The Future of AI Jobs & Cybersecurity Risks from Robots | TSG Ep. 938

Is AI in danger of becoming the biggest paper tiger there ever was? I don't think so. You're watching Text Drunk Gang.

Hi everyone, it's Alan Shimel for Textron Gang. Thanks for joining us. Um, you know, as you may or may not know, we, we usually record the Textron Gang one day before it plays.

So we try to be as current as possible, but no one wants to come in and record on Sundays. So usually Monday shows are recorded on Friday, and since it's, it's Friday, I've gotta take a moment to bask in the glory, right? There's nothing like sending the Red Sox home empty handed.

I, I gotta hand it to this kid. Cam Sch litter, who's a, a Boston kid, a Massachusetts kid, by the way. But man, how sweet it was.

Mike, I, I know you weren't drowning in your beer. No, it was sad. But, you know, my oldest son is a diehard Red Sox fan, so I think it's probably gonna be 72 hours before I hear from him.

I, as it should be. Anyway, a number about baseball, though, we've got a lot to talk about today. Actually, before I do, we should give a shout out to Kimberly Bates and her husband, who are also big time Yankee fans, and we all, we all share in the glory of, of this day.

I don't know what'll happen Saturday we open up against Toronto, but at least for today, there's a lot of joy in Mudville. Um, but let's, let's jump into things. Let me introduce you to our panel for today.

First of all, we have my friend Tracy Reagan from Deploy Hub, the aforementioned Kimberly Bates, Jack Parler, and, and as I said, Mike Vard panel gang members, welcome Mike. What, you know, as usual, there's a lot of AI in the news. There we go.

Well, let's get started with this report from the Yale Budget Lab and the Brookings Institute talking about their analysis of the impact AI has had on the job market. And they find it's been virtually nil and nobody seems to have lost their job because of ai. And they're basically saying, you know, this is another instance where we have some awesome new IT technology that it gets hyped through the roof and it has negligible impact on productivity.

So, Alan, I know you've been following this space for a while, but you know, when I talk to developers and other folks, they'll say AI saves them a few hours a day, but it's really not changing their jobs dramatically, and it's not really having a major impact on GDP. Hmm. So I'm gonna give a very quantum like, answer to this one.

Mike. I agree and disagree at the same time. You know, I, I did a shimmy says on Friday about ai apocalypse not is the title of it.

Here's where I think we are. And I think this report captures it, and I think your comment is insightful into it as well. I think when you look at adoption of technologies, whether it's ai, the cloud, the internet itself, cellular, whatever the technology is, there are rungs of the ladder o of the technology adoption.

I, I think the initial stage is at the individual level, right? It, you know, do the geeks do those early adopters, do the rank of file geeks find it interesting, helpful, worthwhile to experiment with? Then I think you move from the individual level to the team level.

And teams are deceiving. You could have a team of four to six people, you could have a team of 40 to 60 people. I tend to go with the Spotify model, the two pizza, you know, or the Amazon two pizza or the Spotify, I think it's 10 or 12.

I think that's a good team. Um, and then beyond that is the enterprise level. I think what, where we are in the AI adoption curve right now is much as an earlier study that we saw last week.

And I'm, I'm blanking on who, whose it was again, said that, oh, uh, it was the Google Dora, uh, Google Dora study. 90% of developers are using AI in some form or another, some fashion or another. I think this study would show a similar thing, but it's being done at the individual grassroots level.

And Mike, as you say, they are finding it useful. They are finding it interesting. They are experimenting with it to make it better, right?

But at the team level, I think is where the friction is right now. I think some high performing teams are figuring it out at the enterprise level. I don't think it's there yet.

And I think in spite of CEOs saying otherwise, that's what we're really seeing. So yes, I don't think people are losing their jobs to ai. I don't think people are being hired.

I do think people are not being hired because of ai, right? But people aren't losing their jobs If you're looking at it, the unemployment numbers and what's going on with the guys that are just coming outta college. And I think there's two things that they talked about.

One, when they looked at other, other technology changes, it's a long tail, Has a long tail to coming in, although that the uptick with AI is a little bit faster in terms of some job displacement kind of things. Um, and I think it's, Alan, what you just said was really super insightful, so thank you. Um, it's a well said kind of position about where this is.

The, the other piece of it is that they did talk about the three major industries that are impacted, which is the computer science, you know, computer, mathematical areas, which are absolute environments, which are probably, it's easier for the computer to do because it tends to be more specific as opposed to interpretive or analysis. Um, and so that it can, can do things for them that they're beneficial. Um, but that's the impact is there.

It's just a slow moving piece of it. And that goes back to the piece of saying we are still in the early stages about how AI is impacting our world. You know, the AI piece of it that I expecting to see is gonna be coming from, you know, some of the robotics, some of the more, the predictive things.

And that takes a long time to train and to assure that the data and the quality that's coming out of that is really good. And until we can have that level of assurance, the enterprises aren't gonna release those kind of capabilities because of what would happen if they have a bad outcome. It has a huge impact on the company with bad outcomes.

It's not just a little bit, it's a big, big impact. And what this article doesn't say, it doesn't talk about the jobs that AI creates. Mm-hmm.

We have a massive amount of money going into investment in AI with new companies being opened and startups blossoming. 2 million jobs out there were really at risk, which is what, like a half a million jobs, right? That could, could, uh, leave the market.

But if we think about who's in the market now, we do have, uh, an aging population in particular in some of these, uh, very, very important roles in, in technology in particular. Um, DevOps, uh, platform engineering. There's a lot of old folks in the, in those areas, although a lot of young people are starting to go there.

So we need, right now, we need AI to pick up some of the pace for us and AI's making our jobs easier and we're better at it. I just haven't seen a lot of people lose their jobs because of ai. I don't know anybody who has been lay off, laid off because AI took over.

I, I don't know, a single person. However, That's not what we're Heading. That could be the case.

That could be the case in customer support. There are some jobs that could be being impacted that this, this particular article doesn't reference Tracy, I think, and also customer support. You have a huge churn.

So customer support, depending upon the, the market that it's in, customer support or customer service, um, phones are, can be up to a hundred percent turnover in any one year, much lower for the higher capability space. So you might not see the unemployment going on as much as you see the lack of hiring. I think the big risk, and, and Tracy, I'm glad you brought up that sort of the age thing.

I think the big risk is the perception right now is that AI is capable enough to take an entry level job or a junior level job where we don't necessarily need an entry level or junior level job. And right now, while we do have an aging workforce, we also have a very big age discrimination problem in this, in particularly in technology where it's very hard to get hired if you're over 50. And it's actually such a problem in France that France is running advertisements right now trying to convince employers to hire people over 50.

Uh, the French government is, but from a technology perspective, if we don't hire junior people, new college grads and junior people into the industry, they don't become mid-level or senior people. So we run out of the mid-level or senior people, and then we're gonna be in a, in a really sorry shape in three to five, seven years time when we just don't have anybody for these jobs that we need to fill to fill that can't be filled by ai. And that really scare Me.

This is also a channeling indicator, right? Um, because we're talking about usage of AI in the last three years, which is all this copilot kind of stuff, which probably is not nearly as sophisticated as AI agents are gonna be. So this story is not over yet.

It seems to me like the next generation of AI to Alan's point, is gonna be a lot more capable than the first generation. Yeah. But you know, nothing unusual about this, right?

It, this doesn't mean AI is a failure. It, you know, it doesn't, it doesn't necessarily guarantee its success either. But this is the, the way of things grasshopper, you know, it, it, it needs, it goes individual team enterprise To a, to a certain degree, you're, that is true for those of us who are familiar.

But you know, once again, here's the IT industry running around making claims about things and credibility goes through the floor because they don't get realized. And you know what? People stop listening to the IT people because they go and they don't.

Yeah. So I, I, uh, it'll be interesting. It's gonna be interesting for the next two, three years on this front as it continues.

Probably longer than that, Than that. I think it's also like the AI has a huge impact on worker productivity. And I think we were probably, if we look back in history, we would probably see the same things as computers entered the workforce.

And, you know, there's an entire group of people, my parents' agent that were secretaries and uh, uh, administrative assistants that no longer have, there's no career there anymore because most of that work can be done by the individual executive themselves using, uh, you know, email. Nobody has to type out letters anymore, right? Mm-hmm.

So it's not necessarily that AI will eliminate jobs, but it will change the jobs we have. And, and to that point, Jack, and also Tracy also said that similar thing is that shift in changing the jobs you have or changing the industry we have. So I was thinking last night, okay, so we'll go, you know, go back to the Blockbuster to Netflix shift, which is a change of how the entire industry was delivering the, the movies, right?

So it started out with Blockbuster Go and Shop, and then Netflix came on that they were actually shipping you the CD and then became, became the streaming part of it. And so now we would never, you know, yeah, you still have the red boxes that are hanging outside of CVS or something for somebody to check out something, but it completely changed how the industry does. And that takes a long time.

I mean, blockbuster didn't go outta business day one. It took, you know, almost 10 years for that decline to happen all the way through. Fair.

Absolutely. Actually, there is still one blockbuster left I thought, or recently closed or something. I remember seeing an article.

Um, anyway, let's take a break here. We, we will, I'm, I don't think we're done talking about this subject. We'll, I'm sure be coming back to it, but we're gonna come back and talk about platform engineering in the age of ai.

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Contact us today and tell your story to the world in the most powerful way with Textron Group. org have a new study talking about how well platform teams are using ai, which may not come as a big surprise to a lot of folks at this point. But Tracy, let's start with you on this one.

Um, I wonder if the rise of AI doesn't force you down the platform engineering path in the first place. 'cause I need some way to centrally manage this stuff at scale. And are these two things joined at the hip?

I think that the platform engineering movement would've come along without ai. Um, centralizing all of these pieces and parts has been something that we've all tried to do for quite some time. Um, and maybe AI has made it easier to do, but we would've figured it out without ai.

I wanna say that, that, that first. Um, but you know, as a follow up to the, our last conversation, I think that there is a, there's something in this article that, um, kind of references what we were hearing in the last conversation. And that is that there is sort of this gap between, um, these productivity gains that might allow us to lay people off and something that's measurable, uh, that really has improved the way we do work.

Um, when you have, you know, the one thing about platform engineers is they like to tinker and they are very careful with their tinkering. And when they start generating code, they're not going to just use it. They're going to use that as a beginning, a starting point.

So it may make them, uh, make it easier for them to do their work. And while there may be new, new, new tools that are coming onto the market, um, that consolidate this information better and does a better job of managing prompts, we are still seeing a lack of trust in what AI is delivering. So this was discussed in the last, in the last conversation too, when you have, um, when you know when you generate something and you know what's wrong, you may not do it again.

So hallucinations, uh, and literally just the skills GA and prompting is probably slowing down the rate of in which platform engineers trust what they generate or what, what AI they're using. And they may continue doing it on their own until they feel more confident in the product that's being delivered. Uh, I know on, on our side of the house, we we're constantly kind of having jokes about the, you know, the delinquent schoolboy that will just give you any answer because it wants to give you an answer.

But if you're a platform engineer, you don't want that. You want something solid and something you can trust. And there is a big trust issue here.

Yeah, I mean, to your point, all this AI stuff is still probabilistic and we're getting a little bit better at making it deterministic, but that takes a lot of skill, effort and time, and I think a lot of folks don't have the patience for that. And I cannot tell you the number of times where for every one success story that I managed to do with ai, I've probably got 10 where I just threw up my hands and said, you know what? It's faster if I did it myself.

And you know, at platform engineers, DevOps engineers, we are so easy. We, we do that quicker than anybody else. We are like to tinker, we like to script, we like to sort things out on our own.

And, uh, AI sometimes just gives us a bad taste in our mouth because we're like, okay, I just spent all this time working on the script, I gotta just written it. And much of the scripting that we see coming out of the, that kind of scripting when we're talking about platform engineering scripting, that's different than writing code. So I wanna keep that, you know, that should be it's Platform As code we haven't gotten there.

Is it it's platform as code. It's different from writing a piece of source code is way different than, you know, setting up a, I don't know, a frame and some widgets, right? It's very different.

I, I think it's, look, this, this use case right here is the poster child for what I spoke about in the previous segment platform, engineering platform by, by almost definition is enterprise. You don't really need a platform. When you got a handful of developers and two DevOps engineers, you start running in DevOps started running into scalability issues when you started bringing on hundreds if not thousands of developers, and you move DevOps from the team level, right?

Bubbles of DevOps throughout an enterprise into an enterprise wide DevOps, uh, deployment. That's when you started running into sort of scalability enterprise issues, which gave rise to the whole platform engineering movement. Okay?

So by definition, platform engineering is, is enterprise. Now that's, and that's where I think the AI piece of this is, is butting heads because it does offer the promise of making our platforms, um, more automated, uh, more intelligent, even if, if that's how Oxy mark. But it's one thing chase for, for a couple platform engineers, couple DevOps folks to, to do some scripts.

It's another thing to scalable scalability, making that enterprise wide. It reminds me, you know what, Mitchell and I were two of the three co-founders of still Secure, our first product was an intrusion prevention, uh, IP IPS system, intrusion prevention system. The original product was written in Pearl Scripts, right?

And we, and we quickly realized that, that, that you can't go to market, you know, with a bunch of Pearl Scripts. We, we had to convert it to real code and it didn't take long, it took six months or whatever. But that's where we are.

I think Tracy, that's the exact place where we are from the individuals playing around with some scripting, doing some platform as code to saying, okay, how do I institutionalize that? How do I enterprise that? How do I make that scalable?

It can't just be some pearl script And as long, and if we're trying to push AgTech ai, right? To help us make these decisions, and you have AI hallucinating, the last thing a platform engineer wants is something that's gonna make a mistake. So is there a, it's Not, it's not an option.

Is there an I, and this is a question really, Tracy for you or others. Is there a difference between the approach of how this is used? You know, when I think about, you know, legal issues or sales issues, et cetera, if you have one wrong statement, your credibility is just thrown out the door, you just might as well give up and that kind of stuff, because they're not gonna believe anything else you say.

So that's one thought process that when we look at ai, one wrong thing is like, I can't trust it anymore. I can't trust you. I can't trust what's coming out.

The other side of thinking is that when I look at ai, I know, if I know that it's gonna come out with something wrong, but 90% of it, or 80% of it is right, then maybe I look at it differently. I look at it more like a junior person delivering to me their project that I've asked them to do. And knowing that I have to go through that work and make sure I correct it.

And I don't know how much of a heavy lift that is. It depends upon how good it is. But is it a different thinking about how this is used as opposed to saying, I want this to be a hundred percent right when it comes out.

I think that for most platform engineers, they want it to be right. A hundred percent. And and I think that Mike brought up something very early on that is very important is people need to understand the ca how the tool works and the capability of the tools and ai, what we're talking about here is really large language models, and they are by design non-deterministic.

They're actually explicitly designed to not give you with the same answer, with the same inputs every time. And if you don't understand that, you're in for a world of hurt. If you do understand that, you can, I think as Kimberly said, use an LLM and use an AI as a coding agent to give you a foundation upon which to work, right?

If you just take the output and say, I'm gonna put it into my production environment, never look at it, never trust it and just assume it's right. That's the wrong approach with this particular type of tool. It's not a compiler, right?

A compiler's guaranteed to give you the same thing every time. And we believe compilers to give you to be correct, right? They don't make mistakes.

But an AI is designed to give you that non-determinism. And so if you think about it that way, you can say, okay, I don't necessarily understand how, how, how to do this. Or here's, I can use the AI to generate something quick and dirty from which I can then edit and base it.

I use it as the foundation. That might be a better way to treat the tools. Now, I don't know, Tracy, if the, the, the platform engineers look at it that way.

Probably not. I would rather take a template that somebody's already used and I know there's no problems. And, you know, now if AI could take my template and substitute all of my variables, that would be great, right?

But it doesn't do that. And that's, that's how, that's how a platform engineer is gonna think. You've already got a template that everybody's agreed on and it works.

Why generate a new one every time? So there's there, there's something about the, this industry is different, this part of the industry that's different. I think it will get better, but we need to think about it in the same way we think about virtuous cycles and DevOps loops and all that other stuff.

'cause I think what will happen is an AI agent will write a piece of code, it will be verbose and full of vulnerabilities, and another AI agent will review that to identify the vulnerabilities and remediate that. And then another AI agent will be functioning as an SRE that will kinda optimize that code for deployment and the production environment based on Tracy's template. We're not there yet, but I think that that's ultimately how that might play out.

Even after all of that, though, there's two factors. I mean, running that number of processes through AI agents is expensive, and two is, I still might not be at a hundred percent, I might be at 95%, but let's be honest, there's a lot of humans out there who are writing code at 70%. So is that better?

I don't know. Fair enough. Here's the bottom line.

We're we, you know, I used to say we were at the beginning of the beginning of the Ai ai story. Maybe, maybe we're moving to the end of the beginning, but there's still more to this story till we reach the end of the end. And, um, it's gonna be interesting.

That's for sure. I have faith that AI will finally be what we want it to be. It's just not there yet.

And I'm gonna say it again, large language models are not domain specific, which causes the hallucinations. In that article, they said 53% of, uh, platform engineers, you know, struggle with hallucinations. So there you go.

AI folks, Hey, give us some clean data. Start using small language models and maybe we'll get better. I, you better.

Well, I, I think that that is a big key of it, right? Because we, I mean, even things we're looking at here at Tech Trunk, we realize I don't need the whole internet. I, I just need a corpus of knowledge that we're gonna work off of.

But anyway, hey, let's take a break. We're gonna come back and we're gonna, well, we'll still talk about AI in some form or another, but it's, what do they call it? Physical ai.

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Hey folks, we're back and we're talking about robots and security. Jack here has an article up on Security Boulevard that you should all check out talking about what are some of the issues we're gonna encounter, and they are already substantial and some of these robots are even in our homes. So, Jack, what's going on here?

Well, let's start with what we're talking about, which are robots. If anybody is familiar with the, uh, you know, you've seen many of the clips on YouTube about the Boston Dynamics robots that do flips and jumps, or they're one that looks like a dog. This is essentially the same thing.

It's not Boston Robotics. It is a Chinese essentially clone of these companies unitary. But there are many of these floating around, and these are now very sophisticated, um, not toys.

This one is a $16,000 humanoid robot that's about the size of a 10-year-old kid, about four and a half feet tall, 75 pounds, and can do a lot of stuff, Dan, whatever you want it to. Uh, the security side of it is, I think a lot of people would look at these things as iot devices, that they're just, you know, that they're just a, a, a dumb network device, but they're not a dumb network device. They're actually very smart and very complex with a lot of different communications capabilities.

So in this case, this robot has, uh, wifi and Bluetooth plus, it has microphones, it has speakers, and it has the, uh, intel RealSense cameras, which provide 3D imaging and depth perception as well as LIDAR capabilities. So it's able to do physical mapping of the environment and visual mapping and, uh, all a lot of other things. The risk now comes in, in that the security on these things, uh, and this was, this information comes from a research article by a bunch of, uh, researchers that was published on AR Ziv.

And they said it was the most mature from a security perspective that they've ever seen, but it was still very, um, had a lot of vulnerabilities. So some of the vulnerabilities include that the, the Bluetooth encryption was based on a static key that was easily discovered, and that static key is used across their entire family of robots. So once that static key is known, the entire fleet is com easily compromised.

Uh, they use for communication, they use their own version of an encryption rather than using a well-known encryption, uh, algorithm. And that one has already been partially compromised. Um, right.

The robot itself, um, has a fairly complex, uh, fairly capable processor, quad core cortex processor in it, memory, um, uh, onboard storage. And I think the biggest risk here is that it can continuously calls home to the, uh, uh, the home, uh, headquarters, which is in China and is continuously, uh, sending telemetry back home. So from a secure, right?

And now it is an, if you thought we were getting away from AI and discussing robots, you're not because this is an AI enabled robot. So there are essentially, sort of two ways you can think of this from a security perspective or three ways really is one is the robot itself can be compromised, and once it can be compromised, it then can be used as a Trojan horse espionage agent. So this is a robot that can sit in somebody's office or in a meeting room, and it looks very cute and it's very lifelike and it, you know, it's, it's very clean.

It's not an industrial looking robot. There are no wires hanging out. It's completely autonomous and mobile, and it can do audio recording of meetings, it can do video recordings of meetings, it can do document scanning and it can do facility mapping.

So if you weren't in an area where you don't want people to know about that, that's a big risk. The second thing is it has an AI agent built in, or not an agent. It has an AI environment built in.

It uses AI to do its own tasks, but that also can be compromised and used maliciously. So somebody can actually, um, get into the environment and, uh, use, leverage the AI for offensive measures in your network. So this robot has a lot of AI capabilities built in.

It has a AI engine itself that it uses for its own, um, operations. And the researchers demonstrated that you can take over that AI and use it for offensive operations. So they were able to both, uh, exploit, uh, uh, map out, uh, potential exploits of the connections back to the home headquarters, as well as use it to, uh, map out network connections in the local networks and potentially use it for offensive operations.

So the big risk here is they have something that is, you know, really cute and simple and seems benign, but actually represents a huge, uh, Trojan horse security issue for an enterprise environment or a military or top secret facility where people don't really understand the capabilities and how it could be used. So basically when Alan gets that, when Alan gets that robo dog, I can hack into it and chase me around the office. Is that what you're saying?

But, but that was always, I'm right there with you, Mike. I mean, but this has always been a, an issue with, with all IOT devices, and I realize our ro our robots are smarter now. And Tracy, shout out to you with your robot shirt there if you wanna show people your ro robo dragon, uh, there it is.

Um, you know, but here's the thing. You could have always hacked into these robots, whether that robot was in a GM or Toyota car factory or some other high precision factory in China or something, right? You the ability to hack in and make it, in essence, a Trojan horse existed.

I think empowering these robots with AI brings the level of potential harm much higher, much higher. And, and I think there's, there's a another issue with having ai, that these robots are AI empowered. Is it AI on board?

In other words, do they have the horsepower processing power storage to actually run the AI on robot? Yes. This is, that's, that's big risk, or do, or They fall home?

Well, they, they do both, but the AI is running locally on the robot, which means that when it is employed for offensive operations, offensive from the robot's perspective, it is capable of operating autonomously and at a speed that is maybe hard to defend against unless you're using AI to defend against it. I mean, this is attack of the clones. Yes.

So if I look at a acquisition issue, okay, so I can take it a different, slightly different way. Um, and I'm reflecting on a, a friend of mine that presented on the implementation of looking at security and IOT in the city of New York, and she had the responsibility for implementing this. It was a huge effort that they had because when they started looking at IOT in New York, you looked at all the cameras and everything else that was across all the depart divisions.

What she found in terms of working through this is that the IOT devices, like a robot is purchased by a department, not by it, or it has this overlay piece of it responsibility for security making things are secure. Those organizations that are doing that or using however the robot is going to be used do not necessarily have the lens of looking at these for how security is so from an organization, as they're looking, as there were at ramping up the use of these devices, there needs to be an organization that has oversight to look at these items to say, is this secure? Is this not secure?

And, and unfortunately to be a gatekeeper to these, these, these use how these things are used within the enterprises for that reason. Because you can't expect like a warehouse operations guy to have the all the thought process that he has, should he or she should have in terms of security. It's say somebody that like you, Jack, that has been, you know, drenched in this space that's got the ability to look at these things to say, okay, so this is, these are all the elements we need to examine.

So what I'm hearing, it's, you know, we like to point the finger and say, ai, ai, this is the problem with ai. It's really not ai though. What we are worried about in this conversation is the data that's being collected, it's data that is our problem.

Ai, you know, AI is just another way of using data. So it's data that these things are collecting that we don't want that data out there, but we are already getting used to doing that. For the last however long we've been doing TikTok and FA Facebook, we have been sharing personal data in ways that we would've never thought of before, and we're becoming more reliant on the data that gets given back to us through these ai, um, tools.

So for example, I'll just say something really stupid. I did, I was looking for a vet in Albuquerque, I live in, in Santa Fe, so I'm not really familiar with that area. And I'm listening to my Google thinking, Google knows everything about maps, and it took me down a dirt road and I went down that dirt road.

Ooh, I'd done that. I, I, I actually happened to be in Napa Valley. I went it, it took me to the vineyard instead of the winery.

And, and I got like, I'm on the top of the mountain and then a tree fell and it was not a good, I spent a day up there sitting in my car. It's because of the data. Now, in this case, it's even more nefarious though.

It's, it's sitting into our personal, it's in our bedroom essentially, is what Jack is talking about. So what's gonna be done with that data when it knows how to map out our house? And these robots are not that far away in Australia.

There's one called Abby, I don't know ab sophisticated Abey is, but Abby is used for elderly people who are living home alone. Um, so they have somebody to talk to, they can ask questions to it, uh, they can, it will call people for them. Um, and I'm quite certain that Abby's probably pretty networked in, right?

So it's already happening is the data that we should be worried about. Here's the paradox. What a great thing Abby is.

If you, if you have an elderly parent, I who, who's alone to have a companion just to even talk to, let alone to do useful things, you know, I had this conversation I ran in a 5K last weekend, uh, to benefit, it's not wounded warriors, but a similar sort of organization for vets. How many vets, you know, suicide among vets is a crazy number, right? I forget what the statistic is, but how many vets would just have, would benefit from having someone to talk to someone who maybe can even help them navigate the VA and all of that stuff, but also just an outlet to talk to who kind of understand or can, you know, listen, at the very least, these, there's a reason why we look at these robots.

It's not just that they do the nifty Boston Scientific trips. The dog can, you know, fetch a Frisbee, catch a Frisbee, and do somersaults and all that is because the real promise is there's a lot of lonely people. There's a lot of companionship and things these robots can do beyond just welding, uh, cars, right?

Making welds on cars, which is what most robots do in Indus in industry right now. Um, I I saw a thing that, that again, out of a Chinese factory where they're working on a, a robotic face that has like human-like expression, right? Because, you know, and that gets into all this is Asimov stuff, right?

Do you want your robots to look like a human or do you want 'em to look like a robot? Um, but, And what are you gonna do with the DA when it tells you to do something? Are you gonna drive to the top of the hill or down the dirt road?

That is the suspension of our own thought process. Well, having a data turned against, it's not really turning against, the data's not being turned Against us, it's just becoming over reliant. Not thinking for over reliant yourself, not thinking for yourself ab abdicating your, you know, thought process and giving and handing it over to a robot.

Yes. Right. A robot, right, Alan?

I don't, I I'm, I'm not in any way arguing against robots. I'm much No, I know you're not Jack saying, right? I right, but I'm saying that, you know, I think you could sort of look at this sort of like investing, we on the panel here would be considered sort of mature, well knowledgeable people.

There's a whole group of less knowledgeable, less mature organizations and people that wanna deploy these things. And my worry is because of how they look and feel and operate, people will assume that they are benign and not treat 'em as a potential, the potential risk that they are. Right?

It's, and so that it, it, um, it has the potential of erasing, erasing our o our, um, fear factor of it, right? And therefore there's been several And that's the issue. Well, there there's been several Hollywood movies on this subject, right?

The subject. Yeah. But it's a broader discussion.

It's not just robots, Not Really, it really isn't just robots. It's, it's, but It's Alexa, but There is personal response. It's Just the next version of Alexa.

So I just installed the next version of Alexa, and you're right, Kimberly, it is, uh, it got, there Is, there is personal responsibility at work here though. So if you get a robot and you get a firewall for the robot, if you don't get a firewall for the robot, you're kind of an idiot, Maybe. Yes.

But, but I also think There's two sides there. There's what you guys were talk, you know, started going down is the individual use. Yeah, I'm, and how we're using and how, you know, that group of people.

Definitely there's a different there. And then there's the other piece of it is, is Jack was talking about is how the enterprise implements these things. So I think from an enterprise standpoint, because there's a bigger blast radius, you know, when, when I think about an ad here and that kind of stuff, my blast radius is not that big.

So you look at the return on investment for the secur, for the guys that wanna break into it, you know, their return on investment is minimal there, but the return, you know, they can't, yeah, it's gonna disrupt and it can hurt and that kind of stuff, but the return on investment on the other side of the house is much bigger, um, depending upon what they're trying to do. So there's there that risk reward kind of how much do you need to button down versus Not? And then if I can, if I can raise sort of one more risk factor is today these things are connected through the corporate wifi, which gives you at least some semblance of control.

I predict that the next version of these things will be 5G enabled. So there'll be cell phone enabled and then corporate networking will have no visibility into what's going on in network traffic that's going on. So they will become invisible to it and OT and cybersecurity at that point.

Fair guys, we're overtime here. I gotta pull the plug. I'm sorry.

What a great discussion though. I can't wait till we have a robot here on the Textron gang panel. Um, don't laugh, I've looked into it.

All right. Hey, enjoy your Monday, everyone, as usual. We have a full text drunk TV lineup immediately following.

So check that out. If you're not watching this on Monday on our stream, maybe you're on text Drunk TV or the text drum tv, YouTube channel or OTT channel, wherever you're using it. I hope you've enjoyed, stay tuned for more and we'll be back tomorrow with more gang members and more great stuff.

Until then, go Yankees, this is Alan Hummel. We're out.