00:15:43 · Michael Tannenbaum, Anthony Pompliano
[00:15:43] S0: I think one of the reasons Figure has been successful is we put things like tokenization and blockchain in the back and let that be about the infrastructure and the piping to your point. If you go back to 2010, right, and and look at the cloud era, people weren't celebrating... Excuse me. People weren't celebrating the fact that software was in the cloud. They were focused on what they could get from the cloud. Whereas I think with crypto and blockchain, there have been people who do blockchain for blockchain's sake, which is fine, but that's not what Figure does. We use blockchain to add real value in the capital markets, ultimately, to your, you know, to your opening point around affordability and and cutting costs. And so for us, we don't focus on blockchain because, ultimately, people who are in homes and who are looking to save money and save time are not saying, I wanna tokenize, you know, my my mortgage. But they're seeing the value of that tokenization. The capital market sees the value of that tokenization. The real time data, we use stablecoins to settle loans, which which again saves time and money. So there's a lot of benefits to it, but that's more for an institutional audience. So if you go to our website, we're not necessarily preaching, at least on the hero page, the the the virtues of blockchain, but we are very much building that infrastructure, which is powering the future of the capital markets. [00:17:06] S1: Where else can you expand to?
00:17:06 · Anthony Pompliano, Michael Tannenbaum
[00:17:06] S1: Where else can you expand to? [00:17:09] S0: Well, the consumer credit space that we're in is a very large space. I think within mortgage, it's about a 2,000,000,000,000 annual market, and we are doing about 2,000,000,000 a month, as I mentioned. So there is a huge amount of white space for us to go after there and a huge amount of... I think we have about 500 partners. So we're very early innings in growing that marketplace out. That said, we've been at this for about eight years. I think the results are really impressive in eight years, but we, you know, we're 500 partners in after eight years. So we recognize that to expand into all these other asset classes like we've been talking about, into auto, into personal loan and consumer loan, into small business. If we're gonna do that, we're not going to be able to convince every customer to use our software. We're gonna have to take closed loans and bring them into our marketplace and standardize that way. And that's actually where AI has come in. So we built a a pretty interesting loan adapter, which takes closed loans and ultimately adapts them to our marketplace. We took what used to be... Take months to do happens now in weeks because we've been able to standardize those assets and bring them into our ecosystem.
00:18:27 · Anthony Pompliano
[00:18:27] S1: When you think about doing this, it does feel like you can own a relationship with a consumer. So you start to say, hey. We were able to get you a HELOC. Now we're able to help you with x y z loan, then this one, then that one, and let's grow with you throughout your life. Is that kind of the plan, is to have like this direct relationship that you can own?
00:18:45 · Michael Tannenbaum
[00:18:45] S0: It's less the plan because so much of what we do is b to b to c infrastructure, private label, or white labeled with our partners' brands. So we're... What... We're we're not building a direct relationship with the customer pretty much explicitly. Similar to, you know, other marketplaces. Our focus is on serving the marketplace participants, which in our case are the the partners, so the banks, the credit unions, the mortgage companies, the fintechs, as well as the loan investors. But what we are doing that is similar is we're getting a ton of value out of that data. Right? Because... And we're helping our customers in particular use that data, especially with AI, to make intelligent offers, to your point, on customers, to use the power of marketing in today's era where user generated content is so valuable, and we're showing the way. So sometimes I use the the analogy of Google Pixel and Android. Right? Google owns Android and Samsung and all these other all these other phones build on it, but they also have their own first party. We are... Like, they have the Google Pixel, right, their own first party phone. Similarly, we do market directly to customers so we can hone in on the best marketing strategies, the best uses... The best use cases for AI, and bring those to our partners and show them how Figure can be at its best. Great example of that is a loan officer assistant that we actually just built with... So it's essentially a bot that we built with Sierra, which is Bret Taylor. He's on the chairman of the board of OpenAI. And we built this very impressive loan officer assistant. We trained it on our own data, and now we're rolling it out to all of our customers. And what it does is essentially increases conversion by about 20%. So it's a huge deal. And we were able to train that on our own data, and then now partners trust it, and we're able to now roll it out to all the 500 partners that we have. And that's gonna be a huge lever of growth in the coming months.
00:20:54 · Anthony Pompliano
[00:20:54] S1: We've talked a lot tokenization. AI seems like a no brainer for you guys to be using. How are you using that internally?
00:20:59 · Michael Tannenbaum
[00:20:59] S0: Three main ways for us. There's how are we building software, which, you know, we generally use cursor, but we use a lot of a lot of different models actually, which is interesting because actually Grok is a big part of what we do. And so we're definitely using AI internally. I think about 20, 25% of our code at this point is written with AI. And I would call that standard for a tech company. We're also using it in the manufacturing process of the loans. So I think a great example is when you're creating a mortgage, you have to make sure that the loan deed match. Right? That the the way the property is described by the county is essentially the same in your mortgage note, right, in the instrument that you're you're creating. And that process used to be a stare and compare painful process. We've used AI to completely automate that. And that... Those are products that we're building in house. And then on the customer facing side, we're... I think we're implementing AI in in really innovative ways, but we are partnering. I mentioned the partnership with Sierra. We use a product called Decagon, which is pretty popular on the customer service side. So that's where we're leveraging, I think, best in class vendors, but at the same time, using very thoughtful implementations to lower costs and improve the customer support model. I mean, that... That's a big part of why we have been able to take costs down so much on behalf of our partners. And we're at that $1,000 versus the 12. And if you look at who we partner with, they're looking to figure to help them make those AI and technology decisions. A bank today is not necessarily gonna be building its own mortgage chatbot the way that we've done. They're looking to figure to provide that same innovation we brought with tokenization also with AI, and help carry them through this next technology wave.