Introduction: Professor Ibrahim Odeh, Columbia University

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All right, my guest today is Professor Ibrahim O'Day. Ibrahim is the chair of the construction engineering and management program at Columbia University in New York City. He's also the founding director of the Global Leaders in Construction Management Initiative, a program that sends highly selective Columbia fellows directly into the field, working alongside senior leaders at big construction companies like Turner Construction, Bechtel, and a bunch of others. That network has grown pretty significantly. I think you've 5,000 members globally. And one of the stats I read is a million learners on Coursera, which is pretty amazing. And this year, Ibrahim and his team have been deep inside major AEC firms studying the real-world adoption of generative AI for the construction industry with white papers, I think, landing soon. So, Ibrahim, it's my absolute pleasure to welcome you to Wired and Hammond. Likewise, thank you, John. It's uh really great to catch up with you and thank you for the introduction and the bio. It touched base on on a couple of things really uh wonderful to my ears, but it's really took us over a decade just to come to here. So uh thank you. It's genuinely super impressive. Um and and I think even beyond Colombia, uh I I think I first came across your work, it's a while back now, but the the first papers that you contributed to for the World Economic Forum around the future of construction, I think that was pretty formative. How long ago was that? Started in 2015. It was um in collaboration with many colleagues from different parts of the world, from the US, from Europe, Asia, you name it, as well as different entities from academic institutions, public uh uh as well as private uh entities. Uh we all stopped by uh the the WIF in point in time in 2015 asking the question is what is the current status of the industry, as well as what are the mega trends that reshaping the industry? Last but not least, what is the way forward? What can we do to be ready for these changes? So that been for almost 11 years. And like I think the the recommendations that came out of that were all um, I would say sensible in terms of you know the adoption of robotics, good project management practices, software here, good change management, and so all of that sort of the industry was slowly moving towards, right? The interesting part is what we've been discussing almost 11 years ago, a lot of these points are still valid until today, which it's kind of alarming, but at the same time informative to our industry. So it's it's definitely there is a lot of changes that happen in the industry, but not to the speed as we are wishing for.

Theoretical benefit vs. observed benefit: the GenAI gap

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I'm itching to ask you about the role now generative AI capabilities is playing in that sort of more mainstream program, that change program that's really needed to apply these new generative AI capabilities that hopefully we can agree are fairly transformative in their potential. Like, where does that all come together? Basically, in my humble opinion, like we've been studying this for a while now, and for the focus is just to understand what are use cases that we are seeing companies are actually using generative AI tools in uh their day-to-day work. And we found a lot of good industry examples. Now, and that's what I call the first bucket we were focusing on in the last one year. The second bucket is to look at what concerns companies have on the use of Gen AI. And then we focus on the last bucket, which I call it the so what bucket. And uh I'm very excited to publish three white papers on this that coming next week, actually. We spoke with many CEOs and CTOs and CIOs from our industry, and all of them humbly said, from a bottom line perspective, definitely we are in the discovery phase. What are you seeing as the best pathway forward for general contractors? Like, is it is it trying to build this capability in-house or is it relying on more sort of broader suppliers and partners in the industry? Like, what's the right approach, do you think? This is a very good question. There is no one solution fit all. It depends on the if we're talking about contractor and engineering firm, it's a totally different answer. But even if you look at either the C world or the E world, it depends on different factors of the company. From the size of the company, the location of the company, and many other things, the services they are providing. And when you dig deeper into the services, what are the top services that contribute to the revenue of the company is also another key question. Now, what I noticed from without naming names of companies, from our interviewing several of these firms, there are a couple of approaches. And I can highlight some of the lessons learned from this. One is there are definitely a top-down approach where leaders of these contractors they say, like, okay, there is something going on and we have to do something with it. And there is that bottom-up approach where actually it's got enforced in organizations where we see the younger generation that they are using it even before they join any organization. So if you dig deeper into studying the generations active in the industry, we have the baby boomers, we have the Gen X, we have the Millennials, and we have the Gen Z. Millennials are a digital native generation. They are into technology, into innovation, digital solutions, they are big on that. Now, when you look at the Gen Z generation, they are more into an AI generation. It's not a digital generation. So when they are entering into the workforce more and more and more, the expectation is for them to keep using more AI tools. And there is tons of examples that we see in the industry nowadays. So the combination between the top-down and the bottom-up approach, where you see the leadership also implementing what we call the AI champions or the digital champions within the organization, that they have the glue between both groups to make sure that, hey, the message is we need to embrace it, and the message is to collect feedback from everyone to be able to think altogether as an organization. As I always say it, AI with the GI specifically, not AI, generative AI does not make you smarter. It might make you more productive, but as an institution, as a system, I humbly believe it makes it smarter. Not as an individual. So the way forward, as you said, from the internal aspect, this is what we are observing, how it's coming. The external aspect, we see a couple of things. One is either a company, a contractor or an engineering firm, they partner with a rising startup or a consultant to work with them on say, what can we do with you to leverage on your product, or what can we do internally or strategically partner with you as a consultant to help us helping our clients. So until now, I don't see it as there is one solution that will be best for every single company. Some companies, it makes more sense to have it internally, others externally.

Cognitive offloading vs. cognitive surrender

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Yeah, I love I love what you said there about the generative AI and capabilities improving the overall organization, but not making individuals smarter. I heard some terms on this recently that I found really, really on point. And it was like the difference between cognitive offloading and I think it was cognitive surrender. And so I think a lot of the fatalists around gen AI adoption are just thinking that they can press a button and surrender all of the decision making to an AI model, and that's where you get into trouble. It was on this podcast called You Are Not So Smart. It's sort of a psychology podcast, but it's it's very well researched and put together. So they compared it to like using a satellite navigation system in your car. And I think they they referenced some comedic program where you know two different people are driving the car. And so one of the guys uses it gets lost, but he's you know he's able to reroute and he's figuring it out. The other guy who just surrenders to exactly the turn by turn that it's telling him ends up in a lake. So he's driven the car, he's driven the car into the lake because the GPS told him to do that. And so that was a really good parable for generative AI where it is right now, and people really fearful of like, oh, well, it's getting stuff wrong, it's gonna be terrible. And that that can be the case, right, if you surrender completely to it as opposed to think about it as a tool that you can really help to supercharge your work. Yeah, yeah. The thing is, like I would like to comment on this point. Like, you make me think about like when companies as well as the younger generation, my students, or anyone from the industry keep talking, yeah, AI is gonna do this, is gonna do that, it's gonna replace jobs. It's like, and I always push against it for one reason. When you think about the technology or the example of AI at the moment, don't think about it from a futuristic point of view. You have to think about it from two angles. One is the current status of it, what it can do currently at this moment. Second, in the medium term, short and medium term what's gonna happen. Because what we observed from the recent study that we focused on is there is what I call theoretical benefit and observed benefit. The theoretical benefit, don't get me wrong, it is a benefit still. Is it doable? It is doable. But can a company reach out to that benefit? A lot of blocks and barriers prevent the company to reach to that theoretical benefit. And there is the observed benefits where we see it actually happening in the industry and we see it actually being used in the industry. And this observed benefit is much lower than theoretical benefit. Now, here is the point. At the current status, the gap is like this. As we move forward, both benefits are going up. The capability of AI and the theoretical benefit of what we can do with it is also keep improving. And the observed benefits with the capability of both the company financials, infrastructure, technology, even the skills of the labors and the people in the company also is going up. Now, the thing I'm not I'm worried about is what's the speed of freedom this gap as we move forward. So you can reach to this level and everyone keeps talking about this level. They are not talking about the observed level. You know why? Because theoretical, everyone can talk about theory. But when you dig deeper into a company, say, okay, show me exactly what you've done. Show me the ROI on this. Show me what's the value you got for you, for your employees, for your clients, for your collaborators, and so on. Yeah, that's brilliant. I love that. And I just want to take a little step back into your research. And so you've mentioned

Turner Construction and the SafetyCoach GP

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that most are in that exploratory phase, right, in terms of how they can use this. Yeah. I'm going to assume that most have some programs at least running it. Can you did you surface that in your research? Like what percentage of the industry are actually playing around with it? Is it everybody? I can't I can't comment on percentage. I can comment on companies that we talk to. For example, a company we talked to was Turner, which is Turner Construction, doing a fantastic job in this area. For example, what they've done is they did I'm gonna I'm gonna throw two examples about Turner from many other examples they do, just from the time perspective. One, it's something Zeke uh simple, where they registered to to Chat GBT enterprise level and made it available for all their employees. Let them experiment with, let them try to figure out also how we can use it. I think that's actually brilliant because you give them all the tools and let them experiment. So experiential AI is something very important as well. You have, if you have to want to learn it, you have to experiment with it, you have to try it. And through this journey, they collect feedback. This is one. Second, another thing they've done is they develop an AI agent to be connected as a GBT under the ChatGBD platform, and they call it uh safety coach and safe T, capital T and then coach that has a lot of data and best practices from their safety in their Turner projects, which is really top standards, and they train the program. And here's the kick they made it uh open and available and free for everyone in the industry because they said like the more you're gonna put and push for some tools like this in the industry, we're gonna raise the bar. We're gonna get everyone to go to the standards that will help everyone around us when it comes to the area sensitive like safety. So that's why they developed that agent. So that's one example. Another example from the eWorld is Thornton Tomasetti. They're doing brilliant work. And Torto Tomasetti, they have, I forget the name of the lab, and they started to hire more data scientists and AI experts to develop an actual tools to help them with generative design. It became now a spin-off from Turtle Tomacetti and became an entity by itself. Others still inside the umbrella of Turtle and Tomaceti for their own use until it's becoming more and more mature. But what I found it very interesting from their work is they always think as a group between the experienced engineers and the rising engineers that they're working with these tools to shrink the gap between that experienced engineer and non-experienced engineer. Because if you think about it, the younger engineers they depend heavily on historic like I'm talking about younger engineers in connection with data experts and AI experts, where they have all the data from previous projects and they just put it in the program. They follow exactly the structure to make sure that they would generate a design. Nothing wrong with it. But they lack the experience of to tell if the results make sense or not. And that's what the role of an experienced engineer. They can come and they look at the results and say, there is something wrong here. What it is, there is something wrong here. We need to dig deeper, we need to understand it. And then this communication between the two levels of gap generation and skills between the generations of engineers, it helped to improve the program more and more. Let me connect that to the GPS example that you gave. I do remember one of my students told us something. Like when she is with her father driving, she looks at the GPS and she follows exactly what the GPS is telling her. And her father keeps telling her, no, that's gonna be long. Let's go from here, shortcuts. And she told me that almost always he is correct. And I told her, I think she's right, but again, at the moment, the program level of maturity and knowledge is this level. As we move forward and you feed these extra routes and additional routes to the program, it will help the program keep learning and learning to the level that those experienced engineers can feed to the programs and make this more reliable and more mature.

Does AI change the experience mix?

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Do you think it changes the mix of experience levels that is necessary or required on jobs? Because I'm just thinking of the importance of graduates coming out of university that are really well trained and ready for this environment and complemented with these experienced people that are becoming fewer and fewer, right? Since forever, we always depend on the engineer and the PM expertise and skills until they retire. And we look at it as a factory base where they will teach their younger staff how it's been done, and we almost never institutionalize the process in the company. Very, very, very few companies do do that. Now, with the rise of AI and digitizing all these processes, we have a little bit a light at the end of the tunnel. So do we then need more engineers coming in the pipeline? I always argue to say, yes, we do. But the engineer, again, back on the point in time of AI now versus in the medium term and in the longer term, how it is being developed. I definitely can say even the engineering discipline is evolving. So engineer now don't expect to be the same engineer in the next five years from now. And that's connected to the role of academia. What academic institutions are doing to change that, to get them more ready and evolve their skills to make them more connected to that coming wave of new services that will be in the pipeline after they graduate or right after they graduate. Let me give you an example. Here at Colombia, what we do is from our program in construction engineering and management, we introduced a whole new concentration focusing on construction, AI, and innovation. Not only that, we posed looking at every single course we have to ask ourselves the question: what can we do with this change with the trend on AI on the discipline that we are working on in that specific course? And that is something very important to address as our faculty, full-time faculty, as well as as well as adjunct faculty who's working very well with the industry, come to us, think as a group to see how we can even evolve the curriculum.

How Columbia bridges academia and industry

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Yeah, like this is I think it's fascinating what you've built at Columbia, right, between the faculty, between these programs, that you've got your own ideas internally and your research going on, but you're integrated with industry, right? So it's almost like you've got this circular learning pipeline of you're are you testing ideas, getting real feedback from the field, and then that ultimately feeds into how you're developing and changing your courses. Yeah. Yeah. How often is that happening? Is that a continuous process? Let me give you an example. Um it's a yearly thing. Let me give you an example why I'm mentioning that. And I I remember like uh the team from Fronto Tomasetti is core studio. It's a fantastic uh group uh uh led by a very uh good colleague of mine, the CTO of the company. So back to your point and why you said it's a yearly thing, and sometimes less than that, especially in the ever-changing environment we are in. When I started the GLCM, the Global Leaders in Construction Management, main focus of it, the main purpose is to bridge between theory and the practice in education. We pick a group of students and we go outside the classroom and even outside the country to visit other countries around the world to know exactly what are other practices, best practices, trends that we should be aware of. This is one. And when we come back, we address this, we put it in articles, we put it in our website to spread it, we share it with our faculty in a yearly meeting with uh with the faculty at the construction engineering and management. This is one. Second, several of these courses that we have in our program is led by the top companies in the world. These companies, when they come, they don't come only with the knowledge. They come with the practicality aspect of this knowledge area in the field. And because we are very picky to select who to work with, we make sure that these companies, they are continuing learning as well. So they are extracting more and more lessons learned, based practices to feed the courses that they are leading for us at Columbia. So the key aspect, again, as I started a minute ago. To go to bridge between theory and practice in education. That will benefit the student to understand the practicality of the knowledge we are teaching. And that will help us as faculty to keep learning what the environment looks like in the industry and to be forward thinkers to understand what to teach the student to be not ready only for after graduation, but beyond. Yeah, that's very cool. You've got a lot going on there. And you've had a decade and a half to develop this sophistication, right, in your program. And it's uh interested there that you've expanded overseas. What are you seeing in international construction markets that you've been able to take back to the US? The way to do business is so interesting. I spent a couple of weeks in Japan, in Tokyo. I visited a couple of contractors there, and I really enjoyed their investment in technology and innovation. And what can we learn from them to bring back to the industry in the US? That was a great experience for all of us. I worked in Japan for several years, in and out, and doing construction tech in Japan. And there was a big uptick in adoption due to government policy. So there was the eye construction initiatives going, it's going back a while now, but probably around 2015 onwards. And to the point of subsidies for machine owners to put technology on their machines and for site technologies as well, which really sort of spurred on the industry. So interesting to hear you're seeing that sort of on the ground and the fruits of that kind of policy and investment. Is that something that you think generally is a good idea? The government, I I guess you call it intervention, but they're coming in and saying, hey, we need to do better in terms of productivity because we have sharp labor shortages. So if I'm to outline the Japan problem, they're at a sort of an end of demographics where they've got the probably the oldest population in the world or they're thereabouts. So they have all of these problems, but they're a little bit more extreme in terms of supporting aging population, but a lower workforce, fewer people coming into it, which doesn't get backstop by immigration just due to their policies there. But their focus on robotics, assumedly now on AI, all of that, and putting a lot of investment behind that. Are you seeing a similar trend in other countries where you visited? Yes. For example, I really like what the government is doing in Saudi Arabia from pushing innovations and especially the area of AI. Um, how not just like to look at it from one industry, they're looking at it from different industries. And some countries, surprisingly, developed countries, they have more restrictions, more barriers from the government if you want to get an approval on just like uh TERBIT to build a very typical structure, uh like maybe a warehouse, you can go through 25 approvals and say something typical. You can just like automate it, you can just digitize it. No, we do it for ages why to change. Um, so some governments they are a little bit more advanced and others not. So this is one. The second, last year we've done a study to highlight from different countries that what is the level of trust from the user to the AI tools. So every GI that uh generated content, we noticed there is, I think the economist highlighted like a trust index, and that index it was like uh uh informative for for us to notice developing countries have more trust on the outcome than developed countries. This is a changing just because the younger generation of Gen Z, it's the most global generation that came to us. What's that mean is the characteristics of the generation of Gen Z from any different country or region around the world, their characteristics becoming almost the same. If you look at the baby boomers, no, they are not the same in different regions. And the reason for that is social media, more travels, more connectivity globally, and so on. Okay? Um, so I see from different entities, nationalities, countries, regions, it's not about the government only, but also what is the entire population is impacting the usability of these technologies in their day-to-day practices, not just like in companies like construction engineering firms. Is the high trust region gonna have a noticeable improvement in productivity because of that trust in the generative AI? I don't know. My my educated guess, I would say no, because the trust, again, as I concluded my sentence, is shifting from just regions to a generation. Let me elaborate further. When we talk about the active generations we have in the industry, we have baby boomers, we have Gen X millennials and Gen Z. Gen Z is the most global generation we have. That means their characteristics is almost the same. From whatever country you are talking about, millennials also almost the same. Okay? Because of that, who will be who is now dominating the markets in the engineering construction industry? It's more and more coming millennials and Gen Z. So in this case, you are reaching to the level that you are neutralizing kind of the entire reasons of these practices. Because whatever regulations you have from organizations, my take is younger generation are actually the one pushing the use of AI. And we see it from in academia, I see it from students how they are using these tools. In studying, how they are using their tools when they're looking for jobs, when I uh when they are applying for jobs, and how they are using these tools when they're starting the job. So I would I would say um the more we advance in it, the level of trust it will not be a factor. Where it's gonna be a factor is when standards, regulations, the governance started to be more and more implemented from different regions in different ways. So it's not gonna be standard. The failure rate of digital transformation project is really high, right? It's something like 70%, where digital transformation programs do not pan out as expected. Yeah. As we're transitioning to these generations that you're referring to, is it less about transformation and it's more about digital native and now AI native workers coming into the industry?

The three pillars of digital transformation

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Let me um summarize it in the following. For any digital transformation strategy to work, you will need to focus on three pillars. You need to focus on the technology pillar, in the process pillar, and the people pillar. A lot of the misconception, digital transformation more focuses on technology. 10%, maybe 15% depend on comes from technology. The majority, more than 70%, comes from the people. So if you don't have the right people in the organization, you will not then form the right culture to the organization to absorb this digital transformation strategy. And if you don't have the right culture, then the culture will eat your strategy for a breakfast, as we always say. So to dig deeper into that, to connect it to your question, is the people pillar is changing with the organization. And how it is changing is by having more AI native generation entering into the organizations. So it's kind of the force, the company and the leadership to make an action on it. Why? Because one of the studies highlighted that millennials stay in a company, fishing the first jobs for them around 15 to 18 months before and then they end up leaving to another company. So the retainer rate to keep the employees is very, very low. If you're comparing that to baby boomers, they stay in the company between 15 to 20 years and they they move if they move. So if the leadership of these organizations are not taking a serious action of how to retain those younger generations and have a voice for them, it's not gonna go anywhere then.

Winning the talent war with Gen Z

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What are the GCs who are winning the talent war actually doing differently to attract and retain that younger generation? So it's all about like what the company is a provider. Is it the card of technology or the card of freedom? Where, listen, we're gonna bring you here, you are free to certain level to do an experiment with certain tools that will help you to do the job more. So a lot of my students now is like, oh, I will be working with this company and we will be experimenting with an AI tools, left and right, and they get really excited about it. Others, don't get me wrong, they say, we would like to go to a contractor and be in the field. And I want to be the field, I want to understand what's happening in the field. But in point in time, they started to miss. It's like, oh my colleague went to this technology firm and they're making certain amount of benefits and money, and they are using cool gadgets and they're using this kind of specific AI tools. I like it. Why we don't have it here? So, from a contractor perspective, yeah, it depends what uh initiatives are out there for the younger engineer or when when they join the contractor. For example, one appealing, I don't know how to say it, like a program for the younger engineers is the rotational programs, where they bring the student or the fisher graduate and they rotate them into different departments until they find their niche, what they would like to work on, and they give them more an offer to join that specific group. Another GC is that they actually put different initiatives to get the younger generation involved with their leadership on innovation, which I found brilliant. You can do the work, but still in certain time or certain percentage of your time at the company, you need to be involved with us on areas related to innovation. All the others, two other set of companies, they say, you know what, if you have an idea and you want to implement it, no need to leave the company. You can implement it here. We will give you the pilot, we'll give you the support, and in point in time, if you want to spin off from this company, we'll be more than happy to support you. So there is a new innovative business modus, keep rising. That will be the message to those younger generations. Yeah, that's great. This opportunity to explore, to experiment, to ideate, and I think like the freedom, I think was the word that you used there just before. Yeah. That seems really key, right? For especially younger workers in the workforce, right, in these highly technical spaces, to be able to access and leverage the tools that are going to make them do the job because their managers don't know what those are right now, right? And it's all changing so quickly. But what I've seen speaking to companies where we're not allowed to use cloud, you know, we might have been given access to ChatGPT or or they got it through Copilot, but they're not able to use what they're seeing, their friend in another industry using tools and learning how to do stuff way quicker, way smarter. I think like there's still a problem structurally in the industry where even the innovative construction companies, they go, yes, we give you access to this platform, but if another one pops up, you need to be able to give access to that. What would you advise GCs to change structurally or culturally to allow that level of freedom? And remember, there is also more and more tools keep rising. So there is the level of change that's happening is going exponential, and the human adaptability curve is not catching up with this exponential changes in technology. So I think the key is to focus as a leadership, I highly believe, on how to create an agile environment, flexible environment to absorb such new tools and have that creativity from those younger generation, all their employees, whatever it is the generation they are in, to experiment with. So flexibility is the key. And uh and as long as they have clear standards and regulation uh and like clear governance journey, like to be able to govern all the uses of these new tools. So um I would say is is agility for the environment you are in as a company. That's very important. This this is one and and and another thing is to embrace kind of a culture of continuous learning and continuous spotting and scouting for these new tools. But in point in time, you cannot boil the ocean. You cannot bring all the tools and start to your job to experiment with them, and you're missing your actual job, what to do with the tools. Yeah. So what that environment, the leadership gonna create for the company is the next big thing here. Yeah, I think that's a really good point, and it resonates personally. It's it's kind of similar to analysis paralysis, but on the on the doing side or the experimentation side, because there's so much, so many new tools, so much capability that you can get lost in that. But I still think time boxing, time to go and experiment and stuff in a can, like you say, a controlled environment, a secure environment, is hopefully going to lead to a lot of

What the white papers actually conclude

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benefits. Back to your white papers that are coming out. What can you tell me that is in there as a conclusion or a recommendation that might surprise me? Move. Um one thing is it's so interesting when observation, when you are talking with mainly baby boomers, uh, like the more older generation, I hear them always talk about concerns and challenges of the use of Gen AI tools. And when I talk with the younger generation, I always hear them talking about the benefits and the added value of the use of Gen AI generations. So the message here, there is this connection between the two generations. So one major takeaway is how to reduce this gap between the two generations in an organization. One engagement I have with the big GC here in the US, I was working with their CEO, and one of the recommendations I provided to him was to do a reverse mentorship where you have the younger generation mentoring the older generation. And uh in less than a year, I went back to the company and asked, like, oh, how's the strategy? Back then it was amazing, it was fantastic theoretically. Now let's go to the observed value. I noticed it was failing. So I asked why. I went to the younger generation and they were saying, like, when we go to mentor the others, they always tell us, hey, can you show me how you can copy this Excel sheet from this column to that column? Can you help me copy one PowerPoint to that or create this slide? That's not the right way. So the message lost in translation of what is what does it mean when we talk about reverse mentorship? So that's something important. Another thing is one recommendation is the training of how to use Gene AIs in general should focus on all levels of the organization. It's not just an operational level. It needs to educate the leadership as well. You need to get them into training and help them understand the capability of it. And get them to move out from the theoretical benefit to the observed benefit with the idea how to get to that theoretical benefit in point in time. Really looking forward to uh to seeing those papers coming out and trolling through them. To be honest, I'll put them through Claude and then extract the key points.

Generating more, understanding less

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This is something, uh let me let me close with it for everyone to think about it. I'm I'm I'm in point in time experimenting with this article of there is like more and more articles, reports being generated by Genei Tools. Now, the things that worries me is again, when you start the communication with everyone online via emails, everyone started to seem very sharp. Once you take all those who are communicating via emails to the meeting in person, it's a different environment. Or let the phone or the PC computer away from them and let them discuss the topic. It's it's just alarming. So I'm not trying to close the podcast with an alarming message, but I started to notice there is more and more dependence on the outcome without reading the outcome, without even dig deeper into the content, and that's alarming. I'm actually embracing using Gini I even in my classes. And I found many ways, this is maybe another podcast, to highlight what we are doing in academia to embrace it. But to reach to the level that you have fantastic products, fantastic reports, essays, but when you need to discuss it with those who are generating this from an academic institution, from an engineering company, from a construction company, from a public agency, that's when it comes more interesting and alarming. So we are generating more and more content, but we are deeding less and less about this content. It definitely resonates with me. It kind of reminds me back to the point of the cognitive offloading versus the cognitive surrender. Absolute pleasure speaking to you, Ibrahim. Thanks so much for spending your time, your your evening there in New York, and I hope to chat to you again soon. Absolutely, John. I I I hope I hope it added value everyone listening to your podcast. It will help them understand a little bit more about what we're doing in the energy and the construction world. Thank you, John.