Chris Pedregal: Revolutionizing Meetings with AI

Foreign. Hey everyone and welcome to Generative Now. I am Michael McNano. I'm a partner at Lightspeed. And this week on the podcast I speak with Chris Pedregal, the CEO and co founder of Granola. Granola is an AI powered notepad for meetings that works with you by combining your notes with AI generated notes in order to provide you with insights, takeaways and next steps. I speak with Chris about what makes Granola different, the AI startup scene in London, and how he sees the future of AI driven products as human and AI collaborations. Take a listen. So I saw on your Twitter, your ex, rather, that there's a new Banksy in London that I guess what is right near the Granola office A block away.

Yeah, it's one of the perks. We. Our office is in Shoreditch, which is East London. It's the, yeah, the most New York part of London. It's like the creative course side of things. And yeah, a new Banksy popped up two days ago about a block from our office.

So is that a thing? Like, does that happen? Is that a thing that happens in London, like where like famous street artists, they just, their work just happens to pop up all over? I guess it kind of happens in New York sometimes.

It happens a lot in shortage. Banksy kind of goes on. I think every couple of years he goes on a rampage and drops a bunch of artwork. So definitely, it's definitely a thing.

I actually didn't look closely at it. What was the piece?

Swinging Monkeys on a overground bridge. Yeah, some monkeys. It's a bunch of animals. He's putting animals all over the place.

Why, why animals? Is that, Is this a theme?

Don't know.

We don't know. Yeah, Shoreditch is really cool. I haven't spent a ton of time there. I hung out with you there once and yeah, I know what you mean by it feels New York esque.

Yeah, I love it. It's a lot of the tech startups are over here. West London is, you know, beautiful. East London's a bit edgier. It's where all the, I don't know, the chefs who want to experiment new stuff, they'll do all that new stuff that you're testing out and around with creative energy is usually happens on the east side. So it's got a, it's got a nice vibe to it.

What, what, what is it like building a startup in London versus building a startup in, in New York City? You, your, your other startup, Socratic, you built that in New York. The whole journey was in New York team not distributed, not remote. Now you're doing it in London. Same thing. What, what are, what are the differences or what are the similarities? Just what I would love to hear you just talk about it.

There are fewer, let's say consumer or kind of consumer esque success stories out of the UK than there are in New York and obviously the us Right. And I think that kind of permeates. So a lot of the aren't a.

Ton in New York either. Right?

It's true. The fintech, like the financial apps, the monzos, the wise, the revoluts, they really kind of like set the bar in terms of consumer tech in London. So I think a lot of the cultures kind of defined by that, there's amazing technical talent, incredible technical talent in London, specifically in AI. There's less of a startup culture here and I guess there's the startup culture difference and then there's also just British culture. It's a little different. Right. Our team's very cosmopolitan. Yeah. But I'm conscious that I'm, I'm the American, you know, in, in the room and I'm, I might talk about things a little bit more differently, a bit more loudly, a bit more directly than would be the norm in like British culture. But it's, it's also really, it has a little bit of that, in my opinion, the energy that New York had when I was building Socratic. Whereas like there's a lot happening, but it's a little bit, it's a little bit the underdog, if that makes sense. So there's a lot of camaraderie between the startups in London.

So you mentioned there's a ton of technical talent. Like where does, where does that come from? I mean in Silicon Valley it obviously, obviously comes from like decades of these generational companies being built and then people leaving them and starting new companies in New York. It comes I think, similarly from some of these incumbents which have had their roots in the city for many, many years. Like where does it even come from in London, especially on the AI side?

A lot of the big American tech companies, the first office they open is in London, right. So you have the Googles, the Metas, I mean, Stripe has an office here, Snap has an office here. So it's like if you're going to open one office in Europe, it's usually London and you get talent from across all of the EU to come here. Specifically AI, there's like four places. It's basically, there are really good AI programs at Oxford, Oxford, Cambridge UCL and Imperial and then DeepMind was started here. Right. So DeepMind kind of became a center of gravity. Meta opened up an AI research division here. So basically it's kind of been a leader in AI before the recent waves of AI.

I have so much I want to talk to you about with Granola. Obviously you and I talk often. I am, you know, full disclosure for the listeners. I'm very lucky to, to be an investor in Granola on behalf of, of Lightspeed. But I feel like something, when we talk, we, we talk very tactically. Right. That's, that's sort of the relationship between kind of like founder, board member, investor, like we're talking about the next project, the strategy, et cetera. I feel like we don't often have a chance to get sort of into the nitty gritty of like product design or how you made certain decisions about the product. And you know, I think one of the things that I certainly feel about Granola, which I know many other people do as well, definitely many of your users, is that it is this perfect example of an AI application. Right. There's been so much talk and love and investment and capital poured into the foundation model layer, but there are a few examples of really, really high quality products. So things at the application layer of AI. And again I'm biased, I'm an investor. But it feels like people, many, many people who are not investors in Granola have commented on it being one of these great examples. And so that's where I think it would be great to spend some time today talking because I'm sure that you have lots of insights that you can share. Yeah, from, from the first couple of years of the company. I know a Granola is, I'm sure many of the listeners do as well. But for, for the benefit of those who don't like, maybe maybe just start with explaining the story of Granola and what it is.

Yeah, absolutely. Well, I'll start talking about what, what the product is and thanks for all the kind things you said, Mike. So Granola is an AI notepad for meetings. So it's specifically designed for people who have lots of back to back meetings and it looks like Apple notes, it's a simple text editor and you can take whatever notes you want during a meeting. And the difference with Granola, what makes it so powerful is that it's when you're in a meeting, it's also listening to what's being talked about. So it has the whole context of the discussion and the moment the meeting ends, Granola will take whatever chicken scratch notes you've written and rewrite them to be really nice, high quality and understandable to other people. They still feel like your notes though. And it's in a text editor, so you can go in and you can edit, you can change. It's. The whole idea is that it's augmenting your note taking ability as opposed to taking notes for you or on your behalf. That's the basic idea of granola.

Well, how did, how did you get there? I mean, you know, your, your experience, your history before granola wasn't rooted in. Well, I guess you. It was in many ways. You've been working on sort of AI before. It was cool with Socratic, but how do you go from building a product for students and homework and education to building this like, you know, LLM powered.

Super notes in March 2022? I. Is that right? Yeah, March 2022 I played with. It was the instruct version of GPT3, right? And within a week I was just. It. It blew my mind. I was like, this is. This changes things. I think we have all had that moment at different times, you know, different moments. Maybe it was when ChatGPT came it out, but for me it was Instruct update to GPT3. I was like, this is going to change the tools we use for work. This is going to change how we interface with computers. That was really clear to me. I didn't know how, but I wanted to work on it. And in short order I quit my job and started building prototypes and playing around with it. LLMs are extremely good at manipulating information, right? That's like one of their superpowers. And taking information that's in one format and transforming it, translating it into a different format. And that different format might be useful for different needs. Like in our example, it can take 30 pages of transcript, which is impossible to read. I don't know if you've ever. Well, you probably have because of granola, but if you try to read a transcript from a meeting, it's like gobbledygook, right? And LLMs are extremely good at being able to take all of that and turn it into different artifacts that might be useful from that meeting. Anyway, so it became clear to me that LMS can have the super power for manipulating information. I really wanted to work on tools that would help us think better. Like tools for thinking, right? Because that is like in essence what language is, what notes are. And I teamed up with my co founder Sam, because he was in this world also thinking about how can we build tools that help us think better. And when we started interviewing users, real people, we realized, like, the main place where people take notes, and they generally take terrible notes, are in meetings that, like, that is the killer application of this. So if you could build a tool that would elevate the information or your thinking during meeting, but also free up, you know, your cognitive cycles to be a better version of yourself in that meeting, that's like a. It's a double win, right? Because you can be a. You can perform better in the meeting, you can be smarter, pay more attention, but also have higher quality notes and outcomes. So once that became. When it became clear that that was a really big opportunity because we. There's so many AI meeting bots that had already launched at this point, right. So we kind of entered the space begrudgingly, but we kind of got at it from our first principles of, like, how could LLM technology be most useful to people? Like, what is a great application for it? And we entered it and. And then we just spent a tremendous amount of time building prototypes, putting it in front of people, figuring out what works and what doesn't work. I think the fundamental question in Granola was, so you have a text editor, right? And you have all the context that granola is aware of from the transcript or from the calendar events or from the bios of the people who are in the meeting. So there's all this context that the AI knows about. How do you let the user make use of that or interface with all that context without being overwhelming and distracting or pulling you in directions you want to go? So that was. Yeah, that was. That was a lot of trial and error and testing and experimenting and iterating.

It does feel, and you mentioned, it does feel like there are lots of other products that are doing this. There are these meeting bots, there are these things that sit in the zoom meeting. I think Zoom themselves is doing this. I think Google Meet. There are a lot of these things. You don't really hear people talk about loving these products. And it does seem like there's been kind of this outpouring of love for granola. So what is it about Granola that you're doing differently that is sort of leading to that, that user love? Is it just the fact now that you also have the scratch pad that pulls in context? Like that doesn't seem like that big of a difference. So where is the difference? Or maybe that is it?

I don't know. On the surface, Granola and an AI meeting bot, they. They look very similar, right? They help you generate notes from a meeting. I think the moment you zoom in a little bit, you realize they're. They're like different categories of products, right? So I think what an AI meeting bot is trying to do, and I think the premise is actually pretty flawed, in my opinion, is you can say, like, hey, I don't want to take notes. I don't have any control over the notes, right? This. This bot, this other entity is going to join this meeting, and they're going to take perfect notes, and the same notes will work for everybody. And now I don't, like. I don't take notes anymore, right? I've outsourced that to this AI entity. And you know that that's going to be great. And I think if the position we took with Granola is one that was completely different, we don't want to. We don't want to take away note taking from you, right? We want to augment your abilities. So I think, like, a good metaphor here would be, so you're used to riding a bike, right? And you know how to ride a bike, and you know how to. You're really good at bike riding. You've been doing it your whole career. And then all of a sudden, granola comes along. It's like an E bike, right? So now you pedal once, and all of a sudden, boom. Like, the bike goes, right? So you still have total control, but you can get. You can. You can do whatever you want. You can just do it with way less effort, way faster. And I think if you come at it from that place where it's like, how do we augment what you can do so you can be the best version of yourself in a meeting or in the notes that you're trying to capture, you end up with a very different product. Something that's, like, really important to call out is that writing is a big part of the act of thinking. Like, you talk to a lot of people and they're like, no, no. I write notes in large part so that I'm processing information, and. And so I'm smarter. And that's like a really important part of the process. You take that away from people, like, they might not be thinking in the same level that they'd want to be. What we want to do with Granola is we want. There's a very big separation between work that's just busy work and that work that requires judgment. Right. And I think this is like a. Not just a question for Granola, but for all AI products where you want to automate as much of the busy work as possible, but you want to keep the judgment part. Like you want the human in the driver's seat. Everything that's like nuance and judgment because that's why you're in the meeting, right? You're there. Right. So it's very easy to be like, oh, okay, now an LLM can write this thing for me. So it's going to have it write this thing for me. There's the real danger of throwing the baby with the bathwater out. Right. Which is like you want to automate the busy work. You outsource your thinking to a bot. Like, that's not a. I don't think that's a future any of us want to live in. Right. You want the thinking to be done by humans and you want, you want AI to elevate or augment our abilities.

Yeah. So it sounds like what you're saying is it's actually very, very important for the human being, the user in this case, to feel like they're in control. And even though that the, the bot is maybe now doing, I don't know, 50% of the work, maybe even 75% of the work, the human being does feel like they have agency to either drive the quality of the notes or change them or augment them in some way.

Yeah.

And in fact, if we were to go all the way to a hundred percent, well, now we're not even really thinking. Right. We're just kind of, we're, we're pointing the bot in a direction and, and just having it do all of our work, which kind of defeats the whole purpose of, of why we even have our job in the first place.

I think that's exactly right. And I think an example is sometimes the most interesting notes from a meeting are not things that were ever said out loud. Their thoughts that you have. Like if you think about a hiring interview, oftentimes the most interesting notes are like, oh, I noticed this behavior. I'm going to write that down. There's no way a bot can, can read your. Read your thoughts. Maybe one day with a neural link or something like that, they'll be able to. But for now, that high quality stuff needs to somehow still get into your notes.

Sorry, did you mention that the, the, the bot also has context of other elements? Like it looks up bios of the participants and talk a little bit about that. What other signals do you grab to increase the quality of the notes?

We found that the best way to think about LLMs is to think of them as a human. Right. And you have to think like how. What context or what instructions would I have to give a human for a human to do a really good job at this. So in a meeting, if you just say, hey, there are five people in this meeting and they're talking about something versus saying here are the people in the meeting. Like a good example might be like, here's a founder that is pitching an investor right on, like trying to get an investment for their deal. That context really matters in terms of what are the quality of the notes and what you want to focus on. So Granola does. I'm not going to tell you all the different contexts we do, but we're tied into your calendar, right? So like you can think of who's in the meeting, what are their bios, what type of jobs they have.

Yeah, that's super interesting. I actually didn't know that about Granola.

Another thing we do is just, you're aware is different, you know, depending on your job you have different types of meetings. And we're pretty opinionated about, okay, what would good notes for this type of meeting be? Or if, for example, like if you're an investor and you're meeting with another investor versus a founder, whether that investor is a colleague. Right. Or someone you're just gossiping about deals or like an lp. Like those are very different meetings as well. And Granola has an opinion of what, what's important in each of those.

Yeah, that's super interesting. So it sounds like what you're saying is kind of taste and opinion is really, those things are really important. It's not about just like building the rails and handing it over the user. It's about guiding the alarm in like a very opinionated way. And by opinionated, I mean the opinions of, I guess you and your team.

LLMs are really interesting because they're, they're very good at a very wide range of things. Right. Which means that you can build a decent or okay feature experience for an extremely wide range of things. I think what would separates your ability to build something that's like okay or so so to something that's really fantastic requires you to be very specific about, hey, what's the problem? Or the user flow that we're trying to solve for and then be very opinionated in how to do the best job on that user flow. There's this, this trap and I think there's this trap that we, we definitely fell into or flirted with before launch and then we ended up cutting half of the app that we had built where it's very Easy to build features. It's very easy to build features that are like 60, 70% of the way there because with prompts it's extremely, extremely easy to do that. And because it's so easy or so tempting, you're like, oh, it's silly that granola can't do X or can't do Y, right? Like, it's silly. Like right now we don't consolidate all your action items automatically for you in granola, right? That seems like a no brainer, but. And we had that and we, we cut it before launch. And we cut it before launch because it worked okay most of the time, but sometimes it totally failed. And like action items are really important and we can't. You have to get that right and you have to design that in the right way and you can't design and build everything from the get go at a high standard. So you have to be very opinionated about what you, what you prioritize.

So, so it sounds like, you know, what I'm hearing from you is like, constraints are really, really important. You know, you could build a million different things. LLMs, as you've noted, like, they make it easy to build stuff, but you got to resist that temptation. You got to stay focused and you got to stay really, you know, laser, laser focused on, on the most important thing. And I guess in this case it's, it's the notes. It's the notes, right? Is that a way you think about.

It in a world where everything's possible, right. You decide what's important by the constraints you place on yourself and the users. Right. So there's a constraint in terms of we're going to focus on this user flow or this experience and make that great. There's also a bunch of opinions in Granola. You talked about user love. And I think a lot of what resonates with people in Granola is that we make a lot of decisions on your behalf based on what we think is important. So we don't give you too many choices. We don't let you do a million different things. We do let you edit text. Right? But we're right, we really try to reduce the number of decisions you have to make as a user because you're, you're trying to rush this meeting, right? You have your, the method model we have is like you are three minutes late to an important call, right. I don't really want to think about my software. I just want it to work and I want it to be there and support me when I, when I need it. But don't make me think so.

We.

Granola is not the best product for everything. Right. But it's, it's really optimized for people who need to jump into a meeting and capture the important stuff and make sure that their thinking's all there and, and to leave with, with great notes.

Maybe on the topic of great notes, I'd love to, love to pull on that a little bit. You know, one of the things that strikes me about meeting notes and LLMs is that, um, the stakes are really high, right? If you're outsourcing a lot of your work to a bot, you want to make sure it's great. Um, but LLMs, like, as we, as we all know, are not yet imperfect. They hallucinate, they get things wrong, just like humans. How do you manage that? I have to, you know, I, I kind of feel like if the notes were to get something wrong, I would have a hard time trusting granola. So how do you, yeah, how do you make sure they don't get things wrong when we, when we know they, they do all the time.

The most important thing here is that the relationship with the LLM is one of collaboration, right? This, going back to the meeting bots, they just give you the notes and they're like, here are the notes, right? You know, take them or leave them. You had no say on what was important or not, which is like a very different world. Like, I wouldn't, I wouldn't trust a lot of people to take notes for me, let alone an AI bot. So the fact that it's an editor is really important. But there, there are a bunch of principles that we kind of came across, I guess through experimentation that, that matter. So the first is if you see a note that Granola writes, you should, you should be able to see where it came from in the transcript, right? So that's like this idea of being able to view source or view provenance is super important. I guess the high level idea here is LLMs, like you said, will get things wrong or hallucinate or do things that are maybe not exactly what you want. So you have to design for that. And the ability to kind of view the source or view the provenance helps a lot. So in granola, if there's a note, there's a little like a magnifying glass that you can hit and then it'll show you where in the transcript it. Sometimes it's multiple places in the transcript that it pulled together to make a point so you can kind of see the original source and be like, if you agree with that note. The other one is we try to make it clear to you what notes came from you and what notes came from the AI from granola. So notes, because like I said, it's a text editor. You can write notes and then granola will enhance them or add other notes. Your notes show up in black, and the AI written notes show up in gray. So at a glance, you can kind of quickly see, like, where did this come from? And is this something that I, you know, just like, would I want to double check? Just having an understanding of, like, what came from your brain versus what came from, from the system is, I think, super important.

So speaking of that and, like, where the notes are coming from, is it the human or the LLM? I feel like one thing that I hear talked about often in product discussions about AI right now is sort of figuring out, like, what the right interface is for engaging with LLMs. Right? Like, obviously, you know, a chat makes a lot of sense. You know, you're chatting with this agent, but at the same time, chatting is a pretty, like, primitive interface for things. And in many ways it's very. It's. It's very efficient. For some things, like fast communication, it may be very inefficient for getting tasks done.

How are.

How are you and the granola team thinking about designing the interface of interacting.

With an LLM as an industry? Or, like, it's very early days, right. And I think we have, like, the first UIs. I. People often talk about the metaphor of, like, the chat UI is a little bit like the. The terminal was, you know, in the early days of computing. I think the interesting question is how.

What.

What are the UIs where you and the AI are collaborating on the same thing as opposed to working side by side on different things? And I think that's an unsolved problem, and I think that will be different depending on your medium. Right. If you're working on an image or text or a video or what have you, I think if you like, chat is great because it's universal. You can use it to ask all kinds of questions. God, does it feel weird sometimes? Let's say I'm asking ChatGPT to help me write some text. It feels so weird that I have to then write another instruction to make a small change in it as opposed to I can't go and edit what ChatGPT wrote. Right. It feels a little bit like trying to make a sculpture or like I'm doing pottery or something like that. And there's like, the AI next to me building, it's doing its own pot. And we're working on two things next to each other, as opposed to both of us working on the same pot or being like, oh, can you change this on here? And I think that's a lot that needs to be figured out for granola. We're just the early days there. Like the idea that there's one editor, you write text, then the AI takes that, enhances it, but you can still see what came from you and what came from the AI. And I think we're starting to discover some things that seem to click. I think this idea of black text and gray text, which I think was pioneered by IA writer and Bill. Those guys are great. That seemed to be like one of those paradigms that might stick around because it's a. It's like a simple way of, like, quickly seeing provenance. But I think we have, I don't know, five, six, seven more kind of discoveries like that that are going to come along and become. Become ubiquitous.

So in this conversation, we've talked a lot about kind of focus and really focusing on the use case and just nailing that. And I think that makes a lot of sense. How do you think about kind of the future and where granola can go? Like, do you just want to focus on meeting notes or, you know, are there. Are there tangential use cases and how do you even think about tackling those when it is so important to stay. Stay laser focused on the task at hand?

It's a balancing act. I think the right. If you. If you look at this in terms of features, it becomes a really hard decision to make because there are so many features that our users ask us for that we want to build, and we know they'd be useful and they'd be really. Some of them would be really awesome. So it's like, how do you decide this or this or this or this? I think if you zoom out and look at it from a jobs to be done perspective or a user journey perspective, it becomes a little bit easier. Right? So it's like, do we stop? Great question for granola. Do we stop at meeting notes? No. Meeting notes are a stepping point on the path to what you're actually trying to do, and that's actually get the work done. Right? Like, why are you meeting with someone?

Right?

What have you learned? What decisions have been made? What work do you have to do after that? This is. There are lots of dots along this path of you doing the work you need to do. And granola is there to help with that. So I think if you look at that, what granola has is a lot of context, right? In, in a, in a 30 minute meeting you talk about a lot of stuff. If we have a series of meetings between two people, we have a lot of context on the trajectory that you're on and what you're trying to do. I think a natural extension of where we're going is actually try to help you do some of those things that you talked about in the meeting that end up on your to do list. Now again, I think we're gonna, when we approach that, we'll take a very granola esque approach, which is we're not going to try to do everything for you automatically. Right? We'll try to get you like do 50% of it, 70% of it for you maybe, and let you take it to the end and make sure you're in control. And we're also, there's a lot of nuance in, in action items and basically any kind of real work. So we're going to have to design it in a way where you still have total control. Right. And you feel like it's your work that you're doing or just speeding it up and elevating it.

The first thing that comes to mind for me is action items. And maybe it's because you mentioned earlier in the conversation that that was something you had to cut early on. I guess, like how do you even go about executing those action items? You have to adopt some, you know, some new form of agentic model that can go and, and kind of do other things and other tasks besides sort of summarization and language. How do you think about that?

This is a recurring theme. So I, I don't actually think that the hard stuff here is on the technical side.

Okay.

I think the hard stuff here is a lot around the nuance of human interaction and like just the complexity of the world and work that needs to be done. Right. So a good example here is oftentimes in meetings people talk about a lot of things that maybe they should do. Right?

Okay.

There's a very big difference between the stuff you talk about that should be done or maybe you suggest that you're going to do and the stuff that you actually think is important and that you're actually going to do and that once you start looking, it's like very easy to ask an LLM to spit out what are the action items from a meeting. Right. We've all done this, I think, right. Getting the right action items or maybe the unspoken action items and getting the action items that you trust, that's a world harder than that. This is like the classic Twitter demo issue with LLMs, right? Like, oh, it's like, oh, granola spits out action items. Wait, we could do that tomorrow, right? And it would look really cool, right? And I'd be like, oh, now that problem is solved. It's not solved, right? Because what you really want is you want a list of action items that are the ones that you care about and you want it to help you do that. And that's really a human and psychological and like nuanced kind of problem in terms of actually helping you execute. That one's interesting. I'm like, we haven't built it yet, but I'm optimistic because the majority of action items out of a meeting fall under maybe seven or eight canonical actions.

Oh, interesting.

Yeah, it's like, it's schedule a follow up meeting, send a message to this other person, right? Send a follow up email, ask for an intro. Basically you can see a world where it's like if granola was really good at helping you find like scheduling, let's say, right, really good at like recapping a meeting in an email in a specific format that you care about and sending it to the person you met with, really good at kickstarting some other workflow, right? Like maybe you now have a client so you need to update the CRM and you know, put them in the right place. Like those are all things that seem, those actions seem quite doable because they're discrete. We're not going to do the hard thinking. It's like, okay, come up with the strategy for X. Like there, there are a lot of tasks that are like that, but usually that ends up in a to do list, right? So we could help you put that item on your to do list so then you can go do it.

You mentioned that there, you know, there are like however many seven or eight different types of action items. Makes me realize, like, you probably get a lot of insights about meetings in general just by people using the product so much. I mean in aggregate, not in like a micro individual meeting level. Like, like you just said, like, oh, there are seven to eight different types of actions. Like, what else have you learned about, about meetings? I don't know that, that, that, that that either influenced the product or influence kind of the roadmap in the future. I, that must be really, really fascinating.

Something that is, that's super interesting that granola doesn't do a great job at yet, but but will is oftentimes you think of writing notes during a meeting as just, okay, I'm taking notes so that later I have this record of what was talked about or what decisions were made. People use it during the meeting to do real time thinking in organization a lot. So oftentimes people might have before a meeting they might write down some questions they want to ask, they might put in an agenda again for themselves. But then during a meeting they'll, they'll do a lot of thinking, organizing. It's like, oh, they said this thing and I want to ask a follow up question but I don't interrupt them now. And that's a very different type of note than what you would expect. So like there's a lot of building we want to do there other thing that, that's really cool. Not many people know this, but you can create templates in granola. It's a bit of a kind of a beta feature right now. And people have come up with really interesting templates for meetings because like when we first thought about templates it'd be like, oh, I again you're, you're an investor, you're a vc, Mike. So I'm trying to. All the examples I'm coming up with are kind of putting myself in your shoes. But let's say you're talking to a startup for the first time. You might want your notes as in like okay, team, product, problem, you know, like, like traction, things like that. Like what are the questions I didn't ask that I should have asked. Right. Or what are like the best quotes, like specific quotes about user problems or something like that. And it's like a dip. You get like a lens or a filter on the meeting. That's very different than just like information organization.

It almost makes me think like.

The.

Way that people are using granola can sort of dynamically over time inform kind of decisions you make in the product. Or maybe like one specific example, like you can imagine that template for the founder and the VC kind of like dynamically evolving over time based on how people are using granola in real time, if that makes sense.

Yeah, absolutely.

Interesting insights about meetings around like length or I don't know, cadence of speaker or like even speech, like and dialogue. Have you noticed anything interesting about how people speak to one another?

Two things stick out. One is in the era of zoom, people have no time between meetings. It's kind of nuts, right? So if you look, and I didn't realize this, but I think it's become really widespread since COVID where People who do zoom meetings, they tend to do a lot of zoom meetings and they're oftentimes back to back. And what that means is that you have no time between meetings to process any of the stuff that you've talked about. So people's workflows today are oftentimes back to back meeting, back to back meeting, back to back meeting. And every meeting you have is like, you open up all these threads that you don't have a chance to resolve or to do anything with and then at some point at the end of the day, you have to go back and like deal with that stuff. I was surprised to the extent of, of how, you know how back to back people are. The other thing is that again, with everything in life, it's like so much more nuanced and complex than you think when you first look at it. There are all kinds of meetings and with very different goals. So for example, how much people write in a meeting in granola is extremely variable. Right. There can be tons of meetings where users will write nothing and then all of a sudden there's this, this high stakes meeting where they will write like five pages of notes and all of that. And that's like the same person. So it's like, it's, it's a lot less cookie cutter than you would expect at first glance.

I actually would expect that it was very consistent throughout. Yeah, maybe you have these outliers, you have these people that maybe write. Once in a while you get somebody that writes a ton of notes. For the most part, everyone is using sort of the same general template, I guess actually as granola gets adopted more and more and people adopt the templates more and more, you probably can expect to see more and more consistency through how people are taking notes.

Designed Granola for this, it's optimized for meetings, but it's a generic tool to help you think and the thinking you need to do in different meetings or different situations is, is very different. So like an example might be like, okay, when we first started off, we maybe again for the VC cases, like, okay, we're gonna, we're gonna do a really good job for like an investor meeting with a startup for the first time, right? That's like kind of, you can, you can nail the constraints there and try to do a good job there. And then we realized with those beta users that they were, they went off and they'd use Granola for one on ones, they'd go off and use Granola for meetings with their doctors. Like, basically there's like a wide array of moments where you are talking to people and there's important information that you either need to process or remember or like deal with in the moment. One of the things that was very important to us when we designed granola is that it should almost be like a physical object, like a, like a, like a notepad, right? That you can just grab and write stuff on. It should always be there and should always just work, right? So like granola doesn't work as well, but it does work for an in person meeting, right? Like if you're sitting with someone, you can just open up granola and use it as you would like a, like a notepad. It doesn't have to be on a, on a zoom call, for example.

Well, it's an amazing product. Again, I am biased, but I'm a dau. It's part of my daily habit. I'm guessing it's becoming part of many people's daily habits and really, really cool to hear some of the secrets behind the design and the UX and how you're just thinking about building it. Thank you so much, Chris. Looking forward to doing this again sometime.

Thank you so much, Mike.

Thank you so much for listening to Generative Now. If you liked what you heard, please rate and review the podcast on Spotify and Apple Podcasts. That really does help. And if you want to learn more, follow LightSpeed at LightSpeedVP on YouTube X or LinkedIn. Generative now is produced by LightSpeed in partnership with Pod People. I am Michael McNano and we will be back with another fascinating conversation. See you then.