Kubernetes After KubeCon and Google’s AI Agent Workloads | TSG Ep. 967

Hey everyone. Are there some cracks appearing in the feed of the Kubernetes Megalith? You're watching Textron Gang.

Hey everyone, it's a Shiel and welcome to our Friday edition of Textron Gang. We're actually, as you know, we record Textron Gang the day before. So it's actually Thursday.

It's a little quiet here in the morning 'cause the place isn't open yet. But we're on the floor here at Cube Con and we're wrapping up our Cube con coverage On Thursday. Uh, you'll be able to start seeing a, well, if you didn't catch it live streamed.

You'll see it next week, probably on demand. Um, but for today's gang, I'm really introduced pumped to introduce our latest guest here on the gang. He's becoming an official gang member.

He's someone I've known in the tech community for a long time, and I'll leave it at that. My friend Chris Short. Hey Chris, man.

Welcome. Thank you. Appreciate Chat.

We don't usually do this, but me, we're on camera or we're on the side here and it's your first time on. Give people a little bit of you of you are. I'm Chris Short.

I've been in tech pretty much my entire career. Uh, joined the Kubernetes project in 2017 and have been a member of the community ever since. Uh, I'm currently the co-lead of Kubernetes contributor Communications and I also run the open source program office at a startup called CIQ.

Excellent. Thank you Chris. Thank you.

And of course, joining Chris in me is the Dean, Mike Vizard. He's been out doing interviews most of the night, he says, but you could guess. Um, but he's here today and that's what's important.

Mike, what do we got for today? Well, we're leading off with this article that you wrote over on Cloud Native now about the, uh, cracks maybe in the Kubernetes monolith community. And the idea here is that, um, folks are getting a little frustrated maybe on one hand.

And then the other side of it is that the foundation themselves have become, like most non-profits, they wind up kind of servicing their own internal political issues more than they actually wind drive driving the innovation maybe. Yeah. So is it monolith or mega lift?

I think it could be. You know, those might be synonyms if you would find, okay. Maybe.

Well, We'll go with it. But, you know, here, here's, here's the thing though, and it just, it's why I love tech. Right?

Last year, the year before that we were writing about there is nothing on the horizon that's gonna stop this locomotive. Oh yeah. Right.

Maybe, yeah, Kubernetes mm-hmm. It's Pax Kubernetes, right. That that was the world we were looking at.

Mm-hmm. There's nothing, you know, to, to dim the, the bright lights. And I think, 'cause we were looking for something that would replace Kubernetes, but instead I think what we have found is something that Kubernetes may not be able to sort of internalize, and that's ai.

Mm-hmm. Right. We could use AI to try to make Kubernetes better, but can we use Kubernetes to make AI better?

And that fundamentally is gonna be the question of whether Kubernetes, we, we stay in apacs, Kubernetes kinda world. Mm-hmm. Or do we go into a dark ages after Rome fell or something like that.

But, um, but on top of that though, there's some metrics that, that back it up. Chris and I were talking about it earlier. Micah and I spoke about it yesterday.

Look, they're crowing about 15 point something million members mm-hmm. In the cloud native community. Yeah.

That's fantastic. But when you look underneath that, this show has been flat to even slightly down. I saw there was only about 9,000 and something people here.

Yeah. Not to 10,000. So it's been flat to slightly down.

60% of the people here are here for the first time. And that also is a, a kind of a trend steady statistic we've seen, at least in the us Europe's still a little bigger and I don't know about the 60% number. Mm-hmm.

Um, Alright. So from the community side of things, right? If I look at the Kubernetes community and the, the AI things that are happening in the community itself, we see some work groups popping up, AI conformance a number of other AI working groups that the community as said, are needed now.

And that's because Kubernetes has kind of become the platform to do AI at scale. But at the same time, yes. Can we make AI better through Kubernetes?

That's where we're moving towards. Mm-hmm. Now it's a large community and consensus takes time sometimes.

Yeah. So we are seeing some things progress very quickly. We saw an announcement yesterday with my friend Mario Valant, uh, talking about what the AI performance group has done.

And I'm excited about that. But at the same time, I feel like the Linux OS and Kubernetes have a similar journey. We're getting to the point now where you no longer need a Kubernetes specialist on your team.

The skills are becoming more ubiquitous. Right. People understand containers like never before.

People understand, uh, pods, deployments, CRDs, the whole gamut of ways you can instrument something in Kubernetes. So with, with that kind of realization, you know, red Hat for example, you know, a lot of their revenue was from support for RL Sure. Long ago.

Now it's support for OpenShift. Yeah. Which is interesting to me.

Um, and we're seeing a lot more Kubernetes usage in general across industry, which is great to see. So at the same like yes, there are some gaps showing. I think those are more so socioeconomic, oh, sorry.

Than actual like technology concern. I Don't know that I hear the tension along two paths. One is AI workloads are stateful and they need to scale really high.

Mm-hmm. And Kubernetes was never originally designed for that. And we run databases today on Kubernetes, but we don't run 'em at that level of scale.

Right. So people are saying, do we need to fix some of that engine and what goes into that work? Yes.

And then the second thing that people are talking about is, you know, Kubernetes is not the only game in town. And you go talk to the data science community and they like a thing called S slm. It's a job schedule that they're using as an alternative to Kubernetes orchestration because it's more accessible.

And to them this is makes more sense. And they look at the Kubernetes thing and it's like, you know, a bunch of it guys telling them that there's this greatest thing over here to use data scientist looks over at Kubernetes and goes, Doesn't the law running jobs? Well, yeah, exactly.

There is that. Yeah. There is that.

Um, I know from my company's perspective where I work now, yes, slum is a big deal, but underneath that there's this technology called werewolf open source project widely used. It's stateless or state full cluster management at scale was designed for the HPC world Nice. But is suddenly really relevant in the AI era.

Yeah. There you go. You heard it.

Here's extra gag. And that's slim Comes from the same HPC community. Right.

Right, right. So like we're seeing the werewolf project itself, starting to adopt s LM standards and everything else so that you can actually drive those workloads in a more open manner. You know?

But let me, let me not call bs, but let me just bring out statistics that I, or metrics that I've heard around Kubernetes adoption. Mm-hmm. Yes.

In greenfield application deployments, Kubernetes and that whole cloud native stack mm-hmm. Containerized application microservices probably represents 75, 80, 80 5% of, of Greenfield. Right.

You know, critical mass. Sure. It is the standard, it's the compute stack.

Bingo. But yet when we look at the entirety of applications that are running in the world mm-hmm. Not just in cloud Kubernetes, cloud native, you know, microservice architecture.

They represent 15% of the existing applications. And I, and that stat stayed steady now for a while. Mm-hmm.

We have made no inroads into transforming, modernizing, I don't really care what you want to call it. Right. This existing base, which is still larger mm-hmm.

Than, you know, all of the new stuff we come out with all of this time. Well, I think what's happened is people have realized new workloads Yes. Kubernetes, you want that ability to scale fast.

You want that ability to just interact with APIs, but your previous legacy workloads aren't necessarily designed to work like that either and takes their legacy. Right. Re their legacy.

Re-architecting isn't exactly gonna be high on the priority list. I think This legacy to me means money maker. Absolutely.

Right. I don't mess with, don't Mess with that legacy means to me. Yeah.

It ain't broke don't. Right. But I do think That that mentality is starting to change finally.

I think. I think so. Yeah.

I mean, like we, we went through the DevOps era, right. I'm not saying DevOps is dead or anything like that, but we, you know, we've evolved sre Well, you're not saying that Chris. Right.

We still have to go back to those DevOps principles. Yes. Because that's the problem.

We need to be able to scale these legacy applications, but we're still managing them with proprietary network gear. We're not using open source load balancers or anything for that matter on those workloads, which is putting them at a disadvantage. 'cause open source is kind of the concrete foundation of a lot of these workloads.

I don't think anybody really knows honestly how those workloads are actually constructed. That that's a problem too. And so they're hoping maybe these AI tools will help with that.

But if I don't know how the thing is constructed, I can't carve off a microservice off of this mm-hmm. Thing and start slicing it up. Uh, the only way I can get there is, you know, I gotta call consulting firm and then they show up with, you know, 50 kids in a bus who move in for a year and a half.

Agreed. Yeah. Agreed.

Yeah. And not cheap, but let, let me, let me call out an elephant in the room though. Sure.

They call this show Cube Con, the official name of course was Cloud Native Con. Right. But I think they stopped trying to correct people a few years ago and it's just Cube Hunt.

But Cube Con has become a binary star system. Mm. And right over there is OpenTelemetry land or whatever they call it.

Right. Right. And when you take open to hotel mm-hmm.

And you take Prometheus and you take some of these other observability projects that are in CNCF, you, you, you know, it kind of reminds me of the Arthur Clark 2001 where Jupiter becomes a star. Oh yeah. Right.

Yeah. You have a new star in this system and it, and it's, it rivals. Mm-hmm.

Yeah. The old star. And is CNC is this town big enough to two star for two stars?

Yes. I think having two stars in the same foundation is a good thing, right? Mm-hmm.

Um, looking at it from the CCF f perspectives, you know, if I put that hat on, I see it as growth. Mm-hmm. Externally, it's not creating confusion, which I think is Yeah.

Some of the problems with a, you know, two star system, right? Like, which one do I choose? No, no, no one compliments the other.

So that's kind of set up well. Mm-hmm. But the thing that I've noticed is I'm starting to see things like Prometheus used in non-cloud native contexts.

So I'm starting to, you know, see exporters, um, literally. So is that a bad thing? That's Not a bad thing.

That's called maturity. Yep. And if, if someone's not using Kubernetes, but they are using some of the open and underlying components of Kubernetes, that's still a win in my book.

Right. Kubernetes is not the destination, it's a part of the journey. So if we're looking at higher level abstractions Yeah.

Hotel fits right in. And it makes a lot of sense to start using those things in non-cloud native workloads because they're more efficient. Right.

Like we've driven the efficiency into the underlying applications. Yeah. So is there something to be done to jumpstart innovation a little bit in the Kubernetes TOC?

Or is this just the nature of democracy as a messy system and it is what it, is? It, Well, I can't comment on the TOC component of, or creating more innovation, but what we've seen is this very sharp uptick in AI investment. And that's kind of pulling some of those engineers and folks into those projects and not necessarily towards Kubernetes.

Yeah. So how do we make sure that the AI people are in the boat with us is kind of how I'm seeing 2026 play out. Okay.

More so than, um, Or Kubernetes gets in the AI boat Both, or, you know, very little Cross Yeah. Cross, uh, pollination. Yes.

Like, let's work together, let's push these things forward in a more collaborative manner than our normal company based silos. Yeah. Which is good.

Yeah. Let's, Let's last these boats together and have a party. Yeah.

Let's take a yacht outta All this boat. Hey, that sounds good. You Know, you know what, we, we've gotta end this segment, but let me, I'll end it with this though.

Let's keep an eye on Amsterdam Yeah. And see what trends continue or what we can spot from there. So you'll have to wait until March on that.

But, uh, we're gonna be right back here in Textron gang, and we're gonna talk, what are we talking about next? Mike? We're talking about Kerv and open source project.

Hey, native, now moved into the CNCF. Very cool. You're watching Textron Gang.

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A project that was in a different Linux Foundation is now moving over to the CNCF. Kerv is a distributed inference server is one way of thinking about that. And a lot of more of the AI workloads are becoming more distributed.

So that's a good thing. I talked to the, at least one of the maintainers and you know, basically they said they just wanted to be hanging out with the cool kids because, you know, this is where they're gonna do a lot of the integration work going forward. Hmm.

And the other project feels like maybe it's starting to be more of a data science kind of project team. And it's an AI foundation within the Linux Foundation. And those guys are all focused on data and training.

And the inference stuff is just hardware stuff that they're not interested in. So maybe it is better to have this over here, but you're closer to this. What's your take?

Uh, I think, so we've gone through some serious changes in the past, I think five years when it comes to open source or just community events in general. So CubeCon is the one that's attracting a lot of attention. We talked about that in the last segment, but the, the everyone in the same pool model is kind of working, right?

Like we've seen the CNCF landscape drive valuation in companies and now we're seeing it draw in more tooling, which is pretty good thing. I think CNCF landscape thing gives me a headache. I like, yes, They've made some improvements in the past year to make it less headache inducing, but I remember when they used to print those things, uh, and that required a mag.

You needed I was just So you needed a special printer to you do those. Yeah. What do they call those things?

I slipped it out in sheets. Um, but, but here's the thing. So Mike, I think the proper nomenclature, it's not another Linux Foundation, it's a daughter foundation of the Linux UX Foundation.

Right. Same way. Theoretically.

CNCF. Mm-hmm. Um, and I'm just not sure how cool that is.

Is this a bunch of baby birds in a nest? And we just saw a baby bird eaters brother and sister. Hard to say.

But I think, um, er will probably get starved for engineering resources in, in that other daughter foundation, whereas it probably will be able to leverage up more on the core Kubernetes work here. I think, I hope I crossed my fingers. But, um, whether it's case server or not, we gotta figure out a way to make those AI workloads more distributed.

'cause we can't just keep scaling them up. We gotta scale 'em out. We gotta, and that's kind of the big challenge.

Yeah. I don't disagree with that. Mm-hmm.

I, I'm just saying at, at, at a higher level, what's healthier for the Linux Foundation. Ah, okay. Right.

That regard. I would think, you know, fewer foundations, more concentrated work efforts is a good thing, I would think. Right?

Yeah. Because I've seen foundations come and go, so why they Keep giving up so many foundations, Matt Gotta ask Jim. Yeah.

You gotta ask Jim on that one. But I think that's more so, uh, the business model of the Linux Foundation than it is the actual, like industry. If that makes sense.

Yeah. I mean, there are, I don't know how many daughter foundations there Are in, I thought there was like 40 something. Is that all?

Maybe more than, I know the last I looked it was 42, something like that. Okay. Well that makes sense.

42. Yep. But the, the thing I think with K native serving is the K, right?

Kubernetes is the underlying thing. Right? So it belongs here.

It should, it belongs here. And maybe it's the case where it was just put in the wrong place to begin with. It could Have been.

Yeah. And now It's correction. Well, 'cause here's the thing, right?

Like Kubernetes and AI both kind of blew up at the same time, for lack of a better term. At least Nvidia, right? Like when I think about AI workloads, I think 70% of 'em are Nvidia.

Sure. So Nvidia is scaling vertically, not necessarily horizontally right now, but their chips, they're saying, what was it? The Jensen comment was like, a hundred x performance or something like that on their newest GPUs.

Like that's, that's huge. But that is a rip and replace operation, not necessarily making the most of the hardware you have plus. Or you take the old ones and sell 'em to countries that can't buy the new ones.

Well no, actually you say that. But what those countries are actually doing is buying, putting all the data on hard drives, flying the engineers to a data center where they can churn through all that data and then bringing it back. But, but that, and so that's very training specific.

Mm-hmm. Hopefully as we move beyond training the inputs and other stuff, they won't be able to do that. But who knows, We need to move the processing of the data and the, and the inference closer to the network edge where the data's being created and consumed.

'cause otherwise is This other pitch for waso, there's A, there's a thing called latency. There's a thing called latency that gets in the Way. Latency is always gonna be an issue, right?

Like I, I actually talked to a, uh, new contributor at the Kubernetes SIG meet and greet yesterday. And we were talking about the, the, the, the physics of networking Yeah. Are going to start getting in our way.

So how do we work around those physics? Every company supposedly has a solution to that. But what we're seeing now is more mergers and acquisitions than new companies spinning up.

So as things become more pressed against the physical limits of like atoms and, you know, light and things like that, we're going to start seeing some better use cases for older GPUs, for older infra systems. And then scaling them up for today's, you know, examinations and workloads is gonna be interesting. I Laugh 'cause we're going full circle.

Yes. So when I was young, somebody once drove into my head, you should always bring the compute to the data. Nothing good happens when you move the data.

So he, then we did the cloud and we moved data into the cloud. Right? And now we're coming back full circle and saying, you know what?

We gotta bring the compute back out to the data. It's cyclic. This whole thing is, I Got a point of order.

I find that hard to believe someone told you that when you were young. Yeah. Yeah.

His definition of young this is, This is, this is back when I was covering B DP elevens and Oh, vax. So you That's nice. You know, in, in the digital world, we had real computer science, We had real computer science.

Not these guys today. All right. Fair enough.

You know what though? Welcome the project to the, to the CNCF. Yes.

Come on in. There's 200 other people swimming in this pool. Mm-hmm.

And, uh, I, I do think it, this may be a case more of correcting Yes. A misplacement prior than, than anything else. Let's take a break here on the gang.

We'll come back and we have our third topic today, which is Google. Have they lost the love for CNCF or Linux? I don't know.

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Let's revolutionize your tech journey. Contact us today and tell your story to the world in the most powerful way with Textron Group. Hey folks, we're back and I've been at the show all week and there's a lot of things happening here, but I will say that this thing that we're about to talk about is probably the coolest thing I saw since I got here.

Oh. And, and basically what Google's announced is that they've created a sandbox for running your AI agents so that the AI agent can't go and, uh, wild and just start pulling stuff from all over the place. It creates a little bit of a, a barrier around the actual agent, which is good news because right now our biggest problem with AI agents is everybody's deathly afraid that these things are voracious and we don't know what they're gonna do when, so I kind of like this idea.

It's built on that, uh, g visor thing that Google came out with a few few years ago while Yeah. Um, and so that to me was like, Hey, somebody's actually solving a problem we were all talking about. But yeah.

But the issue then becomes, as I was walking away, I was like going, wow, Google's building a lot of cool stuff that they don't seem to be like giving back to the CNCF folks. They seem to be either using that because they think it adds value to their model of GKE Or is it that they just got tired of the politics? Uh, well, yeah.

I can't speak to the politics of it all, but the, I think what we're seeing is we're starting to see folks use AI and they're having interesting challenges with it, right? Like no doubt. I know I had a conversation with a couple engineers a few weeks ago where it was like, yeah, I was using AI to build this tool and ended up deleting my cluster 'cause it thought it could do something better, which failed.

So I reverted back, da da da da. Luckily you were able to revert, you revert Thank you for using gi. Uh, I don't think I've ever said that before in my life, but the, the need for safety is very high right now.

Right. Because you can't think of all the edge cases to tell a, you know, put in a prompt, essentially. Say you're using the underlying infrastructure, don't change it.

Right. Make it work to your advantage as opposed to starting from scratch and saying, I have this problem, help me solve it. Go.

And then all of a sudden everything gets broken in the process of you just saying go. I mean, it's an interesting, there's other ways to get after this too. I was talking about this with Chronosphere and they've kind of taken a, a graph and wrapped it around the LLM and so the LLM only sees what the graph tells it it can see.

Right. And they're using the graph as a controlling function. Yes.

Um, but you know, I think that's a, a good way of making it a little more deterministic. 'cause I'm only limiting the amount of data that I can show this thing, but I still need guardrails and policies. Yes.

I still have to figure out whether or not I trust the output or not, but Right. We seem to be starting to put together, you know, uh, a, a fabric or an ecosystem of things around the LLMs to kind of control the output better. Mm-hmm.

And I think there's good reason for that. Right. We're all a little worried about that use case.

I was speaking of actually like happening in production kind of thing, where you blow away my legacy infrastructure. 'cause you think you can do better, but you don't. So when we look at systems built on safety, I know the CRA air traffic control network has had issues this week.

We've all kind of been delayed getting here to the conference and everything. But that's a system built on safety first. Yes.

Where AI is a system built on innovation first. So it's, it's very easy to innovate, but it's harder to make it safe and you have to build for safety first. Mm-hmm.

Before we had this air traffic control system, though, it was an innovation first system. Right? Right.

Yeah. You know, you gotta walk before your run and you can't make wine before it's time. Mm-hmm.

Right? So I think we're still in that barnstorming stage of ai, if you will, right? Back wing walkers and everything else.

Yes, yes, Yes. Yeah. In that aviation, we'll eventually get there.

But I guys, I think you're missing the story here. The story isn't about the tech. The story is about why isn't Google donating open source projects to foundations anymore, or literally not like what they were.

Mm-hmm. And that might be a political issue, but I don't, I don't know if it's truly it's political, it's financial. It's, you know, it's not that Google's no longer not doing evil, it's also the base, It's also the pace of innovation.

Right? I mean, if I got a committee right, It, there might be that it, it, they feel it's slows down the innovation Consensus can take time. And that's neither here nor there.

That doesn't mean they can't release it later once they've got everything they need in row. But Didn't, didn't you hear authoritarianism is cool again. Come on.

Ah, geez. Yeah, they're bad. Um, Stay tuned.

F shimmy says it two 30. I I talk about this, but no, it, it's, I mean, it's not authoritarianism in that look, hey, Google put the resources into developing this. Mm-hmm.

They're entitled to do what the heck they want with it, right? Mm-hmm. But when you look at, you know, this whole thing built on, on Kubernetes that Google donated and the, and the input and influence it had on even just establishing the CNCF.

Yeah. Are we missing out on the next CNCF on the next community wide industrywide revolution? Yeah.

Because Google's keeping this close to the chest to their breast. I think Google has learned some interesting lessons Yes. When it comes to a cloud b cloud native and then c open source versus closed source versus something else.

Right? Yeah. Like they see an advantage in the middle in, you know, I remember a thing called Anthos from Google that is basically become Google autopilot, GKE autopilot.

Mm-hmm. And I think that serves their customers very well. And some of that you can do with open source, but the actual getting it all the kinks ironed out is proprietary.

Right. Which I think that's where folks are starting to find value is making resilience systems better and making them less error prone and more safe. Going back to our, you know, original count theory.

Democracy is the most inefficient system of government, but it is, but it the best, it is the Best. I Will tell you, I'm starting to hate this phrase that comes out of the valley about go fast and break things. 'cause if you're not on, if you're the guy on the plane, that's not what you want to hear.

No. No one wants to hear go fast and break things on a boat, a plane already. Yeah, yeah, yeah.

I agree. I agree. But you know, it, I, I think so you, you, you look at it from historic, right?

You look at open source. Mm-hmm. So you had your sort of, you know, your, your cathedral and bizarre phase with Richard Storm and Dr.

Richard Stallman and stuff like that. Yeah. Where it was unrealistic Marxism Yeah.

Of, of in a society of atheistic saints, right? Mm-hmm. Yeah.

Nice. But then, but then it went to like this big brother open source where a Google, an IBMA sun. Well, not sun.

They were good. Yeah. Um, whoever a, a company hp mm-hmm.

They, they did open source a particular tool or project, but they, they had their own opinions and ideas and wants for it. Right? So they retained control, but in doing so, it, it, it froze competition or it froze competition out.

Right? So if you were, if you were IBM and I was HP or God darned, I'm not going to, uh, contribute to your success. Right.

Then we had this foundational era of open source mm-hmm. Where IBM and HP could work together along with Apple. Yeah.

And Google and Meta and, and what have you. Are we seeing that era now? Maybe.

That's a great question. And I think, you know, we were talking earlier, all the companies I've worked at that have told me not to work with other certain companies on a, you know, comp, competition based thinking. Mm-hmm.

I've worked with all those companies in the community. Right? So we are now at a point where those companies are saying, okay, our teams are working with these other companies through in open source.

Is that the best thing for us right now? Yeah. And they're rethinking the now not the future.

'cause the future is open, let's face it, right? Yeah. Yeah.

So right now with belts tightening folks having to buy more GPUs and a lot of expenditures on infrastructure. Yeah. You're gonna see things just not get the weight pushed behind them to make them popular and open source.

They're gonna drive the bottom line to increase revenue. True. Absolutely.

Some of these things though, I mean, where they collaborate theoretically at least, least should be on something as non-differentiated value, right? Mm-hmm. It's just an enabling tech.

But I think to your point, it's getting harder to determine what's non-differentiated value. Everyone's freaking out. Today's non is tomorrow's.

Yes. Right. There certainly is that there is that.

See all these people taking pictures of us, I feel like, uh, I don't know. We we're doing something wrong. Anyway, guys, we're about outta time here.

We've, we've got the, the show floors open and we've got interviews and stuff to do. Chris, man, thank you so much for coming in here and popping in. Appreciate it.

We've gotta get you into the rotation. Sure. We had a lot to the conversation.

Thank you. Mike, what do you got planned for the rest of today? I'm Gonna visit more boots and shake some hands and kiss some babies.

I'm running for office. Oh. But we need politician in the CNCF politician.

Great. Thanks. I hope you've enjoyed this Textron gang.

We will be back next week with our normal Dextron gang back in studio, but it's been a hell of a lot of fun doing it here. I'm Alan Shimel. Thanks for watching.

You're out. We're out.