Techstrong Gang – September 4, 2024

Hey, everyone. Happy Wednesday. Does platform engineering have a problem?

It seems it's the thing everyone likes, but no one knows where it is or what it is. Um, our algorithms, the new Monopoly tool. And what exactly, how is AI gonna affect our culture?

All that and more you are watching Textron Gang. Hey everyone, happy Wednesday to you. It's Alex Shemel for Techron and Techron Gang.

We've got an amazing lineup of topics to cover and amazing gang members to cover them with today. Let me introduce you to them right now. First of all, uh, joining us from Mountain Colorado.

He's home this week, I hope, and we fully recovered from his recent health battle there with, uh, our friend and favorite disease to hate. Covid is our C-T-A-C-T-O from Futurum as well as Techstrong. Mitch Ashley.

Hey, Mitchell. How you feeling? I'm doing much better, thank you.

You know, uh, bring, bringing something from h uh, home from vacation, that wasn't what I planned to bring, but it happens. You know, it, it's crazy. It's been speaking to a lot of people, a lot of people traveling, getting sick.

Mm-Hmm. Um, well, I'm glad you're feeling better and, and back on, on track. Thank you much.

Also, joining us today is our friend from the, uh, Charlotte Raleigh area. She's a bit of a developer and DevOps person extraordinaire. Pleasure to have her as part of the gang.

Hope Lynch. Hey, hope. How you feeling?

I am well, Alan. Thank goodness. I'm, I'm avoiding, uh, the summer Covid so far, so, uh, doing great here in, uh, very warm Charlotte.

Yes, it is. It is. It was it.

I'm down here in rainy Florida, but hey, I just got the new Covid vaccine last week, so I'm hoping I'll, I'll skip this round. Thank you very much. Um, but, and then joining us in our Boca Raton Studios where he, he was probably watching a recap of last night's Yankee game.

They, they blew it out at the end there. Our chief content officer, Mike Vizard. Hey, Mike.

Hey. How you doing? I also got a, uh, COVID shot over the weekend and wanna thank Mitch for the, uh, awareness.

My pain is your both your gain, so I'm happy to help You. Exactly. And if it's good enough for Mitchell Mike and then Mike, you'll eat it.

Um, but Mike, the Yanks are back. We're, we're looking like we're getting ready here for this run for the postseason. Let, let's hope it stays that way.

I, I don't know. I was, I got a little queasy with the Cardinal, so, you know, it's up and down. Well, the Cardinals always give 'em a hard time.

But anyway, let's jump on. Be besides the Yankees and Covid, we have some, some really interesting topics to talk about today. Um, our first one is around platform engineering.

Everyone seems to like it. No one knows what it is. There you go.

Sounds like a good reason. Mike, what, what, what's this one about? Well, If you notice almost every announcement these days from any kind of IT vendor, whether they're hardware or DevOps, includes the phrase platform engineering somehow or other.

And, um, when you go talk to folks, though, uh, there's confusion over what that means. For example, um, I was just at the, uh, um, event for the VMware folks. VMware, yeah.

At the VMware folks. And they're talking about tanz now as a platform that is designed specifically for platform engineers. And they're trying to say that they're not really interested in working with the doit yourselfers who are stitching together Kubernetes and their own stacks and things that go with that.

But their definition was interesting because what they're saying is, um, they perceive it to be the APIs that you're gonna put in front of infrastructure that DevOps teams will invoke. At the same time though, you can go talk to the DevOps platform providers, and they will say that DevOps is part of the platform engineering motion, and it is included in the platform. And that, um, you know, it's really DevOps at scale.

We're creating a methodology for that. It also seems though, that not everybody's in love with this idea, because some of the DevOps teams are saying, well, this smells like centralized it one more time. And the reason we embraced DevOps in the first place was to get out from under those folks.

So, hope, I know you've been talking to folks about all this. What are you hearing about, well, what is platform engineering in your mind? So the, the way I like to think about platform engineering, is it being a way that organizations can standardize to whatever degree that may be, streamline the way their developers will build, deploy, and manage their software.

So if you have an organization with thousands of developers, it becomes unmanageable, uh, slows the organization down. If everyone has to figure out what tool, how I'm going to deploy, uh, if everyone is individually responsible for certain aspects of security and software upgrades, platform engineering comes in and hopefully is removing, um, a lot of the overhead, a lot of the, the toil that the developers would be bogged down in, but now gives them self-service capabilities, gives them the right tool to do their work. Uh, perhaps even custom workflows that are suited to their needs.

But also the key with platform engineering is not only are they focused on the developers, but they understand what the goals of the business are. So as they are building this platform, they are trying to make it easier to achieve those business goals through software. Well said.

Fair enough. org. And I, and we, I met you and I have done several DevOps on bounds around platform engineering Yeah.

And had various That's I hope with us too. Yeah, yeah, yeah. Hope was with us on those.

And we, we've had people from the community talk. You know what's interesting, the way I look at it is platform engineering is putting up guardrails that allow developers to go faster. Right?

Now, I think part, part of the issue we have is there are various elements within the platform engineering community who really do believe that DevOps is dead long live platform engineering mantra. They think you can have one, it, it's one or the other. You can't have both.

They don't realize our platform engineering is part of this wider DevOps, agile way of, of developing, delivering and managing our software. Um, now there's another part of that community though, who says, you know, DevOps is okay, but when you, especially for large enterprises, it's hard when you wanna go enterprise y on wide on DevOps for it to scale. It doesn't scale, which is, I find funny.

com, this was always the, the fight is DevOps for startups, or is VE DevOps or enterprise? And, and the consensus was, well, it's hard for startups to do DevOps. They don't have enough resources.

They have people wearing multiple hats, but it's not truly DevOps. The DevOps you get is the DevOps you deserve as, as Andrew Clay Schafer has. Um, so I, I don't necessarily buy into that argument either, but I do think that platform engineering is a way for ops to express or do ops things in a way that it's almost further left than where developers start, if you will.

And, and that's not necessarily a bad thing. Right? And that gets into the whole other thing that we've seen in DevOps for, for as long as I've been, you know, covering DevOps, which is, is DevOps to dev centric?

Is DevOps to ops centric? It's run by the developers? Well, no.

It, it literally takes developers, ops, qa, security, it takes a village to do DevOps and allowing ops folks, 'cause my, a lot of what platform engineering is is still classical ops kind of stuff. But it needs to be put in place if we're going to, you know, go as fast as we can in terms of development, CI/CD, and in order to deploy maybe management. So in, in my world, it's certainly not one or the other.

They're all part of this continuum and, and set of frameworks that also includes ITSM as well as Agile I mentioned earlier in, in how we design, build d deploy and manage software today. And I don't think one's mutually exclusive. And I, as I've said, we are on the verge of, of launching a platform engineering site here at Techstrong.

For anyone watching this who has an idea of, of what they'd like to say or do, or companies in the platform engineering space, we're looking for writers, we're looking for sponsors. If platform engineering is something that's important to you and you want to be involved in this discussion, reach out to us. com.

You know, Alan, I think in a way, there's one way to look at this is we've kind of come, come full circle. There are a lot of reasons why DevOps started. One of 'em.

If you go back to the, uh, to the, uh, host the book, I can't remember now, the, uh, the, uh, project Phoenix project that we got. Oh, yeah. Phoenix Project earlier and, and all.

There we go. And one of the big issues was getting environments, how long you had to wait it, it submitting a ticket and waiting months to get an environment set up for you. And in a way, it, it's, you know, we've had this debate along the way.

Is there such a thing as a DevOps engineer? I think the answer is no. It's a platform engineer that does DevOps and platforms and lots of things.

And, uh, through the DevOps process, we thought a lot about the workflow and the tool chain and how those things all fit together and automation and lots of good things. Um, but I think developers felt like, well, we kind of forgot about us. There's some things we still need.

And one of 'em is rapid access to those things, but also rapid access to configurations that work platforms, things that are maybe hopefully more secure. So I, I see, uh, you and I have talked a lot about this platform engineering is an evolution within the DevOps fold, if you will. It's applying a lot of the, or all the DevOps principles.

It's, it's addressing the infrastructure side of what you need to do development all the way through production, uh, which we didn't address very well, just as part of DevOps standalone. I, I think the interesting, the interesting debate here is, should platform engineering use a platform or should it build a platform? And that, that's part of the tanz announcement, I think is, are you in the business?

Uh, is your platform engineering in the business of creating and stitching together your own platform, if you will, the tool chain, the Kubernetes environment, the software infrastructure, all of that? Or should you go with a platform that's been built, meaning not just an assembly of tools, loose collection things that are to been pre-integrated, share data work together, et cetera. Now, that's not always a realistic option for a lot of organizations because you gotta throw everything else out and now go, you know, full bore on one solution.

And that can be a big nut to swallow. On the other hand, you can also be sick of what you're doing and say, let's just stop that and let's go with one and do something like that. Hope.

Is this a large enterprise problem or, 'cause it sounds like, you know, it's almost DevOps became too much of a good thing where we had all these different little units doing their own thing and they all had different platforms and that led to the friction. Or is this something we'll see be more pervasive than that? I, I think at the moment it definitely is a large enterprise problem.

When you have so many teams, there are some organizations who have 3000, 5,000 developers. When you have that many developers and that many teams and everyone is trying to figure out for themselves what is the right tool, what is the right thing to do? Even, um, uh, for example, there was one organization I worked with in the past where we had microservices, but the team that was building the IO OT platform was saying, do, do we manage these microservices?

Or does the platform team, the platform engineering team manage the microservices for us? Right? That negotiation ended up shifting a lot of that and some of the, let's say, care and feeding of the APIs over to the platform engineering team because there were other teams that were also using, uh, those microservices and those APIs.

So if it's something that is broadly shared, the best place for it definitely is the platform engineering team because you get economies of scale. If it's something that is unique, um, to an individual team, perhaps you have an organization where they have one small data engineering team, maybe they can continue to exist on their own and they don't need to be looked after by the platform engineering team. But for general software development, platform engineering can be a big help.

Uh, Mitch, you said something interesting to me in the sense that, um, in your definition, it seemed to include the IT infrastructure people. And when I look at the history of DevOps, it always seemed to stop short of the, the networking and the underlying storage and the compute. You could argue one of the reasons we went to the cloud is because the cloud exposed an API that people could invoke and developers didn't need centralized it for that.

Now it feels like centralized it, at least in the definition being put forward by broad common, VMware is putting an API in front of all the on-premise stuff and managing it like a cloud. So are we moving to a point where platform engineering is a larger definition in the sense that it does encompass all that infrastructure that previously was kind of to many people outside of the scope of DevOps? Well, um, DYI and, and self-service are not the same thing.

In other words, if you think about the full stack, this obviously has to run an environment. It has to have network and storage and compute and maybe AI capabilities, and then it needs a whole software stack to run on operating system up through Kubernetes and whatever. And, you know, the original DevOps mantra was, developers can do all that.

They do that when they build their environments. Well, yeah. And to hope's point, when you're living in a world of 200, more or less 5,000 developers, at some point people can't move around.

'cause like it's, everything's done differently. It's just chaos. So I, I think that's you.

You've gotta have a standardized environment. 'cause you can't just stop a Kubernetes and say, okay, now what you do under that, we don't care about, you know, do whatever the heck you want. So, you know, ops has, has a big role in playing.

And sometimes OP was cloud engineering. Sometimes that was a separate function and not part of ops, but ultimately it, it, it connects in there because those are the folks that are gonna see the first signals coming out of applications and infrastructure of what's happening. So I think just thinking about it systemically, holistically, yes, it's all of that stuff and all of that is the platform.

Um, and including the application code and all the other software that we're running on it. And you can't say, well, we're only gonna address part of it, and then the rest can be whatever the heck you want. Because at some point that falls apart pretty quickly if you're running in any kind of a complex environment.

One of, one of the other considerations when, when we're thinking about infrastructure is over the past however many years it has shifted. Now it's infrastructure as code, right? You can act as a developer or platform engineer and make changes to the platform, to the platform infrastructure.

Spin up, spin down, uh, configure like you would, uh, just writing code. So it's not someone going to rack and stack anymore. Uh, they, they can manage it the same way.

I think that shift has pushed a lot of the considerations toward the platform engineering team because now they also, uh, in some organizations are looking at the SLOs and the SLAs. They are looking at the resiliency of the platform. They wanna make sure it's stable, they wanna make sure it's scaling as team scale.

Uh, a lot of that infrastructure work will fall to them. Oh, let me ask you one more question about, do you think that AI is essentially gonna force this issue? 'cause as far as I can tell, in order to apply AI to the software development process, I need to pull all this data in from all these different tools in some sort of centralized fashion.

And part of our issue is that none of these tools are integrated anyway. So we're kind of going down the path one way or another if we wanna automate things using ai. Yes.

And I think AI will be revolutionary for platform engineering. If we think about the developer portals that a lot of people say, oh, for platform engineering, you must have a developer portal. But if we think about those portals eventually becoming intelligent, where it knows the work I am doing, it knows the teams I interact with, it knows the tools I need, the documentation I should reference now, that would be an indispensable resource.

And especially if now we have some predictive capabilities alerting other things, uh, integrated AI will be fantastic for that. Uh, if that becomes something that is off the shelf available, I could see a lot more organizations shifting toward platform engineering faster. Even those that are a bit smaller.

So we've got a new flesh here. AI may have a huge impact on platform engineering as well. All right, you're already here.

Hey, what you heard it here, right? Uh, let's take a break here on the gang and let's come back with, you know, the the latest monopoly tool algorithms you are watching Text on gang. All right, folks, we're back.

And it looks like the legal community is figuring out what we've been up to with these algorithms in some of these organizations. 'cause well, they're launching lawsuits and investigations. There's an investigation of an outfit for manipulating Medicaid using algorithms.

Other folks at the DOJ are looking at what landlords are up to in an application that they're using to optimize rents. And um, even Amy Cloer, I think James Buer, um, Senator of Minnesota is calling for an investigation of how we're using all these algorithms. And I think, you know, us in the tech sector have known about this issue for a while, but it seems to finally manifesting itself with some investigations into how algorithms are being used to maybe abuse things.

It's not all for ill, but you know, people are people and it will do things that within the letter of the law versus maybe the spirit. Alan, what do you think is going on here? It's not just people or people companies or companies.

I mean, quite frankly, anyone, anyone who's ever played the Google, uh, AdWords, uh, game, knows how that, how that party works, right? You, you start off barring your keywords for 3 cents, excuse me. And then the algorithms get a hold of it and three months later, those 3 cents is $3 and three months after that they have $30.

And you could sit there and say, oh, Mar, look at this. The whole world discovered my keywords. Well, no, the algorithm discovered your spending habits and, and, and took hold of it.

But in many ways, this is also a problem older than time, right? And that is, if I'm setting a market for something, 'cause that's really what we're talking about here. If I'm setting a market with some for something, if I collude with all the other providers of that service or goods to set a price, it, we're all good, right?

We all benefit as long as everyone sticks to it, right to that price level. And you know, we came up with a word for that in the capital, and that's capitalism baby, right? But we came up with a word for that.

We call it monopoly, a monopolistic, uh, behavior. And we outlawed it. And so you couldn't do that out in the open.

You could set up not-for-profit industry consortiums where you could share that data for the benefit. Mitchell worked for one for a while, um, and, and they get away with it because they, they hide behind their not-for-profit, uh, flag. But in today's world, we don't even need to do that.

We could, there are prior, you know, third party companies who come in and say, I have an algorithm. And based upon that algorithm, you don't have to worry about what your, what you price, you wanna sell your tickets to a sporting event at, or what you wanna rent your house or sell your house at, or what you want to charge, because we'll make sure you charge market rates, right? And how do they determine those market rates?

The algorithm and the way those algorithms work is once we get everyone on board of that market rate eight, we could just inch our way up. I'll look Google AdWords, and, and before you know it, our supermarket prices are really high and our rents are getting higher seeming to defy gravity and inflation. Or even, and, you know, then some politicians say, we gotta do something about this price gouging.

And everyone says, oh, hold on, you can't have, can't do it. Can't do it. Can't have that.

Well, so now we're gonna blame the algorithm. Yes, I think the algorithms are, are part of it, but algorithms are not just run by Skynet and AI just yet. They're run by people in companies who are looking to institute monopolistic practices.

And I, I applaud Senator Amy for doing this. I, I think it's something we need to look into. I, I think, you know, Mike, you and I've spoken to this even about the Google AdWords, right?

Today, companies like Textron, we used to get so much about traffic from Google searches, but Google is keeping more and more of that traffic for themselves by keeping it on the page saying, this is, here's your answer. com to find out about platform engineering or something, right? Because they're manipulating those algorithms.

And some, I look, I, you know, something needs to be done about it. I'll stop right there. It's not just Google, right?

It's Facebook and X and all those guys are all doing the same thing, right? And basically, and Apple. Yeah.

And at Apple, alright, It's time, it's time. I'm declaring we're gonna blow up the algorithm subterfuge because we are using algorithms as a euphemism for lots of things, as in it's not algorithms like in discrete math. Let's break it down for a minute.

That's a, that's a branch of mathematics, right? It's algorithms in logic in computer science. When I took algorithms class, that's what it was all about.

Here's logic patterns and, you know, things that we do to write code. Well, what, what is that? A representation of algorithms are a euphemism of business practices, business policies, goals that you're trying to achieve.

So we hide behind this word algorithms as it's, well, it's just really complex. Well, you know what? Code is complex.

The rest of your business is complex too. You don't get a pass. Algorithms no longer get a pass.

At least not on this show. We're not gonna play that game and hide behind. Well, it's just complex.

So we don't know what to do about it. And yes, Congress will never understand anything about algorithms or anything else about technology from that standpoint to our, to our satisfaction anyway, so get over it. It's business practices that we're talking about, not algorithms, because that's really what those are implementing.

So let's be real, the elephant in the room is no longer needs to be in the room. Algorithms are not the problem. It's business practices.

It's math for evil, I think. But, um, Oh, I think part of the problem we're also seeing is that a lot of the data that's being fed into these algorithms is, shall we say, suspect. And I'll give you an example where there may have been, uh, redlining of real estate areas for decades, and if I just take that data and says, well, that's what the market was before, and I feed that into an algorithm today, I'm still gonna just legitimize, uh, an illegal outcome that was happening for decades and make it look like It's, and, and multiply the impact, Right?

And so we're starting to see that where, um, you know, algorithms are being applied and trained on sets of data that is, shall we say, uh, weighted in one way or another by history. Um, do we need to kind of go back in and look at all that data and say, Hey, where'd this come from? And how was it legitimate in the first place?

I, I, I think we do. Um, and the parallel I can draw is when someone says, oh, we need to take our, our tools and our applications and we need, you know, let's, let's put it in the cloud. So is it a lift and shift into the cloud?

Or are you actually transforming and figuring out how you can take better advantage of the cloud and, uh, align it to your business? A lot of organizations are looking for the least expensive, the least what they feel is the least complicated path. And if they can, um, you know, put a front end or a back end on something and virtualize it and, and connect it to the cloud and leave it the same and someone tells them it's in the cloud, but it's cheaper, a lot would go for that.

But I think, uh, the best due diligence is to examine since the work is happening, um, is is this the best business model? Because one of the other fantastic things that having access to, uh, these types of tools thus make possible, there are ways that you could even examine your business model that you weren't able to do easily, uh, 10 years ago. And in that maybe highlight and find some other opportunities that you weren't aware of.

But yes, I think, um, there is a responsibility that should be placed upon the organizations that are, as Mitch said, uh, furthering their business goals, uh, through technology, through algorithms. And I think they have a responsibility to examine their practices as they would in any other way. Like if I am in the store interacting with a person and they are examining how that person's performance is, how they're interacting with me, um, the legalities around that, it should be the same for the software.

To your point, Alan, Alan said earlier, this is as problem as as old as time. I remember back in the eighties, remember the, uh, online reservation system for airlines, Sabre got accused of putting their American airlines things ahead of everybody else's system. Yes.

What was that? That was an al we call that an algorithm today. The algorithm got you.

You know, it, it's the same thing. It's business practices being put through code. But one, one other, um, thing that I think deserves to be highlighted is for the rank and file person who is applying, you know, for housing, who's applying for Medicaid, there is not enough transparency for a lot of these decisions.

And how can they challenge the decision when the decision was made by a computer system, the, the hoops they would have to through to try and chase down a person, and then the person, maybe a person in the call center, they can't push back because the decision was made by the system. So now you get people in a sort of purgatory where, where they can't escape, uh, what can they do? Um, I think there's still a responsibility there as well to have some transparency into how the decisions were made and give people a way to interact, especially when it has such real human impacts as these two cases, uh, that we're highlighting, Well, those things didn't get there by accident, right?

There's a user story, there's a memo, there's a requirements, A PDF, there's something somewhere along the way a communication developer just didn't make that up to say, yeah, I think we're gonna treat them this way. Let's put this up there in front of that one and prioritize that. So it it is human decisions or human communications that's been put into code.

So to your point, probably not an ops person gonna tell you why it did what it did, right? I need to understand, uh, the person who built the logic and where was that decision made. So it's people that's where it ultimately comes back to someone made that decision or it's an anomaly in error in the code and it did something we didn't expect or want it to do.

I think we got a big issue here that goes beyond just, you know, abusing an algorithm for a profit. A lot of people are starting to not trust big tech. They've come to the conclusion that they can see, um, you know, what they would call su suspicious behavior.

I go look for a flight somewhere and you know, but then I can't make that buy it that second. And then when I flipped back in to look for the price went up by 25% because maybe what the algorithm figured out that I wanted to go there. And suddenly that deal is no longer available.

You hear those stories all the time, how true they are, I don't know. But what is going on is the average human out there is becoming suspicious, and this behavior is starting to manifest in our political discussions. There is always this phrase thrown around now about big tech and can't we trust them?

And this is bad for our industry. And I'm saying, and folks, you know, the more we keep doing this, the more, um, it's gonna become a conversation in a way that's not gonna be what we want out of this industry. And people will come to us and say, we're gonna regulate the crap outta you because you can't be trusted.

You know, I I I think Mitchell has it dead on the, the problem is with the word and it's become a shield and a, and a and a, uh, like a blocker, right? Because here's the fact. We all can talk about algorithms and we understand algorithms as a mathematical principle that you can express in code and stuff like that.

I'm telling you, the majority of people have no frigging clue what an algorithm is. They are just told there's this boogeyman called algorithm and it's in the computer and it's in the tech, and it can't be, it can't be messed with. It's omnipotent.

It's the algorithm. And, and so I think stop calling it an algorithm, start calling it a business process because it's an algorithm that was created to enforce a particular business outcome. And when people get that, instead of trying to wrap their head around what's an algorithm, right?

And, and how does that express in, in decimal points and so forth, they'll, they'll better understand. And if big tech is doing that and hiding behind the algorithm, we'll shame on them and it'll come out. Now, I, I do, I don't think that the, the price of the airline ticket went up 20% just because I just searched for it, right, though, you know, there is that philosophical, I think therefore I am and, and Schrodinger's cat and all of that, once I search for that price for that ticket, yes, someone's searching for it.

And so that drives the algorithm. Um, so it's Always quantum physics, you observe it, so now it's changed. Exactly.

So, so there's, you know, there is that, but I, I think we need to get over the algorithm as the boogieman and that Mitch, that's, I think of what you were expressing and Well, hey, if, if I can convert Alan, you know, we have a, we've got a chance now, Alan and o and Mike, we can change the world. People. Come on.

Let's just Care for, we just should write an algorithm to do it, Mitch. I I have it, it just got this pretty air filled. That's my only problem.

But, you know, well, I, by the way, I don't think there's a problem that politicians solve. With all respect to Senator Amy who I like. Um, we did, you know, we, we've got, it's again, a people education system.

Don't, don't be put off when they tell you the algorithm decided that, right? Or So you wrote that algorithm. What I also think we'll see counter algorithms, right?

Theoretically, um, if real estate companies are creating an algorithm to optimize sales of houses, um, that's one thing. But somebody can create a counter algorithm and aim more it, you know, for the buyer. That's the world we operate in.

My algorithm can beat up your algorithm, Right? And I think That starting to sound like the matrix, Mr. Smith, So that that may be how this all plays out and we just have to learn to whose algorithms to trust, right?

And who's working on our behalf versus the other one, right? We just gotta stop calling it an algorithm. It's a business Practice.

And you got one, one, uh, one other thing I want to highlight that was interesting to me in the reading was one, thinking about algorithmic collusion, right? Um, there is a, uh, a body of thought that organizations could potentially in the future or now develop separate algorithms, but because the math is so similar and the thinking is so similar, they're all gonna reach the same conclusion and it will appear to be co colu, uh, collusion. But it isn't really, it's just that, um, machine learning has taken over, it's reacting to the same signals that are out in the market and everything eventually, uh, starts to converge, like Parallel evolution, if you will.

Um, right, Exactly. Mm-hmm. You know what the algorithm's telling me, we have to take a break right now though, so Yes.

An algorithm. Um, we're gonna, yeah, we're gonna, we're gonna take a break here on Textron gagging. Well, when you come back, let's talk a little bit about how AI might or might not affect culture.

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Contact us today and tell your story to the world in the most powerful way with Techron Group. All right, folks, we're back. And the government is allocating funding to study the impact AI is gonna have on our culture.

There's never been a technology, at least at this scale, that does not fundamentally change the way we think about culture and the humanities. And I guess we'll just jump into this right away, but, um, hope, what's your sense here of, um, the things to study? Are they just the way we're going to, um, interact with ai?

Or is it the way we're going to tell our stories in the future and the way that we will, uh, maybe think AI first before we decide to go do something? We'll be like, whoa, well, let's just have AI do it till now and then I'll jump in and it will change the way we think about our relationship with technology. And as you kind of take a moment, take a giant step back and from 10,000 feet up, yeah.

What are we gonna see here? I I honestly think it's all of the above, but at different stages and different situations because, um, AI already is changing, um, some cultures and one that I think of, I, I have, I have an aunt who is a, a teacher, and we have regular conversations about AI and the classroom and the impacts there and the culture of I as a student must go research, you know, on my own, write my notes, go through all of these things that has been blasted smithereens, probably 70, 80% of students now, when they get an assignment, their first thought is, Hmm, let me go see if the AI can help me with that. That is a huge cultural shift in the way they are thinking about their education and the way that they, uh, must be interacted with by the school administration.

So I think there are diverse and complex issues that are coming some for, for the good potentially and, and maybe some for the bad because AI can reinforce, um, negative values, biases, bad assumptions of societies and the creators overall. But it can also reinforce, uh, really good things and transform cultural norms. But, um, I, I do think it's already very impactful to your point, I Am worried about two things at the opposite end of the spectrum.

Um, well, you know, words maybe not the right word. The good thing is AI theoretically should be able to make it easier to preserve languages for instances, and then that's usually part of somebody's culture. And, um, you know, you could see a proliferation of languages that are kind of been on the fringe and or, or difficult to learn.

The other side of it though is everything could easily become homogenized. We could just wind up, um, having to this previous conversation parallel evolution of AI models that just wind up driving us all to the same thing. And there isn't this diversity and that we've seen in the past.

So, Alan, I don't know, I know we've kind of talked about some of this stuff in the past, but, you know, what's your sense of the cultural impact? Look, AI's gonna have a huge cultural impact, right? Everything we do more of, of this magnitude or even lesser magnitude, how has cell phones had a cultural impact?

Think about life before cell phones. Can you, do you remember those days? Think about life before the internet.

Think about life. Well, the, and that to me is the distinction between something like the cloud and these other two things. And ai one is a technology impact that we feel more in the technology space.

One is a civilization wide impact that I think everyone feels, and I think AI transcends technology to civilization, much like cell phones, much like the internet itself. However, my problem with this one is I'm here from, I'm from the government and I'm here to help in a government that sits with a $33 trillion deficit. And I'm, I'm no deficit arc, but, you know, a $33 trillion deficit, is this something the government should be spending the, our money on?

I, I just, I'm not, I'm not quite sold On that. You know why? All right, my conspiracy theorist, right?

Oh, here we go. Lemme get my two four out. I would think that they would, why I think that they would is when you look at how, uh, how AI already is shaping political discourse in the country, in the us, um, the impacts there, how, um, again, the algorithms right on social media platforms drive similar people into, into the same groups.

Um, that cultural impact can be big because now that becomes to that person or that group of people, this is the reality of the world and every group has their own version of reality. Now, if AI is coming in and further giving an extra push, it only polarizes things more So as a government organization looking years down into the future, um, maybe this would be a good thing to keep an eye on and understand better, uh, just in case. Well, that's the, I think AI is a seminal event like the steam engine, like the automobile, like the airplane, like the internet, right?

That it changes society because of the availability of it and then how it gets used in practice. Um, and, and I'm, my first reaction was yours, Alan, which is I'm from the government, and why, why do you think I would be able to help? But what they did, fact, fact is I think they did the right thing is they issued grants that, uh, to five universities, but the university stu study it.

I'd much rather hear what they have to say and, and especially that they did it with multiple, because they may come with different perspectives and or look at different parts of society and, you know, whatever interesting things they can find, I think that's valuable to us. The hard part is the acceleration of how this is impacting us so fast. Do you think, how long ago was it?

You know, when we didn't have cell phones? Well, cell phones were a nineties thing that started. And then really, I mean, at least in terms of most people getting access to today where everybody has one, virtually everybody does.

I mean, ai, now that we have cell phones, AI is on gonna be, if it's not already on everyone's cell phone too. So it, the, the acceleration of how fast we have access and we use and sometimes don't know, uh, how we're using AI is so, so much more compressed. And I think that's one of the things I would be interested in learning through some of these university organizations is when something can rapidly change things this fast.

Um, how, how do you help society? Probably not prepare for it, but react to it in ways that you don't overreact and do the wrong things as an overregulated, whatever it might be. All right.

I wanna answer Alan's question, which was, you know, before the cell phone you bought a newspaper, you got three quarters back, so you had change for the payphone. Remember that's how you did that And you weren't walking down the sidewalk avoiding everyone's gaze and just staring at your phone. You Look John, like a true journalist.

No, I was just gonna say John Swartz, right? He laments every week of that. He can't find enough places to buy newspapers at, but you can look out at forests of trees that have been saved as a result.

But look, and, you know, but here's the other thing is time, time waits for no one and, and progress marches on. And, and so AI is here, it's gonna have an effect. Another thing I will say though is that I don't think we found, or, or we've coalesced around the killer app for AI yet with killer AppSec for AI yet.

So it's true, it's true. Impact on culture I think remains to be seen. I think it might be a little early to, to start trying to, you know, answer that question.

Um, it will, it will become clear, you know, probably over the next two to five years. But right now, a lot of what ai, you know, the impact ai, I think a lot of people don't even realize they're being impacted by AI right now. Mm-Hmm.

Right. Well, it's, It's Not clear and it's the algorithm. It, it, it's not clear to me either that when we say they're gonna study culture and and assign universities to that, are they talking ultimately about, you know, how we go to theater and plays and that kind of culture?

Or are they talking about the culture that we experience On, on the street level? No, I think they mean like the broader yeah, Sociology culture. It's not Charlene trying to get you some culture, Mike, you know, by making you sit through the ballet or something.

Um, You got blesser for trying, you Know, if that ballet is 15 minutes long, I could be in there. I, I think it, I would optimize the ballet, You know what I mean? Give Mike some culture.

Um, but no, I, I think, I think it's the broader American culture, global culture, western culture, that, that type of thing, how we all interact. Anyway, on, on that note, on that note though, I think we're about outta time for this episode of the Gang. It's been a great conversation though.

Hope, hope. Thank you so much for this. Mitchell said you add so much, it's a pleasure to have you on here.

Thank you. But let me just shout it out. Hope, hope is on the market right now.

If anyone's looking for a fantastic tech person, uh, check her out on LinkedIn, you can get all the particulars there. And Hope Has is a woman of many, many, many talents and and skills. Mitch, I'll be speaking to you later, but it's great to see.

I'm glad you're feeling better, sir. And Monk, we're gonna get you some culture. Don't worry.

We're gonna let me get you a tuxedo or something. Instead Of going to see a movie at the theater, I'm going to see a film at the cinema. Why Exactly Now that's the culture Brow went a little higher.

Yeah, we, we hope you've enjoyed today's Text Trunk Gang. We've got a full lineup of text trunk TV behind this. So stay tuned, stay on tune in, don't tune out.

But until next time, this is Alan Shimel for the Gang. Thanks everyone.