Speaker 1Mortgage is top three in terms of undisrupted industries that exist. This is an industry that hasn't even caught up to 2000s technology yet. That's sort of the size of the opportunity in front of us.
Speaker 2The way that we are going about it as an industry is creating the infrastructure so that it's a continual learning process. Building a system that allows you to continuously run the evals, make sure you're actually continuously improving the model in the direction that is aligned, not just with the way that servicing should be done generally,
Speaker 1but customer-specific. It's $13 trillion of consumer debt that basically runs on a single incumbent that built their legacy system before the internet was invented.
Speaker 2It turns out revenue cycle management is just servicing for hospitals.
Speaker 1Everything is servicing. It's the underlying critical infrastructure that supports basically anything that has money movements, some sort of regulation, and then an operational component.
Speaker 3We started the company before generative AI was really working. What is possible today? That wouldn't have been in your Series A pitch deck.
Speaker 4What is possible today is. Mortgage servicing is a $13 trillion market that still runs in large part on technology designed before the internet. Valen decided to rebuild it from scratch. In this episode, A16Z general partner Angela Strange sits down with Valen's Linda Du and Andrew Wang to unpack what it took to replace decades of legacy infrastructure in one of the most regulated parts of financial services. They discussed why Valen became a mortgage servicer before becoming a software company, how the team turned decades of regulation into code, and why operating the technology themselves was critical to proving it could work at scale. And now, AI is changing what can be built on top. Linda and Andrew explain how agents can handle workflows that were previously too complex to automate, why the underlying system of record still matters, and what they've learned deploying new technology into enterprises with thousands of employees. So Andrew, I think a great
Speaker 3place to start would be from your time at Soros and how you decided that mortgage servicing was going to be a really interesting problem to work on.
Speaker 2So a fun story is that when I got into the world of finance, I actually had zero background in finance. I never took a course in economics. I barely understood what the time value of money was. And so my general interest in finance was actually just in math. And what people don't really know about mortgages, which is just like a very random esoteric fact, is that it's actually a place where there's a lot of stochastic calculus. Prepayment models have stochastic calculus. And so when I started the job, I got very interested in making sure I mastered stochastic calculus. I had taken a course in college. I was going to do all that. And it turns out, it's all a finger in the air. Just guess your way through math. So that wasn't really, yes, yes. You just need to guess some number. You're like, "Yeah, I think it's going to prepay at x speed." Anyways, so that was my initial reason to get into mortgages. What happened thereafter was you end up realizing working with many of the mortgage servicers that it's a big systems engineering problem because there's a ton of data, a lot of money moving around, a lot of entries. They have to constantly be reconciled. And so I spent a lot of time at Soros when I was making these investments, really making sure I understood exactly what was going on and building my own reconciliation systems to make sure the money tied. Turns out, it never tied. And so I spent a lot of time trying to fix that problem internally. And that got me really interested in figuring out, "Hey, how can I actually build better infrastructure? How can I invest maybe in a company, do it with a company and actually ultimately build better systems so that none of this would be a problem?" So it really started originally off of systems and my own pain. Maybe I would say it started off with math. Math became systems, systems became pain, and here we are today.
Speaker 3Excellent. And so discovering a market has a lot of technical problems to solve is one piece of it. Maybe, Linda, from a market perspective, describe the state of the world of the mortgage servicing industry before Valen came in.
Speaker 1Yeah, the way that I think about it is mortgage is probably top three in terms of undisrupted industries that exist. And so it's $13 trillion of consumer debt that basically runs on, for the most part, a single incumbent that built their legacy system before the internet was invented. And so we're not talking about, for most industries, you are fighting with modern incumbents, new startups for the last 20%. And so we're not talking about, for most industries, you are fighting We're talking about the entire, it's like, this is an industry that hasn't even caught up to 2000s technology yet. And so that's sort of the size of the opportunity in front of us. And from a deployment standpoint, these are like the biggest, baddest enterprise deployments to exist. You have to train thousands of employees, you have to basically rip and replace the core operating system. You're actually deploying AI into the real world in a very heavily, heavily regulated industry. And what we always tell the team is that the scale of the industry is going to be the skill set that people will be hiring for in the next decade is the ability to apply AI. And our team's learning that by doing it in the hardest industry possible.
Speaker 3So I think big technical problems, very old industry. I think one of the other interesting things about this industry, and one of the reasons I love investing in infrastructure, is if you unpack crappy consumer or business experiences, it usually often comes down to the plumbing. And so maybe if I'm a homeowner, and my home is being serviced by one of the incumbents versus Valen, what might I notice from just a human perspective?
Speaker 2Yeah, it's a classic problem where the average person who pays on time probably doesn't have to notice that much. I mean, this is a very classic thing where I think whenever people realize that most Americans actually are barely maybe one paycheck away from being able to make their payments, it's a little bit of an astonishing fact. But it's a similar dynamic where in mortgages, again, most people pay on time. But there are people who have certain events that happen to them that are unfortunate, not predictable, and really cause a lot of stress and anxiety. So here's an example of this. One of the key problems with some of the legacy systems is that they don't really model and really capture all of the context associated with the life of the mortgage, right? Meaning you originate the mortgage, they have a snapshot of what's going on right now, but the history isn't fully with really enough fidelity captured. And so a good example of this is when someone passes away, the borrower might end up changing because maybe they were the borrower on the loan before, and then thereafter, there's a new borrower on the loan. Now, that is a normal thing. The systems today do not actually capture all of the changes along the way. And so if you want to look at what happened before that, it's actually very challenging for the servicers because they put it in an ad hoc database on the side. They only have what is going on at any given point in time. And so you might have a situation, as an example, where someone says, hey, or not where a homeowner passes away, their kids are trying to take over, but they no longer have access to the records before. And that might be important because they're trying to file state taxes. They're trying to figure out what happened before. And because of how, again, the systems work, they'll say, well, you weren't that borrower. You don't have access to that history. We're cutting it off at this point in time. And so that causes a lot of anxiety and stress because you have to keep in mind, right? These are like real people at a moment in time where they're trying to figure out something really, really tough. And you have to over and over again, tell them my mom this is why this happened. Please give me the information. And so it percolates in that form and fashion. AI obviously helps a lot of this because you can get that context, but it doesn't change the fact that the information must be represented in the right way so that the systems can perform as expected. And so really the more information represented the better way possible, the better the experience ends up being.
Speaker 3Yeah. And I think too, you guys used to do this every week, sharing a different story of a customer that you helped. Even the more common, simpler case of I can't make my payment this month is something that traditional mortgage servicers can struggle with. So like, how is that different in Valen? Or if it's kind of multiple times in a row, you might need a loan modification.
Speaker 2Yeah, there's all sorts of variations of this. Again, one of them is there is this concept of the escrow when it comes to mortgages. It basically contains both taxes and insurance that need to be paid over time. They generally happen on an annual basis to make sure that homeowners are reserving for the right amounts. They're paying on a monthly basis and you accrue a reserve to pay for it when the time comes. One of the problems right now that everyone faces is actually insurance costs and taxes are actually exploding. And so many homeowners aren't able to make that large payment or change a payment in such a quick fashion. That requires usually a pretty annoying process because one, most systems are designed so that they take the amount and they basically just calculate how much it costs over the course of a year. Even though the regulation and many investor standards allow you to amortize it over 36 months. And so that becomes an ad hoc approval process that has to go to a supervisor as opposed to having really great systems where you pretty much can immediately determine should they be approved, should they not be approved. And being able to configure these things along the way really on the fly because again, regulations change, it might be some big fire happened and so many of the insurance companies are raising prices. Being able to offer that right experience really automated and really give comfort to the homeowners in their time of need. That is really important for a servicer and that ultimately builds the best
Speaker 1customer relationship. And I think it's hard for people to conceptualize how old the existing software is because you have to think about it like when these technology systems were architected, this was the 1960s. And so even something as basic as property insurance, property insurance, mortgage insurance keeps evolving over time. And so the data models fundamentally just don't model the world the way that it behaves today. And so you end up with sort of over a decade, something that someone forced into a system that didn't actually model it correctly. And it's not like the problem's going to happen that week. It's like the problem will happen five years later when someone's trying to make their escrow payment and it compounds. And so that's the part where it's like, you can have the world's best customer experience team and the most empathetic call center agent in the world. But if fundamentally you were charged the wrong amount of money because the system of record was flawed, then you can't fix that homeowner experience without fixing the infrastructure.
Speaker 3Yeah, which is actually a perfect segue. I want to talk a little bit about how Valen got started. And the flip side of these technologically behind incumbent-driven large industries is they can be very hard to break into, right? And so, you know, we had three options. One, build the software and sell it into the incumbents. And that's been successful in many industries, right? Like Harvey and Law is doing that right now. The second would be do some kind of a roll-up, buy a few of the incumbents and then inject them. And then the third, which is what you chose, which is, no, we're just going to build the entire thing from scratch. And so maybe talk through, like, why that option to get started.
Speaker 1Yeah, so we went through all three options. I know, I'm sure. And just to be clear, the path that we went down was the last one because it is the most painful and it takes the longest. But really, option one, which is just building, you know, a traditional normal software company, it doesn't work for mortgage because it's so heavily regulated that no servicer is going to use a DeNovo system. And then even, I would say, from a R&D perspective, it's like if you, let's say that we managed to get one big marquee customer to sign up, we would basically be beholden to how they want to do things, which will look an awful lot like how they do things today. And so you just run the risk that you wake up 10 years from now and you rebuild the legacy system with, like, a really pretty UI. So that crosses out number one. Number two, the problem is IP. So if you buy an existing servicer that's using an existing legacy platform, the nice thing about being able to build software, sorry, I should say convenient, it actually, I think, is not long-term correct, is you can basically swap things out module by module. But one, I think that actually forces you into platform design decisions that probably aren't right. And then two, you have this IP issue. And so really that left us with the choice of we, you know, got the privilege of starting our own servicer and then just building the software from scratch, not looking at anything. And the software got to evolve as the servicer was growing as well. So it's like we started with one loan, which is really nice, and then, you know, ended up with close to a million.
Speaker 3Yep. And then, well, and I think that strategy has proven very prescient now, but to get it started, I started to think of it in three buckets. Like, one, you needed to get licensed. Two, you needed to turn decades of regulation into code. And then three, you needed to convince asset managers, lenders to give this highly regulated asset over to a new company. So I think it's interesting to briefly touch on each of those. Like, I think, starting with licensing, I think it's easy to think, ah, I fill out some long forms, I throw them over a wall, eventually I get a license. Maybe talk us through, like, what was that process, which I think at the time was very painful and now is a very nice moat.
Speaker 2Yeah, I would like to say we were so thoughtful that we already knew all these things going in. There was kind of late looking back, some slightly improvements we could make in terms of the licensing process, but for the most part, like, all of those required. For context, mortgage servicing and many of these licenses have pretty much chicken and egg problems. One of the key requirements is if they tell you, you actually need to be profitable before you get a license. This is true for, like, the state of New York and a couple of other, you know, regulatory approvals like Fannie and Freddie. They'll make some exceptions and you can get around it, but it's quite challenging. And so when you look at it from the get-go, you end up in a situation where you first notice, holy crap, this is going to take a long, long time. And two, then you end up in a situation where you have to really be methodical in plotting out what licenses you get in what order. So for us, what we did was we said, okay, we're going to start out with this type of customer. We need to figure out, because we can't get the major government licenses, we need to look for a specific type of loan that we can get licensed for. It's called, at the time, they're still pretty prevalent today, and they're actually, I think, growing, non-QM loans. They only require you to have state licenses. Here's the problem, though. With state licenses, you need to pick the states that people actually make non-QM loans. One of the biggest ones is the state of California. The state of California actually has pretty stringent requirements. It actually requires you to have a government application or government loan approval in order to service California loans. There is one exclusion for the Department of Real Estate if you're in the brokering business and a real estate brokering business. So now you need to find someone who is in the department or is a real estate broker to actually help you get that license. Simultaneously, you also need to show them that you have five years plus of experience across every part of servicing. That's collections, that's customer service, that's payment processing, that's delinquency, that's foreclosure. So you need to do all these different things just to get going. And like, let's not even get started when it comes to well, once you've done that, how do you actually make sure you get the subsequent approvals and everything else? But it's an extraordinarily long process. Most people forecast this type of endeavor taking three to five years at the minimum in order to, and that's if you take the most literal optimal steps, three to five years to get all of the different approvals because they all stack on top of each other. You can't do one without the other. And by the very end, you end up having to really, like, aggregate not just business, but really expertise across a very, very large set of domains.
Speaker 3There's many years of stories including you going to visit mailrooms of licensed accountants. Exactly, because I'll give you the
Speaker 2classic one, which, by the way, is a funny one. We actually got the approval for New York in a record time of, I want to say, three years. So it was a record time of three years. And we actually never ended up getting approved to license or to originate mortgages in New York. That just tells you how challenging and how difficult it is. And they have this funny thing where, apparently, they will review your application, but if they either miss it or deny it or whatever else, they'll reset the clock like three to six months back. And this was right before COVID happened. And so I knew I wasn't going to be in New York for the moment. And so I didn't want to wait another six months before I came back to New York. And so what I made sure I did was I went to the mailroom and I identified where the package was because they told me my application was missing. And I was like, I have tracking on this. I know it's there. Well, let me go visit your offices to actually track this down.
Speaker 3Fantastic. All right. So we've made it through that full process. And then maybe talking about taking the regulation and putting it into a platform. I think it's really easy to knock on the incumbents, but they've had decades to harden state-by-state regulations changed over time. How did we approach that from scratch?
Speaker 2Honestly, I think the unfortunate reality, which is, again, like you've mentioned before, a little bit of a moat, which is how we make ourselves feel better about all
Speaker 1of it. The pain and suffering is just. It's the greatest moat of all time.
Speaker 2Pain and suffering. But what we ended up doing was reading every single regulation. So there's a whole bunch of federal regulations. There's, you know, RESPA. There's TILA. There's FDCPA. There's GLBA. There's TCPA. There's all these, like, different regulations on the federal level. There's also 50 states where you basically have to cover their mortgage regulations. You have to cover their debt collection practices. You have to cover their real estate foreclosure practices. You have to cover their privacy practices, their escrow practices. So you have to cover every one of these. And you basically go through a very, I'm going to say it just as a fun exercise, refactoring the entire legal code base, if you can put it that way. It turns out many legal statutes are derived off of different states. And so you sit there basically figuring out how to create an abstract framework that handles all of it. And then you put it into your architecture. And then you start to test it. But what really worked for us, quite honestly, was the fact that COVID happened. And when COVID happened, I spent 18 hours a day for six months straight, just every day sitting there reading regulation and annotating and basically coming up with the schematics. And that's what got us through it. It was pure grit, effort, pain, suffering, all these different things. And then obviously you have the five to six years thereafter of testing and making sure it works exactly as you
Speaker 3intended. Which we'll get into in a second. Sets up a very nice ontology for where Valen is going next.
Speaker 2There was a point at which you could cite almost every single state's regulation. And I can tell you what code number it was.
Speaker 3All right. Good quiz. Exactly. Very fun at the dinner party. Amazing. And then okay, so you've got it built, which is a huge challenge. But then convincing customers that probably we're not leading in necessarily to new technology to start off with. How did you get your first loans on the system?
Speaker 1Yeah. So I would say the framework that we use internally for software is a little different than how we started with the loan servicer. So with the servicer, the nice thing is that unit economics and efficiency kind of largely was the product, meaning that the better we made the technology, the more margin we had. And honestly, we just passed that margin through to the end customer. And all of these asset managers who own these mortgage assets, they are economic animals. And so at the end of the day, it's like when you lower price, it's like you can get the market to capitulate. And that just requires the technology being good. From a software perspective, we think about it internally as it's really you always have to balance safety and urgency. And so you get safety because we built a servicer that actually used the technology and proved for the last six, seven years that it's gone through every state exam, every Fannie Mae audit, Freddie, Ginny, all of the different sort of compliance hoops that you have to get through. And so it's safe because we actually took the operating risk of running our own servicer on the system. And then in terms of urgency, it's something that you honestly you know starting to compound something is always the hardest part right um it's creating and generating momentum and so for us the first thing that we did is we took our servicer and we competed in the market and so that's one way of creating urgency because now you're actually directly competing with your software you know with your future software customers and hoping that they'll forgive you down
Speaker 3the line no but it's really jensen's quote of it's not like ai that's your competition, it's your competition using AI faster. Yes. Like what, like from a traditionally very low margin business, Valen turned into a high margin business. Like what was the economic difference that everyone kind of perked up in the industry?
Speaker 1Yeah. So we are about three times as efficient. And so you take this breakeven business and you turn it into sort of a 70, 80% operating margin business. And that margin is what we used initially to create that urgency. And then it compounds, right? So it's like once you get your first big customer to sign up, then the other five big guys call you and say, hey, were you actually serious that you wanted to sell software? And you started the flywheel effect. But without our own servicer in the beginning, it would have been really hard to generate that. Yep. And so you built
Speaker 3Valen servicing, I think in the 200 billion, how big? UPV, yep. Exactly. And then you could have kept scaling that. Yeah. But instead you decided you were actually going to sell it. Just to be clear, this
Speaker 2was the plan. It was what you
Speaker 3were pitched at the beginning. It was in the deck. I agree. I agree. I'm just, I'm just. This is actually one of the funny things
Speaker 2that all of the new employees who joined are like, wow, like you guys like, you know, are selling this thing and say, this is always what we've been meaning to do. But we actually share our Series A deck with our.
Speaker 3To be fair, you had an incredibly detailed Series A deck. I think it was like a 60 page PDF. Yes. The master take. Over plan and then. And then we followed it step by step. Yes. But we were announced, you know, you guys just recently announced the sale of Valen Mortgage over to Carrington. And now you're taking that OS and selling it into the industry.
Speaker 1I think for me and Andrew, we, I mean, we would talk about this every once in a while, as you would expect, right? Where we would sit down and we would say, hey, it is a fully viable path to just build a big servicer. There are $10 billion plus companies that do this. But for us, it always came back to every conversation ended. With we actually want to change the industry. And the thing is, is that if you keep it as a servicer, you're keeping all of the technology and the alpha for yourself. And at the end of the day, you didn't actually change anything. You didn't fix the core infrastructure problem.
Speaker 2Yeah. I think for companies who want to be generational companies, having like a clear purpose and mission, that's not just we're going to make more money. That's great. It's we want to change the world. We want to fix a rather broken world is extraordinarily important for more out the employees and really the direction for the company, right? Because you want people to like. Not just dream. Oh, I make enough money now. It's like, what can I do that's greater? What can I do that's bigger? What can I do to actually solve the impossible? We got to save the world from mainframes.
Speaker 3Wait, but your mission statement is actually that. Yes, exactly. Mainframes, legacy software. And broken critical infrastructure. Exactly. Amazing. So maybe speaking of the bigger opportunity, you started the company before, you know, generated by AI. I was really working. What is possible today that wouldn't have been in your series A pitch deck?
Speaker 2How beautiful of a problem set up mortgage servicing is for what's about to happen, both with AI, but just like overall enterprise and infrastructure in society, which is to say, what is possible today is you can have these extraordinarily complex cases and you can actually research and basically provide the right set of information to the operators so that they're more. More and more in a world where they're effectively operating little mini armies of agents to actually solve whatever set of problems. So I'll give you a really simple example. When a disaster happens, you're supposed to make some phone calls, make sure that the homeowner is actually able to continue to pay the mortgage, what their intentions, what their plans are, if they need any assistance, you have to go through all this process. And that is normal and that is fine. And people have different scripts to like set that up, but changing that on the fly or like, you know, handling very, very specific different notifications or tactics or plans per locale is basically impossible. The only way to do that today as a normal servicer is you basically print out a spreadsheet and you hand a list of loans to one group of people and handle another list of loans to another people and you say, I want you to do A, I want you to do B, and I want you to go figure it out. Versus with our systems and really the power of what AI can do today, you could literally in our product suite say, hey, I want to orchestrate a set of agents to make phone calls to contact homeowners, to provide them these different products. Run these different workflows, and you can do that in sort of like a fire pilot sort of simulation scenario. And that, I think, provides a lot of both power to the underlying servicers and becoming more efficient, but it actually gives them a lot more customizability. And the latter is actually the really, really cool and important part because it allows every servicer to be the best versions of themselves. They can really think about, hey, how do I want to handle a specific scenario that pops up versus just saying, look, I need to run this. I need to run this quote-unquote efficiently as much as possible, running 0% margins. And I thus only have a cookie-cutter way of going about things, and I actually can't change it. It really turns the world from one that is really commoditized to one that is much more specialized and really flavor-driven.
Speaker 3Yep. So as one way to think about it, you've now created this very organized data ontology. And then pre-AI, any of the tasks you needed to do on top were definitely more efficient. Whereas now you can spin up, I think you've got dozens of these different agents. I think you've got dozens of these different agents that can do escrow analysis and sort of any human tasks that you might need to do on top of it so that packaged information
Speaker 2kind of comes in. Yeah, it's a completely different world where before you could only automate things that were clearly deterministic because you could say, hey, I have the right structured data, I have the information, I can automate a very, very small piece of it because I know how to do it. You can still generate really great efficiencies, but it's just not the experience I think that everyone was looking for and desiring, but math still worked out. So what you can do? Today is completely different. Not only can you, one, you know, automate a much, much higher percentage of tasks, the long tail, the really complex scenarios, but really beyond that, it's that you can orchestrate a very wide variety of scenarios for your agents to handle. And as a result of that, you can actually improve and really do champion challenger type tactics to really improve the outcomes of your portfolios, which just was not possible before because of, again, the level of training and level. You have to have a level of specificity. You have to give your human agents to do the work. Now you can just like say, hey, 100 loans. I want to try these different strategies. I want to improve these different outcomes. I want to give these different notifications. Let's see what happens. Just completely different world.
Speaker 3Yeah. It makes your job as a servicer a lot more fun. Yeah. You've got some interesting things on the consumer side to voice agents and sort of other things you've been experimenting with.
Speaker 1Yeah. So I would say the really fun thing that we get to do now and everyone gets to do now is you get to also do a lot of the things that you've been experimenting with. So just rethink how humans interact with systems, right? And so whereas before we were laser focused on building the right system of record and the way that humans interact with that system of record is through defined UIs. And then you actually end up hiring human operators whose skill set is knowing how to navigate the UIs. Now it's like we get to do things like we just build our own harness on top of the system of record. And actually you just have, you know, that classic chat interface. And you get to do this on the back office side. And then when you think about how our home mooners interact with Valen OS, it's like you can do it through a voice AI, you can do it through a chat AI. There are just so many different form factors now. And it's really cool to think about what it could be.
Speaker 3Awesome. And then maybe, Andrew, I think you, well, I know you Moonlight is one of the most productive Valen engineers as well as being CEO. What are some of the interesting technical challenges on the roadmap or things you're thinking about solving?
Speaker 2So there's a lot of things that we're thinking about. There's the classic set of problems when it comes to, as you're building more and more of an enterprise, really industry-specific harness, like decide, do you want to do fine tuning? Do you want to like move, improve your model efficiencies? Where do you want to like invest in terms of like a harness? So, you know, we've done like the classic stuff when it comes to making sure you have like vector databases, when you're trying to make sure you're, you know, pulling the right context, what is the sequence of context you want to put in? And then there's like slight variations of this, which are very industry specific. So in mortgages, as example, we've done like a lot of things like, you know, we've done you care a lot more about correctness. And so you would probably want to align the models as much as possible and really the overall harness outcomes so that if you don't know the answer, it generally says, I don't know, versus let me give you, you know, some hallucinated answer, right? And so there's like small things here and there that really can change how the experience is for the user. But I think the thing I find most fun and really the thing that I think is so amazing about the way that we are going about it as an industry or really as a product. We is creating the infrastructure so that it's a continual, continual learning process. Right. So when you think about a lot of the infrastructure that people build and, you know, they're using obviously quite a bit of data from the user input and whatever else that makes sense when you're doing coding, that makes sense when you have a pretty contained environment. But these are like really complex ecosystems where there's so many different components. When you think about servicing, there's the accounting entries, there's the money movements, there's the customer interactions, there's the regulatory aspects, the different, you know, different vendors that you're interacting with. And so these things change quite dynamically. And that's a very, very challenging problem because you can't say, well, here's like the bounded problem. is how we're going to solve it and we're going to optimize over a very, very clear function. Building a system that allows you to continuously run the evals, pull the data out, make sure you're actually continuously improving the model in the direction that is aligned, not just with what servicing or the way that servicing should be done generally, but customer specific. That is, I think, the part that's super interesting, building a overall set of infrastructure that can handle it at that capacity.
Speaker 3Which segues, well, I know even from the beginning, you both had ambitions broader than mortgage servicing. How does maybe the architecture and how you've set up the Valen platform, set up Valen to expand into other sectors?
Speaker 2Maybe I'll start off with a little known fact, which is servicing is like the nexus of all these different highly regulated enterprises. The fun fact I always like to go through with people is that people think servicing must be residential or maybe loan servicing. And you're like, OK, we talk about Resi and Resi is really great because it's regulated. It's the space that we started out in. It's very impactful. Because of the size of the market. And it's really, you know, near and dear to everybody's hearts, right? It's a home, American homeownership. It's really important. But you can always, you know, take one step left or right, whichever direction you want. And if it's commercial, there's all the data and all the information associated with the real estate, the tenants. Actually, people don't realize this, but as a commercial mortgage servicer, you actually get the information about the tenants' financials and all the data associated with that. So you could say, you know how like Toast and a bunch of people used their platforms to figure out how to do small, business financing. It's like you could kind of do the same thing if you decide to go through commercial. But if you take another step and you say, okay, let me think about this even more broadly, then you even have things like, you know, healthcare where there's revenue cycle management companies. People get very excited about those type of companies. And it turns out revenue cycle management is just servicing for hospitals and their claims and, you know, they're handling their patients. And the data that you get from that is, you know, effectively all electronic medical records. And so it's a very exciting thing to do servicing first because, you know, it gets, it is a very, very, very challenging and difficult problem. It's a very high breath. But when you get into it, you're in a position where you're effectively, you have tentacles across the entirety of the business and every element of what they work with and what they end up doing. And so it just gives you that really expansionary opportunity to say, hey, I want to go help you, you know, not just in making your servicing work, but I want to help you contact your customers. Let's just go back to Resi for a second. People, again, think of Resi servicing as, oh, I'm going to help you you know, collect the payments and interact with the homeowners. Well, whatever we turned it into, I'm going to figure out a way to use this relationship that you guys have with all of your homeowners and I'm going to call them every single month. I'm going to build a relationship with them. I'm going to make sure I have a pulse on what's going on with them every single month. And that's never been done before. And that's not how people think of servicing. But with a mixture of the technology that we've built and really where AI is headed, you can do those things. And I think that's what I find so, so exciting about this. Yes, everything is actually servicing. That could be the,
Speaker 3that's kind of, exactly. It kind of underpins so many things. Yes, it is the,
Speaker 2it is the tentacle that like, or I don't know what you want to call it, but it's the underlying
Speaker 1critical infrastructure that supports basically anything that has money movements, some sort of regulation and then an operational component. And then we just picked mortgages to be first because it's the stickiest. It's actually the hardest and most complicated as well. And so, you know, we thought, why not do that one first and then we'll go conquer the rest.
Speaker 3No, it gets easy from here. Exactly. Perfect. So I want to tell a little bit about where Valen is today. So you built the servicer, recently announced sold the servicer to sell the software platform. And then just recently, Rhythm confirmed that they're going to transfer four million loans over to Valen, which I think is the largest servicing transfer. It's almost 10% of the market. Yeah, exactly. So, and they, you know, they're smart. They baked you off across all the incumbents. What gave them the conviction to make such a big move?
Speaker 1Yeah. So what I would say is that in, in general, as we were, you know, there's always a timing component to anything, right? And I would say, actually, in some ways, Andrew and I didn't decide that we hit product market fit. The market decided that we hit product market fit. And so just to give you a sense of just sort of the pent up demand in the industry, I think within six months of us officially going to market as a software company, we signed over $200 million of deals. And that just tells you how much the industry has been wanting to do. And so from NewRez's perspective, they've partnered with us really closely from the beginning. So Val and Mortgage subserviced NewRez loans. They could see the performance in a very clear, almost like demo room, basically, of this is exactly the performance that you get if you run on the software. Actually, you know, I would argue that's the lower bound of the performance that you get if you run on the software. And then the way that we kind of structure all of our customers is we want to make sure that we're incentive aligned with the customer. I think the second that you lose that alignment, that's when you're kind of going downhill as a company. And so for us, it's like we structure everything as outcomes oriented. And so it's for every dollar that our customer earns or for every dollar that our customer saves, that's when we earn as well. And I think that core alignment was the thing that made it just a no brainer for all of our customers.
Speaker 3Maybe I want to end on and anyone who's in New York, highly encouraged, encourage them to visit the Valen office. This has got a ton of energy. But what, how would you describe what it's like to work at Valen or what might people find surprising?
Speaker 2Yeah, I think, look, plenty of people have really great talent at their companies. There's really smart people, obviously working at a variety of different companies. But I think what's really unique about the people at Valen is they're all the type of people where they want to take matters into their own hands. Because, and I've had this conversation with so many of the people who've both joined and I've asked people who, sort of prior to us actually figuring out why people were joining. But, you know, these people, if you look at the people who've joined Valen, they have previously worked at or they had offers to go to work at, you know, obviously the standard thing, companies, but OpenAI, Anthropic, all those type of things. And what makes a difference for them is the fact that they realize, look, there's a lot of smart people working on these other problems. Those will get solved. Like clearly, like, you know, like at the compute and the underlying model improvements, like it's happening, it's working. Are you going to be the person who actually solves this problem, this really, really important problem? And finding the desire and finding joy in the fact that like you're personally driving that impact in a way that you can so clearly and actionably handle or deal with, that is, I think, what really, really gets the people at Valen excited. And then they realize they joined for the people because they end up realizing, okay, besides the fact that I love like doing this type of work and being very hardcore and making sure, you know, we're fixing these really, really challenging, impossible, challenging problems. You're surrounded by other people
Speaker 1in the same way.
Speaker 2The people really make people stay very long. Really, when we look at the way that our company has been built and formed, I mean, I want to say like their entire, probably 75, 80% of our like management team is filled with people who've been here for five plus years.
Speaker 1Yeah, exactly. Yeah.
Speaker 2It's really a demonstration of, you know, the type of company you have to build, the type of culture you have to build, and really the type of people who, and that's what I've been
Speaker 3working on. What would you say on the deployment side?
Speaker 1I would say, and we're, we're kind of going through this learning curve live, obviously, but I would say doing enterprise, especially large, complicated, regulated enterprise, it is unlike what you would imagine if you were just sitting there thinking about it in the abstract. Because at the end of the day, it's like what we're talking about is really large organizations of thousands and thousands of people. Every single individual has a slightly different objective function. You have layers of the organization that you need to move through. If you had asked me six years ago, I would have told you, you know, this is a technology problem. What I know today is that this is a change management problem. And so what we've spent the last six to 12 months building is really that change management muscle of just how do you navigate organizations? How do you make change happen? And it turns out change is really, really hard at scale. But, you know, it is, I think, for this moment that we're in with AI, the number one biggest value driver for the next decade.
Speaker 3And you've, maybe the last point on that, you've already done a couple of successful deployments. Like, what is the type of person that tends to really thrive in that kind of role?
Speaker 1Yeah. I would say high agency, high ambiguity, high empathy for the customer, and then the ability to kind of really think from different points of view on what's going on and what the objective function is and then figure out what is the global thing that everyone's trying to do, bridge that, and then actually come up with a solution. It's really, I would say, usually there are people who are really, really great problem solvers. And then there are also people who are really, really good with dealing with people. And you need both.
Speaker 4Yeah. Fantastic. Well, everything is servicing. Andrew, Linda, thank you for joining the podcast. Thanks for having us. Thanks, Angela. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X, A16Z, and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. It should not be taken as legal, business, tax, or investment advice. or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com forward slash disclosures. you
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