– English Lyrics

Lyrics & Translation

[English]

AI is continuously developing super

super quickly and that means we need to

do the same. We're finding that as we go

deeper and deeper and deeper in the

entire legal software stack, we're also

seeing that the line between software

and service is blurring. I think that's

been one of our strengths as a company

to say we don't know exactly where the

future's going, but neither do you. So,

let's work together to make sure that

we're both winners in whatever happens.

[Music]

Today, I'm joined by Max Junior Strand.

He's the CEO and co-founder of Legora.

Legora was in winter 24 and they are the

leading AI workspace helping lawyers and

legal professionals do their work.

Welcome, Max.

>> Hey, thanks Gustav.

>> It's been 13 months since you did the

batch. It's been a really busy year for

you.

>> It has. Uh, it feels like it was a

really long time ago. I feel like I've

aged five years in the last one.

>> For those who don't know, tell us about

Legora. Yeah.

>> What are you guys building?

>> At Lagora, we're building the AI powered

workspace for lawyers. We're essentially

transforming the way that they complete

their work. Everything from reviewing,

drafting, researching. Essentially,

within legal, you've had this incredibly

fragmented software space where there

was a lot of point solutions and AI was

never good enough to actually work with

unstructured text, precedent, legal

documents. And when GPT 3.5 got out,

that just completely changed the game.

So we were quick to, you know, build a P

and then now we've scaled that all the

way to an enterpriseg grade system

serving tens of thousands of lawyers

daily.

>> Those point solutions were basically

workflow tools. So what were they

before? Because it's been a history of

of a legal technology industry that

existed before. This is not started

right now.

>> No, I mean legal tech has been a

category for a long time, but um it was

really unsexy for a long time, I think.

And you'd essentially have a broad range

of point solutions. Everything from

templating tools where you would sort of

codify a contract to special translation

tools or redline tools or research

tools. And all of them work with text

somehow. And generative AI came into the

game and just kind of threw up

everything off the off the table. And

then when it landed, you very clearly

saw how you could solve a lot for a lot

of these use cases with the same

underlying tech. So chatp came uh maybe

8 months prior to you guys starting this

company. Describe that moment. Was that

an important moment for the company's

founding?

>> We were playing around in AI and legal

way before Chat GPT and we were using

these early models from BERT coming from

Google. They were decent in English but

they were just horrendously bad in

Swedish and you know the first

observation that kind of sparked the

founding of the company was one of the

co-founders friend who was a lawyer

spent four months during a summer just

summarizing court cases for a big law

firm. We basically saw that GP 3.5 was

released to developers started building

I think the first thing that we built

was a stock option reader that would

explain how a stock option contract

worked

>> useful

>> right you know as startup founders with

no legal background that was seemed

reasonable and then very quickly the the

sort of focus changed to how do we build

this more wall-to-wall or or endto-end

system that every legal professional

wants to work with on a daily basis and

the first product was really quite

simple. Uh especially building for

Europe, you got to go through a lot of

hassle to kind of conform with all the

data processing requirements. So you

know all data hosted within Europe,

nothing for training, no retention,

exemption from human review when you

look at the way Ashure and AWS is

structured. And we we kind of jumped

through all those hoops and just built a

system that was compliant for law firms

to work with. And then very quickly as

the general sort of AI platforms

continued to develop with chat GBT with

cloud with Gemini the requirements for

what we had to build to be much much

better you know continuously increased

>> in some industries or some some

categories like coding or law for

example it seems like the models are

just magical like like they do things

that the people that were in those

industries before could not even imagine

be possible. Could you describe sort of

like the first time you um used Lora um

to do something that was magical for a

customer and and how they experienced

it?

>> Yes, I think the first time was when we

deployed Legora into the biggest law or

largest law firm in the Nordics, man. Um

they their managing partner had a famous

saying in the newspaper that AI was more

artificial than intelligent which was

back from the early models. Yeah. I mean

a lot of firms burnt themselves buying

expensive tools that didn't solve

anything. Mhm.

>> And I came into that meeting, you know,

I bring out my laptop and I just ask

him, you know, put in a query.

>> And he puts in this uh legal research

query and we've tied Legora to Swedish

legislation with a rag system. Yeah. And

it answers perfectly

>> and you know, you kind of see it on his

eyes like it's the aha moment and now

when we're

>> Is that your aha moment as well?

>> No, I think my personal aha moment was

just using chat GPT generally, right?

Like it was amazing. It felt complete

sci-fi that you could talk with the

computer and it talked back and you know

as an entrepreneur you you you kind of

quickly you know from that you

understand that all right we can apply

it in this space in this way and in that

space in this other way and I think for

legal specific the chat experience I

think was always cool but when we took

the the same models and and sort of

applied them differently um one of the

first use cases we did was due diligence

where you have hundreds or or you know a

lot of documents that you want to view

and instead of going through them one by

one by one, we just made this large grid

where essentially every document

represented a row

>> and then you could put your queries in

the columns,

>> right? And as you then put in, you know,

100 employment agreements and you ask uh

does does all of them include an IP

clause

>> where the company protects its

intellectual property and it just starts

to rattle and it goes yes yes yes no no

no yes yes yes and it always links backs

to the citation. and you realize like

holy this is transformational. It's

taking tasks which used to be you know

days or hours and it's turning them into

minutes.

>> By the time this is is live uh you will

have announced that you have raised uh

series B. How much did you raise?

>> We raised $80 million led by iconic and

general catalyst and you know grateful

for YC's continued participation as well

as benchmark and red points.

>> What is the software like? So, so as a

lawyer using Legora, what does my

day-to-day look like?

>> So, it's really broken up into two

pieces. The first one is the web

application and the second one is our

word addin. So, we integrate directly

into Microsoft Word. Right?

>> So, if we start with the web

application, the first thing that we had

was just a simple chat chat over your

own documents and files. This has

quickly developed into its own agent

that's able to use a lot of the other

endpoints in the app and also external

tools to solve more complex sort of

step-by-step workflows. Mhm.

>> So you could imagine saying um hey I

want to write a memo and the first step

of the memo is to go out and do some

research. The second step is to take all

that research and conform it into the

standard language of the firm and the

third step is to write the report and

then output is a report.

>> And does it do all that?

>> It does all of that. Wow.

>> Right. Um, and I think we can talk more

about it later, but MCP and the way that

you can scale the tool usage of these

agents is something that I'm super, you

know, keen on and and that we're leaning

very heavily into because a lot of firms

have different needs in terms of how

they want to adopt the tools to solve

for their specific workflows. And it's

different if you work in intellectual

property or if you work in restructuring

or if you work in corporate or if you

work in uh disputes. The second piece

outside of the chat is uh well the grid

that I talked about before. We call it

tabular review.

>> Y

>> um it's essentially input any number of

files and then input any number of

queries and we sort of cross run that

across each other. And the big

innovation there does not really come

from you know how do you prompt and and

work with the model but it's how do you

make this run at scale

>> right?

>> You know how do you run 100,000 queries

in parallel at the same time and make

sure nothing breaks all the citations

are correct. There's a lot of chunking

sort of rag searching within the

individual documents because sometimes

they're very very long. Y

>> and with legal docs there are certain

intricacies where you need to always

include things like the definitions y

>> and there might be cross references

within each clause to each other. So

taking all of that into consideration

that kind of serves the grid. Looking at

the word add in I think you could phrase

it as cursor for lawyers.

>> Lawyers basically use word that's that's

a like a known fact for a long time.

>> Yeah. I mean they draft and they review

contracts in word or PDF form and

>> what we really wanted to do is similar

to cursor how do we bring generative AI

into the you know existing work

environment of a legal professional and

that means integrating in word

>> now the difference is you can't fork

word and you can't take up all the real

estate you want you're basically

conformed to this sort of right hand

column

>> and then you got to get really creative

it's basically like designing a mobile

app almost because that's all the real

estate you get And the first thing that

we built there was just, you know, how

do we integrate an assistant or a chat

that's able to not only read the

document, but also create edits. So you

might say, I want you to um, you know,

renegotiate this MSA for the buyer. Y

>> and do that using this internal

checklist that I have or this internal

sort of playbook or precedent. And now

we've scaled that to not only work in a

chatby chat basis, but also more

extensive workflows. So you can say,

here's a contract. I want you to take my

playbook that consists of 20 different

steps and make sure we negotiate from

the starting positions and have, you

know, different fallbacks included.

>> Do you have a specific example of

something that was impossible a couple

years ago for a lawyer? Like literally,

you couldn't do it and now you can do

it.

>> Yeah, I mean I think well there's a lot

of it, right? The early ML models were

really bad at legal language and what

they were really bad at was when the

language looked different in across

documents, right? You could train a

system to find let's say a change of

control clause if it looked the same way

across all the documents. Yeah.

>> But it was really frankly bad at

>> finding the meaning of a change of

control if the class didn't look that

way.

>> Mhm.

>> And so what the LLMs have allowed us to

do is to just take tasks where

especially on like large contracting and

large document extraction. So how do we

pull the insights from this? Another one

is just you know redlinining. So

redlinining files within word against a

president or playbook completely

impossible. Or take deep research across

you know hundreds or thousands of

judgments where you need to conform not

only the judgments but also pull in

things like legislation and regulation

all into the same place that since the

cost of intelligence is going down it

also increases the amount of queries we

can do. Right? So, one pretty cool thing

is, you know, embedding making one

search against your own documents and

files. Yeah. Making another one on the

web and making another one against um

court cases and judgments and and

legislation and then combining all of it

to create effectively like a memo that

>> maybe they they couldn't afford to do in

the past. They just like just didn't do

it.

>> No. And similarly with with due

diligence when if you go way back, it

used to be a physical data room. That's

why it's called a room,

>> right? You used to go into the room, you

had all the documents and all the

contracts and then you'd sit down and

read through all of them. Yeah.

>> And you had to mark them with a pen. So

making and doing a due diligence on a

company was really expensive. And now

it's becoming almost a commodity where

you're expected to do it, but

>> clients are also not really that excited

to pay for very simple contract review

when they know that AI can do, you know,

99% of it.

>> Wow. Yeah.

>> So, in the time that I've been at, we

have funded some legal software

companies, but the hardest challenge for

all of them was selling to law firms

>> and selling to legal like most of them

would end up selling to companies cuz

law firms were just like not possible to

sell to. That radically changed um just

like two years ago.

>> Yeah.

>> Can you tell us sort of like what do you

think changed and how do you do it when

you go and sell to uh one of the major

law firms in the world? So for everybody

listening, this was also one of the

questions that I remember you pushing

really hard on during the interview.

>> And I think we were quite contrarian to

say, you know, no, it's different this

time. Trust us.

>> Y

>> I'm glad we were right.

>> I think the way that we approached the

problem was always with this idea of we

win if you win. So let's align our

incentives with saying as a law firm,

this technology is revolutionizing.

you're you're going to need to adopt it

in some sense, shape or form and we want

to be that long-term partner

>> and somehow they know that.

>> Well, so what what happens is um a lot

of legal work is low differentiation.

You know, if you're doing a DD from, you

know, law firm X or law firm Y kind of

getting the same deal

>> and so when you have this perfect

equilibrium of services and somebody

disrupts that by taking a new approach,

>> clients are quick to switch.

>> Yeah. I mean clients are under price

pressure. They want to be effective.

Legal fees are very high. And so if this

equilibrium breaks, you are almost

forced to adopt it. And you you are

incentivized.

>> It's kind of the same as when lawyers

adopted computers,

>> right? If you're billing by the hour,

you could say, well, let's have a person

walk to the library, you know, find the

right book, you know, find the right

cases or the right president and use

that for whatever work we do.

>> Or you press C++ F,

>> right? There's always this dilemma of

you want to serve your client in the

best way possible because that drives

you more revenue over time. Yeah. And

for a lot of a lot of the firms that we

work with their you know brand

reputation trust as always putting the

client first is what matters the most.

And so a lot of the firms also want to

be uh leaders here.

>> Yeah. You know, some of them want to be

fast second movers, but many want to be

first movers because they're

understanding if you have this perfect

equilibrium and you take a simple type

of work that gets disrupted, you should

get more market share by moving down

quicker.

>> Mhm.

>> But then it's not a race to the bottom,

right?

>> It's a question of okay, if we take

>> every country has a ranking of law firms

basically,

>> right? And and it's also not a race to

the bottom in terms of pricing because

if you pull down let's say the cost of a

due diligence, you free up more time to

spend with a board on advising them on,

you know, a really complex merger or a

really complex acquisition. And so what

typically ends up happening is you're

under time pressure. You could do more

work, but you just have all this stuff

that needs to get done. And that's what

AI is really good at. But it's also

serving lawyers in very creative ways. I

mean, we've had use cases where, you

know, we get a call from somebody and

they say, "I played a role role playing

game with Lora, you know, trying to win

this argument and I'm asking it to act

as the other party, right?" Wow.

>> There was this amazing uh situation that

one of the Spanish um partners at firm

called Perorka had where he went into

court. M

>> he had put all the evidence and all the

documents from the opposing party in

Lora and he was actively querying it

during the hearing and during you know

at the time when the other attorney was

speaking because then he could

immediately interrupt if he found

something that was uh that was wrong.

>> Wow.

>> And he phrased it very nicely. He said

when when he goes into the battlefield

having Legora is like having another

piece of armor. And I thought that was

that was very poetically.

>> Could you use Legora to do negotiation

on your behalf? Yeah. So the way that we

built that is I think the LLMs by

themselves are not good enough for that

yet and we can talk about that but it's

it's interesting to to build these

products knowing that the models will

get better. Yeah.

>> And where do you stop?

>> Yeah.

>> Right. On every feature but so so that

feature in the Gore is called playbooks.

>> A playbook is essentially a collection

of rules where you either approve or

disapprove something. So you might say

for the way that you would sign NDAs

here at YC, you always want the

definition within a confidentiality

agreement to look a certain way. So you

provide the rule, you provide some

example language and then you say, "All

right, if the opposing party will not

accept this definition, we have some

fallbacks." So fallback one and fall

back two. And you just open a document

in Agora, you open the playbook, and you

press play. And it goes through every

rule and runs it against the contract

and it marks it up.

>> Yeah. So if it does not conform with

your playbook, it gives you the

suggested language so that it will and

the really cool thing about this is it

scales outside of just legal

departments. So um at Lora every sales

rep is using Lora to negotiate NDAs

before sending it to our legal team. And

we just started working with this very

large bank in the Nordics and it's very

quickly moved from, you know, the legal

team to compliance to risk

>> and now to sales.

>> Wow.

>> Because everybody can leverage the

system. And the cool thing about it is

it's not only faster and more accurate,

but you agree on a standard

>> because the legal team then creates the

playbook and that becomes the standard

that everybody uses. So it actually

increases quality and consistency over

time.

>> YC'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. None of you guys when you

started were lawyers.

>> Oh,

>> so you still are building one of the

largest or fastest growing legal AI

company in the world. How do you do

that?

>> I think at this point I've become a

hobby lawyer. Uh but how we approached

it was being incredibly humble. Humble

for the fact that we did not know the

industry. We were quick to create

relationships with our early partners

where feedback was, you know, happening

daily. Yeah.

>> And I think that's been one of our

strengths as a company to say, we don't

know exactly where the future's going,

but neither do you. So, let's work

together to make sure that we're both

winners in, you know, whatever happens.

And I think now we of course have the

privilege of having hired a ton of

lawyers into the team that work directly

with the product teams and directly with

the customers. Especially in an industry

that is now going through such big

change. It was useful to come in with

more naivveness if you will saying

why does it work this way? You know it

could work this way instead.

>> Let's say you're a founder watching this

right now. You're like I want to build

AI software for logistics or for

insurance or finance. Is your advice

basically you don't need any expertise?

How do you how do you how would you

>> how do you learn about the things you

need to learn though?

>> I think my advice is, you know, learn

about them, right? Like we we went into

this and the first thing I did was I

interviewed 100 lawyers. I had this good

hack on LinkedIn. I I texted them asking

if we could have lunch and I would pay

their hourly rate and I I could def you

know definitely not afford it and none

of them would, you know, impose that.

They would just say, "Oh, that's

amazing. Like I'll have the lunch with

you anyways." One of the attributes that

have been very helpful in in my career

has been that I'm I'm I'm somebody

people want to help.

>> Um I think that's a very underrated

>> skill. I think there there are things

you can do to be more like that. Um you

can be a bit uh fearless in in your

approach to people and you can also be

very veryful and grateful and

appreciative of the work that other

people help you with. If we hadn't done

that, we would not be where we are

today.

>> And then how do you conduct a lunch with

a lawyer when you're starting a startup?

You know not much about law.

>> So you'd sit down like this. Uh you'd go

to somewhere decently nice because again

they make a lot of money. And it took me

some time to even understand that the

way that departments work are

fundamentally different. Like a

transactional lawyer works nothing the

way a lawyer within the corporate

department works. You just ask them a

ton of questions. And I think also

giving them something back. So, you

know, I'd reach out, they see my, you

know, tech background and you try to be,

you know, give them nuggets of, oh,

that's really cool. Have what do you

think about this like you give them

ideas, you make them engaged in wanting

to give you advice and Yeah.

>> And people generally feel good giving

giving founders advice of course like

it's like something that

>> you should take advantage of.

>> Yeah. and something that I'm you know

really happy to do now from the position

where we're at.

>> There are some large companies in in

legal technology. Um are you going up

against all of them or what how do you

think about the existing market of legal

tech?

>> Right. So there's been a lot of sort of

large M&A machines and incumbents in

this space for a long time. They're not

very popular with the end users.

>> Um I think they have very kind of

far faring roots. um there's some

advantages and you know data modes and

so on that that come into play but

effectively what AI has done is really

changed the game in terms of how quickly

you can ship something and it's created

a new category so a lot of again these

existing point solutions were in maybe

suites of of these M&A machines and now

a lot of it is becoming irrelevant very

quickly and the cost of building

software is also going down very very

rapidly So our ability to out ship or

you know uh outd deliver these teams of

you know thousands of engineers with

just 30

>> is insane

>> and so

>> we have instead

managed to build a company with I think

at the at the time of recording it's

about 100 where like our velocity is way

higher than companies you know 100 times

our size. I think that's interesting in

and of itself in terms of how we built

the company over the last year because

when we came out of YC we were roughly

10 people and now we're 100 and that

means we've onboarded on average like

two people a week and hiring correctly

it's really hard like it's a skill you

need to learn

>> and hiring for velocity hiring for you

know entrepreneurship and ownership of

different products and things but also

scale because the company is growing

exponentially so you need your your

teammates to scale exponentially as

well. If people scale linearly, at some

point it's a really large delta and then

you know things aren't working out

anymore.

>> Do these big companies have lock in like

the big legal tech companies?

>> So these big companies have a couple of

advantages but I think the the

disadvantages outweigh uh the advantages

almost 10 to1. There were very large

data advantages and you know being like

an incumbent where you lock in a large

contract.

>> Yeah. But I think the buyers have also

changed aptitude here. So we're not

seeing anybody want to lock in a

five-year contract,

>> right?

>> Because the world is moving so fast.

>> Um so we instead see them, you know,

doing one-year contract.

>> It sounds like a good motivation for

companies moving faster.

>> It is. Yes. And and but even law firms,

right? I mean, they don't want to be

locked in with a vendor. So they're

doing one or two year contracts. And you

know, as we see them now coming up in a

lot of places, they're also looking

outside of their existing alternatives.

So you might have made a bet back in

2023 or 2024 when it was experimentation

days, but now you're looking at what are

we going to deploy, you know, more long

term and there what I'm seeing is yes,

people look at the technology, but even

more so they're they're zooming out and

they're looking at your rate of change.

>> They want to work with the partner

that's going to get them from point A to

point B. And they can be different

things. It might be we want to be AI

first and drive our top line or we want

to drive profitability and you know

streamline our operations. It can be you

know very different motivations.

>> Right.

>> How does your tech stack look like

what's under the hood

>> internally? Yeah.

>> Yeah. So building our infrastructure I

think from the beginning it was pretty

clear that we wanted to be on Azure just

because it was the same that our

customers were were on and in the

beginning I think OpenAI and and GPT was

really the only model that you could

serve via Azure. Now we have much more

um you know options available to us. So

we we use AWS and claude and Gemini and

GPT and Mistrol kind of interchangeably.

The biggest thing there has been how do

we build everything in such a way where

we can hot swap the models whenever we

want and also build it in such a way

that the models become better everything

improves. And now we've also looked into

classification models where you know if

you do a simple query will serve you a

simple model. If you do a complex query

we'll serve you a complex model and

that's just because that's just you know

to keep the margins down but also you

know sometimes you don't need a bazooka

when you just need a you know water gun.

>> So who is the buyer? Uh my understanding

is that law firms have but maybe you

could explain to me like there's a bunch

of partners and there's other people

there too right. How is a law firm or a

legal team at a company generally

constructed and who are there and who

buys it and who uses software?

>> It changes a bit depending on size.

>> Uh so if you start with the with the

biggest firms of course you have the

partner group that kind of runs things

but you very often have an innovation

department which sometimes have more or

less influence. Um if it's a very strong

innovation department, they make their

own choices. They procure software and

they're responsible for the entire

innovation agenda. I've frankly got the

most energy out of working with the

innovation folks who are really smart

about these things. Uh because there's a

lot of people that just want to kind of

check the AI box and then others who

really want to push things forward. And

the interesting dilemma there is, you

know, they're basically driving

efficiency across the across the stack

or across the firm, but they're not the

users themselves, right? However, you

might often have innovation

practitioners that work in the M&A group

or the disputes group or arbitration and

then they will work with those teams to

drive an upskill. So they will have a

very kind like process-minded way of

working and then they might use Lora to

build use cases for the end users

because when you work in a big law firm

you need to hit your building targets.

Yeah,

>> they work a lot. Like we grind as

startup folks, but

>> lawyers

>> lawyers grind as well. And if you know

that there's a way to solve something

and it's going to take 6 hours for you

to do that

>> and you know a way how to do it in 6

hours, you might not take the chance of

exploring a way how you could

potentially solve it quicker or you know

with a with a with a higher quality.

You'll just conform to the way you're

used to working.

>> Yeah. So innovation teams have a huge um

opportunity and frankly you know mission

to drive that across the firm and if you

go down a bit so you have sort of

mid-size firms more often than not you

might not have an innovation department

and so it's the partners who are making

the move or or the decision and

>> what I found is

>> it's hard to get the entire partnership

to buy in. go deeper on this point

because I know a lot of founders is

asking me how do I sell to like a

financial firm or law firm or something

like that and it seems like this is the

the tricky part is like you have to

convince everybody.

>> You have to convince everybody or you

start smaller.

>> Okay.

>> You say let's work with this partner and

their team and make them rock stars.

>> Mhm.

>> And then everybody else looks at them

saying what's that guy doing?

>> Right.

>> That looks awesome. We also want in. And

then you expand. But the key here is to

sell sell sort of like not top down but

sell to the senior people first.

>> Right? So there's it's impossible to do

a bottom up motion in our industry

because you don't uh procure software

individually. You take it through

procurement and you take it through it.

>> Yeah.

>> And there's a lot of security checks.

There's a lot of uh data privacy checks

that you need to go through in order to

actually you know serve client data in

your systems.

>> You were 23 when you co-ounded Lora.

Yeah. Um,

>> by then you've already done a lot. You

had some stints at other YC companies

like multiple different ones.

>> Yeah.

>> What was your background before you

started this company?

>> When I was 18 and it was time to apply

to college, um, I actually had two

options. I was either going to go down

the route of becoming a professional

Dota Dota 2 player or go to college.

>> I knew this

>> and my thinking at the time was okay,

what's the best case scenario in each of

the outcomes? So best case scenario in

Dota would be to win the international

the biggest tournament in the world you

make $10 million that would be amazing.

Uh but then I was thinking what happens

then

>> you know it kind of feels like then life

stops.

>> Yeah.

>> And the best case scenario with going to

college was basically this what I'm

doing now. So I decided to go to

college. Um and when you apply to

college in Sweden um you go to one

school to do one program. So, the

engineering university is completely

separate from the business university,

which I think is really weird. Like, we

don't mix at all, which is bad. But

there was a hack so that you could you

could make an an admission to one of the

schools and then kind of pull the

admission to make another one

>> or pull your application to make another

one and then call them and say that you

messed it up and you wanted to get, you

know, reapplied. So, I I ended up uh

making it so that I could go to both

universities in parallel. It was a

really good timing during COVID to do

that because that means when you have

two lectures at the same time, you can

just have two laptops.

>> Record one. Yeah.

>> Yeah. And there were multiple times

where I had like exams at the same time

with both universities and you would you

kind of sit with one camera over here

and one camera over here pretending that

you were just doing one of the exams.

And so like one or two years into it, I

was working as a programmer. I was

building statistical models for esports

betting and that was really fun. But I

think I I also wanted to kind of see

what the business side looked like. So I

had the the the privilege of working at

a company called Norhen. It's like Y but

for impact and it's based in Stockholm.

And I think I got um a lot of exposure

to other entrepreneurs. And

>> what struck me then was one a few of

them were not super ambitious to build

companies that we're doing now, but they

sort of had this like fiveyear plan to

conquer Nordics. Yeah. So I think

immediately like I had a different take

on it and then did a short stint at

McKenzie um and worked at BAMLO and just

one week at Depict.

>> Depict was one of those companies uh is

is one of those companies that was an

incredible talent magnet. Yeah.

>> Like some incredible people have come

out of depict like an Lovable was one of

the founders. Um but there's a bunch of

others. You're starting the Gora even

though you spend a week there. But is it

it's like kind of cool how you have

these magnets that that that spun off to

a bunch of other cool companies. No,

they're amazing and u you know we're all

good friends in Stockholm. It's a small

ecosystem and it's really fun to kind of

cheer on each other as well.

>> And YC ended in uh April uh last year.

Can you walk us through sort of like the

company growth and your personal

development in this time like you were

10, now you're 100. Um what happened?

>> We grew really fast and we were also

feeling the

>> the the drag.

>> Yeah.

>> You know like we we took the product to

market and you know we would sell it in

a demo. Mhm.

>> And when law firms start to buy things

after one demo,

>> you're doing something right. And so the

rationale was like, we should be doing

more of this and we want to do it

everywhere all at once. And this is also

a space where it's kind of obvious that

legal and LLM is a good fit. And so

there were a lot of other companies in

the industry. I like to say like there

there were so many legal AI assistants

and now it just feels like many of them

have kind of fallen off and they're

emerging a couple of winners. With that

rationale, we wanted also to get

American capital uh on the in the

company because we wanted to be able to

make the move from Stockholm to the US

when the time was right. After we raised

the money during our first board

meeting, we sat down and I remembered

the look on on some of our board members

faces when I basically said, "We're not

going to sell for the next four to five

months." And the reason for that was

when we got the chance to onboard a

client, it took a lot of work. took a

lot of work to get them to a level of

understanding of what they could

accomplish in the platform. And also the

first experience of a of a legal

professional logging in is the one

chance you have. If you mess that up,

they're not coming back.

>> And we had a couple of of of situations

where we'd onboarded a lot of people and

we had done, you know, some misses and

we didn't want to ruin that. So we

worked really hard on reliability,

scalability, got the system to a place

where we could comfortably onboard a

thousand lawyers a day. And once we had

that, we kind of let it rip. And that's

also when we really started to hire. So

we were maybe 25 in the beginning of

October. And just 6 months later, we're

now 100.

>> So what we did was we said, "Okay, we're

now going to scale across every market

in Europe and we're going to start

scaling towards the US." and our initial

conversations in the US some time

because we were a small Swedish startup.

Um so I made you know multiple trips

back and forth to New York and now we

open up hubs both in New York, London,

Stockholm and also have people locally

in Spain, France and Germany.

>> So we've really gone at it and just said

hey we want to do everything everywhere

all at once and let's do it now.

>> And for you personally and Sig like what

was your experience in that? I think the

biggest takeaway and learning is going

from being an IC into delegating. Yeah.

>> And that move, you know, you know how to

do something, but you that's not going

to scale. So, you need to teach somebody

else to do it. And you need to hire

people who are way better than you

>> on a lot of different topics.

>> So, one of the early sort of hires that

we made were actually another YC founder

and we've ended up Jake. Yeah. And we

actually we've scaled the team with a

lot of entrepreneurs. And that's not

only like the skills we're looking for,

but it's also like the way that we built

the company because we're effectively

running multiple companies within the

company.

>> It's sort of like a secret playbook that

a lot of YC companies, some of the best

ones, are all following is that the

first people you want to hire all former

founders. And it's kind of actually an

advice I got from Paul Graham back in

the days is that um sometimes you think

you're a founder that I work in this

company for 3 years didn't go well. Am I

less attractive in the job market? Like

if you're here or if you're in a startup

center, you're actually more attractive

in the job market because people

actually want to work with people like

you.

>> Yeah. And we want to hire them. So um

it's been amazing. And also the agency

and the the attitude to problem solving.

That's kind of what you're looking for.

And then sometimes you need to hire for

scale, right? Like now we have a

significant sales team and you need

somebody who's seen the 100, you know,

10 million to 500 million because that's

the journey that we're on.

>> And my learning from M&B, which probably

I'm sure applies to you, is the culture

in the beginning is the people that you

hire,

>> of course. And when we've now scaled the

hubs, um, we always send a person from

Stockholm with them. It's the best

people from the Stockholm office that

then travels and set up the new hubs.

>> You seem like the kind of person who

embodied the attributes, you can just do

things. So can you tell me how that is

reflected in your company?

>> You can't just do things. And when we

started building this company, we didn't

know anything about law,

>> right?

>> Um I think that was pretty apparent in

our first interview and we you know made

the right moves from then from then to

the second one where we showed that we

could do it.

>> You apply for two different batches.

>> Yeah. The first one didn't go as well.

And so about this attribute, it's

something I look for in others as well.

Um during a lot of the interviews I do I

ask I often ask the question you know

what have you done outside of your role

for the company and here I'm looking for

creativity ability to spot problems and

solve them

>> and to take responsibility for more

things than just the stuff that you're

doing. Right. Right. And I think in

terms of starting companies and you know

building the future because frankly we

need to reimagine a lot of like the

stuff that we're doing we don't want

people who are bogged down by your boss

telling you to do something right we

have a very sort of flat organization

where let's say our marketing team we

want generalists who are using AI to do

10x more work than they could have done

in the past And where you might have

needed a 30 person marketing team, you

now need five.

>> And you want those five people then to

be complete, you know, yes. And to go

out, you know, above and beyond. And

that that characteristic, I think, is

increasingly important as well in an in

an age where if you're really ambitious,

you can get a lot of leverage out of

tools.

>> Absolutely. So, if we fast forward like

five or 10 years, uh how does the

day-to-day job of a lawyer look like?

That's interesting. We think about that

a lot, right? Um I I'm kind of viewing

it as you're more and more entering a

work space of reviewing work than

actually doing it

>> and you are managing the expectations

from your clients and the expectations

and the work from your AI agents, right?

You're you're effectively instructing

them. you're watching them go out and do

work and you're making sure that

everything they're doing is not only

correct and sort of at your standard but

you're also managing how that work gets

delivered to the client because I think

you know you you will always want

somebody who knows their stuff.

>> Yes.

>> On this and there's a big reason for why

we're working with lawyers and not with

the people who might you know use the

legal services because a lawyer is

needed and and necessary to deliver the

end product. But looking 5 10 years

ahead in in these days is also

>> it's hard.

>> It's hard, right? Um if I knew where the

AI models would be 10 years from now.

>> Yeah. We're looking weeks ahead now

right now.

>> Yeah. It's and and that's funny just

with our product road map. Um I tried to

do them kind of like many quarters

ahead.

>> Yeah.

>> Yeah. It's hard.

>> It's really hard.

>> Do you think uh that the large AI labs

are going to try to attempt at doing

law? Maybe not law specifically, but I I

do feel like they're more and more

becoming platform companies rather than

model providers. I mean, Google is

building Google Workspace with Gemini.

Anthropic is running very hard on the

MCP idea of building kind of a universal

entry point into a lot of applications.

>> I think the the expectations on

companies like us are pretty clear. you

know, whatever comes out of a model lab

is kind of expected and then

>> everything else we're adding on top is

kind of like icing on the cake.

>> How does it feel to product market fit?

>> I think the feeling is best summarized

by almost like this drag feeling or kind

of infinite

>> you're being pulled into the market,

>> right? um it like it literally feels

like we have infinite demand and I think

that's it's coming from a point of the

product is working and it's moved from

being in this experimental AI bucket

into we are relying on this for core

work that we are delivering right now if

something breaks you know immediately we

get a phone call saying hey we can't do

this like what's going on right and we

fix it it's basically been this point of

you start out. You hope that what you're

doing is the right thing and you try to

get early partners excited about what

you're doing. And in the beginning, to

be, you know, really frank, a lot of

people got on with us because they

wanted to be on the journey and they

took a bet.

>> Yeah.

>> And I am so thankful and happy that they

did that because now we've taken them

from from point A to point B and we're

continuously scaling from here.

>> So we tell companies to move to San

Francisco

>> generally.

>> Uh you decided to not take that advice.

Can you just like tell us a bit about

the thinking here and and maybe like if

you have some pros and cons?

>> Yeah.

>> About not being here?

>> Yeah. The reason why we stayed in

Stockholm was um we needed a market to

grow in. And if you go to the US, it's

not only more competitive, but I think

it kind of pushes you into becoming a

more narrow company. You start building

really horizontal and then you realize,

wait a minute, we're really good at

this. So you start to scale it in other

markets and you quickly notice ah we're

the best in Finland too and we're the

best in Denmark and we're the best in

Norway and then you scale to Spain,

France and Germany, London and then the

states and at that point we had always

you know we had already done

>> 15 new market entries. The algorithm or

the the method was already kind of

established. Of course, the US is a

bigger undertaking, but we had also then

grown from this small fish in a small

pond to crocodile or a shark in in the

bigger pond now.

>> So, you you've raised $80 million like

in midmay. You open an office in New

York. Uh you launched with one of the

most famous law firms here in the US.

>> Um it seems like you're trying to

position yourself as the category leader

of AI law in the world.

>> Yeah, I mean 100% and I think in many

aspects we're already there. It's for me

more of a question around ambition and

what's next. It's very easy to say, hey,

we see this problem. Let's go solve it.

And then you get satisfied.

>> But it feels to me like every time we

solve a problem, a new one emerges. And

we're finding that as we go deeper and

deeper and deeper in the entire legal

software stack, we're also seeing that

the line between software and service is

blurring. AI is continuously developing

super super quickly and that means we

need to do the same. And so in my mind,

the category leader in the space does

not only build software, they serve as

the strategic partner to these large

firms and they make them win in this

transition because it's a very large

transition. And that's also why we've

basically scaled headcount as as quickly

as we've could whilst maintaining kind

of culture, urgency, and velocity. So, a

lot of founders that I I meet are asking

me questions about how you build a

vertical AI company that seems like the

kind of companies people are building

now. Do you have any general advice you

want to give to those founders who are

just starting out?

>> The first kind of obvious tip is don't

get locked in with a provider and don't

compete with the AI labs.

>> The AI labs ship, right? And so does

companies like Perplexity and and

others. And so, I think you want to be

really clear and honest to yourself

where you're adding value and where

you're adding long-term moat. And this

is something that we've thought a lot

about at Leguire, like how do we build

things as boats so that when the tide

rises, just everything gets better.

>> If you're just starting out, you got to

realize that you do not have the

capacity to outperform any of those

companies.

>> You kind have to find a nar a narrow

category to do it where you know the

models won't get to or

>> either that or finding out a way to

leverage the models very creatively. I

mean in in a way that others haven't

done it. I think take AI scribing is a

good one. Like typical AI scribing is

hard to do

>> and you need to embed a lot of like

custom prompts and ways to get it right

so that it uses the right medical

language

>> which is very similar to law. Like you

needed to write clauses in a way that a

lawyer would write a clause,

>> not just what the model spits out as the

most probable answer.

>> If I'm watching this video and I'm like

I'm thinking about applying for a job at

Agora.

>> Yeah.

>> Uh tell me about what I should expect

either from the application process or

from working there. The things that we

look for are ambition and the

willingness to say we got this huge

problem. There's this huge mountain. How

do we climb it and we're also very

upfront with candidates that this is not

a 9 to5 and we're not the traditional

Swedish working environment? We have the

good stuff. We have the Fica, but uh we

we have a lot more hunger and you know,

frankly, a lot higher expectations and

we we want that not only for ourselves

but for each other because we want to

grow as people and we want to grow as

entrepreneurs and as a company as

leaders and I think there just looking

at like our our application process the

biggest thing we do is a lot of cases

right if you want to come in and work in

our go to market team, you need to come

and pitch us our product.

>> Yeah.

>> And you need to do a really strong

pitch. And you know, if you take the

engineering team, we basically ask you

to build a P of Legora

>> and you know, we want you to work with

AI generated code, but we also want you

to be able to explain it. Yeah. Right.

And to design systems that scale. And I

think

>> Stockholm is a small ecosystem. And so

it's also quite easy to make references

and see who's actually good and who's

who's been in a company and made them a

success. You know, not only was there

the right time exactly and another

really big piece is we're hiring all

over Europe. So um we've had people move

from Madrid, from Amsterdam, from

Germany, from Paris all the way to

Stockholm. uh we tend to not onboard

them in November when it gets nasty, but

I feel like we've started to build this

sort of AI hub together with many other

companies that is not only like super

fun, but also, you know, great companies

come out of it.

>> Thank you so much for coming back to IC.

>> Thanks, Gustau.

[Music]

Key Vocabulary

Vocabulary	Meanings
develop /dɪˈvɛləp/ B1	verb - to grow or cause to grow and become more mature, advanced, or elaborate
super /ˈsuːpər/ A1	adjective - of high quality, great, or excellent
quickly /ˈkwɪkli/ A1	adverb - with speed; rapidly
deeper /ˈdiːpər/ A2	adjective - extending far down from the top or surface
entire /ɪnˈtaɪər/ A2	adjective - whole; complete
software /ˈsɒftwɛər/ B1	noun - the programs and other operating information used by a computer
stack /stæk/ B1	noun - a pile of objects
seeing /ˈsiːɪŋ/ A1	verb - to perceive with the eyes
blurring /ˈblɜːrɪŋ/ B2	verb - to make or become unclear or indistinct
strengths /strɛŋθs/ B1	noun - the quality or state of being strong
company /ˈkʌmpəni/ A1	noun - a business organization
future /ˈfjuːtʃər/ A1	noun - the time yet to come
together /təˈɡɛðər/ A1	adverb - in or into one place, mass, collection, or group
winners /ˈwɪnərz/ A2	noun - a person or thing that wins
workspace /ˈwɜːrkspɛɪs/ B1	noun - an area where work is done
transforming /trænsˈfɔːrmɪŋ/ B2	verb - to change in form, appearance, or structure
reviewing /rɪˈvjuːɪŋ/ B1	verb - to look at or examine again
drafting /ˈdrɑːftɪŋ/ B1	verb - to prepare a preliminary version of a document
researching /rɪˈsɜːrtʃɪŋ/ B1	verb - to study or investigate thoroughly
fragmented /ˈfræɡməntɪd/ C1	adjective - broken into pieces
unstructured /ʌnˈstrʌktʃərd/ C1	adjective - not organized in a clear or ordered way