AI Strategy for CFOs Is a Wild West Without Governance Turn AI Into a Portfolio System – Dave Trier

AI cannot be thought of as a series of experiments. It can't. There's too much cost and risk involved. Just like you would with your investment, they need to be managed with the same discipline that you would manage your portfolio of investments.

Meet Dave Trier, the CEO of Modelop. He leads the company with a clear focus on customer value, product innovation, and enterprise execution. Also the official member of Forbes Technology Council. He brings over 20 years of experience in data science, AI, analytics, cloud, and enterprise software. Dave has a pragmatic, transparent leadership style.

Get away from the Wild West, go into more of a repeatable, industrialized AI delivery process. AI has more complexity in not only the technology, but also the number of parties that are involved. The CFO should be setting some of the financial parameters for going and using AI across the organization.

For our audience listening, CFOs, finance professionals, what's the one thing that they should be doing to improve the ROI for their company?

The one thing I would say is—

Welcome to the Future Finance Show, where we talk about treasury management on arms. Future Finance is brought to you by Qflow.ai, the strategic finance platform solving the toughest part of planning and analysis. B2B revenue aligns sales, marketing, and finance seamlessly. Speed up decision-making and lock in accountability with Qflow.ai. Welcome to another episode of Future Finance. I'm one of your hosts, Paul Barnhurst, and once again I'm, uh, joined by my illustrious co-host, Mr. AI himself, Glenn Hopper. Glenn, how you doing?

Good, Paul. Good to see you.

Good to see you as well. And then we also have a great guest with us today. We're really excited for this conversation. Kind enough to join us is Dave Trier. Dave, welcome to the show.

Thank you, appreciate being here.

Excited to have you. So Dave Trier serves as CEO of Modelop, where he leads the company with a clear focus on customer value, product innovation, and enterprise execution. He has over 20 years of experience in data science, AI analytics, cloud, and enterprise software. He also holds multiple patents He brings deep technical expertise and a pragmatic, transparent leadership style. He is a trusted partner to CIOs, CTOs, and AI leaders. Dave previously served as ModelOps SVP of Product, shaping product vision and strategy. He's also held senior roles at several companies, including Think Big Analytics, Powered by Action, and Accenture Technology Labs. He holds a BS in electrical engineering from Notre Dame. So again, thanks for joining us. Love the background and we're really excited to chat. I think the subject of the decade, is that about right?

AI? Sounds about right.

Dave, I'm going to jump in and I, it's, we were talking right before we, uh, we started recording here. We have more and more of these, it seems, but one of these podcast episodes where I feel like I could be talking, this could either be a podcast or I could be asking you about something I need to implement with a client right now. So with, with that in mind, I'm gonna ask this as a, as sort of a, a client-centric question, but I think for our listeners it'll hit home as well. So right now I'm working with quite a few public companies on their AI implementations, and for all of them, I mean, there's like the 3 main areas that they're concerned about are compliance, data security and trusting the AI. But the biggest one we have to address is compliance and governance. And a lot of people I talk to, I'm coming in through the office of the CFO normally, and they're not yet comfortable addressing this. I think the timing of this recording is interesting too, because I'm sure you've taken probably a deeper look at it than I have, but the Treasury just released, uh, what, it's long, what do they call it? The AI F- It's the Financial Services AI Risk Management Framework. And there's, they've set out some guidelines.

Now try saying that fast 3 times. Yeah.

Yeah. They've got an acronym that I think is hard to say as the full phrase itself. But, and I know you don't focus exclusively on compliance and the CFO's office, but thinking about what ModelOps does and the role that you play in helping companies kind of roll out and manage their AI implementations. I, I guess two questions there. First, are you familiar with the FSAI RMF, and is that aligned with what you guys do? And, and maybe even before that though, explain to our, our listeners what Modelab does.

Sure, happy to. So Modelab, we've been focused over 7 years on, on one thing only, and that's helping large organizations, enterprises, be able to govern, manage, and operate AI at scale across the enterprise. That's all we've ever done. We want to make sure that they can use AI reliably, rapidly, and responsibly as they're starting to think about where and how can I use AI to transform my businesses. So naturally to your question, yes, absolutely. Very attuned to all the different regulatory frameworks, whether it's the, you know, OCC SR 11-7, which was the grandmother of all risk management frameworks in financials, or EU AI Act, NIST put out AI RMF. Canada's got one with E-23. So yes, familiar with all of those. And at the end of the day, and I'm sure you'll ask, is they're just about how can you put the right level of oversight and risk management and mitigation in place so that you can, again, responsibly use AI throughout your organization.

One of the issues right now, I mean, outside of the trust issue and all that, but is the governance. And it's got to be, you know, it starts with with data governance, right? Or well, maybe you tell me, is, do you see AI governance just as an extension of data governance or is it, I think I know the answer to this, but I'm, is it a lot more complex than just data governance, which a lot of companies still are having a hard enough time with that alone?

Yeah, no, it's a common question. They are distinct overall, right? Data governance is focused on all of your enterprise data and making sure you have the right security, understanding taxonomy, oversight management of your data. AI is different and distinct because it's not just data. It's actually a combination of data with software, with a probabilistic approach to using data and software. So there's not always the deterministic answer. There's not always the, if we put in this, we know we're going to get that, right? So that's something that is very, very distinct from anything that we've done in the past, right? It's not like traditional software. It's not like traditional data governance. It's not traditional supply chain, if you will, because you have that intermingling, that mix of data plus software technology, but also the inherent risk that's involved because of the probabilistic nature of these overall algorithms at the end of the day that are being used to make these decisions. So there's an inherent risk factor that is not apparent in data governance as much as others.

That makes a lot of sense to me of that inherent risk factor, right? Data. You know what data is. You can look at it as long as you write the query the same way and nothing's changing the database, you're getting the exact same data back. Not like when you ask GenAI something, or, you know, if you tell the— you can't tell the SQL query, no, you're wrong, I have the right answer and it's this, and it won't come back and say, yes, you are right, sorry, I'm wrong, when it was right all along, right?

Or, or it so confidently answers it and so you believe it, right? So, you know, it's like SQL is ever gonna try to embellish anything. It's just going to give you the answer, right?

Yeah, exactly. Our favorite was we were testing one of the, uh, AI tools. Glenn wasn't on this, but I was testing it and, uh, the balance sheet wasn't balancing. It had built a financial model for us and we asked it to find out why and it kept coming back and all of them will confidently tell you the answer and it's always wrong. And it came back and it was $1.3 million out of balance and said, that's an acceptable variance. That's only 0.3%. It's not how a balance sheet works. Like, there isn't an acceptable variance. So I would love to get your, uh, answer to this. I know CFOs are struggling, right? Just like everybody. We're all trying to figure it out. It's moving so fast. I can't think of anything that moved this fast. You think back to computers and yes, it was quick and you started using them, but there wasn't a 30x improvement that quick with computers. At least it didn't seem like it. I was also a teenager when computers came out. Came out. So might just be my memory, but I would love to know, if a CFO asked you how to determine whether their AI program is creating actual value for the business, what would you tell them? Because I know a lot of people are struggling. This is all moving so fast, they're trying to keep up, but they're wondering, am I really getting an ROI here? Am I getting value?

That's right. Yeah, no, I get that question a lot, and I like to think of it in, in three layers or tiers, if you will. The top tier, which is obviously what every CFO wants to see, is, is there direct financial correlation between the AI that I'm using and some benefit, whether it's cost takeout, rev gen, or just in general operational efficiency? And yes, all the buzz has been around headcount reduction. You know, they have large enterprises laying off a large percentage of their workforce overall,, but there actually are other direct financial metrics. For example, there's reducing time to market for your product, right? Whether that's a physical product, whether that's a service or software, or, you know, we work with a lot of, of large enterprises, especially in the regulated spaces, financial services, pharmaceuticals. Can you speed up drug discovery, right? Using AI. And there was actually just a, a recent publication from the FDA where they are gonna allow them to use analysis and let's just say forego clinical trials in certain situations because the data and the AI is so good now that the FDA is, is allowing that. So you gotta think about it as not just what you see in the headlines is headcount reduction. There actually is absolutely financial metrics around time to market, around sales and helping to grow your pipeline by X percentage, which is obviously leads to tangible impact in terms of your top line. Or automating back office tasks where in the past you had to go and pay an outside contractor to do, and this is more efficient, cheaper, et cetera. So again, that's the top tier, which everybody wants to get. The second tier is, okay, well, we don't have a direct financial correlation, but we know that it's actually providing a lot of value to our internal users or our customers, and that's where you bring in the feedback layer. So you start to collect feedback from your customers, your users, or in the agentic world, who are the overseers of the agents, to see if it's meeting the need. Did it solve the problem? Did an agent do its job without having a human intervene? And how often did it do that? How often did I have to intervene? So that's kind of that second layer that the CFOs can start to think about. How do I judge the value is just getting the feedback. Now the most fundamental layer is just usage. And I know that's not the most exciting thing from a CFO perspective, but if you're dumping a lot of money into it and spending either license money or putting resources and infrastructure into it, you just wanna make sure the thing is used, right? So with all of our customers, it starts with just making sure you have the usage understood. Are all the users using it? Are you looking at it if it's more of a systematic approach? How many transactions is it actually processing and how is that better than what we're doing today? So if you think about those 3 layers, everybody should be tracking usage. You should be tracking that feedback across your customers, users, and overseers of agents., but then also that the top level is absolutely in many cases you can have that direct financial correlation. So that's kind of that 3-tier approach that I typically talk about with executives and CFOs in regards to the value that AI brings and tracking it.

You said just so interesting to me because right now, like there's a million studies out there and there's a new one every week or every day, maybe it seems like that, but it goes back a little bit to the ROI thing, but also just in like the brass tacks of, of talking to companies who are trying to roll out AI. The typical use case I give is a company says, okay, we're gonna do some AI. So they get enterprise licenses, just throw 'em over the fence for their employees and say, go use AI. I argue all the time that if you're just buying, uh, software licenses and talking about an ROI, it's like, What's your ROI on Excel? If you're just buying a, you know, that, so it's, it's a software expense. But one thing that I'm seeing, and this is if you do just throw a bunch of, you know, you get a team account or an enterprise account or whatever and, and throw it out to your employees. When you're trying to measure ROI, the employees may be getting much more productive, uh, and it depends on the environment that you could be scared to say what they're doing with AI. So they're like these, Ethan Mollick calls them like a, which secret cyborgs, you know, doing shadow AI in the distance, and they're doing what used to take them 8 hours. You know, I see this with coders a lot. It now takes them an hour and a half, and then they can hit the golf course or whatever the rest of the day, or maybe they're doing more work and reporting on it or whatever. But the shadow usage and sort of throwing a bunch of pilots out there, it's— I see so many companies in that right now. It's like, yes, we know we have to do some AI, but they don't really know what that means. And I'm, I'm wondering from your standpoint, because I think for ModelOps to really be effective, like, or for companies to be effective with AI, it can't just be a bunch of pilots. So like, how do they move from just whatever we're doing right now, some pilots and pseudo-organized just tests out there to like shifting to where AI is, is treated like a managed portfolio of investments is the way I like to think of it. And I'm wondering like, And again, going back to the CFO perspective, how do you ensure that portfolio is optimized kind of across the company, not just for their organization? Like what is everybody in the company doing?

Yeah, absolutely. And this is, it's, I love that analogy that you have to consider AI just like your portfolio of investments because it is. AI cannot be thought of as a series of experiments. It can't. There's too much cost and risk involved. Obviously the benefits there as well, but there's too much cost and risk involved to think of it as just an experiment. That just like you would with your investment, they need to be managed with the same discipline that you would manage your portfolio of investments. Yeah, some will fail, some will succeed, some, you know, might be a surprise, but that's okay. You, but you still have the management discipline around how you approach them. And what that means is that at the start of it, it's not just go throw spaghetti at a wall and,, you know, get a license for everybody and just, you know, hope, right? Build it and they will come, right? No, you actually, again, with the proper discipline, you go through the process of just analyzing, okay, where are the areas that we potentially could use AI that'll drive the most value through an organization? Which particular processes in that, you know, AI is really suited to improve upon? And then what's that business benefit look like? But then you actually have to, well, cross-reference it just like you would your portfolio of stocks. You cross-reference with what's the risk involved, right? Is there, is it going to be using sensitive data? Is it going to be opening up my firewalls to external parties that could potentially have security exposures? What's the complexity to manage it, right? And are we prepared for that complexity? Also, what's the ability to deliver, especially if it's an internally developed type project where you're creating a custom agent or custom GPT, if you will, Are we able to deliver that? And then the last aspect you gotta look at, again, analyzing it like the discipline rigor of your portfolio of stocks is what's the change management involved, right? Because that's actually one of the often overlooked parts of AI within the enterprise is, well, what changes do we need to put in place? Are we actually changing some of our business processes? Which is not a bad thing, but it means you gotta have the right structure in place to go through that change with your employees, et cetera. So again, to your point around, well, how do I treat AI like a portfolio of investments? It's going through that process. It's going through and understanding where can I see the most benefit and then cross-referencing that with the risk involved, the complexity, my ability to deliver, and then are my people ready to change, which is something that, again, the enterprises really need to focus on overall.

Oh yeah, 100%. And that's, I mean, that, that's what I'm saying is the change management piece because there's, The early adopters out there that are, this is life-changing. I'm doing everything. I've got, uh, I'm running my OpenClaw and I'm doing all these, you know, crazy things. And I've got agents. That would be Glenn.

He's talking about himself, just so you know, Dave. How much do you spend a month on software again? AI software? Are we up to $500 yet?

Uh, I don't, I, I, no comment.

We're around that for mine at this point. You know, then there's the other guys though who, uh, are still, I miss my paper ledger. I don't know, maybe those are few and far between, but the Luddites in the group too. But that said, with the risk and everything, I mean, I understand their position as well, but trying to sort of, trying to wedge AI in between all those people, that's one of the bigger parts of the challenge. It's just that soft skill change management piece.

Yeah, that's right. And again, back to the original conversation we had about, well, how do I get the ROI out of it? You actually do get the most, there's the most opportunity for that transformational ROI that everybody's expecting when you do enhance your processes, transform your processes. So totally agree, but that does involve the change management. So as long as, again, you have the discipline to plan ahead and not just, you know, throw it out there and hit and hope, as they say, right? But you have, again, that particular discipline. And we'll talk about this, I'm sure, in a little bit about how do we go from a one-off, kind of cottage industry of let's just have pockets of innovation, everybody's doing it different, to something that's more industrialized, more industrialized delivery of AI, as I call it, from I've got an idea through all the way through those different rigorous processes, the financial understanding, the complexity, the technical, et cetera, to actually being using that AI itself. So that's part of that discipline that we'll talk about, I'm sure, here.

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It has. And, and that's Frankly, that's why we exist as a company. Our software, we set out over 8 years ago and we knew that it was going to be the Wild West of AI, right? That different teams, different departments were going to start using AI. They were going to take different approaches and use different tools. But you needed to bring some rigor, some discipline to how we can use this at the enterprise level, how we can drive consistency. How do we make sure that they're following the processes? How do we make sure that they're not just doing experiments, right? And something that turns into tangible value overall. So that's what, again, a lot of what we help with our customers and our software drives is going from that one-off Wild West of AI into something that is consistent, repeatable, and make sure that the outcome of which is valuable, profitable, and of course responsible at the end of the day.

So one last question: what role does the CFO play in AI oversight? Obviously they need to make sure it makes financial sense, but you know, as systems start influencing major business decisions, which they're starting to, we're seeing it, how does the CFO kind of keep an oversight there and, and be involved? What's, what's their role? How should they think about these things?

Yeah, so there's a couple points. So first off, the, the CFO should be setting some of the financial parameters for going and using AI across the organization. Meaning that, hey, here is that, as I said, that upfront analysis that needs to be done. What's the business justification? Who are going to be using it? What do we expect as the potential enhancements or improvements to either our current process or to generating new revenue streams? So they should be setting those financial parameters by which you go and judge, hey, this is where we want to put our effort and resources. Here's what we want to prioritize. Second, they should be helping to define the framework for how you're going and looking at the success. And I'll use that in air quotes, the success of a given AI project that you're working on. And that means going in and understanding, okay, well, is this project actually on track or is it vectoring to failure? Is it going in looking like it's going to have a bunch of cost overruns? Does it look like it's going to introduce additional risks that we didn't originally think about? So having, again, that, that cost-benefit analysis, that risk-reward analysis, that's the framework that CFOs can help to put into place, both upfront from understanding the benefit and, and the potential cost, but also the framework to go and get the metrics, get in, I call it instrumenting the process, instrumenting the process so we can judge, hey, is it too much risk, too much cost? Is the benefit in line with what we expect? So those are the types of things that the CFO can help put in place. And then the last thing, of course, is just reviewing that on a regular basis. They should have an enterprise-wide view of all the different AI projects with some of those inputs we talk about to help to rationalize the AI portfolio. We help business leaders to make decisions like, okay, I get it, this looks pretty good, but we got some trade-offs here from a risk perspective. Should we kill that project and instead focus in this area? Right? So that's where the CFO can be very helpful for business leaders of helping to rationalize their investments, rationalize which ones make the most sense to proceed or potentially fail fast, as I like to say.

Yeah, the rationalization point really hits home. We've had to do that with SaaS software, and now we got to do it with AI and projects. Otherwise you get— we're going to have AI sprawl everywhere.

Yep, that's right. The Wild West of AI with sprawl all over the place and people swiping credit cards and just starting to use AI and, you know, you find out later when you get the bill, right?

So Dave, this is gonna, this may sound crazy to you, but as a former CFO and in all of my implementations, I'm selling into the office of the CFO. And there are two types of CFOs out there right now. One is the CFO that leaned into data early and from BI to data science and really understood the value of analytics and kind of expanded their FP&A beyond traditional FPA into using machine learning and being data forward and everything. And these CFOs over the years, several of them have clawed away sort of ownership of, of data. Now CFOs and their domain expertise Most don't have a data science background, certainly not a machine learning engineering background, certainly not a developer background, but because of their familiarity with analytics and because they've been using machine learning, some of these CFOs have taken ownership of AI and for the whole org, not just for the finance group. Some of them know what they want. They may be leaning into CFO, but it's not their domain expertise. They went to business school and got, you know, degrees in accounting and, and, and finance. And, uh, this is a bit of a stretch for him. I'm finding right now that sometimes I'll come into a company, we get all the way up to the CFO, they love the idea, ready to go forward, and then CIO, head of IT, CTO, whatever the role is, will step in in the 11th hour and say, whoa, we're not doing that. And then you have to kind of start all over. And I guess, so two questions here. The first one is, I don't know how many CFOs you've experienced, like the former that I, I described, but beyond the question that Paul just asked you, if you see a universe where, depending on the CFO, where a CFO could sort of own org-wide AI implementation in management, I might be trying to shoehole my own interest into what the CFO should do. And I'm wondering, as someone who does not just work with CFOs, what your thoughts on that are.

Yeah, so what's interesting about this, before I answer the question, is that I've been doing this for over 22 years. I've never seen quite the political land grab ever in my experience working with the, again, enterprises, Fortune 500. I, it's insane. I've seen, you know, again, everybody from COO, CIO, CTO, data analytics, everybody, governance, they're all trying to get the land grab. I, you know, I want to own AI. So I'll just put that as a kind of a starting point, if you will. What I would say just in counsel, it depends on your organization, right? If, if certain, for some enterprises or even just, um, some kind of small medium businesses, you may not have, you know, an, that full gamut of the C's, right? The, the CIO and CDO and CDA, all of those, right? And in that case, then yeah, absolutely. It could make sense for the CFO to own it. And because they, you know, you may not have a, a potentially dedicated CDO or, you know, Chief AI Officer even at that. So that point would make sense. However, what I would say is that it always is a partnership. It has to be a partnership with other executives, the CIO, the CISO, you know, like security is always involved. If there is a head of data, that they're going to be involved as well. So it's always going to be a partnership. But again, I can see to your point for certain companies where you don't have that, level of management, executive level tier there that yeah, the CFO could own it because it is something that is one of the biggest investments and will be one of the biggest investments in your company going forward. So I could see that, but I would just always counsel, always start with the partnership. Otherwise, exactly what you said will happen. It's going to, you know, you'll get to the very end, you get to the finish line and somebody pops up and said, nope, can't do that for security reasons or whatever it may be.

That never happens.

No, no, never, never mixing it.

I've never had those projects die on the vine because of politics. Only everyone ever, it felt like. Yeah, it's a really good point. Appreciate that. Would love to get your thoughts. So for our audience listening, CFOs, finance professionals, you know, what's the one thing that they should be doing to improve the ROI for their companies? If you were to offer them one piece of advice, what What should they be thinking? What should they be doing?

Yeah, the one thing I would say is, as I said, get away from the Wild West and turn something, go into more of a repeatable, as I say, industrialized AI delivery process. And even if you're a small company, it doesn't matter. You can put in the right process, the right rigor, the right discipline to make sure that you're upfront evaluating and prioritizing what makes the most sense. That you are making sure that each of the steps in the process are being followed. Again, what we just talked about, making sure that your data, your security, your IT, your finance, right, architecture and security teams are involved. So making sure the processes are being followed such that when you get to the point where say, great, hey, this looks really good, let's start using it, you're not going to get blocked. You're not going to, you know, told to be go back to go and don't collect $200, right? That you'll be able to go from idea to actual usage rapidly, effectively, with the right level of rigor and oversight so that all parties across all different of the, the different teams are happy with what has been done. They can sign off on it and you can start to use it. But by doing this, by just establishing that, like I said, industrialized process, you can be assured that every team and every user across the organization, whether it's a data scientist or a back office HR person, that they're gonna go through this process and you know and you can trust and you can sleep well at night that this is the right thing to do for a company financially, organizationally, and from a risk perspective overall. So that's what I would say as counsel is it's not too early to go and put in that, again, process and discipline so that you can trust what's being put through the funnel, put through the factory, if you will.

As I think back on our conversation, and maybe here we'll go to our our kind of fun AI section, I couldn't help but think as I was listening to you talk, so much of this comes down to process and change management. And that's so true of so many software tools, right? Implementations often fail. Rarely does an implementation fail because the technologies can't do it. Most technology can do it. There are exceptions, obviously, but most of the time it fails because we don't have a rigorous process. We didn't do a good job in selection, or we didn't manage change it all, all those things. And it feels like, you know, a lot of what you're saying, it's, it's kind of a repeat, but with AI. Yes, there's some different governance, there's different things we need to consider, but just having good process to bring things in and thinking from a company and documenting and managing change management gives you a higher chance of success regardless of ChatGPT, Claude, whatever the tool might be. Am I missing something?

Or it is a lot of it. The only thing I would say is that AI has more complexity in not only the technology, but also the number of parties that are involved. Like if you think about software delivery, normally you have your business stakeholder, you got your software development team, and you got a production team. Great. But that's kind of it. It's those three with maybe a data team. But with AI, you have to pull in at least 10 different people on average. It's, it's 10 different teams that I see, right across IT, legal, risk, compliance, data security, architecture. Project management, production support, right? So because of the nature of AI with inherent risk that's involved, there are more stakeholders involved. So your process has more people involved, but then also it often touches more systems too. On average, I see anywhere between 8 and 12 different systems that one AI solution is touching. So there's just a, a level of additional rigor that has to be put into place because you're touching more teams. More systems as part of it. But, but yes, just in terms of use good process, absolutely. We can learn from the days of DevOps, if you will. But AI has that, that again, that slight nuance. You have to up your game as it relates to risk and security and governance, etc.

No, that's a really good point. The whole probabilistic and the fact that the same tool can be used for everything across the company.

That's right.

You know, in just so many crazy ways for for all kinds of stuff unlike we've seen before. So that's a— the complexity is definitely a good point. So thank you for that. All right, we're going to move into our personal section. So how this works is we use different AI tools. This week I fed it into Claude. We take the questions from the episode, your bio, your LinkedIn profile, whatever it can find on the internet, and we ask it to come up with 25 kind of personal, unique, a little quirky. I put that in there actually.

Okay.

List of 25 questions. And so we're going to, we're going to ask 2. I take one approach. Glenn takes another. Okay. You get one of 2 options. I can use my random number generator to pick a number between 1 and 25, or we can keep a human in the loop and you can pick a number between 1 and 25. And I'll ask you that question.

Oh, okay. Well, I'm an engineer. Let's go with the math. Random, random number generator.

All righty. We get number 2. So let's see what we have. So it says EE major. So electrical engineer major turned AI CEO. Was there a specific moment when you realized you were never going to design circuits for a living and that your future was in software and data?

Yes, that's a great question. And there was a moment actually. And what it was is that out of college, I went and joined an R&D lab, which was fantastic. But in the very first week of work, I was building this large-scale multi-user touchscreen and we had to just do a transfer of data and I had to go and solder an RS-232 adapter in order to be able to transfer from the huge multi-user touchscreen into the actual— at the time it was a computer with NVIDIA chips, which is really cool. I use NVIDIA. 20-something years ago. So that was the point where I said, okay, I don't think I want to do this electrical engineering. I'd like to go more down the route of developing product solutions that are more in the software space. But that was, again, a couple months out of college.

So it sounds like it was early.

It was pretty early.

All right, I know, I know we're pressed for time. So what I do on mine, I take the human completely out of the loop. I figure AI generated the questions., and we'll let AI figure out which one to ask. So I just run Claude to do that. Okay, so Paul, well, you got number 2, I got number 1. This happened in our last week too, where we're, the random numbers are getting to the, they're, they're skewing towards the, uh, the front end here. Oh, you studied electrical engineering at Notre Dame. Are you a diehard Fighting Irish football fan and does game day still rearrange your fall schedule?

Yes and yes. Absolutely. I've been a diehard Notre Dame fan pretty much my whole life. And every Saturday, that's what we do. It's, you know, it doesn't matter if we've got kids stuff going on. It's like, okay, arrange the kids stuff around it. If there's really a kids sports, you know, game or tournament, whatnot, I've got my phone, which is, you know, the beauty of mobile, the mobile era.

So I only have one bone to pick. You didn't play us in the bowl game. I'm a BYU fan.

Oh, okay.

Out. We do get to play next year though, but I was looking forward to that.

I know, I'm looking forward to it too. Yeah, I'll be there. I can't wait. I've never been to BYU Stadium, so I'm looking forward to it.

Oh, you're going to the game in Provo?

I sure hope so. I think tickets are going to be hard to get.

I live here in Utah, so I'll have to grab lunch. Let me know when you come.

Let's do it. Yep.

Well, Dave, I know you've got a hard stop coming up. Really enjoyed having you on the program. And I'm definitely beyond the podcast. I'm definitely checking out Modelab because I see a lot of applications for that. So thanks again for coming on.

Thank you. Love the conversation. Really appreciate you having me.

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