Techstrong TV December 23, 2025

Hey everyone. Nothing witty today. Merry Christmas.

Happy holidays. This is our wrap up. You're watching the gang.

Merry Christmas everyone. It's Alan Shimmel for Textron Gang. I have to apologize.

I did not bring my, my center outfit or anything else, or even Harry Hanukkah or anyone. But, um, we're here for our end of year special. We're gonna do things a little differently today here on Textron Gang.

We're gonna have one se well, not too different 'cause we'll still have three segments. One segment's going to be dedicated to it, stories for the year, and we have our great IT panel. One segment's gonna be dedicated to cybersecurity stories through the year.

And we have a killer cybersecurity panel. And then one segment's going to be dedicated to ai and that's gonna be a free for all. 'cause that's pretty much what AI is these days.

So, um, we, we haven't rehearsed this. It's kind of an improv theater that you're going to see. We hope it'll work.

But let me introduce you to our first panel, which is the IT panel. Joining us is my friend JP Morgenthal, also very good friend, Tracy Reagan and our friend Jack Gold. And joining Tracy, Jack and jp.

I have the, what we call the core four, not quite Derek Jeter, Mario Rivera at Core Four, but John Schwartz, Mike Ard, Mitch Ashley, and myself, gang members. Welcome Mike. It's 2025.

Certainly been a year and it has had its share. What do you got? Well, I think if you look back on it, you know, there's two obvious stories.

One is, you know, all things about ai, and we'll get to that in a, in a little while. But the other story seems to have been a lot of focus on cost optimization. I think a lot of people are struggling with, uh, different economies.

I mean, some people say there's a K economy out there, and it's splitting the marketplace in half. And, um, I think a lot of it people regardless, are under more pressure this year, past year, and probably going in the next year just to make things more efficient and reduce costs. But Jack, what are you seeing here?

I mean, are you catching a different vibe from it lately? No, I, I think you're, you're spot on, Mike, and I think there's a, a k curve in the, the IT industry as well right now. Um, the, uh, K curve for me is the, the cloud-based environment is still hiring.

They're still looking for people. They're still looking for specialists, the traditional it, you know, the, the, the, the guys and gals in the back room managing the server that's going down. Um, and I think you're gonna continue to see that.

Uh, some of that is cost related. Some of that is the assumption. I guess I'm getting ahead of the third panel here that I, AI is gonna take over some of those jobs.

But I think a lot of it is redirecting the, uh, budgets because number one, many companies aren't sure what's gonna happen over the next couple of years. And number two is, what kind of people are we actually going to need in our environment, in our IT groups to manage where we're going in the next two years with Gentech AI coming on board and all kinds of other stuff, uh, cybersecurity issues, there's all kinds of things happening in it. So I think we're seeing a a, a real tendency for a lot of companies to just pull back and say, you know, let's, let's be really, really cautious here where you don't know where our investments need to go.

And, uh, I think that includes getting rid of people, unfortunately. Uh, JP you seeing the same thing? What are you seeing?

Uh, unfortunately, uh, I am seeing the same thing, uh, Jack is seeing, and the, and it is very disconcerting that you have this new technology that is coming into its own and people are starting to make assumptions more than actual, uh, you know, experience with what it can do. So this, I I think part of this is that there are assumptions about what AI will do and what it will fulfill based upon some experimentation. Uh, but you know, it's like, um, it reminds me a little bit of the, uh, the drug industry, right?

I mean, you're making an assumption that this drug is going to perform as expected over time, but you really need five, six years of experience of seeing the impact of it before you know that it's safe for humans. Um, well, the, our industry just feeds the drugs to the humans in hopes for the best. So I do think there will be a falling out at some point, a house of cards that is kind of being built.

Um, the interesting thing is, will AI be able to rapidly be able to hold the house up and put the jacks in place before the whole house falls down, which is, you know, different than other house of card situations because you have this technology that can so quickly respond, uh, and mitigate, uh, mitigate issues. I mean, personally, I have seen that you can point it at a problem and it actually will resolve the issue in in minutes. Mm-hmm.

So, Mitch, let me ask you this then. I mean, we've been talking about automation in it forever, and to be honest, I know everybody's a little concerned about their jobs and their roles, but I don't know, is it better to be in it now than any other time? Because I'm not doing so much Scot work and toil, and maybe, you know, it's just a different mindset in a different way of thinking about what it means to be in it.

I think the good thing about being in it is opportunities. The greatest ones are o often caused when there's great disruption, budget disruption, ai, new technology, cloud, whatever it might be. So automation is certainly one of it.

And we're at this beginning, this transition phase into a hybrid, both procedural and also agent driven kinds of automation. So though it's very early in that space, yeah, so we're, we're, we're still gonna be living with the Pearl and the, and the Python and whatever other types, script, types of scripts that are, that are gonna be automating our processes for quite a while. But I would think we'll increasingly see opportunity there.

I think the other thing that we see saw this year is not just disruption from ai, uh, access to markets, digital sovereignty, sovereign clouds, uh, what does that mean for our business? If you're an international customer, what does it mean for a US business in terms of access to their markets, et cetera? So that causes a lot of disruption, but there's also opportunities.

We see companies introducing new sovereign technologies and sovereign cloud. So even at a macro level, whether you're inside it, I think the biggest disruption though, and I, I think the biggest fallacy was, you know, we don't need any of these people because they're all gonna be replaced by ai. And we seem to be creating a lot of tools for those people that we're, that we don't need.

So we don't, we still need them now, at least for the time being, You know, as, as much as things change, they stay the same. Mm-hmm. There's still some basics here, right?

Mitch, you know, this, I, I've spoken with you about this, right? The CIO was always told that the IT team is here to serve their customer, and the customer is the other employees of the company. The, the function of it is both development, software development, and, and today, you know, and I think this year, but it's been coming for years, the role of software development has become an outsized piece of the entire IT puzzle, right?

'cause we're all software companies. Mark Andreessen said, even though Satya now says we're now intelligence engines. So software development, meaning the platform we develop on, how we develop, what we develop, how we deploy, how we optimize, how we secure, how we, uh, observe feedback and reiterate, that has become, you know, the, the, the biggest chicken, the nest of it, right?

By by huge. But there's still the other function of it, which is, you know, keeping the computers up and the help desk functions and, and those kinds of things that we don't think of necessarily as part of the software development job. Right?

And, and I think in 2025, again, the software development piece of it, that's where the action's been in ai, right? And, and hence that's where a lot of our attention has been. Tracy, I know that's one of your favorite topics.

Well, I think what we have seen in this past year, if we really look back, is one of the largest transformations of operations that we've seen in a very, very long time. And it's moving very, very fast. Um, we are, we are having to, I mean, even, you know, even at the Linux Foundation, Linux is having to, to work to really keep up with the demand of the changes that people need to deliver new kinds of software, faster software, software that requires more processing.

And I feel like in terms of the job market, we have seen, I know many of the developers, some of the developers I should say on our open source community have moved from being developers. They have moved to being platform engineers because they're, and you know, the more I learn about platform engineering, the more I understand that part of it is because of this massive shift that we're seeing and the types of tools that we're using, how the operating system is changing. And we now need a way to do onboarding of new employees down at the developer level as quickly as possible, and be able to implement new tools as quickly as possible.

It is, it's moving very, very fast. I mean, think about the rise in MCP servers over the last year. It's been phenomenal.

But that means that those people on the operations side have to support a lot of new developers, new platforms, new stacks. And I think that this disruption has caused some of the, of hiring to be quite honest. But I also think that the platform engineering has made a big, big difference in how we, how agile we are and how we develop and what we're writing.

But I don't think that AI has reached its peak in any way, shape, or form. I think that we have spent the last year trying to figure out how to build these development environments so we can start thinking about how to implement technology using this new platform that we call ai. I think there's another piece that I've seen as well, and, you know, we talked about the, the K economy for it.

Uh, I'm seeing some companies at least move parts of their IT budgets away from people and into the cloud to AWS or to Azure, you know, Google Cloud. And so what that's forcing is companies to look at it, budgets are going up, but they're not going up by that much, right? They're going up by a couple of percentage points here or there.

And so if you're spending a lot more money with Google Cloud, where is that money gonna come from? So I classify that, clarify that for a second. Do you mean managed services that are handled by those guys, or do you mean me putting my workload up there, but I still gotta manage it?

Both. Okay. Both.

It, it really is both. Uh, it there, you know, there's a reason AWS and Azure and Google Cloud are making a ton of money, right? Uh, those, and, and those budgets are not just coming from line of businesses, they're also coming from IT in, in many cases.

So that's had an effect, I believe as well. It also has an effect in the sense that companies think that if AWS is managing all these clouds, for me, why do I need all these plumbers in, you know, in-house doing the plumbing when I can get it done outside? So it is a, it's a really mixed bag as far as where budgets are, are being dispersed these days.

And the, the, the hyperscalers has certainly had an effect on the hiring. I think there's two things that are true here that Alan's point. One is, developers keep playing with new toys, and then they go build stuff and they get bored with it, and then they dump it on IT people.

That seems to be continuing to happen. And then, um, the second part of this though, is I would also argue that maybe we're looking at the democratization of it, and as we do make these advances, there's whole companies in the mid-market and smaller organizations that don't get to take advantage of all these wonderful things for years. And maybe we're gonna see these organizations implement more advanced it sooner faster.

John, what do you think? So this is taking into account what you've all said is, it's interesting you imagine being inside a company, being a CIO or being in, in charge of the IT plan or the organization where you're being hit with wave upon wave of all these options. You're trying to balance your, your budget.

You're trying to think, figure out where the money's gonna go, what's gonna happen to your employees. I think it's gonna be head spinning. I think it's gonna be a mad dash.

And I think you all see a combination of people mass hirings and maybe a lot of people being transitioned out. And I, I see that, I think about Amazon, I think about how they're using AI and how they're implementing it in within their, IT and I, you just see this kind of chaos going on. And I think Tracy had said, we're kind of just at the beginning and it's only gonna get faster, which presents a lot of problems.

On the flip side, as Mitch had mentioned, disruption's a good thing in terms of ideas, in terms of opportunity. So it's, we're kind of at that, uh, assessment process. And the assessment is overwhelming.

You know, the Other thing, I think 2025 introduced is probably the greatest beginning of the greatest skill shift in our industry. Yes, every job is being not just affected by it, but the skills of people and their ability to use it. Maybe create things with it, maybe operate it, maybe all the above.

That is part of that democratization too. But you look at the skill surveys, they've, they've started to shift into new categories around managing agents or prompting and how, knowing how to, how to instruct and describe things in, in a way that AI can take on, or even looking at roles and jobs and processes and figuring out how to use and apply AI that's outside of it too, not just within it. Mm-hmm.

And Alan, the question I can't answer right now is if, you know, if your child comes home and says, you know, I want to go into it, do you tell 'em that's a good idea right now? Or not? It is funny you should mention that, right?

I've, I've had this conversation not only with my own children, but friends' childrens who come to me because they say, oh yeah, go ask Alan. He's in tech. Um, tech's not going away.

It's not going away. But the skills you're gonna need and what the jobs are, are, as Mitchell said, absolutely changing. And, um, at the end of the day, you know, this whole AI thing is just, it's going to affect it every single job in terms of, um, it's not necessarily gonna replace or take every single job, but if you are not an effective user of ai, someone else will be, and they'll take your job.

And we've been saying that consistently throughout this year. Right. Uh, another aspect I I'll bring out, you know, all of us here are no longer 21 or 22 is, and I, I spoke about this at the, at my shimmy says last week, is the torch being passed to a new generation?

All of us have grown up with 20th century technology, right? We, we've, we've had great careers. Everyone on this show that I'm looking at has had significant accomplishments in their careers.

And we are doing our best to stay abreast. We're swimming as fast as we can, right? To keep up with this AI thing.

But is, is it, is it, it, you know, the boomers are on their way out. The Gen Xs are in the back of the room already. Is it time for the millennials, the alphas, the baiters, whatever they're called, the Gen Zs?

Is it their time to take this baton and run with it and and See where this goes? Yeah, I think we already are. Yeah, yeah, yeah.

I mean, we, this iteration has happened generation to generation. I think just in this example, it's just profoundly larger and faster. It's gonna infect more people.

And, and it's, and again, going back to like the skills and, and what Mitch mentioned, that's why it's so hard to, to follow some of these surveys and, and adoption of certain AI in particular, is that we're at different stages for different skill sets and different development of skills depending on the job. And so it's really hard to get a really hard grasp on it. But is This bringing any different than when we've had transitions in the past?

Yeah, maybe it's coming faster. Maybe it's, it's more effect. Have a greater effect.

But when, when, when we move to PCs, I go, go back to many computers, right? And from mainframes, even mainframes, it always transition, transition was always hard. I Think the big issues, the big salary reset that's gonna occur with this, and I think that is gonna be the problem in the short term for many of these people that Alan's saying, we're passing the torch to their expectations have been set by us, right?

Who have had, you know, looked at for the past, uh, 15, 20 years, you know, entry level jobs in it, of, you know, 50, $60,000 because it was a skill that required a significant amount of training and expertise in order to execute. And now with ai, the, the expectation is that you don't need so much training. You don't need so much experience.

You need how to know how to use ai. But given that the volume of people who will understand how to use AI will be immense, the salary expectations of starting, I think are going to drop significantly in it. And I think we're gonna be back to 25, $30,000 starting jobs again.

And that is gonna be a problem for those who are expecting higher and they're not gonna take the job. And we're gonna see this, this, um, valley, if you will, of where there's this challenge where I want more money. Sorry, we ain't paying you more money.

Uh, and so it settles itself. It's gonna create a very interesting dynamic in the industry. Jake, don't dare you said it, But we have to stay frosty.

Don't spray what everyone else is thinking. And I, I wanna make sure that I get this in because I always like doing predictions as well. I predicted AI agents would suck.

And I'm, I'm, I'm being proven to be right. If somebody was to ask me today, should we go, should I go into it? I would say, yes, it on orbit, go learn as much as you can about satellites.

We are gonna be seeing a disruption in data centers in the next five years. We could go from, we have like 11,000 satellites above us right now today. We could go up to a hundred thousand in five years.

And what are those satellites gonna be doing? They're gonna be our data centers. They're gonna be our data centers, and they require a whole new set of skills to manage those data centers.

That's where we're headed. It's really, really obvious to me when I look at what's happening in the satellite industry. That is where we're going.

We're talking about satellites. So the size of toasters, literally, we're going to space. So technology, hang on.

'cause we're gonna be blasted into space in no time. And it's going to be a busy, busy, busy time with plenty of good salaries. It's not just new person has gone before.

Guys, we, I, I gotta pull the plug on this one. We're over 20 minutes. I got people in the green room it panel.

This is all now recorded for, uh, pros not prosperity me. And we can look at this, but we're gonna take a quick break. We're gonna switch out some panels, and we're gonna come back and look at cyber.

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So, as I said in the outset, you know, a little different with our, our Merry Christmas year end show. Our second segment today is cyber in 2025. And I couldn't think of three better cyber people to talk about it with.

Uh, we've got, and two, Santa Claes and Chris Blas and Fred Wilmont, Fred Wilmont, and the woman behind the, or in front of the brick wall, Terry Robinson. Um, of course joining Mike Mitchell. John and I thank you, my cyber experts.

Um, you know, 2025, like everything else in tech, cyber had itself a year. We saw some amazing acquisitions for big, big money. We continue to see consolidation.

What I found most interesting was after every year of hearing, where's the, the covet, you know, what a vecher is? The who vet saying, where's the innovation in security? Where's the innovation?

All of a sudden, I think we're seeing a little innovation. It's AI fueled, maybe, but innovation, nevertheless, Mike throwing it to you, It's been an interesting year, but every year in security is an interesting year. But I feel like there's been a fundamental change.

And it goes something like this in my mind. It's, um, the pace of the battle is now being fought in real time in milliseconds. And it's changing the way the security people think about their jobs, their roles, and, and maybe even the stress that goes with that.

But since we have, uh, Terry here, I'm gonna go with ladies first. But Terry, what's your thought here on, um, you know, is, is the mindset of the security team changing or has it changed? Or what does the psycho demographic look like to you?

So, yeah, I mean, I, I've had this conversation a number of times with people, you know, recently. It's like some of the issues are the same. Some of the, uh, uh, mitigations and remediations are the same.

But the problem here, or not the problem, but how, what they're having to adjust to is the speed, right? Of everything. I mean, it's, it's, uh, it's crazy.

Now we're seeing exploitations, um, of vulnerabilities, what within hours, uh, of disclosure. Um, that's gonna be minutes. Um, I think it, um, or security, it is like completely stressed over a lot of this.

They just, they already were having problems reacting, right? Or, or certainly not staying ahead of, of everything. But, um, I, I think that if you could get into the psyches of these people, you would, it would be scary.

I mean, I really do. And I, and honestly, I, I, not that I don't think that they'll adjust, I, I think they're going to maybe in 2026, I'm always hopeful about this, is that security will find a way to be more proactive. So they're not always, uh, caught reacting to what's going on in the marketplace.

But, you know, you guys can tell me if that's just a pipe dream. So Fred, we've established that cybersecurity people are scary. Um, but I guess the next question that I might come with is, Did you say that with these beautiful Santa Claus, Here we're the Santa Cla hat cyber guys?

I, I needed to get a Santa Claus hat, and I would've been more rosy, I guess. But, but what are they gonna do to kind of succeed or change? Or is there a, you know, something that they should be doing?

'cause I, it feels like the old playbook just isn't gonna work. So I can sit around and stress out about everything. I need to figure out something to prioritize.

So what is it? So there's three different, uh, forces at work here. Uh, and I think I loved what Terry had to say about it, right?

The velocity and veracity of attacks weeks to days to hours, right? The, probably the biggest RC from 2025 was hours, uh, with React to Shell and the greater than 1% of the internet available for operations on that particular vulnerability. But you have a couple of other interesting and contrasting, uh, forces at work ai, for better or for worse, right?

Uh, skilled talent shortage. But also there is something that, I think there's a, I don't wanna call it a malaise or a LAC that's happening within the industry, uh, but it's within the business, which is to understand the actual risk of what's happening and to treat that risk as a business risk and cyber insurance and the things that go with it. There is no doubt at all that the number of, uh, rce this year is greater than it's ever been.

The number of exploited CEEs is greater than it's ever been. The number of technologies that have been exploited, that our security technologies is greater than it's ever been. But there are an awful lot of really talented cybersecurity people that are outta work.

There are an awful lot of folks that are struggling to implement what everybody's invested, all the financial wherewithal from the venture capital community to make work in the industry. And that hasn't been a wider gap that I've seen in the last 10 years. Good point.

Chris, what, what do, what do you think you've established yourself as the, you know, cybersecurity optimist of 2025? Yeah, we gotta, we gotta understand what story we're part of, right? So lemme take you back to the winter of 1992, right?

And you find a young crisp, uh, Ebenezer Blak, uh, looking at the firewall market where there's a hundred firewalls and start on the planet that typically seven computers a million dollars a year. It was the, it was the application proxy, uh, uh, era. And I had an idea that we'd have a firewall we'd sell thousands of, but we do that, we intercept every packet.

And by nine, the winter of 1998, you find me in, uh, uh, uh, aged, uh, uh, Chris in Utah taking over the, the Cisco having just taken over the Cisco firewalls, which are not proxies. We're at this point where we've blown past that paradigm, it's not gonna work anymore. And now, you know, here we are, you know, the Christmas present back in Toronto again, in a world where, you know, our, our systems are breaking because they don't know.

There, there are people here. We see this everywhere. And security is just a good example.

Compliance, ask any CISO from the beginning of this year to this year, it's just breaking everywhere. Why? Because the systems don't know that Bob and Mrs.

Crochet are there actually trying to use the system. Literally, our systems have no idea we exist. And on every level they're breaking, right?

So the Christmas futures, you know, I think the, the moral of the story, we know pretty well our systems will come to recognize that there are people here and that changes everything. So I think that's that little moral arc. We are part of that story and everything.

You know, Terry and Fred, you just said, we're driving to this. We've hit this point where it takes a change of mind, and I think we know what it looks like. All right, Mitch, the Christmas Carol kind of opens up with Jacob Marley, and he's visiting Ebenezer Scrooge, and he's talking about how I wore these chains in life.

And so I'm now wearing them through eternity. And sometimes I think, you know, cybersecurity feels like that, and maybe Jacob Barley's the patron saint of cybersecurity, I don't know. But how do we break the cycle?

You know, it, it is a perfect metaphor, and here's why. So if we ever thought that the idea that technology could be held back until we were able to secure it before we deploy it, that has been blasted out of the universe. You know, if you wanted an example of how badly will we rush into the next technology AI without worrying about security, we'll rush.

And so it changes the entire psyche, I think for security, for security people, for the mindset, yes, you still have to be the, you know, be the folks that really are looking with great scrutiny and, you know, prove it to me. Let me make sure that this really works, and how do I protect from the bad guys? But I think the big change is, you know what?

We are on a new cycle, and I think it's gonna continue. Whereas it's not about wait till we decide what we wanna do, and then we'll figure out, come to you and figure out how to secure it. You better be on the AI train.

You better be on the data center and satellites in, in space to Tracy's conversation in the last segment. Um, you need to be on where technology is heading and already working, thinking about, all right, if that takes off, what are we gonna do? How, what's our story?

What's our strategy? Or maybe there's an innovation that we're gonna help create to make that happen. So the days of sitting, sitting back and saying, wait for people to come for permission to do things, if there ever really was that, maybe that was back in the 92 era there, Chris.

Um, you know, the, the Christmas future is we're all flying and, and we have to move together. Move very quickly. So when you think about, I was gonna ask you, so when you think about this, looking forward a little bit.

So we have this recurring theme of cybersecurity being this, this evolutionary nature versus AI's revolutionary pace, for lack of a better terms. Does this, and I don't wanna be a doomsdays sayer, but does this inevitably lead to a cataclysmic event that finally forces even cybersecurity to change its ways? And maybe does this lead to like a rash of acquisitions by companies hyperscalers who want to protect themselves and their customers?

I'm just, it just seems inevitable this is happening can happen. Like the cyber Nine 11, is that what we're No, maybe it's a good question though, to the point where does cyber becomes so infused into the platforms and everything else, that it kind of just becomes a feature and it's, yes, it's not its own independent category. Alan, what do you think?

Ba hum. I, I'll wait for that. Yeah, because John, right.

You know, that inability is there, right? You know, so the systems have to get back to relating to humans, right? And it sounds kind of esoteric, but, you know, and as as says, as a longtime security practitioner with quantum computing coming, I actually do see an answer to that.

It's that, of course you have my information, of course you have my documents, you can't read them, you can't use them. You can read them. They're right there.

You have no idea, because that's in the relationship. That information is in relation to Terry and something Terry is doing with Fred. How do you interpret that?

So I think there are ways out of this and, but they are, you know, much more human than the bits and bytes of cybersecurity for the last couple of decades. But they're implemented in that same infrastructure and it's seems to be showing signs of working these days. So there is hope tiny Tim.

Well, and I think Hearken back to what Fred said maybe too, is, um, you know, the whole idea of, of the business proposition, right? And, um, risk, and maybe that's, you know, what, uh, defenders or organizations, uh, focus on because there's just way too much for them to do, right? When you get down to it.

And maybe, um, everybody that I talk to again talks about how we gotta know what's important to us. It's an individual decision organization to organization, you know, what you, uh, protect. And maybe you're protecting the same thing throughout the decades, you know, just maybe writ large or whatever.

But, um, you find a different way to to, to do it. Um, I, yeah, No, I know. It's just, I, my my point I guess is also is that cybersecurity has gone through all these waves, so it's adapted and it's, it's yeah, for severe in many cases.

And I'm just wondering though, given this kind of tsunami like wave coming with through ai, whether it's overwhelmed or their, It's, it'll still adapt. I think fundamentally though, here, here's, here's the deal. Fundamentally, there is a, a schizophrenia in the goal of cybersecurity at, at, at some level.

I don't know if it was Mike or, or, or John who said, you know, security goes away and becomes part of it, built into the fabric, if you will. I think if I asked Fred that, Fred would say, that's a worthy goal. I'd love for that to happen, right?

We, we need to be built in, not bolted on, we need to not be an afterthought, right? We don't need separate security. We don't need the SEC in DevSecOps, it's just DevOps, right?

That's one side of the face. The other side of the face is none of those people give a s**t about security as much as we do. That's right.

And we need, we need, we need to be here to make sure that this whole thing doesn't go to hell in a hand basket. And, and that right there is security personified. Chris, you got your hand up.

You know, in that, in that ghost of Christmas past narrative I gave you, you have to understand the mid nineties, how existential that was because the firewall was security. And the whole concept that we're gonna accept the idea that people, some people will get through was like, apocalyptic. You can't imagine we, we'll no longer have security.

And then we got into it, network awareness, SIM, sim, threat intelligence. My point is, we've gone through these evolutions multiple times. We're at this point where driven to the point, but John, you, you know, call up.

We can, so we will do something, you know, the light will stay on. I, and you know, a lot of people tend to think that it comes down to humans, like relating to the, have the system relate to the human, because actually that gets exponentially harder to crack, easier to deal with smaller. So we're driving ourselves in an evolutionary, uh, again, to a crux where we have to do things differently.

We've done this before. I think this episode next year, we'll, we'll have some alignment and agreement on where that's going. I think it's about people.

Yeah. So Alan, to you, to your point, Terry's point about maybe a little more focusing the conversation on risk. You know, we've been talking about security, people learning how to talk to the business forever and a day now.

And I think maybe some more of them are figuring that. But at the end of the day, you talk to business people and everything they do has a risk. And when you go to business school, it's really about being taught how to manage risk.

So from their perspective, security is just one more risk of many, many. So are we finally getting to the point where maybe the security people realize that, you know, this risk issue is what the business people care about, because they just wanna know, like, yeah, I know it's insecure, but I wanna know, like maybe how insecure and how much money I'm gonna make regardless. Fred, how long have you security?

Uh, it's what, 25 years? 20 plus. Have you been hearing this same argument for 25 plus years?

Absolutely. Chris, you, Oh yeah. We're all gonna die any second now.

Yep. Yeah. Same, same stuff.

Different day. I'm gonna age out of that, that prediction, by the way. We, we are, we're all still here, right?

And this, and again, you know, the, the, it's interesting. I know we're going into AI in a second, but again, these are semantic systems. We're literally talking to them and trying to see what makes sense, which we're literally doing with each other right here.

And, you know, we'll either navigate into a Christmas future where we get to keep doing things, things or we're not. And that binary just continues to drive the fact that we'll come up with solutions. We always hear, I mean, Mike, to, to a, a good answer to your question.

That's why we have CISOs. The CISO is supposed to be the universal translator, translating security to business for the decision makers. They've gotten better at it.

CISOs have become, uh, more widespread and better understood. I also think that they are like, uh, like, uh, building department employees at the New York City building department. You slip 'em a 20 and you get anything you want, but there you go.

I don't know who insulted there. Was that the CSOs or the building department people? No, you take your pick.

It depends where, what side you coming at it from. Yeah. Boat.

That was, that was a grabber on the very unique New York thing. Yeah, that was a New York thing. But let, let, let us, let us take a break here 'cause we're over time.

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Hey, everyone. And we're back. You know, we saved, I don't know if it's the best, but we saved the biggest for last.

And this is, uh, our mega panel as we're calling it, talking about what else? Ai, because as I've said before, and I, I'll say it again, 2025 will go down as the year of ai, right? Where, where we, we moved into this new journey.

Um, for me, it is, it's the start of the 21st century. I think up to this time we plagued with a lot of 20th century technologies, the internet, cell phones, the digital revolution. I think when we look back, this will be the start of the 21st century.

And we'll have robotics or physical AI and, and, and, uh, quantum and all these things. It's gonna be a very different world 30 to 40 years from now. Much as World War I was kind of really the beginning of the 20th century, right?

That was the end of the age of empires and modern nation states and everything else that flowed from it. So as we sit here at the end of this first year of the rest of the century, Mike talk, quantify this. I look at it a little more concrete maybe.

I think if I look back at the year of 2025, it's the year that everybody figured out that the AI industry is having trouble parsing the word is in much the way Bill Clinton did. And, and the issue is they keep saying in the present tense that we can do all these wondrous things. And then you get into it and you find out, well, there's a big gap between what they say we can do with these things, and maybe someday we will, and what we can actually do with them today.

So Tracy, I mean, you're up to your eyebrows and AI stuff this past year, but what's your assessment of what's real, what's not? That's a good question, right? Well, I can say what's real for me.

Um, I have learned a lot about AI in the last, uh, 12 months. I have become far more productive, what, in terms of what I do using ai. Uh, but I still, I feel like it's, we're still pretty basic, right?

Um, sure it can create a par it can rewrite a paragraph for me and make it sound a lot better. Yes. Are we generating more code?

We are definitely generating more code, but we still have to walk through it carefully and verify almost every single line to make sure it's doing what we want it to do. And it doesn't have any bad things in it. And when I look at other countries and, and how they're starting to use ai, if we look at China, they're delivering ai.

And I, I complained about this from the, from the very beginning. AI needs to help people's lives make their lives better. We're seeing a lot of com.

The AI in the US is very focused on businesses and not so much your house. And that bothers me. I still wanna see more AI making people's lives better, more in medicine.

And I'm sure it's out there more in banking. Help me do things that I need help to do other than just write a paragraph or write some code. And maybe that's because that's how I'm seeing it.

So I think we have a lot of room to grow in ai. I think we're just learning about ai. Uh, and agents still suck, guys, and we have a lot of work to do in cyber and ai.

There is so much to do in ai. We are just scratching the surface. We, we haven't even begun really to, to do development in ai.

And I really do think that we have to find more ways to use AI to make the lives of everyday people make their lives better. It's Not, you know, a lesson I learned from my friend Brad felt in technology is that the, the companies that win, the players that win, they win because they suck less. JPI know AI has been a big, a big part of your world this year.

It Has for sure. Uh, you know, I have touched it, uh, and use the technology in many, many ways. I use it, uh, for, uh, research, for writing support.

I have done software development with it. I have done infrastructure management with it. I have built agents.

I find, you know, uh, the technology is continually surprising me with answers. Uh, the most surprising is how it, uh, it seemingly has a life of its own. It does not like to be told what to do.

Uh, much like a, uh, uh, a child, uh, emerging through its teen years, uh, I've called it a teenager multiple times on so many different w levels. It, uh, you it, it tell, it comes back and tells you, I did what you asked. You check it out.

The job's half done. So it's a teenager in that way. Uh, and it's a teenager in that, uh, it, it does, you tell it, this is what I want you to do, and it, it doesn't do what you ask.

However, if you give it some general goal guidelines, like, this is kind of what I'd like to see done. It does amazing things. And, and it will bedazzle you with its creativeness.

So, um, to me that is the, uh, epitome of AI at the moment. Uh, I, I would be very cautious about letting it run amuck and believing that, uh, it, it, its answers are, uh, pristine and, and accurate. Uh, however, uh, I think as a tool that is watched, uh, it, it has produced some incredible, uh, things that, let, let's face it, it's corpus of knowledge is immense, much more than any human could ever maintain in their head.

And on top of that, they have the ability to find patterns between the data in its corpus that most humans would miss. That is its value right now. Right?

John, do you think that the AI vendors just completely missed the boat? Because, and I'm asking the question because, you know, instead of focusing on here was how we're gonna make your job and your life better, they just went out and started banging the drum in Wall Street and said, you wanna eliminate your job. And it's like, well, if you eliminate my job, I don't have a life.

So I generally Don't. Well, they got, they, they got ahead of themselves because they saw the opportunities. They, they're looking down the road at five to 10 years.

This like Mark Benioff and Jensen Wong do. They, they think about life three years from now and how it benefits them. And first and foremost, what they did, the first thing they talked about were these digital workforces of millions of people.

And my first reaction is, what does that mean to the people who have the jobs already? And we are seeing to a small extent what it meant to some of 'em. It was Salesforce, several thousand people lost their jobs.

Um, it's, it's interesting they got ahead of themselves because they pronounced this was gonna be the year of AI agents, which as Tracy pointed out, and I think Gina pointed out in a show last week, is a grow silver ex exaggeration. Um, they got ahead of themselves. They didn't lay it out.

They, instead of kind of giving it to us piece meal wise that they did, is they gave us a grand vision of what they think it will be and where it will be and how it will benefit them financially. And they kind of lost their viewpoint. And I think they scared unintentionally.

I don't think it was intentional, unintentionally. They terrified a number of people and sent them scrambling and created a confusion, which actually hurts them in the short term. So yes, they got ahead of themselves like they always do.

Alan, is this a lie that the bubble's built on, or what? No, the lie that the bubble's built on is that we are going to be able to spend all that money, build all these things, power them, cool them, and then charge for them at a, a rate reasonable enough to return a profit, right? com bubble was built, built on, right?

It cost me a dollar. I sell it for 80 cents. Don't worry, I'm gonna make it up in volume.

Right? No, you still lost 20 cents every time you sold it. You just sold a lot more.

So you lost a lot more 20 senses. It's, it's the same, it's the same thing here with ai, right? When, when the, unless something fundamentally changes the, the power consumption, the cost of doing these computations, it, it's just, that's, that's the bubble.

That's the mismatch. It's actually worse. Technology's terrible.

It's great. Sorry, go ahead. It's actually worse than that.

I mean, various statistics have shown, you know, various research has shown that 80, 85, 90%, um, AI projects in companies don't produce an ROI. If that's the case, and we're talking about basically AI is lipstick on a pig. I mean, if you can't generate money for me, why would I pay you to give me those tools that aren't doing anything for me?

And, and so it's, there's a fundamental disconnect between not that AI doesn't have a future, please don't get me wrong, but there's a fundamental disconnect between what people are promising to deliver today and what's actually producing for me. And if you can't fix that problem, aside from the fact that you're losing 20 cents every time I use your, your AI tool, it's, you're never gonna make any money, Right? We're, we're Insist, and that has still wrap.

I'm sorry. Guess That has a lot to do with that has a lot to do with how we've implemented ai. As to Alan's point, we have, uh, we need, everybody needs to take a breath step back and, and look to see how we can fix some of the problems, right?

I, that's why I keep talking about small language models. We need domain expertise. Maybe we should have a, a system that has a way to have a consi, a consensus between three expert domains, right?

So the, the large language model was a good way to get it out the door, but it's not a good way to go forward. And I'm gonna continue saying that over and over and over. And we have a problem with agents.

We've gotta fix these. This is what's costing us. And like I said in the first segment, look up to the stars because that's where we're gonna have our AI data centers to address some of the costs.

You know, I don't think our problem is ai. I think our problem is we had seven, eight, whatever number tech executives all trying to be Steve Jobs all at once. And they all suck at it.

They're not Steve Jobs, which is really not Overriding problem for Sure. That's been an overriding problem. Yeah.

But even before this, yeah, you asked, And you can, you, you can have your complaints with Steve Jobs too, but a lot of those, these predictions and where they say we're going, that's about setting the vision and setting the quote unquote strategy for where they're taking their companies that's setting stock prices as well as day-to-day performance, right? They're trying to build the roadmap of how convincing investors that they're gonna continue making them money. Um, And if you've bet on a limited amount of funding, you're never gonna get good product.

Yeah. It, it's, it's a different world. It's not AI's the problem.

I think our, our our mouthpieces are the, are are part of the I Too, you know, would've been Interesting to see. I would like to argue the other side of the point here though, and I'm gonna call on JP for this, right? 'cause there are people who are using AI to do amazing things, and they have a lot of skills and a lot of expertise in that space.

And you could argue there might be even a, an AI divide starting to occur because there are people who have the skills to really maximize it. But for the average person, it's not quite living up on the promise. So jp, are we on some sort of timeline curve here in terms of who's using AI to the most benefit and what we might see in the coming year?

I, I think that a lot of people are, uh, overwhelmed by the information that's coming out from, uh, the vendors in the industry as a whole. I think there are people who are, have grabbed a hold of this thing and do a hell of a good job on marketing. I if you're, if you're the type of person who watches reels on Facebook or Instagram or even LinkedIn, you know, you're led to believe, uh, that these people have created these apps that can do amazing things with ai.

And so you start looking into and being able to understand what's really under the hood. I people call their, their application agent-based applications. And then, you know, I, I wrote a, a piece on this, uh, uh, I believe it's a blog or a LinkedIn article, uh, about the AI whitewash, right?

Hey, it's fine to say that your application is an AI application, that it uses AI to perform some of its functions, but calling it an, an agent based AI when it doesn't have any comprehension of what an agent is, and that an agent is this autonomous thing, and there's no autonomy to these things that they built, it's, you know, the lack of separation causes confusion among people who are trying to understand what is this thing and what can it do for me. So we're in that age of, you know, uh, parkers and, uh, you know, uh, used car salespeople, uh, how hawking at you, oh, buy my wares, buy my wares. Uh, eh, this is the greatest thing since sliced bread.

It'll heal you. You know, also, although, you know, can mention something really quickly. Can I, I'm sorry, tr can I just, so that's funny when you said JP, because you know, some people accuse Steve Jobs of being a PT Barnum, but I think he would've been the right person at the right time to explain to us where, why AI should be embraced by us.

And he would do a much better selling job than these other folks. Sorry, Tracy. I think he would've let some of these AI agents Out the door though.

'cause he was kind of finicky about making things actually work. Yeah. Yes, exactly.

But we Got, Tracy and Terry both had their hands up trace, go ahead. Yeah. And Terry, to JP point, we've, this is how we've always seen it.

How long did we have tools that had SQL in the name? And then we did the smart thing and we did a lot of I things for a lot for a very long time. So when we stop putting AI in the name, we know we've arrived Terry.

So, well, mine was just an actual praise the Lord from, uh, jps. Um Oh, okay. You know, he'll.

But I do have something to say about ai you, before we get to the end of this, uh, and, and agents, and I'm gonna go back to the, um, the teenager analogy. And that's where I, I feel like we are with AI agents and I mean, I'm talking about the true things. We don't, you know, uh, ask any parent who's had a teenager, we, we often don't know what, where they are and what they're doing, what they're saying and what they're activating, right?

And this comes from a little bit of a helicopter mom. So, um, that's a confession. But I think that, um, that's one of the problems that we're gonna see going forward.

How do you know what these things are doing and who are doing with and two, and that's, yeah, that's better. We've actually, that's my jam. We've actually gotten better at that.

The providers of the large language models have gotten much better at that. I, I, I think it was Tracy or Harry. I, I've, one of you said earlier about com, you know, committing to small language models, and that's something that I've been predicting for, you know, is needed for years.

We need to get away. You know, we, we, it, it's, it's gotta become a hybrid model where your business is captured in the model, uh, and it's local to you, and it's a small language model. And then when you need to get additional inputs into that model from the larger world, from the outside world, that's when you go to the large language models, uh, and you grant from the corpus.

And, and we need this, you know, tiered, uh, model. And everybody just immediately goes out to the large language models. I think it is a tiered model.

I think you need to build and host your own model that understands you first, and then let that be influenced by additional data that you can then bring in on demand from the larger corpus. I think that's the right approach to using these things. So what I heard JP say, and everybody else kind of echoing a little bit, but is this kind of like the, maybe even the Stanley Steamer era of AI and we're working on the engine next, or, you know, is this kind of where we are?

Jack, kind of kind of wrap it up for us. Yeah, I think it is. Uh, you know, the, the notion that JP had of, and, and Tracy had as well, of building smaller models that are suited to what you need makes a whole lot of sense.

The challenge is, and I've seen this with many companies, the challenge is that I can't get my data together to build those models. I've got data, it's silos, it's scattered all over the place. I don't know who owns it.

Some of it's on pc, some of it's in the cloud. And so, yeah, it makes a lot of sense to build models that are very dedicated to what I need until we get to a point where people are able to actually gather their data together and then build a custom model for my particular needs. We're gonna be stuck with these LLMs there.

There's, that's the only thing many companies have. So there's a lot of maturing that really needs to take place in this marketplace. It's going to take years.

You know, people think AI is going to be here tomorrow. It's not, agents aren't gonna be here tomorrow. That's not gonna happen.

Good agents at least. And so I think ultimately AI is gonna be huge, but we're rushing, you know, we're rushing off a cliff if we keep moving in this direction. All right, folks, I'm gonna wrap it up there.

I think if I look at 2025, it was the year we recognize the potential of ai. That's not nearly the same thing as realizing it. And so hopefully this is the worst AI that you're ever gonna see is the one you got today.

Hey everybody, thanks for sharing your thoughts, not only today, but all year long. I look forward to seeing you all in the new year, and same to everybody who's watching this show. And please stay tuned for the rest of the texture on that TV lineup.

But once again, I hope you guys had a great year. Stay safe, have a great holiday, and we'll see you in 2026. Hey everyone, welcome back here to Techstrong tv.

I'm happy to invite you or introduce you to my next guest from Neo four J. And no, it's not Steven. Um, Steven Chin, of course, is with Neil four J.

We've had the pleasure of interviewing Steven all the time, but sometimes you wanna get a different view. Maybe someone who's a little smarter, maybe, uh, probably not as much hair, but let me introduce you to Sudir. Hospi.

Sudir is the Chief product officer, CPO at Neo four J, and we are lucky enough to have him join us today. Sudir, it's a pleasure to have you on here. Thank you for joining us.

It's great to be here, Alan, and looking forward to the conversation today. Absolutely. And no disrespect to my friend Steven Chin.

We love him. But, uh, UD dear, as I mentioned, you're the CPO at at Neo four J. Why don't we maybe start off with your story before we jump into everything else?

Yeah. So I, I've been with Neo four J for almost two and a half plus years now. Um, I am responsible for all our product strategy, product execution, roadmap, everything.

I work with customers across the globe trying to understand what challenges they have and how do we build our graph intelligence platform that enables them to solve problems. Uh, before coming to Neo four JI actually ran a product for all of data analytics services at Google Cloud. So this meant anything that allowed you to bring your large scale data assets into Google Cloud, how do you process it, how do you actually analyze it?

Uh, and I also was responsible for multiple acquisitions, including Looker. So basically the bi side of the house, so everything. So BigQuery, which is one of the largest products there, that one plus another 10 odd services.

So I ran that for five plus years, uh, at Google before this. Uh, but now I think my opportunity I saw was with large language models just getting ready to take off, I saw there was a huge opportunity for knowledge graphs to provide that intelligence layer or knowledge layer for these systems. And that's why I came to Neo four J.

Absolutely. Um, it's a great story and they're lucky to have you there. You know, we, we, we mentioned Neo four JA bunch of times and, and people who watch text TV all the time.

Thank you. Um, but beyond that, you, you've probably seen us interview Neo four J and we cover them on our various websites as well. But sudir, I'm sure there are people out here who don't know Neo four J or maybe they know a little bit about Neo four J, but you know, not the whole story.

I, if I, if I can bother you, can you kind of, you know, just lay that as a foundation. Give us the Neo four J background. Yeah, so Neo four J the company was started almost 16 plus years back.

Uh, we are in the, we are the category creators for the graph database category. Uh, we now are the graph intelligence platform, uh, that enables, uh, developers to build, uh, agen applications by converting their data into knowledge, right? So all these AI systems need to access enterprise knowledge.

How else are they going to make better decisions? How are they going to be accurate in their, their information and all? And so that's what we enable, uh, customers to do.

Uh, as part of our graph intelligence platform. The core components are, are graph database. That's what we have built over 16 plus years.

We have pioneers in, in the graph space from that perspective. We also have graph algorithms. So we have 65 plus algorithms that you can use to run intelligent, uh, analysis intelligent, uh, decisions on top of the graph data that you may have.

But our new capability allows you to run these algorithms on any data anywhere in the enterprise. So you may have data in Databricks or Snowflake or BigQuery. You can still run these algorithms on top of it.

So that's the core part of our Graph intelligence platform. The layer above that is AI power tools. So you can literally, I have this theme for our product portfolio where I want people to be able to, in five seconds sign up for our service in five minutes, actually use their data and get wowed.

And then in five days, you should get to value. And so in this case, you can literally take your data from Databricks, convert it into a graph data model in like three clicks, and then from there, build agents on top of it within few minutes. So that's the, the AI power tools that we have built out for that.

So automated, uh, graph model generation and all. And then at the top of it is our AI stack, which is our Aura agents. How do you create your own agents and how do you go ahead and, and like, you know, we, we are backbone for many of the memory companies, uh, in the, in the space.

So that's the graph intelligence platform. That's what we provide. Many of our customers use us for various use cases in, uh, agent ai, primarily as the core knowledge layer that can power these AI systems.

But also in financial services. We are big in fraud detection, anti-money laundering use cases. Every supply chain company uses graphs to go ahead and manage the supply chain risk assessment and all, um, all the healthcare life sciences companies use us for the whole, uh, knowledge graph for all of their r and d graphs.

Like, hey, what drugs have been like, you know, uh, identified, what are the things they solve, what enzymes, all of that kind of, uh, actual knowledge graph. Um, US Army uses us for all of their supply chain. So a lot of intelligence analytics and all is done, uh, by me, various intelligence agencies on top of our, uh, our software.

So that's, that's roughly the space we are in. I love it. And, and thank you for taking the time and explaining that, you know, look, we, we live in an, this whole LLM and AI and rag and vector databases, right?

Graph has really kind of found its place, if you will, 'cause it, it, it's great technology and it, you know, as you mentioned, Neo four j's around 16 years. We've had graph databases all these years and everyone knew what, you know, there was so many great use cases, but this may in fact be the killer use case Yes. For, for graph database.

Um, just before we jump into the topic of discussion, for people who may wanna find out more about Neo four J, where, where was, what's the best? Just go to the website or Yeah. com is a good place to go start.

Uh, there are multiple videos we run. One of the things Steven Chin, our friend runs is a dere for US developer relationships, and his team manages this Graph Academy. So if you want to learn about graphs and what graph technology can do, we have Graph Academy that has lots of courses for audiences.

They can go ahead and learn everything. How do we build an application getting started to more advanced courses? Uh, so that's another good resource.

Another resources, if you go on YouTube and search for NEO four J, you'll find tons of content from various of our conferences that we run, including a lot of customer, uh, driven content, customer stories, and all that. Love it. Good stuff.

Alright, let, let's pivot a little bit. You guys recently announced GA of NEO ga, meaning general availability. For those out there who may not be familiar, uh, for the NEO four J fleet manager, which is advertised as the industry's first unified control plane for graph databases.

Give us the scoop here. Sudir. Yeah, so we have tons of customers that actually use Neo four J large enterprises, almost, I think 80 plus percent of, uh, fortune hundred use us.

Uh, and then a lot of these customers have various use cases, like they have been using us for, let's say fraud or anti-money laundering or supply chain and other things that we talked about. And as the agent AI is becoming more and more popular, they have newer use cases, they want to go ahead and use us for, for, for like just the knowledge layer to power these assets, and especially knowledge layer. What I mean by that is you may have data in disparate systems.

Your LLMs are not going to get access to all these systems and be able to make sense of it. So what we allow people to do is take this data and create a semantic layer on top of that data as the knowledge layer, and then your LLMs can use graph rack to go get access to that information in a very secure, trusted manner. And that improves the accuracy, reduces hallucinations.

So as the new use cases were coming, we saw a lot of modern new use cases were getting deployed in our cloud offering, which is Aura. It's a fully managed offering, zero operations. We take care of all the infrastructure, but lot of existing use cases may be running, they run it themselves in one of the clouds.

Or like, you know, 15, 20% of our customers still run their own data centers. And so they're running in all of these, uh, environments. Additionally, we also have databases that are our community edition, which is completely free edition that anybody wants to build on graph.

They can take our open source database and just start building applications. We have a lot of adoption of that, but one of the challenges for IT leadership and CIOs is, okay, when you have that much deployment in an organization, how do you monitor it? How do you manage it?

How do you know what is happening across the fleet? So the fleet manager gives you a single pane of glass to look at all your deployments, whether they are on-prem, on-prem, run by yourself in cloud, or are fully managed Aura offering, whether it is our enterprise offering or whether it's our community edition, which is like completely free open source offering. It doesn't matter.

You go, you can actually visualize and see all your deployments in one single place. You can look at them, you can monitor them, you can operationally figure out what actions you want to take if something goes wrong. It also will tell you any security risks if something is going wrong, like, you know, last time when the lock four J issue happens, something like, if that happens in future, we will be able to alert the CIOs about what the deployments look like.

So I think the most important thing is single pane of glass. Any deployment anywhere in any platform, we can give you complete visibility into it. I love it.

And of course, general availability means it's generally available right now. People can, it's available Now. Any customer who wants to use it, especially if you are in our aura, uh, database user, you will by default start using it.

If you're self-managed, you can register your self-manage databases, start using it in production scale. I love it. Soya, you know, I, I'm doing a few more interviews this week.

We won't be doing any next week as we, you know, break here for the holidays and the new year. You know, it's been a heck of a year right. By any, no matter how you wanna measure it.

Yes. Um, as you look ahead to 2026 with this, is it more of the same? Is it accelerating?

Is it a black hole? We don't know, you know, as part of your role there as CPO, what, what is your, what's your gut telling you? I think I work with a lot of customers, as I said.

Right? One of the things, and just one interesting data point in last, in this year, 2025, I was just doing my measurement. I have been on road for 24 weeks of this, like, Of outta 52, this goal, yeah.

Out of 24, majority of that is actually going out, meeting customers, spending time, talking to them and all. And what I have seen is 2025 has been a pivotal year in the switch from lot of experimentation with agen ai or AI in general, LLMs rag, all that, that happened in 20 23, 20 24, 20 25. We have seen real use cases being deployed by large customers.

Like our recent, uh, graph, like, you know, uh, we, we do these, uh, events with our customers. In the recent one we had Walmart, we had Uber, we have had, uh, uh, Qualis and Brady, which is like an law firm. Like we have had so many different customers actually putting things in production, seeing value from what they're, what they're doing.

No Nordisk has been there. So I think the thing is, I've seen like in 2025, lot of these customers actually getting value and deploying things to production in 2026. I believe that's only going to accelerate because I still believe a lot of our customers have been the early adopters that have transitioned from experimentation to real business value.

But there's this majority that hasn't actually seen this. So 2026, I think it's, uh, it's the acceleration of like, you know, value driven, use case driven implementations of AI that I think will happen. And I think we can really help these customers, especially developers, get value of a from the AI systems.

I love it. Udia, thanks for coming up here on Techstrong tv. You know, I do interview Steven a lot, but anytime you want to come back, you let us know there's a place here for you.

Okay. Thank You, Alan. I really enjoyed talking to you and thank you, uh, for bringing me here.

Also, happy holidays to all your audiences and I'm looking forward to coming back and sharing more as we go into 2026. Thank you. Well Spend a few weeks at home during the holidays at least.

Then it will, if I don't see you on here, we'll see you on the road. Spi, CPO Neil, four J here on Textron tv. We're gonna take a break.

We'll be back. Hey guys, thanks for the throw. We're here with Mike Kelly, who's the CEO of buying plane, and we're having a chat about well open telemetry and all the instrumentation and the data we're collecting.

Well, maybe this is starting to be too much of a good thing and we need to figure out how to manage all of this stuff. Mike, welcome to the show. Thanks.

Thanks for, it was great to talk to you again, Mike. It wasn't too long ago where in my mind we only instrumented a handful of applications because it was hard to do and expensive. And we'd have these a PM platforms, and it was very reserved for the elite, shall we say.

In the last few years, we've seen open telemetry as open source instrumentation software become ubiquitous, and a lot more things are instrumented and a lot more things will soon be instrumented. And we're collecting all this massive amounts of telemetry data now. But I also feel like it comes in a lot of varieties and we have a lot of tools and platforms.

So what's next and how do we get our arms around all this stuff? Yeah, it's a good question. And it's, it's stood on, right?

We know that we've been collecting more and more every year and that there's more data generated. And that's, it's if you think back and you look back 10, 15 years, you see the waves of new, uh, whether it's microservices, ai, it's all generating more data. And then we have platforms that we're using for security, for observability.

They all want all of that. They wanna see all of that. And to get the, the value from those, the most value, you, you really do need to collect all those key signals.

But what happened in that past 10 years or so is we got to a point where there's just an, have a launch of telemetry data, and it is incredibly difficult to manage. And so that's really where this concept of, uh, telemetry pipeline came into play and came, became a, a central component within a, a company stacks. Um, you know, you look at Open Telemetry, that was definitely a, a project focused on let's prevent ourselves from instrumenting everything a dozen different ways by, whether it's open source or proprietary solutions.

Let's, let's fix, uh, or, or solve this with a single solution using standards, telemetry pipelines are a layer on top of that to say, now that we have that, now that we have a a standardized way of collecting the data, let's make sure that we're, we're, uh, filtering, routing and managing the data as it's passing through. So we only allow what we need and we can really reduce the volume and simplify the complexity of, of the telemetry, uh, stack. Mm-hmm.

Is this something that mere mortal DevOps engineers can do? Or do I need to go get a data engineer for telemetry data and add them to the DevOps team? Yeah, that's a question a lot of teams ask, and it, it really depends on where you're at in the, the journey.

So I think a lot of teams start and you have a, um, you know, it's within your DevOps org and you will manage those, those collectors. But at, at some point, you get to a point where the complexity of that and the volume makes it really difficult to do that, uh, without some exploding complexity. So at volume, we think that's, you know, specialized solutions.

And that's what we do at buying planes. We, we develop a telemetry pipeline to manage open telemetry at very high scale and at very high volumes of, of telemetry flowing. Uh, and when you get to the point where you're managing, you know, tens of thousands or hundreds of thousands of individual agents collecting logs, metrics, and traces, it's probably time to think about a, a specialized solution.

Uh, that'll give you the visibility, and it also unlocks a lot of, uh, capabilities and functionality that you, you know, would otherwise be incredibly challenging to, to build out on your own. And some of those, we think about the telemetry pipeline or the stages of that. Um, I think any telemetry pipeline should be able to collect all of those signals, whether it's security related data, observability, AI data that you need.

Then take that and normalize it and also secure it. So you're looking for things that you don't want to pass through, like PII data, uh, you want to normalize it into a standard organization-wide, uh, protocol. Open telemetry, uh, has OTLP for that.

Then you enrich it, then you reduce it by eliminating things that you, you know, you don't need and you can route it to the destination. And if you have that in place, uh, it allows you to do a lot more with, with the data that you have, and really allows you to own the telemetry, particularly if you're using open standards like Open Telemetry. Um, so I'd say, you know, uh, DevOps teams can certainly do this, this on their own at this, this smaller scale.

But once you get into this very high volume, that's when you really run into some challenges, uh, managing that without really overwhelming a team. Mm-hmm. Is there a smart way to do this?

And I asked the question because some teams are like saving everything now, and they're just overwhelmed all this stuff, and maybe they got some of it in an S3 bucket somewhere, but they don't know what to get rid of, so they save everything. And then on the other end of the extreme, a lot of folks are saving nothing because well, it's expensive and just hard to do. So, um, where's the right balance between what I should save and what I should just get rid of?

Yeah, that's, that's one of the tough questions, right? But, um, there are a few guidelines that we provide, um, uh, that I tend to stand by and that, that I think mo apply to most people. It's always gonna dependent on an organization, but one of the things that you can look at are, you know, what are those most critical, uh, signals, uh, that are gonna be relevant for security and for observability, you can look at the, uh, golden signals for observability, for security.

You can go to specific guides, um, that are going to list, these are the events that you should be tracking. The challenge most teams have is that when you do that, or as soon as you start to eliminate anything, you, you have that, that fear that, oh, did I drop something that we don't need? Um, and what we find is if there isn't maybe necessarily that confidence, if you need to go in and investigate that, uh, you will have the data you need.

Uh, probably the most common practice now is to tier your telemetry data. So you'll have the, those critical signals are sent to your security observability platform. Um, but then you'll have a second tier that is sent to low cost storage and the, with the ability to pull that back and send it to, you know, security observability platforms for analysis.

Um, so if you have concerns about, about what you're able to remove, what you're able to reduce, that's, uh, probably the, the, the best practice to maintain flexibility and also significantly reduce your cost. Now, there are always things that we know we can trim, and sometimes you'd be surprised and it's, um, uh, frankly it can be, uh, eye-opening when you really look at what we end up sending along. And a lot of this is, you know, unused or empty fields.

We're sending duplicate data over and over again. So there are a lot of things you can do to compress that and really not lose any visibility, um, in the, the, the platform that you're using to analyze the data. Um, overall, we usually see, you know, it can realistically, uh, reduce by at least 40% of the data.

If you, you haven't already done this, by going and looking at what you can route, what you can eliminate, what you can deduplicate, what you can compress All, um, my approach to AI is somewhat simplistic, but I basically am looking at it and saying, here's a list of things I don't enjoy doing. So maybe there's an AI agent for that, and mm-hmm. I think telemetry data might be at the top of that list.

They're very high up there. So will there be kind of like AI agents to help me manage the telemetry data? Yeah, I mean, I love telemetry data, but I understand that not everybody, uh, has the same affection for it that, that I do.

Um, yeah, I think, you know, AI has impacted telemetry pipelines in, in a really similar way to what you've seen in other areas and in some ways even more. Um, there's the, the first level of let's augment your abilities, make it easier to do, make it easier. And, um, you know, and buying plan, a couple of areas that we've focused on is when you do, uh, instrument your, uh, code base or instrument an application, we will automatically identify what should you do with this?

So you asked, how do we reduce the volume of this data, but we use AI to, to identify the ways that you would reduce that. What should you apply? What filters should you use based on the type of data that you're ingesting?

Um, and that's something that, you know, may not be the, the most fun thing to do, right? Um, it bake in a lot of that knowledge. Um, also makes it much simpler to pull out some simple things like pulling out fields and, and routing and, uh, uh, building in compliance into your data structure.

That can be done with, with ai that's on the, the augmented. So helping you to, to do your job better faster. There's also the, the automation piece of it.

So when you get to a scale of petabytes of data flowing from hundreds of thousands of devices, um, then the question is now how do we, we route this and manage this in a really efficient way? And, uh, we're, we're starting to use AI to look at that as a whole and be much more efficient in a way that would be, you know, very, very time consuming for, for a person to do so. Um, we certainly see it, uh, at, at least as much as we're seeing it in other tech and areas within tech of having a big impact and, and being of a lot of interest with customers.

Is there an opportunity to centralize some of this beyond just what we see in DevOps and app dev? Because if I look across the typical IT organization, the security people are pulling in a lot of telemetry data too. So is there an opportunity to kind of start rationalizing some of this?

'cause we're all kind of collecting the same data for different purposes? Yeah. Um, you know, I've been beating that drum for, for a few years now, and that is absolutely an opportunity I think most organizations have.

Um, what you find is you'll have, uh, frequently have an observability team. They have their own agents installed, they have their own observability solution. Security team also has the, has agents installed, sometimes running on those same systems, collecting the same data.

And, uh, at, at a certain scale, certain size of organization, it's, we've seen between six and 10 different agents collecting the same types of, of information and sending it different places. We know that's not efficient, right? We know that there's a, there's a better way to do it.

Um, open Telemetry has been a, a, a great step in that direction where it's a solution that's designed to, in a vendor agnostic way, collect all of the data that you need, whether it's logs, metrics, traces, or other signals, and, uh, gives you the tools that you need within that open source framework to manage it. Uh, with, with buying plane, we make it much easier to deploy and then get some visibility within those, those very large open telemetry deployments. And we have, uh, you know, I'd say at least half of our customers end up being both, uh, using this for both security and observability, and now expanding into AI use cases as well.

And we expect, uh, more and more folks to start using this for business data. We really, uh, they're huge advantages to having a single source, uh, of data in a standard, uh, standard framework and really letting you turn the, the telemetry data that's within your organization from a challenge into something that is incredibly valuable, uh, and an asset to the organization. Mm-hmm.

What do you see organizations doing that just makes you shake your head a little bit and go, folks, man, we just gotta be a little bit smarter than that. Uh, you know, it's a good question. I think, um, it's where I shake my head because I know the challenges that go into the decisions that are made, and, uh, frequently the, those things that make you shake your head are the things that were the, the finger in the dam to, to solve something when everything was falling apart.

Um, but I think, you know, one of the big ones is, uh, one, one that you just mentioned, right? It's, it's, we've done things a certain way where, whether it's a PM or it's, um, our security deployed proprietary agents of five to 10 different types and, and, uh, really don't have any way to manage those. And now we've gotten so big, uh, and sending so much data that we're really in a tough spot and things are starting to fall apart.

And that's, frankly, that's, that's really common. It's a place people find themselves in. Um, I think standardizing on, uh, open telemetry or, or another open source standard, and there are several out there now, is really a, a smart move for teams, particularly as more and more, uh, options are coming out and, and, uh, new platforms are emerging.

Um, you know, I I think that, uh, that getting to that standard has a big impact on your business and, and your team. Mm-hmm. Um, we mentioned ai, but if I understand this correctly, there's gonna be more AI embedded into our applications, and then there's gonna be more AI agents that are part of the DevOps team, and won't all these things be generating more telemetry data than ever?

So how long before? We are just overwhelmed? They already are.

Yeah. We're, we're seeing it everywhere. Um, uh, whether it's AI agents, uh, logs generated by those, uh, trying to manage the development of those, um, you know, it keeps increasing and, uh, we we're seeing that exponential increase that's been going on for, you know, 10 years now, continue.

So I would say that if, if you haven't been overwhelmed by that, you, you probably will be at some point without really taking it seriously and looking at how are we are going to, to manage and, and, uh, be really thoughtful about what we're collecting and what we're not. You know, talked about the, the waves of this. So when there was virtualization, all of a sudden we saw what looked like a little bit of sprawl, and more and more telemetry was being generated from that.

Then Kubernetes came along and microservices came along. There was more data, more and more data. Um, uh, the E of cloud led to more data.

AI is doing the same thing. So it's just the next step in, uh, the generation of, of more and more of that. Fortunately, this is one technology where we can actually use it to help us, uh, uh, solve the problem that it's creating.

And, uh, uh, at least that's, that's the hope for, for that we're able to use this to, to also reduce the volume and just get what matters the most. Mm-hmm. Um, to that end, um, what is your best advice to folks about how to move this subject up the priority list?

Because there are so many competing priorities and telemetry data doesn't always nail land on the list of 10 sexiest IT projects. So, um, how do you, you know, tell folks in the IT leadership we need to focus on this? Yeah.

Um, you know, what does get it up to the top of the list is when you talk about the, the ROI of managing and reducing the volume, because it's not just a a complexity it or a volume, it is a cost directly associated with how much that is. So if you're sending a terabyte of data to your security platform, you're almost always getting billed on that, that full volume. If you can show that not only are you, are you managing this, you're, you're future proofing, you are, uh, moving to a vendor agnostic solution, you're making it easier for your team to manage this, but you're also reducing the cost of these solutions or allowing you to, uh, preventing a massive increase in the next few years that tends to, to make this a, a much more interesting problem than, than it may otherwise be.

And that's a situation that most folks are finding themselves in. Um, you know, teams, uh, that are, uh, operators are, are finding it's hard to, to manage, uh, with this without a breaking. And so they're looking for a solution, but it is absolutely a budget concern as well.

Uh, and that's why we're seeing so much adoption of this. Uh, it tends to be driven as much from that, the ability to reduce the cost. Uh, and that is a huge focus, uh, with buying plane.

But we wanna suit solve the, the technical, uh, issues and make it really easy for someone in DevOps to be able to deploy and manage and, and filter and route. We also want to keep our eyes on how do, how are we reducing the volume that's being sent to those solutions that, that tend to be a very, uh, get a lot of visibility throughout the organization because they can be so expensive. All cool.

Hey, folks, you heard it here. Every dollar saved on telemetry data is probably another dollar that could be applied to something, well, maybe not more important, but maybe a little more compelling on the business value side of the equation. Hey, Mike, thanks for being on the show.

Hey, thanks so much, Mike. All right, and back to you guys in the studio. Hey, everyone.

Welcome back here to our continuing coverage of AWS Reinvent. You know, we don't do every video interview live at reinvent because there's embargoes, there's other considerations. And so this is one of the videos we recorded at, uh, reinvent in Las Vegas, and we're bringing to you now, just a few days later.

I want to introduce you to my friend, do Laur DOR is, uh, the CEO, I think founder of cid. Yeah, yeah. Co-founder, co-founder Of, of CID db.

I Got help. We all need help. Do's been on with me on text on TV for years and years, but it, it's not often I get to see him.

He's, of course, in Israel. Uh, we were supposed to be in Israel right now, but we're not, uh, for Cyber Week, and it just didn't come together enough. But do's great to see you here in person.

It's great to have you. Thanks. Thanks for hosting me.

It's a pleasure. So, let, let's start with this, though. Not everyone has seen you on Tech Truck tv.

We, you know, we're not, let's face it, we're not CNN or any of those, but yes. Give people a little bit of your journey to, to founding, uh, Silla. Sure.

Um, so I'm a technical founder. Uh, I have a roots in computer science. And, uh, initially in my career, I went to work for a terabit router company that early days tried to take over Cisco's core business in, in 2000, eh, the bubble burst, so it didn't work that much, but we did have a fabulous product and a drop in replacement for Cisco CLI, I'll, I'll come later on with more of the importance of, uh, drop in replacements in products.

Mm-hmm. Um, and later on I did something with the Blade Centers, and then I joined the company, a startup company, where I met my existing co-founder ti and my, uh, existing, uh, chairman who was, uh, the CEO back then. Uh, that setup had had to pivot three times.

This is where, uh, I learned how to pivot Uhhuh. The last pivot, we, uh, came up with the KVM hypervisor, so to, uh, renovate around the new hypervisor, a new approach that, that was the KVM. It worked really well, and Red Hat acquired the company.

We, uh, spent their four years, uh, improving KVM and also the Linux Colonel, and I'm a big fan of it. And, uh, afterwards, we wanted always to have our own startup. So we, we left Red Hat and opened this company.

Uh, originally, uh, it wasn't around databases because we had a lots of, uh, virtualization experience. So we mm-hmm. We started with, uh, an operating system that should have bit, uh, beaten Linux in, in, uh, virtualized workloads.

Uh, the OS exists, uh, still today. And I met a customer yesterday who runs Sila and knows us because of that os Uh, 'cause of that os Really? Yeah.

If you don't mind, what os was this? It's called, uh, OS Suites. It's a Unikernel.

Oh, okay. Sure. Um, They had their moment in the sun.

Yeah. Uh, the Docker kind of sucked all of the air from the room when we around when we launched, but, uh, this is where we, we were familiar with other databases. We, we want to show, uh, the gains when other databases run on top of r os to be faster than Linux.

And we managed to accelerate threads by 70% because we loaded the application into the kernel space was faster. When we did the same with Cassandra, the performance didn't change much. We realized that the overhead of Cassandra, uh, is itself and, and not, and if you replace it with a fast os it, it doesn't change it.

Uh, so we said, oh, that's can be a good idea for a pivot, because we didn't get enough traction. And with why, once we rewrite Cassandra from scratch, keeping the compatibility like the Cisco days, uh, also like the KVM days, it's, it's also about compatibility, uh, with, with other things. Um, and we rewrote Cassandra from scratch.

That's what Sila DB does. Uh, it's also, uh, nowadays compatible with DynamoDB. It's a drop in replacement, and it's a standalone database that can run the biggest, most scalable workloads in the world.

I love it. What a great story. Huh.

And it's also, uh, you know, for, for geeks, right? You're, you're, you're a geek person. I'm a geek person.

A lot of the people out here are, we do this. I mean, it's nice to be able to make a living doing it, but we'd also do it because we love Yeah. Ly playing with this stuff.

And, and this is a great story where your passion led you to, to doing this. Um, it's been now how long with s it's kind of six years, seven years, eight years, how long? Mm-hmm.

Uh, now it's, uh, it's more than 10 years. I tell you, even, uh, our 11th year. Really.

That's, you know, what, and that's something also, quite frankly, to be proud of, right? Mm-hmm. Because, you know, what do they say the average company, if you make it past three years mm-hmm.

It's a big accomplishment. So it, it's, it's all obviously here. Um, now talk to me a little bit about how people engage with Cilla, right?

There's open source parts of it, there's commercial parts of it for people out there saying, you know, we're always looking for better performance, better bang for the buck. What, how, how do they kind of jump into Cilla? Um, so, uh, we started, we were big open source fans.

Uh, we, we started with open source, actually, uh, a year ago. We changed the license, I remember to source available mm-hmm. At the time, a year ago.

I, I was just sitting here. Um, so it's source available. We do have projects which are, uh, open source, like our, Not even source available.

Let me ask you a question. In the year you did that, how many people have asked for the source? Um, so PE people do appreciate, uh, The, that it's available, The source, But it, it, this is, but this is something, look, I've been an open source too for 25 years.

The fact of the matter is, 99% of the people never look at the source code or make a change to it. What not. Maybe not.

99, 90 8% of the people never look at the source code, never make a change, you know? And, and so what they really want is free, Uh, yeah. People like free.

And, and we, we have, uh, a freemium offering right now. We're, uh, now it's source available. It's allows us to, uh, allow people to look at the source and, and also have the, uh, comfortability that the source is, is available for virus cases, uh, for future con continuity.

Uh, but, and we have some control to say, okay, up to this, uh, level, it's free and beyond that level, you need to pay because we are here 11 years on the road. And, and it's a business, Right? Someone's gotta keep the lights on.

I, I agree with you, But, uh, uh, I do understand people, uh, who are passionate about, uh, the source code. And there's a lots of, uh, small things and small changes where things matter. And, and we have, uh, open source, like, like our core engine, it's called csar.

Uh, it is open source and it's license, it is not a GPL, uh, it's license is, uh, uh, Apache because it's important for, for people to use it within their products. And that's why we haven't selected there. There's a ton of no Ly changes.

You know, one of the nice things that I've seen happen in the open source community over the, as I said, 20, 25 years I'm involved, is that most users recognize that though, open source may be free, someone's working on this. Mm-hmm. Someone's entitled to get paid for their time and their effort and everything else.

They may, they may quibble with how much mm-hmm. But you, you know, it, it's ludicrous to think that people are gonna volunteer this outta the pure love and, and not make a living, you know, not be compensated for it. So I think that's been a positive development overall in the open source space.

Mm-hmm. Right. It used to be, oh, you know, you're looking, you're in it for the money.

Everyone's in it for the money. We have to keep the lights on, we've gotta feed our families. But, you know, it's just, it's a fact of life.

I mean, and if you don't wanna recognize that because you're some sort of, you know, like open source zealot mm-hmm. Free is in freedom and free is in beer. Don't use the product.

What can they tell you? And, uh, having, uh, paying users allow us to invest back in the product. Absolutely.

It makes the product better, Product better. Um, so that's primarily what we do. And It's a flywheel As a, as a vendor that, uh, used to, uh, eh, release both open source releases and also, uh, gated product releases.

You double the amount of releases. I was just gonna say, what a pain in the Yeah. You know what that is A hundred percent.

I, I agree with you. So there's, but there is a freemium version. You can go check it out, play with it.

If you do wanna look at source code, and that's your thing, it's available to you as well. Um, Dora, let's talk reinvent here. You guys are here.

It's been an interesting kinda reinvent because, you know, we, when I, I just finished writing an article when I first got here Monday, and I looked at the keynote, you know, agendas and everything. They gave us a press preview. It was obvious, it was all agenda AI all the time, right?

It was all about ai. But over the course of two, three days that I spoke to people and saw things and walked around, see a lot of news about DevOps, cloud native platform, engineering databases, hardware, hardware's, AI stuff too, but hardware, um, you know, it, I maybe didn't hear as much as we normally hear about, like things like S3 or serverless or Lambda or these kinds of things. But the geeks are still here, the developers are still here.

The ops, the DevOps folks are still here in force. What have you seen? Um, so AWS is, uh, a giant, yeah.

E even more, more than that. Um, and, and nowadays they do innovation across, uh, across the year, not just them, also their competition. They, they, they have to.

Um, so our announcement, I think that they're not holding the announcement just for, uh, this event, uh, recently they released a new GRAVITON instances. Yeah. Graviton five is coming.

Yeah. And, and then, and, and the GRA Graviton four was released. Right.

And, uh, we are, we measured graviton four with cila db. And, uh, it, it is offer fantastic, uh, performance and that translate to a better TCO. So for us, it, it's super, that's exactly what we need.

Um, so the, there's a lot of, uh, gradual improvement always on all of these products. Yeah. Um, so it's for, for, uh, for, for, I'm, I'm pleased for that.

It's, it's good enough for us. What about, now I know you're exhibiting, what about like, you know, traffic at the booth, conversations with people? What are you hearing?

Uh, well, the, there's, uh, no shortage of, uh, of traffic at the booth or traffic, uh, here in Vegas. Uh, regarding, um, the entire AWS and, and the ecosystem, uh, it, it's mostly about, about ai. Like, uh, yeah.

Uh, we, we see that a surge in AI use cases. Uh, now about half of the use cases are, uh, directly related to AI Ins, Cilla, Uh, ins. Cilla.

Yeah. Uh, in, in, so Explain that to me. What, what's the use case there?

Um, we can pl split it to, uh, three categories. Uh, one category is the, that we're part of the AI stack. And, and during the, uh, training and also the, uh, serving processes, uh, the, the stack need to just access a tone of objects and, uh, need the fast database for it.

It's part of the AI stack without doing anything, uh, special for it. Like, uh, uh, distributed databases is in demand for high workloads. And, and those are high, very high workloads.

Sure. And, and can be, uh, part of the big LLM uh, companies, or it can be a smaller, much smaller company that started start their AR journey. That's number one.

Uh, number two is a feature store. Uh, feature store is more of, uh, machine learning, but it's, it's part of AI still. And, uh, feature store allows people to classify a users or sometimes agents automatically.

So it can provide recommendations for, uh, e-commerce, for, uh, fraud cases in variety of other cases. And we're big in, uh, feature store case, in feature store needs, uh, a fast database too, to quickly come up with a to, uh, classification that, uh, you as a user was selected and, and what's appropriate for you as a user either to watch on TV or to get an ad, et cetera. Uh, love it.

This is the second one. And the third one is, uh, a vector search, um, to, to do LLM on your private data set set. Uh, that's why, uh, the, the, this whole category of, uh, a rag Right.

Was rag with vector database. Exactly. So, uh, we added, uh, a vector search, uh, eh ourself.

And we, we already have a, a beta that receives lots of interest. And, uh, we, we are going through this month in December, uh, go live with the general availability of our, uh, rag, uh, vector search source, really? Yeah, That's right.

That, so in essence, they could use Stiller as their vector database then. Mm-hmm. They're creating small language models or, or Yeah.

The rag stuff that's gotta be big. No, Yeah. That's, uh, fantastic.

Our, uh, eh, vector search is the most scalable. We can easily run a model with a billion objects. Uh, very few vendors can even get to a billion.

And we can do that with hundreds of thousands of requests per second. So we, we scale, uh, to, to very high numbers. And if, uh, people have, uh, lower or medium demand too, like, uh, mo most will have a model of, uh, 10 million or a hundred million objects, then we can give, uh, the best latency and, and also very low price point.

That's fantastic. Look, there's a lot of people saying that we've scraped all there is to scrape for these LLMs and that, you know, get, making generative AI or even agent AI better by increasing the LLM and the data we have to train is, is diminishing returns. And that the way to go is maybe SLMs more rag, you know, uh, well, there's some people who say, we need to go away from LLMs altogether and go to this world model and stuff like that.

Mm-hmm. Um, but certainly, I, I believe there's gonna be a lot of activity in, in the SLM rag kind of space. And, and not only that, because as we develop AI for specific use cases, I don't need the whole world of the internet.

I just need, especially if it's my own proprietary information, right. And I don't wanna put that out up there. I want it right here.

Just, and so I, I think that's a huge business for you guys. Yeah. Congratulations.

Thanks. Uh, it's, it's, uh, the market demand. Yeah.

It's, Yeah. Well, no, that Is, it's not just an opportunity. It's also a defensive move.

Because if we won't do it, then uh, customers will go elsewhere. Uh, to, to be frank, and yeah, the, the fact that, uh, people would expect, uh, all of the ease of use of LLM on the public data set on the internet, they expect to have the same when they come to every vendor. And to ask in free tech search, uh, your questions in, in one liner, and get immediately the best results without diving into a very complicated ui, that's a power of LLM.

And sometimes it won't be people, but it be agents, right. Uh, that come and, and automate and get the queries automated. So that begs the question, is there a an MCP server in your future, Uh, in the future?

Absolutely. Yes. All right.

Hey, let's fast forward past AWS for a second. People are watching this after the, after the show. Anyway.

You guys have some new announcements that you're previewing here. Mm-hmm. Share, if you don't mind a little bit.

Thank you, uh, for the opportunity. So, um, uh, we'll also move, uh, from beta to general availability. Our X cloud a, a, a managed platform.

Uh, X Cloud is, is, uh, the new generation of our core database with, uh, database as a service management consumption. Uh, the unique thing about it is, uh, our new core architecture, which is called tablets. It's way, way more elastic than any other database or even infrastructure in the industry.

Uh, we, we were okay with regard to, uh, the speed of, uh, increasing the cluster, scaling out, and then scaling in. We were, before this technology were, we were okay, like, like an average vendor, but there was a demand to do it much faster. And frankly, we also compete with DynamoDB.

We're drop in replacement and Dynamo db, uh, was the first NoSQL database. And up to this change was the, the best in the industry. You can easily scale up and down, uh, very easily.

And, and if your workload changes throughout the day, uh, then, then you can, uh, instead of paying for the peak consumption all the time, you can just have the workload follow, uh, uh, the work, the workload should follow the usage, right? Dynamically. So that's exactly what, uh, X Cloud is.

Uh, we, we have, uh, the technology based on components called tablets. We break the gigantic database of, uh, petabyte of data to five gigabytes chunks. Right.

And we can move them around super quickly. Uh, we, we can also even, uh, it allows us, uh, both to scale super fast. We, we can increase capacity, quadruple it in 10 minutes.

Mm-hmm. So you can go from, uh, 500 K to 2 million operation per second in 10 minutes, But could you go back to 500 K and 10 more? And that's right.

So, Because sometimes with these things, it's like blowing up a balloon. Mm-hmm. You know what I mean?

It never goes back to the size it was before you blew it up. So we, we can, it it's not, it, it's, it's a, it indeed. Complicated.

Yeah. But, but we can also go back and, and shrink and, and that's the user workload that, uh, goes, comes and goes, whether it's a Black Friday or, or on a daily manner. Uh, so, so that, that's a big improvement.

Uh, and, and big TCO improvements and, and usability improvement. Sure. Uh, also it's, it's, it's pretty unique.

Uh, we have a child per quart, uh, engine. So let's say if you have, uh, a machine with, uh, 32 cores, we, we'll have 32 independent threads in the server. Wow.

Uh, if you have a 64 machine, then we, we will have 64 threads, uh, in, in engines within that machine, and it'll perform twice as with 32. Now, let's say if you have a 64 way machine, uh, but actually you need, uh, um, 66, uh, threads and you have 64. And now would, would you buy another machine for 64?

It's, it's expensive, right? So instead we, we can mix and match and we can have 1 64 machine together with, uh, a tiny two VCP machine next to each other because of the flexibility and the hard. So It's real distribution And the starting, we, we can combine the two.

Haven't seen any other vendor can do that. No. And what the user receive is efficiency.

Uh, they have exactly what they need. They don't need to buy excessive large servers, which are expensive on AWS, Uh, they're expensive everywhere. It's not just AWS but really what we're talking about here is almost like a finops play, right.

Because that's, I think that's where we are, especially in cloud usage, right? Look, we're talking about spending $5 trillion on data center AI factories, but the fact of the matter is, when I talk to people, they say, I wanna get control of my cloud bill. Hmm.

I wanna du I wanna be more efficient in my use of these resources. And, and that's why I made the joke with the balloon blowing up. That's pretty much how the cloud is, right?

It never seems to go back down. People, they want that ability to have insight to turn that dial, and they want the ability to say, how can I do this more efficiently? Mm-hmm.

Yep. And, uh, our customer success team works with customers. And if we both see, let's say you sometimes utilization, like people can check their database, how much it, it's loaded on an average basis.

Most databases are, are not that loaded. Uh, on, on a, when I'm not talking about the spike, I'm talking about normal, uh, day usage, or overnight it can be 10%, uh, or 20% utilize and you pay for the entire thing. But that was always the pro, that was the promise of the cloud.

That elasticity was a up and down thing. Yeah. It wound up being more of an up thing all the time.

But it's good to know that's there. So this available, well, by the time people are reading this, it'll, or excuse me, by the time people see this, it'll be available. It, it's, uh, today, uh, a avail dated to, uh, a WS conference available as beta and, uh, the time people see it available as general availability.

Excellent. Good stuff. What else from s Um, so it's mostly this.

We, we do have, uh, lots of, uh, things that we develop like tiered storage mm-hmm. Uh, in, in other technology to, uh, reduce the bill. Uh, normally we use NVME for fast source fast performance, and it's also relatively cheap co compared to different alternatives of, uh, of storage.

But, uh, SS three is cheaper. The problem with S3 is that latency is prohibitive big. It's a 50 millisecond, 100 milliseconds.

Uh, and with the storage, uh, we can keep the whole data on fast and VME and automatically move the cold data to S3 and come, come up with, uh, a good solution. 'cause sometimes you keep, let's say 30 days of, uh, of history on, on, on Sila in the NVME, but you'd like to keep one year of data and, and access it through the same API and not develop new access for it. So this allows users to, uh, have one API and, uh, a very cost effective solution.

I love it. Good stuff. You know what, we didn't, we didn't even mention the website, URL for people.

Want to go find all this out on their own. Dig in a little deeper. What's the, what's the best URL to go to do?

Thanks. com, Just as it says underneath his in his lower third. All righty, do.

It was a pleasure seeing you. Safe travels back home. We are wrapping up now.

Again, you, you're seeing this after we were here at, uh, AWS Reinvent, but it's part of our A AWS reinvent coverage. And if you need to find this back on, it'll be listed under the event coverage. But for now, this is Alan Shimel for Techstrong tv.

Thanks for joining. Hey, good morning everyone. It's Alan Hummel, and welcome to our day two coverage of AWS Reinvent 2025.

We're live at the win, uh, right here in Las Vegas, covering reinvent. And, uh, I hope you had a chance to look at some of our coverage from yesterday. We had some really great discussions.

We had a lot of analysts, a lot of different AWS partners. We hope to have some AWS people, I think we have scheduled later this afternoon as well. But let's kick off our day with what, for me, personally, is a highlight.

If you've ever watched our event coverage in the past, this man may be, uh, familiar to you. My friend David DeSanto. David, well, if you know David, you know this, but David ran product at GitLab for five years, Uh, three and a half years.

Three and a years. Cpo O and, and yeah, two and a half before that. So you're either there about five and a half, six years.

Um, always a really smart guy. I always a great interview. He loved talking with him, but he's not here.

This is not David Desto of GitLab anymore. This is David DeSanto. I'm proud to say the CEO of Anaconda.

David, first of all, congratulations, man. Yeah. I'm really happy for you.

Oh, thank you. Yeah, I, uh, truly excited to help Anaconda go into their next chapter. Absolutely.

You know, I, I, I didn't hope didn't embarrass you or anything like that, but I wanted to talk about the GitLab experience because for our audience, which is DevOps and cloud native mm-hmm. And cyber and so forth, that, you know, GitLab is a, is an important company in the ecosystem. Um, and you were an important person in taking that vision and running with it.

Tell us how you wound up at Anaconda. Yeah. So first, yeah, it was a great run at GitLab.

We saw the company grow almost exponentially. It was less than 300 people when I started, and my last day was over 2,600. Right?

And so, uh, the journey to Anaconda does start with GitLab. Going to GitLab. I re-embraced the open source community in a way that I hadn't since ICSA labs many years before that.

And that time was great, you know, uh, our first conversation was me coming out and saying like, we are going to add security and compliance to GitLab. I remember that. Yeah.

And then, uh, the last quarter I was at, that's part of their revenues over 53% of it. So it was a really great run, great company cheering them on. Absolutely.

Uh, but yeah, I was ready for my next challenge. And so when thinking about what I would do next, I explored, do I wanna stay in the DevOps space? Do I wanna go back to security?

And I realized I could do security in AI all in one place. And that was Anaconda. Aha.

There's, there's the word, two minutes in, and we've mentioned ai. Yep. Um, Well, I think I said this once before, but you can't spell David without ai, so That's true.

So yeah, This is true. You have mentioned that My, my wife did say, I have to stop telling that joke, but Well, look, we've got a new audience here. You got a new title.

They may not remember it. So David, some people in our audience I'm sure are familiar with Anaconda, but there's plenty of people who aren't. Let's, let's start real foundational and build our way up.

Give us the Anaconda story. Yeah. So Anaconda came out of a consultancy.

The two founders of Anaconda had a company called Continuum Analytics, and they were doing consulting work for data science within the financial services space. And what they found out was that they were building new Python packages to support the work they were doing, and they decided that, hey, this should be a product company. And so they started Anaconda, the first product's name was Kanda.

And that's what a lot of people think of that provides thousands of trusted, secure data science and AI packages for Python. Uh, but the company has continued to grow beyond that. And one of the reasons why I joined is the story that they are currently on.

Anaconda can help you with secure python development, but we do so much more than that. Uh, earlier in the year, we launched our AI platform that helps you apply security and governance policies to how AI applications are being billed, really. And, and the, yeah, the most recent, which I'm the most excited about, I cannot take credit for it 'cause it, you know, came out I think three weeks after I started.

But, uh, our AI catalyst component of that platform, what it does is provides a curated list of open source models that we have validated or secure. We include the lineage of where they came from, how they were trained. We Oh, I love that.

Yeah. We rate them on performance, and that could be in different quant sizes. And we also then give them the guardrails to make sure that it operates as best as it can.

And so what really excites me about it is we already helping people run inference, and it kind of starts a desktop app before we became a platform and now a, a SaaS offering. Mm-hmm. But the cost to run AI models as part of development is very expensive.

Like I learned that when I was at GitLab. Right. Um, and so what we've done is also make it possible to run a micro in inference on the developer's laptop.

Wow. Which then is that same model that needs to scale. So, So you don't pay the token penalties.

Exactly. And then when you're ready, we can see what you did with the model locally and tune as it gets deployed into production. So, wow.

Yeah. The best way to describe it is, you know, what a GitLab is for DevOps Anaconda is for AI native development. You know, it's funny you mentioned that term.

I was, I was out in Brooklyn actually a couple weeks ago for this AI native Devcon. Hmm. There's this whole burgeoning community, um, you're probably aware of AI native development, uh, uh, guy Ani from sny, who's now, I forget that Tesla is this new company.

They're very active in that community. Um, and I, I went out there, I was blown away. It re it reminded me of going to a DevOps days 10 years ago.

Yeah. Right. That, that same tinkering, geeky, we can make, I love playing with it kind of stuff.

And it was, it was a, it's a great community. Um, let me just kind of shimmy eyes this for, if you don't mind. There.

There you go. So we, we've got Anaconda started as a company providing services on Python scripts And helping companies with their data science development. Yep.

Hence the Python Anaconda connection. Exactly. Okay.

It then shifts to more of a product model, but it's an open source product model, which is still open source today. Yes. Oh, Correct.

Yeah. We have a very healthy, free offering. Mm-hmm.

Uh, it allows people to get in the door using Anaconda and mm-hmm. One of the things that really blew me away as part of the process to join was that 95% of the Fortune 500 use Anaconda today. Really?

Yeah. And we have over 2 million, uh, community contributors. That's great.

And 50 million users. 2 million contributors, yep. Code, yeah.

Code contributors to the open Source source components. Wow. Yeah.

It's actually a really great story. Uh, one of the founders is, uh, Peter Wang Uhhuh known very well in the open source community Sure. And within the data science community, and he's still an active part of the company.

Mm-hmm. Um, you know, he and I talk about what we wanna do next together. Yeah.

And that reach that we continue to have is because he's always out meeting with customers, potential customers. Two weeks he's in Boston for a, uh, meeting around how do you set some AI standards Yeah. As part of development.

And so we continue to lean into that because, you know, that is really the core of the company to your point. Yeah. You know, we started as a package manager con, but now we have the AI platform and we wanna allow people to still come up, get used to using it, get the value out of it, and then want to come and then join and, and pay for either our starter tier or enterprise tier.

I love it. I'm gonna jump into what the store, the, the different tiers are in a bit. I wanna come back to what you were mentioning this newest offering that you're so jazzed about.

Yeah. The AI catalyst. Yes.

The AI catalyst. Now look, you mentioned package managers. It's been a rough couple weeks for package managers, hasn't it?

It has with this shy ude and, and all of that. It sounds like this AI catalyst may be just what the doctor ordered, right. If, if I'm a user mm-hmm.

Of, of package package manage, uh, package packages, I want to make sure that my package manager's giving me something that I'm not Correct. Introducing malware into my, my ecosystem. This only works though with the AI models that you're using, right?

The AI packages, if you Will. Uh, yeah. That and all of the con packages.

Okay. All the kind, Yeah. So because we still use the con package manager, um, we have a very unique build system for building all the packages we provide.

And so we're able to actually take things apart, fix the vulnerability, and say in the binary part of the package, put it back together, and then make it available. And so a lot of people think of Anaconda first as a trusted distribution because we're providing, you know, two thousands of Python packages that we know are secure and are able to scale. Now.

I get it. Yeah. I got it now.

I, it took a little while. Sometimes I'm slow on the uptake. Oh, no.

And, but it, Yeah. If you think about it, there's then that natural transition into the platform, right? It's one thing to start your development, but it's not thing to get that prototype into production.

Absolutely. And, and look, I, you know, just quite frankly, it is, you know, we live in a world of, let's call it Frankenstein software, where software is more assembled than code written, if you will mm-hmm. At some level.

Right. And, you know, and you, you, your security background, you know this, we talk about software, supply chain security all the time and, and how stuff, you know, SBOs mm-hmm. And what have you.

I think the biggest weakness in our system today is the software and packages that we're downloading from all these repos and, and, and depots and what have you. So, you know, the fact that you're do, you're on guard here with the condo packages mm-hmm. Is, is a huge thing.

Give us an idea of scale if, you know, you may not know this off the top of your head, but like how many downloads a day, a week, a month? Yeah. I don't have, don't know that off the top of my head, but Kanda is hit all the time, almost 24 7 with people pulling packages.

So very healthy community. That's how we can have 50 million users really, uh, yeah. Using Anaconda every month.

The thing that is the most incredible to me is what you just touched on, and this is part of that why I joined in the journey. Uh, you can only do so much with the actual packages themselves. Yeah.

But when we're talking about the platform, there's like the starter, which is kind of like, hey, a team's getting together. Uh, but the business tier actually provides what you're talking about. It provides an AI bill of materials, can track vulnerabilities for you.

Uh, we're working on how to help auto remediate those as well. And so the customers that end up on a thing like the business tier or the platform, they're getting, uh, full visibility into their AI life cycle. And that's really powerful.

'cause as you said today, it's very common that vulnerabilities will sneak in some way. And we're heavily reliant on packages that we've not created. We're reliant on our IDs to be secure.

We're, you know, worried about the things that happen after the code is merged. And anacon is just in a really great spot to help with all Of that. You really are.

You're right, you're right at the, the nexus, if you will, of, of where all these come together. I love it. Now you mentioned different tiers.

Mm-hmm. So obviously there's probably a free open source tier where hey, it's open, it's open source, have at it. Then you have, you mentioned the SaaS model.

Yeah. So the product, uh, you can self-host mm-hmm. com.

Mm-hmm. Um, but yeah, the big difference is not necessarily whether you're hosting yourself or using our, our SaaS offering. It's really about the free version gets you up and going, if you're an individual developer, provides you a lot of power.

If you now wanna operate as a team and start having some structure around it, you go into the starter tier, which starts to introduce a lot of that. Um, but when you're ready to talk about AI build and materials security and governance and having policies that prevent malicious packages from being installed, then you end up on the business tier. And that's where all that security and compliance functionality is, including dashboards, policies you can create and so forth.

I love it. Um, to swarm an old school open source guy, what pers 50 million users is a crazy number. Yeah.

You may not know this, you may not be comfortable even saying it. What percentage of those are just pure free open? I mean, usually it's 98, 90 7%.

Yeah. Yeah. So there, uh, a large percentage of it is that open source community.

Sure. Um, but that's something that's very important to us. Sure.

It is. You know, what I learned, uh, working with Open Source, I'm so excited to be, you know, leading a company that has open source first mentality is that like you get more value out of that free tier than you could if you tried to bundle that up and put 'em into a paid tier. And it's ultimately because you get all those contributions, uh, you actually are able to get onto the community, be it events like reinvent Yep.

And have conversations with the actual builders and doers. And that's not something that commonly happens if you only start with a paid option or you're not open core. I love it.

Let's, um, let's talk a little bit about Reinvent. You mentioned it for here. Yeah.

Um, is there like a formal partnership? You know, what, what are you doing at Reinvent? Yeah, so we're in Booth, uh, 1327.

Mm-hmm. So if you're at the show and you wanna check it out, You're watching this live now, you wanna run down there, go run down run, Uh, before we run out of giveaways in swag. Right, exactly.

Uh, some really great swag. But, uh, in our booth, we're actually demoing the AI Catalyst offering and we're showing people all the other things that Icon can do that are not just, you know, being a package manager, uh, but to speak to the partnership AI catalyst this new com Yes. Part of our platform that launch exclusively on AWS and we joint announced it yesterday.

Oh, great. Uh, and it also included that it's now available in the AWS marketplace. You can go and buy it yourself.

You don't have to go through all the hassles of like, the steps to get to that point. I love it. What do, and so yeah, that's a great example of the partnership mm-hmm.

And spill right on top of AWS, but there's so much more we're looking to do with 'em. Uh, you know, we're looking for better integrations into Bedrock customers, like using Anaconda with SageMaker. So getting a nice embed story there.

Yes. We were just talking about SageMaker this morning on Dextron Gang, And so yeah, the partnership is great, but we're just gonna keep on building on top of it because they're a really good partner. You know, I've worked with them across multiple companies, and yes, they're always exactly as great as they seem, and that's really great to have a partner like that.

Absolutely. You, you know what's interesting is I I I, we were talking off camera and I, I mentioned, you know, this year's reinvents a little different. It's very AI focused and everything else, but I'll tell you what it is focused on, it's laser focused on developers.

Mm-hmm. Right. They really are kinda reestablished because when you, you know, you've been around, you know, I know it was the developers who made AWS it was those guys whipping out their credit cards and, you know, building spinning up instances and, and doing stuff that, that made AWS what it is.

And it, there is a renewed focus on the development process. Of course, AI is changing how developers develop, and it sounds like you're, you're responding to that as well at Anaconda, but make no mistake, that's the focus here. Right?

Yeah, no, what I would say is, I, I took away a couple things, uh, just from Matt's opening keynote. Yes. Uh, the first is, it's all about the hardware.

And I think that's something that people don't always think about. You know, we were talking, Well, that was supposed to be the thing about cloud. You didn't have to worry about the hardware.

Yeah, That's a good point. Uh, but I was gonna say the, uh, you know, when you're talking about ai, it kind of starts at that, right? Yes.

You gotta have the right, It's made hardware sexy. Yeah. And so to see that lead off with mm-hmm.

What they're doing to make it a lot approachable for non-developers to get into an environment and know it can work was really good. Uh, definitely the AI lean in mm-hmm. Uh, was very, uh, prominent as well.

But the one thing I would say, uh, and it, I can't believe I'm saying this, like it's my first reinvent, you know, but what it feels like is like if you were to take, uh, a cube con, make it si significantly larger, Four times the size, And it's only about the developers. Yeah. Like that's the, the vibe here.

And it's actually great. Yeah. So this is, I don't know how many, certainly since COVID is the fourth, probably since COVID alone, um, this is pretty much it.

It's, it's, it's a, I mean, you know, it's nice. CubeCon is the, like a perfect size. Mm-hmm.

12,000, 14,000. It's big, but not too big. It's a, it's kinda like building a company, right?

Mm-hmm. You could build a company that has 10 million, 15 million in revenue, and you have one kind of management team. You go wanna build a company that has 75, a hundred million in revenue.

It's a different management team. Mm-hmm. You wanna go build a company that's IPO-ing, it's a totally different animal.

It, it's the same thing with conferences. You get a conference of 60,000 plus people. Mm-hmm.

You, you, you know, hyperscale, it's, it's, it's about scale and they do a great job with it, considering everything that's going on Here. Yeah. No, and I would say too, for those who are watching this and or here but haven't really like, gone over to everything that's going on, it does not feel like there's that many people here.

Like, they've done a really good job keeping Yeah. Well, it's spread, spread out so forth. Yeah.

I, I agree with that. You know what, David, we didn't even mention the website, how to engage. Of course.

I mean, obviously it's open source, you can get it, but what, what is the best website? Yeah. com in there.

It'll point to the dis uh, installers. If you wanna install locally, it can walk you through creating an account for SaaS and getting up and running really quickly. Uh, the other thing is that if you just go to like the doc site as well, to your point, you'll learn about more of the open source focus and how you can contribute code.

com. But ultimately, like what I would say is if you're looking to build AI, and you might not be a developer, or in some cases, you know, I won't say I'm very young, but like I programmed in, you know, c out of college, right? But I don't know how to get into Python.

With Python now being the number one language worldwide, anacon can help you with all that helps you build applications even if you're not technical. Well, AI could help you with it now too, right? Yeah.

I would imagine condo's going to use AI to help. If you don't know how to develop in Python. You don't know Python.

Yeah. To teach it to you and help you develop it. I mean, it's a crazy world we're coming into.

Oh, no, for sure. And what I would tell people is like, it's so easy to get started. I, as part of the interview process, wanted to play with the product and you can get a cloud notebook up and running with one or two clicks, really?

Uh, yeah. The a Anaconda AI system is just there, uh, it's front and square. And I was asking it questions of things that I used to do 10 years ago with Anaconda, like, how do I do this today?

And it was very easy. I felt very, uh, productive and able to actually build something without having, you know, a lot of this, the knowledge that was just built into the platform. So, yeah.

So Lemme ask you a hard question, David DeSanto, do you still consider yourself a developer? Yes, I do. Okay.

I do. And and here's why. There, you know, um, Mr.

Joel for a long time as an engineering leader, uh, people say, I went to the dark side to go into product and mm-hmm. I don't think David graduating from college would know that David would be CEO of the company, right. Company.

But those roots are still really important. And so whether that is me building stuff to play with, uh, me working with our engineering team and finding things that maybe we can make better, you know, it's great to roll up your sleeves and just be in that, especially with a very technical company. And I won't tell you the apps I built are pretty bad, but, you know, But they don't have to be great.

The fact, you know what I am, I said it tongue in cheek. Yeah. But the fact of the matter is, is it, I always tell my team, you gotta be able to walk the walk, not just talk the talk.

And so the fact that you could play with it and make some, it doesn't, doesn't have to be the greatest app in the world, but you could get your fingernails dirty with it gives you a perspective that helps you understand who your customer is, who the users are. Yeah. It's Important.

No, and you're right. You Can't be too abstracted outta that. No.

And what I tell people is like, even though I'm now CEO of a company that's almost 500 people, when we announce our series C, we're at 150 million in revenue. You know, it's still important to me to be thought of as a developer and like a vulnerability researcher. Mm-hmm.

Because all of that is what has helped me be successful in my career. And so what I'd say to people out there who are like, I dunno what I want to do or do I wanna switch roles, go to a different company, you know, find the thing that you wanna do and just do it as best as you can. And it's just so rewarding.

And, you know, Anaconda is there to help people take that journey for themselves. I love it. David, man, congratulations.

Thank you. Best of luck at Anacon. You know, I'm sure now that you're there, we'll be talking a lot, doing more, looking forward to hearing great things.

But this sounds like a great opportunity for Anaconda and a great opportunity for you. It's a good match. You know, thank you very much for having me, and I always love the catch up.

Oh, It was a pleasure. All right. com.

Go check it out. We're live at AWS reinvent. We're gonna be back in just a minute.

We've got tons of great stuff coming up. Stay tuned Is the end of yet another year. And we are here to take a look at all of the big stories from 2025 with a variable degree of snarkiness in this episode of the Tech Field Day rundown.

Hello everyone. Welcome to the Tech Field Day rundown. It is December the 17th.

It is getting very close to the end of the year, and we wanted to take a look back at the last 12 months to kind of give you some highlights of some of the big ideas in the news. We covered a lot of things over 2025. There were a lot of great things going on.

We hope that you are enjoying some pancakes with maple syrup 'cause it's maple syrup day. Um, and it's also national Say It Now Day, which honestly is kind of the thing that we do around here. We just say it like it is now.

Um, well on Wednesdays at least, uh, joining me of course is my co-host Alistair Cook. Alex, good to see you again. It is always a pleasure to be here and particularly on Pan-American Aviation Day.

My first international flights were on PanAm and flying to the United States, so, uh, great to be here on Pan-American Aviation Day. Well, speaking of flying this year really did fly by. Um, we're gonna go ahead and jump into the first set of stories, though Al and I think you're probably the best one to talk about it because I think 2025 will go down as the year of ai.

Yeah, and we kicked off this year of AI with a future and survey of CEOs asking about their AI readiness and the use of AI. And, and the results really painted a picture of unfulfilled promises. We saw paralysis and slower moving firms who had no idea what they were doing with AI in this survey.

And we also saw that more cloud native, uh, companies were still struggling with cohesive AI strategies and cohesive infrastructure. So beginning of the year, AI was definitely, uh, a challenge for some vendors. But Jensen Wang, the rockstar, CEO of Nvidia, was much more bullish.

She presented in CES in January and announced that the Cosmos, uh, was a World Foundation model that, uh, of course NVIDIA's shipping and predicted widespread adoption of AI robotics everywhere. Of course, also in January I saw love the story of a, uh, sticky trap for ai crawlers. Developer was sick of AI data gatherers that don't respect any of the fair use, uh, norms that are happening out on the internet, as well as some of the mechanisms like robots text that limits crawling.

So this developer created a, uh, mechanism to create random cross-link pages with no real content track these AI trawlers in just a little corner of their website, but humor would know not to look at. But AI, not so much. Of course, January brought some really big AI news.

That's when we learned about deep seek. The Chinese ai, uh, trained for far less cost than other LLMs. Uh, also heavily censored dataset.

Don't ask it about Chinaman Square. Uh, and also trained on a bunch of AI generated content. And this was the point at which we thought, hmm, is it wise to feed an AI with AI generated content?

Seems a little bit like some of the problems they had in the UK with chicken, where they were feeding bits of old chicken to brand new chicken and got disease right through the, uh, the feed line. I hope that doesn't happen to ai. Of course, AI being the thing that unifies all marketing departments this year, uh, February brought the news that ER had restructured, was reorienting itself towards a generative AI company.

And at the same time, hammer Space announced that object storage is not the only option for AI training data. This definitely shows us two successful specialist storage vendors confirming that AI is still the essential marketing term for 2025. Heck, we're belly into February and Cisco and Nvidia decided they were gonna partner up as well, bringing Nvidia Spectrum X management for Cisco switches.

And of course, NVIDIA's Bluefield Smartnick into, uh, Cisco devices alongside the Silicon One. While we're talking about hot chips, uh, Broadcom launched their Tomahawk six switches giving over a hundred terabits per second throughput in a single device. Uh, this team presented at the AI infrastructure Field Day, showcasing the Tomahawk Ultra and Jericho.

So as well, we've got massive training networks. You know, we skipped a whole bunch of the run rate kind of announcements around ai, but we did notice that November, uh, AWS talked about a agent ai and particularly using tic AI to help customers migrate from on-premises platforms into the AWS cloud. I think we've seen a lot of progressive tic AI tools through the year, and, uh, hopefully some of that confusion and paralysis that was happening at the beginning of 2025 are starting to clear and companies are starting to get some value out of building AI solutions and finding better tool sets than maybe they had at the beginning of the year.

Another group that's been in the news a lot this year has been Intel. There's been all sorts of trials and tribulations with Intel. Tom, I think they're one of your favorite companies in our, our coverage along the year.

They are. And we've spent a lot of time talking about them because quite honestly, there's been a lot of news. If you remember, we closed out 2024 with a lot of bad news, honestly.

So how did 2025 start out? Uh, honestly, it was a lot more down than up. We found out back in February that Intel was not going to release Falcon Shores.

This was a big hit to what a lot of people considered to be kind of one of their make or break moments. Uh, there started to be some rumors around that same time that Broadcom might be looking to buy out some of those chip designs that Intel had been working on on the cheap. You know how it feels whenever the Vultures start circling it, it's maybe time to hang it up.

And then news came that that big Ohio Fab facility that Steven Foskett has been so excited about was gonna have to be pushed out a few years because of construction, but also because of cash flow issues and some potential changes to the CHIPS Act. And it was not really looking good for them. Now, one might be forgiven for thinking that the appointment of a new CEO in March was gonna be the start of Intel's big recovery.

And lip Bhutan came in really ready to work. He created some big restructuring plans for the organization. He did things like brokering the sale of controlling interest in Alterra to Silver Lake.

You know, that was a, a big thing that Intel was very prideful of, and maybe this was gonna give them some cash to turn the ship. Uh, the rest of the next quarter though, all we could talk about was Intel's layoffs. And, and some of these were confirmed kind of publicly.

Some of them weren't. And, and we were hearing lots of numbers, you know, 15,000 here, 17,000 there. It really, really hurt the industry because those thousands of jobs almost had to be sacrificed in order to get Intel back on track to their core business.

But layoffs are never good for any companies like this. I mean, things were looking pretty bad for Intel. I mean, how could it possibly get any worse?

I know back in August, president Trump said publicly that lip Bhutan had conflicts of interest and he needed to resign as the CEO of the company. I think that's pretty much the point that they hit rock bottom. But I know that that's the case because that really was the beginning of Intel's comeback in 2025, because it took less than a week.

And we started to hear that Intel was securing investments. SoftBank bought in, Nvidia bought in, and the US federal government made a deal. They would see 10% of Intel's proceeds going back to the US government.

After that, everything took off. Intel was no longer the punchline of a joke and also ran in the company. It was more than that because we started to hear rumors that maybe Apple was gonna be looking at Intel again as a partner on chip manufacturing.

We also saw that because of the announcements of all of those investments that Intel decided not to sell off their networking business unit. We just covered that, uh, last week. On the rundown, it seems like money fixes all problems.

Intel, most importantly though, is positioning themselves to be the domestic chip manufacturing giant, because one of the things that we've seen that's kind of been a subtext for everything going on this year is the looming tariffs that are being positioned against companies being used as weapons against other investment vehicles and Intel. Being a domestic chip manufacturer is effectively tariff proof instead of having TSMC need to come over and build a fab in Arizona to beat those tariffs. Intel already has fabs positioned everywhere, and they're trying to do more.

They just need a little bit more runway in order to be able to pull that off. Honestly, at this point, time will tell if this is enough to get them back on the right track, but having weathered everything that they've weathered this year, I can't imagine that Intel has much less position to fall from because they are slowly gaining the momentum, which doesn't seem like a lot against what's been going on in the AI market, but there is an opportunity for them to kind of right the ship. Finally, another thing that made the news this year was the massive spate of outages that really kind of knocked some knowledge workers off track.

Al These outages started fairly small in January. We saw, uh, Utelsat having an outage that left one web's, broadband services offline a lot of the time. These were backup services that were used in locations where the primary internet connectivity wasn't great.

Few places, it was the primary connectivity. So a couple of days without internet, that was pretty significant, but only for the small number of people who are significantly affected. In April, we got a bit of a warmup, uh, to the outages when Zoom.

Uh, zoom got taken offline for 90 minutes and all of us, uh, had breaks from having calls. Uh, we got actually to have some productive time since we weren't on Zoom calls all day for those 90 minutes. Turns out that, uh, GoDaddy, who hosts all of the US domains had for some reason blocked, uh, Zoom's DNS domain.

Hmm, maybe DNS is the root of all errors. Things scaled up quite a lot in June when we started to realize just how much popular applications like Spotify and Discord and Snapchat depend upon. In this case, the Google Cloud.

Uh, Google rolled out a new feature for some, uh, quota policy checks, and they weren't adequately tested. And so Google broke the, uh, the internet for a little while. A bunch of popular applications were often we had to go other places in order to complain that we couldn't get to the internet.

A little later in June, we also saw a record breaking DDoS attack. 3 terabits per second, primarily of UDP frames being sent by the Mariah Botnet, uh, was all being soaked up by CloudFlare, one of our largest DDoS protection networks in the world. And so take everybody offline with it.

But what we did see in October was something big. What we saw in October was that we have become very dependent on both DNS, but also some of the core services inside AWS. And so again, it was a DNS fault that AWS, uh, had put in as an anti DNS record for the Dynamo DB database.

Dynamo DB is the massively scalable key value store database. Uh, without Dynamo db, lots of business applications went offline. Lots of AWS services went offline.

We really saw this lots was out and down and not working. And we realized that when we started using these ubiquitous web services cloud services, we became very dependent on them. And while the individual services are incredibly reliable, when they do go down, consequences are huge.

So these services typically are very reliable yet because everybody uses these highly reliable services, if they have an outage, the impact is immense. And if you've been running your own database on premises, or if you've been running a database inside a virtual machine somewhere, the impact would've been far less. It would've only been on your own services, but the likelihood of that failure would've been a little higher.

People make mistakes. So, uh, sometimes there's a, a reflection here that using these big web services is a bad idea because we see headline news when it goes down. But what you don't see in headline news is just how often services inside organizations go down.

So it's not fair to tar these, um, single points of failure that we've built into our systems as being the, the, the worst thing ever. Um, but nothing is a hundred percent uptime. CloudFlare came back for us in December.

Uh, they forced an outage because they saw an actively exported critical vulnerability. This reactor shell export was, was, um, being used in the wild to run arbitrary code on, uh, these, these websites that were using the React framework. Uh, CloudFlare basically DDoS themselves.

They, they shut down those sites until they could get a more targeted mitigation in place. And so there was a a period of time where CloudFlare was brought blocking access to these vulnerable sites before more targeted reactions can come in place. Uh, I think Reactor Shell is gonna continue to be in the news for us for a little while.

It's gonna be the new version of the Log four J problem where lots of people didn't realize they had vulnerable code because, well, it was a dependency for the thing they actually wanted to have. And it turned up in all kinds of places that they didn't expect it to in terms of outages. We did see a whole bunch of outages this year.

We realized that the cloud is not a golden bullet that takes away all of our reliability problems in our applications. And even if you follow the best practice designs from the cloud providers, you're still not gonna get 100% up time for years and years and years. There is still the possibility for things to truly mess up.

Another company that's been in the news a bit today and a good friend for us here at Tech Field Day has been HPE, uh, that's the enterprise part of what used to be hp separate from the people you buy your, uh, laptops and, and your printers from HPE is the, uh, large scale stuff we put in data centers and Unrun networks around. Tom, you've been across all of this because a lot of this news has been networking related. It has.

We really were kind of curious as to how this thing was gonna start out in the year because we had started to hear rumblings that possibly the US Department of Justice wanted to take a closer look at that proposed acquisition that they had of Juniper Networks. Well, February, almost one year to the day after the big blockbuster announcement, the DOJ decided, no, no, no, we're, we're gonna block this acquisition for the time being. We need to take a closer look at it for, uh, I don't know reasons.

Uh, we also got news that there was potentially some exposure from hacking group Intel brokers getting in. They, they might've gotten away with some source code. That one's kind of still ongoing.

Even to this day. We, we don't know exactly what happened. But then a couple months after that, in April, we got the most terrifying business news that a company can get.

Elliot Management took a position in HPE and immediately we started to hear some questions about Antonio NE's leadership, which is quite honestly kind of part and parcel for how Elliot works. Uh, they wanted to maybe make a change in the CEO chair or potentially get some board members up there that would, uh, look to unlock some of that mythical shareholder value that they seem to keep looking for, but can never seem to find. I think though that Q2 was really the start of HPE really becoming more of a unified company because back in May we saw that they really integrated their Morpheus acquisition into GreenLake and it helped fill out that offering a little bit more completely.

You may be wondering to yourself, well, what does GreenLake offer me? Well, if you're someone who's looking for refuge from the things that have been going on over at VMware by Broadcom, GreenLake has an opportunity to kind of supplant that. And that's how HP has been positioning Morpheus over the year.

They're basically saying, if, if you're not happy with what you're getting there, we have an offering that will work with you and we're gonna provide you the same kind of support that we always have. We're just gonna be working on a different hypervisor, and people seem to be responding well to that. Then at the very beginning of July, the Department of Justice came out with a list of some recommendations that Jennifer and HP needed to do that would allow them to clear that acquisition.

They were a little bit strange. There was some discussion. We even recorded an episode of the Tech Field Day podcast about it, and it took no time at all for the companies to agree that, Hey, we're gonna do that.

And there you go, we're gonna close that acquisition. It's almost like as soon as they found out that this was an option, they're like, book it done. We're out the door.

Let's just do it. No buyer's remorse here. After that, we saw HP and Juniper really ramp up efforts to provide a combined offering, uh, whether it was new agentic AI offerings, integrating some of their product lines.

As we started to hear around the time of HP Discover Barcelona towards the end of the year, including if you were reading between the lines, some very forward-looking language that kind of will show you what the combined offering will eventually look like. But that doesn't mean that networking was the only group that was making advancements. The server side of the house did a lot of integration work and they even picked up a deal to offer private cloud to the US Department of Defense, uh, as a way to kind of, you know, kind of jump in as to some of the remnants of those big projects that we've seen getting bounced back and forth between Microsoft and Amazon for a while.

They also released their Gen 12 server line, which is another big stepping, continuing to refresh the product lines that everybody feels are so critical to HP E's success. They also did a lot in the cybersecurity market. They included things like AI based DDoS protection, and more importantly, they complied with some new European regulations that will require organizations that are undergoing outages or ransomware attacks to be able to isolate themselves from the internet to be able to clean those things up and not create a, a larger impact all over the market.

And as we know from the last couple of years, not being compliant with European regulations is a sure way to get yourself in a lot of trouble real fast because the European regulators do not mess around. Now, November was a bit of a mixed bag for companies that had partnered with HP because we saw that they had dropped support for some of their storage partners like Qumulo Scalability and wca. I think though that this is really more of HP refocusing their efforts on integrating their own storage offerings back into their lines and working with very specific partners in very specific situations.

As we've seen by the breadth of what HP offers, they really do wanna become the OneStop shop. Whether you're doing something in the campus, whether you're trying to build a cloud integration, whether you're trying to work with ai, they want to be the sole source for everything you could possibly need. And if you wanna buy it, they have options that will do that for you.

If you want to rent it through GreenLake, they're happy to do that as well. It's one of those things where we're getting back to the kinds of service offerings that are important to the clients that are out there. It's not just a matter of, give me these parts and pieces, give me the expertise that will allow me to integrate them together and to make something out of this so that I have can have my knowledge workers focusing on outcomes that are business aligned instead of navel gazing about a bill of materials and some pieces and parts that maybe don't make a whole lot of sense.

Now that I, we've kind of dropped all of the drama of whether or not some of these acquisitions are actually gonna be able to be done. I think it's going to allow the leadership at HPE, including current CEO Antonio Neri, to kind of chart a course to not only keep their customers super happy with what's been going on, but keep the shareholders off their backs long enough for certain activists to maybe exit that position in the market. I kind of hinted to it in this story Al, but AI continued to be a huge part of what was going on in 2025, and you have a slightly different take on it with some more news stories.

Yeah. And, and this story comes in two parts and it's about buying, selling the inside baseball side, the who gets what bits of AI and, and the hard will for it and those kinds of things. The, the first part of it, and this started at the beginning of the year, was around who's allowed to buy what hardware and what are the impacts of tariffs.

But as things rolled through into later in the year, the focus shifted towards what looks like a whirlpool of AI funding announcements. So the, the first part began in 2025, beginning of 2025 in, in January when the Biden administration started coming to the end of their, uh, the, the governance were looking to restrict AI chip exports and 20 countries were on, uh, the books of being, uh, places to restrict. Of course there was some responses.

Um, there was the statement from TSMC that, uh, semiconductor trade with, uh, Taiwan and the US was win-win for both. And of course, Nvidia argued that AI expert controls will be detrimental to Nvidia export controls will put in place, uh, in Nvidia high-end. GPUs weren't allowed to be take sent to places that was particularly targeting, keeping them out of China where they might be used to create some sort of, uh, large scale, uh, commercial or even uh, military benefits.

So, uh, we covered some stories around, uh, Singapore, uh, arresting some alleged NVIDIA chip smugglers who were apparently sending, uh, the BA Blackwell GPUs to China. By the time we gotta August tariffs were coming along and US companies were concerned about high cost of imported goods and servers, including GPUs and other semiconductors that were manufactured outside of the yield here. And, uh, the on again, off again, status of these terrorists throughout the middle of the year was a source of some tension.

But by mid-August, Nvidia had agreed to pay the government 15% of its revenue from G Sales to China and return for being allowed to export the medium powered H 20 GPUs. But by December, the US government was allowing Nvidia to sell the powerful H 200 GPUs. That is quite a difference in the year from banning, uh, exports to hundreds of countries while over a hundred countries to actually allowing the, uh, the big enemy to have the most powerful GP news.

I guess it reflects that China didn't actually hang all its hat entirely on getting Nvidia GPUs 'cause they're building their own the AI money go round as the other story. It heated up, I mean it started early on in May Open AI acquired Johnny i's new company IO for $6 billion despite the fact that IO didn't have any product description and that the company name is impossible to trademark and really hard to find on Google. Uh, but by June AWS was announcing they were gonna spend $20 billion building two AI data center campuses in Pennsylvania.

In July, Oracle made a bigger announcement, $30 billion when an unnamed client who are going to buy three times Oracle Cloud's current size. At the time Oracle Cloud was doing about $10 billion a year in, uh, cloud infrastructure. This announcement was 30 billion.

Turns out the unnamed client was open ai. Then in August, meta committed to buying $10 billion of AI compute from Google Cloud. Part of a $72 billion commitment that meta was making to building AI data centers.

That's a lot of money being spent carrying on. In September, we got one of the first of the very circular feeling deals. 3 billion worth of GPUs to Core Weave.

But if Core Weave couldn't sell that GPU time that they had just bought, Nvidia would buy it back off. Seems a little odd following that. We also had, uh, neas and Microsoft, uh, announcing a $19 billion deal in September.

Uh, Microsoft is buying some GPU capacity in Europe to run AI systems to complicate matters. Microsoft also an investor in Core Weave that we just saw doing a deal with Nvidia and NVIDIA's an investor in NEAS that's just doing a deal with Microsoft. And then in October we've got even more serious numbers, uh, Microsoft this time with open ai $135 billion invested by Microsoft into open ai.

And so open AI will mostly be on Microsoft platforms. Uh, apart from that 30 billion that's gonna Oracle Cloud as well. Just to make another circle, in November, Microsoft Anthropic and Nvidia announced a set of investments.

So Nvidia investing $10 billion in anthropic, Microsoft adding another 5 billion and in return Anthropic will buy $30 billion worth of cloud computing from Microsoft, which is their way of procuring a gigawatt. Uh, that must be more than a gigawatt, uh, multiple gigawatts of n of uh, Nvidia GPUs. Uh, I wonder how Anthropic getting all of this money in throwing all this money out, how are we gonna make some profit from these investments?

Of course, at the same time, uh, Nvidia committed to buying $26 billion worth of AI computing from a variety of providers, all of whom get those GPUs from Nvidia. There seems to be an awful lot of money going around in circles or some weird shapes to get between all of these different players. And we're still not sure where air cu and customers are actually going to want to pay for all of these things.

Whether there is going to be business value for all of these billions of dollars running around and these massive data centers that are being built out over time, hopefully 2026, we're gonna start seeing some actual profitability from these AI startups and we're going to start seeing that businesses are being transformed in a positive way by the use of generative AI and massive numbers of GPUs. Of course there could be a fly in the ointment as a new piece of technology comes along and changes everything. Uh, we've gotta be looking for all of the announcements of what's happening in the quantum computing world.

And Tom, you've been following this a little bit this year. I have, and quite honestly, for those of you that are already tired of all of this AI hype, I've got good news for you because the next big thing is on the horizon. Well, if you look closely 'cause it's small, really tiny quantum computing really has been around for decades.

2025 saw some advancements in the technology that we talked about quite a bit. Uh, February ended with a considered to be a blockbuster announcement from Microsoft about the major Ron Quantum chip. It was rumored to have 1 million qubits of capacity and it also came in a really interesting form factor and did some things that people hadn't expected to see out of a quantum chip before.

And that got a lot of people kind of talking about what was, what was gonna be happening. Then we saw some novel new applications. Uh, one of the ones that I was kind of proud of was the fact that people were using quantum super precision for things like navigation.

Uh, big important thing there was because it couldn't be jammed. So if something were to happen that GPS would get taken out. There you go.

Then we saw some announcements from Cisco around quantum networking. I highly recommend you go back and watch some of the quantum networking discussions that we've had with Cisco over the years because they actually have some really cool stuff. Google announced that RSA encryption, which is kind of considered to be the watermark for when quantum will become Supreme, could potentially be broken a lot easier than we were expecting.

Instead of it taking hundreds of thousands or even millions of qubits, it could just be done with a few thousand that were no noisy. The second half of the year focused on other big announcements that were a little bit more in the affordability range because there was a Chinese company called Quantum Ctech and yes, it's not spelled like the one from sneakers, but it's close enough to make you think. And then HSBC coming out with a discussion about how Quantum actually helped boost their trading algorithms, which of course had all the WA the tongues on Wall Street wagging as soon as possible.

And then at the end of October we heard from IIBM and A MD because they were using traditional X 86 based architecture computing to be able to run error correction algorithms against quantum computers. The reason that that's important is because instead of tying up those quantum computers being able to correct their own errors, we can use things that we've already built and deployed to kind of accelerate that. And that is a huge cost savings when you consider how much per watt or per second that these things cost to operate.

Then the US government announced that millions of dollars in investment were gonna be sent to startups in the quantum space through the CHIPS Act, which was kind of bandied back and forth quite a bit over the year, and it was good to kind of see the government stepping up and saying, we want to use this CHIPS act, uh, funding to kind of invest in some of these places. Now, the future of quantum computing looks very bright to people who want to play the long game. I know that AI is getting a lot of these headlines right now, as Al has pointed out in the last couple of stories, that there's a lot of people who are wanting to pour as much money as they can into ai, but no matter what, you've gotta play a longer game because there's still some physics things to overcome here, and there's a lot of other things that need to be considered when you want to do quantum.

I don't care how much quantum foam is being churned up right now. I don't think we're seeing the bubble of quantum being inflated just yet. One of the things that we wanted to get back to, of course, is some more traditional stuff that we, we see and hear a lot, and that's our good old friend in the last hype cycle, cloud computing.

And Al that's your area of expertise. What stood out to you about cloud this year? You know, cloud's full of ai and so there's a whole bunch of AI stories that I'm not gonna rehash here, but all of those AI stories were about cloud.

I liked a story we covered in March where a survey of companies, uh, showed that they're using a lot of cloud financial operations or fops tools and identifying which parts of their estate might be better on-premises than on cloud, as well as optimizing their spend on cloud. I think it's interesting to see that maturity level coming through with applications being repatriated to on-premises, where that predictable costs might align with predictable workloads and clouds being used more as places to put things that are more bursty, more inclined to go up and down in their utilization, or that are more commonly accessed over the internet alone. Uh, oh.

I managed to slip some AI in here 'cause Google announced a whole bunch of AI capabilities at Google Cloud next, along with some nice application platform enhancements for its customers and staying with Google in July. Meta committed to spend $10 billion over six years buying capacity from the Google Cloud. Um, that's interesting in light of all of the other spend on Google Cloud for other purposes.

In the earlier stories, also, in September, a US judge put an into the antitrust case and allowed Google to keep the ownership of the Chrome browser. The browser wars were over years ago, but the lawyers disagreed. One of the most surprising stories, it's a cold day in a typically hot place, uh, in December when AWS and Google announced that they'd built unified software defined network into connects between their competing clouds.

That's a huge win for customers who have ended up with the reality of a multi-cloud and maybe hybrid multi-cloud estate. Uh, now Google and and AWS allow you to glue their clouds together without a huge amount of manual effort. I really do hope we see more cloud providers joining the scheme and that we get a much more unified way of dealing with the hybrid multi-cloud mess that is enterprise it.

You know, there's lots going on in the cloud. We particularly saw a a lot of stories around application building, Kubernetes and all of the tooling that you need to get value out of the cloud over the year. Another thing that's happened over the year is, of course, mergers, acquisitions, purchases.

Uh, we always get interested in who's buying what and from whom. And Tom, you've not had the opportunity to buy a large tech company or sell a large tech company for an exit, have you? Hey, you know, the year is still young.

You know, we, we talk about these, uh, over the, uh, course of 2025. One of the things that I do wanna remind everybody is, is we, we focus on enterprise tech, right? So we're not talking about media companies buying each other.

We're not talking about bidding wars for content libraries, that kind of stuff. We focus on the things that are happening kind of behind the scenes. I think maybe the biggest acquisition this year in enterprise tech had to be Google buying Security Company Wizz back in March for $32 billion.

And it was big not only because of the number attached to it, but also it took a lot of time to make this happen. Analysts were hot for it, and then we heard it might not happen then we're back on it again. This back and forth.

Will they, won't they shipping thing that happened? Not a fan, because it just kind of messes up our rundown stories where one week we're like, oh, they're buying it and then the next week, no, they're not. And then two weeks later, it turns out they are.

So, I, I don't know what to say about that. Uh, security acquisitions continue to be big. We saw Palo Alto Networks picking up protect AI and cyber art to kind of fill out their portfolio.

The data protection market continues to get really interesting. Commvault bought si, Satori Cyber. Veeam also bought a company called Security.

I thought that was kind of neat. Uh, cloud Security group acquired Arc Terra, which you may not know of, but you may have heard the company that they were before. That was Veritas.

Networking was no slouch as well. We saw that Nokia purchased infinera and some of our friends over inventive were picked up by Hubble. Uh, it's maybe not a company that you've heard of, but you've probably heard of one of the other brands that they own a celltech.

So kind of some, uh, consolidation of the antenna market over there. Arista bought VeloCloud to bolster their SD WAN offerings. There was another one of those.

Hey, we're hearing rumors that this might happen, and it took a couple of weeks for it to kind of come to fruition. I, I loved being on a call with some of my Arista friends and kind of asking them casually, jokingly, and they're like, we don't know what you're talking about. Uh, network providers also kind of reduced some competition in the market.

Uh, ISP Cox Communications got bought out by Charter. That's kind of primarily focused in the residential areas. So most of you're probably at home watching us on a link provided by them.

At and t also picked up CenturyLink, so that's kind of more of the, the bigger provider side. And of course it wouldn't be a year without private equity going out and buying some folks. Uh, they bought SolarWinds, they also bought Scale computing and there were several others that got, uh, snapped up and, you know, we're still kind of waiting to see where that comes out.

And everyone's favorite bigs uh, private equity firm. SoftBank bought into Ampere because they wanted to invest in the coming arm data center market AI was very active. Al already kind of mentioned that Open AI bought Johnny i's IO device.

And we also saw some acquisition from, uh, core Weave. They bought Core Scientific and Marmo. Qualcomm wanted to get involved not only in AI but in a, in chips.

And so they bought a company called Alpha Wave. You can tell based on all of this, there's a lot of investor money that's floating around and it is aimed at making some strategic advances in the enterprise technology market. There are a lot of large companies that feel like they're falling behind in some of these areas.

And there's a lot of small startups that are usually founded by people that used to work at those large companies who are aiming to address some very specific pieces. And then usually what happens is, is that those large companies use their war chest or their investment money to go out and buy those companies and continue to integrate them in. But the good news for us is that that tends to create these nice golden colored parachutes for people to go back out into the startup market and kind of attack those areas where they feel there's a gap that can be done.

And this cycle repeats itself over and over again. As long as there are people that are willing to invest in small companies to solve big problems, there are big companies willing to buy small companies to solve those big problems. And the nice thing about that for us is that it makes great fodder for rundown news stories.

Al it's been a really interesting year of, uh, being able to cover the news, uh, being able to see what's been going on. But it's also been a big year for Tech Field Day because when we're not waxing intellectual about the news and things that are going on, we're talking to a lot of those companies who are innovating, who are, um, investing in these markets. What was one big highlight from Tech Field Day that you saw this year that really kind of said out loud to the people, this is something we need to follow?

I think it follows on from my very first story where the beginning of the year companies were struggling to get their arms around what it meant to build AI into their applications. And that progressively as we've come through the year through AI infrastructure field days as well as AI field days, we've seen a lot more reality of actually what does it look like to get value out of ai? What do I need to build for it?

How can I make it easier to build out and get something, some value out of ai? So I'm hoping that that means we're moving beyond straight up hype and and billion dollar funding circles towards actually delivering business value. Tom, I'm sure you've had a different impression because your events, of course are in the security, networking and mobility spaces.

I think for me, security was probably the one that stood out the most is that people are really starting to take it seriously and they're starting to understand the security is an aspect of everything that they do. And we are having the conversations that we need to be having about securing things like agents and, uh, model context protocol servers. But we're also looking at using AI enhancements to make security run better, which is probably a good thing because we're seeing AI being used to create better, faster, more effective attacks.

And one of the things that kind of stood out to me this year was the fact that we relaunched the Security Boulevard podcast myself, along with Alan Shimmel and Fernando Montenegro and Mitch Ashley get to spend a few minutes each week kind of talking about some of the big picture ideas in security. And it never fails to amaze me how intelligent and how uh, learned people in the security space are and the way that they have a perspective that kind of changes the way that you could potentially look at these things is something that I've taken away, especially in this latter half of the year as kind of refocusing what I want to do in 2026. Speaking of which, Al you are the first one up on deck for 2026 when it comes to Tech Field day events.

I'm, I have AI Infrastructure Field Day coming up and the, uh, the last week of January. And, uh, looking forward to having a, a really good crowd of, uh, delegates. I have got, most of my delegates are up on the website at the moment.

Uh, this is gonna be quite network heavy. So Tom, you might wanna tune in for some of these presentations as well. We have a collection of interesting networking companies as well as some, some a little more in the, the storage side of the infrastructure.

Uh, it's, that's gonna be a really fun event. It's gonna be a pretty packed event as well. What is on the Tech Field day website is Cloud Field Day 25.

And so that's my, uh, trip in March to come out to Silicon Valley and spend some time looking at all things cloud. I want us starting to build out with my, uh, collection of wonderful people who will be there with me, both the presenting companies as well as my delegates. Uh, I'm gonna be back in April for Networking Field day.

And just so everybody knows, this is the 40th edition of Networking Field Day. Um, I am very happy to be bringing you something. We're probably gonna have to start calling Xcel 'cause we're gonna, we're gonna start naming a number of them like Super Bowls.

Uh, but this is shaping up to be another really good event. A as al kind of mentioned, you know, he's kicking off in the beginning of the year talking about some AI infrastructure and a lot of those companies are gonna be coming back just three months later to be talking about what they've been doing to a networking focused audience. And we've got some big stuff happening there.

com to find out more Right after that. We've got AI Active Field Day. I mean, it's Active Field Day, but you have to put AI on everything.

So I'm expecting to have a whole lot of interesting coverage of both the tools you use to build AI applications. Um, that's how you're getting value out of all that AI spend, but also the AI tools that help you build line of business applications. Uh, it should be a really fun event and, uh, that we've got a, a whole collection of very different companies that we're lining up for that one.

Absolutely. And then of course, I'm gonna be back, uh, towards the middle of the year with two really great events that I love. Security Field Day and Mobility Field Day.

com for more details, but one thing I do wanna call out Mobility Field Day is consistently one of our most popular events. How popular is that? Well, we're six months away and it's already half full.

That's how popular it is. Everybody wants to be a part of this event and we want you to be a part of it too. com and click on the link for delegates.

And when you do that, you can fill out a short form and you will go to the inbox so that Al and I can kind of check out, uh, what you're about and maybe invite you to a future field event. You can even nominate other people. And we'd love for you to do that because the best recommendation that we get is word of mouth from the people in the community.

Um, just give 'em a heads up that you nominated them because if they get a random phone call from us, they may not know what's up and, you know, maybe we, we wanna help 'em out. One of the things that happens quite a bit on the rundown is that when we are out at a field day event, we're pretty busy hosting things, which means that a lot of times we have to call on some of our amazing delegates and community members to be co-hosts for the event. And we wanna take a special moment to kind of shout them out for all the help that they've put in this year.

Of course, you know, Steven FoST is, uh, one of our favorite co-hosts and he has definitely jumped in to help out with rundown recordings and we love having him on, uh, when time permits and, and we're definitely gonna have him back on in 2026. But I wanna say a special thank you to folks like Jim Rinky, Keith Townsend, Kates Scarsella, Scott Roon, Ned Bevan, Chris Grundman, Jeffrey Powers, Gina Rosenthal, Brad Gregory, Corey Rockney, Romeo Gardner, Ron Westfall for Stepping Into the Chair, uh, learning a little bit about how we do things around here, bringing some snark to the news stories and, and overall elevating the experience. We can't thank you enough for all the time and effort that you put in to making the rundown a huge success.

But once again, I also need to shout out our third hidden co-host for all of the rundown events that we do. And that of course is Corey Derrig. You can't see Corey right now because he's hiding in the background, making sure that our levels are right and that we're making edits to all the little flubs that we do.

But we really can't do this without Corey. We know we've tried a couple of times and he's way better at it than we are. Uh, so when you leave a comment on our, uh, uh, our episodes, when you, uh, let us know the things you liked, you are really thanking Corey at the same time.

And we can't thank you enough. Corey, thanks for putting up with us, uh, getting stories in at the last minute, rearranging things on the fly. Um, thanks for helping line up new co-hosts and everything like that.

Uh, the unsung heroes are usually the ones that need the most applause. Well, I'd also like to thank every single one of you who has been listening, who has been watching as we've gone through this year of the rundown. Uh, for many of you, you know that this is my first year as being one of the two primary hosts on this.

And it has been a pleasure bringing the news to you every week. And, uh, I hope to continue bringing the news to you every week through next year. Do follow us, uh, on your favorite social media, but also subscribe in your favorite podcast application on YouTube.

While we're talking about the awesome things that Corey does, there's a whole collection more podcast that Corey is producing for Tech Field Day and for the wider Futurum group. Uh, I'll make sure that Corey lets you know where you can find all of those and subscribe to those podcasts as well as this one. And wishing you and yours from myself, from Tom Hollingsworth and for the, from the entire team at Tech Field Day and Rum, a fabulous week, fabulous Christmas, a great new Year.

We will see you in January. Hey everyone, nothing witty today. Merry Christmas, happy holidays.

This is our wrap up. You're watching the gang. Merry Christmas everyone.

It's Alan Shimmel for Text on Gang. I have to apologize, I did not bring my my center outfit or anything else, or even Harry Hanukah or anyone. But, um, we're here for our end of year special.

We're gonna do things a little differently today here on Textron Gang. We're gonna have one sec, well, not two different 'cause we'll still have three segments. One segment's going to be dedicated to it, stories for the year, and we have a great IT panel.

One segment's gonna be dedicated to cybersecurity stories for the year, and we have a killer cybersecurity panel. And then one segment's going to be dedicated to ai and that's gonna be a free for all. 'cause that's pretty much what AI is these days.

So, um, we, we haven't rehearsed this. It's kind of an improv theater that you're gonna see. We hope it'll work.

But let me introduce you to our first panel, which is the IT panel. Joining us is my friend JP Morgenthal, also a very good friend, Tracy Reagan and our friend Jack Gold. And joining Tracy, Jack and jp, I have the, what we call the core four, not quite Derek Jeter, Mario Rivera, core four, but John Swartz, Mike Ard, Mitch Ashley, and myself, gang members.

Welcome Mike. It's 2025, certainly been a year and it has had its share. What do you got?

Well, I think if you look back on it, you know, there's two obvious stories. One is, you know, all things about ai and we'll get to that in a, in a little while. But the other story seems to have been a lot of focus on cost optimization.

I think a lot of people are struggling with, uh, different economies. I mean, some people say there's a K economy out there and it's splitting the marketplace in half. And, um, I think a lot of it people regardless are under more pressure this year, past year and probably going into the next year just to make things more efficient and reduce costs.

But Jack, what are you seeing here? I mean, are you catching a different vibe from it lately? No, I, I think you're, you're spot on, Mike, and I think there's a, a k curve in the, the IT industry as well right now.

Um, the, uh, take curve for me is the, the cloud-based environment is still hiring. They're still looking for people, they're still looking for specialists, the traditional it, you know, the, the, the, the guys and gals in the back room managing a server that's going down. Um, and I think you're gonna continue to see that.

Uh, some of that is cost related. Some of that is the assumption. I guess I'm getting ahead of the third panel here that I, AI is gonna take over some of those jobs.

But I think a lot of it is redirecting the, uh, budgets because number one, many companies aren't sure what's gonna happen over the next couple of years. And number two is what kind of people are we actually going to need in our environment, in our IT groups to manage where we're going in the next two years with Gentech AI coming on board and all kinds of other stuff, uh, cybersecurity issues, there's all kinds of things happening in it. So I think we're seeing a a, a real tendency for a lot of companies to just pull back and say, you know, let's, let's be really, really cautious here where you don't know where our investments need to go.

And, uh, I think that includes getting rid of people, unfortunately. Yeah. Jp, you seeing the same thing?

What do you see? Uh, unfortunately, uh, I am seeing the same thing that, uh, Jack is seeing and the, and, and it is very disconcerting that you have this new technology that is coming into its own and people are starting to make assumptions more than actual, uh, you know, experience with what it can do. So there's, I I think part of this is that there are assumptions about what AI will do and what it will fulfill based upon some experimentation.

Uh, but you know, it's like, um, it reminds me a little bit of the, uh, the drug industry, right? I mean, you're making an assumption that this drug is going to perform as expected over time, but you really need five, six years of experience of seeing the impact of it before you know that it's safe for humans. Um, well, the, our industry just feeds the drugs to the humans in hopes for the best.

So I do think there will be a falling out at some point, a house of cards that is kind of being built. Um, the interesting thing is, will AI be able to rapidly be able to hold the house up and put the jacks in place before the whole house falls down, which is, you know, uh, different than other house of card situations because you have this technology that can so quickly respond, uh, and mitigate, uh, mitigate issues. I mean, personally I have seen that you can point it at a problem and it actually will resolve the issue in in minutes.

Mm-hmm. So, Mitch, let me ask you this then. I mean, we've been talking about automation in it forever, and to be honest, I know everybody's a little concerned about their jobs and their roles, but I don't know, is it better to be in it now than any other time?

Because I'm not doing so much scut work and toil and maybe, you know, it's just a different mindset and a different way of thinking about what it means to be in it. I think the good thing about being in it is opportunities. The greatest ones are off, often caused when there's great disruption, budget disruption, ai, new technology, cloud, whatever it might be.

So automation is certainly one of it. And we're at this beginning, this transition phase into a hybrid, both procedural and also agent driven kinds of automation. And so though it's very early in that space, yeah, so we're, we're, we're still gonna be living with the Pearl and the, and the Python and whatever other types, script types of scripts that are, that are gonna be automating our processes for quite a while.

But I would think increasingly see opportunity there. I think the other thing that we see saw this year is not just disruption from ai, uh, access to markets, digital sovereignty, sovereign clouds, uh, what does that mean for our business? If you're an international customer, what does it mean for a US business in terms of access to their markets, et cetera.

So that causes a lot of disruption, but there's also opportunities. We see companies introducing new sovereign technologies and sovereign cloud. So even at a macro level with your inside it, I think the biggest disruption though, and I I think the biggest fallacy was, you know, we don't need any of these people because they're all gonna be replaced by ai.

And we seem to be creating a lot of tools for those people that we're, that we don't need. So we don't, we still need them now, at least for the time being, You know, as, as much as things change, they stay the same. Mm-hmm.

There's still some basics here, right? Mitch, you know, this, I, I've spoken with you about this, right? The CIO was always told that the IT team is here to serve their customer, and the customer is the other employees of the company.

The, the function of it is both development, software development, and, and today, you know, and I think this year, but it's been coming for years, the role of software development has become an outsized piece of the entire IT puzzle, right? 'cause we're all software companies. Mark Andreessen said, even though Satya now says we're now in intelligence engines.

So software development, meaning the platform we develop on, how we develop, what we develop, how we deploy, how we optimize, how we secure, how we, uh, observe feedback and reiterate, that has become, you know, the, the, the biggest chicken, the nest of it, right? By by huge. But there's still the other function of it, which is, you know, keeping the computers up and the help desk functions and, and those kinds of things that we don't think of necessarily as part of the software development job, right?

And, and I think in 2025, again, the software development piece of it, that's where the action's been in ai, right? And, and hence that's where a lot of our attention has been. Tracy, I know that's one of your favorite topics.

Well, I think what we have seen in this past year, if we really look back, is one of the largest transformations of operations that we've seen in a very, very long time. And it's moving very, very fast. Um, we are, we are having to, I mean, even, you know, even at the Linux Foundation, Linux is having to, to work to really keep up with the demand of the changes that people need to deliver new kinds of software, faster software, software that requires more processing.

And I feel like in terms of the job market, we have seen, I know many of the developers, some of the developers I should say on our open source community have moved from being developers. They have moved to being platform engineers because they're, and you know, the more I learn about platform engineering, the more I understand that part of it is because of this massive shift that we're seeing and the types of tools that we're using, how the operating system is changing. And we now need a way to do onboarding of new employees down at the developer level as quickly as possible, and be able to implement new tools as quickly as possible.

It is, it's moving very, very fast. I mean, think about the rise in MCP servers over the last year. It's been phenomenal.

But that means that those people on the operations side have to support a lot of new developers, new platforms, new stacks. And I think that this disruption has caused some of the, of hiring to be quite honest. But I also think that the platform engineering has made a big, big difference in how we, how agile we are and how we develop and what we're writing.

But I don't think that AI has reached its peak in any way, shape, or form. I think that we have spent the last year trying to figure out how to build these development environments so we can start thinking about how to implement technology using this new platform that we call ai. I think there's another piece that I've seen as well, you know, we talked about the, the K economy for it.

Uh, I'm seeing some companies at least move parts of their IT budgets away from people and into the cloud to AWS or to Azure, you know, Google Cloud. And so what that's forcing is companies to look at it, budgets are going up, but they're not going up by that much, right? They're going up by a couple of percentage points here or there.

And so if you're spending a lot more money with Google Cloud, where is that money gonna come from? Yeah. So I clarify that, clarify that for a second.

Do you mean managed services that are handled by those guys, or do you mean me putting my workload up there, but I still gotta manage it? Both. Okay.

Both. It, it really is both. Uh, it there, you know, there's a reason AWS and Azure and Google Cloud are making a ton of money, right?

Uh, those, and, and, and those budgets are not just coming from line of businesses, they're also coming from IT in, in many cases. So that's had an effect, I believe as well. It also has an effect in the sense that companies think that if AWS is managing all these clouds for me, why do I need all these plumbers, you know, in-house doing the plumbing when I can get it done outside?

So it is a, it's a really mixed bag as far as where budgets are, are being dispersed these days. And the, the, the hyperscalers has certainly had an effect on the hiring. I think there's two things that are true here that Alan's point.

One is, developers keep playing with new toys and then they go build stuff and they get bored with it, and then they dump it on IT people. That seems to be continuing to happen. And then, um, the second part of this though is I would also argue that maybe we're looking at the democratization of it, and as we do make these advances, there's whole companies in the mid-market and smaller organizations that don't get to take advantage of all these wonderful things for years.

And maybe we're gonna see these organizations implement more advanced it sooner faster. John, what do you think? So this is taking into account where you've all said is, it's interesting you imagine being inside a company, being a CIO or being in charge of the IT plan or the organization where you're being hit with wave upon wave of all these options.

You're trying to balance your, your budget. You're trying to think, figure out where the money's gonna go, what's gonna happen to your employees. I think it's gonna be head spinning.

I think it's gonna be a mad dash. And I think you will see a combination of people mass hirings and maybe a lot of people being transitioned out. And I, I see that, I think about Amazon, I think about how they're using AI and how they're implementing it in within their, IT and I, you just see this kind of chaos going on.

And I think Tracy had said, we're kind of just at the beginning and it's only gonna get faster, which presents a lot of problems. On, on the flip side is Mitch had mentioned disruption's a good thing in terms of ideas, in terms of opportunity. So it's, we're kind of at that, uh, assessment process.

And the assessment is overwhelming. You know, the other Thing, I think 2025 introduced is probably the greatest beginning of the greatest skills shift in our industry. Yes, every job is being not just affected by it, but the skills of people and their ability to use it.

Maybe create things with it, maybe operate it, maybe all of the above. That is part of that democratization too. But you look at the skills surveys, they've, they've started to shift into new categories around managing agents or prompting and how knowing how to, how to instruct and describe things in, in a way that AI can take on, or even looking at roles and jobs and processes and figuring out how to use and apply AI that's outside of it too, not just within it.

And Alan, the question I can't answer right now is if, you know, if your child comes home and says, you know, I want to go into it, do you tell 'em that's a good idea right now or not? It's funny you should mention that, right? I've, I've had this conversation not only with my own children, but friends' childrens who come to me because they say, oh yeah, go ask Alan.

He's in tech. Um, tech's not going away. It's not going away.

But the skills you're going to need and what the jobs are, are, as Mitchell said, absolutely changing. And, um, at the end of the day, you know, this whole AI thing is just, it's gonna affect every single job in terms of, it's not necessarily gonna replace or take every single job, every single, but if you are not an effective user of ai, someone else will be, and they'll take your job. And we've been saying that consistently throughout this year.

Right. Uh, another aspect I I'll bring out, you know, all of us here are no longer 21 or 22 is, and I, I spoke about this at the, at my shimmy says last week, is the torch being passed to a new generation? All of us have grown up with 20th century technology, right?

We, we've, we've had great careers. Everyone on this show that I'm looking at has had significant accomplishments in their careers. And we are doing our best to stay abreast.

We're swimming as fast as we can, right? To keep up with this AI thing. But I is it, is it it, you know, the boomers are on their way out.

The Gen Xs are in the back of the room already. Is it time for the millennials, the alphas, the baiters, whatever they're called, the Gen Zs? Is it their time to take this baton and run with it and and see where this goes?

I think they already are. Yeah, yeah, yeah. I mean, we, this iteration has happened generation to generation.

I think just in this example, it's just profoundly larger and faster. It's gonna infect more people. And, and it's again, going back to like the skills and, and what Mitch mentioned, that's why it's so hard to, to follow some of these surveys and, and adoption of certain AI in particular, is that we're at different stages for different skill sets and different development of skills depending on the job.

And so it's really hard to get a really hard grasp on it. But Is this bringing any different than when we've had transitions in the past? Yeah, maybe it's coming faster.

Maybe it's, it's more effect. Have a greater effect. But when, when, when we moved to PCs, I go, go back to many computers, right?

And from mainframes, even mainframes, it always transition, transition was always hard. I think the big issue is the big salary reset that's gonna occur with this. And I think that is gonna be the problem in the short term for many of these people that Alan's saying, we're passing the torch to, their expectations have been set by us, right?

Who have had, you know, looked at for the past, uh, 15, 20 years, you know, entry level jobs in it, of, you know, 50, $60,000 because it was a skill that required a significant amount of training and expertise in order to execute. And now with ai, the, the expectation is that you don't need so much training. You don't need so much experience.

You need how to know how to use ai. But given that the volume of people who will understand how to use AI will be immense, the salary expectations of starting, I think are going to drop significantly in it. And I think we're gonna be back to 25, $30,000 starting jobs again.

And that is gonna be a problem for those who are expecting higher and they're not gonna take the job. And we're gonna see this, this, um, valley, if you will, of where there's this challenge where I want more money. Sorry, we ain't paying you more money, uh, until it settles itself.

It's gonna create a very interesting dynamic in the industry. As long as, dare you said it, But we Have to stay frosty. Don't spray what everyone else is think.

And I, I wanna make sure That I get this in because I always like doing predictions as well. I predicted AI agents would suck. And I'm, I'm, I'm being proven to be right.

But if somebody was to ask me today, should we go, should I go into it? I would say, yes, it on orbit, go learn as much as you can about satellites. We are gonna be seeing a disruption in data centers in the next five years.

We could go from, we have like 11,000 satellites above us right now today. We could go up to a hundred thousand in five years. And what are those satellites gonna be doing?

They're gonna be our data centers. They're gonna be our data centers, and they require a whole new set of skills to manage those data centers. That's where we're headed.

It's really, really obvious to me when I look at what's happening in the satellite industry. That is where we're going. We are talking about satellites.

So the size of toasters, literally, we're going to space. So technology, hang on. 'cause we're gonna be blasted into space in no time.

And it's going to be a busy, busy, busy time with plenty of good salaries. It's not just no person has gone before. Guys, we, I, I gotta pull the plug on this one.

We're over 20 minutes. I got people in the green room it panel. This is all now recorded for, uh, pres not prosperity, nuts, prosperity can look at this.

But we're gonna take a quick break. We're gonna switch out some panels, and we're gonna come back and look at cyber. You're watching text on gang.

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So, as I said in the outset, you know, a little different with our, our Merry Christmas year end show. Our second segment today is cyber in 2025, and I couldn't think of three better cyber people to talk about it with. Uh, we've got, and two, Santa Claes and Chris Blas and Fred Wilmont, Fred Wilmont, and the woman behind the, or in front of the brick wall, Terry Robinson.

Um, of course joining Mike Mitchell. John and I thank you, my cyber experts. Um, you know, 2025, like everything else in tech cyber had itself a year.

We saw some amazing acquisitions for big, big money. We continue to see consolidation. What I found most interesting was after every year of hearing, where's the, the covets, you know what a vecher is?

Someone who vets the covets saying, where's the innovation in security? Where's the innovation? All of a sudden, I think we're seeing a little innovation.

It's AI fueled, maybe, but innovation, nevertheless, Mike throwing it to you, It's been an interesting year, but every year in security is an interesting year. But I feel like there's been a fundamental change. And it goes something like this in my mind.

It's, um, the pace of the battle is now being fought in real time in milliseconds. And it's changing the way the security people think about their jobs, their roles and, and maybe even the stress that goes with that. But since we have, uh, Terry here, I'm gonna go with ladies first.

But Terry, what's your thought here on, um, you know, is, is the mindset of the security team changing or has it changed? Or what does the psycho demographic look like to you? So, yeah, I mean, I, I've had this conversation a number of times with people, you know, recently.

It's like some of the issues are the same. Some of the, uh, uh, mitigations and remediations are the same. But the problem here, or not the problem, but how, what they're having to adjust to is the speed, right?

Of everything. I mean, it's, it's, uh, it's crazy. Now we're seeing exploitations, um, of vulnerabilities, what within hours, uh, of disclosure.

Um, that's gonna be minutes. Um, I think it, um, or security, it is like completely stressed over a lot of this. They just, they already were having problems reacting, right?

Or, or certainly not staying ahead of, of everything. But, um, I, I think that if you could get into the psyches of these people, you would, it would be scary. I mean, I really do.

And I, and honestly, I, I, not that I don't think that they'll adjust, I, I think they're gonna, maybe in 2026, I'm always hopeful about this, is that security will find a way to be more proactive. So they're not always, uh, caught reacting to what's going on in the marketplace. But, you know, you guys can tell me if that's just a pipe dream.

So Fred, we've established that cybersecurity people are scary. Um, but I guess the next question that I might come with Is, did you say that with these beautiful Santa Claus here Who's wearing the Santa Claus hat cyber guys? I, I needed To get a Santa Claus hat and I would've been more rosy, I guess.

But, but what are they gonna do to kind of succeed or change? Or is there a, you know, something that they should be doing? 'cause I, it feels like the old playbook just isn't gonna work.

So I can sit around and stress out about everything, and I need to figure out something to prioritize. So what is it? So there's three different, uh, forces at work here.

Uh, and I think I loved what Terry had to say about it, right? The velocity and veracity of attacks weeks to days to hours, right? The probably the biggest RC from 2025 was hours, uh, with React to Shell and the greater than 1% of the internet available for operations on that particular vulnerability.

But you have a couple of other interesting and contrasting, uh, forces at work ai for better or for worse, right? Uh, skilled talent shortage. But also there is something that I think there's a, I don't wanna call it a malaise or a RIE that's happening within the industry, uh, but it's within the business, which is to understand the actual risk of what's happening and to treat that risk as a business risk and cyber insurance and the things that go with it.

There is no doubt at all that the number of, uh, CEEs this year is greater than it's ever been. The number of exploited rcs is greater than it's ever been. The number of technologies that have been exploited, that our security technologies is greater than it's ever been.

But there are an awful lot of really talented cybersecurity people that are outta work. There are an awful lot of folks that are struggling to implement what everybody's invested, all the financial wherewithal from the venture capital community to make work in the industry. And that hasn't been a wider gap that I've seen in the last 10 years.

Good Point. Chris, what, what, what do you think you've established yourself as the, you know, cybersecurity optimist of 2025? Yeah, We gotta, we gotta understand what story we're part of, right?

So lemme take you back to the winter of 1992, right? And you find a young crisp, uh, Ebenezer Blak, uh, looking at the firewall market where there's a hundred firewalls and start on the planet that typically seven computers a million dollars a year. It was the, it was the application proxy, uh, uh, era.

And I had an idea that we'd have a firewall we'd sell thousands of, but we do that. We didn't intercept every packet. And by nine, the winter of 1998, you find me in, uh, uh, an aged, uh, uh, Chris in Utah taking over the, the Cisco having just taken over the Cisco firewalls, which are not proxies.

We're at this point where we've blown past that paradigm, it's not gonna work anymore. And now, you know, here we are, you know, the Christmas present back in Toronto again, in a world where, you know, our, our systems are breaking because they don't know. There, there are people here.

We see this everywhere. And security is just a good example. Compliance, ask any CISO from the beginning of this year to this year, it's just breaking everywhere.

Why? Because the systems don't know that Bob and Mrs. Raett are there actually trying to use the system.

Literally, our systems have no idea we exist. And on every level they're breaking, right? So the Christmas futures, you know, I think the, the moral of the story, we know pretty well our systems will come to recognize that there are people here and that changes everything.

So I think that's that little moral arc. We are part of that story and everything. You know, Terry and Fred, you just said, we're driving to this.

We've hit this point where it takes a change of mind, and I think we know what it looks like. All Right, Mitch, the Christmas Carol kind of opens up with Jacob Marley and he's visiting Ebenezer Scrooge, and he's talking about how I wore these chains in life. And so I'm now wearing them through eternity.

And sometimes I think, you know, cybersecurity feels like that, and maybe Jacob Marley's the patron saint of cybersecurity, I don't know. But how do we break the cycle? You know, it, it is a perfect metaphor and here's why.

So if we ever thought that the idea that technology could be held back until we were able to secure it before we deploy it, that has been blasted out of the universe. You know, if you wanted an example of how badly will we rush into the next technology AI without worrying about security, we'll rush. And so it changes the entire psyche, I think for security, for security people, for the mindset.

Yes, you still have to be the, you know, be the folks that really are looking with great scrutiny and, you know, prove it to me. Let me make sure that this really works, and how do we protect from the bad guys? But I think the big change is, you know what, we are on a new cycle and I think it's gonna continue.

Whereas it's not about wait till we decide what we wanna do, and then we'll figure out, come to you and figure out how to secure it. You better be on the AI train. You better be on the data center and satellites in, in space to Tracy's conversation in the last segment.

Um, you need to be on where technology is heading and already working, thinking about, all right, if that takes off, what are we gonna do? How, what's our story? What's our strategy?

Or maybe there's an innovation that we're gonna help create to make that happen. So the days of sitting, sitting back and saying, wait for people to come for permission to do things, if there ever really was that, maybe that was back in the 92 era there, Chris. Um, you know, the, the Christmas future is we're all flying and we have to move together.

Move very quickly. So when you think about, I was gonna ask you, so when you think about this, looking forward a little bit. So we have this recurring theme of cybersecurity being this, this evolutionary nature versus AI's revolutionary pace, for lack of a better terms.

Does this, and I don't wanna be a doomsdays sayer, but does this inevitably le lead to a cataclysmic event that finally forces even cybersecurity to change its ways? And maybe does this lead to like a rash of acquisitions by companies hyperscalers who wanna protect themselves and their customers? I'm just, it just seems inevitable this is happening, gonna happen.

Like The cyber nine 11, is that what we're Yeah, Yeah. Maybe it's, it's a good question though, to the point where does cyber becomes so infused into the platforms and everything else, that it kind of just becomes a feature and it's, yes, it's not its own independent category. Alan, what do you think?

Ba hum. I'll, I'll wait for that. Yeah, John, right.

You know, that inevitably is there, right? You know, so the systems have to get back to relating to humans, right? And it sounds kind of esoteric, but you know, as, as a longtime security practitioner with quantum computing coming, I actually do see an answer to that.

It's that, of course you have my information, of course you have my documents, you can't read them, you can't use them, you can read them. They're right there. You have no idea, because that's in the relationship.

That information is in relation to Terry and something Terry is doing with Fred. How do you interpret that? So I think there are ways out of this and, but they are, you know, much more human than the bits and bytes of cybersecurity for the last couple of decades.

But they're implemented in that same, uh, infrastructure and it's seems to be showing signs of working these days. So there is hope tiny Tim. Well, and I think Harken back to Chris, what Fred said maybe too, is, um, you know, the whole idea of, of the business proposition, right?

And, um, risk, and maybe that's, you know, what, uh, defenders or organizations, uh, focus on because there's just way too much for them to do, right? When you get down to it. And maybe, um, everybody that I talk to again talks about how we gotta know what's important to us.

It's an individual decision organization to organization, you know, what you, uh, protect and maybe you're protecting the same thing throughout the decades, you know, just maybe writ large or whatever. But, um, uh, you find a different way to to to do it. Um, I, yeah, I know.

It's just, I, my my point I guess is also is that cybersecurity has gone through all these waves, so it's adapted and it's, it's yeah, persevered in many cases. And I'm just wondering though, given this kind of tsunami like wave coming with through ai, whether it's overwhelmed or, and it's, It'll still adapt. I, I think fundamentally though, here, here's, here's the deal.

Fundamentally, there is a, a schizophrenia in the goal of cybersecurity at, at, at some level. I don't know if it was Mike or, or, or John who said, you know, security goes away and becomes part of it, built into the fabric, if you will. I think if I asked Fred that, Fred would say, that's a worthy goal.

I'd love for that to happen, right? We, we need to be built in, not bolted on, we need to not be an afterthought, right? We don't need separate security.

We don't need the SEC and DevSecOps, it's just DevOps, right? That's one side of the face. The other side of the face is none of those people give a s**t about security as much as we do.

That's right. And we need, we need, we need to be here to make sure that this whole thing doesn't go to hell in a hand basket. And, and that right there is security personified.

Chris, you got your hand up. You know, in that, in that ghost of Christmas past narrative I gave you, you have to understand the mid nineties, how existential that was because the firewall was security and the whole concept that we're gonna accept the idea that people, some people will get through was like, apocalyptic, you can't imagine we will no longer have security. And then we got into it, network awareness, SIM, sim, threat intelligence.

My point is, we've gone through these evolutions multiple times. We're at this point where to this point, but John, you, you know, call up. We cannot.

So we will do something, you know, the lights will stay on. I, and you know, a lot of people tend to think that it comes down to humans, like relating to the, have the system relate to the human, because actually that gets exponentially harder to crack, easier to deal with smaller. So we're driving ourselves in an evolutionary, uh, again, to a crux where we have to do things differently.

We've done this before. I think this episode next year, we'll, we'll have some alignment and agreement on where that's going. I think it's about people.

Yeah. So Alan, to you, to your point and Terry's point about maybe a little more focusing the conversation on risk. You know, we've been talking about security, people learning how to talk to the business forever and a day now.

And I think maybe some more of them are figuring that, but at the end of the day, you talk to business people and everything they do has a risk. And when you go to business school, it's really about being taught how to manage risks. So from their perspective, security is just one more risk of many.

So are we finally getting to the point where maybe the security people realize that, you know, this risk issue is what the business people care about because they just wanna know, like, yeah, I know it's insecure, but I wanna know like maybe how insecure and how much money I'm gonna make regardless. Fred, how long have you in security, uh, for 25 years, 20 plus. Have you been hearing this same argument for 25 plus years?

Absolutely. Chris, you, Oh yeah. We're all gonna die any second now.

Yep. Yeah. Same, same stuff.

Different day. I'm gonna age out of that, that prediction, by the way. We, we are, we're all still here, right?

And this, and again, you know, the, the, it's interesting. I know we're going into AI in a second, but again, these are semantic systems. We're literally talking to 'em and trying to see what makes sense, which we're literally doing with each other right here.

And, you know, we'll either navigate into a, a Christmas future where we get to keep doing these things or we're not. And that binary just continues to drive the fact that we'll come up with solutions. We Always are.

I mean, Mike, to, to a, a good answer to your question. That's why we have CISOs. The CISO is supposed to be the universal translator, translating security to business for the decision makers.

They've gotten better at it. CISOs have become, uh, more widespread and better understood. I also think that they are like, uh, like uh, building department employees at the New York City building department.

You slip 'em at 20 and you get anything you want, but there you go. I don't know who even tilted there. Was that the CISOs or the building department people?

No, you take your pick. It depends what Nike you coming at it from. Yeah.

Boat. That was, that was a grabber on the very unique New York thing. Yeah, that was a New York thing.

But let, let, let us, let us take a break here 'cause we're over time. We're gonna come back and do our grand panel on ai. What else should we do at Grand Panel Launch?

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You know, we saved, I don't know if it's the best, but we saved the biggest for last. And this is, uh, our mega panel as we're calling it, talking about what else? Ai, because as I've said before, and I, I'll say it again, 2025 will go down as the year of ai, right?

Where, where we, we moved into this new journey. Um, for me, it is, it's the start of the 21st century. I think up to this time we plagued with a lot of 20th century technologies, the internet, cell phones, the digital revolution.

I think when we look back, this will be the start of the 21st century. And we'll have robotics or physical AI and, and, and, uh, quantum and all these things. It's gonna be a very different world 30 to 40 years from now.

Much as World War I was kind of really the beginning of the 20th century, right? That was the end of the age of empires and modern nation states and everything else that flowed from it. So, as we sit here at the end of this first year of the rest of the century, Mike talk, quantify this.

I look at it a little more concrete maybe. I think if I look back at the year of 2025, it's the year that everybody figured out that the AI industry is having trouble parsing the word is in much the way Bill Clinton did. And, and the issue is they keep saying in the present tense that we can do all these wondrous things.

And then you get into it and you find out, well, there's a big gap between what they say we can do with these things, and maybe someday we will, and what we can actually do with them today. So, Tracy, I mean, you're up to your eyebrows and AI stuff this past year, but what's your assessment of what's real, what's not? That's a good question, right?

Well, I can say what's real for me. Um, I have learned a lot about AI in the last, uh, 12 months. I have become far more productive, what, in terms of what I do using ai.

Uh, but I still, I feel like it's, we're still pretty basic, right? Um, sure it can create a p it can rewrite a paragraph for me and make it sound a lot better. Yes.

Are we generating more code? We are definitely generating more code, but we still have to walk through it carefully and verify almost every single line to make sure it's doing what we want it to do. And it doesn't have any bad things in it.

And when I look at other countries and, and how they're starting to use ai, if we look at China, they're delivering ai. And I, I complained about this from the, from the very beginning. AI needs to help people's lives make their lives better.

We're seeing a lot of com. The AI in the US is very focused on businesses and not so much your house. And that bothers me.

I still wanna see more AI making people's lives better, more in medicine, and I'm sure it's out there more in banking and help me do things that I need help to do other than just write a paragraph or write some code. And maybe that's because that's how I'm seeing it. So I think we have a lot of room to grow in ai.

I think we're just learning about ai. Uh, and agents still suck, guys, and we have a lot of work to do in cyber and ai. There is so much to do in ai.

We are just scratching the surface. We, we haven't even begun really to, to do development in ai. And I really do think that we have to find more ways to use AI to make the lives of everyday people make their lives better.

It's not, you know, a lesson I learned from my friend Brad felt in technology is that the, the companies that win, the players that win, they win because they suck less. Jin PI know AI has been a big, a big part of your world this year. It has for sure.

Uh, you know, I have touched it, uh, and used the technology in many, many ways. I, I use it, uh, for, uh, research, for writing support. I have done software development with it.

I have done infrastructure management with it. I have built agents. I find, you know, uh, the technology is continually surprising me with answers.

Uh, the most surprising is how it, uh, it seemingly has a life of its own. It does not like to be told what to do, uh, much like a, uh, a child, uh, emerging through its teen years, uh, I've called it a teenager multiple times on so many different w levels. It, uh, you it, it tell, it comes back and tells you, I did what you asked.

You check it out. The job's half done. So it's a teenager in that way.

Uh, and it's a teenager in that, uh, it, it does, you tell it, this is what I want you to do, and it, it doesn't do what you ask. However, if you give it some general goal guidelines, like, this is kind of what I'd like to see done. It does amazing things.

And, and it will bedazzle you with its creativeness. So, um, to me that is the, uh, epitome of AI at the moment. Uh, uh, I would be very cautious about letting it run amuck and believing that, uh, it, it, it's answers are, uh, pristine and, and accurate.

Uh, however, uh, I think as a tool that is watched, uh, it, it has produced some incredible, uh, things that, let, let's face it, it's corpus of knowledge is immense, much more than any human could ever maintain in their head. And on top of that, they have the ability to find patterns between the data in its corpus that most humans would miss. That is its value right now.

Right? John, do you think that the AI vendors just completely miss the boat? Because, and I'm asking the question because, you know, instead of focusing on here was how we're gonna make your job and your life better, they just went out and started banging the drum in Wall Street and said, you wanna eliminate your job.

And it's like, well, if you eliminate my job, I don't have a life. So I generally don't. Well, they got, they, they got ahead of themselves because they saw the opportunities.

They, they're looking down the road at five to 10 years, this like Mark Benioff and Jensen Wong, do. They, they think about life three years from now and how it benefits them. And first and foremost, what they did, the first thing they talked about were these digital workforces of millions of people.

And my first reaction is, what does that mean to the people who have the jobs already? And we are seeing to a small extent what it meant to some of 'em. It was Salesforce, several thousand people lost their jobs.

Um, it's, it's interesting they got ahead of themselves because they pronounced this was gonna be the year of AI agents, which as Tracy pointed out, and I think Gina pointed out in a show last week, is a gross silver exaggeration. Um, they got ahead of themselves. They didn't lay it out.

They, instead of kind of giving it to us peace meal wise that they did, is they gave us a grand vision of what they think it will be and where it will be and how it will benefit them financially. And they kind of lost their viewpoint. And I think they scared unintentionally.

I don't think it was intentional, unintentionally. They terrified a number of people and sent them scrambling and created confusion, which actually hurts them in the short term. So yes, they got ahead of themselves like they always do.

Alan, is this a lie that the bubble's built on, or what? No, the lie that the bubble's built on is that we are going to be able to spend all that money, build all these things, power them, cool them, and then charge for them at a, a rate reasonable enough to return a profit, right? com bubble was built on, right?

It cost me a dollar. I sell it for 80 cents. Don't worry, I'm gonna make it up in volume.

Right? No, you still lost 20 cents every time you sold it. You just sold a lot more.

So you lost a lot more. 20 cents. It's, it's the same, it's the same thing here with ai, right?

When, when the, unless something fundamentally changes the, the power consumption, the cost of doing these computations, it, it's just, that's, that's the bubble. That's the mismatch. It's actually worse.

Technology's terrible. It's great. Sorry, go ahead.

It's actually worse than that. I mean, various statistics have shown, you know, various research has shown that 80, 85, 90%, um, AI projects in companies don't produce an ROI. If that's the case, and we're talking about basically AI is lipstick on a pig.

I mean, if you can't generate money for me, why would I pay you to give me those tools that aren't doing anything for me? And, and so it's, there's a fundamental disconnect between not that AI doesn't have a future, please don't get me wrong, but there's a fundamental disconnect between what people are promising to deliver today and what's actually producing for me. And if you can't fix that problem, aside from the fact that you're losing 20 cents every time I use your, your AI tool, it's, you're never gonna make any money, right?

We're, we're insist, and that has skill wrap. I'm sorry. Guess That has a lot to do with that.

Has a lot to do with how we've implemented ai. As to Alan's point, we have, uh, we need, everybody needs to take a breath step back and, and look to see how we can fix some of the problems, right? I, that's why I keep talking about small language models.

We need domain expertise. Maybe we should have a, a system that has a way to have a cons, a consensus between three expert domains, right? So the, the large language model was a good way to get it out the door, but it's not a good way to go forward.

And I'm gonna continue saying that over and over and over. And we have a problem with agents. We've gotta fix these.

This is what's costing us. And like I said in the first segment, look up to the stars, because that's where we're gonna have our AI data centers to address some of the costs. You know, I don't think our problem is ai.

I think our problem is we have seven, eight, whatever number tech executives all trying to be Steve Jobs all at once. And they all suck at it. They're not Steve Jobs, which is really Not an overriding problem for sure.

That's been an overriding problem. Yeah. But even before this, yeah, past.

And you can, you, you can have your complaints with Steve Jobs too, but a lot of those, these predictions and where they say we're going, that's about setting the vision and setting the quote unquote strategy for where they're taking their companies that's setting stock prices as well as day-to-day performance, right? They're trying to build the roadmap of how convincing investors that they're gonna continue making them money. Um, And if you bet on a limited amount of funding, you're never gonna get good product.

Yeah. Yeah. It, it's, it's a different world.

It's not AI's the problem. I think our, our our mouthpieces are the, are are part of the I too. I too, you know, it Would've been interesting to see.

I would, I would like to argue the other side of the point here though, and I'm gonna call on JP for this, right? 'cause there are people who are using AI to do amazing things, and they have a lot of skills and a lot of expertise in that space. And you could argue there might be even a, an AI divide starting to occur because there are people who have the skills to really maximize it.

But for the average person, it's not quite living up on the promise. So jp, are we on some sort of timeline curve here in terms of who's using AI to the most benefit and what we might see in the coming year? I, I think that a lot of people are, Uh, overwhelmed by the information that's coming out from, uh, the vendors in the industry as a whole.

I think there are people who are, have grabbed a hold of this thing and do a hell of a good job on marketing. I if you're, if you're the type of person who watches reels on Facebook or Instagram, or even LinkedIn, you know, you're led to believe, uh, that these people have created these apps that can do amazing things with ai. And so you start looking into and being able to understand what's really under the hood.

I people call their, their application agent-based applications. And then, you know, I, I wrote a, a piece on this, uh, uh, I believe it's a blog or a LinkedIn article, uh, about the AI whitewash, right? Hey, it's fine to say that your application is an AI application, that it uses AI to perform some of those functions.

But calling it an, an agent based AI, when it doesn't have any comprehension of what an agent is, and that an agent is this autonomous thing, and there's no autonomy to these things that they built, it's, you know, the lack of separation causes confusion among people who are trying to understand what is this thing and what can it do for me. So we're in that age of, you know, um, parkers and, uh, you know, uh, used car salespeople, uh, hawking at you, oh, buy my wares, byebye wares, uh, eh, this is the greatest thing since sliced bread. It'll heal you.

Um, you know, also, although, you know, can I mention something really quickly? Can I, I'm sorry. Tr can I, so that's funny when you said JP, because, you know, some people accuse Steve Jobs of being a PT Barnum, but I think he would've been the right person at the right time to explain to us where, why AI should be embraced bias, and he would do a much better selling job than these other folks.

Sorry, Tracy. I think he would've let some Of these AI agents out the door though. 'cause he was kind of finicky about making things actually work.

Yeah. Yes, exactly. Wait, We got Tracy and Terry both had their hands up.

Tracy, go ahead. Yeah, I And Terry, to JP point, we've, this is how we've always seen it. How long did we have tools that had SQL in the name?

And then we did the smart thing, and we did a lot of I things for a lot for a very long time. So when we stopped putting AI in the name, we know we've arrived, Terry. So, well, mine was just an actual praise the Lord from, uh, jps.

Um, Oh, okay. You know, he'll. But I do have something to say about ai be, you know, before we get to the end of this, uh, and an agent, and I'm gonna go back to the, um, the teenager analogy, and that's where I, I feel like we are with AI agents and I mean, I'm talking about the true things.

We don't, you know, uh, ask any parent who's had a teenager, we, we often don't know what, where they are and what they're doing, what they're saying and what they're activating, right? And this comes from a little bit of a helicopter mom. So, um, that's a confession.

But I think that, um, that's one of the problems that we're gonna see going forward. How do you know what these things are doing and who they're doing with, and two, and that, yeah, that's better. We've actually gotten better.

So that's my jam. We've actually gotten better at that. The providers of the large language models have gotten much better at that.

I, I, I think it was Tracy, you or, or Harry, I, I, one of you said earlier about, can, you know, c committing to small language models, and that's something that I've been predicting for, you know, is needed for years. We need to get away. You know, we, we, it, it's, it's gotta become a hybrid model where your business is captured in the model, uh, and it's local to you, and it's a small language model.

And then when you need to get additional inputs into that model from the larger world, from the outside world, that's when you go to the large language models, uh, and you grant from the corpus. And, and we need this, you know, tiered, uh, model, and everybody just immediately goes out to the large language models. I think it is a tiered model.

I think you need to build and host your own model that understands you first, and then let that be influenced by additional data that you can then bring in on demand from the larger corpus. I think that's the right approach to using these things. So what I heard JP say, and everybody else kind of echoing a little bit, but is this kind of like the, maybe even the Stanley Steamer era of ai and we're working on an engine next, or, you know, is this kind of where we are?

Jack? Kind of, kind of wrap it Up for us. Yeah, I think it is.

Uh, you know, the, the notion that JP had of, and, and Tracy had as well, of building smaller models that are suited to what you need, makes a whole lot of sense. The challenge is, and I've seen this with many companies, the challenge is that I can't get my data together to build those models. I've got data, it's silos, it's scattered all over the place.

I don't know who owns it. Some of it's on pc, some of it's in the cloud. And so, yeah, it makes a lot of sense to build models that are very dedicated to what I need.

Until we get to a point where people are able to actually gather their data together and then build a custom model for my particular needs, we're gonna be stuck with these LLMs there. There's, that's the only thing many companies have. So there's a lot of maturing that really needs to take place in this marketplace.

It's going to take years. You know, people think AI is going to be here tomorrow. It's not, agents aren't gonna be here tomorrow.

That's not gonna happen. Good agents at least. And so I think ultimately AI is gonna be huge, but we're rushing, you know, we're rushing off a cliff if we keep moving in this direction.

All right folks, I'm gonna wrap it up there. I think if I look at 2025, it was the year we recognize the potential of ai, that's not nearly the same thing as realizing it. And so hopefully this is the worst AI that you'll ever gonna see is the one you got today.

Hey everybody, thanks for sharing your thoughts, not only today, but all year long. I look forward to seeing you all in the new year, and same to everybody who's watching this show. And please stay tuned for the rest of the texture on that TV lineup.

But once again, I hope you guys had a great year. Stay safe, have a great holiday, and we'll see you in 2026. Hey everyone, welcome back here to Tech Drunk tv.

I'm happy to invite you or introduce you to my next guest from Neo four J and know it's not Steven. Um, Steven Chin, of course is with Neil four J. We've had the pleasure of interviewing Steven all the time, but sometimes you wanna get a different view.

Maybe someone who's a little smarter, maybe, uh, probably not as much hair, but let me introduce you to su di Hospi. Su dear is the Chief product officer, CPO at Neo four J, and we are lucky enough to have him join us today. So, dear, it's a pleasure to have you on here.

Thank you for joining us. It's great to be here, Alan, and looking forward to the conversation today. Absolutely.

And no disrespect to my friend Steven Chin. We love him. But, uh, Sudir, as I mentioned, you're the CPO at at Neo four J.

Why don't we maybe start off with your story before we jump to everything else. Yeah. So I, I've been with Neo four J for almost two and a half plus years now.

Um, I am responsible for all our product strategy, product execution, roadmap, everything. I work with customers across the globe trying to understand what challenges they have and how do we build our graph intelligence platform that enables them to solve problems. Uh, before coming to Neo four JI actually ran a product for all of data analytics services at Google Cloud.

So this meant anything that allowed you to bring your large scale data assets into Google Cloud, how do you process it, how do you actually analyze it? Uh, and I also was responsible for multiple acquisitions, including Looker, so basically the bi side of the house, so everything. So BigQuery, which is one of the largest products there, that one plus another 10 odd services.

So I ran that for five plus years, uh, at Google before this. Uh, but no, I think my opportunity I saw was with large language models just getting ready to take off, I saw there was a huge opportunity for knowledge graphs to provide that intelligence layer or knowledge layer for these systems. And that's why I came to Neo four J.

Absolutely. Um, it's a great story and they're lucky to have you there. You know, we, we, we mentioned Neo four JA bunch of times and, and people who watch Techstrong TV all the time.

Uh, thank you. Um, but beyond that, you, you've probably seen us interview Neo four J and we cover them on our various websites as well. But sudir, I'm sure there are people out here who don't know Neo four J or maybe they know a little bit about Neo four J, but you know, not the whole story.

If I, if I can bother you, can you kind of, you know, just lay that as a foundation, give us the Neo four J background. Yeah, so Neo four J the company was started almost 16 plus years back. Uh, we are in the, we are the category creators for the graph database category.

Uh, we now are the graph intelligence platform, uh, that enables, uh, developers to build, uh, agent applications by converting their data into knowledge, right? So all these AI systems need to access enterprise knowledge. How else are they going to make better decisions?

How are they going to be accurate in their, their information and all? And so that's what we enable, uh, customers to do. Uh, as part of our graph intelligence platform.

The core components are our graph database. That's what we have built over 16 plus years. We have pioneers in, in the graph space from that perspective.

We also have graph algorithms. So we have 65 plus algorithms that you can use to run intelligent, uh, analysis intelligent, uh, decisions on top of the graph data that you may have. But our new capability allows you to run these algorithms on any data anywhere in the enterprise.

So you may have data and Databricks or Snowflake or BigQuery, you can still run these algorithms on top of it. So that's the core part of our graph intelligence platform. The layer above that is AI power tools.

So you can literally, I have this theme for our product portfolio where I want people to be able to, in five seconds sign up for our service in five minutes, actually use their data and get wowed. And then in five days you should get to value. And so in this case, you can literally take your data from Databricks, convert it into a graph data model in like three clicks, and then from there, build agents on top of it within few minutes.

So that's the, the AI power tools that we have built out for that. So automated, uh, graph model generation and all. And then at the top of it is our AI stack, which is our Aura agents.

How do you create your own agents and how do you go ahead and, and like, you know, we, we are backbone for many of the memory companies, uh, in the, in the space. So that's the graph intelligence platform. That's what we provide.

Many of our customers use us for various use cases in, uh, agent ai, primarily as the core knowledge layer that can power these AI systems. But also in financial services. We are big in fraud detection, anti-money laundering use cases.

Every supply chain company uses graphs to go ahead and manage the supply chain risk assessment and all, um, all the healthcare life sciences companies use us for the whole, uh, knowledge graph for all of their r and d graphs. Like, hey, what drugs have been like, you know, uh, identified, what are the things they solve, what enzymes, all of that kind of, uh, actual knowledge graph. Um, US Army uses us for all of their supply chain.

So a lot of intelligence analytics and all is done, uh, by may various intelligence agencies on top of our, uh, our software. So that's, that's roughly the space we are in. I love it.

And, and thank you for taking the time and explaining that, you know, look, we, we live in an, this whole LLM and AI and rag and vector databases, right? Graph has really kind of found its place, if you will, 'cause it, it, it's great technology and it, you know, as you mentioned, NEO four j's around 16 years. We've had graph databases all these years and everyone knew what, you know, there was so many great use cases, but this may in fact be the killer use case Yes.

For, for graph database. Um, just before we jump into the topic of discussion, for people who maybe wanna find out more about Neo four J, where, where was, what's the best? Just go to the website or Yeah.

com is a good place to go start. Uh, there are multiple videos we run. One of the things Steven Chin, our friend runs is a dere for US developer relationships, and his team manages this Graph Academy.

So if you want to learn about graph and what graph technology can do, we have Graph Academy that has lots of courses for audiences. They can go ahead and learn everything. How do we build an application getting started to more advanced courses?

Uh, so that's another good resource. Another resources, if you go on YouTube and search for NEO four J, you'll find tons of content from various of our conferences that we run, including a lot of customer, uh, driven content, customer stories, and all that. Love it.

Good stuff. Alright, let, let's pivot a little bit. You guys recently announced GA of NEO ga, meaning general availability.

For those out there who may not be familiar, uh, for the NEO four J fleet manager, which is advertised as the industry's first unified control plane for graph databases. Give us the scoop here. Sudir.

Yeah, so we have tons of customers that actually use Neo four J large enterprises, almost I think 80 plus percent of, uh, fortune a hundred users. Uh, and then a lot of these customers have various use cases, like they have been using us for, let's say fraud or anti-money laundering or supply chain and other things that we talked about. And as the agent AI is becoming more and more popular, they have newer use cases, they want to go ahead and use us for, for, for like just the knowledge layer to power these assets and especially knowledge layer.

What I mean by that is you may have data in disparate systems. Your LLMs are not going to get access to all these systems and be able to make sense of it. So what we allow people to do is take this data and create a semantic layer on top of that data as the knowledge layer, and then your LLMs can use graph rack to go get access to that information in a very secure, trusted manner.

And that improves the accuracy, reduces hallucinations. So as the new use cases were coming, we saw a lot of modern new use cases were getting deployed in our cloud offering, which is Aura. It's a fully managed offering, zero operations, we take care of all the infrastructure, but lot of existing use cases may be running, they run it themselves in one of the clouds or like, you know, 15, 20% of our customers still run their own data centers.

And so they're running in all of these, uh, environments. Additionally, we also have databases that are our community edition, which is completely free edition that anybody wants to build on graph. They can take our open source database and just start building applications.

We have a lot of adoption of that, but one of the challenges for IT leadership and CIOs is, okay, when you have that much deployment in an organization, how do you monitor it? How do you manage it? How do you know what is happening across the fleet?

So the fleet manager gives you a single pane of glass to look at all your deployments, whether they are on-prem, on-prem, run by yourself in cloud, or our fully managed aura offering, whether it is our enterprise offering or whether it's our community edition, which is like completely free open source offering, it doesn't matter. You go, you can actually visualize and see all your deployments in one single place. You can look at them, you can monitor them, you can operationally figure out what actions you want to take if something goes wrong.

It also will tell you any security risks if something is going wrong, like, you know, last time when the lock four J issue happened, something like, if that happens in future, we will be able to alert the CIOs about what the deployments look like. So I think the most important thing is single pane of glass. Any deployment anywhere in any platform, we can give you complete visibility into it.

I love it. And of course, general availability means availability. It's generally available right now people, it's available Now for any customer who wants to use it, especially if you are in our Aura, uh, database user, you'll by default start using it.

If you're self-managed, you can register your self-manage databases, start using it in production scale. I love it. So dear.

You know, I, I'm doing a few more interviews this week. We won't be doing any next week as we, you know, break here for the holidays and the new year. You know, it's been a heck of a year right?

By any, no matter how you wanna measure it. Yes. Um, as you look ahead to 2026 with this, is it more of the same?

Is it accelerating? Is it a black hole? We don't know what, you know, as part of your role there as CPO, what, what is your, what's your gut telling you?

I think I work with lot of customers, as I said. Right? One of the things, and, and just one interesting data point in last, in this year, 2025, I was just doing my measurement.

I have been on road for 24 weeks of this, like Out of 52, Out of 24. Majority of that is actually going out, meeting customers, spending time talking to them and all. And what I have seen is 2025 has been a pivotal year in the switch from lot of experimentation with Agen ai or AI in general, LLMs rag, all that, that happened in 20 23, 20 24, 20 25.

We have seen real use cases being deployed by large customers like our recent, uh, graph, no, or like, you know, uh, we, we do these, um, events with our customers. In the recent one we had Walmart, we had Uber, we have had, uh, uh, Quas and Brady, which is like an law firm. Like we have had so many different customers actually putting things in production, seeing value from what they're, what they're doing.

No Nordisk has been there. So I think the thing is, I've seen like in 2025, lot of these customers actually getting value and deploying things to production in 2026. I believe that's only going to accelerate because I still believe a lot of our customers have been the early adopters that have transitioned from experimentation to real business value.

But there's this majority that hasn't actually seen this. So 2026, I think it's, uh, it's the acceleration of like, you know, value driven, use case driven implementations of AI that I think will happen. And I think we can really help these customers, especially developers, get value of a from the AI systems.

I love it. Suya, thanks for coming up here on Techstrong tv. You know, I do interview Steven a lot, but anytime you want to come back, you let us know there's a place here for you.

Okay. Thank you Alan. I really enjoyed talking to you and thank you, uh, for bringing me here.

Also, happy holidays to all your audiences and I'm looking forward to coming back and sharing more as we go into 2026. Thank you. Well spend a few weeks at home during the holidays at least then.

And we'll, if I don't see you on here, we'll see you on the road. Spi, CPO Neil, four J here on Textron tv. We're gonna take a break.

We'll be back. Hey guys, thanks for the throw. We're here with Mike Kelly's the CEO of buying plane, and we're having a chat about well open telemetry and all the instrumentation and the data we're collecting.

Well, maybe this is starting to be too much of a good thing and we need to figure out how to manage all of this stuff. Mike, welcome to show. Thanks.

Thanks for, it was great to talk to you again, Mike. It wasn't too long ago where in my mind we only instrumented a handful of applications because it was hard to do and expensive. And we'd have these a PM platforms and it was very reserved for the elite, shall we say.

In the last few years we've seen open telemetry as open source instrumentation software become ubiquitous, and a lot more things are instrumented and a lot more things will soon be instrumented. And we're collecting all this massive amounts of telemetry data now. But I also feel like it comes in a lot of varieties and we have a lot of tools and platforms.

So what's next and how do we get our arms around all this stuff? Yeah, it's a good question. And it's, it's stood out, right?

We know that we've been collecting more and more every year and that there's more data generated, and that's, it's if you think back and you look back 10, 15 years, you see the waves of new, uh, whether it's microservices, ai, it's all generating more data. And then we have platforms that we're using for security, for observability. They all want all of that.

They wanna see all of that. And to get the, the value from those, the most value, you, you really do need to collect all those key signals. But what happened in that past 10 years or so is we got to a point where there's just an avalanche of telemetry data and it is incredibly difficult to manage.

And so that's really where this concept of, uh, telemetry pipeline came into play and came, became a, a central component within a, a company stacks. Um, you know, you look at Open Telemetry, that was definitely a, a project focused on let's prevent ourselves from instrumenting everything a dozen different ways by, whether it's open source or proprietary solutions. Let's, let's fix, uh, or, or solve this with a single solution using standards, telemetry pipelines are a layer on top of that to say, now that we have that, now that we have a a standardized way of collecting the data, let's make sure that we're, we're, uh, filtering, routing and managing the data as it's passing through.

So we only allow what we need and we can really reduce the volume and simplify the complexity of, of the telemetry, uh, stack. Mm-hmm. Is this something that mere mortal DevOps engineers can do?

Or do I need to go get a data engineer for telemetry data and add them to the DevOps team? Yeah, that's a question a lot of teams ask, and it, it really depends on where you're at in the, the journey. So I think a lot of teams start and you have a, um, you know, it's within your DevOps org and you will manage those, those collectors.

But at, at some point you get to a point where the complexity of that and the volume makes it really difficult to do that, uh, without some exploding complexity. So at volume, we think that's, you know, specialized solutions. And that's what we do at buying plans.

We, we develop a telemetry pipeline to manage open telemetry at very high scale and at very high volumes of, of telemetry flowing. Uh, and when you get to the point where you're managing, you know, tens of thousands or hundreds of thousands of individual agents collecting logs, metrics, and traces, it's probably time to think about a, a specialized solution. Uh, that'll give you the visibility and it also unlocks a lot of, uh, capabilities and functionality that you, you know, would otherwise be incredibly challenging to, to build out on your own.

And some of those, we think about the telemetry pipeline or the stages of that. Um, I think any telemetry pipeline should be able to collect all of those signals, whether it's security related data, observability, AI data that you need. Then take that and normalize it and also secure it.

So you're looking for things that you don't want to pass through, like PII data, uh, you want to normalize it into a standard organization wide, uh, protocol. Open telemetry, uh, has OTLP for that. Then you enrich it, then you reduce it by eliminating things that you, you know, you don't need and you can route it to the destination.

And if you have that in place, uh, it allows you to do a lot more with, with the data that you have and really allows you to own the telemetry, particularly if you're using open standards like Open Telemetry. Um, so I'd say, you know, uh, DevOps teams can certainly do this, this on their own at this, the smaller scale. But once you get into this very high volume, that's when you really run into some challenges, uh, managing that without really overwhelming a team.

Mm-hmm. Is there a smart way to do this? And I asked the question because some teams are like saving everything now and they're just overwhelmed all this stuff, and maybe they got some of it in an S3 bucket somewhere, but they don't know what to get rid of, so they save everything.

And then in the other end of the extreme, a lot of folks are saving nothing because well, it's expensive and just hard to do. So, um, where's the right balance between what I should save and what I should just get rid of? Yeah.

Yeah. That's, that's one of the tough questions, right? But, um, there are a few guidelines that we provide, um, uh, that I tend to stand by and that, that I think mo apply to most people.

It's always gonna dependent on an organization, but one of the things that you can look at are, you know, what are those most critical, uh, signals, uh, that are gonna be relevant for security and for observability? So you can look at the, uh, golden signals for observability, for security. You can go to specific guides, um, that are going to list, these are the events that you should be tracking.

The challenge most teams have is that when you do that, or as soon as you start to eliminate anything, you, you have that, that fear that, oh, did I drop something that we don't need? Um, and what we find is if there isn't maybe necessarily that confidence, if you need to go in and investigate that, uh, you will have the data you need. Uh, probably the most common practice now is to tier your telemetry data.

So you'll have the, those critical signals are sent to your security observability platform. Um, but then you'll have a second tier that is sent to low cost storage and the, with the ability to pull that back and send it to, you know, security observability platforms for analysis. Um, so if you have concerns about, about what you're able to remove, what you're able to reduce, that's, uh, probably the, the, the best practice to maintain flexibility and also significantly reduce your cost.

Now, there are always things that we know we can trim, and sometimes you'd be surprised and it's, um, uh, frankly it can be, uh, eyeopening when you really look at what we end up sending along. And a lot of this is, you know, unused or empty fields. We're sending duplicate data over and over again.

So there are a lot of things you can do to compress that and really not lose any visibility, um, in the, the, the platform that you're using to analyze the data. Um, overall, we usually see, you know, it can realistically, uh, reduced by at least 40% of the data. If you, you haven't already done this, by going and looking at what you can route, what you can eliminate, what you can deduplicate, what you can compress.

Alright. Um, my approach to AI is somewhat simplistic, but I basically am looking at it and saying, here's a list of things I don't enjoy doing. So maybe there's an AI agent for that and mm-hmm.

I think telemetry data might be at the top of that list. They're very high up there. So will there be kind of like AI agents to help me manage the telemetry data?

Yeah, I mean, I love telemetry data, but I understand that not everybody, uh, has the same affection for it that, that I do. Um, yeah, I think, you know, AI has impacted telemetry pipelines in, in a really similar way to what you've seen in other areas and in some ways even more. Um, there's the, the first level of let's augment your abilities, make it easier to do, make it easier.

And, um, you know, and buying plain, a couple of areas that we've focused on is when you do, uh, instrument your, uh, code base or instrument an application, we will automatically identify what should you do with this? So you asked, how do we reduce the volume of this data? Well, we use AI to, to identify the ways that you would reduce that.

What should you apply? What filters should you use based on the type of data that you're ingesting? Um, and that's something that, you know, may not be the, the most fun thing to do, right?

Um, it bake in a lot of that knowledge. Um, also makes it much simpler to pull out some simple things like pulling out fields and, and routing and, uh, uh, building in compliance into your data structure. That can be done with, with ai that's on the, the augmented.

So helping you to, to do your job better faster. There's also the, the automation piece of it. So when you get to a scale of petabytes of data flowing from hundreds of thousands of devices, um, then the question is now how do we, we route this and manage this in a really efficient way?

And, uh, we're, we're starting to use AI to look at that as a whole and be much more efficient in a way that would be, you know, very, very time consuming for, for a person to do so. Um, we certainly see it, uh, at, at least as much as we're seeing it in other tech areas within tech of having a big impact and, and being of a lot of interest with customers. Hmm.

Is there an opportunity to centralize some of this beyond just what we see in DevOps and app dev? Because if I look across the typical IT organization, the security people are pulling in a lot of telemetry data too. So is there an opportunity to kind of start rationalizing some of this?

'cause we're all kind of collecting the same data for different purposes? Yeah. Um, you know, I've been beating that drum for, for a few years now, and that is absolutely an opportunity I think most organizations have.

Um, what you find is you'll have, uh, frequently have an observability team. They have their own agents installed, they have their own observability solution. Security team also has the, has agents installed, sometimes running on those same systems, collecting the same data, and, uh, at, at a certain scale, certain size of organization, it's, we've seen between six and 10 different agents collecting the same types of, of information and sending it different places.

We know that's not efficient, right? We know that there's a, there's a better way to do it. Um, open Telemetry has been a, a, a great step in that direction where it's a solution that's designed to, in a vendor agnostic way, collect all of the data that you need, whether it's logs, metrics, traces, or other signals, and, uh, gives you the tools that you need within that open source framework to manage it.

Uh, with, with buying plane, we make it much easier to deploy and then get some visibility within those, those very large open telemetry deployments. And we have, uh, you know, I'd say at least half of our customers end up being both, uh, using this for both security and observability, and now expanding into AI use cases as well. And we expect, uh, more and more folks to start using this for business data.

We really, uh, there are huge advantages to having a single source, uh, of data in a standard, uh, standard framework and really letting you turn the, the telemetry data that's within your organization from a challenge into something that is incredibly valuable, uh, and an asset to the organization. Mm-hmm. What do you see organizations doing that just makes you shake your head a little bit and go, folks, man, we've just gotta be a little bit smarter than that.

Uh, you know, it's a good question. I think, uh, it's where I shake my head because I know the challenges that go into, uh, the decisions that are made, and, uh, frequently the, those things that make you shake your head are the things that were the, the finger in the dam to, to solve something when everything was falling apart. Um, but I think, you know, one of the big ones is, uh, when one that you just mentioned, right?

It's, it's, we've done things a certain way where, whether it's a PM or it's, um, our security deployed proprietary agents of five to 10 different types and, and, uh, really don't have any way to manage those. And now we've gotten so big, uh, and sending so much data that we're really in a tough spot and things are starting to fall apart. And that's, frankly, that's, that's really common.

It's a place people find themselves in. Um, I think standardizing on, uh, open telemetry or, or another open source standard, and there are several out there now, is really a, a smart move for teams, particularly as more and more, uh, options are coming out and, and, uh, new platforms are emerging. Um, you know, I I think that, uh, that getting to that standard has a big impact on your business and, and your team.

Um, we mentioned ai, but if I understand this correctly, there's gonna be more AI embedded into our applications, and then there's gonna be more AI agents that are part of the DevOps team, and won't all these things be generating more telemetry data than ever? So how long before? We are just overwhelmed?

They already are. Yeah. We're, we're seeing it everywhere.

Um, uh, whether it's AI agents, uh, logs generated by those, uh, trying to manage the development of those, uh, you know, it keeps increasing and, uh, we we're seeing that exponential increase that's been going on for, you know, 10 years now, continue. So I would say that if, if you haven't been overwhelmed by that, you, you probably will be at some point without really taking it seriously of looking at how are we gonna going to, to manage and, and, uh, be really thoughtful about what we're collecting and what we're not. You know, talked about the, the waves of this.

So when there was virtualization, all of a sudden we saw what looked like a little bit of sprawl and more and more telemetry was being generated from that. Then Kubernetes came along and microservices came along. There was more data, more and more data.

Um, uh, the E of cloud led to more data. AI is doing the same thing. So it's just the next step in, uh, the generation of, of more and more of that.

Fortunately, this is one technology where we can actually use it to help us, uh, uh, solve the problem that it's creating. And, uh, uh, at least that's, that's the hope for, for that we're able to use this to, to also reduce the volume and just get what matters the most. Mm-hmm.

Um, to that end, um, what is your best advice to folks about how to move this subject up the priority list? Because there are so many competing priorities and telemetry data doesn't always, you know, land on the list of 10 sexiest IT projects. So, um, how do you, you know, tell folks in the IT leadership, we need the focus on this?

Yeah. Um, you know, what does get it up to the top of the list is when you talk about the, the ROI of managing and reducing the volume, because it's not just a, a complexity or a volume, it is a cost directly associated with how much that is. So if you're sending a terabyte of data to your security platform, you're almost always getting billed on that, that full volume.

If you can show that not only are you, are you managing this, you're, you're future proofing, you are, uh, moving to a vendor agnostic solution, you're making it easier for your team to manage this, but you're also reducing the cost of these solutions or allowing you to, uh, preventing a massive increase in the next few years that tends to, to make this a, a much more interesting problem than, than it may otherwise be. And that's a situation that most folks are finding themselves in. Um, you know, teams, uh, that are, uh, operators are, are finding it's hard to, to manage, uh, with this without a breaking.

And so they're looking for a solution, but it is absolutely a budget concern as well. Uh, and that's why we're seeing so much adoption of this. Uh, it tends to be driven as much from that, the ability to reduce the cost.

Uh, and that is a huge focus, uh, with buying plane, but we wanna suit solve the, the technical, uh, issues and make it really easy for someone in DevOps to be able to deploy and manage and, and filter and route. We also want to keep our eyes on how do, how are we reducing the volume that's being sent to those solutions that, that tend to be a very, uh, get a lot of visibility throughout the organization because they can be so expensive. Alright, cool.

Hey folks, you heard it here. Every dollar saved on telemetry data is probably another dollar that could be applied to something, well, maybe not more important, but maybe a little more compelling on the business value side of the equation. Hey, Mike, thanks for being on the show.

Hey, thanks so much, Mike. All right, and back to you guys in the studio. Hey, everyone.

Welcome back here to our continuing coverage of AWS Reinvent. You know, we don't do every video interview live at reinvent because there's embargoes, there's other considerations. And so this is one of the videos we recorded at, uh, reinvent in Las Vegas, and we're bringing to you now just a few days later.

I want to introduce you to my friend, do Laur. Dore is, uh, the CEO, I think founder of cid. Yeah, yeah.

Co-founder, Co-founder of, of CID db. I got help. We all need help.

Do's been on with me on Techstrong TV for years and years, but it, it's not often I get to see him. He's, of course, in Israel. Uh, we were supposed to be in Israel right now, but we're not, uh, for Cyber Week, and it just didn't come together enough.

But Dordt, it's great to see you here in person. It's great to have you. Thanks.

Thanks for hosting me. It's a pleasure. So, let, let's start with this, though.

Not everyone has seen you on Tech Drug tv. We, you know, we're not, let's face it, we're not CNN or any of those, but give people a little bit of your journey to, to founding, uh, Cilla. Sure.

Um, so I'm a technical founder. Uh, I have roots in computer science, and, uh, initially in my career, I went to work for a terabit router company That early days tried to take over Cisco's core business in, in, in 2000, uh, the bubble burst, so we didn't work that much, but we did have a fabulous product and a drop in replacement for Cisco CLI, I'll, I'll come later on with more of the importance of, uh, drop in replacements in products. Mm-hmm.

Um, and later on I did something with the Blade Centers, and then I joined the company, a startup company where I met my existing co-founder ti and my, uh, existing, uh, chairman who was, uh, the CEO back then. Uh, that setup had had to pivot three times. This is where, uh, I learned how to pivot Uhhuh.

The last pivot, we, uh, came up with the K VM hypervisor, so to, uh, renovate around the new hypervisor, a new approach that, that was the KVM. It worked really well, and Red Hat acquired the company. We, uh, spent their four years, uh, improving KVM and also the Linux kernel, and I'm a big fan of it.

And, uh, afterwards we wanted always to have our own startup. So we, we left Red out and opened this company. Uh, originally, uh, it wasn't around databases because we had a lots of, uh, virtualization experience, so we mm-hmm.

We started with, uh, an operating system that sh should have bit, uh, beaten Linux in, in, uh, virtualized workloads. Uh, the OS exists, uh, still today. And I met a customer yesterday who runs Sila and knows us because of that s uh, 'cause of that os Yeah.

If you don't mind, what os was this? It's called, uh, OS V, it's a Unikernel. Oh, okay.

Sure. Um, They had their moment in the sun. Yeah.

Uh, the Docker kind of sucked all of the air from the room when we around when we launched, but, uh, this is where we, we were familiar with other databases. We, we wanted to show, uh, the gains when other databases run on top of r os to be faster than Linux, and we managed to accelerate Redis by 70% because we loaded the application into the kernel space was faster. When we did the same with Cassandra, the performance didn't change much.

We realized that the overhead of Cassandra, uh, is itself and, and not, and if we replace it with a fast RS it, it doesn't change it. Uh, so we said, oh, that's can be a good idea for a pivot because we didn't get enough traction. And with why, once we rewrite Cassandra from scratch, keeping the compatibility like the Cisco days, uh, also like the KVM days, it's, it's also about compatibility, uh, with, with other things.

And, and we re rewrote Cassandra from scratch. That's what cila DB does. Uh, it's also, uh, nowadays compatible with Dynamo Beats a drop in replacement, and it's a standalone database that can run the biggest, most scalable workloads in the world.

I love it. What a great story, huh? Mm-hmm.

And it's also, uh, you know, for, for geeks, you're, you're, you're a geek person. I'm a geek person. A lot of the people out here are, we do this.

I mean, it's nice to be able to make a living doing it, but we also do it because we love Yeah. Playing with this stuff and and this is a great story where your passion led you to, to doing this. Um, it's been now how long with sil?

It's kind of six years, seven years, eight years, how long? Mm. Uh, now it's, uh, it's more than 10 years.

About 10, yeah. Even, uh, our 11th year. Really.

That's, you know, what that, and that's something also, quite frankly, it'll be proud of, right? Mm-hmm. Because what do they say?

The average company, if you make it past three years mm-hmm. It's a big accomplishment. So it, it's, it's all obviously here.

Um, now talk to me a little bit about how people engage with cer, right? There's open source parts of it, there's commercial parts of it for people out there saying, you know, we're always looking for better performance, better bang for the buck. What, how, how did they kind of jump into silla?

Um, so, uh, we started, we were big open source fans. Uh, we, we started with open source, actually, uh, a year ago. We changed the license, I remember to source available mm-hmm.

At the time, a year ago. I, I was just sitting here. Um, so it's source available.

We, we do have projects which are, uh, open source, like our, Not even source available. Let me ask you a question. In the year you did that, how many people have asked for the source?

Um, so PE people do appreciate, uh, the, That it's available, The source, But it, it, this is, but this is something, look, I've been an open source too for 25 years. The fact of the matter is, 99% of the people never look at the source code or make a change to it. Well, not, maybe not 99, 90 8% of the people never look at the source code, never make a change, you know?

And, and so what they really want is free, Uh, yeah. People like free. And, and we, we have, uh, a freemium offering right now.

We're, uh, now it's source available. It's allows us to, uh, allow people to look at the source and, and also have the, uh, comfortability that the source is, is available for virus cases, uh, for future con continuity. Uh, but, and we have some control to say, okay, up to this, uh, level, it's free and beyond that level, you need to pay because we are here 11 years on the road.

And, and it's a business, right? Someone's gotta keep the lights on. I, I agree with you, But I, I do understand people, uh, who are passionate about, uh, the source code.

And there's a lots of, uh, small things and small changes where things matter. And, and we have, uh, open source, like, like our core engine, it's called csar. Uh, it is open source and it's license, it is not a GPL, uh, it's license is, uh, uh, Apache because it's important for, for people to use it within their products.

And that's why we haven't selected there. There's a a ton of No, Absolutely. Changes.

Look, I, you know, one of the nice things that I've seen happen in the open source community over the, as I said, 20, 25 years I'm involved, is that most users recognize that though, open source may be free, someone's working on this. Mm-hmm. Someone's entitled to get paid for their time and their effort and everything else.

They may, they may quibble with how much mm-hmm. But you, you know, it, it's ludicrous to think that people are gonna volunteer this outta the pure love and, and not make a living, you know, not be compensated for it. So I think that's been a positive development overall in the open source space.

Mm-hmm. Right. It used to be, oh, you know, you're looking, you're in it for the money.

Everyone's in it for the money. We have to keep the lights on. We've gotta feed our families.

But, you know, it's just, it's a fact of life. I mean, and if you don't wanna recognize that because you're some sort of, you know, like open source zealot mm-hmm. Free as in freedom and free as in beer.

Don't use the product. What can I tell you? And, uh, having, uh, paying users allow us to invest back in the product.

Absolutely. It makes the product better, product Better. And so that's primarily what we do.

And It's a flywheel Is a, is a vendor that, uh, used to, uh, eh, release both open source releases and also, uh, gated product releases. You double the amount of releases. I was just gonna say, what a pain in the Yeah.

You know what that is a Hundred percent. I, I agree with you. So there's, but there is a freemium version.

You could go check it out, play with it. If you do wanna look at source code, and that's your thing, it's available to you as well. Um, Dora, let's talk reinvent here.

You guys are here. It's been an interesting kinda reinvent. You know, we, when I, I just finished writing an article when I first got here Monday, and I looked at the keynote, you know, agendas and everything.

They gave us a press preview. It was obvious. It was all agent AI all the time, right?

It was all about ai. But over the course of two, three days that I spoke to people and saw things and walked around, see a lot of news about DevOps, cloud native platform engineering databases, hardware, well, hardware is AI stuff too, but hardware, um, you know, it, I maybe didn't hear as much as we normally hear about, like things like S3 or serverless or Lambda or these kinds of things. But the geeks are still here.

The developers are still here. The ops, the DevOps folks are still here in force. What have you seen?

Um, so AWS is, uh, a giant, yeah. E even more, more than that. Um, and nowadays they do innovation across, uh, across the year, not just them, also their competition.

They, they have to, um, so there are announcement, I think that they're not holding the announcement just for, uh, this event, uh, recently they released a new GRAVITON instances. Yeah. Graviton five is coming.

Yeah. And, and then, and the, and the GRAVITON four was released, right. And, uh, we are, we measured graviton four with cila db and, uh, it, it offer fantastic, uh, performance and that translate to a better TCO.

So for us, it, it's super, that's exactly what we need. Um, so there, there's a lot of, uh, gradual improvement always on all of these products. Yeah.

Um, so it's for, for, uh, for, for, I'm, I'm pleased for that. It's, it's good enough for us. What about, now I know you're exhibiting, what about like, you know, traffic at the booth, conversations with people?

What are you hearing? Uh, well, there, there's a no shortage of, uh, of traffic at the booth or traffic, uh, here in Vegas. Uh, regarding, um, the entire AWS and, and the ecosystem, uh, it, it's mostly about, about ai.

Like, uh, yeah. Uh, we, we see that a surge in AI use cases. Uh, now about half of the use cases are, uh, directly related to AI Ins, Cilla, uh, ins.

Cilla. Yeah. In, in.

So explain that to me. What, what's the use case there? Um, we can split it to, uh, three categories.

Uh, one category is, uh, that we're part of the AI stack. And, and during the, uh, training and also the, uh, serving processes, uh, the, the stack need to just access a tone of objects and, uh, need the fast database for it. It's part of the AI stack without doing anything, uh, special for it.

Like, uh, uh, distributed databases is in demand for high workloads. And, and those are high, very high workloads. Sure.

Uh, and, and can be, uh, part of the big LLM uh, companies, or can be a smaller, much smaller company that started start their AI journey. That's number one. Uh, number two is a feature store.

Uh, feature store is more of a machine learning, but it's, it's part of AI still. And, uh, feature store allows people to classify a users or, or sometimes agents, uh, automatically. So it can provide recommendations for, uh, e-commerce, for, uh, fraud cases in a variety of other cases.

And we're, we're big in, uh, feature store case and, and feature store needs. Uh, a fast database too, to quickly come up with, uh, to, uh, classification that, uh, you as a user was selected and, and what's appropriate for you as a user, either to watch on TV or to get an ad, et cetera. Uh, bit, this is the second one.

And the third one is, uh, a vector search, um, to, to do LLM on your private data set set. Uh, that's why, uh, the, the, this whole category of, uh, a rag Right. Was rag with vectored database.

Exactly. So, uh, we added, uh, a vector search, uh, a ourself, and we, we already have a, a beta that receives lots of interest. And, uh, we, we are going to this month in December, uh, go live with the general availability of our, uh, rag, uh, vector search store.

Really? Yeah. That's fta.

So in essence, they could use still, or is their vector database then mm-hmm. They're creating small language models or, or the rag stuff that's gotta be big. No, Yeah, that's, uh, fantastic.

Our, uh, a vector search is the most scalable. We can easily run a model with a billion, uh, objects. Uh, very few, uh, vendors can even get to a billion.

And we can do that with hundreds of thousands of requests per second. So we, we scale, uh, to, to very high numbers. And if, uh, people have, uh, lower or medium demand too, like, uh, most will have a model of, uh, 10 million or a hundred million objects, then we can give, uh, the best latency and, and also very low price point.

That's fantastic. Look, there's a lot of people saying that we've scraped all there is to scrape for these LLMs and that, you know, get, making generative AI or even agent AI better by increasing the LLM and the data we have to train is, is diminishing returns. And that the way to go is maybe SLMs more rag, you know, uh, well, there's some people who say, we need to go away from LLMs altogether and go to this world model and stuff like that.

Mm-hmm. Um, but certainly, I, I believe there's gonna be a lot of activity in, in the SLM rag kind of space. And, and not only that, because as we develop AI for specific use cases, I don't need the whole world of the internet.

I just need, especially if it's my own proprietary information, right? I, and I don't wanna put that out up there. I want it right here.

Just, and so I, I think that's a huge business for you guys. Yeah. Congratulations.

Thanks. Uh, it, it's, it's, uh, the, the market demand. Yeah.

Yeah. It's, yeah. Well, no, That is, it's not just an opportunity.

It's also a defensive move. Because if we won't do it, then, uh, customers will go elsewhere, eh, to, to be frank. Mm-hmm.

And yeah, the, the fact that, uh, people would expect, uh, all of the ease of use of LLM on the public data set on the internet, they expect to have the same when they come to every vendor. And to ask a free text search, uh, your questions in, in one liner, and get immediately the best results, w without diving into a very complicated ui, that's a power of LLM. And sometimes it won't be people, but it'll be Agents, Right.

Uh, that come and, and automate and, and get the queries automated. So that begs the question, is there, uh, an MCP server in your future, Uh, in the future? Absolutely, yes.

All right. Hey, let's fast forward past AWS for a second. People are watching this after the, after the show.

Anyway. You guys have some new announcements that you're previewing here. Mm-hmm.

Share, if you don't mind a little bit. Thank you, uh, for the opportunity. So, um, uh, we'll also move, uh, from beta to general availability.

Our X Cloud, uh, uh, a, a managed platform. Uh, X Cloud is, is, uh, the new generation of our core database with, uh, database as a service management consumption. Uh, the unique thing about it is, uh, our new core architecture, which is called tablets.

It's way, way more elastic than any other database or even infrastructure in the industry. Uh, we, we were okay with regard to, uh, the speed of, uh, increasing the cluster, scaling out, and then scaling in. We were, before this technology were, we were okay, like, like, uh, an average vendor, but there was a demand to do it much faster.

And frankly, we also compete with DynamoDB. We're a drop in replacement, and DynamoDB, uh, was the first NoSQL database. And, uh, up to this change was the, the best in the industry.

You can easily scale up and down, uh, very easily. And, and if your workload changes throughout the day, uh, then, then you can, uh, instead of paying for the peak consumption all the time, you can just have the workload follow, uh, uh, the work, the workload should follow the usage, right? Dynamically.

So that's exactly what, uh, X cloud is. Uh, we, we have, uh, the technology based on components called tablets. We break the gigantic database of, uh, a petabyte of data to five gigabytes chunks.

Right. And we can move them around super quickly. Uh, we, we can also even, uh, it's allows us, uh, both to scale super fast.

We, we can increase capacity, quadruple it in 10 minutes. Mm-hmm. So you can go from, uh, 500 K to 2 million operation per second in 10 minutes, But could you go back to 500 K in 10 more?

And that's right. So, Because sometimes with these things, it's like blowing up a balloon. Mm-hmm.

You know what I mean? It never goes back to the size it was before you blew it up. So we, we can, it, it's not, it, it's, it's, um, indeed complicated.

Yeah. But, but we can also go back and, and shrink and, and that's the user workload that, uh, goes, comes and goes, whether it's a Black Friday or, or on a daily manner. Uh, so, so that, that's a big improvement.

Uh, and, and big TCO improvements and, and usability improvement. Sure. Uh, also, it's, it's, it's pretty unique.

Uh, we have a short per quart, uh, engine. So let's say if you have, uh, a machine with, uh, 32 cores, we, we'll have 32 independent threads in the server. Wow.

Uh, if you have a 64 machine, then we, we will have 64 threads, uh, in, in engines within that machine, and it'll perform twice as good to 32. Now, let's say if you have a 64 way machine, uh, but actually you need, uh, um, 66, uh, threads and you have 64 now, would you buy another machine for 64? It's, it's expensive, right?

So instead we, we can mix and match, and we can have 1 64 machine together with, uh, a tiny two VCP machine next to each other because of the flexibility and the charge real Distribution And the starting, we, we can combine the two. Haven't seen any other vendor can do that. No.

And what the user receive is efficiency. Uh, they have exactly what they need. They don't need to buy excessive large servers, which are expensive on AWS, uh, They're expensive everywhere.

It's not just AWS but really what we're talking about here is almost like a finops play, right? Because that's, I think that's where we are, especially in cloud usage, right? Look, we're talking about spending $5 trillion on data center AI factories, but the fact of the matter is, when I talk to people, they say, I wanna get control of my cloud bill.

Mm-hmm. I wanna reduce, I wanna be more efficient in my use of these resources. And, and that's why I made the joke with the balloon blowing up.

That's pretty much how the cloud is, right? It never seems to go back down. People, they want that ability to have insight to turn that dial, and they want the ability to say, how can they do this more efficiently?

Mm-hmm. Yep. And, uh, our customer success team works with customers.

And if we both see, let's say you sometimes utilization, like people can check their database, how much it, it's loaded on an average basis. Most databases are, are not that loaded. Uh, on, on a, when I'm not talking about the spike, I'm talking about normal, uh, day usage overnight, it can be 10%, uh, or 20% utilized, and you pay for the entire thing.

But that was always the pro, that was the promise of the cloud. That elasticity was an up and down thing. Yeah.

It wound up being more of an up thing all the time. But it's good to know that's there. So this available, well, by the time people are reading this, it'll, excuse me.

By the time people see this, it'll be available. It, it's, uh, today a avail dated to, uh, a WS conference available as beta and, uh, the time people see it available as general availability. Excellent.

Good stuff. What else from silla? Um, so it's mostly this.

We, we do have, uh, lots of, uh, things that we develop like tiered storage mm-hmm. Uh, in, in other technology to, uh, reduce the bill. Uh, normally we use NVME for fast storage, fast performance, and it's also relatively cheap co compared to different alternatives of, uh, of storage.

But, uh, SS three is cheaper. The problem with S3 is that latency is prohibitive, heavy, big, it's a 50 millisecond, 100 milliseconds. Uh, and with theater storage, uh, we can keep the hot data on fast and VME and automatically move the cold data to S3 and come, come up with, uh, a good solution.

'cause sometimes you keep, let's say 30 days of, uh, of history on, on, on Sila in the NVME, but you'd like to keep one year of data and, and access it through the same API and not develop a new access for it. So this allows users to, uh, have one API and, uh, a very cost effective solution. I love it.

Good stuff. You know what, we didn't, we didn't even mention the website URL for people who want to go find all this out on their own, dig in a little deeper, what's the, what's the best URL to go to dor? Thanks.

com. com, Just as it says underneath his in his lower third. All righty, Dora, it was a pleasure seeing you.

Safe travels back home. We are wrapping up now again, you, you're seeing this after we were here at, uh, AWS Reinvent, but it's part of our AWS reinvent coverage. And if you need to find this back on, it'll be listed under the event coverage.

But for now, this is Alan Shimmel for Tech Drunk tv. Thanks for joining. Hey, good morning everyone.

It's Alan Hummel, and welcome to our day two coverage of AWS Reinvent 2025. We're live at the win, uh, right here in Las Vegas, covering reinvent. And, uh, I hope you had a chance to look at some of our coverage from yesterday.

We had some really great discussions. We had a lot of analysts, a lot of different AWS partners. We hope to have some AWS people, I think we have scheduled later this afternoon as well.

We, let's kick off our day with what, for me, personally, is a highlight. If you've ever watched our event coverage in the past, this man may be, uh, familiar to you. My friend David DeSanto.

David, well, if you know David, you know this, but David ran product at GitLab for five years, Uh, three and a half years. Three and half years CP and, and yeah, two and a half before that. Yep.

So you're either there about five and a half, six years. Um, always really smart guy. Always a great interview.

I loved talking with him, but he's not here. This is not David Desto of GitLab anymore. This is David DeSanto.

I'm proud to say the CEO of Anaconda. David, first of all, congratulations, man. Yeah.

I'm really happy for you. Oh, thank you. Yeah.

I, uh, truly excited to help Anaconda go into their next chapter. Absolutely. You know, I, I, I didn't hope didn't embarrass you or anything like that, but I wanted to talk about the GitLab experience because for our audience, which is DevOps and Cloud native mm-hmm.

And cyber and so forth, you know, GitLab is a, is an important company in the ecosystem. Um, and you were an important person in taking that vision and running with it. Tell us how you wound up at Anaconda.

Yeah. So first, yeah, it was a great run at Lab. We saw the company grow almost exponentially.

It was, yes, less than 300 people when I started. And my last day was over 2,600. Right?

And so, uh, the journey to Anaconda does start with GitLab. Going to GitLab. I re-embraced the open source community in a way that I hadn't since ICSA labs many years before that.

And that time was great, you know, uh, our first conversation was me coming out and saying like, we are going to add security and compliance to GitLab. I remember that. Yeah.

And then, uh, the last quarter I was at, that's part of their revenue is over 53% of it. So it was a really great run, great company cheering them on. Absolutely.

But yeah, I was ready for my next challenge. And so when thinking about what I would do next, I explored, do I wanna stay in the DevOps space? Do I wanna go back to security?

And I realized I could do security and AI all in one place. And that was Anaconda. Aha.

There's, there's the word, two minutes in, and we've mentioned ai. Yep. Um, Well, I think I said this once before, but you can't spell David without ai, so That's true.

So yeah, This is true. You have mentioned that my, My wife did say, I have to stop telling that joke, but Well, look, we've got a new audience here. You got a new title.

They may not remember it. So David, some people in our audience I'm sure are familiar with Anaconda, but there's plenty of people who aren't. Let's, let's start real foundational and build our way up.

Give us the Anaconda story. Yeah. So Anaconda came out of a consultancy.

The two founders of Anaconda had a company called Continuum Analytics, and they were doing consulting work for data science within the financial services space. And what they found out was that they were building new Python packages to support the work they were doing. And they decided that, hey, this should be a product company.

And so they started Anaconda, the first products name was Kanda. And that's what a lot of people think of that provides thousands of trusted, secure data science and AI packages for Python. Uh, but the company has continued to grow beyond that.

And one of the reasons why I joined is the story that they are currently on. Anaconda can help you with secure python development, but we do so much more than that. Uh, earlier in the year, we launched our AI platform that helps you apply security and governance policies to how AI applications are being built, really.

And, and the, yeah, the most recent, which I'm the most excited about, I cannot take credit for it 'cause it, you know, came out I think three weeks after I started. But, uh, our AI catalyst component of that platform, what it does is provides a curated list of open source models that we have validated or secure. We include the lineage of where they came from, how they were trained.

We I love that. Yeah. We rate them on performance, and that could be in different quant sizes.

And we also then give them the guardrails to make sure that it operates as best as it can. And so what really excites me about it is we already helping people run inference, and it kind of starts a desktop app before we became a platform, and now a, a SaaS offering uhhuh, but the cost to run AI models as part of development is very expensive. Like I learned that when I was at GitLab.

Right. Um, and so what we've done is also make it possible to run a micro in inference on the developer's laptop. Wow.

Which then is that same model that needs to scale so you Don't pay the token penalties. Exactly. And then when you're ready, we can see what you did with the model locally and tune as it gets deployed into production.

So, wow. Yeah. The best way to describe it is, you know, what a GitLab is for DevOps Anaconda is for AI native development.

You know, it's funny you mentioned that term. I was, I was out in Brooklyn actually a couple weeks ago for this AI native devcon. There's this whole burgeoning community, you're probably aware of AI native development.

Yep. Uh, uh, guy Ani from sny, who's now, I forget, the tes is his new company. They're very active in that community.

Um, and I, I went out there, I was blown away. It re it reminded me of going to a DevOps days 10 years ago. Yeah.

Right. That, that same tinkering, geeky, we can make, I love playing with it kind of stuff. And it was, it was a, it's a great community.

Um, let me just kind of shimmy eyes this for, if you don't mind. There. There you Go.

So we, we've got Anaconda started as a company providing services on Python scripts, Helping companies with their data science development. Yep. Hence the Python Anaconda connection.

Exactly. Okay. It then shifts to more of a product model, but it's an open source product model, which is still open source today.

Yes. Oh, Correct. Yeah.

We have a very healthy, free offering. Mm-hmm. Uh, it allows people to get in the door using Anaconda and mm-hmm.

One of the things that really blew me away as part of the process to join was that 95% of the Fortune 500 use Anaconda today. Really? Yeah.

And we have over 2 million, uh, community contributors. That's great. And 50 million users.

2 million contributors, yep. Code, Yeah. Code contributors to the open source Wow.

Components. Wow. Yeah.

It's actually a really great story. Uh, one of the founders is, uh, Peter Wang Uhhuh known very well in the open source community Sure. And within the data science community.

And he's still an active part of the company. Mm-hmm. Um, you know, he and I talk about what we wanna do next together.

Yeah. And that reach that we continue to have is because he is always out meeting with customers, potential customers. Two weeks he's in Boston for a, uh, meeting around how you set some AI standards Yeah.

As part of development. And so we continue to lean into that because, you know, that is really the core of the company to your point. Yeah.

You know, we started as a package manager con, but now we have the AI platform and we wanna allow people to still come up, get used to using it, get the value out of it, and then want to come and then join and, and pay for either our starter tier or our enterprise tier. I love it. I'm gonna jump into what the story, the, the different tiers are in a bit.

I wanna come back to what you were mentioning this newest offering that you're so jazzed about. Yeah. The AI catalyst.

Yes. The AI catalyst. Now look, you mentioned package managers.

It's been a rough couple weeks for package managers, hasn't it? It has, with this shy ude and, and all of that. It sounds like this AI catalyst maybe just what the doctor ordered, right.

If, if I'm a user mm-hmm. Of of package package manage, uh, package packages, I wanna make sure that my package manager's giving me something that I'm not Correct. Introducing malware into my, my ecosystem.

This only works though with the AI models that you're using, right? The AI packages, if you Will. Uh, yeah.

That and all the con packages. Okay. All the condu.

Yeah. So because we still use the Con package manager, um, we have a very unique build system for building all the packages we provide. And so we're able to actually take things apart, fix the vulnerability, and say in the binary part of the package, put it back together, and then make it available.

And so a lot of people think of Anaconda first as a trusted distribution because we're providing, you know, thousands of Python packages that we know are secure and are able to scale. Now. I get it.

Yeah. I got it now. I, it took a little while.

Sometimes I'm slow on the uptake. Oh, no. And, But it, yeah.

If you think about it, there's then that natural transition into the platform, right? It's one thing to start your development, but it's nothing to get that prototype into production. Absolutely.

And, and look, I, you know, just quite frankly, it is, you know, we live in a world of, let's call it Frankenstein software, where software is more assembled than code written, if you will, at some level. Right. And, you know, and you, you, your security background, you know this, we talk about software, supply chain security all the time and, and how stuff, you know, SBOs mm-hmm.

And what have you. I think the biggest weakness in our system today is the software and packages that we're downloading from all these repos and, and, and depots and what have you. So, you know, the fact that you're do, you're on guard here with the condo packages mm-hmm.

Is, is a huge thing. Give us an idea of scale if, you know, you may not know this off the top of your head, but like how many downloads a day, a week, a month? Yeah.

I don't, don't know that off the top of my head, but Kanda is hit all the time, almost 24 7 with people pulling packages. So very healthy community. That's how we can have 50 million users really, uh, yeah.

Using Anaconda every month. The thing that is the most incredible to me is what you just touched on, and this is part of that why I joined in the journey. Uh, you can only do so much with the actual packages themselves.

Yeah. But when we're talking about the platform, there's like the starter, which is kind of like, hey, a team's getting together. Uh, but the business tier actually provides what you're talking about.

It provides an AI bill of materials, can track vulnerabilities for you. Uh, we're working on how to help auto remediate those as well. And so the customers that end up on a thing, like the business tier of the platform, they're getting, uh, full visibility to their AI lifecycle.

And that's really powerful. 'cause as you said today, it's very common that vulnerabilities will sneak in some way. And we're heavily reliant on packages that we've not created.

We're reliant on our IDs to be secure. We're, you know, worried about the things that happen after the code has merged. And anacon just in a really great spot to help with all of that.

You really are, you're right, you're right at the, the nexus, if you will, of, of where all these come together. I love it. Now you mentioned different tiers.

Mm-hmm. So obviously there's probably a free open source tier where hey, it's open, it's open source, have at it. Then you have, you mentioned the SaaS model.

Yeah. So the product, uh, you can self-host. Mm-hmm.

com. Mm-hmm. Um, but yeah, the big difference is not necessarily whether you're hosting it yourself or using our, our SaaS offering.

It's really about the free version gets you up and going, if you're an individual developer, provides you a lot of power. If you now wanna operate as a team and start having some structure around it, you go into the starter tier, which starts to introduce a lot of that. Um, but when you're ready to talk about AI bill and materials security and governance and having policies that prevent malicious packages from being installed, then you end up on the business tier.

And that's where all that security and compliance functionality is, including dashboards, policies you can create and so forth. I love it. Um, to, so I'm an old school open source guy.

What per 50 million users is a crazy number. Yeah. You may not know this, you may not be comfortable even saying it.

What percentage of those are just pure free open? I mean, usually it's 98, 90 7%. Yeah.

Yeah. So there, uh, a large percentage of it is that open source community. Sure.

Um, but that's something that's very important to us. Sure. It is.

You know, what I learned, uh, working with Open Source, I'm so excited to be, you know, leading a company that has a open source first mentality is that like you get more value out of that free tier than you could if you tried to bundle that up and put into a paid tier. And it's ultimately because you get all those contributions, you actually are able to get onto the community, be it events like reinvent Yep. And have conversations with the actual builders and doers.

And that's not something that commonly happens if you only start with a paid option or you're not open core. I love it. Let's, um, let's talk a little bit about Reinvent.

You mentioned it. Yeah. We're here.

Um, is there like a formal partnership? You know, what, what are you doing at Reinvent? Yeah, So we're in Booth, uh, 1327.

Mm-hmm. So if you're at the show and you wanna check it out, you're Watching this live now, you wanna run down there, go run down, Run, uh, before we run out of giveaways and swag. Right, exactly.

Uh, some really great swag. But, uh, in our booth, we're actually demoing the AI catalyst offering and we're showing people all the other things anticon can do that aren't not just, you know, being a package manager, uh, but to speak to the partnership, AI catalyst this new com Yes. Part of our platform that launch exclusively on AWS and we joined, announced it yesterday.

Oh, great. Uh, and it also included that it's now available in AWS marketplace. You can go and buy it yourself.

You don't have to go through all the hassles of like, the steps to get to that point. I Love it. What do you, and so yeah, that's a good example of the partnership mm-hmm.

And spill right on top of AWS but there's so much more we're looking to do with them. Uh, you know, we're looking for better integrations into Bedrock customers, like using Anaconda with Sage. So getting a nice embed story there.

Yes. We were just talking about SageMaker this morning on Dextron Gang, And so yeah, the partnership is great, but we're just gonna keep on building on top of it because they're a really good partner. You know, I've worked with them across multiple companies, and yes, they're always exactly as great as they seem, and that's really great to have a partner like that.

Absolutely. You, you know what's interesting is I I, I, we were talking off camera and I, I mentioned, you know, this year's reinvents a little different. It's very AI focused and everything else, but I'll tell you what it is focused on, it's laser focused on developers.

Mm-hmm. Right. They really are kinda reestablished because when you, you know, you've been around, you know, I know it was the developers who made AWS Yeah.

It was those guys whipping out their credit cards and, you know, building and spinning up instances and, and doing stuff that, that made AWS what it is. And it, there is a renewed focus on the development process. Of course, AI is changing how developers develop, and it sounds like you're, you're responding to that as well at Anaconda, but make no mistake, that's the focus here.

Right? Yeah, no, what I would say is, I, I took away a couple things, uh, just from Matt's opening keynote. Yes.

Uh, the first is, it's all about the hardware. And I think that's something that people don't always think about. You know, we were talking, Well, that was supposed to be the thing about cloud.

You didn't have to worry about the hardware. Yeah, that's a good point. Uh, but I was gonna say the, uh, you know, when you're talking about ai, it kind of starts at that, right?

Yes. You gotta have the right tructure. It's made hardware sexy.

Yeah. And so to see that lead off with mm-hmm. What they're doing to make it a lot approachable for non-developers to get into an environment and know it can work was really good.

Uh, definitely the AI lean in mm-hmm. Uh, was very, uh, prominent as well. But the one thing I would say, uh, and it, I can't believe I'm saying this, like it's my first reinvent, you know, but what it feels like is like, if you were to take, uh, a cube con, make it significantly larger, Four times the size, And it's only about the developers.

Like that's the, the vibe here. And it's actually great. Yeah.

So this is, I don't know how many, certainly since COVID is the fourth, probably yet since COVID alone, um, this is pretty much it. It's, it's, it's a, I mean, you know, it's nice. CubeCon is the, like a perfect size.

Mm-hmm. 12,000, 14,000. It's big, but not too big.

It's a, it's kinda like building a company, right? Mm-hmm. You could build a company that has 10 million, 15 million in revenue, and you have one kind of management team.

You go wanna build a company that has 75, a hundred million in revenue. It's a different management team. Mm-hmm.

You wanna go build a company that's IPO-ing, it's a totally different animal. It, it's the same thing with conferences. You get a conference of 60,000 plus people.

Mm-hmm. You, you, you know, hyperscale, it's, it's, it's about scale and they do a great job with it, considering everything that's going on here. Yeah.

No, and I would say too, for those who are watching this and are here but haven't really like, gone over to everything that's going on, it does not feel like there's that many people here. Like, they've done a really good job keeping Yeah. Well, it's spread out so forth.

Yeah. I, I agree with that. You know what, David, we didn't even mention the website, how to engage.

Of course. I mean, obviously it's open source, you can get it, but what, what is the best website? Yeah.

com in there. It'll point to the dis uh, installers. If you wanna install locally, it can walk you through creating an account for SaaS and getting up and running really quickly.

Uh, the other thing is that if you just go to like the doc site as well, to your point, you'll learn about more of the open source focus and how you can contribute code. com. But ultimately, like what I would say is if you're looking to build AI, and you might not be a developer, or in some cases, you know, I won't say I'm very young, but like I programmed in, you know, c out of college, right?

But I don't know how to get into Python. With Python now being the number one language worldwide, anacon can help you with all that helps you build applications even if you're not technical. Well, AI could help you with it now too, right?

Yeah. I would imagine Anacon is going to use AI to help. If you don't know how to develop in Python.

You don't know Python. Yeah. To teach it to you and help you develop it.

I mean, it's a crazy world we're coming Into. Oh, no, for sure. And what I would tell people is like, it's so easy to get started.

I, as part of the interview process, wanted to play with the product and you can get a cloud notebook up and running with one or two clicks, really? Uh, yeah. The a Anaconda AI system is just there, uh, it's front and square.

And I was asking it questions of things that I used to do 10 years ago with Anaconda, like, how do I do this today? And it was very easy. I felt very, uh, productive and able to actually build something without having, you know, a lot of this, the knowledge that was just built into the platform.

So, yeah. So Lemme ask you a hard question, David DeSanto, do you still consider yourself a developer? Yes, I do.

Okay. I do. And and here's why.

There, you know, um, I was a developer for a long time. I was an engineering leader. Uh, people say I went to the dark side to go into product and Uhhuh, I don't think David graduating from college would know that David would be CEO of the company, right.

Company. But those roots are still really important. And so whether that is me building stuff to play with, uh, me working with our engineering team and finding things that maybe we can make better, you know, it's great to roll up your sleeves and just be in that, especially with a very technical company.

And I won't tell you the apps they built are pretty bad, but, you know, but They don't have to be great. The fact, you know what I, I said it tongue in cheek. Yeah.

But the fact of the matter is, is it, I always tell my team, you gotta be able to walk the walk, not just talk the talk. And so the fact that you could play with it and make some, it doesn't, doesn't have to be the greatest app in the world, but you could get your fingernails dirty with it gives you a perspective that helps you understand who your customer is, who the users are. Yeah.

It's Important. No, and you're right. And you Can't be too abstracted outta that.

No. And what I tell people is like, even though I'm now CEO of a company that's almost 500 people, when we announced our series C, we're at 150 million in revenue. You know, it's still important to me to be thought of as a developer and like a vulnerability researcher.

Mm-hmm. Because all of that is what has helped me be successful in my career. And so what I'd say to people out there who are like, I dunno what I want to do or do I wanna switch roles, go to a different company, you know, find the thing that you wanna do and just do it as best as you can.

And it's just so rewarding. And, you know, Anaconda is there to help people take that journey for themselves. I love it.

David, man, congratulations. Best of luck at Anaconda. We, you know, I'm sure now that you're there, we'll be talking a lot, doing more, looking forward to hearing great things.

But this sounds like a great opportunity for Anaconda and a great opportunity for you. It's a good match. You know, thank you very much for having me and I always love the catch up.

Oh, It's a pleasure. All right. com.

Go check it out. We're live at AWS reinvent. We're gonna be back in just a minute.

We've got tons of great stuff coming up. Stay tuned. It's the end of yet another year.

And we are here to take a look at all of the big stories from 2025 with a variable degree of snarkiness in this episode of the Tech Field Day rundown. Hello everyone. Welcome to the Tech Field Day rundown.

It is December the 17th. It is getting very close to the end of the year and we wanted to take a look back at the last 12 months to kind of give you some highlights of some of the big ideas in the news. We covered a lot of things over 2025.

There were a lot of great things going on. We hope that you are enjoying some pancakes with maple syrup 'cause it's maple syrup day. Um, and it's also national Say It Now Day, which honestly is kind of the thing that we do around here.

We just say it like it is now. Um, well on Wednesdays at least, uh, joining me of course is my co-host Alistair Cook. Al, it's good to see you again.

It is always a pleasure to be here and particularly on Pan-American Aviation Day. My first international flights were on PanAm and flying to the United States. So great to be here on Pan-American Aviation Day.

Well, speaking of flying this year really did fly by. Um, we're gonna go ahead and jump into the first set of stories, though Al and I think you're probably the best one to talk about it because I think 2025 we'll go down as the year of ai. Yeah, and we kicked off this year of AI with a future and survey of CEOs asking about their AI readiness and the use of AI.

And, and the results really painted a picture of unfulfilled promises. We saw paralysis and slower moving firms who had no idea what they were doing with AI in the survey. And we also saw that more cloud native, uh, companies were still struggling with cohesive AI strategies and cohesive infrastructure.

So beginning of the year, AI was definitely, uh, a challenge for some vendors. But Jensen Wang, the rockstar, CEO of Nvidia, was much more bullish. She presented in CS in January and announced that the Cosmos, uh, was a World Foundation model that, uh, of course Nvidia shipping and predict widespread adoption of ai.

Of course, also in January, I sort of love the story of a, uh, sticky trap for ai. Crawlers. Developer was sick of AI data gatherers that don't respect any of the fair use, uh, norms that are happening out on the internet, as well as some of the mechanisms like robots dot text that limits crawling.

So this developer created a, uh, mechanism to create random cross-link pages with no real content and trap these AI trawlers in just a little corner of their website. But humor would know not to look at. But AI not so much.

Of course, January brought some really big AI news. It's when we learned about deep seek. The Chinese ai, uh, trained for far less cost than other LLMs.

Uh, also heavily censored dataset. Don't ask it about Chinaman Square. Uh, and also trained on a bunch of AI generated content.

And this was the point at which we thought, hmm, is it wise to feed an AI with AI generated content? Seems a little bit like some of the problems they had in the UK with chicken where they were feeding bits of old chicken to brand new chicken and got disease right through the, uh, the feed line. That doesn't happen to ai.

Of course, AI being the thing that unifies all marketing departments this year, uh, February brought the news that WCA had restructured, was reorienting itself towards a generative AI company. And at the same time, hammer Space announced that object storage is not the only option for AI training data. This definitely shows us two successful specialist storage vendors confirming that AI is still the essential marketing term for 2025.

Heck, we're belly into February and Cisco and Nvidia decided they were gonna partner up as well, bringing Nvidia Spectrum X management for Cisco switches. And of course NVIDIA's Bluefield SMARTnet into, uh, Cisco devices alongside the Silicon one. While we're talking about hot chips, uh, Broadcom launched their Tomahawk six switches giving over a hundred terabits per second throughput in a single device.

Uh, this team presented at the AI infrastructure Field Day, showcasing the Tomahawk Ultra and Jericho. And as well, we've got massive training networks. You know, we skipped a whole bunch of the run rate kind of announcements around ai, but we did notice that in November, uh, AWS talked about agen to, and particularly using Agen to help customers migrate from on-premises platforms into the AWS cloud.

I think we've seen a lot of progressive age agentic AI tools through the year, and hopefully some of that confusion and paralysis that was happening at the beginning of 2025 is starting to clear and companies are starting to get some value out of building AI solutions and finding better tool sets than maybe they had at the beginning of the year. Another group that's been in the news a lot this year has been Intel. There's been all sorts of trials and tribulations with Intel.

Tom, I think they're one of your favorite companies in, uh, our coverage along the year. They are. And we've spent a lot of time talking about them because quite honestly, there's been a lot of news.

If you remember, we closed out 2024 with a lot of bad news, honestly. So how did 2025 start out? Honestly, it was a lot more down than up.

We found out back in February that Intel was not going to release Falcon Shores. This was a big hit to what a lot of people considered to be kind of one of their make or break moments. Uh, there started to be some rumors around that same time that Broadcom might be looking to buy out some of those chip designs that Intel had been working on on the cheap.

You know how it feels whenever the Vultures starts circling it, it's maybe time to hang it up. And then news came that that big Ohio fab facility that Stephen Foskett has been so excited about was gonna have to be pushed out a few years because of construction, but also because of cash flow issues and some potential changes to the CHIPS Act. And it was not really looking good for them.

Now, one might be forgiven for thinking that the appointment of a new CEO in March was gonna be the start of Intel's big recovery. And lip Bhutan came in really ready to work. He created some big restructuring plans for the organization.

He did things like brokering the sail of controlling interest in Alterra to Silver Lake. You know, that was a, a big thing that Intel was very prideful of, and maybe this was gonna give them some cash to turn the ship. Uh, the rest of the next quarter though all we could talk about was Intel's layoffs.

And, and some of these were confirmed kind of publicly. Some of them weren't. And, and we were hearing lots of numbers, you know, 15,000 here, 17,000 there.

It really, really hurt the industry because those thousands of jobs almost had to be sacrificed in order to get Intel back on track to their core business. But layoffs are never good for any companies like this. I mean, things were looking pretty bad for Intel.

I mean, how could it possibly get any worse? I know back in August, president Trump said publicly that lip Bhutan had conflicts of interest and he needed to resign as the CEO of the company. I think that's pretty much the point that they hit rock bottom.

But I know that that's the case because that really was the beginning of Intel's comeback in 2025 because it took less than a week. And we started to hear that Intel was securing investments. SoftBank bought in, Nvidia bought in, and the US federal government made a deal that would see 10% of Intel's proceeds going back to the US government.

After that, everything took off. Intel was no longer the punchline of a joke and also ran in the company. It was more than that because we started to hear rumors that maybe Apple was gonna be looking at Intel again as a partner on chip manufacturing.

We also saw that because of the announcements of all of those investments that Intel decided not to sell off their networking business unit. We just covered that, uh, last week. On the rundown, it seems like money fixes all problems.

Intel, most importantly though, is positioning themselves to be the domestic chip manufacturing giant, because one of the things that we've seen that's kind of been a subtext for everything going on this year is the looming tariffs that are being positioned against companies being used as weapons against other investment vehicles and Intel. Being a domestic chip manufacturer is effectively tariff proof instead of having TSMC need to come over and build a fab in Arizona to beat those tariffs. Intel already has fabs positioned everywhere, and they're trying to do more.

They just need a little bit more runway in order to be able to pull that off. Honestly, at this point, time will tell if this is enough to get them back on the right track, but having weathered everything that they've weathered this year, I can't imagine that Intel has much less position to fall from because they are slowly gaining the momentum, which doesn't seem like a lot against what's been going on in the AI market, but there is an opportunity for them to kind of right the ship. Finally, another thing that made the news this year was the massive spate of outages that really kind of knocked some knowledge workers off track.

Al These outages started fairly small in January. We saw, uh, Utelsat having an outage that left one web's broadband services offline. Now, a lot of the time these were backup services that were used in locations with the primary internet connectivity.

Wasn't great few places, it was the primary connectivity. So a couple of days without internet, that was pretty significant, but only for the small number of people who are significantly affected. In April, we got a bit of a warmup, uh, to the outages when Zoom.

Uh, zoom got taken offline for 90 minutes and all of us, uh, had breaks from having calls. Uh, we got actually to have some productive time since we weren't on Zoom calls all day for those 90 minutes. Turns out that, uh, GoDaddy, who hosts all of the US domains had for some reason blocks had zoom's DNS domain.

Maybe DNS is the root of all errors. Things scaled up quite a lot in June when we started to realize just how much popular applications like Spotify and Discord and Snapchat depend upon. In this case, the Google Cloud.

Uh, Google rolled out a new feature for some, uh, quota policy checks and they weren't adequately tested. And so a bunch of popular applications were often we had to go other places in order to complain that we couldn't get to the internet. A little later in June, we also saw a record breaking DDoS attack.

3 terabits per second, primarily of UDP frames being sent by the Mariah Botnet, uh, was all being soaked up by CloudFlare, one of our largest DDoS protection networks in the world. And so hopefully it didn't take everybody offline along with it. But what we did see in October was something big.

What we saw in October was that we have become very dependent on both DNS, but also some of the core services inside AWS. And so again, it was a DNS fault that AWS, uh, had put in as a anti DNS record for the Dynamo DB database, dynamo DB as their massively scalable key value store database. Uh, without DynamoDB, lots of business applications went offline.

Lots of aw WS services went offline. We really saw this lots was out and down and not working. And we realized that when we started using these ubiquitous web services cloud services, we became very dependent on them.

And while the individual services are incredibly reliable, when they do go down, consequences are huge. So these services typically are very reliable yet because everybody uses these highly reliable services. If they have an outage, the impact is immense.

And if you've been running your own database on premises, or if you've been running a database inside a virtual machine somewhere, the impact would've been far less. It would've only been on your own services, but the likelihood of that failure would've been a little higher. People make mistakes.

So, uh, sometimes there's a, a reflection here that using these big web services is a bad idea because we see headline news when it goes down. But what you don't see in headline news is just how often services inside organizations go down. So it's not fair to ta these, um, single points of failure that we built into our systems as being the, the worst thing ever.

Um, but nothing is a hundred percent uptime. CloudFlare came back for us in December. Uh, they forced an outage because they saw an actively exploited critical vulnerability.

This reactor shell export was, was, um, being used in the wild to run arbitrary code on, uh, these, these websites that we're using the React framework. Uh, CloudFlare basically DDoS themselves. They, they shut down those sites until they could get a more targeted mitigation in place.

And so there was a, a period of time where CloudFlare was brought blocking access to these vulnerable sites before more targeted reactions can come in place. Uh, I think Reactive Shell is gonna continue to be in the news for us for a little while. It's gonna be the new version of the Log four J problem, where lots of people didn't realize they had vulnerable code because, well, it was a dependency for the thing they actually wanted to have, and it turned up in all kinds of places that they didn't expect it to in terms of outages.

We did see a whole bunch of outages this year. We realized that the cloud is not a golden bullet that takes away all of our reliability problems in our applications. And even if you follow the best practice designs from the cloud providers, you're still not gonna get 100% up time for years and years and years.

There is still the possibility for things to truly mess up. Another company that's been in the news a bit today, and a good friend for us here at Tech Field Day has been HPE, uh, that's the enterprise part of what used to be hp separate from the people you buy your, uh, laptops and, and your printers from HPE is the, uh, large scale stuff we put in data centers and, and run networks around. Tom, you've been across all of this because a lot of this news has been networking related.

It has. We really were kind of curious as to how this thing was gonna start out in the year, because we had started to hear rumblings that possibly the US Department of Justice wanted to take a closer look at that proposed acquisition that they had of Juniper Networks. Well, February, almost one year to the day after the big blockbuster announcement, the DOJ decided, no, no, no, we're, we're gonna block this acquisition for the time being.

We need to take a closer look at it for, uh, I don't know reasons. Uh, we also got news that there was potentially some exposure from hacking group Intel brokers getting in. They, they might've gotten away with some source code.

That one's kind of still ongoing. Even to this day. We, we don't know exactly what happened.

But then a couple months after that, in April, we got the most terrifying business news that a company can get. Elliot Management took a position in HPE and immediately we started to hear some questions about Antonio NE's leadership, which is quite honestly kind of part and parcel for how Elliot works. Uh, they wanted to maybe make a change in the CEO chair or potentially get some board members up there that would, uh, look to unlock some of that mythical shareholder value that they seem to keep looking for, but can never seem to find.

I think though that Q2 was really the start of HPE really becoming more of a unified company because back in May we saw that they really integrated their Morpheus acquisition into GreenLake, and it helped fill out that offering a little bit more completely. You may be wondering to yourself, well, what does GreenLake offer me? Well, if you're someone who's looking for refuge from the things that have been going on over at VMware by Broadcom, GreenLake has an opportunity to kind of supplant that.

And that's how HP has been positioning Morpheus over the year. They're basically saying, if, if you're not happy with what you're getting there, we have an offering that will work with you and we're gonna provide you the same kind of support that we always have. We're just gonna be working on a different hypervisor.

And people seemed to be responding well to that. Then at the very beginning of July, the Department of Justice came out with a list of some recommendations that Juniper and HP needed to do that would allow them to clear that acquisition. They were a little bit strange.

There was some discussion. We even recorded an episode of the Tech Field Day podcast about it, and it took no time at all for the companies to agree that, Hey, we're gonna do that. And there you go, we're gonna close that acquisition.

It's almost like as soon as they found out that this was an option, they're like, book it done. We're out the door. Let's just do it.

No buyer's remorse here. After that, we saw HP and Juniper really ramp up efforts to provide a combined offering, uh, whether it was new agentic AI offerings, integrating some of their product lines. As we started to hear around the time of HP Discover Barcelona towards the end of the year, including if you were reading between the lines, some very forward looking language that kind of will show you what the combined offering will eventually look like.

But that doesn't mean that networking was the only group that was making advancements. The server side of the house did a lot of integration work, and they even picked up a deal to offer private cloud to the US Department of Defense, uh, as a way to kind of, you know, kind of jump in as to some of the remnants of those big projects that we've seen getting bounced back and forth between Microsoft and Amazon for a while. They also released their Gen 12 server line, which is another big step in continuing to refresh the product lines that everybody feels are so critical to HP success.

They also did a lot in the cybersecurity market. They included things like AI-based DDoS protection, and more importantly, they complied with some new European regulations that will require organizations that are undergoing outages or ransomware attacks to be able to isolate themselves from the internet to be able to clean those things up and not create a, a larger impact all over the market. And as we know from the last couple of years, not being compliant with European regulations is a sure way to get yourself in a lot of trouble real fast because the European regulators do not mess around.

Now, November was a bit of a mixed bag for companies that had partnered with HP because we saw that they had dropped support for some of their storage partners like Qumulo Scalability and cca. I think though that this is really more of HP refocusing their efforts on integrating their own storage offerings back into their lines and working with very specific partners in very specific situations. As we've seen by the breadth of what HP offers, they really do wanna become the one-stop shop.

Whether you're doing something in the campus, whether you're trying to build a cloud integration, whether you're trying to work with ai, they want to be the sole source for everything you could possibly need. And if you wanna buy it, they have options that will do that for you. If you want to rent it through GreenLake, they're happy to do that as well.

It's one of those things where we're getting back to the kinds of service offerings that are important to the clients that are out there. It's not just a matter of, give me these parts and pieces, give me the expertise that will allow me to integrate them together and to make something out of this so that I have can have my knowledge workers focusing on outcomes that are business aligned instead of navel gazing about a bill of materials and some pieces and parts that maybe don't make a whole lot of sense. Now that I, we've kind of dropped all of the drama of whether or not some of these acquisitions are actually gonna be able to be done.

I think it's going to allow the leadership at HPE, including current CEO Antonio Neri, to kind of chart a course to not only keep their customers super happy with what's been going on, but keep the shareholders off their backs long enough for certain activists to maybe exit that position in the market. I kind of hinted to it in this story, Al, but AI continued to be a huge part of what was going on in 2025, and you have a slightly different take on it with some more news stories. Yeah.

And, and this story comes in two parts, and it's about buying, selling the inside baseball side, the who gets what bits of AI and, and the hardware for it and those kinds of things. The, the first part of it, and this started at the beginning of the year, was around who's allowed to buy what hardware and what are the impacts of tariffs. But as things rolled through into later in the year, the focus shifted towards what looks like a whirlpool of AI funding announcements.

So the, the first part began in 2025, beginning of 2025, in, in January when the Biden administration started coming to the end of their, uh, the, the governance were looking to restrict AI chip exports. About 20 countries were on the, the books of being, uh, places to restrict. Of course there was some responses.

Um, there was the statement from TSMC that, uh, semiconductor trade with, uh, Taiwan and the US was win-win for both. And of course, Nvidia argued that AI expert controls will be detrimental to Nvidia export controls were put in place. Uh, Nvidia high-end GPUs weren't allowed to be take sent to places that was particularly targeting, keeping them out of China, where they might be used to create some sort of, uh, large scale, uh, commercial or even uh, military benefits.

So, uh, we covered some stories around, uh, Singapore, uh, arresting some alleged NVIDIA chip smugglers who were apparently sending, uh, the BA Blackwell GPUs to China. By the time we gotta August tariffs were coming along and US companies were concerned about high cost of imported gls and servers, including GPUs and other semiconductors that were manufactured outside of the yield here. And, uh, the odd, again, off again, status of these terrorists throughout the middle of the year was a source of some tension.

But by Midgut, Nvidia had agreed to pay the government 15% of its revenue from GPU sales to China and return for being allowed to export the medium powered H 20 GPUs. But by December, the US government was allowing Nvidia to sell the powerful H 200 GPUs. That is quite a difference in the year from banning, uh, exports to hundreds of countries, well over a hundred countries to actually allowing the, uh, the big enemy to have the most powerful GP news.

I guess it reflects that China didn't actually hang all its hats highly on getting Nvidia GPUs 'cause they're building their own the AI money go round as the other story. It heated up, I mean it started early on in May. OpenAI acquired Johnny i's new company IO for $6 billion, despite the fact that IO didn't have any product description and that the company name was impossible to trademark and really hard to find on Google.

Uh, but by June AWS was announcing they were gonna spend $20 billion building two AI data center campuses in Pennsylvania. In July, Oracle made a big of a announcement, $30 billion when an unnamed client who were going to buy three times Oracle Cloud's current size. At the time, Oracle Cloud was doing about $10 billion a year in, uh, cloud infrastructure.

This announcement was 30 billion. Turns out the unnamed client was open ai. Then in August, meta committed to buying $10 billion of AI compute from Google Cloud.

Part of a $72 billion commitment that meta was making to building AI data centers. That's a lot of money being spent carrying on. In September, we got one of the first of the very circular feeling deals.

3 billion worth of GPUs to Core Weave. But if Core Weave couldn't sell that GPU time that they had just bought, Nvidia would buy it back off. Seems a little odd following that.

We also had, uh, NES and Microsoft, uh, announcing a $19 billion deal in September. Uh, Microsoft is buying some GPU capacity in Europe to run AI systems to complicate matters. Microsoft's also an investor in Core Weave that we just saw doing a deal with Nvidia and NVIDIA's an investor in NEAS that's just doing a deal with Microsoft.

And then in October we got even more serious numbers, uh, Microsoft this time with open ai, $135 billion invested by Microsoft into open ai. And so open AI will mostly be on Microsoft PLA platforms from that 30 billion that's gonna Oracle Cloud as well. Just to make another circle, in November, Microsoft and and Nvidia announced a set of investments.

So Nvidia investing $10 billion in anthropic, Microsoft adding another 5 billion. And in return, anthropic will buy $30 billion worth of cloud computing from Microsoft, which is their way of procuring a gigawatt. Uh, it must be more than a gigawatt, uh, multiple gigawatts of n of uh, Nvidia GPUs.

Uh, I wonder how Anthropic getting all of this money in throwing all this money out, how are we gonna make some profit from these investments course at the same time? Uh, Nvidia committed to buying $26 billion worth of AI computing from a variety of providers, all of whom get those GPUs from Nvidia. There seems to be an awful lot of money going around in circles or some weird shapes to get between all of these different players.

And we're still not sure we and customers are actually going to want to pay for all of these things. Whether there is going to be business value for all of these billions of dollars running around and these massive data centers that are being built out over time, hopefully 2026, we're gonna start seeing some actual profitability from these AI startups. And we're going to start seeing that businesses are being transformed in a positive way by the use of generative AI and massive numbers of GPUs.

Of course, there could be a fly in the ointment as a new piece of technology comes along and changes everything. Uh, gotta be looking for all of the announcements of what's happening in the quantum computing world. And Tom, you've been following this a little bit this year.

I have, and quite honestly, for those of you that are already tired of all of this AI hype, I've got good news for you because the next big thing is on the horizon. Well, if you look closely 'cause it's small, really tiny quantum computing really has been around for decades. 2025 saw some advancements in the technology that we talked about quite a bit.

Uh, February ended with a considered to be a blockbuster announcement from Microsoft about the major run quantum chip. It was rumored to have 1 million qubits of capacity and it also came in a really interesting form factor and did some things that people hadn't expected to see out of a quantum chip before. And that got a lot of people kind of talking about what was, what was gonna be happening.

Then we saw some novel new applications. Uh, one of the ones that I was kind of proud of was the fact that people were using quantum super precision for things like navigation. Uh, big important thing there was because it couldn't be jammed.

So if something were to happen that GPS would get taken out. There you go. Then we saw some announcements from Cisco around quantum networking.

I highly recommend you go back and watch some of the quantum networking discussions that we've had with Cisco over the years because they actually have some really cool stuff. Google announced that RSA encryption, which is kind of considered to be the watermark for when quantum will become supreme, could potentially be broken a lot easier than we were expecting. Instead of it taking hundreds of thousands or even millions of qubits, it could just be done with a few thousand that were no noisy.

The second half of the year focused on other big announcements that were a little bit more in the affordability range because there was a Chinese company called Quantum Ctech and yes, it's not spelled like the one from sneakers, but it's close enough to make you think. And then HSBC coming out with a discussion about how Quantum actually helped boost their trading algorithms, which of course had all the wa the tongues on Wall Street wagging as soon as possible. And then at the end of October we heard from IIBM and A MD because they were using traditional X 86 based architecture computing to be able to run error correction algorithms against quantum computers.

The reason that that's important is because instead of tying up those quantum computers being able to correct their own errors, we can use things that we've already built and deployed to kind of accelerate that. And that is a huge cost savings when you consider how much per watt or per second that these things cost to operate. Then the US government announced that millions of dollars in investment were gonna be sent to startups in the quantum space through the CHIPS Act, which was kind of bandied back and forth quite a bit over the year.

And it was good to kind of see the government stepping up and saying, we want to use this CHIPS act, uh, funding to kind of invest in some of these places. Now, the future of quantum computing looks very bright to people who want to play the long game. I know that AI is getting a lot of these headlines right now, as Al has pointed out in the last couple of stories, that there's a lot of people who are wanting to pour as much money as they can into ai, but no matter what, you've gotta play a longer game because there's still some physics things to overcome here and there's a lot of other things that need to be considered when you want to do quantum.

I don't care how much quantum foam is being churned up right now. I don't think we're seeing the bubble of quantum being inflated just yet. One of the things that we wanted to get back to, of course, is some more traditional stuff that we, we see and hear a lot, and that's our good old friend in the last hype cycle, cloud computing.

And al that's your area of expertise. What stood out to you about cloud this year? You know, cloud's full of ai and so there's a whole bunch of AI stories that I'm not gonna rehash here, but all of those AI stories were about cloud.

I liked a story we covered in March where a survey of companies, uh, showed that they're using a lot of cloud financial operations or finops tools and identifying which parts of their estate might be better on premises than on cloud, as well as optimizing their spend on cloud. I think it's interesting to see that maturity level coming through with applications being repatriated to on where that predictable costs might align with predictable workloads and clouds being used more as places to put things that are more bursty, more inclined to go up and down in their utilization or that are more commonly accessed over the internet alone. Uh, oh, I managed slip some AI in here 'cause Google announced a whole bunch of AI capabilities at Google Cloud next, along with some nice application platform enhancements for its customers and staying with Google in July meta committed to spend $10 billion over six years buying capacity from the Google Cloud.

Um, that's interesting in light of all of the other spend on Google Cloud for other purposes. In the earlier stories also in September, a US judge put an into the antitrust case and allowed Google to keep the ownership of the Chrome browser. Thought the browser wars were over years ago, but the lawyers disagreed.

One of the most surprising stories, it's a cold day in a typically hot place, uh, in December when AWS and Google announced that they'd built unified software defined network interconnects between their competing clouds. That's a huge win for customers who have ended up with the reality of a multi-cloud and maybe hybrid multi-cloud estate. Uh, now Google and and AWS allow you to glue their clouds together without a huge amount of manual effort.

I really do hope we see more cloud providers joining this scheme and that we get a much more unified way of dealing with the hybrid multi-cloud mess that is enterprise it. You know, there's lots going on in the cloud. We particularly saw, uh, a lot of stories around application building, Kubernetes and all of the tooling that you need to get value out of the cloud over the year.

Another thing that's happened over the year is, of course, mergers, acquisitions, purchases. Uh, we always get interested in who's buying what and from whom. And Tom, you've not had the opportunity to buy a large tech company or sell a large tech company for an exit, have you?

Hey, you know, the year is still young. Now we, we talk about these, uh, over the, uh, course of 2025. One of the things that I do wanna remind everybody is, is we, we focus on enterprise tech, right?

So we're not talking about media companies buying each other. We're not talking about bidding wars for content libraries, that kind of stuff. We focus on the things that are happening kind of behind the scenes.

I think maybe the biggest acquisition this year in enterprise tech had to be Google buying Security Company whiz back in March for $32 billion. And it was big not only because of the number attached to it, but also it took a lot of time to make this happen. Analysts were hot for it and then we heard it might not happen then we're back on it again.

This back and forth will they, won't they shipping thing that happened? Not a fan because it just kind of messes up our rundown stories where one week we're like, oh, they're buying it and then the next week, no they're not. And then two weeks later, it turns out they are.

So, I, I don't know what to say about that. Uh, security acquisitions continue to be big. We saw Palo Alto Networks picking up protect AI and CyberArk to kind of fill out their portfolio.

The data protection market continues to get really interesting. Commvault bought si, Satori Cyber. Veeam also bought a company called Security.

I thought that was kind of neat. Uh, cloud security group acquired Arc Terra, which you may not know of, but you may have heard the company that they were before. That was Veritas.

Networking was no slouch as well. We saw that Nokia purchased infinera and some of our friends over at Ventiv were picked up by Hubble. Uh, it's maybe not a company that you've heard of, but you've probably heard of one of the other brands that they own a cell tech.

So kind of some, uh, consolidation of the antenna market over there. Arista bought VeloCloud to bolster their SD WAN offerings. There was another one of those.

Hey, we're hearing rumors that this might happen and it took a couple of weeks for it to kind of come to fruition. I I loved being on a call with some of my Arista friends and kind of asking them casually, jokingly, and they're like, we don't know what you're talking about. Uh, network providers also kind of reduced some competition in the market.

Uh, ISP Cox Communications got bought out by Charter. That's kind of primarily focused in the residential areas. So most of you're probably at home watching us on a link provided by them.

At and t also picked up CenturyLink, so that's kind of more of the, the bigger provider side. And of course it wouldn't be a year without private equity going out and buying some folks. Uh, they bought SolarWinds, they also bought scale computing and there were several others that got, uh, snapped up and you know, we're still kind of waiting to see where that comes out.

And everyone's favorite bigs uh, private equity firm. SoftBank bought into Ampire because they wanted to invest in the coming armed data center market. AI was very active.

Al already kind of mentioned that OpenAI bought Johnny Ives IO device and we also saw some acquisition from, uh, core Weave. They bought Core Scientific and Marmo. Qualcomm wanted to get involved not only in AI but in a, in chips.

And so they bought a company called Alpha Wave. You can tell based on all of this, there's a lot of investor money that's floating around and it is aimed at making some strategic advances in the enterprise technology market. There are a lot of large companies that feel like they're falling behind in some of these areas.

And there's a lot of small startups that are usually founded by people that used to work at those large companies who are aiming to address some very specific pieces. And then usually what happens is, is that those large companies use their war chest or their investment money to go out and buy those companies and continue to integrate them in. But the good news for us is that that tends to create these nice golden colored parachutes for people to go back out into the startup market and kind of attack those areas where they feel there's a gap that can be done.

And this cycle repeats itself over and over again. As long as there are people that are willing to invest in small companies to solve big problems, there are big companies willing to buy small companies to solve those big problems. And the nice thing about that for us is that it makes great fodder for rundown news stories.

Al it's been a really interesting year of uh, being able to cover the news, uh, being able to see what's been going on, but it's also been a big year for Tech Field Day because when we're not laxing intellectual about the news and things that are going on, we're talking to a lot of those companies who are innovating, who are um, investing in these markets. What was one big highlight from Tech Field Day that you saw this year that really kind of said out loud to the people, this is something we need to follow? I think it follows on from my very first story where at the beginning of the year, companies were struggling to get their arms around what it meant to build AI into their applications.

And that progressively as we've come through the year through AI infrastructure field days as well as AI field days, we've seen a lot more reality of actually what does it look like to get value out of ai? What do I need to build for it? How can I make it easier to build out and get something some value out of ai?

So I'm hoping that that means we're moving beyond straight up hype and and billion dollar funding circles towards actually delivering business value. Tom, I'm sure you've had a different impression because your events of course were in the security networking and mobility spaces. I think for me, security was probably the one that stood out the most is that people are really starting to take it seriously and they're starting to understand the security is an aspect of everything that they do.

And we are having the conversations that we need to be having about securing things like agents and, uh, model context protocol servers. But we're also looking at using AI enhancements to make security run better, which is probably a good thing because we're seeing AI being used to create better, faster, more effective attacks. And one of the things that kind of stood out to me this year was the fact that we relaunched the Security Boulevard podcast myself, along with Alan Shimmel and Fernando Montenegro and Mitch Ashley get to spend a few minutes each week kind of talking about some of the big picture ideas in security.

And it never fails to amaze me how intelligent and how uh, learned people in the security space are and the way that they have a perspective that kind of changes the way that you could potentially look at these things is something that I've taken away, especially in this latter half of the year as kind of refocusing what I want to do in 2026. Speaking of which, al you are the first one up on deck for 2026 when it comes to Tech Field day events. I'm, I had AI Infrastructure Field Day coming up in the, uh, the last week of January.

And, uh, looking forward to having a, a really good crowd of, uh, delegates. I've got, most of my delegates are up on the website at the moment. Uh, this is gonna be quite network heavy.

So Tom, you might wanna tune in for some of these presentations as well. We have, uh, a collection of interesting networking companies as well as some, some a little more in the, the storage side of the infrastructure. Uh, it's, that's gonna be a really fun event.

It's gonna be a pretty packed event as well. What is on the Tech field day website is Cloud Field Day 25. And so that's my, uh, trip in March to come out to Silicon Valley and spend some time looking at all things cloud I is starting to build out with my, uh, collection of wonderful people who will be there with me, both the presenting companies as well as my delegates.

Uh, I'm gonna be back in April for Networking Field day and just so everybody knows, this is the 40th edition of Networking Field Day. Um, I am very happy to be bringing you something. We're probably gonna have to start calling Excel 'cause we're gonna, we're gonna start naming a number of them like Super Bowls.

Uh, but this is shaping up to be another really good event. As, as Al kind of mentioned, you know, he's kicking off in the beginning of the year talking about some AI infrastructure and a lot of those companies are gonna be coming back just three months later to be talking about what they've been doing to a networking focused audience. And we've got some big stuff happening there.

com to find out more Right after that. We've got AI Active Field Day. I mean it's Active Field Day, but you have to put AI on everything.

So I'm expecting to have a whole lot of interesting coverage of both the tools you use to build AI applications. Um, that's how you're getting value out of all that AI spend, but also the AI tools that help you build line of business applications. Uh, it should be a really fun event and, uh, we've got a, a whole collection of very different companies that we're lining up for that one.

Absolutely. And then of course, I'm gonna be back, uh, towards the middle of the year with two really great events that I love, security Field Day and Mobility Field Day. com for more details, but one thing I do wanna call out Mobility Field Day is consistently one of our most popular events.

How popular is that? Well, we're six months away and it's already half full. That's how popular it is.

Everybody wants to be a part of this event and we want you to be a part of it too. com and click on the link for delegates. And when you do that, you can fill out a short form and you will go to the inbox so that Al and I can kind of check out, uh, what you're about and maybe invite you to a future Field Day event.

You can even nominate other people and we'd love for you to do that because the best recommendation that we get is word of mouth from the people in the community. Um, just give 'em a heads up that you nominated them because if they get a random phone call from us, they may not know what's up and, you know, maybe we we wanna help 'em out. One of the things that happens quite a bit on the rundown is that when we are out at a field day event, we're pretty busy hosting things, which means that a lot of times we have to call on some of our amazing delegates and community members to be co-hosts for the event and we wanna take a special moment to kind of shout them out for all the help that they've put in this year.

Of course, you know, Steven FoST is, uh, one of our favorite co-hosts and he has definitely jumped in to help out with rundown recordings and we love having him on, uh, when time permits and, and we're definitely gonna have him back on in 2026. But I wanna say a special thank you to folks like Jim Rinky, Keith Townsend, Kate Scarsella, Scott Roon, Ned Bevan, Chris Grundman, Jeffrey Powers, Gina Rosenthal, Brad Gregory, Corey Rockney, Romeo Gardner, Ron Westfall for Stepping Into the Chair, uh, learning a little bit about how we do things around here, bringing some snark to the news stories and, and overall elevating the experience. We can't thank you enough for all the time and effort that you put in to making the rundown a huge success.

But once again, I also need to shout out our third hidden co-host for all of the rundown events that we do. And that of course is Corey Derrig. You can't see Corey right now because he's hiding in the background, making sure that our levels are right and that we're making edits to all the little flubs that we do.

But we really can't do this without Corey. We know we've tried a couple of times and he's way better at it than we are. Uh, so when you leave a comment on our, uh, uh, our episodes when you, uh, let us know the things you liked, you are really thanking Corey at the same time.

And we can't thank you enough. Corey, thanks for putting up with us, uh, getting stories in at the last minute, rearranging things on the fly. Um, thanks for helping line up new co-hosts and everything like that.

Uh, the unsung heroes are usually the ones that need the most applause. Well, I also like to thank every single one of you who has been listening, who has been watching as we've gone through this year of the rundown. Uh, for many of you, you know that this is my first year as being one of the two primary hosts on this.

And it has been a pleasure bringing the news to you every week. And, uh, I hope to continue bringing the news to you every week through next year. Do follow us, uh, on your favorite social media, but also subscribe in your favorite podcast application on YouTube.

While we're talking about the awesome things that Corey does, there's a whole collection more podcast that Corey is producing for Tech Field Day and for the wider Futureum group. Uh, I'll make sure that Corey lets you know where you can find all of those and subscribe to those podcast as well as this one. And wishing you and yours from myself, from Tom Hollingsworth and for the, from the entire team at Tick Field Day.

And Futurum, a fabulous week, fabulous Christmas, a great new Year. We will see you in January.