Future of Work Session 4: Inside the AI Workforce Shift: A Look Inside NVIDIA and HPE

Speaker A: Colorado's workforce landscape is full of reports, rankings and projections. But what do they actually mean for employers trying to hire, retain and grow? Built for executives, HR and talent leaders and workforce and higher ed partners. The Colorado Business Roundtable's recent event, Future of Work From Data Signals to Strategy cuts through conflicting signals and translates the latest labor market and skills insights into practical employer led strategies to keep Colorado competitive. Taking place Tuesday, Wednesday, May 19, 2026 at Empower Field at Mile High. The Colorado Business Roundtable hosted an event with a number of sessions that tackled these issues from a variety of angles. This is session four, titled Inside the AI Workforce Shift. A Look inside Nvidia and hpe. AI is evolving quickly, but what does that actually look like Inside the companies building it? Leaders from Nvidia and HPE offer a behind the curtain look at how AI is reshaping their own workforce. From shifting roles to and in demand skills to real world approaches to upskilling and reskilling. Grounded in real experience, this conversation moves beyond theory to focus on what is changing, what is coming next, and how organizations can respond. We take you now to the president of the Colorado Business Roundtable, Debbie Brown.

Speaker B: All right, thank you. This is our last session for today and then we will enjoy a reception with each other. So I would love for you to lean in, come grab a chair if you'd like. Um, we're going to have plenty of conversation in about 30 minutes, but. But we want to definitely end with a really tremendous fireside chat with these two executives. One from Nvidia and one from hpe. Uh, and I think too, I was checking my Nvidia stock this morning to make sure it was still looking good. But, um, I know when I told my son about our fireside chat, he was very excited to hear what these two gentlemen had to say. But before I have a seat to join in that conversation, I specifically want to thank Andy. If you want more information about the Human Potential Summit, uh, coming up this fall, happy to connect you to that as well. It's really tremendous. So, uh, we will get started. Is there another one?

Speaker C: Okay, great.

Speaker B: Thanks. All right, so this will be a good test. How many saw. I think there was an AI, uh, related CEO, potentially an executive with Google, who got booed at a University of Arizona commencement. Did anybody see that in the news? I see you're raising your hand, Darius. What a life we live in. Isn't it, um, interesting? So I don't see anybody booing yet today, so I think that's a good sign.

Speaker C: I'll Start to cry.

Speaker B: But let me, let me, um, go ahead and introduce to you the executives that are joining us on stage today. Uh, first we have to my immediate left. Sean Young is the Director of Enterprise Industry Marketing at Nvidia we, where he is responsible for go to market strategy with 25 years of design, visualization and simulation experience across automotive and manufacturing. Sean has previously held leadership roles in sales at Nvidia, Business development at HP and product Management at Autodesk. Welcome, Sean. Appreciate you being here. And also, uh, want to welcome Baraji Giallo. How's that?

Speaker D: You did great.

Speaker B: Pass the test. All right. Is an innovation architect at HPE Labs in the office of Chief Technology Officer where he focuses on turning emerging technologies, particularly AI, into systems that people can actually use and trust. Originally from Mali, Baraji works across engineering, business and governance teams to make complex technologies understandable and actionable, helping to shape what gets built and how it's used. So let's welcome our panel, our final panel today. Thank you. I told them that we're the closers so we're going to have some good energy. So we're excited. I feel like people have kind of leaned back in for the close because everything, um, right now around AI brings emotion. I think about the movie Wall E where there is increased productivity and everybody gets fat and they're on a cruise ship and they can zoom around in little buggies because they've lost all their muscle tone because they're. You don't have to work as hard. Or is it a little bit more like the Matrix where we're forced to reconcile with sort of an apocalyptic future. There's anxiety about AI, anxiety about job displacement, um, anxiety about things we don't know. On the flip side of that, there's an incredible optimism about AI in all the things Danny Moore mentioned that's going on in aerospace. How do we build new productive capabilities? Going to the moon, going to Mars, what's going to be unleashed entrepreneurially when we think about all that AI is building for us. So with these two gentlemen, they're on the very forefront of building this future and we want to take a little bit of a behind the scenes look at what it is and maybe they'll say it's a little bit Wall E, a little bit Matrix. I don't know. That's probably a very, uh, JV level of describing it is that for me

Speaker C: it's becoming Wally pretty quickly because I'm spending too much time behind my computer doing AI.

Speaker B: Ok.

Speaker C: It's too exciting.

Speaker B: It Is exciting.

Speaker C: Yeah, it's taking up all my time.

Speaker B: Sean, tell us more about, um, for, for folks too, who don't really understand. Tell us a little bit more about what your company does in this world. And we're going to go into too. Once we start the framework, we're going to definitely go into sort of workplace disruption, workplace enhancement. How are they giving advice to higher ed leaders, talent leaders, other businesses on how to lean in. But let's start with some initial framing. And Sean, we'll start with you. What, what the heck is Nvidia? How are you guys growing at such the pace? Uh, you know, one of the biggest companies, uh, you know, on the planet. Tell us more about what is, what is Nvidia's value proposition for their business?

Speaker C: Biggest in terms of market cap. But we're actually still a pretty small company under 40,000 employees. And um, Nvidia, our origin is in computer graphics. And we got into computer graphics to do something that the cpu. Everybody knows what a CPU is. In all of your phones and computers, you have a cpu. It's a general purpose processor for keeping the, uh, device on, running the operating system. But when you're running computer graphics, like games, it doesn't go fast enough. So we developed a processor called the GPU that can process the frames and all the graphics in your video game quickly enough so you can have a good video game experience. It turns out that that technology for accelerating things that are computationally intensive like graphics, works really well with other things, other use cases like mathematics, uh, weather prediction and simulation and of course AI. So our technology was indispensable in the formulation of early machine models, uh, for deep, deep learning that eventually became, uh, AI. And um, now we make much more than just the gpu. We make cpu, we make new networking technologies and our partner over here, uh, because we don't sell any of that stuff, my job's pretty easy. I don't have to sell because my friend over here does all the selling for us because we sell to HP and HP does all the selling.

Speaker B: All right, that's a good segue, Jello. Do you want to take it from there?

Speaker D: Definitely. I think, uh, as I get started here, one of the thing in my introduction you mentioned Molly, whenever you mentioned that one, I feel like I have to take a moment there, uh, to pause because I was actually reflecting. I think it was Eric on stage mentioned at the beginning to raise your hand if you knew what you would become after high school. That actually got me reflecting while I was sitting there because the Reality is being on the stage like this. I don't take that for granted because I came from a place where access to electricity itself is an issue, let alone being in room like this, uh, to talk about AI, which is a powerful things that change quite a bit. Literally, every panelist talked about AI for the most part, which is exciting. So for me, when we talk about future of work, uh, one of the things before we get started here that I challenge everyone is about opportunity. Because, uh, it reminds me of a quote, Nelson Mandela once said that it's not what you have or what it is what you do with what you have that separate one person from another. Now, where you start, which is very important for me when we're talking about this space, uh, in regard to hpe, a lot of the stuff I do because when we talk about AI is helping organizations actually, uh, move from excitement about AI to what does it actually take to deploy or integrate this system, uh, responsibly and effectively. Because that's what the key is. Because there's so much hype around AI, but what does it truly take to deploy the system responsibly? So one of the things that I realized that when it come to, uh, AI transformation, the problem is not about technology itself. The problem is, uh, adoption. So the mindset shift is so difficult when you're trying to adapt a technology that's so disruptive because everybody are thinking about, can I trust this tool? Can I actually use what it produce? So that one is the key questions everybody is trying to answer. And when it comes to hpe, one of the biggest things we do in partnership with companies like Nvidia is, is trying to build the foundations about how can we move from experimentation to something you can fully trust. Fully trust is a new key word here. And then ultimately being able to adapt this technology in your day to day work. So HPE kind of sit at the intersection and actually building that foundation which is crucial, uh, in this space.

Speaker B: Yeah. Those are two key words. Adoption versus trust. Those feel very different to me. And I meant to do a little show of hands to see if the crowd and the audience fits a little bit of the data that we heard from Accenture. How many of you feel like your organizations raise your hand if you feel like your organization is adopting AI at scale currently, like you're fully in. You've adopted it at scale. Okay.

Speaker C: I expect to see all the Accenture hands up.

Speaker B: How many of you think that you're, uh, in process? In process. And you see some, you're adopting some you're in process, not quite perfectly at scale. Okay. A lot of people in process. Uh, it's interesting. I'm going to push, I'm going to ask you to talk a little bit more about adoption versus trust. I grew up with the Ronald Reagan line, trust but verify. That's kind of how I feel about AI right now. I don't fully trust. I want to adopt, but I don't fully trust. Why don't you tell me how that's the wrong approach?

Speaker D: No, I think that's a good point. The reality is one of the things I will say, accuracy does not equals trust. Because one of the area we all seen around this space, everybody tend to have this benchmark and all these tools that are out there and say, okay, it's 100% accurate. That doesn't tell me much. That reality can be 100% accurate. That doesn't mean I truly trust it. And the other one that I'm actually leaning and pushing toward is not really trust even is proof. Because now we move from uh, showing all this benchmark, all these metrics to can I trust it, but can you actually prove it? So that's where the key, uh, decisions are being made. How do we actually create a system that we can actually prove what is producing is actually what is intended for it to produce? I see. Sean, got something here.

Speaker C: Yeah, I got something to say because I think you have to peel down a layer because it's really important to understand how many people in the room use copilot or chatgpt or something like it. Yeah. Okay, everybody. Awesome. Okay, so when you go to a model behind ChatGPT, um, uh, the Frontier model, ChatGPT open, uh, Copilot uses ChatGPT on anthropic. They make, uh, Sonnet and Opus. You could probably list 100 others. So these models have been trained on the entirety of the Internet. They know they have very wide knowledge, but not very deep because they don't know anything about your organizations. They don't know anything about, you know, you necessarily, uh, they don't know. They don't have a PhD in any specific science or any specific industry domain. They're general purpose models. So sure, if you ask them to like build you a dam, you know, a billion dollar project that's supposed to hold back a river. Yeah, it might make a mistake. You don't want to do that. But what you can do is what's called fine tune. You can take any model, uh, an open source model and teach it new tricks. You could teach it about your business. And you could make sure using technology like Rag, that it doesn't hallucinate, that it gets all its responses from a database or uh, from what's called a knowledge graph. So it's an improper generalization, I would say, to say that it's incorrect or it's going to make mistakes. Because yes, if you ask it to do something it wasn't designed to do, it's going to make a mistake because it's not going to know that it shouldn't. It wants to give you the answer and it's going to give you the answer whether it's right or wrong. Um, what you have to do is use the right AI technology for the right problem.

Speaker B: Sean M. Let me jump in. You mentioned building uh, a dam. That's an interesting analogy. We have a lot of different industry verticals in the room, from construction to banking. Are you seeing certain industry verticals being able to adopt at a faster rate or at a larger scale? Or are you just seeing sort of adoption rates across the board being uniform? Where are you seeing the most scale at play?

Speaker C: Okay. And uh, Bharaji, I'm sure you've worked with customers too, so I'd say it kind of is commensurate with the level of technical sophistication and traditional technical spending for that vertical industry. So for example, leading adopters of AI or uh, financial services is number one. And the reason is because financial services has forever been developing in house technologies, proprietary solutions for banking, for trading, for fraud detection, et cetera. Now it's just a progression, an evolution of technology. They're investing in AI, but they've always made those investments. Second is healthcare. There's always been a massive investment in healthcare at the bottom of the list. One industry that I cover personally is, uh, construction. And that's because the construction industry, $13 trillion industry, has always been laggards in investing in technology, but now there's an impetus not just for construction, but for every industry. To really look, revisit your approach to technology investments because AI is the most disruptive technology of our lifetime. And now's the time to reconsider the approach.

Speaker D: To add on to that, uh, Sean, I think, uh, you touch on a lot of uh, the other industry. One of the biggest, one I personally value beyond all of those, is that humanitarian effort. Because at hpe, those are some of the area we tend to focus on as well. Because the reality is, uh, these tools can be used for a lot more, uh, than of course, revenue generations. There's tools can be used to save human lives and uh, prevent some of those stuff. For example, some of the stuff we do are anti trafficking that we partner with our international rescue committee at HPE or other organization in Thailand. In a way, how can we use these tools? As you start talking about trust, trust is the key big things here. But to uh, be able to detect some of this problem before they occur. And a lot of these organization nonprofits, uh, we have partnered with them to actually deploy this A.I. ah, this agentic tools to able to not uh, only help first line responders but help the survivor themselves. Uh, when it comes to job reintegration which is key in those spaces because a lot of time when people get themselves out of human trafficking for example, the number one way they go back into it is when they're looking for a job. So AI is in a way helping in so many spaces. Uh, but to add on to some of the stuff Sean was saying to me, that's one of the crucial one and to go to one big point you mentioned, how do you trust it? The reality is when you talk about these tools, no matter what sector you're in, I think the number one mistake people make sometimes is just trying to jump into. I want to use AI. You're trying to layer AI into a process that is wasn't designed for. Because one thing about AI, uh, as I'm sure a lot of you guys heard, uh, garbage in, garbage out. So if you don't know your processes, you don't know that data, you don't have the stuff structure properly. And the number one thing you're trying to do is put AI on top of a process uh, that you don't even know or you haven't cleaned it properly. The reality is you're not going to be as successful added to the. But I wanted to add that.

Speaker B: Yeah, I think one industry you mentioned is healthcare. Uh, whenever I get a new acre pain I think oh my goodness, I need AI to fix that for me. And I am excited about the speed of research and data in hopefully solving some of the biggest healthcare issues that we have as a society. It's very, very exciting.

Speaker C: Yeah, I heard um, one of the uh, Peter Diamandis. Does anybody know Peter Diamandis? Peter Diamandis, uh, put on X the other day that he anticipates and common optimist anticipation that our lifespan will reach 150 years because diseases will be a solved problem because of AI, because of all that intelligence that we're going to be applying to uh, medical science, genomics Drug discovery, et cetera.

Speaker B: Okay, then I really need my Nvidia stock to keep.

Speaker C: Well, if that's the case, longevity and health matters more than ever.

Speaker B: So good shifting gears. I think the elephant in the room really is, um, the fear of workforce displacement. And so we're going to talk about that first and then we'll turn to maybe some optimism on where we're seeing workforce enhancement, workforce, uh, you know, addition. There's, there's a lot of reasons to be optimistic. But let's start with the elephant in the room. If you all read Wall Street Journal or any, the uh, Denver Business Journal, you know, there's a lot of layoffs and people want to point fingers to the AI displacement that we are able to get things done potentially with less people now for a long time. We also saw some data earlier today that we've always had a workforce crunch, not enough bodies here in Colorado to get the work done. I'd love for you to talk about some of that, um, displacement, that anxiety about jobs, uh, changing and where you see, and this will be helpful I think, especially for people, the talent leaders and the higher ed leaders here in the room. Where are you seeing work being genuinely, um, quietly eliminated? I mean that's a pretty harsh way to say it. But if you were advising young people today, um, where are you seeing uh, this jobs displacement that might be permanent and might be real?

Speaker D: I can take a first crack at it. Uh, I think for me, uh, sometimes when people start, whenever you have a new transformative technology like AI is, there will always be job changes. But I wouldn't say it will eliminate jobs in the sense of there's always a mindset shift you gotta have in here. Because the question we have to ask ourselves, what is AI actually enabling us to do? Because the reality is it will replace uh, some of those low level tasks that are repetitive. But the reality is it's still a machine. Because I think everybody is thinking is all about probability is predict things. It has massive, massive, massive amount of data that is using to predict the next things. So as humans we shouldn't be trying to compete there. We should try to think about, so it shouldn't be human versus AI. It should be human actually changing how they work because of AI. So, so when you think about how you change how you work as a company, as an organization, we have to think about, okay, what is AI actually enabling this next person to do? Now we don't need to do this manual work, we don't need to summarize all this document we don't need to, uh, do this repetitive work because this can be done a lot faster now with these machines that are way, way powerful. So the question we ask ourselves after that is, what is it enabling us to do? I think that's where the mindset should be, because the reality is, I know Sean, Debbie and I were talking and we're trying to see who said that code, uh, we think it might be Jensen, is that AI is not going to take your job, but those that use AI will replace those that do not. So which is the phase we are in now. So learning is crucial. So the mind shift need to shift a bit. So it's not fully focused on replacing your job. His question is, what does it enable you to do next now? Not that you have AI helping you.

Speaker C: Yeah. I think for me, one of the key words you mentioned there was transition or transformation. And I think, I know people have anxiety, like moment to moment. Um, there's headlines and this and that. But if you take a step back and look at this from an academic perspective and, you know, 500 years from now, this is a historical moment that we're living in. And 500 years from now, they're not going to. The history books, aren't going to talk about newspaper headlines, 100 jobs here and 100 jobs there. They're going to talk about this transformation of our economy. Um, and an analogy of this is, uh, and taking a step back, we have to think about what is coming. What is our future going to look like because of this? Let's take a step back and look at electricity. Um, you know, electricity changed the way we work. I mean, you could say steam did, but let's just talk about electricity. Electricity changed the way we work and it changed the way we live. We got, um, factories, we got, um, mass commerce. Uh, we got cities. Um, jobs change from rural agrarian to line workers. Um, and when the electricity came around and we were all looking around the light bulb and saying, is this all we get all this for a light bulb? You know, but that's not. We couldn't imagine at that time that I'd be talking to you about AI, which wouldn't be possible without a gpu, which wouldn't be possible without a computer, which wouldn't be possible without so on and so forth leading back to the electricity. So, yes, jobs are going to change. The world is going to change. But, you know, this is your opportunity. You're all entrepreneurs, I'm assuming most of you. This is your opportunity to do something about it. This is at Transformational moments, disruptive moments like this, this is where opportunities are born.

Speaker B: And we're not trying to say we should go back to a time without electricity. I heard it framed that way once. And for a non techie person, and I don't know if anybody else can relate to that, it was, it was transformative to me to think about it in that way. This isn't just a new, um, you know, Apple product. This is a complete new system that everything plays within. And so there will be, um, you know, some potential displacement, but really the transformation is what to focus on in terms of being, um, very inspired. I would say there's a handful of entrepreneurs probably in the room, but more than that, there's people who are entrepreneurially minded. So thinking about resilience, what we tell, especially generations younger than me about resilience, and I do use AI every day, by the way. I'm very proud of that. But for my kids, who are all adult workers out there, for them to have resilience even more, to pivot even more quickly, to make sure they have the right certifications, the right training to jump in, I think is the only way they're going to really take advantage of what's coming, don't you think? Like everybody needs to be very prepared for what's, what's not just not coming, but what's here already.

Speaker C: Yeah. I have two kids in university in Colorado, CSU just graduated on Friday, uh, for civil engineering, environmental engineering. Almost home free. I got one more. Um, the other one's at School of Mines and Mechanical Engineering and um, uh, School of Mines in Mechanical Engineering has AI programming with Python and all that integrated in the curriculum. CSU does not yet. And here I am, my job every day is talking to people like you about how you really need to start hiring people who know AI. And my kid is graduating as an engineer. I'm sorry if you have CSU people in the room, but this is your wake up call. You need to incorporate AI into the curriculum because employers are expecting graduates to understand this language. Who else is going to know about how to do this stuff? You know, it's, it's the young people, the AI natives, you know, the kids that were born with an iPad in their hands.

Speaker B: And let's jump into that more. And Bharaji, you'll have a comment here too. But, um, that was the next question for me, especially because we do have a lot of workforce partners, talent creators, higher ed in the room. Where do you see, um, how AI shapes what they're doing? Um, it's gotta be return on investment scale, speed, relevancy, everything related to that training. And to some degree, you've got professors who have maybe been teaching the same way for decades and they, they aren't up to speed on AI. How would you advise some of our higher ed leaders on how to integrate it into what they're doing already?

Speaker D: Yeah, I think what AI has started because if you think traditionally when it comes to learning, a lot of them, um, for the lack of better term is linear versus AI have multiplied everything at this point right now. And the biggest thing for me is about adaptability. So that's the key words I like to use when it comes to that time and space we're in right now. We need to be adaptable and we need to be able to move faster. Because the reality is one thing you learn today, next week, if you're in this space, that will be absolute. So a lot of time I get called into this training, they're like, whatever, you already trained me, you already told me about this thing. I'm like, no, forget what I told you last week. That one is, is absolute. Now m. Now this is the new thing we need to learn, we need to move. So adaptability and flexibility become the key things right now. And especially in our classrooms, the curriculum need to adapt, uh, to be able to meet the demands of how fast this uh, space is moving. Because I don't think we're at a position right now where this is going to stabilize anytime soon. Which is not a bad thing. Because the reality is, I don't know if any of you use these models. Even three, four years ago. Some of them were terrible. But uh, coming fast forward to now, some of them came a long, long way. Me and Sean were talking about developer tools we used to create softwares. And we're just talking about all these different tools we use and not use, why we don't use some of them. So the key now become you need to be adaptable. Not only adaptable. One of the things I mentioned earlier, because one of the biggest mistake I mentioned is people like to just put uh, AI on top of an existing process. That's not the space we're in. You need to redesign your workflow intentionally. Intentionally is the key word here. If you're not redesigning your workflow intentionally, I'll, uh, urge you to pause to say, what are you actually doing? Because if you're just putting AI on top of a process and you're hoping for it to work, it's not going to work, because, uh, you can create those prototypes very quickly, but ultimately, you need to be intentional and adaptable.

Speaker B: Let me push a little bit more on this, and maybe this is for you, Sean. If, again, I think there's sort of an individual responsibility for us to make sure we individually thrive in this new economy. We've got to take some of that on. Look, there's certificates online. Christine, you and I work on some of that. Like, there's, there's even free training out there that everybody could go to tonight and become more fluid, uh, in AI, but specifically for talent leaders, people who are creating systems, a lot of them in the room. Um, how do they. Is part of it just, like, do it, get it in there? Like what? Like, there's no perfect system and it's never going to be on the cutting edge because the cutting edge changes daily. Right, but just get in there.

Speaker C: Yeah. Like Bharagi was saying. I mean, honestly, if you go take a course that was published like three weeks ago, it's outdated and it's crazy.

Speaker B: Isn't that just that frustration? Like, how does a leader. I mean, how do you even do that? Well, you'd be revising your curriculum every Tuesday. That's not practical.

Speaker C: We update our Nvidia, by the way, if anybody's like, serious techies in the room, Nvidia has something called Deep Learning Institute. So if you go to our website, we. There's tons of free courses, lots of tech, and we update them all the time. But what I do, I don't do that. Um, but what I do is, um, I live in Fort Collins. On my way home, if I have no meetings today, I'm gonna turn on audio voice mode on ChatGPT, and I'm gonna talk to it today. My topic is knowledge graphs, and I'm gonna ask it to teach me about knowledge graphs and how I use knowledge graphs and with MCP servers to eliminate, uh, errors and optimize processes. With AI, you can all do the same thing. And the thing is, when you're talking to an AI, there's no stupid questions, you don't have to be embarrassed. And it just about knows everything. And if you're asking it about AI, that's what it knows really, really well. So you're in good hands using AI as your teacher for AI, and you

Speaker D: could just talk to it and, uh, actually to add on to some of the stuff, Sean, uh, as you were talking, that came to mind, especially at hpe, I know there's a lot of leaders in these rooms. And when we're talking about learning, we're talking about institution, or you're talking about your company in general. It shouldn't always be on the individual as well, uh, to learn in regard to, yes, they are responsible for their own journey, for their own careers. But, but one of the key crucial things we need to do is enable that journey, create that path to actually making that a reality. Because when it comes to this space around AI, as we started talking about trust, the key big thing is putting the actual uh, governance process in place. Because at HP that's where we always start about AI ethic. Everyone, when you're working with AI, you need to know what AI ethic. Because this tools are very powerful. They can be used for good, they can actually be used for bad as well very easily because that gap for uh, expertise has shrink quite a bit. So the question is learning about AI ethic, learning about having that governance in place. But you can't just stop there because now that give people confidence about what they're doing is safe. But you need to create that safe experimentation regardless of your organization or, or institutions where people can actually experiment with these tools. Because sometimes you hear use AI, but which one, there's hundreds of thousand of them out there. So it's on the institutions or the organization sometimes to put that learning path forward where people can come and learn and adapt as they move.

Speaker B: Yeah, let me double check on this. I think what you're telling leaders in the room then and again, private sector leaders, anybody who's a leader of an organization is, is show, prioritize that this is important for their team because you're wanting them to build these skills so that they can be productive within the current environment. Make sure they're utilizing their skills to the fullest potentially. But make sure as leaders you allow time, training and some fluidity.

Speaker D: Exactly.

Speaker B: Okay, that's great. Sean, you have something.

Speaker C: Yeah, kind of a counterpoint. I mean, yes, you, you as an organization, you want to think about upskilling, reskilling and training and providing those resources to your employees and certainly sharing out knowledge as you learn. Uh, because if you get off the shelf training, that's interesting, but it's not contextual to your organization. As your teams learn, they should capture what they learn and share it in the organization because the relevance is much greater. But the biggest mistake I see companies want make is waiting till they have it all figured out. Waiting till they have all the governments, waiting till they have all the training programs, waiting till they have all the roles and responsibilities assigned. Waiting for till it figures this, that, and the other thing out. And every day you're waiting, you're falling behind. Every day you're waiting is a day of exponentially less productivity. The thing to do and the thing we do at Nvidia is just make the tools available to your people and let them figure it out. And in general, the tools are quite harmless. I mean, you guys know Chat GPT? It's quite harmless. Okay, so chatgpt, Copilot, the other large language models, they're quite harmless. Make them available to everybody and inspire them to go and figure out how to use them. They teach, they tell you how to use them. You just ask. The first thing you type in is, how do I use you? And they'll tell you. So you don't need an instruction manual with these things. Just ask. And. And, you know, we're at Nvidia getting on to Gentic. AI Bharatji and I were talking about this and, uh, have you, you, you. So what you use Chat gp? Have you started with Claude Cowork.

Speaker B: I'm on the spot.

Speaker C: You're on the spot, Debbie.

Speaker B: I haven't used Claude, but my team has.

Speaker C: Okay.

Speaker B: Uh, they've, they've talked about. It's our new, our, uh, new, um, assistant Claude. So I'm excited to learn about it.

Speaker C: Has anybody in the room used Claude code or Claude co work? Awesome. Okay, all those of you who raised your hand, keep them up.

Speaker B: Okay?

Speaker C: Anybody who's not addicted to it, put your hand down. Okay? You're all addicted, just like me. It's impossible not to be. And in Nvidia, like my colleagues are saying, it's like when I was a kid and got my first computer. It's insane what it can do. And so like, I wake up in the morning and like, I need to, like before all my meetings start, I have to get Claude Cowork Start. It's an agent, so it's doing the work for you. Instead of one question at a time. You say, I need to do a research project. You go do the research and then when you're done, make me a PowerPoint. Send it out for my peers to review, get their emails, review all their emails, make the updates and let me know when you're done. And it could take all day. Whatever, let it go. And it does. And it makes the PowerPoint for you. And you don't have to do anything. You could just watch tv.

Speaker B: Great. It sounded like half the audience, at least was, was already in your camp using Claude.

Speaker D: Um, and Debbie, if I may, uh, Ashan, you were talking. I know we're urging a lot of people in the room, go ahead, use the models, use AI. But one of the number one mistakes uh, I have seen organization made and even internally is ultimately with all these hundreds of thousands models out there, which one of these models fit my specific use case, which one of these models fit this specific task at hand and which one of this model fit my job? Because that's a question people ask. Because sometimes organizations make that mistake saying we have all these tools available to you. You should be AI native by now. Go ahead. Why aren't you AI native? None of these tools actually fit my job functionality, my job needs. So there need to be trainings that happen because that's one of the area I focus a lot on is how do I create recommendation engines, how do I create models that actually tell users what job this model is meant to help you for what job, uh, or what is the task at hand that you should use this model for? Because the reality is if you don't, it can be the most powerful models ever, but it's not needed for that task at end. For example, whenever I am interacting with a chatbot, uh, we're having a conversation back and forth. I love using Gemini because Gemini to me uh, typically is very good. When I'm conversating back and forth whenever I need deep thinking like I truly want you to think, don't just give me an answer, think about it. I like using cloud as we were talking about there and ChatGPT for me is that general purpose models of catch all. But you need to know what is the model for, otherwise you will have a lot of frustration. These models are not trained to say no, they will hallucinate. They will always give you an answer. So it's very, very important to learn what is the models ah is trained for and to help you achieve.

Speaker B: Let's talk quickly about roi. I think you know, you mentioned that the financial industry has been an early adopter, um, you know, with, with AI because they, they have to, they have to make sure they're uh, keeping their system safe. Where, what advice could you give us today on um, where you're seeing ROI show up today knowing that AI investment isn't cheap. Between infrastructure, talent, training, all the things are you seeing as specific example where people could really plug in?

Speaker C: I have an example, I have so many examples, but I'll start off with one from this town. I can't name the company name because they didn't allow me to do it but they bought an HP machine for a computer for $250,000 and they're saving between 250 to $500 million a year. How about that for ROI? So at a certain point the cost is irrelevant. And I think like Bharagi and I were talking about this earlier, it's like when you operate from the perspective of the constraint, if that's your first question as a business leader, uh, what is my cost? And it's prohibitive, that is you're already, you're going to fail. That is not the question to ask. The question to ask is what is the opportunity? What can this do for me? And then the cost is a function of that. So the customer I'm talking about is a uh, construction company. There anybody in the construction industry in the room? Okay, yes. So they're an EPC construction company and they do, they're responsible for the price they quote for like huge projects that could be a billion dollars. So if they can get a better estimate on their quoting system, more accuracy and reduce the number of jobs that they take on that aren't going to be profitable in the end, high risk jobs, um, there's a huge amount of money to be made. They developed a simple AI uh with $250,000 of hardware um, to do automated estimation and through better job selection and better accuracy on their quoting they're saving between 250 to 500 million dollars a year.

Speaker B: And just this crossed my mind but you're saying the technology is changing. How do they work with the changing technology if they have that system today? How do you ensure that it's still going to, you know, is it um, still relevant six months, you know, how does that keep changing again whether it's construction industry or other industry leaders.

Speaker D: Yeah, I think uh, in this AI space there's a lot of focus on the actual models itself versus there need to be a lot of focus on the infrastructure that are put in place, the safeguards that are put in place that human to actually support these models and make decision. I think that's to me where a lot of the investment and decision need to be made and put in place. Because the reality is anything in my opinion that you design need to be model agnostics meaning you should not be type or in my personal opinion to a specific uh, provider. Because this place changes so fast and we're in this space that you need to put the right environment um, in place where you and your peoples are ready for this changing environment. Because a lot of time when we were talking about ROI or Fear of losing jobs. Uh, there's a lot of focus on workforce reduction versus focus on workforce evolution. So because this is the moment we're in right now is to evolve with this technology and to keep learning. So it's not about reductions. The question is how do we evolve and how do we put the right infrastructure in place? How do we put the right safeguards and guardrails in place? Once all that are uh, in place, the models can shift because the reality is we're not at a point where that change is going to slow down anytime soon. But you need to put that right

Speaker C: pieces in place and you know, generally the hardware doesn't get, uh, outdated. Um, so I used to work at hp, uh, and we would sell super high end workstations to engineers. Um, and eventually they would have three years with the engineers and then they would get handed down to the draftspeople who didn't need those at three years of age. They were still good computers, but they weren't the highest end. So then the engineers always got the latest and greatest and they handed them down for another three years to the draftspeople. They had six years at, uh, least of life. It's the same thing with AI. There's always going to be the latest and greatest technology, the new GPUs, but the old GPUs. The old technology still has a function. It's just maybe going to play a different role.

Speaker B: So it comes down to again, we talked a little bit about resilience, learning, making sure people aren't resting on their load laurels. I really enjoyed that phrase, workforce evolution. I hadn't heard it quite that way. I've always thought about workforce transformation. So um, there's, there's some optimism in that as well as we're running out of time. Um, I want to let you both sort of send us off with your best advice for these leaders. Again, of whether it's private, um, sector businesses, uh, talent leaders for institutions. What would be your best advice? And then we'll look forward to having a drink with you later and they can pepper you with what's better. Gemini, Claude, you know what's coming next, but, um, appreciate your best advice. And who wants to go first?

Speaker D: Uh, I guess I'll go. I think for me the question I want to leave the room with is if you can see something but you cannot act on it, do you really understand it? So that's reason I asked that question is when it comes to this space, that challenge is not about awareness. Everybody want to use AI. The Challenge is about connection. So a lot of that stuff we build, we need to build a system that not only connect inside to actions, but that move intelligence, uh, from something fun or something cool to have, but uh, to something you can actually act on to. So my biggest challenge here is we need something we can act on. Because with this AI age, you can create prototypes so quick. But the reality is, I'm sure many of you heard about vive coding or seen people create all this prototype, but it doesn't go anywhere. So my challenge is whatever you're building, ask yourself, who are you building this for? What is it helping to solve? If you can't answer that question, maybe pause because you're just having fun. You're not building anything that will actually be meaningful, that will be valuable. Lastly, I know we started off, opportunity is key because we talk about future of work. I know AI is changing a lot of stuff. Let's take change on others. Uh, the reality is as I started off here, I did not grow up around this technology. I saw my first computer at age 14 and back then I touched a mouse, I thought I broke that whole computer. So that's all go to show about education. So let's take opportunity on each other. So since this is all about future of work, that's what it will take for us to move, uh, and get where we need to be.

Speaker C: I think you kind of need to take, first of all, have an optimistic perspective on the technology, which I know is not natural for everybody in the room, um, and a lot of curiosity, uh, don't make judgments, um, without learning for yourself. And I think that's really what you need to do is go learn for yourself and understand what you're talking about, um, try AI, use AI, um, and understand, you know, the potential impact in your organizations, uh, has. Do you guys know that, um, have you heard about this? The one person billion dollar firm. So nobody's heard about that. So it was, it's been just like artificial general intelligence is predicted to come soon. It's been predicted, uh, that using agentic AI, one individual with no employees is going, using agents as virtual employees is going to be able to reach a billion dollars of revenue. And this has happened a few weeks ago. I could tell you the story, uh, but so if one human can generate a billion dollars of revenue legally, I might add, okay, with a team of agents making websites, doing back office shipping, receiving supply chain management, blah, blah, blah, if one person can do all that, that person has a team of over 100 agents doing all these things. And um, that person is just monitoring those virtual agents. My CEO Jensen Wong says the IT department of today is the HR department of the future. Because we are all going to have virtual agents. So you don't have to be an entrepreneur, uh, or a one person billionaire to benefit from this. If you're in your state or federal or local government organization, your small business, you now have access to hundreds of people that can cut through all the red tape, all the bureaucracy, all the communication challenges, all the budget carry issues and just get something done. Whatever you want them to get done, those agents will get done. You just have to know how to use it. And to do that you just type in how do I use you into the AI and it'll tell you it's not that hard. It's amazing.

Speaker B: All right. I think we're a little bit, uh, energized, a little bit frightened, a little bit excited, but there's coming our way, uh, definitely. Um, want to thank these two gentlemen for their time today. Would you give a warm round of applause to Barachi and Sean for me? Thank you. Thanks gentlemen. You can just stay put. I'm going to share a few closing remarks and then we'll break for our reception. Um, first I want to thank all of our sponsors for today we went through. Hopefully we can throw the list back up there if it's possible. Um, to thank our sponsors. Um, really grateful for your help in making today possible. Um, and top of mind, the reception sponsor tonight is slalom. So we certainly appreciate slalom being a part of the event today as well. Um, a lot of great learning, uh, experience today championing uh, work based learning at scale, inspiring purpose within our work teams, becoming AI curious, AI adopters and looking for growth opportunities and entrepreneurism. A lot of discussion about building partnerships and how we can potentially make some new partners over uh, at a cocktail after the conversation, uh, here on the stage and then mostly thinking about what action you might be inspired to do. Uh, data inspires action. So I'm hoping you're thinking about today whether you're a higher ed partner, a business, a uh, talent producer, how you might bring some action to the different data components that you heard. We will definitely send you slides QR codes on a follow up email. So don't panic, we'll make sure you have all of that and then just wanted to remind you as well if you're interested in uh, two free round trip tickets on Southwest. Be sure to talk about the event today, share some of your insights on LinkedIn sometime this week. In closing I was recently reminded of a quote that I think captures, um, some of the conversation well, today, capital goes where it is welcome and stays where it is well treated. Isn't that true? The banker who said that originally also broadened the thought that capital isn't just cash. It's not just resources. It's human talent, intellectual capital, human talent. And that we need to make sure that we're welcoming talent here in Colorado, growing talent, and treating our talent well. So just something to think through as we're closing today. This, um, is why sound public policy, strong employer engagement matters so deeply. Colorado already has a strong reputation for being one of the best talent ecosystems in the country. We've got a lot of leaders here to thank for that, and we need to do more, um, and business needs to lean in, and that goes back to sort of our founding principle as business as a force for good. So we're excited to bring together a lot of businesses to this conversation today and want to make sure that we're talking about these issues around workforce displacement, social mobility, making sure we're not leaving people behind in Colorado. So for now, that's a lot to chew on. You all have been very patient. We have food and drinks waiting for us. So that concludes our time together, and we look forward to talking at the reception. Thanks so much.