YouSaid · the spoken record

Sanjay Gupta

lines on the record: 65
first: 2025-09-11
most recent: 2025-09-11
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Makes the same amount of money and nobody else makes any money. Like innovation comes with disruption. And if you want the innovation, you have to be willing and actually promote the disruption. And our current regulatory structure, especially conservatorship and the current regulatory regime, discourages all of that. So I think we're finally at a point where given all the financial pressures on the mortgage industry and all the technological innovations and really the brilliance of entrepreneurs like Andrew and Mike to actually drive this change, but we need to make sure that the policy environment, including the regulatory environment, adapts to it as well.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“From engaging in any new products or any new activities without an elaborate regulatory approval process, which includes public comment from all the entrenched vested interests of the current mortgage industry. Like this is all set up to slow innovation. This is all set up to keep the existing power players in their current positions. And what you really want to do is to drive all this innovation, but the ultimate goal of better protecting the taxpayer, making the system safer and better, driving down costs for consumers, that set of conditions is not helpful to that. So what I would say is the conservator and regulator of the GSEs, the Federal Housing Finance Agency, ought to be seriously thinking about not only how to make it easier for innovation to happen, but actually mandating that this innovation”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“One thing that I think is necessary in order to kind of drive all this forward is because the government and the GSEs play such a big role in this system, policymakers, regulators, the management of the GSEs need to be involved in this set of changes. And those conditions don't really exist today to promote this kind of innovation. Fannie Mae and Freddie Mc have been in conservatorship, government conservatorship for the last 17 years. When I joined Fannie Mae, I was told that the board of directors had been assured by the highest levels of the United States government that the future of Fannie Freddie would be recharted in 12 at most 18 months. That conversation was in December 2008. It's now 2025 and they're still in conservatorship. And in addition, in that time, regulations have been passed that prohibit them.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't want to promote a dystopian view of the world. It may come to pass, as Andrew suggests, but I think there'll be a lot of good that comes out of it as well, and there'll clearly be a lot of change.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Encapsulate all their information a little bit stale, but in the form of tokens that can be fed to any originator, right? Like I have all the context about their loan documents. I have their context about their work history, all that type of stuff, and also their pay history. And if you get more and more of that, that gets constantly fed into a machine, you can basically supply any super app or any sort of like customer all of this embedded context about that individual, which means that they can take that, immediately get a new loan, you know exactly what their mindset is, exactly what's going on, and everything becomes instantaneous. It's more dystopian than it is like utopian, but I just think it's going to end up going there because if you look at the sequential steps that take place, that's what's going to end up happening. It's kind of cool to think about kind of scary to think about at the same time, but it is what it is.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“You basically buy it because some people won't refinance when rates go down. So that's sort of like why there's excess return and there's like a call option built into it. And so all this financial mathematics works because there's truly inefficiencies built into the system. When the inefficiencies disappear, that instrument looks very, very different. And so there's like a reckoning at some point in time in the future that will happen because of that. That's more like an interesting financial mathematics exercise that financial geeks will geek out on. The second thing is this, again, more consumer oriented view, which is kind of dystopian. And I just like personally have started to believe that this will happen. If you think LOMs are just going to be able to eat up more and more tokens and have bigger context windows, whatever else, I think what's going to end up happening is that a mixture between Neuralink or people having, if you've ever watched a movie circle, there's like these things that follow around record you everything. Basically, the idea is, look, at the end of the day, today, even today, I have so much information about consumers that I can embody and really.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Angles. I think the two interesting things that I think about are capital markets, and then I think about what the experience is going to be for a consumer. And for the consumer, I think it's more dystopian. The capital market side, I think it's more just like an interesting to think about, right? This is a very interesting, very niche, but important topic. But one of the great inventions of basically the U.S. mortgage market is this 30-year fifth rate mortgage that every big financial sovereign wealth fund institution, whatever else buys from American homeowners and basically earns extremely load spread and returns on relative to the risk that they're taking on in terms of prepayments and whatever else, right, because of this amazing sort of like liquid ecosystem that they've all built. And so as things get more efficient, right, fundamentally what ends up happening is that people should immediately refinance and so basically rates keep dropping. And so this instrument becomes less and less like valuable, right? Today, the way it works is there's a little bit of a spread above treasuries.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“10 years is a very long time, especially in today's technology world. I think that in 10 years, consumers, assuming that the capital market structure doesn't change, which I think is like a debatable assumption in a decade, I think that you'll have consumers really with near instant fully automated one-touch experiences. And you'll still have compliance and disclosure and a whole bunch of things that you have to kind of work through and kind of like opening a deposit account. You still have to sign like a million disclosures to get a deposit account, even though it's like a one session experience. But I certainly think that the fulfillment will be automated. It'll all be source data and the execution will be much better on the back end.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Things that we need to do. There's a specific set of processes, a specific set of steps that you want to be able to follow so that you can explain it. One of my favorite stories is when I talk to the folks at FICO back in the day about like why do they still use logistic regression to effectively come up with FICO scores? And the answer was it's just explainability. Like I need explainability because I need to be able to say this parameter did Y in a very linearly explainable way because anything but that, right? And this is 10 years ago when gradient boosting and whatever else was all the rage, that's not explainable. And then that means when I get in front of the regulators and I get asked all these different questions, I won't have a really great answer. And so I think working with the right platforms where they've structured their solutions and their infrastructure in a way where you can kind of crawl, walk, run, sprint, right? So you can do all of it in the right sort of steps and processes will be the right way and also the fastest way you'll be able to leverage AI for your business.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Try to compete in the sort of middle of the equation, right? Compete on really having the right platform to utilize, or you have to decide you want to be a foundational model company. That sounds like a bad idea, though. And so the question for me is like more so, how do you centralize all the contents you have? How do you have clean data? How do you put as much of the foundational parts of your business in a way that AI can leverage and you can utilize it with the stuff that a lot of people are building, right? And that will make you a big beneficiary in this outcome because ultimately, to me, who are the biggest beneficiaries of AI, right? It's like the customers and it's basically the operating systems that they run off of. It makes it like a really, really great outcome. And so I'd say that's one big thing to think about. And then the second thing about it is you should really think about mixture between the right partners and the right platform that you're working with, right? Because we work in extraordinarily regulated space. And so you need the right guardrails. You know, people thinking about egetic behavior. Agentic behavior is kind of troubling, right? When it comes to a lot of”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think there's a lot of truth to we pursue certain strategies because we think that is true and obviously it's a little bit self-serving because it is the strategy that we pursue. There's two things that I would think”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“The actual innovations and AI as opposed to the people who are kind of like using AI to go fish for customers as a buzzword, which there are a lot of them. And so that definitely, I think, is the hardest part is partner selection.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“There are still lenders out there who have told me they want to train their own models or they want to build their own agents. And for the most part, I think that is probably not a great idea. And then it's really about how do you make sure that you are positioning your technology in a way that you have partners who are going to get better as the models continue to get better and that your business will basically allow be able to take advantage of those models. So how are you changing your processes, changing your staffing, changing the profile of people that you're hiring so that when the technology is much better one, two or three years from now, you're really like oriented towards that. And I think a lot of people see this AI thing. And people always ask, but where will the value accrue in the stack? And they're like, oh, it accrues to the models or, oh, it accrues to the app company. So I have to go build my own apps. I have to go train my own models. What I always want to remind people is, yes, value will accrue to those two places, but most of the value in this AI revolution is going to accrue to the people who use the AI. And so how can you make sure that as a mortgage lender you are really well positioned to be one of the people who use the AI and that you're working with the partners who will bring you”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“I would say there are a handful of things that really matter here. One is that most of these institutions are probably not going to get there on their own. Of course, that's also a little bit self-serving of a statement. But I do often use as a mental model. We've seen like meta throwing around 100 million bucks or whatever for researchers. And so the question I often ask mortgage lenders in general is would someone who can make $100 million from Meta really be in the mortgage industry at all, let alone working at your lender. And so I certainly think there's like a diffusion that we'll have to happen over time. But I certainly think that people that use partners and that find the right technology partners to work with, whether those be existing mortgage vendors, other partners that are more general purpose, are going to be the ones who are able to get ahead first. I often advise people that what you want to make sure you're doing is you're figuring out how to benefit from all of these really smart people, mostly in San Francisco, who are working on making intelligence via API better and better, as opposed to competing with them. And so.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it makes the life of the lender much easier. And in the end, from my point of view, the goal here is to drive down the cost to the consumer. So if you can replace all those people who are doing their best to try as good a job as possible, but nonetheless committing errors and replace that with these technologies, it's better for the lender. They'll be able to improve their cost base, but it's also better for the consumer because they'll end up paying less for it. And in the end, it doesn't increase risk. It actually reduces risk. It should make regulators happy. It should be consistent with what policymakers want to see. Mike, you talk to probably the heads of mortgage across most of the major institutions of the country. They're all wondering, how should I prepare for the future with AI in it? What is the advice that you have?”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“That To the regulatory aspects. Mortg End up being subsumed by AI. This is a way to Rules and processes and procedures required by the GSEs and make them into software code. And not only can lenders deploy it that way, but at the same time, the GSEs and the regulators could actually enforce and monitor compliance with that using those same tools. So much of what happens in the mortgage business is really just a function of human error. There's some fraud, but it's relatively small these days. But there's a lot of human errors that occur. And if you can take the human error out of all of this and replace it with code and be able to monitor it with another set of code,”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“One of the first things that we did in terms of using language models at Fannie Mae when I was still there and I left in 2018. So it's been a while since I've been gone, but we actually took the entire servicing guide Applied machine learning to it and created an inquiry system that allowed. And servicers to submit questions using regular language and get answers without sifting their way through the 1,200 pages of material. Doesn't seem like an amazing thing to do, but seven, eight, nine years ago was really something quite an important step forward. And so I think it's an example of the kinds of things that Mike and Andrew are talking about The technology has advanced so much So quickly”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“I think one of the most exciting things going on here is the software you can now sell in, like the attempts before might have been 2x better. This is now 10x better in that all of the things you wanted it to do, aka document ingestion, now actually work. It can incorporate and scale some of your best employees. And then I think, Mike, to your earlier point of the why now financial services used to be slow at buying. And now there is a tremendous urgency from boards, from executive teams sort of very much embraced the Jensen quote that AI is not your competitor, it's your competitor that adopts AI before you. And so bringing that into your business is a very, very good way to prepare for the future. Tim, I want to ask about AI in regulation. And there is a, like every industry, an increasing amount of regulation in this industry, even just the Fannie Mae selling guide, I think is 1,200 plus pages. How do you think about how I could potentially”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Their best people work on specific workflows, and you can learn from that. You can sample the information that you need, you can reinforce and learn, and you can effectively clone your best people. And so what that basically means in our minds is that you can create a world where by building on Balin, you're building for an AI world by default, right? And you're not only being able to automate a lot of your work, but you're able to clone your best people. And that, to us, is like an extremely exciting thing. And also, honestly, at this point, again, to Mike's point about some really, really great researchers in San Francisco doing a lot of great work for us, that's a world that is actually possible today. And we see that as a near future reality.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“And that's great. That's awesome. They can serve, orchestrate, they can control all of their business processes and have a lot of visibility and auditability, all that type of stuff. What's really cool, though, is that we also have the tasks that humans do when you need a human in the loop in that workflow, which happens to be a really amazing place to get a lot of training data whenever the workflow gets done, right? Think about it as like there's a bunch of things that have to happen, fill out context. There's a task. That's the next step, which is human in the loop. They figure out the answer. They make a decision. The decision gets reported. And now you basically start to create these training sets. And what's really great about that is that then you can automatically start training AI agents based on that specific task, which basically means if people put their entire business process on our workflow engine, they then are able to build automatic AI agents for that specific task. And so you have this like amazing thing where you're able to then get people to not only put their business process and automate it whatever else, but they can have”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Use a lot of the same sort of use cases as Mike. We use LMs to do summarizations. We have voice agents. We parse documents, all type of stuff. I'm going to say some things that will probably drive some amount of industry ferment. What we find super interesting where we're going to really get to, right, is personalized AI agents. And so if you think about what we do as Valen, we basically have big sort of like workflows.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“You could really read super successfully was like W2s. And of course, a lot of the advancements in language models have meant that reading things like purchase contracts or title insurance policies or closing protection letters have become problems that are frankly actually really simple to now hook up because you just plug them into a large language model. Of course, there's a bunch of like orchestration you have to do. You have to do a certain amount of reasoning and consensus across multiple LLMs and prompting and whatnot. But for the most part, it's a much lighter weight effort to basically say, hey, I have an API that can read. And I have a bunch of documents that need to be read. And passing that document there and getting the data out, I would say, is a place where we've seen a lot of returns for customers already. And it's a pretty easy, straightforward case. There's lots of other more interesting and exciting things you can do as you start to unlock computer use and can help co-pilot your configuration. And there's all sorts of things we're thinking about. But the obvious low hanging fruit is the problem that has been there for years and years and years. And it's just some labs in San Francisco gave us an easy way to solve it.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think the obvious use case that people have been trying to do for ages in origination that we've deployed in production is reading documents. It's kind of like a very unfortunately basic use case. But the reality is, if you think about the mortgage origination process, it's basically you've got like a pile of data that describes the borrower situation and a pile of rules that you get from the GSEs for the most part. And a lot of that data lives in documents today. And so a huge percentage of what you're actually paying human beings to do is read the document, extract the data from the document, key that into a system, compare that document to other documents. It's all really like document management. And so for decades, the industry has been trying to figure out how do I take the data out of the document and just put it in my LOS or put it in a schema somewhere that I can manage it. And people have spent tensions, hundreds of millions of dollars like training small models. like training convolutional neural networks even before there were convolutional neural nets like training other stuff to try and like read documents and for the most part the only”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“All of that behind us, and we're turning 100% of our attention now and focused to providing software throughout their services in this space. And that's how we actually get enough market share to have our product affect other people's lives, right? And what's really incredible is that the amount of receptivity we've had because of the work that we've done on the operating side, many of the United States' largest and most prominent servicers have signed up with Valen, and that means we have pipeline of hundreds of millions in AR. And what's more, we now see this really, really large opportunity to not just go after the mortgage servicing business, but really build an OS for servicing in general or regulated financial type businesses. And so it's a really incredible moment from going from a place where we were trying to get a servicer stood up and we just literally wanted to make sure we survived and it worked to a place where we're now in this place where these whales, for lack of better way to put it, are now in a position where they're saying, hey, we've seen what you've done. It's amazing. It's honestly transformative. And now we're really excited to use your.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“A lot of blood, sweat, and tears. So we started the company six years ago, and we went through the process of like, okay, when you start the company, you first have to give the licenses. It turns out when you get the licenses, sometimes they say you actually have to have a certain number of years servicing before you can get the next set of licenses. So you went through that entire sort of, okay, let's do one step at a time. Let's get this first set of customers. Let's do NSA subset of states, et cetera, et cetera, et cetera. And then there's a lot of like stepwise function growth, right? Because once you know how to service a loan, you can probably service 10 loans, 100 loans, but you also mess up on a whole bunch of different things. And then you stop for a while, make sure you fix your core system because Mike, we really, really believe that you have to fix the core to do a good job. And then once you've done that, you can say, okay, let me 10x again. And so six years into that journey, we're a top 10 player. We have 1% of U.S. market share. And we're probably the most profitable service surge ever exist. We are putting...”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“The rebuild the whole thing. Okay, Andrew, you've also chosen not to wrap existing servicers. So you started by not only building your own software, but also being the user of the software or a subservicer in interestly parlance, which is one enabled you to scale very quickly. And then two, maybe talk about how the business of servicing is usually a five percent margin business. You've taken it up to a 50% margin business. So what did it take to build that and where are you going to go from here?”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“That is a surprising fact to pretty much everyone we hire at Vesta, yes. A lot of these things were built on not like SQL databases in many cases, but actually even older, like the flat file databases. And so you have to autonomically save the entire loan file in a save button. And so actually, some people have hacked their legacy LOSs where you can have two people in the file at the same time. But if you actually do that, their saves will overwrite each other to the tune of hundreds of fields and there's no merging mechanism. So yeah, there are like database level constraints that certainly prevent good dynamic parallel processing in existing systems.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. When I was on a demo today with a prospect where they were asking basically, hey, this process is so dynamic, right? Everyone talks about task-based workflow and mortgage origination. And one of the problems that happens is you get like halfway through the loan and then something happens. Their example was then the borrower gets married. I was like, okay, I don't think that many borrowers get married in the middle of the mortgage. But it does happen sometimes. But other examples are like, all right, the appraisal comes back low and that means your LTV is above 80 and now you want to switch from a conventional to an FHA loan. Like that kind of stuff happens all the time. And so for us, to your point, it was like a maniacal focus on getting the right data model and then making sure the entire process was going to be data driven so that you don't have this like weird path dependent workflow that you're trying to sketch out that is super complicated and has to satisfy thousands of pages of rules. And it's much more if you model the data correctly, you can figure out all of the steps that have to be taken looking only at the data, ignoring all of the steps that you've already done in a way. I don't know if that's really an innovation or that's just like an approach that we've taken that's been very successful, but like a very maniacal focus on data.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“One of the other points you made, like for instance, if you notice as a consumer is you apply for a mortgage, it's a very linear type process. And one of the innovations of Vesta is you've completely rethought the workflows. But in order to do that, you also needed to change and adjust the data structures.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“For example, when we're implementing the LOS, as we go into a customer and we go and deploy Vesta and we rip out whatever the existing LOS might be, a huge percentage of the project is actually not deploying the technology. And so it's not building the system. It's not setting up the system for them and configuring it. It's actually the operational transformation of changing the way that these people do work and the way that they think about the process. And you have to achieve that operational transformation kind of almost regardless. And so in some ways it's worked out really nicely for us because being that biggest project, being the fundamental platform and changing that is like the only way really to force a lender to understand that they're also”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“I think that there's a. FinTech has had a debate over the last decade probably around whether you do call it like core replacement or core rappers. And I do think that 10 years ago everyone was in the core rappers camp. My favorite is the company that I don't think was an Andreasen portfolio company that got acquired called Mantle. And it's literally called Mantle because the mantle is the thing around the core, the earth. And I think to me, that's like the pinnacle of the fintech strategy of that version of the strategy. And I think for us, I had the fortune again of working with Tim at Blend. I think for us a blend in many ways was like the wrapper on the LOS. And the question was, hey, can you actually drive a lot of the foundational change in the back office that you want to change without going in and replacing the whole OS? And I think the answer basically was no. Like I think that if we had tried to build something as a wedge around the whale that is encompassed, I think we would have probably ended up in a similar place where we could have added some value, but it would have been very hard to drive the fundamental change with lenders that you need to drive. The other thing that I've noticed.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“So, Mike, I want to come back to building a loan origination system. And anytime you're trying to wedge in as a new company to an entrenched industry, there's generally two strategies. One is build a, these days, interesting agent or other workflow on top and then use that as a wedge into becoming the system of record. The other is, all right, just take the pain at the beginning, build the entire system, and then sell a software that's way better. You chose the much harder ladder strategy. Talk about why.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“To go to an appraisal. And we actually really didn't care very much about the statement of value from the appraiser, which we found to be not as accurate as our own estimate and often was actually quite biased one way or the other and tended to be actually less reliable the higher the loan to values. The more that you were actually looking for the property value to cover the risk, that's when the appraisal was actually the least reliable. So you think about what all that cost the consumer, and that's just one example. This is in effect an indirect tax on all transactions that Policymakers, regulators, the GSEs, lenders, consumers, we should all have an interest in trying to reduce that number to as close to zero as possible.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Every mortgage loan that's underwritten or backed by the GSEs requires an estimate of value of the property. So what does that mean? Well, most consumers end up having to go out and pay some appraiser, typically a guy whose average age is 60 years old, going out with a tape measure and measuring how many square feet there are in a house and verifying the dimensions and the condition of the property. The reality is that they're much faster, easier, more accurate ways to collect that data. And once the GSEs have that data, they have enormous databases of property values, much bigger than any other private party has in the United States. And so at Fannie Mae, we already knew what the property was worth. What we really just wanted was somebody to go out and verify was there and confirm that it hadn't burned down or had been struck by lightning. But there are much less expensive ways of doing that, paying somebody a couple thousand dollars.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Thing. So, our view is we should be consuming the real data. Let's try to do real time, nuanced analysis of that, and be able to underwrite people more effectively, but also more accurately and hopefully actually be able to expand the class of borrowers who could get access to credit because they might be non-traditional borrowers. So that's one example. Another example is that I just look at how much money consumers spend in this process, the mortgage process, without getting a whole lot of real value out of it. So, for example,”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“So, look, so much of what passes for credit underwriting today is basically just like proxies for real data. So what do I mean by that? Today, it's possible to actually deliver people's digital bank account statements to lenders or let's say to Fannie Mae. And we could actually peer into that data and we can see exactly how much money is coming into someone's account every month and how much money is leaving their account every month. And through in that way, whether there are regular W-2 employee or there are gig worker or they're an independent contractor or they own their own business, you can actually get a much clearer and more refined sense of what their true financial picture is than by relying on things like credit reports or credit rating scores or those sorts of things.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“And so, maybe, Tim, do you have a couple examples of either data that's available that potentially the GSEs or others are not able to make use of or things that consumers are historically used to paying for that maybe aren't necessary anymore and evolutions that the industry could go through?”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Of what are known as representations and warranties in the industry that created potential liability for them. So he said, if you deliver the data to us in a reliable way using high quality data, we'll consume that and we'll give you relief around that. The other thing is that from my point of view, everything about the consumer experience and everything for the lenders is like slower, more complicated, more expensive than it ought to be. And so my view was that we should try to drive this digital adoption and make it faster, more convenient, cheaper for everybody, but not compromise any of the risk metrics involved in this business. Like we were not going to take on more risk in order to be able to do this. We actually thought that by using verified digital data, we could actually drive the risk down for the consumer for the”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Those new technology providers would see that there was a good opportunity to gain significant market share. And so we basically pivoted towards trying to drive as much digital adoption as possible and to facilitate our consumption of that digital data. Virtually everything that relates to a mortgage loan is just a piece of digital data. It's about consumer credit characteristics. It's about property characteristics. And today, almost all that information is digital data. And so we put Fanning in a position to be able to consume all that through APIs and try to encourage originators, lenders, and services to be able to communicate with us in that way. And we would facilitate that and we'd actually try to make it easy for them to do it. And we create certain rewards for them to do that, like day one certainty, which gave them relief from certain kinds.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“So, the way we thought about it at Fannie Mae was that there were some things that we could build ourselves, but mostly what we were trying to do was to facilitate the adoption of technologies that were being built by third parties. We recognized that we had certain proprietary things we needed to do. We needed to build the technology to do that, but that most of the industry didn't want to use fan-made technology. It wanted to use its own technologies and it wanted to promote competition and innovation in those technology providers. So what we tried to do was to create the conditions.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Tim, with your former hat on as the CEO of Fannie Mae, right? Like you launched one of the best mortgage innovations, day one certainty under your watch. How did you approach sort of either building new technology in-house or partnering with new technology partners?”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Let us learn how to do this and do it on your dime. That's a lot of convincing you have to do. And we're not even talking about the pain tolerance to just live and build it yourself, right? And so between all of those different things, my honest answer is it's a matter of do you have the patience to execute over extremely long time frames knowing that you can succeed? And the answer is most people don't really like that type of business and that type of journey.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“themselves around doesn't actually fix the problem. You like find a specific process and you're like, I'm going to fix it. Well, it turns out you need to have all this context about all these different things. And there's 25 different systems you have to integrate with just to solve the wedge product or wedge problem. And so what are you actually doing? And so we took a very crazy approach, crazier now that we think about it, but it ended up working was we said, let's go build a servicer, which is, by the way, a heavily regulated business. We got licensed in 50 states, including California and New York, which are extremely regulatory difficult process. And we're like, hey, we don't know how to do this, but you should definitely trust us. It's going to be okay. We got multiple federal level certifications, approval, Fanny, Freddie, Jinny, rating agencies. And once you've done all of that, you've gone the tickets to even be in the business. Now you have to go to these large financial institutions, right? We're also very regulated. And you should say, hey, those hundreds of billions of dollars that you guys have to collect, let us do it.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so the truth is, there's actually been a lot of attempts more than people actually know, and a lot of the ideas that we have in terms of what we should be doing, they're not unique, right? I think people are generally smart. It's a question of can you actually tackle the problem and actually incumbents have spent hundreds of millions of dollars funding spin-offs and there have been startups who've been trying to do it. My deep belief here is there's a class of problems out there where there's this truly entrenched complexity that has suppressed competition long enough that there's this astronomically large opportunity. But you have to be patient, you have to be relentless as a team to actually take over that opportunity or take advantage of that opportunity. And in this case, like Mike said, what we really learned was you need to have a complete overhaul of the underlying systems in the business, which actually also means at the same time you need to understand every single part of it. You need to deeply understand all the workflows, all the aspects, who cares about what, all of those different things. A wedge product, which is, I think, fundamentally what a lot of venture strategies are.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“To go invest in this software, it'll make my life better, and I can go back to doing my job. So I actually try to convince my current CEO to do it, and she told me, no, absolutely not. You're doing this first before I join. And so go try to get it started. And maybe I'll join you thereafter. She ended up joining thereafter. But that's what got me started. And I said, you know what? I should eat my own cooking. How hard could this be? Let me go start a company. Let me start a servicer. This will be fun. Hint, it was not very fun.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“My portfolio and position will be X tomorrow. And when it's all not that, you get yelled at by your team and the back office management saying, hey, your numbers just don't make any sense. Did you make a mistake on your investment? So you get really, really paranoid about the whole thing because then you're like, I got to go dig in. I got to check every single line item. And so then you end up realizing the service source data has a lot of problems. Then I said, okay, this is such a problem. I need to go visit these servicers and go figure out what the heck is going on because this is just like an atrocity. This is ridiculous. So I go visit these servicers and then I realize there's this absolute Frankenstein of all these myriads of systems like I think Wells Fargo at some point had 500 systems just to do servicing and you're like, oh, this is part of my language, a clusterfuck. Like this is ridiculous. And so you're like, okay, there's a technology pop here. This is like a technology problem. I mean, it's a people problem as well, but it's a technology problem. So my brilliant idea was let me go find someone to go start a business.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“A service her. But before I did that, I actually started off as buying only public mortgage bonds, which means you don't have to do anything like someone's handling in the back end. But when we started buying whole loans, like actual loans, you then have to deal with all of the stuff that comes along with that, right? You might have a mortgage loan. You might have bottom mortgage loan, but someone's literally got to go collect that money for you. And so we employed a servicer and I was like, okay, this is kind of annoying, but I'll get the servicer to contractually do this for me. And then when I ended up learning was the firm that I worked at had no back office doing this work. And so they were like, hey, Andrew, if you made this investment, it's your responsibility to figure out how all of this data flows and how we get the money and how this all sort of like reconciles. So my job went from doing investments to very quickly being 50% of my time reconciling the information that we got, the data that we got. And like for me, very personally, when you make these investments, you're like saying, hey, I think it's going to make x return. That means I'll get YK.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“So servicing is everything after origination. As Tim had mentioned, the way that this all works is a lender makes a mortgage and that mortgage generally gets sold to a GSE or some sort of entity. And what that creates is a servicing right, which is the right to service and collect payments on behalf of the mortgage holder, i.e. the government, and remit it to the government and maintain the relationship with the homeowner. The person who does that is the servicer. So you're kind of the person who's in a mixture between a payments business, a collections business, an accounting business, a compliance business, and you kind of do all these different things. And then you're also customer experience. My reason for looking at this whole thing, and it's now, we're foot-flopping. Mike talked a lot about experience, and now I'm talking about my very, very silly reason for starting a company is I used to be a mortgage investor. And so what that meant was I would buy these mortgage loans and I would have to go buy.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Excellent. All right. And so, Mike, if you're more upstream in the LOS, Andor, you're at a different part of the ecosystem. So maybe same question back to you. You also were already in the mortgage industry, decided to start a servicer. Why a servicer? Maybe explain what is servicing, since it's something that many Americans use and don't think of all that often.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source
“Factors, but a big one is actually that the manual process basically like monotonically gets longer and monotonically gets more expensive. There's very little ability to actually reduce the cost of processing, underwriting, and closing alone because they're extremely human-driven processes. And basically people only ever add to those human-driven processes. They never subtract from them. And so that was like a clear existential business problem that these lenders wanted a lot of help with and that we basically determined.”
2025-09-11 · a16z Podcast · Inside the $13T Mortgage Machine · IDENTIFIED FROM THE TRANSCRIPT · source