MSP Dumpster Fire Season 1 Episode 6 - AI in Jobs

Welcome to the MSP Dumpster Fire Series. This is Season 1: The Impact AI Will Have on Your MSP. Welcome to the MSP Dumpster Fire Series, Season 1: The Impact AI Will Have on Your MSP. Today is episode 6, or AI in Jobs, and I'm joined once again by my esteemed colleague and friend Alex Farley. Alex.

Hey Rob, how you doing today?

I'm peachy, man.

Living the dream, right? This, this AI nightmare. Let's, uh, let's dive in and talk about AI taking everybody's job, right? Is that where we're going today?

I think so. It's going to completely take over the world. I've got nothing to do. Other than to find a quiet place on the beach to lay down and do nothing.

There you go.

No, no, no, that's not right.

And have some AI bring me a drink. Like, it sounds like happening.

Maybe, maybe, maybe, maybe. Yeah, like it's where, you know, if you put a little extra engineering time on a Roomba, I'm sure it'll get us there, right?

I feel like we're not that far off.

It feels that way.

So we kid, right? We joke about it, but AI jobs are way different than they were just 3 or 4 years ago. And I think that's what we want to dive into today and talk about some of the differences that we're seeing, some of the new things that are going on. I got a buddy who I went to grade school with who has been a machine learning engineer for Verizon for years. But, and, you know, we talked episodes ago about the differences between ML and AI and some of these generative AIs and some of these other things. We're going to talk about some of the newer jobs, some of the things that have really changed in this field today. Right.

Absolutely, Alex. And I think not only about specific jobs within the AI space, and we'll, you know, we'll touch on, I think, some of the bigger ones. I'm sure there's probably more that are out there that are cropping up every day, but also how AI is changing our jobs as MSPs, as well as affecting the jobs of those people who we support, our clients, right? Be they, you know, senior people there or junior people there or really anybody there.

Yeah, and it seems like no matter what job skill you're in or what role you're in, AI is going to show up in one of your tools somewhere, whether you're ready for it or not. Um, you know, when we first started seeing this stuff come out, it was like, well, you know, we can just block ChatGPT at the firewall, do some DNS and make it go away. And, uh, and then it turned up in Office, and then it turned up in Canva, and then it turned up in every other tool on your desk. And now it's, it's one of those things where it's really, really hard to block and maintain and monitor that. So this gets into something that we'll probably get into in the next episode where we talk about policies and procedures and all the things that butt up against how to manage AI in your business. But now, you know, with it in every tool that we use, it's kind of important that we learn and understand it and that we get better at dealing with it.

Yeah. Resistance is futile, as the Borg say. So there's your AI reference.

Yeah.

Yes, at least the sci-fi portion of it. But in all seriousness, I mean, resistance is futile. Like, it really is just sort of ridiculous to say, I'm not going to embrace this. Imagine any business in the early 2000s, a small business in particular, saying, oh, like, you know, computers, not in my business. We're going to stick with pen and paper. You know, imagine where they might be today. All right, maybe somebody's got an example somewhere that, you know, it's better or it's possible to run a business that way. But, you know, we're talking about such extreme edge cases to resist AI and to not put any focus on how it will impact our specific jobs, regardless of what your job is, whether you're a technician or a senior leader or you're the person who's, you know, operating a cash register someplace, it almost doesn't matter. I think everybody is going to be affected by it at some level.

I think the statement is the future is here and it's time that we embrace it, right? So, so let's get into a couple of these jobs. They get kind of interesting. You want to, you want to take the first one there? Yeah.

So the first one is not only just a job, but it's arguably a skill. And it's not even an argument. It is a skill that someone can possess and probably most people should should possess, but it can actually even be a full-time job, and that is prompt engineer. Now, I know a lot of people who are listening probably have already heard this, but it's important that we unpack this right from the beginning. So, you know, what the heck is a prompt engineer? And these are just people who are skilled at structuring text that can be interpreted by generative AI, right? This just isn't ChatGPT, but it's even some of the the tools, the GenAI tools around video production, audio production, you know, images, and of course also text, right? How frustrating was it for you the first time, Alex, when you first fired up probably OpenAI, I would imagine, and then maybe quickly after that ChatGPT or one of the other versions, and you started prompting it to do things and you were getting all kinds of wonky, weird results not because it wasn't good at what it was doing, but because you weren't asking it the right questions and in the right way.

Yeah, I mean, it takes me back to the days of being an engineer and owning an MSP and having my guys joke about, hey, the difference between us and our customers is that we Google better, right? And but it really does apply here, right? The difference between somebody who's functional and somebody who's not with some of these AI tools comes down to are we willing, are we able to speak to it in a language that gives it everything it needs to spit out a quality piece of feedback?

Yep. And so, you know, I even, I found myself, you know, in the beginning of 2023, you know, trying to use it and kind of like fumbling through it, getting a little frustrated at times. And then I remember being at a conference, I believe it was last June, June of '23, where somebody stood up and said, you know, she was the owner of an MSP and she introduced me, you know, by listening in the audience, she introduced me to this concept of a prompt engineer. And she actually had her daughter with her there who also worked in the MSP. And she says, my daughter is now our prompt engineer. Right. They were very early adopters and, you know, quickly realized that they needed somebody to focus on being able to tell the AI or to direct the AI tools to create the output that they were looking for. It just kind of blew my mind, right? I mean, I know, I know now we talk about it quite a bit, but back then it was such a new concept for me.

I don't remember where I was when I first heard that, that, that job title, but I went, that can't be a— maybe it can. Holy cow. That's a thing, right? And you sit and think about it and you realize how many times you've asked it for something, you didn't get back what you wanted. And that the more advanced these get, when you get into the, you know, the medical research AIs and some of these that are doing the deep learning projects, it's way more complicated than prompting it to ask it to make me a picture or to give me an outline for a talk I'm going to give or something like that. I mean, some of these prompts get to be super detailed.

Well, it's knowing what to do, right? Like how many of us have, you you know, or, you know, probably many of us own cars. I own several cars, right? And you bring your car into the mechanic and, you know, he does one little thing and suddenly your problem is fixed. And it's, you know, that's a valuable service he provided because he fixed the problem so quickly. But it was all that experience and know-how of what to do to get the desired outcome. Right. So, it became very clear to me very quickly. I mean, even in the traditional things that we do in IT, our customers have the same access to all the certifications, the knowledge bases, the Google searches, the technology that we have. They could buy and get and obtain all of the hardware and software and put the time into it. But the end result will be very, very bad, right? That's why you need people with experience and skill to do these things. And that's why it dawned on me that yes, of course, prompts, you know, prompting skills depending upon what it is. For the average user, we should all basically know how to turn on and use the computer and, and have a little self-service and knowledge about like good IT security hygiene as well as how to operate a computer properly. But when things get really serious, you got to hand it off to the professionals. Same sort of thing. I think the average user is going to need some basic to, you know, decent to moderate prompt skills.

Yeah.

And if the project calls for having to bring in a consultant or an expert that has this title, then I think it warrants it.

I think this is yet another place where the younger generation is going to have a leg up on us old guys where it comes to this, right? Because I talked about this in a previous episode where, you know, Wes Spencer, my business partner's son, was working on a project and Wes said, It— I, I wasn't impressed until I realized that he did all of this with ChatGPT. He never asks Google anything anymore. He asks an AI everything. And so they're building that, that muscle, that, you know, interaction muscle that we just don't have yet, uh, that the majority of us don't have. And I think that younger generation is going to be— they're going to grow up with this the same way that we grew up with Google. And I think you're going to see a shift in their capability of getting it to do things just because they have the prompt engineering skill where they don't necessarily, you know, don't live in that job.

It's amazing to see the transformation that's taking place, but I think you're 100% right. It can be generational, but for those of us who are a little bit older on the spectrum, Alex—

I shave off all my hair so you can't see that it's gray. It's okay.

For the record, I think I have a couple of years on you, but aside from that, all of the customers that we MSPs support, right? We have to also recognize that not only are there those generational forces at work and at play within our clients, but that we should also— like, I kind of like to take that opposite tack, too. But, you know, the older generations, those of us Gen Xers or maybe even baby boomers, still have a wealth of knowledge and experience that maybe a younger generation doesn't have. And that's, I think, when you start to marry those two, or you should also feel comfortable to be able to develop those skills. You know, I've, I've, I've even helped octogenarians in my lifetime learn how to use a PC, you know, when they should be not thinking about that. They should be thinking about retirement. But, right. So, so, you know, yes, age is a factor here, but I think we should not just hand off all of that work to the younger generation and let them, let them be.

100%. The more that we find that we use it at Empath, like the more we, the more we dig into AI and the more that we use it, the better we get and the better the outcomes we get. And I think some of that is we're getting better and some of that is the, that the AIs are getting better. And if those two meet at a place where we can really be functional with it, it's pretty impressive.

Yeah, I mean, listen, I know you, I know Al, um, of course you're Alex, um, uh, and that was silly, uh, but between you, Alex, uh, Kyle, and Wes, I mean, you guys have some really amazing experience and knowledge, right? So, um, you know, someone who's right out of school who's really awesome and comfortable with prompting is not gonna supplant that, right? Yeah. So, you know, I think it's, uh, it's just like any other skill. Um, uh, we have to take it in the context in which it's being applied.

Yep. Yeah, it's going to be fun to watch. Totally really fun to watch. So we get into a few other jobs. Let's, let's jump into this data scientist job because this is one, like, the job's been around for a long time, but I think it's changed a lot with the intro to AI.

So I think data scientists can kind of COVID sort of a broad spectrum of jobs, but these are sort of the people behind the AI. And I think the rest of these jobs that we'll talk about are sort of the, you know, the people behind the curtain making all the magic work. But data scientists typically obtain insights from data using statistical knowledge and programming. So, you know, these are people who can leverage AI tools to then draw conclusions, right? And so some of the examples that I've read about have been things around like very large medical databases, right? When looking for trends in populations, or, you know, it could be even like in things like insurance claims, right? Looking for patterns and correlations between things to then drive toward a conclusion. Do we, do we market this message about car safety, or do we send an email out to all our patients to remind them about maybe the timing of their flu shots. I mean, I'm just kind of, you know, drawing on some simple examples here, but this is what data scientists do. They pore over the data, but they're not doing it by hand. They're using the AI tools to get there.

Yeah, I think this is one of those places where, you know, it becomes just faster to do your job, right? You're dealing with massive amounts of data and using computational power to just continue to improve and speed up the outcomes that these guys can get. Statistical analysis should become much much faster with the use of a tool that can spot patterns and really built to, to spot patterns and repeat them back to you, show you exactly where the pattern lies and how that pattern fits into the data.

So they certainly also need contextual experience with whatever field they're in, but you know, they also have to understand data, datasets, data theory. They need to understand even statistical models, right? So there's a multidisciplines there. You're talking about some pretty super smart people here. This isn't just your average bear, you know, looking at a report and drawing a conclusion.

Yeah, that's a unique set of skills.

Totally.

As is probably our next one, right, as we get into AI engineering.

Right, so the AI engineer, this is the gal or the guy who develops the tools, the systems, and the processes that enable AI to be applied in the real world, right? These are the people who are actually building the AI technologies that require a certain understanding of both the AI theory as well as programming skills, and also has an understanding of, you know, again, large datasets, right? We talked about this in one of the early episodes about what AI is. And it's really still just as a reminder for everybody, it's those two components. You've got you know, a lot of good programming, but you've also got a dataset in which that programming can feed off of. But it's also even some of the inputs, right? If you think about like a self-driving car and the integration with sensors and cameras and other telemetry that the car computer is picking up or recording. So that's really kind of what an AI engineer does. Probably not something that we'll see in most MSPs or even our customers, but we should know that they're out there.

Well, and we're probably interacting with the results of their work every time we touch one of these AI models, right? Yeah. You know, we go, we go look at NotebookLM from Google where you can drag 20 or 30 of your documents into a notebook and then ask it questions and it'll use your dataset, right? This is AI engineering. These are, these are the people who built that front end. So that you can dump some data into an AI model and then utilize it for your own research, which NotebookLM, if you haven't played with it, kind of a cool little toy. You know, I was able to take some legal docs and drag them all into NotebookLM and say, hey, kind of summarize these pieces for me because it's 85 pages of legalese and you get an amazing output. Now you got to go back and sanity check it because these things aren't, aren't 1000% today, but at least gave me places to go look. Right? And it's able to say, hey, on page 46, this paragraph right here, this is the thing you should probably worry about. And without data scientists and AI engineers, we wouldn't be able to collect all this data, manage this data pool, and then make some outcome output that a dumb sales guy like me can just hack away at a keyboard and figure out how to use it. Right? They're making it accessible to the rest of us.

By the way, Alex, you've mentioned today again just a moment or two ago and some in some other episodes, great examples of how using AI AI. You know, you've really— it sounds like you've really embraced it.

So yeah, I don't like the idea of being left behind, so I'm begrudgingly embracing it. We'll go with that.

All right, well, we'll hopefully— we'll all get you a little bit more comfortable. You'll find your comfort zone. But, you know, the other last thought that I had on AI engineers is that they're not far removed from us as MSPs. And the clients that we serve, because those AI engineers are likely employed by the vendors that we're using, right? Generally speaking, we may not necessarily ever meet or speak with them, but they are employed there. I mean, it really depends on the company. You may actually have some interaction. And then on the flip side, I mean, think about how many times as an MSP I've been on the phone with, or my team has been on the phone with, the software developer for a line of business application. Well, now, I mean, by extension, that line of business application may have some AI components to it. And now we may be dealing with an AI engineer on that, on that end of the phone as well.

You may actually get to talk to the mythical magical creature that makes this stuff work.

Those unicorns actually do exist. Now, I mean, listen, they're just people, right? They're just people and they've just got a really cool job. Probably much like the way a developer, you know, and certainly have tons of respect for developers. I was a former developer, so I know what goes into that or what it's like to do development work. But, you know, by extension now, it's like AI engineers are sort of like the cool, you know, a cool cousin of software.

Yeah, yeah, yeah, yeah, yeah. Exactly. So, so, so let's, let's kind of wrap this up with one more that we've got, the information research scientists. And yeah, we kind of mentioned some of the research stuff a little bit earlier, but this is a very specific field where we're using large data sets.

Talk to us about this a little bit. Because this, the way this one differs is everything that we've talked about so far is, or our jobs or skills in the practical use of AI technology, even if there's a little bit of development work going on with AI engineers, but they're, they're using tool sets and they're using constructs and theories that are already established, right? But these information research scientists, what they're on, they're really on the cutting edge of things. They're the ones who are sitting in rooms, if you would. And I don't know why we just always have this picture in our minds that, you know, all the people in the power are sitting in a room someplace cooking things up, but, but they're sitting in a room, so to speak, proverbially, and they're developing the theories and the mindsets or the systems to solve the next set of very complex problems, right? They're, they're working on the things that haven't been necessarily invented yet. And so this is not unlike any other kind of R&D research scientist. But they're focused on AI.

That's amazing. And it sounds— so I just imagine a bunch of guys with whiteboards, right, doing math problems I don't understand. But, but, you know, somebody's got to dream all this up and then figure out how to put practical application to the things that we say one day we would love to be able to insert, insert your thing here.

Totally.

Yeah, that's awesome. So, so we've talked about a bunch of the jobs. From, from job, we need to back into how do we get here, right? This isn't like my engineers who just learned Google. Some of us will get there that way, right? We may not hold the fancy job, but we're going to improve the skill set. We're going to polish the skill set. We're going to learn to use AI alongside of us to make us more proficient in our day-to-day job. But if I wanted to be an information research scientist or an AI engineer, how do I go about that today?

Well, I mean, there's a lot of different ways, right? If you're still very early, even pre-career, right? There are AI tracks at universities, right? There's college degrees, there's master degrees, there's specializations. My daughter, who is studying for her master's at RPI, Rensselaer Polytechnic Institute, she's actually, she was heavy, heavy hard science in her undergraduate, but now doing her MBA is in finance. But even within her MBA program, She took a course that was, that discussed a lot of AI concepts. So even someone who might be more on the business side of things already, at least from what I'm seeing through my daughter's experience, is that she is starting to get exposure. I think they're starting to weave the AI conversation into all disciplines. And there are whole new, like, you know, I could see under a computer science department how there's a whole either AI concentration or probably even majors that are starting to form.

That excites me a lot because I feel like for a long time colleges have struggled with technology and they've struggled to be able to bring technology education to the forefront while it's still relevant, right? They really struggle, like with software development back when I was a kid, you know, we're taking RPG and COBOL just as everybody who I know is a developer is going, oh, we're kind of done using those. There's different stuff. We're moving on to the next thing.

We're doing C++. What are you doing a COBOL for?

Yeah, yeah. And so, you know, it's really cool and a little exciting to see that, that we're, that we're at an education level. We're starting to bring things to the forefront that are, that are still in the forefront that really are useful and practical applications in the world.

But that only describes one segment of the population, right? The young generation or the, the up-and-comers, you know, the ones that will take the reins from us, so to speak.

We can't just go quit our job and go back and get a degree in AI.

Well, I'm sure some people are, and maybe I sometimes think about going back and get— getting my master's in something. I just wonder about what's the practicality at my stage of life and business and career, and just does— probably doesn't make a lot of sense.

A lot of folks in that boat.

Yeah. But listen, I mean, I shared with you privately, and don't mind saying it out here, that last year, and I think it's what prompted us to, or one of the prompts of why we're doing this, is because I felt like, oh my goodness, like AI is starting to overtake the world and I don't know anything about it. Like, I know how to spell it. I have some, you know, I kind of remembered some theory back from my college days, but we didn't really go deep into it. It was sort of just this fringe kind of thing that really didn't exist. Um, it was all theoretical. But now it's here, it's front and center. And in fact, I almost kind of felt that pressure that it was going to take over. So what did I do? I mean, I dove into a bunch of online courses, some of which I paid for. Right.

And I think that's important. I think it's important to point out there's a lot of education you can get out there that is— that doesn't lead to a degree, may not even lead to a certification of any kind, but it will leave you in a better place in your job or a better place in your, in your career having this skill set. So there's a way to upskill and build your own toolbox, build your own skill set. Yeah, that doesn't necessarily require having to go back and get a degree. And that is 100% an option for a lot of folks. The words you said that made me hang— latch on to this thing and turn it and get with you to create this podcast was you said, AI is here and now it feels like it's been here a while. And I feel like a lot of folks are afraid to ask questions because it's been here so long. We feel like we should know the answers already.

Yeah.

And I was like, yeah, that's me. I feel like I've let this thing sneak up on me and now it's here. And now it's like that pop culture reference where now you're afraid to go, I think I'm the only one who's never seen that movie. Can you explain that to me? And I spend a lot of my life that way in pop culture because I just don't pay attention to a lot of it. But seeing that in technology and what I think is my career space, I felt like I was kind of afraid to ask the question because I get a lot of people going, hey dummy, you should know this by now. And I didn't.

Well, that's, that's the conundrum that I think a lot of MSPs face, right? Because there's an assumed expected, you're my MSP, you're a techno wizard. And by you, I mean, either as an individual or as a company, shouldn't you just automatically know all of these things? Don't all of you have PhDs in artificial intelligence already? Weren't you just born with that as an MSP? The answer is no. But I think ego kind of gets in the way. And I'll be the first to say this, maybe, I don't know, maybe I'm not the first, but hey, I'm going to, you know, fully admit that we don't know everything. And, you know, and I was a little afraid last year in '23 that this thing was going to overtake us. And I, you know, and I never really honestly felt like, oh, it's going to like completely like, you know, put me, put me out to pasture and force me into retirement or something. I didn't necessarily fear that. I mean, I had some of those little fears, but I felt like I still needed to get ahead of the conversation. And that's why I, I don't know, I signed up for probably anywhere between 12 and 16 hours worth of online training. I took notes. These are these courses where you got to pass, you know, pass a little quiz to make sure you're paying attention. And I paid attention because I wanted to, not because my employee, my employer, which is me, demanded me to do this, but because I felt like I owed it both to my company, I owed it to my clients, and I owed it also back to the industry. And, you know, thankfully you were listening to me that day and we're doing that now here. Do you—

and we're not sponsored or, you know, compensated in any way by any of these organizations. But was there a specific piece of education, a course, something that you want to call out and just say, hey, I got a lot of value out of this one?

Yeah, I mean, you know, if people want to reach out to me, I'm obviously very reachable.

I should have given you that question ahead of time so you could go back and look and figure out the names of the courses, right?

There were two companies that I looked at. One was Coursera and one was Udemy. Okay. You know, I thought—

How about we put a list of some of those in the show notes? If there's anything that you think was worthwhile, we'll throw it in the show notes so that folks can go look those up.

And listen, full disclosure, you know, I mean, you know, they weren't expensive. You know, some of them were like, you know, $10 to $50 or so, you know, somewhere in that range. Maybe I paid $99 for one course. I don't remember. And the quality varied, and some of them were like ultra, like theoretical, and others were less so. But a lot of the knowledge that I have accumulated thus far is sort of an amalgamation of all of that. So, you know, I always see that, like, you know, they're various. I'd rate them all differently, right? But I think together, it was important that I touched on a lot of different courses and there's more that I've seen that I want to take that I just haven't gotten to yet.

Yeah, well, it's all about making time, but I think at this point in the juncture, it's a fair bet to say a small investment in AI and a little bit of time to learn and understand it probably would do a lot of folks good in their career. As we, as we talk about that, we can kind of roll into— and this is going to be a little bit reflective of kind of what we're working on next episode. But do you want to talk just a little bit about using AI in your job and how to do it safely?

Yeah. So try to be concise here because this is again, probably a whole episode at some point. Yeah, yeah, yeah, yeah, yeah. But, you know, it's got to start at the top, right? Leadership has to be behind a project. But here, just I'll say a couple of requirements. I think if you want to start putting your toe in the water, it's got to be a strategic organizational decision, right? And this is some of the conversation I'm having with some of my customers. Who've said to me, hey, we want to start using AI. Like, on what? Like, that's not strategic. That's reactive. Like, you know, like, what, what do you want to, like, solve with AI? Or what do you perceive can be solved? You need to definitely have some change management skills or be aware of that because, like anything new, right? It's almost like, hey, you know, forget AI for a second. Anytime we change anything as an MSP for our customers, we undergo change management, right? We've got to make sure that users accept this, that there's a new interface to the system. There's— it just works differently, right? Like they've got to buy into that and you've got to motivate employees. You've got to get them excited about this. You've got to get their buy-in, right? You've got to address their fears as well as inviting them into the process so that they could potentially identify opportunities that you might not see.

Yeah.

And I got two more for you, at least just in the starting point. The first is ethics and responsibilities, right? We need to make sure that as we undergo an AI project, that we think about the implications of things like hallucination and bias and things like that, as well as protecting the privacy of data confidentiality, which I think is our next episode, which we're going to expand on that in a whole episode itself. And then finally, I would say to somebody, all right, you want to start a project. What does success look like? Right. Even if it's just words, like forget a scorecard with numbers and hard data. But what does success look like? If we can get AI to, you know, reduce the number of calls that we get a day by any amount, it's great. You know, like that's a success. Okay. Does one count? Yes, one, because that's any. So those are just, you know, some of the things to consider when beginning an AI project.

So it sounds like you're saying kind of start with the end in mind, knowing that we have a specific application for AI that we're trying to achieve, because just go use AI, well, it's almost everywhere these days. It's like saying, you know, go use the ground. It's everywhere. So start with the end in mind and then work our way methodically into, you know, a project and make sure that we bring in everybody throughout the organization, different parts of the organization to get their understanding of where this could have intended and unintended consequences to make sure that the outcome is really what we were looking for.

Yep. And this is really, you know, when I reviewed that, I mean, that came through one of the courses that I took and You know, I stitched different pieces of advice together to build that list. But then I thought about it. I'm like, does this only apply to AI? No, it actually applies to quite a bit of any sort of change within an organization. So, but it's good to just kind of like reinforce this. Like this is yet, this is one of those examples where it needs to be managed properly.

This is change control and risk management. Just in a very narrow field, right? This is the stuff that MSPs are used to doing all day, every day. You're good at these things. We've just got AI stuck out in front of it. So maybe a little bit of learning around, around understanding AI. I think this is a great place to leave off. Let's tease kind of where we're going next episode. And then we can, we can leave everybody to be. But next episode, we're going to dive big into confidentiality, where you can use different types of data with your AI, what you should and shouldn't do, maybe a couple of best practices. We're going to talk about open and closed systems and how we use different pieces of data differently in those. So certainly make sure you come back to join us for that one. With AI showing up again everywhere between Microsoft Office and Canva, it's in every tool you use, whether you're in the sales department or the marketing department. There are probably folks in your organization who are going to use it wrong unless you provide them some guidance up front. And so next episode will be all about making sure we understand what guardrails to put in place and how to get out in front of some of those problems. Robert, is that a good place to leave it?

That, I think, is a perfect place to leave it, Alex. I would just say that the confidentiality and privacy piece of the puzzle is really something important not to be overlooked. I think a lot of people want to rush into any kind of AI adoption or project, but you've really got to be careful about this.

Yeah, that's awesome. Well, that's another one in the books, Rob. That is episode 6. We will see you guys back next week when we drop episode 7. Thank you. This podcast was created by me, Alex Farling, and my good friend Robert Joffe. This podcast is produced by Francis Belland. The music and artwork for this series were produced by artificial intelligence and edited by Francis Belland.