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Welcome to another episode of Data Driven where we put the hard hat on

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data and get our hands digitally dirty. Today, Frank

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dives into the world of construction. Yes, actual buildings with

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Amir Berman, VP of industry transformation at

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Builderts. If you thought construction was all bricks and

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backaches, think again. Amir reveals how computer vision

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and data analytics are transforming job sites from chaos to

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code. Think. Fewer delays, more precision, and slightly less

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swearing at blueprints. So grab your virtual safety

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goggles because this episode builds a strong case for AI in

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hard hats.

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Hello, and welcome back to Data Driven, the podcast where we

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explore the emergent fields of data science, artificial

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intelligence, and of course, without data engineering, really not going

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to get very far. And speaking of data

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engineering, my favorite data engineer is not able to make the call today,

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but we've already scheduled this poor guest a couple of times and I don't want

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to push it back another time. So it's just going to be me

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today welcoming Amir Berman,

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VP of industry transformation at Bill Dots. And

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this is going to be really cool because it's really about. He has

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a passion for digitally transforming the

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construction industry. Now, I know the term digital transformation has

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probably left a bad taste in some people's mouth, but I think there's real opportunities

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in the construction space to leverage

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tools from as mundane as predictive maintenance all

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the way to fancy computer vision stuff.

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Welcome to the show, Amir. Thank you. Thanks for having me.

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Cool. And if you, you know, we're going casual today. If you're

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watching this on video, we're both kind of in. One's in a black shirt, one's

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in a gray shirt. And I kind of joked, like, too bad this isn't like

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a hacker call. Like, you know, gray hat, black hat, Andy

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could show up with the white hat. But. But I digress.

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So I remember seeing a video

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from, like, build 2017, 2018

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at Microsoft, big Microsoft conference, where they showed a construction site in

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computer vision where it basically said, you know, hey, where's the

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jackhammer? Oh, jackhammer's here and it's in a dangerous position.

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It's about to fall down. Or Tommy picks up

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the jackhammer and he's not authorized to do it. It'll send an alert to the

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construction manager and it actually sends an SMS and it becomes this

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whole chat thing. Keep in mind, this is pre, like, chatgpt big

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bang moment. Tell me, how far away is that

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vision? You're nodding along, so you may have seen

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this demo. So how far away is the vision of

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a smart construction site? I would say it's pretty

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close because we're practically there. Like you know, for some

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of the audience, I'm pretty sure it's gonna sound like a sci fi

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movie. But bear in mind that

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for once, construction is probably one of the biggest and the

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wealthiest industries out there. I mean if there's like a cool

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tech out there, you've got to be sure that it's being used

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or has been used in this industry. Like personally, I remember

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I've been dealing with augmented reality for

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construction sites back in 2016, I want to say

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so even then. And we were not alone. I mean we were like a few

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startups back in, back in 2016 or 2015,

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I want to say 2015. And we were developing

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augmented reality apps for the job site and

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practically Microsoft was one of our design partners back then

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randomly. So if

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it's, if it's sci fi or if it's like today.

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So it is pretty much like the, the present

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I would say. But I also, but I also think though, like construction

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sites I think are also the ultimate kind of

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test bed for technology. Right. Like you, you're in

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these if you have to wear a hard hat.

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Clearly, clearly it's a, it's a rugged, you have to have a ruggedized

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equipment. You have to have, it has to be reliable. Right. Because

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if, if the system goes down. Right. You have an entire crew of people that

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are billing but not working. Yes.

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So it has to work. Right. So like that's always, I think has that been

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a tension between like, you know, we have this augmented

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reality technology and I understand, I remember seeing the demos too.

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We're probably have seen a lot of the same kind of marketing

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material. Right. Where you know, you put on the headset and like this is where

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the pipes are going to go and this is where the wall's going to go.

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So you know, whoever's on site saying like, oh well you know, we need to

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adjust this, how do we adjust this? But I also

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know like, you know, it's always cool to

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have the new gadget, but that gadget has to work. And it seems like

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construction could be a high pressure environment.

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Yeah. Now whenever we talk about construction, I mean

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people have like tons of different kind of examples running through their head.

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Anything like I've buil a shed. Like

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personally I can guarantee you that I've not built a shed

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that's not in my sweet spot. But whenever we talk about,

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you know, construction. So people have those all sorts of different examples.

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It start by building a shed or like we renovated our house

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or you know, three stories high kind of building

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somewhere downtown, all the way to 40 story

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high, you know, hotel, you know, let's say Austin or

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a data center which is like, I know 2 million square foot or an oil

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rig. Construction is pretty, pretty vast. So

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the cool thing about construction is that the cost

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of running a construction operation is so high, it's like

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ridiculously high. People don't get it like how prices like construction,

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especially like the major projects and at the same time these

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guys and these companies are running like razor thin margins, you

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know, would you, do you want to take a guess? Like what's the margin on

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construction project? I guess it would

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depend on who it is. If it's the real estate developer versus

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the contractor that's pouring concrete versus the guy that's doing the electrical

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or the girl that's doing the plumbing. But I would say, I would say

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probably on a low end, probably like maybe 2%, 1%.

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You're freakishly kind of accurate. I would say like if you're a

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contractor, like a top tier contractor that does like you know, a major

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construction, you're looking at single digits. It really depends on the

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continent, like in the States versus like Europe versus APAC and so on.

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But you're looking at single digits like and if you're saying like let's say

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that we're building a half a billion dollar like

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healthcare facility, right. So 3%

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margins means that you don't have a lot of leeway for R and D.

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Right. That's fair bearing mind. So you have like folks which are like the most

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talented, most devoted people I've ever met. This is like the best industry to

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work for in my opinion. Personally. People are devoted,

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people are like mission driven people like you know, salt of the earth.

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But at the same time, you know, no matter like how good and how solid

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your technologies, you have very little opportunity to prove it

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to them. That's true. Yeah.

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The margins are that thin. Like you have to have a solid story,

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right. Like I don't know what the final price of the HoloLens was, but it

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was something like three, $4,000. Yeah, right. And if I'm, I

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mean if I'm on, if I'm talking to a business owner that has a single

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digit, you know, profit margin number, let's say 2%.

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I have to come in with a really good explanation

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of you buy this and you're not just buying one, right?

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You buy this, it's going to save you X amount of

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money. Yeah, yeah, right. It's you need to

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come with a few things. First of all, at some point we'll need to probably

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educate our, you know, our audience because we're not doing augmented reality.

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You know, we're doing something completely. Right, right, right. I'm just, I don't want to,

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I don't want to get fixed. I don't want to fixate on that. But. No,

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no, no, don't worry, don't worry. I just wanted to make sure that the audience

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are not meeting us instead of the. In case. But I mean, I, I would

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imagine that, I guess that depending on what solution you're selling. Let's,

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let's. Sorry about that. This is what happens, kids, when you have too much

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coffee in the morning.

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I mean, obviously predictive maintenance is probably

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an easy sell for the construction industry.

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I don't know if it's an easy sell. Like, nothing is easy.

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Nothing is easy. Let's go back a few steps. So we said

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it's like a high volume kind of, you

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know, monetary wise. Like it's, it's capital dense, right?

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Margins are super low and all the capital in

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constructions are in within construction projects.

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I mean, headquarters do not have a lot of money, not a lot of capital.

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Why is that? Because all of their capital projects are yielding like low

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margins, you know, let alone like, we're not talking about developers. Developers are doing

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a whole different kind of ballgame. But let's say that you're a general contractor,

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top tier general contractor in the states. You don't have a lot of, you know,

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free money to throw an R and D. And at the same time, because

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construction is such a vast and, you know, major

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industry which has that kind of vibe of

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being late to the party, even though it's not late for the party. From tech

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stack perspective, it means that if I'm coming from a

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contractor side and if I'm the person, you know, if I'm the CIO

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or the person responsible for developing and implementing technology,

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I'm being bombarded by people pitching me constantly.

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So it's not a case where the industry is underserved, but we need

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to have that responsibility as technology vendors that whenever we're

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hitting the market with something, we need to be responsible and respect

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the fact that the other side doesn't have a lot of margin

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to invest in R and D. They do not have a lot of time. They

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need to deliver project asap. So it means that we need to come to the

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market really, really mature and we need to make sure that our

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solutions actually work. And when they do it's terrific. It's

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like magic. It's amazing. Right. I think of that old

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triangle, you know, good, fast and cheap. Right. Like yeah,

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time and money are both constraints, it sounds like in the construction industry.

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So it has to be good, right?

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It has to be good. Money is not always an issue. I

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mean there's some money to invest just because capital

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is huge and the opportunity to gain something

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is vast. I mean if you can take a gc, like a gc, sorry for

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the audience, short for General Contractors.

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So if you're taking a GC and you can kind of help them pave

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the way to break away from the single digits like

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margin, the opportunity is endless. I mean

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those companies are making billions of dollars in revenue, not,

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not profits revenue. So if you can turn like a, let's

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say theoretically take a 5 digit, a 5,

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5% margin company and make it like a 6 or 7%,

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that's, that's tremendous. They're going to be leaders.

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Huge. Yeah, they're going to be leaders in their industry with that type of,

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you know, so, so

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our decisions in the field obviously built a

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construction. So I, I had done some home renovations. My wife is always

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knocking down walls or doing something. So I kind of know

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I would not call myself a construction expert but when we did call in somebody

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to build on an addition to our old house,

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I saw how much that would cost and it was, it was only three stories,

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right. It wasn't like a, you know, a skyscraper or, or data center which I

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would imagine data centers are completely different animal in a lot of

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ways. But are decisions based on

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intuition, right? Because somebody, somebody has a plan,

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right. They have the blueprint, right. And the blueprint seems like,

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you know, if everything works out perfectly but where the rubber meets the

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road, so to speak, is going to be on, on the job site.

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So I mean I would imagine that a lot of the decisions

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historically have been like, you know, the foreman

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or the GC

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superintendent has kind of like an intuition. But like are there

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ways to use data, capture data and make the

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decisions, you know, know where the

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problems are going to be as well as making it more, less intuition based

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and more data, data, dare I say data driven

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type approach. What sorts of tools are there for that?

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Now I think you're hitting the nail on the head because like, you know,

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I think it was like one of my last flights. The reason we

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postponed the, you know, the episode from earlier this week because I caught

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yet another fluke which I'm constantly catching on planes came

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back from London And I think it wasn't that flight, but the previous flight I

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read Thinking fast and thinking Slow. Have you read?

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Yeah, I have, yeah. Yeah, really good stuff. Shout out to. Who

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am I to shout out like Daniel Kahneman. But you know, if you haven't read

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it, go and purchase this either online or read the paperback.

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But you know, he talks in the, in the, in the

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book about, you know, system one, system two, right? Like two kind of system within

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your human brain. I'm far from being expert, but basically you're

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talking about intuition, like the way that we manage ourselves using intuition.

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And what does it mean to have an intuition versus like a deep kind of

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line of thinking and you know, the way that you typically

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would analyze the more complicated, slow thinking process.

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So if you take this back, like this system one to

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construction projects, what does it mean to run based on intuition or

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hunch? Let's take your veteran superintendent, superintendent,

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like the person who really runs the job on the job site

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from the general contractor side, and let's say that he

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or she will have like 20, 25 years of experience. These

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guys can, you know, can sniff, can sense that

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something is wrong. But in reality, you know, without having the technology

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on their side, typically what will happen is that their intuition

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will kick in when it's a bit too late. Why is that?

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Because let's say that you're doing like a 20 story high, you know,

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let's take that 40 story high somewhere building in Austin, Texas, right?

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That's going to be, I don't know, half a million square foot

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of a building. I'm going to have a crew of in between 10 to 20

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people from the contractor side. And there's

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literally hundreds of people working on my building

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installing ductwork, electrical wiring and you know, drywall and

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Sheetrock and you know, you name it and everything changes

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each and every day. And you as a superintendent, even though that you

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have the best kind of experience ever in the job and you have a really

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good intuition, your threshold, right, to

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noticing that something is off, you're only human, so it's

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natural for you to sense that something is off at some point. But what

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technology can bring to the table, and sorry for the very long explanation but

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technology can do, is to lower the threshold for you to be

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sensing that something is off. Let me give you an example. Let's say that

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in, within that same building, you have a crew of people that installing

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ductwork, you know, there's going to be, let's give it like

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an even number just for the example. I'd say like 100,000

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of linear footage of, you know, duck work.

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And they need to do it at a certain pace and to work at a

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certain sequence. And let's say that they're like, by week two or

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week five, they're off by 7%,

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right? They should have done like X and they've done like X minus 7%.

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What is the probability of that veteran super to

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kind of miss that? There's a high chance for them

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to be missing that point. Why is that? Because someone else is yelling. Because

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someone else is like, hasn't been delivering as they should

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be. And the gap is not 7%. They're missing by 50%. Or

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there's a truckload that was supposed to get to the job site that day and

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it hasn't gone there. Or like there's like a design change. There's so many

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moving pieces on the job site and for them to be missing

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the fact that that team is lacking like 7%

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and the week after it's going to be 8%. So you're looking at the kind

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of a snowball effect. So at some point, I know

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by week 20, if, if the ship is like off track,

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right, someone will notice it. But the trick is that you're

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noticing too late. Using

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technology is like, you can combine the system one, the intuition,

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which is basically intuition if you don't know it. Intuition is like experience,

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his knowledge, expertise. It's like how your brain is being, you know,

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rewired as time goes by. But if you combine that

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intuition with technology, that lowers the threshold all of a sudden. You

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don't need to wait until week 20 to sense that you're off by 7%.

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On the second or fifth week, I'm going to say like, hey, you know what,

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you've been doing tremendous work, but bear in mind that you're under delivering by just

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like a tiny bit. Let's go to the root cause of that and figure out

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what we can do together as a team in order to get better, back on

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track before it's being too late. And what I sense that the biggest

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opportunity for construction with technology is exactly that is

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like one of the opportunities is like lower the threshold so we can

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let humans do what they do best and we can let technology do what they

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do best, which is like the heavy lifting, the long tail, like the all that

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kind of boring, quote unquote analysis of this

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situation so that the pros can be like, you know,

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do whatever they do best. Right. And I would imagine too,

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like, I mean, it's Probably a lot easier to, you

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know, once it gets to 7%, right. It's probably

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one level of effort, but if you catch it at 3% or 2%, it's probably

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a lot, you know, like if a concrete shipment, I don't

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know, you know, misses its deadline or is late,

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the downstream effects probably AI is better

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at figuring that out than a person would be. And it's not a, it's

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not, it's just you. Every human on earth is limited by

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human perception. Right. The gateways of that. Right. And, and not that.

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That's. I think, I think you said it best. Like I'm a, I'm a big

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believer in the idea that AI is meant to be an augmentation technology

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for humans because there's things that AI can do better

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and it's, you know,

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and there's things obviously that humans are going to do better than machines for the

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foreseeable future. Right. But I think

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it's interesting is that when you think about, you know, AI and construction, right.

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It's probably, you know, everyone I, you know, immediately like I went to the

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computer vision demo, right. From a few years back, right. But

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it's probably this is. It sounds to me that construction is a very logistics,

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heavy business, right. I need to get people in a place, I need to get

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gear, I need to get equipment, I need to get

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material there and that. And there's probably a certain timing of it, right. It's

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probably very heavy on the waterfall process where you can't put

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ductwork if there's no, you know, I guess the iron skeleton

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on the building or whatever technique, right. If there's no floor, you can't put the

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flooring down. If there's no walls, can't paint them. Right. I mean, it's like from.

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It kind of goes this and I would imagine

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that that creates a very complicated

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web of interconnectedness that.

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Just thinking about it gives me a headache. Oh yeah, yeah.

321
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I think you, you're exactly right. Like it's the knockoff kind of

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cascading effect because everything in construction is sequence. Like

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the most, you know, the easiest kind of example is like you need to do

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the groundwork in order to do, to, to erect the structure. Right.

325
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And once you have the structure, you can start pouring the slabs, which are the

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concrete kind of floors and ceilings. And once you have the structure in place, you

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can start, you know, to, to install all the fit out, you know, all the

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internals. So that would be like the facades and windows and

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externals and guess what? You need the building to be

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what we call wet ready before you can install

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elements which are sensitive to weather. Right. I wouldn't go install

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my precious kind of sanitary work before

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I know that no damage will be caused by weather. And, you

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know, when we're talking about mechanical and electrical and plumbing

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equipment, there's a certain sequence. If you look up, you know the audience. If you

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look up and you have those kind of.

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You can see the ceiling scheme. You know, in office areas, you would

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see that there's, like, a certain pattern in your overhead. Mechanical,

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electrical, and plumbing equipment. Typically, there's going to be high

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difference. So, you know, ductwork, which are the biggest kind of pieces, will

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go first and then sprinklers and then, you know, and so on and so on.

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Mechanical piping, electrical conduits, you typically will go

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last because they're the most flexible. So you're right. There's a certain

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sequence, and once you have kind of a delay or a

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problem in one element, there's going to be a knockout effect to the

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rest of the pieces. And you want to make sure that one. You keep the

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right sequence. And if there's something that isn't ticking the

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right way, you need to fix that asap, because everything that will

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follow will be impacted. And not only that,

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sorry. You want to make sure that you

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keep a certain flow. Like, it's funny, but in

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construction, it shares, like, a bit of, you know, Zen kind of.

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Right, right. Elements. Because bear in mind, there's like,

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dozens of different trades and contractors and supply chain elements that are

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working together seamlessly, and one depends on the other.

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And if I come trade number one, let's say I'm doing ductwork,

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and the next one after me will be the sprinklers guy. If I'm

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late, that's going to affect the other team. And if they cannot

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pull their people to the job, guess what? At some point, they're going to pull

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them off from the job and you, you know, divert them to the next one.

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And me, as a superintendent, is like, the current project will suffer from that.

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So you want to make sure that everyone is working according to pace, according to

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their sequence at a certain flow. And it's really hard.

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It's really hard because, like, you plan your job perfectly

365
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on day one, right. But the minute you started, you're being thrown with

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everything possible, like weather, supply chain issues. The

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owner will change the design because of reason. You know, the

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marvel that, you know, you kind of. You

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wanted to get from Italy, stuck in somewhere in the ocean, like Everything

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will be thrown at you. And you need to have that really

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good data collection system that, you know, keep

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tracks of everything for you so it can raise up all the risks

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and all the kind of flags you need in order to make the right decision.

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So this is kind of the story. You really want to make sure that you

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keep up with the sequence because every kind of grain of, you know, dust that

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goes into that mechanism will

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probably. You know, it seems

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like you can, you can, like you said, like a Zen thing, like it has

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to exist in a certain flow state and the universe is going

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to conspire to make you get out of that flow state.

381
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Right. I, I imagine weather probably plays into it, you know,

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and, and it always fascinated me to see when people would

383
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build homes. I used to live in this big suburban development

384
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in New Jersey. As they were building it, we had these huge blizzards

385
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that winter. And I just remember seeing like the entire frame of the building

386
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was exposed to, you know, the snow and the ice. And I'm thinking

387
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to myself, how is that going to impact, you know,

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00:24:02,780 --> 00:24:06,260
you know, in a one story townhouse or building? It probably is not that big

389
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of a deal. But like, I just wonder like, how do the bigger projects deal

390
00:24:09,700 --> 00:24:13,550
with this, right? If it's a hurricane, if it's this, if it's that. And

391
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I could just imagine a logistics nightmare, especially the bigger the project, because

392
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the bigger the project, the bigger the mart. I mean, the bigger the,

393
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the crews and the bigger all of this. And, and I think you're

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right. Like if, if the ductwork guy gets delayed by a couple of days,

395
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I would imagine like the sprinkler contractors, the

396
00:24:35,030 --> 00:24:38,670
plumbing and all that, they probably have, are working multiple

397
00:24:38,670 --> 00:24:42,450
jobs, right? Like, so they're probably like, oh, I have, and I have

398
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Bob and Tony working on that. But if you're for this week, but

399
00:24:46,210 --> 00:24:49,890
if you're delayed by a week, I got them over here now that screws up

400
00:24:49,890 --> 00:24:53,490
your schedule even further because you can't get those people. And I would imagine

401
00:24:53,490 --> 00:24:57,210
it's logistics nightmare. Yeah, it is, it is,

402
00:24:57,210 --> 00:25:01,010
it is. But, but to be honest, I would say, you know, that industry, this

403
00:25:01,010 --> 00:25:04,010
is how it operates. So it knows how to handle the

404
00:25:04,250 --> 00:25:07,520
unpredictability and how to kind of

405
00:25:08,800 --> 00:25:12,240
change plans at the floor level and

406
00:25:12,400 --> 00:25:15,680
adjust itself. But the key is, and I think that

407
00:25:15,920 --> 00:25:19,640
what's really happening over the past few years, and it's

408
00:25:19,640 --> 00:25:23,440
not just because of AI and technology, I think it's mostly about data structuring

409
00:25:23,440 --> 00:25:26,720
and ability to really represent the project

410
00:25:26,720 --> 00:25:29,200
digitally. So you can represent it

411
00:25:30,080 --> 00:25:33,750
digitally, all the moving pieces. So you can start simulating, you can

412
00:25:33,750 --> 00:25:37,270
start predicting using predictive analytics and so on.

413
00:25:38,070 --> 00:25:40,790
What it offers is, like, it offers. Like, the.

414
00:25:42,630 --> 00:25:46,430
People on the project to kind of work with different options and say, like,

415
00:25:46,430 --> 00:25:50,190
hey, you know what if I'm 7% late? You know, that previous example

416
00:25:50,190 --> 00:25:54,030
on the ductwork, what does it mean for me? Like, what's the end

417
00:25:54,030 --> 00:25:57,870
date for me for that activity? Let's say that I need to have all the

418
00:25:57,870 --> 00:26:01,320
ductwork installed by, I know, December this year.

419
00:26:01,720 --> 00:26:05,240
That's my plan. That's my schedule. Now I'm off by 7%. If you

420
00:26:05,240 --> 00:26:08,680
extrapolate and say, you know, if we continue the same pace, you know,

421
00:26:08,680 --> 00:26:12,280
relatively the same pace, am I going to finish that

422
00:26:12,280 --> 00:26:16,080
on January or February or, you know, what's. What's the knockout

423
00:26:16,080 --> 00:26:19,880
effect? Because once you know that, you can start plan the remedy, and

424
00:26:19,880 --> 00:26:23,640
you can say, all right, you know what. What happened? So far, it's in

425
00:26:23,640 --> 00:26:27,160
the past, but we need to get our, you know, our stuff together. You know,

426
00:26:27,780 --> 00:26:31,620
sorry, keeping my language and, you know, back on track. Sorry, I was

427
00:26:31,620 --> 00:26:35,180
almost there. And you can start having, like an adult conversation with your

428
00:26:35,180 --> 00:26:38,700
supply chain and say, like, hey, you know what, guys, let's go to the root

429
00:26:38,700 --> 00:26:42,500
cause of that. We need to amp our game by,

430
00:26:42,500 --> 00:26:46,260
you know, by that amount. Do we have enough labor on

431
00:26:46,260 --> 00:26:49,740
site? Do we have enough materials? Like, can you. Can you increase

432
00:26:49,740 --> 00:26:53,500
manufacturing of the missing ducts? Maybe? Can I change my

433
00:26:53,500 --> 00:26:57,340
sequence? You know, I have, like, the most amazing example from, you know, a

434
00:26:57,340 --> 00:27:01,060
year and a half ago, we

435
00:27:01,060 --> 00:27:04,860
launched a new product, a new feature about 18 months ago, which

436
00:27:04,860 --> 00:27:08,580
is like a predictive analytics for delays, which is tremendous for

437
00:27:08,580 --> 00:27:12,300
a job site. I remember launching it. And like

438
00:27:12,300 --> 00:27:15,500
everything in life, when we launch a product, we, first of all,

439
00:27:16,060 --> 00:27:19,820
you develop it in the background and you

440
00:27:19,820 --> 00:27:23,260
have early versions of it. And I remember working with

441
00:27:23,500 --> 00:27:26,860
mine, my. My first kind of beta

442
00:27:26,940 --> 00:27:30,620
user for that, one of the projects in the uk, And I told

443
00:27:30,620 --> 00:27:34,020
him, like, hey, there's a coming conference, you know, how about we get on stage

444
00:27:34,020 --> 00:27:37,620
and present together that example from back in the day? And he was like, you

445
00:27:37,620 --> 00:27:41,340
know what, I'm all good, you know, presenting with you, but I have a

446
00:27:41,340 --> 00:27:45,060
new example. I was like, what are you talking about? He was like, you know,

447
00:27:45,060 --> 00:27:48,780
he just released a feature using predictive analytics. And we noticed that my

448
00:27:48,780 --> 00:27:52,510
electrician, he's actually six weeks behind schedule, and it makes zero

449
00:27:52,510 --> 00:27:56,350
sense because he has his whole crew on site every

450
00:27:56,350 --> 00:28:00,070
day. I was like, how the hell are you kind of six week behind schedule.

451
00:28:00,390 --> 00:28:03,590
And the electrician, you know what he tells him, he was like, you know what?

452
00:28:03,670 --> 00:28:07,430
I'm waiting for the elevated floors, right? If you know

453
00:28:07,430 --> 00:28:11,230
what I'm talking about. Those like, elevated floors. I'm waiting for the elevated

454
00:28:11,230 --> 00:28:15,070
floors trade to be finishing in that area for me to getting on there

455
00:28:15,070 --> 00:28:18,870
with my, you know, ramps and everything to be working. And they

456
00:28:18,870 --> 00:28:22,670
stayed together. And I think he was telling me like, why the hell are

457
00:28:22,670 --> 00:28:25,350
we waiting for the elevated flows to be completed? Can we just like have the

458
00:28:25,350 --> 00:28:29,030
electrician go there instead? You know, change the sequence. That's it. And

459
00:28:29,030 --> 00:28:32,670
they change it immediately. And the only reason they could have, you

460
00:28:32,670 --> 00:28:36,510
know, add their kind of experience saying, like, you

461
00:28:36,510 --> 00:28:40,190
know, we just change the sequence. That's it. The only reason they could have done

462
00:28:40,190 --> 00:28:43,990
this because something raised that flag and said, like, hey, you know what? You're

463
00:28:43,990 --> 00:28:47,670
going to be six weeks behind schedule electrical work if you don't

464
00:28:47,670 --> 00:28:51,470
do something right now. So it's, you know, once you're off

465
00:28:51,470 --> 00:28:55,150
track and once you miss something, it's not the end of the world

466
00:28:55,150 --> 00:28:57,470
as long as you kind of address it.

467
00:28:59,070 --> 00:29:02,790
I just, I just love that story because it represents so much of

468
00:29:02,790 --> 00:29:06,310
the industry and its ability to make the best, like,

469
00:29:06,310 --> 00:29:09,800
decision in the split of a second. No,

470
00:29:09,960 --> 00:29:13,200
I think that's a good example of the AI flag something and people kind of

471
00:29:13,200 --> 00:29:17,000
like sat down and talked through and I guess one of the other things

472
00:29:17,000 --> 00:29:20,240
you kind of said was the ability to represent a building

473
00:29:20,240 --> 00:29:23,800
digitally. I would imagine it helps a lot to

474
00:29:23,960 --> 00:29:27,760
have that and then test out different scenarios. Like if

475
00:29:27,760 --> 00:29:31,040
we switch the order this way we'll save two days, right? We'll get back two

476
00:29:31,040 --> 00:29:34,840
days. We change it this way, we'll get back four days. Right. Or,

477
00:29:35,280 --> 00:29:39,120
or something like that. And again, I think the, I think the

478
00:29:39,200 --> 00:29:42,880
construction industry has always had to be resilient for a number of reasons,

479
00:29:42,880 --> 00:29:46,720
right. I think that's something

480
00:29:46,720 --> 00:29:50,560
I don't think people would necessarily appreciate from the outset. Right? Because you always

481
00:29:50,560 --> 00:29:54,320
see, like people always notice when something goes wrong, right? Like, oh yeah,

482
00:29:54,320 --> 00:29:57,920
that building. That building was supposed to go up, you know, in the spring.

483
00:29:57,920 --> 00:30:01,400
Here it is the fall. Or, you know, God forbid there's some kind of

484
00:30:01,400 --> 00:30:05,090
collapse. Like there was. Was it Thailand? I think it was

485
00:30:05,090 --> 00:30:08,730
Thailand. A building collapsed, unfortunately.

486
00:30:10,330 --> 00:30:14,090
So does the sequence of things or the normal sequence of things

487
00:30:14,170 --> 00:30:17,970
change by region? Like is. Is the

488
00:30:17,970 --> 00:30:21,770
US going to have a different order of things or. And like, how

489
00:30:21,770 --> 00:30:24,970
much does zoning affect that? Right. Like, you know, do you have a thing where

490
00:30:25,130 --> 00:30:28,810
you know, well, the local government, the local county or state says you can't

491
00:30:28,810 --> 00:30:31,830
do this before that. Like is that, is that a thing?

492
00:30:34,230 --> 00:30:38,070
Generally, I don't know. I'm pretty sure that there is a zoning kind of thing,

493
00:30:38,070 --> 00:30:41,910
but it's not my. Okay, I was just. But I would say, you

494
00:30:41,910 --> 00:30:45,670
know, think about this one. No building, like most buildings are

495
00:30:45,670 --> 00:30:48,950
not kind of cookie cutter. This is kind of another challenge in

496
00:30:48,950 --> 00:30:52,510
construction. It's like someone, I'm

497
00:30:52,510 --> 00:30:55,830
quoting someone, I can't remember who said it, but it's like you're building

498
00:30:56,070 --> 00:30:59,720
a one time thing, thing, factory. Like you're building a

499
00:30:59,720 --> 00:31:02,840
factory, right? The factory is like the team and the job site that need to

500
00:31:02,840 --> 00:31:06,560
kind of build that building. But it's, it's, it's a factory that

501
00:31:06,560 --> 00:31:10,320
you're going to use once, right? And that factory

502
00:31:10,320 --> 00:31:14,080
needs to build the building. And no building is the same as

503
00:31:14,080 --> 00:31:17,800
the other, right. One will have like a

504
00:31:17,800 --> 00:31:21,600
lowered suspended ceiling, the other one will not. And even like

505
00:31:21,760 --> 00:31:25,340
simple thing like you know, like drywall, like Sheetrock.

506
00:31:25,890 --> 00:31:29,570
Some of them will have insulation, some of them not. Some of them will

507
00:31:29,570 --> 00:31:33,250
have like the two coats of paint. Some of them

508
00:31:33,250 --> 00:31:37,010
will only one. Some of them will have glass walls, some of

509
00:31:37,010 --> 00:31:40,690
them will have brick walls. Nothing is the same. So

510
00:31:40,850 --> 00:31:44,650
sequence changes and varies according to the building that

511
00:31:44,650 --> 00:31:47,810
you're building. Not talking about different verticals.

512
00:31:48,370 --> 00:31:51,890
The healthcare facility is like completely different thing from

513
00:31:51,890 --> 00:31:54,050
residential project. It's a different thing from

514
00:31:55,700 --> 00:31:58,820
an airport or a hotel or data center or an oil reg.

515
00:31:59,540 --> 00:32:02,940
It's like comparing like the, you know, the F1 or in the

516
00:32:02,940 --> 00:32:06,780
NASCAR kind of car to my lousy vehicle that I'm driving

517
00:32:06,780 --> 00:32:10,260
my, my day to day. It's like a completely different animal.

518
00:32:10,419 --> 00:32:14,220
So there's going to be a lot of variations and differences. And this

519
00:32:14,220 --> 00:32:17,860
is like one of the challenges because you only have one shot on

520
00:32:17,860 --> 00:32:21,610
making that building on time and on budget. That's it. You

521
00:32:21,610 --> 00:32:24,290
only have one time. Interesting.

522
00:32:25,410 --> 00:32:27,890
Super challenging. Super challenging. That is industry.

523
00:32:29,170 --> 00:32:32,770
Maybe it's a good example because you know, I promise like the audience just like,

524
00:32:32,770 --> 00:32:36,329
you know, I'll give it like a really short kind of explanation of what we're

525
00:32:36,329 --> 00:32:39,570
doing and then maybe we circle back because like

526
00:32:40,610 --> 00:32:43,730
I think we kept the audience like in the dark for a bit.

527
00:32:45,010 --> 00:32:48,690
Mysterious, like I'm serious about what we're doing. So build outs,

528
00:32:48,690 --> 00:32:52,110
like simplistic. What we do is use computer

529
00:32:52,110 --> 00:32:55,270
vision, right? We use computer vision to Compare

530
00:32:55,510 --> 00:32:59,310
visuals from 360 cameras to your plans

531
00:32:59,310 --> 00:33:03,150
and schedule. Right. Oh, the result is that what we do

532
00:33:03,150 --> 00:33:06,870
is that we analyze the results from the computer vision and we can

533
00:33:06,870 --> 00:33:10,670
programmatically provide you progress

534
00:33:10,670 --> 00:33:14,310
data, like, compared to analytics, like progress

535
00:33:14,310 --> 00:33:18,130
data for your job site. So at any given moment in time, I

536
00:33:18,130 --> 00:33:21,850
can tell you precisely how well are you progressing against

537
00:33:22,010 --> 00:33:25,850
your plans and against your schedule. And it goes down from the very

538
00:33:25,850 --> 00:33:29,650
top level, saying like, you know what, you should have been like 80% so

539
00:33:29,650 --> 00:33:33,450
far in your project altogether. And you just like 75. Or if you're

540
00:33:33,450 --> 00:33:37,290
doing really well, like you're 82 or 80. And it goes down

541
00:33:37,530 --> 00:33:41,370
layer by layer all the way down to the very specific

542
00:33:41,370 --> 00:33:45,090
conduit and specific wiring. Right. You break it down by the

543
00:33:45,090 --> 00:33:48,530
different activities and trades. So Electrical will be

544
00:33:48,530 --> 00:33:52,370
70% out of 75. Ductwork will be so. And so goes

545
00:33:52,370 --> 00:33:55,850
all the way to. On that very floor. It's going to be that percentage

546
00:33:55,850 --> 00:33:59,130
complete and going down to that specific

547
00:33:59,850 --> 00:34:03,610
element type. So it's going to be drywall versus block work or versus

548
00:34:03,690 --> 00:34:07,410
concrete walls and specific wall pieces and

549
00:34:07,410 --> 00:34:11,030
specific floor and so on. And everything is backed by

550
00:34:11,030 --> 00:34:13,030
photos because it's computer vision.

551
00:34:14,710 --> 00:34:18,270
And I'll finish by that. Because the way that we run this

552
00:34:18,270 --> 00:34:21,510
product is that every time you start a new project,

553
00:34:21,830 --> 00:34:25,150
we're going to obtain two things. Your

554
00:34:25,150 --> 00:34:28,630
schedule, which is like a simple Gantt chart. It's far from being simple, but

555
00:34:28,630 --> 00:34:32,430
imagine a Gantt chart. Every major construction

556
00:34:32,430 --> 00:34:36,190
project has a schedule. And the other thing is that

557
00:34:36,190 --> 00:34:39,710
we taking the 3D models, believe it or not, for people who are not part

558
00:34:39,710 --> 00:34:43,170
of the industry. The blueprint that you remember from, you know,

559
00:34:43,170 --> 00:34:46,850
movies, by the way, my first impression of blueprint, have you. Do you

560
00:34:46,850 --> 00:34:50,610
know Die Hard? Yes. You remember him pulling

561
00:34:50,610 --> 00:34:54,450
the blueprints. So there's still blueprints, like in

562
00:34:54,450 --> 00:34:58,050
2D these days. Everything is like still working in 2D, but

563
00:34:58,450 --> 00:35:02,210
major construction and even lower than that are being designed

564
00:35:02,210 --> 00:35:05,850
in 3D, which is tremendous, right? Pretty cool. So we take the

565
00:35:05,850 --> 00:35:09,392
3D models and schedule and we create something that called 4D.

566
00:35:09,548 --> 00:35:13,050
4D model. 4D is like the 3D model that has that

567
00:35:13,210 --> 00:35:17,050
time kind of factor to it. And all of a sudden we have a

568
00:35:17,050 --> 00:35:20,770
digital representation of the project that you're building. Let's say a healthcare

569
00:35:20,770 --> 00:35:24,530
facility somewhere in Jersey. Right. So we know how the

570
00:35:24,530 --> 00:35:28,370
project should be looking like, should behave. Like, what's the sequence?

571
00:35:28,370 --> 00:35:32,130
Who are the trades working there, how many walls, how many pieces of ductworks,

572
00:35:32,130 --> 00:35:35,690
electrical conduits and sockets and so on. And every time,

573
00:35:35,930 --> 00:35:39,760
every time someone takes a walk on the job site with

574
00:35:39,760 --> 00:35:43,120
a hard hat and a 360 camera mounted to the top of it.

575
00:35:43,840 --> 00:35:47,560
Turn the video on and just walk the job. You

576
00:35:47,560 --> 00:35:51,120
walk the job. You then finish it. You upload the video

577
00:35:51,200 --> 00:35:54,960
to our computer, like our servers to our platform. And

578
00:35:54,960 --> 00:35:57,840
we use computer vision to do two things. One,

579
00:35:58,640 --> 00:36:02,240
we precisely locate each and every frame in the video.

580
00:36:02,680 --> 00:36:05,880
You don't need to tell us where have you worked, just walk the job.

581
00:36:06,280 --> 00:36:09,640
We'll figure out the exact positioning of each and every frame in the video.

582
00:36:10,680 --> 00:36:14,480
We're accurately positioning it against the model and against your

583
00:36:14,480 --> 00:36:18,080
plans. The second part is that we use computer vision to

584
00:36:18,080 --> 00:36:21,760
automatically annotate and extract data from

585
00:36:21,760 --> 00:36:25,560
that frame. Let's say that you walk across

586
00:36:26,280 --> 00:36:30,000
a block kind of wall that has an opening. So we know that

587
00:36:30,000 --> 00:36:33,470
that walls in your camera, in your footage kind of is

588
00:36:33,550 --> 00:36:37,310
compared to that wall in the model. Right. We can mark this

589
00:36:37,310 --> 00:36:40,910
as done and we can know whether it was like it has

590
00:36:40,910 --> 00:36:44,750
plaster in that, whether it was coated and so on, so on, so on. So

591
00:36:44,830 --> 00:36:48,390
this is basically what we provide. We provide progress

592
00:36:48,390 --> 00:36:52,230
data, which is equivalent to analytics to the people on the job

593
00:36:52,230 --> 00:36:55,710
site. So that that super. Remember from the previous example,

594
00:36:56,510 --> 00:37:00,150
they know on each and every day whether they're on track or

595
00:37:00,150 --> 00:37:03,950
not. And if not, like, what is the reason for that? Which trades

596
00:37:03,950 --> 00:37:07,630
are behind schedule, what activities are problematic, do they have any quality

597
00:37:07,630 --> 00:37:11,310
issue, what's the predictive analytics says about the end date and what should

598
00:37:11,310 --> 00:37:14,990
they change and to what extent in order to get back on

599
00:37:14,990 --> 00:37:18,110
track and finish the project on time and on budget and

600
00:37:18,270 --> 00:37:22,070
obviously as safe as possible. That's interesting. So

601
00:37:22,070 --> 00:37:25,150
you have this computer vision solution that can be very granular.

602
00:37:25,890 --> 00:37:29,690
It's almost like you have like. What

603
00:37:29,690 --> 00:37:32,250
did you call the person who's in charge of the project? It wasn't foreman, it

604
00:37:32,250 --> 00:37:35,730
was superintendent. Superintendent, yeah. If you're typically.

605
00:37:37,090 --> 00:37:40,890
It's like you have that person on every floor at

606
00:37:40,890 --> 00:37:43,650
all times paying attention to everything all at once, right?

607
00:37:44,290 --> 00:37:47,250
Yeah. And you know what? You can't have this.

608
00:37:48,050 --> 00:37:51,890
You can't have. People are people. People are people.

609
00:37:51,890 --> 00:37:55,660
Yeah. But to be more fair than that is that one. Remember

610
00:37:55,660 --> 00:37:59,260
that 3% or 5% margin, I don't have money

611
00:37:59,260 --> 00:38:01,420
to have enough superintendents on each and every

612
00:38:03,180 --> 00:38:06,700
part. Is that there's a huge shortage

613
00:38:06,860 --> 00:38:09,740
in professional

614
00:38:09,820 --> 00:38:12,780
sophisticated talent in this industry.

615
00:38:13,420 --> 00:38:17,260
The industry is lacking so many people, like all of

616
00:38:17,260 --> 00:38:21,080
the people in the industry are extremely talented, really

617
00:38:21,080 --> 00:38:24,880
smart, really voted. But there's not enough people out there.

618
00:38:25,360 --> 00:38:28,920
And the sad news is that more and more young

619
00:38:28,920 --> 00:38:32,680
professionals are leaving the industry. So you're not only fighting

620
00:38:32,680 --> 00:38:36,000
to recruit people, but also to retain them because

621
00:38:36,160 --> 00:38:38,240
it's a hard physical labor job.

622
00:38:39,760 --> 00:38:43,600
So I wish we could have had like so many superintendents on the job

623
00:38:43,600 --> 00:38:47,210
site, but honestly we can't. But

624
00:38:47,450 --> 00:38:51,290
it's not the end of the world because if you harness technology, you

625
00:38:51,290 --> 00:38:54,770
know, when you combine that technology with the system, one kind of, you know, those

626
00:38:54,770 --> 00:38:58,050
people, all of a sudden you turn them like to, to be more

627
00:38:58,050 --> 00:39:01,770
superhuman in a way. They control more square, square footage

628
00:39:01,770 --> 00:39:04,650
of project. They can know more. They can be,

629
00:39:05,370 --> 00:39:09,210
God forbid, live, you know, early, you know, to be. Right, right, right, right.

630
00:39:09,450 --> 00:39:13,140
To keep their kind of mental health in place because really

631
00:39:13,140 --> 00:39:16,740
kind of it's, it's a hard job. I mean, you need to respect those

632
00:39:16,740 --> 00:39:20,340
people. They working so hard. It seems like it would be very stressful

633
00:39:20,340 --> 00:39:24,140
job, like, especially if when things go wrong and it sounds like things

634
00:39:24,140 --> 00:39:27,940
almost always go wrong a little bit. Yeah, I

635
00:39:27,940 --> 00:39:31,580
would say it's not for me to be speaking about this because I'm. Even though

636
00:39:31,580 --> 00:39:35,380
I've been serving the industry for the past more than a decade, I'm

637
00:39:35,380 --> 00:39:39,220
excellent. So I don't have, I haven't heard. Earned the rights to talk

638
00:39:39,220 --> 00:39:42,860
about this. Right, right, right. Yes. It is known in the industry

639
00:39:43,020 --> 00:39:46,860
that, you know, mental health is an issue. And I think

640
00:39:46,860 --> 00:39:50,580
that if we technology vendor can help just a bit, you know, to

641
00:39:50,580 --> 00:39:53,900
let them go back, spend time with their family and you know, to

642
00:39:54,060 --> 00:39:57,780
decompress for a bit and to be less stressful over the weekend and over,

643
00:39:57,780 --> 00:40:01,540
you know, nights and everything. That's. I would love

644
00:40:01,540 --> 00:40:04,700
that for it to happen. No, I think that's really cool. I think

645
00:40:05,340 --> 00:40:09,110
it's an important. People don't people, I think

646
00:40:09,110 --> 00:40:12,590
under underestimate mental health and things like that. And

647
00:40:12,830 --> 00:40:14,590
to your point, like if there's going to be a

648
00:40:16,910 --> 00:40:20,310
skill shortage. Right. Even if we solve the skill

649
00:40:20,310 --> 00:40:23,710
shortage today. Right. To get that level of experience

650
00:40:23,950 --> 00:40:27,390
that a seasoned like superintendent would have is

651
00:40:27,550 --> 00:40:31,270
going to take. I mean, even if we fix the pipeline today, the

652
00:40:31,270 --> 00:40:34,370
downstream effects and the shortage in the pipeline are going to

653
00:40:35,410 --> 00:40:38,930
cause problems for, you know, a generation potentially. Right.

654
00:40:39,010 --> 00:40:42,130
So how do you, how do you, how do you mitigate that? And I think

655
00:40:42,130 --> 00:40:45,330
this seems like it'd be one way to mitigate that where you could have,

656
00:40:45,810 --> 00:40:49,170
you know, and it's really using AI, I think,

657
00:40:49,729 --> 00:40:53,570
where it's good at. Right. Paying attention to every detail at all times,

658
00:40:53,730 --> 00:40:57,330
everywhere at scale, and then collating

659
00:40:57,330 --> 00:41:01,060
that data and getting to the point where, you

660
00:41:01,060 --> 00:41:04,700
know, AI does a really good job

661
00:41:04,700 --> 00:41:08,300
of, you know, doing the Slow thinking system very quickly.

662
00:41:08,300 --> 00:41:11,540
Right. Like so I think, you know, if we, if we kind of

663
00:41:12,340 --> 00:41:15,420
leverage it that way, I think it's. And I also think too like it's a

664
00:41:15,420 --> 00:41:18,900
very practical use of computer vision. Oh yeah, right.

665
00:41:20,500 --> 00:41:23,940
And I would imagine as time goes on, you'll learn more

666
00:41:24,020 --> 00:41:27,790
about what you said would happen in your system versus

667
00:41:27,790 --> 00:41:31,150
what actually happens. So you have like that training loop probably in place.

668
00:41:31,950 --> 00:41:35,670
There's this training loop and I

669
00:41:35,670 --> 00:41:39,070
would even say, and this is something we're doing already. So

670
00:41:39,310 --> 00:41:43,150
one thing is to optimize the existing project at hand. Right.

671
00:41:43,230 --> 00:41:46,950
Go back to that half a billion healthcare facility

672
00:41:46,950 --> 00:41:50,350
in Jersey. Right. About the next one. I mean

673
00:41:50,670 --> 00:41:54,490
one thing that we've been doing because our computer vision

674
00:41:54,890 --> 00:41:58,650
generates so much data about plan

675
00:41:58,650 --> 00:42:02,330
versus actual, about how actual progress happens on the job

676
00:42:02,330 --> 00:42:06,090
site versus how it was planned. What we're doing right

677
00:42:06,090 --> 00:42:09,930
now is that we look at future jobs and we look at their schedules

678
00:42:09,930 --> 00:42:13,690
and their models and their plans and we can say, well, what is

679
00:42:13,690 --> 00:42:17,290
the probability of different types of risk to happen

680
00:42:17,450 --> 00:42:21,040
on that scheme based on previous historical data that we

681
00:42:21,040 --> 00:42:24,880
have? So let's say that we're not building a healthcare facility in Jersey,

682
00:42:24,880 --> 00:42:28,360
but rather like in, I know, in Indiana. Right, right.

683
00:42:28,520 --> 00:42:31,640
And how many healthcare facilities have we built so far?

684
00:42:32,200 --> 00:42:35,960
How much information have we gained in order to

685
00:42:36,200 --> 00:42:40,040
validate future plans and to de. Risk future plans. Right.

686
00:42:40,120 --> 00:42:43,800
And to build. Right. For the first time. And this is the other example of

687
00:42:43,800 --> 00:42:47,240
how technology and AI can, you know, can kick in because we're not just looking

688
00:42:47,240 --> 00:42:50,880
at the one time factory that we're trying to build, but rather

689
00:42:50,880 --> 00:42:54,560
optimize all the current and future pipeline of our business, which is

690
00:42:54,560 --> 00:42:58,120
tremendous. And all of a sudden you can schedule better. Right?

691
00:42:58,360 --> 00:43:02,040
Because if you look at project scheduling, just to give you like an example,

692
00:43:02,600 --> 00:43:06,200
I've seen construction project schedules with

693
00:43:06,680 --> 00:43:10,280
more than 2 million rows. Right. Think about

694
00:43:10,520 --> 00:43:13,800
a project schedule that has 2 million rows. I've never

695
00:43:14,520 --> 00:43:18,120
seen anything like that personally in my job, you know, for tech company.

696
00:43:19,080 --> 00:43:22,920
So it's beyond human scale. So if you use

697
00:43:23,160 --> 00:43:26,760
historical data and again AI and computer vision and everything

698
00:43:26,760 --> 00:43:30,040
else to kick in to do the stuff that is really hard for human to

699
00:43:30,040 --> 00:43:33,680
do. How did you say it? Like allow computers to do system

700
00:43:33,680 --> 00:43:37,520
two things really fast. Which by the way, I would buy that

701
00:43:37,520 --> 00:43:40,280
T shirt if you get this. I think I'll make that T shirt

702
00:43:41,720 --> 00:43:42,440
make it black.

703
00:43:47,730 --> 00:43:50,930
So all of a sudden you can, you can leverage technology to do other things

704
00:43:50,930 --> 00:43:54,530
as well, like better planning, better scheduling and look at all the other

705
00:43:54,530 --> 00:43:57,810
parts which are heavy lifting tasks that we can

706
00:43:58,130 --> 00:44:01,930
kind of take it from humans not because we want to replace

707
00:44:01,930 --> 00:44:05,570
them, but rather we want to keep their abilities and experience

708
00:44:05,730 --> 00:44:09,410
to do the really hard reasoning and decision making

709
00:44:09,650 --> 00:44:13,160
and, you know, what if

710
00:44:13,160 --> 00:44:16,880
scenarios and so on, and to let technology to kind

711
00:44:16,880 --> 00:44:20,480
of lead the way on the repetitive kind

712
00:44:20,480 --> 00:44:24,120
of hard job. So it's not just about project

713
00:44:24,120 --> 00:44:27,760
control analytics, it's about predictive analytics and better schedulings and

714
00:44:27,760 --> 00:44:31,480
better planning and better kind of de risking for the entire industry, which is pretty

715
00:44:31,480 --> 00:44:34,680
cool. Cool. How did you get into this? How did you get into

716
00:44:35,080 --> 00:44:38,920
it and construction? Oh, that's, you

717
00:44:38,920 --> 00:44:42,320
know what I've, I just 40 about a month ago

718
00:44:42,480 --> 00:44:45,800
and I've been playing with my, you know, lifelong

719
00:44:45,800 --> 00:44:49,520
decisions for the past few years, you know, thinking I'm happy with everything

720
00:44:49,520 --> 00:44:53,360
that I have. But you know, I have been thinking about stuff. So originally I

721
00:44:53,360 --> 00:44:56,480
came from technology, you know, pretty young, about 20 something.

722
00:44:58,160 --> 00:45:01,880
Started in advertising tech back in the day, which then. Still cool.

723
00:45:01,880 --> 00:45:05,690
Yeah. My first kind of role, I remember

724
00:45:05,690 --> 00:45:09,450
I was a product manager for an advertising tool, believe

725
00:45:09,450 --> 00:45:12,730
it or not, as an add on for Flash. Wow.

726
00:45:14,410 --> 00:45:18,050
Yeah, I was like doing some product management for an add on for

727
00:45:18,050 --> 00:45:21,690
Flash and at some point I kind of fell in love

728
00:45:21,690 --> 00:45:25,450
with data analytics. That was my sweet spot, kind of. I know why.

729
00:45:25,450 --> 00:45:29,270
I love numbers, I love reasoning, I love logic. And

730
00:45:29,990 --> 00:45:33,270
I worked in a company called Datorama, which later on was

731
00:45:33,430 --> 00:45:35,830
acquired by Salesforce, which is pretty cool.

732
00:45:37,270 --> 00:45:40,510
Not a lot of credit for me in the acquisition obviously, but you know, it's

733
00:45:40,510 --> 00:45:44,230
just a part of the team. And then I remember getting

734
00:45:44,230 --> 00:45:47,070
a phone call from a friend and that's pretty cool. He was like, you know

735
00:45:47,070 --> 00:45:49,830
what, there's a young startup in

736
00:45:50,310 --> 00:45:54,070
construction tech, you know, looking for the first product manager. Do you want to join?

737
00:45:55,120 --> 00:45:58,720
Believe it or not, my first response was like, no, forget about it.

738
00:45:58,720 --> 00:46:01,880
There's nothing to do there. You know, it's probably going to be boring. But I

739
00:46:01,880 --> 00:46:05,680
took the meeting and you know, eventually I joined the team.

740
00:46:06,480 --> 00:46:10,120
And I remember the first few months I was flying like hell. I was

741
00:46:10,120 --> 00:46:13,880
flying to, you know, Indiana and Boston and New York and Turkey

742
00:46:13,880 --> 00:46:17,120
and Thailand and you know, the UK and France.

743
00:46:17,920 --> 00:46:20,720
And the reason I fell in love with it was people.

744
00:46:21,720 --> 00:46:25,560
Eventually you meet that superintendent and you meet that foreman and you

745
00:46:25,560 --> 00:46:29,360
can see everything in their eyes. It's not just, you know, you're not optimizing

746
00:46:29,360 --> 00:46:33,160
that additional impression on that Google

747
00:46:33,160 --> 00:46:36,840
search ad or whatever, full of respect or everything

748
00:46:36,840 --> 00:46:40,440
dealing with this. But that's not my cup of Tea. I'm a people

749
00:46:40,440 --> 00:46:44,000
person. And you remember, you know, you could have seen everything on their eyes. And

750
00:46:44,000 --> 00:46:47,560
I remember that, like, you know, back in the day, working in the augmented reality

751
00:46:47,560 --> 00:46:51,270
kind of app that I told you about, I was working in a project, not

752
00:46:51,270 --> 00:46:54,870
working. I was like, you know, demonstrating a technology app in.

753
00:46:55,030 --> 00:46:58,590
I think it was Lebanon, Indiana, if no one

754
00:46:58,590 --> 00:47:01,830
knows where it is. They were building a veteran

755
00:47:02,470 --> 00:47:06,070
healthcare facility. I was, like, demoing the app, and

756
00:47:07,030 --> 00:47:10,550
I can't remember why, but I think it was like, they told me that everything

757
00:47:10,550 --> 00:47:14,350
that they build in Diana is built in swampland, that

758
00:47:14,350 --> 00:47:18,080
they need to dig a well into the basement. Like, I know

759
00:47:18,080 --> 00:47:21,560
70ft of well and need to constantly pump the water.

760
00:47:21,960 --> 00:47:25,760
And remember I'm holding, like, a device with augmented reality. And they tell me,

761
00:47:25,760 --> 00:47:28,320
like, hey, can you. Can you come in for a second? I was like, yeah,

762
00:47:28,320 --> 00:47:32,000
sure. And they were telling me, like, hey, you know what? We

763
00:47:32,000 --> 00:47:35,760
believe there's a problem with our well. Maybe it's dislocated or

764
00:47:35,760 --> 00:47:39,560
something. I was like, all right, let's check with the app. Obviously,

765
00:47:41,000 --> 00:47:43,480
spoiler alert. It wasn't working perfectly.

766
00:47:45,610 --> 00:47:49,450
And I'm trying to locate the well, you know, in the model,

767
00:47:49,450 --> 00:47:52,810
in the. In the. In the. In the plans. And

768
00:47:53,050 --> 00:47:56,650
I couldn't see anything. But I had a weird

769
00:47:56,650 --> 00:48:00,410
intuition. I told them, like, guys, what's the probability? What's.

770
00:48:00,410 --> 00:48:04,130
Is it possible that the well is positioned well, but the

771
00:48:04,130 --> 00:48:07,930
diameter is different? Maybe, maybe. Maybe the diameter thing is wrong.

772
00:48:07,930 --> 00:48:11,780
Because they were trying to kind of coordinate the position

773
00:48:11,780 --> 00:48:15,540
of a wall against that well, a kind of a pit in the floor.

774
00:48:16,100 --> 00:48:19,100
I was like, maybe the diameter is wrong, so this is why the wall is

775
00:48:19,100 --> 00:48:22,780
not working against it. And they checked it, and I

776
00:48:22,780 --> 00:48:26,260
don't know how I had this intuition, but I got it right. And the diameter

777
00:48:27,060 --> 00:48:30,580
and the story is that I remember the face of that superintendent.

778
00:48:30,740 --> 00:48:34,580
It turned white immediately. And I

779
00:48:34,580 --> 00:48:38,300
could see that everything is personal. Everything is, like, very human. You're dealing

780
00:48:38,300 --> 00:48:41,640
with, eventually with human that devote their life to. To this industry.

781
00:48:42,280 --> 00:48:46,080
And I just fell in love with this. So I know I'm sold for

782
00:48:46,080 --> 00:48:49,800
the industry. And looking back at my childhood, my

783
00:48:50,040 --> 00:48:53,720
parents, they had this family kind of business for printing 2D

784
00:48:54,200 --> 00:48:57,720
sheets for construction. So my.

785
00:48:57,800 --> 00:49:01,480
All of my summers from age six, probably, I was spending, you know,

786
00:49:01,480 --> 00:49:05,280
folding huge 2D sheets for construction. So maybe, maybe

787
00:49:05,280 --> 00:49:08,840
if, you know, you're looking psychologically, maybe like it's kind of

788
00:49:09,430 --> 00:49:13,230
something that brings me there, but that's definitely my passion. This

789
00:49:13,230 --> 00:49:16,750
is how I got there. So it's A mixture of data analytics, AI and

790
00:49:16,750 --> 00:49:20,470
construction. That's cool. That's cool. Obviously

791
00:49:20,470 --> 00:49:22,790
you mentioned thinking fast and thinking slow.

792
00:49:24,150 --> 00:49:27,710
Audible is a sponsor and there is an audiobook version. So if you go to

793
00:49:27,710 --> 00:49:31,470
thedatadrivenbook.com, you'll get one free audiobook

794
00:49:31,470 --> 00:49:35,270
on us. And if you

795
00:49:35,270 --> 00:49:38,700
get a subscription we'll, we'll get a little bit of

796
00:49:38,700 --> 00:49:42,180
kickback and help support the show. Any other

797
00:49:42,180 --> 00:49:45,940
audiobooks you recommend? I'm not an audiobook

798
00:49:45,940 --> 00:49:48,860
person. I tried it once. Are you doing audio or paper?

799
00:49:50,940 --> 00:49:54,220
I kind of like, I have printed books, I have audio books

800
00:49:54,940 --> 00:49:58,380
and I also recently got a Kindle Scribe which I actually kind of like.

801
00:50:00,140 --> 00:50:03,420
I like it. I like it. If you look at a lot of the.

802
00:50:03,740 --> 00:50:07,460
I've been a big tablet PC fan, like pen computing fan since like

803
00:50:07,460 --> 00:50:11,160
Windows pen in the 90s and even I had an

804
00:50:11,160 --> 00:50:14,920
Apple Newton if you. That's really. Oh yeah, yeah. So I've been a

805
00:50:14,920 --> 00:50:18,600
big believer in that tech for a while. So my,

806
00:50:19,560 --> 00:50:23,120
I saw there's something called the Books which is

807
00:50:23,120 --> 00:50:26,600
basically the actual E ink screen

808
00:50:27,160 --> 00:50:30,800
is A4 size. Oh. So you can drop

809
00:50:30,800 --> 00:50:34,520
PDFs into it and it's like you know, PDF books and it's like

810
00:50:34,520 --> 00:50:37,698
perfect but it's like 6,

811
00:50:37,822 --> 00:50:41,420
$700. So I was like, I don't know if I like it but

812
00:50:41,420 --> 00:50:44,340
I look at the remarkable because I want to be able to take notes in

813
00:50:44,340 --> 00:50:46,540
meetings without being distracted by notifications.

814
00:50:48,380 --> 00:50:51,620
But when I saw the Kindle scribe I was like well I need a reader

815
00:50:51,620 --> 00:50:55,180
and I need a note taking platform and it happens to be the least

816
00:50:55,180 --> 00:50:58,900
expensive of the three. So I'm going to try it out and I like

817
00:50:58,900 --> 00:51:02,700
it. What I really like about it is the

818
00:51:02,700 --> 00:51:06,460
screen's bigger than my other Kindle. Right. I like

819
00:51:06,460 --> 00:51:10,020
the E Ink display because it feels there's no glare, there's no

820
00:51:10,180 --> 00:51:13,900
nonsense like that. I also not staying awake

821
00:51:13,900 --> 00:51:17,660
until like 2:00am or something. Exactly. And you don't have to light on

822
00:51:17,660 --> 00:51:21,340
because it's backlit. You can read it outside but also you can take

823
00:51:21,340 --> 00:51:25,100
notes in the margins. You can open up a different notebook and

824
00:51:25,100 --> 00:51:28,900
kind of write out, sketch out ideas. Yeah, I mean

825
00:51:28,900 --> 00:51:32,350
I'm, I'm a big fan if you're not a big.

826
00:51:32,510 --> 00:51:36,270
And I already have a lot of stuff in the Kindle ecosystem so like it's

827
00:51:36,270 --> 00:51:40,030
not a big loss. I know some people militantly hate the Kindle ecosystem

828
00:51:40,110 --> 00:51:43,710
and that's why like they would go with remarkable or books or something like that.

829
00:51:43,710 --> 00:51:47,310
But you know, which I probably Will end up getting one

830
00:51:47,870 --> 00:51:51,710
if you know when the price comes down. And I go

831
00:51:51,710 --> 00:51:55,040
everywhere now with this little like Kindle and I've only had it like almost a

832
00:51:55,040 --> 00:51:58,800
week and a half. I should probably buy one because I fly

833
00:51:58,800 --> 00:52:01,920
a lot and. Yeah, if you fly a lot. Yeah, I fly a lot and

834
00:52:01,920 --> 00:52:05,680
I, I love reading on planes. This is like the best time usage

835
00:52:05,680 --> 00:52:09,160
ever. You know, if you don't need to work in presentation or to work on

836
00:52:09,160 --> 00:52:12,720
planes, read on plane because it makes you fall asleep faster. Now

837
00:52:12,720 --> 00:52:16,400
that's true. One, another kind of recommendation that I can

838
00:52:16,400 --> 00:52:20,120
give to the audience. First of all, read books, kids. It's important.

839
00:52:20,850 --> 00:52:24,290
Two, have you read the Innovator's Dilemma? No.

840
00:52:24,690 --> 00:52:28,050
So the Innovator Dilemma is like Innovators Dilemma is kind of

841
00:52:28,290 --> 00:52:32,130
one of the best kind of business startup books in my opinion.

842
00:52:32,130 --> 00:52:35,610
It's written by, sorry if I'm not pronouncing it right, I think it was

843
00:52:35,610 --> 00:52:39,370
Clayton Christensen. Look it back. It talks

844
00:52:39,370 --> 00:52:42,850
about why do large enterprises

845
00:52:43,810 --> 00:52:47,490
are late in adopting new technology and

846
00:52:47,570 --> 00:52:50,640
should, should they adopt the new thing

847
00:52:51,200 --> 00:52:54,960
on tech or should they wait? And why are they late

848
00:52:54,960 --> 00:52:58,720
in adopting certain technology? And don't want to give you spoilers but you know,

849
00:52:58,720 --> 00:53:02,440
every time you hear about something new, you know, choose your

850
00:53:02,440 --> 00:53:06,000
current hype, whether it's like vibe coding, I know mcp,

851
00:53:06,160 --> 00:53:09,880
whatever, you know, knocks you out. But sometimes

852
00:53:09,880 --> 00:53:13,560
you think about like why do, why don't Amazon or Google

853
00:53:13,560 --> 00:53:16,750
or you know, Apple or you know, all your top hundred

854
00:53:18,430 --> 00:53:22,230
Fortune 500 companies do not adopt it immediately? You know, why is

855
00:53:22,230 --> 00:53:25,910
that? And there is a certain dilemma. Should they adopt it really

856
00:53:25,910 --> 00:53:29,670
fast before the market, you know, demands it, or should they wait? And

857
00:53:29,670 --> 00:53:33,390
I don't want to spoiler read the book. It's tremendous. It

858
00:53:33,390 --> 00:53:36,950
goes through like research from the 80s and

859
00:53:36,950 --> 00:53:40,190
90s and explained flawlessly like the dilemma of

860
00:53:40,590 --> 00:53:44,230
developing and adopting a new technology right away or should they

861
00:53:44,230 --> 00:53:47,910
wait? There's kind of balance in the middle. I

862
00:53:47,910 --> 00:53:50,070
really recommend it. Awesome.

863
00:53:53,270 --> 00:53:57,070
Awesome. I will definitely check that out. What

864
00:53:57,070 --> 00:54:00,670
about you? What good recommendation do you have that you

865
00:54:00,670 --> 00:54:04,310
read? There's a really good audiobook I'm listening

866
00:54:04,390 --> 00:54:07,670
to now called like 48 days to work. You love

867
00:54:10,710 --> 00:54:12,540
the work you love of

868
00:54:15,020 --> 00:54:18,620
and it's basically idea. I like my job but like you know, it, it, it

869
00:54:18,620 --> 00:54:22,380
really, it's. Anyone from work is listening. No, I

870
00:54:22,380 --> 00:54:25,780
actually do like my job. But like there's like, you know, as you get, you

871
00:54:25,780 --> 00:54:29,540
know, because I'm. I turned 50 not that long ago. Right. And like every time

872
00:54:29,540 --> 00:54:33,260
you have a Birthday with a zero on it. You always have this kind of

873
00:54:33,740 --> 00:54:36,300
how am I doing? You know, tell me about this.

874
00:54:37,500 --> 00:54:40,700
And you know, when I turned 40, I had this crazy idea I was going

875
00:54:40,700 --> 00:54:44,440
to become a documentary filmmaker and long

876
00:54:44,440 --> 00:54:48,280
story, and I went and I really studied up how to do

877
00:54:48,280 --> 00:54:51,840
filmmaking and stuff like that. And then I

878
00:54:51,840 --> 00:54:55,080
realized like how little documentary filmmakers make.

879
00:54:55,400 --> 00:54:59,080
Oh yeah. And I realized, you know, maybe I should because

880
00:54:59,080 --> 00:55:02,440
I was, you know, I was very invested in the Windows

881
00:55:03,000 --> 00:55:06,800
Mobile, Windows phone platform, Windows 8. And then when that kind

882
00:55:06,800 --> 00:55:10,040
of hit was a thud, I kind of realized like, you know,

883
00:55:10,440 --> 00:55:14,280
whatever, you work in technology and like a particular field

884
00:55:14,280 --> 00:55:17,720
kind of flops, you know, that particular niche that you're in kind of flops,

885
00:55:18,040 --> 00:55:21,840
you kind of reevaluate. How did I get here? Right? And it

886
00:55:21,840 --> 00:55:25,320
was almost by chance that I attended a

887
00:55:25,320 --> 00:55:28,520
Microsoft research conference like

888
00:55:28,840 --> 00:55:31,080
over 10, 10ish years ago

889
00:55:32,600 --> 00:55:36,260
where, you know, they were talking about,

890
00:55:36,660 --> 00:55:40,380
you know, AI and like what this is. And at that point I just thought

891
00:55:40,380 --> 00:55:44,220
of, you know, data as SQL and you know, Power BI

892
00:55:44,220 --> 00:55:47,860
dashboards. Like that was my, that was my impression of it. But

893
00:55:47,860 --> 00:55:51,700
when, when I saw that there was an actual engineering discipline to it and

894
00:55:52,020 --> 00:55:55,820
math that will make you go crazy. Like it was

895
00:55:55,820 --> 00:55:59,580
a good technical challenge to get into. And you

896
00:55:59,580 --> 00:56:02,410
know, at the time I was at Microsoft and they were talking about how they're

897
00:56:02,410 --> 00:56:06,170
going to add AI to every Microsoft product, which in 2015 sounded insane.

898
00:56:06,490 --> 00:56:10,130
Yeah, right now, I mean now we see it and like

899
00:56:10,130 --> 00:56:12,970
everybody's adding everything to AI, even if it needs it. Whether or not it needs

900
00:56:12,970 --> 00:56:16,570
it is not really a concern. But

901
00:56:18,170 --> 00:56:21,890
it's, I don't know, like I just. And you know, fortunately that

902
00:56:21,890 --> 00:56:25,730
was the right choice. Obviously people thought I was crazy because I was, you know,

903
00:56:25,730 --> 00:56:29,420
walking away from, you know, years of

904
00:56:29,420 --> 00:56:33,060
like front end development on Windows into a completely

905
00:56:33,060 --> 00:56:36,740
new space and everyone thought I was crazy. But I'm like, nah, there's

906
00:56:36,740 --> 00:56:40,100
something here. And it's, it's fun, it's challenging, it's exciting

907
00:56:40,660 --> 00:56:44,340
and that's that. That kind of explains my current fascination with

908
00:56:44,340 --> 00:56:48,060
quantum computing. Right. Like it's like, you know, it's, it's not quite

909
00:56:48,060 --> 00:56:51,780
there. It's not quite there yet. Right. And people will

910
00:56:51,780 --> 00:56:55,100
argue. Jensen Wong says it'll take 20 years, Bill Gates says

911
00:56:55,100 --> 00:56:58,900
shorter. Some people say three years, five years. It's such in

912
00:56:58,900 --> 00:57:02,080
an stage of a technology development that

913
00:57:04,400 --> 00:57:08,240
we're really barely at the transistor stage. Oh yeah,

914
00:57:08,320 --> 00:57:11,880
here, right. So like it's really like an opportunity to get in and the Math

915
00:57:11,880 --> 00:57:15,639
is hard. The math will give you headaches for sure. But

916
00:57:15,639 --> 00:57:18,960
you don't have to understand all of it to build systems on top of it.

917
00:57:18,960 --> 00:57:22,800
Right. Like, and to understand the impact it's going to have on the industry.

918
00:57:23,120 --> 00:57:26,580
And like, everything. And like everything, the. The smart people will build the

919
00:57:26,580 --> 00:57:30,380
infrastructure layer, and on top of that, you'll have the operation system, the application

920
00:57:30,380 --> 00:57:34,020
layer. And, you know, before you know it, you will build application in an

921
00:57:34,020 --> 00:57:37,860
abstract way without knowing everything that's, you know, underneath the surface. A

922
00:57:37,860 --> 00:57:41,500
hundred percent. You know, at one point, if you were building a computer, you needed

923
00:57:41,500 --> 00:57:45,180
to have an, you know, electrical engineers on staff. Oh, yeah, right. And you

924
00:57:45,180 --> 00:57:48,780
needed to really use those bytes, you know. Well. Right. And how.

925
00:57:48,860 --> 00:57:52,530
How, you know, memory works and how, you know, everything. Efficiency work.

926
00:57:52,930 --> 00:57:56,730
There was one of the mythbuster guys, had

927
00:57:56,730 --> 00:58:00,090
a thing where he talks about a bit from an early computer, and it's about

928
00:58:00,090 --> 00:58:03,850
the size of this water bottle. No way. Something like that. It was. It

929
00:58:03,850 --> 00:58:05,890
was a little smaller than that, but I mean, it was like. And he was

930
00:58:05,890 --> 00:58:09,650
like, you know, it was about that big, and it was somewhere between the size

931
00:58:09,650 --> 00:58:12,850
of this and a spark plug, but it was big. Right. So, like, if you

932
00:58:12,850 --> 00:58:16,330
just think about that, like, and then. Then some other YouTuber did this whole visualization

933
00:58:16,330 --> 00:58:20,180
of what does this look like? What would this look like to have a gigabyte

934
00:58:20,500 --> 00:58:23,620
with those? And it was turned out to be like a skyscraper size thing.

935
00:58:25,380 --> 00:58:29,220
And it was, I don't know, like, to your point. You're

936
00:58:29,220 --> 00:58:32,580
right. Like, the infrastructure layers that we're used to in technology today

937
00:58:33,540 --> 00:58:37,060
are not there yet in quantum. Right. But that also means an

938
00:58:37,060 --> 00:58:40,740
enormous opportunity for those to get in at this level.

939
00:58:40,820 --> 00:58:44,180
You know, whether or not it'll pay off in five years, 10 years, 20,

940
00:58:44,750 --> 00:58:48,390
I can't really say, but it's definitely. I know. It's definitely happening.

941
00:58:48,390 --> 00:58:52,030
Yeah. Well, fun fact. The audience know I know zero about.

942
00:58:52,270 --> 00:58:55,870
Right, right, right. Well, every time I think I understand it, I learned there's a

943
00:58:55,870 --> 00:58:59,710
whole other thing behind it which is both fascinating and, you know,

944
00:58:59,710 --> 00:59:03,230
fun and annoying, but shameless. Plug. I do have another

945
00:59:03,230 --> 00:59:06,510
podcast called Impact Quantum, where we do take.

946
00:59:07,470 --> 00:59:09,750
We do take a look at what Quantum is, where it's at and how it

947
00:59:09,750 --> 00:59:13,430
means, what it means for people's careers and stuff like that. Who knows, Maybe

948
00:59:13,430 --> 00:59:17,190
we'll meet again in decades, talking about. Absolutely. Machinery and construction. There

949
00:59:17,190 --> 00:59:19,710
you go. Well, we'd love to have you back on the show if you're interested,

950
00:59:19,710 --> 00:59:23,350
and maybe talk more about the individual solution, but I really enjoyed our

951
00:59:23,350 --> 00:59:26,990
conversation. Me too. It was a pleasure. Like, thanks for having

952
00:59:26,990 --> 00:59:30,510
me. Thanks for the audience for staying until now. The people who

953
00:59:30,510 --> 00:59:34,310
stayed. Oh, no problem. Oh, one last thing. Where can people

954
00:59:34,310 --> 00:59:37,510
find your company? It's called Bill dots. Yeah. So

955
00:59:37,510 --> 00:59:41,230
buildups.com. like, go to our website, go through everything

956
00:59:41,230 --> 00:59:43,510
that we offer. There's tons of education,

957
00:59:45,110 --> 00:59:48,950
you know, case studies, webinars, you know, we're talking, we're all

958
00:59:48,950 --> 00:59:52,509
the way in social media. Go through LinkedIn to either build

959
00:59:52,509 --> 00:59:56,190
out's profile or to my profile. We're happy to chat

960
00:59:56,190 --> 00:59:59,670
and we're happy to geek out. I mean, eventually we're construction

961
00:59:59,670 --> 01:00:03,030
geeks. Love talking about technology, love talking about

962
01:00:03,320 --> 01:00:06,840
construction. So reach out. We'll have to chat. Awesome.

963
01:00:06,920 --> 01:00:10,520
And it's build. Ots.com, right?

964
01:00:11,160 --> 01:00:15,000
No, it's Build. Like a build. Like to build something. Dots.

965
01:00:15,720 --> 01:00:19,000
Oh, build dots. So two Ds. Yeah. Yeah. So it's got.

966
01:00:20,360 --> 01:00:24,000
I'll make sure that the correct link is in the, in the

967
01:00:24,000 --> 01:00:27,800
description and thanks for your time. And we'll let our AI

968
01:00:27,800 --> 01:00:31,570
finish the show. And that brings us to the end of another episode

969
01:00:31,570 --> 01:00:35,010
of Data Driven, where today we learned that even construction

970
01:00:35,010 --> 01:00:38,810
sites can be smarter than your average smart fridge. Huge thanks

971
01:00:38,810 --> 01:00:42,650
to Amir Berman from Builderts for showing us how computer vision isn't

972
01:00:42,650 --> 01:00:46,490
just for spotting cats on the Internet. It's for keeping billion dollar projects

973
01:00:46,490 --> 01:00:50,330
on track. If your idea of a digital twin was a dodgy sci

974
01:00:50,330 --> 01:00:53,970
fi plotline, well, now you know better. Don't forget

975
01:00:53,970 --> 01:00:57,810
to like, share, subscribe, and maybe send this episode to

976
01:00:57,810 --> 01:01:01,210
the construction manager in your life. Until next time,

977
01:01:01,370 --> 01:01:04,810
stay Data Driven and maybe wear a helmet just in case.