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

AI is going to let humans work

differently, think differently. There

needs to be a tool that supports that

and that's what we want to build. So

this idea of a contextually aware

workspace like AI powered workspace like

that's what we wanted to build with

granola. And we said okay great perfect

we got the vision that's what we want to

build. Where the heck do we start?

>> Hi I'm Matt Turk from Firstark. Welcome

to the Matt podcast. My guest today is

Chris Pedreal the CEO of Granola. In

just over a year since he was launched,

Granola has emerged from the crowded

category of AI notetakers as a bit of a

darling in Silicon Valley. Not just as a

hut AI startup, but as an AI product

that many in tech circles use rapidly

every day and often describe as

life-changing. This episode is a

masterass in how to build a beloved

product in the age of AI, full of

practical tips and lessons that Chris

has learned along the way, including how

to achieve simplicity in product design.

>> We looked at it all and we cut out 50%

of it. We basically redesigned and cut

out 50%.

>> Knowing when to exit stealth

>> in this really, really busy market,

launching something more polished so

that when people use it, they're they're

wowed by it is is a way to stand out.

and what it's like being a let entrance

to a category and then having to compete

with a bunch of big companies including

potentially the open AIs of the world.

>> Open AI is going to try to do everything

to everyone. And I think the question is

can we do something way better for a

specific use case and a specific type of

user?

>> There's a lot to learn for AI builders

in this one. Please enjoy this great

conversation with Chris.

>> Hey Chris, welcome.

>> Thanks Matt. All right. So, not to

fanboy you from the very beginning of

this conversation, but I have to say I'm

a very rabid user of Granola and

actually our entire firm at First Mark

um is and um you know when I start when

I started using Granola a few months

ago, I thought I was pretty cool, a

pretty early adopter kind of kind of

situation. And then there was uh this

article in the information a couple

weeks ago that basically said well

everybody in Silicon Valley uses the

product all the time. So uh maybe not so

much of a of an early adopter from that

perspective after all. Just curious

about how that feels uh as a as a

founder to have a product that's just

widely embraced by our entire at least

little tech ecosystem.

>> It is it feels both amazing and and

daunting is the honest response. I uh we

did this this was last maybe November uh

um so we're based in London, right? And

we went to SF for a board meeting and

someone on the team said, "Hey, should

we rent out a bar and just email users

and say if you'd want to come and we'

like sure, you know, and we thought we

thought like five people would show up."

And like this two-fol bar was just full

of full of people. And um and then they

were just the the level of detail with

which they were talking about the

product or things we should change or

things that they had noticed. It it

really hit me cuz it when you you build

a product for people, but really being

in a room with that community all at

once, it it made me realize there's oh

there's something special that's

happening here that we didn't

necessarily design for. It's kind of

organically happening and now it's kind

of our job to

>> to follow that.

>> Yeah. and and one amazing which uh I'm

sure you've heard tons but it's just my

personal experience and looking online

and talking to people a description of

the granola experience that keeps coming

back is life-changing

uh which is insane but that's again

truly my experience I tweeted that at

some point I was uh you know all my life

a rabid uh notetaker that's how my brain

works and it helps me think through the

meeting or whatever I'm listening to uh

and pretty pretty much overnight that

lifelong habit just disappeared once I

tried granola a couple of times and

trusted it which u again not too fine

for you but uh it's it's been an

incredible experience.

>> I appreciate that. I think um I think

that will I think that speaks to the

moment in history that we're living in

where AI now has all these capabilities

um that are going to transform the way

we work and the way we think and

hopefully you that experience you just

described with granola will keep

happening in many more aspects of your

work life as you're you're basically

able to outsource lower level tasks and

allow you to think higher level um which

is why I think it's such an exciting

time to be to be building and to be

living quite frankly

>> I think when you released your uh team

product a few months ago you used the

expression second brain and it's it's

basically what it feels like and in some

ways a life-changing part does have a

little bit of a daunting aspect as a

user because it sort of feels like

you're outsourcing

your memory to technology and memory is

such a part of who we are as humans and

that's how you know humans survived for

for centuries like whoever was able to

remember facts, was was was able to just

function well in in society. So, it it

does feel like a like a wonderful tech

journey, but like something a little

more than that actually possibly.

>> I completely agree and I think there's

going to be as as uh as AI gets smarter

and we build more and more tools and

workflows on top of it, we're constantly

going to be letting go of things that we

used to do and letting machines do that

for us. And there are times when I think

that's ne incredibly beneficial and I

think there are probably times where

that's harmful, right? Like ne negative

beneficial. Uh the example I always use

is um Google maps, right, on on the

phone. So, it's very clear there were

cities I lived in before Google Maps

came out and I can go back to them. I

can still navigate them without a map

and there are the cities I've lived in

since Google Maps have come out and I

have like there's a very small area of

that city that I can navigate without

without a map. Um, so you could say, oh,

my my navigation skills have atrophied

100%. The number of hours I've walked

around lost in a city trying to find a

place has also gone down dramatically,

right? So it's like I'll I'll take in

that instance I'll definitely take that

trade-off. Um but I think you have to be

thoughtful case by case about what tools

you use and what you decide to

outsource.

>> And do we think long term that uh what

happens like we become we have more time

to actually think and reason but uh it

sort of feels like models are doing that

for us as well. I mean I I think this is

the this is why I'm so excited to be

building Grdola right now to be working

in the space because I I think that

future is kind of up to us right in ter

like there's this great quote which is

we shape our tools and thereafter our

tools shape us and when you think about

AI and um the future world and how it

fits into our society I I think there's

this big question of like what you know

what do we outsource to AI where does AI

replace humans and and where does it

augment humans and um where like me

personally, Sam Granola, we're really

big fans of the augmentation idea. This

goes back to Douglas Anglebart in the

50s, right? Augmenting human

intelligence. And his his view,

honestly, his stuff, I feel like people

don't talk about him enough. It's just

so inspiring. Like, he's known for being

the inventor of the mouse, and I think

the mouse is like the the least

important thing he's come up with. And

and basically, this was um when

computers barely existed. like the

computers that existed were in the

military and the Navy and they they like

filled up whole um uh whole floors,

right? And there are a a bunch of folks

actually who are true visionaries at

that time who imagine this world where

computers would be accessible to people.

They'd be common and they would be tools

for work and tools for thought. And the

way Engelbart talked about it, he was

like, we are becoming, you know, the

world's becoming more globalized. Our uh

the world's becoming more complex and we

need better tools to help us

collectively solve more complex

problems. And that's I mean, talk about

an amazing narrative, right? I feel like

we don't get enough of that today. uh

you know I wish I wish uh whatever I I

have a lot of love for for the tech

world but you know there's something to

be said about some you know

counterculture builders in the '

50s60s7s even like Steve Jobs when he

started Apple like these folks have like

uh you know a counter revolutionary

world view of the world and like how

personal computing and tooling could be

used for that. So in terms of um the

atrophy of the future I like I guess I

was trying to think about examples of

this is have you seen um Wall-E the the

Pixar movie?

>> Yeah. Yeah. You know, the humans, like

the humans are fat and can't walk,

>> people floating in space. Yes.

>> Exactly. So, I feel like that's one

extreme future for humanity post AI,

right? And then there's another one

which is um I think maybe like Jarvis

from Iron Man, right? Which is kind of

like, okay, now I can I can fly. I can

solve things I can never do before or am

I like this fat blob like floating

through space? And I think it's a little

bit

>> what tools we build, what bets we make

as a society, what rules we make. I

think I think that's going to be decided

over the next like 10 years.

>> From an entrepreneurial

journey uh perspective, one of the parts

of the granola story I find fascinating

is that you guys were kind of late to

market in in many ways. The idea of an

AI notepad is not new. There were

several companies doing that. There were

large companies like the Zooms of the

world doing that. So for the builders

listening to this who may look at at a

category uh and see a few companies and

and and try to decide whether they

should build in this category or ignore

it and find a category which is less

crowded. How did you guys think about uh

oh we can come up with something that's

going to be better than all of them?

>> It's a it's a great question. So, um I

think the answer really just comes down

to like what were we trying to build

when we we set off to start Granola.

There have been like meeting

transcription or recording products like

Otter and Fireflies I think are like 9

years old, right? They've been around

for for a long time. Like that's not a

new idea. Um there are all these tools

that like okay we can now record

meetings, right? And and and try to make

something useful there. That's not at

all where we started with Granola. Like

that's not we don't think like a meeting

recorder. That's not what we're

building. We we want to build a tool for

thought. Like the the genesis of Granola

was I uh I quit Google because Google

bought my last startup. So I quit Google

knowing I wanted to do a new startup.

And I came across LLMs for the first

time and they blew my mind. And I was

like this is going to change everything.

this is absolutely going to change the

tools we use for work or productivity

tooling and I met my co-founder who had

come from a tools for thought knowledge

management uh space and we basically

said ah AI is going to let humans work

differently think differently there

needs to be a tool that supports that

and that's what we want to build so this

idea of a contextually aware um uh

workspace like AI powered workspace like

that's what we wanted to build with

granola and when and we said okay great

Perfect. We got the vision. That's what

we want to build. Where the heck do we

start? Right. And and that knows like

okay kind of kind of imagine you can

imagine an assistant that like knows

everything about you. Uh is there where

you're working, gives you suggestions,

learns from you, you know, you can kind

of imagine that. But where do you start

as two people, you know, building in in

2023?

And we we realize that AI is only as

helpful as the context it has about you.

So this is something I think we don't

talk about enough even today in in 2025

like context is so important context

design curation like that's a whole

topic maybe we can talk about Matt but

so it's like okay to be helpful to a

user we need to have their context and

as a tiny startup with you know like we

need an entry point it kind of came down

to email or meetings like those are are

the two places where there was a lot of

useful context uh that that we could

access and then you put the product

building hat on and you say getting

someone to change their email client is

hard, right? That's like a very very

tall ask. Whereas taking notes in

meetings, honestly, the biggest

competitor that Granola had from day one

and even today is Apple notes.

>> It's this idea of, you know, it's like

I'm in a meeting, I'm 5 minutes in, you

say something smart or that I need to

remember, then I'm like looking for a a

pad or paper or something to write it

down. And and Apple Notes is the virtual

version of that. That like that's how we

started with with with meetings. We kind

of begrudgingly entered the super

saturated space, but we really did we

thought like we really did think about

it as a very differently I think from

the companies that were out there like

we were thinking about ganola as a

personal tool for you to help you do

your work better.

>> And while there have been tons of muting

recorders out there I you know I I' I'd

posit that none of them feel like that.

Like when you log into these, they feel

like a meeting repository, like a here

are recordings of meetings or just the

fact that a meeting ends and it it

emails generic notes to everybody that

was in that meeting. It's a completely

different feeling than like here's this

here's this tool for me that is

optimized for me. Like I was to be

perfectly honest, I was surprised that

we were able to break out uh in such a

crowded space. just like there's so much

noise. There's so much happening.

There's so many people doing things and

Granola is by design very quiet. It

doesn't there's no like growth hacks in

there. So, um that was that was a really

pleasant surprise.

>> Amazing. And you mentioned your your

prior startup and your uh co-founder. Uh

another very interesting part I find is

that both of you guys are product

people, right? I mean, I think you have

a computer science educational

background, but like you you is that

fair to say like neither

>> that's accurate. Yeah. Yeah. My my my

co-founder is a designer. I'm a product

person. We can both code. He can code

much better than me, but we're we're

product and design. Yeah. Mhm. And um

where I'm going with this is I'm I'm I'm

uh curious what that means in terms of

again for people builders listening to

this, what that means in terms of um

what kind of team one needs to build an

applied AI company these days, a company

running on top of of an LLM. So what was

your level of sort of technical comfort

working with with LLMs? And at at what

point did you feel the need to start

bringing people to do more technical

stuff? I think the the main thing that's

changed here is that um it used to be

that you would need really strong

technical chops just to build an MVP to

understand if if you this is something

people wanted or not. And I think the

reality now is that um that isn't the

case. you can you can usually figure out

MVP or like is there a there there maybe

even early product market fit

potentially or signs there uh without a

whole bunch of technical acumen um if as

long as you're building on top of the

models like it's a completely different

story obviously if you're building at

the at the model uh layer but if you're

building a a rapper a rapper company

like like we are then it's like you can

you can learn a lot and in those early

phases like when I was looking for a

co-founder I met Sam but I was also I

met all the all the LLM experts from

Imperial and Oxford and Cambridge

because I thought that was DNA we would

need on day one.

>> And as Sam and I started prototyping, we

realized actually there wouldn't be much

for that person to do until we figure

out product market fit until we maxed

out on what like the the base model like

the off-the-shelf models could do and

then we would need that expertise and um

and then we stopped looking for that

person.

>> Mhm. and to the ICL um and generally

London discussion that you mentioned.

It's also interesting and a little bit

of a narrative violation if you will

that uh you guys are building the

company out of uh London in in in a

world where like the default sort of

zeitgeist uh thing that people repeat to

one another is that you can only build

great AI companies uh in Silicon Valley

or San Francisco. What what has that

experience been uh for you? I guess

first of all, why are you doing it? I

think I read somewhere it was for

personal reasons.

>> Personal reasons. Yeah.

>> And and and uh and then more

importantly, what has it been like?

>> It's funny. I um we were here, we we're

in London for personal reasons. My

wife's um my wife's English. Uh we moved

here. I knew I wanted to do a startup.

We chose London because there there's a

there's amazing engineering talent here.

there's enough to there's enough of

ecosystem here to really have a go at as

a startup and then um when I decided I

wanted to build an AI startup I said oh

my god yes like deep minds here like a

lot of like modern AI was like invented

here uh some of the best programs like I

said UCL uh Cambridge Oxford Imperial

they have amazing AI programs and then

and then I had the realization that

actually you know product and design and

and just general product taste and

building is super important so we didn't

need to hire those folks uh early on I

think there trade-offs, right? I think

there there are very real trade-offs.

There's like a center of gravity of

talent in Silicon Valley. I think we're

in a very lucky position to be I'd say

one of the the most um uh visible and

desirable like AI like consumerf facing

AI startups in London. So for there's a

for the continent of people over here

like they they find us which is which is

incredible. And I think I think in an

era of where taste matters and product

sensibility matters, there's the there's

just amazing talent here for that. Um,

as well as amazing engineering talent

from like all the big tech companies.

Uh, and there's a huge uh influx of

Russian uh tech talent that's come into

London. So, we're definitely not in the

eye of the storm, so to speak. The

which which I think is, to be honest,

mostly negative. I think the the the

upside is probably that it's a little

quieter over here. There's so much

noise. There's so much change. There's

so much thrash in AI. Like whenever I

talk to AI founders, especially second

time founders, they're like it's never

been like this before. Like this is like

founding is hard. Founding an AI right

now is like emotionally draining, energy

drain. It's like draining in every

aspect because it's so fast and so and

everything can change, can pivot on a on

a dime. And I think being in London

insulates that uh insulates us from that

a little bit.

>> What's particularly interesting is that

you you're in London but you're a

Silicon Valley darling product, right?

Typically the the trade-off is like yes

we can build great companies outside of

Silicon Valley but typically Silicon

Valley ignores you and I think you

probably maybe was lovable like the or

synthesis you know one of the rare

companies that has sort of broken

through the consciousness.

>> So I guess that was extremely

intentional right. So we're we're we are

the way I talk about internally, we are

an American company that happens to be

in London, right? And um we built for

Silicon Valley, we built for the

American market explicitly. If I ever

see any copy that has English spelling

instead of American spelling that goes

out, I throw a hissy fit because I want

everyone to think we're an American

company. And um and that's what

happened. I also have to say like it

really helped that helps that I built a

my previous company in the US. All our

investors were like our main investors

are based in America. I had that

network. I had like basically it's like

that DNA uh we've transplanted to

London. So it is Granola is a Silicon

Valley DNA company that happens to be

building in London and leveraging that

as much as we can.

>> Tell us about the beginning of the

company. So uh the product itself uh

launched in May of 2024 I believe which

is not that long ago at all uh given

again the level of uh heat and and love

for the for the product but I read

somewhere that before that you were in

stealth or in building mode for about a

year. So again for builders out there uh

how did you think about when to launch

when not to launch? You know there's

this constant tension between like

building public you should be

embarrassed by your first version

otherwise it means you launch too late

but on the other hand you only get one

chance to make a first impression. How

do you think about this?

>> Two thoughts about this. Uh the first is

um a simple way to answer this is what

is the fastest way for me to learn. So

like presumably you start building

something you have some prototype right

uh you have some early version of the

product and you say will I learn faster

if I launch publicly or will I learn

faster if I don't launch publicly and

the answer for us for about a year was

we'd learn faster if we didn't launch

publicly because we were onboarding

users every day onto Granola and it was

painfully obvious what was broken about

it. So launching publicly and getting,

you know, 10,000 people telling us the

exact same thing was actually going to

slow us down rather than just fixing it

based on what users were telling us. Um

there's a there there many costs that

come from launching publicly whereas

like now you have users, you can't ship

things with bugs. You can't you know you

can't if you pivot it comes at a cost.

So um we basically spent a year

onboarding people, learning what was

wrong about it, making fixes to that,

onboarding a new set of people, fixing

it and and iterating. Um and then

basically the moment when we said ah

like we now we now have something that

works and we're going to learn a lot

more by having lots of people use it and

realize who who does it take off with

right like maybe real estate people will

will love it in a way or use it in a way

that we didn't expect like that was the

moment we we decided to launch publicly

on the the general wisdom on this like

MVP non MVP I think today there are so

many products and companies coming out

and vying for your attention that

launching something more polished so

that when people use it, they're they're

wowed by it by it is is a way to stand

out. Uh so I I do think it's tension.

You shouldn't be tinkering in in your

closet for 2 years and the world's

moving very quickly. But there's a lot

of kind of there a lot of MVPs floating

out there, right? So if you want people

if you want to stand out in this really

really um busy market I think you need

to have something a little bit more

polished again when you try to draw

attention to it

>> while you were in that building mode

stay my stealth how did you find those

first um users so you mentioned you're

you're very deliberately an American

company based in London were you also

very deliberately a company targeting

I hate the term but like for lack of a

better term tech elite quote end of

quote of a bunch of like top founders

and VCs. Was that intentional or that

that sort of happened?

>> Yeah, it it it well it's two stages. one

at first we built we were building for

us right and then we were then the first

users are kind of friends and family and

extended network who were knowledge

workers used computers did a lot of zoom

calls um and that got us pretty far and

then there was this moment where users

started telling us different things

they're like oh this this is what's

important this is what's important and

we said okay we we know granola will be

a general product a horizontal product

lots of different types of people are

going to use granola but we should just

choose a user type on day one uh to make

it really good for and then expand out.

And there we kind of like looked around

and we said okay we need a user type

that has a lot of meetings relatively

formula you know like lots of a similar

type of meeting with relatively

formulaic note uh style that they need

that we have easy access to yeah exactly

VCs right yeah so so we said okay and

again

>> let's build for VCs yeah and um

>> and then and then as soon as we launched

we said okay great now we're we're done

with VCs no they're not going to be

we're not going to focus on VCs we're

going to focus on a different user type

And we chose founders just because we

thought they'd be the hardest. Like if

you founders might have a sales call and

then a user feedback call and then a an

interview and and basically thought if

we could build a good product for

founders then we like a great product

for founders would be by default a

decent product for folks in these other

roles and then we could make it better

over time.

>> All right. So getting into the product

itself uh and the general philosophy um

of the how you you you designed the

product. So the the the key for thing

which to me feels like the killer

feature or at least a clear

differentiator is that decision to that

granola should be hidden uh or at least

not apparent to other participants in

the meeting in stark contrast with as

you mentioned earlier bot first kind of

noteakers where you're on a zoom call

and then there's somebody else's uh you

know insert company name um you know

note takingaking bot and I mean clearly

there's a little bit of um sort of

sensitivity here around uh

confidentiality privacy uh and to me it

feels like in retrospect like a little

bit of a certainly opinionated perhaps

gutsy kind of product design decision

curious about the genesis how you

thought about it uh from a product

standpoint but also from a you know

almost societal

>> uh standpoint We always started from the

we always started from the perspective

of this is a tool for you. What what

will make for a great tool? And there

are a few characteristics that are

really important. So a tool needs to be

consistent and reliable. Like if you

pick up a pen and it only works half the

time, that's a terrible pen. You're not

going to use it, right? And um in in the

case of meetings, there's this very real

thing where some of your meetings or

conversations might be on Zoom or meet

or huddles or WhatsApp or or maybe not

even on on on a VC. It might just be in

person. So we started off from this like

tool building perspective of like

granola needs to be consistent and it

needs to work across everything because

we have this like 500 millisecond window

when someone is in a meeting and decides

they need to take a note like what tool

do they open and again we're competing

with Apple notes and Apple notes always

works it doesn't care where you are what

you're doing it always works so that's

where we started and then

>> um

>> from the adding a bump to the meeting

perspective perspective. Well, like

technically just because we wanted to

work everywhere that wasn't a good

option. But if you analyze that a little

bit as a tool, I I it bots make me make

you feel kind of weird, right? Just like

a big black box on the screen. It's like

not a person. Sometimes they show up

before you you join the meeting. It's

it's like a kind of this awkward thing.

Beautiful thing from a growth

distribution standpoint, right? Like you

get a user now, they're exposing

everybody they're meeting with to to

your product. So everyone thought we

were kind of crazy. not not to do that.

Um and then and then the way I think

about um

information capture and usefulness is is

basically uh I'm sure that two years

from now, 3 years from now, everyone's

going to be using something like

granola. Like I'm hoping it's granola,

but if it's not granola, something like

granola just because it is so useful and

will get so much more useful over time.

And I think as a society we need to

figure out like what are what are the

right norms there and I think what you

basically want is you want something

that is um the least invasive for the

maximum useful and that's like the the

right tradeoff here and when we designed

granola we basically said okay cuz all

all the other tools out there they

record audio they record video they save

that stuff at least when we started off

that's how they the tools worked. Um,

and we said that's that again that

doesn't feel right. Like like I don't 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 we we

do not store the audio so we don't

record the audio. Um, which completely

changes the way Granola feels. I I think

Granola feels more like a really smart

enhanced notepad than like a like a

meeting recorder.

>> You store the transcript though and

people can uh review the transcript and

query the transcript. Very important.

>> Exactly. We stored the transcript. We

actually were hoping to not even do that

um or at least not make the transcript

visible. And I guess one of the 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 I don't have to trust the LLM

output. I can kind of go back to the

source

>> and um and of course transcripts get

stuff wrong all the time, but it's like

if I can see if I read the transcript,

I'm like, "Oo, that looks fishy." That's

that's that's important as part of this

uh of the experience.

>> Yeah. the user becomes the human in the

loop effectively. Talk about simplicity.

So my personal experience with granola

as a user is that um 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 um 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 were all these panels

and it's like here's your transcript

here's your super long like here's your

blowby-blow of exactly that happened at

the meeting. Here are your notes. Here

are your private notes. Here's your I

don't know your 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 um

what we did and I think this is probably

probably one of the things I'm proudest

of because it was hard is we we looked

at it all and we cut out 50% of it. We

basically redesigned and cut out 50%.

And um and I think that would have been

impossible or extremely hard to do if we

had been publicly launched. I think

because if we had been publicly

launched, we had all these people had,

you know, grown to love Granola and

whatever weird shape it had been in and

then we cut out half the functionality,

you just get you get so much hate. It'd

be tough. But because we were still um

pre-launch, we only pissed off 150

people instead of instead of the number

of people who use Granola now. Um,

simplicity is really hard. Uh, and it

it's hard because

organizationally,

um, unless you're, you know, the the

founder, like you're solving a problem

and you're in a a little universe and

you're going to 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

which is the problem they're solving and

then you have to have this other layer

which is looking at the product end to

end um and saying sure there's very

clear tangible value in having this

feature and then there's this like very

untangible 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? But

it's and then you look up and you have

10 buttons that are clogging up the app

and then and the app no longer feels so

magical and so zen. Um, and the real

danger there is user requests like you

know people always ask for the things

they don't have. People rarely say, "Oh,

actually can you can you cut out half of

the functionality of of the app?" Um,

even though when people 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 in

the universe who are going to be pushing

for simplicity are are are going to be

the kind of like the design or 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. Um but it's just such

an important job. And to the tangible

versus untangible point, how do you

decide effectively 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

uh what people actually do with a

product? Uh which part is science, art,

taste versus data measurement?

>> Yeah, it it it's all of the above. So

our

our general our philosophy that's gotten

us here and it may not get us there as

we scale is um uh we make most product

and design intuition sorry uh decisions

based on

intuition like what what do we think

makes sense? It's kind of this vision of

the product and that we're headed

towards and um that like we just kind of

make the decisions based on like does

this feel right? because 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 course,

everything we launch we measure and we

look at the graphs and it was like

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 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 they do regular user calls. We we

aim to do I think Sam and I aim to do

four to six calls a week uh with with

users but constantly not like oh we're

we're doing a sprint on this feature.

It's actually we we try to book them

every day always so that there's this

constant context of uh and 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're they want X or they're

going to do of course if we make build

this feature, of course they're going to

use it. And and I think it's only when

you have constant contact with people

and you're like, "Oh yeah, they're so

busy. They have all these other things

to worry about. They don't even know

about what buttons are in granola or

not." Like, of course, they're not even

going to notice that. It's like that

kind of thing. That's that's really

important to do qualitatively.

>> How do you um think about all of this

going forward in a in a context where

presumably you're getting pulled in

different directions? for one direction

uh 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. So on

the one hand and then on the other hand

uh you just launched granola teams

couple of months ago uh and that pulls

you into the world of like enterprise

and so 2 and compliance and and all the

things. So I'm curious about how you

think um about balancing all of this uh

versus uh simplicity and then how you

prioritize uh the road map uh you know

with all that in mind.

>> It's absolutely true. we're we're being

pulled in in a million different

directions and it's very challenging. I

think the overarching point that I have

here is I think the failure mode is that

we optimize for today's world. So we

optimize for today's product and today's

world and today's needs and it's easy

when you just talk to users and you get

these requirements or you talk to

enterprises you just kind of 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 um uh 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 and

maybe you 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. Like what we're very very

excited about a world where people use

granola not just to take notes but to do

all kinds of work. Um, and in a in a in

a way the product we have today is is a

Trojan horse to collect a lot of your

context so that you can then use all the

information in that context to to do

future work. But that is that is hard

because you have users or companies

asking you for future X today, right?

And we have to simultaneously invest in

like oh we're doing a kind of a 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 they're not even

thinking to ask for that. But I

guarantee you that will be a huge part

of of of the of where the world is going

and and and where a lot of value of

granola will will come from.

>> I'd love to uh spend a little bit of

time now on how it all works behind the

scenes, the the tech stack, the

mechanics of it all. So starting with

the models, so seen on the on the

website and as a user um you use lots of

different models. So as a first

question, do you uh exclusively at this

point use third party models? So have

you built some of your stuff from a pure

AI perspective?

>> No, we we're we our philosophy is to use

the best model that is on the market as

quickly as as possible. There's so much

uh 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 like 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 to fine-tune or train models

and we we will do that. Um what we found

is that there's so much alpha in like

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 uh multiodel

world that's open AAI anthropic Google

uh is that right? Are there others open

source uh any specific models that you

currently use as I saw that you just um

announced that you are yes supporting?

>> Yeah. Yeah. Well, I mean, we we we use

we basically test out and the all the

models that come out and um

anyone who spent a lot of time with

models, even users are getting really

sophisticated. You just learn their

abilities. It's like h it's like this

model's like 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 we

while we include all these models in

granola, we set the defaults as uh we

set different default models for the

specific thing you're trying to do in in

the app.

>> Interesting. So you you you provide the

ability to pick a model, but you gently

guide the user towards what's best for

their use case. Okay. How do you think

about um keeping a consistent user

experience in a world precisely where

those models uh one change evolve all

the time, two behave differently and

three as we all know are stoastic not

deterministic?

>> Uh do you uh expect the users as you

just said to like be be smart about it

and that's sort of theirs to figure out

or is that something that you abstract

away for them? we abstracted away from

from people like the the general design

at first we didn't for the longest time

we didn't let users choose their model

right and we only let users choose their

model on chat on the note generation

side we completely abstract it away and

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 for note generation to

provide consistency of experience and an

improvement of experience and there's

significant work that goes into that um

it's I think one of it's one of the

value ads that Granola brings as opposed

to just working with base models is that

we take care of that and we make sure

you get granola feeling or sounding

notes consistently um and that they keep

getting better over time.

>> And uh how does the romping 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, uh you know, what where do

they work, what are they trying to do?

And um we've we've put a ton of work

into uh what are common meeting types

for different with folks with different

jobs and in those meetings, what are the

things that really matter? Uh so for for

example I think the thing that kind of

blew people's mind when granola came out

um was if let's say a VC like an

investor uh and a founder were both

using granola in the same pitch meeting

let's say the notes that granola would

generate for each of them look

completely different right and and it's

based 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 uh coming out of a a pitch meeting

is very different as a founder as it

would be as an investor. Um, and a lot

of that is kind of uh hard-coded

instructions that we we build into the

system.

>> How do you um navigate the context

window constraints? Uh, if you have a a

1hour meeting that's a lot of

information uh and uh I'm curious about

you think about chunking.

>> Sure. So I mean a 1 hour meeting was a

lot of information in in 2023. It's not

a lot of information now compared to

what the models can do. They have um

incredibly this the context window size

increase over the last three years has

just been mind-blowing and and fantastic

for us.

>> Yeah. But call it a you know a board

meeting which is uh four hours right and

and there's like rate limits and all the

things or maybe maybe it's no longer a

problem at all. Maybe

>> no no that's not a problem. The problem

becomes now well okay there are two

things. when notes aren't very notes are

short, transcripts are long, single

meeting is fine. It's when you have lots

of meetings and a large corpus

information that that that's where this

problem

>> uh comes up a lot. And the like the

interesting

the trade-off or the thing that's tricky

here is that if if you care about

information lookup, right, then then you

can do rag, you can do well either like

some form of keyword search or cosign

similarity.

What we found is that a lot of the most

interesting queries that people have um

like would completely fail with that

type of method. So for example, query

might be like what are all the things um

I didn't do a good job explaining

um or or tell me what are all the bugs

that this user encountered uh that you

know in this user call. Um, and the only

way the only way you can you can get a

good answer to that is if the model has

the full context. Um, and so we and this

is very costly, but we we generally tend

to put lots of context into the context

windows. Um, and we are on that side.

Um, and we have some some really cool

stuff in the works where we look at full

context across thousands of meetings

which I've I've told you about. But

again, it's it is costly with today's

technology. like there are trade-offs

and the trade-offs are basically as far

as we've seen it like like money or

quality, right? And um and our our

philosophy since the beginning of

Granola is always to to build for the

world a year from now because by the

time we build it and it gets

distribution um the costs of those

models or those capabilities will will

come down to a reasonable place. Um but

that that's basically I think that the

context the the trade-off there is

around the quality of the the queries

that need a lot of intelligence.

>> Yeah. And the the the cost question is

um particularly interesting and timely

these days uh because of the well

reported um discussion around the cursor

of the world or the coding tools having

negative gross margins. Um, is that is

that a a situation where like

directionally you guys are are are at

where you for now operate on uh 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. Um is

that is that is that is the world of

like meetings different in terms of like

token needs uh versus AI coding. So the

most expensive thing about our business

is actually transcription. Uh and

historically it's been actually

transcription and high quality

transcription versus um LM inference.

And um the we basically use the

best transcription real-time

transcription on the market at any time.

Um and the cost of transcription has has

fallen dramatically uh over the last

couple years and I suspect we'll

continue to do so. Um, so yeah, we're

we're we're not at a negative gross

margins right now. And 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. Uh

it'll be an interesting race to see

like, you know, does does the cost of

inference go down um faster than the

user desire for more complicated, more

intelligent features goes up? talk about

if you will the the parts around the

model. So you mentioned transcription.

Uh obviously there is a big sort of

audio part to what you do with uh you

know there's a lot of problems in that

world. There's the problem of dorization

which means uh figuring out you know

this is Chris speaking or or Matt

speaking uh and there is the problem of

like noise cancellation like how what

work have you guys done which vendors

and solutions have you picked what have

you learned

>> so let's see echo cancellation we we run

ourselves um on on device and the it's

just important because if if someone has

headphones on and they take off the

headphones halfway through the meeting.

That's, you know, it's important for

that for that to work.

>> And it's something you built internally.

>> Yeah. On top of some open source

frameworks and then we and then we built

it. Um we've partnered with Deepgram

Graham and assembly for transcription.

Um they keep pumping out better and

better models. We're always using the

latest and greatest. Um

what else are we doing? Diization.

Unfortunately, real-time diorization is

still in its infancy in terms of in

terms of quality. So that's something

that we're keeping a very close eye on.

But um uh we haven't been able to get

real-time dorization at a at a quality

point where we're where uh we're happy

with. Um and and actually there's a

danger with if you give models are

really smart in ways you don't expect

it. If you give like an incorrect

derization to a model, it'll oftenimes

confuse it more than if it just has to

try to infer who's speaking. So there's

some some interesting like analysis and

evals to to be done there. Um let me

see. Yeah, we use I mean there's so many

like like I can tell you about our whole

tech stack. We use brain trust for a lot

of the uh evals. And

>> how do you think about uh guard rails

make sure that the

system doesn't uh spit out um you know

things it shouldn't for example? I guess

for every product the idea of like what

should the system not what what what

what would be harmful or negative for

the system spit out a little bit

different. I think if you go to um if

you go to something like Google or

ChatgPT and and you ask it for something

it's like an open-ended place where

you're you're looking for guidance or

help or health advice or what have you.

There's some really really uh bad

scenarios there. Um for our case it's

it's it's a little bit different. you're

usually going back and asking questions

over your your your meeting data. So

there there's a question of we get

something wrong or it hallucinates. Um

but what we found there is that the the

best thing to do like you're never going

to get it 100% right. We can never be

like oh you know we make no mistakes you

know you just trust us. Um, so obviously

we do the best we can to avoid uh 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 where like how did

you construct this answer? Like where

what are all the citation? So we spent a

lot of time thinking about citations

about letting you uh view original

transcripts and quotes and 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.

uh switching tax a little bit um away

from the tech stack. Um I'd love to go

into uh growth mechanics and what you've

learned. So you said um among the many

interesting things you said earlier, you

said one on on that topic that caught my

attention. Not having the bot experience

was actually a trade-off in terms of

like virality because you don't have the

built-in um expos product exposure

because there's no bot showing up. Uh so

h what have you done to um uh sort of

overcome that and what are the you know

viral sort of growth mechanics built

into the product. Today

>> we haven't focused on grow basically

what we really focus on is making the

product really good for people and turns

out that that's actually uh led to a lot

of viral growth but that viral growth is

from people telling each other. Uh like

so like an interesting story that this

this I never imagined that this could

have happened but I hear a lot now is um

if you if you have a one like you know

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

what are you doing with an AI bot like

why aren't you on granola yet? you know,

and it's like, oh, wow.

>> The AI bot is now like a conversation

start. Yeah. It's the weird thing. It's

a conversation starter for a human to

bring up granola and to vouch for it,

which is incredible, but I never would

have sat down and and imagined that that

world. Um, so we always start from like

a like a value standpoint like what is

valuable to the users like oh we we

could email your notes to everybody in

the meeting like all the other companies

do but again is that is is that a tool

that you want to use is you know is that

acting like a tool for you or is that

acting like you know like a growth

engine I what what we do have is um we

do let people share granola notes

basically you can share notes on a link

and you can send that link to people and

what's nice about that is that when you

share share the 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 um but to 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 and they grow that way. Um 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 or for your company and um 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 that 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 and what kind of

meetings or context do would not want

shared because there are a lot of

>> a lot of really dangerous uh failure

modes here, right? Like 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, right?" Because like wouldn't

like transparency is a good thing and um

and it's super valuable in the age of

AI, right? Like the more context the

better. And then you actually and then

you and then you actually sit down and

think about meetings or you hear these

horror stories that go viral where it's

like um you know like the the AI meeting

notes were like captured like a 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 I went to a

founder dinner and he was like oh you're

Chris from Granola my company we we just

switched to granola and I was like oh

great like what happened? And 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 going to

get an email with our in-depth

discussion of how we're going to let

this person go. And they and and they

all freaked out and they all said,

"Okay, like if the Wi-Fi cuts out, we're

screwed. If the battery dies, we're

screwed." So, this became like the 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 like they turned it off

and they ended the meeting and they just

sat there for 5 minutes waiting to see

if it was complete disaster. But there's

a lot like turns out like human

relationships are complex and nuanced um

and and if you have like a

one-sizefits-all solution there are a

lot of these cases that get kind of kind

of ugly.

>> Mhm. As you get further into the

enterprise, uh do you get any kind of

push back or questions about um what it

means for every conversation to be

recorded and for it to be a uh I mean

quite literally like a a track record or

of of of everything that was ever said

from a I don't know legal perspective or

any of those.

>> Yeah, absolutely. So I think I think

there's two lines of of questions here.

One is um like it's important in in

enterprise context that everyone knows

that you're using granola, right? And we

we are like we have functionality that

post this in the chat uh 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. But that that's like one line of

questioning and like you should always

tell people use granola. It's like the

right thing to do. um regardless of what

the laws of where you are, you know, I I

think it's like we're also moving

towards a world where like these tools

if they're well-designed and not too

invasive, like it'll be normalized in

certain types of contexts. Um 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? Like

the more you want to at Google, our

emails were deleted after 2 years or 3

years, right? you literally couldn't go

back and search and see why a certain

decision was made on a product, you

know, like why did we do this on Gmail 3

years ago? You you you couldn't find

that in in the email. Um, and and that's

to limit the 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 I talk to a lot of

customers there's the biggest disconnect

that I've seen from uh you know

especially folks who are very like uh AI

forward tech forward thinking about the

future it's like how can everyone in the

company uh uh leverage AI tools to

become better faster smarter and then

the IT team the legal team uh and and I

don't know how that will get resolved uh

I I I honestly don't I think it'll be a

very interesting space to much.

>> So, still in the in the growth uh

mechanics uh world, you have an

incredible uh user retention. Curious if

beyond the sheer quality of the product

uh there is anything that uh you did and

that maybe people can borrow for for

their products uh leading to success in

retention. from my entire career like

building products, you you kind of

learned the hard way that uh getting

users to build a habit of using your

product is is incredibly hard. Like when

I was earlier in my career, I thought,

okay, you just you you build a fantastic

product and then people will use it and

then there's this horrifying uh moment

where you you you you build something

good and you put it in front of someone,

they say, "This is fantastic." Right?

And then the next week they just they

they hit that exact same pain point.

that's the exact same moment and they

don't use your product and you ask why

and and they say oh I just forgot about

it I didn't think to use it you know no

I I still love your product I just

didn't think to use it and that's a

really

it's really heart-wrenching to to to

realize that so now especially when when

people let's say it's like founders come

up to me with their idea like I I urge

them to think really really hard about

what are the the triggers for using your

product and and in those moments and the

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 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 the I think the

combination that granola is useful to

folks and we can send notifications at

the right moment to to start it

>> all right so as we get towards the end

of this conversation uh I'd be remiss

not to ask the uh obvious question about

the competitive landscape So we started

the conversation talking about the

existing note takers. Um the sort of

elephant in the room kind of question

that I'm sure you get all the time is uh

why wouldn't OpenAI do that? Uh and I'm

a VC so that's a I'm a specialist of

asking why Google wouldn't do that and

why wouldn't open do that. Uh but um uh

you know Zoom as a as a as a product um

and it seems to be so fundamentally

important what you what you're doing and

so horizontal and so you know

transformative that it feels like all

those great companies should focus on

this at one point or another. How do you

how do you think about navigating that

tension?

>> Yeah. Well, I guess it was really

helpful that um most of our competitors

had some kind of AI note takingaking

feature but when we launched so it all

that was already the case and and

somehow Granola was able to stand out

and like win people's hearts and and 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 I think

my view is that um notes are 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 is that

Granola will be a year from now, two

years from now will look radically

different from what it is today.

Hopefully, it'll still be very simple,

but it it'll it'll help you do a a lot

of work. And I think um no one no one's

built that yet. Like a lot of people are

racing towards that. And I and I and AI

is an incredibly competitive space. But

when we're talking about really uh when

you're talking about products that are

like native to a new medium, right,

often times uh startups have have an

advantage. But ju just to just to press

a little bit, I'm I'm mostly thinking

about uh OpenAI because uh I do think of

uh Granola and like you don't need

another person to tell you that like

you're the you're the one building it,

but like I do think of Granola as uh you

know building memory for the world and

for for all of us and um so uh that is

incredibly strategic for

>> OpenAI or or Google because that's the

ultimate price, right? Like you're

building as as you said like second

brain. So how do you think about those

players?

>> Yeah, I I would put Google and Open AI

in in different buckets. I I would put

So I like I think a lot more about Open

AI and anthropic than any of the the

legacy players because I think they are

native. They are AI native. They're

they've led the way in in this space. To

me, it all come and again I I don't have

a crystal ball. I don't know what the

future's going to look like, but OpenAI

is gonna try to do everything to

everyone really. And they do an amazing

job at it. It's really, really

incredible. And I think the question is,

can we do something way better for a

specific use case and a specific type of

user? And I think 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 or actually you

know what power tools for this like spec

these specific workflows and just

nailing them because you care more

matters a lot. Um and I wouldn't have

made my bet if I didn't believe that the

quality of the experience and the

tailoring for for for users isn't isn't

going to isn't going to win. Um but

again, I don't have a crystal ball. I I

think kind of like it'll be fun to

watch. I have uh I 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 uh to to

close and and and and zoom out, anything

that you can talk about in terms of the

road map or the future. So, you

mentioned a couple of times the idea of

um searching through the history of of

meetings. So, that's one thing. Maybe

double click on that and or anything

else that uh you can talk about. Yeah,

absolutely. So I I think the the world

we're moving towards is um you have a

bucket of context and then you you

generate documents um or artifacts on on

a per needed basis on the fly and things

the types of things that we're working

on are uh given my entire history of

meetings can you pull out really so for

example a good qu question um you could

ask could be um that we could ask would

be like okay out of everyone we've met

in the last two years like who are the

firms who are most likely uh candidates

to lead our series C. Right? That's a a

question that I can't ask anywhere else

in the world right now, but I have a

version of Granola that will go through

my 2,500 meetings and spit out an

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 or UIs on the fly, can

those be shared? Right? So, this idea of

we have a a folder where all our sales

calls um 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 um 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 it's like a

memo. Um, so there's all these and

there's some use cases I can't 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 of of use cases and

workflows that people aren't even

thinking about right now.

>> And I don't know if that's part of the

road map or whether you can do this to

some extent in the product uh today. But

like for for me clearly uh where this

could be going and where as a user I'd

be like I would find that absolutely

fascinating is um granola as a coach uh

where you know based on um 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. um kind kind of stuff. Not not

that I do that just as a you know as a

>> Yeah. Yeah. I'm I'm I'm curious at a for

you personally who would be is there

like a human who would be the ideal

coach like you know if we're going to go

train a 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 um

you know uh sit down session a little

bit like you would get with psychiatrist

or therapist of of of some sort. Uh if

Granola just knows everything I say in

all meetings, uh after a certain time, I

think Granola is gonna have a very good

idea of uh where, you know, I succeed

and where I could get better.

>> Stay tuned.

>> Okay.

Exciting. Uh very uh very good. Well, uh

it's been a fantastic conversation.

again um you know I'll I'll I'll end uh

where I started which is I'm a huge fan

of the product and it was life-changing

for me. I have to to say as well at a

personal level level you know when we

talk about um feeling how AI is both

enhancing us but also sort of disrupting

us. In my early use of granola, there

was still um it was still just English

uh and uh you guys I think recently or

at least I saw it recently introduced a

multil- language uh feature. And um as a

European, you know, French-born person

who has spent most of his life in the US

at this stage, my secret uh 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, you know,

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. like the machine

does something much better than took me,

you know, a couple of decades to figure

out. So, um I don't know if that's

exciting or terrifying. Probably a

combination of both. Uh but in the

meantime, I really enjoy the the feature

and the product and I very much enjoy

this conversation.

>> Thank you. I guess the the hope is you

make uh the hope is you make better

investments now post granola, you know,

that I think that's the ultimate

question right?

>> Absolutely. You know, hence the the the

coaching part. Um yeah, no look very

excited for uh what you've built and for

the future of the company. Thank you so

much for spending time with us. Uh

plenty of uh uh lessons for builders

around product and growth and uh

building on top of AI. So really

appreciate it. Thank you.

>> Thank you so much, Matt.

>> Hi, it's Matt Turk again. Thanks for

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