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KP Unpacked
KP Unpacked explores the biggest ideas in AEC, AI, and innovation—unpacking the trends, technology, discussions, and strategies shaping the built environment and beyond.
KP Unpacked
AI Research Tools AEC Firms Can't Ignore
AI isn’t just about flashy tools—it’s about transforming how AEC firms research, analyze, and make decisions. In this episode of KP Unpacked - the number one Podcast in AEC, Jeff Echols and Frank Lazzaro dive deep into the power of AI research tools and how you can use them to work smarter, not harder.
Key takeaways from this episode:
- Why AI research tools are changing the game for AEC firms
- Frank’s personal formula for finding 12 minutes of efficiency daily using AI
- Real-world examples of how AI tools outperform traditional research methods
- The top AI tools you should be using: ChatGPT Deep Research, Storm, and Perplexity
- How to avoid AI's common pitfalls in data analysis and research workflows
- Actionable tips to start using AI research tools in your business today
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It’s a space for AEC forward-thinkers are reimagining what’s next. This is where the top minds in the industry are sharing ideas, leading change, and pushing the future of AEC forward.
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Hey, welcome back to KP Unpacked. This is where the biggest ideas in AEC, ai and innovation all collide in one podcast. It's powered by KP Redico and this is the place where we break down the trends, the technology, the discussions and the strategies that are shaping the built environment and beyond. My name is Jeff Eccles. I head up our Mastermind program and our incubator program here at KP ReadyCo. I also record, I guess, just about every version of this podcast and our videos and our live events, etc. And our live events, et cetera. Today I am joined by my friend and colleague, frank Lazzaro, because this is an AI and AEC version of KP Unpacked, or maybe an AI Unpacked version of the podcast. So today, frank and I are going to take another dip into AI how you use it, what it is, how Aec firms can and should be using it, and give you some actionable tips on one more ai topic that, uh, that we can unpack here. So, frank thanks for joining me today.
Speaker 2:Great to see you yeah, great, good, good to be back. I think we're getting a lot of good response with the uh, with the podcast so far, and and I'm excited about this topic in particular, cause this is something that comes up often, um, even internally. I was having a conversation with some of our researchers that that just recently joined and we have a bunch of data. They're doing all these interviews and research and analysis was one of those really big questions that came up and I was like man, that is so timely, that is literally this week's episode and so having to go from not only hearing what our clients are saying but also just knowing that internally we're also exploring how we utilize these tools to kind of really manage large data sets and research and a bunch of other stuff, so I'm really excited about this particular topic.
Speaker 1:Yeah, if you're, if you are listening right now and maybe you've been following the podcast along in all of its different forums, you know that I have been interviewing our research team because they have launched into a brand new research project talking with owners, or something that's spinning out of that, called the integrated owners forum and, as Frank mentioned, we have a whole group of researchers that are having lots and lots of conversations, doing lots and lots of research, digging deeper and deeper into the problems. I guess, if you will, that owners or operators or clients depending on whether you're an architect, an engineer, a contractor, what you call those people but looking at how they feel about services and how they feel about designing, building, operating, constructing, the built environment, and so, as Frank alluded to, the topic for today, the one that we're going to unpack, is AI for research and, as, as he said, I mean this is. This is very timely because all of those people that I interviewed, um, frank, other other Frank, frank.
Speaker 2:Frank.
Speaker 1:Frank, frank Frank um Zig, david Frank Zieg, david Ted Hope. They are all doing the research and they have to be able to have great tools in order to synthesize that and make it usable. So today we're going to expose what we're thinking about and what we're doing, and maybe you can use that and apply it into your work as well.
Speaker 2:So yeah, and it's interesting though, like I, how you kind of framed it up. I think is is is great right, the, the advice and stuff that we give here.
Speaker 2:It's not necessarily just us sitting on a soapbox saying these are all the things that you should be doing right um what we're telling our clients is actually what we're doing ourselves is that you have this ability now, these tools, where you do have your own in-house research, you do have your own like you can do these things to kind of help drive your business forward, whether that's analyzing data or you know. Matter of fact, I was, you know, had a was speaking at a workshop recently and it was one of those things to where they were asking is like, well, we do a lot of public works projects and how do we find out what the city council's saying and how do we get press releases? And we're going to dive deep into some of those tools today. But, yeah, there are tools out there that help you kind of cut through the noise and get to the information that you need yeah, and you know it's.
Speaker 1:I don't think we've talked about this for several episodes now, but I think this is a great time because, as I think about using AI for research and I use it for research every day I also use it to help me synthesize other things. But you have a book called finding 12 minutes unlocking efficiency with generative AI, and we've we've talked about that in past episodes, but I think this might be another great example of what you're talking about in your book, so why don't you recap that for maybe for anybody that hasn't heard us talking about your book in the past?
Speaker 2:Yeah. So it's interesting. I was having a conversation this morning with a global CEO. He was actually in Dubai this morning morning and his company, I think, is based out of Australia, or at least he has offices in Australia, and one of the things that he talks about is like listen, he says ever since I heard you kind of talk about this. He says I have that formula that's sitting in my head Like it's all about heads and hours and every wasted minute could be it's really taking away from billable time. He's like well, if you had an extra week's worth of time that you can find using generative AI, we should be taking advantage of it.
Speaker 2:So he's now looking at his business and his businesses right across the globe about how do I integrate AI to create those efficiencies to find 12 minutes a day. Why 12 minutes a day? Well, 12 times five is one hour, one hour. You know you could get anywhere up to 40 to 50 hours a week of efficiencies per year per person in your firm. So the whole idea is to use this technology to really find just a very, very low bar of 12 minutes per day, and what you're gonna find is that most people are gonna find way more than 12 minutes. It could be 15, 20. I personally find upwards of an hour or more of efficiencies to do more things for myself.
Speaker 1:Sure, yeah, absolutely. I mean, I recently put together a rather large strategy document for one of our initiatives and and yes, I start with a lot of knowledge in that area I you know the prompts that I was using in order to get what I wanted out of it were you know, pages long.
Speaker 1:However, it greatly increased my efficiency and also the quality of my output, if I'm honest about it. And so you know that 12 minutes, that 12 minutes was easy right, it was probably as you said it's, maybe it was an hour, maybe it was six hours, I don't know. It was quite a bit, and I think this area of research in my mind, probably, and maybe that's my own, me being slow in research and things like that. But I think this area of gaining efficiency in the area of research could be huge.
Speaker 1:So, we know that AI can supercharge that.
Speaker 2:One of these tools that we're going to be talking about today came actually from one of our clients. His wife works for a large research company, so you go out there and think about some of those large companies out there that produce reports and surveys and all this other stuff she works for. One of these tools said that what one of these things does in five minutes is typically what takes a human analyst two weeks Wow.
Speaker 2:Right, so being able to access a variety of things. Now, not a lot of people know this and I'll kind of let the cat out of the bag, but you know, I started a doctoral program earlier this year. And when you start thinking about doing research and I haven't done research and haven't written papers, academic papers, in 20 years, having to get back into that I look at some of these tools like Google, scholar and even ChatGBT, being able to find sourced and peer-reviewed documentation at my fingertips. I don't have to go to a library. I have seven libraries in front of me with all of these tools and I'll use deep research and I will say hey, listen, I want to dive deep on this topic. Help me find information.
Speaker 1:No-transcript about this topic for those of you that just said micro, what? Just google it, just google it just google it.
Speaker 2:It's, it's, basically it's it's. The analog internet is what it is um chat or gemini deep research one.
Speaker 1:We'll we'll explain it to you just literally just do it, just do it entertain yourself we.
Speaker 2:We laugh about that because we got some of those gray hairs but it but that is pretty funny a few, yeah, we do, but that is pretty funny.
Speaker 1:A few, yeah, we do, we do. So we know that AI is good for sorting through massive amounts of data, and we know that these tools are great for extracting key insights and being super efficient. And we know that we need to keep up with industry trends, which maybe is easier said than done sometimes if we don't feel like we have, if we're not dedicating enough time to looking into it. But, as you said, some of the information we're going to talk about today came from a client of ours, from client of ours, um, but as we, as we look at at what's out there, how do we keep up to speed with what's going on and what are your recommendations in terms of tools that we should be paying attention to?
Speaker 2:yeah, yeah, there's right now there are about three, three tools that are kind of on my and when I say three, there's probably four, probably four if you really kind of dive into the deep research one right. So ChatGPT came out with their deep research, but Claude came out with their deep research as well, so they all have this like little deep research function. Yeah, what I love about that tool, though, so let's use ChatGPT deep research for it. Some of the pros around that particular tool is the fact that if you already have some data or some research, you can start with your own. So, hey, based on what I've already started, let me augment and build out from there. So say, you're trying to launch a new product, you have a new AEC XYZ widget. Well, you can go and say, hey, listen, we've already done something around. Call it ideal customer profile, we want to sell this, we do all these things. Well, you can start with your base content and then it'll help basically build the research off of that, which is awesome because it's very tailored to what you're trying to do. It's not an open-ended question. Now, downside is is typically those features like that kind of deep research feature does require a paid subscription, so it is separate from the base license, right? I don't think you can get it on the free, but you can definitely get it on the paid versions. Now there are free versions too, right?
Speaker 2:The next one, and this is the tool that just blows my mind, and this one's done by Stanford University. It's called Storm, and when you go to the, if you can Google it and find it, it's a fascinating product and it is more academic. So it does kind of lean towards more scholarly type of content. But all you typically do is you kind of give it a topic, one sentence, and then kind of give it a very short paragraph of why you're creating it, and it'll produce referenced article like a two-page article for you in four minutes, and the best part of that, you know, has hundreds of references. So that's another good one.
Speaker 2:But then another one that's kind of popped up on my radar is I don't Google anymore. I use perplexity, and part of the reason behind that is that it's not giving me static links. You have to understand how does Google make their money, right? Well, it's paid ads, it's paid search, all those things. So they're ranking content based off of what they think that people want or are paying and clicking and all this other stuff. Perplexity kind of doesn't do that. Their big thing is that they'll go out and probably do all the links, but what they're going to do is they're going to synthesize that data and actually give you a summary of it beforehand and then I can interact with it.
Speaker 2:Where Google is right now is very just static. Like I ask it a question, it gives me links and it is. They have put the little AI summary up at top, but I still can't interact with it in a way like I'm having a conversation. And that's where perplexity has come in, which I really, really love. It is one of those things to where, if I was trying to figure out something about a particular job, project or something, I can go there and say you know, let's find all news releases, news articles related to this project. What is the city council saying about this? You know where's the funding come from and what it does is it finds all that public information live and then brings it back to you in a way that it's more digestible. So those are the three big tools, right? Chatgpt, deep research Storm from Stanford University is another big fan of that one and then really kind of elevating my search game by shifting over to Perplexity. For that more well they call it conversational search.
Speaker 1:Sure, yeah yeah. And you know, for those of you that have never tried this and you know, maybe, maybe we'll call you Frank, we'll call you the chat guy and we'll call me the Google guy, because I I have the Google pro edition stuff, so I use Gemini, right. So, very, very similar, for those who aren't aware, it's very similar to chat and I don't.
Speaker 1:I don't even remember what it was that I was. It was something really simple. Having to do something around here at the house Recently, I plugged it into Gemini Deep Research. So, again, very similar to chat deep research, and it came back. Or it comes back with this answer deep research. And it came back. Right, it comes back with this answer. If you've never tried any of these deep research um versions or or uh platforms before check this out, asked a very simple question. Oh, I remember what. I remember what it was.
Speaker 1:Now I was trying to fix a link or a leak sorry, a leak with my kitchen faucet and I narrowed it down. I'd figured out what the problem was, but I couldn't identify um, the, the specific part number. You know, I knew the manufacturer, I knew where the leak was coming from, I had a pretty good idea of what the part was that I needed, um, but I didn't necessarily know the model number of the faucet itself, which you know you need that right To to get down the tree to get to the right part. And so I, you know, I'm Googling. I'm Googling, I'm like I can't figure this out. I can't figure this out. Why not try Gemini deep research?
Speaker 1:So I use deep research, I plug it in, it comes back with a response and it, it, um, it identifies, it uses 40 different sources. It goes all throughout the internet, of course, identifies 40 different sources. Does this? Research comes back, gives me a summary. You know, here's, here's it starts out, starts out here's our methodology, right, here's how we're going to do it. Then it comes out here's an executive summary, basically, and then it goes through, gives me, gives me the ins, the outs, and then it says here's what it probably is. Here's why we can't say definitively what it is, because this changed and that change.
Speaker 1:You know faucets, I don't know three, four years old, right, things, things change, get discontinued, et cetera. But here's what it probably is. What you should do is and it's a Kohler faucet. So I guess, shout out to Kohler or whatever. Maybe they should be sponsoring this episode. But shout out to Kohler. It says you should call this episode. But shout out to Kohler. He says you should call this number. I call this number, use the information that Jim and I told me to use, and within five minutes, kohler is shipping me the part that I need for free, by the way, rather than going to Lowe's or Home Depot or Ferguson's or whatever. And I thought about that because what I was looking at, what I was figuring out on my own, was going to cost me $70, maybe a new faucet, et cetera.
Speaker 2:But think about, think about the intangible cost of time. Oh yeah, like if you would have tried to, if you would have, without that tool, how many trips to Home Depot do you think you would have taken how many? How many? Where's Depot do you think you would have taken? How many? How many? Where's that? Where's your frustration level of actually finding the right solution to what you're doing? So, yeah, there's the, there's the physical hard cost, right, of of them giving you the solution. But I also kind of then start pivoting back towards okay, so what is that soft cost? That? That, that, that that cost of time, time, right, nobody wants to be doing, um, home improvement stuff for three, four, five, six hours on a saturday, right? So I, I, I look at it that way as well, which I think is very fascinating yeah, yeah.
Speaker 1:so I mean that you know, is that aec? No, not in the way that we normally think about it, but I think it's a very, very uh, identifiable, uh relatable example that that many of us go through right when we just have the stuff that we have to get done. And you know, if you think about I- was going to say.
Speaker 2:I had another CEO come up to me recently and he's just like could I take my fee sheet that I start a project with and then pull something out of my accounting system and have it do a comparison analysis of of the fees about how I priced it going forward? I'm like, absolutely, I said so. That kind of research and that type of analysis is kind of inherent to some of these tools, and I say this all the time that people I'm like the power of these tools is not creation. Yes, it can create. It does a really good job at creating the real power and all of this lies in the ability for it to do analysis and that research faster and more effectively than a human being can do.
Speaker 1:Yeah, yeah, well, and you know, you said it before Right, if you have something to start with, right, which is which that example from the CEO that you just shared, I mean, is exactly right. It's exactly that Right, you had something to start with and you know, as you said, can it create? Yes, when I was talking about the strategy document that I needed to put together a while back, I had things to start with. I had a whole knowledge base to start with, experience, et cetera. Was it creating? Yes, was it starting with my years of experience and, like I said, a page long, prompt? Yes, absolutely, but that's where the quality of the output comes from.
Speaker 2:Yeah, right, yeah yeah, and so you know again, these tools also, you know there's there's limitations, and but they get better every time. So if I kind of look internally like what we're doing here at kpr, right, the researchers come back. You know, I was talking with them this morning and it's like, hey, I have large data sets from interviews, this amount of data that they've collected, right, two 300 interviews and is this like the off the shelf version of chats really not doing what we needed to do? Is there another way to do this? And there is. And so that's, you know, using the right tool, also for the purpose of what you're trying to do, is also going to be effective.
Speaker 2:So just know that, just because it says deep research or there's something that says they can do analysis, yeah, but be mindful that sometimes pivoting to a different tool your your go-to chat, gpt may not be the best solution. You may have to look at something else. So when I look at these things, right, it's people are always like what's your favorite AI tool? And the reality is like I got like six. It all kind of depends on what I'm trying to do, right, you know, the propensity could be a great one for, you know, identifying fixes at home from a faucet perspective. But if I'm going to dive deep into some hardcore data analysis, I may be looking at more of that deep research or something along those lines. So for me, it's all about being.
Speaker 2:it comes down to it being purpose-built in a lot of ways right, it's like yep I got a tool that does this and I got a tool that does that, and they all do something different. But I think the data analysis is a great example. I also think research. When I think about research and I think this is one of those underreported things that this industry doesn't do we can use a lot of this deep research and stuff to kind of focus on upskilling our workforce and you want to learn about it within your organization. You can get ChatGPT to create you a learning plan. You can get ChatGPT to kind of help do the research for you to sit there and say give me a summary of the things that I should know about this so that you can get up to speed faster, quicker and easier. So don't dismiss that. Oh okay, I don't do a lot of research. I don't create a lot of white papers Great.
Speaker 1:But don't create a lot of white papers great. But if you're ever looking at upskilling yourself, this is a great way to do it as well. Yeah, you know, we ought to have an episode in the future where we bring in a handful of college students and say okay, how are you using AI to study? You have college students.
Speaker 2:I have college students.
Speaker 1:I know a lot of college students. I have college students. I know a lot of college students and I think many of us you know, I suppose, those of us that aren't so connected to college students but I think many of us would have our minds blown if we understood how college students are using AI to create practice tests to do exactly what you just said in their world Own tutors right, like having a virtual tutor on any subject.
Speaker 2:not only that, but also customized to their learning style, like hey, I'm a more visual learner or it's more, you know, I can read better when it. Like I study better from reading it versus you know hearing it. So even then, you can even customize a lot of these things towards their learning, their learning styles and also having built in tutors.
Speaker 1:It's amazing. Think about how you could build your, your training, your in-house training program around something like that.
Speaker 2:Well, you know, KP and I are kind of working on that right now. That's a super secret project.
Speaker 2:Well, now the cat's out of the bag, the whole concept is how do we take the large data sets that we have around innovation and strategy and consulting to be able to create an AI agent that, as people work with us from an advisory perspective, it's like having their own KP, digital KP right, taking copies of his book, taking presentations, taking meeting notes, taking all this large levels of data but then synthesizing it down to where it's very actionable for the end user.
Speaker 1:Yeah. Yeah, that's the future, all right. So we know that AI speeds up data collection and analysis. We know that it can extract key insights from complex data sources and can find data sources that, frankly, I wasn't going to find right when I was doing my own research. So those are definitely some huge pros to AI research tools. What are some of the cons? What's the downside to some of these things?
Speaker 2:Yeah, typically the research stuff is more of the premium features, so you're going to have to it's just another subscription to it. So, yes, it does have some capabilities on some of the free versions etc. I know that claude may do it, jim, and I may do it, um, but typically if you want access to the more advanced features, um, it is. It is a pay-to-play type of deal you have to, you know, have a subscription. That that seems to be a con with a lot of these tools. Right, we've we've pivoted away from being able to buy software to now we're just basically renting it from a subscription perspective, and that's kind of the same thing here.
Speaker 2:The other thing is you got to understand how it's using your data, right, it's not infallible. It can make mistakes, it can look at data differently than you expect. So I think there's a lot of diligence around making sure that any outputs or analysis that it's doing that you're just verifying and double-checking it. So it's not necessarily a con, but you need to pay attention to this. Don't assume that it's interpreted the data the correct way, particularly if you're looking at large subsets of data. Now, one of the things that I've found with my own experiences is that it doesn't necessarily misinterpret the data. Sometimes it actually leaves some out, so you could be expecting a particular result or expecting a particular answer. Then you realize like well, wait a minute, you kind of skipped over this whole table or you skipped over something. So misinterpretation is an interesting word. It's likely to kind of miss things and you just have to be very diligent on how you're asking it to kind of analyze the content.
Speaker 1:Yeah, yeah, I mean I would call that best practices, right With any sort of AI.
Speaker 1:We have to be really diligent about reviewing the output before we run with it. Okay, the output before we uh, before we run with it. Okay, for our listeners, let's give them one action step or one actionable takeaway, because that's one of the the uh, the big things that we promise here. So, for uh, for the person that's listening and saying, hey, I've never used AI for research, that sounds like something really really useful for me or my team. Uh, what, what do they need to do today?
Speaker 2:You know I would. It's all about just going to try the tools. So what I would do right off the bat low hanging fruit, low risk have it. Create like a LinkedIn post for you around a particular topic that you want to kind of be positioned. So you want to, you know you want to be the subject matter expert around that right. Give it some of your thoughts, give it some of the things that you're thinking and then say, based on what I'm thinking and how I'm, how I'm positioning myself for this, go write me a white paper right Based off of this topic, just to kind of and I and I tell people to do that because it's really low hanging fruit, but I think that gives people the sense and the understanding of the power of what this can do, which then the next logical step is okay.
Speaker 2:How does that, how does that integrate into my business? What am I doing? Am I going to be using the research function from a from a sales and business development perspective? Am I trying to up my white paper and my knowledge expertise? So think about those next steps on how you want to do it. But I would right off the bat is go try it. Do something simple, based on a topic that you're interested in, and just have it do the research for you, just to kind of see the power of what it can do.
Speaker 1:Yeah, a hundred percent, the, the, the I think the possibilities are endless here, right, and I think you know the you mentioned subject matter expert, which many, I think probably many in our audience are subject matter expert on on something right there their specific project type that they, they designed the steel for whatever it is, that they design the steel for whatever it is that they do, right, but the ability to go even deeper or wider, you know, depending Maybe it's extending out in another direction. Ai research tools are a favorite of mine and I'm really, really glad that we were able to pull this episode off today.
Speaker 2:Oh, yeah, I think one of the other things is think about this too is using the research to kind of find adjacent markets that you want to expand into. Right, if you do all your business primarily say in, you know, nashville, tennessee, right, where could you logically expand to? But being like you can guess like well, yeah, nashville, charlotte, maybe very similar, but have it, do the research for you and have it kind of figure out like how do I do market expansion, how do I onboard a new service or solution those are the great things and almost kind of then think about pricing right, start thinking about like how am I pricing my things? But getting it to do that research and ask you for those things. I think that that's where you're going to see a lot of power for some of these firms. Again, doesn't need to be a client deliverable, but you can focus on it from the perspective of helping you gain better knowledge around even just market expansion or something similar.
Speaker 1:Yeah, absolutely, and I like what you're saying there. Right, you start asking it questions. He's done all the research. Let's pull this together. Maybe we are comparing the market in Nashville to the market and in Charlotte. You know what's? What is a prevailing attitude here versus there? What are, what are the the economic factors between here and there?
Speaker 2:What you know there are all kinds of things that you can ask A hundred percent. Then even this asking is like what other markets would benefit from my service? Yeah Right, saying hey, we do this very explicitly, right, where else could I be selling this? And sometimes you'll be surprised. Sometimes it validates what you're already thinking. Sometimes it actually is additive to what you're thinking and makes you and things makes you think a little bit differently. So I think I think it's a very powerful tool to be able to capture some of that stuff.
Speaker 1:Yeah, yeah, a hundred percent. And remember, as always, the better you get at asking questions, called prompts and AI world, but the better you get at asking questions, the better the output you're going get, the better answers you're going to get. Frank, this has been a good one. It's been a fun one. I, like I said, I love using AI for research. I'm glad we could do this. So thanks for thanks for getting together with me today to record this one.
Speaker 2:You know I always look forward to our weekly touch points on this stuff, cause, again, for me it validates a lot of what our clients are telling us. It's validating what we're doing internally and I love sharing it with others so that they can kind of figure out ways to kind of capitalize on this great technology.
Speaker 1:Yeah, and to extend on what Frank's been talking about or has mentioned a few times here, a lot of this, a lot of what we talked about today, came from our work with, or feedback from, our clients. We're also interested in your feedback. So, wherever you're consuming this, first of all, if we talked about anything and go oh, I missed that, what were they talking about? Just go to the show notes. Our production team grabs everything that that needs a link and they put it. Put a link in the show notes below. So remember that, whether you're listening to the recorded version or watching the video version, you can just go to the show notes and find all of those links.
Speaker 1:But the second thing is this is probably easier on the YouTube side. Easier for you to leave comments and questions YouTube side. Easier for you to leave comments and questions. Please question, or maybe even LinkedIn. Leave your questions and comments in the comments section or in the below the show notes, et cetera. Wherever you're consuming this, give us some feedback, ask us questions. It may become an episode in the future, because one of the biggest, you know, cornerstones of this particular show that we do is creating something that's useful and actionable for you.
Speaker 1:So you giving us that feedback helps us tailor these conversations to your needs. So, as always, thanks for watching, Thanks for listening depending on the version and we'll be back again next week. Probably between now and then you'll see KP and I unpack one of his LinkedIn posts or maybe hear from one of our researchers. But we appreciate you, appreciate you listening and watching, and we'll do our best to bring you actionable episodes here in the future. So thanks. This is the KP Unpacked podcast, the AI and AEC version, or the AI Unpacked version. It's where the biggest ideas in AEC, AI and innovation they all collide. It's powered by KP ReadyCo. This podcast breaks down the trends, the technology, the discussions and the strategies that are shaping the built environment. That means where you work, where you live, where you play, where you worship, where you do all the things that you love to do in the built environment and beyond. So thanks for joining us. We'll be back again next week. Thanks everybody.