The Finance Startup Bringing Agentic AI to Wall Street

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 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 invested in or work with is like the ones that kind of do that and really persevere tend to win.

Today, we're here with Arne and Chas Engländer. They are the founders of Model ML from winter 24. 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 YC company before. So I'm super excited to welcome Chas and Arne here to YC. Welcome back.

Thanks for having us.

Thanks for having us. Tell us what you guys are building.

So ModelML is an AI workspace for financial services. So that's our one-liner. What that actually means in practice is we've built a workspace that's akin to kind of the office suite. So 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, You will have access to your files and folder systems, your emails, your CRM, any data vendors that you might use and pay for, real-time publicly available information, public filings, your internal custom data sets, etc.

So then we kind of build this, we call it a cognitive architecture. It's a fancy word of saying, kind of like a brain that mimics what you have access to digitally. And we overlay that with our user interface. 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 things are going?

Vertical. Look, I mean, in the last seven days, we've signed the same number of contracts as we signed in the whole of Q4. Congratulations. Thanks very much. And I think it's really just the turning point, I think, in the sector, whereas as we keep saying, there is clear, tangible value being driven by these products. And it's only going to get better and quickly.

What were people that are using Model ML using before? What were the tools that they were using in their daily work?

So they would have their data sets and then they would spend a lot of time in the office suite or in Outlook. And what that meant was A lot of that process is super manual and super repetitive. I think the key here is we're definitely not saying that humans should never do these tasks, but if you're organizing logos in a PowerPoint presentation and you've done that hundreds of times before, and you're a very well-educated analyst or associate, it's probably not a great use of your time.

I remember from, I think maybe from our interview or sometime in the first office hour, you had a unique story on why you want to solve this problem. Remember what that was?

Yeah, so we sold our first two companies and 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 started whereby we literally were, 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 genetic system that, to be honest, we were just building for fun and kind of produced like a one-pager for us.

But the interesting thing about the one-pager is whilst we receive 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, let's say we got an opportunity to invest in a startup. this thing would go off and look at their LinkedIn and their background and then look at like comparable companies on Crunchbase 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.

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 okay at building stuff and that's how it started.

And that then turned into you selling this product or a new version of that product to the top financial firms in the world, basically.

Yeah. So we can say now that we're about 10% of the largest private equity firms and investment banks in the world use our product. We have other clusters, asset managers, sovereign wealth funds, a bunch of venture firms. But yeah, it's been pretty exciting.

And how would they do their work before you start using Model ML?

So as crazy as it sounds now, right? So let me use an example whereby, let's say you're tracking a public company, right? And every quarter, every time there's a release, maybe as part of your job as an analyst or associate, you'll go to the release, the filings, and you'll put together what's called an earnings 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 fax set and so on and they'll put into these slides. And these things take ages. We're talking probably like days to a 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 ModelMHours, so think of ModelMHours 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 designs, right? And so rather than having to go and gather that information, once that release happens, this one-pager effect, it was actually three pages, a cover page, a main page, and a legal page, just appears in your SharePoint.

or Google Drive, right? And it's kind of 90, 95 percent of the way there. And we think in some cases more accurate because actually trawling through these filings as a human, and because you can also pull from multiple data sets for the same figure, 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. Cause 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 the things that models could do a year ago that were impressive and what can they do today and what are they going to be able to do in the future?

the whole industry or the industry we're in right now, 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 trialing something or looking at 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.

That's good. That's good. That's the most valuable thing he's going to bring into B2B. B2B people don't always know the most important stuff.

100%, which we think is madness. But this year, the whole world went from testing to using. And there was a fundamental shift. I think these are genetic systems. There were small things, like the improvement in things like function calling with some of the newer models. But everything has become considerably better by itself. And that's the interesting thing. 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? And so when you're constantly working on something and you're pushing the boundaries, you know, month to month what's possible is like just wasn't possible the previous month.

I also think the vision models. Yeah. I mean, when they first started coming out and improving, we were seeing next to each other, we were getting 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 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 or a large proportion of it is is kind of where it was even six months 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 put it in a structured format. 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 I think that will take a little bit of time in terms of confidence, but I think some of 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 that this, I'll tell you like the 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 may 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, 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 this is the year when that turns into actual contracts.

Exactly. And 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 thing happened in the last 12 months.

Exactly. And last year was the year of proof of concepts. And now our average contracts are in the years, first of all. And I just think, in general, people are seeing a lot more shorter term or instant value. I also think just on the note of these firms historically, inclusive of law firms and others not buying software, that is I think true. I think the big difference here is this is like number one thing from the very top, this is like the number one thing on everyone's agenda. So we are not selling the next CRM or the next data vendor tool or anything like that.

We are selling what we're describing as the most advanced AI solution for financial services in the world, right? And I think if you are a CEO 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 meetings? Who's the decider? Who decides to buy the software and what are they like?

CEO level, or in general, just the most senior people at the firm, regardless of if you are a top five or top 10 investment bank priority firm, I said I'll solve them up and whatever it might be. Which I think at the start, we found like really strange, because we sort of thought that this would be looked at a team or group level. It's really not. I think it's so important. Firm wide 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 You know from the right person at the top But then you've also got to get buy-in from the people that have ultimately get going to be implementing the tool

So you guys are flying to meet all these firms?

Wherever they are? Wherever they are. So we have a bunch of people working out of Hong Kong and Singapore now. We've just opened a small office in India. About half of our overall team are based in London and we have an office in New York. 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, high use case frequency, right? So then if you think about why they wouldn't sign with you, a big part of that, I think, is trust and building that relationship.

You know, because it's also a different dynamic, you know, it's like a lot of times 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 FaceTime and getting in front of people, obviously, but then spending the time on the demo and how that's justified 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 Lama and Fancy. Before we get into the details of what they were doing, like what are some of the learnings you took from those companies in Jamal ML?

I would say you've definitely got to enjoy it. You've got to enjoy building companies is my overarching thought. What about you? I think you've just got to be prepared for the worst.

You've got to be prepared for the worst and the most ridiculous roller coaster experience. I think All Star at Fountains 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. And as Chas says, just enjoy it because it's fun.

And is there a sense that when you start a third company that 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 calmer. But 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 perseverance, not blind perseverance, but perseverance. And I think there's a clear difference where if something logically makes sense, right, like if you just, if you're 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 not let anything stop you and I think like the consistency that we've noticed that 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 so I think that's probably the biggest learning for us just persevere at all costs.

Yeah, for sure. 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. So hiring was new to me. And I think, to be honest, I didn't really have a clue what I was doing. And we still don't. Yeah, and hiring is always tricky. I think one of the biggest things right now, and we say it a lot, probably the biggest thing that comes up in an interview is how much do you think you enjoy working with that person?

I think, you know, fancy, again, being sort of inexperienced and maybe naive, it was all about where they worked before, you know, what's on their CV. And you kind of ignored the little things that maybe you shouldn't. And now it's kind of the main thing that we look for. We're going to be spending a lot of time with these people. We work a lot and everyone on the team does. You've got to want to work with this person. 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, we hire very slowly and we try and make sure we hire the right people. And it feels like 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 just even more important than it has been before. And yeah, the other aspect that 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. And I think certainly in that initial period that you've got to do that. And I think, you know, our team works six days a week at the moment. 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 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 day today? You know, the question that we ask people is like, you know, what have they been building? You know, regardless of whether they're in engineering, right. And soon as they start to mention things like, oh, they've been doing a bit of vibe coding or they're sat in the other way, 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 at that time it really matters to work hard because they're not the only one trying to go after this market.

Yeah, yeah. We just don't like losing.

Maybe go back to like Winter 17, Fat Llama. Tell us about Fat Llama.

So Fat Llama was a marketplace that allowed people to rent items from people nearby. But the main difference is, you know, you were insured. So 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. That was what we did differently with the model. That model had been tried a lot of times before, but it still took us three years to find product market fit. I sort of defined product market fit loosely as the unit economics add up. People want what you have in terms of product in a sort of relatively large addressable market.

But it took us three years to get there. And I think that was You know, we learned so much during that period. And so, yeah, 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 last mile grocery delivery business, which now everyone's probably bored of speaking about them. But back then, so this was end of 2019, start of 2020. So our model was a little bit different to a DoorDash or an Instacart. we were what's called a vertically integrated model which means 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 out of Turkey but in the UK in particular we were the first ones and really it started as a delivery app for students.

I was a student at the time and it really came about the need. I was really sitting there one day thinking I could do with some Pringles or some a beer to be honest and I didn't want to walk to the corner shop that was like five minutes away. And this was during Covid. So this was just before Covid. This was like you know three months before Covid and we kind of built the app MVP in sort of four to six weeks. I studied computer science at uni and so it was just before Covid and really almost instantly as opposed to Fat Loma We kind of found product market fit almost overnight, which sounds obvious, right?

It's like you're delivering sort of beer, ice cream to students at the same cost as they would get it from the corner shop. So we took off and that was obviously really exciting. And then we got onto YC and then Covid happened. for our business was really that shot of adrenaline. I mean, COVID happened and everyone was staying at home. The business really then started to take off. It was an amazing business. It's an extremely difficult business. I'm sure we'll come onto it in a bit, but we got hit with, we always like to say a lot of cricket bats to the face.

A lot of stuff can go wrong, but it was awesome. So we continued to grow in the UK. We raised money out of YC and then we got an acquisition offer from GoPuff. I think it was about 18 months after we started. And GoPuff at the time, a market leader, they were very, very big in the US and they wanted an opportunity to come into Europe and we were best placed for that. 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. But again i think i was the polar opposite story always talk about with a lot in terms of perseverance was you know i was you know maybe very early twenties obviously and. You know that time when you believe in something and you're raising a small angel around so this is pretty why see right. is we were raising, I think, maybe $50,000, maybe $100,000, which nowadays is like, I suppose back then, at least then it meant everything to us.

We were scraping around, we were doing half an hour pitches with an angel that might put in $2,000. We were just doing everything we could be fundamentally believed in this idea. And Anyway, so we built the MVP, we launched and I started accounting. So I was big on like, yeah, other numbers gonna 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 the rental was for $600. So I'm like over the moon.

I'm updating. 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 to the moon. So that was on Friday, it was due to be returned on Sunday lunchtime. And remember the criticism I got, I probably did a hundred pitches to raise money and maybe one out of a hundred, I must have done close to a thousand in the end. But 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 is going to work. Anyway, so we had, and that's all I'm thinking, it's all we're thinking about at the time. So we had this first rental. Anyway, I got to Sunday and the lender of the item called us and said, I can't get a hold of the borrower of this item. So we're like, okay, it's fine. And then we realized after a couple of hours, he wasn't responding and we start to get a bit nervous. One of the things we did, it was a native app and it's when people search, because it helps with what you share in the search results, if we save or we look at the latitude and longitude, so quite an accurate geolocation of the individual.

So we had that. So I was like, okay, I'm going straight out there. We've got to go and find this item. And this was like an 1800 pound 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 because it's just like, it's that awful. And the door was there, the door was open. I couldn't believe it. So 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 Lenders house. I arrived at the Lenders house. But you've got to picture this, right? What's going through my head is everything that we've pitched, every person I've 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 Arns and I. And they bought into this. And so all I'm thinking is like, I've lied to them.

My whole world was falling apart at this point. And so anyway, I arrived at the borough's house, 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. And he was like, and you're wearing a fat armysuit. And I was like, yeah, we deliver the item back automatically. He was like, this is awesome, man. Like, close the door. And so we it did add up, you know, in terms of the insurance and 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.

And it ended up doing so. But yeah, it was tough.

Both outcomes seems like really, really great, though, like it's like such a well for this company.

Absolutely. I mean, Arns did, what, the first 1200 deliveries, I think.

Yeah, I think with Fancya, from the outside, it looks like a very fairy tale story. Everything after the fact. Exactly. They're like, wow, it's like, I should start a start-up. You should, but really, it doesn't really, it's not all what it seems. You know, with Fancy, we had so many disasters, you know, and so many times where we were like, God, is this really going to work? You know, even when COVID happened, you know, we had to stop delivering for a couple of weeks. You know, there was a point, I remember, I don't know if you remember this, sort of maybe two, three months into the company.

So we were working with Stripe as our payment provider, and one day they just shut us off. Because apparently there was a breach in their terms of service, which later was a misunderstanding. But anyway, they shut us off for probably three or four days. 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, but maybe 500 orders a week.

We didn't have any inventory, so there wasn't a problem with that at least.

Yeah, exactly. And so at this point, what do we do? We've got orders coming in. We can't take payments. So we ended up just taking payments over the phone, taking payments with cash, taking payments with PayPal. 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, It really was an intense situation, but that's really what made it fun, right? I think building a startup, you just become so thick skinned and problems come about on a daily, hourly basis and your mood is like this all the time, but it's epic, right?

On the payments one, we emailed Patrick directly and he responded. He sorted us out. So if Patrick says, thank you, Patrick, he sort us out. But I think on both of those, the general story is the team delivered the first 1500 orders. And so ourselves, so it was very much a case of you're here, but rather than coding at night, we're coding during the day because really most of our orders were kind of in the evening. And but what that meant was we were speaking to a customer for every single order. All right. And so the customer feedback wasn't just like real time that you hear about.

It was at 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 just 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 customer centric, 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. How are you going to avoid that?

Well, I mean, you speak to everyone you can talk about on the product side. I think 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 a sales stage, but also a pure customer feedback stage. So naturally, and I'm 100% involved with that. And it's also what I enjoy as well. Spending time. 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. But you're also doing customer feedback calls constantly, right?

Yeah, I mean, you know, it's always a wisy mantra. I think, you know, you've 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? And it's that constant iteration, right? We always say, you know, the quicker we can ship things, the quicker we can learn.

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 and in the details.

YC's next batch is now taking applications. Got a startup in you? Apply at ycombinator.com slash 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 group office hour topic where we talk about what motivates you to build a company. 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 first two companies and then maybe what it is today?

You know, when we have our one-to-ones, we're not brainstorming how to raise money. 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 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 that really has been the story across all the companies. I think what's been nice about this and surprising about this is we questioned ourselves going from two consumer brand heavy type businesses into a B2B.

But actually, particularly in the world of AI, the most important element is the B2C element. And that is, to us, the most rewarding element. 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. Because they've never seen that before. And they've never seen before. They've never seen it.

It's 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.

Like Chad said, you just, you know, it's priceless. I've noticed this with AI products that often the customers cannot even imagine what the solution will look like because the models are so good and they're so advanced right now. And very often, you build it for them first. You can't really figure out what the problems are because it's not a specific problem you're solving. It's just like a whole other league of solutions.

Yeah, and the difficult thing as well is you do have to rethink things slightly from the ground up. So I do think that at least as of today, our product, 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 type of technology, will remain quite consistent. In other words, like a long-form document, a storytelling presentation, and tabular across Excel, Word, and PowerPoint. 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 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 right 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 tasks will happen entirely autonomously.

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. 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. Tell us what you've learned about the most important thing. And maybe for the audience of founders who are thinking about finding a company, what should I be looking for in my co-founder? And what's important and what's not important?

I think we mentioned it previously around hiring. I think this is sort of tenfold when it comes to your co-founder. You're going to spend so much time with this person, more time that you spend with your family, with your partners, with your friends. So I think first and foremost, Is that person someone that you want to spend a lot of time with?

We're still not sure. I think if you were not sure, you wouldn't say that.

Exactly. Otherwise, it would be really awkward. So look, we've always had a really good relationship. I think not all brothers working together, I think is a good idea. I think we've been fortunate enough, we've always had a good you know, personal and working relationship. One of the things that makes things really easy, I think is, and again, 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. There can't be any miscommunication, lack of communication.

We all know one of the biggest reasons, if not the biggest reason why startups fail is found to fall out. And I think with us, we're always very good at communicating, trusting each other.

Another thing on the trust piece then is the way that we describe our Venn diagram. I think I'm an engineer, but I'm not actually an engineer. I think I am. I only studied computer science. I studied accounting. So our Venn diagram, and I think if you're thinking about another co-founder, I'd really consider this, is Arnie obviously handles sort of engineering products and I handle kind of finance commercials and products as well, kind of in the middle, right? So the overlap in our Venn diagram is basically 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 love, but we also love all the other stuff. And I think like that clear 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, for example, Arnie may have studied CS. But if I really am interested in wanting to basically do Arnie'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. And that tends to last for a long time.

as well. I got this email, I think it was yesterday from a person who was in the 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 with AI, but it doesn't seem to motivate me enough. I want to make something much bigger. Like when I have a bigger impact in 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, or you're thinking about starting a company, but want to do something big, what would you tell them?

I think we're probably biased, because we probably favor starting a company before going to work at a big corp. I think first and foremost, we'd be very honest with that. As we mentioned before with our fancy story, you look from the outside and it might seem like sunshine and rainbows. But it's really hard. Building a business is really, really, really hard. It's really fun if you enjoy that stuff, but it's really hard. So really, you've got to say to yourself, look, you might be building a business for the next five, 10, 15 years. They might not go anywhere.

And you've got to be OK with that reality. 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. And if that sounds good to you, then build a startup. And if that doesn't sound good to you, then I probably recommend probably getting a job somewhere else and then develop over time. And then if you feel like you're more ready to jump into the startup world, then do that.

So maybe if you're a builder and you enjoy seeing 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 enjoying that. I then think another different way to look at this is think about, I know it's maybe slightly morbid if that's the correct word, but if you're lying on your deathbed and you look back at your life, what do you want from your life? And I think we do that a lot. And basically, what we conclude is we just want to have been impactful with our time. We want to have left the world a better place, but in a way that we've just built things that people enjoy using. And I think that's really what motivates us.

But I think really that one point of, you've really got to enjoy the journey. And we just love building.

It 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.

Go all in. Go all in. Like all in. And I think if anything, more is more in general.

I remember when I met with Paul Graham sometime around the beginning of the batch, and I was doubting whether it just doesn't work out. Like maybe my career is fucked. And he said something like, well, if you're 26 and you're poor, that would be the worst outcome and probably most 26 euros don't have any money anyways.

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 gonna learn so much over such a short period of time and that is the worst case because often when you compare this to like you know we often now when 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 year.

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, they sort of like five 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 the 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.

first principle thinking. They just got to move faster. 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 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, we need this now. So it's things like that where you get, you know, caught up in the I guess the big company thinking, which makes sense in certain environments. But when you're working at 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, I think you in particular really try and just say, look, we need this now, instead of all these spreadsheets and slides.

I think that is the first lesson. It's like everything that you have learned for five years, try and unlearn immediately. I think just in a sense, it's different. You just got to move so much faster. I think building a plane going down the runway is the best way to describe things. I think that is the quickest way to learn. It's different when you're, you know, where we are now, it's different. You know, the business, you know, we have large customers in production, 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.

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 better way to build a company.

And you guys chose to do YC three times. Can you describe, are you thinking here?

The second and third time it wasn't a financial thing. We still get asked. We've had loads of teams, people that work 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 way I sort of answer this is you cannot replicate that. I remember very clearly, Michael Seiborn in 2017, the first office hours we had, a young English lad, 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. The second aspect which definitely cannot be overlooked is the instant support network that you get. I think us being from Europe, working seven days a week, it's very unusual to say the least. There's not many people that we can call on. 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.

Whereas in the Bay Area, particularly, and driven by YC and through YC, you get this instant network that you cannot get anywhere else.

And you ended up being in the batch with 24, which was sort of like one of the first really big AI batches where most companies were building companies similar to you. 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, again, working side by side with people building really cool products, working seven days a week, and that buzz around the office, around San Francisco, it's really, really hard to replicate. And we absolutely loved it, didn't we?

Yeah. It's interesting you mention that because that really was That was like the first batch where it was like through and through just, you know, AI companies pretty much. And like, it felt like in that Slack group, didn't it? It felt like almost daily or hourly, that was like a scientific breakthrough. It was like phenomenal to be a part.

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. I was in London a year, year and a few months ago. It's from European founders is where they should be based and where they should be building their companies. I don't have the right answer. It's a controversial topic. You guys are based in London. But I'm curious what you think as you're thinking about this question.

Go on then. This is a tough one. It's tricky.

I think we absolutely love San Francisco. We've said it many times every time we come here. It's the best place of all. There's something different about it 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 Fancy, we were a remote batch, right? So we didn't come to San Francisco. And then when we came here again 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 Z. And in the UK that just doesn't happen. Clearly. Clearly. So look, we love the UK. If we were to start a company again, I mean, we're trying to convince people to move to SF for this company. We think it's an incredible place to build a company. And like we say about work ethic, it is really challenging, we think, in the UK in particular, to find people who frankly want to work this art.

I think in San Francisco, it's a lot more common. I think in Europe, it's just not. One of the positive things about Europe, and we always said this, is talent and particularly engineering talent, which might sound counterintuitive. I think you've got amazing engineering talent 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.

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, I'm going to give you advice. 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. London is one of them. But Europe is not one country. So it's like a big culture shift to, or maybe not big, but it's a big change to move from, I don't know, Spain 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 hiring process earlier, You've just got to be really rigorous with your hiring process. There are amazing people and amazing talent in these cities. You've just got to find them. So I think don't settle for second best. You've really got to find those people that are self-motivated, hungry. Chances are if they know about YC, they've got the right attitude.

And I suspect a lot of your customers in the US.

Pretty much all of them. I'd say 80% of our customers are US.

How do you handle that?

Well, I spent the bulk of my time in New York, Hans spends the bulk of his time in London, and then kind of split between Hong Kong 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, you know, back when we made the decision to move the engineering team to London, that would have influenced things.

But yeah, my view for what it's worth is I think people should do what it takes 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, you know, folks that are also building stuff. Awesome. Thank you so much for coming. It was great to see you guys. Yeah, great to see you again. Thanks very much.