Andreas Cleve (Corti): Why constraints can be an advantage in vertical AI

Speaker A: But I think that sort of evades the market a little bit here. Pre IPO frenzy in the US everybody's just talking about bigger models. But although their models have got like an order uh, of magnitude or 2 bigger, they're not closer to BFDA compliant. Right. Whereas today that same hospital might have a surgery for a very, very small body part. That's specialization. That's great because that means we all get better healthcare if we can get access. So the thesis here is very much healthcare. Healthcare is not one market, it's many markets. There are billion dollar markets, they're very deep. If you are like spelling one letter, one word wrong, the entire situation changes. That's created a situation where like 90% like kind of good looks great in demos. What you can then do is what, what the best application guys are doing out there. They're building massive scaffolds around their, their platform to make sure that their exact corner like core market can, can, can manipulate the AI to get to, to a certain space. So I think our, our compliance team is bigger than our HR team. My advice would just be. I know it sounds daunting. It is really not that daunting. The moment you get out of the sort of the advisory industry that wants to tell you it's the hardest thing in the world, it's definitely much easier than building your company. It's actually taking it upon yourself to own it as much as you can and find the sort of the past path of least resistance. And it's probably the path with least amount of people involved.

Speaker B: In 2026 every AI company gets the same advice. Build the platform, be the horizontal layer, maximize the tam. The guest today went the opposite direction. Eight years ago he co founded Cordi in Copenhagen as a healthcare research company. Today It's a healthcare AI developer platform. 2000 builders on the platform. 12 to 15% week on week growth, 100 million raised. Competing directly with OpenAI and Mistrala model quality just in one domain healthcare.

Speaker C: This show is not investment advice and the hosts of this episode may be invested in the funds and companies featured.

Speaker B: To those that don't already know you, please give a quick intro to Corti and then we can jump into the thinking behind going vertical.

Speaker A: Yeah, great, thanks for having me. We are a healthcare uh, developer platform. We orchestrate models, uh, train models to models and deliver agency infrastructure to help AI become ah, table stakes in healthcare.

Speaker B: Quick stats on you. What have you done before? How much have you raised? I think the big important thing about Europe is that most people don't know the incredible champions we have and you're one of them.

Speaker A: Thank you. Um, we started in 17 as a research company. Our 17 theses funded by Sunstone. Now hardcore was context of our language models. Spent three and a half years in research and uh, clinical RCT proving we could have low latency. This is a different era of language models but language models running low latency in real time to help clinicians make better decisions. You can find the research out there. Cordi became into being as a commercial company selling APIs during COVID build a nice double digit AI SaaS business around our own models because nobody wanted APIs or AI uh, company from Copenhagen during COVID and in 23 we started seeing more and more traction around our APIs ChatGPT moment had happened and we could get back to our ethos of and our founding sort of thesis of building infrastructure and training models. So that's our thesis. We're growing, I don't know, 12, 15% a week. We have around 2,000 builders building on Cordi. We released the building platform last summer. 25. What we help with is if you want to have clinical grade AI running in production or you're building for clinical grade type AI workloads, you're probably going to want to have help from somebody who sees thousands of cases, have compliance bespoke models that actually work for healthcare that thinks about nothing else. And that's exactly what we do.

Speaker B: Beautiful. Andres, tell me. I want to dive into this decision to go vertical at a time where it felt like everything was about scale and thus also you wanting to go horizontal. So dive into that. What was the first thinking around why you should go vertical?

Speaker A: So our sort of founding ethos obviously from all the way back to all of the people out there who remember the deep learning days was very much trying to find data, uh, modality that really served as well as we started to build deep models. So we need a lot of data. We needed quite well labeled and quite well understood and obviously we wanted to build a company around healthcare. Where I had built a company before and my co founder was passionate that had the thesis that we thought would be the most impactful, which was there's not enough clinicians, there is simply not. And the quality of access to clinicians is also worsening even in the best parts of the world. So too few people, too little access to them. And us as consumers, we just want more, more healthcare, more health, more, all of it. And that's sort of the supply demand dynamic we saw when we found in 17 would get worse and obviously has got quite a lot worse. So the thesis was could we build real time AI that actually understood real patient conversations? And what we just learned quite fast was that general purpose is great, like healthcare will have a great time using general purpose models of 2026 for healthcare marketing and beyond and a lot of great use cases there. But healthcare is so deep, if you look at the size of the market and the need for really governable compliant AI, that we thought somebody has to focus on this full time because these models never got there.

Speaker B: If we start with what it gave you, then we can go afterwards into what you had to give up. But what did this decision give you, so to say, what did it enable you to do that no one else could?

Speaker A: Yeah. So obviously first things first. What we Deliver is an API. It's a platform console studio like you have on OpenAI. We don't see ourselves as competing. Our job is not to build the best horizontal models, make sure you actually can get something as good as you would want so you can get it live. Because healthcare mostly don't have AI life. So what it gave us was a chance to focus. So I think the uh, dominating narrative of AI today is that train time compute so pre training and more data will solve all the problems of the world. And if you really study what has transpired so far, at least in the current paradigm, is that although we're plowing many, many billions into these models, they actually obviously become a lot better. A lot of the latest ooms, as Leo, Palesch and Brenner calls them, so order of magnitude improvements, they actually come from not from pre training but from a mixture of other things that compound together. So I think reasoning models, the 030, uh, one moment a couple years back was a real moment. I think a lot of people didn't actually see that it wasn't about a new model. It was the same model as before, but it worked now as a reasoning model. Multimodal and multi expert interactions. And this mixture of experts and much, much more has been compounding layers on top of great pre trained models. But in reality, as we see today with harness engineering, controlling models for specific paradigms, for great outcomes is a lot more about understanding the realm you're in and understanding the models and understanding how to navigate them versus just plowing more GPUs into pre training. But I think that sort of evades the market a little bit here. Pre IPO frenzy in the US Everybody's just talking about bigger models. But although the models haven't Got like a, uh, order of magnitude or 2 bigger. They're not closer to BFDA compliant, right?

Speaker B: Yeah. So that's what it gave you then. What did it cost you? Did you have a time where you felt like this is costing us too much in terms of one is that we all know the importance of being able to build a narrative that allows you to raise. You're raising in a market where everyone's talking about go horizontal, you got to go bigger and you're doing the opposite. Did you feel at times we probably should adapt our model to allow us to raise more so that we could keep pace

Speaker A: again? So we are, we're an AI model provider. We compete with other models coming from very, very big horizontal labs. So first things first, when you only focus on one market, you only have that total addressable market. Right? And obviously we believe healthcare spends more than a trillion a year in the US alone on admin. That's sort of the easy market. More importantly is just we believe there is so much more to be spent on just us getting more healthcare. And I think reasoning models and beyond will play a big role there. So we believed our market was big enough. But obviously if you don't invest in healthcare like the investment thesis falls short already there. Right. Secondly, we believed that it's not going to be, uh, a one model solves it all. We believe that healthcare is actually not one market at all. We believe it's many, many markets. So radiology is so, so, so different from dentistry or psychology. And although we'd like from a technology perspective to standardize and treat them all the same, you should not, and we can talk later about this being, I believe, the hardest professional user group in the world to deliver software to. But it's not because they as one are hard, it's because they in their specialty are specializing more. So if you go back 80 years to a rural hospital in mid America, they might not have a lot of surgeons and those surgeons will do a lot of different types of surgeries. Whereas today that same hospital might have a surgery for a very, very small body part. That's specialization. That's great because that means we all get better health care and if we can get access. So the thesis here is very much healthcare. Healthcare is not one market, it's many markets. They're billion dollar markets, they're very deep. So yes, you're giving away 10. And next up, you have to get very deep in the tech stack. If we just came here and said, hey, the only thing you can build on cordi Is one of these AI scribes we wouldn't last long because you need to actually believe you can have your entire roadmap running on Cordi and you have ambitions. Right. So our sort of attack vectors, the surface you have to cover doesn't feel very narrow when you sit inside healthcare because we have to cover all the basis of. Okay, you want to build agents, here's an agent framework that's better than, than LangChain. Okay, you want to build text generation, here's text generation that's much, much more trustworthy than what you get on OpenAI. Ah, okay, you want to build speech to text. Here's something that can beat 11 labs. So our sort of spectrum here is, is quite wide. So we're giving up TAM for a very deep battle controlling all the way down to sovereign infrastructure or to GPUs. Right. So a lot of risk assumed and a lot of work to be done and obviously a uh, temp constrained situation.

Speaker B: Were there any places where you would say that going vertical felt like we can't do it, it's cutting off too much. Or how did you approach that in terms of the trade offs? And how did you talk to both your investors but also your founding team about it?

Speaker A: The challenge for us was first off we were both building a nice application SaaS business and we were building our infrastructure. So our own SaaS business was running on our own infrastructure, our own models, and so were some of our customers. And the first things first was obviously we had to tell our investors and the parts of our employees that the customers that was running on our SaaS products, they were going to run on hopefully partners or builders running on cording the future. We were not going to build, keep building on that SaaS platform and that obviously is a new dynamic. If you are a VC with the thesis of investing into software as a service. We're saying we're going to build at the model layer, the infrastructure layer and it's a different thesis, right? Secondly, there is a completely different need on resourcing. So obviously we've always trained models back to our founding, but God knows there is a big need for control and we have a quite large portfolio of offerings today. So uh, a completely different approach to talent acquisition, where to build talent, how to attract them and then GPUs, do we have infrastructure enough? Do we have the infrastructure to partner with from the hyperscalers and what's the sort of GPU per head we have access to? So we know that each brilliant individual can be meaningfully creative. Right. So there was a lot of sort of navigation of who we are and what that means. And I think for many in Europe there was sort of um, I think there have been for the last, what, I don't know, 8, 10, 20 years maybe this feeling that oh, ah, this is tough. Right? Probably not for Europeans. Right?

Speaker B: Yeah. Or at least uh, not this is tough but this is too capital demanding. I think most would say we do have the talent. Most would say that it's not that it's too complex but it's just that we don't have the depth of capital. Maybe you can talk a bit of that capital fundraising journey. You've raised massive amounts and you're obviously on a path that will require even more money raised and you're doing it out of here. Can you comment a bit on that?

Speaker A: Uh yeah, of course. So we've raised $100 million which I find very uh, slim. I think we're at the most both per head and relative to revenue. I think we're the most uh, lean AI lab, maybe among some Chinese ones in the world. And luckily we have great partnerships with Nvidia and Microsoft so it wouldn't be doable at all. So our core innovation is a lot around uh, test time, compute, thesis, the reasoning, the understanding of the market, the multi agent space and building and tuning and training there and obviously around a uh, large swath of data and insights and a great data pipeline, a synthetic pipeline that we've built ourselves that allow us to innovate as we've sort of proven. And that was the thesis of the year. If we can become state of the art, so if we can become soda on the core infrastructure of medical reasoning and control, the medical intelligence layer, then we're going to invest a lot into that thesis which is, was something we're going to announce much more about this autumn. So yeah, it's a capital intensive journey. Right. We are a lab and we train models and obviously health care is a deep uh, market as I said. So yeah, it's a completely different journey and obviously we're somewhere where we're going to look very internationally to accrue enough capital to make that happen.

Speaker B: Are you building as much in Europe as you would want to or are you starting to see that you need to move to the US more and more.

Speaker A: So commercial headquarters where I sit is in New York. We have our uh, commercial team as well and back office in London and then we have RD in Copenhagen and we've been worried uh, as hell around whether or not we can keep doing that not because of the talent. Talent is crazy good. But I think we're, I don't know, 20 nationalities in Copenhagen sub 100 people and we're very bound still to Copenhagen continuing to be an amazing place to live so we can attract people from all over the world to come and live. And then obviously the legal paradigms softening to recruitment of our style. So that's at least something I worry about generally for Europe is if we're heading in a direction where talent acquisition increasingly will become easier because we definitely need that. We've actually chosen to double down on Copenhagen in Europe. So not only do we have one of the best technical universities, if you go back through the history, PHP or Bluetooth contributions and so on, there's so many great scalps and milestones coming out of the tech ecosystem and before that the physics ecosystem in Copenhagen around Sport Institute. So as you said, talent is there, but I don't think a lot of people have been on a growth journey as they've been lucky to do more in Stockholm and London. So there is not a lot of, especially commercial talent and talent who's used to scaling things at a large scale so that we obviously have to go find elsewhere.

Speaker B: Uh, first question, are you trying to build a hybrid organization where you're going to be able to attract talent from across Europe that then maybe once a week or something like that have to come into Copenhagen? Or are you trying to build an organization where everyone has to be in Copenhagen and that's for R and D as an example,

Speaker A: we'd like everybody to be in Copenhagen more especially if they're building. But I also think it's, it's just a pure fact that if you are 100 people or you're sitting in two floors and the moment you're on two floors you don't, you're remote, you don't meet everybody every day and there is a uh, distance at least that's created. So I don't think sort of where we're sort of absolutist, you have to like have eye contact a couple times a day. But I think for creative work especially which, especially on the emergence of capabilities on these models, it's really, really meaningful to have everybody in a room every day. So certain teams were pretty religious and other teams were much more relaxed about where they work from.

Speaker B: How do you balance this, Andreas? How do you think about building uh, up the capacity in Copenhagen and the environment uh, that will allow you to bring the best from Paris, to bring the best from the many of The Eastern European countries are obviously leading when it comes to developing talent in this space.

Speaker A: I think first and foremost, I think it's a lot about obviously, do they feel our agenda is ambitious, the targets ambitious, the goal is ambitious, that we want to compete with the best. Generally, people we like to attract are people who want to compete, who want to see how far they can take it, but want to stay and live and build from Europe or have a, uh, clear thesis of wanting to help build something in healthcare. So most employees at Cordi have healthcare, uh, experience in their backpack that wasn't great and have an ambition of contributing. So obviously we're hammering a lot on like that's a place to come where you get GPU per head, you get access to fantastic colleagues who can work everywhere. But they're here because they believe in the mission. And then it's trying to make sure that we double down on making an ecosystem around not only Cordi, but the AI, uh, ecosystem in Copenhagen and the office to make sure you land not just with us, but with everyone else who's thinking about building something big with AI.

Speaker B: Are you finding it generally difficult to bring people from the rest of Europe to Copenhagen or are you finding that. No, you know what, they are driven by the mission enough. They're driven by what we are building. So we're not yet there where it's a problem.

Speaker A: I believe Copenhagen specifically has a massive talent acquisition problem. So when I'm in the us, everybody knows Copenhagen, everyone wants to go visit and it's known as a fantastic place. Could do a sabbatical in Copenhagen. I don't think we have an environment, uh, currently where the tech industry is booming. I don't believe our local champions stayed or built deeply long enough from like unity to send this Fantastic founders, fantastic companies. I could go on. There's quite a few, but they build elsewhere. And I think that's one of the most fascinating things about Stockholm is like how long and how big Klarna and Spotify and so on has built and stayed in Stockholm. So I think the ecosystem is really lacking. And that's obviously an issue if I'm a builder. And I know I'm moving to Klarna and Stockholm, I know there's four or five other companies I'd love to work for around the corner, so. So I'm okay moving from wherever to Stockholm because I know I'm landing in an ecosystem. Whereas in Copenhagen, if you want to work for an AI lab, you can come to us, but where would you go next? There's obviously great AI companies, but you might not know them. Right. So I think Copenhagen has that problem. But then we sort of, I don't think we have still to sort of accept the fact that technology and increasing demand will I think reverberate through our industries. So Copenhagen is still very anchored in life science and pharma to some extent logistics and I don't think yet sort of the excitement around AI has taken a foothold. People are still very wary about the impact of AI. They're I think conflating it quite a bit with social media and the worries that's created. So I think Copenhagen's environment right now is tough in general for building a big tech company.

Speaker B: How do you navigate it as a founder recruiting?

Speaker A: Um, I think we focus on what we can control. We've been lucky to prove that our technology is top notch all the way from our big launch last year on mechanistic interpretability. So understanding these LLMs, what goes on inside, find it on hugging face and all the way through our latest launch in so agentic ready speech to text. So I think across the stack we just really try to make sure that each team are state of the art and they compete with the best globally. And then we focus a ton on just really, really hammering on every problem that is real for healthcare. So our builders feel that we're abstracting away some of the difficulties of building right. And that attracts people and we control that, then obviously we're going to raise more, get more GPUs per head and keep on trying to at least guide the market forward. And I think that at least uh, resonates with the ton of builders out there.

Speaker B: Okay, now I want to go back a bit to understanding how you're building for accuracy in a space that is notoriously difficult. Maybe uh, you can talk first about the importance of accuracy in the health care market. Obviously there's very little room for mistake in the surgeon's room, but I imagine that it's the same thing across the sector.

Speaker A: Yeah. And I think uh, to a lot of people it would come as a shock how difficult different um, healthcare is to other professions that would be deemed liable, highly detailed oriented industries. So we were last week together with one of the fastest growing companies in the world. They're in the legal space and we talked about how the legal space is very different from healthcare. So if you are creating a synthesis, uh, or a summary of a conversation, the lawyer might feel it's pretty good. Like I don't like your commas, but I'm Fine. Whereas a doctor would be like, oh, that one comma, that's wrong. Can't use it. And why is that that important is because everything in healthcare is based on a language that people actually don't know exists. Healthcare has its complete own language. So there is more clinical terms in one of the many coding standards than there are words used in English. So if you take one of the big coding standards called SNOMED or icd, these are types of abbreviations and coding systems that allow other people in healthcare to understand what happens with the patient. If you are spelling one letter, one word wrong, the entire situation changes. That's created a situation where like 90% like kind of good looks great in demos. What you can then do is what, what the best application guys are doing out there. They're building massive scaffolds around their, their platform to make sure that their exact corner, like core market, can manipulate the AI to get to a certain space. It's obviously very expensive, it's very hard on margins and it's very tough to control this massive little bit shaky scaffold. Right. In our case, we're an API infrastructure provider, so when you come to us, you have to believe that you can get to state of the art yourselves. And many of our customers are all the way down to real clinicians building for themselves. And obviously they haven't spent five years at Meta after their PhD from MIT on AI, so they're not comfortable with many of these contexts. But. Right. So not only do we have to prove, which is why we benchmark a lot publicly, that we, you can come to us for, for, for state of the art, but we also increasingly are learning how much we need to co build with you to get stuff, right, get stuff live and leverage all the learnings we're getting from thousands of builds because one company building for radiology with us, which is one of the toughest verticals as far as I see it, on accuracy, these people will have no tolerance and they all do their abbreviations, their terms, the way they document a little bit differently. They won't just like take the basic Scribe 101 standard and say, that's great, I'll riff on that. Ah, they'll need something very, very concrete because they're sitting every day for hours talking to a screen with images and it needs to be on point every time. So to build with them, we're leaning much more into it. We're allowing them much more bespoke opportunities. We're building a ton of software around the models which Obviously, general purpose model providers won't that are specifically aligned with, like, how do you tell your EHR platform in plain voice, although they are very different, that it should do certain things while you're talking to a screen full of radiology images. It's a very concrete engineering problem on top of these very jagged, uh, LLMs. So there is just a lot to do to get you to accuracy. That's way beyond training, way beyond data. It's about making sure you can embed it into a reality that is completely different from one radiology to the next, or one radiology department to the next department or radiology to pathology or pathology to psychiatry. Right.

Speaker B: Am I right in saying that you just described perfectly why you ended up going for the pure API foundational model layer and said, we cannot do the UI side, we cannot do the SaaS model side of this because it is too complex. Each case will be too complex to solve at the same time as we're trying to be state of the art, uh, on the foundational layer.

Speaker A: Absolutely. And to be absolutely honest, we look at some of the people building on Cordi and some of the coolest companies and startups out there who are also building the app, right? They're not building the infrastructure. They might be on us, they might be on OpenAI or Gemini or Mr. Allah or whatever. And sometimes you really envy them. Oh, because the last mile of, like, how do you highlight or the microphone that app developer have used to record something probably shouldn't have put it behind the computer, right? You should probably should have put it in front of the patient. But we don't control that, right? We don't control how they told the story, how it's orchestrated. And obviously sometimes you want to, you want to control more. Every time we've interfaced one of these problems covering, I don't know, 50 specialties in healthcare today that all expect us to be the best in the world at anything from psychiatry to, uh, obstetrics. We have to learn how to make everything a product problem because we don't get to be there in the room, we don't get to be there in the last mile. So now we have an API endpoint that allows you to actually check if the microphone you bought for your customers, if you are an AI scribe in healthcare, if that microphone quality is high enough. Obviously we're not building microphones, but we learned that a lot of the build wasn't just about becoming state of the art. It was becoming the easiest place to build and the easiest place to get things Approved. If you don't solve those two too, you can have all the token maxing going on in the world, but you won't get there.

Speaker B: Am I right in describing your model a bit as the in terms of go to market and creating customer success, similar to what Anthropic did as an example with forward deployment engineers or whatever they call it. That's the same model you have with your partners.

Speaker A: Yeah, we're all learning from Palantir. Right? Palantir is the goat of getting complex data products live. I think where we see our job a little bit differently than the horizontal providers is obviously we can hire and build specifically for clinical reality and we can get much deeper in clinical reality and we actually want everybody co building with our customers inside Cordi. Whereas I think some of the AI labs have such a scale that they're building these external vehicles or companies with a lot of funding, a lot of model access to go build. I think what we really cherish is the intimacy we can have with our customers and the proximity to their problems. So we have them all inside, but obviously we're all, I think, building on sort of. What did Palantir learn? It's that the vast majority of technology success in deep data driven products is happening at the edge. And given that we're not allowed to build a product at the edge, I think the next best thing is sitting at the same screen sometimes when the customers go live. Right,

Speaker B: I'm going to ask you a question now. That's probably going to be difficult. Can you talk a bit about adoption across healthcare in general? Where do you see movements happening fastest, that type of thing?

Speaker A: I get a question asked a lot from VCs which is sort of what's happening, obviously. Can you tell me a bit about what you see at the data layer? And we think about, uh, hierarchy there is the big cornerstone of the admin. That's where most of the action is happening. Most of the admin action is happening mapped m directly to the workflows that's happening the most. The workflow we see happening the most are what's always happened in healthcare. Interviews with patients and dictation. That's where we see a lot of action. So a new modality in interviews is these AI ambient scribes who are almost always outpatients or you go to the same doctor sitting at the same desk, that desk is increasingly having a microphone. But dictation still is massive. So doctors with small machines or talking to a computer or a telephone dictating, that's massive. But this Whole bundle of like, how do I capture massive, massive startup, uh, uh, hotbed increasingly invaded by the big ehrs, the big classic healthcare companies. On top of that we see the first sort of realm of these augmentation products. So it's much more about using an LLM to write a note or write an email or summarize something or make a patient transfer note or send a referral. They're happening on top of these layer 1 admin workflows for capture, but they're increasingly being adopted, especially in the us, slowly in Europe, but the curve is crazy. And then finally we're seeing the first adoption of agents. Agents being instead of sending a referral note, shouldn't we just refer them? Like what would it take to refer them more? Do I have authority prior off to be able to give the patient what I want to give them? Can an agent go check and do that? I think the vast majority is happening at the bottom still, but the vast majority of value is going to happen at the top.

Speaker B: Uh, and from a partner level, is it primarily startups, is it primarily scale ups? Is it primarily the established players that are now moving really quickly?

Speaker A: So I think in our case, I think one of the benefits of being uh, quite a lot of people on the ground is that we can work with some really big players. So that's where we spend most of our time is with uh, the big labs, companies, EHRs, risk, PECs, whatever they're called, all the abbreviations of healthcare. So imagine a big system who has hundreds of thousands of specialty doctors or nurses or whatever. That's where we spend a lot of our time because the compliance standards for rolling out something at that scale is massive. And they probably have multiple countries, multiple jurisdictions. That's where we spend most of our time. Obviously we have a PLG motion, everyone can go sign up right now, you get $50 free. And we have a ton of small healthcare startups who are running free. We also have an incubator for them. We spend less time there and many of them are much more tech enabled and obviously have a much simpler life because they're smaller. They don't have to ask as much for permission, but we work on both ends. Time spent mostly on the enterprise guys, but I think the volume is happening at the startup level.

Speaker B: What have you learned from navigating these larger enterprise partners both in terms of how you get into them? One is plt. Obviously that probably doesn't work with the big ones in this space. Another model of go to market is of course the forward deployment that we spoke about. I'm also curious to understand, are you finding that this is the land of milk and honey when it comes to readiness to pay or are you seeing. No, they're being quite conscious about cost as well.

Speaker A: So I think healthcare in volume is one of the biggest markets in the world, but in margins it's low. So there is no place in the healthcare value chain that has flush amount of cash beyond pharma and life science. Different beast. So I think generally nobody would tell you that you're going to have an easy time selling to anybody in this value chain. But in volume they obviously many of them have a lot to spend, right? A hospital is a large volume revenue organization, right. So if you do it right, there's a lot of volume and you can have a great company and build great margins if you build something really meaningful. But meaningful is very different. And I think that's why many of the fastest growing companies are coming in as challengers, saying that we're going to replace what's already there because we can control that ecosystem ourselves. What we've learned a lot with these big players is a lot about. I think what everybody's trying to figure out is like what does the future look like post AI and, and I think in some industries where there is zero, uh, barriers to large language model providers like Claude or sorry, Entropic and OpenAI, we already see that play out on public markets. I think in healthcare it's still like verdict is out, like what is the job? How many builders, what will they own? Is EHRS a thing? Is it not a thing? Is it headless? Or is it like a software piece with clicks and cursors? Right. And I think that's still many bets on it. I think increasingly what we're seeing is that uh, especially in Europe, the contracts are long, the changing costs are crazy. So get a hospital with like zero downtime or under pressure to say like hey doctors and nurses, we're gonna run out a test like ehr, maybe it's cool, like that's not happening, right? So the certainty level you need to have that it's great is high. The amount of like POVs, POCs you want to run before doing anything is real. The time they believe they have for this transition is longer than anybody in Silicon Valley believe. And the contracts actually allow them to think that. And I think the change management readiness on most of like non private health care is real. Private healthcare in the US is maybe a little bit differently because they're obviously driven by some different Incentives, but probably they won't start with the clinical reality. They're going to start with like medical coding and billing and invoicing and all that stuff. Right.

Speaker B: Uh, I'd love to understand from you navigating regulation in this for you, how have you done it? What have you learned? How would you advise other founders to go about navigating regulation in this space?

Speaker A: Yeah, this is obviously something we think about, uh, a lot. And first and foremost, I think that the narrative about regulation is really wrong from a startup perspective. So what I always hear is this, uh, Europe is behind. They're stupid. They want to regulate their way out of it, but it's not happening here in the US and regulations just get rid of it and we'll have a better healthcare market. I just think like, the first principles here is that healthcare is not here to use technology. They're here to care for people. They use technology to care for people. That's great. And healthcare is the last resort. Health as a wider industry has a lot of like pre hospital stuff that happens to make sure you never get to the hospital, but you actually end up in a clinic. Even at your doctor's office. It's probably already not great. Like, you won't go there unless, like, it's not like, I'm gonna sit at the doctors today and hang out. Just like the waiting room is so cozy. Like, coffee is great. Like, nobody does that. So I think the narrative of like, oh, it's so stupid, all the regulation, I think it forgets one thing, which is when you and I as patients are in the hospital and we're lying on the bed and we're waiting to get whatever treated treatment right, we don't want to be asking like, are you guys ISO certified? Is the LLM sort of accountable? Or like, who's accountable if it makes mistakes? Or that surgery robot, is that like FDA cleared or is like something you're testing out of China? Like that, I wouldn't want to go there. You wouldn't want to go there. Nobody wants to go there. And like, even worse, we both have kids. Send your kids there. Like, no way. So the permanent thesis of healthcare has to be that whenever you're in healthcare, the actual reactive part of the system, everything has to be so you never have to question it. But if we never want to question it, any of us, when we consume it, it has to live up to a really high bar. And that bar has to be pretty standardized. And that's why we have regulation. It's not because Europe doesn't like American technology or Chinese technology, it's because we want one standard. So, you know, if you get treated in Sweden or you get treated in Denmark, we have the same thing. Or in Europe, if you're in Germany and you go to France, you know, you get decent treatment. So how we're thinking a lot about this every time we look at the regulatory narrative is are we heading in a direction that will sort of make it better and easier for clinicians to feel in healthcare that they have less risk and they can do more, then it's probably going to be a permanent factor. It's probably going to stay permanently. If it's a, uh, idea for regulation that is much more at the edge or changes workflows that might not be helping doctors, it might go through a 10 year process to get into reality. Right? So we try from first principles just to look at like what will make it easier for healthcare to consume risk, have a better time treating patients and feel a clear standard for all their vendors. That's probably going to be reality. And what we're seeing right now in the US even is that state by state, AI regulation is changing. There's increasingly, even under trump, adoption from European and especially UK AI standards. And I think the FDA and the European authorities have worked together before. They might want to do it again in the future. I don't see any path where regulation doesn't happen in healthcare. Just it has to focus on making the clinical reality better. So that's been our core focus.

Speaker B: So in other words, to summarize, founders should reset their thinking around regulation, seeing them as first and foremost instrumental for giving us a safe health care system. That's one thing. Then my question to you is how do you navigate it? What would you say have been your three biggest learnings when it comes to navigating the regulation of the healthcare system?

Speaker A: I think first and foremost everybody should remember the moment, you know, you have to get um, uh, approval from German authorities or ever French authorities or go on. There is an entire industry that's created to help you do that. And the service part of the industries is selling. If you look at their business models and then their incentives, it's selling hours. If I am selling you hours, I probably want to sell you a lot. That's a great business. So I'm making it pretty complex. So our learning is very much that a lot of startups, they end up raising a seed round or any round. Some of them come to us, they're in the middle of whatever approval kind and they've been swallowed up by a ciso, uh, who is like external and working a company. And it's very complex, all of it. And there's an entire sort of compliance company apparatus around them saying that if you don't do it like that, you probably don't get it. And usually these companies get very slow to market and they haven't really, some of them at least taken it upon themselves to really own the minutia. So I think what my co founder did very early was that he hired the entire compliance team inside Cordy. We don't use external as much as we can, uh, if ever. We don't do that across what. We're in 11 countries now, so we cover quite a lot of ground, but we do it all internally. So I think our compliance team is bigger than our HR team and our goal here is not either or is bigger. It's just to deliver what we need to deliver. And we're trying to productize it more and more. So even our customers, some of our biggest EHRs, they come to us when they have to go into a new country and like, do you guys, have you built a stack we can build on? So now we're increasingly turning it into something where, yeah, like use our entire stack and we'll help you work with the government. Right. So I think as we've. My advice would just be. I know it sounds daunting. It is really not that daunting. The moment you get out of the sort of the advisory industry that wants to tell you it's the hardest thing in the world, it's definitely much easier than building your company. It's actually taking it upon yourself to own it as much as you can and find the sort of the past path of least resistance. And it's probably the path with least amount of people involved.

Speaker B: It's actually a surprisingly simple though I imagine for many counterintuitive learning there. But you'd always say that you would want to control and own the success in any space. That's absolutely instrumental to you. And we always refer to, uh, healthcare as a heavily regulated industry and that's why you will not succeed as a founder in it. That's been the spiel against it. But obviously given that being the constraint you want to own it, then it makes a ton of sense. Andres, let me ask you a different question. And I'm going to go super wide now and instead of focusing on Cordy so much, then focus on your learnings from raising inventor and being inventor for a long time. You are of course not Only the founder of Cordi. You've got past successes behind you. You've raised hundreds of millions. I would love to ask you, if you were to speak bluntly to our founder audience about how they should be thinking about navigating venture, what would be your biggest things that you would say to them?

Speaker A: I think the advice I give the most these days, and I think founders are less excused than ever before, is, um, the sort of the canonical get to product market fit. I think everybody knows it, but, like, what does it mean? I think what I say the most, like, get to actual product market fit. And I don't think there's more or less any excuse for not being capable of getting a really nice POV to some level of quality where you can get that kind of feedback. There's ample amount of VCs available in the world and they would definitely invest in you, but I think the bar has just increased so much, and I think that's fair and maybe only good. So if you look at the macros as I see them, more money is going to the winners. They accumulate faster. And it's getting harder and harder for laypeople getting to A and B round because ultimately you need so much more firepower to be able to. And I think, uh, the sort of daunting challenge for many is like, oh, I need to quit my job. I need to make all these sacrifices. And I think the navigating of VC is like, VC should never be sort of your jump, parachute. It doesn't feel like at least the founders I know that's not the ones who have the most successful, like, needed that to jump, build, build for the jump. And the moment you feel that your parachute will hold, you want wind in your parachute. Like, that's when you get VC money. So I think navigating that, like, there's a lot more pressure on founders today. They should build a lot more, they should get a lot further. And the best is a global problem. Right? When I raised my first round in 17, 16, whatever, there was much fewer people building. They were still formidable. The best were great, but now I'm competing with people who can place money elsewhere. They have much more access, much more visibility on deals. And founders come from all over the world and they're formidable. So I think the local problem of being a local winner in Scandinavia is great, but you're in a global player, you're not for VC money, and you're against a global hoard of companies, or you're not.

Speaker B: How about we shared off the line, we talked about how some VCs behave. Would you care to share a bit about how you would recommend founders or advise founders to to manage VCs and what's okay behavior, what's not okay behavior, what's just not okay. But you got to just accept it because this is the name of the game.

Speaker A: It's a critical service industry. If you're building in deep tech you absolutely need support of fantastic VCs and I think for my own experience we've been very lucky to have some fantastic ones. I think our last round was led by Atomicon process and I think there's not a week where I don't pester Atomicon M for something and they're fantastic. So I think first things first, I think it's much more about being very thesis driven as a founder, which is a luxury you only get if you get to a certain level of success. But that's why you need to build for it. Because I think the vacuum between the median VC and the best VCs are widening and you want the best because they unlock capital, a different kind of growth journey, different kind of exit scenarios, whereas I think the median VC do not. So I think the first thing first, I think just founders need to make every problem including building VC relationships, founding funding and leading a board and a group of VCs they need to make it a product and ah, a founder and a builder's problem because ultimately capital allocation obviously is the name of the game and you're trying to allocate your other people's money as best you can. So I think generally, especially in Europe, there is a lot to be learned still on how to play a game with international money allocators at scale and living in that kind of pressure and being hyper aggressive as a founder and CEO, which I think many European startups still are learning. But I think there's a big vacuum in sort of what we do and how we work in Europe versus the us. It's not increasing at all. Like the best companies in Europe are fantastic today and they're doing it more and more. But I think generally there is a lot to do and I still think there's also a lot to learn from European VCs on what sort of best in class kind of behavior

Speaker B: fast should comment on that, which I think I must. What I would say is that you're absolutely right about your point about the best and the non best. So to say I agree that the uh, I don't know if I agree that the uh, chasm is increasing. I Actually maybe think, meaning between the good and the less good. I maybe actually think that it is not. It definitely is in terms of being able to raise large funds and thus, uh, stay competitive. But I think that behavior in Europe from VCs towards founders in is increasing for the better. There's just a difference between uh, who will be successful in fundraising and create the funds that are of a size where it's truly relevant. I would actually say that we're in a better position from that perspective. But I think you're absolutely right about the importance of optimizing for the best names and the people that really know what you're doing. Uh, I'm definitely seeing that that is getting harder to diligence because you need to not only diligence whether the person on the other side feels strong and good, but you also got a diligence whether this fund is on a trajectory that is good because that will. If you, as you said, are building in a deep tech space, you need someone who can fund you quite a long way, um, because you're going to need that money later.

Speaker A: And don't you think like when looking at sort of the, um, the leading funds in the U.S. let's take healthcare. The leading funds in healthcare, like General Catalyst, they're calling themselves a platform, which in my universe is a P kind of term. It's not a VC term. But all of a sudden you're trying to blend categories, which I think is really smart. But I see Andreessen do the same thing. They're also blending, talking about platforms like General Catalyst bought a hospital, like if you mean healthcare, and you have a thesis that's ultimately conviction, right? Roll out all the VC investments you've done in your own hospital. So, uh, my point being, I think we're catching up to where our VCs in the US were. I think the VC category in the US is moving as well. And I increasingly just think that there is a lot of power law going on when you can take a lot of capital, maybe even more, maybe marry it with GPUs. I think that's something we've been pitching to many VCs in Europe. Like, what's your GPU strategy? We've done that for years. Most VCs in Europe, especially the big ones, still don't have one massive mistake. In my point of view. If they've done it earlier, they could have been somewhere else.

Speaker B: Uh, elaborate that. The GPU.

Speaker A: So if you give a pre seed company $3 million to build a vertical model for generating Ad spend or you know, you're going to spend a dozen of dollars on inference with one of the big models. But they need to build something, tune something, build their own and they need to go to Nvidia and buy themselves. I think the VC perspective in the US has been like we're just going to buy GPUs ourselves, then we're going to start distributing like Andreessen have done that many others have as well. I think, um, Ned Friedman is I think a leader there. Angel font turned VC fund. They had Andromeda, one of the biggest VC angels, angel clusters in the world of GPUs they ran themselves, gave it to their investments alongside money, obviously that kind of thing on scale. I think ultimately if you control the electricity and the tokens for the generation of AI startups to come, you have a completely different offering than just sort of passive money. And the argument about, oh, we're like, we're here to help. But I think sort of a lot of VCs forget that every other VC says that to a precede company, we're here to help. We have network, we work with founders that build great companies, but everybody do that, right? And I can find the same founder for M, like as a venture partner in multiple funds. So I think ultimately, as we head into a new paradigm, the narrative, the category blend in the US feels real. The accumulation of money is bigger than ever. They're betting much harder on their winners. They come with other things in money like GPUs, and they're embedding themselves much more into ecosystems in the market they play in, like healthcare.

Speaker B: The last point is of course one that's very strong around verticalization of VC and specialization of vc, however you want to frame it. I think it's incredibly interesting to hear you talk about the GPU strategy and the importance of that. I completely agree. It's not something I've seen in any way being led by a lot of European VCs. And then I think that you're absolutely right when you're also talking about the importance of this merging of asset classes. It might sound to the, to the typical founder listening to this, why are you talking about this? But one of the really big problems that we have is this stage of going from venture funded to private equity funded. And that's of course because as most founders will, most especially deep tech founders will relate to the problem of not being able to go from the university setting or the grant funded setting to raising your first private money. You have the exact same problem happening at the end of the stage, at the end of the journey where you're getting from venture into private equity, where a venture still underwrites you as a venture case and loves you for that. But private equity is finding it very difficult underwriting your stat. And for that reason these platforms that are operating across that spectrum are very interesting to get funded by.

Speaker A: Yeah, and I think we had sort of this European uh, narrative a couple of years back when we were still doing pre A round. I remember there was this period you might recall where everybody was like, oh, we're going to build a seed fund. Like Everybody had a $100 million seed fund. All of a sudden it was like there was no money for seed. Which in my point of view was felt. Always felt a little bit weird seeing it from founders perspective. So like obviously if you like there was a lot of companies still needed funding. Totally agree. And there was ecosystems in Europe like uh, Vilnius, like great city, fantastic town, had less access. Totally true. But that was also true in the U.S. right. There was also a time when like Boulder wasn't attracting as much capital. Austin still felt like a weird place in Texas for most people but like now it's just Austin and it' awesome. But that's also the reality for Europe. There was a maturing of sort of the ecosystems. But I think everybody was investing in seed and now everybody's saying the different thing which is like what do we do at growth? What's the ABC realm? What's the platform beyond how do we step, like how do we build a platform into public markets? I think we have a really big problem with. It's almost impossible to have a meaningful IPO in most European countries at this point. So there is no path there. That means investment banking quality and talent is less. And obviously we have some great ones in Copenhagen. But there is a massive gap here that Copenhagen maybe cannot continue to attract the best uh, um, investment bankers in the world to help people go public on a local nasdaq. Less people than do that means the next generation doesn't have any references of. That was a great going IPO or dual IPO on Copenhagen. They go to London, they go to us. And ultimately that's I think the trajectory of like there is so much pressure that uh, because the top part of the uh, growth funnel doesn't work very well and there is no path to local IPOs. And I think that's a massive hindrance for local ecosystems. And if we don't plug it really fast and finding meaningful ways of helping people to Scale beyond just. We have great advice then I think ecosystems will cap out and more people will continue to move to London and especially us.

Speaker B: How do you advise fellow founders about going to the US because it's obviously one thing many have to do for the commercial side. Some do it for the fundraising side first. Maybe share. Why did you do it? Why did you choose to move the commercial headquarters to New York and yourself as well? And what does it unlock for you?

Speaker A: In 1617, when we were creating what became Cordi, we were already in San Francisco. So we actually moved back to Europe for talent and for data access and due to local partnerships with healthcare. So we had actually not planned on building from Europe but ended up doing it. And we've been very happy. We did for many reasons. Uh, we've already always had a presence in the US and during our time the vast majority of our customers have been American. But being on the west coast versus being on the east coast, massive difference in time. So when we started the New York office it felt meaningful. We were there. Our two biggest customers are American still. So we wanted to be there, wanted to scale that, build the team out there. We got fantastic leadership now. So that was sort of our case. And obviously we already always planned on working out of the US and both of us love it and still very passionate about Europe and Denmark for that matter. But that was the art plan. And I think what I say to people is obviously like, I speak a lot with seed levels like seed stage founders, Aron founders who are like told by their seed stage VCs like go to the US. You have to go to US. And I think a. I think um, the sort of the SaaS logic of the European market being too TAM constrained I think is not true. I think look at in our space we have a fantastic startup up in Sweden called Tandem and Healthcare. They're doing great across Europe and they have no plans of doing US and all power to them. They're building a fantastic company. I could go on. There's quite a few. That doesn't mean you shouldn't go to the us Just like the sort of the VC fantasy of everything is easier in the US and it's a much bigger tenant that will help you a lot. True, but tough. The cost is also much higher. The talent acquisition much higher. You don't have any network there to help you talent acquire and keeping them happy. So I think post product market fit with real scale and sort of a platform to open new markets makes sense. Or if your cornerstone customers like ours are there makes sense, but I think that rushing to the US today, like 26, when Europe is awakening, I'm not sure it's the best for everyone.

Speaker B: Let's land on that sentence. I love that. Andreas, thank you so much for joining me on the podcast today. We did a episode earlier that was about an announcement of yours. Now we did the bigger deep dive. Thank you so much for that.

Speaker A: Thank you, sir. It's great having you.