TRANSCRIPT 🔗
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Realistically, factory as a service is almost a distraction.
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What this is, is really, it's really an option that D.R.
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Horton is exercising. I was going to say, if I may real quick,
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the examples you gave, Dusty and Rugged and, well, let's start there,
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they prove the limits of robots.
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And so translating what a $25 billion start means to,
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the local workforce is probably the first step because it doesn't mean it's
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not the same as $25 billion in tilt-ups happening, right? And these are just tools.
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As smart as they sound, these are just tools. So it's still the human's job.
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It's still the PM's job. It's still the company's job.
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Alright, so here's the rundown. We've got six stories. I don't know if we're
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going to get to all of them.
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One is we've got a regional starts report where mega projects are skewing kind
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of regional growth numbers.
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We've got D.R. Hortons, the nation's largest home builder, taking a bet on robotic
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home building, which introduces us to a concept, factory as a service, which is kind of cool.
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We're talking through kind of what insurance are actually saying about AI mistakes
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on construction projects, who's got the liability.
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And then we've got input prices are up and yet we've still got contractors kind
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of expecting their margins to shrink so,
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interesting conversation there and then a housing starts that kind of undercuts
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some of the automation story that we're seeing and then we've got construction employment growing,
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while the residential side of it shrinks even as i walk through that there's
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no way we can draw all this we're going to go for it so i think let's start
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with the money so we've got construct connects regional report came out,
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and you know it says essentially what we are all kind of feeling right seven
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of nine census divisions are up here to date on non-residential starts i think the big,
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news when you kind of dive behind that data though is they're all being kind
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of driven by really large projects and so i think,
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maybe the the opening question aaron would be you know if regional starts are
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doubling because of these really large projects within each of the regions,
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i think how should how should contractors in these regions be responding to that growth?
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Are they feeling the competition of...
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All of a big project kind of drawing away all of those resources that they need
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to manage their own business? Or are they maybe already involved with it themselves?
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Well, I think one of the first things is to really normalize,
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figure out what those numbers actually mean, right?
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What does a $25 billion start mean for a mega project, especially in a business
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where most of that is probably in like lithography equipment and,
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other, you know, basically owner supplied equipment that's coming into the building.
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That's a really big question that they all need to ask. If you look at that,
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like the micron clay scenario that I guess broke ground in January of,
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2026, the peak workforce on a $25 billion project was about 4,500 workers.
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And so translating what a $25 billion start means into the local the local workforce
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is probably the first step because it doesn't mean it's not the same as 25 billion
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in tilt-ups happening right,
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so but yeah i mean it's gonna the biggest thing that it's gonna do is is it's
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gonna drive most of these have a pla involved so thinking about what that is
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you know might that particular pla micron's pla,
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was an 80 local labor target and all union hall first hiring so that's kind
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of that's definitely a crowd out written like you,
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so if that's if that's where your labor comes from and that's where your your your pricing starts,
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you know that's the place to that it's definitely going to disrupt any of the
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other business happening and probably drive up all those other local prices,
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Yeah, and it would seem like if you have a project like that coming to your
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region, I think the ability for contractors to kind of stay diversified can be challenged, right?
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If one project's consuming all of your resources. Obviously,
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depending on what kind of outputs from a project like that in terms of demand
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for the supply chain, but that would seemingly be a challenge.
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Yeah. Yeah. Hugh, when you talk to some of
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these regional contractors,
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are, is there a recognition of, like, are they, is any bit of their strategy
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being informed by these kind of regional growth statistics in terms of where
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to invest and where to resource themselves?
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I'm not hearing it like that. I'm hearing it more like we could maybe do some of that too.
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Like healthcare contractors saying, you know, I could probably become part of
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a JV for a data center, right?
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Like the kind of bigger story is that lots of times people don't have enough
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people to do the data center job, so they're JVing all over the place.
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And that starts to open up partnerships that they might not have otherwise done.
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So I'm hearing about people from, you know, the East Coast that are setting
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up shop in Texas. Now, we'll see what happens, whether that's a pull is quite
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to Texas as much as it was, but you get my point.
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So I think it's a little bit more like people thinking outside of their region,
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because there's just so much demand in these pockets where, you know,
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the combination of permitting and other factors are driving primarily data centers.
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It's just skewing the whole market. Like this amount of money going into something
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is just warping literally everything.
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You know, how long is that going to last? Certainly another year or two.
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I mean, even if the new Congress throttles it a little bit, they're not going
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to throttle it that much.
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And it's just going to make that what was happening already with transparency
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and sort of water and electricity use was kind of happening already.
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It's just going to now be regulated more than it was.
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And, and, and Hugh, do you, do you, don't you think that it's like drawing the,
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best and the most valuable workers away?
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I mean, I think about who is, who's capable of putting their wife in an RV and driving to Texas.
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Like the guy, the guy that's going to do that has, he, he's got a family to
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support. He's got some kids that are in college. He's like, like,
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this is a major commitment, right? It's a big life change to drive across the
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country and take on a job for two years.
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It's not the 20-year-old apprentice in San Diego that is going to community
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college to try to become an electrician.
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I wonder about that. I wonder about that because, again, the volumes are so big.
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I'll give you an example. I do some work with SmackDown, and not them,
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but a related group that they're part of just got a bunch of money and,
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in fact, have been offered, and we're talking millions.
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From, I believe it was Meta, but anyway, one of the hyperscalers to really drive
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a lot of training, right? And they've gotten the same type of offer from apparently
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from other people. I don't know the details. I just literally met people at
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their conference last week.
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So no, I think it's pulling everybody. If anything, I would say some of the
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senior folks don't have to.
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People with a lot of experience where the move is, I don't want to pull my kids
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out of school or whatever.
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They may be less likely to do that. Whereas maybe it is the 25-year-old who's
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more likely to go because they can.
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I don't know. I think it's, that'd be the kind of thing you really want to see
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the data on because there's probably more than one thing going on.
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Man, they're desperate for folks at, you know, at scale. So I think,
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I don't know. I think that'd be a good thing to see some data on.
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I was curious, this was something I think we talked about last week,
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Stephen, or last month, Stephen, is like the statistics were completely skewed
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towards these megaprojects, but everything else seemed like it was down.
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So like anything outside of megaproject territory or like data centers and we'll
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say like industrial type projects, that was down. Has that remained kind of
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true? Did we see that anywhere this month?
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Yeah, I don't think I pulled the same data source, but I think it would be interesting.
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And I think anecdotally, I think that is true. And I think the question there
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is, is it because owner demand is down in those verticals, or if just all the
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resources are being drawn away from those verticals, and they can't support them?
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I think cost of capital is absolutely going to be part of that because we already
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have had interest rates go up, which, you know, that's just kind of across the economy.
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But also there's just so much capital that's going to be invested in anything
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real estate related. And if you've got this much demand, it's just crowding
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things out. I'm sure that has something to do with it. Yeah.
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Construction finance is so weird anyway, you know, like the bank does a little,
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and then this guy does a little and that does a little.
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So, you know, it already is a, is a kind of a complicated picture.
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And I have a feeling that between all of this other, other demands on capital,
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it's just crowding some of it out. So things that were on the,
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on the borderline, maybe get pushed back.
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Yeah. So, well, the, the, the thing for sure, I I've been watching the census
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numbers on this stuff for a couple of years and we saw in march this year by
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march this last this year there was,
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major indicators that the that the flip was underway right that that,
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these large industrial and we're not just talking about data centers we're talking
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about chip fab a little bit other re-industrialization right,
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compared to non-residential or compared to residential starts being down compared
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to all of these home builders having to give major discounts on,
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on home purchases and all that stuff you know they're trying everything they
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can to keep some of these other markets afloat from a capital point of view
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and it's just still not it's not holding.
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Sponsor break if you spend any time running a project you know the answer is
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almost always sitting in the contract document somewhere,
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catch is that it's almost never in one document It's split across the contract,
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the specs, and some flow-down clause three layers deep that nobody connects
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until it's already cost money.
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That's the whole premise behind Document Crunch. That risk doesn't live inside
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any single document. It lives in the gaps between them.
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And most teams never look at all of them together until they're standing in a dispute.
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The average construction dispute in the U.S. now runs about $60 million,
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according to Arcadis. It's a lot of money.
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And the root cause they point to isn't fraud or bad intent. It's people not
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fully understanding what they agreed to.
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So Document Crunch builds a platform around closing that gap.
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Crunch AI reads the documents, surfaces the risks, and it cites the exact page and clause that's big.
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And the whole thing runs on your own company standards. The office and the field
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You mentioned how this might impact kind of the labor pool long term as we kind
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of optimize for a very specific maybe variety of build,
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go out on a limb like do you have any kind of hypothesis on what that looks like of,
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for the workforce as we kind of optimize for speed and optimize for these very
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intricate kind of mission critical oriented workflows yeah i'm not sure that
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it that i meant that that it optimizes for that that,
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project type so much as it's just people are making a lot of money doing this
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yeah so i think you know in five or ten years somebody who came in as an electrician or as an hvac,
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you know worker they're going to have all that capability and they'll have dealt
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with really complicated projects that were fast and like like really tense so
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these are going to be they're thoroughbreds compared to you know someone that
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may have come up in something else,
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and i think there's a there's I wouldn't call it a perfect storm,
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but there's more than one thing going on.
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Like how many, how many, I got a nephew who's, who's 15.
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Does it make sense for him to think about college? I, you know,
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like maybe, but it's much more of a question than it was.
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You know, you look at what it, what it's going to cost. I mean,
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it's not crazy to think it's 400, $500,000 to get.
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--> 00:12:58.588
I mean, obviously you're talking a pretty expensive school, but not a crazy one either.
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You know, think about a half a million dollars to get a degree in,
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you know, in studies of something or whatever. Like, I just think that the automatic
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let's go to college thing is just not what it was.
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And what that's going to do from, you know, to the trade workforce,
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I think it's just like you look at someone who doesn't have that.
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They're making $150,000 when they're 22 or whatever.
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I mean, like some of it, especially when you add in these crazy bonuses,
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you know, thinking about what you were talking about, Aaron,
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where they force people to use local, what do you call it, local resources who may not want to come.
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So they're giving them like a morning bonus and then an afternoon bonus.
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I mean, you hear these nutty stories about keeping people engaged.
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Like, that's a lot of money by the time you're done. So I just feel like we're
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going to have more of a workforce than we otherwise might have.
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But we've had a secular problem with our workforce shortage for like 50 years. It's not new.
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So I don't know that this is going to completely solve it. I think it's just
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going to make it better than it would have been.
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I think something else that gets lost in the data is because there's been so
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much growth in data centers and some element of manufacturing,
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it kind of shadows a lot of the infrastructure work that is actually going on.
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You know, I've talked to a number of infrastructure GCs in the last couple weeks,
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and there's projects across the country, and they're large projects,
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but they're in the billions, but they're not in like the 25 billion, right?
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And so I think rewind 10 years ago, and that would have been the project on
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the face of Construction Dive.
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And now just because of the scale of how Mission Critical has grown,
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that's now maybe a non-mega-mega project, which is interesting.
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Let's move to robotics. So Promise Robotics announces a multi-year agreement
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with D.R. Horton to support more
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than 1,250 homes. Horton is the biggest builder in the U.S. by volume.
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The model is factory-as-a-service. Horton doesn't own the plant.
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I think that's interesting. So I guess Horton does about 85,000 homes a year.
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So this is certainly not a majority of their build per year.
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It's about 1.5%.
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So very much in the pilot kind of phase.
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But I think it's an interesting kind of model that we're seeing.
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So I guess, Hugh, the question is, we've talked about robotics quite a bit.
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I think as model kind of capabilities have improved and maybe the brain side
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of robotics has improved, is this a breakthrough moment for kind of the business
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model associated with robotics?
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Or is this just kind of one more iteration towards something else?
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I would probably say it's the latter, but it's probably an important one.
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You know, like, I think breakthrough is a, is a strong word only because construction
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is so complicated in terms of who's going to buy what and what project is this
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right for and so on. But I think the intuition you're getting at, which is.
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The ability of a facility, a factory that has robotics in it to be a lot more
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flexible than it was and much more.
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Able to reconfigure for, you know, something different is clearly better than
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it was and going to get even better, you know, pretty soon.
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I mean, whether it's, and we see a lot about humanoid robots,
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but I think the point is what goes into those doesn't need to be humanoid.
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It's still that those advancements can live in something that's a little more specialized.
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So thinking about factories that are are you know much much more like you know,
00:16:25.758 --> 00:16:31.324
anyway customizable i think is is absolutely right the business behind it like one of i remember when,
00:16:31.980 --> 00:16:34.989
there was a couple of companies right before covid,
00:16:35.582 --> 00:16:39.558
spent some money out of chicago one in particular in chicago and they had trouble
00:16:39.558 --> 00:16:44.478
with it because i'll just say who was it was skender but that but they had they
00:16:44.608 --> 00:16:47.378
built a factory and it was pretty cool.
00:16:47.378 --> 00:16:50.146
And it was, there was a lot of smart people that were involved in it.
00:16:50.715 --> 00:16:54.488
It wound up running into trouble because of financing, not because of,
00:16:54.488 --> 00:16:57.258
I mean, the demand wasn't what they hoped it would. And obviously
COVID had
00:16:57.258 --> 00:17:01.218
an impact on that, but the point was, it wasn't that it failed in the market
00:17:01.218 --> 00:17:03.466
is that it failed in the financing.
00:17:03.850 --> 00:17:07.721
So the problem a little bit with stuff like this, that is a really different business model,
00:17:08.203 --> 00:17:12.978
is sometimes they, they, they struggle with some of the things that make it possible to do, to do,
00:17:13.458 --> 00:17:17.028
the business you know and maybe it's financing maybe it's something else maybe
00:17:17.028 --> 00:17:20.198
you know what i mean maybe it's you know the architect's ability to deal with
00:17:20.198 --> 00:17:24.201
it who knows but the point is there's so so much complexity that goes into,
00:17:24.741 --> 00:17:26.715
something like this that that.
00:17:27.438 --> 00:17:30.749
They may have had on the horton side had to do an awful lot to make it work.
00:17:31.614 --> 00:17:35.298
That that that limits how much this means for the rest of the industry in the
00:17:35.298 --> 00:17:39.238
short run so i think it's a really important thing and and it Maybe there's
00:17:39.238 --> 00:17:43.724
five more steps like this before it becomes the way that housing gets built.
00:17:45.388 --> 00:17:48.262
Well, I think, you know, how they, in one of the press releases,
00:17:48.262 --> 00:17:49.922
I think they talked about D.R.
00:17:49.922 --> 00:17:54.012
Horton kind of passing on drawings for the different templates of houses and
00:17:54.012 --> 00:17:58.992
Promise Robotics kind of consuming that and building out a plan and a factory
00:17:58.992 --> 00:18:03.892
to respond and create these kind of components that get assembled on site.
00:18:03.892 --> 00:18:06.962
And so, you know, variability kills that plan.
00:18:07.113 --> 00:18:08.982
And so I think that's why they're starting regionally, right?
00:18:08.982 --> 00:18:10.109
They're rolling out in Phoenix.
00:18:10.875 --> 00:18:14.466
You know, Tyler, you've kind of leaned into AI and agents. Yeah.
00:18:15.092 --> 00:18:19.702
On a, in your personal life. Have you like any humanoids? What's your,
00:18:19.702 --> 00:18:22.907
you have any robotics? I'd call them ever. No.
00:18:23.499 --> 00:18:26.976
I mean, yeah, we're seeing massive investment over into the robotics space.
00:18:27.423 --> 00:18:33.772
The, the AI side of this conversation within DR Horton, I kind of look at it
00:18:33.772 --> 00:18:37.922
and I'm like, okay, I mean, I can see how AI might be useful in some of this for sure.
00:18:37.922 --> 00:18:42.562
Like from like having the robotics, being able to tap in to something, but I don't know.
00:18:42.562 --> 00:18:49.212
I go back to when i was detailing steel and when i was working with fab shops that would have to,
00:18:49.712 --> 00:18:53.419
run robotics in their fabrication shops and the amount of time,
00:18:53.883 --> 00:18:59.682
it took to just set up the way everything flowed through the shop and i
think
00:18:59.682 --> 00:19:03.762
it's really interesting too that i think hugh you brought it up it's like almost
00:19:03.762 --> 00:19:05.121
like this this factory could.
00:19:05.947 --> 00:19:07.521
Shift around depending on what
00:19:07.521 --> 00:19:10.718
it was building right and so like the lines could change and everything,
00:19:11.310 --> 00:19:18.025
and it'd be really hard to optimize a factory like that but i guess you could potentially use ai,
00:19:18.501 --> 00:19:22.461
to identify where the bottlenecks are occurring and then re kind of configure
00:19:22.461 --> 00:19:26.501
how your shop is set up for that too so i don't know it's just kind of an interesting
00:19:26.833 --> 00:19:30.581
interesting angle this like factory is a service thing that could be reconfigured
00:19:30.581 --> 00:19:31.627
depending on what you're building,
00:19:32.162 --> 00:19:36.881
but also like the modular side of things has been talked about for years and
00:19:36.881 --> 00:19:40.911
years and years is this thing that's going to change the game and like i've
00:19:40.911 --> 00:19:45.139
had umpteen thousand conversations about it and i've yet to see it actually,
00:19:45.749 --> 00:19:50.971
change the game because it requires people to slow down and think and people
00:19:50.971 --> 00:19:55.451
don't like to slow down and think they just like to go go go a lot of times
00:19:55.451 --> 00:20:00.250
especially in our industry so and yeah to the point like how many people in the,
00:20:00.819 --> 00:20:05.151
in the accountable chain from you know owner through design through engineer
00:20:05.151 --> 00:20:10.757
and all that have to change to make this thing work right you know that's what i so who knows,
00:20:11.418 --> 00:20:17.804
well well here's the thing yeah okay so at dr horton nothing has to change.
00:20:18.931 --> 00:20:24.531
As a publicly owned company dr horton is primarily operating as a transactionalist
00:20:24.531 --> 00:20:27.971
they don't even they don't even own all the lots they sell.
00:20:28.791 --> 00:20:35.268
They built Four Star in 2017 as like a way to shift the capital heavy land development off.
00:20:35.680 --> 00:20:41.051
And this is just an opportunity and a test to figure out how to shift manufacturing
00:20:41.051 --> 00:20:45.381
or how to shift the production of the construction site, right?
00:20:45.381 --> 00:20:48.971
So really what they're doing is buying an option.
00:20:49.531 --> 00:20:53.169
From D.R. Horton's point of view, they're buying an option on production.
00:20:53.477 --> 00:20:57.231
I mean, as soon as they announced this deal, within two weeks,
00:20:57.231 --> 00:20:59.891
they announced a 45% reduction in starts.
00:21:00.824 --> 00:21:05.691
Hmm. Right. So what they're really and then what that does is that says,
00:21:05.813 --> 00:21:09.018
wait, Promise just made this deal. Did they know that Horton was going to be
00:21:09.018 --> 00:21:12.018
doing a 45 percent reduction in starts?
00:21:12.372 --> 00:21:17.338
If they did, they probably designed that factory in Phoenix specifically to
00:21:17.338 --> 00:21:20.678
be at an 85% utilization rate, right?
00:21:20.678 --> 00:21:23.708
Like they're, they're designing, that's a micro factory in Phoenix too.
00:21:23.708 --> 00:21:28.524
That's nothing like the ones in Montreal or Alberta or potentially Toronto that,
00:21:28.978 --> 00:21:34.388
that promise adjacent folks have been built, have been building.
00:21:34.388 --> 00:21:38.338
So realistically, factory as a service is almost a distraction.
00:21:38.338 --> 00:21:47.049
What this is, it's really an option that D.R. Horton is exercising or purchasing.
00:21:47.304 --> 00:21:52.528
And the question is, what does the contract actually look like? Who is short on the put?
00:21:54.251 --> 00:21:57.716
Fascinating so i think the point is,
00:21:58.471 --> 00:22:04.166
they see a slowdown and they're essentially like hedging their risk exposure
00:22:04.271 --> 00:22:05.844
is is kind of another way of saying that,
00:22:06.465 --> 00:22:10.368
yeah that's exactly what's happening they they either see a slowdown or generally
00:22:10.368 --> 00:22:15.558
they want to be asset light as a as a business and don't want to be correlated
00:22:15.558 --> 00:22:20.488
to the housing market in the the production side of the housing market, right?
00:22:20.488 --> 00:22:24.078
They don't want to be correlated to the asset ownership, which again,
00:22:24.078 --> 00:22:27.558
looking at the bigger picture here is really been the challenge, right?
00:22:27.558 --> 00:22:32.108
Like Katera's problem was that they invested in all these assets and couldn't
00:22:32.108 --> 00:22:37.160
respond to the lumpy demand that is like, that is so common in construction.
00:22:37.862 --> 00:22:41.246
What's Katera? I've never heard that name. I'm just kidding.
00:22:42.251 --> 00:22:44.127
There's a base crater that we can take you to.
00:22:46.466 --> 00:22:47.447
I mean, I do think.
00:22:49.967 --> 00:22:53.369
There is, you know, to the point you made previously, Aaron,
00:22:54.367 --> 00:22:58.777
I think the maturity moment for robotics is when we stop talking about the robot
00:22:58.777 --> 00:23:02.860
and we start talking about the output, right? And I think this is on that path.
00:23:03.440 --> 00:23:08.177
And I, you know, obviously we'll see how it goes.
00:23:08.177 --> 00:23:15.047
But I think if you talk about Dusty, you talk about Rugged, you talk about all
00:23:15.047 --> 00:23:18.807
of the different robot applications, I think the success moment is when we stop
00:23:18.807 --> 00:23:21.367
talking about the robot and we just talk about their productivity,
00:23:22.387 --> 00:23:23.886
the cost profile, et cetera.
00:23:24.345 --> 00:23:28.777
And so I think that's what I'm interested to watch here is not so much the robot
00:23:28.777 --> 00:23:31.822
since more of the outcome. Well, and it's so much the
timing.
00:23:32.322 --> 00:23:35.793
Yeah, go ahead. I was going to say, if I may real quick, the examples you gave.
00:23:37.067 --> 00:23:40.081
Dusty and Rugged and, well, let's start there.
00:23:40.540 --> 00:23:42.984
They prove the limits of robots.
00:23:43.593 --> 00:23:47.977
Because if you think about it, they had to choose something where there's nothing there.
00:23:47.977 --> 00:23:53.227
Like the one time when people sweep the floor and clean it up and remove all
00:23:53.227 --> 00:23:57.697
the complications and so on. Like it's the one, it's the cleanest,
00:23:57.697 --> 00:24:01.449
most simple environment in, on a, on a construction site. Do you see what I mean?
00:24:01.838 --> 00:24:06.187
Like the fact that that's the one that's working illustrates the limits of,
00:24:06.187 --> 00:24:09.390
of robotics, at least as they are now, you know?
00:24:09.687 --> 00:24:12.747
And again, factories, the whole point is you can control the environment or
00:24:12.747 --> 00:24:15.567
even a fab shop in a way that you can't on a, on a job site.
00:24:15.567 --> 00:24:19.886
But anyway, just, it's really interesting. The ones that work illustrate why
00:24:20.043 --> 00:24:21.593
it's a, not a general solution. Yeah.
00:24:25.349 --> 00:24:29.519
Even in the fab shops, proving that a robot is going to support the work that
00:24:29.519 --> 00:24:33.619
you're doing almost always depends on measuring how much of the output that
00:24:33.619 --> 00:24:36.595
you're going to get, how much standardized output you're going to get on that one thing.
00:24:36.862 --> 00:24:40.147
Because if you still have to shift your line and do a lot of these,
00:24:40.420 --> 00:24:42.489
you have to kind of redesign what a robot looks like.
00:24:42.489 --> 00:24:46.289
It's still often much better to just keep people moving around.
00:24:46.289 --> 00:24:49.309
If you finally get to a place where you have enough demand, enough throughput
00:24:49.309 --> 00:24:55.723
on a piece, now it makes sense to put machine tending in or to roboticize and fully automate a line.
00:24:57.076 --> 00:25:02.829
It's interesting i'll throw in there the the humanoid angle of this if we play
00:25:02.829 --> 00:25:06.969
that out like let's say okay humanoids come into the industry and they're starting
00:25:06.969 --> 00:25:10.107
to you know walk around our job sites and do work for us,
00:25:10.719 --> 00:25:15.969
i think the the issues then flow back upstream to the planning side right which
00:25:15.969 --> 00:25:20.152
is okay we have to be able to tell the robots,
00:25:21.109 --> 00:25:26.689
with some level of clarity what they're supposed to do and so this idea of like
00:25:26.689 --> 00:25:31.149
oh we'll just beat it to fit paint it to match out in the field like robots
00:25:31.149 --> 00:25:36.018
are they they may do it but they're not going to have the intuition of a human to say,
00:25:36.690 --> 00:25:39.807
okay here's how we're going to beat it to
fit and paint it to match,
00:25:40.499 --> 00:25:44.589
as readily available i don't know maybe it'll change eventually but i see this
00:25:44.589 --> 00:25:45.559
kind of tagline for ugc tyler what's that.
00:25:47.789 --> 00:25:52.433
Beat it to fit, paint it to match. Oh, that's literally every subcontractor's.
00:25:53.869 --> 00:25:57.769
I mean, that's been the whole line for years in steel is like,
00:25:58.129 --> 00:26:01.385
we don't know, just send it out and we'll beat it to fit, paint it to match.
00:26:01.890 --> 00:26:04.773
Like that's kind of where we're at in some ways.
00:26:06.774 --> 00:26:09.924
And it's that, and it's that ability, usually the reason you're having to beat
00:26:09.924 --> 00:26:13.504
it to fit is because you had to build something before you actually really understood
00:26:13.504 --> 00:26:15.634
the site conditions or, right.
00:26:16.063 --> 00:26:21.834
And, and, and unless you start to, when, when we start to get to real-time data
00:26:21.834 --> 00:26:26.144
information and we're, and we're accurately comparing expected versus ad built
00:26:26.144 --> 00:26:28.404
as built on a real-time basis.
00:26:28.404 --> 00:26:32.954
And we're getting we have an information system that's capable of knowing exactly
00:26:32.954 --> 00:26:36.594
where the people are and where the robots are and what work needs to be done
00:26:36.594 --> 00:26:40.774
and and adjusting decisions and being able to make changes at
00:26:41.154 --> 00:26:47.244
a rate of 10, 10 changes an hour, because that's the kind of things that that
00:26:47.244 --> 00:26:49.104
that humans can do on a job.
00:26:49.995 --> 00:26:55.054
The robots and the humans are going to always be out of sync and reality is
00:26:55.054 --> 00:26:56.556
going to be out of sync with the robots.
00:26:57.411 --> 00:26:59.894
I mean, what we just described, though, is back to your point,
00:26:59.894 --> 00:27:04.348
Hugh, robots do really well when there's a lack of variability and when there's a clear plan.
00:27:04.753 --> 00:27:11.234
So it seems like residential is kind of the perfect environment for an increase
00:27:11.234 --> 00:27:17.204
in robotic kind of engagement, but maybe not the kind of R&D incentive.
00:27:17.383 --> 00:27:22.994
Well, I think it's also worth asking, if we assume, which we kind of just did,
00:27:22.994 --> 00:27:26.324
that they're not replacements for what a human can do, and I think Tyler put
00:27:26.324 --> 00:27:29.762
it perfectly about intuition and just ability to problem solve,
00:27:30.379 --> 00:27:36.074
does that not mean that the role that they play is not what a human would,
00:27:36.074 --> 00:27:37.374
but a slice of what a human would?
00:27:37.374 --> 00:27:40.234
And maybe things that you wouldn't normally pay a human for,
00:27:40.234 --> 00:27:43.754
like, I need you every night, go through the site, clean things up,
00:27:44.520 --> 00:27:46.064
check this, check that, whatever.
00:27:47.241 --> 00:27:50.587
You know, things, if you think about what we use AI for, even sometimes it's
00:27:50.587 --> 00:27:51.557
stuff a human would do with.
00:27:51.557 --> 00:27:54.486
Sometimes you're doing things that if I had, if I had, you know,
00:27:54.776 --> 00:27:57.577
budget, I'd have people do it, but I can have AI do it for me.
00:27:57.577 --> 00:28:01.377
So I feel like there's going to wind up being an opportunity to say,
00:28:01.719 --> 00:28:04.837
we don't have to look at what a human would do. We can say, what is something
00:28:04.837 --> 00:28:07.457
that I would have a human do if it were cheap?
00:28:07.937 --> 00:28:10.728
And now that it is, I can, I can have the robot do it.
00:28:11.059 --> 00:28:13.973
That may not be everything, but I think that's something even back to,
00:28:14.467 --> 00:28:20.617
if you think about what rugged and Dusty do, what they lay down on the slab
00:28:20.617 --> 00:28:23.150
is way more complicated than what it replaces.
00:28:23.638 --> 00:28:28.827
So it allows for things that a human wouldn't have done that helps the job in other ways.
00:28:29.164 --> 00:28:31.910
So I have a feeling that over time, you'll see some of that too.
00:28:32.217 --> 00:28:36.707
You know, you'll see that let's keep the robot away from live work and live
00:28:36.707 --> 00:28:40.385
building, but there's still an awful lot that goes into a job site that it can support.
00:28:40.973 --> 00:28:45.857
So what you're saying is that we might reduce counseling bills for people going
00:28:45.857 --> 00:28:50.637
forward for like you know my dad could have used a robot one day to get it to
00:28:50.637 --> 00:28:52.455
hold the flashlight in the right place,
00:28:52.943 --> 00:28:56.497
like we could have just used a robot for that and so then i'm not going to counseling
00:28:56.497 --> 00:29:00.917
as a result so i love this i think this is great that's so funny we're saving
00:29:00.917 --> 00:29:03.317
society over here yeah no not.
00:29:04.037 --> 00:29:08.677
There still one of these episodes it's gonna be like an episode of mari i'm
00:29:08.677 --> 00:29:12.717
gonna bring in papa campbell just like midway through the episode and just be
00:29:12.717 --> 00:29:15.117
like, Hey, Papa Campbell.
00:29:15.577 --> 00:29:20.695
I don't even know his actual name, but not Papa Campbell. That's for sure.
00:29:20.927 --> 00:29:25.497
Oh gosh. All I hear are great stories that need to be brought to the fore of your youth.
00:29:26.457 --> 00:29:28.627
How come no one's seen Tyler for a couple of weeks? I don't know.
00:29:28.977 --> 00:29:31.137
I have no idea. I was ever against that podcast.
00:29:33.522 --> 00:29:40.912
All right. So from robots to maybe AI, let's talk about we've got adoption of
00:29:40.912 --> 00:29:45.002
AI in different levels throughout the industry, but at an individual level,
00:29:45.002 --> 00:29:47.022
it's happening. We've seen that, right? I think so.
00:29:47.782 --> 00:29:52.222
E&R
back in October kind of went, I think through a survey and a couple other
00:29:52.222 --> 00:29:56.932
means, went out to insurers and brokers and kind of asked how the adoption of
00:29:56.932 --> 00:30:00.634
AI was potentially influencing different exclusions that were showing up in
00:30:00.745 --> 00:30:02.178
construction liability policies.
00:30:02.746 --> 00:30:09.582
And the answer was not yet. I guess there were some professional liability policies that were being,
00:30:10.182 --> 00:30:12.472
and the professional liability policies were not showing up,
00:30:12.472 --> 00:30:18.302
but the general liability, at least according to Gallagher, some exclusions started to appear.
00:30:18.302 --> 00:30:25.616
So I think the question is, Hugh, if your team uses AI to save a bunch of time,
00:30:26.245 --> 00:30:30.563
and that leads to maybe a multi-million dollar mistake...
00:30:31.279 --> 00:30:35.472
How do we adjudicate that mistake in today's reality?
00:30:35.756 --> 00:30:40.505
Yeah, I don't think that's even a question. And I'm glad you bring it up because
00:30:40.505 --> 00:30:44.436
it is a question people ask. But we have to keep in mind, these are just tools.
00:30:44.881 --> 00:30:48.435
As smart as they sound, these are just tools. So it's still the human's job.
00:30:48.435 --> 00:30:52.869
It's still the PM's job. It's still the company's job. I mean, a funny analogy.
00:30:53.171 --> 00:30:59.635
There was two court cases recently about whether if I'm talking to ChatGPT or
00:30:59.635 --> 00:31:04.295
whatever, in prep for a meeting with my lawyer, is it covered,
00:31:04.295 --> 00:31:06.490
does that covered by attorney-client privilege?
00:31:07.053 --> 00:31:10.315
Which if you think about it is a really weird question to ask.
00:31:10.315 --> 00:31:14.385
It's like, well, if I was writing notes in, I don't know, in Microsoft Word,
00:31:14.705 --> 00:31:15.836
yeah, it wouldn't be covered.
00:31:16.405 --> 00:31:21.395
You're just using a digital tool, right? But the fact that these AI can kind
00:31:21.395 --> 00:31:24.195
of sound like a human sometimes, it throws people off.
00:31:24.195 --> 00:31:27.556
And the crazy thing is one of the, these two cases, one of them,
00:31:27.910 --> 00:31:31.817
the judge found that, yes, It is covered because it's like you were talking to a human.
00:31:32.159 --> 00:31:35.675
That's insane. And I can't imagine that's going to survive. The other one,
00:31:35.675 --> 00:31:38.635
the person said what I just said, which is, in fact, I'm taking what they said.
00:31:38.635 --> 00:31:41.476
This is like using Microsoft Word. So back to this.
00:31:41.859 --> 00:31:45.425
At the end of the day, just because AI was involved doesn't absolve anybody
00:31:45.425 --> 00:31:49.275
of any responsibility. And if anything, you know, hopefully there won't be big
00:31:49.275 --> 00:31:53.562
court cases. At some point, something will happen because it's construction and stuff happens.
00:31:54.195 --> 00:31:57.495
Hopefully it's not too big a deal and no one gets hurt. But you're going to
00:31:57.495 --> 00:32:02.275
wind up having examples of people thinking that, you know, they let it,
00:32:02.275 --> 00:32:03.565
they didn't check the AI.
00:32:03.565 --> 00:32:08.260
And I think it's so easy to forget that it is still imperfect.
00:32:08.260 --> 00:32:12.445
The whole reason it works is because it can handle kind of probabilities.
00:32:12.445 --> 00:32:14.285
Well, you pay for that on the way out.
00:32:14.616 --> 00:32:18.807
And I think as people are learning to be vigilant, I see a lot of people that are not.
00:32:19.022 --> 00:32:23.635
I see a lot of people that are enamored with how great what comes out of it
00:32:23.635 --> 00:32:25.065
can be and they don't check it enough.
00:32:25.506 --> 00:32:30.158
So yeah, to me, there's not even a question there. And I think hopefully,
00:32:30.709 --> 00:32:35.028
certainly owners of contractors are not under that impression.
00:32:35.764 --> 00:32:35.868
But,
00:32:37.908 --> 00:32:40.718
this is really a question about the insurance companies, though,
00:32:41.873 --> 00:32:49.618
and either how they can create ambiguity in this conversation or reveal preferences
00:32:49.618 --> 00:32:51.258
and actually understand what's really going on.
00:32:51.258 --> 00:32:58.104
Because an insurance company's biggest problem isn't a couple of one-off cases, even severity.
00:32:58.562 --> 00:33:02.928
Their problem is correlation. Are we going to start seeing a bunch of common
00:33:03.248 --> 00:33:09.789
mode failures happening that we could go in and potentially deny.
00:33:11.594 --> 00:33:15.198
Because these teams are relying too much on AI?
00:33:15.198 --> 00:33:20.188
So, yes, there is a conversation here to be had about how we treat it and how
00:33:20.188 --> 00:33:21.207
we look at it and how we...
00:33:22.223 --> 00:33:28.700
How we babysit it basically the but the bigger question is is how are insurance companies going to,
00:33:29.680 --> 00:33:32.809
find ways to get out of some of these dno claims,
00:33:33.634 --> 00:33:37.240
yeah i mean are you saying insurance companies do that aaron i well they're
00:33:37.240 --> 00:33:41.670
more pressed to think of yeah i mean there's a great point there's kind of three
00:33:41.670 --> 00:33:45.460
things right which is like if you are an ai first company and you're automating
00:33:45.460 --> 00:33:46.753
the crap out of everything,
00:33:47.217 --> 00:33:52.680
insurance companies probably need to completely change how they approach the work that you're doing,
00:33:53.515 --> 00:33:58.291
and they don't have an actuarial approach to that yet the other thing is this like,
00:33:59.220 --> 00:34:02.860
ai ai can't really be defined today without going,
00:34:03.540 --> 00:34:07.050
with a fine tooth comb through everything that's happening right because like
00:34:07.050 --> 00:34:11.320
you said like when you when
we were introducing ourselves you we've been building
00:34:11.720 --> 00:34:16.640
machine learning tools and ai tools and you know Essentially, it's stochastic,
00:34:17.540 --> 00:34:19.618
prediction about something, right?
00:34:20.920 --> 00:34:25.855
That exists. Honestly, the software that contains the most of that in construction today is CAD.
00:34:26.070 --> 00:34:30.580
And just in how it finishes lines and how it connects, you know,
00:34:30.580 --> 00:34:32.816
there's machine learning all over that tool.
00:34:33.188 --> 00:34:38.568
So what it's really doing is it's creating where it's shifting the cost of litigation
00:34:38.835 --> 00:34:40.880
back to the contractor and,
00:34:41.413 --> 00:34:48.222
the professional to prove and to actually litigate what AI means. Hmm.
00:34:49.952 --> 00:34:55.826
I mean, Tyler, you are knee-deep in helping contractors, one,
00:34:55.826 --> 00:35:01.410
make sense of their data, building AI kind of centric workflows.
00:35:01.874 --> 00:35:06.256
How much of this, two questions, how much does this go into how you orient your
00:35:06.256 --> 00:35:09.926
contract or contract with those contractors? And also, how are you setting them
00:35:09.926 --> 00:35:14.539
up to maybe protect themselves? Or has this kind of come up in any of those conversations yet?
00:35:15.062 --> 00:35:17.476
I don't know, man. And I've been working through this a lot because like,
00:35:18.196 --> 00:35:20.579
I think, yeah, the accountability component.
00:35:21.556 --> 00:35:26.736
I've been wondering if we're going to find ourselves contracting people out
00:35:26.736 --> 00:35:31.323
more and more instead of just having like these massive teams of W-2 employees.
00:35:31.857 --> 00:35:34.127
Because like, think about it. If I come work for your company,
00:35:34.464 --> 00:35:39.905
I'm transparent and I'm saying, hey, listen, I use AI tools to get the job done. Then I screw up.
00:35:40.389 --> 00:35:44.406
Right. I mean, it's pretty easy to cast blame on me at that point.
00:35:44.592 --> 00:35:48.836
And so like, hey, it was Tyler, it was his team, and he was the problem here.
00:35:48.836 --> 00:35:51.208
He didn't check his stuff before he implemented it.
00:35:51.748 --> 00:35:55.546
So I almost wonder if there's that lens of like people are going to start doing
00:35:55.546 --> 00:35:57.025
more contract type labor.
00:35:57.906 --> 00:36:01.814
We all have like professional liability in our sub. Yeah, I don't know.
00:36:02.534 --> 00:36:08.844
But then also with a W-2 worker, I think there are two angles of this.
00:36:08.844 --> 00:36:13.384
There's like the AI that you're using day to day that you're chatting with,
00:36:13.384 --> 00:36:16.064
right? Like you're talking to and collaborating with.
00:36:16.517 --> 00:36:22.055
But then there's also the agentic AI, which is going out and doing things proactively.
00:36:22.328 --> 00:36:29.903
And that's, I feel, the biggest fear within this story here
is the agentic side,
00:36:30.275 --> 00:36:37.074
the proactive AI that nobody is really saying yes to or no to.
00:36:37.074 --> 00:36:41.274
So I don't know, man. It's just kind of an odd spot.
00:36:42.514 --> 00:36:46.764
I was just interested to hear what Hugh and Aaron had to say about this topic,
00:36:46.764 --> 00:36:50.494
too, because I think everybody's kind of going, it's kind of up in the air right now.
00:36:51.874 --> 00:36:54.849
I think that if I can tee off of some of what you just said,
00:36:55.121 --> 00:37:00.234
the idea that you have an agent that completes something, I don't think anybody's
00:37:00.234 --> 00:37:02.684
ready for it. And I don't, I know the technology is not ready.
00:37:02.684 --> 00:37:04.094
I don't care what vendors say.
00:37:04.454 --> 00:37:08.554
It's just not what, what you, the better way of thinking about it is our,
00:37:08.954 --> 00:37:11.194
an agent will automate up to 80%.
00:37:11.194 --> 00:37:13.934
Like it gathers things, it drafts things, it pulls things. I mean,
00:37:13.934 --> 00:37:16.981
there's stuff like, you know, let's have an agent that pulls all the,
00:37:17.498 --> 00:37:21.576
you know, details from, from invoices. I mean, you know, stuff like that. Sure.
00:37:22.029 --> 00:37:26.264
But, but, you know, you come down to the question of, can I create a checkable standard?
00:37:26.264 --> 00:37:30.411
Can I verify it? And so little is actually verifiable. So as a result,
00:37:30.754 --> 00:37:33.743
before you even get into the conversation of who's liable for what,
00:37:34.074 --> 00:37:36.134
it's just not something you can reliably automate.
00:37:36.134 --> 00:37:42.094
So I think this, this change and talking about, you know, agents as if they
00:37:42.094 --> 00:37:45.484
are going to complete things, A, I think overstates what they can do,
00:37:45.484 --> 00:37:49.886
but also is a great way to freak people out, as opposed to saying the stuff you hate doing.
00:37:50.537 --> 00:37:54.512
That allows you to do your thinking. Like that's where AI helps is it brings
00:37:54.512 --> 00:37:57.392
it together. Maybe it drafts the thing, maybe it gives you a version or,
00:37:57.392 --> 00:38:01.322
or, and you know, a take or a critique or whatever, but ultimately it has to
00:38:01.322 --> 00:38:03.572
leave a human's hands for, for most.
00:38:03.892 --> 00:38:07.179
And I think we also underestimate how much judgment people bring to things.
00:38:07.806 --> 00:38:10.942
I think you're right here.
00:38:10.942 --> 00:38:13.762
I think the judgment thing is there, but you know, I've, I've,
00:38:13.896 --> 00:38:19.682
there's these things that databases can do in terms of mutual exclusivity and
00:38:19.682 --> 00:38:23.492
collective exhaustiveness that humans are incapable of doing.
00:38:23.492 --> 00:38:28.722
We're great at judgment, but when it comes to massive amounts of information
00:38:28.722 --> 00:38:33.667
and sorting through huge complex scenarios, we aren't that good at it.
00:38:33.772 --> 00:38:38.162
And it's possible that if we actually get more discreet with some of these things,
00:38:38.162 --> 00:38:42.857
and we're not just talking about, you know, judgment scenarios,
00:38:43.147 --> 00:38:46.456
that the machines are going to be way better than us at it.
00:38:46.891 --> 00:38:49.822
It's just most of the inputs aren't that good. That's, that's been to your,
00:38:49.822 --> 00:38:51.172
maybe that's what you meant by disagree.
00:38:51.172 --> 00:38:54.602
That's exactly, that's exactly my point is that most of the inputs aren't that
00:38:54.602 --> 00:38:59.432
good, but if the inputs were actually digital and immutable and, and,
00:38:59.792 --> 00:39:04.372
you know, we had software that wasn't destructive and had traces and logs and
00:39:04.372 --> 00:39:07.104
all that kind of stuff, the inputs would be a lot better. Yeah.
00:39:08.219 --> 00:39:13.293
So I think one of the challenges with the kind of human in the loop model,
00:39:13.293 --> 00:39:17.393
other than that it's maybe dismissive of our humanity, is,
00:39:18.193 --> 00:39:22.573
when you have outputs that are plausible, which these AIs produce,
00:39:22.573 --> 00:39:25.918
agents produce, I think, to maybe your point, Aaron,
00:39:26.551 --> 00:39:30.633
often the human is kind of not really doing a proper review.
00:39:30.633 --> 00:39:34.039
Or there hasn't been definition of what proper review means.
00:39:34.527 --> 00:39:40.833
And so I think maybe, Aaron, put yourself in the seat of you're managing a large GC.
00:39:41.917 --> 00:39:46.243
As you see kind of AI adoption happening at individual level,
00:39:46.243 --> 00:39:52.653
at project level, how are you maybe carrying this conversation with your insurance provider?
00:39:52.653 --> 00:39:57.532
Are you being more proactive internally or trying to put the hedges in place externally?
00:39:59.523 --> 00:40:05.543
I mean, I definitely think as a large GC, you have the benefit of working with
00:40:05.543 --> 00:40:07.573
your insurance provider.
00:40:08.013 --> 00:40:12.603
And that's a big part of this, right? Because the whole thing that the insurance
00:40:12.603 --> 00:40:15.203
provider wants to know is what's actually going on.
00:40:15.203 --> 00:40:18.553
And most of the time they're putting these governance docs out there and they're
00:40:18.553 --> 00:40:23.663
doing this stuff in the renewal and the disclosure forms because they haven't
00:40:23.663 --> 00:40:26.993
been able to build a statistical model. They haven't been able to see what's
00:40:26.993 --> 00:40:29.605
happening to understand where the risks are, right?
00:40:29.964 --> 00:40:36.033
And so if you can cooperate with the insurance company now, everybody's afraid of what happens.
00:40:36.033 --> 00:40:41.123
You know, we start putting the little trackers in cars and pretty soon our rate
00:40:41.123 --> 00:40:44.303
doesn't come down, but everybody else's
goes up and, you know,
00:40:44.303 --> 00:40:49.274
just the whole it's there's definitely a lot of competition things to consider there.
00:40:50.506 --> 00:40:56.476
But yeah, if you can, if you can reveal the right information at the right time
00:40:56.476 --> 00:41:01.562
and work with them, both, both parties are going to make it work.
00:41:02.019 --> 00:41:05.096
Yeah. Cause they want, they want to see your business grow. You know,
00:41:05.096 --> 00:41:09.576
they want to see the business grow. They want to see efficiency and productivity
00:41:09.576 --> 00:41:14.673
come to the business because it ultimately means more work for them too. Yeah.
00:41:15.729 --> 00:41:18.466
We're running out of time, but I do want to get to this next one.
00:41:18.466 --> 00:41:24.756
I think, so input prices rose 11.2% year over year.
00:41:25.354 --> 00:41:29.076
And I think that's no surprise. I think, you know, you go to a grocery store,
00:41:29.076 --> 00:41:31.896
you talk to your wife, like everything's getting more expensive.
00:41:31.896 --> 00:41:35.727
And there's, you know, there's the Iran war, there's some tariff tailwinds.
00:41:36.193 --> 00:41:40.286
I think that's not the surprising part. I think the interesting part of this
00:41:40.286 --> 00:41:45.620
kind of ABC analysis reference that one in four contractors,
00:41:45.968 --> 00:41:47.986
as a result, expect lower margins.
00:41:47.986 --> 00:41:51.036
And I think there is a narrative out there that when prices go up,
00:41:51.292 --> 00:41:57.131
those prices are, that increasing cost is passed on to the customer, in this case, the owner.
00:41:57.709 --> 00:42:01.076
But at least 25% of the contractor community doesn't think that.
00:42:01.453 --> 00:42:04.286
I guess maybe to start a variety of this conversation...
00:42:05.432 --> 00:42:08.329
Why do we think that isn't always just passed on to the owner?
00:42:08.789 --> 00:42:14.599
Is there a cap on some of these verticals in terms of what we can charge from
00:42:14.599 --> 00:42:15.869
like a square footage standpoint?
00:42:15.869 --> 00:42:23.419
Or are these contractors who are seeing a squeeze in their margin not incorporating
00:42:23.419 --> 00:42:28.179
the appropriate business model response to increased costs? Do we have any hypothesis
00:42:28.179 --> 00:42:29.946
there? Aaron, you want to start us off?
00:42:30.949 --> 00:42:35.189
Well yeah i mean we spent 20 years before you know up up until what basically
00:42:35.189 --> 00:42:40.506
up until covid we spent 20 years in basically a low low inflation environment
00:42:40.936 --> 00:42:45.369
yeah maybe 25 years right where business models were like were,
00:42:46.069 --> 00:42:50.309
not thinking about escalation unless they were just in the annual union,
00:42:50.909 --> 00:42:55.609
labor escalation scenario right and so i think there was some tools that were
00:42:55.609 --> 00:43:02.016
lacking and And then obviously, in COVID, it was like everything was this force majeure conversation.
00:43:02.623 --> 00:43:07.129
And then finally, now we're having to actually put these kinds of cost escalation
00:43:07.569 --> 00:43:13.620
clauses or some kind of mechanism into the business to reclaim that.
00:43:14.788 --> 00:43:18.693
I think the real number here is that those inputs are up 11.9%,
00:43:19.099 --> 00:43:24.018
but over the same period, bids are only up about 5.4%.
00:43:24.018 --> 00:43:32.108
And so there's really about a 6.5% wedge or leakage in this number.
00:43:33.688 --> 00:43:36.028
And I don't know if it has to do with where it's coming from.
00:43:36.028 --> 00:43:38.848
It may be that a lot of it's coming from diesel fuel.
00:43:40.328 --> 00:43:45.828
Some long lead times, unexpected escalations that are beyond what was already
00:43:45.828 --> 00:43:49.578
in a clause, and it's gotten into a point of sharing.
00:43:49.875 --> 00:43:54.318
So a lot of this really has to do with, like, are you structuring your escalation
00:43:54.318 --> 00:43:59.288
clauses in a way that is actually consistent with what's going to happen in the market?
00:43:59.288 --> 00:44:04.978
And I think owners are probably just more financially savvy about this stuff
00:44:04.978 --> 00:44:06.686
and about the range of risks.
00:44:07.310 --> 00:44:11.140
The other thing that happens here is the question about whether or not trades
00:44:11.394 --> 00:44:16.642
and GCs are both aligned in the way that they manage the escalation causes.
00:44:17.810 --> 00:44:22.768
That was actually going to be my question is, I think, drilling into what's
00:44:22.768 --> 00:44:23.876
under a number like that.
00:44:24.027 --> 00:44:28.628
Like the problem always with numbers in construction is it covers an awful lot of different things.
00:44:28.628 --> 00:44:32.498
And I wonder if the leakage is happening on the trade side more than it is on
00:44:32.498 --> 00:44:36.386
the GC side, and if there's specific trades more than others where,
00:44:36.980 --> 00:44:40.628
you know, there's either something unexpected or they tend to be small companies
00:44:40.628 --> 00:44:42.028
because of the type of trade it is.
00:44:42.468 --> 00:44:47.118
I just, I wonder a little bit about what's, I mean, if it's a number that big,
00:44:47.118 --> 00:44:49.518
it's reflecting a lot of companies. So it can't just be like,
00:44:49.518 --> 00:44:51.052
you know, only the drywall guys.
00:44:51.441 --> 00:44:56.015
But nevertheless, it'd be interesting to understand what dynamic is going on under it.
00:44:56.335 --> 00:44:59.748
And where that squeeze is coming from, right? Like the squeeze could be coming
00:44:59.748 --> 00:45:03.948
from the fact that a bunch of residential firms are now competing much more,
00:45:04.448 --> 00:45:09.705
much more aggressively for, for work or, you know, all the trouble in the,
00:45:10.348 --> 00:45:13.415
in the other commercial and multifamily markets, right?
00:45:14.804 --> 00:45:18.389
That's a good point. Yeah, that was going to be my flag here is,
00:45:19.075 --> 00:45:24.604
you know, if we're looking at our margins getting pressed, it has to be,
00:45:24.964 --> 00:45:27.929
yeah, I mean, the competition has to be driving some of this.
00:45:28.376 --> 00:45:30.310
It's just a very competitive marketplace.
00:45:30.855 --> 00:45:34.344
And then they're just trying to get the next job, right?
00:45:34.344 --> 00:45:38.814
So I'm going to lower my prices in order to get the next thing,
00:45:38.814 --> 00:45:42.721
because if I don't have something, then I'm going to go out of business,
00:45:43.145 --> 00:45:48.404
right? So it's better to have something that I can work with than nothing at all. So,
00:45:50.776 --> 00:45:55.864
especially if the pressure is labor, right? I mean, keeping your bench alive
00:45:55.864 --> 00:46:00.041
is one of the hardest things that a trades contractor does.
00:46:00.395 --> 00:46:04.374
Well, okay. On that too. So if we look at the hyperscalers and kind of back
00:46:04.374 --> 00:46:07.174
to what we were talking about earlier, if these younger kids are able to go
00:46:07.174 --> 00:46:11.673
out and get a job there and they're getting primo money for that,
00:46:12.298 --> 00:46:15.844
like the expectation goes up so yeah as a result if you're not changing your
00:46:15.844 --> 00:46:20.887
prices then you're getting like just that compression that's gonna naturally occur there,
00:46:21.524 --> 00:46:25.374
a corollary might be it'd be interesting after the fact to see if change orders
00:46:25.374 --> 00:46:29.824
went up like we lost it in the bid and we made it up later because that never
00:46:29.824 --> 00:46:31.515
happens in construction so who knows.
00:46:32.504 --> 00:46:36.564
I mean the study i'd like to see then maybe i'll submit this to you aaron because
00:46:36.564 --> 00:46:41.295
you have like a you have a monster computer that's just churning through economic data, I believe.
00:46:41.446 --> 00:46:49.772
But the data I'd like to see is what's happening to, let's just focus on builders of data centers.
00:46:50.137 --> 00:46:53.234
Is there margins going up over the last 10 years? Because it should.
00:46:53.234 --> 00:46:55.895
If it's not, that's probably a problem, right? And,
00:46:56.867 --> 00:47:00.478
I mean, simple economics as demand rises. Yeah.
00:47:00.838 --> 00:47:05.398
Well, there's some confounding factors that are going on there because a lot
00:47:05.398 --> 00:47:08.629
of those builders also have brought on a lot more self-performed labor.
00:47:09.203 --> 00:47:14.323
So absolutely, most of those top tier GCs, their margins are increasing.
00:47:14.665 --> 00:47:19.378
But what's interesting is their margins on self-perform are actually lower than
00:47:19.378 --> 00:47:23.304
the trades contractors would have had in that context.
00:47:23.722 --> 00:47:26.848
And that makes sense because they're right at the learning curve.
00:47:26.848 --> 00:47:28.428
They're coming into a new business,
00:47:28.428 --> 00:47:30.938
that kind of thing. They don't know how to price for it the same.
00:47:31.758 --> 00:47:35.098
I mean, I can imagine some of that, right? And they don't know how to plan for it either.
00:47:37.236 --> 00:47:43.599
When you don't have that sort of natural tension between the trade and the GC, the GC wins.
00:47:43.750 --> 00:47:47.948
And so the GCs for a lot of these self-performing, now not in every case,
00:47:47.948 --> 00:47:52.058
but in a lot of cases, the GC actually lowers the margin of the trade because
00:47:52.058 --> 00:47:56.187
they're forcing them into bad schedules and they're forcing them to cover for
00:47:56.496 --> 00:47:58.718
like, right. I mean, they just basically become a relief.
00:48:01.118 --> 00:48:04.101
Yeah. I feel like that's a classic one.
00:48:04.269 --> 00:48:10.388
It's like always the low bid, but why, why is it? Yeah.
00:48:11.318 --> 00:48:15.408
Well, we're out of time guys. I've enjoyed this. We missed two stories, but I'm impressed.
00:48:15.408 --> 00:48:20.878
We got through four hugh where can people find you linkedin tiktok yeah link,
00:48:21.418 --> 00:48:26.551
not tiktok linkedin is great my name you see okay aaron,
00:48:27.450 --> 00:48:31.419
Yep. You can find me, you can find me actually find me on LinkedIn.
00:48:31.419 --> 00:48:36.111
And then also you can see what we're doing at Lou L U H dot tech,
00:48:36.587 --> 00:48:41.523
or you can also find me at the Asher hub.com.
00:48:42.331 --> 00:48:46.119
The Asher hub. That's a cool name too, man. You guys got a corner market for
00:48:46.119 --> 00:48:48.209
cool names. Tyler. What was you?
00:48:48.759 --> 00:48:53.039
I have one called a AI edge dot construction. That's that's so since we're giving
00:48:53.039 --> 00:48:54.619
out websites, that's a good name.
00:48:54.822 --> 00:48:57.279
No, you can catch me on Broadway.
00:49:00.419 --> 00:49:05.619
Short run yeah very short run this isn't i don't i don't think it's not meant
00:49:05.619 --> 00:49:09.119
to be mean but i could actually see you doing broadway i could see you doing
00:49:09.479 --> 00:49:13.879
some quantity of drama what are you saying i'm dramatic what are you saying that,
00:49:15.299 --> 00:49:16.278
way,
00:49:17.892 --> 00:49:21.779
you can find me on linkedin broadway you can find me on linkedin you can find
00:49:21.779 --> 00:49:27.979
me at fieldproof.com all one word field proof is a great website i highly recommend
00:49:27.979 --> 00:49:32.699
field proof it's a new it's a new website too so go check it out it's got animations all right guys,
00:49:33.318 --> 00:49:36.745
no more tyler that's enough self-promotion and another thing check,
00:49:38.419 --> 00:49:40.738
it i just end the recording all right thanks guys.