The 5 Hidden Rules Behind Successful AI Products | Chris Pedregal (Granola)

I think the number one lesson is you shouldn't work on any problems that aren't going to be problems in the short to medium term there are problems in your product that might get solved by the next model drop and then there are other problems that are always going to be a problem you know things worthwhile no matter how smart the models get and I think that the easiest mistake to make is to focus on the thing that users are screaming about but that the next version of the LMS will we'll just do for you now I think as a product person is like you talk to your users and they complain about something like hey I can't use granola for meetings that are over 30 minutes right and like everything inside of you saying that's ridiculous grola should work for meetings that are more than 30 minutes this has got to be our number one priority but something that seems like it's really simple all of a sudden to do well does take a bunch of time and then like all that time would have been wasted because the next version of the model had a large context window we could just stick the whole meeting in there and I think this is something that's like it's just so easy to get this wrong also because it's hard to predict the future right which should you be building and investing in now so that you know 12 months from now when LMS can do that at a reasonable cost Point your product will be fantastic all right well welcome everyone my guest today is Chris pedig Chris is the CEO of granola which I think is the best AI notepad for me meetings and I'm excited to chat with him about his hard one lessons from building one of the most successful and retentive AI apps in the market so welcome Chris thank you so much Peter thank you for having me yeah all right man so let's start with some spicy

Tes yeah so you know you being a Founder twice already right and you have also me a formal Google PM twice so I had to start this question like what do you wish Google and other big company PMS did more of you know now that you built these successful startups what they did more of oh you have to give me more context there what do you have in mind well I mean like how is it different being a startup founder and being a Google pm and what do you wish the Google PMS or like any other big company PM like you being one like what do you wish that they did more of and what they wish they did less of to be more efficient yeah I mean I think the reality is that there are so many constraints in place when you're a PM at a large company it's just like a fundamentally it's like a different sport you know what I mean like if I don't know if being a Founder just choose a random sport is like American football and like being a PM coul like uh soccer you know what I mean they're like totally you can't play one game with the strategy or the rules of the other game I think what I found at Google when I was a PM is that there's so many different types of work that a PM does right there's like project management type work there's like planning there's like being a leader there's and then part of that is also you know product design or how should the product work and what should it do and my experience at Google was that was just an extremely small percentage of your time and that's just the reality because there are all these demands on your time all these constraints all these meetings you have to go to that there was really very little time at least for me to do the deep thinking on actual like product work I don't you can't it's just the nature of the job I think I mean you have you probably have a lot of thoughts on this Peter yeah it's almost like you're kind of forced to do the thinking in meetings with other people which I guess has some benefits MH but also just like a lot of time wasted just like preparing like communicating upwards like having all the meetings and presentations and stuff right like I totally like I like to protect my craft time like I actually decline meetings is probably not good for my career but yeah I'm like you know I need to think through the problem I can't really meet with you right now yeah I mean like I remember you know at Google my day was just back toback with meetings right so that meant that if I was actually going to do deep product thinking I'd have to do it on the weekends or in the evenings you know or wake up super to do it and that's just the reality but it is kind of sad if you're a PM and you have to like do the actual product work on weekends you know but I think that's just the reality of a larger or and maybe that's why you started granola like can you walk us through after you left Google like why you how did you start grola yeah so I joined Google after my last startup Socratic was acquired and I was I ran Socratic for five years so I had another co-founder shance and I and we ran for 5 years when we required and I was pretty burnt out by the time the acquisition happened like I was you know 5 years is a long time on any project and you know startups are intense and when that happened I think my son was six months old at the time so I was also learning that whole set of skills or try figuring out how to be a parent so I was pretty thankful to post- acquisition to just have a bit more breather a breathing room but in the back of my mind I knew I'd probably do a startup again and after a year I was just I was kind of counting down that days a little bit in my mind I told it would make sense for me to do that again so I ended up quitting Google in I think March of 2022 and I knew I wanted to do a startup I knew like I by that point I had moved to London so I knew I wanted to do it in London I knew I wanted to do a startup and I didn't have an idea a co-founder so I started playing around with stuff and almost instantly I fell in love with g PT 3 at the time like when I had been at Google I hadn't had the time to play with it properly so I'd kind of been like ah I don't know about this thing and once I had some free time I started playing with it and it just I was like it blew my mind I was like this is different from other stuff that I've seen and I basically spent a year building prototypes to get a feel for the technology because the interesting thing about LMS I think we've a lot of us in Tech like normalized to it but like the first time you interact with one Le less so now cuz they're smarter but like back then especially they'd be like really good at something that maybe like only a college level you know college student level educated person could do and really crap at stuff that like my 5-year-old could do you know and that was like a really confusing thing and yeah and then ended up meeting my co-founder Sam exploring a bunch of different ideas and then settled on what ultimately became granola and so why did you decide to

tackle the meeting problem or was that I guess we entered it kind of reluctantly just because they so many different products out there built for the meeting space what I guess two things kind of convinced us well I'll tell you the great things about meetings ready are one people have a lot of them and there are they're scheduled on a calendar so like sending notifications to use your product when you have a meeting actually feels helpful and not annoying like most products if like granola will like send like I don't know like six or seven push notifications a day for a user if they have six or meetings that we're going make sense so it's a really nice user need to build a habit around because they're like these natural hooks so that's one two llms I think have a few like super power applications right and I think we you know one I think is cenation two I think is like search right and another thing that they're incredible at is taking something super long and unwieldly like a transcript and turning it into something useful so I think that was wow there really is so much value here and then man we tried all these different ideas and we put them in front of people and no one people are just not interested in all of them and their lives their eyes lit up when we showed them a thing of like first version of granola because they were like I hate taking notes you know so it was just a combination of things we like okay I think alms can really I think there's a product here to be built that really is going to be helpful to people and I think if we build it you know there's a path here where we can get people to use it and keep using it but it was like it was a little scary because there's so many people doing meeting relating stuff yeah that that's what I thinking too right so like there's you know like zoom and Google meet I think both offer AI Meeting those now but I think people just love using granola like I don't know if you can share like how many people come back like after first week but like what makes people love Yeah much well yeah it's like 70 like basically if you use granola like 70% of people come back the next week and then they kind of stick around why what makes why do they like it so much was that the question or like what what's different from the big ones I think I'd say over the overarching reason is that it's simple and convenient to use I I really think that's like the main one there's not a bot that joins your meeting you don't have to open a special UI it's like a it's an app on your computer right that looks like a notepad it looks like Apple notes and it kind of you can open it up and use it if you want and if you don't you do is use it you just close it and you know you're really in control of it and I think it kind of integrates seamlessly into people's lives like I think that's the main reason I think it sound when you say it that way it sounds kind of silly it took a lot of work to get to that like we had to build a ton of things and cut them out and we had to like figure out like what is at the core of the product right and there's some insights I can talk about there but I think ultimately it's because it's a thing that doesn't get in your way and that people like spending time in okay so let

let's think a little bit more but let's talk about it from the framing of your top lessons from building AI products right like you just publish this great blog post so maybe why don't you start by just listing what the lessons are like what are your top four yeah sure and I might mess them up for memory but um I guess like the number one lesson I think so basic I think what like the defining these are hard earned lessons so these are like mistakes we made and kind of learned from I think the defining characteristic of building an AI is that the underlying technology the models are evolving so quickly that it's a and that affects the entire environment so I think the number one lesson is you shouldn't work on any problems that aren't going to be problems in the short to medium term and by that basically there's there are problems in your product right that might get solved by the next model Drop Like GPT five or six or what have you and then there are other problems that are always going to be a problem you know things worthwhile no matter how smart the models get and I think the easiest mistake to make is to focus on the thing that users are screaming about but that the next version of the LMS will will'll just do for you naturally and we have a bunch of examples there language support is one like context window like you can only use granola on like short meetings at first so like that's one big lesson do you want me to go to the next one Peter do you want talk no let's go a little bit deeper in that one so okay so like as an example you mean like users are like why doesn't work for longer meetings but your point is like you know the next model will have a bigger context wayd though so it will work yeah exactly and like so and what's weird about that I think as a product person is like you talk to your users and they're they complain about something like hey I can't use granola for meetings that are over 30 minutes right and like every everything inside of you is saying that's ridiculous grola should work for meetings that are more than 30 minutes this has got to be our number one priority but you know the way to do that at that time it like sounds easy but actually it's like okay now you need to chunk up like the meeting into several chunks and summarize different chunks right that but now you need to reconcile the summaries right and something that seems like it's really simple all of a sudden to do well does take a bunch of time and then yeah all that time would have been wasted because the next version of the model had a large context window we could just stick the whole meeting in there and I think this is something that's like it's just so easy to get this wrong also because it's hard to predict the future right like what like llms are I mean I still think of them as primarily text based right with that's wrong right LMS are going to take any kind of input then you're already seeing that with I mean obviously voice but photos and video soon and like when an L1 can take all that context in real time like what's that mean for products what should you be building and investing in now so that you know 12 months from now when LMS can do that at a reasonable cost Point your you know your product will be fantastic well what's something that you mentioned that like your 5-year-old can do that an LM can't do like something that actually is important to solve now yeah well I mean that was so that was a reference to like when I started like two years ago was like basic like math you I mean like basic math stuff was like really weird or just also just like you know like the classic like positional stuff you know what I mean it's like Sy go on top of this thing or whatever like they've gotten a lot smarter now but that was the kind of stuff I had in mind got it in terms of predicting what Al

can do in the future like I guess you can look at a few broad trends like hopefully the cost of inference will come down it's going to become multimodal bigger context maybe they can do more work like more agent flows as opposed to you prompting them all the time those are kind of the big trends right yeah exactly I think there's two things you can do I think the simple thing to do is basically look at what is the what can the most cuttingedge expensive model do today right and then just assume that will be cheap and accessible in not too much time and build for that and I think everyone should be doing that right so I think we should be you should be everyone should be preparing for a world where you can feed like live video into an llm and that's like doable and cost effective I think if you try to predict past that it gets really tough right I think it gets that gets into like sci-fi territory if you're like okay like what does you know like what will the next generation of like Cutting Edge models do I think that's a lot harder to imagine got it let's go to your uh next principle like okay you have this principle called go narrow and go deep and I love that cuz it's about like focus and prioritization so we talk a little bit more about that too yeah I think obviously General advice is really hard so this is all grounded my experience with granola but like general purpose tools like Claude and chbt are really good you know like surprisingly good at a whole swath of tasks so I think if you are building a startup you need to be 10x better than that right at least 5x better than that and I think the only way you can really do that is by choosing a narrow use case a narrow path and trying to make that experience really great and I think when you do that something that's interesting that comes out is that a lot of the work that you need to do to make that narrow experience really great sometimes has nothing to do with AI sometimes a lot around like the rapper like examples for us for granola would be we spend a ton of time so basically you have to do people might have headphones on they might not have headphones on right if you don't have headphones on the audio stream from the speakers goes into the microphone phone so you have to do this Echo cancellation thing and basically like long story short like to make that really seamless we had to like roll our own Echo cancellation which sounds like super silly that we'd have to do that in like 2024 but we did and that took a bunch of time and effort and has nothing to do with any like the core like note writing of the product but it had all everything to do with granola just being the seamless thing you don't have to think about and I think that's like when you go narrow you like there's all this kind of like parallel work that needs to be done or like that you can do to make that core experience great how did you approach this with did you like map out the customer Journey for like doing a meeting and then you kind of like figured out the little friction points along the way or um yeah uh we did but we so we were in we had a closed like a closed beta for we were in closed beta for a year and we basically had a small by the end it was like 100 people that we were building with but we started off with three and it became really obvious once you gave it to real people that there are like some basic things that you need to do for them to trust you to take notes for them I think it was just a lot of like giving it to people and spending a bunch of time getting feedback from them and not mapping too far into the future I think it's very interesting because with non- a products you can you know design something you can ship it get feedback right but with this stuff like the output is like non-deterministic so it's almost like not like is this good or bad it's almost like is it good 80% of the time or like you know there's like a threshold where you think it's actually good enough to ship like has that been your experience all right yeah I mean I think what it makes it just it makes it harder I find to get a handle on what the actual user like the quality of your user experience is right because it's deterministic and for better for worse it's meant we've relied more on Direct user contact rather than quantitative metrics on user experience quality like I feel like you can on other more traditional flows you might be like okay what percentage people made it through this flow and you know is that good or bad or what not whereas here it's a lot harder to actually measure that so we end up just spending a lot of time talking to people and let's get a sneak peek into how this thing works so it's not just like taking someone's like transcription like you know using some prompt to summarize it right it's actually more than that like can you talk a little more about that or yeah sure so like I guess I mean I can tell you the thinking I can to tell you how it works now I might leave some details out I think what we figured out is that when we started off while we started off like writing the prompts for granola the prompts were very instruction based it was like okay if this is the case like write notes like this or if this is the case include this and don't do that and what we kind of quickly figured out is that the reality of the world is that it's really nuanced and complex and these kind of like binary instructions map very poorly to that world because it's okay it's like you know it's like okay if there's this type of detail like this type of information is important to add right don't make the notes too long and it's like okay now how the heck do I know which of those two are more important right if I have these conflicting instructions so what we found like our mental model shifted uh to a different one it's basically think of the model like the llm as an intern on their first day at work which is basically a smart person right who has no idea on how you do things and doesn't have any context on what you do and give that intern all the context they need to do a good job so a lot of the work that we put into granola is basically like how do we give you the intern the right context to do a great job in this meeting so those are things like who are you meeting with right like who are they what companies do they work for what jobs do they have for a meeting like this with these type of people and these types of roles like what are they probably optimizing for right like what's going to get them promoted like that you know like if you start thinking about that way and then you think about who you are then you can pull in a bunch of contexts and kind of give the model a lot of Direction on what would actually be valuable to you when you read those notes okay so that's

like uh I think your third principle like context is King right yeah but that's actually very interesting so your context is not like oh you got to list like three action items and like you know five takeaways it's more like it's more like teach him some general principles about how meting not work yeah it's general principles but it can be specific to the people so like a good example there is there are let's take I don't know like let's take in like VCS like investors right like they do a lot of startup pitches right there are a set of things that are really important to investors to make sure that their notes capture when a startup pitches them right and that's very kind of like specific to that use case so we don't like actually tell you say exactly hey write these things but we do articulate okay like you need to make an investment decision right these are the types of things that are important when you're making an investment decision like write the notes to match so it it's basically you have to give it the context of what's valuable for those people but you don't have to be too prescriptive on specifically what to write got it and in doing this what's a day in a life like is it just like you're like constantly up to your prompt and or maybe you're using retrieval or something and then do you have like an eval process going or you just kind of relying on user feedback yeah we have a very to be perfectly transparent manual evil process going that we're you know we're systematizing I think that you can having automated eval or like we're actually taking the same approach to our eval process as we do with granola which is we want our eval framework to make it much easier and faster for humans to eval things as opposed to fully automate the human and the reason for that is like there's just so much Nuance there's so so much Nuance in whether like notes are important because like notes are basically it it's kind of like it's like stack ranking of like all this information what's the most important information for you that's like an extremely hard problem so yeah so our internal tooling is very human- centered and aiming at making our humans faster yeah I always have like some doubts about those like synthetic eval stuff or like you know they get the other LM to give the LM a score like who knows if the score is like made up or got this stuff yeah so that goes into well okay I want to finish covering your principles so you have another principle called your cost is my opportunity yeah can you talk about that yeah so like as I was like you know since I've like become conscious or like aware of the internet and you know started hacking on stuff like the principle of the internet has always felt so crazy and so powerful was that if you built something online you put up a website like millions of people could go to that website right it's like yeah it would cost more take a little bit more energy but like the marginal cost of an of serving an additional user was you know BAS basically zero or close to zero right and that's amazing right because that means if you build something good you know it can scale to tons of people but it also means that if yeah it just means that like the flip side of that is like you know if a Google or Microsoft build something then like you know scale to millions of people in AI because of the cost of like these models are so expensive to run still right that it doesn't work that way right like the the marginal cost of every user is constant so like for granola basically we pay money every time we generate meeting notes right so yeah our transcription and our bills map linearly to you know one to how many users we have right so the interesting thing there is that as a tiny startup you probably don't have that many users so it's actually possible to use really like Cutting Edge models like and whereas you know my friends at Google who like that I worked with before I left you know if you're on Google Drive you have so many users and so much data and if you actually want to roll out a like a Cutting Edge feature to all those users it's just not feasible it's not feasible from a financial standpoint or from a compute standpoint so basically you're saying that you can build like you can use the best models even though it cost a lot to deliver like the Best in Class experience to a small group of users and they expand from there is that yeah because like I think you know best case scenario as a startup your user base grows exponentially right and if you like if you look at the cost basically the cost of insurance like say like oh models haven't gotten cheaper no models have gotten smarter right over time but if you keep the level of intelligence fixed like what it costs to like run like I don't know gpt3 level intelligence today is nothing compared to what it was three years ago right so hopefully as your startup scales if even if you're growing exponentially the your inference costs should decrease exponentially and you kind of hope the math works out in the long term got it that makes sense okay

so let's talk about your I'm not sure it there a principle but I really like it it's you should build products that feel like they have a soul what is a soul like what do you mean by have a soul yeah it's a good question and I think soul is basically this does a product give you a sense of cohesiveness when you use it and that's one of those things where it's like if something is kind of frankensteined together you know by a lot of like different people then when you use it you don't necessarily know what the essence is or what this thing is like it's a little bit like I don't know I think we think of I think on some level we interact with products the same way we interact with people right you kind of attribute attributes and you have like a relationship with it so I think that's anyway yeah I'll pause there no so it's like well I mean you talking about Frankenstein like you know that's pretty typical at larger companies you have different ORS trying to build on the same surface and you just like the like what was the point of the surface like there's like five different things that you can do on the surface right so you see kind of like being focused and like actually solving like solving a problem or having that emotional connection yeah I think it's maybe a way to put it is sometimes you use a product and you can kind of feel the person who designed it like you can feel the people on the other end who made that product and you can kind of hear what they're saying or what they felt or what they wanted you to feel and other times you don't feel that at all right it's like a that feeling is absent and I think it's like one of those things you don't you know if you stop and you pick up an object you'll kind of you'll know if you think about it but like you know it's like one of those subconscious things like I I like I don't know like early Mac products always felt like that like I always felt like I could feel like the people behind like in copertino probably working their butts off right and like what they they're trying to pour a lot of themselves into this thing and it felt very like they're humans on the other end of that I also with my last startup I was building for high school students and like early versions of Snapchat also really felt that way you know completely different you know product UI but like they there was a real like point of view on the world you know and you could tell that they're like again there's something that they're like really like pouring themselves into it whereas most products you interact with you don't get that or maybe you feel people talking about like hey we got to hit this metric this quarter so gotta put this big banner here yeah how do you balance between listening to customer feedback and your own product intuition like do you think the two kind of feed off each other or do you think no yeah I think there's a few different schools of thought here I basically think like on one end there's I'm an artist right I'm going to go off and I'm going to come up with like the perfect design and it doesn't really matter what people think right I'm just going to like understand this problem that's one end and I think on the other end there is extremely customer feedback driven right just listen like the customer is always right like listen to what they say and what they want and like my view is that I think for a product to have soul it needs to be designed with cohesion so I think it's very important to design based on your intuitions and your instincts and for that to come from you as opposed to like I said Frankenstein from a different you know and you need have like a consistent worldview but I think the problem is that it's extremely hard as a human to put yourself in someone else's shoes so you need to constantly be getting the context of what other people are thinking so like our view at granolas is not actually like to make a list of here are the product requests or the pieces of feedback that people give us and therefore we're going to build x what we do is we inundate ourselves with product feedback right like we try to do I try to do a user call a day every day we have a screen that will Flash the feedback that users uh send us like real time on the wall we get these Dodges so basically it's like I want us to be immersed everyone on the team to be immersed with what customers are saying but then when we go design kind of from first principles of like how we think a thing should work yeah I love that it's uh it's kind of like a prompting LM right you want to have the context like yeah cuz you want to put yourself the customer shoes at the end of the day like and like some of it might not be like what they're saying but like actually watching them try to use the product or something you know yeah 100% And I think like you want it to be as like I think our brains are really good at filtering information right so it's like if you're constantly immerse in what users are saying or thinking or feeling then you don't have to analytically say oh okay well there you know 15 reports of this and there are 50 therefore you know it's like you'll feel it you'll be like no clearly this is more important you know like I can feel it it's like an emotional thing like it hurts exactly dude that resonates me so much man like I that is also why I started this interview with the big company thing cuz like you just don't have time to be immersed man like you just have so many other meetings with internal stakeholders it's so hard to be immersed some big companies like yeah

so yeah and the other thing that's maybe not talked about a lot is like it takes a lot of like infrastructure and tooling to be immersed right so like so my last startup was cratic right and it was a it was an AI tutor it was like a mobile app right you stuck on a homework problem you could take a photo try to teach it how to do your homework problem getting immersed and feedback there was incredibly hard it took us a lot like it took us like I think a year and a half to figure it out eventually what we did was so we were based in Manhattan so every Tuesday we had a class of high school kids come into our office and just hang out in our office for three hours so they'd come in after school and they'd hang out do homework do whatever and on Tuesday it was always the same kids so it was the high school down the block and the idea there is like we get to know them we get to see their Journey they'd get to trust us and then on Thursdays every week we'd have a different set of kids we've never met before right and we could do that in New York because like you know Manhattan St there are lots of kids and once it's like it's a ton of effort to put that in place right you have to Source the kids you have to get them signed disclaimers they're all minors like it's a huge pain in the butt right but like once that machine was churning oh man the whole team like we would ask them questions about everything right like we would show them prototypes we would like just shoot the you know and talk about stuff and that like immersion like helped the team tremendously but it took a ton of time and effort to get it I do that through you know like Discord communities or online communities and yeah you have something to go through a bunch of internal approvals but it's totally worth it because you can just like randomly ask some questions throughout the day yeah that's great like you know like my product development feedback loop now is like there's three circles there's like internal stakeholder feedback there's customer feedback through my community and then there's AI feedback like I also ask all my questions yeah that's interesting how's that what have you found the AI to be helpful with versus not so helpful with when you're asking question it's pretty good for like obviously it's very good at summarizing stuff but like in terms of asking questions it's like you have to give it a ton of context like you have like pasting like entire slack threads or like you know my entire document and then I start asking questions then it's a lot better if you just randomly ask a questions it's probably not very good yeah yeah but like it's never going to you can ask any dumb question you want yeah yeah yeah I have this so I have this go link I don't know is go link does everyone know what a go link is or is that just like a Google like a big tech company thing do you guys have go like a short link right it's like a short yeah it's just like you can like a mini URL you know like and I have one for a Claud project that has a ton of context about like granola and my job at granola right and what I'm trying to do and like who I work with and like what our Tech stack is and like all these things so that when I'm asking a question at least it has all that like that General context otherwise it'll just you know might just go off totally in the wrong direction and I find that it helps a lot some of the time and other times it just you know it over indexes on that on the context awesome

so let's kind of wrap up by I would love to hear about what's next for canola you know I've been using it it's pretty awesome no cing app I love how it combines my notes with the AI notes I kind of wish it let me or maybe it already does this like does it let me upload my own temp templates yeah for format does it's just really hard to find okay so you can yeah after a meeting has happened you can select from there a few templates and but you can also create your own but it's really indisc discoverable right now so we're definitely going to make that better that might be because like with my prompts like the more examples I give it of stuff that I want the better it becomes that might be an interesting idea ping a bunch of past meeting notes yeah absolutely yeah so you're like where we're headed and what we're going to focus on yeah yeah I think um I think right now gral is really focused on giving you good notes right and having those notes feel like they're your notes and you mentioned this I actually don't know if we explain it basically I think what the big difference I think about granola from all the other like AI note taking apps out there is that it's a text editor right and you can still take notes and then when the meeting ends it'll flesh out your notes but it really anchors on what you've written and like I think big picture A lot of the value that granola is g to provide in the future isn't going to be on the Note helping you get good notes it's going to be on the okay now the meeting's over what's all the work you have to do as a result of this meeting and kind of helping you do that work because the reality is again we were talking about the importance of context you know the context from the meeting and maybe the series of meetings that you've had like on that project leading up to that meeting like that context is super important and I'm a big believer that a lot of the work like action items like all the stuff that comes off of like the back of a meeting GR should really be able to take away a lot of the repetitive tasks there got it so it's like you know all obvious just like next steps own nurse but actually following through following up see if they actually do their job or you know that kind of stuff right yeah I guess this is like one of those kind of philosophical discussions where I think like I think there's kind of obviously the reality is like uh Blended but two extreme there's do you want AI to replace the human who's doing stuff right or do you want AI to give the human superpowers so it can do it so you want like Jarvis you know from Iron Man where it's like you're still driving you're still in control but like now you can do so much more than you could do before or do you want something that's like fully automated that goes and does stuff for you and like we're at granola we're very much a believer in like giving people superpower so it would be less like a go off and like do it but it would be more like there's all these classic examples right like follow-up emails like that's like a classic thing where it's like a lot of writing a follow-up email is kind of wrote and then there's like a few decisions that are strategic that you really want to make sure you get right and like the human should be doing those but like all the specifics of like what was agreed upon in the meeting that an AI could write if does that make sense yeah that makes a lot of sense I mean like you know we talked about before interview like you know I was in a meeting and everyone else was like furiously taking notes and I was able to just sit there and have granola take my notes I put some notes there some sometimes but like then I can actually think about what's going on in the meeting right so I'm just like furiously taking notes yeah so that already helps a lot yeah there's uh yeah man I think there's like it's an exciting time to be alive right like I really think the tools like you know the famous saying is like it was like we shape our tools and thereafter our tools shape us right and I think that's true and I think with AI it's like the potential for the tools to like shape our thinking is exponentially higher right so I think it's like and that's good and bad right it's like it can oh it's like you can in kind of push people to think a certain way or not to think about certain stuff or that you could really Elevate the types of the level that people are thinking at right like I see a future where AI is taking care of all like the boring details and you get to think the higher level about like what's really that what really matters right and then you that's where you influence it's going to let the Google PMS operate like Founders man that's I don't know I think there's more structural issues there but yeah yeah so do you have any like closing words of advice for people who want to build AI apps or you know get into stuff yeah I think it is an extremely exciting time to build like I don't know when Sam my co-founder and I started off was kind of pinching myself I'm like oh man I get to be alive now like it feels like one of those moment like it a little bit like if you go back to the early Computing Pioneers in like the 50s and 60s like Eng goart and alen K and all those guys like it feels kind of similar now except for those guys who are looking at the technology of the time you know they're looking at this computer that'll take up a whole room right and cost like a million dollars and be like one day everyone will have their own computer and you have to be super Visionary right to imagine that whereas like we get to be like one day you know AI is going to be able to do this and it's like six months later can do it you know so it's like the fact that we get to live through that facee is incredible I think the I don't know any advice would be like the world's moving quickly and I think the right move is to like adapt to it quickly as opposed to like I think it's really hard to predict what's going to happen right so kind of roll with it and like as every time you get a big technical change it unearths a bunch of opportunities and sometimes it's not obvious what those are until you kind of just play around with it so I think there's like a lot of Alpha and just playing with the latest stuff and just kind of seeing what you know what you discover and where the value might be yeah I totally agree just like thinking of stuff without any kind of goals or you know just like spending time to actually play through with these tools that that's really important yeah it's so hard to do that when you know you have a busy job and those things you're trying to accomplish because it's like a different mindset right you want to be like a kid playing with toys right and just seeing what kind of comes out of it just to clown the me meetings man it's not yeah no no and sorry and and uh for for people watching this where can they find granola where can they use granola yeah oh yeah so granola. bar speciic use case very well so congrats to you and the team and yeah hopefully more people play with yeah Peter thank you so much it's been a pleasure sure and I really appreciate you taking the time all right