Hugh Seaton

Embark on a transformative journey with Hugh Seaton, CEO of The Link, as we traverse the changing landscape of construction technology. Having worked across continents and industries, Hugh brings a wealth of experience to the table, from his early days in Hong Kong and Taiwan to reshaping the construction world. This episode peels back the layers of traditional construction practices to reveal how data-driven specification management and forward-thinking IT strategies are revolutionizing the industry. Armed with a case study from The Link, Hugh illuminates how actionable data can streamline construction processes, moving us beyond the paper-laden methodologies of the 1970s.

As we venture further, the conversation turns to the sophistication of AI and its burgeoning role in construction. Language models like GPT-3 are under the spotlight, with the RICE Framework guiding us through crafting precise inquiries to elicit the most relevant information. Within the industry's technological tapestry, we find innovation teams working diligently to integrate new tools and BIM, changing the face of project delivery and embracing the strategic side of IT beyond mere support. Hugh shares how these expert teams are not just adopting but also selectively tailoring software, ensuring that the pace of technological change matches the industry's unique demands.

Looking toward the horizon, Hugh and I contemplate a future where learning and technology are inextricably linked within construction. From AI's potential in generative design to the liberation of the workforce from mundane tasks, we discuss an industry on the cusp of a new era. Technology's role in providing real-time, on-the-job training promises to revolutionize how we approach building and design. Join us for an episode that not only reveals the current state of construction technology but also charts a course for its exciting future.

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Chapters

• 0:27 Construction Technology
• 11:30 Innovating Specification Management Platforms
• 18:43 Advancements in AI and Construction Technology
• 24:01 Elevating IT in Construction Industry
• 37:18 Future of Construction Technology and Learning

Transcript

Speaker 1: 0:27

Welcome builders and innovators to another episode of the Future Construct podcast. I'm your guest host, mark Oden, and today we have the privilege of diving into the world of construction technology with a true visionary in the field. Our guest today, for the third time on our show, is none other than Hugh Seaton, the CEO of the Link, a software company that combines cutting-edge data science and artificial intelligence to make construction documents consumable for contractors. They've spent years testing and refining to ensure that teams get the information they need when they need it, in the most useful form possible. Hugh is a seasoned professional who has dedicated his career to uncovering the core truths in diverse realms, from navigating the intricacies of advertising metrics to understanding business culture in China and tackling the complexities of startup financing.

Speaker 1: 1:13

Hugh first made an appearance on the Future Construct podcast on season one, episode 14, where the conversation centered around the future of AR and the tech evolution. His second appearance was on season three, episode three, where Hugh discussed enabling software and internal systems to easily translate data. Hugh's current passion lies in a domain that's relevant to all of our listeners the construction industry. As the general manager of the Crosswalk business line at CSI and Symmetry, hugh gained invaluable insights into the fundamental workings of construction. He delved deep into how companies leverage data to drive their projects forward. Not stopping there, hugh wears multiple hats in the construction realm. As the host of the Constructed Futures podcast, he engages in thought-provoking conversations with two construction rock stars every week, offering a glimpse into the future of the industry. His expertise doesn't end with discussions. Hugh authored the Construction Technology Handbook, providing a much-needed primer on technology for today's construction professionals. So join me in this fascinating conversation as we explore Hugh's journey, the future of construction technology and the groundbreaking solutions that the link brings to the table.

Speaker 1: 2:22

Welcome back to our show and thank you for all of your many contributions in the AEC space, hugh. Thanks, mark, it's great to be here again. So happy to have you To get going. I'd love to dive into your background even more than the past couple of episodes. In your first feature on our show, you spoke about growing up in the 80s and spending a lot of time in Hong Kong and Taiwan before moving back to the US and working in advertising within the technology space. What experience or moment catalyzed you to pursue a career and technology to begin with you?

Speaker 2: 2:50

Yeah, when I first came back from round one of China in 1998, I got a job with Philips Electronics the ad agency for Philips Electronics and I've always been a bit of a nerd. When I was a kid I studied lasers and studied electronics and, like a lot of people, tried to make some things with electronics and succeeded. So it sometimes failed with others. But it really lit up when I was able to work with Philips Electronics I just saw a lot of what they were doing. They were actually partnered with the old Bell Labs, a company called Lucent, so we were seeing some pretty cutting edge stuff and that just got me really excited and it's continued ever since.

Speaker 1: 3:28

That's amazing. You've had a diverse career, delving into areas such as advertising, metrics and, as mentioned, the business culture of China start-up financing, before focusing on construction industry. How have these different experiences shaped your approach to analyzing and sharing information about the construction sector here?

Speaker 2: 3:45

Yeah, I mean what I'm doing now isn't so different from what I did. The very first job I had was translating specifications from English into Mandarin in Southern Taiwan. So there's been a through line of how do people make better decisions, how do people create Like, how do you brief somebody, how do you create the conditions for someone to do their job really, really well, and that's a lot of what specifications are. You know, sometimes that took the form of briefing a creative team. Sometimes that took the form of managing a team.

Speaker 2: 4:21

There's been a bunch of ways that that's shown up, but I've got to be honest, I love watching people do great things. I love watching people create amazing stuff, whether it's a business plan, it's a product, it's something creative in the advertising world, it's just something about human capability that I really am excited about. And, in fact, my last startup prior to this one, we did a lot with e-learning, and a lot of it was that A lot of what e-learning is supposed to be doing is helping adults to become better than they were at whatever it is they're interested in. So there's a bit of a through line in really loving to create the conditions for people to do what they do best.

Speaker 1: 5:01

Yeah, let's talk about your current startup, the link. So it's, from what I understand, a construction specifications management platform that pulls data and insights from construction specifications for project teams and, as you mentioned, it seems to be very well connected to your past. Can you walk us through a specific example or case study where the link has significantly streamlined the construction process for a project team?

Speaker 2: 5:23

Yeah, let me talk about the way it all started. So I was lucky enough to be recruited to go work for CSI Actually, the company that held my W-2 is supplier to CSI, a company called Sympatry, an amazing group and I was asked to go and help them to grow and continue to create their technology and their software offering. That gave me a lot of visibility into a few things. One of them is some of the standards that they use, but the other one was how specifications have evolved over time and how they kind of haven't evolved as other things have. So specifications in construction right now are still documents. Fundamentally they behave the same way they did in the 70s when they were being couriered around to each other. Yes, they're PDFs, yes, they get emailed and so on and so forth, but there isn't the same reason for them to be all in one document quite like there was. For a number of reasons it's going to stay that way for a bit, and one of them is just legal they're all in one place that someone can stamp and put their reputation behind.

Speaker 2: 6:34

There are ways in software to do it differently than that, but that's one thing at a time. So, to answer your question, you wind up getting especially in a classic design bid build project, but even in design, build and a number of other project delivery methods, you get a bunch of specs all at once. The project team has to onboard these specs really quickly understand them, start to write contracts for subcontractors and so on, and it's really not that easy. It's talking about 2,500 to 4,000, sometimes more pages, a pretty dense, legalese text not always legalese, but certainly technical text. That's hard for people to navigate. It's hard for them to get through it in a way that is coherent. They manage, but they usually manage by putting bodies on the problem and by getting it, getting through it pretty painfully. So what we do is the very first thing is just to do. Some of the outcomes early in the project that you want are like a submittal log and some other things. So the first thing that we do is make the onboarding of the spec faster and easier.

Speaker 2: 7:37

So the next piece, though, mark, that more clearly answers your question, is the idea that after the beginning of the project, people tend to look at the spec a lot less than they did In the beginning. You don't have a choice. You have to go through and understand what you're required to do and what you're on the hook for. But later, once you're building, people have a tendency to look at the drawings a lot more than they do the specs. The problem is someone said this once and I love it is lawyers don't read drawings, they read specs. So at the end of the day, if there's a claim or a problem, they're going to go to the specs, not the drawings.

Speaker 2: 8:12

So what we do now is we help people to go from this older kind of mentality where it's almost like an old map right, like a Rand McNally map that you only use if you're going really far or if you're lost in the middle of a desert. And the analogy in construction is if it's a big deal or it's a disaster, okay, we'll go through the specs. But otherwise people on the project know how to build, they know how to pour concrete, they know how to install windows. So the temptation is to go with muscle memory and then things get caught expensively and redone with rework or they don't get caught and then you have a claim or even a lawsuit. So what we look to do, and what we've been doing, is going from that paradigm of an old map to Google Maps Now it's so easy or ways or whatever you name the digital map.

Speaker 2: 8:58

But the point is, if you're even a little unsure, you pull it up and go check and that's the big shift that you see. In for project teams is a superintendent, a PE, the PM. If they're even a little unsure, they're like yeah, I'm pretty sure I know this, but the barrier is so low to just ask, because our system will then go into the specs, find the answer, get it to you in. You know two seconds right, and what that allows you to do is really reduce the risk of people going on autopilot or going, more accurately, on muscle memory, and instead they can go consult the contractual document and say, all right, we're sure we're right, we're covered.

Speaker 1: 9:52

That's awesome. I'm so curious about it. Can you speak a little bit about you know training the model or using LLMs, and you know the data set and all of that, yeah.

Speaker 2: 10:03

And we do use LLMs. Specifically right now we're using OpenAI GPT-4 plus, but you know, every 15 minutes there seems to be another one. But that's okay. What we didn't do is train a model. What we do is take the model as it is and fine tune it, because that space is moving so fast and there's no way we're going to I mean, they're raising billions of dollars. So the state of the art in AI models is not something a startup at our size is going to engage in.

Speaker 2: 10:32

What we're engaging in is how do we take these emerging capabilities and make them useful, make them into products, because chat GPT or, yeah, chat GPT is interesting and it's a generic problem, like Google is. But if you want a specific product, something that has security, something that has workflows around it, you need to build actual software. So that's what we focus on. An example of that is most PMs and superintendents often reference the last job or two jobs ago when they're just thinking about their current job. What we allow them to do is upload those specs to and point spec GPT at that also, so they can find the thing that there's on the tip of their tongue or what did we use in that last one. Well, they can go find it like that and then continue to use past experience to influence and really help them to execute the project at the right minute level.

Speaker 1: 11:23

I love that you and I'm super excited about diving even deeper with you. Would you highlight some unique features that the links, that's apart from other specification management platforms? If any others exist, I'd love to hear.

Speaker 2: 11:35

Well, actually, let me start with the last half of your question, because that's a really good place to start. People have spent a lot of time making drawings better, like whether it's planned grid or it's pro-core. A bunch of things have been done on the drawing and on the modeling side, but specs have really not had a lot of attention. There's a couple of pretty good spec writing platforms, but they're really more about writing them and not so much about managing. They're really more for the architect side of the contracts, so to speak, as different project delivery methods have grown. The fact is, you don't always get all of the specs at once and they may change and things you know grow, design build being an easy example of that. You get about a third of your drawings and you get or of your specs, excuse me and you get going and then you fill it in over time. So one of the things that we think is absolutely missing is the ability to manage specs as they change, as they get amended, but also just the fact that accessing them and effectively using them it's a really archaic system that people have right now, whether it's a viewer, whether it's even if you get a submittal log, that's not the whole spec. That's just the submittals. I'm getting a little deep into my own story here. So the net net is we think that it's really a ripe time for not just a platform that can manage specs on the contractor side, but also really do we need to have them as documents? These are collections of data that relate to each other and we've been doing databases, you know, everywhere for 40 years now. Turning those documents into data is sort of the fundamental thing that we look to do at the link, and we actually spent a bunch of time with a guy who works for the Air Force I believe excuse me, his PhD is being funded by the Air Force who does taxonomies and ontologies. What that means is he helps us to look at specs and start to create buckets so you can break them down into data. Whenever the project is, it'll all be the same kind of buckets Took a lot of work. We put thousands of specs through it and that's the beginning of us starting the process of changing what is fundamentally a document into fundamentally, a database, because that's what it should be.

Speaker 2: 14:00

If you think about what BIM is, bim was originally meant to be it still is meant to be a database of information about a building. That is, of course, related to a geometric model, but the point is it was meant to be data, not just geometry, and similarly, specs are supposed to be the narrative of the project what you're building, how you're installing it, how you're preparing before installation, how you're inspecting it, how you're paying for it, you know, how you're meeting about it, all this sort of thing. There's no reason that needs to be all in a document, and one of the problems we have I mentioned before these are long documents. Part of the problem is you need to include everything that's going to be needed for a two-year project into the same document, which means, at any given moment, you only need you know 5% of what's in that doc, probably less than that. Again, it's a very archaic way of doing things. It assumes that you have a set of documents that get stamped and that's the legal agreement.

Speaker 2: 14:56

Well, you can do things. You don't even need blockchain. Just normal, you know, just normal software. You can see who did what. You can audit what was changed when it was changed, way more efficiently than a PDF that has you know markup on it, because you can't do a lot with a markup. You certainly can't do it at scale. Someone has to physically look at it.

Speaker 2: 15:15

So the point I'm making is you know you started this with a great question about spec management platforms and the first thing to do to make these things more manageable is to kind of change the way you interact with them, and that's the first thing we did. So we pointed in LLM at them, and now you can. The way you interact is now much more conversational, much more directed, much more intelligent. But then, downstream, what you need to do is say great, these documents, for reasons that are beyond this podcast, they're probably going to stay being produced the way they're produced for the next little while. But once they're handed over to the project team, why do they have to stay a document? Why can't we turn them into data and then do all the wonderful things we know how to do with data, whether you're searching, recombining things, networking them with other information like standards and RFIs, and change orders and product information and so on.

Speaker 2: 16:08

We have a whole network of information that goes into the building of a product or a building of a building, and specs often reference other things. But if we turn them into data, you don't need to just do the reference anymore. You can have it all in one place, and that's a long-winded way of saying that. We're on the beginning of a journey where we think there's a new way to think about specs and the sort of building requirements that they're a part of, so that it's a little bit less siloed. You're asking a lot less of people when they're busy to find things and execute against them, but also, over time, what happens when regulations change? What happens when you're being asked what's the carbon content of this building?

Speaker 2: 16:51

Right now it's hard to do because you have to go hunt through data. Sorry, you have to go hunt through documents. Well, that should be a quick search that should be very fast to say here we have this EPD. These are documents that come from the environmental EPA as well as building product manufacturers. I ought to be able to reference EPDs automatically just based on the fact that I have data and I have this EPD coming in. It shouldn't need to be document against document. That's just one example of how we see this changing. Again, once you have things in the form of data, it unlocks a ton of downstream innovation and ability to do smart things with your project.

Speaker 1: 17:35

Yeah, really, I can just see so many use cases and simplifying the interaction with the data or the specs and where it makes so much sense to create a spec GPT using an LLM like chat GPT and something the audience may be aware of chat GPT has been. There's a common word called hallucinations, where it may just create an output on an input. So I'm wondering how are you tackling hallucinations when it comes to spec GPT?

Speaker 2: 18:07

Yeah, that's a great question. There's a number of ways that you answer that. The very first one is the underlying models themselves are getting better all the time. So the OpenAI's and the Metas and the Mistros in France and Google, of course, they were aware of this time last year, so there's been a lot of work to make the models better and better. In fact, what isn't commonly talked about is prior to November 2022, when chat GPT really broke out into the scene.

Speaker 2: 18:37

The underlying models have been around for a couple of years. Gpt-3 had been around for at least a year. The problem was it produced a range of answers that weren't that useful. The big innovation that made all this happen is OpenAI, among others, innovated a way of training them to only give you the best answer. So an extension is called reinforcement learning with human input. The extension of that is to continue to train the models to give answers that they know came from somewhere, as opposed to they're just filling in what looks like the best answer. So the first line of defense is the underlying models continue to get better. In fact, gpt-4 plus or turbo I believe it's turbo. It was out last week, so, and if this podcast comes out in a month. That's probably still going to be true because it came out with another one. They're just doing it so quickly. That's point one.

Speaker 2: 19:31

Point two is asking better questions. So if you treat spec, gpt, chat, gpt, any one of the LLMs as if you're talking to Google, you're going to get much more variable answers than if you ask a better structured question. Typically, you want to be separating out parts of the question like who are you asking as? What's the context? What are you looking for? Are there any examples? Depending on what you're asking for, we bake that. You don't see that as the user. Well, we bake that into the way your question gets asked. So you type in your question but we fill in some of the other things so it'll answer the problem in a more narrow way.

Speaker 2: 20:11

A general rule of thumb for all AI, whether it's deep learning or whether it's LLMs or even the vision models the smaller you make a problem, the narrower you define the problem, the better it's going to be able to answer the question and, by the way, that's true for humans too. One of the most famous admin ever said give me the freedom of a tight brief, and his point was the better you define this is David Ogily. The better you define a problem, the more I'm able to put resources against solving that instead of trying to figure out what you meant. So the same thing is true with LLMs If you can make the problem smaller by defining it, by saying who's the persona, what's the thing you're looking for? Is there any context that helps? Are there any constraints I should know about, and do you have an example? That's actually a framework called the Rice Framework R-I-C-C-E.

Speaker 2: 20:58

That is just a better way of writing prompts. In two weeks I'm actually doing the whole webinar just on how to write prompts, because I think the industry and just everyone in the economy is on a path of learning how to interact with LLMs more effectively, not as if you're talking to software and not as if you're talking to Google, but an entirely new class of cognitive systems that are a little way more powerful than what we're used to, but they can also be a lot more variable than what we're used to. So, just like if you were talking to a human, the better you brief them, the better your answer is going to be. So hallucinations are just like a human. They will still make mistakes, but the degree to which those mistakes are made is becoming much less than it was. But the final way we deal with that is by giving people access to where the answer came from. So in our interface you can click on a button and see the part of the spec that that answer came from. So that's the ultimate point is go look for yourself.

Speaker 1: 22:01

That's brilliant. Thank you, Hugh. I appreciate that and this is certainly a shared passion between you and I, and I'm sure you and I can and we'll talk for hours on this. To transition out of the link specifically and out of spec GPT, We'd just love to talk a little bit more broader sense. What do you feel are the current technological challenges in the construction space, above and beyond the link that you've been looking at?

Speaker 2: 22:26

Yeah, I think there's some things everybody knows about, and that is BIM should be more a part of how projects are managed. I think ProCore has done a pretty good job of bringing that into the field. They're not the only ones to have done it, they're just seeing a lot more of that. I think construction has had a pretty heavy couple of years where a lot of money and a lot of options have been presented to contractors and along the way they got pretty good at assessing and integrating and internally marketing new ideas. So you've seen, even since I wrote the book, the growth of innovation teams. They're not always called that, but let's just for the moment say that you know a team whose job it is to survey what's out there but also survey what's needed by their. So constantly talking to project teams to find out what they're missing, what they could do with, what processes they could do better and trying to match those and then, once they've done a couple of pilots and worked out kinks, then figuring out how to market it internally so that it gets adopted, because it's pretty infrequent that some central authority tells everybody you absolutely have to use this. That happens for things that you don't have a choice, like the accounting software or your project management software, but usually that those are most of the time those are the only two. I mean, sometimes it depends on the contractor. So I think that you know a lot of things are going better than they were. I will tell you.

Speaker 2: 23:55

I think the biggest opportunity for the industry totally is for IT teams to get elevated to a strategic role, so to have a real CIO who is C-suite, not just the IT director with a better title, and that there's. You're seeing that. You know Albert Ricci did a great job pretty early. Actually, they're not at all alone. Dpr is famous for being good at this. You know Suffolk has been fantastic at this. I'm going to forget to mention some folks, our friends at Ellis Don, who I've got a lot of affinity for. They were wise enough to choose us as part of their accelerator. The degree to which they have a fully functional internal software team is surprising. It's pretty amazing, and you know they spent a lot of time and a lot of money getting there. But the point I'm making is I think across the industry, contractors need to spend more money on IT, because that's how they're going to unlock a lot of the value that things like LLMs and the link or you name it, because if we are alone helping a process, that's great, but if we're part of an integrated set of processes, the value we can add is literally exponentially more than it was. So I think that's going to be the big one.

Speaker 2: 25:05

In all honesty, cyber security is such a thing right now that I'll bet you a lot of them are breathlessly trying to keep up with the threats they face. So, as much as they probably want to be more strategic than they are, I understand why it's slower than everybody wishes it were. You know, the problem with the contractor is unlike some other fields of endeavor or other professions if your documents get locked up, it's really really hard to proceed. So the more digital they are, ironically, the more that's true. So they're getting really good at fighting against ransomware and some other attacks, but nevertheless I have a feeling that's slowing things down. But I think over the coming years you're going to see the role of IT and CIOs become more strategic than has been true in the past and probably be separated so that there's an infrastructure team who makes sure that the light stay on, the emails work and the ransomware gets taken care of, and then the second half. That is really looking forward to how these technologies can help manage risk and deliver projects.

Speaker 1: 26:10

Thanks, and you did mention some industry titans there that are setting incredible examples to all of us, so testament to their investments and their forward thinking. So, looking at your formal position as a general manager of the crosswalk business line at CSI and Symmetry, and having gained the firsthand experience at a fundamental level of the construction industry, can you share specific instances where the industry has given you a unique insight into how companies utilize data to run projects?

Speaker 2: 26:43

Yes, I think it's a very, very big question. I think that there's a few things. One insight that maybe is counterintuitive to the question you asked is what construction teaches you is the limits of what data can do versus human intuition and judgment. So you really are finding yourself saying the job of data isn't to make decisions, it isn't always even to define what the decision is. It's to support the decision for sure and help guide the intuition of someone who's seen this problem 40 times before, and often it's to make sure that the person realizes this isn't the 41st time. It's actually either new or it's different. So I think that what you see in construction is because of the complexity of what's really going on and all the interconnectedness of it. So if there's a thing that I would say was really a revelation for me, is the incredible interconnectedness of construction, and ask anyone who's tried to manage a construction schedule. They'll tell you that it's so. I mean, every day there's another ding on it and it moves here, and it moves there, and, oh my gosh, we have to send it out to the subs, and so on. That speaks to the fact that one thing is late because somebody has a flat tire and all of a sudden it has 15 impacts that you have to then manage. That sets an upper limit on what certain types of data can help you with, because that complexity means a human has to kind of manage it and make trade-offs, because it isn't just that you move one thing and then everything moves accordingly. You're now making trade-offs. Am I going to accept a little more risk by having two trades in the same room with, or three or whatever? Am I going to accept risk of trade stacking? Or am I going to accept risk of delivery time by not doing that? There isn't really an equation for that. Data doesn't get you there. What data does do is start to help with benchmarking, help people to understand how high is up and what is good and what actually we should be doing better. Then I think it helps people to understand the problem a little bit better, and definitely less on the decision side than on the operation side. The more visibility people have to what's going on, the better they can apply that intuition and say we're going to do better than this. An example of that is a number of companies have gone and started to automate. Things like this is a little more on the trade side, but things like the fab shop. There's a lot of the same thing going on. It's not quite a manufacturing well, it is manufacturing technically, but it's not like an assembly line. Nevertheless, there's enough repeatability that you can start to really tighten up how you're doing what and how you're making what decisions. I think the number one place that you see data helping construction companies is visibility. The next thing after that is starting to really think about process and process redesign. Frankly, I don't see a ton of that Seeing it more in the fab shops, a little less elsewhere, but it's happening.

Speaker 2: 29:37

The final one that is the holy grail is predictive analytics. I think that predictive analytics in construction is not impossible. It just takes more creativity to think about what you're predicting and what's it really telling you. I think what it's not going to do is tell you how things are going to work out. It tells you where the risks might be and what you should pay attention to. You're seeing some folks that are making some headway there.

Speaker 2: 30:03

The problem is, as always, finding the same data that you can aggregate together into big enough numbers to run math against it, much less train. A deep learning model is always going to be tricky. You really have to ask yourself what are we training? How complicated is the thing we're trying to predict? I mentioned before a general point with AI is the more you can narrow the problem, the better it's going to perform. Frankly, if you think about things like surveys, if I want a survey to be valid, I want to have a big group of people and I want to have as few questions, as few cross tabs as possible. It's a little like that the more of the same thing you have against the narrower problem, the more accurate it's going to be. Well, that's a challenge for data for us. Just to recap again, I think the first thing is visibility. That makes the existing intuition and experience that much more valuable. Then, after that, you get the process redesigned and you ultimately get to some predictive analytics. It's all coming one way or the other.

Speaker 1: 31:05

Yeah, thanks so much. You're definitely highlighting. One of the largest challenges in terms of training the LLM, or predictive analytics, is how to find enough data and how to make sure that you're training the model. How do you handle finding the data and ownership of data and transporting the data?

Speaker 2: 31:23

It's a bunch of things in there. I'll tease apart. The first one is the value of an LLM. Why are they so transformative is you don't need to train the model, you need to fine tune it. So it goes and finds just the thing you want. It comes to you with literally trillions of parameters already. That's really transformative, because now, all of a sudden, it's going to be useful for construction. The way you fine tune it is you have people go through the exercises you want and point out to it it should have been this, it should have been this, it should have been this. Now you're talking about hundreds and thousands of data points, which, while annoying, is absolutely doable. That's point one is you want to make sure you're selecting the right model. The truth is that the top end of them are all pretty good. The next is do I need to fine tune it? If so, how do I create a repeatable process for people to train it? There's a couple of ways around that.

Speaker 2: 32:22

You also talked about storing data and how do you handle it. It's a really big point, and I think you were also asking about who owns it. That's actually a bigger point. Contracts right now are not written for this. It's really something that people are starting to get smarter about, but the classic AIA contract didn't need to deal with this Not really, because they didn't think about derivative data. The way the magic really matters right now isn't the project data, the plans and the specs and all that. That's pretty clear cut. It's funny that it often is the architect that owns it, but that's not what you're asking about. While operating on that, I'm creating new data and metadata and so on. That's still being figured out.

Speaker 2: 33:06

Frankly, I'm not sure there should be a standard there. I think that should be something that gets negotiated depending on what both parties want. I think what has happened, what I've seen, is contractors gave up a lot of data four or five years ago to start-ups who then went on to raise huge rounds. They felt a little bit taken in. Wait a minute. We gave you all this data, which wasn't free for us to organize and produce. We put effort and resources into this. We got to thank you from you and off you went into the sunset. Some of these companies didn't succeed, so it is what it is. It did leave a bad taste in contractors' mouths. Now they want to know either they own it or there's some compensation for the effort they put in. That's more than just paying for hours. How do we take part of this upside? I think the ownership thing is going to take a little while, not least because, like I said, I think that should be negotiated in the contract, depending on what the different goals of the parties are. But the other one is on the contractor side.

Speaker 2: 34:09

You have very accomplished but very focused lawyers who are really good at construction contracts and have had no reason to be good at IP law. So a lot of them are struggling right now to figure out how to write an IP policy and how to think that through. And you know they're smart people that really understand their business. Probably don't love the idea of bringing an IP specialist in, but the funny thing about IP is, almost anywhere you go, whether you're a startup or you're, you know, a non-construction corporation, IP is so specialized that you very often bring in an IP specialist, even if you're, you know, general counsel at Proctor Gamble, Because it's just a thing that people really get good at.

Speaker 2: 34:49

So I think the IP question is just starting to get asked and properly thought through and I don't think the I don't even think we understand where the problem is going to be in coming years, because what if you've automated a process and now the automated process is creating metadata? Who owns that Like? So I think that it's going to get a little squirrely, but we'll figure it out. And that's where, again, I don't think a standard is a great idea. I just think it's going to depend. Some contractors are going to care, some owners are not going to care. Some owners are going to really care. You know, if you're putting up one building a year, you care less about the IP than if you're. You know target. You put up, you know, 50 projects a year. You see what I mean. Like, for one group it might be something that really matters for their competitiveness and for another it may not. So I think it'll be an area for discussion.

Speaker 1: 35:42

I love it, hugh, and you're always the best one to start the discussion. And, speaking of, you're the host of the Constructed Futures podcast. We're on a weekly basis. You host two rock stars in the construction industry and you're the author of the Construction Technology Handbook, which provided a primer on technology for Didey's construction professionals. We'd love you to tell me a little bit more about the Construction Technology Handbook. What was your inspiration and what did you find that helped you transform construction practices along the way?

Speaker 2: 36:10

Yeah, I think what I found was I wanted to write a book because I wanted to dive deeper and dive deeper into the industry and talk to the most amazing people I could find, and it was kind of early. I was still pretty new to the industry. I'd done some things in the background, but not in the middle of it, and the cool thing about writing a book is almost no one says no to a request for a book interview, so I got to talk to some pretty amazing folks. I'm actually doing a second book, which, not surprisingly, is called AI and Construction, so I'm interviewing a little bit less this time. I'm starting from a different basis.

Speaker 2: 36:44

The point of that book, though, and sort of the core insight of the first one of the Construction Technology Handbook, was people talk over each other too much. So folks who spent 20 years figuring out how to pull a building out of the ground probably don't spend a lot of time wondering what API stands for, and yet people are throwing this word at them all the time. So I actually spend the first third of the book defining terms. I mean, I literally define what Wi-Fi is Understanding.

Speaker 2: 37:11

Most people know what it means, but have probably never had a discussion about it so that people walk away saying, look, I may have known some of that, but I know all of it now, or at least I've heard all of it now, because I wanted to level the playing field so that consumers of technology would be able to make better decisions than if they have to kind of shut the brain off and get through the part with all the words they don't know and then have to feel a little stupid asking questions because it's on them to know whether the thing they're buying is what they need.

Speaker 2: 37:40

But if they're not armed with the right terminology and understanding of what's going on, they're really hands-strong and that slows everything down, because now people are like, look, maybe this would help me, but I don't understand it well enough and I can't take a risk with this project. So if you give people at least the table stakes of what these words mean and thinking about how software is built and so on, they're better able to make a decision which helps both sides it helps the contractor and it helps the vendor of software and it helps the people managing the whole thing. That was the big deal.

Speaker 1: 38:13

So, in addition to I mean I'm really excited to hear about this new book you have in addition to the success of the last one and the new book that you're working on, and I'm sure it all ties together to this question, which is what is your vision for the future of construction and how do you see the work you're doing contributing to the evolution of the construction, best construction practices and project management?

Speaker 2: 38:33

Yeah, I think that it's important not to get focused on one part of what's going on. I think there's a lot of change and there's a lot of new ideas and a lot of new ways of doing things that are being worked out and tried out. It is such an intricate as I said before such an interconnected endeavor to build a building that it's really hard to change one thing without it impacting others. So they're all inching forward. But I think what you're going to see and what I, to the degree I have a vision about this is we start to differentiate buildings and think about how to build them in the way that's most appropriate to them. So things like industrialized construction, which absolutely relates to specifications and absolutely relates to AI, especially on the generative design side, but some buildings are going to be better for that and some are not, and apartment building is probably better for that, multifamily maybe less so, or a factory definitely less so. So you see what I mean. Like, I think that we have a tendency sometimes, when we're talking about construction, to try to continue to lumping it together, and I think you're going to find specializations more and more be the case. I think that the degree to which a human needs to be doing. Lots of the minutiae in construction, I think, is going to go away. If there were a vision, it would be. We need fewer people who are doing more value at, more of the intuition and thinking that goes into making a great building, and less of the moving stuff around and less of the paperwork and less of the stuff that people typically hate now anyway.

Speaker 2: 40:11

And the interesting thing, though, is often when you say things like that, veterans in the industry will tell you yeah, but how are people going to learn if they don't go through and do 15 submittal logs? Well, the answer is the same. Technology that automates. Some of this is fantastic at creating little micro-trainings, so whereas before there really wasn't a ton of training unless maybe you're talking about safety, and even that is kind of recent you're now able to have the LLM or whatever the software platform is, create little snippets of learning along the way, so you're learning what they call learning in the flow of work.

Speaker 2: 40:44

So the other thing I think you're going to find is the tools that automate and the tools that make it easier for us to not have to do all the Mimousha that, again, people don't usually love will be the same ones that are the source of learning and the source of upskilling people, not only to where they would have been 20 years ago, but, as things continue to change, they're able to be there to do what in the learning world, we call scaffolding learning, so supporting learning.

Speaker 2: 41:11

So you're not taking on too much at once, you're just learning a little piece each time, but pretty quickly you get really good at whatever the new thing is. So I think, rather than automation, necessarily meaning that our skills go away, I think there's actually opportunity to be thinking and saying these things can also write learning. So let's do that, let's have them do that and deliver learning in a way that's optimized for whoever the learner is. I think you're going to see a revolution across manufacturing, across construction, across pretty much every service business, where learning in the flow of work it comes from the tools you're using, not necessarily some learning management system that no one pays any attention to.

Speaker 1: 41:52

Yeah, and these lessons that have been learned are very costly to the business too, right? So if there's a way to avoid that the cost in the learning I think that would be revolutionary as well.

Speaker 2: 42:05

One interesting point really quickly. Almost every project manager I've ever talked to has a list of lessons learned. They often need a little bit of translation because they're usually reminding themselves or they're delivered by them to someone else, but the point is there are huge repositories across the industry where people have taken notes. They've taken notes on projects, they've taken notes on specs, on drawings, so on. There's a lot of context out there that these new AI tools have the promise to be able to distill into lessons that make some sense, especially when you combine them with the work that the International Code Council and CSI and ASTM and some others. They have amazing repositories of information about how to build a building and I think that those things start to become unlocked. We're going to be able to think about buildings in a much more sophisticated, comprehensive way than any one human's brain could handle.

Speaker 1: 43:01

So, looking into the future, as you and I just were, I would like to transition into the final question of the show and the tradition of our podcast, future Construct. If you could project yourself out 25 years and wanted to have any device or technology that would benefit you personally, what would it be and what would it do?

Speaker 2: 43:17

25 years is an awfully long time. I want a spaceship that'll take me to the moon, where I can hang out. I'm kind of coming up with that one on the cuff. It doesn't really relate just to construction because, frankly, in 25 years I think the way we build buildings will be, some of it will be unrecognizable and some of it will look a lot like right now. But me personally, yeah, I like the idea of personal transport that can I don't know about the moon, but that can take you out into space, and I love the idea of experiencing space and I think I'm young enough that that's a reasonable expectation. I'm not so sure about Mars. I moved to Austin and say I never want to be culled again and I don't know that Mars is going to satisfy that.

Speaker 1: 44:07

Well. Thank you so much for your time. It was an incredible conversation with you, hugh. Your journey from the 80s in Hong Kong to becoming a construction tech visionary is truly captivating. The link specification management platform, especially with spec GPT, seems like a game changer, streamlining processes for project teams. Your insights into the industry, challenges, your unique offerings, such as your constructive future podcast and the construction technology handbook and your upcoming book they all provide a rich perspective of looking ahead. Your vision for the construction industry and the links role in shaping the future is insanely inspiring. Thank you, hugh, for being a thought leader and to our listeners, stay tuned for more insights on the future construct podcast. Hugh, it's been an absolute pleasure having you on the show. Thank you so much for your time. A thanks for having me. Absolutely Thank you.