How Valon Rebuilt a $13 Trillion Industry From Scratch

a16z General Partner Angela Strange sits down with Valon’s Andrew Wang and Linda Du to unpack what it takes to rebuild the infrastructure underneath a $13 trillion mortgage market that still relies heavily on systems designed before the internet. Linda and Andrew explain why Valon chose the hardest path: becoming a regulated mortgage servicer, translating decades of federal and state regulation into software, and proving the platform on its own loans before selling it to the industry. That foundation made Valon roughly three times as efficient as traditional servicing and created the system of record it is now using to bring AI into complex mortgage workflows. They also discuss what AI makes possible on top of that infrastructure, from agents handling long-tail servicing tasks to voice interfaces and more personalized customer experiences. And they explain why deploying the technology into large regulated enterprises is ultimately as much a change-management challenge as a technical one.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Mortgage 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.

• 0:13Speaker 01

  The 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 a direction that is aligned, not just with the way that servicing should be done generally, but customer-specific.

• 0:30Speaker 00

  It's $13 trillion of consumer debt that basically runs on a single incumbent that built their legacy system before the internet was invented.

• 0:39Speaker 01

  It turns out revenue cycle management is just servicing for hospitals.

• 0:43Angela Strange?

  Everything is servicing.

• 0:44Speaker 00

  It's the underlying critical infrastructure that supports basically anything that has money movements, some sort of regulation, and then an operational component.

• 0:52Angela Strange?

  You started the company before generative AI was really working. What is possible today that wouldn't have been in your Series A pitch deck?

• 1:01Speaker 01

  What is possible today is...

• 1:03Erik Torenberg?

  Mortgage servicing is a $13 trillion market that still runs in large part on technology designed before the internet. Valon decided to rebuild it from scratch. In this episode, A16Z general partner Angela Strange sits down with Valon's Linda Do 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 discuss why Valon 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

• 1:48Erik Torenberg?

  that were previously too complex to automate, why the underlying system of records still matters, and what they've learned deploying new technology into enterprises with thousands of employees.

• 2:01Angela Strange?

  So Andrew, I think a great place 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.

• 2:09Speaker 01

  So 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'd 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...

• 2:45Speaker 00

  Just need to pick a multiple.

• 2:46Speaker 01

  Yeah, 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,

• 3:31Speaker 01

  and actually ultimately build their system 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.

• 3:44Angela Strange?

  Excellent. 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 Valon came in.

• 3:55Speaker 00

  Yeah, 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%. 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.

• 4:38Speaker 00

  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 skillset 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.

• 5:01Angela Strange?

  So 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 Valon, what might I notice from just a human perspective?

• 5:26Speaker 01

  Yeah, 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 it really causes 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,

• 6:10Speaker 01

  but the history isn't fully with really good 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 says, something occurs to them. They end up in a situation where a homeowner passes away.

• 6:53Speaker 01

  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 far away. 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 passed away. 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.

• 7:36Speaker 01

  And so really, the more information represented the better way possible, the better the experience ends up being.

• 7:43Angela Strange?

  Yeah. And I think 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 Valon? Or if it's kind of multiple times in a row, you might need a loan modification?

• 8:03Speaker 01

  Yeah, 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. You sort of pay it 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,

• 8:46Speaker 01

  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 automate it, 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 customer relationship.

• 9:21Speaker 00

  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.

• 10:06Speaker 00

  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.

• 10:14Angela Strange?

  This is actually a perfect segue. I want to talk a little bit about how Valon 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 Loz 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?

• 10:54Speaker 00

  Yeah, so we went through all three options.

• 10:55Angela Strange?

  Oh, no, I'm sure.

• 10:56Speaker 00

  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 de novo system. And then even, I would say, from a R and 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.

• 11:39Speaker 00

  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.

• 12:23Angela Strange?

  Yep. And then, well, and I think that strategy has proven very prescient now, but to get it started, sort of 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, 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.

• 13:05Speaker 01

  Yeah, 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, slight improvements we could make in terms of the licensing process. But for the most part, like all of those required. For context, mortgage servicing in 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, you know, true for like the state of New York and a couple of other, you know, regulatory approvals, like Ben 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

• 13:50Speaker 01

  you have to really be methodical in plotting out what licenses you get and 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 needed 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. 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.

• 14:33Speaker 01

  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 who'll actually help you get that license. Simultaneously, you also need to show them that you have five years plus of experience across, you know, 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,

• 15:18Speaker 01

  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 aggregate, not just business, but really expertise across very, very large set of domains.

• 15:34Angela Strange?

  There's many years of stories, including you going to visit mailrooms of licensing.

• 15:39Speaker 01

  Exactly, because I'll give you the classic 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.

• 16:24Speaker 01

  Well, let me go visit your offices to actually track this down.

• 16:27Angela Strange?

  Fantastic. 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. Like, how did, how did we approach that from scratch?

• 16:47Speaker 01

  Honestly, 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 this.

• 16:56Speaker 00

  The pain and suffering is just.

• 16:57Speaker 01

  Greatest moat of all time, pain 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, of refactoring the entire legal code base, if you can put it that way.

• 17:38Speaker 01

  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 like annotating and basically coming up with the schematics. And that's what got us through it. It was, it was pure grit, effort, pain, suffering, all these different things. And then obviously, you have the five to six years thereafter of like testing and making sure it works exactly as you sort of, you know, intended.

• 18:17Angela Strange?

  Yeah, which we'll get into in a second, sets up a very nice ontology for where Valen is going next.

• 18:22Speaker 01

  You know, there was a point at which you could cite almost every single state's regulation, and I can tell you what code number it was.

• 18:29Angela Strange?

  All right, good quiz.

• 18:30Speaker 00

  Yeah, we'll do a pop quiz with you.

• 18:31Angela Strange?

  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, you know, probably were not leaning in necessarily to new technology to start off with. How did, how did you get your first loans on the system?

• 18:47Speaker 00

  Yeah, so I would say, you know, the framework that we use internally for software is.

• 18:56Speaker 00

  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, you know, 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

• 19:39Speaker 00

  and proved for the last, you know, six, seven years that it's gone through every state exam, every Fannie Mae audit, Freddie, Ginny, you know, 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 it, 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? 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 the line.

• 20:22Angela Strange?

  No, but it's like Jensen's quote of it's not like AI that's your competition. It's your competition using AI faster.

• 20:28Speaker 00

  Yes.

• 20:29Angela Strange?

  Like what it like from a traditionally very low margin business, Valon turned into do a high margin business. Like what was the economic difference that everyone kind of perked up in the industry?

• 20:39Speaker 00

  Yeah, so we are about three times as efficient. And so you take this break even 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.

• 21:08Angela Strange?

  Yep. And so you built Valon servicing, I think in the 200 billion, how big?

• 21:14Speaker 00

  UPB, yep.

• 21:14Angela Strange?

  Exactly. And then you could have kept scaling that.

• 21:17Speaker 00

  Yeah.

• 21:17Angela Strange?

  But instead, you decided you were all going to sell them to customers.

• 21:22Speaker 00

  And we're going to go do software.

• 21:25Speaker 01

  Just to be clear, this was the original.

• 21:27Angela Strange?

  This was the plan. It was the original plan.

• 21:29Speaker 01

  It was what you were pitched at the beginning.

• 21:30Speaker 00

  It was in the doc.

• 21:31Angela Strange?

  I agree, I agree. I'm just, I'm just.

• 21:33Speaker 01

  This is actually one of the funniest things that all of the new employees who joined, they're like, wow, like you guys like, you know, are selling this thing. Say this is always what we've been meaning to do, but we actually share our series A deck with our new book.

• 21:45Angela Strange?

  You had an incredibly detailed, yeah, series A deck. I think it was like a 60-page PDF of the master takeover plan. And then it was definitely.

• 21:53Speaker 00

  And then we followed it step by step.

• 21:55Angela Strange?

  Yes, but we are announced, you know, you guys just recently announced the sale of Valon Mortgage over to Carrington. And now you're taking that OS and selling it into the industry.

• 22:04Speaker 00

  I 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.

• 22:34Angela Strange?

  Yeah.

• 22:34Speaker 01

  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 morale of 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, you know, solve the impossible?

• 23:00Speaker 00

  We gotta save the world from mainframes.

• 23:02Angela Strange?

  Wait, but to the full, your, your mission, mission statement is actually, is actually that?

• 23:07Speaker 00

  Yes, exactly.

• 23:08Angela Strange?

  Mainframes, legacy software.

• 23:10Speaker 00

  And broken critical infrastructure.

• 23:12Angela Strange?

  Exactly.

• 23:13Speaker 00

  Yeah.

• 23:14Angela Strange?

  I mean, maybe, maybe speaking of the, the bigger opportunity, you started the company before, you know, generative AI was really working. What, what is possible today that wouldn't have been in your, your series A pitch deck?

• 23:29Speaker 01

  How beautiful of a problem setup 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 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.

• 24:14Speaker 01

  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 hand 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 plans, run these different workflows. And you can do that in sort of like a fighter pilot, sort of simulation scenario.

• 24:59Speaker 01

  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 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.

• 25:37Angela Strange?

  Yep. So is 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 that can do like escrow analysis and sort of any, any human task that you might need to do on top of it so that packaged information.

• 26:00Speaker 01

  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 could 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. 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

• 26:44Speaker 01

  to really improve the outcomes of your portfolios, which just was not possible before because of, again, the level of training and 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.

• 27:03Angela Strange?

  Yeah, it makes your job as a servicer a lot more fun. You've got some interesting things on the consumer side too. Voice agents and sort of other things you've been experimenting with.

• 27:13Speaker 00

  Yeah, 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 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 skillset 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.

• 27:55Speaker 00

  And then when you think about how our homeowners interact with Valon 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.

• 28:07Angela Strange?

  Awesome. And then maybe, Andrew, I think you, well, I know you moonlight as one of the most productive Valon engineers as well as being CEO. What are some of the interesting technical challenges on the roadmap or things you're thinking about, thinking of solving?

• 28:21Speaker 01

  So 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 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, 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 hallucinating answer, right?

• 29:06Speaker 01

  And so there's like small things here and there that really can change how the experience is for, for the user. But I think the. 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 suite 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

• 29:51Speaker 01

  aspects, the 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 problems, 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 a 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.

• 30:24Angela Strange?

  Yep. Which segue as 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 Valon platform, set up Valon to expand into to other sectors?

• 30:38Speaker 01

  Maybe I'll start off with a little known fact, which is servicing is like the nexus of all of 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, okay, 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 could 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

• 31:21Speaker 01

  get the information about the tenant's 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 decided 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 electronic medical records. And so it's a very exciting thing to do servicing first because it gets, it is a very, very, very challenging and difficult problem. It's a very high breadth. But when you get into

• 32:04Speaker 01

  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

• 32:49Speaker 01

  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.

• 32:55Angela Strange?

  Everything is servicing.

• 32:57Speaker 01

  Yes, everything is actually servicing.

• 32:59Angela Strange?

  That's kind of, exactly. It kind of underpins so many things.

• 33:03Speaker 01

  Yes, it is the, it is the technical that like, or I don't know what you want to call it, but.

• 33:06Speaker 00

  It's the underlying critical 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.

• 33:26Angela Strange?

  No, it gets easy from here.

• 33:27Speaker 00

  Exactly.

• 33:28Angela Strange?

  Perfect. So I want to talk a little bit about where Valon 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 Valon, which I think is the largest servicing transfer.

• 33:47Speaker 00

  It's almost 10% of the market.

• 33:48Angela Strange?

  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?

• 33:56Speaker 00

  Yeah. So what I would say is that 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 change. And so from NewRes's perspective, they've partnered with us really closely from the beginning. So Valon Mortgage subserviced NewRes loans, they could see

• 34:41Speaker 00

  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 customer engagements 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.

• 35:24Angela Strange?

  Maybe you want to end on, and anyone who's in New York, highly encourage them to visit the Valon office, this has got a ton of energy. But what, how would you describe what it's like to work at Valon or what might people find surprising?

• 35:36Speaker 01

  Yeah, 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 Valon 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 had joined prior to us actually figuring out why people were joining. But, you know, these people, if you look at the people who've joined on, they have previously worked at or they had offers to go to work at, you know, obviously that's the standard thing, companies, but OpenAI, Anthropic, all those type of things. And what makes the difference for them is the fact that they realize, look, there's a lot of smart people working on these other

• 36:21Speaker 01

  problems. Those will get solved. Like clearly, like, you know, like at the compute and the 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 actionally handle or deal with? That is, I think, what really, really gets the people at Valon 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 problems.

• 36:59Speaker 00

  You're surrounded by other people.

• 37:01Speaker 01

  Yes.

• 37:02Speaker 00

  Exactly the same way.

• 37:03Angela Strange?

  Yeah.

• 37:03Speaker 01

  The 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, 8% of our like management team is filled with people who've been here for five plus years.

• 37:21Speaker 00

  Yeah, exactly.

• 37:22Angela Strange?

  Yeah.

• 37:22Speaker 01

  It's really a demonstration of, you know, the type of company you have to build, type of culture you have to build, and really the type of people who thrive and work at Valon.

• 37:30Angela Strange?

  What would you say on the deployment side?

• 37:32Speaker 00

  I 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 an, 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

• 38:15Speaker 00

  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 it's, it is, I think, for this moment that we're in with AI, the number one biggest value driver for the next decade.

• 38:36Angela Strange?

  Yep. And you've made a last point that you've already done a couple of successful deployments. Like what is the, the type of person that tends to really thrive in that kind of role?

• 38:46Speaker 00

  Yeah. I would say high agency.

• 38:48Angela Strange?

  Yeah.

• 38:49Speaker 00

  High ambiguity, high empathy for the customer. And then the ability to kind of really think from different points of view on 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.

• 39:17Angela Strange?

  Yeah. Fantastic. Well, everything is servicing. Andrew, Linda, thank you for joining the podcast.

• 39:24Speaker 00

  Thanks for having us.

• 39:25Speaker 01

  Thanks, Angela.

• 39:28Erik Torenberg?

  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.

• 39:53Erik Torenberg?

  As a reminder, the content here is for informational purposes only, 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/disclosures.