Exploring Innovative Trends in AI-Driven Mortgage Solutions: A Conversation with Vishal Garg
Vishal Garg, Chief Executive Officer and founder of Better.com, joins John Furrier at the theCUBE studio in the New York Stock Exchange to discuss transformative trends in AI-driven mortgage solutions. The conversation offers a deep dive into Better.com’s innovations and the broader implications for finance and advancements in artificial intelligence. This session, hosted by SiliconANGLE Media, Inc., illuminates the convergence of technology and traditional financial practices.
In this insightful discussion, Garg, a pioneer in AI applications in finance, shares their expertise on the creation of AI-native mortgage systems. By implementing advanced algorithms and innovative technology such as the MCP server and rules engine, Better.com revolutionizes mortgage approvals, reducing the time required from days to minutes. theCUBE Research and hosts explore the company’s strategic partnerships and the challenges of transforming finance with new technologies.
Key takeaways from this engaging session include Garg’s perspective on how AI and tokenization reshape financial services, ultimately benefiting consumers by lowering costs and improving efficiency. They also highlight the potential of tokenizing real-world assets such as mortgages, paving the way for a democratized financial landscape according to industry analysts. This approach not only offers substantial consumer savings but also promises long-term changes in financial services.
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Vishal Garg, Better.com
In this theCUBE + NYSE Wired: Mixture of Experts segment from the New York Stock Exchange, theCUBE’s John Furrier sits down with Raj Verma, CEO of SingleStore, to unpack how the intersection of technology and finance is shaping enterprise strategy. Verma shares why SingleStore is “on course” for the public markets, reflects on brand-building through the company’s partnership with golf Hall of Famer Padraig Harrington and connects that ethos to how SingleStore helps organizations fix struggling data “swings.” The discussion zeroes in on what’s next as Wall Street watches the AI infrastructure buildout: after chips and systems, the software and data layers set the pace for value creation.
Verma outlines why enterprises must modernize “brown” data estates into “green” ones to safely bring corporate context, governance and compliance into LLM workflows via RAG – and why commoditized data-at-rest puts the advantage at the query layer that unifies data in motion with data at rest. He predicts agentic AI will gain reasoning capabilities in roughly 18 months, cites industry indicators like Google reporting ~25% of its software now built by AI and argues that high switching costs will give way to disruption as buyers reassess legacy vendors. The conversation closes with concrete momentum: ~33% YoY growth, ARR in the ~$135M range, gross dollar retention ~98%, cloud NDR ~130, ~50% of business now in the cloud, landing ~3 new customers per day, a path to cash-flow breakeven in the next two quarters and a teaser for AI-related announcements in the next two months. Listeners will find notable stats, real-world use cases and forward-looking views on how databases power reliable AI at enterprise scale.
>> Welcome back to theCUBE here at our New York Stock Exchange studio. I'm John Furrier, host of theCUBE. This is part of our mixture of experts here. We have an expert, but also a double win. We also have a crypto trailblazer here. Vishal Garg is the CEO and Founder of Better.com. Vishal, great to have you on theCUBE. You're a twofer, you've got a little mixture of experts and you're kind of the crypto trailblazer. You hit both of our episodes.
Vishal Garg
>> Oh no, thank you so much. Such a pleasure to be here.
John Furrier
>> You guys are doing some pretty fascinating things, obviously, on the home side, but talk about Better.com. What do you guys do? I want to set the table for what you guys do and then I want to jump in with some questions.
Vishal Garg
>> Yeah. We're the first AI-native mortgage and home equity finance company in the US. We've built an MCP server called Betsy on top of Tinman, which is our rules engine based matching engine between consumer data, property data, which we containerize and then match with institutions' credit criteria for making a home finance. So Fannie, Freddie, 45 other investors, home equity loans and beyond. And so we're enabling consumers to transact and finance their mortgage in minutes rather than days or weeks like a traditional mortgage company. And then from there, once we've done that and containerized the mortgage itself, we're then able to do any number of things beyond with it.
John Furrier
>> So you take that and put it to work.
Vishal Garg
>> Yes.
John Furrier
>> So you're targeting consumers, not institutions or both?
Vishal Garg
>> Yeah, we're targeting both because we have consumers and then the business that we've built for ourselves and the platform we've built for ourselves over the past two years, as the incumbents are now sort of feeling the meteor that is artificial intelligence and agentic AI, they're all coming and saying, "Hey, can we use your platform?" And so we've announced some very large partnerships. One of the largest personal financial services sites in the country, the top five mortgage originator, they're all moving to our platform.
John Furrier
>> When did you start the company?
Vishal Garg
>> I started it about 10 years ago.
John Furrier
>> All right. So this is again, maybe a threefer here with a cloud-native angle. So you mentioned containers. It's interesting with AI. With AI done right on the AI-native side, there's certain patterns. One is, are you in the cloud, are you using data effectively? The way you're handling the mortgage, you're isolating it out as an asset, putting a container around, you mentioned MCP server. You're obviously cloud-native?
Vishal Garg
>> 100%.
John Furrier
>> Again, the stack's fully scalable.
Vishal Garg
>> Yep.
John Furrier
>> Cloud-native is as boring and scalable now as we've ever seen with Kubernetes, really just good DevOps.
Vishal Garg
>> Exactly. And it's the first new platform in 25 years in the mortgage industry. The last platform was built on Windows 95 architecture. Only one person can be in the consumer file at a time. So how are you going to put agentic AI when you've got to go ask Becky, the underwriter, to get out of the file, like we used to back in the SharePoint days. So that's what we're dealing with.
John Furrier
>> Triggering moments, SharePoint.
Vishal Garg
>> That's right. Yeah.
John Furrier
>> Well, you're also pointing out the fact that you've kind of set the table, you've kind of almost had the world spun in your direction with AI. Take us through what happened next because now that you have this kind of discrete instrumentation, if you will, of the asset, what happens next? What does AI do for you? How does that play out?
Vishal Garg
>> Well, we've taken the process with just a machine learning driven rules engine. We took the process from making 55 days to make a mortgage approval, took it down to 1 day mortgage, right? So we got there. Now, we're able to do mortgage approvals in two minutes that used to take, the rest of the industry 15 to 21 days, used to take us a day. And so what we're doing by effectively creating the ability for people to finance their houses in the way that you can finance equities on a margin loan like instantly, you basically are liquefying real estate and making it a much more liquid asset. You've got $64 trillion of residential real estate globally, 39 trillion in the United States, and that asset is a dormant asset and now you can allow people to draw home equity against it instantly. You can allow people to pay for other things, consolidate their debts, pay off their credit cards. The average user on Better's platform today is saving $1,100 a month and having their credit score go up by 37 points if they get a home equity line from us.
John Furrier
>> Yeah. So it's a solution that gives them more leverage for themselves-
Vishal Garg
>> Yeah....
John Furrier
>> versus somebody else.
Vishal Garg
>> That's right.
John Furrier
>> Talk about the AI piece of it because now you have the AI-native, you start to see products roll out. And one of the hottest areas we're covering in our AI section is these vertical models. You're starting to see verticalization. You play in a very interesting vertical. There's assets, Real-world assets. That's another buzzword. Real-world assets on chain is coming, it's here. So you got the verticalization. So a lot of people participating in that market and you've got some market shifts on the tech side.
Vishal Garg
>> Yeah. So what you're doing is by deploying vertical AI into effectively an expert labor-driven task business, which is like mortgage loan officers, mortgage underwriters, you're basically taking all of the transactional friction out. Also, the output file is something that is not stored in PDFs, it's actually a data string, which then allows for real-world tokenization, and you can take the data string. So investor A wants to buy assets in zip code 11375 with these parameters of FICO, credit, DTI, LTV. That has never been possible before. Now, you can go and sell them a piece of it and a token. Now you today go... We go to a bank and we put our money in the bank and we get 2%. And what does the bank do? The bank goes and lends it out at 6% against a government-guaranteed mortgage. Well, what if you could just own a couple of tokens of that mortgage? You suddenly change the entire definition of what it makes. Can you make a bank yourself? Can you say, "Hey, can I have JP Morgan?" "Oh, JP Morgan's got 19% of its portfolio in auto loans, 12% in corporate credit, 40% in mortgages. Well, press this button and I've got my AI advisor and he'll replicate the JP Morgan portfolio," but instead of like 400 basis points going to the banking system, that 400 basis points is coming to you.
John Furrier
>> Yeah. So this is like one of the biggest, I think, most important under-reported topics, which is the power of the data the users have goes to them. I'll never forget when people started to discover how Facebook worked and the cliche went, you're the product, 'cause they're selling advertising against you. In a way, that's what the middlemen were taking off the table in your business.
Vishal Garg
>> Yes.
John Furrier
>> You just shift that paradigm and say, "Hey, you own your data, you own your asset."
Vishal Garg
>> Yes.
John Furrier
>> Why aren't you putting that in your pocket?
Vishal Garg
>> The data is the asset in consumer credit. All consumer credit and all consumer credit pricing is based on your attributes or the asset's attributes. And you control that as a consumer. So if you can then effectively allow an institutional marketplace just to bid against it for extending you credit against your future income, against the value of this asset, against the rental income you get, it's a whole new world.
John Furrier
>> So you guys got a lot going for you. So I want to ask, just give some stats, put a plug in for some of the stats on customers and just some numbers you can share.
Vishal Garg
>> Sure. We funded 111 billion of loans on the platform, 600,000 families. Average user has saved $21,000 on a refinance, $30,000 on a purchase. If you get a Better mortgage, you're saving about $1,200 a year on a $400,000 mortgage versus the competition.
John Furrier
>> That's real money.
Vishal Garg
>> And that's real money for an average consumer.
John Furrier
>> And the other things too, I'd point out, anyone who's done a mortgage has gone through the knothole, it's a fricking hassle.
Vishal Garg
>> Yes.
John Furrier
>> It is a pain in the butt.
Vishal Garg
>> Yeah.
John Furrier
>> How do you guys make that less frictionless? Give us an example of kind of the time it takes.
Vishal Garg
>> Well, we're taking the data from third-party sources. We're compiling all of that. We do the work for you. That's the difference. I think V1 of the internet was all these marketplaces like Expedia for flights and hotels and things like that, they show you choice. LendingTree, they show you choice. The AI era is it shows you the choice, it gives you a recommendation, and then it does it for you. So we take like what someone gets at like a Goldman Sachs Private Wealth Management and we bring it down to the average American family. So I call it like giving them a Four Seasons experience at a Four Points price.
John Furrier
>> This is the benefit of technology. I interviewed a guy named Jeff Hammerbacher, founder of Cloudera, 2010, and he ended up quitting the company because he was a PhD and he was a super smart math guy. His comment was, well quoted, "All the brightest minds in the industry are working about how to have an ad show up better on the screen, hence the recommendation engines you were pointing to." Now we're shifting to a world where if you own your data, you get the value and now with the US regulation around crypto, you're seeing the decentralized infrastructure, Bitcoin, Ethereum, the infrastructure, and the crypto as currency, digital currency blend in with the physical world. And if you look at companies like Nvidia, physical AI is what they talk about now, a little bit different context and they have different tokens for different things, but this physical-digital convergence is happening. What is that going to do for your business 'cause you got the GENIUS Act, the CLARITY Act, well, clearing the lines of sight into unit economics with the stablecoin. How is that impacting your services?
Vishal Garg
>> I think the future holds, where almost every major financial services company, all the fintechs, like you don't need to be a bank anymore, right? You can provide a variety of coins, so you can have your own stablecoin, which is basically, okay, fine, you take it to deposit, provide the rewards in the form of whatever product you do. But then as you get consumers to adopt your stablecoin, from there with frictionless commerce, frictionless paying your contractor, frictionless paying your gardener, frictionless paying you all those people, right, they're not getting a 3% scrape from Visa, MasterCard or whatever and they're earning yield along the way, you then go to the next step. Okay, fine, can I tokenize my mortgage? Can I now take this 6% rate and bring it back to the consumer at a much lower cost of funds. The lower the cost of funds, the lower the cost of funds back to you as a user and you start to like... And all of these are government-guaranteed assets. Then, eventually once you get that user base comfortable, well, home equity-
John Furrier
>> Yeah....
Vishal Garg
>> and everything else.
John Furrier
>> One of the things I think is encouraging the way you're talking is there's a lot of mainstream press talking about the gloom-and-doom of many things, but one of them is home-ownership and it's proven that if people have home-ownership, they have solid families. But people aren't buying homes now until they're like 40. So that's a huge problem versus say in the 20s. Do you see this potentially because most of the people under the age of 30, they know what crypto is? So they can probably grok the idea of store of value, productive assets, real-world assets on chain. Do you see a future where that might turn around?
Vishal Garg
>> I think so totally because I think what you have with the power of tokenization, 27% of the cost of building a home for a home builder is financing costs. So with tokenization, you can get financing to buy a piece of land. With tokenization, you can get financing to build these materials from a house 'cause you have completely transparency as to where the money is going. So much of the financing cost excesses in all of the process of making a home or making new homes is built around information and efficiency and fraud. And so if you, with tokenization, you get rid of all of those things, then you dramatically lower the cost of building houses, which means more houses get built, which means more people can own homes.
John Furrier
>> More access. Yeah, it's a good thing. You mentioned earlier taking friction out of the process. One of the things I see in AI right now and good use cases of tokenization is that these vertical apps are really targeting friction, operational friction. What's your thoughts on that, comments?
Vishal Garg
>> I think if you look at financial services, the entire business of financial services works on operational friction, right? I remember after the credit crisis, I went to go see a bank at the United Kingdom and he said, "Well, operational friction is our method of credit underwriting," meaning if you're really willing to be able to get through this process, then you're probably a good credit.
John Furrier
>> Outlast, outwit.
Vishal Garg
>> Yeah.
John Furrier
>> Yeah. It's like a game of survivor. Oh my God. So I want to ask you about entrepreneurship because this is more of a personal question. Since I opened up the studio from Palo Alto, I noticed that there's a New York scene here in finance, especially With the DeFi and TradFi or I call backend, front end, but there's almost like a financial entrepreneurship I've never seen in my career. Financial entrepreneurship has been around, I call it hustle entrepreneurship, ride some payment rails, make some money, buy a boat, buy a house in the Hamptons, not too shabby, but not structural take-down territory. You're starting to see like product management, new kinds of products and services emerge from these new efficiencies. So the question is, what do you see as entrepreneurial opportunities or white spaces or even big space for people to come in and take territory? What's going to be enabled? Because if you make things faster, simpler and easier to use and take away middlemen, a new form might come in to accelerate. It's not friction, it fits. What's your view on that financial, what products emerge? What do you see as the next product management role in finance products?
Vishal Garg
>> I think the interesting thing is is that New York City, if you were smart and hungry, you'd go work on the street, you'd go work at a private equity fund, you would go work at a hedge fund. And I did a lot of those things and I just felt each time I was like helping rich people get richer and there's nothing more empowering than taking those same tools and then saying, "Hey, I can containerize this product that I'm building. I'm containerizing this model that I'm building and I'm actually taking that power and distributing it to a million consumers." And instead of those million consumers, me making like a billionaire an extra billion dollars, I'm making these million consumers a thousand dollars richer, and that's a lot more meaningful. And I think that's what's possible here. Global banking system profits, which is taking money from consumers, guaranteed by the government and lending it to consumers, mostly guaranteed by the government, is $1.2 trillion.
John Furrier
>> And taking a big cut.
Vishal Garg
>> Yeah, $1.2 trillion of global banking system profits for intermediating PDFs and data.
John Furrier
>> Yeah. I think when you have domain expertise, which is one of the buzzwords we hear a lot in all the AI, domain intelligence, you could be like four years working at Goldman Sachs in it's Credit Department and be like, "Hey, I could actually go do it better."
Vishal Garg
>> Yeah.
John Furrier
>> Take a unique, specific process and categorically change the industry.
Vishal Garg
>> Yeah. Those of us who work on Wall Street and work 16 hours a day, right, there's one thing that the AI does better than what we do. It doesn't have to stop. It works 24 hours a day.
John Furrier
>> Make money while you sleep.
Vishal Garg
>> Yeah. It keeps asking the marginal question. It goes one step deeper, one step deeper. And that's what you learn in early days of finance is like oh, okay, there's a discovery premium, there's a hustle premium. You can keep going deeper and deeper, but once you train the machine to do that, the machine can just do it over and over and over again. And so you can take the process that would go into like project finance for a power plant, for a billion-dollar power plant and you can take that same process and bring it down to an American consumer and say, "How can I get this person approved for a loan?" So today on Better's platform with Tinman, you are not allowed as a human underwriter to reject a loan until Betsy has reviewed it and exhausted all options.
John Furrier
>> It's like guardrails in the other direction.
Vishal Garg
>> Yeah.
John Furrier
>> I think this is one of the biggest things that people don't talk about because I think creativity, intelligence, AI scales intelligence, if you think about it. So if you can get line of sight on some unit economics, doesn't have to be a massive go-big-or-go-home kind of Silicon Valley play, you can just like knock down a category. The long tail of wealth creation, especially with agentic coming, you can certainly automate, but now you can repeat or refactor.
Vishal Garg
>> No, and you can train it on the best of us, not the average of us, but the best of us. And then it can take the process that the best of us does and create it repeatable for everyone.
John Furrier
>> And also get at use cases that aren't general purpose. We're seeing that in healthcare, just in x-ray scans, for instance, on cancer treatment. They can actually have AI look at things and have this fast discovery at massive scale.
Vishal Garg
>> Right.
John Furrier
>> That's awesome. Well, I'm excited for you. I think you guys got the nice cloud native. You got the crypto trailblazer. You got the mixture of experts on the AI side.
Vishal Garg
>> Thank you so much.
John Furrier
>> World's looking good for you. What's the forecast for you guys? What's on your plate? What are you optimizing?
Vishal Garg
>> Well, we've told the street we're going to double our business in the next six months and then continue to grow beyond. We're the fastest-growing home equity finance company in America today. So we're just helping consumers live a better life by getting a better mortgage or a better home equity loan.
John Furrier
>> Just keep getting better. Keep iterating. Thanks for coming on. Appreciate you.
Vishal Garg
>> Thank you so much.
John Furrier
>> All right. We got a mixture of expert and trailblazer here in theCUBE. Again, the world is changing. The physical and digital worlds are coming together. The money system, the value propositions, and our experiences with the world we live around us is completely changing and working on our behalf. That's the big trend here. Of course, theCUBE's doing its part to bring that to you. Thanks for watching.
>> Welcome back to theCUBE here at our New York Stock Exchange studio. I'm John Furrier, host of theCUBE. This is part of our mixture of experts here. We have an expert, but also a double win. We also have a crypto trailblazer here. Vishal Garg is the CEO and Founder of Better.com. Vishal, great to have you on theCUBE. You're a twofer, you've got a little mixture of experts and you're kind of the crypto trailblazer. You hit both of our episodes.
Vishal Garg
>> Oh no, thank you so much. Such a pleasure to be here.
John Furrier
>> You guys are doing some pretty fascinating things, obviously, on the home side, but talk about Better.com. What do you guys do? I want to set the table for what you guys do and then I want to jump in with some questions.
Vishal Garg
>> Yeah. We're the first AI-native mortgage and home equity finance company in the US. We've built an MCP server called Betsy on top of Tinman, which is our rules engine based matching engine between consumer data, property data, which we containerize and then match with institutions' credit criteria for making a home finance. So Fannie, Freddie, 45 other investors, home equity loans and beyond. And so we're enabling consumers to transact and finance their mortgage in minutes rather than days or weeks like a traditional mortgage company. And then from there, once we've done that and containerized the mortgage itself, we're then able to do any number of things beyond with it.
John Furrier
>> So you take that and put it to work.
Vishal Garg
>> Yes.
John Furrier
>> So you're targeting consumers, not institutions or both?
Vishal Garg
>> Yeah, we're targeting both because we have consumers and then the business that we've built for ourselves and the platform we've built for ourselves over the past two years, as the incumbents are now sort of feeling the meteor that is artificial intelligence and agentic AI, they're all coming and saying, "Hey, can we use your platform?" And so we've announced some very large partnerships. One of the largest personal financial services sites in the country, the top five mortgage originator, they're all moving to our platform.
John Furrier
>> When did you start the company?
Vishal Garg
>> I started it about 10 years ago.
John Furrier
>> All right. So this is again, maybe a threefer here with a cloud-native angle. So you mentioned containers. It's interesting with AI. With AI done right on the AI-native side, there's certain patterns. One is, are you in the cloud, are you using data effectively? The way you're handling the mortgage, you're isolating it out as an asset, putting a container around, you mentioned MCP server. You're obviously cloud-native?
Vishal Garg
>> 100%.
John Furrier
>> Again, the stack's fully scalable.
Vishal Garg
>> Yep.
John Furrier
>> Cloud-native is as boring and scalable now as we've ever seen with Kubernetes, really just good DevOps.
Vishal Garg
>> Exactly. And it's the first new platform in 25 years in the mortgage industry. The last platform was built on Windows 95 architecture. Only one person can be in the consumer file at a time. So how are you going to put agentic AI when you've got to go ask Becky, the underwriter, to get out of the file, like we used to back in the SharePoint days. So that's what we're dealing with.
John Furrier
>> Triggering moments, SharePoint.
Vishal Garg
>> That's right. Yeah.
John Furrier
>> Well, you're also pointing out the fact that you've kind of set the table, you've kind of almost had the world spun in your direction with AI. Take us through what happened next because now that you have this kind of discrete instrumentation, if you will, of the asset, what happens next? What does AI do for you? How does that play out?
Vishal Garg
>> Well, we've taken the process with just a machine learning driven rules engine. We took the process from making 55 days to make a mortgage approval, took it down to 1 day mortgage, right? So we got there. Now, we're able to do mortgage approvals in two minutes that used to take, the rest of the industry 15 to 21 days, used to take us a day. And so what we're doing by effectively creating the ability for people to finance their houses in the way that you can finance equities on a margin loan like instantly, you basically are liquefying real estate and making it a much more liquid asset. You've got $64 trillion of residential real estate globally, 39 trillion in the United States, and that asset is a dormant asset and now you can allow people to draw home equity against it instantly. You can allow people to pay for other things, consolidate their debts, pay off their credit cards. The average user on Better's platform today is saving $1,100 a month and having their credit score go up by 37 points if they get a home equity line from us.
John Furrier
>> Yeah. So it's a solution that gives them more leverage for themselves-
Vishal Garg
>> Yeah....
John Furrier
>> versus somebody else.
Vishal Garg
>> That's right.
John Furrier
>> Talk about the AI piece of it because now you have the AI-native, you start to see products roll out. And one of the hottest areas we're covering in our AI section is these vertical models. You're starting to see verticalization. You play in a very interesting vertical. There's assets, Real-world assets. That's another buzzword. Real-world assets on chain is coming, it's here. So you got the verticalization. So a lot of people participating in that market and you've got some market shifts on the tech side.
Vishal Garg
>> Yeah. So what you're doing is by deploying vertical AI into effectively an expert labor-driven task business, which is like mortgage loan officers, mortgage underwriters, you're basically taking all of the transactional friction out. Also, the output file is something that is not stored in PDFs, it's actually a data string, which then allows for real-world tokenization, and you can take the data string. So investor A wants to buy assets in zip code 11375 with these parameters of FICO, credit, DTI, LTV. That has never been possible before. Now, you can go and sell them a piece of it and a token. Now you today go... We go to a bank and we put our money in the bank and we get 2%. And what does the bank do? The bank goes and lends it out at 6% against a government-guaranteed mortgage. Well, what if you could just own a couple of tokens of that mortgage? You suddenly change the entire definition of what it makes. Can you make a bank yourself? Can you say, "Hey, can I have JP Morgan?" "Oh, JP Morgan's got 19% of its portfolio in auto loans, 12% in corporate credit, 40% in mortgages. Well, press this button and I've got my AI advisor and he'll replicate the JP Morgan portfolio," but instead of like 400 basis points going to the banking system, that 400 basis points is coming to you.
John Furrier
>> Yeah. So this is like one of the biggest, I think, most important under-reported topics, which is the power of the data the users have goes to them. I'll never forget when people started to discover how Facebook worked and the cliche went, you're the product, 'cause they're selling advertising against you. In a way, that's what the middlemen were taking off the table in your business.
Vishal Garg
>> Yes.
John Furrier
>> You just shift that paradigm and say, "Hey, you own your data, you own your asset."
Vishal Garg
>> Yes.
John Furrier
>> Why aren't you putting that in your pocket?
Vishal Garg
>> The data is the asset in consumer credit. All consumer credit and all consumer credit pricing is based on your attributes or the asset's attributes. And you control that as a consumer. So if you can then effectively allow an institutional marketplace just to bid against it for extending you credit against your future income, against the value of this asset, against the rental income you get, it's a whole new world.
John Furrier
>> So you guys got a lot going for you. So I want to ask, just give some stats, put a plug in for some of the stats on customers and just some numbers you can share.
Vishal Garg
>> Sure. We funded 111 billion of loans on the platform, 600,000 families. Average user has saved $21,000 on a refinance, $30,000 on a purchase. If you get a Better mortgage, you're saving about $1,200 a year on a $400,000 mortgage versus the competition.
John Furrier
>> That's real money.
Vishal Garg
>> And that's real money for an average consumer.
John Furrier
>> And the other things too, I'd point out, anyone who's done a mortgage has gone through the knothole, it's a fricking hassle.
Vishal Garg
>> Yes.
John Furrier
>> It is a pain in the butt.
Vishal Garg
>> Yeah.
John Furrier
>> How do you guys make that less frictionless? Give us an example of kind of the time it takes.
Vishal Garg
>> Well, we're taking the data from third-party sources. We're compiling all of that. We do the work for you. That's the difference. I think V1 of the internet was all these marketplaces like Expedia for flights and hotels and things like that, they show you choice. LendingTree, they show you choice. The AI era is it shows you the choice, it gives you a recommendation, and then it does it for you. So we take like what someone gets at like a Goldman Sachs Private Wealth Management and we bring it down to the average American family. So I call it like giving them a Four Seasons experience at a Four Points price.
John Furrier
>> This is the benefit of technology. I interviewed a guy named Jeff Hammerbacher, founder of Cloudera, 2010, and he ended up quitting the company because he was a PhD and he was a super smart math guy. His comment was, well quoted, "All the brightest minds in the industry are working about how to have an ad show up better on the screen, hence the recommendation engines you were pointing to." Now we're shifting to a world where if you own your data, you get the value and now with the US regulation around crypto, you're seeing the decentralized infrastructure, Bitcoin, Ethereum, the infrastructure, and the crypto as currency, digital currency blend in with the physical world. And if you look at companies like Nvidia, physical AI is what they talk about now, a little bit different context and they have different tokens for different things, but this physical-digital convergence is happening. What is that going to do for your business 'cause you got the GENIUS Act, the CLARITY Act, well, clearing the lines of sight into unit economics with the stablecoin. How is that impacting your services?
Vishal Garg
>> I think the future holds, where almost every major financial services company, all the fintechs, like you don't need to be a bank anymore, right? You can provide a variety of coins, so you can have your own stablecoin, which is basically, okay, fine, you take it to deposit, provide the rewards in the form of whatever product you do. But then as you get consumers to adopt your stablecoin, from there with frictionless commerce, frictionless paying your contractor, frictionless paying your gardener, frictionless paying you all those people, right, they're not getting a 3% scrape from Visa, MasterCard or whatever and they're earning yield along the way, you then go to the next step. Okay, fine, can I tokenize my mortgage? Can I now take this 6% rate and bring it back to the consumer at a much lower cost of funds. The lower the cost of funds, the lower the cost of funds back to you as a user and you start to like... And all of these are government-guaranteed assets. Then, eventually once you get that user base comfortable, well, home equity-
John Furrier
>> Yeah....
Vishal Garg
>> and everything else.
John Furrier
>> One of the things I think is encouraging the way you're talking is there's a lot of mainstream press talking about the gloom-and-doom of many things, but one of them is home-ownership and it's proven that if people have home-ownership, they have solid families. But people aren't buying homes now until they're like 40. So that's a huge problem versus say in the 20s. Do you see this potentially because most of the people under the age of 30, they know what crypto is? So they can probably grok the idea of store of value, productive assets, real-world assets on chain. Do you see a future where that might turn around?
Vishal Garg
>> I think so totally because I think what you have with the power of tokenization, 27% of the cost of building a home for a home builder is financing costs. So with tokenization, you can get financing to buy a piece of land. With tokenization, you can get financing to build these materials from a house 'cause you have completely transparency as to where the money is going. So much of the financing cost excesses in all of the process of making a home or making new homes is built around information and efficiency and fraud. And so if you, with tokenization, you get rid of all of those things, then you dramatically lower the cost of building houses, which means more houses get built, which means more people can own homes.
John Furrier
>> More access. Yeah, it's a good thing. You mentioned earlier taking friction out of the process. One of the things I see in AI right now and good use cases of tokenization is that these vertical apps are really targeting friction, operational friction. What's your thoughts on that, comments?
Vishal Garg
>> I think if you look at financial services, the entire business of financial services works on operational friction, right? I remember after the credit crisis, I went to go see a bank at the United Kingdom and he said, "Well, operational friction is our method of credit underwriting," meaning if you're really willing to be able to get through this process, then you're probably a good credit.
John Furrier
>> Outlast, outwit.
Vishal Garg
>> Yeah.
John Furrier
>> Yeah. It's like a game of survivor. Oh my God. So I want to ask you about entrepreneurship because this is more of a personal question. Since I opened up the studio from Palo Alto, I noticed that there's a New York scene here in finance, especially With the DeFi and TradFi or I call backend, front end, but there's almost like a financial entrepreneurship I've never seen in my career. Financial entrepreneurship has been around, I call it hustle entrepreneurship, ride some payment rails, make some money, buy a boat, buy a house in the Hamptons, not too shabby, but not structural take-down territory. You're starting to see like product management, new kinds of products and services emerge from these new efficiencies. So the question is, what do you see as entrepreneurial opportunities or white spaces or even big space for people to come in and take territory? What's going to be enabled? Because if you make things faster, simpler and easier to use and take away middlemen, a new form might come in to accelerate. It's not friction, it fits. What's your view on that financial, what products emerge? What do you see as the next product management role in finance products?
Vishal Garg
>> I think the interesting thing is is that New York City, if you were smart and hungry, you'd go work on the street, you'd go work at a private equity fund, you would go work at a hedge fund. And I did a lot of those things and I just felt each time I was like helping rich people get richer and there's nothing more empowering than taking those same tools and then saying, "Hey, I can containerize this product that I'm building. I'm containerizing this model that I'm building and I'm actually taking that power and distributing it to a million consumers." And instead of those million consumers, me making like a billionaire an extra billion dollars, I'm making these million consumers a thousand dollars richer, and that's a lot more meaningful. And I think that's what's possible here. Global banking system profits, which is taking money from consumers, guaranteed by the government and lending it to consumers, mostly guaranteed by the government, is $1.2 trillion.
John Furrier
>> And taking a big cut.
Vishal Garg
>> Yeah, $1.2 trillion of global banking system profits for intermediating PDFs and data.
John Furrier
>> Yeah. I think when you have domain expertise, which is one of the buzzwords we hear a lot in all the AI, domain intelligence, you could be like four years working at Goldman Sachs in it's Credit Department and be like, "Hey, I could actually go do it better."
Vishal Garg
>> Yeah.
John Furrier
>> Take a unique, specific process and categorically change the industry.
Vishal Garg
>> Yeah. Those of us who work on Wall Street and work 16 hours a day, right, there's one thing that the AI does better than what we do. It doesn't have to stop. It works 24 hours a day.
John Furrier
>> Make money while you sleep.
Vishal Garg
>> Yeah. It keeps asking the marginal question. It goes one step deeper, one step deeper. And that's what you learn in early days of finance is like oh, okay, there's a discovery premium, there's a hustle premium. You can keep going deeper and deeper, but once you train the machine to do that, the machine can just do it over and over and over again. And so you can take the process that would go into like project finance for a power plant, for a billion-dollar power plant and you can take that same process and bring it down to an American consumer and say, "How can I get this person approved for a loan?" So today on Better's platform with Tinman, you are not allowed as a human underwriter to reject a loan until Betsy has reviewed it and exhausted all options.
John Furrier
>> It's like guardrails in the other direction.
Vishal Garg
>> Yeah.
John Furrier
>> I think this is one of the biggest things that people don't talk about because I think creativity, intelligence, AI scales intelligence, if you think about it. So if you can get line of sight on some unit economics, doesn't have to be a massive go-big-or-go-home kind of Silicon Valley play, you can just like knock down a category. The long tail of wealth creation, especially with agentic coming, you can certainly automate, but now you can repeat or refactor.
Vishal Garg
>> No, and you can train it on the best of us, not the average of us, but the best of us. And then it can take the process that the best of us does and create it repeatable for everyone.
John Furrier
>> And also get at use cases that aren't general purpose. We're seeing that in healthcare, just in x-ray scans, for instance, on cancer treatment. They can actually have AI look at things and have this fast discovery at massive scale.
Vishal Garg
>> Right.
John Furrier
>> That's awesome. Well, I'm excited for you. I think you guys got the nice cloud native. You got the crypto trailblazer. You got the mixture of experts on the AI side.
Vishal Garg
>> Thank you so much.
John Furrier
>> World's looking good for you. What's the forecast for you guys? What's on your plate? What are you optimizing?
Vishal Garg
>> Well, we've told the street we're going to double our business in the next six months and then continue to grow beyond. We're the fastest-growing home equity finance company in America today. So we're just helping consumers live a better life by getting a better mortgage or a better home equity loan.
John Furrier
>> Just keep getting better. Keep iterating. Thanks for coming on. Appreciate you.
Vishal Garg
>> Thank you so much.
John Furrier
>> All right. We got a mixture of expert and trailblazer here in theCUBE. Again, the world is changing. The physical and digital worlds are coming together. The money system, the value propositions, and our experiences with the world we live around us is completely changing and working on our behalf. That's the big trend here. Of course, theCUBE's doing its part to bring that to you. Thanks for watching.
John Furrier