me a little bit more? And I've seen some comments from you in other places around system 1, system 2 thinking. But could you maybe describe a little bit more, like, what is the— obviously people are busy, right? Everybody's short on time. But what does that translate to in terms of what they are unable to do that you are specifically trying to design around?

Yeah, I think us tool builders, myself included, 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. 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 in over time. And I think 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 work for most people who work on a computer is way more reactive and way more chaotic. And so much of the time, people are not operating with their rational, methodical parts of their brain. They're in like a reactive, like the system 1 kind of brain, you know, if you're familiar with that, where you're, you know, where really like you wake up in the morning, you check your inbox, you're like, ah, fuck, there's 3 things there 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 juggle all of this? 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 more, you know, I think just that's the reality for a lot of people at work. And as software designers, we need to kind of assume that reality and design for our software to fit in that reality.

Yeah, I mean, that immediately calls into question the way that most people do User testing, right? Where you're like, it's been a while, again, 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 gonna interfere at all. But you are immediately putting them in a state of full focus on this cuz they don't wanna 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?

Yeah, it's a great point. Yeah, I feel like I fall into the same— I feel like I'm guilty of the same thing all the time too. You wanna watch someone use your thing and get validated that they understand it. Yeah, I think—

What helps?

I think 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 does Granola fit into it? And this is hard with like watching them use the products live, but for example, 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'd just go like, go to their Granola home screen where they have a list of all the meetings they've done. And we'd go meeting by meeting and I'd 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 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, you're in like a— yeah, you're getting the person's imagined view of themselves, which is often so different from how they actually behave in the real world. I 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 discovery, 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 how it feels for us to use it ourselves. And now the team's a little bigger, how we can observe people in the team using it 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.

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 it might have to get through the university review board before you can deploy something like that.

Yeah.

Yeah.

Hey, we'll continue our interview in a moment after a word from our sponsors. 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 2, 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 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 1 and helping them manage that reality? Or do you think you are ultimately like helping them get into that System 2 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 2 competing lines of thought?

It's a good question. It's a good question. I think both. I feel like we'll be able to make progress in some areas faster and in other areas not there. I think, for example, there are so many tools out there trying to be like a work executive assistant for you for work or like an agent helper that understands what's going on and helps you do your knowledge work. And I've tried a lot of them and I think the thing that they struggle with every time is like knowledge work is just so messy and nuanced. And I just think like, We're still a long way off, I think, like AI's 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 for that person and how should I, do I rank that, like the importance of what they're asking for versus the importance of the other things they're 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 going to really help me. But then when you actually engage with the content, so often they just if they miss the mark even slightly, then they're useless. They're like, ah, it's almost there, but I'm just not quite comfortable using that. And but 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 we want to be playing in that space too. And so the game for us is how do we— I think a principle that I think we've found helpful often with this stuff is to almost be surprisingly unambitious in the kind of tasks we promise to help you with. 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 because it's not that important and also, and there's a lot of it and it's boring. 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 2 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 though. 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, 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.

Yeah, I like the, um, 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 take— 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 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 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 5 years, all my Slack messages from the last 5 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, diarisation, 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, 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 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, with a little tweak, it'll get there. So I feel like I'm starting to see the promised land. If I, I wouldn't necessarily say I've entered it, I can see over the hill at least a bit. What do you think is gonna make that slow for most people? Is it gonna 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 gonna try to hoard their data and prevent people from exporting it all and doing that. Like exporting stuff 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, that keep that? from happening, say, in the next quarter or 2?

Yeah. 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. I think, I mean, a lot's possible nowadays, like on a personal level, getting all your stuff into one place. I think 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, you know, you've gotta 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. There are definitely, like, a lot of Granola customers are, like, surprisingly progressive, I think, in how much they're willing to let employees 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 you're gonna get left behind really quickly. Yeah, but I think context is a big, big one. And then just like the, level of personalization and memory that's necessary for the agent to do a good job, I think, is maybe underestimated by a lot of software. I think the agent just has to know a lot about you, right? A lot about you, about the people you work with, the projects you're working on, the priority level of things, and I think it's possible with OpenCLaw or any of the general-purpose agents to teach them those things if you're extremely proactive today. But average computer user is not extremely proactive in setting these things up. 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 don't have to think and you just get useful stuff put in front of you every time you turn on Granola.

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

Yeah.

Yeah. I think, um, it's just like, I feel like innovation on, in, in this area would like unlock so much for, 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, Granola defaults to private for everything. So like every note you start is only visible to you unless you take an action to put it in a shared space. And it's because like, conversations are potentially really personal, right? And you only have to say one personal or dodgy thing to kind of pollute a transcript and make it not okay to share with the wider organization. But that means that gives us a really tricky design problem in that you gotta, so much of the value of 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, you know, while also not putting information that shouldn't be shared into places, into public places? 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 do a lot better still.

Yeah, that's interesting. How many— this is an obviously AI-pilled take on the question, but my first instinct would be to run the transcripts through. And I 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 of the direction that you're headed? Try to use the AI to help surface these potentially sensitive things for people?

No, we're tinkering with this a bunch at the moment. Yeah, I feel like there probably is a world where we could 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, uh, Granola 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.

Yeah, I hear better models are coming soon, so 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.

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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. 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 gotta 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 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 gonna use this many things. As far as I have experienced, it's always been just use it and it seems, it feels like it's unlimited. So maybe tell me if I'm like, 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? When 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 organise your folders with this really full contextual awareness, you're starting to spend real money, right? You start 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?

Yeah, yeah. I think where we're at today is really a function of where we're at as a company and how we've prioritized things over the last couple years. When we were tiny and pre-launch and around launch, I think we explicitly had, we explicitly told ourselves, like, there is no budget. Like, whatever makes us create the best product, we should work on that. And just creating a good product is gonna be hard enough. Like, when the user numbers are small enough, you have, like, it'd be a waste of time to focus on optimizing cost. And so, yeah, Granola was, I mean, Granola was actually really expensive, like, when we launched, 'cause, So much of the cost was in transcription APIs, and 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 early 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 cost, you know, like the number of meetings someone does, has like physical limits to it and it averages out over the course of a month and you can figure out what margins you're comfortable with there. I do think like as granular, like sophisticated users who have lots of meetings and wanna be able to chat and do stuff with large amounts of meetings do rack up pretty big bills, which we swallow at the moment. And yeah, I think over time as Granola kind of transitions to kind of more directly doing work for you, like rather than just being a kind of companion or aid or whatever, like 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 something very nice and transparent about, like, paying a price and getting a thing. And especially while 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 the core of it, people really like the simple seat-based thing.

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 yeah, there's a lot of chance 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 6 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 was like, this is like 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'll probably, we might, the pendulum might actually swing back again as we do more LLM stuff. Work assistant type stuff over the coming months, probably get more expensive for us. And I think at some point, like, that's gonna trigger a, we need, we should think about usage-based pricing. But yeah, for now we're comfortable with where we're at.

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

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

Yeah, so far at least. I do think at some point, I think of this as being an Emad Mostaque 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, 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 a large majority of general knowledge work cases, and the best models will really be kind of doing the genius in a data centre thing? And do you really need a genius to turn your meeting notes into a follow-up email, or is there a point where you can kind of 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 could you tell us about how transcription works, or what have you learned about transcription that you think maybe is underappreciated?

Yeah, the way it works is pretty simple. 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 and we work with a couple of those. And yeah, 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. 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 one of the— 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 speaker separation is something that's If we can make that better in the product, we'll just make so much, like everything else, 10 times better. And so I can also see us just chasing whatever provider or solution is gonna get us to that. So yeah, I don't know. We have to stay nimble. We've built, 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.

I don't know if you wanna 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'll—I have a whole skill of skills after we record. I'll get the audio out of Riverside and send that over to Deepgram and transcribe, and then downstream of that is 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 underappreciated or anybody would want to shout out as being particularly good?

Deepgram and Assembly are the 2 main providers we work with. Both are great. We've used Deepgram. I think we settled on them before launch and every time we evaluate, the models are good and we can't find a better one. Assembly, I think 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 or better quality all the time?

Yeah, totally. Makes sense. Even just for API, we're definitely entering a world where uptimes 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 wanna 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.

Yeah. People, people really squeal when Granova goes down. It's, it's just, you notice very quickly and it's a very painful thing. Yeah. The redundancy's important.

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— give me all the layers. But 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. Entering this realm where certain powerful entities are indicating their interest in processing large amounts of data on individuals 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. 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 Gradle 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 how you should talk about it. And 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, 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's not immediately obvious that I should be 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.

This has been like one of the, this is a sticky question. Like I think we ended up with to 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 like an 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. We're interested in, like, at the beginning, actually, we were really, we thought notes is, we're just gonna 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? It was 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 so we keep the transcript. But I think we're not, we're not interested in having a word-for-word legal, 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 Grinnell 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 an S3 bucket and we were like, we're not gonna use it now, but at some point in the future, 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 were like, nah, this is too creepy. It feels, it makes the product feel heavy and 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 Brunello 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, 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 Granola should be hidden. Like, it's in our interest for Granola 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've 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 because it's in everybody's interest for that to be out in the open. Yeah. And I think that's where we're at today. I do think Granola 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 get a lot more questions about privacy and consent when we started 2 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 than a personal context. Like I think the, Always on wearable thingamajigs like that, they have a real—they're gonna have a real hard time like navigating the privacy question in people's personal lives. We have it much easier. Meetings are like a set piece where you sit down and you all agree that you're going to 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 that doesn't exist in the rest of your life. Yeah, that's a. That's roughly how we're thinking about it today, but we still have a lot to figure out there.

It's interesting how much you have seen a shift already. And I do feel like you're probably right that there's just going to be more and more shift because the value is really hard to pass up. You were commenting about potentially you've got kind of 3 layers, right, of raw audio, which you don't even keep, transcript, 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 product today, there's like grounding of answers and notes to specific moments in the transcript, which I think is huge for confidence building, if nothing else, and ability to dive in and sanity check. And then the most kind of superficial, in theory, should have full value, but sometimes doesn't, is the notes itself. I wonder, how do you think about that in the context of just the ever more AI-fied future? One thing I'm doing right now is, 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 of all my contacts that I mentioned previously lives. And that's where I've got Claude Code is kind of my go-to Because I do trust it the most, certainly relative to God knows what OpenCLAW model with who knows what model in there with this kind of— and also on this computer, I'm logged into everything too. So there's another consideration. But I basically run 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 doing super long running loops or waking up when I'm not around. 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 about me? One of the things I'm experimenting with is basically creating summaries, right now going through this full 5 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, overall time, what were the projects that kind of were discrete things and which of those are active? And then relationships across the last 5 years, 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 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 inbox, and it's going to have its own email, and it's going to have the ability to kind of send email perhaps without my review. So maybe that a more abstracted understanding 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 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 adversarially robust to attacks 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?

No, I don't know either. I think you're already more sophisticated than me though. I like thinking about it like humans that I would be interacting with, like really helps me think about it, I think. So we have an executive assistant on our team who works with Chris and me with a bunch of stuff. And you work with an executive assistant, like 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 being outside of the relationship we have. And and that comes at a cost of the of our executives not having the full picture 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 baked into all of us as humans for the fidelity of our memories to drop off pretty quickly. I feel like 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 a transcript or from your emails or from anything if stuff gets fuzzy pretty quickly in the agent's memory.

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 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 very good in its niche. But yeah, but over time, as it gets smaller and smaller, you get efficiency out of that, and you also get the assurance that it really can't do anything else at some point. And I do think there's a lot of design space 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 the everything AI. And I really wish we were doing a lot more breadth of search before going so deep on one particular thing, because it just seems like 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 just different people probably would want to make decisions very differently, such that I do wish we had a more breadth, wider view before we get to AGI with, like, the— 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 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, has got to be something that you are holding the line on incredibly purposefully, because obviously in today's world, we can vibe code our way to new features 10 times before launch, right? In a world where you could code anything, you guys have made the very, again, disciplined choice to keep the number of features to a relative minimum. How do you think about that? Why are there not more features? Obviously, this has something to do with people being 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?

Yeah, that's a good question. I think you're right, though. 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 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 paranoid of adding stuff to that view. 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 less of a calm place or less of a place that you want to spend time in. Which is tricky. It either means that we like Barry, 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 is a 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 in 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 I think when you're in a meeting, we can expect to have two percent of your attention and that the other ninety eight percent is on the person you're talking to. Whereas in when you're chatting with Granola, I think we can have more like eighty percent of your attention. And therefore we can afford to throw more stuff at you. And, 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.

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 engineer on my own that obviously the vast majority of people can't. 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 2 side of my life.

Maybe.

So I guess the 2 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?

Yeah. Yeah. Recipes have been really interesting. And yeah, I think we've like a lot of use cases like the one you described have come out of the woodwork and weren't really things that we were anticipating when we built the 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 and so we built recipes which says 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 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 corral that messy transcript into something useful. So our CX team who are fielding bug reports and customer issues all day long will pretty regularly like 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, for 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. 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 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 it's written by Matt Mockery, who's a famous CEO coach, and we'd like ask someone to. Get on 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 dint of being with you in all the conversations. And I'm continually surprised by the depth of its understanding there.

That reminds me of another one that I thought was really cool, which was from Dan Schipper, 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 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, are you putting a laptop on a table in a conference room and just 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.

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 put, turn Granola 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. You can, people can just air out their thoughts, debate things. Leave it up to the robots to figure out the structure and how to format that information.

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.

Our 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. 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 it 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 can get the AI to come up with pretty punchy principles that are worded in a more interesting, different way than me trying to capture the average of everything everyone was saying.

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

I think recipes do well on 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 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 as quick and seamless as possible. And yeah, the amount 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 thirty 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. 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 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 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.

You only need one viral growth mechanism if it really works. That's One of the profound lessons 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 roles? How much of those blur 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, 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?

It feels like a slowish change on the day-to-day, but then if I like 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 3 product people, 3 designers, 3 designers, 3 designers plus me, a couple of like design engineers, and then everyone 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, actively playing with Cloak 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 mockup 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? Ergonomically to have it open on a meeting and be 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 a meeting. And so there we're really biased towards using AI coding tools to 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 in Figma and evaluating it that way still makes sense. The stuff around our paywall, like how do you sign up to Granola and what's the sequence of screens we show you and what information and what things do you need to understand as you do that? It's just really useful being able to see the whole thing in its entirety. And the design problems are all around copywriting 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 a canvas where you're not having to throw everything into the product. So yeah, it's a mixed bag, but the lines are really blurry these days. Like designers are submitting code, building prototypes in code, engineers are doing design as you would've thought of it like a few years ago. Yeah, it's a real, like, it's a real hodgepodge in a good way. I'm, I like having more fun, I think, than I ever have as a designer-builder person.

Yeah, I'm right with you on that. It's unbelievably empowering and unbelievably fun. How would you say it has impacted, I don't know if you have metrics on this sort of thing, or you could just gimme 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.

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? So the Granola Notepad, 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 that's the trigger point for being like, oh yeah, shit, this is obvious. We should do this. Was 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 really sped up the time from idea to a thing that we can start using and feeling in the app for ourselves. I think there are 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.

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

I think they've got their work cut out. So at least for me, Figma's just become a more specialist tool. It's like a, it's, 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 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 coding it and implement that, and then Rinse and repeat, we'll 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. Um, it's, it's way more of an ad hoc thing that you pick up and put down.

Okay. I'm not sure where, where that bottom lines out for me in terms of the future of Figma, but, uh, yeah, I don't know. It does, if I'm Figma, makes me a little nervous. Yeah, I'd be nervous too.

Yeah, I like, yeah, yeah. The question is, can they, I feel like, I don't think that need's gonna go away. If they can hold onto 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.

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 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 random ideas that people have and vibe code to things that actually get launched has got to be. I mean, correct me if I'm wrong, but it feels like it's got to be like 100 to 1, which in a sense is maybe good. I certainly can see the argument that that's what users ultimately benefit from and deserve. From the individual engineer's standpoint, or the individual person who came up with the idea standpoint, I could also kind of imagine it being frustrating. And I guess I'm wondering, But then you also said when something really hits, if you can get it 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 that you gotta keep in mind too is like, How do I make sure people keep actually trying? 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? It's probably not going to work. So I see a bunch of competing challenges there. I'm wondering how you're dealing with that from a team culture and management perspective.

Yeah, it's definitely a thing. I think one, I think we're very mindful of Like Granola is, as a company, is 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 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, 2 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 come up with good ideas, but also have the humility to assume that 9 times out of the 10, your idea's not gonna work. Like, when it hits the ground and it's put in front of real people, that's— and that's not, I think it's not because Chris or I says it won't work, but because it just doesn't land with the end users. Yeah, so 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 the next thing is just, like, 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 what, 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 yeah, there's no kind of like waterfall effect or anything going on there. And so I think this 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, 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, 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? It's like this. What if what if transcription worked in this radically different way? And people are trying stuff like that all the time. And I think 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, take what you learned 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 it's, that ends up putting us in a good place.

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 everything we've just been talking about. How do you get real responses? How do people feel like their ideas got a fair shot, etc.? How are you using the beta program?

Not enough, honestly. It's probably a bit disappointing. It's big. I think we have 10,000-ish people on it. And I think we make product decisions almost exclusively qualitatively. Like, 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 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 Either from us using it and talking about it internally, or 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 high 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 beta, 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.

That's interesting. I do wonder if we're gonna 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 launching a tonne of stuff to a small number of users and kind of seeing what happens. And then 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 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. Can AI do it? 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?

Definitely. That'll be the moment it takes my job. Yeah, I don't know. I feel like if humans— we'll see. But if humans are the ones making the decisions still, I feel like humans are going to want to be the ones You're gonna want pretty raw input to feel good about making that decision, I think. Who knows? That might be very outdated thinking very quickly, but I think that's where I'm at today. Yeah. I could see features where the usage is really disparate. People use it for many different things, like chat, for example, like open-ended 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. 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.

What scares you the most in terms of Granola's future? And I'll give you just kind of one candidate, which is my friend Andrew Critch 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 materials 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, clone what you've built and even potentially offer it at subsidised 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?

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'll be much happier building Granola in the meantime until that singularity. But I also think, I don't know, I feel like we, especially in tech, we 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 remember feeling this about Alexa and I think even Facebook Messenger had some chatbot. Like, I don't know, in 2013 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 through 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 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 architect 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 the next version of Claude or GPT comes out, then I think over time we're cooked. We have to go with the rising tide and just be a little bit better than them in our particular area and 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.

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 my common refrains is the scarcest resource is a positive vision for the future. And so I'd love to hear yours.

Yeah, I think when you sit down and study somebody's day, like work life, If you're like a laptop email knowledge worker or whatever, so much of it is you being like a slave to your computer. Like, you're 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 menial 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, I think that sounds like it can move work towards being a much more interesting place for a lot of people. And I don't like, 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 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 future.

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 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 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.

Thanks so much, Nathan.

Oh, they're stacked and I can't breathe. Talking fast, but I can't see. Scribbling hard. Falling behind. Half the words just slip my mind. 2%, that's all I need. 2% and I take the lead. A calm place in a crazy day. Let the notes fall where they may. Oh, not the flash, not the grand design, just the quiet, kept right on time. Simple things, simple things. Simple things done beautifully. That's the secret no one sees. Oh, 2%. That's all I need. 2%. And I take the lead. A calm place in a crazy 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. Help me look them in the eye. Let the weight fall. Let me fly. A little more present. A little more free. A little more free. That's all I ever wanna be. Oh, 2%, that's all I need. 2%, and I'll take the lead. A calm place in a crazy day. Let the notes fall where they may. 2%. That's all I need to pass it. Oh, let it breathe.

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