Carter Malloy: Why better land data is the foundation of better housing supply

HousingWire

On this sponsored episode of Power House, Zeb Lowe sits down with Carter Malloy to discuss why better land data may be one of the most important and overlooked pieces of solving the housing supply crisis.   Malloy explains how his experience in land investing exposed a major industry problem: builders, developers, and investors were still making massive decisions using fragmented systems, outdated information, and hyper-local knowledge. That challenge eventually led to the creation of Acres, a platform designed to bring transparency, speed, and intelligence to land acquisition and development.   The conversation explores how better visibility into zoning, infrastructure, ownership, and growth patterns can help bring housing to market more efficiently. Malloy also breaks down the difference between AI hype and AI that actually creates operational value, arguing that the real opportunity lies in combining high-quality geospatial data with intelligent systems capable of analyzing land-use variables at scale.   Related to the episode:   ⁠Zeb Lowe’s LinkedIn⁠  https://www.linkedin.com/in/zebulon-lowe-a02353a4/  Carter Malloy's LinkedIn  https://www.linkedin.com/in/carter-malloy/  Acres  https://www.acres.com/   The Power House podcast brings the biggest names in housing to answer hard-hitting questions about industry trends, operational and growth strategy, and leadership. Join HousingWire’s Zeb Lowe every Thursday morning for candid con

Transcript

00:00In this sponsored episode of Powerhouse, I'm joined by Carter Malloy, CEO and founder of Acres.

00:05We dig into how better land and housing data can reshape the way our industry builds,

00:09invests, and plans for the future. We'll talk about why transparency at the land level has

00:14been such a persistent blind spot, how to separate real AI innovation from the noise,

00:18and what it takes to turn high-quality data into truly actionable insight for home builders,

00:23capital markets, and technology leaders.

00:37Carter, thank you for joining me.

00:39Thank you, Zip.

00:40Yeah, so I want to talk to you a bit. You and I chatted up quite a bit before this

00:46recording started,

00:47so I feel like I know the most important things about you. We both play guitar, and we both have

00:53an affection for gypsy jazz guitar, which is a really small sample size of people.

01:00But I'd like to learn a little bit more about you. So before we dive in, can you walk me

01:04through

01:05your journey to where you are now at Acres?

01:09Yeah. Personally, I grew up here in Arkansas and grew up around land and farmland.

01:16My career was really around investing and then turning that into land investing.

01:20So skipping past me and talking about the business, which is more fun for me to talk about,

01:26we began, I began personally 15 or so years ago investing in land and then turned it into

01:33a business seven or eight years ago to go help others deploy capital into land.

01:38Was it land investment only or was it for construction?

01:44Okay.

01:44That's correct. So just thinking about land. Today, same as a business, we are land only.

01:50We are obsessed with that specific problem. And as a company, we were building this as land

01:55investing platform. Our whole shtick was generate alpha through data science, right? We were going

02:00to have some nerds on staff, some geospatial sort of PhD type of folks that help us to go and

02:07understand land at scale and find good deals. And we ultimately, we got to a pace where we were

02:12investing hundreds of millions of dollars into land. And we kept banging into this problem, which is

02:18despite being, you know, sort of self, we like to call ourselves land nerds. It was still really hard

02:25to actually do that. The data was bad. The data was desperate. We had eight different software systems

02:31and logins we were using. And so as we were building this tool, which today is known as Acres,

02:39we, we hoped someday to commercialize it. We began commercializing that.

02:43Can I ask you a question? Can I ask you a question about that? Why was the data so bad?

02:47Was it because

02:48the technology wasn't there? Or is it because there wasn't anyone really investing in it?

02:51Yes. All of the above. So the primary system that folks use is Esri or ArcGIS. These are amazing tools.

03:00If you're a scientist or an engineer, if you're a business person like me, and like most people who

03:05actually buy and sell land, they are unusable. Again, it's not an insult. It's just not the purpose

03:11those tools were built for. So a lot of companies over the years that have stitched together little

03:15point solutions, but there was no real encompassing solution for land in the U.S.

03:21Couple that, that's sort of the application layer. Couple that with lack, general lack of availability

03:26of data, whether that's good topo, good sewer lines, good zoning, et cetera, et cetera. So that's

03:33really what we, what we went after. Okay. And so I know Acres is, you know, a position around

03:40bringing transparency both to land and housing data. And I'm curious to know why that's been such a,

03:47a blind spot or persistent, persistent blind spot in the, in the industry.

03:51I think a lot of it's been, the business has been hyper local is one. And two is look, you

03:58know,

03:59the folks get into a habit. Like I'm, I'm used to driving around in my truck. I'm used to going

04:02and buying land this way. And so, and frankly, that's, that's a business challenge for us,

04:07which is showing folks like, Hey, you spend an hour at your computer and save a week on the road.

04:11Right. So that, that, that's a transparency is, which is in our, our mission as a business is what

04:19we're all about, which is I genuinely believe that markets function more effectively and people,

04:24the individuals participating in those markets have way better outcomes when the playing field

04:29is, is even by, by data and transparency. Was there, have you experienced, I mean,

04:35to me, this seems like a win-win kind of a solution here, but have you experienced resistance to

04:41wanting to bring the sort of transparency and with, with data to the, to this industry?

04:48Yes. I, everyone thinks they're getting a deal and that's often the case, but, you know,

04:55there's usually a losing side as well that you may or may not be on. And that may be a

05:00small part of

05:01it. A bigger part is really, as we started rolling out our competitive indexes and unmasking what people

05:07own around the U S that is, has caused a lot of consternation and we've gotten some real pushback.

05:14And what I mean there is, right. You, you've seen the stories of, oh, Bill Gates owns all this farmland

05:19or, oh, this, this person owns land around the U S. Well, the folks who are writing those reports are

05:25using acres to, to do the research at this point. And, and so we, we took that step further and

05:31built

05:31indexes. So housing is a great example where you can now track well over 90% of the production builders

05:38in

05:38U S every one of them, every one of their entities and LLCs and what they're buying and selling in,

05:44in near real time and acres. And not everybody likes that.

05:47Right. Right. And how much of that I'm curious as about, cause the way that I, the way that I

05:53understand it, you'll have larger builders that depending on the state or the region,

05:56they'll buy under an LLC or something that ultimately rolls up into the larger organization.

06:01And you, you know, if you're a smaller, if you're a smaller builder, are you, you know,

06:06you want to build a, or high-end homes, you may not be interested in buying a, you know, land

06:11that

06:11is 50, a hundred yards from I guess, you know, a rental unit, right. Or an apartment building.

06:18And I, I, how much of that is it, it's just the way that the industry has evolved or it's

06:26not,

06:26not arbitrarized isn't the right, isn't the right word, but there is a level of, of, uh, uh, I guess,

06:34like a secrecy, uh, at play where, you know, other there's, there's a very specific reason why some

06:38builders do not want other builders to know where they're building and, and, and, and relative

06:41location to them. Does that, does that make sense? I don't know if I've phrased that correctly.

06:46It, it, it does. And I think ultimately, you know, I'll, I'll go to the most extreme examples,

06:50uh, which we, we track data centers, right. And, and for, you know, very real pushback,

06:55some of them are getting, um, in, in, in a lot of city councils around the U S. So we

06:59track the

07:00sentiment of the city councils for building and rezoning for home builders, data centers, uh, et cetera,

07:05um, through, through a company or a partner of ours, Hamlet, but the, the, the idea of tracking where

07:12companies are going, like you can see the same thing in retail. We help you understand where

07:15your competitors are going and they want to keep that under wraps. The same is true in,

07:20in home building, where a lot of times you do want your competitors to know actually,

07:24because they trade lots of one another. And then other, other times, uh, you, you've, you know,

07:28you found something unique or special, or you're trying to create an assemblage. And so, uh, that,

07:34that can, you know, have potentially negative business implications, but the reality is, is the,

07:40the, the extreme majority of the time is people just view that as, as, um, you know, that that's

07:44mine, right. You don't, you don't get to know that. And, uh, I, I disagree simply. I think again,

07:52if we all have more information, we can all be more rational actors within the market.

07:56Right. Right. There's the, I mean, over the past, well, I mean, ever since the, the, the great rate

08:04rise post post COVID, uh, there's been so much conversation about, you know, rising rates and,

08:09um, and affordability and just inflation, I mean, a myriad of, uh, factors at play,

08:16but if you sit down and really it's, you know, in any industry that's tied, it's under the housing

08:21industry umbrella, it's, it gets to, uh, inventory, right? Like we have a housing shortage in this

08:30country and the more homes that we can get out into the market, everything, all the other variables

08:36that people kind of are, are upset about or are attacking or kind of railing on, they, it's not

08:40like they're not important, but they become less important. And so inventory is kind of like the,

08:46the, the bottom line of, uh, of this, of every conversation. And, you know, I think that, um,

08:54the earliest stages, or it seems obvious to me that the earliest stages of, uh, of, of like land

08:58acquisition tie into, um, getting more homes on, uh, on the, on the market and having more inventory.

09:05Can you kind of like, can you share or walk me through how you think, uh, industry leaders should

09:11be, begin kind of rethinking or reassessing that land acquisition as its overall place in the,

09:16in the formula of more inventory, more affordable housing. I believe on this theme of transparency is

09:24again, the more we know, the better, the better you can, uh, place, right? So where you're doing a

09:31deal, uh, when you're doing a deal and what you're pricing that deal at, uh, the more information you

09:36have, the more accurate you can be in, in assessing those, those, the variables there, uh, so that you

09:42have the best outcomes. And so not stacking on top of one another, like, you know, we, we want more

09:47housing supply. We also don't want, uh, home builders to have a bad business model, right? It's important

09:52that these folks can, can all deliver, uh, appropriately around, around the U S and all

09:56the right pressure points. So that's where, where we think, you know, measuring, uh, or

10:01sorry, uh, uh, layering all that data, right? So understanding where the income growth is

10:06happening, understanding where the new plants and manufacturing is going, where the new job

10:10growth is headed, where the growth corridors are, where that new grocery store is, is going

10:14before the market knows that having all that data can help deliver more housing, more effectively.

10:21Uh, one of the things I wanted to talk to you about, and you actually, you gave me a really

10:24cool, like very short, uh, demo, uh, was AI. And before I want to talk to you about what

10:31we'd say genuine or real AI versus what you and I were talking, calling it bullshit AI.

10:36Um, one of the problems I think that we have in, in, in, in our industry is that AI is

10:41just

10:41this very generic term. There's not a, an agreed upon definition of what AI is specifically

10:47as it's tied to any one industry. So within this industry, how, what, how would you define

10:51what AI actually is within this industry?

10:55Yeah. I think the, the problem you name is across all industries, right?

10:58Yeah, of course.

10:59And every, every person you meet like me that does anything with data or software

11:03says at this point that they are AI powered, right? Or AI native AI first.

11:08Uh, and, and so I think, uh, you know, first we can define that, right?

11:12Like AI for a long time has meant machine learning, like, which is really great statistics, more

11:18or less, um, and, and decision trees, things like that. Um, there's computer vision, which

11:23is looking at things and trying to understand the application there, which is quite applicable

11:27in the world of satellite imagery as an example. And there's LLMs, which is, you know, the AI

11:32of the moment. And, and for obvious reasons, it is a big deal. Um, and, and so the application

11:38of those things, you know, to, to me as a, as a user, um, you know, should be seamless

11:44and should feel magical, right? If, if, if, if the, if something is called AI, but it doesn't

11:50feel that way and it's not delivering you insane value, uh, then, then that's probably all you

11:56need to know about working with, with that company.

11:59Can this, this sounds like a silly question. Can you describe that feeling? What does that

12:04mean? Whenever you say, when you're interacting with something, that's, I know that sounds really,

12:07the awkward sounding question, but I described that feeling. What does magical feel like when

12:11you're interacting with AI to you? Um, you know, these are things that are hard to put into words,

12:16but every person listening today has had that moment where they typed in a question and just

12:21took a breath, like had a gasp, like, Whoa, right. This, this robot just did something pretty

12:27magical for me. And that's the moment we're looking for when, when we talk about AI, like,

12:32you know, every company, their developers are using it to build software. Cool.

12:36Uh, lots of companies are using it to go gather data, to go help them deliver things

12:40faster. And even some companies have, you know, what I would call lazy wrappers,

12:45right? Like there, there's an LLM that's involved in their product, but doesn't do

12:48anything for you. Right. Um, uh, I go type some things and it applies some filters.

12:52Okay. Yeah. Like a real application, a real application of AI, I, I wish I had a better

12:59way to, to describe that, but it should save you such an amazing amount of time.

13:04Right. Usually what it boils down to is that, holy cow, like, I can't believe this was able

13:10to produce this result for me that quickly.

13:12I know you and I are going to come, uh, just by the nature of what we do for a

13:15living, uh,

13:16going to come to this from two very different angles, but I'm going to ask you, I'll give

13:19you, I'm gonna give you my moment with AI. And, uh, if you, if you can recall yours, like

13:24the first time that you had that experience, like, Oh wow, holy shit, this is like, this is

13:28really something. Um, so it was, uh, I was, I did a LinkedIn post and I was, I was making

13:36fun of people that use AI to just generate their posts, which are obviously like predictable,

13:42um, you know, AI, it's AI slot. And so I had, uh, I had chat generate, uh, a, uh, an

13:51AI post

13:54where it adopted the tone of Hunter S Thompson, like fear and loathing in Las Vegas, fear and

13:58loathing in the campaign trail. And my post was something along the lines of like, if you're

14:01going to use AI to generate your, you know, your social content, I, you know, I suggest,

14:07uh, you know, adopting a tone. I like Hunter S Thompson. So I, and it was this article written,

14:11it could, it could have been a page from fear and loathing in Las Vegas or chapter fear

14:15and loathing in Las Vegas. I posted it. And like, uh, and it was a, it was a joke. I

14:21mean,

14:21it was awesome. It was the, the content was perfect. And I tweaked, I had to tweak it just

14:25a little bit, but the, but nobody got, maybe these people don't get my sense of humor, but

14:29everybody, it was so people thought that I actually wrote the thing that I was, that was

14:35just done by AI. I was like, wow, this is really, um, one, maybe I have a skewed sense of

14:40humor,

14:40but two, this is really, this is really something special. Like the ability to really, if you spend

14:45a little bit of time with it, adopt a person's, adopt a person's tone to where it really is

14:51quite indistinguishable. Um, but I would assume that a data person, you'd kind of come with that

14:55from a different angle. Do you remember like the first time that you had that, that kind of like

14:59wow experience and we were like, well, I got, we got to, we got to hop on this train.

15:03Yeah. You know, uh, look for, for far too many years, if somebody, and, uh, if somebody

15:10said the word Bitcoin or token or AI to me, I immediately did not take them seriously.

15:15I like dismissed them as a, as a non-serious human. And, you know, I remember like when,

15:20when GPT three, five came out the end of November 2022, and that was, that was definitely the holy

15:27shit moment of, okay, first of all, woe, I've been wrong. And, and, you know, the, the implications of

15:32this and how quickly four, uh, rolled behind that. It was a sort of the scary moment of,

15:37Hey, this is, this is moving so fast that I'm not sure we are, we are ready for it, uh,

15:41as

15:41people. So, uh, you know, and since then, you know, it's been pretty consistent. I think last

15:48year, Opus four, five came out from Claude. That was a big deal. And then quite frankly, what our

15:53developers showed up with, uh, three months ago, which we released today, funny enough, uh, that

15:58was the big moment for me. We, we've been trying to, uh, crack geospatial AI, which is a whole

16:04new set of challenges, right? We're no longer extrapolating ones and zeros and, and, uh, you

16:09know, sentences, basically we're trying to do math about, you know, a hundred thousand or a

16:15million features like lines or points and polygons bent around the globe, right? The LLMs fall over dead.

16:22And the moment earlier this year that they showed up and said, Hey, like, we, we think we, we've done

16:26something here. It was, um, like a hair raising moment internally for all of us of, oh my gosh,

16:32like we, we've, we have found something really special. Yeah. I think that, uh, that term bears,

16:37I guess, uh, repeating and maybe, uh, re-explaining or, or, or stating again, uh, geospatial AI unpack

16:45again, please. Uh, what exactly, what exactly that is. Yeah. So when we say the word geospatial,

16:52we're just talking about, you know, maps, right. And you have to remember that maps are not flat.

16:56They are, they are, uh, they're wrapped around the earth. And so it presents very real compute

17:02problems. Like even something as simple as calculating the acreage of a parcel, uh, when you take into

17:08account the, the varying geometries, uh, of, of any individual parcel. And so, uh, AI has a much more

17:15difficult time exploring those data sets. And so, uh, what, what you can't do today at all is, is have

17:24complex AI queries inside of any of the frontier, uh, geospatial models. So what, what, what we do is,

17:30is we've built, uh, you know, pretty incredible system of, of tools and tricks and fast, uh, uh, geospatial

17:38tools, right. And we then have a number of AI agents inside of that, that understand how to operate

17:45and have context and, and weightings or trainings in various parts of our compute stack, uh, so that

17:51they can go and, and complete operations for you as a user very quickly. That, I went off on a

17:58tangent.

17:58Here's what it does. Hey, go find me all the parcels over 40 acres near sewer with low topography.

18:06They're developable without flood that are either zoned R2 or reasonable in these three counties.

18:13And it comes back and does all that work for you and highlights them on a map. Go show me

18:17the lot

18:18pricing for all the lots, both finished and raw and been sold near Atlanta in the last five years.

18:23It uses all of our data sets and proprietary data to deliver you that, uh, in a couple of minutes.

18:29So does that, that's what, yeah, that's, um, what you were showing me the, the, just a brief

18:34demo that you were showing before was wild. Does that require in order to do that or to deliver

18:39that, does that require a relationship or an ongoing relationship between both like your,

18:44your programmers as well as your software developers along with geologists, or is this,

18:50is this all a computing at this age? Is it now all a computing issue?

18:55Well, we, we certainly have, um, you know, PhD and master, uh, you know, geos master's program,

19:02geospatial, uh, data scientists on, on staff. So you, the, the, the expertise there is, is very,

19:08very niche and, and very important, uh, to be able to, to build the tooling, uh, that you can then

19:14basically, you know, guide these robots through to go, go teach them how to operate. And you have to

19:19couple that with very real LLM. And we also use, I mentioned earlier, you know, computer vision and

19:25machine learning. We use those forms of AI as well. Uh, but you, you've got to have some real

19:29expertise to go do the trainings and the weightings. And, you know, in our, in our case, that, that means

19:34like actual compute, uh, and GPUs here in our office, kind of a room, you know, full of our, a

19:41couple of

19:41our robots, right? That have really stupid names. Um, but, but we use, uh, to, to be able to sort

19:47of stay

19:47there on the, on the bleeding edge as the stuff is moving so fast and the rules of engagement change

19:52so frequently. Right. Well, I know that, uh, I mean, it's, uh, pretty common sense that, you know,

19:59the, the, the, uh, the cleaner, I guess the data or the higher quality of the data, you know, that,

20:05that's essentially it's a foundation, right. For any of your AI driven insights. So what do you,

20:11how do you separate, you know, beyond like the, the magical, like, wow moment, how do you separate

20:17this like actionable data and insights from, I guess, the noise, like how should, uh, decision

20:23makers or leaders, how should they go about evaluating, you know, tools that they're investing

20:28in that are really delivering value versus like, what's like, what's like, what's bullshit.

20:33Yeah. Look, like the, the, the best analyst in the world, if you give them really bad data,

20:39they're going to produce your really crappy spreadsheet. Right. And the, the same rules absolutely

20:44apply garbage in garbage out with, with AI, right? Like there, there's an adage of like,

20:48show me, uh, bad data and amazing AI and I'll tell you which one wins. Right. So I'm glad you

20:54called

20:54this out. And it is, we, we identify as a data company first and foremost, right? So that, that is,

20:59uh, what, what most of our team is focused on. Most of our investment to date has been all about

21:04is bringing together high quality or high resolution data, right? So breadth, meaning like how many counties or,

21:11or cities or states or, or, uh, places do we cover, uh, depth, right? So how, how deep does that

21:17information go? How much do we know about every parcel on the US? Uh, and then, then a recency,

21:22right? That's the, the, the, the third incredibly, uh, component, uh, incredibly, uh, important component

21:27there, uh, to sort of wrap all that, that context for, for, for data quality and data accuracy.

21:34Uh, so that, that's what we spend most of our time on still today is pulling all that information

21:39together for our clients to interact with and, and now for the AI to interact with on their behalf.

21:44I was going to ask you about earlier in our conversation, you'd said that you had experienced,

21:49um, like resistance, right. Well, with, uh, bringing the, uh, transparency of data to market

21:56and that there is kind of a, like, this is how my granddaddy did it, drive around in the truck.

22:01Are you at a, are you at a point now to where most people that you're interacting with on and,

22:08and either showing this or, or trying to, you know, sell this, or as you're trying to expand

22:12where people, cause it's, you know, AI adoption is, it's, it's different though. I was just reading

22:17an article just the other day and in, in, in home building AI is used, uh, surprisingly more

22:23and adopted surprisingly more than, than I would assume is much more than in real estate and,

22:27and the mortgage, uh, industry. Are you at a point where you're with the people that you're

22:32interacting with potential customers that say, um, they, they get it. Like they, they're,

22:37they know that they need to become more, uh, not even tech friendly, but they need to get

22:41on the AI train or do you still experience that hesitancy or skepticism? Yeah. Look, our, our,

22:47our customers are cross industry working with land. Right. And, and we still see a healthy

22:52amount of skepticism, but, um, you know, I'll, I'll speak to the home builder vertical. I just came

22:56back from, from ULI and, and, you know, I'm, I'm always thrilled to hear the, the various use cases

23:03people have found for AI inside of their businesses. I think when it, when it comes

23:07specifically to land, uh, I, I, we are seeing it, right. We show people and, you know, the, the

23:14responses are usually some four or five letter words and like, Hey, that, that was my job five years

23:20ago. Right. That's, and that's true about a lot of AI is it's, it's not, um, a super positive thing

23:25for entry level jobs. Uh, but at this point, like it is a matter of you can adopt this and

23:32use it,

23:33uh, or you can be at a competitive disadvantage. Do you see this as a opportunity and which I don't

23:39know how, um, uh, I have no idea how, uh, let's say guarded the, this industry is, or your industry

23:48is specifically, but do you see an opportunity or do you see this democratizing perhaps like land

23:54acquisition to where, uh, people that, you know, uh, would not be drawn to the industry prior to

24:03this sort of technology, you know, this sort of transparency of data, do you see it opening

24:07up the industry, bringing in new, uh, new, maybe new talent, I guess is what I'm trying to say.

24:13I do because it makes it easier. Right. And that's, you know, as a, as a company, it's always a

24:19challenge for us. How do we make these tools really friendly, right? Like you, you open your screen,

24:23what do you do next? It should be blatantly obvious. And so, uh, we, we've worked pretty

24:28hard and think, think we're, you know, we've made a lot of progress to this idea of I can

24:32hop right in at this point and begin speaking to the robot and, and get a really good sense

24:36of what I should and shouldn't be doing. Now, importantly is it does not replace experience,

24:42right? Like, like, as we all know, trust, but verify, right? Like there, there's some amazing

24:48output and the same is true for, for what, what our AI produces like the, you know, the, uh, zoning

24:53report you and I were going through earlier, the, the, the outputs are, are great and it's going to

25:00save you a lot of time. It's going to get you to say no to deals much faster, but you're

25:04also

25:04probably not going to just go make a multimillion dollar decision based on a robot. There's very real

25:10human judgment necessary, uh, to, to move these things along. Right. Well, we skip, I'll go back to my

25:16first question. We kind of skipped over this, uh, or fast forwarded right to, to acres. Uh,

25:20what got you into land acquisition? Like your first deal? You just dropped my piece of property.

25:27Like that looks like a good, like a good place to buy it. Like where, where did that come from?

25:30Oh, my dad. No, I learned it the way my daddy did it. Just like I said earlier. So I

25:35am,

25:36I am certainly one of, one of those people. Uh, so yeah, my, my, uh, dad has been farming my

25:41whole life.

25:41And so that got me on the country a lot of the kid and,

25:45uh, got me to appreciate a sense of place and, uh, own a land, you know, that's, that's the,

25:50that's the dream. Excellent. Yeah, for sure. Um, okay. Let me ask you that. So looking ahead,

25:55um, where do you see, or where do you feel most, uh, optimistic about the, uh, increasing adoption of

26:03AI and the advancements of, uh, technology in this, in the housing ecosystem at large? Where are you,

26:10where two part question, where are you most, what are you most optimistic about? And then what

26:14what gives you, uh, what, what gives you pause or gets you some concerns that you think people may

26:21not be, uh, thinking through or acknowledging. I'll, I'll maybe zoom out further. So last year,

26:28a couple of really opposing books came out on AI. One was called, uh, AI snake well,

26:33and it's just about LLMs and hitting, hitting their ceiling and having really bad, uh, financial models,

26:39which is somewhat true, especially in the latter part. And the other was, uh, called if anyone

26:43builds it, everyone dies. And you know, you can imagine what that one was about. And I read a

26:49similar one, uh, it was called, uh, against the machine by Paul Kings North. And it made me want to,

26:53uh, retreat to the, to the mountains. And I, I look, I think it is important in, you know,

26:59anything you do in life to, to try to get an understanding of the opposing viewpoints as,

27:03as wild as they may seem sometimes. And, and that worry on that side is, is so material. It's

27:09so existential that we, even if it is a tiny, tiny, tiny fraction of a percentage of a threat,

27:15like it's something we should all be taking very seriously. Uh, so, so look, there, there is that,

27:20that, um, concerning side of, of, of a lot of technologies being built today, uh, just as there

27:25was in the forties when we started building things that could destroy cities. Uh, and, and inversely though,

27:32like the, the unlocks that come from this technology are absolutely insane, right? Even

27:38if the technology stops evolving, and this is truly the, the best model we ever get,

27:42uh, the implications are, are, are huge. And as, as we, as companies and as individuals figure out

27:49how to use it better and better, I think in housing, look, that means we, we reduce the friction,

27:54we reduce losses, right? So, so we help folks do better deals and think about just about land,

27:59but I, although that implies like better outcomes for the consumers,

28:02there's better outcomes for the businesses there and a, and a better functioning market.

28:05So I'm, I'm really, really excited about what it does for the companies, what it does for

28:11individuals and, you know, giving them quality of life because they're not having to waste their

28:14time on bad deals. Like I, and this is, it's an exciting moment we're in as me as, as acres

28:20.com.

28:20And, and, and for all of us as an industry. All right. All right. Thank you so much for joining

28:25me. I really appreciated our conversation. I have, we have a music stuff to talk about offline.

28:30Yeah, we do, man. Thanks, Sam. Thank you.

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