AI Failures in Construction for Business Leaders to Fix Workflow Gaps with Connor and Hugh

0:00 [SPEAKER_02]: On this week's show, how to do AI with Connor Schmelling and Hugh Seaton from Quantum Rock. [SPEAKER_02]: Alright, that's right. [SPEAKER_02]: It's one host. [SPEAKER_02]: It's two guests because if you're gonna figure out how to do AI, it takes more than just me. [SPEAKER_02]: I hope you guys got that.

0:25 [SPEAKER_02]: It was tongue-in-cheek on the way in. [SPEAKER_02]: This is gonna be a fun one. [SPEAKER_02]: You see, it's good to see you back at the hard cast microphone with me. [SPEAKER_02]: How are you? [SPEAKER_02]: Good. [SPEAKER_02]: Good. [SPEAKER_02]: Glad to be back. [SPEAKER_01]: Where are you [SPEAKER_02]: Austin Texas where I'm happy to say it's 82 degrees in sunny see I I can't say I want it to be sunny or I want it to be warm it's cold and man Do we need snow up here in the mountains we just got a little more so hopefully see some of you folks out here for ski and later But Connor welcome to the show first time or how you doing appreciate it Jeff doing wall.

1:03 [SPEAKER_02]: Thanks for having me on where we got you coming in from [SPEAKER_00]: We're at the Quantum Rise Headquarters in Chicago, Illinois. [SPEAKER_00]: So not quite the eight degrees, but we're good. [SPEAKER_02]: Shit, Chicago. [SPEAKER_02]: One of my favorite places for conferences, one of my favorite places in the summer, not on the winter, in the summer.

1:19 [SPEAKER_02]: You know, actually, there's like this perfect window time that we seem to go there. [SPEAKER_02]: So, uh, but one of, not on the last one of my favorite, and if you're an architecture buff, then awesome place to go for architecture, man. [SPEAKER_02]: Take the architecture tour, right? [SPEAKER_00]: It runs right past my condo on the river, [SPEAKER_02]: that's awesome well listen you know we'll dive in you know we'll have a lot of fun but before we do y'all know the deal but I'm gonna be honest with you you're not doing it as much as I would like you to do it so go to youtube.com forward slash at the content crew hit the subscribe button hit the like buttons give me some feedback tell me you love it tell me you hate it just tell me you're there all right we're back

2:03 [SPEAKER_02]: Here I'm going to let you kick this off because it was a brain child between you and I that made us want to come on and you know sort of talk about what's going on right now. [SPEAKER_02]: So talk a little bit about, you know, where you been and where you are right now. [SPEAKER_01]: Yeah, so I've been talking about AI for a while and working with the guys to quantum rise for gosh nine months now and have joined more formally, you know, this is an amazing time for AI course I'm going to say that, but you know, I like to make the analogy of the internet where.

2:31 [SPEAKER_01]: Everyone thought the internet was amazing and had a lot of potential in like 1998-1999, but it was nowhere near where it got to be in 2007. [SPEAKER_01]: And we're kind of there. [SPEAKER_01]: We're kind of like in the 1999 era where there's some, you know, a lot of promise, a lot of interesting things the fact that a billion people are using chat bots is obviously a good sign.

2:51 [SPEAKER_01]: But we're still really early, and there's a lot of figuring out what going on right now. [SPEAKER_01]: You know, jokingly said, how to do AI because we get that. [SPEAKER_01]: We get people saying, look, we know we need to do AI. [SPEAKER_01]: Can you help us out? [SPEAKER_01]: And a lot of what we wanted to do is taking that big question and turning it into something useful.

3:06 [SPEAKER_01]: And there's a lot of that going on. [SPEAKER_01]: There's a lot of people that feel [SPEAKER_01]: some anxiety that we're going to fall behind, which is, you know, it's justified to some degree, but I'll often say, this is so early that you're doing the right thing by getting into it now, by trying it out yourself, by using it on projects, by using it, you know, doing internal implementations, which we'll get to a little later.

3:25 [SPEAKER_01]: Now's the right time. [SPEAKER_01]: No one's really behind yet, you know, unless you're a big foundation model in which case, you know, it's a whole other thing. [SPEAKER_01]: This is a good time to be thinking about processes, to be thinking about how it might fit in. [SPEAKER_01]: That's a lot of what I spend my time doing.

3:39 [SPEAKER_02]: I think that's really important because the distinction there is if you're building AI models, et cetera, you're a high, sure. [SPEAKER_02]: But that's a select group of people, and in fact, it's getting even more select and shrinking down. [SPEAKER_02]: What you're saying is if you are thinking about operationalizing AI and really bringing it into your organization, you're not nearly as far behind as the [SPEAKER_02]: thing, which is part marketing hype.

4:05 [SPEAKER_02]: Like at some point, people got to pay for these things in the end. [SPEAKER_02]: So there's going to be some marketing hype. [SPEAKER_02]: And that's why, you know, I think what's interesting here is that you brought Connor with you. [SPEAKER_02]: So Connor talk a little bit about the end to the Yang with you and what you're up to it at quantum rise and a little bit about yourself.

4:22 [SPEAKER_00]: Yeah, absolutely. [SPEAKER_00]: So we work with a ton of construction clients that we work from general contractors, asphalt paid in subcontractors, et cetera. [SPEAKER_00]: And as you said, these clients come to us and say, we want to do AI. [SPEAKER_00]: But there's clients all over the place on where they are in their AI journey.

4:37 [SPEAKER_00]: You've got people that have started experiment with POCs or get cuts stuck in POC mode. [SPEAKER_00]: They don't know where to start or can't even spell AI. [SPEAKER_00]: So it's a lot of education, just instilling the fundamentals in these organizations. [SPEAKER_00]: So these teams from [SPEAKER_00]: the back office, project management, core operations, just understand the basics of how to use some of these tools, whether it's copilot, chat, GPT, Gemini, or other AI tools, and then really helping them understand their processes deeply, their data, their systems, and how we can actually make this a reality for them, because a lot of the clients will think that AI is just a magic one.

5:10 [SPEAKER_00]: It's going to solve all their problems, but it's only going to work if it's purpose fit [SPEAKER_02]: minimal disruption is possible in the work flows because otherwise you're going to have a change management nightmare down the road so keeping those those users in the loop so you're using some of those now I know I brought you using some of those sexy words like proof of concept change management not really that's not what the high folks like to hear [SPEAKER_02]: that it's hard to do, and it takes time, and maybe even some POC.

5:41 [SPEAKER_02]: How did you get here, though, Connor? [SPEAKER_02]: Because you know, everybody listening in likes to know a little more about our folks. [SPEAKER_02]: So what's your history like? [SPEAKER_02]: Because you're running the sort of the vertical obstruction over there, but how did you get here?

5:52 [SPEAKER_00]: Yeah, so I've spent most of my career in the consulting, technology, digital transformation world started my career at KPMG and of doing off to the CFO work, everything from large scale ERP implementations, some RPA work, operating all designed to help back in the day. [SPEAKER_00]: So labor arbitrage moving companies, operations to share services and centers.

6:12 [SPEAKER_00]: From there, worked for a FinTech startup and brought me to Quaramrise. [SPEAKER_00]: I'm just a problem solver. [SPEAKER_00]: So I like getting deep, really understanding the clients, problems of processes, getting their trust early. [SPEAKER_00]: So because if you're just coming at them with outside ideas, with no understanding of the business, they're never going to want to work with you.

6:27 [SPEAKER_00]: So really becoming kind of one with the client to integrate yourself within their operations and understand what's going on. [SPEAKER_00]: So we can make the right recommendations, but then move those recommendations into real outcomes down the road and help them implement or build these solutions that are solved in the right problems for them.

6:42 [SPEAKER_02]: Yeah, I mean, you said this pre-call, you know, I get it, you know, Connor's got to cash the text that you write over there on what you guys can do and how it can help companies because I think, you know, in your journey here, that's what you've spent the last couple of years, especially just [SPEAKER_02]: I'd say, you know, learning and educating like it's a strange moment right where you're you're learning and educating at the same time and identifying the problem.

7:07 [SPEAKER_02]: So talk a little bit about, you know, last couple of years of this process and sort of what drove you here. [SPEAKER_02]: Where are you on the correct problem set? [SPEAKER_01]: That's a great point and, you know, one of the things that kind of brought up that I'll kind of tease out is, you know, [SPEAKER_01]: We're finding, not surprisingly, that AI implementations are more about the non-AI part than they are about the building.

7:30 [SPEAKER_01]: You spend 80% of the time, I don't know if that's quite right, but you spend a lot of time getting in and understanding what people actually do and where they want to go and how do you get from where they are now with what they currently have to where they want to be. [SPEAKER_01]: And I think to answer your question about how the past couple years have gotten me here, [SPEAKER_01]: I've been doing things with AI for a decade now, and it kind of went quiet because it became normal software, and that's interesting because I think it'll happen again.

7:58 [SPEAKER_01]: Somebody smart said, we call it AI when it doesn't work. [SPEAKER_01]: I think that's a bit of an over-claim, but the point is it's all, you know, the educating while learning is because it's changing so fast, and if anything, the last quarter has been remarkable and how busy it's been in terms of new things and new changes.

8:14 [SPEAKER_01]: Even the last two weeks, [SPEAKER_01]: What Claude Code has shown itself to be able to do, it's been around a little longer than that, but you're starting to hear some pretty interesting comments about how this is just different from what was there before. [SPEAKER_01]: And this isn't the last time you're going to hear that.

8:28 [SPEAKER_01]: So there isn't a body of knowledge that you can study up on and then off you go and teach people. [SPEAKER_01]: You have to be [SPEAKER_01]: really paying attention to what's going on now and asking yourself what it means and building things yourself and trying to do things and so on and so forth.

8:41 [SPEAKER_01]: So we're at a place now where it's good enough that you can do real things, you can get through reliable things. [SPEAKER_01]: But to do that, you need to understand the problem really well. [SPEAKER_01]: And again, that's where someone like Connor comes in to spend a lot of time listening and a lot of humility to say, we don't have a hammer and we're going to go find the nails.

8:59 [SPEAKER_01]: It's what are you doing and let's spend a lot of time on that. [SPEAKER_01]: It's the difference between [SPEAKER_01]: Where, you know, you see some folks out there who maybe have a team overseas and they'll build some stuff for you and that's wonderful But maybe they build a thing you don't need or maybe they build a thing that doesn't quite work or they build a thing that no one's ever gonna use Getting all that right to make sure it's a priority, make sure it fits with what you're doing now That's the key thing for AI now.

9:22 [SPEAKER_01]: We're past the part where [SPEAKER_01]: It's, you know, cool custom GPTs, that may be a solution for the point is we're past cute little magic tricks and now it's it's industrializing so at CES this year we really saw that that it's industrializing in a way that it just hadn't been before it was actually kind of Nvidia's show where they talked a lot about this being another another kind of stage where we spent last year realizing we needed to do a lot more tooling to make things work.

9:50 [SPEAKER_01]: That's not in the rear view, but it's pretty established how to do some of that stuff. [SPEAKER_01]: So now we have to make it so that it's a lot cheaper to use. [SPEAKER_01]: It's a lot more reliable in kind of every part of the stack. [SPEAKER_01]: And this year there's going to be more of that.

10:02 [SPEAKER_01]: There's going to be more real things being done because we've we've passed some of the early uncanny valley points. [SPEAKER_01]: And now we're ready to actually do stuff. [SPEAKER_02]: Yeah, and I kind of want to double down on that because I've been saying that for a while and I've said it with a few other things with the easy analogy is like We don't talk about our cars as diesel engine.

10:20 [SPEAKER_02]: You don't talk about it as a gas engine. [SPEAKER_02]: You just call it a car, right? [SPEAKER_02]: It's a thing. [SPEAKER_02]: You don't think about the thing that runs it. [SPEAKER_02]: You know, you put gas in it, but it's not a gas [SPEAKER_02]: power, you know, definitely you talk about it.

10:31 [SPEAKER_02]: That's when AI gets to that point where you're just using x, y, or z, then it's we've reached that moment and that's an important moment. [SPEAKER_02]: But I really want to to double down and talk to Connor about this part because the biggest failures that I've seen and the biggest problem through this art that I've seen is organizations feeling that [SPEAKER_02]: And we want to do AI.

10:56 [SPEAKER_02]: And they gather a very expensive team together. [SPEAKER_02]: And they solve for a problem that just doesn't have the return. [SPEAKER_02]: You're like, why did you solve that? [SPEAKER_02]: Well, because we could. [SPEAKER_02]: Okay, I could solve a lot of problems, but should I that that so Connor I want to I want to hear from you because I by the way to the listeners out there this is not just in construction like I've heard it in construction quite a few times, but I have a preview outside and I've seen it in other large organizations I've you know had people pull me aside and say wow my company's doing this and we're trying to automate an AP process that takes me five minutes.

11:39 [SPEAKER_02]: And really, there's all this stuff, there's stuff like I didn't need it. [SPEAKER_02]: And it doesn't work all the time. [SPEAKER_02]: So talk to me about Furma construction perspective, you know, what are some of the big misses? [SPEAKER_02]: And then how are you resetting people to go down this process with you?

11:54 [SPEAKER_00]: As you hit on, there's so many companies out there that think they're behind, some might be, some might not be because there's so many organizations that are in the same spot of just starting to toy around with some of these, but again, to your point, they don't know where to start, and they just latch on to a problem and go try and solve it and, oh, well, it failed, it didn't realize the value, the users didn't like it, and then people get they get pissed off, the organizations a little averse to change.

12:19 [SPEAKER_00]: So where we typically start with this is we do what we call rapid AI breakthrough assessment. [SPEAKER_00]: And it's you kind of said it's it's not just an AI strategy it turns into a business strategy. [SPEAKER_00]: We sometimes don't even talk about AI solutions until week two of these products that we do with these clients.

12:38 [SPEAKER_00]: So we start casting a pretty wide nut understanding okay from a business perspective. [SPEAKER_00]: What are your top 10 top 20 problems that you're trying to solve or [SPEAKER_00]: metrics or KPIs that are lagging, that could be an AP process. [SPEAKER_00]: It could be an Estabating issue, it could be a scheduling issue.

12:53 [SPEAKER_00]: It could just be, you got a bunch of turn across the organization. [SPEAKER_00]: We don't know why our employees are turning. [SPEAKER_00]: So it's not just construction field operations. [SPEAKER_00]: It's just core-back-off operations, too. [SPEAKER_00]: So. [SPEAKER_00]: Once we typically gather a ton of not a ton but leaders across the organization so we can speak the common language and it's not just a mandate from the CEO or the board of directors so then everybody feels like they're part of this process and kind of co-shapian and co-building this the strategy where we want to attack and then once we understand those important KPIs or business outcomes at those.

13:24 [SPEAKER_00]: our clients are trying to improve that's when we would all it down to start to go deep with the client stakeholder subject matter experts, the people who are actually doing the work. [SPEAKER_00]: So getting deep and understanding the processes, the systems, where things fail, where there's a kind of a gut decision or maybe we can't trust the data and then work backwards from there to figure out what that best fit solution is for them.

13:46 [SPEAKER_02]: I then we build it and then you build it right yeah, and then we build it But that's and that's the that's the magic sauce, but the what I like there is you hit two huge things The gut I've said this from the beginning and and you really I'm like I want to take us back into like a time machine Because you said it you like pre internet and then the internet I like to use the SaaS kind of rise of SaaS because I was so ingrained in that and I went through it [SPEAKER_02]: where it was the same thing.

14:14 [SPEAKER_02]: It's like, there's fundamentals. [SPEAKER_02]: Understand the problems, understand the systems, understand what's happening. [SPEAKER_02]: Like, really look for what are we looking for as an organization and then dig on where are the problems? [SPEAKER_02]: And then none of that is fun.

14:27 [SPEAKER_02]: And really none of that has changed right at all in the process of understanding. [SPEAKER_02]: The tools you're looking at, the things you're dealing with, they're much more advanced now. [SPEAKER_02]: You know, the way we build is more sure all that's changed, but the fundamentals are still true in understanding and finding those problems and then finding those things that would make sense.

14:47 [SPEAKER_02]: in going slow to then figure it out. [SPEAKER_02]: Now, what we're going to be capable of doing in future with this new technology? [SPEAKER_02]: Well, I would say that's very powerfully different and game changing. [SPEAKER_02]: Don't disagree. [SPEAKER_02]: Just believe fundamentals don't go away.

15:03 [SPEAKER_02]: I love that because, you know, one of the places I said in the beginning for analytics. [SPEAKER_02]: Was you want to know where you need analytics? [SPEAKER_02]: You want to know where you need data? [SPEAKER_02]: That is when you're sitting in a room and someone says, I feel like my history here is no.

15:20 [SPEAKER_02]: Uh-uh. [SPEAKER_02]: I feel my gut. [SPEAKER_02]: They're bad influencers. [SPEAKER_02]: They're really bad because we're real positive people or we're real negative people. [SPEAKER_02]: But we go on the spectrum. [SPEAKER_02]: So I love that you hit the gut. [SPEAKER_02]: I love that you hit the I'm sure it's where manual entry and stuff is happening right there's the opportunity so and I know I cut you off there you but I wanted to make sure people understood that like you're not the fundamentals are still true it's just the tool sets that we now have available it I mean.

15:51 [SPEAKER_02]: You used to use a hammer when you're putting, now I'm watching somebody put in a roof across me, so I'm gonna use this. [SPEAKER_02]: You used a hammer, you know, a hammer all of that in the nails. [SPEAKER_02]: That guy's got an impact drill up there or drill, nail gun, I'm watching him.

16:06 [SPEAKER_02]: What he's doing didn't change the tool that he's using didn't, he's way faster, I can see it. [SPEAKER_02]: So talk a little bit about that to you because I think that's important to understand like a fundamentals of true, what Connor and his team [SPEAKER_02]: What are you seeing as transformational and what are you seeing like the future be able to look like?

16:27 [SPEAKER_01]: Going back to in video again, I'm going to quote Jensen Juan, he's been talking, I really like this framework. [SPEAKER_01]: The difference between a purpose and a task. [SPEAKER_01]: And I think what you're starting to see is people realize exactly what you just said that AI is tool that will help you to achieve your purpose, often by automating little tasks that are in there.

16:46 [SPEAKER_01]: So I think you're starting to see people go past the, you know, the height phase. [SPEAKER_01]: There's going to be plenty of hype that LinkedIn apparently. [SPEAKER_01]: That's all it does now. [SPEAKER_01]: And if anything, people should just recognize that what you're seeing on, especially LinkedIn, but really in a lot of places.

17:01 [SPEAKER_01]: It's just people trying to make hot takes. [SPEAKER_01]: It's not real. [SPEAKER_01]: You know, there's real work being done. [SPEAKER_02]: So except the content crews, you know, linked in, am I linked in? [SPEAKER_01]: Obviously, obviously, everything I put up there is completely legit.

17:14 [SPEAKER_01]: But honestly, they're just starting to shift the way it should, which is we're starting to get serious about this and really think about [SPEAKER_01]: We're not replacing the entire what a person does and what their purpose is. [SPEAKER_01]: We're saying you could get to what you're really there for a lot faster because you're not messing around.

17:31 [SPEAKER_01]: You're not second guessing day to the same way. [SPEAKER_01]: You're not hunting for things. [SPEAKER_01]: You're not unsure about something because you can look it up so quickly. [SPEAKER_01]: But we are still early. [SPEAKER_01]: It's worth keeping in mind that we are still early. [SPEAKER_01]: There's a lot of innovation that will be ahead of us.

17:45 [SPEAKER_01]: One of the things that we spent a lot of time trying to reap both reassure people, but also in how we engineer things is to say, [SPEAKER_01]: that whatever we put in now, there's going to be things that slot in later that might make it work even better, whether it's a different model, usually it'll be a different model.

18:01 [SPEAKER_01]: But knowing that, you're able to architect things in a way that there is future proof as they can be. [SPEAKER_01]: Certainly not. [SPEAKER_01]: You're not going to wake up in nine months and be like, oh my gosh, what I just spent money on. [SPEAKER_01]: There's a limit to how much you can promise that.

18:13 [SPEAKER_01]: But I think that you can't we have enough history with AI now to have a pretty good sense of how good it'll be in a year. [SPEAKER_01]: If anything, some of the pieces that they could good now will make it better later. [SPEAKER_01]: As opposed to, again, you waking up and saying, oh my god, we got to swap it out and we got to change the model.

18:29 [SPEAKER_01]: You're not installing software. [SPEAKER_01]: You know what I mean? [SPEAKER_01]: You're building a solution that has pieces to it that are as modular as they can be, which is pretty modular. [SPEAKER_02]: Yeah, that's, I mean, that's one of the critical components is to why, you know, I think specialty groups like yours that are designing and implementing and working with them.

18:47 [SPEAKER_02]: And aren't selling one individual thing, you're selling a set of solutions to a set of problems that are customized. [SPEAKER_02]: You said this and I want to really, really focus on it. [SPEAKER_02]: Like you said, automating little tasks. [SPEAKER_02]: So much of that height that you hear on LinkedIn or I mean, LinkedIn's I'd say it's a little better than say like a Facebook or an Instagram or an X, where they're like, you'll wake up tomorrow.

19:15 [SPEAKER_02]: We need universal income because the bots will do everything. [SPEAKER_02]: Yeah. [SPEAKER_02]: That's not true so much. [SPEAKER_02]: No, and it's actually, maybe sure. [SPEAKER_02]: I'll sit with some of the great people and they could probably convince me that in 50 years that's where we might be.

19:33 [SPEAKER_02]: In fact, those big, big things, it doesn't do them, and it doesn't do them well, and you can't trust it. [SPEAKER_02]: And you nailed it. [SPEAKER_02]: I love, I think it was quick-banks, who did a study last year or the year before. [SPEAKER_02]: And it was 75% or more of people in the field don't trust the data that they're looking at.

19:53 [SPEAKER_02]: That's a huge problem. [SPEAKER_02]: That creates uncertainty. [SPEAKER_02]: Construction hates uncertainty, just solving for little things together stacked makes for future. [SPEAKER_02]: So I wanna bring it to Connor, like maybe you have some examples of those little things that like free somebody up to A, have trust and B, make decisions.

20:16 [SPEAKER_00]: Yeah, absolutely. [SPEAKER_00]: And I think I saw similar to your commentary on the little tasks of AI's great at the boring tasks. [SPEAKER_00]: So sometimes it might, our clients will try and latch on to the fancy problems. [SPEAKER_00]: But if you really decompose the problem, it's like what's actually the root cause of it.

20:30 [SPEAKER_00]: So one example here in our work to actually talking about the other day is subcontractor via subcontractor prequalification process. [SPEAKER_00]: kind of a check the box, but manual effort for legal and finance teams today to just make sure yeah, our subcontractor is financially liquid, they're being our safety requirements, et cetera.

20:48 [SPEAKER_00]: But if you're if you're leveraging the data, it could be more of a strategic advantage for you on your projects, your risk, et cetera. [SPEAKER_00]: If you're pulling in, subcuse drywall company has been [SPEAKER_00]: His head and his with any change orders, they're always hitting their schedule and they're always on time and on budget.

21:04 [SPEAKER_00]: Let's use that for more of a subcontractor performance reporting so we can use it as a strategic advantage of what subcontractor is going to be the best trade and partner for us on this job. [SPEAKER_00]: So being able to leverage that data and you might not be able to trust 75% of the data, but there's a lot of data there that is under leverage today to help the business make better decisions at the time of estimating at the time of buyout.

21:24 [SPEAKER_02]: So moving up more from just to check the box activity to more of a competitive advantage for you as you're going to bid and preparing your project plans and schedules their fear of the data or their unsure of the data is that they're not seeing it all right they're not getting it all they're only capturing like a little bit of it so they don't they don't trust it they want to know that it's the entirety so they want to know that like that drywall contractor didn't actually you know just like we didn't notice that the two times they defaulted or they were later whatever [SPEAKER_02]: And so that's the point is that you're giving them the understanding that we have it in it's entirety and now we're making it available to you and it's entirety to make a really well informed decision and drive your business and that's ultimately what we want, right?

22:08 [SPEAKER_02]: This is that I love that it's great at boring tasks because we don't have enough people, we don't have enough time and we certainly don't have enough experience. [SPEAKER_02]: to go around to make those informed decisions and that's what, you know, things like this do. [SPEAKER_02]: And I mean, there's so many things in what construction companies do that are menial little tasks that are necessary.

22:34 [SPEAKER_02]: Like, I think people hear that and they think, oh, it's unnecessary. [SPEAKER_02]: We'll just, you can't skip it. [SPEAKER_02]: It's necessary. [SPEAKER_02]: But that doesn't mean it's fun. [SPEAKER_00]: It's so funny on that too, because I'll talk to so many like superintendents, project managers, guys that are in the field to start off our projects.

22:48 [SPEAKER_00]: And they'll be so resistant to start. [SPEAKER_00]: They'll say, hey, I can't help you with my job. [SPEAKER_00]: And then I ask them a question because what's corporate bugging you about? [SPEAKER_00]: Or what annoys you about the information that corporate's bugging you for? [SPEAKER_00]: And the floodgates open up and they start listening off 10, 20 different ideas of things that can potentially make their lives easier, because they want to be in the field building.

23:05 [SPEAKER_00]: They don't want to have to do all the paperwork, the status updates. [SPEAKER_00]: financial reports and budgets and stuff like that. [SPEAKER_00]: So it's eye open for some of them sometimes. [SPEAKER_01]: And Jeff, that speaks to where we look at the role of AI is to elevate the human judgment, intelligence, not replace it.

23:21 [SPEAKER_01]: Is you're saying we want to pull away the stuff that stops you from doing what you're really there for. [SPEAKER_01]: It's necessary for the project and for the company to run. [SPEAKER_01]: But it isn't building. [SPEAKER_01]: It isn't adding value. [SPEAKER_01]: So how do we pull that away?

23:34 [SPEAKER_01]: So you have more time to think about what you're doing. [SPEAKER_01]: Talk to your teammates. [SPEAKER_01]: Talk to your subs. [SPEAKER_01]: Whoever might be, but really elevating the human not replacing them. [SPEAKER_01]: I mean, it's really funny. [SPEAKER_02]: You go to it's been a lot of attempts at this over time to make us more human.

23:48 [SPEAKER_02]: And it's really interesting because, you know, there's so many people that AI makes us less human. [SPEAKER_02]: I think done correctly. [SPEAKER_02]: It makes us more human. [SPEAKER_02]: What is construction based off of relationships? [SPEAKER_02]: Always has been always will be. [SPEAKER_02]: It's a face-to-face.

24:02 [SPEAKER_02]: It's a hand-to-hand putting work in getting things built. [SPEAKER_02]: And I used to say this like, it's so crazy. [SPEAKER_02]: Because maybe this is 10 years ago where I was like, you're pushing too much to the field. [SPEAKER_02]: And you're really, you're inundating them. [SPEAKER_02]: They're dabbling in all of your things, but, you know, somebody in finance in a CFO and others in operations were like, we need that.

24:25 [SPEAKER_02]: And I'm like, you're not wrong, but, gosh, is there easier way to do it? [SPEAKER_02]: We've tried so many of you things along the way that now I think we're in a position where we have an opportunity to deploy a set of tools that can achieve that. [SPEAKER_02]: And then ultimately, hopefully help the relationship building [SPEAKER_02]: help the decision-making problems.

24:45 [SPEAKER_02]: It's not going to hear rid of a problem, right? [SPEAKER_02]: If your AI stack tells you that your switch gear is late, it can't fix that. [SPEAKER_02]: The key is it tells you that. [SPEAKER_02]: you have to decide as a team, how you're going to adapt to that and handle that. [SPEAKER_02]: And the earlier the signals happen, the sooner you can do that.

25:08 [SPEAKER_02]: And man, that's where this stuff is truly transformative. [SPEAKER_02]: And I want to dig back to one thing about the future-proofing hue, cause I want, [SPEAKER_02]: You know, there's there's a lot of this like am I going to go to one big am I going to go buy open AI and I'm going to do all that or I'm going to build it all you know there's always been that build first by right talk about the difference here because you talked about switching out models and I think that may have gloss over for a few people what that even means so talk about how you guys are approaching this problem of the rapid change that's going to happen.

25:45 [SPEAKER_02]: it's happening today. [SPEAKER_02]: It's happening tomorrow. [SPEAKER_02]: How are you stacking this to solve that, you know, sort of, you're not fully future proof. [SPEAKER_02]: Nobody, you know, you didn't say that. [SPEAKER_02]: You just said future proof fish, you know, what's much as you can.

25:58 [SPEAKER_01]: There's a few things in there that are worth talking about. [SPEAKER_01]: One of them is. [SPEAKER_01]: Basically every month, whether it's Google or it's OpenAI or it's someone else, there's a new model that's coming out. [SPEAKER_01]: And model, by that I mean, it's kind of like the engine of AI.

26:11 [SPEAKER_01]: It isn't what you're using. [SPEAKER_01]: It's the thing under what you're using back to your car analogy. [SPEAKER_01]: It literally is like the engine of the car, but you don't drive the engine. [SPEAKER_01]: You drive the car. [SPEAKER_01]: So what we're doing a lot of is building all of the things that make it functional and useful and then you can swap the engine out easier than you can swap a real engine out and it will still work and it's still interconnected the way you know the old one was but it'll be a little faster a little smarter a little bit more capable or often what you're really going to see.

26:39 [SPEAKER_01]: Because the models now are good enough that most of what you need them to do, they're probably good enough already. [SPEAKER_01]: They're going to start to get a lot cheaper. [SPEAKER_01]: So the running costs, the gas, is going to be cheaper and cheaper. [SPEAKER_01]: And I think dramatically so over the next couple of years.

26:52 [SPEAKER_01]: The other thing though, is because our team has been doing this, in some cases for 20 plus years, I'm much more on the technical side. [SPEAKER_01]: Everything isn't in LLM and every solution doesn't mean you need a chatbot for it. [SPEAKER_01]: There's vision models, there's this kind of model, there's analytics models and so on.

27:09 [SPEAKER_01]: And asking yourself, do I need to use the big OpenAI one? [SPEAKER_01]: And honestly, people often don't want to use one that's public for a number of reasons. [SPEAKER_01]: And not so much public, but you know, pinging API. [SPEAKER_01]: Maybe you want to have one that's hosted, it's just for you, probably hosted or whatever.

27:22 [SPEAKER_01]: Or maybe you needed a tune, something so that it works with the data you've been given. [SPEAKER_01]: There's a bunch of different ways to look at this. [SPEAKER_01]: The LLMs have really given everybody a lot of focus and a lot of energy, which is great, but honestly, we've had pretty good models for certain things for a long time.

27:37 [SPEAKER_01]: So depending on what you need, it may or may not be a model that's ever going to change, because it's already as good as you need it to be, and why mess with it later, or it works, let's just move on. [SPEAKER_01]: Just keep in mind that there's a lot of different engines, just like there are with engine engines.

27:50 [SPEAKER_01]: There's electrical ones, there's gas ones, there's two [SPEAKER_01]: And you're starting to see even for the big frontier models people hear about, they're getting their own personalities as well. [SPEAKER_01]: So, Claude has a ton of its own, a chatchipit has a ton of its own, and some areas where it's better and worse.

28:09 [SPEAKER_01]: Claude has been making a lot of progress in the coding area. [SPEAKER_01]: Now, so is OpenAI. [SPEAKER_01]: But the point is, depending on what you're trying to do, [SPEAKER_01]: They're not interchangeable as much as maybe they were a year ago. [SPEAKER_01]: So a lot of that goes into what Connor and the technical team will do is, what do you really need here?

28:25 [SPEAKER_01]: And the other thing I want to touch back on the role of humans is and this idea of data that you don't trust. [SPEAKER_01]: If it's opaque, you're not giving them any more trust, even if there's AI in there. [SPEAKER_01]: So a lot of what we also do, and that's this humility of talking to people, is how we're building in verification, so you can look at the source documents really quickly.

28:44 [SPEAKER_01]: And maybe they're highlighted or whatever it might be. [SPEAKER_01]: But understanding that a human is using this, [SPEAKER_01]: is what makes a really good engine into a really good car. [SPEAKER_01]: You know what I mean? [SPEAKER_01]: Like how many times have you seen when people tricks out, people trick out and not so great car with an amazing engine?

28:59 [SPEAKER_01]: It's like, oh, okay, I don't want to drive that thing, but golly, it's fast. [SPEAKER_01]: That's a rough analogy for what you might do here is if you're not giving people the comfort to use the thing. [SPEAKER_01]: doesn't matter how good the tech is. [SPEAKER_01]: And we've seen that in SaaS to where people over automate and you feel like I don't have a handle on my risk.

29:17 [SPEAKER_01]: So there's you're automating too much and I don't want to decide at the end or approve at the end. [SPEAKER_01]: I wasn't part of the work along the way. [SPEAKER_01]: I don't have contexts to make that decision. [SPEAKER_01]: I can't approve this because I don't really know what you were looking at and I don't really know what's being worked on.

29:30 [SPEAKER_01]: So all of that kind of comes together in one big thing, what you're saying, how am I creating something that gives the user comfort that there's no new risk? [SPEAKER_01]: And then senior management comfort that they didn't just make an investment that they're going to have to, you know, replace in 12 bucks.

29:45 [SPEAKER_02]: Yeah. [SPEAKER_02]: I mean, you hit on a couple of things that I want to pull over to Connor, and I'm going to use my own analogy in here because you talked about the trick-out car that doesn't look good, but goes fast. [SPEAKER_02]: I just had this experience where it was traveling in a rented car, we we got upgraded.

30:00 [SPEAKER_02]: I did not just rent this car straight out, but it was a it was a it was a range Rover sport and it was too much. [SPEAKER_02]: There was a number of features that I could not free I mean it was like I got to get where I'm going like what the heck I know it's got it's got to have a heated wheel it's got to have this it's got.

30:17 [SPEAKER_02]: I got my kid in the back trying to figure it out and I will tell you like my experience was not great because it was just like yeah, I mean I'm in a captain's chair and it's comfy in this thing's a giant but what is it doing how do I make it and and I got this giant screen I got to look at the road like so I'm with you you can go both ways right you can underdo it and you can overdo it so it corner how do you make sure that it's that creamy middle just right because people have to use it.

30:47 [SPEAKER_00]: It's exactly the importance of us trying to understand the processes what the outcomes they're trying to do because it's a classic. [SPEAKER_00]: I might need a Toyota. [SPEAKER_00]: I don't need a Ferrari or in your case a Range Rover Sport. [SPEAKER_00]: I just need a reliable basic car to get me from point A to point B.

31:00 [SPEAKER_00]: So once we really understand the processes, it's, okay, this might be AI isn't just an LLM or the latest and greatest reasoning model from OpenAI or Clot or Gemini. [SPEAKER_00]: It could be a simple automation of just moving data around or transforming data or trying to use all the rich history that you have to predict the potential outcome, so it could be schedule risk, it could be labor run rates, or to want to use the engine example we built a predictive maintenance or predictive diagnostics model for heavy equipment for paving companies.

31:30 [SPEAKER_00]: helping use that rich work order history, fault codes, engine failures, resolutions, et cetera, to help when maybe an engine is about to fail, or when something does fail, what parts do I need to go fix that? [SPEAKER_00]: Because we want that machine back up and running, otherwise it's going to really impact our project timeline.

31:45 [SPEAKER_00]: We're going to eat a lot more costs, maybe more rental costs, or. [SPEAKER_00]: something else that we need to do because we need to get that very important piece of equipment up and running. [SPEAKER_00]: So it's really trying to decompose what that problem is and then we'll come back and say, okay, it might be a mix of an automation, an LLN, and maybe a predictive model, or it could just be a basic automation and move data from one place to the other, help to make decisions.

32:06 [SPEAKER_02]: Yeah, and to say with the car analogy, now you put them in the driver's seat, how do you make sure they drive the car and they don't just get out and go do it the old way, walk to where [SPEAKER_00]: Yeah. [SPEAKER_00]: I mean, we're working with these guys week over week every time we make a new update to the solution we're building or making sure that what the user experiences and user journey is something that they would actually use.

32:28 [SPEAKER_00]: So it's not just you give us a problem. [SPEAKER_00]: We go away for a few weeks and come back with a solution and say use it. [SPEAKER_00]: We're going to cheat back from them along the way, call it an iterative or agile process to make sure that this is up to their standards, it's muting the expectations and outcomes that they expect this thing to do.

32:43 [SPEAKER_00]: And then once we give them the keys, they're ready to drive it. [SPEAKER_00]: They don't have to go figure out the fancy touchscreen on the Range Rover Sport and actually what capabilities are. [SPEAKER_01]: But also, there's a change management piece to this. [SPEAKER_01]: That's also part of a lot of engagements.

32:57 [SPEAKER_01]: We have actually an instructional designer. [SPEAKER_01]: We went that far to say, how are we creating materials? [SPEAKER_01]: So people can onboard and get going quickly. [SPEAKER_01]: It's not just the test group that you want to get moving. [SPEAKER_01]: You want the whole organization.

33:12 [SPEAKER_01]: So not only are you starting with, [SPEAKER_01]: a pretty rigorous analysis of what people have said or they need, what the tech team has said, here's our current stack, here's the software that we already used that we want you to connect with. [SPEAKER_01]: Also though the financial side of what's the ROI, but then you're saying after we've gone through all that.

33:31 [SPEAKER_01]: We have a lot of material to go to the rest of the company and say, Fred really liked it. [SPEAKER_01]: You know what I mean? [SPEAKER_01]: And we talked to 20 of your people, here's how to use it, here's what to expect and so on. [SPEAKER_01]: So it isn't just here the keys, it's also here's the map, how's that beating up a metaphor.

33:48 [SPEAKER_01]: But the point is, you can't just leave people with that. [SPEAKER_01]: It's that everybody has had that problem with SAS, where you dump it on people and they get frustrated. [SPEAKER_01]: Happily, what we create tends to be the point of it a lot of times is it's already a lot easier, because it's automating little pieces and so on.

34:03 [SPEAKER_01]: But also just making sure that people aren't left to figure it out. [SPEAKER_01]: Number one reason for churning SAS is lack of adoption. [SPEAKER_02]: 100% they just they never get past the point of using it never get past pilot you never get full saturation it just doesn't work and and that's why it's critical to have you know a group like yours that you're not tied to anyone particular thing and when I you know we were talking really specifically about AI agents and.

34:28 [SPEAKER_02]: LLMs and computer vision and other things, but ultimately you're working side of a stack too, so you're working with their PMIS systems, you're working with their financials, you're working with what other, you know, maybe other tool sets that they have and you're sticking in the middle there, right?

34:45 [SPEAKER_01]: And connecting dots, I mean, a lot of what we'll do with, you know, great example is one of the projects was cost to complete like, where are we, you know, we're in the middle of the project. [SPEAKER_01]: I don't know how many, you know, other change orders outstanding are through this or that and everybody's, you know, nervous about where are their surprises lingering.

35:03 [SPEAKER_01]: And some of that information or all that information is somewhere. [SPEAKER_01]: It's just not usually in the same place and that usually in a way that you can do something with it. [SPEAKER_02]: And I'll steal from that, too, is that like there's a lot of computer vision out there that we know is a lot further along than some of the other stuff, but it doesn't actually work really well with the data right now to say, okay, what is the computer vision saying we are where we are and how does that relate to the financials in the billing and other things so I can really get to a true, like, I feel pretty good about a cost to complete or a percent [SPEAKER_02]: Well, we build 50, so we're 50, you know, uh, you know, that's what you're combining systems together, Connor, and that's part of, you're not just working with people, you're working with the tech stack, too.

35:52 [SPEAKER_00]: Like the computer vision examples, perfect. [SPEAKER_00]: There's great computer vision capabilities out there, but I think a lot of the clients that we talk to, it's like, oh yeah, cool. [SPEAKER_00]: We're doing some drone photos. [SPEAKER_00]: We're doing some walkthrough videos with the helmet cans and stuff, but we're not actually using the information from those, those videos and actually the computer vision [SPEAKER_00]: update the project plan, update the schedule, update the budget, go get a change order because we thought this is going to be 50% done by month three of the project and oh, we're only 25% done based on what the computer vision and lock-throughs are telling us.

36:24 [SPEAKER_00]: So, that's the important key I think moving forward is how do we actually leverage all these great tools that are being used in the field, but they're kind of just sitting in silos and that's a lot of valuable information to get me fed back to the teams. [SPEAKER_02]: Well, I think that's critical too, because we've seen this in litigation too, because some things that I've heard recently are like, I don't think I want to do that walk anymore, because by the way, when something goes wrong down the road and I go back and I go crap, I had it.

36:51 [SPEAKER_02]: And I didn't know it, that I'm liable for it. [SPEAKER_02]: And that is not a reason to not doing. [SPEAKER_02]: That is a reason to connect it. [SPEAKER_02]: So it can signal sooner. [SPEAKER_02]: That's the thing that the computer vision hook to the other things connected together can create signals that tells that, soup, PM, superpowers.

37:14 [SPEAKER_02]: Hey, floor three, west wing, [SPEAKER_02]: way off early, early signal, we're going to be in trouble. [SPEAKER_02]: What do you do? [SPEAKER_02]: You get out there and you look at why? [SPEAKER_02]: Why might be a number of different reasons? [SPEAKER_02]: Oh, my lift's been down for a few days or I don't have the material or the framer, the drywall or the, you know, the crews behind.

37:36 [SPEAKER_02]: Whatever it might be, but we can even know it. [SPEAKER_02]: And we need to early signal to it because that's what we truly want. [SPEAKER_02]: I don't want people to chuck things away, but I've heard it now. [SPEAKER_02]: So, you know, these are real, real solid [SPEAKER_00]: And even from a safety perspective to using some of those drone surveys to identify where there's main material that's dangerously stacked in a hydrophic area or something like that to avoid those problems before it becomes an issue.

38:00 [SPEAKER_00]: Like it's those early signals of what's figured out day off rather than when the incident actually happened. [SPEAKER_02]: Well, I mean, how about trenching, you know, like how many trench collapses do we see how awesome would it be if that drone was picking that up and just, you know, it's an easy measurement right it's height, it's length, those two things, the walls collapse like sound the alarm, you know, and people think, oh, [SPEAKER_02]: just read another report of, you know, unfortunate accident with another group in a trenching incident.

38:29 [SPEAKER_02]: Like, these things happen and we have these tools that can protect people. [SPEAKER_01]: But they gotta be all working together and connected and, you know, and coming back to the point we made earlier about automating the staboring stuff, you have time to process those signals when you're not burning up time on paperwork.

38:46 [SPEAKER_01]: You know, like you have time to think about your project, people are worried about it already. [SPEAKER_01]: It's not like they're not doing their jobs, but if you're overwhelmed with all this stuff, you blow a couple of days on paperwork or whatever it might be, you know, things stack up.

38:58 [SPEAKER_01]: So having the time and space for people to do what they're really there for, which is to manage things and execute things. [SPEAKER_01]: As opposed to doing the back end support, that's essential, but man, it can take a lot of time. [SPEAKER_02]: Yeah, and if it's taking time away to do that, you're not keeping your eyes on the job and your eyes on the job or what we need.

39:16 [SPEAKER_02]: We'd love those computer vision signals to show it too, but then we don't just want to slap those people. [SPEAKER_02]: I want seasoned foreman to go out and sit down with a team that's digging that trench and say, listen, like when you started, you were here, we're bringing in new workforce, right?

39:31 [SPEAKER_02]: We don't have a ton of experience and we need to train them. [SPEAKER_02]: So this is an opportunity to not worry about your daily report, not worry about the other thing right now. [SPEAKER_02]: and sit down with some new team members and say, listen, when you start it and you were, you know, 10 feet, you're okay, but you've now continued down your 20 feet long and you're building with the same walls and those walls of the first 10 are putting just as much pressure.

39:56 [SPEAKER_02]: You're not giving yourself enough space. [SPEAKER_02]: Those are things that season and by the way, I would not know that if I had that wasn't a real incident where I actually got talked to by somebody explaining this to me that this stuff this is how this works, so this is valuable learned over time experience that can then be [SPEAKER_02]: codified and told to people and explained that we can just get up.

40:18 [SPEAKER_02]: We can take less time doing paperwork, wartime trading the future, wartime folks on the job, wartime get work in, and that's, you know, that's the power of this. [SPEAKER_02]: So I just love to see practicality. [SPEAKER_02]: That's why I said, how do you do AI? [SPEAKER_02]: But really, we've talked about it as a tool.

40:35 [SPEAKER_02]: We've really gone to the fundamentals [SPEAKER_02]: I do think there's an opportunity here done correctly that you guys are on the forefront of starting to connect our ecosystem in a way that it can effectively change. [SPEAKER_02]: And that's pretty powerful. [SPEAKER_01]: That is the plan, one company at a time, but really starting to connect what the potential is.

41:02 [SPEAKER_01]: And how does it make you rethink what you're doing? [SPEAKER_01]: At first, we automate the boring stuff, but then you realize we don't need to do it quite like that in terms of what we do when or what order that we do things in. [SPEAKER_01]: I mean, that's a little further out, I think there's a lot to do right now, but the ultimate goal in all of this is if you had more time or you didn't need to throw bodies at that problem, what might you do differently?

41:25 [SPEAKER_01]: How might you control risk? [SPEAKER_01]: How might you win bids on time, not just money? [SPEAKER_02]: Yeah, I mean, those are powerful things because, you know, we talked about this in the SaaS times and I think it's more true now than ever. [SPEAKER_02]: And we would make this software do those things and now we can make AI do some of those things and connect where it wasn't connecting because I was mainly having to go from E and pick up ABC and put it into F right well okay so now we're solving some of that but what if we have the time to think about what do we really need BC DE maybe we can get from A to F [SPEAKER_02]: systematically.

42:08 [SPEAKER_02]: As long as these are not decisions, these are automations, these are things that would naturally come together. [SPEAKER_02]: Okay, let's skip to death. [SPEAKER_02]: As long as we're not removing human decision-making and creating assumptions in there, right, and we're just automating a process, like you said, we can rethink work completely.

42:30 [SPEAKER_01]: How many documents in construction were kind of [SPEAKER_01]: right a lot. [SPEAKER_01]: So okay, does it still need to be like that? [SPEAKER_01]: Maybe for legal reasons you need to have that version somewhere, but to someone need to be looking at a PDF specification. [SPEAKER_01]: Not sure they do.

42:48 [SPEAKER_01]: You know, and there's a hundred, I happen to know, specs better than other things, but there's a hundred little things like that where does it really need to be the equivalent of paper? [SPEAKER_01]: Surely we can do it some other way. [SPEAKER_01]: And so I think there's a lot of scope for that.

42:59 [SPEAKER_01]: It's important to do these things without creating new risks and not overstepping areas that are there for a reason. [SPEAKER_02]: But that question I think will be something there's no answer to right now it's a process that we're all in the middle of war in some cases the beginning of yeah and I think that's your I mean I could go on forever with you guys about this but I think that's your moment of saying like here's where we are today here's where we can do here's what we're looking down the path of one partner with you and get down there and I think that's what's different about quantum rise is you're not selling me one thing you're selling me a partnership and a relationship and you're going to go down this with me and you know

43:34 [SPEAKER_02]: I get to leverage your expertise as an organization to know that you're going to guide me, not just down the right path, but towards the path of my problem. [SPEAKER_02]: Because you've got general contractors, you've got asphalt papers, you've got drywallers and framers, you've got a little bit of everybody and they're all need different things.

43:52 [SPEAKER_02]: So that's also what, you know, there's this, oh, I need to do it in house. [SPEAKER_02]: Listen, we subcontract for a living in this business, right? [SPEAKER_02]: At GCCs, you don't build much. [SPEAKER_02]: You know, you self-perform one or two things, but you outsource a lot of it. [SPEAKER_02]: So again, find those trusted partners like quantum rise and, you know, on that note, like Connor, where can they go?

44:14 [SPEAKER_02]: Learn a little bit more about you, connect with you, and then I'll throw it over to you. [SPEAKER_00]: Yeah, feel free to find me on LinkedIn, Conner Schmelling, or Link will be shared with this. [SPEAKER_00]: And then at quantumrise.com, we're here. [SPEAKER_00]: We're problem solvers looking to make contractors, subcontractors, that's home favors, when their business and transform what they are.

44:32 [SPEAKER_02]: I love it, I love it. [SPEAKER_02]: Hugh, always great to sit down with you. [SPEAKER_02]: Let people know where they can go learn a little bit more about you and about quantum rise as well. [SPEAKER_01]: Yeah, LinkedIn, again, is probably the best place. [SPEAKER_01]: Quantumrise is at quantumrise.com.

44:45 [SPEAKER_01]: We've got a lot there. [SPEAKER_01]: We'll have a lot more about what we're doing. [SPEAKER_01]: Yeah, excited for it. [SPEAKER_02]: I was excited to sit down when I heard what you guys were up to. [SPEAKER_02]: I think this is the type of organization that's type of thing we need in the industry.

44:55 [SPEAKER_02]: And I just can't thank you enough for coming on the show and wrapping that was uh that was one of the most sound AI episodes we've done guys. [SPEAKER_02]: So to both of you, I appreciate that a lot. [SPEAKER_00]: Thanks for having us on. [SPEAKER_00]: Appreciate it, Jeff. [SPEAKER_02]: Thank you for tuning in the day to geek out for our interview with Connor Smelling and Hugh Seaton.

45:13 [SPEAKER_02]: To read all our new stories, learn more about our guests and listen to the show, visit the contentcrew.com. [SPEAKER_02]: This is the contentcrew signing out. [SPEAKER_02]: Until next time, enjoy the ride, geek out.