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

28:07Like, 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? Like, I actually need good notes, right? That 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 like a meeting recorder. You store the transcript, though, and people can review the transcript and query the transcript.

28:44Exactly. We stored 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 it's 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 and I'm like oh that looks fishy, that's important as part of the experience The user becomes the human in the loop, effectively.

29:22Talk about simplicity. So my personal experience with Granola as a user is that it's incredibly simple. It's incredibly frictionless. But as we all know, simplicity from a product design perspective is very hard to do. So I'm curious about how you think about it and perhaps what you decide to deliberately not include that would have ruined that simplicity feel. we had an event the other night and and we were reminiscing over beers about the versions of granola we built before we launched publicly and basically what happens like we were in stealth for a year and uh 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 there was this version of granola where you could you could kind of swipe and there are all these panels and it's like, here's your transcript.

30:15Here's your super long, like here's your blow by blow of exactly that 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, like, you know, 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 have been impossible or extremely hard to do if we had been publicly launched.

30:49I think because if we had been publicly launched, we had all these people had grown to love granola and 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. um simplicity is really hard uh and it's hard because organizationally um unless you're you know the founder like you're solving a problem and you're in a little universe and you're gonna 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're not 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 is just the problem they're solving.

31:45And 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 like very intangible, hard to measure cost to launching it, right? And every, on a one by one basis, it always looks like you should launch the feature, right? 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?

32:25Even though when people talk about Granola, what they love about it is that it's simple. So basically the only person, the only people in the universe who are going to be pushing for simplicity are going to be 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. And to the tangible versus intangible point, how do you decide effectively even to play back some of what you just said, the 50 % that you need to cut? Are you looking for qualitative feedback or do you look at quantitatively what people actually do with the product?

33:09Which part is science, art, taste versus data measurement? Yeah, it's all of the above. So our 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 like what what do we think makes sense is kind of this vision of the product and that we're headed towards and um that like we just kind of make the decision based on like it 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 um user feedback user opinion as possible and some of that is quantitative of of course everything we launch we measure and we look at the graphs and it was like oh people 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 um and you know 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 it's like sam and i and i'm talking about sam and i because we work very closely together but uh like most of the team, actually they do regular user calls.

34:31And we, we, 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, 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, abstract away a human. So, so, so easy. It's like, it's, it's, 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.

35:07And 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 about 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. How do you think about all of this going forward in a context where presumably you're getting pulled in different directions? So one direction presumably is the fact that you've been very successful with the tech Silicon Valley crowd, but then you're going to go into lots of different industries with people with different experiences, different needs, and perhaps different levels of expectation or comfort with technology and AI.

35:50So on the one hand, and then on the other hand, you just launched Grunala Teams a couple of months ago. And that pulls you into the world of enterprise and SOC 2 and compliance and all the things. So I'm curious about how you think about balancing all of this versus simplicity and then how you prioritize the roadmap with all that in mind. 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.

36:35And 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, very excited about a world where people use granola not just to take notes, but to do all kinds of work.

37:18And 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 like, oh, we're doing a kind of this really incredible, like deep research mode across that can look at thousands of meetings in a matter of seconds and pull out these insights. And, you know, that's that's not something that our enterprise customers are asking for right now because not even thinking to ask for that.

37:55But I guarantee you that will be a huge part of of of the of where the world is going and and where a lot of value of granola will will come from. I'd love to spend a little bit of time now on Heidel works behind the scenes, the tech stack, the mechanics of it all. So starting with the model, so I'm seeing on the website and as a user, you use lots of different models. So as a first question, do you exclusively at this point use third-party models? So have you built some of your stuff from a pure AI perspective? No, our philosophy is to use the best model that is on the market as quickly as possible.

38:42There'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 so 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 then to fine-tune or train models and 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. And so that multi-world, multi-model world as OpenAI, Anthropic, Google, is that right?

39:17Are there others, open source? Any specific models that you currently use I saw that you just announced that you are supporting. 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, it's like this model's really good at writing these types of things. This model's 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 different default models for the specific thing you're trying to do in the app.

40:00Interesting. So you provide the ability to pick a model, but you gently guide the user towards what's best for their use case. How do you think about keeping a consistent user experience in a world precisely where those models evolve all the time, two, behave differently, and three, as we all know, are stochastic, not deterministic? Do you expect the users, as you just said, to be smart about it and that's theirs to figure out? Or is that something that you abstract away from them? We abstracted away from people. Like the general design, at first we didn't, for the longest time, we didn't let users choose their model, right?

40:39And we only let users choose their model on chat. On the note generation side, we completely abstracted 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. So it's, I think, one of the value adds that Granola brings, as opposed to just working with base models, is that we take care of that. We make sure you get Granola feeling or sounding notes consistently and that they keep getting better over time.

41:16And how does the prompting work behind the scenes? 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 do they work, what are they trying to do? And we've put a ton of work into what are common meeting types with 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 mind when Granola came out, it 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?

42:02And it's 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 is very different as a founder as it would be as an investor. And a lot of that is kind of hard-coded instructions that we build into the system. How do you navigate the context window constraints? If you have a one-hour meeting, that's a lot of information. and I'm curious about you think about chunking. Sure. So, I mean, one-hour meeting was a lot of information and in 2023, it's not a lot of information now compared to what the models can do.

42:48They have incredibly... The context window size increase over the last three years has just been mind-blowing and fantastic for us. Yeah, but call it a board meeting, which is four hours, right? And there's like rate limits and all the things. or maybe it's no longer a problem at all. 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. Single meeting is fine. It's when you have lots of meetings and a large corpus information. That's where this problem comes up a lot. And the interesting, the 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, Well, either like some form of keyword search or cosine similarity.

43:39What we found is that a lot of the most interesting queries that people have, like 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 that, you know, 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.

44:29But 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, like money or quality. 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. Yeah, and the cost question is particularly interesting in time these days because of the well-reported discussion around the cursor of the world of the AI coding tools having negative gross margins.

45:17Is that a situation where, like, directionally you guys are at where you for now operate on lower gross margins. And I will not ask you for any specific numbers. But again, you're building for the world of tomorrow where you have higher gross margins. Is the world of meetings different in terms of token needs versus AI coding? 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 and we basically use the best transcription, 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 we'll continue to do so.

46:10So 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 data sets. It'll be an interesting race to see, does the cost of inference go down faster than the user desire for more complicated, more intelligent features goes up? Talk about, if you will, the parts around the model. So you mentioned transcription. Obviously, there is a big sort of audio part to what you do with, you know, there's a lot of problems in that world. There's the problem of diarization, which means figuring out, you know, this is Chris speaking or Matt speaking.

47:00And there is the problem of like noise cancellation. Like what work have you guys done? Which vendors and solutions have you picked? What have you learned? 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. And it's something you built internally? Yeah, on top of some open source frameworks, and then we built it. We've partnered with DeepGram and Assembly for transcription. They keep pumping out better and better models. We're always using the latest and greatest.

47:42What 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 if you give models are really smart in ways you don't expect if you give like 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 like analysis and evals to be done there let me see yeah we use I mean there's so many like I can tell you about our whole tech stack we use brain trust for a lot of the evals How do you think about guardrails?

48:38Make sure that the system doesn't spit out things it shouldn't, for example? I guess for every product, the idea of what should the system not... 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 like 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, there's a question of, we get something wrong or it hallucinates.

49:21But what we found there is that the best thing to do, they're never going to get it 100%, right? We can never be like, oh, you know what? We make no mistakes. you know, 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, you design your product needs to let the user kind of like, like view source, kind of look behind the curtain and be like, wait, where, like, how did you construct this answer? Like where, what are all the citations? So we spent a lot of time thinking about citations, about letting you view original transcripts and quotes.

49:52And there's a lot more we want to do there, but that's really been the, the, the way you solve for this, at least so far. Switching tags a little bit, aware from the tech stack, I'd love to go into growth mechanics and what you've learned. So you said, among the many interesting things you said earlier, you said one on that topic that caught my attention, not having the bot first experience was actually a trade-off in terms of like virality because you don't have the built-in product exposure because there's no bot showing up. So what have you done to sort of overcome that? And what are the viral sort of growth mechanics built into the product today?

50:39We haven't focused on growth. Basically, what we really focus 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. So an interesting story. I never imagined that this could have happened. but I hear a lot now is if you have a one-on, like 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? Like, why aren't you on Granola yet? And it's like, oh, wow. That's so interesting. The AI bot is now like a conversation starter.

51:18Yeah, 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. I never would have sat down and imagined that world. So we always start from a value standpoint. What is valuable to the users? 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.

51:57And 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, but 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, like our goal, the next step there is to be kind of a second brain for your team or for your company.

52:32And this is how, obviously at granola, that we have that. And it is pretty incredible what you can do when you have all that shared context. It 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. It's very easy to sit down and be like, oh, you know what? I want all my meetings shared. All the meetings in the company should be shared because transparency is a good thing and it's super valuable. In the age of AI, the more context, the better.

53:07And 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 were like captured like a sensitive meeting uh on the wrong calendar invite and like emailed the whole company or oh i was at a founder dinner the other day okay this is another this is a great story of how how we get customers um 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 like what happened he's like well i walked in on my co-founder and my cto having a question a discussion about letting go of this key person and i noticed that i think it was like the google meet recorder was on and it was on uh the all hands which had happened just before in that meeting and and we had this awful realization that the moment we 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 they and and then they They all freaked out and they all said, okay, like if the wifi cuts out, we're screwed.

54:15So if the battery dies, we're screwed. So this became like 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 like some like undocumented setting in the workspace admin, like data control thing. And they were able to turn it off, but they're like, 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, like, it turns out, like, human relationships are complex and nuanced. And if you have, like, a one-size-fits-all solution, there are a lot of these cases that get kind of ugly.

54:49As you get further into the enterprise, do you get any kind of pushback or questions about what it means for every conversation to be recorded? than for it to be quite literally a track record of everything that was ever said from a legal perspective or any of those? Yeah, absolutely. 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. We have functionality that posts this in the chat right now. you can turn this on and be like, whenever you join a call, like 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.

55:37But that's like one line of questioning. And like, you should always tell people use granola. It's like the right thing to do. Regardless of what the laws of where you are, you know, I think it's like, we're also moving towards a world where like these tools of their well-designed and not too invasive, like it'll be normalized in certain types of contexts. Then the other question is like, And this is not just a question for Granola, but this is for AI in general, which is like, 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.

56:14Why did we do this on Gmail three years ago? You couldn't find that in the email. And that's to limit the liability footprint. In a world of AI where all that stuff's actually really useful, we think has the promise of being really useful in the future, there's a lot of tension there. Especially when we talk to a lot of customers, the biggest disconnect that I've seen from, especially folks who are very AI forward, tech forward, thinking about the future. It's like, 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.

56:55I honestly don't. I think it'll be a very interesting space to watch. So still in the growth mechanics world, you have an incredible user retention. Curious if beyond the sheer quality of the product, there is anything that you did and that maybe people can borrow for their products leading to success in retention? 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. Like when I was earlier in my career, I thought, okay, you build a fantastic product and then people use it. And then there's this horrifying moment where you build something good and you put it in front of someone.

57:42and they say, this is fantastic, right? And then the next week they just, they hit that exact same pain point and 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. You know, no, I still love your product. I just didn't think to use it. And that's a really, it's really heart-wrenching to realize that. So now, especially when people, let's say it's like founders come up to me with their idea, like I urge them to think really, really hard about what are the triggers for using your product in those moments.

58:16And the beautiful thing about meetings is that there's a, you know, meetings are on a calendar and there's a very specific moment where we know like you're going to do a meeting. And that's not enough, right? If granola weren't actually useful, then, you know, that wouldn't lead to retention. But it's like, I think the combination that granola is useful to folks and we can send notifications at the right moment to start it. All right. So as we get towards the end of this conversation, I'd be remiss not to ask the obvious question about the competitive landscape. So we started the conversation talking about the existing note takers.

58:53The sort of elephant in the room kind of question that I'm sure you get all the time is, why wouldn't OpenAI do that? And I'm a VC, so I'm a specialist of asking why Google wouldn't do that and why wouldn't OpenAI do that. But, you know, Zoom as a product, and it seems to be so fundamentally important what you're doing. And so horizontal and so transformative that it feels like all those great companies should focus on this at one point or another. How do you think about navigating that tension? Yeah, well, I guess it was really helpful that most of our competitors had some kind of AI note-taking feature when we launched.

59:37So 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 it is 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 going to work in the future. And the way we're going to 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.

1:00:20Hopefully 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. Like a lot of people are racing towards that. And AI is an incredibly competitive space. But when we're talking about really, when you're talking about products that are like native to a new medium, right? Oftentimes, startups have an advantage. But just to press a little bit, I'm mostly thinking about open AI because I do think of Grinola, and you don't need another person to tell you that. You're the one building it. But I do think of Granola as building memory for the world and for all of us.

1:01:08And so that is incredibly strategic for OpenAI or Google because that's the ultimate prize, right? Like you're building, as you said, a second brain. So how do you think about those players? 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 AI native. They led the way in this space. To me, again, I don't have a crystal ball. I don't know what the future is going to look like, but OpenAI is going to try to do everything to everyone, really. and they do an amazing job at it. It's really, really incredible.

1:01:53And 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 there are, it's hard to visualize that world, right? There's a world where like, oh, actually, you know, it's not that specific. The upside is not that great. Or actually, you know what? 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 isn't going to win. But again, I don't have a crystal ball. I think it'll be fun to watch.

1:02:34I have high hopes for a lot of the stuff that we haven't launched yet and how different that's going to feel. So a little bit on that note to close and zoom out. anything that you can talk about in terms of the roadmap or the futures? You mentioned a couple of times the idea of searching through the history of meetings. So that's one thing. Maybe double-click on that and or anything else that you can talk about. 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 really?

1:03:24So for example, a good question that we could ask would be like, 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 2500 meetings and spit out and remarkably intelligent answer to that in 20 seconds it's like a deep research mode right um the other thing that we've played around with is like if you're if you're dynamically generating artifacts or uis on the fly can those be shared, right?

1:04:00So 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 like today. And every time you reload it, it's up to date, but it's like a, it's like a memo. So there's all these, and there's some use cases I I can't talk about just yet, but basically like 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.

1:04:41And I don't know if that's part of the roadmap or whether you can do this to some extent in the product today, but for me, clearly where this could be going and where as a user I would find that absolutely fascinating is granola as a coach, where, you know, based on, you know, everything I do all day, it could be, Matt, you need to ask better questions. Those are the three questions you never ask you need to ask. Or, you know, you spend a lot of your time on stuff that doesn't really move the needle kind of stuff. Not that I do that just as a hypothetical. Yeah, yeah, yeah. I'm curious, for you personally, who would be is there like a human who would be the ideal like you know if we're gonna go train a granola coach on a person is there a specific human in mind that you'd like um yeah and look i'm not i'm not very qualified because i never had one but i think the entire coaching industry right of like people that you spend uh you know one hour a week and you talk about how you use your time uh and what issues you encounter at work where you could where you do well, where you don't do so well, and you have this kind of, you know, sit down session a little bit like you would get with a psychiatrist or therapist of some sort.

1:06:01If Granola just knows everything I say in all meetings, after a certain time, I think Granola is going to have a very good idea of where, you know, I succeed and where I could get better. Stay tuned. Okay. Exciting. Very, very good. Well, it's been a fantastic conversation. Again, you know, I'll end where I started, which is I'm a huge fan of the product and it was life-changing for me. I have to say as well at a personal level, you know, when we talk about feeling how AI is both enhancing us, but also sort of disrupting us, in my early use of granola, There was still just English. And you guys, I think recently, or at least I saw it recently, introduced a multi-language feature.

1:06:56And as a European, French-born person who has spent most of his life in the U.S. at this stage, my secret kind of a little trick ability that I had was always to listen to a conversation in French and take notes in English directly in real time. And I was very proud of that. And it took me a lifetime to achieve the level of fluency I'm able to do it. And then one day that feature appeared on Granola and I was, there you go. The machine does something much better than it took me a couple of decades to figure out. So I don't know if that's exciting or terrifying. Probably a combination of both. But in the meantime, I really enjoyed the feature and the product.

1:07:37And I very much enjoyed this conversation. Thank you. I guess the hope is you make better investments now, post-Canola. I think that's the ultimate question, right? Absolutely. Hence the coaching part. Very excited for what you've built and for the future of the company. Thank you so much for spending time with us. Plenty of lessons for builders around product and growth and building on top of AI. So really appreciate it. Thank you. Thank you so much, Matt. Hi, it's Matt Turk again. And thanks for listening to this episode of the Mad Podcast. If you enjoyed it, we'd be very grateful if you would consider subscribing if you haven't already or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from.

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