Product Talk | Chris Pedregal | Granola

SA I'm looking forward to this conversation. We have Sarah Drinkwater here who runs a solo GP firm called Common Magic, and we've got Chris Pedroville, who is of course our co founder and CEO. And we'll leave you guys to it. So thank you very much.

Let's have a round of applause for. Thanks, Zach. Hi, everyone, I'm Sarah. I'm really delighted to be having this conversation as a long term Granola user and fan. Before we get started, can I ask you to put your hand up if you work in product right now. Yes. Who is a founder right now? You can be both, of course. Who is an engineer right now? Who is everybody else doing cool things? Great.

Who wants to be a founder?

Yes. Come and chat to me later. So we're going to have about half an hour's worth of questions and then we're going to go over to Q and A. So please be thinking about things. We really want to make this an informative, useful conversation anchored around product and granola. And I want to start by who's using granola daily now. Great. For those of you that aren't, how would you describe granola? Chris?

Yeah. Granola is an AI notepad for people in meetings. So it's a lot like Apple Notes. It's an app on your computer and when you open it up, it'll listen to what people are saying in the meeting and it'll take whatever notes you've written and then at the end of the meeting enhance it and try to fill in the things you didn't write down. Yeah.

Can we talk a bit about the origin story? So this is your second startup. You sold the last one to Google. How did you decide to build something, particularly in a space that you could argue is competitive? You know, obviously you're based in London already, but could you tell us a bit more about the genesis of Granola?

Yeah, absolutely. So my last startup was called Socratic. It was an AI tutoring app back before this wave of AI. So it was really, really basic compared to what you can do today and after it was acquired by Google. So after four years at Google, I quit. I knew I wanted to do a new startup and I didn't know what that would be and I started exploring and I very quickly stumbled upon GPT3 at the time. So this was about seven, eight months before ChatGPT came out and I just started playing with it and everyone here probably had this moment at some point in the last couple of years. I was just like blown away that this technology is different than anything I've seen before. And I think as I started building, I just built all these personal projects on top of LLMs to get a feel for it. And as I was doing that, I just kind of became convinced that a lot of the tools we use to do like knowledge work on our computers were going to get reinvented on top of LLMs. I didn't know what that would look like. I still don't know what that looks like to, to be honest. But I had this like deep conviction that that was going to be the case and I just wanted to be a part of that. So I started looking for a co founder. I was very lucky to find Sam, who's an amazing co founder. He's a designer. He can do everything, but he's a designer by, by training and had come from the Tools for Thought world and we started iterating and one thing led to another and going on well.

And that's a, you know, you know, a shared passion you and I have is the tools to Thought space. Tell me more about that for those of you, for those of us in the audience that may not be so familiar with that kind of sphere.

Totally. I. So I'm a huge, like, I love tools. You know, there's the famous quote, which is we shape our tools and thereafter our tools shape us. So like, I think they're from a product building perspective, just super interesting, super fun space to be in. I think we, like, we were just talking about, you invested in Vermont, right? And like the founder, Paul is like super into like the history of computing. And like, I think I'm like, we're really inspired by Douglas Engelbart and this idea of augmenting, augmenting human intelligence. And like all those ideas were like floating around when we started playing with alarms. I mean, it's, it's not surprising. It's kind of like you, you come across this new way of this new AI and it feels. It's like this new technology, like you discovered fire, right? And you're like, what are you going to do with this? It's probably good, it's probably bad. It's probably all these applications are hard to think about. It's just like, what vision of that do you want to put into the world? And that's really the space where Sam and I started with this. We didn't say, oh, we want to build a meeting notes app. We said, oh, we want to let people like our vision. We're far from it, but we're building towards it. It's like, we want to build tools that let people do more, let them be smarter, let them solve problems they couldn't solve otherwise, like literally expand like human capabilities and LLMs. And the future generation of AI that is coming is like the perfect tool. Like if, if the personal computer is like a bicycle for the mind. You know, to paraphrase, paraphrase Steve Jobs, like, AI should be the jetpack for the mind. Right. It really should let us do more and it should let us do more collectively. But anyway, so that's the big vision. We're like, oh, where the heck do we start?

What I was going to ask you next is like how you get into the product process and choices.

Yeah. And there are two. So like, if you're building with AI, AI is only as useful as the context it has about you. That's something that I believe very strongly. And if you're. We were thinking, okay, we're a startup, you know, we need to start somewhere. How do we get a lot of context about someone quickly so that we can be useful? And we only really saw two paths. We saw emails, right? Like, you know, you could become an email client and then you have the history of emails. You have a lot of context or conversations and meetings, right? Because just, just one 30 minute meeting is like 40 pages of transcript. There's so much nuance of so much information in there and out of those two we're like, oof. Getting someone to change their email client seems really, really hard. But people use all sorts of different tools to take notes during meetings. And it's usually the thing that's easiest to grab like five minutes into the meeting when you're like, wait, shit, that person said something important. Like I should write it down. And then from my previous experience building products, the thing that I've learned that is almost impossible to do is to build a habit in using your product. Like you get someone to try your product, they say, I love it. And then they forget it exists. They never come back to it. And beautiful thing about meetings is that they're in your calendar and we can send you notifications. So it seemed like a really from a habit building perspective and from a strategic perspective, very rich place to start because of the context. Unfortunately, it's an incredibly saturated space. Tons of players, you know, like, like the biggest ones are like eight or nine years old now. So they've literally been building for almost a decade. So we entered it kind of eyes wide open, but we were a bit daunted by that.

How do you think about If I compare Granola to, say, some of those other competitive products, there were some foundational product and design choices that you made, you know, such as the frictionlessness of how Granola feels on the desktop, you know, the choice to kind of not store voice data. Like, how did you come to these? Tell me about the process of kind of coming to these decisions.

Absolutely. It's funny, it's like looking in hindsight, a lot of these seem like really smart decisions. At the time it was like not, not so obvious. It all kind of comes down to this idea of granola is a tool for you, right? It's like a tool for you in a moment where like the prototypical user we have is you're running three minutes late to a meeting, right? And you need to get into that meeting and you want to cap. You want to be your best self in that meeting. You want to capture the most important stuff from that meeting and you're late and you're stressed and you're just in another meeting that was, that went over and we're building for that moment and that user and we want to be a tool for you and we want to enable you to do more. Right? So that's the, that's the perspective with which we came to Granola. And if you have that perspective, a lot of decisions become kind of simple. So one is, well, if you're running three minutes late to a meeting, I can't be opening up a web browser, figuring out what tab Granola is in. It needs to be really, really easy to open up. I can't worry about whether it's going to work on Zoom, but not on my slack huddle or actually this is an in person meeting and there's no Zoom link. So I can't be thinking about that. I just need a tool that my goal is to be as easy to use and as reliable as that, right? It's like a pencil and a pad of paper, storing the audio. Like 99% of your work meetings, you don't need to go back and listen to the audio. If you're a journalist, it's different. There are use cases where it's useful, but most of the time for our target user, you just care about the takeaways, the notes. So storing the audio just makes it worse. Way more invasive, way heavier. Makes me wonder, oh, should I be using it? Not. Whereas if it's just notes, it's much lighter. So I think all those decisions kind of stemmed from that point of view of it being a tool for the person and I think if you look at a lot of the meeting bot like companies out there, they don't feel like personal tools, they feel like meeting.

Like what, impersonal tools?

Yeah, I would say that they feel like meeting artifacts like capture systems. You know, it's like, here's a. I log in. It's like, here's a recording of all these meetings from my team. It feels very different from like this is something that I'm using for myself to do to achieve my goals.

How did you come to the original, like, I remember doing a user feedback session with you and Sam a long time ago. How did you come to your first Persona? When you're thinking about that person who stream at a slave for meetings, which those of you in the audience that have done meetings with me may recognize occasionally, how did you come to that Persona?

So I think it's. The short answer is we started building granola for ourselves and it's a lot easier to build a product that you want or that you want to use. And then we kind of found the most extreme version of that in terms of who has the strongest pain point. Right. And there are just so many people in back to back meetings. And if you. It's kind of, you know, oxo the right. It's like the company is basically like for people who don't know the story, it's like they would build kitchen utensils for people with arthritis. Right. And because it's like can be really painful, like peel a potato or something if you're arthritic. And then turns out they're just more comfortable for everybody. So they're actually like really nice tools for everybody to use. It's a little bit like that, no?

On that note, my, I've got a son with one hand. And his question for you, speaking of tools that work in particular use cases because lots of products built for people like him are better for all of us. He asked me, why did you call the company Granola?

I mean that's Sam's brilliance. Like so. So Sam's last project was called Custard. So I feel like the story he told me was like, oh, I named the git repo this morning. And I'm like, oh, why'd you call it Granola? It's like I was eating granola at the time, time. But I think, I think the interesting question is why do we kept the name? Right? And like, I think we fell in love with the name very quickly. A lot of productivity tools are like super this, like AI that and something about Granola is just kind of, you know, it's like a pleasant part of your everyday life. It's kind of like friendly but healthy.

You know, and it's a good habit.

Yeah, I like, exactly. It's like I. I've also eaten granola every day since we started the company. Because staying on brand, we actually, at the beginning, we didn't want to use the word AI anywhere. We were like, oh, AI is not the point, right? The point's the product. And we did all these user interviews on our landing page and they would literally stare at it for three minutes. And it's like meeting notes, blah, blah, blah. And they'd be like, how is it doing it? And then they'd be like, wait, is it using AI? And I was like, oh, God. And then it's like, actually it's very useful to use AI. So our domains now, granola, AI.

I love it. But how do you think about models? You know, from what I'm understanding, you're kind of multimodal. You're using the best model at the time. How did you come to that decision? Does it matter what model? You know, if we're thinking about loyalty in the modern.

Right. So I guess, like, we started a couple years ago and we started a couple years ago, the big question was like, train your own model. Fine tune your model. Like how? Like, I think the narrative at the time was you have no moat if you don't have a technical moat. Right? GPD wrapper was dirty word. And we kind of, again, from the. We just took a very user centric view and we said, okay, what's. What's stopping this product from actually being really useful to people. And the answer was never the model. It was a lot of the other stuff around it. We also took a view that the model, like, I think it's. There's a lot to talk about. But, like, I think a huge thing if you're building an AI is what's the timeframe that you're building for, Right? Because I think if you're saying, oh, I'm building this, I'm building this product for five years from now or 70, at some point you're like, We've crossed the AGI line. Who knows? Like, robots might be running the world. It's like really hard to build for that future world. And then if you build for today, like, you kind of miss the boat because you want to build for. The puck is going not where it is today. And things are moving so quickly. So we, early on, we kind of made this decision, we said we're going to assume models will continue to get better, faster, smarter, cheaper, quickly, and we're going to build for that world in like one to two years. And in one to two years when the models are smarter and faster, what's going to be holding back the product? And that's like the long list of things are like, are the notifications popping up and like seamless enough? Is it like in Granola, you can take your head, put your headphones on and take them off and it just works. But there's all this stuff that we have to do behind the scenes around echo cancellation for that to be the case. So we made like a very strong decision to not try to innovate at the technical level, at the model level, until it was absolutely necessary. So yeah, that the byproduct of that is we use the best model on the market at any given point. We flip flop, ideally as quickly as possible and we use the most expensive models because we think most of our users are in the future and we basically want to be building for tomorrow. And the best way to build for tomorrow is by using cost prohibitive models today that will get cheaper and more accessible tomorrow.

Like, relatedly, how do you think about, when you think about your product roadmap and building for people that live in the future, how do you think about sequencing? So for example, you know, I was really intrigued by granola launching. Like I'm now using the mobile app very often when I'm having an in person conversation and I would not have predicted that in my behavior a year ago. Like how do you think about choice and kind of, and kind of choosing your version of granola's future in such a kind of uncertain time.

So I think the, I think the really hard thing when you think about Roadmap and is in this space is how much do you build for the local maximum of today and how much do you build for whatever the global maximum or the local maximum of tomorrow? And that's an incredibly hard question. It's like we take it day by day. It's hard to know. I can tell you. I do believe that if we go back to the origin story, this idea is this, this tool that augments your abilities. Like that's something that's way beyond meeting notes, right? And if we don't invest and build on that today, like we might never get to that. And that is, I think extremely important because the industry is moving very quickly. And I also think that is the biggest, I think this idea of software that can assist augment you across all your work tasks using your personal context. That's kind of why we get up in the morning and that's what we're excited about building. That said, there's like a million bugs in our current product today that we want to fix and you kind of need to do both. And what's hard about that, it takes a very different mindset. So the mobile app is. You could justify it in both ways. You can justify it. People have thousands of meetings in Granola, they're on the go, they need to access that information. They need to include as a companion app for what's currently happening. But then there's this view of oh, actually in the future, if Granola, you're going to want all your conversation context in one place so that you can query it so that you can run agents on top of it. And from that perspective, if all your in person meetings are just invisible, that's a huge problem and we need to start investing in that now.

How do you think about the evolution of products inside the company? So I heard earlier you're hiring, obviously you're a product leader, so you've been running products so far, but I heard earlier you're hiring one of your first PM roles right now. How do you think about what time it is in terms of your hiring? This is a critically important point made to me earlier. Granola is hiring.

Yes.

How do you think about what time it is and what you'd be looking for or in this first person given that we have a room full of many PMs.

Yeah. So I guess first off, we've tried to do a bit of a linear model where every engineer we hire, we expect them to have strong product sensibilities. We actually interview them for that. So it's a little bit like making Granola a great product is everyone's job, it's not one department's job. And then my co founder is a designer. So it's like product design is kind of in the central DNA of the company. I think the reality is our ambitions are extremely large and we just need to be working on a lot of really big things in parallel. And I believe that it's impossible to build good product if you can only give it fractional attention. And each of these big things we're working on require full attention from a person to really, really push it forward. And so it's like up until now I felt like we could get away without it and now we do need those product folks and we need, we need a Product person. Well, you can call that the titles are hiring for right now are AI Growth and Consumer. But really across the board there's just so much we need to do do.

So how does it differ building a company now versus your first company? Like what are there. What similar. What's different?

Yeah, so there are a lot of differences that are specific to my two companies. I mean my first was in education. Now I wanted to invest in education products. It was, we had to really, really fight to get attention. It was like a slow burn. We just, it took us five years to really break out until we got lots and lots of users. Whereas granola is a very different story. I do think, I think building products in the age of AI is different. Like if you are building AI native products or products that are primarily based on LLM, there's just like a different set of characteristics there.

How do you think about when we're thinking about other AI native products you love, what would you pull out? How optimistic.

Yeah, so I, I always struggle with this question which then sends me into some like, like deep existential crisis of why I don't have a good answer to it. And here's what I think. I think we are, I think we're in the stone age of AI products. And I think in the stone age of AI products it's all about technical capabilities. Right. So, and I think that will change over time. But right now it's like, oh, if this model now has, it can now take in context from the web, right? And it can include search. Like that's a huge capability. I'm going to go and I'm going to use that. Or this model does the same quality response but does it three times faster. Like I'm just going to do that. And sure that's speed and quality are huge parts of product, but it really is driven by the technology technical innovation at the base layer. So all my favorite products are really like when deep Research came out that was a big deal. But to me that's like a technical innovation at the technical layer. That's not so much at the product layer. And we could debate this and I'm sure we're having drinks afterwards, so please come up and debate this with me. I feel like we have seen remarkably little UI innovation and user experience innovation in AI and befuddles me. Like I think the only explanation I have is that things are moving so quickly technically that everyone is just running to keep up with that and they don't have the time head space to. And to be fair like product, like ui innovation is a time intensive and low chance of success endeavor. Right. So it makes sense that you're not going to lock yourself in a room and spend six months trying to come up with a new UI on top of text. If you there's all this low hanging fruit of other things you can be doing. That said though, I feel like you see this glut of products that all look very similar and oftentimes don't work that well. And I think there's this like the shiny ball of because everything is now technically possible or feels possible, it's very easy to put out a first version, but it's very hard to put out like a great version of a product. So I think we're kind of like in this uncanny valley middle ground. So another way to say is I think the great AI products are yet to come outside of coding. I think coding is the tip of the spear. I feel like the coding products are probably like 12 months ahead of any other function that I've seen.

Yeah, it kind of goes back to the timeline piece we discussed earlier. In terms of building an AI requires trying to hold in your head the tension between building for two years ahead, but not AGI because that's too intimidating. And I think what we discussed a little bit earlier was around, you know, we're seeing these companies rise and fall really fast, building air really fast, but the products themselves feel very lightweight. If you're someone that really likes great products, like how do you think I feel like products in its early embryonic years kind of often owned the sort of user interface, often owned the kind of design portion of the product. And I think now how do you think the field of product has evolved? Has it evolved?

Yeah.

No.

I mean, yeah, great question. I think here are certain realities of building an AI startup today we maybe talk about the product field and definitions, but one is I think in a world where you have ChatGPT and Claude out there, you have like very good general purpose tools. That means that you need to build a product that is really focused and way better than those and that's like a, that's a hard thing to do. Right. It also means that in a world where there are lots of copycats product that is 20% better, like that's, that's meaningful and people will notice that and people will change their behavior because of that. I also think so I. We can go through my history, but I was a PM at Google for a while and I think my old product hat kind of training makes me think like, historically I'd be like, like you said, the UI is the product, the content is not. Right. It's like, if I'm YouTube, I'm like, the UI of YouTube is the product. Sure, there's a ranking algorithm, but like, I don't control the videos. That's, that's something else. In a world where a model is generating the content, well, you control the content, right? And a lot of products, if you look at the percentage of the pixels on the screen, a lot of it is model output, right? And it doesn't really matter where it's coming from. If you really care about building a great product and like, you need to make sure that is great. So all the tools that go into making great AI generated output now is part of the, I think the product job description. And that goes from understanding models, understanding prompting, that also goes to understanding what context to give those models. Right. And I think this should be the golden age for product managers. Like, like, if you look at like the jobs to be done today, there's a lot, if you know, like in the ethos right now, there's like a lot of the idea of taste, there's the idea of like, to get good content, to get good outputs out of a model, you need to give the model the right context, right? And then there's a question, well, what's the right context? And at the end of the day, those questions all end up being the same kind of product. Questions of like, if I can put myself in a user's shoes and I can think about like, what, what would make me, me achieve my goal, feel happy, feel good. A lot of the same process that you would apply to like a UI or what buttons you'd have. You can apply to context selection, you can apply to model selection. It's all the same tool set. I don't know if I'm like, I actually don't know. It'd be interesting to see are most founders out there. Do a lot of them have a strong product background versus an engineering background? And how is that changing in the age of AI? Like, it'd be an interesting question.

Yeah, I think what's really interesting is the blank lend a background. So like Malvika from Schmidt, one of my founders who's over here from New York, is a designer who can code. And I think what I observe is that there are so many more all rounders and I just personally, like, I'm curious, when you think about the granola team you talked earlier about, when you hire engineers, you want them to have product sensibility. What is the archetype of a granola hire? Like what is that kind of person? Like, what do they care about, think about across the board in terms of function. Sorry, I'm not going to call you out. I just thought you're a great example of an all rounder.

Yeah, I mean it's more what makes a granola person I think is more cultural. Right. So there are three things. Basically. One is the people who join granola should be friendly is maybe the wrong way to say it, but basically like push come to shove, you should be willing to put the team ahead of yourself. And I think this collaborative, like easy to work with, excited to invest in your relationship with other people. Like that's super important. The second one is people should be really self motivated and ambitious. Like we think it's like this incredibly exciting time to be building and we want people who want to come and do the best work of their life at granola and who think they can level up and who are self motivated. And then the last one is that they really, they, they really care about product and they really are excited about what can be, what can be built in this moment in time in product. That's cross function. Right. Like I think we want that to be true of anybody who joins and then ideally functional. You're just excellent or have the ambition to be excellent. And I will really quickly.

I love that I'm going to come to the audience really soon for Q and A. Please be thinking of questions for all of the early career product folks in the room. What advice would you have them in the quest to become excellent, which all of us should want?

Yeah, what advice for earlier?

How would you think about if you were them right now? But in 2025, I'm, I.

Every day I need to overcome intuitions that were formed pre AI. Right. And I'm very intentional about it. And I still, I still fall, might see myself falling into like these old intuitions, these old, old ways of doing things. So I far early in my career in product, I would, what would I be doing? I would be immersing myself into what would like what does building an AI mean? What does working with AI mean? And I'd be leaning into that really heavily. And then I would. It almost doesn't matter what you choose, but I would choose something, some problem for someone and I would just see how well can I solve that myself. Right. Like no, nobody else. Like no engineers. If you're an engineer, great. But basically the idea of like oftentimes and not necessarily an idea that's like, oh, this is going to be a great startup idea, like if you want to build great product muscle. We didn't talk about this, but a huge thing about building product at Granola is how often we talk to users and how that informs our product building process. So maybe we should talk about that.

I was going to ask you about that next, Sean.

Yeah, yeah, yeah. But yeah. So to answer the previous question though, just choose something and then just see how far you can take it, right? And the fundamentals of building product are oftentimes you look at something, you try to figure out from your intuitions what's good or bad about that. And then you put that in front of other people and you figure out what's good and bad about that from what other people say. And then you just try to make it better. And then you just do that like 50 times, right? And as you do that 50 times, your intuitions should be getting better and better and you should be able to do that. Instead of 50 iterations, you should be able to do that in 10 iterations. It'll never be zero iterations, it'll never be one, but it might be like five or 10 instead of 50. And the beautiful thing about this moment is nobody really knows how to do this. So there aren't people to go out there to learn from. You can do this. You have to teach yourself from first principles. And if you do that, it'll be a lot easier for you to go further than me because I have all this other stuff I need to forget, which was true. I think in the pre AI world. I'm 100% serious here. And I think that, like, you see people who are very young, not everybody, but very young in their careers, who kind of like grew up with these tools and then use them to an extent that is like, it's like very surprising for folks like me. And I think that that will just compound, right? And those intuitions, those learnings over time will compound and there'll be a delta and then there'll be very few people out there who are like, have built a native products that feel great. And it could be a prototype. Like, it doesn't matter if it's like commercially viable, if it's something people are using, it's like, you should be able to send someone a link and say, try this, right? And it could just be a really complicated prompt. Like, that's fine. Like it doesn't matter what's under the hood, it's what's the output what's the experience that matters? And I would just, I would just try to build something I'm really frickin proud of. And once I'm really freaking proud of it, I think that's like then email it to me and come work here, you know, like that's like. Or email it to anybody and go work there. Because it's like a very, very rare skill today.

Yeah, I love that. And I feel like the bar to build stuff now is like to your point also had a whole career pre AI and so I think losing all of the ideas around building MVP takes time. I think it's always surprising to me now when I meet somebody looking for a job and they're not already building stuff and using the stuff they're building as a way to get a job. Because to me that feels so obvious. Going back to your point around user feedback, you know, I'm an ex journalist, you've got a master's in journalism. The old rubric of product was always the tension between listening to users deeply and synthesizing that information and kind of having intuition and you know, kind of like developing your own gut, your own taste versus being purely driven by what you're hearing. Like how do you think about that here? What have you.

Yeah, like our view on it is very simple. So I think there's. So one of the spectrum is basically you just, you talk to users and you build exactly what users ask you to do. The other end of the spectrum, as you say, like I'm a designer, like I, I, you know, people don't know what they want, they'll ask for faster horses, I'll just figure out the person and I think it's completely, it would be completely impossible for me to build a good experience on either end of that spectrum. So the question then becomes how do you blend it? And it can be very, very hard to blend because if you talk to a lot of users, they'll ask for lots of different things and just categorizing and organizing that information becomes very hard because what you end up doing is you end up listing here are the feature requests, right? And feature requests are usually the wrong way to talk about building and product. The right way to do that is, I think it's like, it's pain points, like what are like needs jobs to be done. So basically what we do is we talk to a ton of users and then we let that context inform our decisions. So like we have an opinion of what granola should be and the future we're building for. And how it should work. Right. And I think we have. It's very important for product builders to have an opinion. Otherwise you don't. You're probably not going to build something great. Like it's got to be something you believe should be in the world and you should believe you, you want it in the world because it should work a certain way or it should have certain characteristics. It's just very easy to go off the deep end if you're just like talking to yourself or thinking about what's really good for you. So you just need to immerse yourself in user feedback. So like an example, like something that really helped at my last company, Socratic was like we were kind of wandering in the product wilderness for a couple years until we set up this thing where we were building for high school students. And this is like pre zoom. So it was like really hard to talk to them where on Tuesdays and Thursdays we had like 10 high school students in our office every Tuesday and Thursday. And sometimes we had nothing to show them and it didn't matter. We would pay them and they would go off and they'd be super happy. Other times we would show them tons of stuff. Right. And the moment we changed the process so we had easy access to students, we could ask for their opinion on all kinds of things, whether it's the copy on this thing or this feature or like how do you feel when your parents see you using Socratic or whatever it may be. It made our intuitions 10 times better. So like the way I think about it is like user feedback should sharpen your intuitions and you should build based off your intuition.

I love that. Okay, final question then we're going to hand over to you a product that you love. You said to me that products should have soul. It doesn't have to be AI native. We've already said that they're not great yet. It can be a physical product, it can be a digital product. What's a product that you really love?

Great question. Until you said the word soul, I knew what I was going to say.

And without solemn with so well, there's a.

When, when, when I first interviewed at Google, I was asked like what's your favorite product? And I was, I entered a my. I had a Moleskine notebook which I had recently discovered at like the age of whatever, 21 or something. And I just gushed about this thing. But there was something really incredible about simple physical products that get out of the way but get give you an emotional reaction. That's what you want? I weirdly came across this Nespresso machine a couple weeks ago and fell in love with it, which I never thought I would say because I like historically have always hated them. But this Nespresso, there's like, there was not one unnecessary thing on this machine. And that I thought was incredible. I had two buttons. There wasn't an on button. There's literally a button for one shot or a two shot and that was it. And like I literally took it apart and I was like, could I, what could I get rid of? And I couldn't figure out anything. It's a bit like you only understand the genius of Hemingway when you try to take away like a word from a paragraph of his books and you. It's very, very hard to do that. So I don't know, I found, I was like, wow, whoever, whoever is responsible for this thing. Like, yeah, good, good on you. Yeah, I don't know if those are the most.

No, I love that. That was amazing. Okay, please don't be shy. We've got about 25 minutes. Questions. Okay. Over there, that was the first hand. And please say your name at the start and what you're working on. If you want or if you're looking to be hired, that's also fine.

My name is Luke, so I am just happily employed software right now. You talked about a lot of things like checking to graph and being a dirty word. And you think about things very user focused and for example, one hand building. Like whatever someone tells you this is, I know exactly what it is.

Yeah.

I feel like a very mainstream opinion is that the model provides most of the value for companies. Like this is kind of like what is important for an AI product. And it feels very confusing because is it the model, is it the distribution, is it. You really understand the workflow very well. So I'm curious if we can use Granola as you could explain how you see Granola's quotes.

Yeah, I'll. I'll tell you how I think about this. Can the microphone. Can everyone hear that? Do I need to say the question back? Or like what's the moat for AI products? Or specifically like model wrapper products? And what is it for granola specifically? Right. I think about this a little bit like when products move to the cloud. Right. So think of like Google Docs moving to the cloud. Cloud. And a lot of stuff had to be rewritten for that. So that wasn't trivial. But the cloud was kind of accessible to everybody. Right. No one was like, oh my moat is the cloud. Right. People would say like well maybe the moat for Google Docs is collaboration because the cloud enables that. But it's not like the cloud itself is going to bring the moat because everyone can move to the cloud. And, and that's how I think about things in AI basically I think the moats are the same that existed before AI. I think for whatever reason we thought there, there's this expectation that it would be different post AI. But I think what's happened, this wasn't clear like three years ago, is that so far and I believe this will continue because of the amazing competition between the leading model providers and the open source community. This like incredible intelligence is available to everybody equally. Right. And that's and amazing for product builders. But what it means is like access to the model I don't think is a moat. I don't think fine tuning models, I don't think data is a moat. Like if anything I think the importance like you need less and less data because the base models are getting smarter and smarter to like get fine tuned results. So I think the answers are, are the same as before. So switching costs, right? If you have, if there's like a social graph where the values is a consequence of how many people are on there and I think, I think for granola it's two things. I think the more meetings you do in granola, the more context we have about you, the more useful we should be able to be. And the second one is like if context is important to be useful than like switching that to another provider is just a pain, right? And that's like the same type of thing that would keep like, like this, this idea of switching costs or integration. Switching costs is the same thing that like makes zapier a pain in the butt to change. Even though it's totally doable, it's just like kind of hard to do. So that's what I believe. I think there's this other thing which is AI is AI space is moving so quickly that if you're not innovating and staying ahead then it's very easy to fall behind. And if you fall behind then like then everyone's going to leave. So it's like assuming you can stay at the forefront then I think it's all the traditional like product like most.

And there was one, yeah.

AI.

Or non AI.

Okay. So the question is like if only work on LLM features or non LLM features, when will that switch? So I guess the, I don't like the way the question is formed is the answer. It seems like A false dichotomy. Here's my thinking. Like, we should work on what's going to deliver the most value for the user, right? Or whatever's going to deliver the most value for the business. Like, if we can build a thing that will help us grow five times faster, we should also build that. I think likely today a lot of that is going to come from, like, new capabilities unlocked by AI. That said, a lot of what made granola look good had nothing to do with AI, right? It was a lot of like, polishing paper cuts. I think now that we kind of have. I think we do a decent job with meeting notes and a lot of those paper cuts have been fixed. It's a lot about like, oh, now we have all this context about you. How can we make your life 10 times easier, right? And like, that's the next forefront for us. And that's all based. Like, that's all based. That's. That's even like that stuff that hasn't landed yet because, you know, we have so much data, there's like no way to process that amount of data and the speed and quality that we need today, but will be possible within the next six months.

Lucy was next. I think I was wondering how to place.

Like, consequence.

So the question is, like, do I have any non work habits that will help with clarity of thought, like meditation or whatnot? So I think at some point I said, to be able to build great product, I think you need to be able to give it, like, undivided attention and lots of it. And I think what's been very challenging for me, to be quite honest, is that granola has scaled significantly over the last year. Like we launched a bit over a year ago. And with that there's a tremendous amount of complexity. Like we were four people when we launched granola. We're like 28 people now. And as there are more and more and more things happening that fall on my plate or take up my headspace, it becomes a lot harder to do the product focus. I don't come into the office for the first few hours. Every morning I go somewhere, literally, people can't find me. I try not. And then depending on. And that's like another Hemingway reference, but it's kind of like do the hardest thing you need to do first thing in the morning. And that's where I tried to do my deep product thinking. I try to open as little technology as possible. And sometimes that's not possible. Sometimes you need to go into the analytics or whatnot. But I find that the moment you open that floodgate, there are more and more things crying for attention and becomes much harder to. My dream would actually be if I previously printed out the 10 things I need to look at the day before and then I just looked at that analog and did my writing analog or did my thinking analog and then moved into the digital world. But yeah, hyperscaling startups, it's like a, it's a new class of hard thing. And I think I'm invest, I'm like, I'm investing in trying to learn how to manage myself and how to like build the team around me to scale. Anyway, this is like what every, every founder ever has said when their company started growing and things started breaking, they're like, oh, I don't know how to do this. But it's like an active. It's a thing I'm like actively investing in because otherwise it's not going to go well.

I do that at the back.

So.

Hi, my name is Song and I'm a software engineer. I am currently. So I have two questions about ask alignment. So you mentioned a lot about context being really important to.

Yeah.

How have you made sure that you are getting like, what do you want to consider it to be? Good context? So at the moment you're getting the context from meetings. Have you thought about getting the context Nl on your music? And then how do you juggle that with like digitality and.

Keeping user, what, happy? Yeah. So yeah, I think in the age of LLMs, like context. I don't know if context design is a thing yet, but it will be if we don't use that term. We use it internally. Context design is a really big deal. I think there's two kind of questions. One is like, what are the sources of context? And you said like right now granola is primarily notes. Not exclusively. We also pull in information about people and companies. So like, one of the, I think one of the things that like blew people's minds when granola came out was that if a founder and an investor were both using granola in the same meeting, their notes looked completely different. Right. And they look completely different because, I mean, we know like you probably don't care so much about what you said in the meeting. You probably care more about what the other person instead. But we also know that, hey, if you're a founder, if you're an investor, it's pretty clear if it's like a pitch you care about, what's the idea, what's the traction, what's the market size, how much have you raised. It's like a clear set of things. Whereas if you're a founder, it's like, what did they seem not convinced by?