Ep 121: Navigating Technology Convergence to Create Sustainable Abundance with David Kilzer

[00:00:09] Hey everyone, it's Bob. Welcome back to Elevate Your AIQ, your go-to source for insightful conversations on human-centric AI readiness, talent transformation, responsible innovation, and the future of work. Today's guest is David Kilzer, founder of Strategic Transformation Advisors, a mechanical engineer, and MBA with a career spanning General Electric, Digital Equipment Corporation, and decades of entrepreneurial work at the intersection of operations and emerging technology.

[00:00:36] David and I explore the convergence of AI and humanoid robotics, how organizations can build a data-first mindset to unlock real AI value, and why the path to sustainable abundance runs through human collaboration with machines, not displacement by them. David brings a grounded, hands-on perspective shaped by living through every major technology shift of the last 50 years, and this conversation will challenge you to stop waiting for perfection and start getting your hands on the tools that you can use.

[00:01:07] David Kilzer, Thank you so much for tuning in, and I hope you enjoy this episode. David Kilzer, Hey everyone, it's Bob Pulver. Welcome back to the show. Today, I have the pleasure of speaking to Mr. David Kilzer. How are you today, David? David Kilzer, I'm doing well, thanks. Yourself, Bob? David Kilzer, I'm doing great. I'm doing great. Looking forward to this conversation. We are going to get into the weeds a bit on technology convergence and AI and robotics and some of the other fun things that you've been doing in your career.

[00:01:34] So, before we get into it, why don't you just give my listeners a little bit about your background and some of your early career and some of the projects and things that you're working on these days. David Kilzer, My background. In my core, I'm a North Dakota farm boy. And the hands-on experience early in life has served me well through my career. I went from off the farm to North Dakota State University with a bachelor's in mechanical engineering and added an MBA from Indiana University.

[00:02:03] And my earliest career started with General Electric Company. And they put me to work in an area I had a lot of fun with, which was designing automation for manufacturing processes. And went from there to helping in their manufacturing management programs. And ended up being responsible for relocation of manufacturing plants to a first-ever non-border maquila in Chihuahua, Mexico. Oh, wow.

[00:02:31] So, it was a lot of fun. And then segued from there to a little bit higher tech, at least in terms of what I was doing, with a company called Digital Equipment Corporation. Ended up doing some facility expansions with them. Had a lot of fun with them. They actually won the 1982 Modern Materials Handling System of the Month for the design and implementation of an automated system in that company's manufacturing operations in Phoenix.

[00:02:59] Went to Europe. Did a lot of things with advanced manufacturing engineering in Europe. Came back to the U.S. to a tech corporate. Found out that that wasn't a good fit. So, I jumped into the entrepreneurial world and had a lot of fun there since. Been there since. Yeah, I definitely want to hear about some of those projects. And Digital Equipment Corporation brings back a lot of fond memories growing up just a few miles from their headquarters. So, we probably crossed paths at some point.

[00:03:27] Well, first, I want to just hear a little bit about your current advisory practice, strategic transformation advisors and some of the work that you're doing. That's the entrepreneurial edge that I mentioned. So, we started a firm there. And what we're really aimed at is, as a company, we're focused on operations. If you touch, move items, especially in a discrete space, that's where we focus. We help clients articulate their goals and objectives for their business.

[00:03:56] And then we develop tools that can help them achieve those goals and objectives. Primarily internal transformation. Though we do get involved with mergers and acquisitions from a due diligence standpoint, really what we're, our core is internal operations within a company and helping them develop paths to develop those organizations so it aligns with their business goals and objectives. That's our lodestar. Business goals and objectives.

[00:04:26] And then, you know, one of the things that I think is relevant, like I sort of teased at the beginning, was around like technology convergence, right? So, I imagine some of your engagements start, well, like a lot of engagements start with some type of discovery process.

[00:04:43] But I imagine a lot of people just don't necessarily think, you know, long term about the way that individual technologies, let alone, you know, the intersection of technologies is going to impact their, you know, longevity and some of their strategies, right?

[00:05:00] Yeah, but I mean, we have to keep that in mind because if we take a look at the history, you know, we're not to look very far, very deep before we see the explosive impact when technologies combine and converge. I mean, some of the great examples that, you know, we're living through today, most of the people that are listening to this today will have lived through the convergence of GPS with high-speed mobile computers and giving us the smartphones.

[00:05:28] Yeah, I go all the way back to the convergence of technologies around integrated circuits and transistors. And, you know, the bird, the PC. So, my career stretched backs to pre-PC day, you know, when RelayLogix was done was really with relays. And so, I've gone through these convergences and one that, you know, in deck days.

[00:05:53] I was there in the middle of deck when the TCP IP protocol, you know, was merged with packet switching. And I think it was about 83 when, you know, the ARPANET converted to that. And hence, we are using today, you know, pervading, conducting this conversation over the internet that resulted from that.

[00:06:16] Yeah, you're just, you're reminding me of some of my early memories of my dad, who was a Raytheon veteran, 44 years at Raytheon. I'm talking about like integrated circuits and microprocessing and all of these things that were way over my head at the time. And I guess in some ways still are, but I'm starting to have, you know, an appreciation for just, you know, what it was like back then.

[00:06:44] Because back then, that was sort of the latest and greatest, you know, technology. And yeah, they were working on some pretty cool stuff. So, you've talked to a lot of people, not just clients, about, you know, technology, convergence. I was curious to hear a little bit about your TEDx talk that you gave a while back in South Carolina, was it? Yeah, it was in Greenville, South Carolina. The TEDx organization is called Unity Park, or that's what they named their event.

[00:07:14] And a great organization, great, great group of people. If anyone's considering doing a TEDx talk, I would encourage them to consider giving TEDx Unity Park a chance, you know, provide them a script. But that was a particularly challenging time for me because I began writing that script for a November presentation in April or May of this year. Or last year, rather, sorry.

[00:07:43] And I ended up rewriting it weekly. It is changing so fast. It's, you know, you can't, you know, open up a YouTube, at least with my history in YouTube, without something new and earth-shaking coming through almost on a daily basis.

[00:08:04] So the talk was started to, you know, originally aimed at looking at the last mile of e-commerce package delivered. And it morphed into a much more general discussion of that set of technologies. And you can't say it's a single technology, but it's a convergence of AI in humanoid robotics and what it's doing to shape the world.

[00:08:32] Now, I made a, might not be a bit hyperbolic statement in there, but, you know, I said, you know, this is going to be looked at as the next biggest thing for humankind since fire. That may be a bit hyperbolic, but it gives you the scale of what I see the impact coming from that. And the 10X platform is a great platform.

[00:08:57] You know, if you have time, it's a great place to learn about ideas worth sharing. Yeah, I hope to do one of those someday. I got to work on my public speaking, perhaps a little bit first and find a good chapter. But yeah, I would love to do that someday.

[00:09:15] There was a futurist I heard just the other day on a webinar who was making similar hyperbolic statements about, you know, the impact of some of these technology convergences, including AI and robotics. So you're not alone in making those bold statements. You're in pretty good company there. I will say that I find this increasingly fascinating for a couple of reasons.

[00:09:45] You know, one of them is, as you and I discussed, I do spend a fair amount of time in the talent space, looking at talent technologies and how AI can augment, you know, human beings in their day-to-day work and the capabilities that they have. How do we make humans better? How do we, you know, complement the human capabilities that we have with AI?

[00:10:11] But I've been largely thinking about that in a sort of knowledge work kind of context and not necessarily exposed as much to, you know, some of the blue-collar professions in manufacturing and elsewhere. Not even just blue-collars, like, you know, retail, you know, hourly work. And so I find this particularly fascinating.

[00:10:38] I mean, certainly some of the, you know, Boston Dynamics, you know, robotics have found their way into different manufacturing facilities or warehouse facilities and things like that. And they've got, and obviously we've seen, you know, car manufacturing and things like that. I think, you know, BMW down in South Carolina might be one example.

[00:10:58] But we're starting to see some really significant advancements in some of those capabilities in terms of the dexterity of the robots, the injection of more, I guess, I don't know if it's LLM-based AI, but more AI and more almost seemingly like thinking machines in some of these endeavors as opposed to more automation,

[00:11:27] like rules-based, you know, robots that need to follow very specific rules or they're, you know, they're not walking around necessarily. They're the giant, you know, arms and they're doing intricate, you know, things with the circuits and stuff like you were alluding to before. So I guess I was curious to get your perspective on the sort of trajectory of this technology convergence,

[00:11:53] specifically, you know, expanding on what you talked about at TEDx, like, you know, humanoid robots and AI. Bob, I really like, you know, some of the key points you made there. And one of the key points is that when done's right, you know, it's not humanoid robotics replacing humans. It's augmenting, collaborating with humans. And that's where I see the future going is, you know, humans are going to maintain firmly in control,

[00:12:23] but we'll leverage that technology to get to the next generation of humankind, which is, you know, you know, the hyperbolic statements was sustainable abundance. Maybe not hyperbolic, you know, that's the aspect of, you know, what we aspire to. There's going to be some real challenges that we have to address getting from here to there, but it's nothing that, you know, it's not within our realm to conquer and really fully exploit that technology for the benefit of all humankind.

[00:12:51] So the path to get there, you know, there's a lot of different elements on that path that people can start preparing themselves to follow. You know, I think the key thing there is don't be intimidated by it. Don't think that it's beyond your grasp.

[00:13:18] Get in there, get, you know, that North Dakota farm boy hands-on approach. And, you know, at the same time, take advantage of what's available out there to learn from that. And, you know, some of the Coursera work is some great places to start. Some of the stuff that NVIDIA is providing free of charge is great place to dig in and start.

[00:13:45] Getting your personal grasp on this technology. Yeah, I mean, I think you're echoing a lot of the points that I bring up pretty consistently. And I did my first solo episode the other day and just kind of emphasizing that I don't want people to feel like they're being displaced or that they're a victim, right?

[00:14:08] I want people to sort of take control of their own destiny and sort of look at the trajectory of some of these technologies and figure out how you can learn some of these AI skills. Or maybe in this case, it's AI, a little bit of AI skills and learning a little bit more about, you know, robotics and where that's going. Certainly, your company, every company is going to sort of move forward and adopt these technologies at a different pace.

[00:14:37] But I just want people to be proactive and prepared so that they can be one of the... Maybe if you work in manufacturing, you can be one of the people that helps to train the robots and making sure they're acting, you know, ethically and making sure they're not bringing, you know, harm to anyone or anything. And I just... Every circumstance is going to be different, but I just... I don't want people to get caught flat-footed or feel like they have no control over their own future. Yeah.

[00:15:06] There will be a lot of... A lot of press has been spent on that. And some of it has been steered by some of the, you know, the dystopian literature, you know, and movies that have been out there. But, you know, humans create AI. AI. Humans create the robots. Hey, y'all. I'm Lee Cage Jr. And I'd like to invite you to listen in to my podcast, 15 Minutes With.

[00:15:33] 15 Minutes With rising stars and seasoned disruptors, thought leaders and change agents who are sharing how they're reshaping work, rethinking worth, and reimagining what's possible. This is fast-paced, hard-filled, and unfiltered. These aren't just conversations. They're catalysts. So tune in, elevate, share the shift, 15 Minutes With, wherever you get your podcasts. Humans will direct AI into robots, collaborate with them. We'll be much larger.

[00:16:03] The reach of what we can accomplish is vastly expanded with these tools. But at the end of the day, they're tools. And we have to make sure that humans maintain control over those tools. So I really like your touch just a few minutes ago on ethics and keeping that in the core of every discussion.

[00:16:26] When people are looking to the future of AI, the ethical boundaries and guardrails that get established are critical, that we take what's in place today and expand on them. As we learn more about this technology and its capabilities to expand, we have to adapt and expand with it. But that's the human contribution, if you will, or critical role that we play in that.

[00:16:56] How do you think the U.S. is doing in some of these endeavors versus China and other areas? I mean, are we going to be a leader in developing these kinds of robots? I don't know if it's Tesla or another big tech company or the next Boston Dynamics or whatever. But I don't know.

[00:17:18] I guess I'm concerned a little bit about what's going on in China and their appetite and investment in these types of technologies. I think it's a good reason to be very concerned with that. If we want to, I think it would be definitely in the world's best interest that we maintain leadership in this technology. And they're making some huge investments.

[00:17:42] Currently, they're a little bit handicapped by their access to some of the latest silicon-based technologies. You know, what NVIDIA is producing. It's at the core of the whole AI revolution and rapid evolution. But they have been incredibly adaptive at working around some of the technological limitations that they have. So the Chinese are very formidable competitors.

[00:18:13] And we need to keep focus on, you know, the collaboration across the different elements of our society if we're going to compete with them. And leading robot manufacturers in the world today, you know, if I take a look across the U.S., I see what figure AI is doing in California, what Tesla is doing with the Optimus machine.

[00:18:36] And certainly with Boston Dynamics, they've been incredibly high performance in terms of they're able to convert their machine from hydraulic to full electric. And, you know, whether or not they're an American company right now, you know, because they're 88% ownership by Hyundai, you know, some people might question it. But they're in the core, their developments are still done in the Boston area.

[00:19:01] And they're, you know, really contributing a lot to the rapid evolution of the robotics space today. So it's an exciting place. And you can't discount what China's doing. I mean, they have some of the leading machines out there today. And it's going to be a competitive race.

[00:19:22] And the major benefactors of that race are going to be humankind as we get to take advantage of these wonderful machines to improve our lives. Do you see, I was thinking about the, like some of the use cases for these robots.

[00:19:39] And I'm thinking about some of the areas where it's like, are we, I guess I'm thinking about whether we're prioritizing the right use cases and in the sense, in a human centricity kind of sense.

[00:19:54] What I mean by that is, are we prioritizing not just speed and scale, but maybe doing some of the jobs that humans shouldn't do because of, because it's dangerous. Yeah. Right. Like, you know, testing, you know, chemical, certain chemicals or I don't know, even like firefighting or building the next international space station.

[00:20:22] I mean, shouldn't we send robots to do that and not people, right? Who need to be rescued and, and run into all kinds of just, you know, carbon-based life form challenges. Yeah. I mean, there's, there's a lot of areas where, where robots without that carbon-based limitation, they have some, you know, insurmountable advantages compared to the, you know, to our frail human bodies.

[00:20:47] So, you know, the use of them, the application of them where they were, they will, you know, contribute to that. And those are certainly not just a conjecture, but they are, they're already, I mean, there's been robotics in space and there will be an expansion of that. You know, the big push, you know, by getting people to Mars, you know, the first fleet of workers that are going to show up right there, I don't think they're going to be carbon-based.

[00:21:13] They're going to be a robotic, you know, ambassadors for humankind on that, that planet. And they're going to be, you know, there to prepare the way for humankind, build the structures and the shelters that allow humans to live there when we arrive there.

[00:21:32] So, yeah. I mean, I'm a sci-fi fan, so maybe I've seen too many movies, but it just seems like these are some of the sort of very human-centric, not only human-centric to, you know, for safety reasons and logistical reasons or, you know, whatever it is, but just to also not take jobs away from humans that are perfectly capable of doing certain work.

[00:21:56] But, you know, we don't need people getting trapped in coal mines and we don't need people, you know, going into harm's way in a lot of places. But, yeah, I just, I don't know how far we are away from some of those use cases, but certainly we've seen, I mean, even, you know, FEMA, I could see someday having, you know, a fleet of, you know, AI-powered robots.

[00:22:21] Yeah, I mean, robots have been out there in emergency rescue for some time. I mean, there's, you know, I think we can all remember, you know, the pictures of the robot crawling through the remains of an earthquake and discovering through listening and other sensing devices, you know, where there were, people were still trapped and having them safely rescued from that.

[00:22:46] Yeah. Was that the, was that the four-legged one or was it a, it wasn't a bipedal robot, right? It was like a, one of the spot kind of robots? It was actually tracked. It was a hybrid. Tracked, okay. A tracked vehicle that could go on there and had the ability to compress down to where it was only a few inches tall and squeezed through some very narrow spaces and bring in listening devices and cameras to help locate, you know, trapped people.

[00:23:16] So it's, it's out there and expanding daily, thank God. And so these are use of a different type of robots. And the application of AI is, is starting to be added to those, those machines as well. So, you know, the human doesn't have to control every little direction that they take.

[00:23:39] There's certainly a collaboration between the human controller and the machine, but the machine using its algorithms can take some initiative in terms of the direction they take while going through a disaster area. You know, it's generative in a sense that they will learn from their experiences and improve their capabilities as, is their, their use cases are promulgated.

[00:24:05] You know, we're not, we're not, we're not hoping for emergency situations to, to advance that technology. But, you know, when, when they do occur, of course, you know, the learning capability use advance there. And certainly drones are being used every day and, and all kinds of situations, you know, from, you know, the battlefield to, you know, firefighting and other emergency response as well. The, the other thing I was thinking about was somebody, somebody the other day brought up Rosie from, from the Jetsons.

[00:24:35] And I don't know how, I don't know how far away we are from having the average house have a, an actual, you know, robot, you know, walking around as opposed to just a Roomba or an Alexa. But I do wonder if that will happen in my lifetime. I, I don't think there's any doubt that, that it's going to happen. I think if you take a look at what, what was just, you know, exhibited at CES, where, you know, we talked about Optimus and, and, and its development.

[00:25:05] One of, one of the key demonstrations that they provide there is, is it folding. And I, it's actually, you know, something that we all do, but it's an incredibly difficult task if you try to program a robot to do something like that. And the amount of learning that they have to be done so they can adapt to every different garment being a different, the different case, if you will. And a different starting point in terms of how it's been tumbled out of the dryer.

[00:25:30] It's, it's, it's the, the, the range of dexterity that the machine has to have, the intelligence about creating the folds. It's, it's, it's testbed that is not trivial in terms of what it contributes to the technology overall. Oh, and it, and it, you know, there was, there was, you know, at CES, you know, a lot of, a lot of pictures or, and also around the internet of robots stirring pots is, is, you know, food is being cooked.

[00:25:58] Robots flipping burgers in, in fast food joints. It's, it's coming, but, you know, the, you know, the focus on, on, on, on jobs, which is the, the ones that are drudgery, that are dirty or dangerous. Those are the ones that, that, that robots will be first applied to. No, I hope, I hope that's the case because otherwise, you know, I, I talk a lot about like using AI where you, where you should not wherever you can.

[00:26:26] Right. So let's, let's focus on where it makes the most sense. And I think one of the things that I think about, you know, just going back to like blue collar kinds of jobs. I mean, right now trade jobs are some of the most sort of AI proof, at least for a while, but it doesn't mean you shouldn't understand how some of these technologies are being used.

[00:26:53] I mean, if we just think about technology more broadly, I mean, I just had, I just had the HVAC guy here yesterday. He's got, I got automated texts when he was on his way. He showed up with, with a tablet and these fancy cameras that were able to give me like dozens of pictures of the inside of my HVAC system.

[00:27:15] So he could specifically show me where heat is not being distributed, you know, properly where there's rust building up inside some of the internal components. I mean, imagine your car mechanic had giving you that kind of tour. So you could actually see that he's not making stuff up and fixing stuff that doesn't need to be fixed.

[00:27:35] Right. So, but he was able to navigate the, you know, the, the tablet, everything from, you know, signing up, signing for your, you know, paying for your bill and that type of information to, you know, a tour of, of the equipment to, so you got to know there's all these, even if it's not, I'm not using AI to actually, you know, do my actual, you know, plumbing work or carpentry work or whatever it is.

[00:28:04] There's still all kinds of parts of running a business and operating a business that is using technology. So, so there's no, there's nobody I can think of that isn't affected by technology in some way. And therefore everyone should upskill themselves to, to some degree with, you know, how technology is being integrated more and more into our lives.

[00:28:27] Yeah. And, and this year I'll make a not so bold prediction that then in 2026, the generative AI is going to shift from, from experiment, from the experimental use of it to really a backbone technology.

[00:28:43] And so we need to really treat it as a core operating system, you know, and each one of us, we've got, we've got to learn, we got to, you know, the hands-on approach to North Dakota Farm Boy, get your hands on it. Start using it and plot measurable wins. And as an organization, we have to build flexible teams around that.

[00:29:06] So each component of the team, human or machine, you know, has, has a domain of expertise that, that they contribute to the effort. And critical is we start, don't wait for perfection. And I think companies got to start experimenting this quarter and that will keep us on top of the technology. You know, keep humans in control of the technology. You know, don't let us, let us be overrun by the technology.

[00:29:35] And, and that, that starts, starts with that hands-on approach. I absolutely agree. You're, you're speaking my language. You can be my wingman anytime, dude. So, yeah, I mean, I guess the other thing that I think about is, you know, not just the people that are going to, you know, use it and try to, you know, sort of augment their own capabilities. But the people, you know, perhaps like, like yourself and your clients, like, you know, being responsible by design, right.

[00:30:05] And making sure that we're thinking about these types of things. I mean, I guess in some ways it ties right to the first law of, of robotics, right. Like do no harm to, you know, humans. So, but making sure that there's perhaps added context, right. When we say do no harm, we don't just mean, you know, physical harm. That there's other things to, to consider when it comes to an impact to, to humanity.

[00:30:32] I mean, I don't, it's not about like robots having the autonomy to decide, you know, completely what, what to do. I mean, there's always these human, you know, guardrails and that's why we need to stay in charge. But I just think, I think about anyone who's building anything in the, in the AI space. And that includes, you know, AI empowered, you know, robotics.

[00:30:57] That they think about those things, especially if it becomes something that's mass produced and therefore is available for the average consumer to interact with, you know, directly. Just like when ChatGPT introduced the world or most of the world to. Get ready to turn up the volume on your HR game. I'm Jay Arnold, former musician turned HR leader and hosts a backstage pass to HR Rockstar.

[00:31:25] Each episode pulls back the curtain on key HR topics. From leadership lessons and bold career pivots to the latest in HR tech and talent strategies featuring the rock stars within their industries. If you're ready to shake up the status quo and amplify your impact, this is your all access pass. You can catch the show in the Work Defined Podcast Network and wherever you listen to podcasts. Subscribe now. Let's rock the future of work together. LLMs and Generative AI. Right.

[00:31:51] That made it, you know, by introducing the world to it, that meant the world's got to what we just discussed, got their hands onto it. And that's the way I see this is best handled from an individual professional standpoint. Understand what you need to accomplish as a business with it. Map out what those steps to get there and to get started. Get up close and personal with it.

[00:32:15] And then you start that by, you know, the first build AI literacy. So you have that capability. Corsair has got that, what is it, generative AI for everyone. And then you get that bonding, you get the hands on using a cloud or, or grok or, or any of the industry specific ones.

[00:32:40] If you were in a technology space like a Siemens industrial co-pilot, some of the other ones, get involved, get your hands on, learn that way. And then when you look to adapt to it, you know, this is humans employing it, not, not humans being run over by it. Humans employed by, by looking at, at, at what you need to accomplish primarily as a business and, and look for high impact use cases.

[00:33:07] You know, you could target, you know, robots are in there a lot, you know, so you got hard automation right now. Gen AI is great for describing motions with human language, with, with the English language description of what do you need a machine to accomplish. And Gen AI will convert that into the outlines of the program that are going to drive the machine.

[00:33:32] So now this is, this is, you know, again, not displacing humans. It's making humans much more productive in terms of creating that, that initial lines of code. You know, you know, we all had the, the programming experience where you forgot to put a, a colon in some place or a slash or something. You know, that gets, you know, in a lot of sense, that's the drudgery part.

[00:33:55] You know, what's the, what's the human part is the eventive part of it, figuring out what needs to happen and describing that in a language and having the machine do that drudgery part. You know, you're breaking it down to all those little elements and making sure that all the, the format and, and, and, and line spacing is, is, is, is correct to make, to make it actually work. So that, that's, that's how we could do that. I mean, there's, there's other ones, you know, there's generative designs there.

[00:34:24] There's, you know, where you got to check out all, all little boxes, you know, you know, the, the. Doer's type of things in terms of, is it the least amount of parts in that, or is there some other function that that part can play? All those tests could be done by AI at this point. And back in my, my design days, that, that, that was a part I didn't enjoy. I liked the grand concept, not, not taking apart all the little pieces and seeing if there's any optimization that, that needs to be done.

[00:34:53] That's AI can, can help out in that area of drudgery as well. Yeah, if we can take some of that off our plates, there's other things, there's better things that us humans could be doing. Yeah, you had me having a little flashback there to my computer science class in, in college and just had to be so, so detail oriented with every little, every character mattered, right? If one, one little thing could throw it off.

[00:35:20] And nowadays, if something didn't run, program didn't run properly, just ask AI, go fix this. Yeah, I go all the way back to, to, you know, programming in my collegiate days was stack of punch cards. Okay. You know, and the, and the fear that you're going to get, you know, inadvertently shuffled them incorrectly. Exactly. Oh yeah. That, that, that, that, that's a, you know, it scarred me for life, I think. Yeah.

[00:35:50] I mean, a little, a little before my time, but I spent quite a bit of time at IBM headquarters where they had all of that old equipment, basically a museum and headquarters here in Westchester. Yeah. Now that was looked at as very high tech at the point, at that point in time, but in retrospect, we identify it for the drudgery that it was.

[00:36:10] So, you know, I think we have to have a similarly flexible mindset when looking at the application of AI and how this technology will, will be used and evolved. And, and I believe will very much make human life better on balance. I agree. There will be dislocations, but, but. Yeah.

[00:36:32] I think it also, we've just got to stay on this like responsible and human centric path, right? Because there's always going to be, you know, bad actors or people looking for, you know, a quick buck first mover advantage or whatever, who are just throwing stuff out there because the technology is capable of doing it without thinking, without, you know, using, you know, first principles and without thinking about, you know, what's, what's best, you know, for people.

[00:37:00] Am I doing this for people or am I doing this to people? Right. So I think that's one of the fundamental questions, but even the concept of, of human centricity is, can go in many different directions. It's, you know, obviously about, you know, fairness and bias, you know, mitigation and things like that, but also long-term impacts of central, you know, job displacement.

[00:37:22] And then thinking about your own, just your own sense of, of identity and who you are and where you derive meaning and your own sort of, you know, wellness and all these, you know, sort of very human, you know, attributes and characteristics. And we can't, we can't, we can't lose that.

[00:37:42] So we've just got to, you know, I guess, hold each other accountable as we move forward and make sure that we have that foundation of, of trust as we continue to advance technology as technology has always advanced. Yeah. Yeah. Certainly that's the case. The, you know, humankind is, is, you know, greatly benefited from the advance of technology.

[00:38:03] You know, then, you know, today, you know, we, there is not a, a regal person in the middle ages that had the, the benefits that the most normal person has today, you know, in terms of air conditioning and heating and, and, you know, internal, you know, plumbing and everything else. So, and that was a technological advance from then till now. So what has driven the most rapid technology advances is, you know, the competitive forces in business.

[00:38:33] So I don't think we can mute those competitive forces either. You know, we have, we have to recognize, you know, their contribution, you know, to, to our wellbeing at the same time as you, you so broadly pointed out earlier, you know, make sure that the ethical boundaries are clear. The guideposts are in place.

[00:38:56] The guardrails are also there to, to, you know, steer the error to detect and steer the error back on a, on a, on a path. Yeah. So when I take a look at companies that are making this and looking at this investment to improve their competitiveness, I think that's a net, net benefit for mankind, for our society. That, that, that, that will drive us forward on a competitive basis.

[00:39:25] That, that there's some real positive contributions from that. There's always some, some of the negative dislocations that will take place. But if we take a look at different systems, that, that has been the most productive for mankind in the world.

[00:39:40] Yeah, no, I totally agree. And I, I just hope, I don't want, I don't want this to turn into any kind of, you know, sort of political statement, but we just, we just need the right people with the right decision-making authority to, to listen to, you know, diverse voices when it comes to the impact of all of this.

[00:40:05] The impact on, the impact on, on humanity, the impact of course, on our general sort of national competitiveness, not just, you know, company to company, but how is the U S, you know, being, being selfish since we're both Americans. You know, how, how are we, how are we doing, how are we educating people to build the skills of tomorrow? How are we building the technology that's going to take us through the next century and beyond?

[00:40:30] And how are we investing in the right places along with the right sort of guardrails and, and regulations so that people stay safe? And that it's, that it's basically put forth with this, this human centricity as an underpinning of, of the premise and why these rules exist. Yeah. There's some internal tension in what you just described there in terms of keeping our society on the forefront, but the forefront is where risks are.

[00:41:00] And so, you know, balancing, you know, that, that societally is, is really, you know, one of, one of the things that's, that's going to be determined of success or, or less than success. So we have to continue to take risks as a society, all the components of society, companies have to continue to take risks to, to advance.

[00:41:29] As soon as they become complacent or stay, you know, the, you know, the world will bypass them. So we, we can't be either complacent or, or, or, or stuck in our, in the current. And so company, company's got to move forward. And one of the things that, you know, it sounds like drudgery, but it turns to be foundational.

[00:41:50] The companies, when they think about converting, not converting, but taking advantage of AI and its convergence with the technology, humanoid robots, what they're going to find is, is that the, there's a very much of a foundational element that everybody gets a little bit bored by. And that's the company's data.

[00:42:09] You got to develop a data first mindset and, and you got to invest in, in cleaning and, in, in prioritizing data as a company and making sure that, that accuracy is, you know, the primary goal, not the bulk data. Make sure that all the data that you're preparing is highly accurate and then expand from that point.

[00:42:36] That, that's what, you know, data drives AI. That's, you know, certainly the, the, the cases that we can talk about with AI hallucinations, often you can point back to some critical data element that, that, that, you know, the corrupted its directions of headache. So. Yeah. I'm glad you brought that up, David, because that came up, I mean, it comes up a lot and some people just don't necessarily think too deeply about that just because they,

[00:43:05] I don't know, I guess when it comes to the user interaction, that seems a couple of degrees removed from them. But, but this is why we tell you to don't take AI's output at base value because you don't know how it was trained and you don't know. It might sound confident in its response, but that doesn't make it any more true. You know, it, it's always very confident in its response, you know, generically. It is not always correct.

[00:43:36] And, and, and it does, you know, if it can't find in its models some particular piece of data, you know, AI has proven its abilities to invent. Yeah. That information. So. Yeah. Someone, someone brought, made a comment like, you know, enterprises haven't, most enterprises haven't adopted AI because there's an obvious hallucination problem. Right.

[00:44:01] And I was like, well, that's a little bit of a naive statement. I mean, you're sort of conflating two, two things, right? Like, yes, AI hallucinates, it hallucinates often, but for an enterprise to truly put something into production, they have, if not eliminated, but drastically mitigated the likelihood of it hallucinating.

[00:44:25] And if they did it properly, they would have human oversight over anything that feeds into any kind of decision making or a decision support system. Right. So, so I just thought it was a little bit of a naive, you know, comment. I'm not saying it's not true, but ideally enterprises before they, or as they built their AI strategy, they think about all the components of that strategy. What do we have the talent with, with the right skills?

[00:44:55] Do we have AI and automation at least lined up to, you know, take on certain, you know, tasks and activities? And have we, you know, streamlined some of our processes before we tried to, you know, automate them or stick AI agents in there? And what is the status of our data? Because all bets are off if our data is terrible. I mean, that's, that, that, that will, that's the breeding ground for, for AI hallucination is, is, is, is bad data.

[00:45:23] But the, you know, if we take a look at where it's been successfully prior in, in, in, you know, measurably. So, you know, some of the supply chain planning and scheduling algorithms, you know, have got great results. So, I mean, an AI can, you know, keep, you know, 10,000 data points clearly in focus. Humans are not so good at that. Right, right. And so, so they can, they can, they can see patterns that, that, that are not visible to us.

[00:45:53] And they can detect alternative paths through mazes that, that we may, may not have the, all the combinations and permutations they're making way through there and have the ability to conceive every one of them. They can test each and they can choose the best and the optimum. And then they've been proven to be tremendously powerful in that space, in that application space. And it, it, it, it's uses in business is going to expand from there. Yeah. Has will be expanding from there.

[00:46:22] David, this has been a fascinating discussion. Thank you so much for spending so many of your valuable time with me. No, this is, this is great. I really, I really wanted people to get a sense of that AI. Yes, it's, it's powerful, but you have, you know, to take control of your own sort of destiny. And as you sort of navigate your, your career path and your skills growth, you've got to really think about how you're going to work with AI, not against AI.

[00:46:52] And I also wanted to make it clear that AI isn't necessarily existing in isolation, that it can be sort of married with other advanced technologies to take things in a different direction, to solve new, you know, problems. But also in some cases, you know, accelerate the advancement of some of the things that we're seeing, you know, personally and professionally.

[00:47:16] So, so thank you for opening our eyes to some of the impacts of that and how that all, that all works. So it's been great. I've enjoyed it. Let's keep it optimistic and keep on focusing on, you know, what's the best for, for humankind. Absolutely. I am a cautious optimist. I'm an enthusiastic optimist, so. Oh, there you go. Even better. Awesome, David. Well, thank you again for your time. Really appreciate it. And thank you everyone for listening. We will see you next time.