Y Combinator Startup Podcast - The Finance Startup Bringing Agentic AI to Wall Street

Episode Date: July 31, 2025

Brothers Chaz and Arnie Englander started Model ML after building and selling two YC companies. What began as a tool to help them analyze deals has grown into a full AI-powered workspace purpose-built for financial services, empowering firms to create automations and workflows that reflect exactly how their teams operate. And it's already being used by 10% of the world's top investment banks and private equity firms to automate everything from client-ready PowerPoint decks to deep-dive research and due diligence—by orchestrating AI agents that work like expert team members. In this conversation with YC Partner Gustaf Alstromer, they discuss going from internal tool to production platform, the power of perseverance, and their ambition to build a billion-dollar company with just ten people.

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 are 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 Arnie and Chas Englander. They are the founders of Model ML from one to 24. Prior to model ML, they started two other YC companies that both were successful and sold, Fancy and Fat Lama. 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 Arney here to YC. We'll come back. Thanks for having us. Thanks very much. Thanks for having us.

Tell us what you guys are building. So Modlamel 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 agenic system that kind of mirrors what a human has access to at the firms we work with. So quite specifically, if you're a human at firm X, right, you will have a human at a firm 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, you know, 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. Wow. Congratulations. Thanks very much. And I think it's really just the turning point, I think, in the sector, whereas as we keep saying, it's like, 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 their tools that they were using in the 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, right? And, you know, what that meant was, you know, a lot of that process is super manual and super repetitive. You know, I think, you know, the key here is we're definitely not saying that humans should 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. Right. And I remember from, I think maybe from our interview or sometime on the first officer, you had a unique story on why you wanted 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 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.

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 GENCic system that, to be honest, we would just build. for fun and kind of produced like a one-pager for us. But the interesting thing about the one-pager is whilst, you know, we received some information via email, what was in the one page, probably 90% of that information was unrelated to what we would have received via email. In other words, you know, let's say we got an opportunity to invest in a startup, all right? 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's 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, you know, we can say now that we're about 10%. about 10% of the largest private equity firms and investment banks in the world use our product. You know, we have other customers, asset managers, sovereign wealth funds, you know, a bunch of venture firms. But yeah, it's been pretty exciting. And how would they do, how would they do in their work before, before you start using MLML? 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 and 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 fact set and so on. They'll put into these slides. And these things take ages. We're talking probably like days to the entire company,

just to pull together these slides, right? Because everything has to be linked back to source and checked and so on. And what you can do in Model Amower, so think of Model Amower as 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 page or effectively was actually three pages. cover page, your main page, and a legal page, just appears in your SharePoint or Google Drive, right? And it's kind of 90, 95% in the way there. And we think in some cases more accurate because actually trawling through these filings as a human, because you can also pull from multiple datasets for the same figure. Yeah. Yeah, it can be more accurate. I'm assuming that you built something in the beginning and there was a model that could do some amount of this work. Because you did Y.C how long ago? A year ago. A year ago. So the models have probably progressed a lot in that year.

What were things the models could do a year ago that were impressive and what can they do today and 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. So everyone wanted to make sure they had some sort of exposure, like as in they were they were trialing something or looking into something, but it was still testing, right?

And us having ran consumer companies, we don't really care about revenue figures. We've always been obsessed with like usage and retention. Right, right. That's good. That's good. The most valuable thing he's going to bring into B2B. Like B2B people don't always know the most important stuff. 100%, which we think is madness.

But this year, everyone kind of, the whole world went from testing to using. So there were, and there was a fundamental shift. I think these agenetic 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, right? It's like I think even if we do nothing, theoretically our product will improve, which is a pretty interesting world to be in.

And so when you're constantly working on something, you're pushing the boundaries, you know, month to month what's possible was 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 We were going crazy, weren't we? It was like the stuff that we could do with the vision models, if you think analyzing files as an example, you know, OCR, okay, it 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 kind of where it was even six months or so ago, right? You know, what we're seeing today is, you know, if you look at some of these data providers, they've got humans reading information from, say, public filings and put in that in structure 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. I'll tell you, like, at YC, the evolution went through at the same time. So two years ago we would say, oh, you can't really sell software to investment banks or private equity funds.

They don't really buy software or if they do, they buy one piece of software every 10 years. And the same was true for lawyers. But something happened last year, like they all got very AI curious. Yeah. Curious about AI, they were like, 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't, you can no longer say these companies don't buy software.

don't buy software, that's wrong. They're all buying software now, and that's because this thing happened in last 12 months. Exactly. And last year was the year of kind of proof of concepts. And now, you know, our average contracts are in the years, first of all. And I just think in general, people are seeing, you know, a lot more kind of shorter term or instant value. You know, I also think just, you know, 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 like the next CRM or like the next data vendor

tool or anything like that. We are selling what we're describing as the most advanced sort of AI solution for financial services in the world, right? And I think if you are a 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. You describe some of these sales is like who's the decider like 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 ten investment bank priority firm I said or sovereign wealth and whatever it might be which I think at the start we found like really strange because we all we sort of thought that this would be looked at 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 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. And then you've also got to get buy-in from the people that have ultimately going to be implementing the tool. So you guys are flying to meet all these firms where they are? 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, you know, high use case frequency, right? So then if you think about, like, 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 time you're speaking to folks that, you know, if they make a wrong call here, they could get fired, right? And so we spend a lot of time building that trust. I think the 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, Fat Lama and Fancy. Before we get into the details of what they were doing, what are some of the learnings you took from those companies into model ML? I would say you've definitely got to enjoy it. You've got to enjoy building companies.

It's 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 start-up 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. Definitely. But there is certain things that you just cannot prepare yourself. You know, I think like, you know, like it just in general,

So 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 would 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. We still don't. Yeah, hiring is always tricky. I think one of the biggest things right now, and we say it a lot, that's 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 an experience 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, you know, that maybe you shouldn't. And now it's kind of the main thing that we look for. You know, we're going to be spending a lot of time with these people. You know, we work a lot and everyone on the team does. And you've got a lot of work with this person. So we often meet in person multiple times and make sure that the right cultural fit. And it really makes a different. And I think right now, you know, 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, dare I say, 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've started to ask more questions around, look, all the other sort of standard stuff that has to be done. I think it's important to have the kind of standard

interview type structure. But are they going to enjoy their day today? You know, the question that we are ask people is like, you know, what have they been building, you know, regardless of whether they're in engineering, right? As soon as they start to mention things like, are they've been doing a bit of vibe coding or this, that and 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 that at that time, it really matters to work hard. Yeah. Because like they're, they're not the only one trying to go after this market. Yeah, yeah. And we just don't

like Lizzie. Maybe go back to like Winter 17, Fat Lama. Tell us about Fat Lama. So Fat Lama was a marketplace that allowed people to rent items from people nearby. With 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 you know, the unit economics add up, people want, you know, what you have in terms of product in a sort of relatively large addressable market, right? 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 Lama 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. 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. So 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

are the first ones. And really it started it as a delivery app for students. You know, I was a student at the time, and it really came about the need. You know, I was really sitting there one day thinking, God, I could do with some, you know, Pringles or some beer, to be honest. And I didn't want to walk to the corner shop. There was like five minutes away. And this was during COVID. So this was like, you know, three months before COVID.

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, which, which sounds obvious, right? It's like you're delivering sort of beer, ice cream to students in at the same costs as they would get it from the corner shop. Yeah, so we took off and that was obviously really exciting. And then we got onto YC and then COVID happened. COVID, you know, for our business was really that shot of adrenaline.

I mean, COVID happened, everyone was staying at home. You know, 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 on to it in a bit, but we got hit with, we always liked 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 place 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, right, I'd say. But again, I think Fat Lama was the polar opposite. The story I always talk about with Fat Lama in terms of perseverance was, you know, I was, you know, maybe very early 20s, obviously. And, you know, at that time, when you believe in something and you're raising a small angel around, so this is pre-YC, right, is, you know, we were raising, I think, maybe $50,000, maybe $100,000, which, you know, nowadays I suppose back then, at least then it meant everything to us. You know, we were scraping around. We were doing half an hour pictures with an angel that might put in $2,000, right?

You know, 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, you know, are the numbers going to make sense, right? So I had my like forecasted average transaction value, my retention, everything else. So we launched and pretty much straight away, like within the first day, we got a rental. And the rental was for $600. So I'm like over the moon.

I'm update. One of the first things I did is I'm straight into the financial model and I'm updating the average order value. And this thing is looking crazy. I'm like, we're going. We're going to do. We're going to do. Right. And so that was on Friday.

It was due to be returned on Sunday lunchtime. And remember the criticism I got, you know, I probably did a hundred. hundred pitches to raise money and maybe one out of 100, you know, I must have on close to a thousand in the end with T.S. But all the other 99 was like, no one's going to lend out an item because everyone's going to steal them. And even if you have insurance, the insurance is going to work, right? Anyway, so we had, and that's all I'm thinking. You know, it's all we're thinking about at the time. So we had this first rental. Anyway, got to Sunday and the lender of the item called us and said, like, I can't get hold of the borrower of this item. So, okay,

it's fine. And then we realized after a couple of hours, like he wasn't responding. And we start to get a bit nervous. One of the things we did, it was, you know, it was a native app and when people search, because it helps with what you show in the search source, so 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, yeah, I'm going straight out there. We've got to go and find this item. And this was like an 1,800 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, 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, is what's going through my head is everything that we've pitched, every person that I pitched, family and friends, we had spent months, probably close to a year at this point, convincing people that this would work. And they believed us. And they believed Darns and I, and they bought into this. And so all, All I'm thinking is like, I've lied to them.

You know, this is like my whole world was falling apart at this point. And so anyway, I arrived at the borrower's house, or the lender's house rather, open the door. And he was like, hey, you're not a lender? And I was like, oh, I know. I gave him the drone. He was like, and you're wearing a fat alarm tissue. And I was like, yeah, we deliver the item back automatically. He was like, this is awesome, man.

Like, close the door. And so it did add up, you know, in terms of the insurance. And we focused on verification, but it fundamentally came to that perseverance piece. This 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 said you have well for this company. Absolutely. I mean, Arns did what the first 1,200 deliveries, I think. Yeah, I think with fancier, from the outside, it looks like a very, very tell story. Everything like, like after the fact. Exactly. Exactly. And they're like, wow, it's like, I should start a fairytale story.

start up and I was like, you should, but 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 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 shut us off because apparently there was a breach in in their terms and service which later it was a misunderstanding but anyway they shut us off for probably you know 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 you know maybe 500 orders a week in inventory so there wasn't a problem with that at least yeah exactly and so at this point well 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 from 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, you know, problems come about, you know, on a daily, hourly basis and, you know, kind of your mood is like this all the time. But it's epic, right? On the payments one, we emailed Patrick directly and he responded. Like within hour, he sorted us out. Yeah, it was awesome.

So if Patrick Lise says, thank you, Patrick, he's sorted us out. But I think on both of those, the general story is, and the team delivered the first 1,500 orders, right? And so, you know, ourselves. So it was very much a case of, you know, you're here. But we, rather than coding at night, we're coding during the day because really, most of our orders were kind of in the evening, right? And but what that meant was we were speaking to a customer for every single order.

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 maintaining the foundations of being incredibly customer centric, things tend to go okay. 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'd learn from customers.

How are you going to avoid that? Well, it's on, I mean, you speak to everyone. You can talk about on the product side. I think on the cell side particularly, because also we have a proof of concept face still, right? So we still have a trial phase, which is this nice blend between kind of a cell. stage but also a pure customer feedback stage. Right, right. So naturally, I'm 100% involved with that and it's also what I enjoy as well, right?

You know, spending time, you know, I don't like demoing on a screen. I like sitting with a user with a laptop, how they're going to work and working on the product together. And that also is the best way we found to sell. But you're also doing customer feedback calls constantly, right? Yeah, I mean, you know, it's always a wicy mantra. I think you've got to speak and listen to customers and that's really what we try and do 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 of a sour topic where we talk about what motivates you to build a company. And it's mostly to surface sort of the motivations for a family. 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 we 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. You know,

and 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 like the B2C element. And that is to us the most rewarding element. like when you do a demo to someone or they click run and they do something and it is just there's no money that can buy those moments

because they've never seen that before they've never seen four they've never seen it it's like with our previous companies with fancy when you'd be at the door with their delivery sort of 10, 12 minutes off they ordered it and then you saw their face like what the hell's going on you're already here like chas said you know it's priceless I've noticed this with AI products that they often the customers cannot even imagine what the solution will like because the you models are so good and they're so advanced right now and very often you just have to build like you build it for them first like you can't really figure out

right figure what the problems are because like it's not a specific problem you're solving it's just like it's like a whole other like a whole other league of solutions yeah and and you know the difficult thing as well is you you do have to rethink things slightly from the ground up so I do think that you know at least as of today if our product we made it we made a call that we would kind of to rebuild our version of like PowerPoint Word and Excel because 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 would stay the same. So you're not trying to get people to change the current... We're not trying to get... Because we just think that that's what people are very used to. However, we think what's, if we look at what we're seeing this year, a lot of what's happened up until now is sort of humans are still coming into these systems and they are like clicking run on something, basically, right? We think the biggest shift this year is going to be that even that element won't happen. And therefore, elements of the user interface, we think will be less important. In other words, these 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-funders, too. Tell us what you've learned about the most important thing. And maybe, like, for the audience of founders who are thinking about finding a company, like, what should I be looking for in my co-founder?

and 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 know, you're going to spend so much time with this person. You know, more time that you spend with your family, with your partners, with your friends. So I think first and foremost, is that person someone that you want to spend a lot of time with? You know, obviously, we're still not sure. Yeah, we're still not sure.

I think if you were not sure you wouldn't say that. Yeah, exactly. Exactly. Otherwise, it would be really awkward. It would be really awkward. So look, you know, we've always had a really good relationship. I think not all brothers working together, I think, is a good idea. I think, you know, we've been fortunate enough.

We've always had a good, you know, personal and working relationship. One of the things that make things really easy, I think is, and again, not all siblings are like this, but there's no filter between us. You know, we're very transparent. We're very honest. And I think, you know, with your founder, you need to be exactly that. You know, there can't be any miscommunication, lack of communication. Like we all know one of the biggest reasons, if not the biggest reason, like, you know,

startups fail is found a fallout. And I think with us, we're always very good at, you know, communicating, trusting each other. Another thing on the trust piece then is the way that we describe our Venn diagram, you know, I think I'm an engineer, but I'm not actually an engineer. I think I am. Arnie 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 Arne 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, like for example, Arnie may have studied T.S. And I may have said, 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 start by 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 narrowing to AI. But it doesn't seem to motivate me enough. I want to make something much bigger.

Like when I have a bigger impact on the world, like if you got that email, if you would give him advice, Like, what would you advise someone who's in their really early 20s or in school and they're thinking about their career right now or maybe in the world of AI or we're thinking about starting a company, but want to do something big? Like, what would you tell them? I think we're probably biased because, you know, we probably favor starting a company before going to work at a, you know, a big corp. I think first and foremost, we'd be very honest with that, you know, as we mentioned before

with our fancy story, you know, you look from the outsider that might seem like, um, you know, you know, 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 okay with, you know, 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'd 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 you stuff in people's hands, the small of the company you work for, maybe you're even your own company,

the faster it gets than the customer's hands. 100%. And enjoying that. I then another different way to look at this is, is, you know, think about, I know it's maybe slightly more, but if that's a correct word, but, you know, if you're lying on your deathbed, you know, and you look back at your life, like, what do you want from your life? And I think we do that a lot. And basically, you know, what we conclude is we just, we just want to have been impactful with our time. You know,

we want to, you know, left the world a better place, but, 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 know, you've really got to enjoy the journey. And we just, 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. Hell no. 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'll 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 going to 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 hard to change when you're 30 and you have commitments. Definitely. Way harder. Yeah. When you're hiring people, people that come from finance or quant backgrounds, are they things that they have to unlearn about

their job, either about the company they're working for, about their industry in order to work for a tech startup serving the same industry? First principle thinking, eh? They've 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, well, they won't, but they will try, and 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 say, look, we need this now, instead of, you know, 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 the sense it's different. You know, you just got to, yeah,

you've just got to move so much faster. I think building the plane going down the runway is the best way to describe things. You know, I think, you know, you know, that is the 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 IC three times. Can you describe sort of like how you're thinking here? second and third time it wasn't a financial thing. We still get asked, like still, we've had loads of teams that, you know, 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 manage 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. You know, I remember very clearly, Michael Cyberwin, on in 2017, you know, the first office hours we had, you know, a young English lad, you know, we report on our numbers and I reported monthly. And I remember Michael been 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, all right? That's the fact, the second aspects which definitely

cannot be overlooked is the instant support network that you get. You know, I think, us being from Europe working seven days a week, it's very unusual to say the least. And there's not many people that we can call on. And in fact, a lot of the people that we surround ourselves with outside of work, they fundamentally disagree with the way that we work and how we're building and why we're building, particularly when we don't need to from a financial sense. Right. I think that's different. Whereas in the Bay Area, you know, particularly and driven by YC and through YC, you get this like instant network that you just, you cannot get anywhere else. And you ended of being in the batch went to 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's just crazy. I think I think again working side by side with people building really cool products working seven days a week and that buzz, you know, around the office around San Francisco, it's really, really hard to replicate. And we absolutely loved it, didn't I? Yeah. And it's like, it felt, 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. Yeah.

It was like phenomenal to be 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,

is from European Papua. founders is where they should be based and where they should be building their companies. And 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, and like as you're thinking about this question. Go on, then. Go on, 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, well. 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 would 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, 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 mean, give 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 a hiring process earlier, you've just got to be really rigorous with the hiring process. You know, 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, and 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 one 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 our London is out of San Francisco. So I think if we'd known more about that, you know, back when you 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 1 city and try and be as close to, you know, folks that are also building stuff. Awesome. Thank you so much for coming. It's great to see you guys. Yeah, great to see you again. Thanks very much.