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 work days. In practice, as you'll

hear, the keys to Sam 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 dog fooding 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 frescos 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. 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 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 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 a back 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 to see yourself among that company is like a it's a great feeling and it's very validating so Um yeah, I mean like I I feel like uh uh Granola is growing fast and it's it's like almost entirely through um word of mouth, people recommending it to each other or sharing notes with each other

and uh um I guess we kind of you know that when we 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 uh uh it's I mean like took us a while to to unlock some of those viral loops and to get that to start happening. But, uh, I I feel like the thing's really starting to snowball at the moment and, um, and and we're growing a lot a lot month over month. 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? 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 we're 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 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 loads of time fiddling with. How do we help those people like access the power of this new technology? and um 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 of a 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 like archetypal person that emerged as like a good target user for us over time was like somebody basically somebody who's in backto-back meetings all day. And that could be like many kinds of jobs. That could be like an 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 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 a great analogy. My my co-founder Chris uh uh kind of brought and and and I really like which was um there's this uh kitchen utensils brand. Um I'm going to [ __ ] it up. I think it 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 was like had struggle like holding like gripping utensils or only had 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 end up creating a very kind of friendly easy to use product for the rest of us too. Um and uh yeah, this is like um I think if you work in like accessibility in in 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 toback 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 the 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 longtail 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 on 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 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 yeah yeah I yeah I think um uh I think us tool builders 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 um be capable of like stringing together quite complicated sequences and maneuvers in the software to do what they want to do. Um uh yeah, you know, and and and therefore, and because of that, you

know, we we we feel like we can get away with like pretty sophisticated, complicated bits of software that people have to learn and and um develop mastery in over time. And I think like sometimes that's the case, but I I I think it's probably way less than we would like to think. And um 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. Uh um where you you know where where really like you you wake up in the morning, you check your inbox, you're like, "Ah, [ __ ] there's three things there that that are that are like burning fires and I got to deal with it right now, but I also have a meeting starting in 20 minutes and like oh [ __ ] how am I gonna how am I going to juggle this all of this? And then um and then just kind of in a constant state of like feeling behind, feeling overwhelmed. Um never quite getting on

top of things. Uh and I think 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. That immediately calls into question the way that most people do user testing, right? Where you're like, it's been a 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 realw 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. I Yeah, I feel like um I I I fall into the same I I

feel like I'm guilty of the same thing all the time, too. you know, like you uh you want to watch someone use your thing and get validated that that they can they understand it. And uh yeah, I think what helps I think uh one one thing we found incredibly helpful um 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 um when we were when we were thinking about introducing folders in Granola or like some kind of organizational principle, um the way we started the 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 on 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 did they what's 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 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 I did 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 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. >> 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. >> One of the recurring themes we explore on this show is the gap between AI that thinks and AI that acts. We're seeing massive progress in large language models, but the real final frontier is embodied AI, giving machines the ability

to navigate the messy, unpredictable physical world. Rooflow is a company pushing the boundaries in embodied AI. I was recently reading about how a robotics company used Rooflow to tackle one of the oldest problems in automation, the bin picking problem. For decades, if you wanted a robot to pick up a part, you had to spend millions retrofitting the factory to make sure that that part was always in the exact same place every single time. Why? Because robots couldn't see a random pile of unstructured objects. It's effortless for a human toddler, but it's been a brick wall for industrial robotics until they used Rooflow. By leveraging Rooflow to train and deploy state-of-the-art models, robotics companies can move past the limitations of traditional automation systems and build robots that can look into a random heap of parts, reason about which one is on top, and pick it up with submillisecond precision. They didn't just build a better robot. They removed the need to hardcode the physical world. This is a great example of what I mean

when I talk about the cognitive revolution. It's the shift from machines that follow a script to machines that perceive, adapt, and learn. If you're building AI for the physical world, Rooflow is the best infrastructure to power your visual AI pipelines. If you want to move your project from observation to action, you need to check out Rooflow. Go to rooflow.com to read the full robotic story and see why the most innovative teams in the world are building on Rooflow. That's roofflow.com. Support for the show comes from VCX, the public ticker for private tech. For generations, American companies have moved the world forward through their ingenuity and determination. And for generations, everyday Americans could be a part of that journey through perhaps the greatest innovation of all, the US stock market. It didn't matter whether you were a factory worker in Detroit or a farmer in Omaha. Anyone could own a piece of the great American companies. But now that's changed. Today, our most innovative companies are staying private rather than going public. The result is

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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 to 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 one and helping them manage that reality or do you think you are ultimately like helping them get into that system two 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?

>> It's a good question. It's a good question. I like um I think both like I feel like we'll be able to make progress in some areas faster and in other areas not there. like um uh I think for example there are so many tools out there trying to be like a work uh assistant you know executive assistant for you for work or like a um you know agent helper that understands what's going on and helps you do your knowledge work and um 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 and and uh um 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 and uh uh 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 the content, so often they just if they miss the mark even slightly, then they're useless. They're like, "Uh, 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 want to 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 granola 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. Uh yeah. So I I think like I 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 though. Exactly. 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 backto-back meeting moment and means we can do different things design-wise. >> Yeah, I like the um multi-meating

feature a lot. It was one of the biggest things that I 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 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, diorization, 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 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 exporting 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 two? Yeah. I I mean I mean everything you've said makes me I feel like it's the things that you've done that you that you're doing like uh uh connecting like the machine is only as good as the context you've given it as you saying like uh um I think I mean a lot's possible nowadays like on a personal level getting all you know getting all your stuff into one place like I think um it's much tougher if you work at a company and a lot of the the context that needs to happen is like you know inside a company with all of the complicated permissions and and you know you got to get get security to sign off on connecting like you know all the tools together and things like that. Um I think things are way more fragmented there still for most people. Um, it's it's um there are definitely like I a lot of Granola customers are like um uh

like surprisingly pro progressive I think in you know how how much they're willing to let employees like uh 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 uh you're going to get like left behind really quickly. Um yeah, but I think so I think context is a big one and then and then um just like the level of personalization and memory like that's necess necessary to like for the agent to do a good job I think is maybe underestimated by a lot of software like uh I think the Asian just has to know like a a lot about you right a lot about you about the people you work with the projects you're working on um the priority level of things and and uh I think it I mean it's possible you know with like open core 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 an extremely proactive like uh you know in in setting these things up um 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. >> 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 me 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 going to 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 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 and things like that, you know, like um uh we like granola granola defaults to private for everything. So like every every note 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 uh uh you only have to say one personal or dodgy thing to like kind of pollute a transcript and make it not okay to share with with um the wider organization. Um, but that means that gives us like a really tricky design problem in that like you gota, you know, so much of the value of 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? Um, you know, while also not putting information that shouldn't be shared into places into public places. Um, it's a it's a tricky one and we haven't nailed it yet, you know, like we're we're trying stuff and and it's getting better, but we could we could do a lot better still. >> Yeah, that's interesting. How this is an obviously AI pill 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 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 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 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 back end 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 with more work and with better models. >> Yeah, I hear uh better models are coming soon. though uh that won't uh 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. >> One of the best pieces of advice I can give to anyone who wants to stay on top of AI capabilities is to develop your own personal private benchmarks, challenging but familiar tasks that allow you to quickly evaluate new models. For me, drafting the intro essays for this podcast has long been such a test. I give models a PDF containing 50 intro essays that I previously wrote, plus a transcript of the current episode and a simple prompt. And wouldn't you know it, Claude has held the number one spot on my personal leaderboard for 99% of the days over the last couple years, saving me countless

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cogrev for 50% off your first month. The activation link is in the show notes, so give it a try at tasklet.ai. 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 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 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 seem 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 context right 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 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 Antarctic 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 the futuristic value that you're describing? >> Yeah. Yeah. I think I think um 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 years like uh 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 there's it's a real waste of time to focus on optimizing cost. Um, and so yeah, granola was I mean granola was actually like really expensive like uh when we launched cuz um 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 technologies got more commoditized and cheaper. Uh so yeah and and I think that

that was kind of the early days. I think today where we're at is like um most usage of the product is like is the notes and that's a pretty predictable um predictable cost you know like the the number of meetings someone does has like physical limits to it and uh and the it averages out over the cost of a month and you can you know figure out what margins you're comfortable with there. Um, I do think like as granola like you know sophisticated users who have lots of meetings and want to be able to chat and do stuff with the you know do stuff with large amounts of meetings um do rack up pretty big bills which we which we swallow at the moment. Um and and uh yeah, I 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 aid or whatever like uh um I think I 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

or claw model makes a lot of sense like uh you know but I I think there is um there's something very nice and 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 not 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 sepas thing. >> Yeah. >> 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 on that >> the line of burn rate we 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 how much the product costs there 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 a this is like a kind of distressing to look at. But the transcription costs have gone down a lot for us and that that was I think there was a time where half of our burn rate as a company was going on transcription. That's it'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 LM stuff and 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 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 counterveailing forces there too in the same way there has been with transcription obviously a lot of downward price the trend in in model cost 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 like 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 Immad Mustach um 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 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 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 I 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? >> Yeah. Yeah. We the way it works is pretty simple like we we record audio from the microphone and the system audio of your computer and we pipe those to realtime 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 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 to only be keeping a transcript and not the 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 than if we were to send all the audio in one go and get and wait a minute for the transcript to come back. Um the quality is a bit worse. The speaker separation is a lot worse.

There's a lot of trade-offs, but I overall I think it's the right move for us. Yeah, I we're we're kind of like constantly looking at transcription because it's one of the it's it's maybe like the most fundamental of the technologies in Granola and if you improve that by 10% and there are a lot of knock-on effects to the rest of the product. Like 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'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 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 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 want to name names, but I'll tell you on my end, I'm a pretty happy Deepgram Graham 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 Dgram 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 Sunseo that all come including key phrases, fun phrases out of the transcript. So I'd say Deepgram Gram 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? >> Deep Graham and Assembly are the two main providers we work with. Both are great. Like we've we've used Deepgram Graham. I think we settled on them before launch and they just every time we evaluate the models are good and we can't find a better one. So, and assembly I think assembly we've been using for a while now too. They I think we actually split traffic in the desktop app between the both of them because they're comparable and it's helpful for to have redundancy with it because it's such a core infrastructure thing. Yeah. No, I think we're really we're really happy with that. It's just that we 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 Dgram on the API uptime front to be clear. But 98 point something is you want to have a fallback 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 whenever goes down. It's it's you notice very quickly and it's a very painful thing. Yeah, the redundancy is 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 You 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 the way people are thinking about AI and being surveiled. 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. Everybody has seen and I actually use both in today's world. I use both a call joiner notetaker and then I also have grid on my computer and they they both do their thing. And again, this is something I do cuz 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's it stands out right as the sort of it works on your computer, 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, 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 cuz 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 a sticky question and like a thing we ended 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 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. Uh we're interested in like at the beginning actually we were really we thought notes is we're just going to get notes that's all. 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, but I think we're not we're not interested in having a word for word leg 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 granola needs is the it needs the notes of what happened and it 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 we initially I think this lasted a week. We like when we first started giving the product to people, we stored the audio in a S3 bucket and we were like we're not going to use it now, but at some point in the future, there will probably be like we could probably train a model on all this audio and it's going to 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 now 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. 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 granola 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 when 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 build 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 cuz 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. Uh, and I think we and others will learn, we'll figure out how to help mitigate the downsides of it that it it will become a default. Like we used to 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 and a personal context. Like I I think the always unwarable thing like that they have a real they're going to have a real hard time like navigating the privacy question in people's personal lives. We have it much easier. A meeting is a 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 out in the rest of your life. Yeah, that's a that's roughly how I think about it today, but we still still have to figure out that >> 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 is really hard to pass up. your comment about like you know

potentially you've got kind of three 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 you know specific moments in the transcript which I think is huge for confidence building if nothing else and you know ability to to dive in and sanity check and then of the most 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 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 clawed code is 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 you know with with this kind of and that also on this computer 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 co-pilot model. It's agentic in the sense that it can do stuff, but it's co-pilot 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 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 discreet things and which of those are active and then relationships you know across the last 5 years like what 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. Um, and it 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 of me is the right thing. Or maybe there's m 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 uh

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? >> No, [laughter] 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 they're uh 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 exe 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 it's it's there's it's I like being able to talk with just Chris in the room and to 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 of everything we talked about 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 a elegant way of diffusing the dangerous stuff from from a transcript or from your emails or from anything if stuff gets fuzzy pretty quickly in the agents memory. >> Yeah. 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 it to 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 I do think there's 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 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 [snorts] decisions on tradeoffs 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 such that I do wish we had a more breadth uh wider view you know before we get to AGI with like the

you know don't um [snorts] 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've 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 launch right 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? >> Yeah, it's a good question. I think you're right though. What you said about us like 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 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 and I think it's got to be really useful to to justify the expense of 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. Yeah. Which is 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 is 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 uh 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 2% of your attention and the other 98% is on the person you're talking to, whereas in 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 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 common zen but sometimes at the price of like getting good things out quickly. >> One thing you mentioned at the top was viral hooks working or I don't know if you use 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 but 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 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? >> 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 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 um and so we built recipes which 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 there are a bunch of cases where uh 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 [ __ ] talk about it and 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 [snorts] 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 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 timesaving. The other one that 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 for me Matt recipe, which is it's like uh it's written by Matt Mockery, who's a famous TE coach, and we'd like ask someone to to get on a call with us and then go to their recipes page and click this recipe and then we 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 dinner 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 uh 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 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, are you like putting a laptop in a like on a 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.

>> 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 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. >> 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 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 to be written in the language that our team understands and like it to feel like it's kind of 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 every everyone was saying. >> So are these recipes the thing that is driving the growth or are there other um viral feature design principles you could share? I think I mean like recipes do well on like social media and or 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 v 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 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? 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 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 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 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 the slowest 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 three product people, three designers, three designers, three designers plus me, a couple of like design engineers, and then and then every everyone not on the product engineering design side. And yeah, I think we I as I've been hiring designers especially like I guess I guess I'm closest to design and 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 CL 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 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 in the meeting. Uh and so they're like they're we really biased towards using AI coding tools to like hack stuff together into the real app. Uh and all of our designers are doing that at this point. I think there are other parts where like it's I think the more traditional like mocking up a flow in Figma and evaluating it that way still makes sense. The like the stuff around our pay wall like how do you like assign 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 copywriting and um 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 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 who designers are submitting code, building prototypes in 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 host podge 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 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? >> 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 notepad the chat floats at the bottom of the app basically everywhere in the app now right and in the same way and uh 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 after the trigger point for being like, "Oh yeah, [ __ ] 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

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 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. Uh 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. At least for me, Figma's just become a more specialist tool. It's like a it's I use Figma now for ideiating. 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

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 and it and implement that and then rinse and repeat but 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 line's out for me in terms of the future of Pigma, but >> 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 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 going to I presume Claude code is going to run at it from their direction and other people are going to 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 going to 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 got to be I mean correct me if I'm wrong but it feels like it's got to be like 100 to which in a sense is maybe good like that, you know, I certainly can see the argument that that's what users, you know, ultimately benefit from and and deserve from the individual engineers standpoint or the individual, you know, person who came up with the idea standpoint. 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 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 you got to keep in mind too is like how do I make sure people keep actually trying right 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 and I'm wondering how you're dealing with that from a team culture and management perspective. >> Yeah. Yeah. It's 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 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 going to 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 going to work like when it hits the hits the ground and is put in front of real people. That's and that's not I think it won't 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 I 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 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 needs 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. Uh, 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 users 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 a and it's sometimes it's stuff that's on the road map 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 or what if what if transcription worked in this radically different way and people were 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 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 it's that ends up putting us in a good place. As [snorts] 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 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, etc., etc. How are you using the the beta program? >> Yeah, not enough. Honestly, it's probably a bit disappointing. We It's big. Like I think we have 10,000ish 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 and AB test but um 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 then and is a core feature right tend to happen either from us using it and talking about it internally

or or us as working with very specific people. Like I think uh 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 to iterate on. Um and 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 senseing we didn't, you know, [ __ ] up some obvious thing. Yeah, 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 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 con 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. Hannah, 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 or maybe dreading that day if it comes? >> Yeah, definitely. That'll be the moment

it takes my job. Yeah. [laughter] 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 going to want to be the ones you're going to 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 that's I think that's where I'm at today. Yeah, I could see features where like the usage is like really disperate. 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 are 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 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, 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? >> That's the main one, I think. Yeah, I feel like I've always worried like I've worried about this since day one. At the moment chat GPT came out, you could like plot some like handwavy line into the future where like the just this one tool does everything and no startup or specialized service ever

needs to exist again. [laughter] 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 if even if we are marching towards a texting big tech singularity, I would 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 like love to assume that the next big thing that comes along is going to swallow everything and going to be the thing that takes over. And I feel like I'm really feeling this about Alexa and I think I think like Facebook Messenger had a some chatbot like I don't know 2013 or back in the day and I remember thinking damn like interfaces they've cooked like we're just going to 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 overindex on one solution being the

thing that's going to 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 pro 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 backto-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 uh you'll have a a place in the future. How about just your general positive vision for the AI future? One of the 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 that's 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 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 going to have help and so much of that so much of that like kind of menial mindless work is going to be taken care of or like we're going to have we're all just going to have help with it. And I Yeah, I think I just I think that sounds like it could move work towards being a much more interesting place for a lot of people. No, I don't like I 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 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 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. [music] >> [music] >> Oh, day is stacked [music] and I can't breathe. Talking [music] fast but I can't see. Scribbling hard, falling behind. Half the words just slip my mind. [music] 2% that's all I need. 2% and [music] I take the lead. A calm place in a crazy [music] day.

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