412- Nuclear Bombs, F-35s, and Herding Cats w/Stephen Salaka

Mike Kelley: welcome to another episode of You've Been Heard. Today, we've got Doctor Steven Salaka, and he's coming to us with an interesting take on not only AI, but just technology and the utilization of technology within the organizations. Steven, if you'd like to introduce yourself, please, sir. Yeah.

Stephen Salaka: Thanks for having me, Mike. So my name is Steven. For a long time ago, in a galaxy far, far away, I wanted to build nuclear bombs, but unfortunately, that really didn't pan out. There's a little bit of an accident and a couple injuries along the way. so I pivoted to computer science. I've always wanted to do stuff with computers. I found myself in Japan, and that's when I learned my real superpower. It's herding cats. I learned that I'm really good with technology, but I'm even better making sure that people know exactly where they need to do. So I went out there, got my MBA, and then a little thing, a PhD in industrial organizational psychology. This is where it gets interesting because when you're in the technology world, everybody thinks of, technology problems and software and hardware. But the real trouble that most organizations face is the people. And so with that degree in I o psychology, now all of a sudden we're looking at ways of how can we create those better requirements? How can we better engage those teams? So that's where my career has led me, across multiple continents, leading global organizations. and, recently working on projects like the Tesla supercharger network and the F-35. So lots of interesting things over my years, for sure.

Mike Kelley: Yeah. I noticed those things on LinkedIn, on Tesla and the F-35. And then, that post that you put out there a little bit earlier around, the genie, that interesting fact. I was born and raised in Los Alamos, the home of the atomic bomb, not the nuclear, but I mean, it began that whole journey. yeah. So when my original interview, when I was talking to Phil, I was telling him about Railguns and he had no idea what a railgun was. So It was kind of fun. I also noticed that you like to do, was it science fiction? You're a science fiction writer too?

Stephen Salaka: Yeah I do. Last year I was able to put out my first trilogy. I'm in the process of writing another book now.

Mike Kelley: Right on. Very interesting. And, I must say that you're the first one that I've come across with psychology. So which one was chicken? Which one was the egg?

Stephen Salaka: So, I've always been into studying and learning things. And so after I completed my MBA, the question was what do I do next? Well, you could do a DBA. and, understand the business at a more technical level, or you could do like a PhD in computer science. Well, a lot of the computer science stuff was really like research oriented and looking at the hard math behind it. And that's interesting stuff, but I wanted something more practical. So DBA really was broad my mind. But then I also saw that the university was offering a PhD in psychology. I'm like, hey, that's kind of interesting. It's the industrial organizational psychology, the psychology of the workplace. What do we do as managers? How can we better lead our people? And that's where the psychology piece really sort of appealed to me. It's like, oh, how can I take this? And now leverage that to to help my teams become even better?

Mike Kelley: So I'm interested in how you ended up as CTO with that, because that seems more like, I don't know, it almost seems like it would lead you towards HR or a different C-suite role than technology.

Stephen Salaka: Oh yeah, of course. But the big thing with I o psychology, it's all about the change management. And so what this has allowed me to do is all of my projects that I've run, everything that I've done have been extremely successful because I've been managing those people, because I've been taking care of that psychological orientation. So as an example, we had a contract with UPS to roll out their new maintenance system, and they had failed for years trying to get these things out there. But one of the approaches I took in there with my team was not just putting the technology in, not just, doing the migrations and all the data pieces, but we built a really robust change management piece where we retrain the people. We're creating contests for them to go through the process. So they're really encouraged about the new platform. So when it went live, now everybody's using it and everybody's out there jumping on and getting in there. And so it was a successful implementation because we employed the change management piece. And at the end of the day, they're like, hey, you're great at what you do. Let's, have you lead the whole division. And you know, I've got raving fans, for everybody who's working with me because it's different than just being technical.

Mike Kelley: Yeah. And, that's one of the largest challenges that I've run into in my career. throughout the years, that training piece of it, that rollout piece of it. But I've also found, and I wonder what your thoughts are on this, the revisiting of that training. The training seems to be something that needs to be redone every six months, three to six months, depending on what it is to be able to keep it enforced and keep that top of mind and keep everybody utilizing whatever you created or whatever you've delivered. optimally. thoughts on that retraining? And because I know a lot of technical teams that say, okay, we got it up, the lights are blinking. It does what it does. We'll make sure it continues to run training somebody else training. It's just like web content, you know? Well, we're great at standing up websites, but crafting the content within the web page not always our forte.

Stephen Salaka: Yeah, for sure. think about it like CPR certification. You've got to redo those every two years. You know, if you're a paramedic, you're on the box, you're doing compressions on a regular basis. You still have to recertify every two years. And a lot of times that stuff doesn't change. It's just a skills refresher. It's like, oh, I got to get my BLS and ALS renewed. But at the end of the day, you're practicing those skills. And as the medical advice changes, that's going to change how we deal with, our patient care. It's the same thing in the tech world. We may put out a platform and everybody's using it on a daily basis, but if you don't have that refresher in there, it really does become stale now. Instead of forcing people to sit through huge amounts of retraining, we found tools like Walkme are extremely helpful in this regard. You know, you'll have a tool that will do the initial training. You create all these Scorm packages, you create all this material, the simulations, all this great stuff. But then after you go through that, maybe they forgot how to, reset an aircraft or attach this component to it to a new bomb, whatever you're trying to, to do there. Walk me will help guide you through that. So it's really that refresher that constantly does that. But the cool thing about technologies like Walk Me. Is it also captures what you're doing and sort of guides you in that direction. So if people have discovered a workaround or there's a better way of doing something, it'll adapt as you go. through the process. So you don't necessarily have to worry about constantly refreshing all that training. And it's just really baked into the end user experience. So it's not just, hey, here's an interface, and then some pointers and guidance on how to continue using it. Yeah. I think from a technology perspective, if the technologist is doing this, they've usually got a lot of those process pieces already in place. So like, hey, I do my code, I pass it through penetration testing, I do some vulnerability scanning. I do all of these things as part of my natural process. So I'm not too concerned about that pathway. It's these folks who are outside of that who don't know that those pieces exist, where those problems come up. we understand, package injection, we understand MPN poisoning and things along those lines. So we're very aware of what packages and libraries we use. In fact, we probably have tools out there that say, hey, you got a CV on this package. Need to update it. X, Y, z. But when you're talking about everyone else, they're just like, hey, I'm so happy I got this widget. Now that I've always been looking for, it's doing what I needed to do. They don't really looking at the architecture, they're not looking at those things underneath it and those things underneath it may be, vectors for infection, like the example I, I put in the post, Klein was a package that a lot of people were using. Somehow the, it had been poisoned and it allowed the person to install cloud code on about four thousand machines out in the wild, just because the the package was there. So if you were using Klein and didn't understand any of that, you know, you're going to have an infected computer on your thing. And the whole way of getting there, especially now with AI is kind of crazy. AI, we're moving out of the time of search engine optimization where you had to, get this page ranks and now it's just looking at relevant articles and relevant things. So if you get a PR, a press release going out, it's going to look at that. It's something really high. So if you put a PR saying, hey, I got a new package out, that package might actually start getting recommended inside the build process. when Claude is out there saying, I need a package for something. It'll pull that particular package in. Then all of a sudden your npm stats start going up and all of a sudden it starts building in and of itself. And it's great for you as a developer because you're getting that stuff out there. But think of that as a nefarious actor. something that looks innocuous, it looks like it does what it needs to do. It's out there, everybody's using it. And then all of a sudden you get some injection when with the latest update and now you've got a botnet suddenly sitting out there because it's not vetted, it's not verified and validated. I mean, even though it's open source. Sure. I mean, the great thing is it does provide them with a lot of flexibility. It's going to pull a lot of those low hanging fruit off our plate. Like, hey, I need this report updated or I need this tool to do X, Y, Z and it's going to, it's going to allow them to dip their toes in there. The problem isn't so much the individual using it. It's the issue of like, oh, look, Frank over in finance has done something. And now Sam goes over there and uses it too. And now all of a sudden you got so many people using it. So this one micro tool becomes spread out through the organization. So the problem shadow, it becomes compounded exponentially because now it's not just Frank over here. You've got somebody over there in ops, you got somebody over there in shipping and receiving, and they all got these little tools that are now making their way throughout the organization.

Mike Kelley: Environment.

Stephen Salaka: Control over it. There's no, you know, understanding of what actually is out there. And then if one of those things breaks that they built a process around, they're not going back to Frank who built it, they're coming over to it, say, hey, our thing is completely broken. Or worse, it creates this vector for an attack surface. And all of a sudden, you've got a cyber security incident because they weren't abreast of the cybersecurity policies or procedures. no shame on them because, that's not really their job. But at the end of the day, these tools are making it. So it's sort of has to be, we have to have some framework or some tooling or some sort of security assurances, like a sandbox or something that we can make sure that these things aren't becoming those surfaces.

Mike Kelley: And I haven't seen those tools or those best practices yet. I, you know, we know some of these innately, just like you and I are talking about, of, trying to stop that injection and the supply chain injection kind of things. And that's the way you've presented it is a very obvious way of getting Ahold of some of these nefarious packages. but I'm not seeing and nor am I hearing from any of the research groups ways of stopping that, let alone ways of empowering Frank and Steve to take care of the low hanging fruit for themselves without having to have a group of fifty. IT people drop into all of the different parts of the organization to try to increase the speed or the volume, or do more with less. all of those things. And then the thought while you were talking about it, of how easily it's going to be for Frank to hand that off to Steve without anybody ever knowing about it until Steve starts bragging about it. And, it's going to have hit five to ten other people or many more than that before, the guys in the back corner finally hear about it.

Stephen Salaka: Yeah. You know, the big issue with email when it first came out is like, oh, people are going to be able to send anything to anybody. And they're like, oh, we're going to have company secrets exposed. and then, we got training on it. People had some common sense around it. And yet we still have people falling for phishing attacks. every day it's like you got to do the phishing training, people fall for it. And that's just, plain easy social engineering. Well, now, it's not just email, it's all these other things. I mean, when I first came out a couple years ago, when, when ChatGPT became the hot thing, people were loading their corporate documents into ChatGPT, saying, synthesize this for me, do this for me. And so many passwords and keys and corporate secrets got out there in the public internet as a result of that. So a lot of folks just said, yep, we're going to shut down access to those things, not let anybody get out there and do that. And, since then, we put in some training in place and hopefully people are a little bit more cognizant of what they're, leveraging the public models for. But it's still happening. People are turning this stuff out. It's, it's getting out there. And, you can query Claude and there are places and you'll find out probably some active keys as you go through it. So it's going to need to take a deeper level of training for folks. I mean, when we rolled out power BI at Smtc, one of the issues is, we wanted to have a self-service model where people can go out and do things on their own so they wouldn't have to involve a lot of it. The problem with that is they can corrupt data. They can make reports all wonky and all of that stuff. So it was a matter of if you want to be able to do this, you've got to go through and pass all of this training. we're going to certify you as a power BI super user and things along those lines. So they never got the access until they actually committed to going through, taking all that material and making sure that they at least had the awareness and understanding to do that. And that set off a lot of people in terms of like, hey, I'm not or put off a lot of people. We don't necessarily want to do this anymore because it's too much work. but you had a couple people that bubbled through and now they became the SMEs for power BI throughout the organization. They're doing really well in terms of what they do. And there's that connection with that it that was established. So now, hey, I'm trying to do this and I want to check to make sure it's secure and safe. So there's been that locus of control. But what we really need to prevent is that wild West mentality, which we sort of have now of everyone just randomly doing stuff and, Winding up with a mess that's going to be too hard to clean up down the line.

Mike Kelley: Yeah, hearing this and thinking about one of my last interviews and they're leveraging of it.

Stephen Salaka: there's been a huge shift in how we've leveraged AI. So at meal match, we're building out a meal planning software for low income folks so that they can go out there and find available meals, recipes that they can use that are within their budget based on access, based on the local grocery stores. And, it gets rid of the burden of meal planning. So initially we had coded this, all by hand because this was back in, two thousand and four time frame really wasn't any agents out there at the time. And they really weren't up to par. So we were doing a lot of the coding by hand. And we're integrating AI as part of the Rag model. As part of that. Well, as we've gone through, we've actually shifted away from a lot of hand coding. We're now using genetic programming to actually do a lot of the work. And so we had to shift all of the models we had to go through. use AI to help create some of that, spec driven development pieces. And now it's able to produce the code and work through the pieces that we're doing. So we're able to accelerate with with a lot fewer folks. I mean, we're a startup as it is. and then we actually took a lot of what we did inside of the rag model and we converted that back into, standard queries on the side of the database. So once we figured out what those parameters were and how that matching was occurring, we converted that into regular statistical formula so that they could pull that out. And now the AI piece is more of the interface like natural language. Hey, I want to plan this. I want to do this and then converting that into something that they can use in the back end. So that's helped bring down the cost of the tokens across the operation. So instead of tokens for the query tokens for the processing tokens for this, it's tokens for translate this into something that we can fetch. And now do the fetching in the back end. And it's, not only sped things up, not having to wait for response. It's made the end user experience a lot better. So it's that AI of where it was then, querying was what it was with the rags to where it is now, being able to use it to build and have AI where it needs to be as opposed to just AI everywhere.

Mike Kelley: Okay, so it almost sounds like radically different approaches. So internally, the team is using it to generate the code and to add and augment the application and present and build. But then you're also taking and what you're providing to the consumer, the user of the application, you're optimizing your token usage and allowing them to, not hit as many portions of it, but just taking that, converting it and then allowing for The optimized queries or the optimized, response Generation.

Stephen Salaka: Yeah. And this is where that vibe coding, if you want to call it that, really comes into play because it gets you to the point of seeing the problem space a lot more clearly, because a lot of times you don't know what you need to put together and you don't know all these different pieces of moving parts that are there. So AI lets you get to get there quickly. You can use AI for those boxes that like, hey, I need you to do this. Hey, I need to do that. Once you've got those pieces figured out, the next step is taking it from that prototype or that MVP and saying, well, now how can I do that with just database queries? How can I convert that into a Kafka queue? How can I, take AI as the processor and now spread that out and actually, build regular components in the back end to handle those things. And what I'm seeing with a lot of the vibe coding, coming out of lovable folks out there is they just leave it at that prototype stage. So they're churning out a million credits a day because, everybody's just, sticking with that AI piece rather than saying, hey, AI needs to be here and needs to be here because that's what the intelligence is. The rest of it is just, functions and querying and pulling data in and out. And so when you do that, next step, that's where that software engineering, I think has, has evolved into. No, I don't think so at all. I mean, you think of this analogy, Excel came out, what, in nineteen eighty three or eighty four or whatever that was. Well, at the same time, you had Visual Basic. And so people could code their own version of Excel. They could, instead of paying the licensing fee for Microsoft, it's pretty easy to create your own sort of spreadsheet, but nobody did. Why? Because there's a maintenance cost associated with the support costs associated with it. if you don't get the coding right, you got all these bugs you got to work through. So there's a huge cost in terms of maintaining it. So companies didn't, hire developers because of Excel. Excel is just a thing. And there never was that replacement. Well, it's the same thing with these these big SaaS programs. They're complicated. They're huge. There's a lot of different connections that happen and trying to replicate and recreate all of those pieces. It's going to be nearly impossible for an agent to do that. I mean, it collapses under a weight of, a couple thousand lines of code and it starts hallucinating. Now you've got millions and millions of lines across, thousands of different modules. It's way too convoluted for an AI to properly manage now. Being able to, dive in and do bits and pieces and focus targeted things there. Yeah. No problem. I think what's going to happen from the SaaS perspective is now that we've got AI agents, you're not going to have to have three hundred seats of a SaaS model for somebody to use it. They're just going to spin up an agent, which is going to be doing all the querying from the SaaS model. So you're still going to have the integrations on your end. But instead of having John and Sam and Stephanie, all of them over there with their own licenses to Salesforce, you're going to have one license that goes to the agent and the agent's going to be doing all these queries and things and things for them. So I think what's going to change is the per seat model that has been baked into these applications for a long time is going to go away. and it's going to be on a per use basis. So based on worker throughput and things along those lines. So I don't think vibe coding is going to replace the applications. I think it's just going to cause a shift in the way that those licenses are going to have to be handled from an enterprise perspective going forward.

Mike Kelley: Yeah, and in one sense, that's kind of scary and invigorating at the same time to go from three hundred licenses down to one and have that agent handling all of that work and that integration piece of it. the integration in between the data, well, the data flow and the workflow and handling all of that and optimizing that piece of it, making it that much faster. and then switching to a per transaction, basically to a token model, almost, within them. And so it's going to be interesting how they're going to recognize when it's an agent versus a human doing stuff. and, or, just API calls from regularly written programs.

Stephen Salaka: Yeah. You know, a perfect example of this is, one of the startups, we're using Jira on there, but we're using their ten dollars a month, ten user type of thing. But we've got way more than ten devs who are in there. And a lot of organizations have been doing this for a while where they're, sharing credentials not supposed to, but it's happening. And so, we actually built an interface that will actually help spit out and, truncate the pieces so we can actually have that, multiple user experience with only a few licenses. So there'll be tags inside of the tasks themselves that'll indicate users and assignments and things along those lines, those get maintained. And then that way, we can say, hey, this is the actual assignments versus what we're seeing inside of JIRA. So now we can get by with the ten dollars for ten even though we've got, eighteen developers all using it and still have the flexibility of, the regular assignments that you see with the JIRA pieces there. And it goes pretty quick to put that layer on top to allow us to do that. the Chrome plugin, Chrome extension. And now we're able to do that. That's the type of, things that are going to happen now. It works for us. I don't think that's going to work for a massive organization because there's going to be too many requests in terms of, all the different users and this and keeping track and whatnot. But for us, it's the Chrome extension. It works fine. Make that.

Mike Kelley: Prediction.

Stephen Salaka: the collapse of the AI bubble and how we're trying to replicate a lot of what I did for a lot less expensive pieces, things like deep seek and some of these other, bigger models that are coming out there. Some of the open source will be some of that replacement that come out.

Mike Kelley: Okay. any thoughts on what we will be doing or what that collapse is going to look like?

Stephen Salaka: So I think open AI is going to be the linchpin here. open AI has been the main GPT that a lot of the consumers are using. unless, you've got technologists and people on the technology side where we're dealing with all this other stuff, but your regular everyday consumers, you know, they've mainly been using ChatGPT. Well, now people are leaving that. There's issues with future financing for the Nvidia and the Oracle pieces. So I think those pieces are coming down to the point where they're going to have to start raising prices in order to maintain their revenue. I mean they're already losing money. And I think that pressure there is going to cause this cycle. Just like Uber. When Uber first came out, it was highly, heavily subsidized. It was like, oh, I can get cheap rides everywhere. Everybody's loving it. It put a lot of taxi businesses and small operators out of business. and then they started raising the prices. They started taking the subsidies out so they could get to that profitability piece. And I think, just the cost of processing the cost of electricity just where we are today and in the next five year horizon, we can't just turn on a new power plant. It takes, years for those things to get out there. So we're going to be limited on that fresh water supply limitation. And then just see the chip and hardware limitation is going to be the point where they're not going to be able to subsidize that anymore. The investors aren't going to be continuing to pump money into it. And then all of a sudden they're going to start raising prices. And so companies that have baked in AI as part of their core business model are going to have to raise prices. So if they're not able to compete at that level, they're going to have to end up having to pull that out and replace it. Now, AI is not going to disappear. The process of technology is still going to be there. We're still going to have, text processors, chat bots, all of that stuff that we have just maybe not, access to all these frontier models and having AI as a service like we do now, it will probably be, folks having to spend their own up, building their own server parts or leveraging parts of their server infrastructure to spin up some of these things or even, more beefier workstations to handle some of the local AI processing.

Mike Kelley: Yeah. I've seen multiple, instances or tutorials on how to build your own contained, infrastructure using some containers and the like and setting up the database on the back end so that you can run everything locally on a single PC and continue to use that PC for everyday usage at the same time.

Stephen Salaka: Yeah, I think they'll definitely be a lot more of that. A lot of, maybe there'll be a new market for AI PCs. Like I know Intel is trying to put out, an NPU and their AI PCs, but that really didn't take off because we were like, oh, I just I just pinged Claude. I just pinged stuff in the cloud. Well, if that collapses, that becomes too expensive. Now the local hardware becomes the bigger piece. And Intel's already working on PCs and AMD is, is following up with that as well. And so I think instead of all the memory and all the chips going out to the data centers, there's going to be a hard reset. And now all of a sudden it's going to be baked back into devices that are going to get more powerful laptops, more powerful workstations, etc., so people can roll their own at home using, the models they find on Hugging Face or, deep sea stuff. The biggest thing is, how can we leverage AI to make this as optimal as possible in terms of, our developer and our developer processes? Because, when you're at a larger organization, you've got more resources, you have the ability to go out and get, Visual Studio licenses and access to tooling and things. So up here in the startup world, it's like, what can we do? Free open source, using a lot of the Google stuff, using a lot of, free credit time and cloud, run AWS and Azure and be able to shift those things around as the credits run out. and just really the focus on deliverable versus scaling. when you're at these bigger organizations, it's all about scaling, your stability. You've got process pieces, but at a startup, you're starting from zero. So you've got to come up with that process framework. You've got to come up with that tooling. You've got to come up with all of these things that, you usually take for granted are already there. And unless you build that in, as you do that startup, you're going to be floundering around, at the very beginning, we had, a bunch of developers, this person did this thing over here, another one's doing this over here. It's like, no, we need to be in a repository. So we put in and get, okay, now that we have git, now we've got to have a pipeline to actually build that stuff out. So setting up the, the CI CD pipelines, putting in a process where we've got a JIRA board, we've got a task tracking. So that way we're keeping track of what we're doing. And all of these little tiny things really play a huge impact on getting the organization. It takes a while to get that set up. And until it does, everybody's just like going crazy.

Mike Kelley: So so this sounds this sounds perfectly within your wheelhouse because again, we're right back to the change management and the psychology of how to do things more optimally and repeatable. And because the startups, they're just doing, or I imagine a lot of those guys are out there. Those developers are, like you said, once Steve and Frank are off in their corners doing their little thing and they're not thinking about checking all of that in or making it so that it can be, it'll, I always call it the beer truck test. somebody gets hit by the beer truck. What now? And that repository and the CI CD and the pipelines and all of those pieces so that you can continue on. If somebody just says, I'm done.

Stephen Salaka: Yep. And, it's funny because a lot of large organizations that have gone into don't have any of that stuff to start with there. it's Wild West. It's cowboys. They've still gotten there. There's been a lot of mergers and acquisitions over time. They've never integrated stuff. And so that's where I come in and say, all right, what are you doing today? And just making sure that you've got an understanding like this is how development is happening here. Development is happening here. Then you say, okay, well, instead of tracking it in your Excel sheet, in your access over here and your this now I want you to put it over here in Jira or whatever that tool is. So now all of a sudden they're still doing what they're doing, but now they're using a centralized tool. Okay, cool. Now that you're doing that, now, instead of putting your code and saving it here, let's also save it over here into git or whatever the version control we're doing. So now they're doing that. So it's that process of leading them through, doing what they're doing today and then migrating into a central piece. And once you get enough of that pieces there, that's when you can start looking at the economy of scale, saying, okay, now, Mark, I want you to teach Jen about this. Jen, I want you to see about this. So now they can start swapping pieces and you can get that distribution of knowledge. And that also takes care of the documentation piece because, Marx went over there for, thirty years he's been doing his own thing. He's been awesome at what he does, but it's all in his head. Well, now he's training Jen over here doing a piece over there. She doesn't know anything. So now she's taking her notes. He's comparing notes, and now all of that, you've got this, document. So when the next hire comes in, you say, hey, go through the documentation and set up guides that we now have that we didn't have before. And then they go out and start building that. And so you're building this process as you're still able to deliver stuff as things are still happening. And then one day it's just like this switch, you know, it's six months later and all of a sudden you're in this process piece. The people are doing these things and now you've got metrics, you've got tracking, and things are going faster because it's not this chaos anymore. Everybody's, marching along in the same direction. And it's an amazing shift and AI is just going to accelerate that. documentation is going to be quicker. Being able to migrate stuff to central places is going to be quicker. So that whole process is going to be accelerated. But the concern is the pace of change for an individual from a psychological perspective. You know, some folks have an issue of, too much change, too much issue can cause them to burn out. it can cause a spike cycle. They can be heavily resistant against it, not because they know it's not right. They know they need to do it. It's just overwhelming.

Mike Kelley: Alright. Radical shift in questions. What's something in it or in technology that you hear CIOs, CTOs talking about that you think is just fundamentally wrong, that you want to just kind of reach out Mike, what are you thinking?

Stephen Salaka: Well, I think the biggest misconception is AI is going to be a panacea for everything. It's going to be able to do everything. It's going to be AGI. I think there's this myth that out there that AI is super powerful. As long as you have enough tokens, you can get it to do everything. And I think that misconception is really feeding a lot of pain in just everywhere we go, because the speed that they're expecting from the payments that they're making is nowhere near where, the pieces you think of software development for decades now, we've optimized things to the point where we've already automated most of it, and now we're seeing it's not AI and the speed of the code that's the issue. It's these upstream requirements. And those have always been the issue up to now. Now we're being able to focus on those pieces. But because the CTO's and the CEOs are out there saying, oh, we're going to throw AI, it's going to work. They're assuming the whole process, including this requirement piece, including all of these human in the mix pieces that AI is not replacing. I think their confusion is AI can just replace everything and it's like, no, it's sped up coding a bit. we've got some improvements here, especially in Greenfield and focused development. But for all those other pieces, we still need to have those things there because it's messy. It's not something that I can just drop in and do.

Mike Kelley: I'm seeing kind of a disparity in the thoughts around what it can do and what's going to happen with it, and that the C-suite and the leadership is thinking that it's going to be solving everything just like you were just talking about. And then the rank and file are thinking, oh, it's taking my job and I don't want that in there. and then there's some of us in the middle of it trying to figure out how best to make both sides happen.

Stephen Salaka: And this is really where that people in I o psychology piece comes in deeply because, from the, rank and file like, oh, it's going to take my job. Well, no, it's not, you're still going to be doing your job. You're just going to be doing it differently. Now. You're going to have new tools for it, because the backlog of work that everybody has to do is not slowing down. I mean, if anything, the backlog continues to grow. So they're going to have to be more people to do that work. Well, now you're going to be able to get through it faster, but the backlog is just going to increase as a result of it going faster. So maybe you're not over there monitoring and writing fifty different reports a week. now you're able to do, that reporting in, fifteen minutes a week, because you've got agents being able to take care of it, but there's still all those other pieces that you didn't do or you put on the side or you neglected because you're focusing so much on the reporting. Now you're, shifting from just doing reporting to, doing those other pieces of the analysis, the discussions, all the other pieces,

Mike Kelley: Or just the work that comes from the reporting. Because if you're generating enough information out of the report, now, the new activities that need to be done to achieve the goal that you're trying for and or you can focus in on specific things. So now that amount of work needs to come out. a lot of the discussion that we've been having in the last five, ten minutes has also made me really think about, the Phoenix project and the goal and how, there's one quote that I grabbed out of that, that I latch on to a lot and think of optimization anywhere, but the constraint is illusion. And really, what you're talking about of all of the different places, we'll just throw AI at it. Well, we may speed up the front half of the process, but the back half is still as slow, if not slower than it was. So now all we're doing is creating more of that backlog like you were talking about.

Stephen Salaka: Yeah. it goes back to a post a couple of days ago, we were talking about, when I was at Virginia Tech, we were marching around and we were just trying to get everything done and doing it. So Colonel Wesley Fox came out and he said, hey, slow down. Get it right. Because. when you think about it. Slow is smooth and smooth is fast. and I think it's also a Navy Seal quote along the lines of, well, exactly. At the end of the day, it's that smoothness that we need to be shooting for, removing the friction in the processes, the automation, the speed, all of that is going to come as a result of it being smooth. So if you get it smooth, all those other things come into play. So when we're throwing automation over here and saving fifteen seconds over here, the goal isn't speed. The goal isn't automation. That's what too many people are focusing on. The goal is where is the bumps and the friction in the process. And if we smooth that out now, all of a sudden everything is working faster. It's just happening a lot more effectively. There's no stops along the line, refactoring and doing things and so slowing it down, making it smooth. Like you said, focusing in on those, pain points. That's what we need to do, not just make something faster for the sake of making it faster. So if you're interested in this whole psychology and how that plays out in the real time and the impacts of the world, that's where my first trilogy comes into play. It's actually about a tech manager who suddenly invents a way with infinite energy, infinite computer processing power. and, Star Trek type replicators. but it doesn't solve things. There are problems that come in. It's that thought experiment of what would happen if we had that magic wand. Well, it doesn't solve things because there's problems on the edge. And then you solve those problems and more problems pop up.

Mike Kelley: Alright. Well, truly enjoyed the conversation. Doctor Salaka, thank you very much for joining us on You've Been Heard.

Stephen Salaka: Yeah. Thanks, Mike.