Techstrong TV December 22, 2025

Hey everyone. Welcome. You know, you need two jacks or better to start Textron Gang today, but we've got 'em.

You're watching Textron Gang. Hi everyone. Happy Monday.

It's the Monday before Christmas. What are we doing here? Well, we still got today and tomorrow we won't be doing a show Wednesday, and then we won't have live shows probably till after the first of the year after that.

But nevertheless, savor these last couple shows of 2025 because 2025 is going to go down as a really a boundary layer year. And if you'd want check out my shimmy says from last Friday where I talked about this. Um, I do believe it's the first year of the 21st century.

I know you think my math's crazy, but it's true. Um, let me introduce you to our gang today. We got some hard cuts.

I mentioned a pair of Jackson. Better to to go over stuff. Got our friends Jack Poller and Jack Gold as, as well as Mike Ard and myself.

And we've got some interesting stuff to go over today, gentlemen, welcome. As we get ready here for Christmas and New Year's and the holiday break, the news doesn't stop 20, as I said earlier, 2025 is gonna go down in the record books as a, an interesting year to say the least. Mike, what do we got for today?

Well, at a time when everybody's trying to figure out how to gain AI skills, there's a lot going on. 5 billion. IBM is partnering up again with Pearson to kinda revamp their entire online learning category log using AI agents.

Sorry about that. And um, we also have some senators probing some other stuff. We'll get into that later.

But the issue seems to be the following. It's clear that we need to re-skill a huge part of the workforce, but it's not clear to me that organizations are willing to invest in that just yet. At the same time, we don't have time to go sit in formal programs anymore for, I'm gonna sit around for three weeks to learn something.

So we're trying to use AI to deliver the right content at the right time. So the AI agent's gonna observe what you're doing and notice that you might be struggling and then pop up with some things that say, Hey, um, you might be interested in learning this now because it seems like you're trying to figure out how to do this particular thing, ma'am, in some quarters that might be creepy and others might say it's useful, but the whole way we train people seems to be in need of a revamping. Jack, what's you take on what's going on here?

I sometimes feel like organizations are kinda like the Stein branders. All they want to do is hire the best and they don't wanna invest in training. But do we need a farm team for ai?

Yeah, we do. We certainly need more than we've got, uh, you know, various statistics out there showing it's what 80%, 90% of, of AI projects are failing at at large enterprises to, to achieve the, uh, TCO or RROI goals that they've set. There are a number of reasons for that.

Some of it is because, you know, there, there are not, you know, they're, they're not being able to pull in the corporate data into the AI models to make it work properly. Um, and uh, the other issue of course is that they're not able to redirect or, or, or, or redo their workflows to make best use of ai. But a lot of it also has to do with the fact that we're not training people to use AI effectively.

You know, when Office first came out as an example, we just kind of threw it at people and they said, here, you know, here's word, here's Excel, here's PowerPoint, here's all this great, these great tools. Go to town, go use them. But it took several years before people really got good at using those tools.

We're, if we don't train people with new methods of, of work with new methodologies, with new applications solutions, we shouldn't expect them to be good at what they're doing in using those things. And so training is going to become one of those issues for large organizations and small that will be a deterministic, uh, or determinator of how well AI is gonna work within my organization. You can't just throw these things at people.

Um, AI is good if you know which questions to ask it and how to ask them. That's really going to be providing the results that, that you want. As we move to Ag agentic ai, it gets even worse because now we're expecting AI to be able to replace Mike or Jack or other Jack or Alan, uh, with actual actions.

If you aren't able to tell it what actions you wanted to take precisely what you wanted to do, then it's gonna take actions that aren't gonna be very good. Uh, and I I think you're going to see a real effort by large organizations to start doing a lot more training. You're seeing it of course with the, uh, the issues that you just mentioned, Mike, but I think you're gonna see a lot more of that.

I think you're gonna see a lot more money spent there, which is also, by the way, why Coursera and IBM and others are beefing up their AI training capabilities. 'cause people are gonna actually have to buy those technologies, those capabilities from them. Mm-hmm.

A what's your take on what's the right balance here? Because there's certainly a certain amount of personal responsibility here, right? I need to be relevant and I need to get my own skills and it's part of my career, but at the same time, organizations need to invest some level of training.

So is there a balance to be struck here or what's your sense of what's going on? Well, sure, you need to take some personal initiative for sure, but the question I that I would have for large organizations is do you let that kind of go out there on its own, uh, initiatives, uh, you know, let people go do their training on their own. Some will do it very quickly, some will do it over six months, nine months, 12 months a year, two years, or do you try to impact that training?

Most companies forget about AI for the moment, but most companies have some kind of training re-skilling capability in-house anyway. And so, uh, they realize that training people to do their jobs better presents really good ROI and I think you're going to see that come about with, with AI as well. I think it's not gonna be just about personal training.

Um, me going out and trying to figure out how to make this work, I think it's gonna be about kickstarting that training within organizations, and maybe it's driven by hr, maybe it's driven by other groups that are going to implement training sessions to try to get the efficiency of people up. That's, that's how you get corporate efficiency to go up. It's, it's on a personal level, not just on the application level.

Mm-hmm. Alan, do you think that this is a national priority issue? Is this something we should be addressing at a country level?

Because ultimately it comes down to our ability to compete, Frankly, gentlemen, I think you're looking at this all wrong, right? I I have a decent amount of experience here. Having founded the DevOps Institute and we got that up to 65,000 people becoming CER certified in DevOps in about six, seven years before we sold it to people.

Cert one of the largest training companies in the world, right? Um, you gotta know something about the education market. Jack, to your point, there's a difference between reskilling and upskilling.

What you are talking about is upskilling. In other words, I got a developer and I'm gonna make him an AI savvy developer. I upskilled him.

That's very different than having a marketing product, marketing manager whose job has been eliminated by ai and they gotta go become a coder or a, or a car mechanic or something that's reskilling, not upskilling. And, and the real crux of what we're gonna face in this world over the next year or two or three years, is it an upskill or a reskill? Are people, is that QA person gonna stay a QA person to just be harness AI to become an upskilled QA person?

Or is he gonna throw the towel in and say, QA is the, is the land of AI will rule that I'm gonna go become a developer or a, or a platform engineer or something else that, that's number one. Number two, prior, even prior to ai, over the last 10 years, we've seen watershed moment in, in technical skill education and training. We went first from doing these things in person, right?

There was a time Jack and Jack where if you wanted to go get a cert, you went to a class, there were 20 people in the class. It was usually two days a class, 16 hours, 15 hours, and you got a piece of paper that suitable for framing that said you were certified. Well, 10, 15 years ago that changed.

We went to virtual training, right? Where instead of being in person, you could do a virtual and then at your own pace, it's prerecorded and you just take tests at the end of every chapter and you gotta score an 80% or whatever to move to the next chapter. That became dominant.

What we're on the verge of seeing here now is AI monitored, managed instruction AI training where it's no longer an instructor doing the curriculum. It's going to be ai. Who's gonna train you to use ai?

It sounds a little crazy, but that, that's AI becomes the trainer, right? Is that letting the fox into the head house? I don't know.

But AI is the trainer, not the person, not the individual company. So I look at this Coursera and Udemi deal, and they're both big traditional trainers who now do a lot of stuff virtually. What, what the, the other change was like people cert, for instance, DevOps Institute, we at DevOps Institute, I had 175 partners around the world who delivered that training, both in person and virtually.

Now with the web Coursera Udemi, they deliver that training directly. They killed that whole channel. A whole business economics bus, you know, economy was killed off there in the training channel because people go direct to these people.

But there's a reason why this is an all stock deal training as we know it is dying. If it's not already dead, this model of Coursera and Udemi, my AI will gimme that training without them. Why the hell would I pay them?

Because I'm going to get the cert from them. There was a time where to go get a job as an ops person, you needed an idle certification, right? A lot of jobs said idle certification, mandatory security, Jack, right?

You needed that C-I-S-S-P certification. Those days are done, those days are done. Ai, you just go to AI and say, Hey, make me smart.

I want to take a a security class draw, draw me up a class and it's gonna draw you up as class as good as udemi or Coursera's people cap, they're dead men walk in. I wouldn't take that stock and use it for wallpaper in my bathroom. Now see Alan, I thought I was gonna be the only one who's gonna be contrary and think this is the insanity.

This is an insane deal. It's Over, it's over, it's over. It's, It's Over.

It was if, look, there's, there's, there's two parts of this. One is, Alan, you're a hundred percent right in that we went from in-person training to online training. And that's about to change again.

Um, if you look at what's motivating Udemy and Coursera to sell is that they had a big spike in online learning during COVID when nobody had to go do to work. They didn't go into their office, they went and did a whole bunch of training. The big problem has been all along is, who pays for this?

And how did they, you know, how does it happen? Right? And the reality is, those companies now realize that people aren't going to pay for this because they have alternative means, as you said, AI in the web and companies will start paying for an AI enabled course and then quickly realize that none of their employees take it because none of their employees ever do this.

And it becomes, uh, it's a people process problem as we like to talk about. Is that a company that goes and says, I'm gonna pay a hundred thousand dollars to Coursera to give my, all my employees access to all these courses will do that, but they won't go the next step and say, I'm going to give all of my employees a month of time that they can go and spend on this instead of their own personal time. They can spend work time to go take their courses.

And unless they do that, nobody's ever gonna take the courses. And then next year the company's gonna say, we spent all this money on this and we have three people take this course, and we got nothing out of it because they took this course upskilled and left. Because now they have this.

And, And LinkedIn really killed their market too, Jack, Right? And, and LinkedIn bought Linda, that was, I dunno what, 10 years ago, something that was a long time ago. And all those courses are available.

If you buy LinkedIn premium, then they're all available for you. And they're all if you need. So let me just add, wait, Mike, let me just add one thing.

This is not uniform across the world, right? I learned another lesson I learned at DevOps Institute. So in the US less companies pay for training, right?

A lot of, in the US a lot of people, individuals pay for their own training. 'cause they want to be more valuable in the job market, not at their present job, but for their next job. In Europe, the the model is that companies do plan for training because in Europe, those employees often stay for their entire career and only one or two companies.

And so they are truly uplifting their, their employees, right? And, and oftentimes in Europe, you've gotta sign that. If you're taking the cla the course through the employer, you've gotta stay at least three years afterwards.

So they in essence get their money back. Then you've got India. India is a whole world unto itself, right?

It's a gravity well in the amount of people who are there working in it. And for whatever reason, the folks in India love their certifications. They love taking these courses, they love getting certified.

They don't like paying for them. So we had, we had different pricing. We had worldwide pricing and then India pricing.

India pricing was 60% less than Europe and, and North America off the bat, off the bat. And then trying to get the money was, was a lot of fun. So, you know, if you have the India market, which I say is a gravity well, and they're saying, should I pay for it or use the AI to make it?

It's, I I think there's A couple things at play here that we didn't mention. One is, I think a lot of people are getting training on YouTube now, and it's free and it's a video and somebody watches that. And that's part of the thing.

I'm not sure I agree with the stock not having value though. 'cause these companies at least theoretically, should have content that's behind a firewall somewhere that is of value that people will pay to get access to. And it will not necessarily be something that chat GPT has seen.

And if they don't, They should. Well, Mike, what are they gonna have that chat? GPT doesn't have?

Well, I'll give you an example with, uh, AWS who's talking about the following thing. They're revamping their certification programs as we speak, and all of that stuff sits behind our firewall, But there's nothing proprietary about it other than the certification itself. I've been down AWS offered DevOps training eight years ago, nine years ago.

We looked, we had people take the course. It wasn't, it was, it was a, it was very AWS specific, but nothing that you couldn't learn from just being on AWS What you couldn't get was a certificate from AWS unless you paid them. And that's what, it's the same thing with the C-I-S-S-P.

You've got plenty of security qualified people. They don't got the sheep skin as the old commercial used to say. Right?

So the, the sheepskin, as you described it, is also changing though. And what AWS is talking about now is, um, smaller certs for specific skill sets that you can demonstrate. And you're not gonna take some, you know, six month certification program.

They're gonna give you, you know, a a, a micro cert essentially that says, you know, yes, you've proven that you have the ability to provision this and here's your gold star or whatever you want to call it. And I think that's gonna be more of that in the age of ai, because A lot Only, but Mike, only if employers demand it. If employers don't demand it, it's useless.

How many of you raise your hand if you go through LinkedIn and you see your friend's post? I just obtained a new certification. I, I I presented at the Linux Foundation event, or I took Alin course on, on OAS pop 10, the FD BFD.

It's a nice LinkedIn post. I'm sorry. There is one issue though, Alan, and, and, and I think we need to, to, to look at this as well.

AI training isn't free either. So, you know, Coursera is making money, obviously by offering you a course, you can go to chat GPT, but you're not gonna get a good course if you're not a subscriber to chat GPT 20, 20 bucks a month a lot cheaper than that course cost course. Well, and, and, and I will, I will argue that as well, which is that there are plenty of people we know who are experts in these things who will gladly put up a YouTube thing for free and take the YouTube ad money that whatever money they get, because that's the way the, the information wants to be free.

I mean, this goes back to the days of the early days of the internet and information wants to be free. And it is. And how many of you subscribe to 20 different online news sites?

We don't. Right? Because if you, if you go and you look at it and there's a New York Times article on something and you're not a New York Times subscriber, you say, okay, let me Google that and see what other news article I can find that has the same info for free.

And that's the way the world works. And unless I agree with Alan, unless somebody demands a cert for a job, and we see this, we still see this a huge amount in the security environment. Um, but you know, I, I consult with the SANS Institute, and this is an issue for organizations that's existence, is to certify people.

It's how do you convince an employer to allocate the time and money and d for the certs so that they can demand it and have somebody qualify and need it? And do they really need it to do the job? And that's the big questions everybody's struggling with.

Do you need a cert from chat GPT to be able to use chat GPT? Well, I can guarantee you that every, all of our parents here are using it without getting certs and, you know, Right? And then also, and you know, when I talk to DevOps folks, you know, they're of two minds.

One is they get the cert, although they hate it because they had to pay for it, and they're only doing it so they can get past some sort of search engine on an HR system somewhere that's looking for keywords. But, you know, when you go talk to people who hire folks, you know, they'll tell you every time, if if it comes down to somebody they know or met at a conference that they know has the skills and can demonstrate it, they'll pick them over no matter how many certs they got. It.

This hence why we sold, oh, hey, we're 22 minutes into this one, man, we, we could talk about it all day, but we can't. We've got other stuff to cover. Let's take a quick break.

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Hey folks, we're back and we've been talking on this show about AI and power for a while, and now some senators are starting to ask some difficult questions about, well, are all these AI data centers increasing the actual cost of electricity for the average household? I think we've been kicking this around for a little bit, and I'm not sure that the evidence is solid yet, Jack, but what's your take on what's going on here? Well, I think, you know, first off, there's politics going on and then there's the rest of the world.

In the real world on the political side, you have, this is an issue that Senator Elizabeth Warren can get ownership box and say, Hey, look, I wanna hold inquiries and say, you know, the big bag corporations are doing in the common man. So that's one part of it. The other part of it is, and and still politically related, it's how much power is AI using and how much power do we have and how much power do we need?

If you look over, and I did a quick search and research for this, uh, segment in the last nuclear power plant the United States approved was in 2012, so that's what, 15, 14, 15, 16 years ago, something like that. In that time since then, we've added, uh, about 500 gigawatts of power. Now you look at what AI has committed for and, and ai, sorry, open ai, open AI itself wants something like 30 gigawatts in the next year.

So that's, um, almost 10% of what we've done in the last 14, 15 years. They wanna consume in the next one to three years. They wanna build data centers and do that from a data center perspective.

So, and, and by The way, that's just open ai, right? That's Just open ai, right? So if you look at, and, and that was just an easy one because they're very public about what they've committed to do.

And that's, you know, um, if you look across all of that, you can figure that that's probably not four and a half or five gigawatts, but probably, or sorry, 30 gigawatts is what they wanted. It's probably more closer to a hundred to 150 or 200 gigawatts across all of the major, uh, AI data centers. That's close to a quarter.

Again, you know, getting up there in the amount of power we've added over the last few years, at the same time, our government has made it very, very hard to open power plants, and in particular the one type of power plant that we really need, the cleanest and most efficient power plants that we can do, which is nuclear. So if we're not gonna do nuclear and we are going to expand our power requirements, then we have to build coal and natural gas, which are dirty and noisy and et cetera, et cetera. And regulations.

And getting new power plants built is very costly and time consuming, getting through the government regulation process. So you have more demand chasing after a restrictive supply. Prices are going to have to go up.

It's not necessarily evil power companies that are trying to do in the consumer. It's basic economics of supply chasing demand, or sorry, demand chasing supply that isn't caught up yet, and the government not allowing us to build quickly enough. There's another piece to this as well, and that is not the, not in my backyard phenomena.

Yes. And I, and I, and I've talked about that before. When you go outside of Manassas, Virginia, where all the big data centers are outside the nation's capital, and the noise from these things is truly horrendous.

Uh, data centers are very, very large. They consume a lot of land and they produce a lot of noise both from the fans, the backup generators, and the humming of the electricity lines and the transformers. And they employ very, very few people.

So it's can be, um, have a very large impact on local communities, particularly in, and they also Pay property tax. Yep. Yeah.

Which is another piece. I gotta step up on my soapbox here a second. I'll step down, Alan, so you can step up, step up.

So Jack, you're right, there is political theater and then there's the reality. But every once in a while, political theater matches the reality at the heart of the political theater. Here is this basic principle.

You wanna build AI data centers, you wanna power AI data centers. You are gonna make a lot of money off of those AI data centers. Why the hell should my electric bill go up?

That's real. That's real, number one. Number two, you know what, I'm all for nuclear power.

And I agree we should, we should look at authorizing and investigating. But let's not rush to bring nuclear power on without proper safeguards, without proper inspections, without proper guidelines. Otherwise, let's build it in Chernobyl or next to Love Canal so we could get ready the next generation of irradiated cancer survivors, right?

I'm not saying nuclear's not safe, I'm saying you don't cut corners for safety, right? This is out of the movie. What was the movie, Karen?

Uh, well, China Syndrome was one the three mile out. No, but Silkwood Silkwood wasn't it, wasn't it Silkwood the movie in the book True Story. We, we don't want that.

And Jack, when we talk about, hey, let's do some clean, efficient energy, well, why the hell is our government putting the kibosh on wind and solar and renewable clean energy that you talk about, our capacity growing, our capacity has grown from those energy sources, right? So if you, if, if the political theater is, you've got a com, a country that just, the president went online and said, Venezuela stole our oil, right? When this isn't about oil.

Yes. Next gen nuclear maybe. Yes.

To solar, yes to wind truck media themselves. Yes. They announced that they're merging with a company to go build fusion energy plants five years from now, 10 years from now.

It's always five to 10 years from now. That's the real issue here. I don't, Elizabeth Warren isn't wrong here.

I let open AI pay for their energy, let Meta and Google and Microsoft and Amazon pay for the energy that they wanna power these AI plans for. I'm already paying enough for my electric. It's wrong.

And I'll actually give you a real world example here in Massachusetts. Every month I get a bill from the electric company. It's also true on gas where they have added, the state has mandated an additional cost for upgrading the infrastructure.

Now, who's gonna benefit from that infrastructure upgrade? It's probably not going to be me. The, you know, the lines on my street are 50 years old and they're, I'm not getting up updated anytime soon.

But the data center that they wanna build, you know, down the street or, uh, you know, uh, open AI putting in something nearby, those are the folks that are gonna benefit from that. It's the new construction Absolutely. That they benefit From.

So on my bill, so we have FPL far to power and light. Here's an interesting thing on my bill. Every month they show me how much of my energy consumption was, was done using solar, right?

And it, and it goes up a little bit every month. This, this is a fundamental issue we're gonna have to face as a nation here. And I'd make it an election time issue.

Why should we pay for the tech bros data centers? I don't understand why. If they want their data center to be approved, they just don't show up and tell everybody in the town that they're gonna pay their electric bill as part of the deal for building The data.

'cause they're too busy telling them all the jobs it's gonna create. Well, that, and they're not making any money to begin with. Where's the profitability for these guys?

Vin Krishna, the CEO of IBMI, I wrote about this about two, three weeks ago. He had a dead on guys. Jack, to your point, about $8 trillion isn't going to need to be spent to build and power these data centers worldwide.

That's a worldwide number. And when you look at the economics of what the return will be on that $8 trillion, understanding that every five years, you need to, you need to regen those data centers, right? Re re you know, upgrade the, the, uh, infrastructure.

The emperor has no clothes here, guys, unless something fundamentally changes. This is a losing proposition. Yeah.

They need 800, $800 billion a year just to pay the debt. Can I tell you something that happened here in Westchester last week? So there was a meaning about the powering down of the Indian Point reactor, and one of the people there stood up and said, well, you know, we're powering this down and maybe you guys might wanna use this land for data centers.

Well, the whole room flipped out. Oh, I'm sure. But, but Mike, this is a perfect ex example.

I lived in New York. I remember when Indian Point came online and, and, and everything, we knew that it was end of life thing. Why didn't we build a replacement for Indian Point that went through the regulatory process and is ready to come online instead of saying, oh my God, we gotta do something right now.

Cut all the regulations, flip the switch. It's just so, I it's p**s poor planning. Well be, because the answer is because people like you, Alan, who say, I don't want nuclear because it's scary.

No, no. And the reality is, Jack, Hear me out. I didn't say I don't want nuclear because Yeah, Yeah.

You know, nuclear, if you're gonna Silkwood and Meryl Streep and Nuclear and all of this stuff, that's, I think next gen nuclear is very different than what's an Indian point right now, or what was in three Mile Island, right? And all I'm saying is don't cut corners on regulatory practice. And I don't, I don't think anything anybody is arguing for that.

I just think, and, and I'm not, I Absolutely are arguing. No, absolutely. I mean, that's just what the Senate, there are bills in the Senate and the, the administration is pushing to cut and they're, and they're preventing states from putting in regulations on this.

They wanna cut, turn, drill, baby drill, turn it on right now. And, and the answer is because, you know, look, I, my, my former home was in the, the wonderful state of California where nothing can ever be built because of the regulations they Still have. You can't build, they still have, you can't build power plants.

They still, the biggest economy in the world, they're doing pretty good compared to the red states Because they're buying their power from their importing power rather than generating it themselves. And at some point when the, I mean, the power's gotta come from somewhere to, to drive Silicon Valley data centers. Jack, you, you're say, you're saying, okay, let me show you the extreme case.

I'm telling you, bring next gen nuclear power online. Make sure it's, it's done right. Don't cut corners.

There is another piece of this guy, so let me, let me kind of throw something in here. One of the problems with nuclear is it generally takes five to 10 years to get a plant built and up and running for five to 10 years. The power companies are spending billions of dollars to get that place up and running with no revenue to show for it.

And so, from a financial perspective, if it really takes and forget about regulations, regulations are not, and, and, and Alan, I agree with you, it has to be safe and all of that stuff. But if it takes five to 10 years to build a new plant, get it up and running and generate no revenue, but still have debt that I've gotta pay down, who's gonna pay for that? We do.

But we always have. We always have. And that's okay.

No, that's the pushback though. People don't want to pay that, that, that shows up in your bill. Well, the, the and, and the, the, the alternative is to pay forward dirty energy like, uh, uh, gas or coal or, or solar.

Solar is not clean. So here in Florida, Jack FPL has invested a ton in these solar farms or whatever you wanna call 'em. Um, guys, we need, you know, the shimmy says that I did on Friday is that 2025 was the first year of the 21st century.

We're done for the first 25 years of this century. We've lived off of and worked in stuff that was done in the 20th century. We need energy for the 21st century.

Obviously, if we want to do this AI stuff, if we want to live without glowing at night, if we want to, you know, realize the promise of what's before us, we need a plan for the 21st century. And it's not drill, baby drill. That's the issue.

We, and we gotta do it right. I don't suppose We could spend any time, I don't suppose we could spend any time making the existing grid system more efficient than it is. 'cause that don't, That has to be done too.

And better batteries and all of these things. We need a 21st century energy plant, not, not John Rockefeller's plant. And, and, and people overlook the fact that the current energy grid in this country loses about 20 to 25% of the electricity just in transmission.

I think it's more jagged tion, Be more sure. But that's a significant chunk. You know, I don't wanna get emotionally involved here.

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And we're talking about the, well, the AI bubble again, apparently Oracle has had a setback on funding for a $10 billion data center deal. And it's raising questions. And at the same time, you know, I also happen to notice that Morgan Stanley is funding some sort of data center initiative using junk bonds.

So I guess, Alan, are we running outta money here or are people saying that, you know, we don't wanna fund these 'cause maybe we're over provisioned. What's your take on what's happening here? Well, you know what, this is Mike.

That's the world's smallest violin playing hearts and flowers for Larry Ellison. Um, look, the man, uh, increased his net worth by about 40% when, when a a, a bunch of these, uh, Oracle Cloud AI deals were, were announced with Nvidia and open AI and, and Blue Al and et cetera. And now, you know, what goes up must come down.

Gravity is no one defies gravity in spite of the song in the movies. You can't defy gravity. I I, I wrote about this a couple weeks ago as well.

The real issue is, and it goes back to our last segment, no, not the nuclear stuff, but the, but the stuff about, uh, what Arvin Krishna wrote about the economics of these things, of these data center factor of these AI factories. They're, they're spacious, if not downright fraudulent. I mean, it just, it, the number not fraudulent in terms of a crime, but the numbers just don't add up it from an arithmetic point of view on given current, you know, the economics of this currently.

It just, you know, whether you're talking about tokens and what tokens are really gonna cost, whether you're talking about where the energy comes from, whether you're talking about what you can charge for, for this stuff, I, I think we, we got a little ahead of our skis and, and now, you know, gravity set in All Jack Gold. If I take away all the investments that are being made in ai, what is your sense of what's the real state of the economy and what are people kind of actually spending money on? Because from what I could see, at least the numbers that suggest that AI accounts for half of the GDP growth.

So maybe we're not growing as much as we should be because, well, everybody else is not in the AI Business. If we had real numbers, we'd know, wouldn't we? Yeah, I was gonna say the same thing, Alan, if you look at it, I mean, it's all circle financing, right?

Uh, Nvidia in invests in my cloud company for a billion dollars, but makes me buy a billion dollars of your chips. Essentially. You're giving me your chips for free for my company.

Uh, so it, it's really, really, really hard to tell, uh, until we get to a point where the investment is more transparent and less circular, then we'll really know. By the way, there is another issue around all of this that, that, that people tend to gloss over. If we're spending all this investment, all this money, and there's a lot of money out there, let, let's, let's be clear.

But if we're spending all this money on ai, what technology, what future technologies are we not spending on? What's it taking away from? And there's always the risk that if we're spending so much, you know, it's, it's like the, the, the, you know, the, the, the web, uh, craziness, right?

Of the, of 20, 25 years ago. Uh, everyone is spending all kinds of money, you know, a OL and Time Warner, great example, right? Wh where's a OL today?

Uh, but there are new technologies that are potentially coming in line that we're not investing in, in, um, in healthcare, in, you know, like the last segment in, in nuclear power that we can actually deploy quickly and cheaply. There's a lot of stuff that's not happening. So it's not clear how all of this money that's being spent, uh, is going to, uh, help us.

And by, and by the way, Alan, you mentioned earlier that these data centers have to be upgraded every five years. It's more like two years. If you're an AI data center, you need the latest NVIDIA chips, or you're not a data center anymore.

And they're only, they're only good for a year or two. In fact, they, I saw a statistic that said about 30% of them are failing per year because they're driven so hard. Yeah, That's A lot of, Lemme piggyback on what you were talking about there though, Jack, here.

Here's the situation. The fact of the matter is, this first phase of AI expansion that we've lived through, let's say over this last two, three years, was primarily funded by the cash reserves of the Mag seven. Well, minus Apple.

I don't think they've spent a dime on AI to speak of. I'm being, yeah, I'm being face, I'm being face facetious. But, you know, it was primarily spent by the, by the cash reserves of the Mag seven.

And, you know, you could chalk this up to fools in their money, right? It's their money. They could do what they want with it.

But what we're seeing now is a lot of this AI spend or plan spend is coming from VC money funds, money, debt financing, you know, more traditional ways of, of, uh, capital expenditure here rather than just, you know, because the fact is coming outta COVID, the Mag seven, were sitting on hors hordes of cash, you know, collectively in the trillions of dollars. But as that pile goes down and we start financing this stuff with Wall Street money, you know, pension fund money and stuff, do they have pensions? They're still pension funds, public employees and Middle East money.

But when that, you know, when things don't work out like this deal here and, and the stuff hits the fan, well, then it becomes a little bit more painful, doesn't it? Right. Because you're not just saying, yeah, Google will make it back.

Right? Ellen, I think, I think there's, there's another part that's very important to layer on top of that, which is financially all of the, the Mag seven, or at least the, sorry, let's look at this way. The big three, the cloud providers, Google, Amazon, Microsoft, all have cash engines, cashflow engines that are just throwing off cash left and right.

That's something that Google does not have at the same scale. So from a, sorry, not Google. Um, Oracle does not, sorry, Oracle.

Oracle does Not have, well, I wouldn't take up any collections or you know what they, but No, no, but not, not, they don't have the ability to replenish the coffers in the same way that, you know, Google has that, that Google, Microsoft, and Amazon have. Now, I bring that up and I got tongue tied over Google because Google's going to be the next one hit by this because the entire search engine business is changing completely. And I think we're going to see an interesting impact on Google's cash flow in the next one, two years as people are massively changing from going to Google for searches to going to AI for searches.

And what is that gonna do for Google's ability for, to get Wall Street money to finance this stuff? Well, Well, what they're doing, who's Google's competition? Open ai, perplexity, anthropic, let's call it generative search.

And Google is betting that they're gonna become a generative search company. But you know, Jack, it's funny, I, I did a, I did a shimmy says last Thursday, aren't sure we break up big tech. Now, I will tell you historically, I'm not a big breaker upper, I, I don't believe in that.

I, you know, the only one I've seen in my lifetime of sides was the at t breakup. And you know, it, it worked out okay, but I watched a debate over at Johns Hopkins University, uh, I, the Starrs, nca, Argo, whatever, you could check out my article and text strong it. And they, this wasn't your presidential debate where you have one guy who can't put two words together and another one that lies all the time.

It was smart people arguing both sides of this. Take Google for instance, if you took the Google search business without the AI business, it's not long term. I'm not putting a lot of money into it, but if you, but if you give it ai, it, it, it will compete with the best of them.

But does it need YouTube? 'cause YouTube's the second biggest search engine, right? If you spun YouTube out of Google, you spun Gmail out of Google, you, you know, you spun some of the, and then Google Cloud out of the search business.

And the same thing if you spun AWS out of the Amazon retail business, if you spun Instagram and WhatsApp, WhatsApp out of Facebook, those are gonna be all viable companies. You're gonna have a lot more competition. And maybe we'd all be better off.

No. Hey, I don't, we've seen that. I mean, Oracle's gonna now have TikTok.

There's gonna be a lot of money to be made there as well. I know everybody's passionate about this, but I think I'm just gonna have the last word here 'cause we're running outta time. Go.

So go. Hey Ai brother, can you spare die or megawatt? Yeah.

It's crazy stuff. Megawatt. There you go guys, thank you very much.

I appreciate you coming on today. Thank you for watching. I hope you get as jazzed and juiced about these things as we do.

Um, listen, it's Monday Christmas is coming this week we have one more show tomorrow. It's gonna be a special event where we're gonna have an extra large gang panel with a lot of our favorite gang folks. But we're only, it's gonna be a different panel for each segment.

One segment will be on ai, one will be on it, and one will be on cyber. So stay tuned for that. Stay tuned for Techron It Do go check out those Shimmy says episodes I spoke about.

They're pretty cool. Read it. You know, we, Mike and his team do an amazing job.

We publish a ton of stuff on our text Drunk sites, as well as on text drunk tv. If you wanna stay in the know, that's your way to do it. Don't have to go pay for some bogus education.

Um, until then, until tomorrow though, there's Alan Shiel for Text Drunk Gang. Thanks for watching. Hey everyone, welcome back here to Tech Drunk tv.

My next guest is Jeffrey Zu. Jeffrey is the VP of Product of Pine Cone. It's his first time here on Tech Drunk tv, so let's welcome him and, and get to know him.

Hey, Jeff, how are you? I'm doing great. How about yourself?

Good. Um, I mentioned you a VP of Prime, uh, of Pine Cone, Jeff and Jeff. Is it, do you prefer Jeff or Jeffrey?

Let's, Jeff is usually good, so Jeff is good. Yeah, Absolutely. Truth be told, my middle name's Jeffrey too, so I, but not many people know that.

But anyway, um, so Jeff, a lot of people know Pine Cone, especially, you know, in this era of ai. And when generator of AI and, you know, op uh, open AI first kind of exploded, you know, pine Cone really kind of got to people's consciousness in front of their faces. Um, we're gonna talk about that, but before we do, let's talk about you a second.

What, what, what's, what's your arc? What's your story? How, how did you wind up here?

Yeah, I'm happy to do that. So, you know, I've spent most of my time working really in large scale distributed systems. Like, uh, I, before applying Fund, I spent eight years at Microsoft working in Bing.

So I was running a lot of their machine learning AI infrastructure platform there as a platform pm So I was doing things like, you know, training large scale language models before chat GBTI was doing things like accelerating GPU inference, uh, using Cuda and asics and things like that as well, really, but most relevant. I also built their vector search platform from scratch. So I was actually running, you know, hundreds of billions of like vectors, you know, for their web scale search engine itself.

And that was, you know, that was obviously maybe like, you know, started seven, eight years ago. But that was one of the most really exciting things, you know, before Vector Search became so popular across the industry. You know, it was already a very, very integral part of search systems and, you know, it was locked away to a really, really small set of, you know, uh, companies.

Right. But honestly, that's kind of what ended up motivating me to join Pine Con in the first place because I saw what Pine Con's mission was, which was really, you know, to make AI knowledgeable through vector search and enabling that through, you know, enabling any developer to take it on, right? And so that was really kind of what brought me to Pine Con in the first place.

'cause I saw how hard of a problem it was, how challenging it was, and then, you know, and how integral of a technology was going to be. And then, uh, you know, you know, the stars aligned and I found a startup that was in the same mission that I wanted to do. Excellent, man.

You know, a lot of people, so I, I've founded, co-founded four or five companies over the years, you know, and I always say people don't realize, you know, their, their 10 and 12 year overnight successes, right? Open AI and chat GPT exploded on the scene and everyone thought, oh my God, what a, this is an overnight success. All of a sudden it's here.

Yesterday it wasn't. Right. Well, maybe to you it wasn't, but there've been people working on, on the technology behind this.

I i in the case of, of LLMs and, and, and, you know, uh, uh, artificial intelligence, I mean, this has been going on for 30 years, some of this stuff, right? They really Exactly. I mean, if not more.

Uh, and then the whole vector search thing, a as you said, right? Microsoft Bing was doing it. There was a small group who had that use case of a huge, you know, basically cataloging the internet, right?

Yeah. And this was the logical way to do it. So, you know, when you see these things that explode on the scene, don't assume it was like some eureka moment that happened six months ago.

It, there are people who toiled their, almost their entire careers Absolutely. To make this stuff available to us. Um, now of course, look, tech Techstrong AI is one of our sites.

We, we talk about AI a lot, but not everyone. Jeff, I think is familiar with the role that Vector databases, vector search, you know, the whole way of injecting data, whether it's Rag or for s SLMs or, or what have you, you know, the role Vector plays for those who may be my cybersecurity friends who are smart as heck, but don't know, don't play with Vector a lot. What, how would you explain it to them?

Yeah, I think the simplest way is to think around, you know, what vectors really do are they allow you to represent, you know, instead of in the classic search world of like text, right? Like, oh, I know a keyword, right? This specific keyword mm-hmm.

Like, you know, is exactly, exactly what I'm looking for. But what vectors really allow you to do is kind of represent more concepts, right? Rather than necessarily that exact word, that exact text at any given time, right?

And so that's really powerful because all of a sudden, you know, you can have, you know, I think the classic, you know, example, they always give in like the classic MLO courses is like, you know, man and woman and queen and king right? As concepts, right? Where if you represent them in vectors, you know, man and king would be closer together than let's say, you know, uh, you know, woman and king, right?

Right. And so idea here is that you're able to, because these models essentially are trained on huge corpus of data these days, honestly, I don't even understand how much of a train on these days that, that it's actually mind boggling, you know? Mm-hmm.

How they're being trained. But like, they're able to essentially learn these relationships between the words, and then they represent those relationships. And essentially in vectors, which is, you know, for most people it's just, you know, it looks like a, like a series of numbers, right?

But within those numbers, that distance between those vectors is actually what we call the semantic similarity or the distance, right? Which ultimately allows you to say, is this similar to this particular other concept? Right?

And of course, when you go into the world of LLMs and natural language and how we interact with AI today, that's super important, right? Because we don't speak as human beings. We don't interact with like perfect text, you know, recall of exactly the same language.

We operate in concepts as well. So this is what it really enables you to do, is represent concepts and speak in that language, and ultimately be a little bit more closer to how we as humans actually even talk and, and kind of interact with each other as well. Very cool.

That's a great kind, almost layman's way of, of explaining it to people. I think they get it. And, you know, and a lot of that goes towards, you know, you hear people say, well, generative AI is just picking the next word based on the previous word, right?

It, it, it's not really, you know, look, there's an automagical element to this. You show it like, to my wife's family, they think it's a person in there. You know, I and, and others say, well, no, that's not a person there, it just picks the next word based on the last word.

And, and it, and it just goes on like that. And I don't think that's a hundred percent true either, obviously. But it, it does give you this idea of how, how vectors work, right?

And how, what makes sense and, and, and how these things go together. If you can, Jeff, obviously as we, we've talked about with the, with the advent of, of, of AI and generative ai, pine cones had a massive explosion. Tell us a little bit what that ride's been like and what's been going on.

Yeah, for sure. I think, uh, you know, when I joined Pine Cone, I actually joined roughly nine months or maybe eight months before chat GPT. So honestly, like before, before, right?

So I will say, you know, I don't wanna call myself a hipster or anything like that as well, but, you know, I was, I was there before it was cool. Mm-hmm. And, uh, and no, I mean, part of it was that, you know, uh, you know, at the time of, you know, when I was first joined Pine Conan and the types of use cases that we were thinking about, obviously were very in the classic world of information retrieval, exactly what I was doing at Bing, right?

Mm-hmm. It was all about how do I do semantic search as in doing, improving my search results and search relevance through this? How do I build recommender systems, right?

And so obviously when Chat g BT came out, that completely exploded because all of a sudden, right now what was really interesting about these LLMs right, is that they were really good quote unquote reasoning engines, right? But they really, what they needed to do to be accurate and, you know, you know, and actionable and, and you know, useful to people is that they needed the right context, right? And that's ultimately where kind of rag came in as a concept, you know, retrieval, augmented generation, where the idea here is obviously that you're able to take, you know, the only relevant, you know, snippets or pieces of information and feed that as part into the LLM, right?

And, you know, I think, you know, that was obviously the very early days of how, you know, RAG was treated. And so, I'll be honest, I think everyone at the time of really didn't understand rag. And so the first couple years of where Pine Cone was was really all around honestly, educating the market and helping people understand like, what are the basics around kind of this, you know, vector search capability for, and how does it relate to AI and LLMs, right?

But I think over time, right, as people got more comfortable with Vector search and capabilities, you know, I think once some of the evolution was that, you know, how do I not only, you know, there's obviously very basic ways to do RAG in terms of just like a very simple embedding, you know, retrieval just shove it into a prompt, right? But obviously as the LLMs capabilities grew, obvi, the, the other kind of the, the other part of the market kind of grew in, in what they call context engineering, right? As in like, how do I then not only just do basic retrieval, but more complex and more interesting ways to ensure that I'm giving the most relevant information back, right?

And so as part of that Pine Cone has also evolved, right? Is that of course, by the way, you know, our Core Vector database is massively scalable, massively, you know, cost performing things like that as well. It's really, really important for that to be, I would say, a base foundation layer.

But, you know, as we've looked to kind of where we see the future of kind of the evolution of Pine Cone going, you know, our mission at Pine Cone is to make AI knowledgeable, right? Is not actually to be the best, best vector database in the world. We see it that the Vector database is a component, it is a very, very integral piece to the puzzle to actually make AI knowledgeable and to produce knowledge for them.

But is, you know, only one component of that. Right? And so as we kind of look towards the future, you know, we still care a lot about the Vector database and serving our customers there, but, you know, now that people are building agents, right?

And not only just, when I say not only just developers, I mean just almost anyone these days can build agents themselves. And that's kind of where we really wanna play a role in the future where, you know, it's all about making AI agents and LMS knowledgeable, which means more than just pure vector surge. But, you know, we have a, we have a product called pinecone Assistant, which allows you to essentially take in files taken, uh, you know, PDFs directly and actually get the relevant context out of it.

So instead of using vectors as the interface, we're actually using something like, you know, PDFs and chat and words, right? Which is much more natural to anyone building Sure. Agents today.

So I think that's kind of been the interesting ride where, you know, of, of course, the Vector database is a super integral piece of the puzzle, but I've, as kind of the world has evolved and the, and the capabilities that LLMs and agents has evolved, we also want to continue evolving beyond the Vector database, but more into the knowledge space itself. Right. That makes total sense to me.

You know, it was about two years ago, we did like a hackathon down here at Tech Trunk. Hmm. And you know, where DevOps dot com's one of our sites, right?

And then, yeah, I started it 12 years ago, 13 years ago. Um, and we had a bunch of, like the OGs from DevOps, you know, Patrick dubois and John Willis and Damon Edwards, and of a lot of people who started the DevSecOps Movement, um, just really smart, good people, and they were operationalizing ai. Hmm.

And back then, two years ago, the only way to get anything in was you, you had to basically vectorize it, if, if we could call it that. Right. Put it into the Vector db.

And, and so they were here. That's when I first really became aware of Pine Cone and, and what role it was playing in there. And I was like, wow, they're kind of the only, they're the ferry boat that gets you from this side of the island, you know, back to the mainland, into the, into the ai.

But I always felt like we had to democratize that. We had to make it easier for people to get the data in it, you know, converting it into a, a Vector DB while, you know, my, my developer friends was like, oh, this is easy, and I just write a little script. It sucks it in, boom, boom, boom, that, you know, it was going to get easier.

And it sounds like pine cones kind of realized that and is making it easier. Yeah. And I, and I do think that's the future too, right?

Because as you said, these LLMs are so massive, Jeff, we scraped well, we, from a publicly available information perspective, we've scraped everything there is to scrape, there's a lot of stuff that's not publicly available that we need to scrape, but I don't think the way we built the LLMs is the way we'll scrape that, right? Mm-hmm. I think Exactly.

We need better input. Um, people who wanna get more information pine, about Pine Cone, where, what, what's their best kind of on-ramp? Honestly, uh, the best OnRamp here is, uh, to just go to our website, pine ca io and sign up.

So, so we have a very generous, essentially a starter tier, which is completely free. Uh, there, you don't need to put in a payment card whatsoever. You can get started with both the Vector database if you're interested, you know, getting deep in the Vector database, but also, as I mentioned with Pin con assistant, with a little bit more of a simple, if you ever just wanna chat with your docs, right?

Drop in a PDF start chatting with it, or even, you know, like retrieve the relevant snippets from it, all of that is available in our starter tier. And so just come to the website and sign up. The way that I always like to think about it is that there's no better way of learning about it than actually doing, right?

I mean, I can show you the docs, I can show you the blogs and hon honestly, but I always come back to just get in there and try. And I promise you, like, one of the things that we absolutely emphasize here as a, as a core design principle, is a developer experience and user experience, right? We wanna make it dead simple, easy, and as fast as possible to see value.

So just come on through our website and try it. I promise you'll be super fast. Very cool.

Um, alright, let's, let's pivot. Pine Code recently announced something called dedicated read notes, DRN full disclosure, it's in public preview right now. Maybe you can tell us more on that timeline, but first let's just, you know, define what do we mean by dedicated read notes?

Yeah. So, you know, before I dive too deep into the weeds of it, I think one of the things around just like vector search, so this is in the vector database world of vector search world, just to be clear, uh, the, one of the things around vector search is that it's all about, you know, in many ways the primary, you know, dimensions you consider it is around accuracy, performance, and cost, right? Those are, I would say, when it comes down to it, you can talk about some other bells and whistle, but when it comes down to what people really care about for a database, it is those three things, right?

But, you know, one of the things is that for, but what we found over time was that, you know, there's a lot of different types of workloads, you know, that are especially in the higher scale area that are starting to stress exactly what vector search workloads look like, right? So on in one dimension, we have what we call, I would say like recommender systems, right? You can, this is the classic e-commerce.

Every single time my page loads, I want to send, you know, find the most relevant. You know, you may be also interested in items, but this is talking about, you know, thousands, tens of thousands of queries per second, right? But with really, really short latencies, right?

Because you're on a page load, we can't be waiting, we can't be slowing that down, right? So like under 50 millisecond agencies, right? Um, but then, and then on the, you know, in on the other end, what we have is like customers who wanna do very, very large semantic search workloads, I have a corpus of let's say, decades of news articles, right?

And I wanna be able to enable search over that, right? So you have a huge, let's say, billions of vectors that you wanna search over, but you wanna be really, really accurate for those, right? And then last but not least, kind of what's been around, it's like, uh, what we call multi-tenant rag or agents is that you have maybe millions of really tiny independent, you know, completely isolated like agent memory context that you essentially wanna be constantly writing to.

But, you know, you don't need this to be super fast because, you know, it has an LOM in it. I don't, hundreds of milliseconds is probably fine, it's not a big deal, right? But I think what we realized when we were working with all of our customers is that to do the optimal cost performance trade off for each of these, you actually need to serve it in a different way.

Right? So I would say maybe around a year ago, we kind of embarked on our transition to what we call the slab architecture, which is essentially an object storage based architecture, where essentially the ground truth, instead of using everything on SES and memory, which a lot of the, you know, existing industry does, we're gonna be object storage based, right? Instead, where our ground truth lives in object storage, and then that allows us to serve use cases like multi-tenant rag and agents very, very cost effectively, right?

Because if you don't need it, we just leave it in object storage, and now we have a completely serverless kind of operation, right? That allows you to be very sporadic. You don't just pay for what you use.

It's a really, really good scale, cost, performance offering for you, for, for our customers, right? But what that doesn't work well for though is, is something like a recommender system. Because if you think about a recommender system, I need to send a single index, a single, like, you know, node itself, tens of thousands of queries per second, right?

It needs to be essentially dedicated hardware so that we can actually serve it and fully saturate it and actually give you the best latency and performance instead of like, trying to overcomplicate it somewhere else, right? And so that's kind of where, you know, this new offering is what we call dedicated re nodes, is that instead of being a multi-tenant architecture, what we're doing is that we're actually allowing customers to select and say, Hey, for this given index, I'm gonna dedicate five nodes to this one, and I just want to, you know, fully saturate it. You're the only customer on it, and it really enables very high performance and, you know, high throughput for really, really good cost performance, right?

But really kind of the intuition here is that we en and we enable people to really fully saturate the hardware instead of really like, you know, dispersing it and kind of working in a multi-tenant fashion. So that's kind of the key enablement around what dedicated Read Notes does. And, you know, we actually have like pretty massive scale, you know, use cases on top of it.

4 billion vectors. Uh, you, you know, 6,000 queries per second, right? With like a P 50 of 25 milliseconds.

So like, this is really, really cost perform and high scale hardware. But of course, as I mentioned, the thing that's really interesting that I do want to call out is that Pine C supports both models, right? You can be really, really sporadic, very cheap pay for operation, or we also support the ability to do essentially these dedicated hard work profiles, which honestly, to my knowledge, is fairly unique in the industry.

No other kind of vector database offering kind of provide both at the sa at the same time. So really it's all about flexibility for our customers and really enabling them to find the right exact hardware profile for their actual use case And just for people out there. So Pine Con's offering this kind of as a service.

Mm-hmm. Absolutely. Right?

And that, that's the important thing I want people to know. Jeff, we mentioned it's in public preview. When do you think it might be JG eight?

Yeah, we're looking right now at a Q2, so sometime in April, I believe targeting roughly April's timeframe for a, you know, a, uh, GA release. Uh, in the meantime though, I think it is something where, you know, we do have, even though it's in public preview, we do have a couple of customers running on production in it, as, as we always do. So I would really recommend if you guys want to give it a try and, and, you know, and let me know what the feedback is.

We're actively continuing to improve the performance, the efficiency, the capabilities of this. And you know, we're really eager to see this kind of powering massive scale use cases in the future. Very cool, man.

Hey, Jeff, we're about outta time. I want to thank you for coming on here. It was a great discussion.

I think our audience definitely learned something, and that's the best thing about it. Um, come back, you know, we don't get enough information like this. We are lucky to have you, and we'd love to hear more as Pine Cone has more news and more, you know, breakthroughs that they're making here.

Thank you very much. Yeah, thank you. Alright, Jeff z VP product pin cone here on Textron tv.

We're gonna take a break. We'll be right back. Hello and welcome to the latest edition of the Text Drawing AI leadership series.

I'm your host, Mike Ra. Today we're with Frank Ada, who's C-I-O-C-T-O for Zider TruCare. And we're having a chat about the impact tech and AI in general is gonna have on maybe, hopefully improving the state of healthcare.

Frank, welcome the show. Thanks. Really appreciate being here, Mike.

Uh, look, we looking forward to our discussion. I feel like something is fundamentally changing here in the healthcare sector because for years when it came to tech, healthcare was a laggard. I mean, outside of maybe some fancy new machine that would save someone's life, you know, the whole backend of healthcare was always something of, uh, you know, a lot of legacy technology that was hard to support.

And now when I look around, especially in the age of ai, I almost feel like they're at the forefront of this because, well, maybe they have the data and maybe they've had instruction enough to make use of all this stuff. But what's your current assessment of the state of healthcare? And it, It, it, it's definitely evolving from an AI perspective.

And, um, zider TrueCare, um, we focus, um, on the, the clinical, um, aspect of, uh, healthcare with care management, uh, utilization management, a lot of population health. An easy example of that is when you go to the doctor or go to the doctor, you need a procedure. You need to get a prior authorization approval.

Our utilization management software handles that. Same with on, uh, kind of like a a a case management side when you're on a care plan, um, working with physicians, uh, and, and nurses to, to, to review that, to, to give you the outcome that you need. And, um, you know, we also developent AI products that are embedded in these ecosystems that are changing the way business process within large, uh, payers.

Insurance companies work, definitely a ton of data. Um, but it's connecting that ecosystem, redefining business processes. And going back to your original question, there's been a lot of great technology deployed over many years, um, with less than optimal outcomes.

And what we do through our, our products that have been around for about 20 years, we have 44 million lives, um, supporting our customers on our platform is to reshape, um, and connect that ecosystem and redefine those, um, business process that deliver outcomes, uh, in, in, in the world that is evolving, given the cost pressures, um, all of the regulatory mandates from, um, entities like CMS, et cetera. To your point about cost, there's a lot of conversation about that, uh, all up and down the line from Congress all the way down to the local dinner table probably, or diner. Um, what impact can it have on those costs?

Because some of them are kinda baked into the system, but it's not clear how many of them are actually directly relatable to the fact that the data itself is problematic to manage. Mm-hmm. Yeah.

Yeah. And, and great question. And, um, that's the, the, the business we're in of solving with, um, you know, our, our, our product suite.

And we look at the cost of, of care is going up, the cost to administer, um, workloads are going up, data is spread across these large enterprise ecosystems. In some case, disconnected very or very hard to connect a lot of one-off solutions. And the healthcare costs rising population is not, um, getting any healthier.

And what we're able to do is go in and we sell our, our product in terms of piloting. We show, um, hey, how we can, uh, re-engineer these processes, um, with our agentic ai, take human out of the loop, and also improve outcomes. Um, and, you know, we're looking at it from, Hey, we can give you a, a 20% performance improvement with operational optimization increase.

Um, you know, the, the, the outcome that you are delivering to, um, your, your customers, the, the folks that you insure and really, um, e extract that cost from the business at, at a lower price point. And that's what you have to do, because there's been excellent technology implemented for the past 15 to 20 years in these companies. And the outcomes aren't changing.

The costs keep increasing, and you have to really get in there, um, connecting the ecosystem through agent ai, taking risk from these payers, insurance companies, turning that into opportunity, um, not only from better care delivery, lower prices on plans, um, but also technology outcomes. So what is the appetite for agen ai? And I asked the question because on the one hand, there is a lot of data to navigate and it is part of the problem.

But, um, there are also trust issues with agen ai and I have to put the right controls in place. Mm-hmm. I have the right context to get there and outcome.

Mm-hmm. And healthcare doesn't like probabilistic solutions. They want it to be right a hundred percent of the time.

Yep. Mm-hmm. Mm-hmm.

Yeah. And that's a, a, a great question. Um, and, you know, some of the challenges and opportunities we have in front of us, if you look back at the beginning of 2025, nobody was talking about ag agentic ai.

And now there's products and companies that solely focus on it. Yesterday I read an article that, um, the Salesforce CEO will take cloud out of his VO vocabulary and only speak in agentic interfaces. So this is, um, you know, where we're at, it evolves.

But, you know, there are, um, core challenges to model hallucination bias. And our approach when we come in, especially on the payer side, on the provider side of healthcare, there's been a little more uptake, um, a little bit more quickly, a little bit more in, in public sector too. Um, but, you know, we bring, um, a, a set of capabilities when we go to market and, um, we call it our recode philosophy with a model office.

So we have PhD MDs that support and build our technology as well as your traditional engineers. And we go in, we, we prove it out. Um, we're able to train models.

We're able to bring, um, a diff different models in to, um, increase evidence-based outcomes and provide confidence scores with, um, how we deliver AI through workflows, reshape workflows. And it's a, a, a, a barrier. Like sometimes it's a barrier, sometimes it's an opportunity.

But you have to involve, um, you know, chief medical officers compliance, uh, folks when, when you're rolling this out to, to overcome those challenges. And it's also working with ecosystems that are API enabled where you have agents to agents doing the work, and an agent in the background might be going and pulling data solving problems and delivering them to you, where that has to be accurate, you know, on, on a customer side as well as within your product. But I think as we train more, we look at eliminating bias, hallucinations, and, and focus on refining our models.

It'll have larger and larger uptake. 'cause it's very serious in terms of you have people's health and, and lives at risk and you can't have things, um, you know, deviate in that process. So it's a, um, a like, we look at it as a, a measured scale up where we could come in and we look at a line of business and a percentage of workflow and, and go from there.

Um, but it's definitely new. It's where the market is going is at right now. And we'll eventually, um, drive that, that change in innovation and trust.

Um, and, and supporting that from a a product perspective. We have the Zider Institute at Carnegie Mellon, where we work with them on, um, agen ai, um, challenges like this in the greater industry, um, governance, compliance, uh, efficacy from, you know, uh, the medical side of the house as well. Is this an opportunity to fix something?

And I'm asking the question because I seem to remember the time when, you know, electronic medical records were gonna cure what ails us. And we got in there and everybody seems to have these systems. And yet every time you go to a physician and you gotta move from one to the next to the next, especially if you're older, um, they still don't know, you know, what's in the record from one to the next to the other.

And, you know mm-hmm. It's that level of interoperability that we were trying to achieve never seems to have been realized in a way that resulted in a better patient experience. So, is AI gonna finally deliver on that promise?

Um, not by itself. Um, we have, uh, a set focus on interoperability services, um, within our products, in, in services that, uh, we deliver. Because you can't just take all of this data that's in a disconnected, um, environment, multiple environments, mesh it together and, and bring it in.

Um, it's a real philosophy, um, and it's real work to have interoperability in place. So when you bring the data in from these disparate systems into your tology, um, and you start really, um, implementing this, this type of software and, and, and work within the customer ecosystem, it has to be correct. And it's not a, a, a AI is not a panacea for it.

It, um, you have to work with your customers to really, um, en ensure that, um, you know, this, i, this interoperability how you like your data, how it's updated, um, where it comes from is correct. We can accelerate that, um, pace. We can provide better outcomes, but it's still something that needs to be worked at.

And if you look at the landscape with what AI and agentic AI is, uh, doing to the marketplace, there are now net new startup companies popping up to focus on this interoperability challenge. Um, um, because it's not just on the intake. It's like you said, when you move from one physician to another, uh, it, it, it spans EHRs, it spans, uh, clinical software companies like us, uh, claims core admin software companies, uh, CRM, it's a, uh, a, a very large, uh, ecosystem that you have to get working correctly.

But the opportunity is there to connect these in a agentic way where it can be very seamless, uh, very forward looking and less, i, I would say, siloed, um, to a particular vendor, or even in some cases monolithic. But it's, you know, it's there every day solving for that challenge. Do you think we might also, because we can get to the data better, see some actual medical breakthroughs as a result?

And I'll ask the question in this regard, and I realize you're not a physician per se, but it's clear there are things like, uh, cancer clusters in specific regions, and there's should be some sort of common root cause it just has alluded us all these years. But, you know, is the answer to a lot of these questions somewhere in the data? Yeah, I, I think, uh, uh, it is, it is rooted in data.

Um, it is, I think AI helps. Um, you know, we've published our, um, VP of AI innovation has published or co-published a, um, uh, uh, papers with Mayo Clinic regarding this. And, um, you know, this kind of how AI impacts, um, you know, the, the world, uh, in, in terms of, of solving these challenges.

And it, it's there, it's an accelerant. It's it, you know, helping speed things along 'cause you want better outcomes from this. And whether it's medical devices to cancer research, to, um, you know, uh, uh, utilization management and AI plays a big factor there.

And I think, uh, you know, we saw what, you know, cloud and, and hyperscalers have done in the past 10 to 15 years. We're just dipping our toe in the water, uh, on what a AI could bring, um, in, in terms of, of benefit in to society in that regard. They're smarter people than, than than myself working on these challenges every day.

But I, I think it's definitely, um, you know, going to really accelerate how some of these, um, diseases get cured or variants of diseases as well. So it's tremendous opportunities, tremendous forward outlook. There are, of course, some healthcare organizations that are simply larger than others, and they have more money to spend.

But is there something you're seeing amongst them, regardless of size that says that they're more successful with IT and AI in general than others because of something they do or a cultural issue? Or, or is there something that, you know, a pattern that you see among those organizations that you just go, yeah, those folks get it. Mm-hmm.

Yeah. You know, that, that's a another excellent, uh, observation in question where, um, you find where people treat technology as an asset rather than a cost. Um, those organizations, no matter, um, how big or small, always have an advantage because they're always looking to something with, um, and accelerator, right?

It, it doesn't matter if it's AI or, you know, some type of different technology where it's embedded into the, the ecosystem. It's delivered in conjunction with, um, type operational integration, um, joint decisioning and, you know, realistic strategies followed by very pragmatic execution. Um, those organizations really succeed, um, because a lot of what, you know, I've seen in, in, in my 25 years are, uh, you know, the organizations that have that, um, you know, really just move along, right?

Um, they're able to adapt, upgrade when business throws kind of a curve ball in and they have to change, it's, uh, they're prepared to change. It's when you look at it as purely a cost or, um, I need this for that, where the business isn't leveraged or, you know, strategy is, is not realistic, um, in terms of what you actually are encountering and delivering on a day-to-day basis. Those, those tend not to, to do really well.

Um, so we're at the end of the year, you know, what is your, you know, outlook for the coming year, 2026, you know, what are you looking most forward to? Um, just really, um, getting leaps and bounds into our agentic delivery quicker, faster, um, because we have the scale and really seeing how that evolves and where the, the, the market is going because, um, you know, we're looking and, and how we sell and go to market is completely different than a, a lot of other companies maybe a year ago, because we're able to write, um, software, uh, in a very much more prolific way with the tool, the AI tools that we incorporate into our engineering, our testing. Um, and you know, how we del and our delivery process and just continuing to build and optimize that, tackling new challenges, um, that are coming our way.

Um, we still have, um, you know, some legacy products that we have to, to, to, to pull forward. Um, but 2026, the outlook is, uh, uh, really good. And I, I think too, um, all these different startups in our space and, and what they're doing, um, lend a lot to us that we can, um, utilize to move and shape, um, what we're doing in our own space.

So, um, you know, the, uh, I, I think we're, we're kind of like on a three month iteration cycle. I think it's going to be that fast or quicker where we have to, uh, uh, adapt and adjust and, uh, you know, try to, um, keep our position, uh, within the market and not being, uh, caught from behind. Hey, folks, I think we all realize at this point that AI will help from it and all kinds of other automation is having a profound impact on our lives and everything that goes along with that.

But in terms of benefiting society, well, I think in the healthcare space, we're about to see some amazingly profound things. Hey, Frank, thanks for being on the show. Well, thanks for having me, Mike.

Uh, really enjoyed it. All right. And thank you all for watching the latest episode of the Techstrong AI Leadership Insight series.

Find this episode and others on our website. We invite you to check all those out. Until then, we'll see you next time.

Hey, everyone, welcome back to the Techstrong tv. My next guest is Casey Marks. Casey is the Chief Operating Officer at ISC two.

Now, I know what a lot of you are thinking, isn't it? ISC squared? No, it's not ISC squared, but two years ago they, they changed the nomenclature or whatever naming here, and they've gone to ISC too.

But don't worry, I'm here on your behalf. So I immediately asked Casey and his team, why'd you change Casey? We, you know, I, we, this real investigative journalism at its best.

Why did you change? Oh, it's certainly a talker than answering questions about the detailed content outlines. Alan, thank you.

First of all, it's, it's wonderful to be here. It's wonderful to be able to talk with you about, uh, ISC two and all the great things that we're doing. And, uh, again, my name is Casey Marks, I'm the Chief Operating Officer, and what are we doing with this?

So, yeah, a couple years ago, we wanted to make it easy. Technically, ISC squared was absolutely correct in terms of the initials of the full organizational name. Um, but just, uh, you know what?

People got really tired of typing out Super scripts and ens, and let's just call it ID two and make it easy. You know what, that's where the world's going. Make it easy.

That's right. But you, you can always ask just AI to type it out for you, and, you know, we'll dumb it down. Yeah, we'll dumb it down.

Casey, you're the, as I mentioned, you're the COO over at ISC too. But give us a little bit of your background. How long have you been there?

What'd you do before that? Yeah, sure. Uh, thanks, uh, well, first of all, you know, I've been at ISC two now for, uh, well, it's more than 10 years.

Uh, I've been saying almost 10 years, almost 10 years for so long. But I've now exceeded 10 years. 10 years.

My 11th year. Uh, I originally started in the organization to computerize, adaptively, uh, transfer the C-I-S-S-P from a linear exam to an adaptive exam. And so that was, that is the reason why I started at the organization.

Um, I am not a cybersecurity expert, and I don't pretend to be one. I am a psychometrician by education, and I've been doing high stakes certification and licensure exams for now, unfortunately, into 30 years. And so everything from nursing licensure, um, uh, language testing, English language testing in, uh, certifications of all types in the private space.

And so, uh, very proud to be with IAC two during this time, and seeing the growth of, uh, of all of our certifications, uh, C-I-S-S-P and the rest of the portfolio. And so, uh, just been doing that a long time. Um, also responsible for our professional development.

So anything with regard to our CPE opportunities and our, our certification education and, and a lot of the research that we do, um, you know, for, for, for the profession, uh, we do things like, uh, the code of professional conduct, our unified body of knowledge, and just a lot of, uh, you know, uh, generation of, uh, professional materials, uh, to, to support, uh, the profession. Excellent. You know, one of the companies I co-founded over the years was the DevOps Institute, and we were certification education provider.

So I have a little bit of experience, you know, dealing in that. And it's, it is a fascinating field. And, and the truth of the matter is, you don't have to be a subject matter expert in what you're certifying people on just the whole certification process and implementation and making sure curriculum is correct.

And, you know, ours was maybe a little different and that we had all these channel partners around the world teaching classes based on our curriculum, but, you know, they, they dabble, let's say they deviate. So it was, it was, it was a learning experience. I'll say that.

Um, tacy, you know, you mentioned C-I-S-S-P and some of the others. Of course, I think most people in our audience are familiar with ISC squared for the C-I-S-S-P, which is kind of the gold standard in, in, uh, cyber security certifications. I was before cyber became anything I security.

Yeah. But what are some of the other certs that you guys offer? Sure.

Yeah. So, you know, we do, we do lots of stuff at IC too, and, you know, to your point, and talking about, um, uh, DevOps and yeah, y you don't want me and you don't want an expert doing this stuff. 'cause you don't want someone putting their thumb on the scale.

You want someone who knows the art and science and how to be able to, to make the machine turn and get the, uh, the right subject matter experts who matter to contribute. And, and that's what we do at SISC two. We are, we are member focused.

We are member driven. Um, everybody, every single person who is certified as a member, every single member is a certified individual. And we have nine certifications in the entire portfolio.

Everything from the, the newest and the most entry level, which is the certified in cybersecurity, the cc, um, which is a no experience, uh, requires certification, professional certification requires CPE, gotta maintain it, all that good stuff, um, up to what we call the concentrations and, and engineering management. Um, and those are, uh, a post C-I-S-S-P more experienced, a little bit more focused. But, you know, we have the CCSP, which is in cloud and LP is secure development.

And so we have a ton of different stuff. I, I, I certainly recommend for folks who are, who are interested in certs, and, you know, it sounds odd coming from the guy who does certs and, and things around certs that they're not for everybody. Uh, they are aspirational.

They are for those who wish to be able to have them, we believe that they have value, but there's lots of ways to demonstrate value. But should you wanna go in that direction, we got a lot of information, um, about all of them on the website to be able to pick and choose what might be valuable to you. Excellent.

Yeah. All right. Let's pivot a little bit.

You guys recently released your 2025 cybersecurity workforce study, and you know, it's funny, I'm doing a lot of my year end wrap up for the next week to 10 days that we'll run here at techron. Look, I think historically, when we look back on this year, this is, this is a, a defining year in a lot of ways. I think in some ways this is really the first year of the 21st century, right?

Free from the the 20th century shadow, right? Because I mean, the internet was really a 20th century thing, though. It came into its own here over the last 25 years.

But it, it was a 1990s thing. So much of what we, you know, secure, well, the cloud, I guess was a 21st century thing, but Right, right there. Clearly now with AI and everything that this year has brought, we're, we're in kind of uncharted waters.

Yeah. So I am, I think our studies are gonna start to reflect these kinds of churn. Wondering what you guys saw in your study?

Yeah. Well, let me, let me, let me jump out, Eddie. We've been doing this for a while.

Um, it is something that, uh, even, uh, pro professionals that maybe aren't certified or don't hold any, any certs, they, they know about these things because for years mm-hmm. We've been talking about, uh, uh, you know, the workplace and trying to describe, uh, kind of like the match between skills and persons and, uh, and the demands and how, and how companies are seeing, uh, how to fill those gaps. And, uh, you know, we do survey over 16,000 people globally.

Um, those fluctuates, you know, over time. And we try to get better samples all the time and more people and more places and, and really try to, to get in depth. Um, you know, you know, we look at, you know, uh, how the practitioners is feeling about these things, how they're responding to challenges, how, how, how, how the employer is, is demanding things of folks.

And, and it really does highlight a lot of the challenges with regard to, you know, staffing and skills match and, and kinda like ongoing. And, uh, it's, it's, it's been a really nice, uh, you know, study over time. And, and, and to your point, I mean, this, this may be one of the AI and, and everything that we'll, we'll talk about here in a moment, the first big, uh, first big, uh, bump in the road, I think, uh, after, after years of talking about the workplace in a very specific way, we've, uh, we've had a little bit of a change.

Yeah. To say the least. To say the least.

So, Casey, well, I should mention, we're, we're going to talk about a couple key findings and some other stuff, but for people who want to maybe get, get their hands on the study and dive deep, what's their best way to do that? org. Um, under the insights and research section of the website, you could find this study along with tons of others.

Uh, we do employer surveys. We do the workforce study. We will replace, uh, uh, a number of different, uh, work products that members provide and experiences.

And, uh, there's a lot of good, rich information that talks about, uh, boots on the ground experience. Excellent. Alright, let's dive into this study though.

What would you say some of the key findings are? Uh, so, uh, do you want me to just say AI and be done, or do you want me to, do you want the detail? Well, you know, it's funny, we're doing a virtual event next month.

Uh, we do every year predict. Yeah. And our, our thing is, uh, it's sort of a pseudo time magazine cover with a, an AI thing on it.

Yeah. You know, it's, but you do, you call the person of the year, I don't know, entity of the year, whatever, but yeah. Be, but, you know, let's peel the a in the I off for a second and see what lays underneath that.

Well, it, it is, as I was alluding to earlier, it is, we have had a big change, uh, right. So for, for years we've talked about deficiencies in the workplace and, and in the workforce and talking about people, um, somehow, uh, from the assumption, you know, more people, uh, will get better outcomes and to a certain extent that it's absolutely true. But this year is the first year that, you know, it really got more nuanced in talking about what people and what they bring to the table and the skills match rather than just brute force.

Um, and, you know, you're still gonna have the determinations. Do you have enough people, do you have enough assets? Do you have, do you have enough tooling to be able to, to to, to secure what's important to you and what's required from your business process?

But, you know, we're, we're, we're thinking about it differently. And so, um, the, the number one and the single biggest change it was, was the talk about skills and the skills match. And to be specific, you know, it, it's, uh, over 95% of the organizations, um, that we were send, that they've had at least one critical skills gap in terms of what they were looking at in this past year in terms of being able to do what they wanted to do.

And we didn't get terribly specific on, on, on what those means, but if they identified as important and they couldn't do it, it was a mismatch for them. And, and 88%, so almost 90% said that that deficiency led to, um, let's just say I, I incident is strong, but maybe just say an outcome that they didn't really care to have. And so I think that it's really impactful to talk about how this gap is really impacting the performance of individuals.

Yeah. I, I agree with you. It is, um, I mean, of course the question is, is that only going to rapidly increase this next year?

And the, you know, or, but let me ask you another question. What is, what is A-C-I-S-S-P to do? What is a security person to do?

Well, this, that's really interesting. So at the individual level, at the organizational level, kind of like, you know, so what are people looking for? So, um, in terms of like, uh, knowledge, uh, areas and tooling concerns and hiring managers in particular, you know, at cloud, we talked about cloud a little bit, still incredibly important.

The nature of cloud and what, and, and, and, and, and deployment looks different, slightly different in some cases now than before. Hyperscalers and, and, and, and, and, and, and mass, uh, uh, integration is certainly important. There's regionalization happening, um, that is, is in competing in some ways that's been, been doing some things different in terms of having, um, uh, localization of cloud and in different clouds and, uh, being able to be more robust.

Um, and so some of the thinking is definitely changing, uh, ai, uh, artificial learning, uh, and, and machine intelligence. Um, while everyone who you speak to in the practitioner base will talk to you about that, you know, it's, it's not new, but to your point earlier, it's the velocity is changing. And this is the first time where maybe it's starting to, uh, to, to take over and start to, uh, tee it up.

Um, you know, certainly, uh, security engineering concerns, um, uh, is certainly skill based gaps in terms of, of how to think about how you deploy solutions. Um, GRCA lot of governance risk and compliance issues. And this, this is an area that we keep seeing popping up.

So when we talk about, uh, uh, skills that are in demand and kinda like what gets impacted by AI first, and it, 'cause it's all around us and it's, it's so prevalent, but, um, you know, those high frequency, low volume kind of like, uh, you know, high automation type tasks, and you, you have an awful lot of that within the, a compliance framework environment. And so AI can be a very assistive in that process. And so we're seeing a lot, seeing more, um, in the way that I like to talk about it, uh, professional codification of practice, uh, within the GRC space.

And that really is where AI is impacting things. Agreed. Um, I, I, so all of these things are great.

You get, I, I told you I was working on year end stuff. I can't help but think though that a lot of the people watching this out there, a lot of the people who are CISSPs or who get information from IC two, it, it becomes personal for them. Casey, a lot of these people are thinking, my friend got laid off.

Yes. I've seen big cuts in all of these companies, especially tech companies, right? Big layoffs and, you know, people being quote unquote replaced by ai.

What does my job look like? What's my, how is this affecting my career arc? Should I be getting, should I be getting smarter about ai?

Should I be taking classes Yeah. Education to make me a better cyber individual, a better cyber worker, because I am AI empowered in how to use it. And I think people are think because you can't blame, it's not being selfish.

It's, you know, charity starts at home, it starts at their home. Think. And then, well, what does that mean for ISC two?

Do you have an obligation, not an obligation, yeah. Maybe an obligation to say, Hey, let us keep you upskilled for this next generation, for this next wave iteration of what you need to, to be a security pro. Well, you know, that, that's, that's a really interesting question.

So, you know, we do feel we have a responsibility, uh, to the profession and to our members to make sure that we're doing things that are, are timely, they're important, they're impactful, and across the board. And that there's many different, uh, avenues that, that goes in. Um, staying current and staying on top of things, you know, that really is one of the hallmarks of professional certification versus other areas of, um, a skills validation.

So different than an educational degree, different than a certificate. Um, all learning is good learning, uh, to stay on top of things. It's different strokes for different folks that wouldn't criticize anybody who's interested in bettering themselves and being able to develop their skills.

Um, what you need to do is probably based on your personal preference, and, you know, obviously we love to be able to help people and provide things, uh, with regard to, um, uh, having training that's, that's fit for purpose and certifications, uh, at this point, yeah, I would say you're out there in the security space and for as much as anybody who's there and anybody who wants to be there, you better get ahead of this stuff with, uh, with ai. Um, you have to stay current. Technology does change all the time.

15, you know, 10, 15 years ago, um, if you wanted to be at the bleeding edge, you, you, you had to get involved in, in cloud, you had to understand what was going on. Even if we weren't doing it, you were gonna get impacted by it. I think it's the same thing going on right now.

Um, we do have those things. We do have courses, we do have certifications. Um, there's, there's information all over, but you can't pretend it's not gonna happen.

It's coming. It, it's not gonna be stopped. And, and you know what, Alan, it's an opportunity.

Um, e even, you know, some of the latest and greatest I was watching, uh, any, uh, uh, uh, an interview with the CEO from, from IBM, you know, very clearly saying, you know, we're not gonna see, um, we're gonna see ups and downs with regard to employment, but in the long run, we're gonna see opportunity. Um, and, you know, this is gonna be a tool that is gonna change a lot, much like other transformative technologies that, you know, the, the economy grows and opportunity grows with it. Absolutely.

Casey, another question for you. What did this year's study kind of, you didn't have on your bingo cart, right? It was like, wow, I didn't see that coming.

Oh, Yeah. Um, you know, I, and this is, uh, maybe not gonna be as, it's quite as whizzbang, um, in ter in terms of things that, you know, I, I don't think that we expected the pivot to skills over persons. Uh, we did expect, we did expect, and maybe, maybe that's the AI impact.

Um, uh, you know, who, who did you say? Um, and I, I think that that was just the, the characterization, how the problem was framed changed, and that was probably the most impactful decision, hers dcb uh, outcome and finding Fair, right? org.

You could find there under research, right? Yes, Exactly. I remember.

Yeah. Um, and then I guess I should ask, so this is the 2025. We, we probably won't see a full-blown report like this till the end of next year, huh?

That's correct. Uh, we'll, we, we're gonna do the same thing again. We're gonna learn from what we have here.

We're gonna learn from employers, we're gonna learn from the folks out in the field who're gonna try to improve this. It will be interesting, uh, predictably in terms of what's gonna happen. Uh, where will the, uh, the AI euphemism, what will it evolve into, what specifically, where will we go with that?

And of course, we'll be looking out all along the way and figuring how that impacts, you know, facts, p certs and PXI education impacts the learning opportunities, um, engagement opportunities. Uh, but it's, it, it's gonna be engaging and just gonna try to figure out what gets prioritized. Agreed, man.

All right. You got an invitation. Come back next year on this, unless something good happens in between, we could talk about Unless we solve it all.

Perfect. Thanks, Alan, I appreciate that. All right.

KC Marks Chief operating officer of ISC two here on, uh, text Drunk tv. We're gonna take a break, we'll be back in a minute. Hello and welcome to the latest edition of the Techstrong AI Leadership Insight series.

I'm your host, Mike Bazar. Today we're with Rich Waldron, who's CEO for trade ai, and we're having a chat about, well, AI agents and how to track their performance and measure their value. Rich, welcome to the show.

Thanks, Mike. How you doing? I'm well, I'm well.

I think everybody went from what is an AI agent to pretty quickly saying they're gonna be billions of them. And I'm not quite clear how we're gonna afford all that, but, uh, at the very least, maybe we're each gonna have a dozen of these to manage and try to figure out. But, you know, there'll be dozens and dozens of these over the spread of an organization, and then it becomes, well, how do I know which of these AI agents is doing the right thing performing well?

And for that matter could wind up being more trouble than it's worth, but how are we gonna evolve from here? So yeah, I think it's a, it's a interesting question because you are totally right in the speed at which people went from, Hmm, I think I might need an agent to, I now have a lot of agents, what do I do about them and how do I figure out, uh, how effective they're being? I think there are kind of two ways to look at impact or effectiveness, and often they get conflated.

So on one hand there's the kind of technical side of it, which is, is the agent doing the thing that it should be? Like, is it technically sound? Is it carrying out the actions in a verifiable or accurate way?

Can I determine that the sort of implementation or the execution of this agent is, uh, is up to par? And then the second question is, is it actually doing anything valuable? And that, that latter piece is far more around figuring out what the impact of the agent is, and that that's not dissimilar to kind of taking on any sort of consultant or thinking about any line of work that we do today.

In that you sort of start with, here's the thing that I'm trying to solve, uh, what will determine whether or not I've been successful. And so I think there's, there's sort of two ways to kind of look at the effectiveness of a, of an agent. One is technical execution, and the other is, uh, overall impact or business impact that it has.

Mm-hmm. When I talk to folks, at least early adopters right now, there seem to be in some sort of mindset that says, well, the AI agent never quite does the same thing the same way twice. So they ask it to do something about four or five different times, and then they pick the one outcome that they seem to think is the best suited for their task.

But that seems kinda an expensive way of going about doing that, shall we say, because now I'm, I don't know, let's say I'm, I'm throwing out 80% of the output that I created for the 20% that I have preferred, and ultimately the cost of that gets a little crazy. So how do I kind of like start thinking or putting some sort of, um, controls or governances around that, that activity in a way that won't break the bank? Yeah, so the, I think that falls into the first camp that I was describing, which is the evaluation of the technical implementation and the way in which it, it's executing as a, as a function.

And I think really the, the challenge here for a lot of folks is that we used to kind of buying software or interacting with software in a way where once it's set up, it kind of just works and it often stays the way that it, you know, we implemented it, um, until further down the line, something else in the business changes. Whereas an agent kind of needs constant nurturing. You know, if you think of a agent, much like, uh, the, the analogy I use is it's the, it's the intern that kind of knows everything, but what the intern doesn't have is necessarily the experience.

That's the thing that you are, you are implementing or, or kind of educating that, that intern on over time, which is how they ultimately become more effective. And so there are, there are a couple of sort of basic, um, uh, actions I think people need to take. The first one is really the feedback loop.

Uh, and this is, you know, probably the lowest hanging fruit. And one of the most important things, which is to handle the thing that you just described, really, you're trying to get a feedback loop going whereby you are indicating back to the agent whether or not it handled the, uh, question or the action in the way that, that you wanted it to. And that feedback loop then kind of feeding that insight back into its memory, meaning that the next time around it's gonna perform in the, in the way that you want it to.

And so that, that kind of loop of, um, helping to sort of train and, and, and push the agent in the right direction is necessary. And the speed at which you can make these agents more productive, um, is partly through a feedback loop. And then it's secondarily through figuring out what scope you are providing the agent in the first place.

So how these things end up being expensive is if you're providing too broader scope, uh, and expecting an agent to carry out a much wider range of, um, uh, actions. Therefore it has a much bigger aperture to, to kind of, you know, get wrong or, or not, not acting the way that you like. So keeping that relatively limited scope, introducing those feedback loops is the fastest way to getting a, an effective agent from a technical standpoint.

Yeah. So to your point, we've established that there's costs to these things and we can put some controls in and maybe best practices to minimize those costs, but there's a difference between cost and value. So how do I understand what the value of the AI agent was to the business?

Yeah, and I think that the, the value piece is this is something that, um, uh, you know, we actually have a lot of experience at. You know, I, there, there hasn't been a, uh, uh, a software project that I've ever worked on, or at least a good one that didn't have some semblance of how we measure ROI at the end of it. And I think the, the agent implementations are very much the same.

So a a bit of a classic example is if you think of, um, using a support agent of some kind, maybe it's a IT support agent or a, or a customer support agent, the way in which you're measuring impact is, um, through things like number of tickets, deflective or, or responses handled, and then you're effectively, you, you know, you are then being able to measure that against what your expectation is, um, uh, prior to the agent being around. And what did that allow you to accomplish that you, you weren't able to accomplish before once you start getting some of these business metrics in place? You know, another example being, um, we've worked with a, a, an organization that's implemented a, an agent that basically does call preparations for, um, account managers before they get on a customer call.

And what they discovered is account managers in general, we're spending between 45 minutes and an hour sort of researching an account before going and, and getting on the call. And they were often going to the same places, getting the same sources, setting up in the same way, so that they were able to put some simple time tracking in place to figure out, okay, well how much time is this agent saving each, you know, account rep, uh, uh, per week? And then you can figure out, well, what does that look like over the entire workforce or over the entire group?

And then you're starting to get to a place where you can figure out, okay, well how effective is this agent being, like, what, what impact is it happening, uh, on, on the bottom line? And then ultimately the top line? Because in theory, you're in a better position to support your customer.

So I think that that business outcome angle, um, is a little bit tangible and, and kind of based on, uh, e each individual business. But we, we should be thinking about this in the same way that we would in with any sort of project that, that people are embarking on. Mm-hmm.

There's a lot of debate about, um, how best to pay for the AI agent. And some people are saying, you know, it's token, others are per seat and others are re length at time of usage, et cetera, et cetera. Um, but the value of the AI agent will differ widely per business and per task.

And so do we need to rethink how we're gonna price the way we consume AI agents? Is there another way to think about this? I think in, uh, you know, what, 25 years of SaaS delivered software and, and many years before that of, uh, on-premise and ERP delivering software, I think the, the pricing model has kind of constantly evolved.

You know, we, I think e even SaaS itself, it was pretty standard to charge per seat. Then consumption started to be introduced because the workloads or the payloads began to change. And I think with ai, it, it, we're gonna see a similar sort of oscillation between pricing models.

I think quite early on there was a lot of discussion around outcome-based pricing, but to your point, everybody's outcomes look slightly different. Um, the value of the outcome is slightly different. You know, if you've, if you've got an agent working on a, uh, account that's worth a million dollars or, um, a hundred accounts that are worth $10, like, how do you think about the effort required or the way in which you sort of break that down to be able to develop a pricing model on a, on an individual basis?

I think what I'm commonly seeing right now is more of a sort of consumption slash token based approach, which companies are then using to get a sense of what their normal course and speed looks like. And then, then you're able to start trying to, you know, push that into, um, uh, models or, or at least ways of thinking about pricing that are slightly more applicable to the, to the action that's being taken. Um, I think, long story short, I don't think we're quite far off enough along yet where, um, uh, enough of these have been stood up at a level for us to be able to say, Hey, this is the de facto pricing model.

And, and, and that's the way that that, that this should be oriented. Correct me if I'm wrong, but I think I'm also starting to see early signs of what I might call an AI agent divide. And it goes something like this, people who have a lot of expertise in a space, for instance, are able to manage multiple agents performing tasks in parallel.

Mm-hmm. And they get that whole superpower kind of value. Then there's other folks who, you know, are mere mortals and they basically can't handle the cognitive load of all that parallel processing in their heads and how to bring all that together.

Yep. And they're likely to get a different value out of AI agents, and there's probably those who can maybe only handle the concept of an AI agent, you know, performing tasks in a more sequential fashion. Is that gonna impact the value that we get out of it?

And therefore, you might have some organizations that are like, we're deriving an awesome amount of value based on the skills of our people versus, uh, others that might not. Without a doubt. I think the, um, I think it goes deeper than that, which is, I think that during this sort of initial phase, there was a lot of kind of, uh, what I would describe that as throwing the agent at the problem, right?

Which is an agent may not be the right solution, but people felt like that's the thing that they should be trying to, to solve whichever problem it was that that emerged. What I've seen through the, uh, uh, customers that we have that have kind of gone a bit further down the maturity cycle, they're starting to get a bit of a balance between, um, agents that are delivered to their, uh, employees or, or kind of, or, or provided externally to, to customers. And those are built out in a more kind of traditional, um, uh, like software delivery, um, pattern.

Uh, they're very thoughtful about how those are built out. There's a, there's a lot of, um, additional modeling that's gone into figuring out what the right execution is gonna be in some cases. There, there are multiple agents that are working together so that they, they solve some of the problems that you described, which is how you handle feedback and supervision and, and, and elements such as that.

But secondarily, they recognize that actually, um, some solutions need something a little bit more deterministic, and part of that flow will actually be ag agentic. So that's more of like a agentic workflow model. It's not leaving everything up to open interpretation and, and, and for an agent to go and figure out.

And I think at the other end of that spectrum, there are companies that have kind of said, Hey, look, we'll open up the budget and we'll open up the tools and we'll let everybody experiment, and you're gonna get a pretty mixed bag of results. Because in the same way, uh, your average employee perhaps isn't that great at prompting, they also don't necessarily know, you know, how to solve their own problem or, or, or, or how to construct an agent to go and do it in the first place. So how you approach that as a company has a, um, significant impact on the overall outcome.

You know, the, the technology is no doubt astounding and evolves at a rapid pace. How we harness it is really, you know, where the, where the difference is made. It almost seems like there's two vectors to kinda master here.

Um, one is, I gotta make sure that the data that I show the AI agent in the first place is of sufficient quality to drive the outcome I'm looking for. But secondarily, it seems like the AI agent more so than the copilot, needs more context. And you hear the phrase context engineering.

Mm-hmm. And much of what you just described in my mind comes under the heading of context engineering. But yeah, there's an art to that, right?

Because I also talked to other folks and they're like, you know, starting to use phrases like context rot because they gave the agent too much context and then it went off and did all kinds of things. So how do we kinda strike a balance here? Yeah.

I think a good way to think about an agent is, an agent is as effective as the knowledge you give it and the, uh, tools that it has available to itself. So if, uh, if, if you know, AI's uh, overarching skill is reasoning, then the knowledge that you make available to that, um, uh, to, to the LLM to be able to reason it significantly impacts its ability to, to react or make the right decision. And then secondarily, the tools that it has available limit or enable the impact that it could have.

And I think what a lot of people have discovered is that agents that have a more specific purpose are far more effective. You know, if they've got, uh, less knowledge that they're working from that is very rich and they have specific tooling, which allows 'em to carry those tasks, then they can be extremely effective. Uh, I think the, the mistake I often see is this idea of like a kind of super agent, uh, uh, whereby I'm gonna give it access to everything that it could possibly know, and I'm gonna give it as many tools as it could have.

And that has a, a kind of paralyzing impact, right? You, you, you get into that whole context rot debate, and you get into really a bit of an inability to take an effective action. That's why you hear a lot about the agent to agent model, because then you have agents that have specific knowledge and specific skills, but they're aware of what other agents around them can do, and therefore you are kind of limiting the scope, but opening up the aperture in a way that's far more effective.

Mm-hmm. Are you at all concerned that we might therefore encounter something that feels like a little bit like the trough of disillusionment when it comes to AI agents? Because, well, while 10 to 15% of the population has the cognitive skills to master them and become super humans, the rest are gonna be, you know, are basically gonna conclude that this may be just, you know, yet again, the IT industry overhyping something and it doesn't quite deliver that value.

So we might spend a year where people are kind of like, yeah, this stuff is, you know, it's, it is mildly helpful. I, I think it very much depends on, um, how each organization approaches their utilization of ai. Um, and I think that kind of like any tech cycle, you know, you, the, the people that are the builders aren't necessarily, um, uh, always gonna be the, the consumers.

What I, what I mean by that is it matters how you construct these agents. It matters when how you think about their effectiveness. It matters, um, how you think about, um, uh, the ongoing, not just governance of them, but ultimately how they're gonna evolve.

And that is a skill that does require, um, um, a skillset and, and it requires having people that kind of think in, in, in that manner or, or in that sort of method of delivery. I think the disillusionment that you described is ultimately when we are just opening up the technology without thinking much about the richness of knowledge or without thinking about what tooling these things, uh, uh, uh, are given and expecting a huge audience just to go and be successful. I think that that might, you know, where, where I think the, um, dare I say it, um, over hype or the over excitement is, um, I think things like vibe coding are a, are a, and I'm talking in a generalist way, are a great way for people to get an idea of what's possible, but not necessarily going to lead to, you know, production quality evolved outcomes.

And so really you're gonna see the, the companies that break out are the ones that take that approach, really think about how they're gonna harness their technology, really think about doing it diligently and well, and the others that are kind of expecting to just turn this stuff on and, and it's gonna produce magic, uh, I think will be disappointed. So would organizations be well advised therefore, to create something that feels like, you know, a center of AI agent excellence, where they're gonna go and, you know, work all these issues out and then kind of teach the rest of the organization how to master it? I think most organizations already have that department, and that is the IT department, you know, and in if, if I think back to, you know, my, my years in the integration world, the best integrations were always a handhold between IT and a department.

And the department brings the, the main knowledge and the, uh, business problem and the idea of, of, of, of what is required. And the IT department has the expertise in developing solutions and knowing how to, how to utilize the technology and harness it in the right way. And so I think, uh, a lot of companies have these steering committees and they're ultimately made up of, uh, line of business expertise.

And then, you know, we're, we're hearing about kind of, you know, forward engineers or, or, or, or, um, AI engineers that can work within, within these departments. I think that's very much a, um, a model for, for a lot of people to follow, because it means that not only are you building in a way where you're thinking carefully about effectiveness and ROI, but also you've got an awareness of what else exists. Because then the power is when these agents are, you know, are aware of each other and can tap into each other's capability.

If you're building in a silo and each department's trying to do stuff on their own, you know, then you're really not gonna get the value that, that, that could be available to you. Mm-hmm. So how will this all play out ultimately?

Let's assume that everybody's got a dozen AI agents, and then the organization will probably have a bunch of AI agents that are assigned their particular set of tasks that they will do on behalf of everybody else, and all this stuff will need to be orchestrated somehow or other. So where does the intelligence lie for coordinating all the activity of the AI agents who are all reasoning across some set of tasks, but, um, how are they sharing their information with each other and with us? Yeah, so I think there are protocols emerging, um, that, that help with this, uh, protocols like agent to agent for, for example, uh, which is one that we at Tray have adopted.

And it, it all comes down to how you think about that centralization. Um, uh, you know, I think realistically there are going to be organizations that will adopt agents from multiple vendors. So this idea that there is some central, um, orchestration that a, there is a protocol that enables agents to communicate with each other.

And really it comes down to how well described are the agents and their capable actions, because that's then what is used for, for these, uh, uh, uh, for the agents to be able to figure out, okay, what is available to me? Where do I need to go for this? How do I, um, uh, how do I utilize the, the skills or the capabilities of, of the agents that surround me?

And so, yeah, we, we at t Tray have, have been building out this model, this sort of AI orchestration platform, really with that thought line in pro, uh, in the top of our mind because it's the, it's the same thing we saw from the, uh, integration world, which is, all right, well, I've bought 3, 4, 500 applications. Well, how the hell do I get these things to communicate with each other? How do I get some sort of centralization?

What do I need to be the glue that sits between these organizations? And that historically has always been a integration platform as a service. I think the evolution for, for, um, uh, for our, for our business is very much in that realm.

And I think you'll see more and more, um, uh, vendors kind of sit up and, and approach it in a similar way. All right, folks, you heard it here. There's no magic wand for AI agents.

You still gotta do the work. You gotta figure out how to a, teach them, train them, monitor them, check their behavior, and then figure out how to make 'em all play nice with each other. Hey Rich, thanks for being on the show.

Thanks Mike. Alright, and thank you all for watching the latest episode of the Techstrong AI Leadership Insight series. You can find this episode and others on our website.

We invite you to check them all out. Until then, we'll see you next time. Hey everyone, welcome back to our Tech Drug TV coverage of AWS Reinvent 2025, sponsored by our good friends at suse.

You know, we've been doing a couple of panels. I love doing the panels 'cause we get a lot of points of view and we've got some really, really smart people that I, I enjoy learning from. Let me introduce you to our smart panel for today.

I'm gonna start on the far right with Rick and I'm gonna let each of them introduce themselves because I'm not smart enough to remember all their names and titles. But Rick, why don't you go first. Sure.

My name's Rick. I'm the general manager of the Linux team at suse. Wonderful, Excellent.

Uh, Manuka Kar, I lead all of our business and Linux application partnerships at AWS And Margaret Dawson. I also love Linux, but it's not in my job title. And I'm the Chief Marketing Officer at suse.

Excellent. Thank you all. Rick.

The other two folks gave their last name. I'm going to call you out. Sure.

My name is Rick Spencer. That's what we want to hear Rick. 'cause there's someone at home who says, that's my dad, my husband, my someone I Think I kids, mom, my kids probably know my name, but name if you wanna find me online, I'm Rick Spencer.

Three, all one word on all my social media. There we go. Who's Rick Spencer?

One and Two. My grandfather and my father. Oh, that's cool.

Okay. Really? Yeah.

Very cool. Well be that as it may though. We're here to talk about something really important today.

You know, I wanted to start this conversation off with, I, I've been been a user and a a my name is Alan. I am an AWS user, um, for a long time. And you know, it's funny, when you start, you first start, I don't know how many of you have been, I assume you have, have done on your own AWS journey, but you whip out your credit card, you open your first instance, it's easy peasy.

Mm-hmm. Right? Pick.

I'll go with the default Linux and I click that. I'll click one of these, eh, gimme two of those. And, and it's very simple, but these things have a way of, of like being like rabbits where they breed and, and, and, and at each level it becomes more complex and more complex.

And then your, your cohort at work, he's on his own journey and she's on her own journey. And then someone says, how many instances of AWS are we running on this table? Everyone gives the face.

I don't know. It becomes a complex kind of thing because maybe you didn't pick the same Linux I'm running. Maybe you didn't pick the same configuration manager mm-hmm.

Program mano. I'm sure at AWS you guys know this journey well where one day you wake up, you know, and then give, gave rice the whole finops movement as well. Right?

That was part of it. Yeah. Right.

What do I, my goodness, what am I managing here? What do we got? How do I know what we have?

How do we, how do we bring order to this chaos? Right? And it, it's a problem.

It's a real problem, right? For, for most organizations, especially at the enterprise level. Rick, I don't know, do you see this problem on-prem as much at ssa?

Like, 'cause SUSE has a lot of on-prem enterprises as well? Or is this kind of a, a cloud specific? Um, I would say it's not cloud specific.

So what we see is that in a typical enterprise, they're abs absolutely managing a multi Linux estate as we, as we put it. And, um, there's different reasons that lead to that as, as you mentioned, like the ease on a cloud provider, like, you know, picking a workload, launching it without, you know, giving thought to what you know, what you're gonna do in two days when there's time to apply updates and et cetera. Um, also, um, you know, different, uh, different ways of running workloads.

One company might acquire another company and that company had standardized in a different way. Uh, so this is one of the reasons that our multi Linux manager tool is so popular. 'cause that allows you to manage any Linux anywhere at any scale.

That's very useful in AWS environments. 'cause you know, EC2 customers may be managing, you know, workloads from, with all different, um, oss, not just suse, of course, Amazon Linux, you know, other, other, other linuxes. And so that's, uh, a really good option for if you're an EC2 customer, you want to get like a pane of glass that'll help you make sure things are being up to date.

Multi Linux manager will help you apply, uh, policies. It'll help you mirror repositories so that you can, um, you know, operate with the utmost safety, et cetera. I Think this goes to some things we've talked about a lot this week already, where an AWS and SUSE are very aligned in providing that choice.

I mean, people may not realize that when they spin up an instance in AWS you can choose from a multitude of different linuxes or, or Kubernetes. I know we're gonna move to that in a minute. But, um, I think the importance is that we're now very, very focused on how do you help people understand those different environments?

How do you help people manage those different environments? Have more of a, you know, single control plane. I don't actually believe in a single pane of glass.

I don't think we can even do that. But like, we can help you have better visibility. How do we understand the cost better?

How do you integrate finops different things? So that's really where we are today. And then, you know, part of that is then how we integrate ai because that's helping you automate, helping you do things more easily.

So I think the original demand and ease of use that came out of AWS you know, initially that allowed developers to spin all that up and have that choice. Now people want a little bit more control, a little bit more management, a little bit more visibility while still giving developers, while still having choice, correct. While still having choice.

They're not giving choice up. That's correct. And still, and ease of use.

Developers wanna build an app, they wanna build cool things. So how do we not take away any of the ease of use? We don't wanna add friction, but for the ops people, for the management, for all the other people that are leveraging, um, that incredible cloud infrastructure, how do we give them more visibility and more management, more control?

And I think a part, uh, a part of coming here at Reinvent with all the 60,000 developers and, you know, people who are part of the A Ws ecosystem, I think it's also to put a flag, not, uh, right now, but like what's gonna happen over the next 12 months. And I think a part of this conversation of managing complexity is also, you know, some of the stuff that, uh, Matt shared yesterday in the keynote around the work that we are doing with our frontier models. Yeah.

And I think that, uh, the, the whole idea is that we are trying to abstract the complexity of it. Mm-hmm. So there is a level of, you know, abstraction, the complexity that is happening, what Rick mentioned, uh, at the, uh, the Linux manager level.

Mm-hmm. But as we progress over the next 12 months, I think we need to talk about how we are bringing the MCP servers as part of this conversation. How do we bring the, you know, the, the big announcement yesterday was around Quick Suite.

Yes. So I think the, the thinking here is that yes, we are having this, uh, SUSE Linux manager that is going to manage a lot of the, what is your security posture? What's your packages?

What is the profile? That stuff is, you know, that's beautiful Now from a developer who's just getting started, you know, the people who are doing one servers or two servers, they're not really super sophisticated into the nuts and bolts of the Linux, or as well as for the AWS for that matter. I think we are moving to this natural language processing interface, like a chat bot.

You know, you talk to the machine and they response back. So I think that's where it is very exciting over the next 12 months. I think where we see is integration of the SUSE manager with Quick Suite.

So customers can actually just speak or, you know, in natural language discuss, Hey, what is, you know, how is my overall the next distribution? What is my cost? How do I manage it?

What do my security posture? And I think that really makes it much more accessible and democratizes and then also gives you more control and you have more control, more choice as Well. Right?

Yeah. And it also brings in like the Agentic capabilities. And so in some ways I look at the Quick Suite as like sort of almost an, an agentic orchestrator.

And so you need the primitives there, which are those NPC servers, for instance, the, you know, right now you can go get multi Linux manager, you can install the MCP server, try it out. Right now you can go spin up SLES 16. Our, our latest release we released a few weeks ago install the MCP server, of course rancher, um, install rancher, get your Kubernetes clusters in control mm-hmm.

And install your MCP server, which is great. But Quick Suite really can like help you take it to a next level by allowing you to, you know, run agentic jobs, which maybe, you know, if it detects an issue, the LLM can like make some decisions about maybe I need to log a ticket, maybe I need to use, you know, the other services that are available as part of Quick Suite. Maybe enhance the information with some other things that Quick Suite would know, like, you know, who owns that server in your organization.

Mm-hmm. Is it mission critical or is this something that can go down? And those kinds of things.

So I think the combination of like the SUSE infrastructure, uh, using EC2 and EKS and Quick Suite is going to, it'll just be totally different in 12 months how people are managing their infrastructure at scale. And there'll be like more uptime, more efficiency. Um, I'm, I'm really excited to see what, uh, happens over the next 12 months.

You know, scale's a funny word, right? Your scale may be different than my scale. Yes.

Right? And, and so it's relative, but you know, they don't call a WSA hyperscaler for nothing. Right.

Some of this, the scale of, of the, of the install base right. Of, of, of enterprises on AWS is truly massive, massive scale. Mm-hmm.

And to me, it seems like this is a perfect use case for a AI and agent AI and MPC servers, right? Because how else are we going to get our hands around this? I mean, and, and when you think about why do we want to use ai, right?

It's, it's to do things that the mundane things, yes. But it's to do things that we, we can't not easily get our hands around. Mm-hmm.

And, and so I, I think it's important now, we spoke in the last panel about the strategic nature of the relationship now mm-hmm. Between Linux, uh, between, excuse me, between s and a and AWS around Linux, around Rancher, and around Kubernetes and all of this. But I don't think you can mention those three or four things without now mentioning AI as well, because this is an AI assisted model.

Mm-hmm. We've seen a lot, as you mentioned, man, there was a lot of, uh, announcements around AgTech and a AgTech AI and AI in general yesterday. Rick, I'm gonna throw it to you and then we bounce it around the panel here.

How, you know, 'cause this is a 12 month roadmap, let's say, but who knows? This goes real quick. How quickly do you see suse, you know, taking what was announced this week, internalizing it, if you will, and, and reflecting it on what's available in the marketplace?

Well, I, I would argue it's happening right now. And that SUSE is uniquely positioned for this. I think only SUSE has a multi Linux management tool.

Only SUSE has a multi Kubernetes management tool. Um, only SUSE also has a, a Linux distribution that's made to work with that. Um, now, um, if I may, I, I would caution people to think about, like, if you're just grabbing an MCP server, hooking it up to your LLM and letting it, like do what it wants to do, that's probably not a good idea.

Because if, you know, if you just take the approach of just exposing the API to an LLM, we've all read the tragedies of like, LLMs deleting production databases and stuff like that. So the thing I would say is that, you know, if you look at SUSE's history over the last, what, 25 years, you know, we've been really focused on like security, safety, compliance. So we're very carefully building those agentic capabilities so that they fit into a real, real world workflow, you know?

And, um, and I, I think we're probably the most trustworthy company to actually like, bring all of the, uh, power of the quick suite and all that agentic orchestration that people are doing, like, into the operation space. I think I just wanna call. Thanks, Rick.

I think I wanna call back to your point about scale. Like a, a company scale is different from B'S company. I wanna highlight, uh, that SUSE and AWS have been partnering for two decades now.

Mm-hmm. And so we have thousands of customers that are running mission critical workloads, both on the SUSE rancher offering as well as stress. And these are, you know, company, you know, workload, they high performance computing, SAP.

So, so customers are really, they love the fact that the two companies have been working together. There's a level of trust and we are supporting mission critical workloads for a long time. So I think Agen is just the next chapter in this partnership.

I think we spoke already about managing the complexity of Linux, which is distributed across AWS and across your multiple, uh, multi-cloud environments as well as on premises. How do we bring this back into, um, you know, a quick, uh, suite view, and then we can actually do real time analytics on that? We spoke about that.

I think that then this entire narrative of abstracting complexity mm-hmm. Then extends to container workflow. So with, you know, the, the rancher manager, the SaaS application that underlie underneath uses, um, uh, bedrock.

So then, again, similar to what we discussed in Linux, in a natural language interface, customers can say, Hey, what's the status of my clusters? Uh, what is the cost associated with it as you discussed? That's a big, uh, that's a big concern as well.

And then, uh, how do I manage it? And so I think, again, it's all about democratizing, making it easy, uh, for our customer. And, you know, we have a track record of doing it for two decades.

And, uh, we just going, this is the next evolution of, I think there's one other piece of this strategic relationship is that we're doing this, you know, we talked about choice across very heterogeneous environments in terms of multi Kubernetes, multi Linux. Like, we're kind of embracing that together. What the AI does is take all of those, you know, very, um, disparate sources almost of data and is does such a great job of bringing that together, just like we make it easier to manage all those disparate, you know, flavors of Kubernetes and Linux.

So it, you know, there's these kind of thematic ideas that both companies are embracing, and it is about lack of complexity, but it's also about ease of use, ease of management. Um, and then I think the other piece that SUSE brings is that open source heritage into AWS that came with the supplemental packages on Amazon Linux. So it comes back to whether you're using SUSE Linux or Amazon Linux, how does SUSE support that and make it even better, bring you the technologies and tools you want as you're using Amazon.

You know, how does Amazon allow you to have a choice around that? And I think that is a very unique and differentiating, um, partnership. Yeah.

Right? Because we're bringing you the stability, the security, the scale. Nobody scales like Amazon, right?

Nobody brings open source technologies in a secure or more stable way. And then we're also giving that choice and control of heterogeneous, heterogeneous environment. So I, I think that combination of all those things is really powerful for the customer.

Absolutely. I, I, I couldn't say it better. Margaret.

We spoke MPC service. Yeah. Everybody has an MPC server today, it seems.

Right? How many MPC servers do M-C-P-M-C-P, excuse me. Mm-hmm.

I forgot what MPC thinks. I Don't know. We should make something Multi player gaming.

It is. It's multiplayer console. That's what I'm saying.

That's what, well, that's where my head's not Bring out the Xbox. Okay. Yeah.

But you know, the, the problem I like, in my mind, I'd like to see Amazon come out with an open source version of the server that everyone could standardize on. And then sui, you could build your special sauce on top of this. In other words, how many different, like right now, how many MCP, right?

Yeah. Mm-hmm. Now you have me questioning myself.

Uh, how many MCP services? So I think one of the things we discussed, um, uh, so first of all, I think the standard spec for M CCP is, is open source. Yes.

Yes. It's Similar with a two A in terms of how the agents think. Yes.

Open source. Um, more than, uh, you know, we launched this CP server slash agent tech slash ai marketplace back at the New York Summit in July. So we have thousands of ISVs as well as products that are listed.

So I think now, at this point in time, um, the customers are really transitioning from piloting over the last year. I think that's our, our thesis is over 2025. A lot of the piloting, a lot of testing, and 2026 customers are really going into production around that.

So I think, uh, so everything that Rick and we discussed in terms of exposing the capability of, for example, the thing we discussed about either rancher, uh, um, you know, manager, or whether the SUSE Linux manager, the MCP is just like, you know, uh, is just an interface into managing that. Uh, you know, and how does it talk to an LLM. Mm-hmm.

And then we bring that all of that, integrate that into Quick Suite. So that's where the customer is, customers actually doing a natural language conversation with quick suite. Either it is text or it is voice.

And then, uh, we abstract the complexity way or through the MCP server in the MCP client in the background, all of that insights, whether it is the state of your Linux operating system, whether it is how your Kubernetes cluster is doing, what is the security, what is the posture, the cost, all of that comes and says, Hey, I can you give me, and for example, a question would be, can you tell me how many Linux distributions that I'm using across my state here? Mm-hmm. What is the status of my Kubernetes?

Just gimme an overall view. How is the cost trending over the last three months, six months, nine months? Just gimme a and what can I do to optimize?

Those are really powerful things right now. It would take, you know, days and weeks for somebody to pull that entire view together. I think we are in the next 12 months we are heading is to consolidate the view two one, I, I would like to, um, well, first of all, if I may, if there's any viewers who are like, what is exactly MCP and how it's working, so think of it as basically like a shim between like any tool and an LLM.

Mm-hmm. And there's two main parts to think about. And the first part is like context, right?

You tell the MCP server tells the LLM how to ask for context, right? So how do you query for logs and that kind of thing. And then the other part is tools or tasks that the MCP ser that the LLM can actually take, right?

You, you tell it, you tell the LLM, you can actually go do these things through the MCP server. Mm-hmm. And, um, so it's actually relatively trivial to stand up an MCP server in the same way it's tri trivial to stand up a, a website.

Mm-hmm. Right? However, designing one that is usable in production in the way that we're talking about is non-trivial.

And so, um, that's why, uh, we're like really excited to be working with Amazon to expose like our very careful painstaking work to like, you know, bring those natural language queries and those automated actions, like into the operations and administration space. And then exposing that back up to, to the, the quick suite where it can bring in other contexts that, you know, were are not being provided by our tools and other actions that, you know, are, are exposed to other tools. Like think of something as simple as like logging a ticket in your Atlassian or whatever mm-hmm.

Your GitHub or et cetera, right? So it can like, ask our tools for, you know, is everything looking okay? And it might say it's looking okay, but a little shaky.

Let me log a ticket so that when your admin wakes up in the morning or it's looking really bad, let me use your PagerDuty and Wake something, somebody up. You know, these are the kinds of like very trivial examples of the kind of workflows that people are gonna be able to build in their, um, you know, using the, the, uh, quick suite along with our MCP servers for our management tools. So mm-hmm.

Mm-hmm. I wanna return to money. So it's, it's always on top of everyone's money.

Ronnie, you mentioned a little bit, we, we can actually through Quick Suite ask the ai what can we do to optimize, you know, you wanna call it thin ops or whatever you wanna call it, but optimize spend cost. Um, how, I mean, it's one thing to ask and get some suggestions. It's another thing to say, okay, you gotta close this, move that, do this, do that.

How close are we to automating that? Like, so for instance, when I write something with ai, right? It says, do you, would you like me to do this?

Yes. Mm-hmm. And it does it, right?

That's, to me, that's the money shot, right? Can we do that now with, with the Linux, with this relationship and, and with what we have there? I mean Sure.

Like, do you want to though, is question. Yeah. So the way that we actually have implemented like our logic is like, you wouldn't let an LLM do something that you wouldn't let a team member do.

Like no sane SRE team lets somebody just like log in and make a massive change without a code review, right? So if you look at some of our demos and, you know, come to our booth, we can show you, you know, for it can, you know, write the recipes and scripts, check it in for you, start the code review process, you know, which is what you would expect a human to do. The only thing is it can be done a lot faster with a lot more context.

You know, the LLM handles complexity in a different way than a human does. But, um, that's to go back to what I was saying before, like that's really why I think you wanna partner with a company like suse. 'cause we take that very, very seriously.

Like, you know, making sure that your, uh, infrastructure is protected and compliant and following all your compliance guidelines, while also giving you all the efficiency gains that, you know, admin teams are so on. I think like absolutely, I think in the enterprise setting, this is still, uh, it's still a, uh, A problem that everybody's working on. Mm-hmm.

I think if you saw, again, Matt's a keynote yesterday. He talked about everything that we are doing around guardrails and policies. Mm-hmm.

So I think there is a lot of active work that we are doing both at the LM level as well as through quick suite in terms of how do we manage and police, uh, you know, the, the actions that the LL m's taking on behalf of the customer. I think have we, are we there fully? I think to Rick's point, um, you know, we can create guardrails.

We can train data. There is already like LLM looking at the LLM to make sure that, uh, you know, we are in the state and narrow here. Uh, and I think that's where we, we discussed like 2025 was a lot about experimenting.

If we have to get to the scale in 2026, get into production, this is probably the most critical, uh, thing that we have to, you know, cross the chasm as they say, uh, to get, to make, uh, enterprises comfortable with, you know, taking that leap. Yeah. And I think the question is, when do you want the LLM to take that action versus you still want a human in the loop, right?

And I don't think we're done with that conversation, right? There's still gonna be times where you want a human to push the button, so to speak. Um, you're not gonna give the LLM access to your source code necessarily, right.

Um, even if you want it to help you develop in your way. So I, I think there's still some, um, ongoing discussion and of where the human in the loop is, um, even with all the guardrails and all the, the, the governance that happens. But I think the more you can automate all of those things and bring us intelligence faster, better, cheaper, right, then we can make better Decisions.

Yeah. I mean the, I mean, we all agree though. There's a massive push to get into that direction.

I think the, the value and the opportunity is, is tremendous. Mm-hmm. Yeah.

And so that's why everybody's just, uh, you know, kind of run, you know, running for, well, I, you know, so you ask yourself why, why, why, why must there be a human in the loop? Well, there's two reasons. Number one, is the underlying technology good enough where I could, I I, because it comes outta to a matter of trust, right?

Mm-hmm. Can I trust it by itself? You know, I, I was driving my car on the highway a couple weeks ago going to the airport and it said, Hey, why don't try our, it's A-B-M-W-B-M-W assisted driving.

I said, all right, let me try it. I'm by myself. No one will yell at me.

I, I, I hit the button and it started driving it, it take your hands off the wheel, take your feet off the pedal, just sit back and watch. And I, I, I found myself like this. Yeah, right.

Sitting ready to pounce on that wheel. And you know what? I never had to, it, it took the turns.

It, if I put on my signal to switch, if there's a car there, it tells me no. If there's no car, it speeds up and switches. It's a very similar experience to rusting the, the LLM, the the AI to do these things.

It, it's gonna take time until you take your hands away from the steering wheel and put 'em on your lap. And we'll get there one day. I think, I don't know if I'll be here, but we'll get there.

And, um, it's, it's, it's coming for sure. Like, there's no doubt in my mind this is the way of where we're headed. I mean, I think it depends on how you look at it, right?

Because if you think of like the driving analogy being that, um, the L LMS is changing your infrastructure like directly, like that makes sense. But if you think about the way people actually manage your infrastructure through like Ansible and SALT and other tools, like we're there right now mm-hmm. Like the absolutely can generate like perfectly serviceable Ansible scripts that can go into your, into your whole GI ops workflow.

And people can, can, you know, your other GI ops team members can look it over, other LLMs can look it over and make sure, like, does this meet our security compliance and et cetera. So, um, I, um, I think the technology there, as they once said on a TV show, we have the technology. Yeah.

But do we have the Trust? I think like a lot to explain a lot of that is deterministic in the sense we know there's like A to B2C, everything is fully track. I think the LLMs is non-deterministic, some part of it.

Uh, so I think like once the technology is there, there's already a pattern here that, you know, the customers trust and you know, yes. This, they're already deploying mission critical workloads through, you know, salt scripts and such. I think the, I think the, the LLM in itself has to, has to evolve to get there, right?

And I think right to Margaret's point, we are still, I think for 2026, we still foresee the human in the loop is gonna be play a very critical, uh, thing. Yeah. Critical role.

Yeah. Agree. I mean, there are, there are patterns in like in for instance, Kubernetes, you can set it to like auto scale and different things.

Mm-hmm. So like I think, um, a lot of it is like what RAC, role-based access controls do you give the LLM And this is already a very common pattern, right? Like a lot of platform teams will say like, Hey, our ops team is allowed to scale out, but they can't scale down.

Or similar things like that. And so I think we'll see over the course of just literally the next 12 months, like patterns like that being brought into like this is a tool that we can give the LLM to just go ahead and do it. 'cause they can't do much damage.

Like they could create a new table, but they can't delete a table. I think the question is like, do you use the ZLM put out patches to all the unpatched systems in your, like, do you trust it enough to do that or not? Do you let it roll back a feature update or not?

Right? So I think there's like a level of specificity Or certain kinds of patches, right, exactly. Maybe do Yeah, exactly.

Or not, right? Yeah. So, and I think just one last point in the race to get to that determination, like, you know, the best state here where we can start trusting the LLM apart, a lot of it is actually training and what data that we are training the LLMR.

And in fact, and in fact that, you know, just SUSE has decades of, uh, data in terms of how customers are using both their Linux and the Kubernetes distributions for AWS you know, what Matt discussed yesterday, the frontier models around developing code on deploying code and running security. So we are running, as you know, is said, hyper scaled, right? So we have like tons of, you know, supporting millions of customers, uh, over a couple of decades now.

So that data, and then what is the learning that we have done as a function of that, that informing some of the, the new AI or the new elements we are developing, specifically what that specific workload, I think that is probably the path to getting our customers more comfortable into deploying it and production use cases. I think that seems like the, the road to get there. Mm-hmm.

So there's been a great discussion, but you, you know what I'm worried about people looking at home, if they're watching this, they're probably not here. I, how can we get them started? How do we, these are all great things that we spoke about, right?

Really kind of job changing if you're a, a Linux administrator or an AWS administrator, Rick, I'm gonna start with you. Sure. Where do people go?

How did they get started? Where's the on-ramp here? Sure.

So, um, I would say for a call to action, if you want to get involved, go to the marketplace. You can get multi Linux manager there. You can get SLES 16 there.

Go ahead and install the tech previews for the MCP servers, start giving us feedback, you know, what's working for you, what's not working for you. It's exactly why we put it out in, uh, in as a tech preview so that we can iterate quickly with the user base. Mm-hmm.

And same is true for a rancher. And, uh, my call to action is do everything what Rick is. That's an easy one.

Plus, uh, uh, you know, try out the new kiro, uh, software developer platform, uh, that is becoming now generally available. Also, go look at, uh, you know, some of the new stuff that we are doing here with, uh, the frontier models, both on the DevOps side as well, security side. I think that is really gonna start, uh, pulling the story together in terms of what, you know, Rick is talking.

And then yeah. And no coincidence. Those are the three agents that were announced yesterday, right?

The kiro agent and security agent, and The DevOps agent. And the DevOps agent. So you get all of those.

Margaret, I wanted to give you the last word. Wow, I don't see this. Now you've got me speechless.

No, I think I'm just really No, no, no. I want that down. I was just gonna say that, that last two days, which feels a lot longer than two days, but there is just so much energy in this show.

There's so much energy around Amazon, around ai, around just how to collaborate. And, and again, I'm gonna go back to, I know this is becoming a repetitive theme, but, you know, continuing to look at the ecosystem around AWS and I would say it's our ecosystem and a W s's ecosystem and the power of how all these companies and all these technologies are coming together to help people, you know, build applications that are better, faster, smarter, more secure, um, and, and deploy them, you know, what works best for them. So it's just exciting.

Like there's just a lot of enthusiasm. Great tech At at great scale too. At Great scale and great technology.

Rick, Manu. Margaret, thank you so much for joining us on our Tech Trunk TV coverage. We're gonna take a break here on Text Trunk tv.

We've got lots more coming at you today and tomorrow, so stay tuned, but we'll be right back. We're back here with some more, uh, coverage from our AWS reinvent recent, uh, video stand. Uh, if you haven't seen some of our other AWS reinvent, uh, coverage, you know what, at this point, most of the videos are up.

You can catch 'em on Textron tv, on the drug tv, YouTube channel, or on our text drug TV OTT app. If you've got Amazon Fire or Roku or Apple tv, or even iOS or Google Play, I, you can get the OTT app there. Um, but let me introduce you to our guest here.

His name is Robert Chichi. Silla. Silla.

Yes. Close. Rob, I was close.

I left the S out. Robert Cilla, first of all, Robert, welcome to Text on tv. Thanks you for having, it's the first time he's been on, so glad to have him on.

Robert, you're with suer as, unless you just took the shirt. It is a great shirt. Possible, sir.

It is a great shirt, but I am with su. Okay. And tell us what, what's your role at c?

So I am the Director of Technical and community Marketing. So I handle our community efforts around, mostly around our consumer community. And we, 'cause we have multiple communities, um, it's like that with any tech company.

So direct to consumer kind of stuff versus, uh, No, when I say consumer, it's people who consume our technology Okay. Is the primary focus. And then our secondary focus is people who contribute.

And on the open side, their focus is slightly different. Where they focus on contributions and less on people adopting, you know, it, they, you know, they kind of build it, it they will come open on that side. So they cater to making sure the project package maintainers are taken care of.

Um, and the needs of these two communities, don't, they overlap, but they're not the exact same. I love it. You know, we've, over the course of AWS reinvent, I bet you I interviewed a half a dozen to 10 Sosa people.

Mm-hmm. Not one of them really spoke about the, they mentioned the community, but they never really spoke about the community. And so let's start right there if we can.

When we talk about the Sousa community, and you mentioned there are different facets, aspects of the community, but how do you define this community? Can you give us sizes? Give us, you know, I don't even know how you would define it.

We, I, I define our community as a large group of practitioners who enjoy the technology and that is the binding glue that brings them together in our community. Um, to count it, it's hard, um, because you people are in certain channels and they're not in others. And we estimate anywhere between, you know, 45 to 65,000 people, um, who are active, who, um, they participate in Rancher Academy, which is a LMS platform.

We put out, we want people to learn about our projects that, that are out there, or they're in our Slack channel, or they're engaging with us on social media and we understand there's crossover. So that's why it's an estimation 'cause Right. I don't, we don't track exactly who's who.

That's just kind of creepy. We just want you to show up for, it's just, well, But that's, that's part of that open source mantra, right? We, we don't track.

Yeah. You know, we're not looking for your blood type or DNA samples like that. We don't Wanna know what your kids' names are.

We don't exactly Like that. Or even your birthday. Yeah.

But, um, so a lot of it is online it sounds like. But then like in an event at AWS Reinventor, are there any kinda suse community activities tied to it? We Do a few videos that we post out the community, um, does crossover with AWS slightly.

Um, when it comes to some of the projects, AWS does have a, a large user community and there's, there's some crossovers there with that. And we see it more so on the consumer side, very little on the con contribution. Um, for us here, it's just, you know, showing what's the latest and greatest on AWS because we understand that comm there are community users who, you know, they're not customers, but they use our, our projects in AWS and we wanna make sure that we, I don't wanna say meet their needs, but know we acknowledge that that's where they're at.

And you know, That's portal they, and they matter. They, they matter. I get that.

What about in-person events in the community, not just at AWS three event, but, So when we have any large event that, that we try to attend, that piggybacks where, what our comm, where our community's at, whether it's here at Reinvent or Coup Con or Open Source Summit, we like to engage with our community, let 'em know that we're there. Um, we always have community team members on staff at these events to ensure that, you know, like they can meet the people that they talk to online. Like these, these are kind, I don't wanna say they're, they're rock stars in my mind because they're, they're great individuals on our community team, but I, I wanna make sure that they can connect, you know, in person just 'cause, you know, it's post COVID world, you know, having that interpersonal connection is, is nice sometimes.

Sure. Absolutely. Let me, um, I, I, I, one of the companies I had started was called the DevOps Institute.

We sold it about three, four years ago. Mm-hmm. But we had a, a nice community.

It was very simple. It was very easy. Well, it wasn't that easy, but one, one part of the community, the people who actually had taken our certification classes and our courses mm-hmm.

And those, we did know their children's name and their date of birth and all that. 'cause we knew who they were. They had a, you know, they took classes and they were certified.

The bigger part of the community though, were just people who maybe, you know, didn't take a, a real certification class, but somehow consumed our content or, or what have you. And it was always the discussion we always had at the exact level is why would those people want to be in our community? What would, like, what, what's the advantage of being in a community, if you will?

Uh, Well, I, I'd like to, I will speak, I mean it in any community, but I wanna speak towards the, the technical community. 'cause you know, it's what we're talking about and it's fairly relevant, is that individuals have to take some of these skills to work. And they don't want to know that.

They don't want people to know. They don't know. So, being anonymous, being able to go and adopt, learn and grow outside of your normal work environment, to come back in and say, I, I, I don't, I know this so I can talk to it.

I'm, I'm participating in it. And I think that's where you see it. And it does cross over to non, I'm a, I'm an avid cook.

I love cooking, I love cutlery. I'm in, you know, a community about, you know, cooking and so, you know, new knife skills or something like that. 'cause I want to learn and grow and not think, my wife thinks I don't know what I'm doing in the kitchen.

But that's just the same thing. It's the same adoption that you want to have. And it's not judgmental.

Someone comes to the community, they don't know. It's like, can we point 'em in the right direction? People love to come in and answer questions for them, and they take that back to work, or they take it back to school.

Absolutely. So there is the, the, the help you grow, and especially from a work related mm-hmm. Point of view.

There, there, look, there are plenty of people who are hobbyists when it, especially things like open source and Linux Yep. And, and so forth. Um, but it is, it, it, it's a way to advance your personal career path.

Let's, let's call it that way. I, I, you know what else? I, and this is me talking now.

I don't have anything to back it up. Sure. But I think it's part of human nature to, to want to feel part of something, part of a community.

I, and, and as you said, it could be cuddly, it could be cooking, it could be anything. But you always wanna feel like, I'm not the only one who feels this way, who has this problem, who, you know, is working on things, solutions to a particular issue. I, I think there's, there's something intrinsic to humanity that wants us, that, you know, drives us to be part of community.

Yeah. It's a, it it's a sense of belonging. Yeah.

So when you, you, you talk to people like we have our regulars in the community, and you talk to 'em and sometimes they will just wanna say hi. Yeah. And, you know, and or they will bring you something that they did and they want, they wanna show it off.

And I love that because you're seeing someone who has the same type of passion. And it makes me feel better. 'cause it's not me going, like, I'm just a nerd here.

There's, there's other nerds like me out there. Love it. Absolutely.

Look, I built my whole business here on those nerds. Right? I mean, they're, they're the people who watch our love and, and consume this.

But it, it's, it's part of being in a tribe. Yeah. Right?

It's tribal at, at it's very nitty gritty. It's tribal. Right.

These are people who are in my tribe. It, it, it may not be a tribe that I live with or, or something like that, but we share that common bond, that common interest and, and they become part of your tribe. And It goes down, it goes even down further where it's like, I, I only like Linux.

I don't like Cloud native. Yeah. And, and that's okay.

And we have a lot of people who are like that. And that's, and it kinda, and you know, there's always rivalries in any type of community, so, you know, we're better than you kind of thing. Mm-hmm.

And it's, I it's akin to sports fans. Right. And then as a Cleveland Browns fan, you know, I don't really fully understand what it's like from a sports perspective, but I'm sure like Eagles fans or someone else out there, you know, with, you know, a better team behind them would understand that level of, you know, rivalry that you have with the technology.

Yeah. My sympathies to you by the way. Thank you.

Okay. Looks like you can have a good pick at a quarterback. Again, though, this I'm only, let's not get into football.

Let's, I'm a Steelers fan. I'm my own trouble. But, um, and I, I actually, my, when my brother who's is, he's one of a fire toing guys, and I say, Hey, be careful what you wish were, 'cause look at the Cleveland Browns, right?

Mm-hmm. But it's all relative. But it is, we are, we're tribes.

It, it, football fans are definitely community and tribal. Yeah. We, we still love, it's in that crossovers.

We, we each love our teams, the Steelers and Browns. We would, we would love our teams and we have those rivalries and we can say, oh, we do this better. And you have, we even have it in the Linux communities where, you know, they don't, there's, there's certain schisms that you have and sometimes they get toxic because, you know, we're in an online community.

Right? Yeah. And when you don't have the interpersonal things go get, they get dark, but they usually recover the, and that's what the beauty of a community, it, it, like naturally recovers.

I I think part of that though is, is, and, and you hit on something when you have a virtual community. Mm-hmm. You know, it's easy for people to sit behind a computer and say something that they would never say in person.

Yep. And it's easy to misinterpret what someone else wrote and may not, they may not be the greatest written communicator, and they, maybe you're taking it the wrong way, or they just wrote it the wrong way. And, and this le look, I've been in online communities for a long time, maybe 40 years.

And, um, well You also for, you didn't mention, but you know, we're international. Right? Right.

And you Right. You guys Are, and there's, and so there's, there's language things. There's language really, I really, barriers, but, you know, this Is No, no, but there's, there's miscommunication All the time.

And sometimes I come into Slack and I'm like, what's going on? Why is there a dumpster fire today? And I'm like, oh guys, he mis like, he meant this.

Like, that's not that word that you think It is, but it doesn't take, It doesn't take long. Doesn't take much. Nope.

It Does not. There's people over the edge. Robert, let me, we're, we're running lower on time.

But for people out here who say, you know what? I've been a SUSE fan. I, or I've been a Rancher fan.

Mm-hmm. Both or, or what have you. I'd like to be more involved in the community.

Sure. What's the best on-ramp farm? io, you can go sign up and you, you get dumped into our general chat and people, and we see, we see people who get put in there and just say hi.

And someone from the community team or someone from the community will do, and explore what they have going on in there. There's, there's a lively chat. Um, there's random stuff that people, you know, post, there's technical checks.

So, you know, if they wanna learn more about K three s or rancher specifically, um, those, that's generally the, the best way. And, you know, I am, I'm in that slack more than our work Slack. So really, that's my world.

Well, that is, that is, that's your work. That's my world. So I come, I go back to work.

It's your tribe. I, it's, yes. And I go back to the work one when I have to, but that's where I, you'll catch me, um, is that, that's probably the best way.

And again, this is for the consumer side. When you're getting started in a community, uh, you don't have to come and contribute right away. I always tell people that just come and say hi and, you know, find where you want to connect.

You know, and it doesn't have to be contributions right away. It doesn't have to be consuming right away. It's just showing up and just being, just taking part.

Excellent. Is this your last show of the year? This is my last show.

Me too. Um, I'm getting, uh, a very busy with a, and I'm gonna do a shameless plug on Scon coming up April 20th through 23rd in Prague chea. Um, that's what's consuming most of my time now is the planning for that, um, on the CFP committee.

So I'm going through, um, uh, being in a group of individuals at suse, going through a ton of talks with a lot of great topics. So if anyone is in Europe can make it. I do.

Well, I hope to see you there. I'm hoping to be there as well. Okay.

I'm thinking maybe I should submit something. Has anyone submitted anything on AI yet? Oh, I'm kidding.

Kidding. That one right there is, uh, I think, I think that's the, the vast majority. And I think when I saw, I saw one that wasn't AI related, I was excited.

I was like, wow, I, I get that way too. It Was brave enough to put that one in, Put something in, not with, it's a crazy time to be alive. I know.

It is. Everything's ai. But yes, if anyone can make it, I would love to see you there.

com, find out more information about that. I Love it. Rob, thanks for coming on and with us today, man.

This is great. Hey, go check out the rest of our AWS reinvent videos. Sussan is coming, I believe it's April 20, the 23rd, as Rob mentioned in Prague, which is a great city.

You don't have to be in Europe to go to that, though. They do have planes that come from here to there. Yep.

And, and, uh, it might be worth your while. It's, uh, I've done sus actually, the last scon I did was in Orlando near our house. Yep.

But it was a great event as well. So highly, highly recommend it. But that's it for here.

I hope you've enjoyed our AWS Reinvent coverage. There's Alan Shimmel for text on tv. Hey everyone, it's Shimmy.

Thanks for joining me. I know this is a little rapid. We just did one yesterday, but we're doing another one today.

Welcome to what is my last shimmy says of the year. And, you know, I, I really put a lot of thought into this one and, uh, I call it Yellow Flags in New Dawns. And it's, you know, it, it's, I think it's a balanced look.

It's fair and balanced as we look back at this year and look ahead to what's ahead of us. Let me start off though, though, when I say we're looking ahead and looking back, we're looking back not at, you know, we're gonna talk about macro things. We're gonna talk about big trends and megas and all that.

But it's really about you. It's you, the people who watch this. It's people like you and I I talk to people like you all, all, all, uh, year long, right?

Whether you're a DevOps person or a security, uh, pro or a cloud engineer, a cloud native person, a platform engineer, whether you're a founder or executive C level, or a director, or just a single practitioner who reads on various text strong sites. What I'm about to say really is about you the individual. That's who I wanted to reach today, right?

It's been a crazy year, right? And I think you are not alone. If you are thinking, boy, this has been a crazy year.

I think we're all thinking about this. And it's been a crazy year for a lot of reasons, right? There's so much going on in the world, there's so much going on in technology.

But let's talk about bringing it home personally to each of us, right? Because we, we were asking what will AI do to the jar market? But you know what, we're ending the year asking, what will AI mean to my job?

What will it mean for me? We're not, you know, we all, were wondering how companies are gonna react and how they're gonna adjust. But now we're wondering how are we going to adjust?

And right there, that shift from worrying about other people and other things and big things to about our own livelihoods and our own futures. And what is, what makes this both scary, exhilarating. And, and so god damn important.

So let, let's start right here. You know, I call this a yellow flag moment. It's kind of like the yellow flag they put out in an indie race or a NASCAR race when there's oil on the track.

And then they gotta clear things up before we could go full speed ahead. Well, we're going full speed ahead anyway, but we are very much sort of in this yellow flag area where we're at this boundary zone where things are happening, right? You're doing your job, you're delivering your work.

But is that gonna be enough going forward in this brave new world? You know, we're seeing layoffs all over the place, especially in tech. I have so many friends who come to me and ask to help for finding new jobs.

It may not be your company that's doing the layoffs, but you're hearing about it from your friends and on other companies. You know what, even your managers, they're trying to give you the, the, the optimistic everything's gonna be okay. But don't think that they're not sharpening up their resumes and checking LinkedIn to find out what's going on, just in case.

What, what's behind all this? Well, for the most part, it's ai, right? Not AI in the abstract, not AI for the industry, but AI as it relates to you, your jobs, your career, your livelihood, your stability.

You know what, this is the first year where AI stopped being a cool story, have got really personal. It became a force that you've had to reckon with you didn't recognize. You didn't ask yourself.

If you haven't asked yourself, rather, at least once, where do I fit in going forward in this AI future? You weren't paying attention. 'cause I think we've all asked ourselves that.

But this isn't a sign of fear. It's a sign of awareness. If you did right, you asked yourself that.

'cause you recognize the writing on the wall. It's a sign. You understand the gravity of the moment we're living in.

And this is where, what it feels like when a new era arrives, and folks, this new era has arrived, right? It's been a such a strange choppy year, right? Because we have ai, let's, let's zoom out a little bit, right?

The year has been, you know, uncertainty to say the least. The global, global economics have, you know, flickered like a neon light at a cheap motel. Um, the markets have shook.

Forecast is, forecasts have shifted. Yeah, the markets have gone up. But a lot of it is strictly due to seven or eight companies, 80% of the growth in the s and p 500, right?

The logic coming out of the halls of power. Sometimes they feel less like policymaking and more like performance art. At the same time, beyond ai, we're dealing worldwide with things like wars, diseases that we thought were done with, or back unrest, instability.

Sort of an authoritarian bend that was seeing across the world that doesn't bode well for personal freedom on top of a crazy technological revolution. The likes of which we haven't seen before. It's almost, you know, I was, I've I was a child of the seventies in music, you know, the moody blue song Night and white satin.

There was a point where breathe deep. The gathering gloom. Red is gray and yellow's white.

But we decide which is right and which is an illusion. That's where we are today, which is right, which is an illusion, which is real. Which way should we go?

Who should we be with? Because a lot of it does all feel like illusions right now. It's all shifting.

Nothing seems solid. But here's the part that too often gets lost. This isn't just a moment of, of uncertainty.

It's a moment of transition. And that's what I keep coming back to. This is a moment of transition.

I always, I said it, I wrote it in this article that accompanies this to me. 2025 feels like the first year of the 21st century. For the first 24 years of this, of the two thousands we've lived off of what we did in the 20th century.

We've lived off technologies like the internet and the cell phone, right? And, and the digital revolution. But those were really 20th century kind of inventions.

And they've powered us for this first 25 years of the 21st century. But you know what, think about it. We're a quarter of the way through this century.

That's a good chunk of the way through this century this year with ai. And a lot of what we're seeing, we are, we are moving forward into the next century. We are clearly in the 21st century, and it's time we stopped thinking our long, 20th century thoughts and start thinking really 21st century thoughts.

And this, whether we're talking about what we're going to use for energy consumption, maybe what we've used in the 19th and 20th centuries doesn't cut it. I'm talking about fossil fuels. You know, whether we're talking about digital and quantum and robotics, n ai and all these things.

These are 20, they're gonna be 21st century technologies and whoever masters those are gonna rule the 21st century. And whoever they will be, who will rule it. It's not gonna be people like me, right?

I'm, I'm at the far end edge of the baby boomers, the last of the boomers. But our days passed, even most of my friends who are Gen Xers, we're in the back of the room. It's time, you know, not to go all John Kennedy on you, but it's time for the torch to be passed through a new generation to the Gen Y, the Gen Zs, the millennials, the alphas, and whatever you call 'em.

It's these people who are going to make the decisions, who are going to utilize these new technologies, these new powers that are going to determine what your life is like in the next 25, 30 years for the bulk of the 21st century, right? Um, and here's the good thing. This new generation, they're not waiting for my permission or your permission.

They're stepping up. They're redefining what work looks like. They grew up in nine 11.

They grew up with COVID, they grew up with the internet, and they're native, and now they're accepting ai. They want to define for themselves what's normal, right? They don't care what we spent decades building up is normal.

They're pushing the boundaries that we thought were unbreakable. And you know what? I for one, love it.

I think it's time we do it. We need to do it. I spend a lot of time talking to people of this generation, including my own two sons.

And let me tell you something, they're different. And they're different in the best of ways. They're not intimidated by the complexity or, or importance of the moment.

They're not nostalgic for the old ways and the old music and the old everything. They're not romanticizing the inefficiencies that we've spent our entire careers trying to patch over. They are ready to take the baton and enter the world that is coming, not the one we're leaving behind.

And they have the tools for it to them. AI isn't a threat. It's a partner, a lever, a multiplier.

It's something that frees up their time so they can focus on what humans actually do best. Creating man imagining, connecting and building. And it's not just ai.

They can't wait for quantum. They're embracing robotics. They're embracing change.

You know, we thought we embraced change, but at a very only at our speed. But let's talk about AI directly. Yes, it's changing jobs.

Yes, it's redefining work. Yes, it's unsettling. But this isn't the first time that technology has shifted the landscape.

And every single time it has, the shift ultimately created more opportunity than it destroyed. Maybe not immediately, but eventually not smoothly, but inevitably. So, AI may take some of our jobs.

It will take certain tasks, it'll define certain roles, it'll elevate certain people, and it's gonna displace a lot of us. But you, humanities, unique value, creativity, judgment, leadership, empathy. That's not changing in this new century, in this new era.

We're being repositioned, not replaced. And that's something that we can shape. And it's something that this next generation wants to stay, wants to shape.

We gotta stay engaged. We stay curious, we stay adaptable. But you know, the old saying, right, if you don't learn the lessons of history, you're bound to repeat their mistakes.

So my advice to this new generation is learn from history. Don't repeat these mistakes. Build something better, stronger than was built before.

And that's why right now it feels like we're at the forge with the Black Smith Station. And everyone knows you can't make steel without heat and without some banging. And I feel like that's what we're doing.

You know, part of the craziness in the world. Like I'm sure you guys all heard, you know, this insanity of Rob Reiner and his wife being killed potentially by his own child, by their own son. And it is just nuts.

And it caused me to really think back on Rob Reiner. I, I loved Rob Reiner's work. I I didn't know him personally, but from his time as the meathead on, on Archie Bunker, all in the family, all through his acting career, the Wolf of Wall Street, you know, he was, uh, Jordan's dad.

And even directing movies. I mean, what a great talent Rob Reiner was. And I'm reminded of a movie that he directed.

Many of you may have seen the American President. Uh, it's starred Michael Douglas. And if you haven't seen that movie, it's an amazing movie and it's a great Rob Ryan movie.

Go see it. But there's a scene in that movie where he, Michael Douglass is the president, finally steps up and basically lays down the gauntlet. And I'm gonna paraphrase kind what he said, because I think it's really right off for where we are today.

And it's this, you know what the bad guys, the, the crazies, the cronies, the stuff we've been dealing with, they've had their moment in time. They've had their 15 minutes. It's time for them to get out of the way and let serious people get to work.

And you know what? You are the serious people, my friends, colleagues, tech workers who are working it. We are the serious people.

We may not be the loudest voices, the flashiest, we're not the doomers of the heart or the hype artist, but we're the people who day after day are building this new tomorrow, building the system, the world, the systems that the world's gonna live on. You'll decide what this new era becomes. You are the ones who are gonna shape how AI is used.

You are the ones who are gonna keep us anchored in reality while we push forward. 'cause there is no other way. The moment isn't calling for spectators, it's calling for contributors.

I'm asking you this 21st century be contributors. Turn the page. We're heading into a new year, right?

Take this week, take a breath, rest, recharge. We've earned it. It's been, it's been a year for sure.

But come January, it's time for all of us together to get back to work. Not just to keep the pace with the world, but to help shape the world. Because the future isn't something that happens to us.

The future should be something we build. So I'm gonna get off my soapbox in just a second here. On behalf of myself, my family, everyone here at Techstrong Group, our colleagues at the FU Group, I wish you all peace.

I wish you health. I wish you quiet in the middle of this loud insanity that is our world today. And I wish all of us, the wisdom and courage to step forward into what's next with purpose.

Because this adventure is just beginning. It's far from over. And folks, here's to going another one of my, uh, favorite TV shows, right?

Here's to going where no person has gone before. Peace, happiness, happy New Year, everyone. I'm shimmy, I'm out.

I'll see you next. YearMy says, Hey everyone, welcome. You know, you need two jacks are better to start Textron Gang today, but we've got 'em.

You're watching Textron Gang. Hi everyone, happy Monday. It's the Monday before Christmas.

What are we doing here? Well, we still got today and tomorrow we won't be doing a show Wednesday. And then we won't have live shows probably till after the first of the year after that.

But nevertheless, savor these last couple shows of 2025 because 2025 is going to go down as a really a boundary layer year. And if you want check out my shimmy says from last Friday where I talked about this, um, I do believe it's the first year of the 21st century. I know you think my math's crazy, but it's true.

Um, let me introduce you to our gang today. We got some hard cuts. I mentioned a pair of Jackson.

Better to, to go over stuff. Got our friends Jack Poller and Jack Gold as, as well as Mike Ard and myself. And we've got some interesting stuff to go over today, gentlemen, welcome.

As we get ready here for Christmas and New Year's and the holiday break, the news doesn't stop 20, as I said earlier, 2025 is gonna go down in the record books as a, an interesting year to say the least. Mike, what do we got for today? Well, at a time when everybody's trying to figure out how to gain AI skills, there's a lot going on.

5 billion. IBM is partnering up again with Pearson to kinda revamp their entire online learning catalog log using AI agents. Sorry about that.

And um, we also have some senators probing some other stuff. We'll get into that later. But the issue seems to be the following.

It's clear that we need to re-skill a huge part of the workforce, but it's not clear to me that organizations are willing to invest in that just yet. At the same time, we don't have time to go sit in formal programs anymore for, I'm gonna sit around for three weeks to learn something. So we're trying to use AI to deliver the right content at the right time.

So the AI agent's gonna observe what you're doing and notice that you might be struggling and then pop up with some things that say, Hey, um, you might be interested in learning this now because it seems like you're trying to figure out how to do this particular thing, ma. And some quarters that might be creepy and others might say it's useful, but the whole way we train people seems to be in need of a revamping. Jack, what's you take on what's going on here?

I sometimes feel like organizations are kinda like the Stein Breaders. All they want to do is hire the best and they don't wanna invest in training. But do we need a farm team for ai?

Yeah, we do. We certainly need more than we've got, uh, you know, various statistics out there showing it's what 80%, 90% of, of AI projects are failing at at large enterprises to, to achieve the, uh, TCO or RROI goals that they've set. There are a number of reasons for that.

Some of it is because, you know, they're, they're not being able to pull in the corporate data into the AI models to make it work properly. Um, and uh, the other issue of course is that they're not able to redirect or, or or redo their workflows to make best use of ai. But a lot of it also has to do with the fact that we're not training people to use AI effectively.

You know, when Office first came out as an example, we just kind of threw it at people and they said, here, you know, here's word, here's Excel, here's PowerPoint, here's all this great, these great tools. Go to town, go use them. But it took several years before people really got good at using those tools.

We're, if we don't train people with new methods of, of work with new methodologies, with new application solutions, we shouldn't expect them to be good at what they're doing and using those things. And so training is going to become one of those issues for large organizations and small that will be a deterministic, uh, or a determinator of how well AI is gonna work within my organization. You can't just throw these things at people.

Um, AI is good if you know which questions to ask it and how to ask them. That's really going to be providing the results that you, that you want. As we move to Ag agentic ai, it gets even worse because now we're expecting AI to be able to replace Mike or Jack or other Jack or Alan, uh, with actual actions.

If you aren't able to tell it what actions you wanted to take precisely what you wanted to do, then it's gonna take actions that aren't gonna be very good. Uh, and I I think you're going to see a real effort by large organizations to start doing a lot more training. You're seeing it of course with the, uh, the issues that you just mentioned, Mike, but I think we're gonna see a lot more of that.

I think you're gonna see a lot more money spent there, which is also, by the way, why Coursera and IBM and others are beefing up their AI training capabilities. 'cause people are gonna actually have to buy those technologies, those capabilities from them. Mm-hmm.

Yeah. What's your take on what's the right balance here? Because there's certainly a certain amount of personal responsibility here, right?

I need to be relevant and I need to get my own skills and it's part of my career, but at the same time, organizations need to invest some level of training. So is there a balance to be struck here or what's your sense of what's going on? Well, sure, you, you need to take some personal initiative for sure, but the question that, that I would have for large organizations is do you let that kind of go out there on its own, uh, initiatives, uh, you know, let people go do their training on their own.

Some will do it very quickly, some will do it over six months, nine months, 12 months a year, two years. Or do you try to impact that training? Most companies forget about AI for the moment, but most companies have some kind of training re-skilling capability in-house anyway.

And so, uh, they realize that training people to do their jobs better presents really good ROI. And I think you're going to see that come about with, with AI as well. I think it's not gonna be just about personal training.

Um, me going out and trying to figure out how to make this work, I think it's gonna be about kickstarting that training within organizations, and maybe it's driven by hr, maybe it's driven by other groups that are going to implement training sessions to try to get the efficiency of people up. That's, that's how you get corporate efficiency to go up. It's, it's on a personal level, not just on the application level.

Mm-hmm. Alan, do you think that this is a national priority issue? Is this something we should be addressing at a country level?

Because ultimately it comes down to our ability to compete, Frankly, gentlemen, I think you're looking at this all room, right? I I have a decent amount of experience here. Having founded the DevOps Institute and we got that up to 65,000 people becoming CER certified in DevOps in about six, seven years before we sold it to people.

Cert one of the largest training companies in the world, right? Um, you gotta know something about the education market. Jack, to your point, there's a difference between reskilling and upskilling.

What you are talking about is upskilling. In other words, I got a developer and I'm gonna make him an AI savvy developer. I upskilled him.

That's very different than having a marketing product, marketing manager whose job has been eliminated by AI and they gotta go become a coder or a, or a car mechanic or something that's reskilling, not upskilling. And, and the real crux of what we're gonna face in this world over the next year or two or three years, is it an upskill or a reskill? Are people, is that QA person gonna stay a QA person to just be harness AI to become an upskilled QA person?

Or is he gonna throw the towel in and say, QA is the, is the land of AI will rule that I'm gonna go become a developer or a, or a platform engineer or something else that, that's number one. Number two, prior, even prior to ai, over the last 10 years, we've seen watershed moment in, in technical skill education and training. We went first from doing these things in person, right?

There was a time Jack and Jack where if you wanted to go get a cert, you went to a class, there were 20 people in the class. It was usually two days a class, 16 hours, 15 hours, and you got a piece of paper that's suitable for framing that said you were certified. Well, 10, 15 years ago that changed.

We went to virtual training, right? Where instead of being in person, you could do a virtual and then at your own pace it's prerecorded and you just take tests at the end of every chapter and you gotta score an 80% or whatever to move to the next chapter. That became dominant.

What we're on the verge of seeing here now is AI monitored, managed instruction AI training where it's no longer an instructor doing the curriculum. It's gonna be ai who's gonna train you to use ai. Sounds a little crazy, but that, that's AI becomes the trainer, right?

Is that letting the fox into the head house? I don't know. But AI is the trainer, not the person, not the individual company.

So I look at this Coursera and Udemi deal, and they're both big traditional trainers who now do a lot of stuff virtually. What, what the, the other change was like people cert, for instance, DevOps Institute, we at DevOps Institute, I had 175 partners around the world who delivered that training both in person and virtually. Now with the web Coursera who, Demi, they delivered that training directly.

They killed that whole channel. A whole business economics bus, you know, economy was killed off there in the training channel because people go direct to these people. But there's a reason why this is an all stock deal training as we know it is dying.

If it's not already dead, this model of Coursera and Udemi, my AI will give me that training without them. Why the hell would I pay them? Because I'm going to get the cert from them.

There was a time where to go get a job as an ops person, you needed an idle certification, right? A lot of jobs said idle certification, mandatory security, Jack, right? You needed that C-I-S-S-P certification.

Those days are done, those days are done. Ai, you just go to AI and say, Hey, make me smart. I want to take a a security class draw, draw me up a class and it's gonna draw you up as class as good as udemi or Coursera's people cap, they're dead men walk in.

I wouldn't take that stock and use it for wallpaper in my bathroom. Now see Alan, I thought I was gonna be the only one who's gonna be contrary and think this is the insanity. This is an insane deal.

It's Over, it's over, it's over. It's, it's over. It was if, look, there's, there's, there's two parts of this.

One is, Alan, you're a hundred percent right in that we went from in-person training to online training. And that's about to change again. Um, if you look at what's motivating a Demi and Coursera to sell is that they had a big spike in online learning during COVID when nobody had to go do to work.

They didn't go into their office, they went and did a whole bunch of training. The big problem has been all along is, who pays for this? And how do they, that, you know, how does it happen?

Right? And the reality is, those companies now realize that people aren't going to pay for this because they have alternative means, as you said, AI in the web and companies will start paying for an AI enabled course and then quickly realize that none of their employees take it because none of their employees ever do this. And it becomes, uh, it's a people process problem as we like to talk about.

Is that a company that goes and says, I'm gonna pay a hundred thousand dollars to Coursera to give my, all of my employees access to all these courses will do that, but they won't go the next step and say, I'm going to give all of my employees a month of time that they can go and spend on this instead of their own personal time. They can spend work time to go take their courses. And unless they do that, nobody's ever gonna take the courses.

And then next year the company's gonna say, we spent all this money on this and we have three people take this course and we got nothing out of it because they took this course upskilled and left. Because now they have to, and, And LinkedIn really killed their market too, Jack, Right? And, and LinkedIn bought Linda, that was, I dunno what, 10 years ago, something that was a long time ago.

And all those courses are available. If you buy LinkedIn premium, then they're all available and They're all you can need. So let me just add, wait, Mike, let me just add one thing.

This is not uniform across the world, right? I learned another lesson I learned at DevOps Institute. So in the US less companies pay for training, right?

A lot of, in the US a lot of people, individuals pay for their own training. 'cause they want to be more valuable in the job market, not at their present job, but for their next job. In Europe, the the model is that companies do plan for training because in Europe, those employees often stay for their entire career in only one or two companies.

And so they are truly uplifting their, their employees, right? And, and oftentimes in Europe, you've got a sign that if you're taking the cla the course through the employer, you've gotta stay at least three years afterwards. So they in essence get their money back.

Then you've got India. India is a whole world unto itself, right? It's a gravity well in the amount of people who were there working in it.

And for whatever reason, the folks in India love their certifications. They love taking these courses, they love getting certified. They don't like paying for them.

So we had, we had different pricing. We had worldwide pricing and then India pricing. India pricing was 60% less than Europe and, and North America off the bat, off the bat.

And then trying to get the money was, was a lot of fun. So, you know, if you have the India market, which I say is a gravity well, and they're saying, should I pay for it or use the AI to make it? It's, I I think There's a couple of things at play here that we didn't mention.

One is, I think a lot of people are getting training on YouTube now, and it's free and it's a video and somebody watches that. And that's part of the thing. I'm not sure I agree with the stock not having value though.

'cause these companies at least theoretically shouldn't have content that's behind a firewall somewhere that is of value that people will pay to get access to. And it will not necessarily be something that chat GPT has seen. And if they don't, they Should.

Well, Mike, what are they gonna have that chat? GPT doesn't have? Well, I'll give you an example with, uh, AWS who's talking about the following thing.

They're revamping their certification programs as we speak, and all that stuff sits behind our firewall. And, But there's nothing proprietary about it other than the certification itself. I've been down AWS offered DevOps training eight years ago, nine years ago.

We looked, we had people take the course. It wasn't, it was, it was a, it was very AWS specific, but nothing that you couldn't learn from just being on AWS What you couldn't get was a certificate from AWS unless you paid them. And that's what, it's the same thing with the C-I-S-S-P.

You've got plenty of security qualified people. They don't got the sheep skin as the old commercial used to say. Right?

So the, the sheepskin as you described it, is also changing though. And what AWS is talking about now is, um, smaller certs for specific skill sets that you can demonstrate. And you're not gonna take some, you know, six month certification program.

They're gonna give you, you know, a a, a micro cert essentially that says, you know, yes, you've proven that you have the ability to provision this and here's your gold star or whatever you want to call it. And I think that's going to be more of that in the age of ai because a Lot only, but Mike, only if employers demand it. If employers don't demand it, it's useless.

How many of you raise your hand if you go through LinkedIn and you see your friends post? I just obtained a new certification. I, I I presented at the Linux Foundation event, or I took a Linda course on, on awas top 10 B-F-D-B-F-D.

It's a nice LinkedIn post. I'm sorry, Jack, Jack. There is one issue though, Alan, and, and, and I think we need to, to, to look at this as well.

AI training isn't free either. So, you know, Coursera is making money, obviously by offering you a course you can go to chat GPT, but you're not gonna get a good course if you're not a subscriber to chat GPT 20, 20 bucks a month. A lot cheaper than that Corsair cost, of course.

Well, and, and, and I will, I will argue that as well, which is that there are plenty of people we know who are experts in these things who will gladly put up a YouTube thing for free and take the YouTube ad money that whatever money they get, because that's the way the, the, the information wants to be free. I mean, this goes back to the days of the early days of the internet and information wants to be free. And it is.

And how many of you subscribe to 20 different online news sites? We don't. Right?

Because if you, if you go and you look at it and there's a New York Times article on something and you're not a New York Times subscriber, you say, okay, let me Google that and see what other news article I can find that has the same info for free. And that's the way the world works. And unless I agree with Alan, unless somebody demands a cert for a job, and we see this, we still see this a huge amount in the security environment.

Um, but you know, I, I consult with the Sands Institute, and this is an issue for organizations that's existence, is to certify people. It's how do you convince an employer to allocate the time and money and for the cert so that they can demand it and have somebody qualify and need it? And do they really need it to do the job?

And that's the big questions everybody's struggling with. Do you need a cert from chat GPT to be able to use chat GPT? Well, I can guarantee you that every, all of our parents here are using it without getting certs and, you know, Right?

And then also, and you know, when I talk to DevOps folks, you know, they're of two minds. One is they get the cert, although they hate it because they had to pay for it, and they're only doing it so they can get past some sort of search engine on an HR system somewhere that's looking for keywords. But you know, when you go talk to people who hire folks, you know, they'll tell you every time, if if it comes down to somebody they know or met at a conference that they know has the skills and can demonstrate it, they'll pick them over.

No matter how many certs they got, It did this. Hence why we sold, oh, hey, we're 22 minutes into this one, man, we, we could talk about it all day, but we can't. We've got other stuff to cover.

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Well, I think, you know, first off, there's politics going on and then there's the rest of the world. In the real world on the political side, you have, this is an issue that Senator Elizabeth Warren can get on our soapbox and say, Hey, look, I wanna hold inquiries and say, you know, the big bag corporations are doing in the common mail. So that's one part of it.

The other part of it is, and it's still politically related, it's how much power is AI using and how much power do we have and how much power do we need? If you look over, and I did a quick search and research for this, uh, segment in the last nuclear power plant the United States approved was in 2012, so that's what, 15, 14, 15, 16 years ago, something like that. In that time since then, we've added, uh, about 500 gigawatts of power.

Now you look at what AI has committed for and, and ai, sorry, open ai, open AI itself wants something like 30 gigawatts in the next year. So that's, um, almost 10% of what we've done in the last 14, 15 years. They wanna consume in the next one to three years.

They wanna build data centers and do that from a data center perspective. And by The way, that's just open ai, right? That's just Open ai, right?

So if you look at, and, and that was just an easy one because they're very public about what they've committed to do. And that's, you know, um, if you look across all of that, you can figure that that's probably not four and a half or five gigawatts, but probably, or sorry, 30 gigawatts is what they wanted. It's probably more closer to a hundred to 150 or 200 gigawatts across all of the major, uh, AI data centers That's close to a quarter to again, you know, getting up there and the amount of power we've added over the last few years.

At the same time, our government has made it very, very hard to open power plants and in particular the one type of power plant that we really need the cleanest and most efficient power plants that we can do, which is nuclear. So if we're not gonna do nuclear and we are going to expand our power requirements, then we have to build coal and natural gas, which are dirty and noisy and et cetera, et cetera. And regulations.

And getting new power plants built is very costly and time consuming, getting through the government regulation process. So you have more demand chasing after a restrictive supply. Prices are going to have to go up.

It's not necessarily evil power companies that are trying to do in the consumer. It's basic economics of supply chasing demand or sorry, demand chasing supply that isn't caught up yet and the government not allowing us to build quickly enough. There's another piece to this as well, and that is not the, not in my backyard phenomena.

Yes. And I, and I and I've talked about that before. When you go outside of Manassas, Virginia, where all the big data centers are outside the nation's capital and the noise from these things is truly horrendous.

Uh, data centers are very, very large. They consume a lot of land and they produce a lot of noise both from the fans, the backup generators, and the humming of the electricity lines and the transformers. And they employ very, very few people.

So it's can be, um, have a very large impact on local communities, particularly in rural areas Where they built and they also don't pay a lot in, in property tax. Yep. Yeah, which Is another piece.

I gotta step up on my soapbox here a second. I'll step down, Alan, so you can step up, step Up. So Jack, you're right, there is political theater and then there's the reality.

But every once in a while, political theater matches the reality at the heart of the political theater. Here is this basic principle. You wanna build AI data centers, you wanna power AI data centers.

You are gonna make a lot of money off of those AI data centers. Why the hell should my electric bill go up? That's real.

That's real, number one. Number two, you know what, I'm all for nuclear power. And I agree we should, we should look at authorizing and investigating.

But let's not rush to bring nuclear power on without proper safeguards, without proper inspections, without proper guidelines. Otherwise let's build it in Chernobyl or next to Love Canal so we could get ready the next generation of irradiated cancer survivors. Right?

I'm not saying nuclear's not safe, I'm saying you don't cut corners for safety, right? This is outta the movie. What was the movie Karen?

Uh, well, China Syndrome was one the three mile out. No, but Silkwood Silkwood wasn't it, wasn't it Silkwood the movie in the book True Story. We, we don't want that.

And Jack, when we talk about, hey, let's do some clean efficient energy, well, why the hell is our government putting the kibosh on wind and solar and renewable clean energy that you talk about our capacity growing, our capacity is grown from those energy sources, right? So if you are, if, if the political theater is, you've got a com, a country that just, the president went online and said, Venezuela stole our oil, right? When this isn't about oil.

Yes. Next gen nuclear maybe. Yes.

To solar, yes to wind truck media themselves. Yes. They announced that they're merging with a company to go build fusion energy plants five years from now, 10 years from now.

It's always five to 10 years from now. That's the real issue here. I don't, Elizabeth Warren isn't wrong here.

I let open AI pay for their energy, let Meta and Google and Microsoft and Amazon pay for the energy that they wanna power these AI plans for. I'm already paying enough for my electric. It's wrong.

And I'll actually give you a real world example here in Massachusetts. Every month I get a bill from the electric company. It's also true on gas where they have added the state has mandated an additional cost for upgrading the infrastructure.

Now who's gonna benefit from that infrastructure upgrade? It's probably not going to be me. You know, the lines on my street are 50 years old and they're not getting up updated anytime soon.

But the data center that they wanna build, you know, down the street or uh, you know, uh, open AI putting in something nearby, those are the folks that are gonna benefit from that. It's the new construction. Absolutely.

That's the benefit. So on my build, so we have FPL far to power and light. Here's an interesting thing on my bill.

Every month they show me how much of my energy consumption was, was done using solar, right? And it, and it goes up every a little bit every month. There's a fundamental issue.

We're going to have to faces a nation here and I'd make it an election time issue. Why should we pay for the tech bros data centers? I don't understand why.

If they want their data center to be approved, they just don't show up and tell everybody in the town that they're gonna pay their electric bill as part of the deal for building The data. 'cause they're too busy telling them all the jobs it's gonna create. Well, that in, they're not making any money to begin with.

Where's the profitability for these guys? Vin Krishna, the CEO of IBMI, I wrote about this about two, three weeks ago. He had a dead on guys.

Jack, to your point, about $8 trillion isn't going to need to be spent to build and power these data centers worldwide. That's a worldwide number. And when you look at the economics of what the return will be on that $8 trillion, understanding that every five years you need to, you need to regen those data centers, right?

Re re you know, upgrade the, the, uh, infrastructure. The emperor has no close here, guys, unless something fundamentally changes. This is a losing proposition.

Yeah. They need 800, $800 billion a year just to pay the debt. Can I tell you something that happened here in Westchester last week.

So there was a meaning about the powering down of the Indian Point reactor. And one of the people there stood up and said, well, you know, we're powering this down and maybe you guys might wanna use this land for data centers. Well, the whole room flipped out.

Oh, I'm sure. But, but Mike, this is a perfect example. I lived in New York.

I remember when Indian Point came online and, and, and everything. We knew that it was end of Lifeing. Why didn't we build a replacement for Indian Point that went through the regulatory process and is ready to come online instead of saying, oh my God, we gotta do something right now.

Cut all the regulations, flip the switch. It's just so, I, it's p**s for planning. Well be, because the answer is because people like you, Alan, who say, I don't want nuclear because it's scary.

No, no. And the reality is, Jack, Hear me out. I didn't say I don't want nuclear, because Yeah, Yeah.

You know, if you're gonna up silk good and Mery Streep Nuclear and all of this stuff, that's, I think next Gen nuclear is very different than what's an Indian point right now, or what was in Three Mile Island. Right. And all I'm saying is don't cut corners on regulatory practice.

And I don't, I don't think anything anybody is arguing for that. I just think, and, and I'm not, I, we Absolutely are arguing. No, absolutely.

I mean, that's just what the Senate, there are bills in the Senate and the, the administration, which is pushing to cut, and they're, and they're preventing states from putting in regulations on this. They wanna cut, turn, drill, baby drill, turn it on right now. And, and the answer is because, you know, look, I, my, my former home was in the, the wonderful state of California where nothing can ever be built because of the regulations they still Have, can't build, still have the, you can't build power plants.

The biggest economy in the world, they're doing pretty good compared to the red states Because they're buying their power from their importing power rather than generating it themselves. And at some point when the, I mean, the power's gotta come from somewhere to, to drive Silicon Valley data centers. Jack, You, you're saying, you're saying, okay, let me show you the extreme case.

I'm telling you, bring next gen nuclear power online. Make sure it's, it's done right. Don't cut corners.

There is another piece to this guy. So let me, let me kind of throw something in here. One of the problems with nuclear is it generally takes five to 10 years to get a plant built and up and running for five to 10 years.

The power companies are spending billions of dollars to get that place up and running with no revenue to show for it. And so, from a financial perspective, if it really takes and forget about regulations, regulations are not, and, and, and Alan, I agree with you, it has to be safe and all of that stuff. But if it takes five to 10 years to build a new plant, get it up and running, and generate no revenue, but still have debt that I've gotta pay down, who's gonna pay for that?

We do. But we always have. We always have.

And that's okay. No, that's the pushback though. People don't want to pay that.

That, that shows up in your bill. Well, The, the and, and the, the, the alternative is to pay for dirty energy like, uh, uh, gas or coal or, or solar. So here in Florida, Jack FPL has invested a ton in these solar farms or whatever you wanna call 'em.

Um, guys, we need, you know, the shimmy says that I did on Friday is that 2025 was the first year of the 21st century. We're done for the first 25 years of this century. We've lived off of and worked in stuff that was done in the 20th century.

We need energy for the 21st century century. Obviously, if we want to do this AI stuff, if we want to live without glowing at night, if we want to, you know, realize the promise of what's before us, we need a plan for the 21st century. And it's not drill, baby drill.

That's the issue. We, and we gotta do it right. I don't suppose We could spend any time, I don't suppose we could spend any time making the existing grid system more efficient than it is.

'cause I don't think That, that, that has to be done too better batteries and all of these things. We need a 21st century energy plan, not, not John Rockefeller's plant. And, and, and people overlook the fact that the current energy grid in this country loses about 20 to 25% of the electricity more just in transmission.

Think It's more, I think it's more Jack. It's highly in. It may be More Sure.

But that's a significant chunk. You know, I don't wanna get emotionally involved here. Let's take a break and come back to our next thing.

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Contact us today and tell your story to the world in the most powerful way with Textron Group. Hey folks, we're back. And we're talking about the, well, the AI bubble again, apparently Oracle has had a setback on funding for a $10 billion data center deal.

And it's raising questions. And at the same time, you know, I also happen to notice that Morgan Stanley is funding some sort of data center initiative using junk bonds. So I guess, Alan, are we running outta money here, or are people saying that, you know, we don't wanna fund these 'cause maybe we're over-provisioned.

What's your take on what's happening here? You know what, this is Mike. That's the world's smallest violin playing hearts and flowers for Larry Ellison.

Um, look, the man increased his net worth by about 40% when, when a a, a bunch of these, uh, Oracle Cloud AI deals were, were announced with Nvidia and open AI and, and Blue Al and et cetera. And now, you know, what goes up must come down. Gravity is no one defies gravity in spite of the song in the movies.

You can't defy gravity. I I, I wrote about this a couple weeks ago as well. The real issue is, and it goes back to our last segment, no, not the nuclear stuff, but the, but the stuff about, uh, what Arvin Krishna wrote about the economics of these things, of these data center factor of these AI factories.

They're, they're spacious, if not downright fraudulent. I mean, it just, it the number for not fraudulent in terms of a crime, but the numbers just don't add up it from an arithmetic point of view on given current, you know, the economics of this currently. It just, you know, whether you're talking about tokens and what tokens are really gonna cost, whether you're talking about where the energy comes from, whether you're talking about what you can charge for, for this stuff, I, I think we, we got a little ahead of our skis and, and now, you know, gravity set in.

All right, Jack Gold, if I take away all the investments that are being made in ai, what is your sense of what's the real state of the economy and what are people kind of actually spending money on? Because from what I could see, at least the numbers that suggest that AI accounts for half of the GDP growth. So maybe we're not growing as much as we should be, because, well, everybody else is not in the AI business.

If we had real numbers, we'd know, wouldn't we? Yeah, I was gonna say the same thing, Alan, if you look at it, I mean, it's all circle financing, right? Uh, Nvidia in invests in my cloud company for a billion dollars, but makes me buy a billion dollars of your chips, Ben, essentially, you're giving me your chips for free for my company.

Uh, so it, it's really, really, really hard to tell, uh, until we get to a point where the investment is more transparent and less circular, then we'll really know. By the way, there is another issue around all of this that, that, that people tend to gloss over. If we're spending all this investment, all this money, and there's a lot of money out there, let, let's, let's be clear.

But if we're spending all this money on ai, what technology, what future technologies are we not spending on? What's it taking away from? And there's always the risk that if we're spending so much, you know, it's, it's like the, the, the, you know, the, the, the web, uh, craziness, right?

Of the, of 20, 25 years ago. Uh, everyone is spending all kinds of money, you know, a OL and Time Warner. Great example, right?

W where's a OL today? Uh, but there are new technologies that are potentially coming in line that we're not investing in, uh, in healthcare, in, you know, like the last segment in, in nuclear power that we can actually deploy quickly and cheaply. There's a lot of stuff that's not happening.

So it's not clear how all of this money that's being spent, uh, is going to, uh, help us. And by, and by the way, Alan, you mentioned earlier that these data centers have to be upgraded every five years. It's more like two years.

If you're an AI data center, you need the latest NVIDIA chips, or you're not a data center anymore. And they're only, they're only good for a year or two. In fact, they, I saw a statistic that said about 30% of them are failing per year because they're driven so hard.

Yeah, That's A lot of, lemme Piggyback on what you were talking about there though, Jack, here. Here's the situation. The fact of the matter is, this first phase of AI expansion that we've lived through, let's say over this last two, three years, was primarily funded by the cash reserves of the Max seven.

Well, minus Apple. I don't think they've spent a dime on AI to speak of. I'm being, yeah, I'm being, I'm being face facetious.

But, you know, it was primarily spent by the, by the cash reserves of the Max seven. And, you know, and you could chalk this up to fools in their money, right? It's their money.

They could do what they want with it. But what we're seeing now is a lot of this AI spend or plan spend is coming from VC money funds, money, debt financing, you know, more traditional ways of, of, uh, capital expenditure here rather than just, you know, because the fact is coming outta COVID, the Max seven, were sitting on Horts horts of cash, you know, collectively in the trillions of dollars. But as that pile goes down and we start financing this stuff with Wall Street money, you know, pension fund money and stuff, do they have pensions?

They're still pension funds, public employees and Middle East money. But when that, you know, when things don't work out like this deal here and, and the stuff hits the fan, well, then it becomes a little bit more painful, doesn't it? Right.

Because you're not just saying, yeah, Google will make it back. Right? Alan, I think, I think there's, there's another part that's very important to layer on top of that, which is financially all of the, the Mag seven, or at least the, sorry, let's look at it this way.

The big three, the cloud providers, Google, Amazon, Microsoft, all have cash engines, cash flow engines that are just throwing off cash left and right. That's something that Google does not have at the same scale. So from a, sorry, not Google.

Um, Oracle does not, sorry, Oracle. Oracle does Not have, well, I wouldn't take up any collections or you know what they, but No, no, but not, not, they don't have the ability to replenish the coffers in the same way that, you know, Google has that, that Google, Microsoft, and Amazon have. Now, I bring that up and I got tongue tied over Google, because Google's going to be the next one hit by this because the entire search engine business is changing completely.

And I think we're going to see an interesting impact on Google's cash flow in the next one, two years, as people are massively changing from going to Google for searches to going to AI for searches. And what is that gonna do for Google's ability for, to get Wall Street money to finance this stuff? Well, well, what they're doing, who's Google's competition?

Open ai, perplexity, anthropic, let's call it generative search. And Google is betting that they're gonna become a generator search company. But you know, Jack, it's funny, I, I did a, I did a shimmy says last Thursday, aren't sure we break up big tech.

Now, I will tell you historically, I'm not a big breaker upper, I, I don't believe in that. I, you know, the only one I've seen in my lifetime of size was the at and t breakup. And you know, it, it worked out okay, but I watched a debate over at Johns Hopkins University, uh, I forget the Starrs, n Carlos, Argo, whatever, you could check out my article on text Strong it.

And they, this wasn't your presidential debate where you have one guy who can't put two words together, and another one that lies all the time. It was smart people arguing both sides of this. Take Google, for instance, if you took the Google search business without the AI business, it's not long term.

I'm not putting a lot of money into it, but if you, but if you give it ai, it, it, it will compete with the best of them. But does it need YouTube? Because YouTube's the second biggest search engine, right?

If you spun YouTube outta Google, you spun Gmail out of Google, you, you know, you spun some of the, and then Google Cloud out of the search business. And the same thing. If you spun AWS out of the Amazon retail business, if you spun Instagram and WhatsApp, WhatsApp out of Facebook, those are gonna be all viable companies.

You're gonna have a lot more competition. And maybe we'd all be better off. Uh, hey, I don't, we're already seeing that.

I mean, Oracle's gonna now have TikTok, so there's gonna a lot of money be made there as well. I know everybody's passionate about this, but I think I'm just gonna have the last word here 'cause we're running outta time. So, Hey, AI brother, can you spare a die Or megawatt?

It's crazy stuff. Megawatt. There you go.

Guys, thank you very much. I appreciate you coming on today. Thank you for watching.

I hope you get as jazzed and juiced about these things as we do. Listen, it's Monday Christmas is coming this week we have one more show tomorrow. It's gonna be a special event where we're gonna have an extra large gang panel with a lot of our favorite gang folks.

But we're only, it's gonna be a different panel for each segment. One segment will be on ai, one will be on it, and one will be on cyber. So stay tuned for that.

Stay tuned for Text Drunk It Do Go check out those Shimmy says episodes I spoke about. They're pretty cool. Read it.

You know, we, Mike and his team do an amazing job. We publish a ton of stuff on our text Drunk Sites, as well as on text Drunk tv. If you wanna stay in the know, that's your way to do it.

Don't have to go pay for some bogus education. Um, until then, until tomorrow, though, this is Alan Schl for Textron Gang. Thanks for watching.

Hey everyone, welcome back here to Techstrong tv. My next guest is Jeffrey Zoo. Jeffrey is the VP of Product of Pine Cone.

It's his first time here on Text Drunk tv, so let's welcome him and, and get to know him. Hey, Jeff, how are you? I'm doing great.

How about yourself? Good. Um, I mentioned you a VP of Pro, uh, of Pine Cone, Jeff and Jeff.

Is it, do you prefer Jeff or Jeff? Let's, Jeff is usually good, so Jeff is good. Yeah, Absolutely.

Truth be told, my middle name's Jeffrey too, so I, but not many people know that. But anyway, um, so Jeff, a lot of people know Pine Cone, especially, you know, in this era of ai. And when generative AI and, you know, uh, open AI first kind of exploded, you know, pine Cone really kind of got to people's consciousness in front of their faces.

Um, we're gonna talk about that, but before we do, let's talk about you a second. What, what, what's, what's your arc? What's your story?

How, how did you wind up here? Yeah, I am happy to do that. So, you know, I've spent most of my time working really in large scale distributed systems.

Like, uh, I, before Pine Fund, I spent eight years at Microsoft working in Bing. So I was running a lot of their machine learning AI infrastructure platform there as a platform pm So I was doing things like, you know, training large scale language models before chat GBTI was doing things like accelerating GPU inference, uh, using Cuda and asics and things like that as well, really, but most relevant. I also built their vector search platform from scratch.

So I was actually running, you know, hundreds of billions of like vectors, you know, for their web scale search engine itself. And that was, you know, that was obviously maybe like, you know, started seven, eight years ago. But that was one of the most really exciting things, you know, before Vector Search became so popular across the industry.

You know, it was already a very, very integral part of search systems and, you know, it was locked away to a really, really small set of, you know, uh, companies. Right. But honestly, that's kind of what ended up motivating me to join Pine Con in the first place, because I saw what Pine Con's mission was, which was really, you know, to make AI knowledgeable through vector search and enabling that through, you know, enabling any developer to take it on, right?

And so that was really kind of what brought me to Pine Con in the first place. 'cause I saw how hard of a problem it was, how challenging it was, and then, you know, and how integral of a technology was going to be. And then, uh, you know, you know, the stars aligned and I found a startup that was in the same mission that I wanted to do.

Excellent, man. You know, a lot of people, so I, I've founded, co-founded four or five companies over the years, you know, and I always say people don't realize, you know, they're their 10 and 12 year overnight successes, right? Open AI and Jet GPT exploded on the scene, and everyone thought, oh my God, what a, this is an overnight success.

All of a sudden it's here. Yesterday it wasn't. Right.

Well, maybe to you it wasn't, but there've been people working on, on the technology behind this, in, in the case of, of LLMs and, and, and, you know, uh, uh, artificial intelligence, I mean, this has been going on for 30 years, some of this stuff, right? They really Exactly. I mean, if not more.

Um, and then the whole vector search thing, as you said, right? Microsoft Bing was doing it. There was a small group who had that use case of a huge, huge, you know, basically cataloging the internet, right?

Yeah. And this was the logical way to do it. So, you know, when you see these things that explode on the scene, don't assume it was like some eureka moment that happened six months ago.

There are people who toiled their, almost their entire careers Absolutely. To make this stuff available to us. Um, now, of course, look, tech Strong AI is one of our sites.

We, we talk about AI a lot, but not everyone. Jeff, I think, is familiar with the role that Vector databases, vector search, you know, the whole way of injecting data, whether it's Rag or for s SLMs or, or what have you, you know, the role Vector plays for those who may be my cybersecurity friends who are smart as heck, but don't know, don't play with Vector a lot. What, how would you explain it to them?

Yeah, I think the simplest way is to think around, you know, what vectors really do are they allow you to represent, you know, instead of in the classic search world of like text, right? Like, oh, I know a keyword, right? This specific keyword mm-hmm.

Like, you know, is exactly, exactly what I'm looking for. But what vectors really allow you to do is kind of represent more concepts, right? Rather than necessarily that exact word, that exact text at any given time, right?

And so that's really powerful because all of a sudden, you know, you can have, you know, I think the classic, you know, example, they always give in like the classic MLO courses is like, you know, man and woman and queen and king, right? As concepts, right? Where if you represent them in vectors, you know, man and king would be closer together than let's say, you know, uh, you know, woman in king, right?

Right. And so, idea here is that you're able to, because these models essentially are trained on huge corpuses of data these days, honestly, I don't even understand how much we're trained on these days, that, that it's actually mind boggling, you know? Mm-hmm.

You being trained. But like, they're able to essentially learn these relationships between the words, and then they represent those relationships. And essentially in vectors, which is, you know, for most people it's just, you know, it looks like a, like a series of numbers, right?

But within those numbers, that distance between those vectors is actually what we call the semantic similarity or the distance, right? Which ultimately allows you to say, is this similar to this particular other concept? Right?

And of course, when you go into the world of LLMs and natural language and how we interact with AI today, that's super important, right? Because we don't speak as human beings. We don't interact with like perfect text, you know, recall of exactly the same language.

We operate in concepts as well. So this is what it really enables you to do, is represent concepts and speak in that language, and ultimately be a little bit more closer to how we as humans actually even talk and, and kind of interact with each other as well. Very cool.

That's a great kind, almost layman's way of, of explaining it to people. I think they get it. And, you know, and a lot of that goes towards, you know, you hear people say, well, generative AI is just picking the next word based on the previous word, right?

It, it's not really, you know, look, there's an automagical element to this. You show it like, to my wife's family, they think it's a person in there. You know, I, and, and others say, well, no, that's not a person in there.

It just picks the next word based on the last word. And, and it, and it just goes on like that. And I don't think that's a hundred percent true either, obviously.

But it, it does give you this idea of how vectors work, right? And how, what makes sense and, and, and how these things go together. If you can, Jeff, obviously as we, we've talked about with the, with the advent of, of, of AI and generative ai, pine cones had a mess of explosion.

Tell us a little bit what that ride's been like and what's been going on. Yeah, for sure. I think, uh, you know, when I joined Pine Cone, I actually joined roughly nine months or maybe eight months before chat GPT.

So honestly, before, before, right? So I will say, you know, I don't wanna call myself a hipster or anything like that as well, but, you know, I was, I was there before, it was cool. Mm-hmm.

And, uh, and no, I mean, part of it was that, you know, uh, you know, at the time of, you know, when I was first joined Pine Con and the types of use cases that we were thinking about, obviously were very in the classic world of information retrieval, exactly what I was doing at Bing, right? Mm-hmm. It was all about how do I do semantic search as in doing, improving my search results and search relevance through this?

How do I build recommender systems, right? And so obviously when Chachi BT came out, that completely exploded because all of a sudden, right now, what was really interesting about these LLMs right, is that they were really good quote unquote reasoning engines, right? But they really, what they needed to do to be accurate, and, you know, you know, and actionable and, and, you know, useful to people was that they needed the right context, right?

And that's ultimately where kind of rag came in as a concept, you know, retrieval, augmented generation, where the idea here is obviously that you're able to take, you know, the only relevant, you know, snippets or pieces of information and feed that as part into the LLM, right? And, you know, I think, you know, that was obviously the very early days of how, you know, RAG was treated. And so, I'll be honest, I think everyone at the time of really didn't understand rag.

And so the first couple years of where Pine Cone was, was really all around honestly, educating the market and helping people understand like, what are the basics around kind of this, you know, vector search capability for, and how does it relate to AI and LLMs, right? But I think over time, right, as people got more comfortable with Vector Search and capabilities, you know, I think once some of the evolution was that, you know, how do I not only, you know, there's obviously very basic ways to do RAG in terms of just like a very simple embedding, you know, retrieval just shove it into a prompt, right? But obviously as the LMS capabilities grew, obvi, the, the other kind of the, the other part of the market kind of grew in, in, what do they call context engineering, right?

As in like, how do I then not only just do basic retrieval, but more complex and more interesting ways to ensure that I'm giving the most relevant information back, right? And so, as part of that Pine Cone has also evolved, right? Is that, of course, by the way, you know, our Core Vector database is massively scalable, massively, you know, cost performing things like that as well.

It's really, really important for that to be, I would say, a base foundation layer. But, you know, as we've looked to kind of where we see the future of kind of the evolution of Pine Cone going, you know, our mission at Pine Cone is to make AI knowledgeable, right? Is not actually to be the best, best vector database in the world.

We see it that the Vector database is a component, it is a very, very integral piece to the puzzle to actually make AI knowledgeable and to produce knowledge for them, but is, you know, only one component of that, right? And so, as we kind of look towards the future, you know, we still care a lot about the Vector database and serving our customers there, but you know, now that people are building agents, right? And not only just, when I say not only just developers, I mean, just almost anyone these days can build agents themselves.

And that's kind of where we really wanna play a role in the future where, you know, it's all about making AI agents and LMS knowledgeable, which means more than just pure vector surge. But, you know, we have a, we have a product called Pine Cone Assistant, which allows you to essentially take in files, take in, uh, you know, PDFs directly, and actually get the relevant context out of it. So instead of using Vectors as the interface, we're actually using something like, you know, PDFs and chat and words, right?

Which is much more natural to anyone building agents today. So I think that's kind of been the interesting ride where, you know, of, of course, the Vector database is a super integral piece of the puzzle, but I, as kind of the world has evolved and the, and know capabilities that LLMs and agents has evolved, we also want to continue evolving beyond the Vector database, but more into the knowledge space itself. Right.

That makes total sense to me. You know, it was about two years ago, we did like a hackathon down here at Techstrong. Hmm.

And, you know, we're DevOps dot com's one of our sites, right? And then, yeah, I started at 12 years ago, 13 years ago, um, and we had a bunch of, like the OGs from DevOps, you know, Patrick dubois, John Willis, and Damon Edwards, and of a lot of people who started the DevSecOps Movement, um, just really smart, good people, and they were operationalizing ai. Hmm.

And back then, two years ago, the only way to get anything in was you, you had to basically vectorize it, if, if we could call it that, right. Put it into the Vector db. And, and so they were here, that's when I first really became aware of Pine Cone and, and what role it was playing in there.

And I was like, wow, they're kind of the only, they're the ferry boat that gets you from this side of the island, you know, back to the mainland, into the, into the ai. But I always felt like we had to democratize that. We had to make it easier for people to get the data in it, you know, converting it into a, a Vector DB while, you know, my, my developer friends was like, oh, this is easy, and I just write a little script.

It sucks it in, boom, boom, boom, that, you know, it was going to get easier. And it sounds like pine cones kind of realized that and is making it easier. Yeah.

And I, and I do think that's the future too, right? Because as you said, these LLMs are so massive, Jeff, we scraped well, we, from a publicly available information perspective, we scraped everything there is to scrape. There's a lot of stuff that's not publicly available that we need to scrape, but I don't think the way we built the LLMs is the way we'll scrape that, right?

Mm-hmm. I think exactly. We need better input.

Um, people who wanna get more information pine, about Pine Cone, where, what, what's their best kind of on-ramp, Honestly, uh, the best on-ramp here is, uh, to just go to our website, pine c io and sign up. So, so we have a very generous, essentially a starter tier, which is completely free. Uh, there, you don't need to put in a payment card whatsoever.

You can get started with both the Vector database if you're interested, you know, getting deep in the Vector database, but also, as I mentioned with Pin con assistant, with a little bit more of a simple, if you ever just wanna chat with your docs, right? Drop in a PDF start chatting with it, or even, you know, like retrieve the relevant snippets from it, all of that is available in our starter tier. And so just come to the website and sign up.

The way that I always like to think about it is that there's no better way of learning about it than actually doing, right? I mean, I can show you the docs, I can show you the blogs and on, honestly, but I always come back to just get in there and try. And I promise you, like, one of the things that we absolutely emphasize here as a, as a core design principle, is a developer experience and user experience, right?

We wanna make it dead simple, easy, and as fast as possible to see value. So just come on through our website and try it. I promise you it'll be super fast.

Very cool. Um, all right, let's, let's pivot. Pine Code recently announced something called dedicated read notes, DRN full disclosure, it's in public preview right now.

Maybe you can tell us more on that timeline, but first, let's dis, you know, define what do we mean by dedicated read notes? Yeah. So, you know, before I dive too deep into the weeds of it, I think one of the things around just like vector search, so this is in the vector database world of the vector search world, just to be clear, uh, the, one of the things around vector search is that it's all about, you know, in many ways the primary, you know, dimensions you consider it is around accuracy, performance, and cost, right?

Those are, I would say, when it comes down to it, you can talk about some other bells and whistle, but when it comes down to what people really care about for a database, it is those three things, right? But, you know, one of the things is that for, but what we found over time was that, you know, there's a lot of different types of workloads, you know, that, especially in the higher scale area that are starting to stress exactly what vector search workloads look like, right? So on, in one dimension, we have what we call, I would say like recommender systems, right?

You can, this is the classic e-commerce. Every single time my page loads, I want to send, you know, find the most relevant. You know, you may be also interested in items, but this is talking about, you know, thousands, tens of thousands of queries per second, right?

But with really, really short latencies, right? Because you're on a page load, we can't be waiting, we can't be slowing that down, right? So like under 50 millisecond latencies, right?

Um, but then, and then on the, you know, in on the other end, what we have is like, customers who wanna do very, very large semantic search workloads, I have a corpus of let's say, decades of news articles, right? And I wanna be able to enable search over that, right? So you have a huge, let's say, billions of vectors that you wanna search over, but you wanna be really, really accurate for those, right?

And then last but not least, kind of what's been around, it's like, uh, what we call multi-tenant rag or agents, is that you have maybe millions of really tiny independent, you know, completely isolated like agent memory context that you essentially wanna be constantly writing to. But, you know, you don't need this to be super fast because, you know, it has an LOM in it. I don't, hundreds of milliseconds is probably fine.

It's not a big deal, right? But I think what we realized when we were working with all of our customers is that to do the optimal cost performance trade off for each of these, you actually need to serve it in a different way, right? So I would say maybe around a year ago, we kind of embarked on our transition to what we call the slab architecture, which is essentially an object storage based architecture, where essentially the ground truth, instead of using everything on SSCs and memory, which a lot of the, you know, existing industry does, we're gonna be object storage based, right?

Instead, where our ground truth lives in object storage, and then that allows us to serve use cases like multi-tenant rag and agents, very, very cost effectively, right? Because if you don't need it, we just leave it in object storage, and now we have a completely serverless kind of operation, right? That allows you to be very sporadic, you know, just pay for what you use.

It's a really, really good scale, cost, performance offering for you, for, for our customers, right? But what that doesn't work well for though, is something like a recommender system. Because if you think about a recommender system, I need to send a single index, a single, like, you know, node itself, tens of thousands of queries per second, right?

It needs to be essentially dedicated hardware so that we can actually serve it and fully saturated it and actually give you the best latency and performance instead of like, trying to overcomplicate it somewhere else, right? And so that's kind of where, you know, this new offering is what we call dedicated read nodes, is that instead of being a multi-tenant architecture, what we're doing is that we're actually allowing customers to select and say, Hey, where this given index, I'm gonna dedicate five nodes to this one, and I just want to, you know, fully saturate it. You're the only customer on it, and it really enables very high performance and, you know, high throughput for really, really good cost performance, right?

But really, kind of the intuition here is that we en and we enable people to really fully saturate the hardware instead of really like, you know, dispersing it and kind of working in a multi-tenant fashion. So that's kind of the key enablement around what dedicated Read Notes does. And, you know, we actually have like pretty massive scale, you know, use cases on top of it.

4 billion vectors, uh, you know, 6,000 points per second, right? With like a P 50 of 25 milliseconds. So like, this is really, really cost perform and high scale hardware.

But of course, as I mentioned, the thing that's really interesting that I do want to call out is that Pine C supports both models, right? You can be really, really sporadic, very cheap pay for operation, or we also support the ability to do essentially these dedicated hardware profiles, which honestly, to my knowledge, is fairly unique in the industry. No other kind of vector database offering kind of provides both at the sa at the same time.

So really, it's all about flexibility for our customers and really enabling them to find the right exact hardware profile for their actual use case And just for people out there. So Pine Con's offering this kind of as a service. Mm-hmm.

Absolutely. Right? And that, that's the important thing I want people to know.

Jeff, we mentioned it's in public preview. When do you think it might be JG eight? Yeah, we're looking at right now at a Q2, so sometime in April, I believe, targeting roughly April's timeframe for a, you know, a, uh, GA release.

Uh, in the meantime though, I think it is something where, you know, we do have, even though it's in public preview, we do have a couple of customers running on production in in it, as, as we always do. So I would really recommend if you guys want to give it a try, and, and, you know, and let me know what the feedback is. We're actively continuing to improve the performance, the efficiency, the capabilities of this.

And, you know, we're really eager to see this kind of powering massive scale use cases in the future. Very cool, man. Hey, Jeff, we're about outta time.

I want to thank you for coming on here. It was a great discussion. I think our audience definitely learned something, and that's the best thing about it.

Um, come back, you know, we don't get enough information like this. We are lucky to have you, and we'd love to hear more as Pine Cone has more news and more, you know, breakthroughs that they're making here. Thank you very much.

Yeah, Thank you. All right. Jeff z VP product Pine Cone here on Tech Trunk tv.

We're gonna take a break. We'll be right back. Hello and welcome to the latest edition of the Text Drawing AI leadership series.

I'm your host, Mike Zora. Today we're with Frank Ada, who's ccio CTO for Zider TruCare, and we're having a chat about the impact tech and AI in general is gonna have on maybe, hopefully improving the state of healthcare. Frank, welcome to show.

Thanks. Really appreciate being here, Mike. Uh, look, we looking forward to our discussion.

I feel like something is fundamentally changing here in the healthcare sector, because for years when it came to tech, healthcare was a laggard. I mean, outside of maybe some fancy new machine that would save someone's life, you know, the whole backend of healthcare was always something of, uh, you know, a lot of legacy technology that was hard to support. And now when I look around, especially in the age of ai, I almost feel like they're at the forefront of this because, well, maybe they have the data and maybe they've had instruction enough to make use of all this stuff.

But what's your current assessment of the state of healthcare and it, Yeah, it, it's definitely evolving from an AI perspective. And, um, zider TruCare, um, we focus, um, on the, the clinical, um, aspect of, uh, healthcare with care management, uh, utilization management, a lot of population health. An easy example of that is when you go to the doctor, go to the doctor, you need a procedure, you need to get a prior authorization approval.

Our utilization management software handles that. Same with on, uh, kind of like a, a a case management side when you're on a care plan, um, working with physicians, uh, and, and nurses to, to, to review that, to, to give you the outcome that you need. And, um, you know, we also develop agen AI products that are embedded in these ecosystems that are changing the way business process within large, uh, payers.

Insurance companies work, definitely a ton of data. Um, but it's connecting that ecosystem, redefining business processes. And going back to your original question, there's been a lot of great technology deployed over many years, um, with less than optimal outcomes.

And what we do through our, our products that have been around for about 20 years, we have 44 million lives, um, supporting our customers on our platform, is to reshape, um, and connect that ecosystem and redefine those, um, business process that deliver outcomes, uh, in, in, in the world that is evolving, given the cost pressures, um, all of the regulatory mandates from, um, entities like CMS, et cetera. To your point about cost, there's a lot of conversation about that, uh, all up and down the line from Congress all the way down to the local dinner table probably, or diner. Um, one impact can it have on those costs, because some of them are kinda baked into the system, but it's not clear how many of them are actually directly relatable to the fact that the data itself is problematic to manage.

Mm-hmm. Yeah. And, and great question.

And, um, that's the, the, the business we're in of solving with, um, you know, our, our, our product suite. And we look at the cost of, of care is going up, the cost to administer, um, workloads are going up, data is spread across these large enterprise ecosystems. In some case, disconnected very or very hard to connect a lot of one-off solutions and the healthcare costs, rising populations not, um, getting any healthier.

And what we're able to do is go in and we sell our, our product in terms of piloting. We show, um, hey, how we can, uh, re-engineer these processes, um, with our agent ai, take human out of the loop, and also improve outcomes. Um, and, you know, we're looking at it from, Hey, we can give you a, a 20% performance improvement with operational optimization increase.

Um, you know, the, the, the outcome that you are delivering to, um, your, your customers, the, the folks that you insure and really, um, e extract that cost from the business at, at a lower price point. And that's what you have to do, because there's been excellent technology implemented for the past 15 to 20 years in these companies, and the outcomes aren't changing. The cost keep increasing, and you have to really get in there, um, connecting the ecosystem through AgTech ai taking risk from these payers, insurance companies, turning that into opportunity, um, not only from better care delivery, lower prices on plans, um, but also technology outcomes.

So what is the appetite for ag agentic ai? And I asked the question because on the one hand, there is a lot of data to navigate and it is part of the problem, but, um, there are also trust issues with AgTech ai, and I have to put the right controls in place mm-hmm. And mm-hmm.

Have the right context to get there an outcome. Mm-hmm. And healthcare doesn't like probabilistic solutions.

They want it to be right a hundred percent of the time. Yeah. Mm-hmm.

Mm-hmm. Yeah, and that's a, a great question. Um, and, you know, some of the challenges and opportunities we have in front of us, if you look back at the beginning of 2025, nobody was talking about AgTech ai, and now there's products and companies that solely focus on it.

Yesterday I read an article that, um, the Salesforce CEO will take cloud out of his VO vocabulary and only speak in agentic interfaces. So this is, um, you know, where we're at, it evolves. But, you know, there are, um, core challenges to model hallucination bias.

And our approach when we come in, especially on the payer side, on the provider side of healthcare, there's been a little more uptake, um, a little bit more quickly, a little bit more in, in public sector too. Um, but, you know, we bring, um, a, a set of capabilities when we go to market and, um, we call it our recode philosophy with a model office. So we have PhD MDs that support and build our technology as well as your traditional engineers.

And we go in, we, we prove it out. Um, we're able to train models, we're able to bring, um, a diff different models in to, um, increase evidence-based outcomes and provide confidence scores with, um, how we deliver AI through workflows, reshape workflows. And it's a, a, a, a barrier.

Like sometimes it's a barrier, sometimes it's an opportunity, but you have to involve, um, you know, chief medical officers compliance, uh, folks when, when you're rolling this out to, to overcome those challenges. And it's also working with ecosystems that are API enabled where you have agents to agents doing the work, and an agent in the background might be going and pulling data solving problems and delivering them to you, where that has to be accurate, you know, on, on a customer side as well as within your product. But I think as we train more, we look at eliminating bias, hallucinations, and, and focus on refining our models, it'll have larger and larger uptake, because it's very serious in terms of you have people's health and, and lives at risk, and you can't have things, um, you know, deviate in that process.

So it's a, um, a, like, we look at it as a, a measured scale up, where we could come in and we look at a line of business and a percentage of workflow and, and go from there. Um, but it's definitely new. It's where the market is going, is at right now, and will eventually, um, drive that, that change in innovation and trust.

Um, and, and supporting that from a a product perspective. We have the Zider Institute at Carnegie Mellon, where we work with them on, um, agen ai, um, challenges like this in the greater industry, um, governance, compliance, uh, efficacy from, you know, uh, the medical side of the house as well. Is this an opportunity to fix something?

And I'm asking the question because I seem to remember the time when, you know, electronic medical records we're gonna cure what ails us. And we got in there and everybody seems to have these systems. And yet every time you go to a physician and you gotta move from one to the next to the next, especially if you're older, um, they still don't know, you know, what's in the record from one to the next to the other.

And, you know, mm-hmm. It's that level of interoperability that we were trying to achieve never seems to have been realized in a way that resulted in a better patient experience. So is AI gonna finally deliver on that promise?

Um, not by itself. Um, we have, uh, a, a set focus on interoperability services, um, within our products, in, in services that, uh, we deliver. Because you can't just take all of this data that's in a disconnected, um, environment, multiple environments, mesh it together and, and bring it in.

Um, it's a real philosophy, um, and it's real work to have interoperability in place. So when you bring the data in from these disparate systems into your ontology, um, and you start really, um, implementing this, this type of software and, and work within the customer ecosystem, it has to be correct. And it's not a, a, a AI is not a panacea for it.

It, um, you have to work with your customers to really, um, en ensure that, um, you know, this, i, this interoperability how you like your data, how it's updated, um, where it comes from is correct. We can accelerate that, um, pace, we can provide better outcomes, but it's still something that needs to be worked at. And if you look at the landscape with what AI and agentic AI is, uh, doing to the marketplace, there are now net new startup companies popping up to focus on this interoperability challenge, uh, um, because it's not just on the intake.

It's like you said, when you move from one physician to another, uh, it, it spans EHRs, it spans, uh, clinical software companies like us, uh, claims core admin software companies, uh, CRM, it's a, uh, a very large, uh, ecosystem that you have to get working correctly. But the opportunity is there to connect these in a agentic way where it can be very seamless, uh, very forward looking and less, i, I would say, siloed, um, to a particular vendor, or even in some cases monolithic. But it's, you know, it's there every day solving for that challenge.

Do you think we might also, because we can get to the data better, see some actual medical breakthroughs as a result? And I'll ask the question in this regard, and I realize you're not a physician per se, but it's clear there are things like, uh, cancer clusters in specific regions, and there's should be some sort of common root cause it just has alluded us all these years, but, you know, is the answer to a lot of these questions somewhere in the data? Yeah, I, I think, uh, uh, it is, it is rooted in data.

Um, it is, I think AI helps. Um, you know, we've published our, um, VP of AI innovation has published or co-published a, um, uh, papers with Mayo Clinic regarding this. And, um, you know, this kind of how AI impacts, um, you know, the, the world, uh, in, in terms of, of solving these challenges.

And it, it's there, it's an accelerant. It's, um, you know, helping speed things along or 'cause you want better outcomes from this, and whether it's medical devices to cancer research, to, um, you know, uh, uh, utilization management and AI plays a big factor there. And I think, uh, you know, we saw what, you know, cloud and, and hyperscalers have done in the past 10 to 15 years.

We're just dipping our toe in the water, uh, on what a AI could bring, um, in, in terms of, of benefit to society in, in that regard. They're smarter people than, than than myself working on these challenges every day. But I, I think it's definitely, um, you know, going to really accelerate how some of these, um, diseases get cured or variants of diseases as well.

So it's tremendous opportunities, tremendous forward outlook. There are, of course, some healthcare organizations that are simply larger than others, and they have more money to spend. But is there something you're seeing amongst them, regardless of size that says that they're more successful with IT and AI in general and others because it's something they do or a cultural issue?

Or, or is there something that, you know, a pattern that you see among those organizations that you just go, yeah, those folks get it. Mm-hmm. Yeah.

You know, that, that's a another excellent, uh, observation in question where, um, you find where people treat technology as an asset rather than a cost. Um, those organizations, no matter, um, how big or small, always have an advantage because they're always looking to do something with, um, an accelerator, right? It doesn't matter if it's AI or, you know, some type of different technology where it's embedded into the, the ecosystem.

It's delivered in conjunction with, um, tight operational integration, um, joint decisioning and, you know, realistic strategies followed by very pragmatic execution. Um, those organizations really succeed, um, because a lot of what, you know, I've seen in, in, in my 25 years are, uh, you know, the organizations that have that, um, you know, really just move along, right? Um, they're able to adapt, upgrade when business throws kind of a curve ball in and they have to change, it's, uh, they're prepared to change.

It's when you look at it as purely a cost or, um, I need this for that, where the business isn't leveraged or, you know, strategy is, is not realistic, um, in terms of what you actually are encountering and delivering on a day-to-day basis. Those, those tend not to, to do really well. Um, so we're at the end of the year, you know, what is your, you know, outlook for the coming year, 2026, you know, what are you looking most forward to?

Um, just really, um, getting leaps and bounds into our agentic delivery quicker, faster. Um, 'cause we have the scale and really seeing how that evolves and where the, the, the market is going because, um, you know, we're looking and, and how we sell and go to market is completely different than a, a lot of other companies maybe a year ago, because we're able to write, um, software, uh, in a very much more prolific way with the tool, the AI tools that we incorporate into our engineering, our testing. Um, and you know, how we del and our delivery process and just continuing to build and optimize that, tackling new challenges, um, that are coming our way.

Um, we still have, um, you know, some legacy products that we have to, to, to, to pull forward. Um, but 2026, the outlook is, uh, uh, really good. And I, I think too, um, all these different startups in our space and, and what they're doing, um, lend a lot to us that we can, um, utilize to move and shape, um, what we're doing in our own space.

So, um, you know, the, uh, I, I think we're, we're kind of like on a three month iteration cycle. I think it's going to be that fast or quicker where we have to, uh, adapt and adjust and, uh, you know, try to, um, keep our position, uh, within the market and not being, uh, caught from behind. Hey folks, I think we all realize at this point that AI will help from it and all kinds of other automation is having a profound impact on our lives and everything that goes along with that.

But in terms of benefiting society, well, I think in the healthcare space, we're about to see some amazingly profound things. Hey, Frank, thanks for being on the show. Well, thanks for having me, Mike.

Uh, really enjoyed it. All right. ai Leadership Insight series.

Find this episode and others on our website. We invite you to check all those out. Until then, we'll see you next time.

Hey everyone, welcome back to the Techstrong tv. My next guest is Casey Marks. Casey is the Chief Operating Officer at ISC two.

Now I know what a lot of you are thinking, isn't it? ISC squared? No, it's not ISC squared, but two years ago they, they've changed the nomenclature or whatever naming here, and they've gone to ISC two.

But don't worry, I'm here on your behalf. So I immediately asked Casey and his team, why'd you change Casey? We, you know, I, we, this real investigative journalism at its best.

Why did you change? Oh, it's certainly a talker than answering questions about the de detailed content outlines. Alan, thank you.

Uh, first of all, it's, it's wonderful to be here. It's wonderful to be able to talk with you about, uh, ISC two and all the great things that we're doing. And, uh, again, my name is Casey.

Marks, I'm the Chief Operating Officer, and what are we doing with this? So, yeah, a couple years ago, we wanted to make it easy. Technically, ISC squared was absolutely correct in terms of the initials of the full organizational name.

Um, but just, uh, you know what? People got really tired of typing out super scripts and perens, and let's just call it ISC two and make it easy. You know what, that's where the world's going.

Make it easy. That's right. But you, you can always ask just AI to type it out for you.

And, you know, that's the next We'll dumb it down. Yeah. We'll dumb it down.

Casey, you're the, as I mentioned, you're the COO over at ISC two, but give us a little bit of your background. How long have you been there? What'd you do before that?

Yeah, sure. Uh, thanks. Uh, well, first of all, you know, I've been at ISC two now for, uh, al well, it's more than 10 years.

Uh, I've been saying almost 10 years, almost 10 years for so long. But I've now exceeded 10 years. 10 years.

I'm 11th year. Uh, I originally started in the organization to computerize, adaptively, uh, transfer the C-I-S-S-P from a linear exam to an adaptive exam. And so that was, that is the reason why I started the organization.

Um, I am not a cybersecurity expert, and I don't pretend to be one. I am a psychometrician by education, and I've been doing high stakes certification and licensure exams for now, unfortunately, into 30 years. And so everything from nursing licensure, um, uh, language testing, English language testing, and, uh, certifications of all types in the private space.

And so, uh, very proud to do with IAC two during this time, and seeing the growth of, uh, of all of our certifications, uh, C-I-S-S-P and the rest of the portfolio. And so, uh, just been doing that a long time. Um, also responsible for our professional development.

So anything with regard to our CPE opportunities and our, our certification education and, and a lot of the research that we do, um, you know, for, for, for the profession, uh, we do things like, uh, the code of professional conduct, our unified body of knowledge, and just a lot of, uh, you know, uh, generation of, uh, professional materials, uh, to, to support, uh, the profession. Excellent. You know, one of the companies I co-founded over the years was the DevOps Institute, and we were certification education provider.

So I have a little bit of experience, you know, dealing in that. And it's, it is a fascinating field. And, and the truth of the matter is you don't have to be a subject matter expert in what you're certifying people on.

Just the whole certification process and implementation and making sure curriculum is correct. And, you know, ours was maybe a little different in that we had all these channel partners around the world teaching classes based on our curriculum, but, you know, they, they dabble, let's say they deviate. So it was, it was, it was a learning experience.

I'll say that. Um, ta you know, you mentioned C-I-S-S-P and some of the others. Of course, I think most people in our audience are familiar with ISC square four, the C-I-S-S-B, which is kind of the gold standard in, in, uh, cyber security certifications.

I was before cyber became anything I security. Yeah. But what are some of the other certs that you guys offer?

Sure. Yeah. So, you know, we do, we do lots of stuff at ISC too.

And, you know, to your point, and talking about, um, uh, DevOps and yeah, y you don't want me and you don't want an expert doing this stuff. 'cause you don't want someone putting their thumb on the scale. You want someone who knows the art and science and how to be able to, to make the machine turn and get the, uh, the right subject matter experts who matter to contribute.

And, and that's what we do at US. ISC two, we are, we are member focused. We are member driven.

Um, everybody, every single person who is certified as a member, every single member is a certified individual. And we have nine certifications in the entire portfolio. Everything from the, the newest and the most entry level, which is the certified in cybersecurity, the cc, um, which is a no experience, uh, required certification, professional certification requires CPE, gotta maintain it, all that good stuff, um, up to what we call the concentrations and, and engineering management.

Um, and those are, uh, a post C-I-S-S-P more experienced, a little bit more focused. But, you know, we have the CCSP, which is in cloud and LP is secure development. And so we have a ton of different stuff.

I, I, I certainly recommend for folks who are, who are interested in certs and, you know, it sounds odd coming from the guy who does certs and, and things around certs that they're not for everybody. Uh, they are aspirational. They are for those who wish to be able to have them, we believe that they have value.

But there's lots of ways to demonstrate value. But should you wanna go in that direction, we got a lot of information, um, about all of them on the website to be able to pick and choose what might be valuable to you. Excellent.

Yeah. Alright, let's pivot a little bit. You guys recently released your 2025 cybersecurity workforce study.

And you know, it's funny, I'm doing a lot of my year-end wrap up for the next week to 10 days that will run here at techron. Look, I think historically, when we look back on this year, this is, this is a defining year in a lot of ways. I think in some ways this is really the first year of the 21st century, right?

Free from the the 20th century shadow. Right. Because I mean, the internet was really a 20th century thing, though.

It came into its own here over the last 25 years. But it, it was a 1990s thing. So much of what we, you know, secure Well, the cloud Yeah.

I guess was a 21st century thing, but Right, right there. Clearly now with AI and everything that this year has brought, we're, we're in kind of uncharted borders. Yeah.

So I am, I think our studies are gonna start to reflect these kinds of churn. Wondering what you guys saw in your study. Yeah.

Well, let me, let me, let me jump out at you. We've been doing this for a while. Um, it is something that, uh, even, uh, professionals that maybe aren't certified or don't hold any, the certs, they, they know about these things because for years mm-hmm.

We've been talking about, uh, uh, you know, the workplace and trying to describe, uh, kind of like the match between skills and persons and, uh, and the demands and how, and how companies are seeing, uh, how to fill those gaps. And, uh, you know, we do survey over 16,000 people globally. Um, those fluctuates, you know, over time.

And we try to get better samples all the time and more people and more places and, and really try to, to get in depth. Um, you know, you know, we look at, you know, how the practitioners is feeling about these things, how they're responding to challenges, how, how, how the employer is demanding things of folks. And, and it really does highlight a lot of the challenges with regard to, you know, staffing and skills match and, and kinda like ongoing.

And, uh, it's, it's, it's been a really nice, uh, you know, study over time. And, and, and to your point, I mean, this, this may be one of the AI and, and everything that we'll, we'll talk about here in a moment, the first big, uh, first big, uh, bump in the road, I think, uh, after, after years of talking about the workplace in a very specific way, we've, uh, we've had a little bit of a change. Yeah.

To say the least. To say the least. So, Casey, well, I should mention, we're, we're going to talk about a couple key findings and some other stuff, but for people who want to maybe get, get their hands on the study and dive deep, what's their best way to do that?

org. Um, under the insights and research section of the website, you could find this study along with tons of others. Uh, we do employer surveys.

We do the workforce study. We will replace, uh, uh, a number of different, uh, work products that numbers provide and experiences. And, uh, there's a lot of good, rich information that talks about, uh, boots on the ground experience.

Excellent. Alright, let's dive into this study though. What would you say some of the key findings are?

So, uh, do you want me to just say AI and be done, or do you want me You got the detail. Well, you know, it's funny, we're doing a virtual event next month. We do every year predict.

Yeah. And our, our thing is, uh, it's sort of a pseudo time magazine cover with a, an AI thing on it. Yeah.

You know, it's, but do you call the person of the year? I don't know. And of the year, whatever, but yeah.

Be, but, you know, let's peel the A in the IR for a second and see what lays underneath that. Well, it, it is, as I was alluding to earlier, it is, we have had a big change, uh, right. So for, for years we've talked about deficiencies in the workplace and, and in the workforce and talking about people, um, somehow, uh, from the assumption, you know, more people, uh, will get better outcomes and to a certain extent that it's absolutely true.

But this year is the first year that, you know, it really got more nuanced in talking about what people and what they bring to the table and the skills match rather than just brute force. Um, and, you know, you're still gonna have the determinations. Do you have enough people, do you have enough assets?

Do you have, do you have enough tooling to be able to, to to, to secure what's important to you and what's required from your business process? But, you know, we're, we're, we're thinking about it differently. And so, um, the, the number one and the single biggest change it was, was the talk about skills and the skills match.

And to be specific, you know, it, it's, uh, over 95% of the organizations, um, that we were said that they've had at least one critical skills gap in terms of what they were looking at in this past year in terms of being able to do what they wanted to do. And we didn't get terribly specific on, on, on what those means, but if they identified as important and they couldn't do it, it was a mismatch for them. And, and 88%, so almost 90% said that that deficiency led to, um, let's just say I, I incident is strong, but maybe just say an outcome that they didn't really care to have.

And so I think that it's really impactful to talk about how this gap is really impacting the performance of individuals. Yeah. I, I agree with you.

It is, um, I mean, of course the question is, is that only going to rapidly increase this next year? And, you know, or, but let me ask you another question. What is, what is the C-I-S-S-P to do?

What is a security person to do Given this? That's really interesting. So at the individual level, at the organizational level, kind of like, you know, so what are people looking for?

So, um, in terms of like, uh, knowledge, uh, areas and tooling concerns and hiring managers in particular, you know, cloud, we talked about cloud a little bit, still incredibly important. The nature of cloud and what, and, and, and, and, and deployment looks different, slightly different in some cases now than before. Hyperscalers and, and, and, and, and, and, and mass, uh, uh, integration is certainly important.

There's regionalization happening, um, that is, is in competing in some ways that's been, been doing some things different in terms of having, um, uh, localization of cloud and, and different clouds and, uh, being able to be more robust. Um, and so some of the thinking is definitely changing, uh, ai, uh, artificial learning, uh, and, and machine intelligence. Um, while everyone who you speak to in the practitioner base will talk about that, you know, it's, it's not new, but to your point earlier, it's the velocity is changing.

And this is the first time where maybe it's starting to, uh, to, to take over and start to, uh, tee it up. Um, you know, certainly, uh, security engineering concerns, um, uh, is certainly skill-based gaps in terms of, of how to think about how you deploy solutions. Um, GRCA lot of governance risk and compliance issues.

And this, this is an area that we keep seeing popping up. So when we talk about, uh, uh, skills that are in demand, and kinda like what gets impacted by AI first, and it, 'cause it's all around us and it's, it's so prevalent, but, um, you know, those high frequency, low volume kind of like, uh, you know, high automation type tasks, and you, you have an awful lot of that within the, a compliance framework environment. And so AI can be very assistive in that process.

And so we're seeing a lot, seeing more, um, in the way that I like to talk about it, uh, professional codification of practice, uh, within the GRC space. And that really is where AI is impacting things. Agreed.

Um, I, I, so all of these things are great. You again, I, I told you I was working on year end stuff. I can't help but think though, that a lot of the people watching this out there, a lot of the people who are CISSPs or who get information from ISC two, it, it becomes personal for them.

Casey, a lot of these people are thinking, my friend got laid off. Yes. I've seen big cuts in all of these companies, especially tech companies, right?

Big layoffs and, you know, people being quote unquote replaced by ai. What does my job look like? What's my, how is this affecting my career arc?

Should I be getting, should I be getting smarter about ai? Should I be taking classes Yeah. Education to make me a better cyber individual, a better cyber worker, because I am AI empowered and how to use it.

And I think people are think, because you can't blame, it's not being selfish. It's, you know, charity starts at home, it starts at their home, think. And then, well, what does that mean for ISC two?

Do you have an obligation, not an obligation that maybe an obligation to say, Hey, let us keep you upskilled for this next generation, for this next wave iteration of what you need to, to be a security pro? Well, you know, that, that's, that's a really interesting question. So, you know, we do feel we have a responsibility, uh, to the profession and to our members to make sure that we're doing things that are, are timely, they're important, they're impactful, and across the board.

And that there's many different, uh, avenues that, that goes in. Um, staying current and staying on top of things, you know, that really is one of the hallmarks of professional certification versus other areas of, um, a skills validation. So different than an educational degree, different than a certificate.

Um, all learning is good learning, uh, to stay on top of things. It's different strokes for different folks. I wouldn't criticize anybody who's interested in bettering themselves and being able to develop their skills.

Um, what you need to do is probably based on your personal preference, and, you know, obviously we love to be able to help people and provide things, uh, with regard to, um, uh, having training that's, that's fit for purpose and certifications, uh, at this point, yeah. I would say if you're out there in a security space, and for as much as anybody who's there and anybody who wants to be there, you better get ahead of this stuff with, uh, with ai. Um, you have to stay current.

Technology does change all the time. 15, you know, 10, 15 years ago, um, if you wanted to be at the bleeding edge, you, you, you had to get involved in, in cloud, you had to understand what was going on. Even if we weren't doing it, you were gonna get impacted by it.

I think it's the same thing going on right now. Um, we do have those things. We do have courses, we do have certifications.

Um, there's, there's information all over, but you can't pretend it's not gonna happen. It's coming. It, it's not gonna be stopped.

And, and you know what, Alan, it's an opportunity. Um, e even, you know, some of the latest and greatest I was watching, uh, any, uh, uh, uh, an interview with the CEO from, from IBM, you know, very clearly saying, you know, we're not gonna see, um, we're gonna see ups and downs with regard to employment, but in the long run, we're gonna see opportunity. Um, and, you know, this is gonna be a tool that is gonna change a lot, much like other transformative technologies that, you know, the, the economy grows and opportunity grows with it.

Absolutely. Casey, another question for you. Why did this year's study kind of, you didn't have on your bingo cart, right?

It was like, wow, I didn't see that coming. Oh, Yeah. Um, you know, I, and this is, uh, maybe not gonna be as, as quite as whizzbang, um, in terms, in terms of things that, you know, I, I don't think that we expected the pivot to skills over persons.

Uh, we did expect, we did expect, and maybe, maybe that's the AI impact. Um, uh, you know, who, who to say. Um, and I, I think that that was just the, the characterization, how the problem was framed changed, and that was probably the most impactful decision, her DCB, uh, outcome in finding Fair.

Right. org, you could find there under research, right? Yes, Exactly.

I remember. Um, and then I guess I should ask, so this is the 2025. We, we probably won't see a full blown report like this till the end of next year, huh?

That's correct. Uh, we will, we're gonna do the same thing again. We're gonna learn from what we have here.

We're gonna learn from employers, we're gonna learn from the folks out in the field. We're gonna try to improve this. It will be interesting, uh, predictably in terms of what's gonna happen.

Uh, where will the, the AI euphemism, what will it evolve into? What specifically, where will we go with that? And of course, we'll be looking out all along the way and figuring how that impacts, you know, facts, impacts the certs, impacts the education, impacts, the learning opportunities, um, engagement opportunities, uh, the, it's, it's gonna be engaging and it's gonna try to figure out what gets prioritized.

Agreed, man. All right. You got an invitation.

Come back next year on this. Unless something good happens in between, we could talk about Unless we solve it all. Perfect.

Thanks Alan, I appreciate That. All. Alright, KC Marks Chief Operating Officer of ISC two here on, uh, text Drunk tv.

We're gonna take a break, we'll be back in a minute. Hello and welcome to the latest edition of the Techstrong AI Leadership Insight series. I'm your host, Mike Bazar.

Today we're with Rich Waldron, who's CEO for trade, do ai. And we're having a chat about, well, AI agents and how to track their performance and measure their value. Rich, welcome to the show.

Thanks, Mike. How you doing? I'm well, I'm well.

I think everybody went from what is an AI agent to pretty quickly saying there're gonna be billions of them. And I'm not quite clear how we're gonna afford all that, but, uh, at the very least, maybe we're each gonna have a dozen of these to manage and try to figure out. But, you know, there'll be dozens and dozens of these over the spread of an organization, and then it becomes, well, how do I know which of these AI agents is doing the right thing performing well?

And for that matter could wind up being more trouble and it's worth, but how are we gonna evolve from here? So yeah, I think it's a, it's a interesting question because you are totally right in the speed at which people went from, Hmm, I think I might need an agent to, I now have a lot of agents, what do I do about them and how do I figure out, uh, how effective they're being? I think there are kind of two ways to look at impact or effectiveness, and often they get conflated.

So on one hand there's the kind of technical side of it, which is, is the agent doing the thing that it should be? Like, is it technically sound? Is it carrying out the actions in a verifiable or accurate way?

Can I determine that the sort of implementation or the execution of this agent is, uh, is up to par? And then the second question is, is it actually doing anything valuable? And that, that latter piece is far more around figuring out what the impact of the agent is, and that that's not dissimilar to kind of taking on any sort of consultant or thinking about any line of work that we do today.

In that you sort of start with, here's the thing that I'm trying to solve, uh, what will determine whether or not I've been successful. And so I think there's, there's sort of two ways to kind of look at the effectiveness of a, of an agent. One is technical execution, and the other is, uh, overall impact or business impact that it has.

Mm-hmm. When I talk to folks, at least early adopters right now, there seem to be in some sort of mindset that says, well, the AI agent never quite does the same thing the same way twice. So they ask it to do something about four or five different times, and then they pick the one outcome that they seem to think is the best suited for their task.

But that seems kinda an expensive way of going about doing that, shall we say, because now I'm, I don't know, I'd say I'm, I'm throwing out 80% of the output that I created for the 20% that I have preferred. And ultimately the cost of that gets a little crazy. So how do I kind of like start thinking or putting some sort of, um, controls or governances around that, that activity in a way that won't break the bank?

Yeah. So that, I think that falls into the first camp that I was describing, which is the evaluation of the technical implementation and the way in which it, it's executing as a, as a function. And I think really the, the challenge here for a lot of folks is that we used to kind of buying software or interacting with software in a way where once it's set up, it kind of just works and it often stays the way that it, you know, we implemented it, um, until further down the line, something else in the business changes.

Whereas an agent kind of needs constant nurturing. You know, if you think of a agent, much like, uh, the, the analogy I use is it's the, it's the intern that kind of knows everything, but what the intern doesn't have is necessarily the experience. That's the thing that you are, you are implementing or, or kind of educating that, that internal on over time, which is how they ultimately become more effective.

And so there are, there are a couple of sort of basic, um, uh, actions that I think people need to take. The first one is really the feedback loop. Uh, and this is, you know, probably the lowest hanging fruit.

And one of the most important things, which is to handle the thing that you just described, really, you're trying to get a feedback loop going whereby you are indicating back to the agent whether or not it handled the, uh, question or the action in the way that, that you wanted it to. And that feedback loop then kind of feeding that insight back into its memory, meaning that the next time around it's gonna perform in the, in the way that you want it to. And so that, that kind of loop of, um, helping to sort of train and, and, and push the agent in the right direction is necessary.

And the speed at which you can make these agents more productive, um, is partly through a feedback loop. And then it's secondarily through figuring out what scope you are providing the agent in the first place. So how these things end up being expensive is if you're providing too broader scope, uh, and expecting an agent to carry out a much wider range of, um, uh, actions.

Therefore it has a much bigger aperture to, to kind of, you know, get wrong or, or not, not act in the way that you like. So keeping that relatively limited scope, introducing those feedback loops is the fastest way to getting a, an effective agent from a technical standpoint. Yeah.

So to your point, we've established that there's cost to these things and we can put some controls in and maybe best practices to minimize those costs, but there's a difference between cost and value. So how do I understand what the value of the AI agent was to the business? Yeah, and I think that the, the value piece is this is something that, um, uh, you know, we actually have a lot of experience at.

You know, there, there hasn't been a, uh, uh, a software project that I've ever worked on, or at least a good one that didn't have some semblance of how we measure ROI at the end of it. And I think the, the agent implementations are very much the same. So a a bit of a classic example is if you think of, um, using a support agent of some kind, maybe it's a IT support agent or a, or a customer support agent, the way in which you're measuring impact is, um, through things like number of tickets, deflective or, or responses handled.

And then you're effectively, you know, you are then being able to measure that against what your expectation is, um, uh, prior to the agent being around. And what did that allow you to accomplish that you, you weren't able to accomplish before once you start getting some of these business metrics in place? You know, another example being, um, we've worked with a a, an organization that's implemented a, an agent that basically does call preparations for, um, account managers before they get on a customer call.

And what they discovered is account managers in general, were spending between 45 minutes and an hour sort of researching an account before going and, and getting on the call. And they were often going to the same places, getting the same sources, setting up in the same way, so that they were able to put some simple time tracking in place to figure out, okay, well how much time is this agent saving each, you know, account rep, uh, uh, per week? And then you can figure out, well, what does that look like over the entire workforce or over the entire group?

And then you're starting to get to a place where you can figure out, okay, well how effective is this agent being, like, what, what impact is it happening, uh, on, on the bottom line? And then ultimately the top line? Because in theory, you're in a better position to support your customer.

So I think that that business outcome angle, um, is a little bit tangible and, and kind of based on, uh, e each individual business, but we, we should be thinking about this in the same way that we would in with, with any sort of project that that people are embarking on. Mm-hmm. There's a lot of debate about, um, how best to pay for the AI agent.

And some people are saying, you know, it's token, others are per seat and others are re length at time of usage, et cetera, et cetera. Um, but the value of the AI agent will differ widely per business and per task. And so do we need to rethink how we're gonna price the way we consume AI agents?

Is there another way to think about this? I think in, uh, you know, what 25 years of SaaS delivered software and, and many years before that of, uh, on-premise and ERP delivered software, I think the, the pricing model has kind of constantly evolved. You know, we, I think even SaaS itself, it was pretty standard to charge per seat, then consumption started to be introduced because the workloads or the payloads began to change.

And I think with ai, it, it, we're gonna see a similar sort of oscillation between pricing models. I think quite early on there was a lot of discussion around outcome based pricing, but to your point, everybody's outcomes look slightly different. Um, the value of the outcome is slightly different.

You know, if you've, if you've got an agent working on a, uh, account that's worth a million dollars or, um, a hundred accounts that are worth $10, like how do you think about the effort required or the way in which you sort of break that down to be able to develop a pricing model on a, on an individual basis? I think what I'm commonly seeing right now is more of a sort of consumption slash token based approach, which companies are then using to get a sense of what their normal course and speed looks like. And then, then you're able to start trying to, you know, push that into, um, uh, models or, or at least ways of thinking about pricing that are slightly more applicable to the, to the action that's being taken.

Um, I think long story short, I don't think we're quite far off enough along yet where, um, uh, enough of these have been stood up at a level for us to be able to say, Hey, this is the de facto pricing model. And, and, and that's the way that that, that this should be oriented. Correct me if I'm wrong, but I think I'm also starting to see early signs of what I might call an AI agent divide.

And it goes something like this, people who have a lot of expertise in a space, for instance, are able to manage multiple agents performing tasks in parallel. Mm-hmm. And they get that whole superpower kind of value.

Then there's other folks who, you know, are mere mortals and they basically can't handle the cognitive load of all that parallel processing in their heads and how to bring all that together. Yep. And they're likely to get a different value out of AI agents, and there's probably those who can maybe only handle the concept of an AI agent, you know, performing tasks in a more sequential fashion.

Is that gonna impact the value that we get out of it? And therefore, you might have some organizations that are like, we're deriving an awesome amount of value based on the skills of our people versus, uh, others that might not. Without a doubt.

I think the, um, I think it goes deeper than that, which is, I think that during this sort of initial phase, there was a lot of kind of, uh, what I would describe at as throwing the agent at the problem, right? Which is an agent may not be the right solution, but people felt like that's the thing that they should be trying to, to solve whichever problem it was that that emerged. What I've seen through the, uh, uh, customers that we have that have kind of gone a bit further down the maturity cycle, they're starting to get a bit of a balance between, um, agents that are delivered to their, uh, employees or, or kind of, or, or provided externally to, to customers.

And those are built out in a more kind of traditional, um, uh, like software delivery, um, pattern. You know, they're very thoughtful about how those are built out. There's a, there's a lot of, um, additional modeling that's gone into figuring out what the right execution is gonna be in some cases.

There, there are multiple agents that are working together so that they, they solve some of the problems that you described, which is how you handle feedback and supervision and, and, and elements such as that. But secondarily, they recognize that actually, um, some solutions need something a little bit more deterministic, and part of that flow will actually be age agentic. So that's more of like a agentic workflow model.

It's not leaving everything up to open interpretation and, and, and for an agent to go and figure out. And I think at the other end of that spectrum, there are companies that have kind of said, Hey, look, we'll open up the budget and we'll open up the tools and we'll let everybody experiment, and you're gonna get a pretty mixed bag of results. Because in the same way, uh, your average employee perhaps isn't that great at prompting, they also don't necessarily know, you know, how to solve their own problem or, or, or, or how to construct an agent to go and do it in the first place.

So how you approach that as a company has a, um, significant impact on the overall outcome. You know, the, the technology is no doubt astounding and evolves at a rapid pace. How we harness it is really, you know, where the, where the difference is made.

It almost seems like there's two vectors to kinda master here. Um, one is, I gotta make sure that the data that I show the AI agent in the first place is of sufficient quality to drive the outcome I'm looking for. But secondarily, it seems like the AI agent more so than the copilot, needs more context.

And you hear the phrase context engineering and much of what you just described in my mind comes under the heading of context engineering. But Yep. There's an art to that, right?

Because I also talked to other folks and they're like, you know, starting to use phrases like context rot because they gave the agent too much context and then it went off and did all kinds of things. So how do we kinda strike a balance here? Yeah, I think a good way to think about an agent is, an agent is as effective as the knowledge you give it and the, uh, tools that it has available to itself.

So if, uh, if, if you know, AI's uh, overarching skill is reasoning, then the knowledge that you make available to that, um, uh, to, to the LLM to be able to reason it significantly impacts its ability to, to react or make the right decision. And then secondarily, the tools that it has available limit or enable the impact that it could have. And I think what a lot of people have discovered is that agents that have a more specific purpose are far more effective.

You know, if they've got, uh, less knowledge that they're working from that is very rich and they have specific tooling, which allows 'em to carry out those tasks, then they can be extremely effective. Uh, I think the, the mistake I often see is this idea of like a kind of super agent, uh, uh, whereby I'm gonna give it access to everything that it could possibly know, and I'm gonna give it as many tools as it could have. And that has a, a kind of paralyzing impact, right?

You, you, you get into that whole context rot debate, and you get into really a bit of an inability to take an effective action. That's why you hear a lot about the agent to agent model, because then you have spec agents that have specific knowledge and specific skills, but they're aware of what other agents around them can do, and therefore you are kind of limiting the scope, but opening up the aperture in a way that's far more effective. Mm-hmm.

Are you at all concerned that we might therefore encounter something that feels like a little bit like the trough of disillusionment when it comes to AI agents because well, well, 10 to 15% of the population has the cognitive skills to master them and become super humans. The rest are gonna be, you know, are basically gonna conclude that this may be just, you know, yet again, the IT industry overhyping something and it doesn't quite deliver that value. So we might spend a year where people are kinda like, yeah, this stuff is, you know, it's, it is mildly helpful.

I, I think it very much depends on, um, how each organization approaches their utilization of ai. Um, and I think that kind of like any tech cycle, you know, you, the, the people that are the builders aren't necessarily, um, uh, always gonna be the, the consumers. And what I, what I mean by that is it matters how you construct these agents.

It matters when how you think about their effectiveness. It matters, um, how you think about, um, uh, the ongoing, not just governance of them, but ultimately how they're gonna evolve. And that is a skill that does require, um, um, a skill set.

And, and it requires having people that kind of think in, in, in that manner or, or in that sort of method of delivery. I think the disillusionment that you described is ultimately when we are just opening up the technology without thinking much about the richness of knowledge or without thinking about what tooling these things are, uh, are given and expecting a huge audience just to go and be successful. I think that that might, you know, where, where I think the, um, dare I say it, um, over hype or the, the over excitement is, um, I think things like vibe coding are a, are a, and I'm talking in a generalist way, are a great way for people to get an idea of what's possible, but not necessarily going to lead to, you know, production quality evolved outcomes.

And so really you're gonna see the, the companies that break out are the ones that take that approach, really think about how they're gonna harness their technology, really think about doing it diligently and well, and the others that are kind of expecting to just turn this stuff on and, and it's gonna produce magic, uh, I think will be disappointed. So would organizations be well advised therefore, to create something that feels like, you know, a center of AI agent excellence, where they're gonna go and, you know, work all these issues out and then kind of teach the rest of the organization how to master it? I think most organizations already have that department, and that is the IT department, you know, and, and if, if I think back to, you know, my, my years in the integration world, the best integrations were always a handhold between IT and a department.

And the department brings the, the main knowledge and the, uh, business problem and the idea of, of, of, of what is required. And the IT department has the expertise in developing solutions and knowing how to, how to utilize the technology and harness it in the right way. And so I think, uh, a lot of companies have these steering committees and they're ultimately made up of, uh, line of business expertise.

And then, you know, we're, we're hearing about kind of, you know, forward engineers or, or, or, or, um, AI engineers that can work within, within these departments. I think that's very much a, a, a model for, for a lot of people to follow, because it means that not only are you building in a way where you're thinking carefully about effectiveness and ROI, but also you've got an awareness of what else exists. Because then the power is when these agents are, you know, are aware of each other and can tap into each other's capability.

If you're building in a silo and each department's trying to do stuff on their own, you know, then you're really not gonna get the value that, that, that could be available to you. Mm-hmm. So how will this all play out ultimately?

Let's assume that everybody's got a dozen AI agents, and then the organization will probably have a bunch of AI agents that are assigned their particular set of tasks that they will do on behalf of everybody else, and all this stuff will need to be orchestrated somehow or other. So where does the intelligence lie for coordinating all the activity of the AI agents who are all reasoning across some set of tasks, but, um, how are they sharing their information with each other and with us? Yeah, so I think there are protocols emerging, um, that, that help with this, uh, protocols like agent to agent for, for example, uh, which is one that we tray have adopted.

And it, it all comes down to how you think about that centralization. Um, I, you know, I think realistically there are going to be organizations that will adopt agents from multiple vendors. So this idea that there is some central, um, orchestration that a, there is a protocol that enables agents to communicate with each other.

And really it comes down to how well described are the agents and their capable actions, because that's then what is used for, for these, uh, uh, uh, for the agents to be able to figure out, okay, what is available to me? Where do I need to go for this? How do I, um, uh, how do I utilize the, the skills or the capabilities of, of the agents that surround me?

And so, yeah, we, we at t Tray have, have been building out this model, this sort of AI orchestration platform, really with that thought line in pro, uh, in the top of our mind because it's the, it's the same thing we saw from the, uh, integration world, which is, all right, well, I've bought 3, 4, 500 applications. Well, how the hell do I get these things to communicate with each other? How do I get some sort of centralization?

What do I need to be the glue that sits between these organizations? And that historically has always been a integration platform as a service. I think the evolution for, for, um, uh, for our, for our business is very much in that realm.

And I think you'll see more and more, um, uh, vendors kind of sit up and, and approach it in a similar way. All right, folks, you heard it here. There's no magic wand for AI agents.

You still gotta do the work. You gotta figure out how to a, teach them, train them, monitor them, check their behavior, and then figure out how to make 'em all play nice with each other. Hey Rich, thanks for being on the show.

Thanks, Mike. All right. ai Leadership Insight series.

You can find this episode and others on our website. We invite you to check them all out. Until then, we'll see you next time.

Hey everyone, welcome back to our Tech Drug TV coverage of AWS Reinvent 2025, sponsored by our good friends at suse. You know, we've been doing a couple of panels. I love doing the panels 'cause we get a lot of points of view, and we've got some really, really smart people that i, I enjoy learning from.

Let me introduce you to our smart panel for today. I'm gonna start on the far right with Rick and I'm gonna let each of them introduce themselves because I'm not smart enough to remember all their names and titles. But Rick, why don't you go first?

Sure. My name's Rick. I'm the general manager of the Linux team at suse.

Wonderful, Excellent. Uh, manup, I lead all of our business and Linux application partnerships at AWS And Margaret Dawson. I also love Linux, but it's not at my job title and I'm the Chief Marketing Officer at suse.

Excellent. Thank you all. Rick.

The other two folks gave their last name. I'm going call you out. Sure.

My name is Rick Spencer. That's what we want to hear Rick. 'cause there's someone at home who says, that's my dad, my husband, my someone.

I think My kids probably know my name, but I, if you want find me online, I'm Rick Spencer. Three, all one word on all my social media. There we go.

Who's Rick Spencer? One and Two. My grandfather and my father.

Oh, that's cool. Okay. Really?

Yeah. Very cool. Well be that as it may though, we're here to talk about something really important today.

You know, I wanted to start this conversation off with, I, I've been been a user and a a, my name is Alan. I am an AWS user, um, for a long time. And you know, it's funny, when you start your first start, I don't know how many of you've been, man, I assume you have, have done on your own AWS journey, but you whip out your credit card, you open your first instance, it's easy peasy.

Mm-hmm. Right? Pick.

I'll go with the default Linux. I click that, I'll click one of these, eh, gimme two of those. And, and it's very simple, but these things have a way of, of like being like rabbits where they breed and they, and, and, and at each level it becomes more complex and more complex.

And then your, your cohort at work, he's on his own journey and she's on her own journey. And then someone says, how many instances of AWS are we running on this tape? Everyone gives the face.

I don't know. It becomes a complex kind of thing because maybe you didn't pick the same Linux I'm running. Maybe you didn't pick the same configuration manager mm-hmm.

Program mano, I'm sure at AWS you guys know this journey well where one day you wake up, you know, and then give, give rise to the whole finops movement as well, right? That was part of it. Yeah.

Right. What do I, my goodness, what am I managing here? What do we got?

How do I know what we have? How do we, how do we bring order to this chaos? Right?

And it, it's a problem. It's a real problem, right? For, for most organizations, especially at the enterprise level.

Rick, I don't know, do you see this problem OnPrem as much at suse? Like, 'cause SUSE has a lot of on-prem enterprises as well? Or is this kind of a, a cloud specific?

Um, I would say it's not cloud specific. So what we see is that in a typical enterprise, they're s absolutely managing a multi Linux estate as we, as we put it. And, um, there's different reasons that lead to that.

As, as you mentioned, like the ease on a cloud provider for like, you know, picking a workload, launching it without, you know, giving thought to what you know, what you're gonna do in two days when there's time to apply updates and et cetera. Um, also, um, you know, different, uh, different ways of running workloads. One company might acquire another company and that company had standardized in a different way.

Uh, so this is one of the reasons that our multi Linux manager tool is so popular. 'cause that allows you to manage any Linux anywhere at any scale. That's very useful in AWS environments.

'cause you know, EC2 customers may be managing, you know, workloads from, with all different, um, oss, not just suse, of course, Amazon, Linux, you know, other, other, other linuxes. And so that's, uh, a really good option for if you're an EC2 customer, you want to get like a pane of glass that'll help you make sure things are being up to date. Multi Linux manager will help you apply, uh, policies.

It'll help you mirror repositories so that you can, um, you know, operate with the utmost safety, et cetera. I think this goes to some things we've talked about a lot this week already, where, and AWS and suse are very aligned in providing that choice. I mean, people may not realize that when they spin up an instance in AWS you can choose from a multitude of different linuxes or, or Kubernetes.

I know we're gonna move to that in a minute. But, um, I think the importance is that we're now very, very focused on how do you help people understand those different environments? How do you help people manage those different environments?

Have more of a, you know, single control plane. I don't actually believe in a single pane of glass. I don't think we can even do that, but like, we can help you have better visibility.

How do we understand the cost better? How do you integrate finops different things? So that's really where we are today.

And then, you know, part of that is then how we integrate ai because that's helping you automate, helping you do things more easily. So I think the original demand and ease of use that came out of AWS you know, initially that allowed developers to spin all that up and have that choice. Now people want a little bit more control, a little bit more management, a little bit more visibility while still giving developers, while still having choice, correct.

While still having choice if they're not giving choice up. That's correct. And still, and ease of use.

Developers wanna build an app, they wanna build cool things. So how do we not take away any of the ease of use? We don't wanna add friction, but for the ops people, for the management, for all the other people that are leveraging, um, that incredible cloud infrastructure, how do we give them more visibility and more management, more control?

And I think a part, uh, a part of coming here at Reinvent with all the 60,000 developers and, you know, people who are part of the A Ws ecosystem, I think it's also to put a flag not, uh, right now, but like what's gonna happen over the next 12 months. And I think a part of this conversation of managing complexity is also, you know, some of the stuff that, uh, Matt shared yesterday in the keynote around the work that we are doing with our frontier models. Yeah.

And I think that, uh, the, the whole idea is that we are trying to abstract the complexity away. Mm-hmm. So there is a level of com, you know, abstraction, the complexity that is happening, what Rick mentioned, uh, at the, uh, the Linux manager level.

Mm-hmm. But as we progress over the next 12 months, I think we need to talk about how we are bringing the MCP servers as part of this conversation. How do we bring the, you know, the, the big announcement yesterday was around Quick Suite.

Yes. So I think the, the thinking here is that yes, we are having this, uh, SUSE Linux manager that is going to manage a lot of the, what is your security posture? What's your packages?

What is the profile? That stuff is, you know, that's beautiful Now from a developer who's just getting started, you know, the people who are doing one servers or two servers, they're not really super sophisticated into the nuts and bolts of the Linux, or as well as for the AWS for that matter. I think we are moving to this natural language processing interface, like a chat bot.

You know, you talk to the machine and they response back. So I think that's where it is very exciting over the next 12 months, I think where we see is mm-hmm. Integration of the SUSE manager with Quick Suite, so customers can actually just speak or, you know, in natural language discuss, Hey, what is, you know, how is my overall the next distribution?

What is my cost? How do I manage it? What my security posture?

And I think that really makes it much more accessible and democratizes and then also gives you more control and give you more control, more choice as well, right? Yeah. And it also brings in like the Agentic capabilities.

And so in some ways I look at the Quick Suite as like sort of almost an, an agentic orchestrator. And so you need the primitives there, which are those MPC servers. For instance, the, you know, right now you can go get multi Linux manager, you can install the MCP server, try it out.

Right now you can go spin up SLED 16. Our, our latest release we released a few weeks ago, install the MCP server, of course rancher, um, install rancher, get your Kubernetes clusters in control, install your MCP server, which is great, but Quick Suite really can like help you take it to next level by allowing you to, you know, run Agentic jobs, which may be, you know, if it detects an issue, the LLM can like make some decisions about maybe I need to log a ticket, maybe I need to use, you know, the other services that are available as part of Quick Suite. Maybe enhance the information with some other things that Quick Suite would know, like, you know, who owns that server in your organization.

Mm-hmm. Is it mission critical or is this something that can go down? And those kinds of things.

So I think the combination of like the SUSE infrastructure, uh, using EC2 and EKS and Quick Suite is going to, it'll just be totally different in 12 months how people are managing their infrastructure at scale. And there'll be like more uptime, more efficiency. Um, I'm, I'm really excited to see what, uh, happens over the next 12 months.

You know, scale's a funny word, right? Your scale may be different than my scale. Yes.

Right? And, and so it's relative, but you know, they don't call a WSA hyperscaler for nothing. Right.

Some of this, the scale of, of the, of the install base right. Of, of, of enterprises on AWS is truly massive, massive scale. Mm-hmm.

And to me, it seems like this is a perfect use case for a AI and agent AI and MPC servers, right? Because how else are we going to get our hands around this? I mean, when you think about why do we want to use ai, right?

It's, it's to do things that, the mundane things, yes. But it's to do things that we, we can't not easily get our hands around. Mm-hmm.

And, and so I, I think it's important. Now, we spoke in the last panel about the strategic nature of the relationship now mm-hmm. Between Linux, uh, between, excuse me, between SUSE and a and AWS, they're around Linux, around rancher and around Kubernetes and all of this.

But I don't think you can mention those three or four things without now mentioning AI as well, because this is an AI assisted model. Mm-hmm. We've seen a lot, as you mentioned Minor, there was a lot of, uh, announcements around AgTech and AgTech, AI and AI in general yesterday.

Rick, I'm gonna throw it to you and then we bounce it around the panel here. How, you know, 'cause there's a 12 month roadmap, let's say, but who knows? This goes real quick.

How quickly do you see suse, you know, taking what was announced this week, internalizing it, if you will, and reflecting it on what's available in the marketplace? Well, I, I would argue it's happening right now. And that SUSE is uniquely positioned for this.

I think only SUSE has a multi Linux management tool. Only SUSE has a multi Kubernetes management tool. Um, only SSA also has a, a Linux distribution that's made to work with that.

Um, now, um, if I may, I, I would caution people to think about, like, if you're just grabbing an MCP server, hooking it up to your LLM and letting it, like do what it wants to do, that's probably not a good idea. Because if, you know, if you just take the approach of just exposing the API to an LLM, we've all read the tragedies of like LLMs deleting production databases and stuff like that. So the thing I would say is that, you know, if you look at SUSE's history over the last, what, 25 years, you know, we've been really focused on like, security, safety, compliance.

So we're very carefully building those ag agentic capabilities so that they fit into a real, real world workflow, you know? And, um, and I, I think we're probably the most trustworthy company to actually like, bring all of the, uh, power of the quick suite and all that, a gentech orchestration that people are doing, like, into the operation space. I think I just wanna call.

Thanks, Rick. I think I wanna call back to your point about scale. Like, uh, a company scale is different from B'S companies.

I wanna highlight, uh, that Suzy and AWS have been partnering for two decades now. Mm-hmm. And so we have thousands of customers that are running mission critical workloads, both on the SUSE rancher offering as well as stress.

And these are, you know, company, you know, workload, like high Performance Computing, SAP. So, so customers are really, they love the fact that the two companies have been working together. There's a level of trust and we have supporting mission critical workloads for a long time.

So I think Agen is just the next chapter in this partnership. I think we spoke already about, uh, managing the complexity of Linux, which is distributed across AWS and across your multiple, uh, multi-cloud environments as well as on premises. How do we bring this back into, um, you know, a quick, uh, suite view, and then we can actually do real time analytics on that?

We spoke about that. I think that then this entire narrative of abstracting complexity mm-hmm. Then extends to container workflow.

So with, you know, the, the rancher manager, the SaaS application that underlie underneath uses, um, uh, bedrock. So then, again, similar to what we discussed in Linux, in a natural language interface, customers can say, Hey, what's the status of my clusters? Uh, what is the cost associated with it as we discussed?

That's a big, uh, that's a big concern as well. And then, uh, how do I manage it? And so I think, again, it's all about democratizing, making it easy, uh, for our customer.

And, you know, we have a track record of doing it for two decades, and, uh, we just going, this is the next evolution of, I think there's one other piece of this strategic relationship is that we're doing this, you know, we talked about choice across very heterogeneous environments in terms of multi Kubernetes, multi Linux. Like, we're kind of embracing that together. What the AI does is take all of those, you know, very, um, disparate sources almost of data, and is does such a great job of bringing that together, just like we make it easier to manage all those disparate, you know, flavors of Kubernetes and Linux.

So it, you know, there's these kind of thematic ideas that both companies are embracing, and it is about lack of complexity, but it's also about ease of use, ease of management. Um, and then I think the other piece that SUSE brings is that open source heritage into AWS that came with the supplemental packages on Amazon Linux. So it comes back to whether you're using SUSE Linux or Amazon Linux, how does SUSE support that and make it even better, bring you the technologies and tools you want as you're using Amazon.

You know, how does Amazon allow you to have a choice around that? And I think that is a very unique and differentiating, um, partnership. Yeah.

Right? Because we're bringing you the stability, the security, the scale. Nobody scales like Amazon, right?

Nobody brings open source technologies in a secure, more stable way. And then we're also giving that choice and control of heterogeneous, heterogeneous environment. So I, I think that combination of all those things is really powerful for the customer.

Absolutely. I, I, I couldn't have said it better, Margaret. We spoke MPC service.

Yeah. Everybody has an MPC server today, it seems. Right?

How many MPC servers do M ccp, MCP, excuse me. Mm-hmm. I forgot what MPC thinks.

I don't know. We should make something Multiplayer gaming. It's, it's multiplayer console.

That's what it's, that's what, well, that's where my head's at. But bring out the Xbox. Okay.

But you know, the problem I like, in my mind, I'd like to see Amazon come out with an open source version of the server that everyone could standardize on. And then suse, you could build your special sauce on top of this. In other words, how many different, like right now, how many MCP, right?

Yeah. Mm-hmm. Now, now you have me questioning myself.

Uh, how many MCP services? So I think one of the things we discussed, um, uh, so first all, I think the standard spec for M CCP is, is open source. Yes.

Yes. It similar with a two A in terms of how the agents thing Yes. Open source, um, more than, uh, you know, we launched this CP server slash agent tech slash AI marketplace back at the New York Summit in July.

Yes. So we have thousands of ISVs as well as products that are listed. So I think now, at this point of time, um, the customers are really transitioning from piloting over the last year.

I think that's our, our thesis is over 2025. A lot of the piloting, a lot of testing, and 2026 customers are really going into production around that. So I think, uh, so everything that Rick and we discussed in terms of exposing the capability of, for example, the thing we discussed about either rancher, uh, um, you know, manager, or whether the SUSE Linux manager, the MCP is just like, you know, uh, is just an interface into managing that.

Uh, you know, and how does it talk to an LLM. Mm-hmm. And then we bring that, all of that, integrate that into quick Suite.

So that's where the customer is, customers actually doing a natural language conversation with quick sweep. Either it is text or it is voice. And then, uh, we abstract the complexity way or through the MCP server and the MCP client in the background, all of that insights, whether it is the state of your Linux operating system, whether it is how your Kubernetes cluster is doing, what is the security, what is the posture, the cost, all of that comes and says, Hey, I, can you give me, and for example, a question would be, can you tell me how many Linux distributions that I'm using across my state here?

Mm-hmm. What is the status of my Kubernetes? Just gimme an overall view.

How is the cost trending over the last three months, six months, nine months? Just gimme a and what can I do to optimize? Those are really powerful things right now would take, you know, days and weeks for somebody to pull that entire view together.

I think we are in the next 12 months where we're heading is to consolidate that view on two one, I, I would like to, um, well, first of all, if I may, if there's any viewers who are like, what is exactly MCP and how it's working, so think of it as basically like a shim between like any tool and an LLM. Mm-hmm. And there's two main parts to think about.

And the first part is like context, right? You tell the MCP server tells the LLM how to ask for context, right? So how do you query for logs and that kind of thing.

And then the other part is tools or tasks that the MCP ser that the LLM can actually take, right? You, you tell it, you tell the LLM, you can actually go do these things through the MCP server. Mm-hmm.

And, um, so it's actually relatively trivial to stand up an MCP server in the same way it's tri trivial to stand up a, a website. Mm-hmm. Right?

However, designing one that is usable in production in the way that we're talking about is non-trivial. Mm. And so, um, that's why, uh, we're like really excited to be working with Amazon to expose like our very careful painstaking work to like, you know, bring those natural language queries and those automated actions, like into the operations and administration space.

And then exposing that back up to, to the, the quick suite where it can bring in other contexts that, you know, were are not being provided by our tools and other actions that, you know, are, are exposed to other tools. Like think of something as simple as like logging a ticket in your Atlassian or whatever mm-hmm. Your GitHub or et cetera, right?

So it can like, ask our tools for, you know, is everything looking okay? And it might say it's looking okay, but a little shaky. Let me log a ticket so that when your admin wakes up in the morning or it's looking really bad, let me use your PagerDuty and wake something, somebody up.

You know, these are the kinds of like, very trivial examples of the kind of workflows that people are gonna be able to build in their, um, you know, using the, the, uh, quick suite along with our MCP servers for our management tools. So mm-hmm. Mm-hmm.

I wanna return to money. So it's always on top of everyone's money. Ronnie, you mentioned a little bit, we, we can actually through quick Suite s the ai, what can we do to optimize, you know, you wanna call it finops or whatever you wanna call it, but optimize spend cost.

Um, how, I mean, it's one thing to ask and get some suggestions. It's another thing to say, okay, you gotta close this, move that, do this, do that. How close are we to automating that?

Like, so for instance, when I write something with ai right? It says, would, do you, would you like me to do this? Yes.

Mm-hmm. And it does it, right? That's, to me, that's the money shot, right?

Can we do that now with, with the Linux, with this relationship and, and with what we have there? I mean Sure. Like, Do you want to though, Is your question.

So the way that we actually have implemented like our logic is like, you wouldn't let an LLM do something, right? That you wouldn't let a team member do. Like no sane SRE team lets somebody just like log in and make a massive change without a code review.

Right? So if you look at some of our demos and, you know, come to our booth, we can show you, you know, for it can, you know, write the recipes and scripts, check it in for you, start the code review process, you know, which is what you would expect a human to do. The only thing is it can be done a lot faster with a lot more context.

You know, the LLM handles complexity in a different way than a human does. But, um, that's to go back to what I was saying before, like that's really why I think you wanna partner with a company like suse. 'cause we take that very, very seriously.

Like, you know, making sure that your, uh, infrastructure is protected and compliant and following all your compliance guidelines, while also giving you all the efficiency gains that, you know, admin teams are so hungry. I think Like absolutely, I think in the enterprise setting, this is still, uh, it's still a pro, uh, a problem that everybody's working on. Mm-hmm.

I think if you saw, again, Matt's a keynote yesterday. He talked about everything that we are doing around guardrails and policies. Mm-hmm.

So I think there is a lot of active work that we are doing both at the alumni level as well as through quick suite in terms of how do we manage and police, uh, you know, the, the actions that the LL m's taking on behalf of the customer. I think have we, are we there fully? I think to Rick's point, um, you know, we can create guard rails, we can train data.

There is already like LLM looking at the LLM to make sure that, uh, you know, we are in the state and narrow here. Uh, and I think that's where we, we discussed like 2025 was a lot about experimenting. If we have to get to the scale in 2026, get into production, this is probably the most critical, uh, thing that we have to, you know, cross the chasm as they say, uh, to get, to make, uh, enterprises comfortable with, you know, taking that leap.

Yeah. And I think the question is, when do you want the LLM to take that action versus you still want a human in the loop? Yes.

Right. And I don't think we're done with that conversation, right? There's still gonna be times where you want a human to push the button, so to speak.

Um, you're not gonna give the LLM access to your source code necessarily. Right. Um, even if you want it to help you develop in your way.

So I, I think there's still some, um, ongoing discussion and of where the human in the loop is, um, even with all the guardrails and all the, the, the governance that happens. But I think the more you can automate all of those things and bring us intelligence faster, better, cheaper, right, then we can make better decisions. Yeah.

I mean, the, I mean, we all agree though. There's a massive push to get into that direction. I think the, the value and the opportunity is, is tremendous.

Mm-hmm. Yeah. And so that's why everybody's just, uh, you know, kind of run, you know, running for, well, I, you know, so you ask yourself why, why, why, why must there be a human in the loop?

Well, there's two reasons. Number one, is the underlying technology good enough where I could, I I, because it comes down a matter of trust, right? Mm-hmm.

Can I trust it by itself? You, I, I was driving my car on the highway a couple weeks ago going to the airport and it said, Hey, why don't try our, it's A-B-M-W-B-M-W assisted driving. I said, all right, let me try it.

I'm by myself. No one will yell at me. I, I, I hit the button and it started driving it, it take your hands off the wheel, take your feet off the pedal, just sit back and watch.

And I, I, I found myself like this. Yeah. Right.

Sitting wait, ready to pounce on that wheel. And you know what? I never had to, it, it took the turns.

It, if I put on my signal to switch, if there's a car there, it tells me no. If there's no car, it speeds up and switches. It's a very similar experience to trusting the, the LLM, the, the AI to do these things.

It, it's gonna take time until you take your hands away from the steering wheel and put 'em on your lap. And we'll get there one day, I think. Mm.

I don't know if I'll be here, but we'll get there. And, um, it's, it's, it's coming for sure. Like, there's no doubt in my mind this is the way of where we're headed.

I mean, I think it depends on how you look at it, right? Because if you think of like the driving analogy being that, um, the LLM is changing your infrastructure like directly, like that makes sense. But if you think about the way people actually manage your infrastructure through like Ansible and SALT and other tools, like we're there right now, like the absolutely can generate like perfectly serviceable Ansible scripts that can go into your, into your whole GI ops workflow.

And people can, can, you know, your other GI ops team members can look it over, other LLMs can look it over and make sure, like, does this meet our security compliance and et cetera. So, um, I, um, I think the technology is there. As they once said on a TV show, we have the technology.

Yeah. But do we have the Trust? I think like a lot to explain a lot of that is deterministic in the sense we know there's like A to B2C, everything is fully track.

I think the LLMs is non-deterministic, some part of it. Uh, so I think like once the technology is there, there's already a pattern here that, you know, the customers trust and you know, yes. This, they're already deploying mission critical workloads through, you know, salt scripts and such.

I think the, I think the, the LLM in itself has to, has to evolve to get there, right? And I think right to Margaret's point, we are still, I think for 2026, we still foresee the human in the loop. It's gonna play a very critical, uh, thing.

Yeah, yeah. Critical role. Yeah.

Agree. I mean, there are, there are patterns in like in for instance, Kubernetes, you can set it to like auto scale and different things. Mm-hmm.

So like, I think, um, a lot of it is like, what RAC, what role-based access controls do you give the LLM? And this is already a very common pattern, right? Like a lot of platform teams will say like, Hey, our ops team is allowed to scale out, but they can't scale down.

Or similar things like that. And so I think we'll see over the course of just literally the next 12 months, like patterns like that being brought into, like this is a tool that we can give the LLM to just go ahead and do it. 'cause they can't do much damage.

Like they could create a new table, but they can't delete a table. I Think the question is like, do you use the ZLM put out patches to all the unpatched systems? And you're like, do you trust it enough to do that or not?

Do you let it roll back a feature update or not? Right? So I think there's like a level of specificity Or certain kinds of patches, right, exactly.

Maybe you Had to do Yeah, exactly. Or not, right? Yeah.

And I think it's just one last point in the race to get to that determination. Like, you know, the best state here where we can start trusting the LLM apart, a lot of it is actually training and what data that we are training the LLM on. And in fact, and in fact that, you know, just SUSE has decades of, uh, data in terms of how customers are using both their Linux and the Kubernetes distributions for AWS You know, what Matt discussed yesterday are the frontier models around developing code on deploying code and running security.

So we are running as, you know, as said, hyper scaled, right? So we have like tons of, you know, supporting millions of customers, uh, over couple of decades now. So that data, and then what is the learning that we have done as a function on that, that informing some of the, the new AI or the new elements we are developing, specifically what that specific workload, I think that is probably the path to getting our customers more comfortable into deploying it and production use cases.

I think that seems like the, the road to get there. Mm-hmm. So this, there's been a great discussion, but you, you know what I'm worried about people looking at home.

If they're watching this, they're probably not here. How can we get them started? How do we, these are all great things that we spoke about, right?

Really kind of job changing. If you're a, a Linux administrator or an AWS administrator, Rick, I'm gonna start with you. Sure.

Where do people go? How do they get started? Where's the on ramp here?

Sure. So, um, I would say for a call to action, if you want to get involved, go to the marketplace. You can get multi Linux manager there.

You can get SLES 16 there. Go ahead and install the tech previews for the MCP server. Start giving us feedback.

You know, what's working for you, what's not working for you. That's exactly why we put it out in, uh, in as a tech preview so that we can iterate quickly with the user base. Mm-hmm.

And same is true for rancher. Yeah. And, uh, my call to action is do everything what Rick is.

That's an easy one. Plus, uh, uh, you know, try out the new kiro, uh, software developer platform, uh, that is becoming now generally available. Also, go look at, uh, you know, some of the new stuff that we are doing here with, uh, the frontier models, both on the DevOps side as well, security side.

I think that is really gonna start, uh, pulling the story together in terms of what, you know, Rick is talking. And then, yeah. And no coincidence.

Those are the three agents that were announced yesterday, right? The kiro agent and security agent, And the DevOps agent. And The DevOps agent.

So you get all of those. Margaret, I want you to give you the last word. Wow.

I don't see this. Now you've got me speechless. No, I think I'm just really No, no, no.

I mark That down. I was just gonna say that the last two days, which feels a lot longer than two days, but there is just so much energy in this show. There's so much energy around Amazon, around ai, around just h how to collaborate.

And, and again, I'm gonna go back to, I know this is becoming a repetitive theme, but, you know, continuing to look at the ecosystem around AWS and I would say it's our ecosystem and AWS as ecosystem and the power of how all these companies and all these technologies are coming together to help people, you know, build applications that are better, faster, smarter, more secure, um, and, and deploy them, you know, what works best for them. So I, it's just exciting. Like there's just a lot of enthusiasm at great Tech.

At, at at great scale too. At great scale and great technology. Rick, Manu.

Margaret, thank you so much for joining us on our techron TV coverage. We're gonna take a break here on Techron tv. We've got lots more coming at you today and tomorrow, so stay tuned, but we'll be right back.

We're back here with some more, uh, coverage from our AWS reinvent recent, uh, video stand. Uh, if you haven't seen some of our other AWS reinvent, uh, coverage, you know what, at this point, most of the videos are up. You can catch 'em on text Drunk tv, on the text, drunk tv, YouTube channel, or on our Text Drunk TV TT app.

If you've got Amazon Fire or Roku or Apple tv, or even iOS or Google Play, I, you can get the OTT app there. Um, but let me introduce you to our guest here. His name is Robert Cilla.

Sia Silla. Yes. Close.

Rob, I was close. I left the out. Robert Cilla.

First of all, Robert, welcome to Tech Drug tv. Thanks for having it. It's the first time he's been on, so glad to have him on.

Robert, you're with suer is, unless you just took the shirt. It is a great shirt. Possible shirt.

It is a great shirt, but I am with su. Okay. And tell us what, what's your role at Suse?

So I am the Director of Technical and Community Marketing. So I handle our community efforts around, mostly around our consumer community. And we, 'cause we have multiple communities, um, it's like that with any tech company.

So direct to consumer kind of stuff versus, uh, No, when I say consumer is people who consume our technology Okay. Is the primary focus. And then our secondary focus is people who contribute.

And on the open SU side, their focus is slightly different. Where they focus on contributions and less on people adopting, you know, it, they, you know, they kind of build it, it they will come Yeah. Open on that side.

So they cater to making sure the project package maintainers are taken care of. Um, and the needs of these two communities, don't, they overlap, but they're not the exact same. I love it.

You know, we've, over the course of AWS reinvent, I bet you I interviewed a half a dozen to 10 SUSE people. Mm-hmm. Not one of them really spoke about the, they mentioned the community, but they never really spoke about the community.

And so let's start right there if we can. When we talk about the SUSE community, and you mentioned there are different facets, aspects of the community, but how do you define this community? Can you give us sizes?

Give us, you know, I don't even know how you would define it. We, I, I define our community as a large group of practitioners who enjoy the technology. And that is the binding glue that brings them together in our community.

Um, to count it, it's hard, um, because you people are in certain channels and they're not in others. And we estimate anywhere between, you know, 45 to 65,000 people, um, who are active, who, um, they participate in Rancher Academy, which is a LMS platform. We put out, we want people to learn about our projects that, that are out there, or they're in our Slack channel, or they're engaging with us on social media and we understand there's crossover.

So that's why it's an estimation. 'cause I don't, we don't track exactly who's who. That's just kind of creepy.

We just want you to show up for just, Well, but that's, that's part of that open source mantra. Right? We, we don't track, you know, we're not looking for your blood type or DNA samples.

We don't wanna Know what your kids' names are. Exactly. We don't Like that.

Or even your birthday. Yeah. But, um, so a lot of it is online, it sounds like.

But then like in an event, AWS reinvent, are there any kinda suse community activities tied to it? We do A few videos that we post out the community, um, does crossover with AWS slightly, um, when it comes to some of the projects, AWS does have a, a large user community, and there's, there's some crossovers there with that. And we see it more so on the consumer side, very little on the con contribution.

Um, for us here, it's just, you know, showing what's the latest and greatest on AWS because we understand that commu there are community users who, you know, they're not customers, but they use our, our projects in AWS and we wanna make sure that we, I don't wanna say meet their needs, but know we acknowledge that that's where they're at. And the, And they, and they matter. They, they matter.

I get that. What about in-person events in the community? Not just at AWS re event, but, So when we have any large event that, that we try to attend, that piggybacks whether what our comm, where our community's at, whether it's here at Reinvent or Coop Con or Open Source Summit, we like to engage with our community, let 'em know that we're there.

Um, we always have community team members on staff at these events to ensure that, you know, like they can meet the people that they talk to online. Like these, these are kind of, I don't wanna say they're, they're rock stars in my mind because they're, they're great individuals on our community team. But I, I wanna make sure that they can connect, you know, in person just 'cause, you know, it's post COVID world, you know, having that interpersonal connection is, is nice sometimes.

Sure. Absolutely. Let me, um, I, I, I, one of the companies I had started was called the DevOps Institute.

We sold it about three, four years ago. Mm-hmm. But we had a, a nice community.

It was very simple. It was very easy. Well, it wasn't that easy, but one, one part of the community, the people who actually had taken our certification classes and our courses mm-hmm.

And those, we did know their children's name and their date of birth and all that. 'cause we knew who they were. They had a, you know, they took classes and they were certified.

The bigger part of the community though, were just people who maybe, you know, didn't take a, a real certification class, but somehow consumed our content or, or what have you. And it was always the discussion we always had at the exact level is why would those people want to be in our community? What would, like, what, what's the advantage of being in a community, if you will?

Uh, Well, I, I'd like to, I will speak, I mean, I it in any community, but I wanna speak towards the, the technical community. 'cause you know, it's what we're talking about and it's fairly relevant, is that individuals have to take some of these skills to work. Yeah.

And they don't want to know that. They don't want people to know. They don't know.

So being anonymous, be able to go and adopt, learn and grow outside of your normal work environment to come back in and say, I, I, I don't, I know this so I can talk to it. I'm, I'm participating in it. And I think that's where you see it.

And it does cross over to non, I'm a, I'm a avid cook. I love cooking. I love cutlery.

I'm in, you know, a community about, you know, cooking and so, you know, new knife skills or something like that. 'cause I want to learn and grow and not think my wife thinks I don't know what I'm doing in the kitchen. But that's just the same thing.

It's the same adoption that you want to have. And it's not judgmental. Someone comes to the community, they don't know.

It's like we point 'em in the right direction. People love to come in and answer questions for them, and they take that back to work, or they take it back to school. Absolutely.

So there is the, the, the help you grow, and especially from a work related mm-hmm. Point of view. There, there, look, there are plenty of people who are hobbyists when it, especially things like open source and Linux Yep.

And, and so forth. Um, but it is, it, it, it's a way to advance your personal career path. Let's, let's call it that way.

I, I, you know what else I, and this is me talking now. I don't have anything to back it up. Sure.

But I think it's part of human nature to, to want to feel part of something, part of a community. And, and as you said, it could be cuddly, it could be cooking, it could be anything. But you always want to feel like, I'm not the only one who feels this way, who has this problem, who, you know, is working on things, solutions to a particular issue.

I, I think there's, there's something intrinsic to humanity that wants us, that, you know, drives us to be part of community. Yeah. It's a, it's a sense of belonging.

Yeah. So when you, you, you talk to people like we have our regulars in the community, and you talk to 'em and sometimes they will just wanna say hi. Yeah.

And, you know, and or they will bring you something that they did. And they want, they wanna show it off. And I love that because you're seeing someone who has the same type of passion.

And it, it makes me feel better. 'cause it's not me going, like, I'm just a nerd here. There's, there's other nerds like me out there.

Absolutely Love it. Look, I built my whole business here on those nerds. Right?

I mean, they're, they're the people who watch our stuff and, and consume this. But it, it's, it's part of being in a tribe. Yeah.

Right? It's tribal at, at it's very nitty gritty. It's tribal.

Right. These are people who are in my tribe. It, it, it may not be a tribe that I live with or, or something like that, but we share that common bond, that common interest and, and they become part of your tribe.

And it goes down, it goes even down further where it's like, I, I only like Linux. I don't like Cloud native. Yeah.

And, and that's okay. And We have a lot of people who are like that. And that's, and it kinda, and you know, there's always rivalries in any type of community, so, you know, we're better than you kind of thing thing.

Mm-hmm. And it's, I it's akin to sports fans. Right.

And then as a Cleveland Browns fan, you know, I don't really fully understand what it's like from a sports perspective, but I'm sure like Eagles fans or someone else out there, you know, with, you know, a better team behind them would understand that level of, you know, rivalry that you have with the technology. Yeah. My sympathies to you, by the way.

Thank You. You Looks like you're can have a good pick at a quarterback. Again, though, this I'm, let's not get into football.

Let's, I'm a Steelers fan. I'm my own trouble. But, um, and I, I actually, my, when my brother who's is, he's a one of a fire toggling guys, and I say, Hey, be careful what you wish were, 'cause look at the Cleveland Browns, right?

Mm-hmm. But it's all relative. But it is, we are, we're tribes.

It, it, football fans are definitely community and tribal. Yeah. We, we still love, we, it's in that crossovers.

We, we each love our teams, the Steelers and Browns. We would, we would love our teams. And we have those rivalries and we can say, oh, we do this better.

And you have, we even have it in the Linux communities where, you know, they don't, there's, there's certain schisms that you have and sometimes they get toxic because, you know, we're in an online community. Right? Yeah.

And when you don't have the interpersonal things go get, they get dark, but they usually recover the, and that's what the beauty of a community. It, it, it like naturally recovers. I I think part of that though is, is, and, and you hit on something when you have a virtual community.

Mm-hmm. You know, it's easy for people to sit behind a computer and say something that they would never say in person. Yep.

And it's easy to misinterpret what someone else wrote and may not, they may not be the greatest written communicator, and they, maybe you're taking it the wrong way or they just wrote it the wrong way. And, and this le look, I've been in online communities for a long time, maybe 40 years. And, um, Well you also, for you, you didn't mention, but you know, we're international.

Right? Right. And you Right.

You guys Are, and there's, and so there's, there's language things. There's language really, I won't say barriers, but you know this. No, no.

But there's, there's miscommunication All the time. And sometimes I come into Slack and I'm like, what's going on? Why is there a dumpster fire today?

And I'm like, oh guys, he missed, like, he meant this. Right? Like, that's not that word that you Think, but it doesn't take, It doesn't take long.

It's too much. Nope. It does not.

There's people over the edge. Robert, let me, we're, we're running lower on time. But for people out here who say, you know what?

I've been a Souse fan. I, or I've been a Rancher fan. Mm-hmm.

Or both or, or what have you. I'd like to be more involved in the community. Sure.

What's the best OnRamp farm? io, you can go sign up and you, you get dumped into our general chat and people, and we see, we see people who get put in there and just say hi. And someone from the community team or someone from the community will do and explore what they have going on in there.

There's a, there's a lively chat. Um, there's random stuff that people, you know, post, there's technical checks. So, you know, if they wanna learn more about K three s or rancher specifically, um, those, that's generally the, the best way.

And, you know, I am, I'm in that slack more than our work Slack. So really, that's my world. Well, that is, that is, that's your work.

That's my world. So, um, I come, I go back to work. It's your tribe.

I go, it's yes. And I go back to the work one when I have to, but that's where I I you'll catch me. Um, is that, that's probably the best way.

And again, this is for the consumer side. When you're getting started in a community, uh, you don't have to come and contribute right away. I always tell people that just come and say hi and, you know, find where you want to connect.

You know, and it doesn't have to be contributions right away. It doesn't have to be consuming right away. It's just showing up and just being, just taking part.

Excellent. Is this your last show of the year? This is my last show.

Me too. Um, I'm getting, uh, a very busy with a, and I'm gonna do a shameless plug on Scon coming up April 20th through 23rd in Prague chea. Um, that's what's consuming most of my time now is the planning for that, um, on the CFP committee.

So I'm going through, um, uh, being a group of individuals. Atsa are going through a ton of talks with a lot of rate topics. So if anyone is in Europe can make it.

I do. Well, I hope to see you there. I'm hoping to be there as well.

Okay. I'm thinking maybe I should submit something. Has anyone submitted anything on AI yet?

Oh, I'm kidding. Kidding. That one right there is, uh, I think, I think that's the, the vast majority.

And I think when I saw, I saw one that wasn't AI related, I was excited. I was like, Wow, I, I get that way too. He was brave enough to put that one in, Put something in, not with It's crazy time to be alive.

I know. It is. Everything's ai.

But yes, if anyone can make it, I would love to see you there. com, find out more information about that. I Love it.

Rob, thanks for coming on. Thanks for having and talking with us today, man. This is great.

Hey, go check out the rest of our AWS reinvent videos. Ichan is coming, I believe it's April 20, the 23rd, as Rob mentioned in Prague, which is a great city. You don't have to be in Europe to go to that, though.

They do have planes that come from here to there. Yep. And, and, uh, it might be worth your while.

It's, uh, I've done Susko actually, the last scon I did was in Orlando near our house. Yep. But it was a great event as well.

So highly, highly recommend it. But that's it for here. I hope you've enjoyed our AWS Reinvent coverage.

This is Alan Shimmel for Textron tv. Hey everyone, it's Shimmy. Thanks for joining me.

I know this is a little rapid. We just did one yesterday, but we're doing another one today. Welcome to what is my last shimmy says of the year.

And, you know, I, I really put a lot of thought into this one and, uh, I call it Yellow Flags in New Dawns. And it's, you know, it, it's, I think it's a balanced look. It's fair and balanced as we look back at this year and look ahead to what's ahead of us.

Let me start off though, though, when I say we're looking ahead and looking back, we're looking back not at, you know, we're gonna talk about macro things. We're gonna talk about big trends and megas and all that. But it's really about you.

It's you, the people who watch this, it's people like you and I I talk to people like you all, all, all, uh, year long, right? Whether you're a DevOps person or a cybersecurity, uh, pro or a cloud engineer, a cloud native person, a platform engineer, whether you're a founder or executive C level, or a director, or just a single practitioner who reads on various techstrong sites. What I'm about to say really is about you, the individual.

That's who I wanted to reach today, right? It's been a crazy year, right? And I think you are not alone.

If you are thinking, boy, this has been a crazy year. I think we're all thinking about this. And it's been a crazy year for a lot of reasons, right?

There's so much going on in the world, there's so much going on in technology. But let's talk about bringing it home personally to each of us, right? Because we, we were asking what will AI do to the jar market?

But you know what, we're ending the year asking, what will AI mean to my job? What'll it mean for me? We're not, you know, we all, were wondering how companies are gonna react and how they're gonna adjust.

But now we're wondering how are we going to adjust? And right there, that shift from worrying about other people and other things and big things to about our own livelihoods and our own futures. And what is, what makes this both scary, exhilarating.

And, and so god damn important. So let, let's start right here. You know, I call this a yellow flag moment.

It's kind of like the yellow flag they put out in an indie race or a NASCAR race when there's oil on the track. And then they gotta clear things up before we could go full speed ahead. Well, we're going full speed ahead anyway, but we are very much sort of in this yellow flag area where we're at this boundary zone where things are happening, right?

You're doing your job, you're delivering your work. But is that gonna be enough going forward in this brave new world? You know, we're seeing layoffs all over the place, especially in tech.

I have so many friends who come to me and ask to help for finding new jobs. It may not be your company that's doing the layoffs, but you're hearing about it from your friends and on other companies. You know what, even your managers, they're trying to give you the, the, the optimistic everything's gonna be okay.

But don't think that they're not sharpening up their resumes and checking LinkedIn to find out what's going on, just in case. What, what's behind all this? Well, for the most part, it's ai, right?

Not AI in the abstract, not AI for the industry, but AI as it relates to you, your jobs, your career, your livelihood, your stability. You know what, this is the first year where AI stopped being a cool story and got really personal. It became a force that you've to reckon with you didn't recognize.

You didn't ask yourself. If you haven't asked yourself, rather, at least once, where do I fit in going forward in this AI future? You weren't paying attention.

'cause I think we've all asked ourselves that, but this isn't a sign of fear. It's a sign of awareness. If you did right, you asked yourself that.

'cause you recognized the writing on the wall. It's a sign. You understand the gravity of the moment we're living in.

And this is where, what it feels like when a new era arrives and folks, this new era has arrived, right? It's been a such a strange choppy year, right? Because we have ai, let's, let's zoom out a little bit, right?

The year has been, you know, uncertainty to say the least. The global, global economics have, you know, flickered like a neon light at a cheap motel. Um, the markets have shook.

Forecast is, forecasts have shifted. Yeah, the markets have gone up. But a lot of it is strictly due to seven or eight companies, 80% of the growth in the s and p 500, right?

The logic coming outta the halls of power. Sometimes they feel less like policymaking and more like performance art. At the same time, beyond ai, we're dealing worldwide with things like wars, diseases that we thought were done with, or back unrest, instability.

Sort of an authoritarian bend that we're seeing across the world that doesn't bode well for personal freedom on top of a crazy technological revolution. The likes of which we haven't seen before. It's almost, you know, I was, I've I was a child of the seventies in music, you know, the moody blues song Night and White Satin.

There was a point where breathe deep. The gathering gloom. Red is gray and yellows white.

But we decide which is right and which is an illusion. That's where we are today, which is right, which is an illusion, which is real. Which way should we go?

Who should we be with? Because a lot of it does all feel like illusions right now. It's all shifting.

Nothing seems solid. But here's the part too often gets lost. This isn't just a moment of, of uncertainty.

It's a moment of transition. And that's what I keep coming back to. This is a moment of transition.

I always, always, I said it, I wrote it in this article that accompanies this to me. 2025 feels like the first year of the 21st century. For the first 24 years of this, of the two thousands we've lived off of what we did in the 20th century.

We've lived off technologies like the internet and the cell phone, right? And, and the digital revolution. But those were really 20th century kind of inventions.

And they've powered us for this first 25 years of the 21st century. But you know what, think about it. We're a quarter of the way through this century.

That's a good chunk of the way through this century this year with ai. And a lot of what we're seeing, we are, we are moving forward into the next century. We are clearly in the 21st century, and it's time we stopped thinking our long, 20th century thoughts and start thinking really 21st century thoughts.

And this, whether we're talking about what we're going to use for energy consumption, maybe what we've used in the 19th and 20th centuries doesn't cut it. I'm talking about fossil fuels. You know, whether we're talking about digital and quantum and robotics, n ai and all these things.

These are 20, they're gonna be 21st century technologies and whoever masters those are gonna rule the 21st century. And whoever they will be, who will rule it. It's not gonna be people like me, right?

I'm, I'm at the far end edge of the baby boomers, the last of the boomers, but our days passed even most of my friends who are Gen Xers who are in the back of the rule, it's time. You know, not to go all John Kennedy on you, but it's time for the torch to be passed through a new generation to the Gen Y, the Gen Zs, the millennials, the alphas, and whatever you call 'em. It's these people who are going to make the decisions, who are going to utilize these new technologies, these new powers that are going to determine what your life is like in the next 25, 30 years for the bulk of the 21st century, right?

Um, and here's the good thing. This new generation, they're not waiting for my permission or your permission. They're stepping up.

They're redefining what work looks like. They grew up in nine 11. They grew up with COVID, they grew up with the internet and their native, and now they're accepting ai.

They want to define for themselves what's normal, right? They don't care what we spent decades building up is normal. They're pushing the boundaries that we thought were unbreakable.

And you know what? I for one, love it. I think it's time we do it.

We need to do it. I spend a lot of time talking to people of this generation, including my own two sons. And let me tell you something, they're different.

And they're different in the best of ways. They're not intimidated by the complexity or, or importance of the moment. They're not nostalgic for the old ways and the old music and the old everything.

They're not romanticizing the inefficiencies that we've spent our entire careers trying to patch over. They are ready to take the baton and enter the world that is coming, not the one we're leaving behind. And they have the tools for it.

To them. AI isn't a threat. It's a partner, a lever, a multiplier.

It's something that frees up their time so they can focus on what humans actually do best. Creating a man, imagining, connecting and building. And it's not just ai.

They can't wait for quantum. They're embracing robotics. They're embracing change.

You know, we thought we embraced change, but at a very only at our speed. But let's talk about AI directly. Yes, it's changing jobs.

Yes, it's redefining work. Yes, it's unsettling. But this isn't the first time the technology has shifted the landscape.

And every single time it has, the shift ultimately created more opportunity than it destroyed. Maybe not immediately, but eventually not smoothly, but inevitably. So, AI may take some of our jobs.

It will take certain tasks, it'll define certain roles, it'll elevate certain people, and it's gonna displace a lot of us. But humanity's unique value, creativity, judgment, leadership, empathy. That's not changing in this new century, in this new era.

We're being repositioned, not replaced. And that's something that we can shape. And it's something that this next generation wants to stay, wants to shape.

We gotta stay engaged. We stay curious, we stay adaptable. But you know, the old saying, right, if you don't learn the lessons of history, you're bound to repeat their mistakes.

So my advice to this new generation is learn from history. Don't repeat these mistakes. Build something better, stronger than was built before.

And that's why right now it feels like we're at the forge, the Black Smith station. And everyone knows you can't make steel without heat and without some banging. And I feel like that's what we're doing.

You know, part of the craziness in the world. Like I'm sure you guys all heard, you know, this insanity of Rob Reer and his wife being killed potentially by his own child, by their own son. And it is just nuts.

And it caused me to really think back on Rob Reiner. I, I loved Rob Reiner's work. I I didn't know him personally, but from his time as the meathead on, on Archie Bunker, all in the family, all through his acting career, the Wolf of Wall Street, you know, he was, uh, Jordan's dad and even directing movies.

I mean, what a great talent Rob Reiner was. And I'm reminded of a movie that he directed. Many of you may have seen the American President.

Uh, it starred Michael Douglas. And if you haven't seen that movie, it's an amazing movie and it's a great Rob Reiner movie. Go see it.

But there's a scene in that movie where he, Michael Douglas is the president, finally steps up and basically lays down the gauntlet. And I'm gonna paraphrase kinda what he said because I think it's really right off for where we are today. And it's this, you know what the bad guys, the, the crazies, the cronies, the stuff we've been dealing with, they've had their moment in time.

They've had their 15 minutes. It's time for them to get out of the way and let serious people get to work. And you know what?

You are the serious people, my friends, colleagues, tech workers who are working it. We are the serious people. We may not be the loudest voices, the flashiest, we're not the doomers of the heart or the hype artist, but we're the people who day after day are building this new tomorrow building the system, the world, the systems that the world's gonna live on.

You'll decide what this new era becomes. You are the ones who are gonna shape how AI is used. You are the ones who are gonna keep us anchored in reality while we push forward.

'cause there is no other way. The moment isn't calling for spectators, it's calling for contributors. I'm asking you this 21st century be contributors.

Turn the page. We're heading into a new year, right? Take this week, take a breath, rest, recharge.

We've earned it. It's been, it's been a year for sure. But come January, it's time for all of us together to get back to work.

Not just to keep the pace with the world, but to help shape the world. Because the future isn't something that happens to us. The future should be something we build.

So I'm gonna get off my soapbox in just a second here. On behalf of myself, my family, everyone here at Techron Group, our colleagues at the Futurum Group, I wish you all peace. I wish you health.

I wish you quiet in the middle of this loud insanity that is our world today. And I wish all of us, the wisdom and courage to step forward into what's next with purpose. Because this adventure is just beginning.

It's far from over. And folks, here's to going another one of my, uh, favorite TV shows, right? Here's to going where no person has gone before.

Peace, happiness. Happy New Year, everyone. I'm shimmy.

I'm out. I'll see you next year.