Chris Pedregal + Sam Stephenson: Making Meetings More Effective with Granola
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Hey, everyone, and welcome to Generative Now. I am Michael Magnano. I'm a partner at Lightspeed. This week on the show, I spoke with the co-founders of Granola, Chris Pedrigal and Sam Stevenson. Granola is a powerful note-taking app that uses AI to compile and summarize meeting notes.

Chris and Sam and I talked about their journey as co-founders, how they have quickly become a must-have tool for many companies in tech, sales, recruiting, and beyond, and the areas where Granola might be trying to grow next. We spoke in front of a live audience at the Granola headquarters in London, and it was a lot of fun. So let's get into it. Thank you.

Hey guys. Thanks, Mike. Thanks for hosting us in your amazing office. Thanks for coming. Yeah. This office is like brand new for you guys, right? A couple months. Something like that. Yeah. Sam just carried all the plans in himself. Is that true? Not quite. A few other people.

a lot of tricks. Like actually, you went to the plant market at like 4 a.m. with like... We did do that, yeah. ...a truck and like carried it up all the steps. That's like a great example of building a startup, right? Yeah. That was like the least hard thing he did that. All right. Obviously, everyone, I would hope,

Most people in this room are familiar with granola and we want to get into the app layer and what it means to build at the app layer. But tell us about the product. Tell us about the company. How'd you guys get started? How'd you end up doing what you're doing right now? Give us a little bit of the origin story. So this was three years ago. I quit Google. I knew I wanted to do a startup in London. I didn't know what I was going to do. And, um,

And within a week of quitting Google, I started playing with GPT-3, the Instruct version of it that had just come out and was blown away, as I'm sure everyone here at some point in the last five years has been with alarms. And I was like, okay, this is new. This is different. I don't know what it is exactly. So I started messing around with it. I knew I wanted to start a startup, so I also started looking for a potential co-founder.

And I basically was like, he missed his two things at the time. I'm like, okay, maybe if I need a technical co-founder, it should be someone who knows how to train models, which I changed my mind on later. And then the other really hard thing I was like, oh man, there's a bunch of new UI that's going to have to be designed. Like that's AI native. So like I need a, someone who's really thoughtful at that. And yeah.

I started exploring these like tools for thought forums and I stumbled across this online meetup called like Tools for Thinking. What are tools for thought? Just to help us all understand what that means. All right, you don't have to cut me off. Tools for thought are basically like humans are toolmakers, right? Like my friend Paul was here and like taught me that. It's like humans are toolmakers. It's one of the things that sets us apart from animals, right?

And basically what we are able to do is really limited by the tools we have available to us, right? So classic Steve Jobs, Bicycle for the Mind, right? But like the original tool thought it's language. It's like written language, right? Then you can look at like mathematical notation, right? Like if you're using like Roman numerals, you can only do so much math in your head. Whereas if you use

whatever we use today, you can do much more complicated stuff with paper and pencil. And basically every development of human toolmaking has meant that humans can do more and more and more. And I think AI is the ultimate turbocharger of tools for thinking. Anyway, so I found this guy online in a tools for thinking meetup group.

I didn't even meet him. I just saw his profile and I sent him an email being like, hey, do you want to grab a beer sometime? And somehow he said yes. Yeah. So I think, yeah, we basically like both were very aligned from the beginning on. Like we...

AI is going to change the landscape of the tools we use. And either the tools that exist right now are going to have to change everything about what they do, or new people are going to come in and take over. And so that felt like an exciting place to be starting a startup from. It's an opportunity that's just opened. From both of our prior experiences where Chris talked about how we think building a really good product experience is going to be a lot of what matters in making something successful in this space. And I think

To do that, we were both very keen to have a very specific and painful user problem that we could be designing around. You want to be able to picture a person in your head and picture them struggling with a thing so that you can make the tool that solves the struggle. And so we spent a while just open-mindedly wandering around looking for that struggle, talking to people about their days, trying to figure out where are the pain points, what sucks about people's jobs that we could possibly make easier.

And like, I think a thing that came up again and again was like people whose job revolves around meetings and talking to people. Every time you have a meeting with somebody, that meeting tends to create a kind of pile of follow-up work, whether it's simple stuff like just writing up notes that you care about or sending a follow-up email to the person you met, or whether it's complicated stuff like

updating 20 different fields in a CRM and triggering a workflow and an email campaign to somebody, things like that. A lot of people who have calls have a version of those kind of things after a meeting, and they all universally hated doing them. It's all kind of menial work that isn't what you get energy from in your job. And it felt like the kind of stuff that AI was primed to be able to help with, if not then when we were doing it, at least in a few years' time.

And so we started pushing on that, on like, how can we kind of make a tool that is in your meeting that eventually will be able to help you do

a lot of this kind of menial work that happens around meetings. Chris said that it's going to be so important with AI to build a great product experience. So I totally agree. But I would say that for a lot of people in the AI community, in tech, in VC, in startups, that was not obvious to many people a year ago. I'm sure you guys remember a year, year and a half ago, there was all this talk about, oh, if you're building the app layer of AI, you're just a wrapper on

GPT-4 or GPT-4, you know, whatever, anthropic clod.

Now it feels like we've done a 180. There's so much excitement about the app layer. And again, you know, Granola is an example that's often cited as being, you know, one of the potential winners. What changed? Why is there a bit... Why do you think... I know you guys haven't changed, but why do you think everyone else has sort of changed their mind about this opportunity? I think a few things happened. One is these models just kept getting better and better, faster and faster. And it became very clear that...

it made way more sense to just use the best frontier model out there than try to train your own thing. We're always going to be slower, right? That's one. Two, super hard and expensive to train your own model. So it's going to be a couple big shops that are going to do that. And then three, man, you get all the benefits from those models. So if you can apply it to the right use case, it's really powerful. And I think our view of that from the beginning has always been

low frequency use cases that are maybe non-critical are going to be eaten up by the general agents. So I think if it's like a consumer use case that you do like twice a month, it's definitely going to go to ChachiBT or Anthropic. If you are doing something like really matters, like it's like professional tooling where your performance really matters and you want to optimize for that use case,

then bespoke tools that are optimized for that are going to be way better. And I think that's what you're starting to see things like, like cursors valuation, like Windsurf just got acquired. I think it's like prototypical, like they're just like wrappers on, on Sonnet 3.7 or whatever. Right. But actually they're like amazing. And there's so, it's hard to build great software and the Delta of, if you're using one of those products, it's how much more productive you can be really matters. And I think now the market's kind of,

I think this thing is like a pendulum, right? We get really excited about one thing and then it's like, oh, there's a glimmer of the future might be different and people get very excited about that. We think that has sticking power, right? Because we think the tools you use matter and we think the professional tooling has always been a thing, right? And if it makes you 10, 20, 30%, 50% better at your job, that's going to always have a lot of value and economic value.

It does mean we have to be quite selective about the things, like the challenges that we choose to bite off and which we choose to leave alone. When we started, it was like GPT-3 time and real-time transcription had kind of just become a thing that was available by an API. But it wasn't great. You know, like transcription was obviously bad in a bunch of ways, like it would kind of miss things. The notes that we wrote weren't amazing just because the models weren't amazing. We had tons and tons of conversations about like, you know, like,

Should we, you know, what should we be investing our time in? Because like the, some stuff is just going to keep getting better without us doing anything. You know, the quality of the AI output, the speed, the cheapness, all of those things. And some things are not going to get better unless we like push really hard and then try and figure out what a good solution is. And so I think a lot of the game for us has been like picking our battles and like,

knowing what to innovate on and what to just like wait for it to get better, you know? Give us some examples of some things, you know, real-time transcription is one of them, but like what are some other things that you, you know, very intentionally decided not to work on and maybe what are one or two things that you did? You're like, oh, this is our job to solve. Granola can be the best at this. The obvious one to me was language support. Like when we launched Granola,

it immediately was like the most requested thing for Granola was to support multiple languages. I think it still is. And we spent like a week working on it, like trying to figure out a good interface. It was like kind of available in one of the transcription providers. I guess it wasn't great, the language situation now, which meant that we would have had to

it was looking like it was going to be like a few weeks to a month long project to make a good interface to help you pick the right language for the right time that the meeting that you're in um which is a huge investment like a month of time right and that to me feels like a thing that there are dozens of companies out there really incentivized to figure out multi-language real-time transcription models and um

If we just wait, like it's going to happen and the experience will be way better in that world than anything we can kind of think up to like hack around the fact that it's not good right now. Yeah. Another example is like context window links. It was too small when we launched. You can only do like 30 minute meetings and we could have done a bunch of work to like, okay, try to chunk that or we just...

You just wait a little bit and the context window's got larger. It just keeps getting bigger. Yeah, exactly. Another one's RAG, actually. Can you explain to people what you mean by RAG? Retrieval Augmented Generation. Basically, the idea is like, so context window...

Most people here probably know this, but a model can only take so many tokens, so much context into its memory. And if you have more-- let's say you have, in our case, a repository of meetings that are larger than a context window, you have to figure out which of those do you put into the memory. And there's all these naive approaches where you basically do a search across those and you choose a subset. Doing that well is hard. Doing it not well is super easy. Doing that well is really hard.

But complex windows keep getting bigger. So you can get away with by just sticking a lot of stuff in there, which is like, and in some ways it's like an unintuitive, I think if you put your engineering hat on, you're like, oh, that's wrong. You know, we should, we should engineer this. Throwing more stuff into the moment. Unstructured. Like AI breaks intuitions, man. It's like, sometimes you're like, oh, it's like, it's very imprecise. We're putting all this stuff in there. But like, I think these models are,

smarter and more intuitive than we expect. And sometimes though, the moments where I'm like, oh shit, are usually where it picks up on something I wouldn't have expected a machine to pick up on. You know, it'll be like, actually in that meeting six months ago, you said this thing. And then now you said this. And like, we wouldn't put that in the rag if you stick it on the context window. Like sometimes magic comes out. Right.

Let's talk about the business model of building at the app player. It feels like so many companies right now are basically just charging for inference, right? We see all these products that charge for credits. Yeah. And really what those credits go to are just

just hitting the model, right? How do you guys and Granola think about the business model? Like, is that the type of business model these companies should be pursuing? Should they not be pursuing? What's the opportunity to build like a huge business at the app layer on top of the models? I think a lot of the ways we think about it is probably not too, like our business model is probably not too different, like pre or post AI. Like I think ultimately we're trying to make a tool that's like

valuable enough that a company will give us money for it. And there are things that we want to push on to kind of make the thing feel more valuable, which are

not really to do with AI, to do with like team app building, I guess. Like if we can, if we can unlock network effects in Granola where like there's a, the Granola gets better the more people in your team are using it and it becomes like this valuable repository in and of itself. I think that's the thing that we have a lot of signal that companies will pay good money for. And it's kind of independent from AI, I guess, although the AI enables you to do cool stuff with that. If you're just monetizing the AI, you're effectively just like a reseller for open AI. Whereas like,

If you charge for the repository, you're charging for granola, something only that granola can provide, right? Yeah. I do think we're at an interesting moment in history because it's kind of a land grab right now. There's new products that are possible that couldn't exist before.

And we know that the cost of running these products two years from now will be vastly cheaper than they are today. So there's like, and maybe that will always be the case, but it's kind of easier for me to just think about the next few years. So we're in this world where it's going to be cheap to run granola or I don't know, pika or whatever you want two years from now, but it's quite expensive to run now, but there's a lot of user demand. So like, what do you do in that kind of situation? And I think it's the kind of thing where you have to have, I think you have to build for the future, right? And you have to,

You have to figure out how to make that work for your company. Because if you build for today, I think you'll make all the wrong optimizations, which does mean it's a capital intensive play. Right. When we forecast our finances, you know, into the future, like if you don't account for things getting cheaper, then it gets like really fucking expensive really quickly. Well, with exponential, with like 10% we can already grow. Yeah, yeah, yeah. And so, I mean,

part of the company's bet, I guess, is that this stuff is going to get cheaper and there's going to be ways. Some things will always want to be on the frontier of, I think. I could see being able to do chat and document creation on top of the huge body of all of your company's meetings is the kind of thing where just the more power, the better. But transcription, I could see hitting a ceiling where there's a point where it's good enough and then like

and then the game piece is just like cool let's now get that transcription for like

as little money as we can so that that's our main running cost. Like that can kind of go away. Lightspeed was one of the first investors a couple of years ago. And I will say it was so fun for me and I know everyone else on the team watching you guys build from day one, just like from zero lines of code to what it is today. And I remember the launch moment. May 22nd. May 22nd. So we're coming up on a year. Yeah. That launch moment. It was amazing. It felt like almost...

instant product market fit, which is so rare, never happens. I got to ask, like, tell us about the process of

of building the first version of Granola so that you could have that day you launched, which again, it's nearly impossible to do. I don't know. We try and be deliberate about things, but something's kind of happened by accident too. But I think for things that we were deliberate about, where the hard thing about Granola was probably going to be figuring out what's like an interaction or what's that lets a user get the stuff they care about out of a meeting in a way that feels really natural and really effortless.

like figuring out that is like, it's just a huge part of what we have to do to make the product successful. And so I think the first, I don't know, six, nine months of granola were like just experiment after experiment as experiment, like trying things to figure out what that might be. You know, we build a thing, put it out in the world, uh, be constantly talking to new users and watching how they react to it and how they use it. And, um,

And over time, we threw away a lot of the things we tried and were able to hone in on something that felt like it could work. The first six months was like...

a gradual growing of complexity in the thing as we threw more ideas into it, trying this and that and this and that. At some point, I think we felt like we'd maybe found a thing that could work. Just like, you know, type your notes at the end, Granola fleshes it out on the same piece of paper, that kind of thing. And then we kind of went through this process of cutting back and streamlining everything until it was really just that feature.

And that's what we launched with. Throughout this, we were kind of like, the goal was to build a daily habit for our users. Like, can we make this a daily use product in the small number of beta testers that we had? And we had this chart called the dot plot, which is like, you can see each individual user that uses Granola day by day and how many meetings they did on a given day. And that helped us be really honest with ourselves about like, is someone...

reliably picking this up and using this in their meetings or are they just kind of dipping in and out or is it kind of random? Yeah, we were in closed beta for a year and we had about 150 people that we had onboarded by the time we decided to launch and we manually onboarded all of them at that point. And I guess looking back on it, it's so funny. We never, like, that's not only Connect looking back, but

I didn't really think of New World's Reddit when we launched it. Mike pushed us to launch. Typical VC. Actually, he'd been pushing us to launch for about nine months before that. And we held him off for nine months. But at that point, all we could see were the things that were wrong with it, which is an interesting lesson, right? Because once we put it out in the world, it actually hit a bunch of cores, but we didn't necessarily...

appreciate the depth of that until we put it out there. Sam, I've heard you talk a little bit about your design process and about how the team really thinks about designing for what people actually need, not what they think they need. I've heard you use the term lizard brain. Explain. In building software, it's really easy to, I speak as someone who's done this over and over and over again on things I've worked on. Like,

It's really easy to get theoretical about what a user might want. And like, this thing would be cool. I've got such a good feeling about this. I'm going to, you know, I think this is how the app should be. And when you interview users, you know, they can tell you all of their great ideas for the product. And it's really easy to just build what they want because they're asking for it. One thing that we were kind of paranoid about from the start was, I guess, especially in our use case, like meetings are a super high stress situation in that

when you're in a meeting especially like a back-to-back meeting where you know where you're you're maybe it's two minutes past the hour you're already late for your next meeting you're like shit you know trying to make excuses to get off the call um and then you get off the call and then you've got to rapidly get into the next one as quick as you can and then you're like oh my god who am i talking to why are we doing this you know all that stuff you have so little brain space for for a piece of software at that moment to try and help you like you you're just trying to deal with the basics of

getting the next person in front of you. We just have like this, I don't know, 1% of your brain to play with, you know, as people designing a product. And I think keeping that in mind, like keeping the kind of

stressed out back to back kind of moment in our heads as we were designing it, like helped keep us honest to what's going to fly, what's going to work in this. And I think people often talk about how simple granola is and how it feels nice because of that. I think that's just a function of like, we really can't put many buns in front of you when you're in that situation, you don't have the headspace for it. That's really cool.

Chris, you built and scaled and sold another company before this, Socratic. I had the pleasure of watching you do that as well, because the company that I was building was on a street right behind you. Yep. In New York. That was a while ago. And, you know, one thing I often think about is, especially with you guys building this, is wondering, like, what's it like to build a company now with AI versus...

building a company that didn't have AI? What's the difference in building companies across these two eras? Ask me that in two or three years. I think I'll have a much better answer. Well, I guess one is, so Voss, our CTO, who is not here right now, like,

I look to him because I think he's the best at this on the team, but he really, really pushes us internally to use AI as much as possible. So it's like an active goal to reduce the number of lines our engineers write every day. And I think that you actually do need to push people for that because we all have habits. We've been working, we've been doing stuff for a while and the world's changing very quickly. So if the org isn't doing that, then you're missing out. The other thing is I think

People ask a lot about like, okay, what's the makeup of a company going to look like in this post AI world? How big does it have to be? My view there is that like,

I don't know what it'll be like in five years, but for us, the product is core. So we need a really, we need a bunch of really thoughtful, you know, best, best in class people working on the product. There are other functions where I like in the past, we might've built a really big customer success function where I don't expect us to do that. I expect us to use like whatever the best and greatest like AI tooling is. And we'll still have a great team there. It's just like,

how the team spends their time. And they might look more like engineers in a way in terms of building systems, even though they might not be writing code. And the last one is the world is changing and everyone's watching and interested in wanting to try stuff out. That wasn't the case with my last startup. I'm used to startups being like a slog if you like,

you're fighting so hard to get people to care about what you're doing. And, um, I kind of feel like the rug got pulled under me out from under me with granola because we put it out there and we're like, all right. So I'm like, no, I'm going to care about this. You know, I have to like keep working on it, keep working, keep working on it. And all of a sudden, like it just started growing and then, and then stuff just started breaking and trying to like mentally prepare for that. Um, so I, that's like macro environment questions. Like

those things change quickly. But that's been a defining part of this journey. It's just trying to keep up with the change and keep up with the growth. And I think that inevitably forces you guys and really any team building in AI right now to just move so freaking fast, which inevitably creates a different type of challenge for the company. It's like, how do you maintain quality? How do you maintain taste, right? Like taste has been this thing. I feel like

It's become this really annoying word, actually. You gotta have taste. But I think, again, granola gets cited as one of these products that just like, oh, it's beautiful, it's great design, amazing taste. Like, how do you think about maintaining that when you're moving stuff out and when you're building a team? It becomes so important, I imagine, with each and every person you bring on. I think we do all right at this, but I think there's much more we can do to make this better. Yeah.

But I think things that I think we do well over the workforce, we screen engineers as part of the interview process for product thinking, I guess. Can you think from the point of view of the user? And when there's a technical problem put in front of you, get to the why of why is this a problem for the user? And that helps you make the right trade-offs in cutting the scope and really just building the thing that's going to solve the person's problem, not this beautiful technical masterpiece of an execution.

There are types of features where once we have good systems set up, like the UI of Grinnell is kind of figured out, you can just ship and iterate and push stuff out very quickly there. And we don't need to be so cautious about that stuff. You can always roll it back a couple of days later. And then that way we can kind of reserve our judgment and the

taking the time to kind of pore over the details on the things that really matter or kind of like in the core flow of someone using Granola? New primitives in the app. Basically, we're trying to get better at the one-way door versus two-way door. Yeah. So it's like if it's a two-way door, can we just...

shift changes quickly, see what people think and go from there. That said, I think what people love about Granola is that it's simple, minimal, Zen, gets out of your way. And you add 50 buttons in there with new features, you kind of kill the golden goose, right? And I think we're figuring out how to find that balance because we do have to move quickly, but we also need to keep the soul of the product intact. I want to talk a little bit about building a team here in London. Granola, I will tell you, in the States, I mean, you guys know this, in New York, you

in Silicon Valley, I mean, people are obsessed. It's kind of like you're building a Silicon Valley startup in London. Is that intentional? And what is that like? It is intentional. Hopefully you guys can meet some of our team. And I think what you'll find when you meet them is

everyone on the team kind of wants to have a really ambitious, like classic startup journey. Uh, and we just happened to be in London and that's like a pretty, I think it's a pretty beautiful twist on it because, um,

you know, get to get to be in London, but you also kind of get to live the Silicon Valley dream. And that's pretty rare. But I think there's like, like the reality is there's like a most successful tech companies come out of Silicon Valley. Right. And there's like, there's a culture and like learnings and best practices about how to build a hyperscale tech startup that were kind of invented over there. And I'm not saying we got wholesale copy all of that, but I think our, our DNA, and you can hear my accent, our DNA is kind of comes from the Valley.

That said, there's amazing talent in London, right? And it's an incredible, it's a pretty fantastic group of people and perspectives that are here. So I think there's like a real big opportunity like for us to build a Silicon Valley style startup, like in London with the talent that's here. And I think something that kind of benefits us is at the app layer, there aren't that many kind of like buzzy AI app companies in London. There are some pretty impressive ones,

the foundation layer, right? Like you have the 11 labs and parking parks, but all the way back to like deep mind, right. Where, um, so there's incredible AI talent in London, but at the app layer, we're kind of like a bigger fish in a smaller pond compared to if we were in Silicon Valley, which is so much stuff going on. So we're kind of a magnet for a certain type of person. So it's probably like a bit of a strategic advantage when it comes to hiring, building the team, being in a different market is actually helpful. Yeah. There are trade-offs with everything, right? Um, I, I feel like we definitely get, uh,

access to incredible people here, that those people will be lots of different companies will be trying to hire those people in Silicon Valley, whereas here they would kind of get first dibs on them. There's also a lot of stuff happening in Silicon Valley, right? So it's important for us to stay current, to understand what's going on there. It can also be a full-time job to keep up with what's going on in AI, right? So you want to strike the right balance of like,

keeping your finger on the pulse, but don't get distracted, right? Because there's so much noise and so much trash. At the end of the day, all that really matters is building something that's useful, that's going to grow. What other, like, are there other London-based companies or products or teams that you guys take inspiration from when you think about that, when you think about building a team here in London? I think the Adio folks have done a great job at attracting a bunch of good talent. People from 11 Labs I've met playing, I think, and building, like, a really...

Great user experience, you know, on a product category that's existed for a long time. Yeah, I think they're like Monzo's wise. Yeah. There's all the fintech ones. Like, I think my view is basically like, it's too easy if you're in London to think about the UK and to think about Europe.

And like my general view is that in this AI space that's so competitive, you need to be competitive in the US because otherwise someone will win the US and then you're going to have to fight them in Europe. Whereas there's no reason why you can't go after the US market from here, right? Like most people don't know granola is a mug. Like users don't care. Like they all think it's an SF company.

So it's like, I think it's a question of ambition, right? And I think in AI, the prize is so big. There's going to be so much competition. You have to have that high ambition level or you're going to get eaten anyway. So we're taking a very like world view from the get-go. And we just happen to be base here, but we're not like doing all our user interviews with folks and we're doing them all over the place. Maybe last question for me. And then I want to open up to everyone in the room.

What is the ultimate ambition of Granola? We know it as the note taker for people in back-to-back meetings. You said you want to build, you're building with Silicon Valley type ambition. What does it become? What is the, you know, what is the massive Silicon Valley like success version of Granola? Other professional categories have already figured out their like power tools that people spend their day in and it kind of helps them get their best work done. Um,

uh designers have figma or photoshop back in the day engineers have ids like cursor or vs code if you if you're an engineer or designer for example like seven or eight hours of your day is spent in those tools and they amplify what you can do

by a huge multiplier. Up until now, like people, folks who work in like, I don't know, doing like people stuff, I guess, you know, talking to people, whether that's like sales or customer facing stuff or managing or investing, like you've not really been able to have one of these workspaces because like the

the kind of fundamental unit of your work is natural language and conversation and that's just too squishy for like uh traditional software to deal with it's not it's not code and it's not pixels but i think we're at this exciting point where like

we can finally, like computers can finally make sense of natural language and organize it. And so I think we have a shot at creating that kind of workspace that people who do people stuff kind of live in and it amplifies them, makes them work better and work faster. I agree with all of that. But if I do even more, I think,

We're so lucky to be alive at a moment in history where we talked about humans as toolmakers, like the tools that humans use to think and to do work are being reinvented. And I really do think AI is like if computers were a bicycle for the mind, like AI has the potential to be a jet pack for the mind. So like my ambition is

can we build tools that help people actually think smarter, work better, do better things? It's like be a multiplier on human capability. It kind of harkens back to, I don't know how much of you have studied Dungos Engelbart, but there are all these ideas at the birth of computing, basically of what impact it's going to have on human society and our ability to do great things that we could never do before. I think computers did do that. And I think now it's like the second chapter of that. What are the new heights that we can reach

Awesome. I could ask questions all night, but I know people here probably have lots of questions. Hi, I'm Emily. I'm working on something new and I'm really curious because I'm very early days on how you guys approach when you were early, your feedback loops and your early iterations and

I think something I'm trying to think through is like, how do I know when I have enough data to move forward? And also if I don't have enough data to move forward, what kind of data am I looking for? Is it qual? Is it quant? How much do I need? So I would love your guys' thoughts. I have like a philosophical view on this. Basically, it's all qualitative. Like my view is like in the early days, it's actually not even that. It's like you need to go off of your intuitions.

I believe that deeply. If you don't fundamentally feel the product or the need deep down inside, then that's a real problem. I'm not saying go off in a closet and just work in isolation for six months. I think talking to users and people is paramount. You should do it every day, basically. But you shouldn't... It's not the ask people if they want to build faster horses thing. It's you...

By spending time with users and watching them try to do stuff and fail, you are honing your, you're giving your mental context, like your brain, all this really relevant context so that your intuitions are better honed. I think if you're looking for anything qualitative, it's almost impossible in the early days. Yeah.

I think everyone here would love for Granola not to become a CRM. So my question would be about to create the sort of jetpack of the mind, what does the future of actually design look like for the jetpack of the mind? I hear you with the CRM thing. Yeah.

I think the way we think about it is, one, the thing that has served us really well so far is putting the individual user and the particular moment that they're in when they're using Granola above everything else and designing a great experience around that. And so when we're talking about how to spend our time and what things to build, that user has come first. And yeah, companies pay for us, but it's not the thing that's driving every product and feature decision.

make granola great for for the user i guess there's kind of two directions we i think it was pushing in like um we've seen when teams use granola together there is like a lot of value in um having the kind of shared context in one place that where you can you can kind of look at not just the one meeting you had then but every meeting that your team has had around a specific subject and do stuff with that if you're a salesperson trying to get better at your job uh then then

Being able to look back at every call the sales team has had for the last week and query, like, why are we losing deals? And what things have people said that helped us win when we thought we were going to lose? And things like that. It's super helpful to the individual. Yeah, I guess just adding to that, in my mind, it's all about AI is as good as the context as it has, right? And then the UI that lets you do useful things on top of that context.

And right now, Granola looks like it generates meeting notes. And that's what it does for people. That's what people like for it. If you saw the versions we had internally, Granola is all about using all this context we have about you to help you do work. I don't know. I think Sam and I were both like, okay, meetings are going to be a good wedge because there's a lot of information in meetings, whatnot. So I think we get a little bit of credit for it. But actually looking back, meetings are freaking incredible because the amount of data in transcripts is nuts.

And meetings are really just to start, like we'll have to add emails, we'll have to add Slack, we'll have to add all this context for you to be able to do useful stuff. But I think meetings are a really powerful training ground because, for example, if like you're a VC, like I want every VC in the world writing the first draft of their investment memo in Granola, right? Because we have all the, we should be the best tool for that.

Full stop. Every follow-up email, every strategy document. If you're going to rework your company, like you should do that in Granola because we know the most about what's going on in your company. Jim, who maybe is here, he built this demo the other day and it blew my mind. Again, like I've been working on Granola for two years. There's so much data in these meetings where he built like a self-writing wiki for Granola. Like it writes itself and it's always up to date, which is nuts, right? And it was, have you guys seen like WebSIM? It's like basically...

What it'll do is it'll generate an HTML page. You give it a URL, and it'll have an LLM hallucinate an HTML page. So this wiki worked the same way. So I could be like, what's our work-from-home policy? And it wrote it based on all the meetings that we have internally. So it's just this crazy new world that it's hard to imagine all the value that's going to come out of it until you start playing. But you should come by. We'll show you some demos. Hi, I'm Sandeep. I'm with Automation Anywhere.

I used to work in financial services. And one of the things you observe about meetings with potential customers is, hey, I don't want to share this information and that to be recorded. So just out of curiosity, in engaging with users, what have you found about human preferences, about having information stored, transcribed, that lets you put the tools for thought in action? Tools like Granola.

are already useful and will be so useful in the future that they will be expected in work situations. I think the private social sphere is a different question. That one's a big question mark to me. But in the work sphere, I think it's going to be normal. I do think for the companies in the space versus society, there's a conversation around

What are the specifics and how invasive are those tools? Right. So granola from the get-go never stored the audio. It only stores the transcripts, right. Which limits how useful we can be, but it makes it way less invasive than like the other AI meeting bots out there. And I think the conversation is going to shift from whether or not something is transcribed to how

who has access to that transcripts, right? Is it just me? Because there are lots of meetings. I don't want anybody else to have access to that transcript. Is it my team? Is it my company? Is it the world? Is it? And I think that that will really, really matter. And I think the defaults companies build

there will have a lot of downstream consequences. It's like someone's discovered fire. No one's going to be like, we're not going to use fire. It's like, we're not going to heat ourselves or cook food. It's so damn useful. We're going to use it. But how do we use it in a thoughtful way with good norms that actually minimize potential bad situations for the most upside? Let's have a big round of applause for Chris and Sam. We'll find this in Granola.

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