How to Build a Beloved AI Product - Granola CEO Chris Pedregal

Thanks, Matt.

It feels both amazing and daunting, is the honest response. We did this last, maybe, November. So we're based in London, right? And we went to SF for a board meeting, and someone on the team said, "Hey, should we rent out a bar and just email users and say, if you'd want to come?" And we were like, "Sure," and we thought like five people would show up, and this two-floor bar was just full of people. And then they were just—the level of detail with which they were talking about the product, or things we should change, or things that they had noticed.

It really hit me because when you build a product for people, really being in a room with that community all at once made me realize there's something special that's happening here that we didn't necessarily design for. It's kind of organically happening, and now it's kind of our job to follow that. Yeah.

I appreciate that. I think that speaks to the moment in history that we're living in, where AI now has all these capabilities that are going to transform the way we work and the way we think. And hopefully that experience you just described with Granola will keep happening in many more aspects of your work life, as you're basically able to outsource lower-level tasks and allow you to think higher level, which is why I think it's such an exciting time to be building and to be living, quite frankly.

I completely agree. And I think as AI gets smarter and we build more and more tools and workflows on top of it, we're constantly going to be letting go of things that we used to do and letting machines do that for us. There are times when I think that's incredibly beneficial, and I think there are probably times where that's harmful, negative. The example I always use is Google Maps, right, on the phone. So it's very clear.

There were cities I lived in before Google Maps came out, and I can go back to them and I can still navigate them without a map. And there are cities I've lived in since Google Maps came out, and there's a very small area of that city that I can navigate without a map. So you could say, "Oh, my navigation skills have atrophied 100%." The number of hours I've walked around lost in a city trying to find a place has also gone down dramatically, right?

So it's like, I'll take—in that instance, I'll definitely take that trade-off. But I think you have to be thoughtful, case by case, about what tools you use and what you decide to outsource.

I mean, I think this is why I'm so excited to be building Granola right now, to be working in the space, because I think that future is kind of up to us, right? There's this great quote, which is, "We shape our tools and thereafter our tools shape us." And when you think about AI and the future world and how it fits into our society, I think there's this big question of what do we outsource to AI? Where does AI replace humans, and where does it augment humans?

And we're like, me personally, Sam, Granola, we're really big fans of the augmentation idea. This goes back to Douglas Engelbart in the '50s, augmenting human intelligence. And his view, honestly, his stuff, I feel like people don't talk about him enough. It's just so inspiring. He's known for being the inventor of the mouse, and I think the mouse is the least important thing he's come up with. Basically, this was when computers barely existed. The computers that existed were in the military and the Navy, and they filled up whole floors.

There are a bunch of folks, actually, who are true visionaries at that time who imagined this world where computers would be accessible to people, they'd be common, and they would be tools for work and tools for thought. And the way Engelbart talked about it, he was like, we're becoming—the world's becoming more globalized, the world's becoming more complex, and we need better tools to help us collectively solve more complex problems. And that's—I mean, talk about an amazing narrative, right? I feel like we don't get enough of that today.

I have a lot of love for the tech world, but there's something to be said about some counterculture builders in the '50s, '60s, '70s, even like Steve Jobs when he started Apple. These folks had a counter-revolutionary view of the world and how personal computing and tooling could be used for that. In terms of the atrophy of the future, I guess I was trying to think about examples of this. Have you seen WALL-E, the Pixar movie?

The humans? The humans are fat and can't walk.

Exactly. I feel like that's one extreme future for humanity post-AI. Then there's another one, which is maybe like Jarvis from Iron Man, which is kind of like, okay, now I can fly, I can solve things I could never do before, or am I like this fat blob floating through space? I think it's a little bit what tools we build, what bets we make as a society, what rules we make. I think that's going to be decided over the next 10 years.

It's a great question. So I think the answer really just comes down to what were we trying to build when we set off to start Granola? There have been meeting transcription or recording products like Otter and Fireflies, I think, for like nine years, right? They've been around for a long time. That's not a new idea. There are all these tools that are like, okay, we can now record meetings, right, and try to make something useful there.

That's not at all where we started with Granola. We don't think like a meeting recorder. That's not what we're building. We want to build a tool for thought. The genesis of Granola was I quit Google because Google bought my last startup. So I quit Google knowing I wanted to do a new startup, and I came across LLMs for the first time, and they blew my mind. And I was like, this is going to change everything.

This is absolutely going to change the tools we use for work or productivity tooling. I met my co-founder, who had come from the tools-for-thought knowledge management space. And we basically said, AI is going to let humans work differently, think differently. There needs to be a tool that supports that. And that's what we want to build. So this idea of a contextually aware workspace, like an AI-powered workspace, that's what we wanted to build with Granola. And we said, okay, great, perfect.

We got the vision. That's what we want to build. Where the heck do we start? Right. And I was like, oof, okay, well, kind of imagine, you can imagine an assistant that knows everything about you, is there where you're working, gives you suggestions, learns from you. You can kind of imagine that, but where do you start as two people building in 2023? And we realized that AI is only as helpful as the context it has about you.

And this is something I think we don't talk about enough, even today in 2025. Context is so important. Context design, curation, that's a whole topic maybe we can talk about, Matt. But it's like, okay, to be helpful to a user, we need to have their context. And as a tiny startup, we need an entry point. It kind of came down to email or meetings. Those are the two places where there was a lot of useful context that we could access.

And then you put the product-building hat on and you say, getting someone to change their email client is hard, right? That's a very, very tall ask. Whereas taking notes in meetings, honestly, the biggest competitor that Granola had from day one, and even today, is Apple Notes.

It's this idea of, it's like I'm in a meeting, I'm five minutes in, you say something smart or that I need to remember, then I'm looking for a pad or paper or something to write it down. And Apple Notes is the virtual version of that. That's how we started with meetings. We kind of begrudgingly entered this super-saturated space, but we really did think about it very differently from the companies that were out there.

We were thinking about Granola as a personal tool for you to help you do your work better.

And while there have been tons of meeting recorders out there, I'd posit that none of them feel like that. When you log into these, they feel like a meeting repository, like, here are recordings of meetings, or just the fact that a meeting ends and it emails generic notes to everybody that was in that meeting. It's a completely different feeling than, here's this tool for me that is optimized for me. To be perfectly honest, I was surprised that we were able to break out in such a crowded space. There's so much noise, there's so much happening, there's so many people doing things.

And Granola is, by design, very quiet. There's no growth hacks in there. So that was a really pleasant surprise.

That's accurate. Yeah. My co-founder is a designer. I'm a product person. We can both code. He can code much better than me, but we're product and design.

I think the main thing that's changed here is that it used to be that you would need really strong technical chops just to build an MVP, to understand if this is something people wanted or not. And I think the reality now is that that isn't the case. You can usually figure out an MVP, or, like, is there a there there, maybe even early product-market fit potentially, or signs there, without a whole bunch of technical acumen, as long as you're building on top of the models. It's a completely different story, obviously, if you're building at the model layer.

But if you're building a wrapper company like we are, then you can learn a lot. And in those early phases, when I was looking for a co-founder, I met Sam, but I also met all the LLM experts from Imperial and Oxford and Cambridge because I thought that was DNA we would need on day one. And as Sam and I started prototyping, we realized actually there wouldn't be much for that person to do until we figured out product-market fit, until we maxed out on what the base model, like the off-the-shelf models, could do.

And then we would need that expertise. And then we stopped looking for that person.

Personal reasons, yeah.

It's funny. We were here in London for personal reasons. My wife's English. We moved here. I knew I wanted to do a startup. We chose London because there's amazing engineering talent here. There's enough of an ecosystem here to really have a go at it as a startup. And then when I decided I wanted to build an AI startup, I said, "Oh my God, yes. DeepMind's here. A lot of modern AI was invented here. Some of the best programs, like I said, UCL, Cambridge, Oxford, Imperial, they have amazing AI programs."

And then I had the realization that actually product and design and just general product taste and building is super important. So we did need to hire those folks early on. I think there are trade-offs, right? I think there are very real trade-offs. There's a center of gravity of talent in Silicon Valley. I think we're in a very lucky position to be, I'd say, one of the most visible and desirable AI consumer-facing AI startups in London. So for the continent of people over here, they find us, which is incredible.

And I think in an era where taste matters and product sensibility matters, there's just amazing talent here for that, as well as amazing engineering talent from all the big tech companies. And there's a huge influx of Russian tech talent that's coming to London. So we're definitely not in the eye of the storm, so to speak, which I think is, to be honest, mostly negative. I think the upside is probably that it's a little quieter over here. There's so much noise.

There's so much change. There's so much thrash in AI. Whenever I talk to AI founders, especially second-time founders, they're like, "It's never been like this before. Founding's hard. Founding in AI right now is emotionally draining, energy-draining. It's draining in every aspect because it's so fast and everything can change, can pivot on a dime." And I think being in London insulates us from that a little bit.

So I guess that was extremely intentional, right? We are, the way I talk about it internally, an American company that happens to be in London, right? And we built for Silicon Valley. We built for the American market explicitly. If I ever see any copy that has English spelling instead of American spelling that goes out, I throw a hissy fit because I want everyone to think we're an American company. And that's what happened. I also have to say, it really helps that I built my previous company in the US.

All our investors, or our main investors, are based in America. I had that network. I had basically that DNA we've transplanted to London. So Granola is a Silicon Valley DNA company that happens to be building in London and leveraging that as much as we can.

Two thoughts about this. The first is: a simple way to answer this is, what is the fastest way for me to learn? So presumably, you start building something, you have some prototype, right? You have some early version of the product, and you say, will I learn faster if I launch publicly, or will I learn faster if I don't launch publicly? And the answer for us for about a year was we'd learn faster if we didn't launch publicly because we were onboarding users every day onto Granola.

And it was painfully obvious what was broken about it. So launching publicly and getting 10,000 people telling us the exact same thing was actually going to slow us down, rather than just fixing it based on what users were telling us. There are many costs that come from launching publicly, where it's like, now you have users, you can't ship things with bugs. If you pivot, it comes at a cost. So we basically spent a year onboarding people, learning what was wrong about it, making fixes to that, onboarding a new set of people, fixing it, and iterating.

And then basically, the moment when we said, ah, we now have something that works, and we're going to learn a lot more by having lots of people use it and realize, who does it take off with, right? It's like maybe real estate people will love it in a way, or use it in a way that we didn't expect. That was the moment we decided to launch publicly. On the general wisdom on this, MVP, not MVP, I think today there are so many products and companies coming out and vying for your attention that launching something more polished so that when people use it, they're wowed by it is a way to stand out.

So I do think it's a tension. You shouldn't be tinkering in your closet for two years, and the world's moving very quickly. But there are a lot of MVPs floating out there, right? So if you want to stand out in this really, really busy market, I think you need to have something a little bit more polished, again, when you try to draw attention to it.

Yeah, well, it's two stages. First, we were building for us, right? And then the first users were kind of friends and family and extended network who were knowledge workers, used computers, did a lot of Zoom calls. And that got us pretty far. And then there's this moment where users started telling us different things. They're like, "Oh, this is what's important. This is what's important." And we said, "Okay, we know Granola will be a general product, a horizontal product."

Lots of different types of people are going to use Granola, but we should just choose a user type on day one to make it really good for and then expand out. And there, we kind of looked around and we said, "Okay, we need a user type that has a lot of meetings, relatively formulaic—lots of a similar type of meeting with a relatively formulaic note style that they need. VCs, same question. That we have easy access to." Yeah, exactly.

VCs, right? Yeah. So we said, "Okay, let's build for VCs." Yeah. And then as soon as we launched, we said, "Okay, great. Now we're done with VCs. We're not going to focus on VCs. We're going to focus on a different user type." And we chose founders just because we thought they'd be the hardest. Founders might have a sales call and then a user feedback call and then an interview. And basically thought if we could build a good product for founders, then a great product for founders would be, by default, a decent product for folks in these other roles, and then we could make it better over time.

We always started from the perspective of, this is a tool for you. What will make for a great tool? And there are a few characteristics that are really important. So a tool needs to be consistent and reliable. Like, if you pick up a pen and it only works half the time, that's a terrible pen. You're not going to use it, right? And in the case of meetings, there's this very real thing where some of your meetings or conversations might be on Zoom or Meet or Huddles or WhatsApp, or maybe not even on a VC.

It might just be in person. So we started off from this tool-building perspective of, Granola needs to be consistent and it needs to work across everything, because we have this 500-millisecond window when someone is in a meeting and decides they need to take a note: what tool do they open? And again, we're competing with Apple Notes, and Apple Notes always works. It doesn't care where you are, what you're doing, it always works.

So that's where we started. And then from the adding-a-bot-to-the-meeting perspective, technically, just because we wanted to work everywhere, that wasn't a good option. But if you analyze that a little bit as a tool, bots make you feel kind of weird, right? Just like a big black box on the screen. It's not a person. Sometimes they show up before you join the meeting. It's kind of this awkward thing. Beautiful thing from a growth distribution standpoint, right?

Like, you get a user now, they're exposing everybody they're meeting with to your product. So everyone thought we were kind of crazy not to do that. And then the way I think about information capture and usefulness is basically, I'm sure that two years from now, three years from now, everyone's going to be using something like Granola. I'm hoping it's Granola, but if it's not Granola, something like Granola, just because it is so useful and will get so much more useful over time.

And I think as a society, we need to figure out what are the right norms there. And I think what you basically want is something that is the least invasive for the maximum usefulness. And that's the right trade-off here. And when we designed Granola, we basically said, okay, because all the other tools out there, they record audio, they record video, they save that stuff. At least when we started off, that's how the tools worked. And we said, again, that doesn't feel right.

Like, I don't want to be recording. I don't want to have video recordings of all my meetings. That feels very invasive. Like, what do I actually need? I actually need good notes, right? Most of the time, I actually just need good notes. And so we made another decision early on, which was, even though we could store the audio and that would be useful, we do not store the audio. So we don't record the audio, which completely changes the way Granola feels.

I think Granola feels more like a really smart, enhanced notepad than a meeting recorder.

Exactly. We store the transcript. We actually were hoping to not even do that, or at least not make the transcript visible. And I guess one of the things we figured out, and has become a design principle for us, is that in the world of AI, where AI makes mistakes, transcription makes mistakes, it's really important that I don't have to trust the LLM output. I can kind of go back to the source.

And of course, transcripts get stuff wrong all the time, but it's like, if I can see, if I read the transcript, I'm like, ooh, that looks fishy. That's important as part of the experience.

We had an event the other night, and we were reminiscing over beers about the versions of Granola we built before we launched publicly. And basically what happened is we were in stealth for a year. And as I said, we were onboarding people every day, learning about what was wrong. And we kept adding things and adding features and adding views. By the end, there was this version of Granola where you could kind of swipe, and there are all these panels, and it's like, here's your transcript, here's your super long, like, here's your blow-by-blow of exactly what happened in the meeting.

Here are your notes, here are your private notes, here's your, I don't know, your notes in another language. It was like, really, and you could see how you got there because we learned about all these pain points, all these use cases. And then what we did, and I think this is probably one of the things I'm proudest of because it was hard, is we looked at it all and we cut out 50% of it. We basically redesigned and cut out 50%.

And I think that would've been impossible or extremely hard to do if we had been publicly launched. I think because if we had been publicly launched, we had all these people who had grown to love Granola in whatever weird shape it had been in. And then we cut out half the functionality. You just get so much hate. It'd be tough. But because we were still pre-launch, we only pissed off 150 people instead of the number of people who use Granola now.

Simplicity is really hard. And it's hard because organizationally, unless you're the founder, you're solving a problem and you're in a little universe, and you're going to design for something that's going to optimize to solve the problem that you're fixing, whatever that is, right? But you don't have the full context of the product in mind. You don't have the full context of the strategy, and you end up with lots of different people going for whatever is a local maximum solution based on the worldview that they have, which is the problem they're solving.

And then you have to have this other layer, which is looking at the product end-to-end and saying, sure, there's very clear tangible value in having this feature. And then there's this very intangible, hard-to-measure cost to launching it. And on a one-by-one basis, it always looks like you should launch the feature. And then you look up and you have 10 buttons that are clogging up the app, and the app no longer feels so magical and so zen. And the real danger there is user requests.

People always ask for the things they don't have. People rarely say, "Oh, actually, can you cut out half of the functionality of the app?" Even though when people talk about Granola, what they love about it is that it's simple. So basically the only people in the universe who are going to be pushing for simplicity are kind of like the designer-product leaders in the org. And it's kind of a lonely job, right?

Because you kind of make everyone angry, or everyone's unhappy with you. But it's such an important job.

Yeah, it's all of the above. So our general philosophy that's gotten us here, and it may not get us there as we scale, is we make most product and design decisions based on intuition. And so, what do we think makes sense? It's kind of this vision of the product that we're headed towards, and we just kind of make the decisions based on, does this feel right? Does it feel like it's in line with the vision?

And what we do to make sure we're not divorced from reality is think of us as like an LLM. It's like we try to fill our context with as much real user feedback, user opinion as possible. And some of that is quantitative. Of course, everything we launch, we measure and we look at the graphs, and it's like, oh, a lot of people were asking for this and not that many people use it. It's like, oh, it must have been the loud minority.

And that's a very important tool in the tool chest. But what I think is even more important is constantly talking to people. And that's something where Sam and I—and I'm talking about Sam and I because we work very closely together, but most of the team actually—they do regular user calls. And we aim to do, I think Sam and I aim to do, four to six calls a week with users, but constantly, not like, oh, we're doing a sprint on this feature.

It's actually, we try to book them every day, always, so that there's this constant context of—and the thing is, when you're building product, it's so easy to abstract away a human. So, so easy. It's what our brains always push us to do. And when you abstract away the user, it becomes very easy to convince yourself that they want X or they're going to do—of course, if we build this feature, of course they're going to use it. And I think it's only when you have constant contact with people and you're like, oh yeah, they're so busy.

They have all these other things to worry about. They don't even know what buttons are in Granola or not. Of course they're not even going to notice that. It's like that kind of thing that's really important to do qualitatively.

It's absolutely true. We're being pulled in a million different directions, and it's very challenging. I think the overarching point that I have here is, I think the failure mode is that we optimize for today's world. So we optimize for today's product and today's world and today's needs. And it's easy when you just talk to users and you get these requirements, or you talk to enterprises. It's easy to make this assumption that's like, "Oh yeah, I'll come up with a plan assuming the world stays static."

And we are in one of the fastest-moving moments in tech history right now. So I think the main failure mode for Granola is not to invest enough in building for the world of tomorrow. And maybe you can kind of infer some of this from what I said earlier about Granola is not about meeting notes. It's actually a tool for thought to help you do work. We're very excited about a world where people use Granola not just to take notes, but to do all kinds of work.

And in a way, the product we have today is a Trojan horse to collect a lot of your context so that you can then use all the information in that context to do future work. But that is hard because you have users or companies asking you for feature X today, right? And we have to simultaneously invest in this really incredible deep research mode that can look at thousands of meetings in a matter of seconds and pull out these insights. And that's not something that our enterprise customers are asking for right now because they're not even thinking to ask for that.

But I guarantee you that will be a huge part of where the world is going and where a lot of value of Granola will come from.

No, our philosophy is to use the best model that is on the market as quickly as possible. There's so much value in focusing on the product, like low-hanging fruit when you focus on the user experience today. And the base models are getting so much better and smarter quickly. Our strategy has been to use the latest and greatest. And when we feel like we hit a wall and the only way to make the experience better is to fine-tune or train models, then we will do that.

What we found is that there's so much alpha in the improving models and making sure you get the most out of that, that that's kept us busy thus far.

Yeah, yeah. I mean, we basically test out all of the models that come out. And anyone who's spent a lot of time with models—even users are getting really sophisticated—you just learn their abilities. It's like, oh, this model's really good at writing these types of things. This model is really good when you stick a ton of information in the context window; it can pull out the right stuff. So, while we include all these models in Granola, we set the defaults.

We set different default models for the specific thing you're trying to do in the app.

We abstract it away from people. At first, for the longest time, we didn't let users choose their model. And we only let users choose their model on chat. On the note generation side, we completely abstract it away. And the reason we do that is every time a new model comes out, we have to completely change or tweak the prompts that we use for note generation to provide consistency of experience and an improvement of experience. And there's significant work that goes into that.

I think it's one of the value adds that Granola brings, as opposed to just working with base models, is that we take care of that and we make sure you get Granola-feeling or -sounding notes consistently, and that they keep getting better over time.

Granola takes in a bunch of signals about you. So who you are, what kind of work you do, where you work, who you're meeting with, where they work, what they're trying to do. And we've put a ton of work into what are common meeting types for different folks with different jobs. And in those meetings, what are the things that really matter? So, for example, I think the thing that kind of blew people's minds when Granola came out was if, let's say, a VC, like an investor, and a founder were both using Granola in the same pitch meeting, let's say, the notes that Granola would generate for each of them look completely different.

Right. And it was based on something as basic as, I mostly care about what you said in the meeting, not what I said. Sometimes I care a little bit about what I said, but it's usually what the other person says. But also, the kinds of things that I care about coming out of a pitch meeting are very different as a founder than they would be as an investor. And a lot of that is kind of hard-coded instructions that we build into the system.

Sure. So, I mean, a one-hour meeting was a lot of information in 2023. It's not a lot of information now compared to what the models can do. The context window size increase over the last three years has just been mind-blowing and fantastic for us.

No, no, no, that's not a problem. The problem becomes now, well, okay, there are two things. One, notes are short; transcripts are long. A single meeting is fine. It's when you have lots of meetings and a large corpus of information that this problem comes up a lot. And the interesting trade-off, or the thing that's tricky here, is that if you care about information lookup, right, then you can do RAG. You can do either some form of keyword search or cosine similarity.

What we've found is that a lot of the most interesting queries that people have would completely fail with that type of method. So, for example, a query might be, like, what are all the things I didn't do a good job explaining? Or tell me, what are all the bugs that this user encountered in this user call? And the only way you can get a good answer to that is if the model has the full context. And this is very costly, but we generally tend to put lots of context into the context windows, and we err on that side.

And we have some really cool stuff in the works where we look at full context across thousands of meetings, which I've told you about. But again, it is costly with today's technology. There are trade-offs, and the trade-offs are basically, as far as we've seen it, money or quality, right? And our philosophy since the beginning of Granola is always to build for the world a year from now, because by the time we build it and it gets distribution, the costs of those models or those capabilities will come down to a reasonable place.

But that's basically, I think, the context. The trade-off there is around the quality of the queries that need a lot of intelligence.

So the most expensive thing about our business is actually transcription. And historically, it's been actually transcription and high-quality transcription versus LLM inference. We basically use the best real-time transcription on the market at any time. And the cost of transcription has fallen dramatically over the last couple of years, and I suspect will continue to do so. So yeah, we're not at negative gross margins right now. But what I do expect is I expect the cost of inference to stay the same or go up as we allow users to do much more complicated queries over much larger datasets.

It'll be an interesting race to see: does the cost of inference go down faster than the user desire for more complicated and more intelligent features goes up?

So let's see, echo cancellation, we run ourselves on-device. And it's just important because if someone has headphones on and they take off the headphones halfway through the meeting, it's important for that to work.

Yeah, on top of some open-source frameworks. And then we built it. We've partnered with Deepgram and AssemblyAI for transcription. They keep pumping out better and better models. We're always using the latest and greatest. What else are we doing? Diarization. Unfortunately, real-time diarization is still in its infancy in terms of quality. So that's something that we're keeping a very close eye on, but we haven't been able to get real-time diarization at a quality point where we're happy with.

And actually, there's a danger with—models are really smart in ways you don't expect. If you give incorrect diarization to a model, it'll oftentimes confuse it more than if it just has to try to infer who's speaking. So there's some interesting analysis and evals to be done there. Let me see. Yeah, we use—I mean, there's so many. I can tell you about our whole tech stack.

I guess for every product, the idea of what would be harmful or negative for the system to spit out is a little bit different. I think if you go to something like Google or ChatGPT and you ask it for something, it's an open-ended place where you're looking for guidance or help or health advice or what have you. There's some really, really bad scenarios there. For our case, it's a little bit different. You're usually going back and asking questions over your meeting data.

So there's a question of if we get something wrong or it hallucinates. But what we found there is that the best thing to do—you're never gonna get it 100% right. We can never be like, "Oh, you know what? We make no mistakes. You just trust us." So obviously, we do the best we can to avoid those mistakes, but really what's important is the way you design your product needs to let the user kind of view source, kind of look behind the curtain, and be like, "Wait, how did you construct this answer?"

Where, what are all the citations? So we spend a lot of time thinking about citations, about letting you view original transcripts and quotes. And there's a lot more we want to do there, but that's really the way you solve for this, at least so far.

What we really focused on is making the product really good for people. And it turns out that that's actually led to a lot of viral growth. But that viral growth is from people telling each other. An interesting story that I never imagined could have happened, but I hear a lot now, is if you have a one-on-one, you're meeting with someone on a Zoom call and your AI bot shows up, and you basically are told, like, hey, what are you doing with an AI bot?

Why aren't you on Granola yet? And it's like, oh, wow.

The AI bot is now a conversation starter. Yeah. It's the weird thing. It's a conversation starter for a human to bring up Granola and to vouch for it, which is incredible. But I never would've sat down and imagined that world. So we always start from a value standpoint: what is valuable to the users? Like, oh, we could email your notes to everybody in the meeting, like all the other companies do. But again, is that a tool that you want to use?

Is that acting like a tool for you, or is that acting like a growth engine? What we do have is, we do let people share Granola notes. Basically, you can share notes on a link.

And you can send that link to people. And what's nice about that is that when you share the Granola link, the other person can chat with the transcript and ask questions. So it's kind of like unlocking all the AI capabilities to the person you're sending it to. And we see a lot of link sharing there. And then a lot of times people then say, oh, this thing seems interesting. What's this? And they go and they download Granola and they grow that way.

The thing we're working on right now, and it's still early days, is if Granola acts as a second brain for you, our goal, the next step there is to be kind of a second brain for your team or for your company. And this is how, obviously, at Granola, we have that. And it is pretty incredible what you can do when you have all that shared context. That does bring up a lot of questions again of, like, what kind of meetings, what kind of context do I want shared with whom?

And what kind of meetings or context do I not want shared? Because there are a lot of really dangerous failure modes here.

Right? It's very easy to sit down and be like, oh, I want all my meetings shared. All the meetings in the company should be shared, right? Because transparency is a good thing and it's super valuable in the age of AI. The more context, the better. And then you actually sit down and think about meetings. You hear these horror stories that go viral where it's like the AI meeting notes captured a sensitive meeting on the wrong calendar invite and emailed the whole company.

Or, I was at a founder dinner the other day. This is a great story of how we get customers. I went to a founder dinner, and he was like, oh, you're Chris from Granola. My company, we just switched to Granola. And I was like, oh, great. What happened? He's like, well, I walked in on my co-founder and my CTO having a discussion about letting go of this key person. And I noticed that I think it was the Google Meet recorder was on, and it was on the all-hands, which had happened just before in that meeting.

And we had this awful realization that the moment we hit end on the Google Meet, everybody in the company was gonna get an email with our in-depth discussion of how we're gonna let this person go. And then they all freaked out and they all said, okay, if the Wi-Fi cuts out, we're screwed. So if the battery dies, we're screwed. So this became the sacred computer. And I think they had like 10 people trying to figure out, and they were able to change—they figured out there's some undocumented setting in the Workspace admin data control thing, and they were able to turn it off. But they turned it off and they ended the meeting and they just sat there for five minutes waiting to see if it was going to be a disaster.

But there's a lot—it turns out human relationships are complex and nuanced. And if you have a one-size-fits-all solution, there are a lot of these cases that get kind of ugly.

Yeah, absolutely. So I think there's two lines of questions here. One is, it's important in the enterprise context that everyone knows that you're using Granola, right? We have functionality that posts this in the chat right now. You can turn this on and be like, whenever you join a call, post in the chat, let everyone know I'm using Granola. And we're going to launch a whole bunch of stuff that makes that better. But that's one line of questioning.

And you should always tell people you use Granola. It's the right thing to do regardless of what the laws are where you are. I think we're also moving towards a world where these tools, if they're well-designed and not too invasive, will be normalized in certain types of contexts. Then the other question is, and this is not just a question for Granola, but for AI in general, there's your liability footprint, right?

At Google, our emails were deleted after two years or three years, right? So you literally couldn't go back and search and see why a certain decision was made on a product. Like, why did we do this on Gmail three years ago? You couldn't find that in the emails. And that's to limit the liability footprint. In a world of AI where all that stuff's actually really useful, or we think has the promise of being really useful in the future, there's a lot of tension there, especially when I talk to a lot of customers.

There's a big disconnect that I've seen, especially from folks who are very AI-forward, tech-forward, thinking about the future. It's how can everyone in the company leverage AI tools to become better, faster, smarter? And then the IT team, the legal team. And I don't know how that will get resolved. I honestly don't. I think it'll be a very interesting space to watch.

From my entire career building products, you kind of learn the hard way that getting users to build a habit of using your product is incredibly hard. When I was earlier in my career, I thought, okay, you build a fantastic product and then people will use it. And then there's this horrifying moment where you build something good and you put it in front of someone, they say, "This is fantastic," right? And then the next week they hit that exact same pain point.

That's the exact same moment, and they don't use your product, and you ask why. And they say, "Oh, I just forgot about it. I didn't think to use it. No, I still love your product. I just didn't think to use it." And that's really heart-wrenching to realize. So now, especially when founders come up to me with their idea, I urge them to think really, really hard about what are the triggers for using your product and those moments.

And the beautiful thing about meetings is that meetings are on a calendar, and there's a very specific moment where we know you're gonna do a meeting. And that's not enough, right? If Granola weren't actually useful, then that wouldn't lead to retention. But I think it's the combination that Granola is useful to folks and we can send notifications at the right moment to start it.

Yeah.

Yeah, well, I guess it was really helpful that most of our competitors had some kind of AI note-taking feature when we launched. That was already the case. And somehow Granola was able to stand out and win people's hearts and grow. I think, again, the failure mode here is to think about the world as it is today and the product capabilities as they are today. And I think my view is that notes are kind of useful. I think they are a stepping stone to the way we're gonna work in the future.

And the way we're gonna work in the future is with AI that has really deep personal context about you.

And the product experience that Granola will be a year from now, two years from now, will look radically different from what it is today. Hopefully it'll still be very simple, but it'll help you do a lot of work. And I think no one's built that yet. A lot of people are racing towards that, and AI is an incredibly competitive space. But when you're talking about products that are native to a new medium, right, oftentimes startups have an advantage.

Yeah, I would put Google and OpenAI in different buckets. I think a lot more about OpenAI and Anthropic than any of the legacy players because I think they are native. They are AI-native. They've led the way in this space. To me, it all comes—and again, I don't have a crystal ball. I don't know what the future's gonna look like—but OpenAI is gonna try to do everything for everyone, really, and they do an amazing job at it.

It's really, really incredible. And I think the question is, can we do something way better for a specific use case and a specific type of user? And I think it's hard to visualize that world, right? There's a world where, actually, it's not that specific. The upside's not that great. Or actually, power tools for these specific workflows and just nailing them because you care more matters a lot. And I wouldn't have made my bet if I didn't believe that the quality of the experience and the tailoring for our users is gonna win.

But again, I don't have a crystal ball. I think it'll be fun to watch. I have high hopes for a lot of the stuff that we haven't launched yet and how different that's gonna feel.

Yeah, absolutely. So I think the world we're moving towards is you have a bucket of context, and then you generate documents or artifacts on a per-need basis, on the fly. And the types of things that we're working on are, given my entire history of meetings, can you pull out... So, for example, a good question that we could ask would be, okay, out of everyone we've met in the last two years, who are the firms who are most likely candidates to lead our Series C?

Right. That's a question that I can't ask anywhere else in the world right now, but I have a version of Granola that will go through my 2,500 meetings and spit out a remarkably intelligent answer to that in 20 seconds. It's like a deep research mode, right? The other thing that we've played around with is, if you're dynamically generating artifacts or UIs on the fly, can those be shared, right? So this idea of, we have a folder where all our sales calls get put into, and they're shared within the company.

And then we have this artifact that you go to the URL—it doesn't have to be in the Granola app—and it'll tell you, here are the most important things our enterprise customers are telling us today. And every time you reload it, it's up to date, but it's like a memo. So there's all these—and there's some use cases I can't talk about just yet—but basically, manipulating information for you on the fly based on your context and increasingly large context, I think, will unlock all kinds of use cases and workflows that people aren't even thinking about right now.

Yeah, I'm curious. For you personally, is there a human who would be the ideal coach? Like, if we're going to train a Granola coach on a person, is there a specific human in mind that you'd like?

Stay tuned. Okay.

Thank you. I guess the hope is you make better investments now, post-Granola. I think that's the ultimate question, right?

Thank you so much, Matt.