– English Lyrics

Lyrics & Translation

[English]

In the last seven days, we've signed the

same number of contracts as we signed in

the whole of Q4. There is clear tangible

value being driven by these products,

and it's only going to get better and

quickly.

Ultimately, you've got to be very

passionate about what you're building.

You've got to have that perseverance.

And if that sounds good to you, then

then build a startup. If something

logically makes sense, you should

probably continue doing that thing,

right? And not let anything stop you.

And I think like the consistency that

we've noticed of founders that we've

some of that we've invested in or work

with is like the ones that kind of do

that and really persevere tend to win.

[Music]

Today we're here with Arie and Chaz

Englander. Uh they are the founders of

Model ML uh from Winter 24. Uh prior to

Model ML they started two other YC

companies that both were successful and

sold, Fancy and Fat Llama. And this is

probably the first time I've worked with

a company where both of the founders had

had a previous successful uh YC company

before. So I'm super excited to welcome

Chassen Arie here to YC. Uh welcome

back.

Thanks for having us.

Thanks very much.

Thanks for having us.

Tell us what you guys are building.

So Monal is an AI workspace for

financial services. So that's our

oneliner. What that actually means in

practice is we've so we built a

workspace that's akin to kind of the

office suite. our own version of Word,

PowerPoint, and Excel with the major

difference that it's built on top of an

agentic system that kind of mirrors what

a human has access to at the firms we

work with. So quite specifically, if

you're a human at firm X, right, you

will have access to your files and

folder systems, your emails, your CRM,

any data vendors that you might used and

pay for, uh, real-time publicly

available information, public filings,

your internal custom data sets, etc.

Right? So then we kind of build this, we

call it a cognitive architecture. It's a

fancy word of saying, you know, kind of

like a brain that mimics what you have

access to digitally digitally. um uh and

we overlay that with our user interface.

Um the general idea being well, if you

had an Excel spreadsheet that was

already connected into those data

sources, you'd probably spend less time

going and gathering information and

analyzing it.

I can tell that you guys are excited

about how things are going right now.

Would you put some words on how how

things are going?

Vertical.

Uh look, I mean, in the last seven days,

we've signed the same number of

contracts as we signed in the whole of

Q4.

Wow. And

congratulations. Thanks very much. And I

think it's it's really just the turning

point I think in the sector whereas as

we keep saying it's like there is clear

um tangible value being driven by these

products and it's only going to get

better and quickly.

What were people using MML using before?

What were their tools that they were

using in the daily daily work?

So they would have their um data sets uh

and then they would they would spend a

lot of time in the office suite or in

Outlook, right? Um, and you know what

that meant was, you know, a lot of that

process is super manual and super

repetitive. You know, I think, you know,

the key here is we're definitely not

saying that humans should like never do

these tasks, but you know, if you're

organizing logos in a PowerPoint

presentation and you've done that

hundreds of times before,

uh, and you're a very well educated, you

know, analyst or associate, it's

probably not a great use of your time,

right? And I remember from I think maybe

from our interview or sometime on the

first office hour you had a unique story

on why you wanted to solve this problem.

Remember what that was?

Yeah. So we um so we sold our first two

companies and um after we sold our

second company we did a bunch of kind of

investing ourselves which I think on the

whole we were pretty bad at. But we

became very interested in automating as

much of those processes as you possibly

could, right? And so it it started

whereby we we literally were um when we

would receive an opportunity via email

and having sold a company you kind of

tend to receive a bunch of opportunities

every day. It would then enter into this

like a genensic system that to be honest

we were just building for fun um and

kind of produce like a one pager for us.

But the interesting thing about the one

pager is whilst you know we received

some information via email what was in

the one page probably 90% of that

information was unrelated to what we

would have received via email. In other

words, you know, let's say we got an

opportunity to invest in a startup,

right? This thing would go off and look

at their LinkedIn and their background

and then look at like comparable

companies on Crunchb or S&P and so on

and kind of produce this one pager that

you would probably go and do as a human

as your first port of call, you know,

little things like if it was a consumer

company, it would go and look at review

websites just as an example. Um and

yeah, we thought it was pretty cool and

then people became interested in it and

then we kind of agreed that we were

quite bad investors and uh okay at

building stuff. Um and that's how it

started

and that then turned into you selling

this product or new version of that

product to the top financial firms in

the world basically.

Yeah. So um you know we can say now that

we're about 10% of the largest private

equity firms and investment banks in the

world use our product.

um you know we have other customers

asset managers sovereign wealth funds um

you know a bunch of venture firms but

yeah it's pretty it's been pretty

exciting

and how would they do how would they

doing their work before before you start

using model ML

so as crazy as it sounds now right so

let me use an example whereby

um let's say you're you're tracking a

public company right and every quarter

or every time there's a release um maybe

as part of your job as an analyst or

associate you'll go to uh the release

the filings and you'll put together

what's called an earning summary. So

think about it like a beautifully

present like a slide effectively a

single slide with a bunch of information

but all that information is coming from

the same piece of information. So for

example you might always get certain

numbers from the filing itself. You

might get other numbers like consensus

or similar from a fact set and so on and

they're put into these slides

and these things take ages. We're

talking probably like days you company

just to pull together these slides,

right? because everything has to be

linked back to source and checked and so

on. And what you can do in model is so

think of model as you know being used as

the building blocks for this. So you

have an Excel spreadsheet that's already

connected into these data sources and

you might want to export that into your

designs, right? And so rather than

having to go and gather that information

once that release happens, this one

pager effectively was actually three

pages, a cover page, your main page, and

a legal page just appears in your

SharePoint or Google Drive, right? And

it's kind of 90 95% of the way there.

Wow. Um and we think in some cases more

accurate because actually trolling

through these filings as a human uh and

as because you can also pull from

multiple data sets for the same figure.

Y

yeah can be more accurate.

I'm assuming that you were you built

something in the beginning and there was

a model that was could do some amount of

this work because you did YC how long

ago?

A year ago.

A year ago. So the models have probably

progressed a lot in that year. What were

things the models could do a year ago

that were impressive and what can they

do today and where are they going to be

able to do in the future? the whole

industry or the industry we're in right

now.

Yeah.

Um really from January

is going straight line up. And I think

the main difference that we've seen this

year versus last year is last year was a

year of testing, right? So everyone

wanted to make sure they had some sort

of exposure like as in they were they

were triing something or looking into

something but it was still testing,

right? And us having ran consumer

companies, we don't really care about

revenue figures. We've always been

obsessed with like usage and retention.

Right. Right. That's good. That's good.

That's that the most valuable thing you

want to bring into B2B. Like B2B people

don't always know the most important

stuff.

We which we think is is madness. But

this year everyone kind of the whole

world went from testing to using. So

there and there was a fundamental shift.

I think these agentic systems there was

small things like the improvement in

things like function calling uh with

some of the newer models but everything

has become considerably better by

itself. And that's the interesting

thing, right? It's like I think

even if we do nothing,

theoretically our product will improve,

which is a pretty interesting world to

be in, right? Um and so when you're

constantly working on something and

you're you're pushing the boundaries,

you know, month to month, what what's

possible is like just wasn't possible

the previous month.

I also think the uh the vision models.

Yeah. I mean, when they first started

coming out and and improving and we were

sitting next to each other, we were

going crazy, weren't we? It was like the

stuff that we could do with the vision

models. If you think analyzing files as

an example, you know, OCR, okay, was

amazing. You combine that with vision

and its ability to uh read information

from tables and charts, it really just

changed the game.

Yeah. And also I think it's a

misconception, right? So like I think

the industry still thinks that AI um or

a large proportion of it is is kind of

where it was even 6 months or so ago,

right? You know what we're seeing today

is, you know, if you look at some of

these data providers, they've got humans

reading information from say public

filings and putting that in structured

format. Um, you know, the bulk of the

work that we're doing, what we're seeing

is models are already more accurate than

humans in those sorts of tasks. And so

uh I think that will take a little bit

of time in terms of confidence but but I

but I think some of the the more lower

level more kind of data gathering and

presenting type tasks are fully already

being automated at the top firms.

This is interesting. This I'll tell you

like they at YC the evolution went

through at the same time. So two years

ago we would say oh you can't really

sell software to investment banks or

private equity funds. they don't really

buy software or if they do they buy one

piece of software every 10 years and the

same was true for lawyers but something

happened last year like they all got

very AI curious they're curious about AI

they were like let's

let's do pilots to play with the

software and try it out and you were

saying that like this is the year when

that turns into actual contracts

exactly

and you can't you can no longer say

these companies don't buy software

that's wrong they're all buying software

now and that's because this this thing

happened in the last 12 months

exactly and last year was the year of

kind of proof of concepts and now you

know our average contracts are in the

years first of

Um and I I just think in general um

people are seeing you know a lot more

kind of shorterterm or instant value you

know I also think just you know on the

note of uh these firms historically

inclusive of law firms and others not

buying software that is I think true um

I think the big difference here is um

this is like number one thing at from

the very top this is like the number one

thing on everyone's agenda so we are not

selling like the next CRM or like the

next like data vendor tool or anything

like that. We are selling what we're

describing as the most advanced sort of

AI solution for financial services in

the world, right? And I think if you are

a a CEO or or or an exec in general,

you've kind of got to take that call,

you know what I mean? And I think that's

really played to the advantage for all

startups for sure.

Can you describe some of these sales

meeting like who is the decider? Like

who decides to buy the software and and

what are they like? CEO level um or or

or in general just the most senior

people at the firm regardless of if

you're a top five or top 10 investment

bank priority firm as I said I'll sub

them up and whatever it might be um

which I think at the start we found like

really strange because we we sort of

thought that this would be looked at at

a team or or group level it's really

not. I think it's so important um

firmwide that everyone has to be

involved from from the very top. And

actually what we found is you've really

got to get that buy in um you know from

the right person at the top but then

you've also got to get buyin from the

people that are ultimately get going to

be implementing the tool.

So are you guys are flying to meet all

these firms where they wherever they are

wherever they are. Wherever they are. Um

so we have a bunch of people working out

of Hong Kong and Singapore now. Um we've

just opened a small office in India. Um

uh about half of our overall team are

based in London and we have an office in

New York. Um again I think like it's

clear the product is impactful, right?

Because when we're demoing we're like

laptop out we're showing real use cases

with real data

uh you know high use case frequency,

right?

So then if you think about like why they

wouldn't sign with you, right? A big

part of that I think is trust and and

and building that relationship, you

know, because it's also a different

dynamic. You know, it's like a lot of

time you're speaking to folks that, you

know, if they make a wrong call here,

they could get fired, right? And so you

spend a lot of time building that trust.

I think a big part of that trust is is

FaceTime um and getting in front of

people obviously, but then spending the

time on on the demo and how how that's

justified and and really investing in

things that are specific to the

customer. I want to go back a little bit

to the two previous companies that you

guys ran, Pat Llama and um Fancy. Before

we get into the details of what they

were doing, like what are some of the

learnings you took from those companies

uh into Mal ML?

I would say you've definitely got to

enjoy it. You've got to enjoy building

companies. Uh is my overarching uh

thought. What about you?

I think you've just got to be prepared

for

the worst. Yeah, you got to be prepared

for the worst and the most ridiculous

roller coaster experience. Uh I think

all startup founders will say the same.

I think it's going to be a lot of ups

and downs, a lot of the time downs and

you've just got to be prepared for that

and know it's coming. Um and as Chess

says, just enjoy it because it's fun.

And is there a sense that when you start

the third company that you you've gone

through that before, so you've gotten

used to the ups and downs and you can

just kind of like be calmer about it?

You can definitely be calmer. definitely

karma but I but but there is there is

certain things that you just cannot

prepare yourself for you know I think

like you know like it just in general no

matter how much you've kind of said to

yourself this is normal you're going up

and down there'll be the odd thing it

will be employee related or product

related whatever that will still

surprise you the fundamental thing that

I think we realize that we've at least

brought into this company is this

concept of of perseverance not blind

perseverance but perseverance and I

think there's a clear there's a clear

difference where if something logically

makes sense, right? Like if you just if

in an unemotional way you're approaching

a problem and it logically makes sense,

right? Then you should probably continue

doing that thing, right? And um not let

anything stop you. And I think like the

consistency that we've noticed that of

of of founders that we've some of that

we've invested in or worked with is like

the ones that kind of do that and really

persevere tend to win. So I think that's

probably the biggest learning for us.

Just persevere at all cost.

Yeah, for sure. Uh I mean one of the big

ones for me in particular is definitely

hiring. I think when you know I was CEO

of Fancy I was 22 23. Um so hiring was

new to me and I think

to be honest I didn't really have a clue

what I was doing

and I think we still

Yeah. Okay. And hiring is always tricky.

Um, I think one of the biggest things

right now and we say it a lot and it's

probably the biggest thing that comes up

in an interview is is how much do you

think you enjoy working with that

person? I think you know at Fancy again

being sort of inexperienced and maybe

naive it was all about where have they

worked before you know what's on their

CV and you kind of ignored the the

little things that you know that that

maybe you shouldn't. And now it's kind

of the the main thing that we look for.

You know we're going to be spending a

lot of time with these people. you know,

we work a lot and everyone on the team

does and you've got a lot of work with

this person. Um, so we often meet in

person multiple times and make sure

they're the right cultural fit and it

really makes a difference. I think right

now, you know, we hire very slowly. Um,

and we try and make sure we hire the

right people. Um, and it feels like we

we've got better at that as time's gone

on. And humans are just so much more

impactful now as well.

So, I think it's it's just even more

important than it has been before. Um,

and yeah, the other aspect I said is

work ethic. I think we, you know, I

think we're known for it. I mean, right

now we're still working seven days a

week as we said and we have done for

about 18 months. Um, and I think

certainly in that initial period that

you've got to do that and I think it,

you know, our team work 6 days a week at

the moment. Um, look, that may change

I'm sure as time goes on, but I think

part of that point about enjoying

working with the person is they've also

got to really enjoy what they do, right?

So, we we've started to ask more

questions around look all the other sort

of standard stuff that has to be done. I

think it's important to have the kind of

standard interview type structure, but

are they going to enjoy their dayto-day?

You know, the question that we ask

people is like, you know, what have they

been building? You know, regardless of

where they're in engineering, right? And

soon as they start to me me me me me me

me me me me me me me me me me me me me

me me me me me me me me me me me me me

me me me me me me me me mention things

like oh they've been doing a bit of vibe

coding or this that and the other we're

like okay they're going to enjoy what

they're doing right and I think that's

really important

it feels like there's a moment in a

company's lifetime where

it's suddenly it's working

but you haven't won the market yet it

sounds like

that at that time it really matters to

work hard

because like they're you're not the only

one trying to go after this market

yeah we just don't like losing

um maybe go back to like uh winter 17

fat llama Um, tell us about Fatal Lama.

Um, so Fatal Lama was a marketplace that

allowed people to rent items from people

nearby with the main differences, you

know, uh, you were insured. So if I let

if you're my neighbor and I lent you a

camera, a $10,000 camera, I'm insured.

If I lent you a drill, the same thing.

Um, that was what we did differently

with the model. That model had been

tried a lot of times before, but it

still took us 3 years to find product

market fit. I sort of define product

market fit loosely as um, you know, the

unit economics add up. people want, you

know, uh uh what you have in terms of

product um in a sort of relatively large

addressable market, right? But it took

us 3 years to to get there and I think

that was, you know, we learned so much

during that period. Um and so yeah, that

that was Fat Llama and I can talk more

about that in a second, but what about

Fancy?

Yeah, so Fancy again consumer business.

So we were a a last mile grocery

delivery business uh which now

everyone's probably bored of speaking

about them but back then so this was end

of 2019 uh start of 2020 uh so our model

was a little bit different to a a Door

Dash or an Instacart. So we were what's

called a vertically integrated model

which means uh we had our own

warehouses. We held the stock ourselves

and this was relatively new in Europe. I

think there was one other player doing

it um out of Turkey, but in the UK in

particular, we were the first ones. And

really, it started it, you know, as a as

a delivery app for students. You know, I

was a student at the time. And it really

came about the need, you know, I was

really sitting there one day thinking,

God, I could do some, you know, Pringles

or some a beer to be honest and I didn't

want to walk to the corner shop that was

like 5 minutes away.

And this was during CO.

So, this was just before CO. So, this

was like, you know, 3 months before CO.

We kind of built the app MVP in sort of

four to 6 weeks. Um I studied computer

science at uni and so it was just before

co and really almost instantly as

opposed to fat lama we kind of found

product market fit almost overnight

which is that sounds obvious right it's

like you're delivering sort of beer ice

cream to students in

at the same cost as they would get it

from the corner shop.

Uh so so we took off and that was

obviously really exciting. Um and then

we got on to YC and then CO happened and

co you know for our business was really

that shot of adrenaline. I mean co

happened everyone was staying at home

you know the business really then

started to take off. Um it was an

amazing business. It's an extremely

difficult business. Um I'm sure we'll

come on to it in a bit but we got hit

with we always like to say a lot of

cricket bats to the face. You know a lot

of stuff can go wrong. Um but it was

awesome. So we continued to grow in the

UK. Um we raised money out of YC. Uh and

then we got an acquisition offer from

Gouff I think it was about 18 months

after we started. Um and Gopuff at the

time a market leader um they were very

very big in the US and they wanted a um

an opportunity to come into Europe and

we were best place for that. Um so it

really was an incredible ride.

Yeah. Yeah. It was like the at least for

that amount of time felt like the

perfect kind of startup ride I'd say. Uh

but again I think Fat Lama was the polar

opposite. The story I always talk about

with that llama in terms of perseverance

was you know I was you know maybe very

early 20s obviously and

you know at that time when you believe

in something and you're raising a small

angel around so this is preyc right is

you know we were raising I think maybe

$50,000 maybe $100,000 which um you know

nowadays is like I suppose back then at

least then it meant everything to us you

know we were scraping around we we were

doing half an hour pitches with an angel

that might put in $2,000

you know, we were just doing everything

we could. We fundamentally believed in

this idea and um anyway, so we built the

MVP, we launched and I started

accounting. So, so I was big on like you

are the numbers going to make sense,

right? So, I had my like forecasted

average transaction value, my retention,

everything else. So, we launched and

pretty much straight away, like within

the first day, we got a rental and um

the rental was for $600. So, I'm like

over the moon. I'm update. The one of

the first things I did is I'm straight

into the financial model and I'm

updating the average order value and

this thing is looking crazy. I'm like

we're going

we're going the moon,

right? And um so that was on Friday. It

was due to be returned on Sunday

lunchtime. All right. And remember the

criticism I got, you know, I probably

did 100 pitches to raise money and maybe

one out of 100, you know, I must have

done close to a thousand the end would

say yes. But all the other 99 was like

no one's going to lend out an item

because everyone's going to steal them

and even if you have insurance, the

insurance isn't going to work, right?

Anyway, so we had and that's all I'm

thinking, you know, it's all we're

thinking about at the time. So, we had

this first rental. Anyway, got to Sunday

and the the lender of the item called us

and said like, I can't get hold of of

the borrower of this item. So, okay,

it's fine. And then we realized after a

couple of hours, like he wasn't

responding and we started to get a bit

nervous. One of the things we did, it

was, you know, it was a native app and

when people search because it helps with

what you show in the search results is

we save or we look at the the latitude

and longitude. So, quite an accurate

geoloc. So, we had that. So, I was like,

"Okay, I'm I'm going straight out there.

We got to go and find this item." And

this was a this was like an £1,800

drone, right? So, for us, this was like

everything, right? So, we went up.

Anyway, so I arrived on this random

street in North London, and there was

just a door open to this house. I don't

think we've ever told this story cuz

it's just like it's that awful. And and

the door was there. Door was open. I

couldn't believe it. As I opened the

door, there it was. The drone was just

there on the side. So, I picked up this

drone. I got back in the car and I just

went straight down to the to the

lender's house. I arrived at the

lender's house. But you got to picture

this, right? Is what's going through my

head is everything that we've pitched,

every person that I pitched, family and

friends, we had spent months, probably

close to a year at this point,

convincing people that this would work

and they believed us and they believed

and I and they bought into this. And so

all I'm thinking is like I've lied to

them. You know, this is like my whole

world was falling apart at this point.

And so anyway, I arrived at the

borrower's house uh or the lender's

house rather, opened the door and he was

like, "Hey, you're not the lender." And

I was like, "Oh, I know." I gave him the

drone. He was like, "And you're wearing

a fat alarm t-shirt." I was like, "Yeah,

we we deliver the item back

automatically." He was like, "This is

awesome, man." Like, closed the door.

And so we it did add up you know in

terms of the insurance and we we focused

on verification but it fundamentally

came to that that perseverance piece is

like we believe this is something that

should exist and we believe from a tech

from a tech perspective we could make it

work. Uh and it ended up doing so. Um

but yeah it was tough.

Both outcomes seems like really really

great though like it's like set you up

well for this company.

Absolutely. I mean an did what the first

1,200 deliveries I think. Yeah. I think

I think with Fancier from the outside it

looks like a very fairy tale story.

Everything like after the fact.

Exact. Exactly. Exactly. And they're

like, "Wow, it's like I should start a

startup." And almost like you should,

but really it doesn't really it's not

all what it seems. You know, with Fancy,

we had so many disasters. Uh, you know,

and and so many times where we were

like, "God, is this really going to

work?" Um you know even when COVID

happened uh you know we had to stop

delivering for for a couple of weeks you

know there was a point I remember you

remember this sort of maybe two three

months into the company uh so we were

working with Stripe as our payment

provider and one day they just shut us

off uh cuz apparently there was a breach

in in their terms and service which

later was a misunderstanding but anyway

they shut us off for probably you know

three or four days uh bear in mind we

couldn't take any payments right and at

this point I can't remember how many

orders we were doing maybe not massive

you know, maybe 500 orders a

week in inventory. So, there wasn't a

problem with that at least.

Yeah. Exactly. So, at this point, what

do we do? We've got orders coming in. We

can't take payments. Um, so we ended up

just taking payments over the phone,

taking payments with cash, taking

payments with PayPal. Uh, and there were

so many of these stories. And if you

think of our business, you know, the

amount of issues we had with delivery

drivers, with warehouses. Um, it really

was, uh, an intense situation, but

that's really what made it fun, right? I

think um you know building a startup you

just become so thick skinned and you

know problems come about you know on a

daily hourly basis and you know kind of

your mood is like this all the time but

you know it's epic right on the payments

one we emailed Patrick directly and he

responded like within he sorted us out

yeah it was awesome

so if Patrick listen says thank you

Patrick he sort us out um but I think on

both of those the the general story is

and an you know the the team delivered

the first 1500 orders right and So u you

know ourselves so it was very much a

case of you know you hear but we rather

than coding at night we were coding

during the day because really most of

our orders were kind of in the evening

right

and but what that meant was we were

speaking to a customer for every single

order

right and so that the customer feedback

wasn't just like

real time that you hear about it was

like every order we knew what was

working what wasn't working right which

meant that you know when we started to

implement drivers

you know we always say that you you knew

exactly how long it was going to take

from the warehouse to that house because

you've done the route about 50 times

before and I think across all three

businesses if you sort of set up and

maintain the foundations of being

incredibly customercentric things tend

to go okay

yeah there is a pattern though with

startups like as they grow they move the

builders further and further away from

customers and there's a bunch of other

roles in between and eventually you kind

of have to interpret the information you

learn from customers like how are you

going to avoid that Well, it's on I mean

um I mean you speak to everyone you can

talk about on the product side I think

on on the sales side particularly

because also we have a proof of concept

phase still right so we still have a

trial phase

which is this nice blend between kind of

uh a sales stage but also a pure

customer feedback stage

so naturally uh and I and I'm 100%

involved with that and and it's also

what I enjoy as well right you know

spending time you know I don't like

demoing on a screen. I like sitting with

a user with a laptop how they're going

to work and working on the product

together. And that also is the best way

we found to sell. Um, but you're also

doing customer feedback calls

constantly, right?

Yeah. I mean, you know, it's always a YC

mantra. Why I think, you know, you got

to speak and listen to customers and

that's really what we try and do, you

know, on a daily basis. You know, really

figure out what are their pain points

with using model ML, what does their day

look like? and then think internally,

you know, how can we then productize

that and get that into their hands for

them to try. Uh, and it's that constant

iteration, right? We always say, you

know, the quicker we can ship things,

the quicker we can learn. Um, and really

we want to try and stay as lean as

possible. You know, we always have this

theory. We want to be the maybe not the

first, but, you know, one of the first,

you know, 10 person billion dollar

company. And it's just about staying so

close to the customer uh, and in in the

details.

Y's next batch is now taking

applications. Got a startup in you?

Apply at y combinator.com/apply.

It's never too early and filling out the

app will level up your idea. Okay, back

to the video. So, during YC, we have

this um group office hour topic where we

talk about the what motivates you to

build a company. Um and it's mostly to

surface sort of the motivations for

founders and their co-founders.

So, when things are really tough, you

know why you're there. Do you guys

remember what was your initial

motivation when you started the the

first two companies? than maybe what it

is today.

You know, when we have our oneto ones,

we're not brainstorming how to raise

money or we're brainstorming actually

how we can do this for the rest of our

lives because it is so rewarding. You

know, building something for

individuals. Making money, I think, you

know, it just gets boring so quickly,

right? You know, it's not it's not

really that motivating, you know,

whereas building something that makes

people smile that's very impactful and a

lot of people are using it, that's just

so motivating to us. And I think like um

that really has been the story across

all the companies. Um you know and I

think what's been nice about this and

surprising about this is we were we

questioned ourselves going from a two

consumer brand heavy type businesses

into a B2B but actually particularly in

the world of AI the most important

element is like the B2C element. Uh and

and that is to us the most rewarding

element like when you do a demo to

someone or they click run and they do

something

and it is just there's no money that can

buy those moments

cuz they've never seen that before

and they've never seen before. They've

never seen it.

It was like with our previous companies

with Fancy when you would be at the door

with their delivery sort of 10 12

minutes after they ordered it and then

you saw their face like what the hell's

going on? You're already here. Uh like

Chaz said you just you know it's

priceless. I've noticed this with AI

products that they often the customers

cannot even imagine what the solution

will look like

um because the models are so good and

they're so advanced right now

and very often

um you just have to build like you build

it for them first like you can't really

figure out try to figure out what the

problems are because like it's not a

specific problem you're solving it's

just like it's like a whole other like a

whole other league of solutions.

Yeah. And and you know the difficult

thing as well is you you do have to

rethink things slightly from the ground

up.

Mhm. So I I do think that you know at

least as of today if our product we made

it we made a call that we would kind of

rebuild our version of like PowerPoint,

Word and Excel because we we do believe

at least for the time being that the way

in which people interact with technology

regardless of of type of technology will

remain quite consistent. In other words,

like a long form document, a

storytelling presentation and tabular

across XL Word and PowerPoint. Um we

think that will stay the same.

So you're not trying to get people to

change the current We're not trying to

get because we just think that that's

what people are very used to. However,

we think what's if we look at what we're

seeing this year, a lot of what's

happened up until now is sort of humans

are still coming into these systems and

they are like clicking run on something

basically, right? We think the biggest

shift this year is going to be that even

that element won't happen and therefore

elements of the user interface we think

will be less important. In other words,

these these tasks will happen entirely

autonomously. You know, as you arrive in

the morning and the things that you

would normally have had to go and

trigger and click run, they will already

be there. They will already be done.

Right. Right. Right. You guys are

siblings. Um, historically in YC, we've

known that this is a pretty good recipe

for a good co-founder relationship. Tell

us, I mean, you've been co-founders of

multiple companies. You've had other

co-founders, too. like tell us what

you've learned about the most important

thing and and maybe like for the

audience of founders who are thinking

about finding a company like what should

I be looking for in my co-founder and

like what's important and what's not

important. I think we mentioned it, you

know, previously around hiring. I think

this is sort of tenfold when it comes to

your co-founder. You know, you're going

to spend so much time with this person.

You know, more time that you spend with

your family, with your partners, with

your friends. Um, so I think first and

foremost, is that person someone that

you want to spend a lot of time with?

Uh, you know, obviously with us,

we're still not sure.

Yeah, we're still not sure.

I think if you were not sure, you

wouldn't say that.

Yeah. Exactly. Exactly. Otherwise, it'

be really awkward. It would be really

awkward. Um, so look, you know, we've

always had a really good relationship. I

think not all brothers working together,

I think, is a good idea. I think, you

know, we've been fortunate enough we've

always had a good, you know, personal

and working relationship. One of the

things that make things really easy, I

think, is and again, not not all

siblings are like this, but there's no

filter between us. You know, we're very

transparent. We're very honest. And I

think you know with your founder you

need to be exactly that you know there

can't be any miscommunication lack of

communication like we all know one of

the biggest reasons if not the biggest

reason why you know startups fail is is

founder fallout and and I think with us

we're always very good at you know

communicating trusting each other.

Another thing on the trust piece then is

the way that we describe our ven

diagram. You know I I think I'm an

engineer but I'm not actually an

engineer. I think I am. Anie studied

computer science, I studied uh

accounting. So our ven diagram and I

think if you're thinking about another

co-founder I'd really consider this is

um Anie obviously handles sort of

engineering product and I handle kind of

finance commercials and products as well

kind of in the middle right so the

overlap in our vend diagram is basically

um customers and product right which we

think is just like the most important

piece anyway right and that bit we both

really enjoy and we and we love but we

also love all the other stuff and I

think like that clear uh segregation of

duties and interests from day one I

think is really important. So I think

one of the things that we think that we

look at is I really emphasize that point

of interest because actually you may

like for example Anie may have studied

CS and I may say but if I if I really am

interested in wanting to basically do

Anie's job and write the bulk of the

production code then we've probably got

a problem. You know what I mean? And so

I think it's like really think about

that. Uh and and and that tends to last

for a long time as well.

I got this email uh I think it was

yesterday from a person who was in their

early 20s. They were basically asking me

for not startup advice but life advice.

So what do I do? I'm like 21 and I think

he was writing something along the lines

of like I know I can go and build

something B2B that's quite narrow in AI

but it doesn't seem to motivate me

enough. I want to be something much

bigger. Like I want to have a bigger

impact on the world. like if you got

that email, if you would give him

advice, like what would you advise

someone who's in their really early 20s

or in school and they're thinking about

their career right now or maybe in the

world of AI um or thinking about

starting a company but want to do

something big like what would you tell

them?

I think we're probably biased because

you know we'd probably favor starting a

company um before going to work at a you

know a big corp. Um, I think first and

foremost we'd be very honest with them.

You know, as we mentioned before with

our fancy story, you know, you look from

the outside and it might seem like uh,

you know, sunshine and rainbows, but

it's really hard. You know, building a

business is is really, really, really

hard. It's really fun if you enjoy that

stuff, but it's really hard. Uh, so

really, you've got to say to yourself,

look, you might be building a business

for the next 5, 10, 15 years,

right?

It might not going any go anywhere. And

you've got to be okay with, you know,

that reality. Uh, and ultimately, you've

got to be very passionate about what

you're building. You've got to have that

perseverance that Chad spoke about

earlier. Uh, and if that sounds good to

you, then then build a startup. And if

that doesn't sound good to you, then,

you know, I probably recommend probably

getting a, you know, a job somewhere

else and then develop over time. Uh, and

then if you feel like you're more ready

to jump into the startup world, uh, then

do that. So maybe if you're if you're a

builder and you enjoy seeing your stuff

in people's hands, the smaller the

company you work for, maybe you're even

your own company, the faster it gets in

the customer's hands. 100% and and

enjoying that. I then think another like

different way to look at this is is um

you know, think about I know it's maybe

slightly more, but is that if that's the

correct word, but you know, if you're

lying on your deathbed, you know, and

you look back at your life, like what do

you what do you want from your life? And

I think we we do that a lot. And

basically, you know, what we conclude is

we just we just want to have been

impactful with our time. You know, we

want to have, you know, you know, left

the world a better place, but but but in

a way that we've just built things that

people enjoy using and and I think

that's really what motivates us. But I

think really that one point of um you

know, you've really got to enjoy the

journey and we just we just love

building.

Just sounds like if you were in his

shoes, you would give yourself basically

the advice to the path that we ended up

on.

Yeah. Go all in. It wouldn't change

anything.

Hell no. Goal in. Goal in like all in.

And I think if anything uh more is more

in general.

I remember when I met with Paul Graham

sometime in around the beginning of the

batch and I was doubting whether if this

doesn't work out like maybe my cur is

And he was some he said

something like well if you're 26 and

you're poor uh that will be the worst

outcome. And probably most 26 year olds

don't have any money anyway. So doesn't

make a big difference.

Yeah. Yeah. It's also just like the way

I describe it is like well the worst

case like the very worst case is you're

you're going to learn so much over such

a short period of time and that is the

worst case cuz often when you compare

this to like you know we often now when

we're we're hiring people we talk about

their opportunity costs right

I think a lot of the time if you take a

step back those jobs or those roles

they're still going to be there in a

year's time right so like the actual

worst case is you've just learned a lot

over a here. And I think if you frame it

like that, then a lot more people would

just get out and build stuff.

And a lot of people that end up on these

tracks, um, they sort of like 5 years

into investment banking. It's hard to

step out of that.

Super hard.

But like it's just the risk that you

want to take, you want to take early on

because it's just like much harder to

change when you're 30 and you have

commitments and

definitely way harder.

Yeah. When you're hiring people, people

that come from finance or quant

backgrounds, are there things that they

have to unlearn about their job? Either

about the company they're working for,

about the industry in order to work for

a tech startup serving the same

industry.

Yeah. First principle thinking, eh,

they just got to move faster. Okay. Um,

and again, this is probably more your

realm than mine, but what we notice is,

you know, we'll ask someone to do

something and well, they won't, but they

will try and, you know, spend a day, two

days putting together some sort of

presentation, slide deck,

and we're like, what the hell's going

on? You know, uh, you know, we need this

now. Um, so it's things like that where

you get, you know, caught up in the, I

guess, the big company thinking, which

look makes sense in certain

environments. Um but when you're working

in a startup and you're wearing so many

different hats, frankly there's no time

for that. So we really try and from day

one uh I think you in particular really

try and just say look we need this now.

Um instead of you know all these

spreadsheets and and slides I think that

is the first lesson. It's like

everything that you have learned for 5

years try and unlearn immediately. Um I

think just in the sense it's different

you know you just got to yeah you just

got to move so much faster. I think

building the plane going down the runway

is the best way to describe things. You

know, I think you know you know that is

the the quickest way to learn. It's

different when you're you know where are

now it's different. You know the

business you know we have large

customers in production that that can't

be the case but in terms of like the way

that you think or the way that you go

about your work the fundamentals you

know still need to remain the same. Um

you know we are big on this first

principles thinking which I know is very

much the terminology that is overused

but I think in general that way of

looking at the world is a is a better

way to build a company

and you guys chose to do YC three times.

Um can you describe sort of like how

you're thinking here

the second and third time it wasn't a

financial thing. We still get asked like

still we've we've had loads of teams

that you know people that have worked

for us that have gone on to even start

YC companies or gone on to build some

great companies and we still get asked

how we managed to maintain that and I

quote YC culture throughout the life

cycle of the company right and so the

the the way I sort of answer this is you

cannot replicate that you know I I

remember very clearly Michael Cyborg on

uh in 2017, you know, the first office

hours we had, you know, a young English

lad, you know, we report on our numbers

and I reported monthly and I remember

Michael being like, "What the hell are

you doing?" Like, let's talk weekly if

not daily, if not hourly. And I think

that culture never leaves a company,

right? That's the second aspect which

definitely cannot be overlooked is the

instant support network that you get.

You know, I think us being from Europe,

working seven days a week, it's very

unusual to say the least. And there's

not many people that we can call on. And

in fact, a lot of the people that we

surround ourselves with outside of work,

they fundamentally disagree with the way

that we work and how we're building and

why we're building, particularly when we

don't need to from a financial sense,

right? I think that's difficult. Whereas

in in the Bay Area, you know,

particularly uh and and driven by YC and

through YC, you get this like instant

network that you just you you cannot get

anywhere else.

And you ended up being in in the batch

winter 24, which was sort of like one of

the first really big AI batches where

most companies were building companies

similar to you.

Um how did that feel? Like was there

something

like just being in the Bay Area like 18

months ago?

I mean it was just crazy. I think I

think again working side by side with

people building really cool products

working seven days a week and that buzz

you know around the office around San

Francisco it's really really hard to

replicate um and we absolutely loved it

didn't we

yeah and it's like it felt it's

interesting you mentioned that cuz that

really was

that was like the first batch where it

was like through and through is just you

know AI companies pretty much and like

it felt like in that Slack group didn't

it felt almost daily or hourly there was

like a scientific breakthrough. It was

like phenomenal to be a part of.

One question I get I was in Munich and

then I was in Zurich on a trip recently

and I was in London. I saw you guys uh I

was in London um a year year and a few

months ago is from European founders is

where they should be based and where

they should be building their companies

and I don't have the right answer. Um

it's a controversial topic. You guys are

based in London. So um but I'm curious

what you think and like as you're

thinking about this question.

Go

this is a tough one. Uh

it's tricky. I think

we absolutely love San Francisco. Uh

we've said it many times. Every time we

come here,

it's the best place in the world.

There's something different about it.

And and we always thought, you know,

being from the UK, this San Francisco

buzz, Silicon Valley,

it's a bit of a myth. At least that's

what I thought growing up. And then I

think what was interesting with uh with

Fancy uh we were a remote batch, right?

So we didn't come to San Francisco. Um

and then when we came here again for for

model ML's batch, just being in San

Francisco and being a part of the

community, it was crazy. You know, Chaz

always tells a story when, you know,

you're at the gym during the batch and

you just had people on the treadmill on

their laptops, you know, on Zoom calls,

you know, talking about funding rounds

and X, Y, and Zed. And in the UK that

just doesn't happen. Um, and look,

clearly

clearly,

um, so look, we love the UK. Um, if if

we were to start a company again, I

mean, we're trying to convince people to

to move to SF for this company. Um, we

think it's an incredible place to build

a company. Um, and like we say about

work ethic, it is really challenging, we

think, in the UK in particular, um, to

find people who frankly want to work

this hard. Um, I think in San Francisco

it's a lot more common. I think in

Europe it's just not um one of the

positive things about Europe and we

always said this is is talent and

particularly engineering talent which

might sound counterintuitive I think you

got amazing engineering talent you know

in the Bay Area

the problem is it's very very expensive

and the competition is just so so high

you know you're going up against

you end up hiring from your own network

anyway you're trying to bring people

over from the UK exactly um whereas in

the UK I think the level is still really

really strong but the competition is

less. So your ability to hire that top

class talent

I think is probably stronger in Europe.

It's a trick question. If you're based

in Europe you I've been given advice.

Okay. If you decide to stay in Europe

try to move to one of the few cities

where there are other really ambitious

people and there's just not that many of

them. Uh London is one of them. But

Europe is not one country. So, it's like

a big culture shift to to or maybe not

big, but it's a big change to move from

um I don't know uh Spain to to London.

It's not that big of a difference to

move from Spain to San Francisco.

Yeah. And I also think as we spoke about

our hiring process earlier, you've just

got to be really rigorous with your

hiring process. You know, there are

amazing people and amazing talent in

these cities. You've just got to find

them. Um so, I think don't settle for

second best. You really got to find

those people that are self motivated,

hungry. Uh, chances are if they know

about YC, they've got the right

attitude.

And I suspect a lot of your customers

are in the US.

Pretty much all of them. I'd say 80% of

our customers are US.

How do you handle that?

Well, I spend the bulk of my time in New

York. An spends the bulk of his time in

London. Um, and then kind of split

between Hong Kong and and San Francisco.

Interestingly, actually, for finance, as

I mentioned this when I came in earlier,

there's a surprising number of decision

makers for global firms around sort of

technology implementation in San

Francisco. It's not out of New York or

out of London. It's out of San

Francisco. So, I think if we'd known

more about that um you know back when we

made the decision to move the

engineering team to London, that would

have influenced things. But uh yeah, my

view for what it's worth is I think

people should do what what it takes to

to move to San Francisco if that's not

possible or that's not where your

customers are based at the very least as

you said, move to a tier one city and

try and be as close to uh you know folks

that are also building stuff.

Awesome. Thank you so much for coming.

It's great to see you guys.

Yeah, great to see you again.

Thanks for

[Music]

Key Vocabulary

Vocabulary	Meanings
contract /ˈkɒntrækt/ B1	noun - a legally binding agreement verb - to enter into a formal agreement
tangible /ˈtændʒɪbəl/ B2	adjective - perceptible by touch
passionate /ˈpæʃənət/ B1	adjective - showing strong feelings or enthusiasm
perseverance /ˌpɜːrsəˈvɪərəns/ B2	noun - steadfastness in doing something despite difficulty
logically /ˈlɒdʒɪkli/ B2	adverb - in a way that is based on clear reasoning
consistency /kənˈsɪstənsi/ B2	noun - the quality of being consistent
founder /ˈfaʊndər/ B1	noun - a person who establishes a company
invest /ɪnˈvɛst/ B1	verb - to put money into something to make a profit
cognitive /ˈkɒɡnɪtɪv/ C1	adjective - relating to cognition or the mental process of knowing
architecture /ˈɑːrkɪtektʃər/ B2	noun - the design and structure of a system
manual /ˈmænjuəl/ B1	adjective - done by hand, not by machine
repetitive /rɪˈpɛtɪtɪv/ B2	adjective - repeated frequently
automate /ˈɔːtəmeɪt/ B2	verb - to operate by machines or computers
opportunity /ˌɒpəˈtjuːnɪti/ B1	noun - a set of circumstances that make it possible to do something
comparable /kəmˈpærəbəl/ B2	adjective - similar or equivalent in certain characteristics
sovereign /ˈsɒvərɪn/ C1	adjective - having supreme power or authority
accuracy /ˈækyərəsi/ B2	noun - the quality or state of being correct or precise
retention /rɪˈtɛnʃən/ B2	noun - the act of keeping or continuing to have something
functionality /ˌfʌŋkʃəˈnælɪti/ C1	noun - the quality of being suitable to perform a task
implement /ˈɪmplɪmɛnt/ B2	verb - to put into practical effect