AI’s Impact on Society, Business, and Regulation | Utilizing AI Podcast Ep. 3

Everybody's trying to figure out how AI fits into the political, social, uh, individual. And of course, it landscape people are trying to get out ahead of it. Uh, people are, some people are trying to step out of the way and, and see what develops.

That's what we're talking about on this episode of utilizing ai. Welcome to utilizing ai, the podcast focused on practical applications of artificial intelligence from the Futurum group. Each episode brings together a diverse perspectives to explore news and use cases in the way in which AI is transforming enterprise IT and the industries it serves.

I'm your host, Stephen Foskett, president of the Tech Field, a business unit here at the Futurum Group. Before we dive into the discussion, let's meet who's on the panel today. Hi, I'm Nick Patience.

I'm the AI practice lead, um, at Futurum Research. Hey everybody. I'm Mike Baard, chief Content Officer for the Textron Group when we publish Textron AI among other things.

Absolutely. And, um, you know, Mike, uh, you and I are on the Textron Gang quite often talking about, uh, news and what's going on in the industry. Um, and of course, AI is just everywhere.

It's become, uh, front page news for basically everything that's happening in the world today. Uh, Nick, uh, let's kick things off by talking a little bit about the ways in which governments are trying to get involved in ai. Sure.

Yeah. I think it's been apparent, um, for a long time that AI will be a regulated industry of some, uh, uh, to some extent, to a large extent or another. It already is in, in, obviously in in China, uh, to a much greater extent.

But one thing that kind of caught my eye, um, was, uh, sort of talk here in, in Europe. Uh, I'm in London, but you know, we still consider ourselves part of Europe. Um, the, the, um, European Commission is thinking of watering down somewhat.

The EU AI Act, which has already passed, hasn't been fully implemented in every, every, uh, every country yet, or all the 27 countries. But they're talking about that, and it, you know, there was a story, I think it was in the FT originally, and they were, they were saying about, you know, the commissions come under a bit of pressure from big tech companies, um, not surprisingly. Um, and they're talking about having grace year long grace periods before certain aspects have to be, uh, implemented.

Those you, you may remember, has a kind of, um, a risk hierarchy of like, you know, unacceptable risk, high risk, medium risk, low risk types of, uh, applications, and low risk would be spam filters. So everybody uses it, no problem at all. High risk and things like that would be, um, you know, facial recognition and unacceptable risk things, facial recognition.

So it's, it's an interesting if that's, if that's gonna happen. I mean, there's been a lot of talk over the years when the EU was trying to figure this out, that they were trying to, um, put the cart before the horse somewhat and regulate something that hadn't come out yet. In fact, just before they, they passed it, um, you know, or they were, they were negotiating chat, GPT came out, and that caused 'em then to rewrite drafts.

Um, you know, we've better to get this, this generative AI stuff in there. Um, and so they were trying to react to that. And now of course, you know, we've got a gen coming up, uh, and, and those kind of things.

But if for enterprises, for those that, you know, this is utilizing ai, so it's, you know, it, it could be relatively good news. Uh, and obviously this is not just EU headquartered companies. This is companies doing business within the eu.

Um, so it could help them, it could, it could help enterprises, you know, reduce their, uh, immediate term compliance needs and compliance costs, uh, and things like that. So it's a, it's something we'll, uh, I'll certainly, uh, be keeping my eye on because, you know, compliance and sovereignty and, and all these kind of things are, are really, really key issues in ai. You know, I'm having a hard time wrapping my head around this whole thing, and I'm hoping you can gimme some insights here, because on this side of the pond, it kind of looks like, well, you know, the EU for some folks was gonna save us from ourselves and institute all these rules.

But there's also people who say maybe there is a legitimate case being made here for not having so many prescriptive rules so early. And other folks, of course would say, you know, those folks in Brussels will just overregulate everything, and they're basically gonna be like, you know, just standing in the way. So what is the mood over there?

I think the, you know, especially in, you know, I may be in London, but especially in continental Europe, it is quite a fundamentally different way of looking at things. Um, and certainly on the data privacy issues, that's where obviously GDPR, which came into that, came into, um, effect in 2018. Um, you know, those kind of data privacy issues, privacy, privacy, call it what you will, um, is extremely embedded in the culture.

And so it, it does seem a little bit, um, weird, I think to, to, to, to folks in North America that, that kind of obsession. But, you know, these things go back with a, a long and a somewhat troubled history. But, uh, I think it's, you know, I think there is a, certainly a case to be saying they were trying to, they were, you know, as I said, what the cart before the horse, the flip side of saying that is, I think there was a 75 year gap between the Model T Ford, um, the first model T Fords being shipped and seat belts being mandatory in the us, you know, and obviously hundreds of thousands of people died in car accidents between, you know, the, the first and the second thing.

This is not the same thing. You know, I'm definitely not on the kind of, um, munging, um, AI is gonna kill us all, um, side of the argument. I think there is certainly though, um, you know, data privacy issues that are look completely legitimate about, um, about, you know what, it's not so much what companies collect because I think people that kind of cat out the bag.

It's, it's whether the government can have access through back doors, um, you know, to your, to, to your data. And I think that we're seeing, we're seeing a lot of, uh, issues with that, you know, in here in the UK with Apple, um, you know, and, and the government, you know, having, negotiating with them on that. Um, but the, but the, uh, the EU act is, is pretty, is pretty broad, and to say it's very sort of risk focused.

Well, the, on the GDPR front, that's a really interesting point that Mike makes though. Um, how do they square that on that side of the pond, uh, with all of the intense data collection that's gone into the building of these AI models and the kind of data collection that's gonna be feeding these AI applications in the future. Um, are they expected to, uh, abide by GDPR rules?

Or is it sort of like a, eh, well, except ai, I think, except, yeah, I mean, LLMs and, and how they vacuum up stuff on the web, I, I guess a lot of that doesn't really count as, um, data that's private. So, you know, vacuuming up publicly available data is, is, uh, is not really covered by it. Um, and so I think it's from that point of view, while some people worry about it, um, it's also quite difficult to figure out obviously, what the training sets were or are because, um, yeah, the companies are not exactly particularly transparent in, in, in what they're doing.

Um, so I think it's, I think that doesn't, the LLM kind of aspect how LLMs were built originally, um, isn't so much of a, of a, of a GDR issue. Um, but, but yeah, it certainly, and there hasn't been, let's face it, a huge amount of massive, um, cases. You know, it hasn't been all these kind of, because obviously there's, there's, uh, revenue, there's, you, you can be fined up to a certain percentage of revenue, and there hasn't been these kind of, you know, 300, 400 billion Euro fines, um, um, administered.

So you could argue that, um, you could argue that it's doing its job because that has not, or you could argue that it wasn't quite as needed as, um, as some in, uh, Europe thought it might be. Yeah, it's pretty surprising really that there hasn't been more cases, because of course, people have, um, legitimate concerns about the kind of data that, uh, AI is, is vacuuming up. But I guess, uh, that kind of leads to this whole question.

Is AI a different animal from all other IT applications, or is this just a wild west still that hasn't been regulated? Um, do, do you think that AI is, um, fundamentally a different animal and needs different types of supervision? I think the, If you kind of contrast to something like, you know, just a relational database, um, that's obviously, you know, where a lot of this data is stored.

We don't have, um, relational database legislation. Uh, but that's, but that's partly because that's a very, um, you know, that takes humans to put the stuff in there. It's structured and, you know, and findable, et cetera, et cetera.

I think there is something a bit different about AI in the sense that it's, um, you know, it's obviously, you know, if you think about the phrase machine learning, it learns, it adapts, it evolves. And that's where, you know, the, that's where the difference comes. So we've gone from kind of, um, you know, just sort of predictive models that, that they're gonna predict an outcome, um, based on a kind of fixed data set to things that are evolving using enormous, um, training sets that, you know, we're not sure what's in them.

And so, you know, and as they change and, and evolve and models drift and model decay, you know, I think it then becomes issues around explainability become very hard. You know, how can you sit across from your, if you are, you know, if you're a bank, how can you sit across from your regulator and explain exactly how every single uh, decision that you are you are using AI for was made? You can't.

Um, so I think it, I think it is, I think it is different. The volumes of data are just, just so much larger, um, than, than anything else. And I think it's just been, been evolving as we've gone from machine learning predictive models to generative AI to, you know, eventually agent, um, and things like that.

So I think, I think it is a bit, it is a bit different. I mean, you haven't had kind of, um, you know, I mean, you have regulations around search engines, but not for the reasons of, you know, necessarily, you know, what they're sucking in. It's more like yeah, monopolies and, and things like that.

I think the regulations may play out a little bit differently too, rather than having some overarching set of rules, I think you're gonna see, like the regulations that apply to various vertical industries will get tightened around ai, and we'll see extensions to whatever rules we have in place for finance, whatever rules we have for healthcare, as those people get a better understanding of how AI is applied. And that may be more effective ultimately, because each vertical use case is different anyway. Yeah.

As suppose. Yeah. But it, it is interesting to think though that, um, you know, for example, in the copyright world, um, there's been a lot of trouble with, uh, generative ai, um, violating copyright on a wide scale, essentially being able to regurgitate, I don't remember what the, what it was, it was like 87% of Harry Potter or something like that when prompted to, and I've seen similar situations where it has, um, generated, um, specific photos from, um, you know, registered photos from photographers and things like that.

Um, if given the right prompts, so that data is in there. Um, I wonder if there's personally identifiable information that it could be coerced into, uh, spitting out, even though, uh, Nick, as you say it, it's theoretically only been trained on public data, but, uh, I think maybe some of our readers might be yelling, or listeners may be yelling at their phone right now to say, no, it's not just public data, because I think people have a suspicion that it goes well beyond what it should have been trained on. Yeah.

I think people also are, um, amazingly willing to put public information on the, on the, on the web about themselves. So, I mean, this is not just sort of LinkedIn things, but, but o but other things that, that, you know, you could piece together. So I think it's, uh, yeah, I think, I think you're right.

The copyright stuff is interesting. Obviously they've, there's been a lot, bunch of lawsuits and there's been some settled, um, for, for publishers, you know, when, when the, some of the, the, the early, um, LLMs vacuumed up, um, you know, hundreds of thousands of books, um, which are, you know, some were under copyright, some weren't. Um, but, uh, yeah, and there's been some settlements in, in that regard.

And then you've seen all the, the big publishing companies, you know, you know, New York Times and BBC and all this have kind of agreements, um, with, with some of these LMS that they can or cannot, uh, use their, use their data for purposes of training. So I think it was, it was very much in the early days, and by the early days, we of course mean, uh, just under three years ago, um, you know, extremely wild west, like, and then, you know, within a few months it, um, there was, there was some, you know, uh, some, some, some, some agreements put in place, but they're very much kind of one-on-one. You know, this is not, you know, legislation, uh, uh, based this is just, uh, you know, companies settling or, or not, as the case may be.

Mm. I don't know how feasible it is, but I always thought about it this way. I mean, they may have the data, but the issue in my mind at least is that they enable people to use the prompts to tease that data out.

So if we were better at maybe putting the guardrails in place around the prompts so that people couldn't tease out Harry Potter, then maybe we wouldn't have a crime in the first place, right? Yeah, I mean, that's Unfortunately, yeah, that's easier said than done. Yeah.

Yeah. I think it's kind of, because the generative AI has fundamentally changes the nature of security cybersecurity, doesn't it? Because you can't, um, whereas previously with machine learning, uh, you, you could control the input 'cause the input was, you know, your bank, you know, what the input into it is gonna be.

And there's no point with a kind of a decision tree chat bot and trying to manipulate it into, say, into swearing or saying something, you know, nasty. Because all it'll do is, you know, do you know your account number, date of birth, et cetera, et cetera. It's not gonna tell you anything else.

Not gonna tell you Harry Potter, there's no point. And, but now with generative AI in the, in the chat box, you know, you can't, you know, as the owner of it, you can't really control the input. People will, will, you know, put prompts in, um, you know, with, you know, trying to trick the things and, and put code in there and stuff like that.

And then of course, you can't control the output so much. So it does change the, you know, the threat vectors as, uh, cybersecurity professionals, uh, like to say, Which I guess begs the question, is AI going to be an IT application or is it going to be, again, back to my original state, is this thing a di fundamentally different animal than the other types of applications and, and, uh, data sources that we've relied on in IT for, for many years? Do you see it differently?

I see it in this regard. Um, and people are talking about that here in Atlanta, CubeCon, but there's a difference between training and inference, right? And there's training, that is one thing.

And then the inference side of it seems to be moving back towards IT as a deployment model, and they're managing the infrastructure. And Nick, I don't know if you see it differently, but in my mind, a lot of this sovereign cloud conversation that's happening in Europe is very much tied to the fact that these AI workloads need to be centrally managed, secured, and protected. And all of this seems to me, screams we need some adult supervision from the IT folks.

Yeah, I think it's, it's interesting as to whether it's different. Uh, I think the, I think there's certainly shiftings, uh, there's been shifts happening, uh, towards the, the, the buyer changing, obviously as, as there used to be. I didn't like the cliche of every company becoming a technology company a few years ago.

I thought it was way overblown. Um, but I don't really think it is anymore because I think, I think, uh, you know, any, any sizable company, um, everybody has the ability to, to use, uh, gen ai. Obviously there's, you know, certain things are locked down, um, but they've got other, other devices connected the internet, um, that can, couldn't do things.

And I think, but I think the, you know, the kind of vibe coding shift, no code tools and things like that, um, and obviously the gen AI coding tools enable, you know, so many people to be able to, you know, develop a small app, micro apps and things like that. I think it is shifting the buyer pattern. Um, so we are getting more line of businesses, uh, involved, young line of business people, by which we mean, I always, it's, again, it's a funny expression.

It means everybody other than the IT department. So in most companies, that's, that's virtually everybody. And so you're getting, yeah, many, many, many more inputs into it.

Um, certainly in the early days of, of Gen ai and now in the early days of agen ai, you're getting those people who are driving the experimentation. The issue comes is when we move from experimentation to implementation, and that's where, um, I think it, um, you know, sort of pulls everything back in to a certain extent. Doesn't matter if it's in cloud, on-prem, hybrid, anything, um, and, um, and, and get, and gets heavily involved.

So I don't think it's, um, you know, like that famous, uh, essay that was wrong 20 years ago, 20 plus years ago. It doesn't matter. I don't think that's that's the case at all.

But I do think there's gonna be many more people involved in the kind of, um, you know, the top of the funnel stuff in terms of the buying, the buying patterns. I, I think one of the biggest challenges with AI is just, you know, it, it is a very different type of application. Um, it, it works very differently from the way that it is useful.

It tends to be very reductionist in its thinking. It tends to say, you know, here's the data, here's the network, here's the servers, here's the application. You know, really lining things up along conventional ways.

I don't know that AI applications really align nicely to that old way of thinking. I mean, in many ways it's very similar to the transition to personal computers, the transition to mobile, the transition to cloud to, um, as a service. You know, AI just takes that to another level in terms of, um, we don't really know what it is.

We don't know really know where it is. We don't really know what data it's using, maybe. Um, and that makes it, I guess, a little less amenable to the conventional IT mindset Maybe.

But I would argue that a lot of these, you know, business apps that are gonna be built by end users using natural language will suffer the same issues we saw with low code, no code, right? We're gonna get a lot of ugly applications, they're gonna be insecure, and they won't scale, and that's when they're gonna call for the IT folks. So IT folks, then they're gonna show up and, and say, well, here's how you build this.

So I don't, maybe they're not, and Somebody's gonna know how to screw these things together and, and, you know, not gonna be the business units. There's gonna be, but there's gonna be a whole beautiful agent orchestration layer that we just haven't seen yet. Um, certainly load of load of, uh, every vendor of any size is coming out with those kind of things.

Um, which is, I mean, I guess, you know, I, I, I know what you mean. I think the, the EENT stuff, if it does come off is, uh, again, a shift. And when these kind of shifts happen, um, it, it's like, it's not like everybody, everybody stops what they're doing before and moves to the new thing.

It is a, it is an evolution. Um, and I think it would be, it'd be, it'd be foolish of us to kind of think that, you know, the, you know, the ENT stuff is not gonna happen to some degree or another. This is not robotic process automation where you wrote a script, um, but a human wrote a script and just tell a computer to go off and just keep doing the same thing over and over again.

This is where, you know, with, with, with the agents, um, which I think there definitely will have to be, um, orchestration frameworks, which will have to be sanctioned by it, and that will happen because yeah, if you think it's, if you think it's kind of scary, um, having users type type things, imagine if you've got other applications deciding whether our applications, what to build and building their own applications. And, you know, you kind of got the, uh, the ultimate, um, insider threat, um, there from a cybersecurity point of view. So I think, yeah, I think it, um, yeah, it will always be around and always be a, a role for it.

I think the, it is just, uh, I think there's a lot, you know, just expanding the footprint of people who, um, consider themselves users of, uh, of, of, of technology in some cases fairly advanced technology. And I liken it to, you know, I could diagnose my own issues, right? I'm using chat GT to figure out what's ailing me, but I'm probably better off if I get a professional to tell me to.

Yeah, I had an interesting conversation, uh, last week with a company, uh, SE four AI about how they're trying to do agentic applications. And, and their idea was that instead of using a conventional approach, um, you know, or instead of trying to build prompt engineering that we would use runbooks essentially document, uh, the, the, the state document, the process, document what you're trying to get out of it, throw that into the, uh, AI model and see what the AI model can build that achieves your goals. I, I felt like that was actually a pretty interesting insight, not because it was such a novel approach.

I mean, we've been using runbooks in it for decades, but because it actually kind of meets the users where they are and where they need to be in terms of interacting with these applications, um, Do, Do you think that, uh, that signals that AI is not going to be an it, a conventional IT application? I certainly think if that kind of stuff comes past, and you, and you're right, obviously in IT itself, we've been doing it and Red Hat with an Ansible is a good example, um, and how they've added a AI to, to Ansible Runbooks. And I think they've done a really, a really doing some really interesting stuff, but it's only to say within that domain, I think, I think yes, I think there will be, um, if you mean now, are there gonna be ways that people using natural language will be able to describe what they want and have something get built and go off and do it?

Yeah, I think there will be, and I think, but there will obviously be guardrails where you hope there would be, um, that says, you know, you know, I'm, you know, almost effectively, I'm sorry, Dave, I can't do that. Um, but it's, but it's gonna be, those, those kind of things are, are, are gonna be in play. But I think, yeah, I think it opens up, I think no code has opens up the, the i the idea of building apps to, to, to so many other people.

No code has been a bit of a disappointment, but I think this is slightly different, you know, with natural language interfaces. I'm not sure the runbook is the right means for managing all this in the future. And, and I say this because the application environments themselves are gonna be much more dynamic.

They're gonna be a lot more applications built, and they're gonna be updated more frequently. And I think the runbook is a little bit of a more of a static concept. So I'm wondering at some point, you know, I a it will be AI agents, I'm just not sure they're gonna be invoking some sort of runbook that a human created as much as maybe there's some other means for managing it that is equally dynamic as the environment.

Well, you've been in this space forever, uh, and you're there at CubeCon, you know, you're looking at all this cloud native stuff. Um, what do you think, um, I, I know it's probably too much to ask, you know, what's the answer, man? What, what do you think of the direction that, uh, the IT industry is heading in terms of getting their hands around this?

I think everybody is betting heavily on AI agents being able, providing the ability to scale, because part of our issue has always been we didn't have enough people to manage the applications, so we didn't deploy as many as we might have possibly could. And everybody's got this huge backlog of applications they theoretically wanna build and deploy. But the, the limiting factor has always been, well, where do I get the software engineers and the IT people to manage all this?

So if we can manage all this stuff at scale using AI agents, that will be great. I just don't think we should abandon first principles of good software engineering because we have a bunch of AI agents out there. I think we need to think that through, make sure those AI agents are trained and AI agents are voracious and they will do things, you know, unless you specifically tell them not to.

And if that's the case, then somebody's gotta sit there and orchestrate and manage and, you know, take care of this army of AI agents. So I just think the future of it is, you know, humans plus AI agents, and there may be thousands of those AI agents, but it requires somebody who actually understands what the objective is of the army. Oh, Yeah.

I think it's, I think you're right, Mike. I think there's no, as, uh, it's more important than ever to not abandon the, the, the principles of software engineering. I think it's, uh, because of, because of the kind of force multiplier effect of all this, of agents calling agents, calling agents, you know, there has to be, you know, that that kind of, uh, discipline in place otherwise, you know, truly chaos, uh, will rain As one way one said to me, you know, it's one thing to be wrong, it's another thing to be wrong at scale.

Well, and that's, that's, you know, kind of bring this conversation around full circle then, um, you know, I guess the question is, do we want to risk getting our cart in front of the horse like the EU may have done with their AI regulations? Do we want to be very, uh, reactive like it has often been in the past? And, um, or, or do we wanna somehow strike a balance in terms of getting our hands around the growth of this technology and, and what does that mean to business users?

So, uh, that's a lot. Um, Mike, what do you think? Do we wanna get in, get out?

Should it get out in front of this? Or is the eu, did they make a mistake by trying to get out in front of it before chat GPT was even launched? Yeah, I think there's a big cultural divide here that you're poking at, right?

Because over in the valley that they'll say things like, go fast and break things, but you know, they don't actually manage and do anything. They're just providing the tech. And for folks who are, um, you know, I'll just put a, a, you know, a small little simple example in place, but, you know, if you're manufacturing yogurt and suddenly the AI is making, you know, 50,000 gallons of yogurt that you got no place to ship to, it's a problem.

And I don't think people will wanna see that. And I think business leaders are gonna be one saying, you know, well, hold on there folks. You know, this is real money we're talking about.

Yeah. I think even though AI is, um, is a general purpose technology like electricity is or was, it certainly does. I think it's, I think it's, um, it will have to come under the purview.

You know, again, we always make mistake, Don, we've, every time there's a new wave of, oh, we gotta get some of that new wave, rather than what is the problem you're trying to solve and work back to backwards towards the technology? It happens every, every single time. Um, you happen with cloud happen, mobile happen happened with Java in the nineties.

Um, and so I think, um, and now look at that, you know, you can consider a lot of these things like Java and cloud and mobile, just, just table stakes. And I think AI will get like that. I do think that, um, there is an old joke in ai, and I, I may even used it before on this podcast, that, you know, that AI is whatever hasn't been invented yet.

And so people, you know, there's a kind of, you know, you, you have a problem to solve, use ai, and, and, and then it gets implemented, rolled out and becomes commonplace. And everybody goes, that's not ai, that's just software, or, well, it is still, but you just don't think it like that. And then AI is trying to solve the next problem along.

So I think it is, um, I think we're gonna see a, you know, a long, um, you know, evolution and constant, um, you know, and constant, uh, innovation, um, for, for, you know, decades to come, I think. But it will have to serve, serve the business, otherwise, um, it won't, it won't be much use to anybody. I'm laughing to myself now.

I'm the days when Wang Labs document. Exactly. Yeah.

Technology is, uh, anything that was invented since you were born? Uh, I've heard that one. Um, yeah, it, well, I, I will say too that I suspect in answer to kind of this whole question, I suspect that there's going to be sort of a bifurcation in terms from governments, from companies, from consumers, and, um, you know, each of us individually, uh, some of us are gonna try to get out ahead of it and stand in the way.

Some of us are gonna be, take a very passive approach. Uh, some of us are going to adopt aggressively, some of us are going to adopt conservatively. And, uh, ultimately I think that what's gonna win the day is the practical benefits that we get from this technology or not, and that will spell whether this is going to have the impact that we expect it will.

Uh, I think that's a theme that we've heard, uh, uh, the, each of the episodes so far of, uh, utilizing ai. And I suspect that we're gonna continue to hear that, that, you know, really at the end of the day, um, I, I was, I was talking to Daniel Newman this morning, and, uh, one of the things that he and I were talking about was the fact that ultimately, um, the success is it's all, is, is the barometer of success. In other words, ultimately if you're selling, if uh, customers are buying what you're selling, then you know, you have a success.

And there's really no other metric for success beyond success. And that's, I think, gonna spell what's gonna happen with AI as well. I think there's different forms of ai.

When I think about gen AI in particular, I, I kind of think you should work backwards from the business process. So if the process itself needs to be done the same way every time, then maybe it doesn't lend itself to Gen ai, which never does anything the same way twice. But if the thing is, you know, I don't know, creating a marketing newsletter or something where it doesn't really matter if it's done the same way every time, then gen AI might be perfect.

And I think there's a spectrum of those things and people need to kind of sit down and figure it out. Yeah, exactly. Right.

Um, Mike, there's, you know, hallucination might, might have, might get a bad name, but in, in creative amongst creative professionals, that's exactly what you want. Um, you know, so, so you want it to hallucinate if you say, if you're doing, um, yeah, marketing collateral, but you also want to, if you just want to resize, um, 50 bits of marketing collateral to 50 different sizes, so they, they work differently in format, you don't need a whole load of creativity in that process. You just want it to be correct.

And so, yeah, I think there's, there's gonna be, um, yeah, there's gonna, there's gonna be, you know, use cases for all kinds of AI and, um, obviously non-AI technology And the, and the risk level on that newsletter going out being wrong is minimal, right? I might be embarrassed a lot. The company's not gonna go under, you know what I'm saying?

Uh, thanks a lot for joining us on this episode of utilizing ai. Uh, Nick, it's great to see you again. Uh, Mike, welcome to this, uh, exciting little circus here that we're gonna be doing every Wednesday for our listeners, uh, before we go, Mike, um, give us a little pitch.

Where can we continue the conversation with you and where can people find your content? ai, and that website is also one of a series. com cloud native now, security Boulevard, and we also have, uh, tech Strong it, and they all have an amazing amount of AI content because well, AI's everywhere And Nick.

Sure. Yeah. com.

Um, you can find me on, uh, Twitter X at at nick Patience. Um, and so yeah, we're con constantly looking at this space and, and updating all the research for our clients. Yep.

And as for me, uh, yeah, you'll find me at s Foskett on most social media networks. Um, I'm on Textron Gang pretty much every Tuesday, though, not this week because I was traveling. And, uh, also of course, uh, you'll find me here at, uh, utilizing ai.

Thanks everyone for listening to this episode of the Utilizing AI podcast. If you enjoyed this episode, uh, please let us know. Drop us a line, uh, also maybe subscribe.

You'll find us on YouTube or your favorite podcast application. Uh, this podcast is brought to you by the analysts and experts from the RUM Group where Insight meets ai. For show notes and more episodes, head over to Textron ai, uh, the utilizing AI YouTube channel or textron's TV app.

Thanks for listening, and we will catch you next week.