Chris Pedregal + Sam Stephenson: Making Meetings More Effective with Granola

[Music]

Hey everyone and welcome to Generative

Now. I am Michael Mcnano. I'm a partner

at Lightseed. This week on the show, I

spoke with the co-founders of Granola,

Chris Pedrical and Sam Stevenson.

Granola is a powerful note-taking app

that uses AI to compile and summarize

meeting notes. Chris and Sam and I

talked about their journey as

co-founders, how they have quickly

become a must-have tool for many

companies in tech, sales, recruiting,

and beyond, and the areas where Granola

might be trying to grow next. We spoke

in front of a live audience at the

Granola headquarters in London, and it

was a lot of fun. So, let's get into it.

Hey guys. Thanks, Mike. Thanks for

hosting us in uh your amazing office.

Thanks for coming. Yeah. This office is

like brand new for you guys, right?

Couple months. Something like that.

Yeah. Yeah. Sam just carried all the

plants in himself. Is that true? We're

going quiet. Almost a few other people.

Like actually you went to you went to

the plant market at like 4 a.m. with

like We did do that. Yeah. People sold

us a truck and like carried it up all

the stuff. That's like a great example

of building a startup, right? Yeah. That

was like the least hard thing he did

that day. All right. Obviously, everyone

I I would hope most people in this room

are familiar with Granola and we want to

get into the app layer and what it means

to build at the app layer, but tell us

about the product. Tell us about the

company. How'd you guys get started?

How'd you end up doing what you're doing

right now? Give us a little bit of the

the origin story. So, this was three

years ago. I quit Google. I knew I

wanted to do a startup in London. I

didn't know what I was going to do. and

um like within a week of quitting Google

I started playing with uh GPT3 the

instruct version of of it that had just

come out and was blown away as I'm sure

everyone here at some point in the last

5 years has been with all and I was like

okay this is this is new this is

different I don't know what it is

exactly so I started messing around with

it I knew I wanted to start a startup so

I started looking for a potential

co-founder and I basically was like

convinced there's two things at the time

I'm like okay maybe if I need a

technical co-founder it should be

someone who knows to train models. Um,

which I changed my mind on later. And

then the other really hard thing I was

like, "Oh man, there's a bunch of new UI

that's going to have to be designed like

that's AI native." So like I need a

someone who's really thoughtful at that.

And I started exploring these like tools

for thought forums and I stumbled ac

across uh this online meetup called like

tools for thinking rocks. What are tools

for thought? Just to help us all

understand what that means. All right.

All right. You're going have to cut me

off. Tools for thought are basically

like like humans are tool makers, right?

Like my friend Paul was here like taught

me this like humans are toolmakers. one

of the things that sets us apart from

animals and basically what we are able

to do is really limited by the tools we

have available to us right so classic

Steve Jobs uh bicycle for the mind right

but like the the original tool thought

it's language it's like written language

right then you can look at like

mathematical notation right like the if

you're using like Roman numerals you can

only do so much math in your head

whereas if you use what are Arabic nu

like whatever we use today like you can

do much more complicated stuff with

paper and pencil and basically like

every development of like um human tool

making has meant that humans can do more

and more and more and um I think AI is

like the ultimate tippercharger of tools

for thinking. Anyway, so I found this

guy online in a tools for thinking

meetup group. Uh I didn't even meet him.

I just saw I just saw his profile and I

sent him an email being like, "Hey, do

you want to grab a beer sometime?" And

somehow uh he said yes. Yeah, we uh so I

think yeah, we were we basically like

both were very aligned from the

beginning on like we AI is going to

change the landscape of like the tools

we use and either the tools that exist

right now are going to have to change

everything about what they do or new

people are going to come in and and take

over and and so that felt like an

exciting place to be starting a startup

from like it's a it's a opportunity

that's just opened from both of our

prior experiences where you know Chris

talked about how we think um building a

really good product experience is going

to be a lot of what matters in making

something successful in this space. And

I think to do that, we were both very

keen to have a very specific like and

painful user problem that we could be

designing around. Um like you want to

have us you want to be able to picture a

person in your head and picture them

struggling with a thing so that you can

kind of like make the tool that that

solves the struggle. Um, and so we spent

a while just just like open-mindedly

kind of wandering around looking for

that struggle, talking to people about

their days, like trying to figure out

where are the pain points, what what

sucks about people's jobs that we could

possibly make easier.

Um, and like I think a thing that came

up again and again was like people whose

whose job revolves around meetings and

talking to people, every time you have a

meeting uh with somebody, that meeting

tends to create a kind of pile of

follow-up work. Whether it's simple

stuff like just um writing up notes that

you care about or uh sending a follow-up

email to the person you met or whether

it's complicated stuff like updating 20

different fields in a CRM and triggering

a workflow and and an email campaign to

somebody, you know, things like that. A

lot of people who have calls have a

version of those kind of things after a

meeting and they all universally hated

doing them. Uh it's all kind of menial

work that isn't isn't what you get

energy from in your job. Um, and it felt

like the kind of stuff that AI was like

primed to be able to help with, uh, if

not then when we were doing it, at least

in a few years time. Um, and so we

started pushing on on that on like how

can we how can we kind of make a tool

that is in your meeting that eventually

will be able to help you do a lot of

this kind of menial work that happens

around meetings. Chris said that it's

going to be so important with AI to

build a great product experience. So I

totally agree. Um, but I would say that

for a lot of people in the AI community,

in tech, in VC, in startups, that was

not obvious to many people a year ago.

I'm sure you guys remember a year a year

year and a half ago, there was all this

talk about, oh, if you're building at

the app layer of AI, you're just a

rapper on GPT4 or GPT4, you know, what

whatever anthropic claude. Um, now it

feels like we've done a 180. There's so

much excitement about the app layer and

again you know granola is an example

that's often cited as as being you know

one of the one of the potential winners.

What changed? Why is there been Why do

you think I know you guys haven't

changed but why do you think everyone

else has sort of changed their mind

about this opportunity? I think a few

things happened. One is these models

just kept getting fa better and better

faster and faster and it became very

clear that it was just it made way more

sense to just use the best like frontier

model out there than try to train your

own thing. You're always going to be

slower, right? Um that's one. Two, super

hard and expensive to train your own

model. So it's going to be a couple big

shops that are going to do that. And

then three, man, you get all the

benefits from those models. So like if

you can apply it to the right use case,

it's really powerful and I think our

view of that from the beginning has

always been low frequency use cases that

are maybe non-critical are going to be

eaten up by the general agents. So I

think if if it's like a consumer use

case that you do like twice a month,

it's definitely going to go to catch PT

or anthropic. If you are uh doing

something that like really matters like

it's like professional tooling where

your performance really matters and you

want to optimize for that use case then

bespoke tools that are optimized for

that are going to be way better and I

think that's what you're starting to see

things like um like cursors valuation

like windsurf just got acquired I think

it's like prototypical like they're just

like rappers on on summit 3.7 or

whatever right um but actually they're

like amazing and there's so it's hard to

build great software and the delta of if

you're using one of those products is

how much more productive you can be

really matters. Um and I think now the

market's kind of I think this thing is

like a pendulum, right? We get really

excited about one thing and then it's

like oh there's a glimmer of the future

might be different and people get very

excited about that. We think that has

sticking power, right? Because we think

the tools you use matter and we think

the like profession professional tooling

has always been a thing, right? Um and

uh if it makes you 10 20 30% 50% better

at your job like that's going to always

have a lot of value and economic value.

It does mean we have to be quite

selective about the things like the the

challenges that we choose to bite off

and which we choose to like leave alone.

When we started which was like GPT3 time

and um real-time transcription had kind

of just become a thing that was

available by an API. Uh, but it wasn't

great. You know, like transcription was

obviously bad in a bunch of ways. Like

it would kind of miss things. The notes

that we wrote weren't amazing just

because the models weren't amazing. We

had tons and tons of conversations about

like, yeah, you know, like should we,

you know, what should we be investing

our time in because like the

um some stuff is just going to keep

getting better without without us doing

anything. Um, you know, the quality of

the AI output, the speed, the cheapness,

all of those things. Um, and some things

are not going to get better unless we

like push really hard on it and try to

figure out what a good solution is. And

so I think a lot of the game for us has

been like picking our battles and like

knowing what to innovate on and what to

just like wait for it to get better, you

know? Give us some examples of some

things, you know, real-time

transcription is one of them, but like

what are some other things that you, you

know, very intentionally decided not to

work on? And maybe what are one or two

things that that you did you're like,

"Oh, this is our job to solve. Granola

can be the best at this." The obvious

one to me was um language support. Like

when we launched it immediately was like

the most requested thing for granola was

to support multiple languages. Um I

think it still is and we spent like a

week working on it like um trying to

figure out a good interface. Uh it was

like kind of available on one of the

transcription providers. I guess it

wasn't great the the what the language

situation now which meant that we would

have had to it was looking like it was

going to be like a few weeks to a month

long project to make a good interface to

help you pick the right language for the

right time that the meeting that you're

in. Um which is a huge investment like a

month of time, right? And that to me

feels like a thing that there are dozens

of companies out there really

incentivized to figure out multi-

language real-time transcription models.

And um if we just wait like it's going

to happen and and the experience will be

way better in that world than anything

we can kind of think up to like hack

around the fact that it's not good right

now. Yeah. Another example is like

context uh window length. It was too

small when we launched. You could only

do like 30 minute meetings. Um and we

could have done a bunch of work to like

okay try to chunk that or we just just

wait a little bit and the context

windows got bigger. Yeah, exactly. Yeah.

Another one's rag actually. Can you

explain to people? Retrieval augmented

generation. Basically the idea is like

so context window most people here

probably know this but uh uh a model can

only take so many tokens so much context

into its like memory. Um, and if you

have uh more, let's say you have like in

our case a repository of meetings that

are larger than a context window, you

have to figure out like which of those

do you put into the memory. And um, like

there's all these naive approaches where

you basically kind of do a search across

those and you choose a subset. Doing

that well is hard, right? Doing it doing

it not well is super easy. Doing that

well is really hard. Um, but context

windows keep getting bigger. So you can

get away with by just sticking a lot of

stuff in there. uh which is like and and

in some ways it's like an unintuitive I

think if you put your engineering hat on

you're like a that's wrong you know we

should we should engineer this throwing

more stuff but it but it it unstructured

it it like AI breaks intuitions man it's

like um sometimes you're like oh it's

like it's very imprecise we're putting

all this stuff in there but like I think

these models are smarter and more

intuitive than we expect and sometimes

they'll the moments where I'm like oh

are usually where it picks up on

something I wouldn't have expected a

machine to pick up on. You know, it'll

be like actually in that meeting 6

months ago, you said this thing and uh

and then now you said this and like we

wouldn't put that in the rag if you

stick it all in the context window. Like

sometimes magic comes out, right? Let's

talk about the business model of

building at the app layer. It feels like

so many companies right now are

basically just just charging for prints,

right? We see all these products that

charge for credits. Yeah. And really

what what those credits go to are just

just hitting the model, right? How do

you guys and Granola think about the

business model? Like is that the type of

business model these companies should be

pursuing? Should they not be pursuing?

What's the opportunity to to build like

a huge business at the app layer on top

of the models? I think a lot of the way

we think about it is probably not too

like our business model is probably not

too different like pre or post AI. Like

I think ultimately we're trying to make

a tool that's like valuable enough that

a company will give us money for it. And

um there are things that we want to push

on to to kind of make the thing feel

more valuable which are not really to do

with AI but to do with like app team app

building I guess like if we can if we

can unlock network effects in granola

where like there's a the granola gets

better the more people in your team are

using it and it becomes like this

valuable repository in and of itself. I

I think that's a thing that we have a

lot of signal that companies will will

pay good money for and um it's kind of

independent from AI, I guess. Although

the enables you to do cool stuff with

that. If you're just monetizing the AI,

you're you're effectively just like a

reseller, right, for Open AI, whereas

like if you charge for the repository,

you're charging for granola, something

only that granola can provide, right?

Yeah. I I do think we're like an

interesting moment in history because

it's kind of a land grab right now.

there's like new products that are

possible that couldn't couldn't exist

before and we know that the cost of

running these products 2 years from now

will be vastly cheaper than they are

today. So there's like and maybe that

will always be the case but it's kind of

easier for me to just think about the

next few years. So, we're in this world

where um it's going to be cheap to run

granola, I don't know, PA or whatever

you want um 2 years from now, but it's

quite expensive to run now, but there's

a lot of user demand. So, like what do

you do in that kind of situation? And I

I think it's the kind of thing where you

have to have I think you have to build

for the future, right? And you have to

you have to figure out how to make that

work for your company because if you

build for today, I think you'll make all

the wrong optimizations. Uh which does

mean it's a capital intensive play right

now. when we uh forecast our finances,

you know, into the future, like

uh if you don't account for things

getting cheaper, then then it gets like

really expensive really quickly.

Exponential with like 10% we can regrow.

Yeah. Yeah. Yeah.

And so and so I mean part of part of the

company's bet I guess is that is that

this stuff is going to get cheaper and

you know and there's going to be ways

some things we'll always want to be on

the frontier of. I think like I could

see like

um uh being able to do chat and document

creation on top of the huge body of all

of your company's meetings is like the

kind of thing where just the more power

the better. But like transcription I

could see hitting a ceiling where where

there's a point where it's good enough

and then and then like and then again be

it's just like cool let's now get that

transcription for like as little money

as we can so that so that you know

that's our main running cost like that

can that can kind of go away. Lightseed,

you know, was one of the first investors

a couple years ago. And I will say it it

was so fun for me and I know everyone

else in the team watching you guys build

from day one just like from zero lines

of code to what it is today. And you

know, I remember the launch moment 22nd.

May 22nd. We're coming up on a year.

That launch moment, it was amazing. It

felt like almost instant product market

fit, which is so rare, never happens.

Yeah. I got to ask like tell us about

the process of building the first

version of Granola so that so that you

could have that day you launched which

again it's it's it's nearly impossible

to do. I don't know you know we try and

be deliberate about things but something

can happen by accident too but I think

the things that we uh we're like

deliberate about were the hard thing

about granola was probably going to be

figuring out what's like an interaction

or what's that lets a user get the stuff

they care about out of a meeting in a

way that feels really natural and really

effortless. um like figuring out that is

like is just a huge part of what we have

to do to make the product successful.

And so I think the first I don't know

six nine months of Granola were like

just experiment after experiment after

experiment like trying things to figure

out what that might be. You know we'd

build a thing put it out in the world uh

be constantly talking to new users and

watching how they react to it and how

they use it. And um and over time we we

you know threw away a lot of the things

we tried and were able to hone in on

something that felt like it could work.

The first 6 months was was like a

gradual growing of complexity in the

thing as we like threw more ideas into

it. You know, trying this and that and

this and that. And at some point I think

we found we felt like we maybe found a

thing that could work. Um this like you

know type your notes at the end Granola

fleshes it out on the same piece of

paper that that kind of thing. Um and

then we kind of went through this

process of like cutting back and

streamlining everything until it was

really just that feature. Um and that's

what we launched with throughout this we

were kind of like the goal was to build

a daily habit for our users like can we

make this a daily use product in the

small number of beta use beta testers

that we had and um we had this uh this

this chart called the dot plot which is

like uh you can see each individual user

that uses granola um day by day and how

many meetings they did on a given day

and uh that helped us be really honest

with ourselves about like is someone

reliably picking this up and using this

in their meetings or are they just kind

of dipping in and out or you know is it

kind of random? So yeah, we we were in

closed beta for a year and uh we had

about 150 people that we had onboarded

by the by the time we decided to launch

and we had manually onboarded all of

them at that point. Uh and I guess

looking back on it it's so funny we

never like the dos only connect back but

I didn't really think was ready when we

launched it like Mike pushed us to

launch. No, really. And like typical

VC actually been pushing us to launch

for about nine months before that. We

held them off for nine months. But I,

you know, it's like at that point all we

could see were the things that were that

were wrong with it. Um, which is like an

interesting lesson, right? Because uh

once we put it out in the world, it it

just kind of it actually hit a bunch of

quarters. Uh but we didn't

necessarily appreciate the depth of that

until we we put it out there. Sam, I've

heard you talk a little bit about your

design process and about how the team

really thinks about designing for what

people actually need, not what they

think they need. I've heard you use the

term lizard brain. Um, explain. In

building software, it's really easy to

um I speak as someone who's done this

over and over and over again on things

I've worked on. Um, like it's really

easy to get theoretical about like what

a user might want and like um this thing

would be cool. I've got such a good

feeling about this. I'm going to, you

know, I think this is how it how the app

should be. Um, and when you interview

users, you know, they can tell you all

of their great ideas for the product and

and it's really easy to just build what

they want because they're asking for it.

One thing that we were kind of paranoid

about from the start was um I guess

especially in our use case like meetings

are a super high stress uh situation in

that when you're in a meeting especially

like a backto-back meeting where you

know where you're you're maybe it's 2

minutes past the hour you're already

late for your next meeting you're like

you know trying to make excuses to

get off the call um and then you get off

the call and then you got to rapidly get

into the next one as quick as you can

and then you're like oh my god who am I

talking to why are we doing this you

know all that stuff you have so little

brain space for for a piece of software

at that moment to try and help you like

you you're just trying to deal with the

basics of getting the next person in

front of you. We just have like this

this I don't know 1% of your brain to

play with you know as like as a people

designing a product. Um and I think

keeping that in mind like keeping the

kind of stressed out backtoback kind of

moment in in our heads as we were

designing it like helped keep us honest

to what's going to fly what's going to

work in this. I think people often talk

about how simple granola is and how how

it's feels nice because of that. I think

that's just a a function of like we

really can't put many buns in front of

you when you're in that in that

situation. You don't have the head space

for it. That's really cool. Chris, uh

you built and scaled and sold another

company before this Socratic. I had the

pleasure of watching you do that as well

because the company that I was building

was on a street block right behind you.

Yep. Um in New York. That was a while

ago. And you know, one thing I often

think about is especially with you guys

building this is is wondering like

what's it like to build a company now

with AI versus building a company that

didn't have AI? Like how what's the

difference in building companies across

these two eras? Ask me that in two or

three years. I think I'll have a much

better answer. Well, I I guess one is uh

so boss, our CTO, who um who's not here

right now, like he's like I look to him

because I think he's the best at this in

the team, but he really really pushes us

internally to use AI as much as

possible. So, it's like an active goal

to reduce the number of lines our

engineers write every day. Um and I

think that you like you actually do need

to push people for that because we all

have habits. We've been working we've

been doing stuff for a while and the

world's changing very quickly. So if

like the org isn't doing that um then

you're missing out. The other thing is I

think people ask a lot about like okay

what's the makeup of a company going to

look like in this post AI world? How big

does it have to

be? My my view there is that like I

don't know what it'll be like in five

years but but for us the product is

core. So we need a really we need a

bunch of really thoughtful you know best

best-in-class people working on the

product. There are other functions where

I like in the past we might have built a

really big customer success function

where I don't expect us to do that. I

expect us to use like whatever the best

and greatest like AI tooling is and

we'll still have a great team there.

It's just like how that team spends

their time and like they might look more

like um like engineers in a way um in

terms of like building systems even

though they might not be writing code.

And the last one is the world is

changing and everyone's watching and

interested and wanting to try stuff out

and that wasn't the case uh with my last

startup. I'm used to startups being like

a slog of you like you fighting so hard

to get people to care about what you're

doing and um I kind of feel like the rug

got pulled under me out from under me

with granola because we put it out there

and we're like all right. So I'm like

I'm like no one's going to care about

this you know we have to like keep

working on it keep working keep working

on it and all of a sudden I'm like it

just started growing and then and then

stuff just started breaking internally

because we weren't like mentally

prepared for that. Um, so I that's like

macro environment questions like those

things change quickly. But that's that's

been a defining defining part of this

journey is just trying to keep up with

the change and keep up with the growth.

And I think that inevitably forces you

guys and really any team building an AI

right now to just move so freaking fast,

which inevitably creates a different

type of challenge for the company. It's

like how do you maintain quality? How do

you maintain uh taste, right? Like taste

has been this thing. I feel like it's

like become this really annoying word

actually. Gotta have taste. Uh but but I

think again granola gets cited as one of

these products that just like oh it's

beautiful, great design, amazing taste.

Like how do you think about maintaining

that when you're moving so fast and when

you're building a team? Uh it it becomes

so important I imagine with with each

and every person you bring on. I think

we do all right at this but I think we

we like there's there's much more we can

do to make this better. Um but I think

things that I think we do well or that

of WordPress um we screen engineers as

part of the interview process um for

like product thinking I guess like can

you can you think from the point of view

of a user and like uh you know when

there's a technical problem put in front

of you like get to the why of like why

is this a problem for the user and you

know that helps you make the right

trade-offs in cutting the scope and

really just building the thing that's

going to solve the person's problem not

like this beautiful uh technical

masterpiece of an execution. There are

there are types of features where you

can where like um once we have good

systems set up like you know the UI of

Granola is kind of figured out you can

just kind of like ship and iterate and

push stuff out very quickly there and

and we don't need to kind of be so

cautious about about that stuff. Um you

can can always roll it back you know a

couple days later. Um, and and then that

way we can kind of like help like

reserve our judgment and the taking the

time to kind of pour over the details on

the things that really matter or kind of

like in the core flow of of someone

using Granola. New primitives in the

app. The basic we're trying to get

better at the one-way door versus

two-way door, right? So, it's like if

it's two-way door, can we just ship

changes quickly, see how what people

think and go from go from there. That

said, I think what people love about

granola is that it's simple, minimals,

and gets out of your way and you add 50

buttons in there with new features, you

kind of kill the the golden goose,

right? And I think we're figuring out

how to find that balance because we do

have to move quickly, but we also need

to keep the soul of the product like

intact. I want to talk a little bit

about building a team here in London.

Um, Granola, I will tell you in the

States, I mean, you guys know this in

New York in Silicon Valley, I mean,

people are obsessed. It's kind of like

you're building a Silicon Valley startup

in London. Is is that intentional? And

what is that like? It is intentional.

Hopefully you guys can meet some of our

team. And I think what you'll you'll

find when you meet them is everyone on

the team kind of wants to have that like

really ambitious like classic startup

journey. Uh and we just happen to be in

London. And that's like a pretty I think

it's a pretty beautiful twist on it

because um you know get to you get to be

in London but you also kind of get to

live the Silicon Valley dream and and

that's pretty rare. Um but I I think

there's like like the reality is there's

like a most successful tech companies

come out of Silicon Valley, right? And

there's like there's a culture and like

learnings and best practices about how

to build a hypers scale tech startup

that were kind of invented over there.

And I'm not saying we wholesale copy all

of that, but I think our our DNA and you

can hear my accent. Our DNA is kind of

comes from the valley. That said,

there's amazing talent in London, right?

And it's an incredible it's a a pretty

fantastic group of people and

perspectives that are here. So, I think

there's like a real big opportunity like

for us to build a Silicon Valley South

startup, but like in London with the

talent that's here. Um, and I think

something that kind of benefits us is at

the app layer, there aren't that many

kind of like buzzy AI app companies in

London. There are some pretty impressive

ones the foundation layer, right? Like

if the 11

labs all the way back to like deep mind,

right, where u so there's incredible

like AI talent in London, but at the app

layer, we're kind of like a bigger fish

in a smaller pond compared if we were in

Silicon Valley. There's just so much

stuff going on there. So we're kind of a

magnet for type of person. So it's

probably like a bit of a strategic

advantage when it comes to hiring,

building the team, being in a different

market is actually helpful. Yeah, there

are trade-offs with everything, right?

Um I I feel like we definitely get uh

access to incredible people here. Uh

that those people will be lots of

different companies be trying to hire

those people in Silicon Valley whereas

here they would kind of get like first

first dibs on them. You know there's

also a lot of stuff happening in Silicon

Valley, right? So it's like it's

important for us to stay current,

understand what's going on there. Um can

also be a full-time job to keep up with

what's going on in AI, right? So you

want to strike the right balance of like

keeping your finger on the pulse but

don't get distracted, right? because

there's so much noise and so much trash.

At the end of the day, all that really

matters is building something that's

useful that's going to grow. What other

like are there other London based

companies or products or teams that that

you guys take inspiration from? When you

think about that, when you think about

building a team here in London, I think

the AIO folks have done a a great job at

attracting a bunch of good talent. Uh

people from 11 Labs I've met um plane I

think at building like a really great

user experience, you know, on a product

category. listed for a long time. Yeah,

I think that like Monzo's wise card, all

the fintech ones are like I think my

view is basically like it's too easy if

you're in London to think about the UK

and to think about Europe. And like my

my general view is that in this AI space

that's so competitive, you need to be

competitive in the US because otherwise

someone will win the US and then you're

going to have to fight them in Europe.

Whereas there's no reason why you can't

go after the US market from here, right?

Like most people don't know granola.

like users don't care. Like they all

think it's an SF company. Um so it's

like I think it's a question of

ambition, right? And I think in AI the

the prize is so big, there's going to be

so much competition, you have to have

that high ambition level or just, you

know, you're going to get eaten anyway.

So we're taking a very like world view

from the get-go. Uh and we just happen

to be base here, but we're not like

doing all all our user interviews with

folks in London. We're doing them all

over the place. Uh maybe last question

for me and then I want to open up to

everyone in the room. Um what what is

what is the ultimate ambition of

granola? We we know it as the the

notetaker for people in backto-back

meetings. You said you want to build

you're building with Silicon Valley type

ambition. What does it become? What is

the you know what is the massive Silicon

Valley like success version of granola.

Other professional categories have

already figured out their like power

tools that people spend their day in and

it kind of helps them get their best

work done. Um uh designers have Figma or

Photoshop back in the day. engineers

have IDs like cursor or VS code. If you

if you're an engineer or designer for

example like seven or eight hours of

your day is spent in those tools and

they amplify what you can do by a huge

multiplier. up until now like people uh

folks who work in like I don't know

doing like people stuff I guess you know

talking to people whether that's like

sales or customerf facing stuff or

managing or investing like uh you've not

really been able to have one of these

workspaces because like the the kind of

fundamental unit of your work is natural

language and conversation and that's

just too squishy for like uh traditional

software to deal with. It's not it's not

code and it's not pixels. But I think we

are at this exciting point where like we

can fin like computers can finally make

sense of natural language and organize

it and so I think we have a shot at

creating that kind of workspace that

that people who do people stuff kind of

live in and and it amplifies them makes

them work better work faster. I agree

with all of that. Uh, but if I zoom out

even more, I think we're like we're so

lucky to be alive at a moment in history

where we talked about humans as

toolmakers. Like the tools that humans

use to think and to do work are being

reinvented. And I really do think AI is

is like if computers were a bicycle for

the mind. Like AI has a potential to be

a jetack for the mind. So like my

ambition is can we build tools that help

people actually think smarter, work

better, do better things. It's like be a

multiplier on on human capability. um

kind of hearkens back to I don't know

how much of you have like studied like

Douglas Anglebar but there are all these

ideas that the uh birth of computing

basically of like what impact is going

to have on you know society and our

ability to do great things that we could

never do before I think computers did do

that and I think now it's like the

second chapter of that like what what

are the new heights that we can reach

to. Awesome. I I could ask questions all

night but I know people here probably

have lots of questions. Go right here.

Hi, I'm Emily. I'm working on something

new and um I'm really curious cuz I'm

very early days on how you guys

approached when you were early your

feedback loops in your early

iterations. I think something I'm trying

to think through is like how do I know

when I have enough data to move forward?

And also if I don't have enough data to

move forward, what kind of data am I

looking for? Is it qual? Is it quant?

How much do I need? So I would love your

guys' thoughts. I I have like a

philosoph philosophical view on this. Ba

basically it's all qualitative. Like my

my view is like in the early days it's

actually not even that. It's like you

need to go off of your intuitions. I I I

believe that deeply. If you don't

fundamentally like feel like the product

or the need in like deep down inside,

then that's a real problem. Uh I'm not

saying go off in a in a closet and like

just work in isolation for six months. I

think talking to users and people is

paramount. You should do it every day

basically. But you shouldn't, it's not

the like ask people if they want to

build faster horses thing. It's you by

spending time with users and watching

them try to do stuff and fail. You are

honing your you're giving your mental

context like your your brain all the all

this uh really relevant context so that

your intuitions are better honed. I

think if you're looking for anything

qualitative um it's it's almost

impossible in the early days. I think

everyone here would love for Granola not

to become a CRM. So my question would be

about to create the sort of jetpack of

the mind. What does the future of

actually design look like for the

jetpack of the mind? I hear you with the

CRM thing. Yeah. Uh uh I think the way

we think about it is um one the thing

that has served us really well so far is

like putting the individual user and the

particular moment that they're in when

they're using granola like above

everything else and designing a great

experience around that. And so you know

when we're talking about how to spend

our time and what things to build that

user has come first and and like and

yeah companies pay for us but it's not

kind of the the thing that's driving

every kind of product and feature

decision. It's like make granola great

for for the user. I guess there's kind

of two directions we we I think of this

pushing in like um we've seen when teams

use granola together. There is like a

lot of value in um having the kind of

shared context in one place that where

you can you can kind of look at not just

the one meeting you had then but every

meeting that your team has had around a

specific subject and do stuff with that.

If you're a sales person trying to get

better at your job, uh then then being

able to like look back at every call the

sales team has had for the last week and

query like why are we losing deals and

what what things that people said that

helped us win when we thought we were

going to lose and things like that. It's

it's super helpful to the individual.

Yeah, I guess just adding to that it's

in my mind it's all about AI is as good

as the context as it has, right? And

then the UI that lets you do useful

things on top of that context. And right

now AI like granola looks like it

generates meeting notes and that's

that's what it does for people. That's

what people like for it. You saw the

versions we had internally. Um AI sorry

granola is all about using all this

context we have about you to help you do

work. I don't know. I think Sam and I

were both like okay meetings are going

to be a good wedge because you know

there's a lot of information in meetings

whatnot. Um so I think we get a little

bit of credit for it but actually

looking back meetings are freaking

incredible because the amount of data in

transcripts is nuts. And meetings are

really just to start like we'll have to

add emails, we have to add Slack, we

have to add all this context for you to

be able to do useful stuff. But I think

meetings are a really powerful training

ground because for example, if like

you're you're a VC like I want every VC

in the world writing the first draft of

their investment memo in Granola, right?

Because we have all the we should be the

best tool for that. Full stop. Every

follow-up email, every strategy

document. If you're going to reorg your

company, like you should do that in

Granola because we know the most about

what's going on in your company. Jim,

who maybe is here, he built this demo

the other day, and it blew my mind.

Again, like I've been working on granola

for two years. There's so much data in

these meetings where he built like a

self-writing wiki for granola. Like it

it writes itself and it's always up to

date, which is nuts, right? And it was,

have you guys seen like web uh websim

web? It's like basically what it'll do

is like it'll generate an HTML page. You

give it a URL and it'll have an LLM

hallucinate an HTML page. So this wiki

work the same way. So I could be like

what's our work from home policy and it

wrote it based on all the meetings that

we have internally right which so it's

like it's just this crazy new world that

it's hard to imagine all the value

that's going to come out of it until you

you start playing but you should come by

we'll show you some demos. Hi, I'm

Sundep. Uh I'm with Automation Anywhere.

I used to work in financial services and

one of the things you observe about

meetings with potential customers is hey

I don't want to share this information

and that to be recorded. So just out of

curiosity in engaging with users what

have you found about human preferences

about having information stored

transcribed that lets you put the tools

for thought in action? tools like

granola are already useful and will be

so useful in the future that they will

be expected in work situations right I

think the private like social fear

sphere is is a different question that

one's a big question mark to me but in

the work sphere I think it's going to be

uh normal I do think like for the

companies in the space rest of society

there's a conversation around what are

the specifics and how invasive are those

tools right so granola from the get-go

never stored the audio. It only stores

the transcripts, right? Which limits how

useful we can be, but it makes it way

less invasive than like the other AI

meeting bots out there. And I think the

conversation is going to shift from

whether or not something is transcribed

to who has access to that transcript,

right? Is it just me? Because lots of

meetings, I don't want anybody else to

have access to that transcript. Is it my

team? Is it my company? Is it the world?

Is it I think that that will really

really matter. And I think the the

defaults companies build there will have

a lot of down like downstream

consequences. It's like someone's

discovered fire, you know, like no one's

putting like no one's going to be like

we're not going to use fire. It's like

we're not going to heat ourselves or

like cook food. It's so damn useful.

We're going to use it, but how do we use

it in a thoughtful way with good norms

that actually, you know, minimize like

potential bad situations for the for the

most upside? Let's have a big round of

applause for Chris and Sam.

If you like this episode, please do us a

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People. I am Michael McDano, and we will

be back next week with another

conversation. See you then.