The a16z Show - a16z Podcast: Construction Under Tech -- The Build

Episode Date: June 14, 2018

with Martin Fischer (@fischermartin), Saurabh Ladha (@ladhasaurabh), Chris Rippingham, and Hanne Tidnam (@omnivorousread) Continuing our series on how tech is changing construction -- one of the industries most resistant to change (and facing declining productivity) -- this episode of the a16z Podcast looks at what happens when you go from planning to actually putting boots on the ground. How can tech translate rich data sets into the just-right types, amounts, and levels of information for each different piece of the incredibly complex, dynamic, time-and-space problem that is a building site? Martin Fischer, professor of civil and environmental engineering at Stanford; Saurabh Ladha, cofounder and CEO of Doxel, which uses AI to real-time measure progress and inspect quality on construction projects; and Christopher Rippingham, who leads technology and innovation leadership for nation-wide commercial contractor and manager DPR Construction discuss with a16z's Hanne Tidnam how AI is introducing something fundamentally -- no, foundationally -- different for the construction industry: the feedback loop. The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information. Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The content here is for informational purposes only, should not be taken as legal business tax or investment advice or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. For more details, please see A16Z.com slash disclosures. Hi and welcome to the A16Z podcast. I'm Hannah and in this episode, we continue our series on how tech is changing construction, looking at what happens when you go from the planning phase to the building phase. The conversation includes Martin Fisher, Professor of Civil and Environmental Engineering at Stanford, Saurab Luddha, CEO of Doxel, a company that uses artificial intelligence to measure progress and inspect quality on construction projects in real time, and Christopher Rippingham, who works on technology and innovation leadership for DPR construction. We've talked a little bit about the lay of the land already in the construction industry, some of the big challenges and new technology around the flow of information.

but what happens when you get to moving into building the buildings, when you get into execution on the ground and productivity and performance management? One of the biggest challenges we face right off the bat is trying to take the rich data sets that are already inefficient from pre-construction and migrate that into something that somebody on a construction job site can use or read or fabricate from. And what kind of data sets do you mean by that? Like the 3D models, building information models, that kind of information, how do we make that information accessible in the first?

field all the way down to RFIs, you know, written information to plans and specs, you know, from the architect and engineers. All that information is either dumbed down usually or put in a format that is not quite as rich or usable or has the dynamic state that, say, a dataset would have within pre-construction. And when it's dumbed down, is stuff stripped out of it? Sometimes, yes. It depends on what kind of information's in there. What we try and do is only give the information that's needed to do that specific task.

The goal is to give location awareness, you know, role awareness, that kind of information to a specific worker on the job site. So you're kind of like fuzzing out the irrelevant stuff and crystallizing the relevant stuff. Right now we're basically giving an entire set of plans over to a construction worker in the field and that could be thousands of pages of information that maybe only a handful, you know, 100 pages are useful for that one specific person. So an electrician goes into a site. Yep. And he has literally the entire set of documents. Yeah. And so different useful technologies now are helping make that data set smaller and smaller. But meanwhile, the job site is a very dynamic environment. It's really, frankly, crazy if you think about it. You have a group of architects and

engineers that create drawings or even 3D model information just in case, just so they can sort of show we did a lot. And then the reality, they have to go through there and filter out information that is relevant to them. And then any uncertainty in the information that exists from the design phase gets multiplied in construction. It doesn't get simpler, right? Because then you have to figure out how to allocate resources, what sequence you were going to work the site logistics. Imagine that now for 20, 30 trades coming together on any given day. And that's supposed to work like clockwork.

But there's uncertainties, vagaries, and everybody's variability and everybody's schedule there. And then you compound that with uncertain design information. And that's why we end up with this kind of unproductive chaos that we often see. What is the human result of that? Are there people sitting around waiting for deliveries? Are there people not coming up to their jobs and they're not ready for them? How does that actually look on a human scale? I mean, there's a whole concept of onboarding to a job site.

It's hard for workers just to come out and start working that day one. That's true. It's like starting a whole new environment. They come out and they're off to the races, but that's not really the case. They really have to dig into the information, find out what they need to look for, make sure there's not other details that have been created somewhere else, make sure that the details that they usually use are actually incorporated into the set of documents. There's this issue of trust on a construction job site because, like an engineer doesn't always trust that. The subcontractors know what they're doing.

And so they put in all these details that they think are the best details that work. but they've never installed anything in the field. Meanwhile, a field worker has to come through and look at those details and say, I can't actually build it this way. I have to do it this way, which means we have to put in a request for information in an RFI in construction terms, and then it has to have more communication back and forth with the architect or the engineer, which adds more and more complexity onto this already complex work environment. So this trust situation, that's really interesting.

How variable is that? Are there different sites where there's a higher degree of trust, or is there a general point of low trust across the board? There's definitely different contracting types that help with that. But the traditional design, bid, build atmosphere doesn't lend itself to that trust. But when you get towards integrated project delivery or design build type contracts, there's a lot more trust that has to be established with the design team. There's a lot more opportunity for subcontractors to be involved in the process earlier,

which allows them to provide those details that are constructable that they'd actually use in the field. This has been something that has been a bit of a surprise to me, because I would say I have seen more initial adoption of advanced technologies in projects with little trust. Somebody says, ooh, this is a really good tool. I can defend myself if necessary. I can be on top of the food chain

because I have all the information that the others don't have, etc. That's really surprising because you would think that... Yeah, you would think that on collaborative projects, they would say, we can do all of this great stuff.

It's not what I have experienced on project. Then, from there, it does grow into people then realize, oh, wait a minute, we could actually work together. Well, let me ask maybe a kind of dumb question, but isn't the job of the general contractor to kind of be like the central, the heart of that trust. So it's a lot easier said than done.

And if you actually zoom out and take a look at the construction industry, 98% of construction projects above a billion dollars run on average 80% over budget. That's so much over. I know, right? Don't you just feel the dollars leaving? Right. It just feels so sad. But the reality is that general contractors get a lot of flack for that

and understanding how challenging the job is physically with human limitations is important. There's a very popular notion that, hey, manufacturing has doubled in productivity since 95, and construction has stayed flat. In manufacturing, they're doing the same thing again and again, and they've got real-time feedback. So if something doesn't go as planned, you can intervene in three minutes and fix the problem. Whereas in construction, you're building this massive thing once with different people and materials every time, and you're expected to get it right. And every step is different. Every step is different, and you have to do it all without any real-time feedback. If something is installed half an inch off, it could cause a massive ripple effect on the dependencies.

So there are just some human limitations in assessment of quality and progress just because of the amount of stuff that's going on on a construction site. Well, let me ask you about when you say without any feedback, I mean, isn't the contractor standing there assessing, keeping track of things, even with a clipboard? There's an attempt at feedback, wouldn't you say? Or no? There is an attempt at feedback. Yes, we do have quality assurance programs that are put in place, and we do have people doing visual checks. And we do have a lot of walks with the architect. You know, we have things called rolling completion lists that are things that we need to complete that the architect would like to see happen. But there's a lot of human interaction there that doesn't always capture the true intent of the design. You know, we have the same of measured twice, cut once. And it's like, do quality check that twice after that. Right, right. And it's eyeballs and voices right now, basically. We do a lot of model-based layout and trying to double-check that the models meet that, but it is still a fairly manual process.

So in the study of 19 projects from 19 different companies, who just span the globe literally. And our research has shown that people don't have feedback loops, period. Wow. I mean, just informal. No formal. And certainly not on a project, not from week to week, not on projects, not across projects, not across the company, not one,

whether it was on schedule, whether it was on performance, on quality, we could not find it. There was a lot of use of building information modeling. There was a lot of using construction management methods. But nobody actually really formally closed the loop. To really close the loop. You've got to have a plan that matches how you're going to do the work. And that is actually much harder to do because of the scale and demand of nature.

And that's where really the technology we're seeing now emerging will make it easier to create a plan that matches how you can, collect data in the field. So you're saying that that initial fracture kind of happens at the very beginning because of plan versus execution. And then that ripples out in a million ways and makes it impossible to get feedback. DPR tries very hard to create a plan from the initial set of documents or at the very beginning of the project. But then to continuously execute on that plan has to be revised because the job site is so dynamic and there are changes coming down. Like what kind of changes? How often? So if you were building a hospital or something, you might have a job walk with one of the

doctors the day before you're supposed to be installing overhead equipment. And he says, no, I don't want that boom here. I want it here. And that boom actually affects all the structural steel that's above. It affects all the MEP that's above. And so there's a lot of that dynamic changing. Wow, that sounds really miserable. That's the role of general contractors to really try and manage those design changes really work with the owner to make the building what they really want it to be within the timeframes that they have within the budget they want and with the quality that they want. So what are some of the kinds of technologies that we're starting to see come in to start knitting that initial fragment or that are helping in that feedback loop.

The big difference really in the last two years has been the ability to sense what's going on on a construction site. That's something we have never had in the history of construction, right, 5,000 years of building things by humans, from visual to Fitbit type things, to vibration, to, you name it. That's basically the core of what Doxel does. And Doxel is essentially a computer vision solution that uses. autonomous robots to scan both the indoor and the outdoor of a construction project on a daily basis. And then we use deep learning to automatically extract how much work has actually been done

and how much of that work has been done correctly. So what you start getting is this dashboard which tells someone in finance how many dollars of work has been installed today versus how much money are people asking me to pay. People can compare schedules, right? How are we doing in actual versus plan, and then of course you get the quality piece, which is stuff installed correctly. So it's that real-time feedback system of manufacturing brought to construction. We cannot possibly maintain manually a plan at that level of detail every day. So you literally tried this kind of manually?

We have tried this manually and we realized that's not really possible. Because you drown in the data? Well, because you've got a way to late in the day to learn what happened that day. And then you got to create a plan for the next day in like, you know, seconds quickly while the managers of the site are still there. So they can look at the plan for tomorrow and say, yeah, this is good or this is bad. Because otherwise they're not going to stand in front of the people the next morning at 6 and say, this is what we're going to do today.

I mean, if there's one mistake in there, like, Pat, you already built that. Or that needs rework. Don't you know this? Back to the trust, right? Yeah. Their credibility just dies. And so that's where we just couldn't keep it up, right? We were able to keep it up for two or three days and then just eventually the reality and the plan fell apart.

What I'm excited to see is the combination, the tools that automate the planning. And the sensing and the technology to abstract what really happens on the site into actionable, a managerly actionable information that can be matched against the plan to actually bring the daily feedback and the reality. Like this work really takes this long back into the planning so that we can make better. And increasingly make better predictions. Like not just on a daily level, but on a gigantic planning level. Eventually, we will have all of this data, you know, like a baseball coach has this kind of data for who to send out for pitching in various situations.

But we don't have it in construction. Oh, that's so funny to think about, yeah, how accessible it is in other certain enclosed finite scenarios. What are the new kinds of data that you're factoring into your decision making? We're just trying to use historical data is a big thing for us. You know, prior it was just, here's a sort of plans. go build it and on to the next one when you're done. And now it's like, how can we leverage that data to make our business better?

So productivity rates and stuff like that, if we can have very accurate productivity rates, it helps us on our estimating projects for the next projects. If we're getting schedules from our subcontractors saying it's going to take two months, but in reality, we know we have this much to install with this kind of productivity rate, we can now say, well, it should actually be this. This illustrates very nicely how, in my experience, the technology actually builds trust. Everybody functions on the beliefs and the things they remember. If we have the data, we can stall to gain trust in each other.

One of the big challenges is just getting the right data, because let's say that you know that a particular assembly takes six weeks to install, right? You have no idea at an object level what happened in those six weeks. You've got human reports that I got 10% down, 20% down 30%. You have no idea if that's true or not. But what do you mean at an object level? Well, a construction project consists of millions and millions of objects. and every one of those objects takes a different amount of time to install.

It requires a different components parts. It's reminding me of the escape room I did on Friday. Yeah, pretty much. Find the key to insert in. Exactly. Building off of that analogy, if you knew exactly how long it takes for each step in that process and you see a new escape room, which has half of those components but doesn't have the other half, you can now suddenly use that data because you have a history on it.

On the other hand, if all you know is that, you know, escape room allows. Ladin took six weeks and escape room pirate took eight weeks. What good is that for your estimation? So a big thing in construction right now is how do we get hyper-granular data on how long things are taking to get installed, how much labor it's taking to get them installed and things like that. So it's surprising that you can get so much just visually. Is it just visual this data that doxel is collecting or are you also collecting? Because like how do you know if a switch has been installed, the wiring has been connected in the wall? Like how do you get that granular from just a vision point. Yeah, that's a good question. So we use vision as well as

LIDAR, which tracks stuff to two millimeters of accuracy. We can't yet detect things like wire pulls. Having said that most of the issues as well as the progress challenges, the progress management challenges in construction take place at the MEPF stage, the mechanical electrical plumbing and fire stage. And the reason why that happens is because multiple trades own the site and have to share it. So, backstory, right? You start construction with excavation. Pretty much one trade owns the entire site, right? Then you move to foundation, structural, so on and so forth. There's just one trade that owns the whole site. So things move. They're running the show in that phase. It's just them. The moment you get to MEPF, it's this ballet, which has to work

perfectly well. If I'm a day off on getting my fire sprinklers installed, I could hinder the duct guys from coming in and doing their job. And is that because of efficiencies of timing largely, or are there actual mechanical issues as well that when everything is not perfectly choreographed? Oh, both. It's like, you know, not to sound too trippy, but it's a very time and space kind of problem. If you don't install what you promised at the right time and at the right place, which could sometimes be half an inch, quarter inch accurate, you could create like a massive ripple effect. And sometimes these issues are discovered two or three weeks later.

There was a project on which the Robinson deck was installed incorrectly. Someone put in the wrong deck, poured the concrete. The concrete had all set, and basically the entire floor was like wrong. How often does the ripple effect get that big? So 20% of construction costs are usually reworked costs. Wow. It seems on most projects, there's not a single project where things in the right place land for the furniture.

And then, of course, the people notice, and so all the furniture is wrong. It's so true. Even on like minor home, I had a friend redoing a bathroom, redoing his bathroom. Exactly. In Palm Springs and it looked gorgeous until he sat down on the toilet and his feet couldn't touch the floor. And then he was like, okay, wait, this is weird. Something's off here. I can't imagine when you extrapolate out to projects that are hundreds of million dollars.

Another key difference between manufacturing and construction. I mean, there's also many similarities. We create physical products and so on. But in manufacturing, you don't have to deal with the workspace. in construction you have to allow for the workspace that the work crew needs and the space is changing over time. In construction, it changes every day. The workspace you have, the workspace you create, the workspace you need,

for many different reasons, safety, you know, just productivity and so on. So that's something that is very unique to construction. That's very challenging. And if you think back to manufacturing, what made the improvement possible there is to look at the installation of every object, of every part. And then to really align the design and the manufacturing methods

to support each other. I just see this incredible improvement possible not only for the construction process but then for the synergies of design and construction that have been created by having this granular data. And so to jump on to the 20% of construction is always rework, what excites me

is that you can catch that a lot earlier. And so maybe we're able to cut that 20 down into 10% because we've caught errors in a day instead of waiting two weeks to find out when the next trade is trying to install and that can save us a lot of time, you know, cut down that rework. And so there's a lot of potential there. And that's what gets us excited about that feedback loop. Right. You don't necessarily even have to solve the problem perfectly. You just have to shave a little bit off that 20%. Yeah.

It's getting that feedback loop to know there's an issue and then take care of it. So what does this actually look like on the job site? Describe to us how this data is being collected. It's an autonomous vehicle. How does it move around the different terrain? Sure. What does it need to know in this constantly shifting environment? Also, how is it making decision because presumably at the end of the day, you're still presented with this volume of data. I mean, how is that parsable in a moment of five to ten? I don't know how long it takes to change your plans for the next day to shave off, you know, a little bit of that percentage. So, first of all, you're not presented with the volume of data. That's what the AI's job is.

It's to translate the data into a digestible format. So on DPR, for example, DPR is one of the fastest moving general contractors that we've worked with. I'm not buttering you up. That's very honest. And by fast as moving, you mean to completion of a project? Schedule-wise, it's just the furious space, which we've not seen before. So usually we do like once-a-week reports, and many contractors as well as owners have told us that's a little bit of overkill, right? Because we can't move the field that fast. DPR is doing two reports with us a week, and they're saying, can you do it every day? So what do you attribute that fast-moving culture and pace to? Well, the pace is generated by our ability to plan. And so the job that Sorab is mentioning is to lab job.

We modeled the entire job all the way down to, you know, three-quarter-inch conduits. So everything in the wall was modeled, everything above the ceiling was modeled. So that way everything was being fabricated off-site, and we have a lot of shared rack systems that are coming with mechanical, electrical plumbing, and fire protection systems already installed on it. And so these systems come out pre-manufactured. And so it's getting us more towards a manufacturing where it's more of a kit of parts that we're installing now, as opposed to, you know, cutting every piece of conduit in the field and then having to measure it and install it. Like Legos. Exactly. We're trying to get more towards an entire Lego set to put a building together.

And so we're doing all that through the use of virtual design and construction or building information models. And that's how we're getting to that really rapid pace of getting MEP installed. And then now once we get a feedback loop of making sure that it's installed correctly, we're able to make sure we're not missing something or find those errors early in the process. So that way, if we're running fast and we have an issue on day one, you know, if we wait until day five to find that, it's, you know, it could have been, you know, day 15 on a normal project. What are the kinds of things that you catch? It'll be things like, you know, main duct work is installed two inches off from where it should have been.

Just inches a lot of the time. But you only have inches. Yeah. Because otherwise you have to build your building bigger. That means more structure. That means in an earthquake zone. I mean, it's just the ripple effects on cost are just tremendous. Especially on an additive process of known as construction.

You know, an inch here and inch there adds up to two inches, which could ultimately be an issue. We have found enough that it's been valuable for our team to know exactly what's installed and when it's installed. The biggest piece which I think seems to be grabbing the most eyeballs is the schedule update. Construction schedules look like they're basically Gant charts, right? They're these massive Gant charts which are showing you dependencies, right? And what our tool does is once these robots go in and do their thing and capture the data, the AI updates that schedule completely automatically and shows you in the same Gant chart exactly where you stand on schedule. So the general contractor isn't standing there reading a report.

It's direct to schedule. Exactly. They're looking at the same schedule that they look at every week in their schedule planning meeting, except that this time it's updated with superhumanly accurate reports from the field generated by AI. Is it clear why? Do you get sort of reports on like, well, this is why? Because this electrician slept late. Is it clear or is it invisible behind the scenes?

So that piece really is decoded very quickly by the superintendents on the site. Our job is to tell them that there is a problem. and oftentimes they don't even know that there's a problem. Like there have been multiple conversations, and this is just in general, going back to the trust issue, right, where someone says something is installed or someone thinks that something is installed and it happens to not be installed, right?

Maybe there was a communication gap or whatever led to that. But it's very, very important for the top decision maker who the buck stops with on that project to know that it's not installed so that they can reorganize the field based on that. So that's basically the value of the AI that it translates it into a readable, format rather than giving you pictures or laser scans or something of that, which is just

counterproductive. How are you training your AI to recognize different kinds of iron beams and different kinds of flooring? Do you send it out into, is it learning as it goes? What are the challenges behind that kind of AI? So the state of the art in computer vision prior to say deep learning is either one of the most popular techniques is a thing called SVM, right? Support vector machines. So support vector machines. So support vector machine's SVM is basically this machine learning-based approach, which trains a computer to recognize a certain type of object with a certain type of sensor in a certain type of environment. And it takes a smart engineer maybe two to three months to program it for something super tough. Sometimes it

could take just a week, but the point is it's customized programming for every type of object. And the moment you change the sensor type or the type of environment changes, it doesn't work. So, for example, if you train a system to recognize a pipe, but when you go into a different type of construction site where that pipe looks a little bit different and it's blocked and you can't see the entire pipe. Right. This does not sound like it would work. Exactly. The computer would just be like,

oh, this is an alien object. I've never been trained to recognize this. Deep learning was just beginning to show a promise in 3D computer vision. And the value of deep learning is that you can achieve a higher layer of abstraction where it can train itself on recognizing all these, different types of objects in different environments with different sensors. So you don't have to now program 20 different types of objects for every construction project. You've got the same neural network that's carrying over learnings from construction site A to construction site B. And furthermore, it can do that with different sensor types. So you're not bound to the same sensor. As sensors evolve,

the same neural network can adapt and can learn and can use learnings from sensor one and translate it to usage from sensor two. What were some of the choices? What were some of the choices? the wah-wah blueberry muffin challenges that you guys struggled with? I think the biggest challenge, frankly, was the fact that there was no publicly available work, which had shown that the same quality of recognition could be achieved with 3D data as with, say, 2D data. Like in 2D data, image net was the state of the art.

So a giant reservoir of visual representation of construction sites, basically, would have been ideal. It's not just that. It's also about the core logic of how, computers learn how to deal with 3D shapes because it's completely different data. And just on the core algorithm level, there wasn't as much research out there as with, say, 2D computer vision. And then the second challenge was how do we get all the data?

We have the highest accuracy in 3D and construction environments in any publicly available project that I know of at this point. So in my unfair ratting out of the electrician sleeping late, could it feel that way on a human level? Like, do you have this sort of robot following you around reporting on I've done this correctly. I've done it on time. It definitely could, but if we go around just pointing fingers at people saying you did something incorrectly, they're never going to want to work with us again, and we're going to go out of business sooner rather than later. What it is useful for is to facilitate those conversations, because if we don't catch something, then it's going to become a bigger issue down the road that could lead to, you know, in the worst case, litigation.

And so if we present the data in a way that it's more collaborative of, hey, we're coming back, we found this, let's go talk about this real quick, get it fixed now. So that's not a larger issue down the road. You gain the trust of people at that point. And then also, you know, after you've created that kind of culture, people actually want to bring some of the issues to you? Or a lot of the time we talk about... Oh, it's a culture shift. Yeah, definitely. And like, we always talk about go-back work and construction of, you know, the electrician installed everything, but that one box because he didn't have it on site that day.

And he's going to go back in three days and go install it. Usually, you wouldn't catch that as something that happens, but that one box being installed could affect the HVAC guy that's installing that day. They know it's not installed. It's going to show up on a report as not installed. So instead of waiting, now they're coming to us saying, hey, I don't have this on site right now. What can we do to make that shift? And so we can have that discussion with all the other subcontractors in the room and say, hey, can the electrician get into install that box on this day?

Is it going to cause an issue yes or no? And so it creates more of a trust throughout the entire job site, as opposed to deteriorate as long as you use the information in the right way. It's a big benefit to find the mistakes, absolutely. But it's actually, I think, also benefit to document all the things that were installed correctly the first time. So then you can actually get a quality score for your subcontractor. We focus a lot on the mistakes and we have to. But I think it also good to know, hey, this subcontractor installs on our projects, 99% of the things correctly the first time.

This other one only 95% or whatever. And you can show the owner creating that trust as well, I think, is important and that data. And from a business perspective, I think this technology does really two things. It improves the predictability. And the second thing, it reduces time to market. And then in the kind of technology complex projects that the EPR is building, that is super important. Because if your time to market is too long, the technology will change. then you have to do rework because of that.

Can you give an example of what that might look like? So they take a hospital and they commit to a particular MRI machine and then it takes a long time to build the down thing and by the time six months before the hospital turned over a new one comes on the market and like they don't want to open the hospital with an MRI machine from three years ago. But then that one will have different foundation requirements, different electrical connections, etc.

And you are having a lot of fun with... Trying to manage that. And so if you can shrink that time to market, then the client still gets what they ordered in a time frame that's useful to them. If you look at anything in the world, trust issues exist because two people have different versions of the same facts, right? If both parties trust a third party to come up with that version of facts, which they believe is more objective, it inherently increases trust if both parties begin trusting that. Not just for general contractors and owners, but also for subcontractors. because if you actually look at how subcontractors get affected by this lack of trust, they often get affected in the form of cash flow.

A subcontractor says, hey, Hannah, I'm 25% done. Could you pay me 25% of my budget? Of what you owe me? And Hannah is thinking, damn, if this guy is 24.9% done and there's like one pipe installed in the wrong place, I'm going to be liable for a three-week schedule delay when I discover it four weeks later. So Hannah doesn't pay Mr. Sub-contractor. Despite the fact that the sub is claiming.

And then trust is eroded again. Exactly. Now instead, if Mr. Subcontractor comes up and says, I have this 2mm accurate laser scan and the world's most sophisticated artificial intelligence, shameless plug, saying that I'm 24.86% complete. Could you pay me? It gets a lot more objective. So it goes far beyond just the job itself. It starts affecting project financiers. It starts affecting project insurers who get more transparency on the job. We see just all of these wasted efforts, frankly, from many, many players, from subs to GCs to owners, designers, we see all these wasted efforts that will go away. With AI, I can do more things more consistently that I'm already doing. So that is, let's say, in this case, apply a particular inspection routine more consistently.

Then I can do it better because I can... over time, realize, okay, this works 99.8% at the time. The feedback loop. And then I can do new things that I wasn't able to do otherwise, because I can learn. We cannot look across data from 50 projects and make sense of it. That's super cool. From the construction side, one of my goals is to make our teams more efficient

and to get our teams and our people solving problems instead of doing non-value-ad tasks. And going around and looking at a model to compare it against what was installed doesn't necessarily a value-ad task. If we can automate that, we can get the people that would be doing that job out in the field, solving more problems, working through more changes, making sure the owner is getting what they want, all those kind of things that would trickle down from there. And so for me, that's a huge benefit.

Maybe we can quickly touch on the regulation. Is there, you know, regulation is notoriously difficult around construction sites and introducing new technologies even down to. We talked in the other podcasts about like even when you wear your glasses and what it's like to bring an iPad into the field. what's it like to bring an autonomous robot into the field? How does the regulation work around that? As far as the actual robotic equipment that we're bringing in, the hardware is nothing new. We're using sensors that have been used on construction sites in the past. We're using things like cameras. So it's not rocket science. The hardware isn't particularly novel. It's pretty much off the shelf. So from that perspective, you know, from a regulatory standpoint, we haven't had as much of an issue.

Of course, we also capture data with drones. So there, of course, you have a standard process, which is, you know, FAA Part 107, and you have to have a certified pilot and things like that. But other than that, you know, we've not faced as much of any challenge. When you use the drones is using the airspace of a construction site, isn't that a challenging thing in terms of also coordination and timing? Yeah, it is, actually. FAA guidelines don't allow you to fly over anyone who's not in direct operation of the drone.

So we only do that after hours. Now, having said that, it's a very little known fact, but 80% of construction money is spent indoors anyway. So the 20% that's captured with drones, it just goes by super fast. Well, that makes sense, actually, when you think about what a building is like. Yeah, exactly. Now, to touch upon a point you made about regulatory authorities in construction, it's actually a huge deal. And, you know, we do a lot of work with healthcare, which is one of the most regulated construction spaces around. And a lot of the events in the construction site are actually planned around when the inspectors.

will show up. My kind of dream, be able to have as much trust in our technology as general contractors and owners do to have that same level of trust, you know, exist with the inspectors. Because now all of a sudden they can remotely take a look at what's getting installed, what's not getting installed, trust the quality that things are getting installed by, ensure that it is indeed the plan that they had stamped off on. So you don't feel like you're showing up and you're going to trial one day. It's just there's a constant flow of information and you've been doing things right all along. It's just about the logistics of managing an inspector showing up on site.

I mean, every site that I go to, there's a board that says reserve for inspector. Like, that's a reserve parking spot for the inspector, right? Because they show up on site, and then sometimes you'd have to have some parts of your wall exposed. Usually right next to the red carpet. I'm sure sometimes you guys don't close up walls and stuff so that inspectors can come in and take a look at it. Especially the Office of Statewide Health Planning and Development Work. They're the most stringent regulatory agency, I think, in the world, based on the seismic requirements for a state of California. That's why building hospitals has been so expensive in California for so long.

Because we actually want to use them after an earthquake. Now, imagine if, you know, Oshbott trusted tools like this and just said, okay, yeah, cool, you can cover it up, right? Just keep doing your work. We have a system of record, which is showing us this stuff got done correctly. Gathering this kind of information must feel like a gold mine is opening up. What are the kinds of questions that this kind of new data makes you want to ask? If we go way past how efficient is this job site and we turn all these job sites not into little labs by day

but multiplied by tens of hundreds, what are the kinds of questions, the big questions that we might start to get answered? I think from a societal perspective, if you can cut 20% of the rework and then you could make construction 20% more efficient in other ways, which seems entirely within the realm of feasibility.

You're talking improving construction by 40%. In most economies, let's say a rough number is construction represents 10% of GDP. So imagine freeing up 4% of GDP to do something else. Yeah, that's a powerful new resource. It's crazy what you could do with that amount of money from there. Now, from a project and company perspective, I think being able to connect the business purpose of hospital, fabrication plant, whatever, is being built. and translating that into actions that take place,

strategic actions that you have to do every day on the project to meet that business purpose. I mean, that's a connection we don't have today. And those are, I think, the kinds of questions we can ask. What kinds of designs will enable this kind of production or what kind of flexibility you seek maybe? So connecting really the client purpose with design and construction and aligning those, that's where I see the foundation being created.

It actually, it's interesting that it sounds like an opportunity to rethink a lot of the foundational assumptions we've been making for a long time. I think once we have these feedback loops, we will have a construction industry that we haven't known so far. The one thing I would add is to be able to leverage this data from a construction standpoint in applying that data that we find, such as productivity rates or how long, you know, procurement, lead times, et cetera, into objects that we then use for design purposes. And that creates an even bigger design loop of now if we provide construction objects to a design

to use in their design, now we know what the cost is, what the schedules associated, or productivity rates, whatever it's going to be. And that way we can almost start utilizing that information right off the bat to start building estimates and schedules with the design information. So like when an architect or an MEP engineer is going through to create their design, whether it's a linear foot of duct work or if it's a door, if they can populate a door that knows the lead time on this door is six weeks. You know, the frame is seven weeks.

And if we need to have that door there in two weeks, we should probably not be using that object. And so if we can provide that kind of information up front of the design, there could be a lot of benefits there. Or if an architect is starting to create their design, they drag a wall and see that the wall is, you know, $100, $200, $300, $300, $100. All that context is right there. And now they can start using cost information to help create their design as opposed to create a design, wait for an estimate, go back and revise the design to try and meet an estimate. You know, there's a lot of that back and forth that could be eliminated if we start providing that real-time feedback with this giant feedback loop of cost and schedule.

Change's decision-making from day one. We did actually a test of that. For steel structure, we were able to get the data up. pull together and we were able to cut the cycle for feedback from eight weeks that you have today in terms of creating a design, you know, getting the estimates for fabrication, et cetera, getting it back to a few hours. I think today it could be minutes and reduce the cost of a structure by 13% and make it 20% faster to build. I think what both of these guys are talking about is fundamentally how data captured in the

field is going to make planning even better. Every time a construction design is built, the estimate of how much something costs and how much each object costs is completely fresh. Ultimately, any construction project is a trade-off between budget, schedule, and quality. So as you start getting these insights from the field, it just becomes easier and easier where you can optimize better for your owner. Like maybe it's an owner that doesn't care as much about budget, but cares a lot about schedule and quality. So to go from design to reality, to how you measure reality back to design again.

Exactly. Yeah. That's a big impact. Well, thank you guys so much for joining us on the A16C podcast. Thank you. Thank you.