Invest Like the Best with Patrick O'Shaughnessy - Max Simkoff - Redesigning the Home Buying Experience - [Founder’s Field Guide, EP. 47]

Episode Date: August 19, 2021

My guest today is Max Simkoff, Founder and CEO of Doma. Max founded Doma in 2016 after experiencing the pain and manual process associated with title insurance and real estate transactions. With a background in predictive analytics, Max built Doma to bring a digital-first approach to a historically manual and labor-intensive process.    In our conversation, we cover the history behind mortgage closings, where title companies fall into that process, and how Doma is using technology to improve the client experience. We also discuss Max’s formative experiences at his previous venture, Evolv, and the lessons he’s learned from taking Doma from an idea to a public company. There are many great lessons in this episode, and Max’s entrepreneurial energy shines throughout. Please enjoy this great conversation with Max Simkoff. For the full show notes, transcript, and links to mentioned content, check out the episode page here. -----   This episode is brought to you by SnackMagic. SnackMagic is the only 100% customizable snack and swag service that allows recipients to build their own snack stash. Whether you want to thank your global team, need goodie bags for your upcoming hybrid event or want to stock your office pantry, the menu of over 1,000 types of snacks and sips covers just about every preference. To learn more and get 10% off your first order with code Patrick at snackmagic.com/patrick.   -----   This episode is brought to you by Vanta. Vanta has built software that makes it easier to get and maintain your SOC 2, HIPAA or ISO 27001 reports at a fraction of the typical cost. Founder’s Field Guide listeners can redeem a $1k off coupon at vanta.com/patrick.    -----   Founder's Field Guide is a property of Colossus, Inc. For more episodes of Founder's Field Guide, visit joincolossus.com/episodes.   Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.   Follow us on Twitter: @patrick_oshag | @JoinColossus   Show Notes [00:03:17] - [First question] - What Doma does and what they do for customers [00:04:38] - What Title is and why it sits at the center of such a large transaction [00:08:41] - Overview of the business economics of this space  [00:13:55] - How the ecosystem works writ large [00:18:05] - The formative business experiences he had that led him to today [00:23:03] - What it means to be great at this whole process [00:26:53] - The thing Doma tries to predict and the inputs that allow them to do so [00:32:50] - Defining his biggest roadblocks and how they’ve changed over time [00:36:02] - Managing stakeholder expectations and perception [00:40:22] - Learning to walk to the line of having a large vision and communicating it [00:42:51] - What his loose screw is as a founder [00:45:06] - The square-peg-round-hole they encountered during the pandemic [00:51:20] - What the counterproductive byproduct of his genius is [00:53:35] - Figuring out where to take the company next  [00:56:52] - The big lessons learned from interacting with capital markets [00:59:30] - Other entrepreneurs he feels are maniacs that he respects [01:00:55] - What will be the biggest contributing factors to their success over the next decade [01:02:44] - The key ingredients for building a winning team [01:04:34] - The kindest thing anyone has ever done for him

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I'm Patrick O'Shaughnessy, and this is Founders Field Guide. Founders Field Guide is a series of conversations with founders, CEOs, and operators building great businesses. I believe we are all builders in our own way, and this series is dedicated to stories and lessons from builders of all types. Founders Field Guide is part of the Colossus family of podcasts, and you can access all of our podcasts, including edited transcripts, show notes, and resources to keep learning at join colossus.com.

Patrick O'Shaughnessy is the CEO of O'Shaughnessy Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaunacy Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of O'Shaunoshae Asset Management may maintain positions and the securities discussed in this podcast. My guest today is Max Simcoff, founder and CEO of

Doma. Max founded Doma in 2016 after experiencing the pain and manual process associated with title insurance and real estate transactions. With the background in predictive analytics, Max built Doma to bring a digital first approach to a historically manual and labor-intensive process. In our conversation, we cover the history behind mortgage closings, where title companies fall into that process, and how Doma is using technology to improve the client experience. We also discussed Max's formative experiences at his previous venture evolve and the lessons he's learned from taking DOMA from an idea to a public company. There are many great lessons in this episode and Max's entrepreneurial energy shines throughout. Please enjoy this conversation with Max Simcoff.

So where to begin this conversation? I think we're going to go all over the place. Probably to level set with the audience, it would be good for you to describe what DOMA does, just at a very high level for those that haven't heard about it. And then we'll probably go all the way back to your early entrepreneurial days and dive into things like machine learning and modern product and all sorts of fascinating topics. But first, just tell us what DOMA does and what you do for customers. DOMA has built a technology platform that at its core uses machine learning to remove most of the friction, frustration, time, and now expense involved with closing mortgage. We're structured in the footprint today of a title company, title company. title companies are responsible for manage residential real estate closings, which is an odd thing

for me to kind of get my arms around when I was researching, you know, where is the ownership sit of closing a mortgage? But because title companies are responsible for basically closing the transaction, that means everything, not just underwriting this weird arcane product called title insurance, which we can talk separately about, but setting up the escrow, funding yourness money, doing the disbursements, paying the realtors commission check, getting all the docs, signed, recorded at the county reporter's office, title company's responsible for all of it. So we're in the business of applying technology and ML to that entire process and making it go much faster and be a lot easier than it's ever been.

Probably nobody really knows what title is, even though they've heard that a lot or they had to deal with it one or two times in their life and got confused by it. What the hell is title? Why is this this weird thing that sits at the center of this big transaction that people do a couple times in their life? So mini history lesson back in the, I think it was like mid to late 1800s, there was a Supreme Court case in the United States where somebody sold a piece of property to somebody else. They knowingly sold that piece of property with liabilities attached to it that they did not tell the new buyer of. New buyer discovered those liabilities and said, what the hell?

Why did you not tell me about this? Cellar had the audacity to say, well, it wasn't my responsibility to tell you about that. you should have done your diligence. It went to the highest court in land, and the Supreme Court decided that in this country, it is actually buyer beware. The seller is not necessarily responsible for establishing clear ownership on the property. And not that long after the first title insurance company was founded, which was these businesses. And you have to, again, it's actually kind of fascinating to understand how this industry developed was when those companies were started, obviously no technology. And even insurance as a concept was relatively,

unsophisticated. So the way that they said they would ensure title, which is the clear transfer of ownership from one party or set of parties to another, was that they'd just do research. The first title companies were effectively groups of lawyers who would, when a residential real estate transaction was happening, they'd examine the records and they would make sure that there were no outstanding ownership interest in the property conferring to the buyer. Now, fast forward all the way to today, that process is actually, not that much different in the traditional sense, albeit mostly it's not lawyers who are doing the work. These large incumbent title companies up until DOMA came along for every residential real estate

transaction that took place every year, virtually everyone, they were basically doing these many research projects to go to the county recorder's office and go to the county assessor's office and make sure there's no outstanding wills or trusts that have claims to the property so that they could effectively insure it, and I would put insure in quotes, because basically what their insurer on title is saying, we've exhaustively looked at the records and we can tell you with your certainty that you will be the new effective owner of this property. And so you pay an insurance premium. The insurance premium is a one-time fee that you pay when you're buying or refinancing a home. And here's where things, again, for me, got really confusing and it's why we started

business. I kind of understood why you get title insurance on a purchase. You're buying a property from someone else. You could have bad actor behavior or someone to decide they don't want to split things. but you also need title insurance on a refinance. So now you own the property. Here's where things are going to have another twist. When you own the property, you've already paid for title insurance when you bought it. If you want to do a refinance, you have to get title insurance again. But it's not for you.

So the insurance policy that you're paying for when you do a refi is to the benefit of the new lender, as it's called a lender's security policy. That title insurance policy basically indemnifies the new lender against anything from having happened to cloud their ownership interest between the time when you purchase the property and refund. I had this like hilarious. I mean, I wasn't hilarious at the time.

I was actually very annoyed. But by the time I did like a second rate in term refi on the same property that I own with the same bank. And they told me yet again, they were like, hey, here's the clothes that's going to work. By the way, you owe $1,500 for, you know,

title insurance. I was like, hey, I've got an idea. How about I pay you $50? And you send me a certified affidavit that, I will sign under penalty of perjury that says I have not originated any new ownership interests in this property since the last time I did a refi and I paid you $1,500, right? And by the way, you're my bank.

So you have all my bank. You have all my money. I'm certifying under penalty of perjury. And if I'm wrong, just take my money. Don't charge another $1,500. So the title companies, as they exist today, are probably worth just explaining, like, what the simple P&L looks like for a title company and like why this became an interesting space. So you're paying a equivalent of sort of a one-time premium. The general concept is the investigation

to see that there's no other nonsense going on. So the buyer, whether that's bank or the person, is sort of like, quote unquote, insured. And that costs $1,500. Is it cost $700 to service that? And the gross margins 50%. Like, what are those businesses look like? How big are they? Anything related to the home is a huge business, it seems like. What I just described as the title insurance piece. Now, there's another fee stream that title companies get. When I talk about title companies, I'm referring to the at-scale, what I'll call full-stack providers, like DOMA, they have an insurance carrier to write the insurance policy. But they also, once they've written the insurance policy, and this is why it's just such a fascinating business,

because they're on the front end of receiving all this information related to a closing contingency, they also morphed over times now managing all of the closing activity for the lender, for the realtor, for the buyer, the borrower, seller. There's a title insurance piece, and then there's what's called escrow and settlement. And so getting into the economics of the business, title insurance you pay a premium, an insurance premium for a one-time fee. You also pay what's invariably referred to as an escrow settlement or closing fee. And there's separate fees, but it's the same company that's charging them.

It's just like ones for one and the others for the other. On the high end, a typical purchase transaction is probably total title and escrow fees of, I think, like $2,500 to $3,000. bucks. The pricing model is usually priced off of the value of the transaction. And then our refi, again, it just varies. It could be if it's like a retail refy transaction, so like no volume discounts for the lender, it's a one-off kind of thing. It could be $1,200 maybe ish. And again, I'm benchmarking to call it a $3,000 to $400,000-ish-dollar refi. Bulk, like the lowest the lowest the price can go is basically what DOMA charges for what we call our DOMA enterprise customers.

that fee can be, call it $700-ish total of premium and escrow. And so to answer your question about how the economics work, if you took a purchase transaction, those are the most profitable transactions there are in the industry by virtue of the fact that they have the highest revenue. So one of the fascinating things about the P&L and the unit economics business is the work that's actually done, which drives the bulk of your cogs, doesn't vary that much from a refi to a purchase transaction. There's a few differences, but we've described it as basically,

it's like 80% one-to-one overlap of the steps that need to get done. You need to check the records. You need to set up the escrow. You need to accommodate this first-meas, you know, the line of closing day, get the docs sign. It's the same stuff versus, you know, for a person's versus refund. But a person has like two X the revenue. So you basically get a massive amount of operating leverage as a traditional title business on a purchase transaction just by virtue of the fact that it has a lot

more revenue and not a lot of significant differences in process from a refinance transaction. So if you look at one of those, the bulk of your. gross revenue dollars still get consumed by OTHX, and most of the OPEX is a very easy business to understand. So all that stuff that I described, all those steps, both in Title A and escrow, today for traditional title companies, they're all many. It's a person who has to examine the county records and put together something called preliminary and title report. And it's a person who then needs to fix all the issues that they found. And it's a person who's like setting up escrow accounts. You just think about the process and what's happening. That person in a title company

gets an order from a lender or a realtor, and they're asking a title company, they're like, he calculate all the fees. And all the fees means not just like prorated interest on the loan that needs to be paid off, but the local settlement reporting taxes, homeowners insurance premium, everything needs to be included in that closing statement of title companies. And then a bunch of stuff changes. And then there's emails going back and forth. So you can imagine that person scrambling to figure out to get back to the unit economics.

I think incumbent title companies, it's hard because they don't actually break it out on purchase versus refi, you kind of just need to look at their P&L. Generally speaking, last time I looked at this, I think it's roughly 80-ish, low to mid-80s percent of gross revenue goes to OPEX. Then you have to add their loss ratio, because again, their insurance companies. Their loss ratio, by the way, is next to nothing. I mean, it typically, it traditionally runs three to five percent. So if you put that on top of the mid-80s, like a decent incumbent probably runs mid-to-high 80s combined ratio, which means they've got 10, 11, 12 cents of pre-tax left. And a bad one can be probably in the low 90s kind of area. And again, then they don't

break out for you, by the way. They don't want, or maybe they don't want to break out for you. What's in that labor in the office? How much that is people working in the back office to do all the work they're talked about? It's a lot. How much is going to management salaries? They're high, by the way. How much? How much. goes to sales marketing, not a lot as far as we can tell, which is another misconception about this space is people are like, oh, isn't the title? Like they spent a lot of money on acquiring customers. They actually don't. It's not hard to mark customers in this business. That's generally how it works. So long wind way of saying they run an okay pre-tax margin. They don't break out their

gross margins. And most of their cogs is back office labor. So how should we think about what you do, especially, it's interesting you say it's not hard to acquire customers. Like usually that is a problem in almost every business. So how does that whole ecosystem work? are you partnered with mortgage lenders primarily? Do you think of yourself as like a B2B company that just happens to serve consumer transactions? It's kind of a fascinating business. So it's easy to describe the go-to-market motion as false. If it's a refy transaction, it's a lender that you're effectively running your go-to-market motion around.

And if it's a purchase transaction, it's typically a realtor. And so on the lender side, it is much more B-to-B than it is anything else. The reason I wouldn't call it pure B-to-B is because at the end of the day, lender is actually recommending to the borrower what to use. But the borrower follows the lender's recommendation because they're trying to get a refinance done. This complicated transaction, they trust their lender, as well they should. Most lenders have their customers fiduciary interest in mind. But it's a weird thing where it's like the lender's not actually paying the cost. They're just saying, why should we recommend this product to our borrower on transaction?

Now, thankfully, if you're a large centralized lender like Chase or Penny Mac or Wells Fargo, all of which are customers bars, what we have pitched them on was we said, if you send your orders, when the mortgage is ready to close, you send an order, we will close it so much faster with such a better outcome for the customer. And oh, by the way, it will also be the cheapest for them. Really, there's no reason for you to send that business anywhere else. And the way that they're set up is they allocate volume. They're not making them. They're not making the money. They're not making decision on every transaction, they basically decide if our solution provider like DOMA is able to blow away their metrics, then we will send them X percent of our national volume. I mean, they can

flip a switch overnight and send you a massive amount of volume. So that is much more like a B2B sales motion. We have our DOMA enterprise sales force. These are folks who are well versed in the complex sale. They explain the value proposition unlike any title companies ever, even. These lenders are used to title companies coming in and saying, we've got the best service. And we come in and like you plug it in and your loans are going to close a week faster, your NPS is going to go up by a standard deviation and your customers pay the cheapest price. And by the way, here's what it means for your P&L and your own productivity and your own digital mortgage initiatives. It's very much like an enterprisee sell. On the realtors side, it's somewhere between B2B to C and just straight B2C

because a lot of realtors are much more like individuals than they are, they're entrepreneurs. They're like running their own kind of business practice. And so you're kind of selling, it's like you have to sell them on why your solution is going to help them manage their own business practice better. because the transaction is closed faster with higher certainty. Today, the volume that we have on the full stack technology solution, which uses all the machine learning applies to refinance only. And we are bringing purchase transactions onto the platform later this year, which is something we're very excited about.

But that's generally how it works is through each of those channels. I'll do like an oversimplified summary and you tell me where I'm missing something. We'll soon end the stage of like the basics of the business. So let's say I'm a refi lender. I view DOMA as almost like an API. I just get to like send you a request and really high quality, good, fast, cheaper thing comes back to me that I used to deal with this legacy 150 year old company or something. That's possible, which we'll get to in a minute here. You're using data and algorithms and software to effectively replace the work of that 80% cents on the dollar that was being done by back office and front office labor.

Absolutely correct. The way that we get it is through a direct digital integration with lenders use these big systems called, and origination systems is kind of their system of record for managing the transaction. We've built digital integrations with all those systems. We also have our own public API. So if a lender is using a system that we haven't integrated with or it's an up-and-com lender, we say, look, we make this programmatic, we make it easy. And what's so important, and it does get to the machine learning stuff, is we don't ask

them to conform to some new standard of how they pass that information back. Take another use to giving it. You send us P.S. through the digital integration if you want. We will consume it. our machine will make sense of it, send it back to you in the format that you want. Genius. Now I think it's good to take a step back and ask the question,

what formative business experiences or experiences you had prior to DOMA that set your worldview? And maybe we could talk about evolve here around the power of data and machine learning because every time I encounter a company like yours, I'm like, oh, that must be the last company that's finally like fixing some old pen and paper problem. But there just seemed to constantly be more to tackle. also your experience and your formative lessons, I think, could be valuable for other companies. So talk us through. What were those formative experiences prior to DOMA? They sort of made you the right person to start the company. I think the biggest one as it relates to evolve was I got a

front row seat to understanding how it is going to be an oversimplification, but applying statistics or econometrics against reasonably structured data could yield dramatically better business outcomes in a function where people thought you can't apply data to that. That's got to be intuitive, experiential thing. In our case, this meant hiring. If all of business was focused on helping the largest employers in the country, or hiring lots of entry-level hourly workers, which happened to make up, you know, at the time, it was two-thirds of the nation's workforce. It's probably higher now. And they had all this structured data to tell them whether or not those people were successful, namely, how long did they stay on the job? The reasons that they left,

so every HR system in these large employers, when a termination date would get entered for somebody, whether somebody terminated, they would terminate either voluntarily or involuntarily, meaning they quit or got fired. But they would also track what are called reason code. Did someone quit voluntarily because they found a better job? My favorite was they had a reason code called involuntary termination due to punctuality and attendance. That one was a hard one to parse because it was like, oh, they stopped, they were showing

up late too much as you fired them. And they're like, well, actually, a lot of these, they just stopped showing up for work. And we had to term them in the system. And so the code we used was involuntary. Who knows what happened to them? And it was punctuality-intentioned-related because they didn't show up for work. That's like the worst case of not being punctual, right? And so when we saw this data, I think the aha moment for us, and I think we were really

on the front end of this was we said if we were going to try and determine reasonable signal from this data and then transform it into something that we could use on the front end of a hiring process, how would we do that? And we got a group of what are called industrial organizational psychologists, a group of econometricians. And the industrial organizational psychologist said, here are the constructs that you could measure on people inside of an online job application, or conscientiousness, punctuality, these kinds of things. The econometrician said, here is this level of statistical validity that you should look for changes in the numbers on the back end to tell you that the constructs

you're measuring are, in fact, predictive. Then we started doing some really amazing stuff, which was, Again, because our employers were hiring so many people, I mean, some of these companies hired tens of thousands of people for a single job category a year. And we got really good at measuring things in the front end of our online job application that were highly predictive of somebody terminating for any given reason or for somebody not getting to a certain level of productivity or not getting to a certain level of promotability, right? We could predict it all up front.

Now, it was very, in hindsight, it was all very rudimentary. Like a lot of this, the way that we were building these, what became. models were using a combination of off-the-shelf statistical software like SPSS, with econometricians working in it and being like, okay, we found like the models working. And then talking to the IEO psychology team and saying, can you guys create new questions or measurement mechanisms for the front end application? The engineering team had the thankless task of like, then they got the survey questions from the IO team and the models from the econometrician team.

And the engineering team had to build the software. They had to actually make it all work in production. And to add insult to injury, a lot of the computing power is a lot more expensive. And so that, you know, it just was harder to do. But it was seeing that those outcomes were like some of our customers, Xerox, eBay, AT&T, I mean, they plugged in our solution. And over months or even years of time, they cut their attrition in half. Their productivity went out by like 10 or 20%.

And it's huge. I mean, these people are handling customer calls. They're doing sales. That foreign of experience was, oh, if you have structured data, and somebody's not paying attention to how it should be used, you can put data science on top of it, and you can unlock a huge amount of what I'd call latent profitability in the business. That informs my view probably most for starting DOMA. And the big thing that we could talk about with DOMA is now we live in a world where you can do that same stuff with unstructured data.

The structured data we got were forms and fields from the HR system that told us the termination data, the reason code whatever, is all very binary. Now we can put natural language models against free tax. in a deed sitting at a county recorder's office. And we can pull out pieces of information that are extremely relevant to predicting the likelihood that there may be a title defect risk. Let's talk about what it means to be great at that function. Because I think, as you said, before we hit record, you being good at this is kind of like the ballgame. Like the whole point is that you do this faster, cheaper, more reliably, and that you use data science and machine learning to do

that for your customers. So you have to be really good at this. So what does it mean if you were to sort of abstract to a generic description, like, what does it mean to be really good at this process of defining the outcome you're trying to model for getting structured and unstructured data well, building a team around this? What does Great look like? Great looks like you have to have a very clear picture of what the outcome is. You have to know what it is that you want to predict. And it sounds like a simple, it's like, yeah, of course. It's like, you know, and it's like, no, but like when you want to get in the case of Evolve, if you want better employment outcomes, Everybody wants, yeah, I want better.

What does that mean? What does success look like? In that case, we got very granular about it. We were like, well, everybody even wants to reduce their attrition or their turnover, but even that was a loaded stat. What we ultimately boiled it down to to be great, we had great business people who are like, what's the business value of tenure? Because there's like good tenure and bad tenure.

You have someone who stays in the job for six years, but they've plateaued to a level of productivity, that's 70% of somebody who's been there for six months. Like, that's not good. So first, the ingredient is great business people to find the outcome. The next ingredient is great data scientists applying the best in what's currently available from academic research. And I think that's a really important component. Again, in the case of all, we had a chief data science officer who has now become, you know, a friend of mine. And he really pioneered for us. he said, guys, the way we should do this is we should borrow models from epidemiology and biostatistics. And what we ultimately looked at was something that sounds very morbid,

but we looked at something called a survival curve. So instead of looking at an absolute attrition number of people, it's just like just tells you at a point in time, do more people leave than stay, this individual said, we should look at a continuous spectrum starting at 100%, like on day one, ideally you've got all of your people there. And then you look at a continuous curve over time. By the way, these survival curves are used in epidemiology to study, the impact of disease. And we applied them against people's tenure on the job. And then we applied them on productivity. They all worked the same. They give you the continuous measure. So you know, is the curve trending in the way that you want? Ideally, if you're impacting attrition in the

best way, you should see that curve come up. You should start at 100. You should be losing less people at any given point in a curve going all the way out to the point of maximum tenure. So great business people to find the outcomes and then great data science people borrowing as best they can from academic research. And that is something that, again, I really took the heart when we started DOMA. I would argue that our chief data science officer hire was certainly one of the most important in the company's history, if not the most important because the business is built around it. We took that search extremely seriously. And the person who clearly stood out is Andy Modavie. He's still our chief data science officer. And his luck would

have it. He'd spent most of his career as a tenured professor of astrophysics. He was and remains it's like one of the most unbelievably creative, but still yet practical individuals about like, hey, if you don't pay attention to the constraints of what everybody else is doing and look to academic research or whatever is the most cutting edge stuff that's being used and apply that, experiment with it, get a data science team underneath you that's creative and willing to do those things. He really has set the tone for how we build out that approach. So those two ingredients, I think, were critically important for me in the past and we've carried them forward to DOMA. So if we think about any modeling exercise as define the outcome that you want to predict,

be really creative and smart and clever about what features or inputs go into that model. And then the model itself sort of use the cutting edge, whatever that might be at a given time, whether it's an epidemiology model or some boosted tree model or whatever, the processing layer. What are the start and ends for DOMA? So what are you trying to predict? And what are the interesting kinds of inputs that help you do that? So this is what I love about our business because the business output stuff is like, So what are we trying to predict?

And when we're underwriting a title insurance policy, we're trying to predict the risk that there is some outstanding ownership interest in the past that would cause a loss. And so that one's pretty straightforward. Where we got lucky, quite frankly, is remember the whole construct of the industry before we came along was all these companies were spending time doing all the work and documenting all the issues. There was an answer key for every transaction that title companies were very proud of. They're like, we're the best doing the research. We found all the answers.

We're like, great, we'll take those answers and we'll plug them into a monitor. model, right? On the input side for the title insurance piece, underwriting, what we've done is very unique is we use only property specific characteristics. So a number, hundreds of input variables, none of which are unique to the individual in a property transaction. And that's important because once you start getting data on individuals, you can run into a number of challenges, especially in the insurance context, discriminatory underwriting decisions, prejudicial stuff, all that kind of stuff. So our data science team built only a property characteristic specific model. What are some of the inputs? Examples, number of bedrooms and bathrooms and bathrooms and a home.

And again, you might hear that one be like, oh, that's like pretty simple. Well, it turns out that when we built our initial model at scale on hundreds of thousands of completed what are called preliminary title reports, once that had all the answer key information, we found that in particular parts of the country, the number of bedrooms and bathrooms in a home was linearly predictive of certain categories of title defectress. to a point, and then they invert. So it's like, okay, two bedroom homes have more risk than one on certain categories. Three bedroom homes have more than two. Four bedroom homes have less than three. And then you have a data science background, so you probably had many times where then people want to talk about the causal related. They're like, what's the reason for this?

Me not being a data scientist and being more on the business side, I'm usually one who's like, guys, we don't care about the risk. We're not in the causation business. We're in a correlation business. If that thing's got enough statistical predictive capability, at 100,000 data points, like put in the model, right? Yeah. Other stuff that we found is actually kind of interesting is census tract data,

which is a surprisingly robust data source. I forget what the time series is like done every five years or something like that. But they collect a bunch of stuff. And so we found, for example, and this I think is a perfect example of how dramatically different what we're doing is from the traditional way, we found that from the census track, they create something called the housing price index, which is basically just an index of like,

how much the average or the median home price in any given area appreciates. And they do it at the zip code level. We found signal in that dataset on the compound annual growth rate from one census data set from previous at the zip code level HPI. So we basically were saying the five-year CAGR on the zip code level housing price index is strongly predictive of certain categories of title defectors. Now, I just want to put this in perspective because this is about getting now back to the 50,000 foot level. there's probably not a single title examiner at any one of the other incumbent title companies

that have been doing this for 100 years, who ever, when they're pulling their reports, says, you know what, I'm just going to look at one more thing because I'm just, I'm not sure that I found everything. I'm going to go look at the five-year CAGR on the zip code level HPI. And if it looks like it's off, I think I'm going to pay special attention to this title report. Now, I'm always looking at it on every single transaction. on the escrow side, the closing side, this is where our inputs are much more unstructured. Our inputs are typically PDFs that we get from the lender, closing statements, information on the transaction.

And we're having to apply the models that we're using now are primarily, first, many times we have to use optical character recognition, just because sometimes the PDFs aren't even structured. There's just a flat image file that we get. So we have to use an OCR model to actually just pull out the text from the image. And then we are typically using natural language models to understand the syntax of what's being said in the text. And we're training them to understand the syntax that somebody working in a title company would have understood to know that certain information about the vesting of the property

might confer certain tasks that need to happen, for example. And then the last thing we've recently started using, which I'm really, really, this is what, again, I just so fascinated by this is now, because we have so much. information coming to us from our customers, we've started applying computer vision as our model construct on the same unstructured data we get. So this is literally a lot of the same PDFs. Now, instead of just using an NLP model, a natural language model to understand language syntax, we use computer vision to understand the geographic placement of certain bodies of text or phrasing on pages to operate in much the same way that the human mind works when it looks like

a title person who's looked at thousands of closing statements, they don't read all the text on every line and say, well, this says there's a fee of this amount due of X and there's that. And so it must be a closing statement because I read the language to understand. The human line looks at the document and it says, I see tables in a certain format. I see shading in a certain way. And I see a signature line at the bottom. That must be a closing statement. Now I'm going to pick out the fees that matter. And we're training our computer vision model to be the most accurate it can be at immediately understanding from the document what it is and then kicking out all the fees based on the geographic placement, not on reading text. The outputs then being for that model are much more accurate fee information. So we make less errors in calculating closing fees. Getting the information just turned faster. That sounds silly, but like when it's a human being doing it, it needs 30 minutes. When it's a model doing it, it needs less than three seconds. So those are some examples of the inputs and the outputs for the various models that we use. As you think about company building, I'm always interested in the stages and the chapters and the challenges for a CEO at different chapters.

So maybe chapter one was just like, how the hell do we get V1 of the model going? Like, where do we get the data and how do we get our first customer? As that has progressed, how would you define like the ways in which your biggest roadblock or bottleneck or responsibility has changed as the company has scaled? You know what the biggest one has been? It's really basic. I think it helps to keep in mind is the proviso here. It's like this long ago stopped being a concern, particularly on the back of us going public.

But for a long time, your primary concern is don't let the company run out of money. It's just such a simple thing that a lot of people miss. A lot of founders, especially, I think, are so they fall in love with their idea so much that they don't realize they're running a business. Again, they didn't start a faint tank. They started a Delaware C corporation. Right. The shareholders were expecting a return.

And so it is to me, it's like a lot of these businesses, they get very focused on, like, we're so passionate about delivering the best thing. we'll figure out the business. It's like, but you might not. And if you don't, you just raised a bunch of money from investors and you have nothing to show for it. And so that work served us well, like, especially in a business where we were taking real balance sheet risk. We had a predictive model we wanted to apply to underwriting title insurance transactions initially a totally different way, which meant that after we'd gotten permission to do it from people like Fannie Mae and Freddie Mac and we needed to be capitalized in a certain way such that we would have plenty of runway to do what we needed to do.

So, like, we found a reinsurer who became a strategic investor in a company. Like, they put cash into the business alongside a reinsurance agreement we had. We partnered with Lanar. We bought their title business from them in early 2019. And it brought a massive amount of data with it, which is great. It brought a massive amount of scale. But it also brought a cash flow stream. It was a business at scale.

We wanted for a variety of reasons, but it helped us really build like a going concern business. That would probably be the biggest one. that early days I certainly focused a lot on. And then, look, again, it's a truism, but like, you have to get the best people and those people, by definition, they need to bring something new to the table as people. If you just hire people from the industry or you're playing it safe, you're going to be constrained by inertia and why things can't be done. And we dealt with that, like, our COO, when we hired him, he was one of the youngest people. to make partner at McKinsey, and he was in their digital insurance transformation practice.

And so, like, one might say at the time, they're like, you need, why are you hiring this individual who's never operated a business to be your COO? You need an operator. We didn't need an operator. We needed somebody who was going to be really creative and then who was going to just out-execute everyone else and set an example for the people who they hired. I think the reality is he probably just ended up working twice as hard as any seasoned operator, because he needed to figure it out. I talked about our chief data science officer. Everybody brought something new to the table. And I think that was really important because there were so many naysayers, wrong way. How do you manage a million different stakeholder perceptions of you and the business?

I know this is something that you spent some time thinking about. I think it's a fascinating topic of just like, it's very hard to control how someone other than you sees something, but it's very important and can be very powerful. So managing perception is like this weird, Jedi mind trick skill. Tell me your thoughts here. Like, what have you learned about this? Why are you interested in that? The trickiest part about it is retaining confidence in the long term while also allowing the people who justifiably are trying to challenge certain assumptions that you have to show them that you're listening. And so like the balance between I'm listening to this thing that you're telling me that you think is important. And oh,

shit, if this thing that you say is important is true, everything I've been saying about what happens in the future is false. So, like, how do I balance that? And look, I'll give you a couple examples of, like, in a positive sense, there were people early days who were saying to us, once we got the business kind of working, like we were instant underwriting refinanced federal insurance policy, but we're doing it in a handful of states with a small number of customers. And there were a lot of people who are like, look, you need scale, you need distribution. If this is working, this is an industry where it's basically, they've locked everyone else out. There's four providers. They're all publicly traded. You're not going to organically grow fast at like a typical software company.

You're sitting there and you're like, well, but I told everybody that I raised my series A that this was like going to be a software business basically and like would scale organically. And in that sense, the way that we bridge the gap was we're like, well, who could we, is there a unique partner that we could find to help us solve this problem and still maintain the long-term vision. Because the problem was we go and buy a title company. That was the obvious. It's like, we'll go buy a title company, buy distribution by day. But if you bought one that's for sale, again, the reality of M&A is that like it's highly risky. And especially a startup buying a large company is just like it won't work. And so we had to figure out, well, is there somebody who is not

trying to sell a title company and who if we were to buy it from them, we could make them a large shareholder and be part of the journey. And that was what Lenar became. And so we found the way of making the bridge where it was like, look, guys, long term, actually now, and the best way you then manage perception there is you like, good news. Now the long term vision is even better than was. I told you a year or two ago when you were joining and your partner was telling you were crazy for leaving your big company job to join the startup that the company could get to 50 million in revenue within a couple of years. Well, guess what? Now it's going to be like a couple hundred million revenues sooner. That's better, right? Now we just got to do

with the challenge of like integrating this company and transforming the operations. That's when it worked out well. I'll give another example. It's like when you have people in the organization who have made it clear that their perception is that you're not on the right track and they don't get on board. They need to be fired. And I wish someone had told me that sooner. And hopefully it doesn't happen often, but I can think of one instance for early days when we had an individual was like, I just don't see it. They're like, I don't see how we're going to be at X or how this strategy is going to do Y. And I try to get them there a little bit. And then you realize, this is, I mean, this is something that's horrible to realize in your small organization.

But if that's what that individual is telling you, the founder and CEO, you are high. If you think that they're not having drinks with your entire team, they're out there being like, this is crazy. Why are we, guys, should we really be here? Like, why don't we all just quit in unison? This is things. And so, and the only thing you can do is just as you tell them, you're not a believer. And if you're not a believer, this is a bad place for you to be.

Find a place where you do believe in the long term. And again, that's an issue of perception. Like, what prevents CEOs from doing that, I think, is this other perception thing where they're like, oh, no, if this person's important and I ask them to leave, how are people going to think about me? Are they going to think that I have my head in the sand? Are they all going to quit? You can't think about it.

That's a decision you have to make with confidence in the future and completely disregarding whatever negative perception there might be in the short term. The big part of your job is to, one, understand the opportunity and sort of have your own, just personal private vision of what this could become. And then also communicate like the right amount of that so that it doesn't sound insane. It sounds like attainable, but it also sounds like a stretch. like, yeah, wow, if we do this, this is going to be pretty wild. What have you learned about, like, walking that line and setting the right amount of vision without overdoing it?

Mostly the things I've learned, I hate to say it, but like I've ignored a lot. Like, you set aggressive goals for the business, and the earlier you are, the more aggressive they should be. If you miss some of those goals along the way, it does not mean you should set less aggressive goals. That is a mistake. You're touting to, like, normalcy. Startups are impossible. founders are not normal people. They've got to screw loose, all of them.

It's just a matter of where it's loose. Is it on how they tell the story? Is it on sales expectations? What I have learned, and what I think is positive is that, like, you should always set aggressive goals. You should know when it's time to change the goal. There's no benefit to setting an aggressive goal at the beginning of the year. And if you're halfway through the year and you're like so far off, there's not even

a chance you're going to make it. It is damaging to morale to just be like. the results be damned. Like we set a goal and we say we're going to do it. But until that point, you just can't give in. And there will be a lot of quite like you set an aggressive goal and you get like, usually the way it works is, you know, everybody's happy in November. You're like finalizing the budget and people are feeling good.

The sky's the limit. December, the board's approving the budget. And you're kind of getting a little nervous because in the board meeting, you're like, are you guys sure that this is then? You're like, yeah, sure, right? People are looking at each other? Like, are we going to get our credibility damage from the board halfway through the year? It's usually like February and March.

where there's a lot of people being like, should we do a re-forecast? I think your job is to be like, is there still an ability to hit these goals for the end of the year? If so, no. Like, we're not doing a re-forecast. You now have new information. You're a couple months into the year.

Like, you're trending ahead of plan on certain areas. Like, that's great. How do you do that in other areas? You have to be a cheerleader and you have to will it. If you start having conversation about, well, yeah, that's right. Like, maybe that won't happen. Then you just start second-guessing everything. So bottom line is, like, you got to set aggressive goals.

and you should have a culture where people know that the process of setting the aggressive goal and the ability to achieve ideally sometimes outperform, but many times fall short of them. It's productive and healthy. It shouldn't be a reason that you get more conservative a goal setting. Well, I like the screw loose concept. One of my favorite visual representation to that is in Peter Thiel's book where he says, like, the instinct would be if there's a bell curve of people, you would just expect like the tails of the curve for founders to be like higher. But he's like, actually no, it's actually inverted.

It's an upside down bell curve. And so the screws loose are like actually essential. They only work because there's a specific screw loose. So what is your loose screw? How would you describe your loose screw? My loose screw is that I refuse to take for an answer that something is not achievable on a reasonably short time frame. Anything. And look, I think last year, there's some, and I used this example last year, you know,

our business had an extraordinary year last year. And there were many things that, you know, as we planned for this year, We were trying to set aggressive goals and stuff. And it was very fortunate that it happened to be the same time that not one, but two companies for the first time ever created a vaccine inside of 12 months to solve a national health crisis. You get in your own head and we're like, well, how are we going to do X or Y for our instant digital mortgage closing business? And I'm like, guys, like, look at what these companies just did.

And I don't try to bag on us, but like, that is nearly impossible. And they did it inside of 12 months. because they had to, because they got purpose and they had clarity of vision and they worked well as a team and they got it done. So nothing is impossible. People are sending rockets in the space. They're making cars that don't use gas. I mean, just go back 10 and 15 years and think about the things that people thought would be impossible then, getting something delivered to your doorstep in two hours or less. It's all possible. I've learned to try and be careful on this one. The minute someone tells me, I don't think we can do this. My default is like,

you're wrong. Like, that can be done. And we're going to figure it out. And the reason it has to be tempered some is because, like, sometimes people are telling you, they're either telling you something that can't be done, maybe, or they might be telling you something else. If your initial reaction is like, I'm shutting down, you're wrong and like, well, we got, it can be done. You might miss some other important piece of information that's leading them to say that it can't be done. Is it that like something's missing from our sales deck? Is it that our product doesn't have this feature. Like, what is it? And you won't get it out of them if you just immediately cut them off. You're making me think of my all-time favorite movie scene in Apollo 13, where to save the crew,

they have the square peg in the round hole that they have, you know, we have to make this fit into this using nothing but this and we've got 30 minutes. If you took that movie scene and transposed it onto DOMA, what sequence or a little event stands out most in your memory of, holy crap, we've got to solve this really hard problem really fast, and then you ended up doing it. One of them actually was last year when pandemic hit. We'll never forget this. It was on March 13th that we closed our Series C financing. It was all earmarked for like R&D investment and accelerating our product roadmap and vision. But we did it on the backs of like pretty healthy real estate market where we had a significant revenue stream that was subsidizing some of our plan. The country went into lockdown and the real estate market dropped off a cliff. And in that one it was we need to. We need to. to very quickly, inside of a week or two, figure out how we do not compromise our ability to invest in the business, in the way that we wanted to, without introducing the kind of risk to our company that could cause us to go out of business. Real estate transactions stopped.

The revenue stream that we had depended on to subsidize some of our plan, we had no certainty as to how it would come back and at how large. And we needed to get very clear on how we would change the focus of the business and the prioritization of how we're spending resources to accommodate effectively investing through the cycle. And we made some tough decisions. We had to lay off a number of people, which is a very hard thing to do in general. It's especially a hard thing to do when you're also saying, by the way, we're going to accelerate spending in a part of the company. We're having to let people go at the same time that we've just closed a new financing and that that financing is earmarked for ramping up, you know, hiring of certain other kinds of people, one that needed

to happen. Usually that's the kind of thing that you want to do over like a month or two. It's like, okay, we got to play and it. And it was like, no, no, no, no, it's DefCon 5. And we need to get this right because the other thing you can't do is whatever you do now, if you didn't get it right and you find that you have to do something again in six to eight weeks, that's a disaster. So that was one. And then another was we started trying to sell customers initially in the business. That was emotion and I kind of was like, no, wants to be your first customer. So fine. You got to find somebody who do a pilot and PR value, whatever. We got all that and we found one. And they were like, hey, you know, the only thing is

you're like, we'll be your first customer or finally or whatever, but like the instant underwriting thing you're doing for title insurance is accepted by the GSE, Fannie and Freddie, because we sell all over loans to them, just like every other lender. And we were like, well, yeah, we think it's, we think they accept it, right? They're like, well, could you show us on the selling and servicing guidelines, which is like the Bible for Fannie. They're like, could you go to the title insurance department, selling services and guidance, tell us how you meet these criteria. And we pulled it up. And it's like, the title insurer must prove that the mortgage is in first lead position. We're like, I mean, I don't know what we do that. And the second thing was like, the title insurer must

have a credit rating of at least whatever BDB plus or better. And so we're like, okay, we got this first thing. We've probably got to talk to Fannie about, but let's also, let's go get a credit rating at least. Let's get that service. So we reached out to these rating agencies and they were like, okay, you want rating. No problem. You just need to show us that you've been in business for two years underwriting title insurance. And we're like, what? How does that work? I know, but we can't do it unless you give us the rating. So why don't you just, well, here's our financials. We got all investment. You give us the rating. We'll do business. And they're like, no, that's not the way to work. So there was that catch 22. And then we went to Fannie and that look, we have a huge debt of

gratitude to Fannie Mae for being very innovative at the time. We were like, look, the way we read you're selling in servicing guidelines. It says the mortgage must prove. Title insurance must prove the mortgage in the first-in-position. I'm like, yeah, that's okay. And based on how your model works, we've got to understand it more. And we're like, but technically a traditional title insurer doesn't do that. They're like, what do you mean?

We're like, well, they have a loss ratio. They make mistakes. It's not a big loss ratio. But why would you let them make some number of mistakes? And not also let our model make some number of mistakes. Now, it might be some number higher than the traditional title company makes, but that would just be unfair for you to let them do. it, not us. And basically they were like, yeah, that's right. Just so long as if you're going to make

potentially more mistakes, you need to carry more capital on your balance sheet. And more capital for that means like hundreds of points of dollars. It's not like that. And that's why we got reinsurance. So we're like, okay, well, but if we got reinsured and we used someone else's balance sheet, would that be good enough? And they're like, yeah, but you'd have to be a pretty airtight reinsurance contract. So we went and got one. We got the reinsurer to invest in us. And then we launched our first customers. It was a pilot program, effective. with one of the GSEs where they said, we're going to write a contractual document

that says we understand you don't meet the guidelines. And as long as this is ring-fenced in a very controlled manner, we'll let you launch this first customer to show that it's working. And once we did that, we then went back to the credit bureau. People are like, look, it's not two years of history, but we're operating business, we're writing policies, and we've got this reinsurance agreement. It's like unlimited balance sheet.

And we got one of them to, issue us a rating. But all is that one of the things was like, we didn't get a credit rating, we would have been dead. If we didn't get the GSCs to accept what we were doing, we would have been dead. It had to happen quickly because the company was burning cash. Am I stretching it too far? Because that sounds like this interesting series of brick walls that needed running through. Is it stretching it too far to say that like that's not a bug? It's a feature. Like as a founder of a company challenging a big incumbent, like if you're not running through a consecutively harder brick wall, you're probably not doing anything interesting.

That gets back to the screw loose thing. Like that's probably another screwloose. that I have, which is like, I get some sick pleasure out of this stuff. Like when that credit agency was like, you can't have a credit rating because you're not having been in business. Most people are like, oh, my God. And I was like, oh, this is a good one. I was not expecting that. Cusha. Yeah. And you just got to run through those walls. It's like my wife, she was saying the other night, we were talking about our team and like how great this starts. Like our management team is really amazing. And she was like, yeah, I'm going to estimate this team at your own risk.

There's always two sides to everything. My friend Graham Duncan always reminds me this amazing idea that like anyone with genius, their dysfunction always sits right next to their genius. Like almost everything is always a double-edged sword. So if your genius is this ability to the screw loose or whatever, what's the dysfunction? And how do you manage it? Oh, I can tell you. That one's easy.

We have a great board at Doma. Great. I mean, some unbelievable people, Matt Zanes, who is our board chairman, is a former CEO of J.P. mortgage hits. Stuart Miller, who is the executive. chairman at Lenar, but was there a 23 or 26-year CEO? I mean, he built the business into what it is today, and he's done some incredible things. Larry Summers, who's former secretary of the treasury, Karen Richardson, who was on the board of World Pay when it got sold for $43 billion on the board

of British Petroleum. Our board directors are really amazing. And so back to you're like, what's the fringe of the genius that becomes problematic? Each one of these people has seen so many different situations. They've managed them with grace and poise, and they can manage stress. They never lose their cool. And without exception, I have gotten each one of them to the point where they were screaming. To the point where like probably all of them had to call after me like, I'm really sorry. I lost my temper. And I had to be like, no, no, no, you don't need to apologize. It's just that's the only way that I'm going to understand that I'm pushing it too hard. And you get like very accomplished people who are like, I don't know if that's a good idea.

And they're like, well, that person's very accomplished. And they're saying it's not a good idea, so I should probably back down. And I'm just missing that to the point where like I told Stewart the other night. And it's funny now, but it wasn't funny at the time. We had a board meeting probably a year and a half ago or something. And I don't even remember what it was about that I got crossways with him on about something. But I just wouldn't let it go. I refused. I was like, I have to do this whenever. And he came in my office after and he yelled at me. And I remember it very distinctly because the crux of what he was yelling about was he basically was saying, Max, I've enjoyed a lot of success in my career, and I've enjoyed

solving a lot of challenges. But across both challenges and success, I've always made a point of having fun. And what you did today is getting me to a point where I'm not having fun right now. Amazing. I think in every interesting founder's case, there's something like this, right? Like, there's some twin genius dysfunction that's fascinating to study and everyone's got their own and people should ask themselves, like, what's mine? And how do I make a lot out of the one and manage the other? How do you apply that same mindset and advantage, I would argue, to where the company goes from here? So I'm always fascinated by, you've done something impressive. It's going to be a public company. Like, it's a real thing. It's a going concern, as you said. And then it's always like,

okay, well, now what? Do we just try to just keep getting better at the thing we already do and just win more market share? Do we try to expand our product lineup and do lots of other stuff around the home? How do you deliberate with your team about where to take this thing? It's like the dog that catches the car. Like, you caught the car. Like, now what? Yeah. The short answer is you do both.

You get better at your core business. You stay dogmatically committed to being truly excellent at what you're doing. And we have a long way to go. And if we were just truly excellent in our core business already, we'd have 40% market share and high gross margins. all the like, we'd be done with that part and we could focus on our stuff. So you have to continue remaining relentlessly focused to your core business. And in a way that you don't lose the, again, the like the startup entrepreneurial DNA. I do a weekly pipeline review for the DOM enterprise part of the business and also for what we call DOMA Local, which is

where we're originating purchase business along global refies. And I look at every metric. To the point, like I look at, we have a customer health dashboard. Our customer success team It's a weekly report. Every customer success manager writes a weekly report on each customer informed now by a composite score that's automatically pulled from a variety of systems, looking everything from like changes in volume, changes in sentiment that's ranked on a 10-point scale, changes in any change in SLAs that are basically measured by the minute with customers. Like, how long does it take for our system to respond to any request, any number of seconds or minutes? It's like I look at that customer success dashboard.

And as the head of DOM Enterprise would tell you, because I just did this with him yesterday, I don't look at the top of the list. I look at the bottom. I don't care about the customers that are at scale where everything's going great. And whenever I go straight to the like, what's this outlier here? Why are we not perfect for them? Why is this particular component score of yellow instead of green? And so you have to do that for your core business.

And then you have to expand the scope of your ambition. The way that we've done this, which we've communicated via all of our public-facing investor materials is, We've identified two adjacencies in the form of appraisal and home warranty that are other problematic, non-digital filled with friction experiences on either side of the title and closing piece that we should own. We should have a solution for them so that we can have a truly integrated horizontal instant digital home ownership experience. You've got to go even harder at your core business and remain committed in the same way that you were when you were startup.

and then you have to expand the scope of your ambition because we're public company and we're going to have a lot more cash on our balance sheet and our new set of investors who I'm very focused on making this one of the best investments they've ever made are expecting us to deploy that capital in a creative way for them. What have you learned about the finance side of the business, specifically around cost of capital, raising capital, good capital partners? You mentioned M&A and how important it is. What are the big lessons that stand out from this side in terms of your interaction with capital markets, I'll call it. Look, we've been fortunate, I think, in that I think we've selected our capital partners

across the board reasonably well, like from our Series A lead foundation capital, all the way through to the investors in the pipe for our go public transaction, folks like BlackRock and Fidelity and Hedafia. So the first thing I've learned now over time is you have to really work at finding investors who are truly long-term focused. There are a lot of investors in the world today. We live in a world of instant gratification and OCD. They pay attention to stuff by the day.

If we pay attention to the value of the company on a daily or weekly or monthly or quarterly basis, we're screwed for long-term returns. Actually, we might be okay for small movements in shareholder value, but there's a mindset of investor that I think I've gotten better at recognizing where they're like they look at, for example, they're the ones who remember that the stock performance of Square or Facebook in the six to 12 months after the company price. No one remembers this, by the way. No one ever talks about something. But if you take yourself back in time and you remember, breaking the IPO, yeah. Yeah. These are the people that are like, you could have looked at it and been like,

it's done. And like Square, their stock price, I think, declined by, I don't know, maybe 20 or 30 percent over a month or two period to six-month period after their IPO. It's a blip. That company is now worth a hundred times more. It blew people's minds. You got to pardon with people who are long-term in nature. I think we've gotten better understanding that we live in a very sophisticated capital markets time, but there's many different instruments you can use to facilitate many different goals for the company. To start the company, it's preferred equity from a venture capital fund. Fund the balance sheet to get approval from Fannie Mae, reinsurance is basically a form of financing. I see it that way. As we approach the public markets, we put in place a senior secured credit

facility that gave us the capital to execute a self-funded plan and communicate to the public markets as we're going to that process. Here's where we're self-funded plan looks like. Money's already on the ballot sheet. We think we've structured it well. This does not anticipate the use of proceeds from the go public transaction. So there's this like stack of capital instruments that you can use that it's really important to pay attention to how they can be optimally used for any given decision. As you think about other entrepreneurs, you and I met originally through Asaf at Hippo, and he checks a lot of similar boxes, I think that you do, which is just this awesome, you're kind of entrepreneur that I love spending time with. If you think about other entrepreneurs

that have, if you're running through brick walls, they're running through steel walls, like who comes to mind as just like a maniac that you respect and look up to? Asoft would be number one on that list. I love how principled he is. I have. find that I'm disappointed a lot, but people, this is horrible, didn't say, but like, people disappoint me a lot because they basically don't meet my ridiculously high. I know it's ridiculous. But you're still disappointed. Yeah, I'm still like, why didn't they do this thing? The self is the one who might be slightly worse than I am, had a call to me the other day. I was like, whoa, dude, I thought I was principal, but like, this is like a really principal

issue for you. Eric Wu at Open Door, I wish I could have his temperament. He's like running through some major barriers. And he's able to do it in a way where he's, he's just a really one person and like very thoughtful and doesn't lose as cool. So often Eric are probably the ones that come to mind and I'll give it some thought. I mean, then there's like people like Elon Musk, people that, again, on that, back to that issue of perception, like they just don't care what people think of them. Sometimes it's a line. It's a thrill's been.

But like not caring is actually one of the most, if it's rained in and point of the right areas, it's like a really powerful thing to be able to employ. With that in mind, If we look forward 10 years and this business is 10, 20 times the size of this today, what do you think will be the most important contributing factors to that kind of outcome? Unquestionably, the people that we're building business with. It is the, and look, it's, again, the people who build our, if we have the best data science team in the industry, we're going to build the best data science.

And that's something, again, we take very seriously. Like, we want data scientists to join our company who are building a career. We want them to be known for a unique application. of their craft. If you want to build most cutting edge application of computer vision to financial documents, we want you here. And if we can get you here and you can do something remarkable, it has exponential benefit for the company. And that holds true in our industry, and I would describe the industry of real estate industry broadly, not just title and escrow. It's an industry that is historically underinvested in technology. You don't hear about like

the new CXO at Microsoft coming from a real estate company. Right. It just doesn't happen. I think what will make this company great in 10 years, beyond the fact that we'll have facilitated the ability to sign a purchase contract on a home on a Friday evening and move in on a Monday morning. With everything done in the background, completely magically well, all of your docs signed, all of your appliances protected and connected,

integrated with great other companies in our space to provide just it'll be a seamless experience. It'll be that ideally we will be known as a place that hired and helped people start their careers where they then went on to do other great stuff and they'll point back to it. But they're like, oh, that person was a donor. And people are like, well, I said, yeah, then we should talk to them. We should consider them for this role or we should try and recruit them to do this thing. Does doing that well, meaning successfully building a team which can then lead to these big outcomes, in your mind, does that just boil down to being a place where you give a lot of autonomy? me to solve really hard problems. What are the key ingredients to being successful at building that kind of team? Everyone wants that kind of team, hard to build one. That's one part of it. It needs to be a

place where they feel like they're solving problems they've never been able to solve before, and they're fun and challenging. But other parts are like recruiting. Like how do we recruit people? How do we treat people in a recruiting process? People make decisions to join a company often off of first impressions. Did they like the call they got from a recruiter? It sounds silly, but it's like we measure the NPS of our recruiting process. We're constantly looking at that and saying, did this candidate have a great experience? Or did this candidate who turned us down?

Did it have anything to do with the way that we manage this process? And we're not perfect. Sometimes it does. Sometimes it's like, yeah, you know, I feel like ideally you would have gotten back to me a day sooner. It's like, that's a problem. We need to be all over that. Because recruiting is sales, by the way, time kills all deals.

The longer you let people fit of good people, the more opportunities they're going to find, the more offers they're going to get. Like, you've got to swarm people. and make sure that they know how important they are to the future of the business. So that's an important part of it. And then, of course, people they work with is also important. That applies not only to their manager, but to the other people on their team.

They want to feel like these are people that they want to spend time with. And that's a hard thing to get right, especially when you're hiring really quickly, trying to balance. This person's a really talented individual, and we'd be lucky to have them. But like, how are they going to interact with the other people? on this team and how are they going to interact with other people on other teams? Are people going to look up to this person? Are they going to think they're a jerk? So those things, you've got to kind of try and get right.

Well, there's been so much fun. I won't forget that phrase time kills all deals. It's totally true. I do this mostly so I can have conversations like this one. I search for people that love business and building stuff as much as you clearly do. I ask everybody the same closing question. What is the kindest thing that anyone's ever done for you? My wife supported me to build this company. And when I say support, I mean, like, you know, early days, like, well, income was either non-existent or low in startup days. And like, she started her own business and she's a successful entrepreneur in her own right. And she was supporting the family. She was putting aside a lot of the amount of stress that I was dealing in. Like, so the fact that she was

shouldering all this stuff solely for this purpose because she was like, I want you to do this and I want to support you. That's definitely the kind of thing I can think of that comes to mind. Fantastic. I absolutely love it. Max has been so much fun. Thanks for your time. Yeah, thanks, Patrick. This episode was brought to you by Snack Magic. In this four-part miniseries, I sit down with founder and CEO, Seanuk Amin, to talk about the origins of the business, how they manage the complex logistical operation, and what lies ahead in the future. In this week's episode, Seanuk and I discuss breakthrough moments during the product pivot and how he thinks about allocating capital after a recent funding round. In the early days of this massive pivot, what were the big breakthrough moments?

Were there big memorable events or customers that you want or orders that were placed? What stands out in your memory as early signs that this new model was going to work? Yeah, so right out of the gate, you know, we just put the website up without any marketing, email, anything. First day, I don't know what people were doing on a website, but we got a few orders, which I did not expect. So that was the first thing. And then the next breakthrough moment was when people said, can you include other things like swag in the box? I think that took our business to a whole other level. So that happened in month two.

And by month three, I think like if you look at our calendars, we were doing 16, 18 meetings back to back every single day without us doing any outbound marketing. Because the product has virality built into it, right? You're sending the link out to everybody and then it spreads that way. So yeah, so when that happened, I mean, it took us six years to get to that point at stadium. And this happened in month three, I think we thought that was something special. I noticed a note from David Sacks' firm who made an investment in your company more recently.

He was a recent guest on the show. And they mentioned incredible growth numbers. It's public. I'm sure you won't mind my sharing something like zero to 20 million and, you know, less than a year. Incredibly fast growth rate for revenue. How do you think about using that investment? So you've taken outside capital, you've got a lot of revenue to fund some of what you're doing. What does the future look like and what do you intend to do with that investment?

So yeah, on the revenue, we were profitable in December and have been profitable since. But the reason we took money is, yeah, we had the same. We wanted to apply this business model to other verticals, right? Like there are so many things we can do with the same concept, which was the main reason we took the funds. So the first new brand that we recently launched is Swag Magic, where you as the organization, can send a link out and give them a budget and everybody can pick their own sweat. So if I want a water bottle and a tumbler and a T-shirt, I can get that branded with your company logo or someone else can get something else.

And then next, I think in two months, we are launching a concept around ready-made desserts and prepared food, which can be shipped from across the U.S. So we're going to open up and then early next year we're going to launch white. And it's the idea that basically everything is homegrown. So, you know, in the case of swag, I assume I'm just sort of sending you a logo, you know, in the case of these other things, you're managing all the logistics with the suppliers. Be really curious how you interact with suppliers. You had a lot of experience at the prior company doing this, I'm sure. But what's the secret sauce there? I think it's both. So some of it is we are fulfilling on the snack magic side, the snacks and beverages. We have a warehouse in Long Island City where all the picking, packing, boxing happens. That's one of our strengths on how it's. efficient we can do that. But then there are other areas that we just pass the orders to our partners and they drop ship it. Just a combination of it. If you enjoy this episode, check out join colossus.com. There you'll find every episode of this podcast complete with transcripts, show notes, and resources to keep learning. You can also sign up for our newsletter, Colossus Weekly, where we

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