Techstrong TV December 4, 2025

Hey everyone. You know what a kiro is. You're going to, you're watching Textron Gang.

Hey everyone, good morning to you. It's Alan Hummel for Textron Gang. If you couldn't tell, well, you could see the logo back here, but if you couldn't tell, we are in Las Vegas continuing our AWS reinvent coverage of, uh, reinvent 2025.

It's been a great show. Uh, you are watching this already on Thursday. So we've had two days of coverage here.

We hope you've been able to catch 'em. If you haven't, don't worry. They'll be available on Text Trunk tv or the Text Drunk tv, YouTube channel and OTT channel probably by early next week or so.

But our, our crackerjack video team is busy, busy editing these as we're shooting them. So good for that. Um, we've got a, a good panel of, uh, we've got the core panel here, core Three used to say the Core four, but that would be the Yankees Core three.

We've got Mitch Ashley on, on my far right, Mike Ard in the middle. And of course, I'm Alan Shimmel. Gentlemen, welcome to the gangs.

Great to have you on this morning. So, you know, continuing our, uh, I feel like Sherman marching through Atlanta, continuing our Agent AI campaign through Las Vegas. Shoot, That, Um, I thought we'd start off with a little kiro.

Yeah. So we are definitely on the wrong end of the fire hose here, but a lot of stuff coming down the pike from these folks. Yes.

But Kero is this AI coding tool that AWS created and is now updating to add something called Kero Powers, which are basically a set of specialized agents that have been trained to perform specific tasks, including autonomously with hooks and links to an MCP server to call things like Datadog and whatever else do we want to, uh, invoke as part of a, a workflow for the developer In a lot of ways, I think, you know, what we're seeing is a lot of shifting further left of various DevOps tasks into the developer, I guess what we might call the interloop. And, um, what's interesting about all these new AI agents is that it continues a conversation you and I were having yesterday about the fact that they only load when needed. And so they're reducing the total cost of using these AI agents because there's sort of one AI agent that keeps track of what the developer's doing and then automatically invokes these specialized agents when needed.

But Mitch, you're closer to this than I am. You know, what's your take on what's going on here with AI coding tools? 'cause I feel like they're getting a whole lot smarter, faster than we thought.

It's, It's the move, if you want use the term agentic, DevOp, DevOps. Basically, we w we wire these things together now today, whether it's A-C-I-C-B process or plugging in, scanning for vulnerabilities or deploying, whatever it might be, and observability, things like that. And you do see a lot of effort today around hooking in observability security earlier in the development process.

And that's part of what they're doing through these agents. Uh, it's also pre-integrated. I mean, the agent knows how to talk to Dynatrace and, and, uh, you mentioned, uh, Datadog.

There's others. I think they've been launching with about six or seven companies. Oh, Horseman's in there.

And a bunch of others. Yeah. So wait a second though.

I, I, I got a question here. Yes, gentleman in the far left of the room. So what you're trying to tell me is that we're teaching a agent AI to shift left.

Finally, somebody's shifting left. Okay. I thought shift left was passe.

I thought we went through our shift left period. Well, now it does bring up an interesting question because just because you can do something doesn't mean people will. So it may wind up just being too overwhelming for somebody to cognitively keep track of.

'cause you're, all these things now are happening in parallel. So if I'm writing code and suddenly, you know, an AI agent's popping up going, Hey, you know, you want me to run this, or we need to do this, it's kind of like, well, I, I think when you're writing code, it's kind like, even with the help of ai, it's still as much art as it is science. So how much of this stuff winds up just interrupting the flow?

Very, very, you could get pestered just like popups and notifications and et cetera, I think is your point. Absolutely. Well, it's gotta be managed through some kind of a control plane, whether that's inside the IDE, more likely, it's probably part of something like, uh, like Agent Core or something's got to orchestrate and control what the agents are doing.

I, I doubt that's gonna start that way. I think it's gonna start by the developers running, doing their thing, leveraging these agents. They'll build 'em into their workflows.

If they're bothersome, they'll get, they'll kick 'em out or make 'em more useful. Mm-hmm. Well, let me give you Shimmy's take on this one.

So, you know, when you talk to my friends of Plat in platform engineering, they tell you one of the reasons that platform engineering Rose is because in DevOps, the whole shift left thing just really didn't work, and it didn't work as well as intended because it just put too much on the developer's plate. So by putting too much on the developer's plate, the developer did what developers do, which said, the heck with this, I'm not doing it right. I, I want to code.

And, and so we, we've seen this whole rise of platform engineering to put a platform together to allow the developer to do what he wants to do within the garage, guardrails and policies that we want. So along comes this idea of saying, Hey, instead of letting the developer do it, we're gonna have an autonomous agent, AI do these tasks. And so the developer won't have to, he could just code, or he or she could just code like they want to.

And now we're saying, wait a second, this thing is gonna be API A, it's just gonna pop up and do all of that. You don't think the developer's gonna say the same thing and say this, there's A-P-I-A-I, I'm not gonna pay attention to it. I think there's gonna be some balance in that equation.

But I also think, you know, it goes back to what we were talking about a couple of days ago with AWS. They clearly think that they are gonna be the platform engineering team for the developers. And so they're more than just an infrastructure provider in their minds.

They're saying, we're gonna provide various types of DevOps AI agents, and they are gonna be the manager of the DevOps workflow and the platform engineering team for all these developers out there. And that's how they're starting to think of DevOps as more of a managed service rather than something that, um, you know, a bunch of software engineers are trying to support on behalf of a hundred developers. They're kind of inserting themselves into that process.

Do you, is that how you see it? I, I think so. I definitely, I do see it that way.

And I think you, you skip the step, which is the developers are the people who experiment with things, right? Maybe engineers do, but developers definitely do. Not all of them, but most are, they'll get on, they'll try these agents, they'll see what's if, what's useful, but they'll get to a point where they say, okay, now the hassle factor, I don't wanna deal with this anymore.

Can someone take this? And now that we have platform engineering, I don't think it has to be, oh, the CIS admin who, or the who, who runs the build who can take this. It's the platform engineering team can work with the development team and say, all right, you guys, try this stuff out.

See what's helpful. We will make sure it's wired the right way into our tool set, and we'll keep the things that are helpful to us. But I think your point also is correct of AWS is to figure out how to compete with GitHub.

You know, how do we provide not just the tool to write code, how do we create an environment for creating mm-hmm. Software and agents integrating all these things. We've got all the rest of the infrastructure from, you know, hardware all the way up to databases and security, and that let's start to build an environment.

You can really kind of pre integrate these things together. My, my problem with it is you can't please all the people all the time. We all can't be right here.

One of us is wrong. Well, clearly it's, thanks for stepping up, Alan. No, I, and I'm not idiot fan.

I know. Nothing. Love it.

Well, he's such a giving guy. He's really just sticking. But here's, here's the thing.

We talked about platforms and platform engineering. You know, when platform engineering first came out, and we talked about platforms, if you managed your Kubernetes, you were a good platform engineer, right? You had a nice Kubernetes workflow, you had your CICD pipeline cooking, and wow, you're a genuine platform engineer.

And then a funny thing happened, platform engineering shifted focus to IDPs, right? Internal developer platforms backstage and, and it's ilk. Um, are you saying that these hero agents, these agent AI agents that, uh, Amazon's coming are gonna form a new IDP, they're gonna run in the existing IDP backstage or what have you, because is that really where we want them there?

Or are they after the fact? 'cause if we want them in the IDP and they're generating the code and they're doing all of these things, we are fundamentally changing the role of the developer. The developer becomes less the person who develops code and more the person who's gonna manage the agents through develop code.

Yes. That is, that is, I'm asking my developers out there, raise your hand if that's what you want your job to be. So, so hold on a minute before we, we jump fully off the bridge.

Let's stop about halfway down. So here, here's where we're, it's not American, and I won't stand for here. Let's, let's where we're gonna march if we're, let's, let's protest.

I mean, look at what's happened so far. Um, we, we've gone from the, you know, every tech executive in the world saying, yeah, we don't need developers anymore. 'cause I can build a snake game on my computer, right?

Mm-hmm. We don't need people to write software. What are, where are all the tools that are coming out?

They're tools for developers to use AI to build software. So are you gonna write less code? Hey, I'm happy not to write all the code.

If it's for good code, it's, I'm happy not to write every piece of code if I know it's gonna work and not cause me more work. Right? As AI gets better at doing these tasks, generating some of the code, doing code reviews, now it has to be done in a way, it can't be the intrusion prevention of work system, right?

Like we saw with I Yes. Is, and I IEPs, but developers do these things anyway already. They already orchestrate work.

They already work on design. They already craft software. They write code, but that's actually a small part of what they do.

The 80, the 80% of what they were doing, that's not writing code, is all this stuff. Yes. So we're trying to automate that while they're sitting there.

But your point is well taken, right? Because on the other side of it, developers are gonna pick or have a lot of influence over which of these AI coding tools that they're gonna use. And some of these decisions may be made by, you know, a CIO who's gonna sit there and say, this is our standard folks and this is what you're gonna use.

And that definitely happens, right? And then there's gonna be folks who are gonna, where the developer has more influence over which tool they're gonna want to use, and they may like open AI or Microsoft or whatever it is, or philanthropics coding tools better for some reason. And then there'll probably be some middle ground between turning on every platform engineering AI agent there is, and me wanting to be comfortable automating a couple of functions, like observability of my code as I'm writing it, versus still relying on a platform engineering team.

So I don't think that this is, you know, an an either or, it's a spectrum of things that will vary widely depending on the nature of the organization using it. So it's a spectrum. It is a spectrum.

Well, We know developers anything to do with vaccines. No. Um, but, but, but, But, but as we have, but as we have noted on this show many times, we are all on the Spectrum.

Yes, we are. So I appreciate the conversation. Appreciation.

Let me, let me bring this home to a business. Mm-hmm. Conclusion.

We spoke yesterday about to a certain degree, and I, I spoke about it last night with Daniel Newman. We record, actually, we, we think we recorded that. Yes.

This reinvent has been a big pivot for AWS because the perception was they were trailing Google and Microsoft and ai. Mm-hmm. And they came out guns blazing with ai, AI all the time.

All ai all the time. You said something before about Microsoft, you haven't mentioned Google. Google had a great week last week in the stock market, right?

Yeah. Because I think the market recognizes that Google may have the best vertically and horizontally integrated stack among the hyperscalers. They have it all.

Microsoft has some great tools though too. They have GitHub. They have the developers in their back pocket, 150 million of their friends, right?

And they always had a great developer channel before GitHub even. Exactly. But they've got GitHub, they've got it.

Azure, they have the open AI relationship as well as their own copilot AI things that they also own the enterprise. And they own the enterprise formidable, formidable competition here. Mm-hmm.

Now, AWS look, developers built the AWS that we see here. There's no doubt they were developer developers love to whip out their credit card and, and open up instances on AWS, but they didn't have a GitHub analog. They don't have Google's stack.

Yep. Agreed. And so what AWS is doing here is this is their claim to a stack, or at least the beginning of an AWS stack that'll run on train and processors as well as on Nvidia.

And, and they'll have an analog to a GitHub or some sort of developer IDP and they love open source. They can take backstage. Yeah.

And they, and they'll go one step further. They'll say that rather than just focusing on Gemini or open ai, they're gonna say, you, you Can plug anything in Bedrock. You can plug anything you want in here.

And we will automatically optimize the right AI model for the job based on the cost and the level of accuracy that you want. And it will just be something I tune rather than something that I decide that I'm gonna be like all in on open ai, or I'm gonna be all in on Gemini three. And they're not completely out of the model game.

They have the Novo models. I'm not saying that's where they're Right. They have no where the others are, but hey, you know, they're at least attempting to make a play in that.

We'll see. But they're also giving you the ability through Nova Forge, forge to make your own models to a certain extent. But we So crazy like a Fox.

Yeah. We talked about that the other day though. It's gonna be, you know, Nova's kinda like, you know, the generic version of the prescription drug, right?

It's does it enough and it's cheaper. And it's, It's the A WSA way. It's the AWS way.

80% for 20%. Right. And it is the way, And we're gonna, and we're gonna, this Is the way, this is the way, and We're gonna talk a little bit more about NOVA in our next segment.

All Right. Let's take a break on text on gang. We'll be right back.

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Contact us today and tell your story to the world in the most powerful way with Textron Group. So we are gonna shift the gear here a little bit. 'cause, uh, AWS is talking about, um, making it simpler to, uh, retrain AI models and also customize them.

So the retraining is a function of bedrock where they're kind of streamlining that whole process so that I don't have to a PhD to go fix the AI model, and I can fix the AI model on two parameters. One is I can make it more accurate, or I can also make it less costly. And so they're trying to make the whole play, uh, simpler for mere mortals, or I wouldn't call it everybody, but we're getting past the point where I need a data scientist to do all this stuff.

And what was interesting to me about this particular capability is that, um, it shows up first on Nova models, oddly enough, and then it's gonna show up on all this other stuff later. Mm-hmm. So they're starting to send a signal that says that maybe NOVA is not a, just an also ran, it's gonna be like their preferred platform.

And then everything else we'll see later on other models at some point. And then they're also talking about, uh, within SageMaker, which is their managed servers for building AI apps, um, that they're gonna add some ability to have a serverless capability for customizing the models. So you get a little more granular control over today, what is pretty much, you know, a take it or leave it kind of approach to build out an AI model.

And I think that's a fair assessment of what they're talking about and thinking about doing Good coverage of it. Um, From your perspective, as you look at all that, are we kind of getting closer to the democratization of the building of AI models so that I don't need to have a ma whole bunch of data scientists to do every little thing? 'cause that seems to be the gating factor in my mind.

I can't find enough of those people and they're expensive, and, you know, they work for me for six months and somebody steals 'em. Mm-hmm. Well, there's a duality here, which is bring your ai, bring your models.

We will, we'll use whatever you, and you, of course, you can use our, our nova, our AWS Nova models. On the other hand, what, what, what they're doing with Forge is bring your data and train your, our, our models, your instance of it, if you will, on your data, rather than doing everything through A MCP access. Right.

Well, what does that create? I'm not gonna move Nova over to somebody else's service. I'm gonna run that in.

Aw. It's a stickiness right there. Mm-hmm.

I think that that's the brilliant move behind not just giving there is Yes. Making it more alluring, the bar to be able to train models yourself and not have to be a genius. I love a duality, Mitch.

You know, I'm all about the duality. So I looked that word up. Speak back to a, it's my word of the day.

It was in my, on my tongue. No, no. We, I learned about duality in St.

John's, but in a theology class. Oh, you did? Yes.

Okay. Yeah. Light and dark and, and all of this eroticism.

I wasn't doing the evil Good thing, but, you know. No, but there's a duality. But, but here's, here's the, I guess for me, the crux of it with this forge tool, I'm not looking to create a model that's gonna take on anthropic or open or any of this.

To me, forge, you know, when we did our, um, hackathon almost two years ago mm-hmm. On operationalizing ai, we had all those really smart people, a lot of brain power in that room. It was clear to me, listening to them that large frontier LLMs were not the answer for everything.

They're good for consumers to play around with and, and do a lot of things. And maybe for marketing and analyst people, you know, but not for everything. And we were going to need small language models, right.

SLMs, we were going to, you know, and back then they would, you know, Patrick and John and those folks were doing vector databases to introduce just the beginning of that for MCP and what became MCP, what became MCP rag stuff like this. Mm-hmm. So to me, is forge a better mousetrap to build my SLM?

And then do I maybe plug that in a bedrock and I still have an open AI or a frontier model behind it? I think we're gonna go back to the word spectrum. So there's four tiers to this thing, right?

I can build a foundational model, I'll, uh, open AI and all those things, then I can use, but you Really can't. I, well, I know, but I'm just saying as the spectrum is that's the top end of the world. Then you use Forge to build your own foundational model.

You're not really building a SLM with that. You're building, and it's for global 2000 companies that are gonna build a fairly large model. Then there's all these tools underneath in Bedrock that I can use to, um, I don't have to be as big an expert, and I'm gonna use those to build SLMs, and I'm gonna use those to kind make that more accessible to a broader number of people.

And then underneath that is SageMaker, which is kind of this total managed service where I just basically show up and describe what it is that I want done, and SageMaker will automatically generate it for me. And they're all kinda like, on a different set of classes of things and customers. It's Like Skittles in the rainbow, I guess.

Yeah. Taste the Rain. But yeah, it, it gets a little complicated.

But, you know, from their point of view, there are different classes of companies using different levels of ai. So there, I gotta disagree with you. I think companies will use different classes.

Not, it's not, this class goes to that company. Yeah. One company, in one instance may want to use the Forge or Nova and, but at another instant, or in another instance, or in another team, or at another juncture that we don't need that right now.

I just need the SageMaker thing. This is true. This, Right.

And I, I think that's what we gotta understand is this is not, as AI continues to develop, this is not a one size fits all. And it's not, I just need a one solution. I need the whole spectrum.

Well, the LJ would use is if you're not a, a frontier model company, which AWS is not a, not not at the, the level of course of a open AI or philanthropic, what do you do? You sell people, you're an arms dealer, you're a picks and shovel dealer. You're selling the tools.

Right? And I think that's to your point about the spectrum of depending on what you want to do, how, how invested you want to be in creating your own models versus using models versus customizing access to 'em. That's the strategy here.

Yeah. No, I got a question regarding chip usage in this spectrum, though, as we go to that front on that spectrum, from left to right, right from ultraviolet to infrared, where do I need my GPUs? And where do these traum chips fit in?

Mm-hmm. It, it, it remains to be seen, because, you know, right now, I think most people are using Traum as an alternative for inference engines. I can though, you know, AWS is talking about Traum four here, which, you know, right now doesn't exist.

It's just a piece of paper with some specs on it. But, uh, you are right. But let's be fair, they came out with Traum too.

They said, we're coming out with tra they deliver, and they, yeah. So the point is, I think a significant portion of the AI training will move to AI accelerators, and for two simple facts. One is GPUs are more expensive, and two is GPUs have a lot of code in there for graphics that have squat to do with ai.

And they're not the most efficient way to do that. And they only happily, accidentally wound up being the platform for AI because they existed. And somebody went, well, that's the best thing for training in parallel.

So here we go. But if we have processors that are designed for the tasks specifically, and it's not just training 'em, there's a small army of these things starting to show up more and more of those training and inference sets are gonna wind up on things other than GPUs. And they're gonna be less costly to run.

They're gonna consume less energy, and it's gonna have an interesting impact on stock valuations. I think it's all about economics and availability. Mm-hmm.

I mean, that's the, that's the game right now, right? Yeah. If you can provide an, an alternative, maybe it's not as fast, maybe it's not as quite as cutting edge as some things about Nvidia.

Yeah. On the other hand, the other thing they did with, with the, the training environment is they opened source, the SDK, they came out with an SDK for that. Uh, well, that, to, to fight against basically the, you know, the non-open source version of what, uh, Nvidia does with their SD K.

So there's another track. I thought NVIDIA's, SDK is open. I don't think it's open source.

I, I'll double check, but I don't believe it's open. I I think it's not within a consortium, but I believe like large aspects of, It's not a Linux Foundation, you think. Right.

But it's, it's open. It could, it could be open. It may, maybe it is, because I've seen Cuda libraries showing up in other products.

CO is definitely open. Yeah. So there's pieces of that that are probably open.

But, um, it's just a question of who's open, who's writing all the, My, my feeling though is this, if you're AWS every time you gotta write those big checks over to Jensen, you wince a little bit, right? Mm-hmm. That's, You Know, Jensen was here doing the thing, man, but They're partners.

Yeah. They're partners. You know, that makes no difference, right?

I mean, you know, the, what's his name? Who wrote the, the Yard of War? Sonya.

Not Sonya. Sun. Sun Sue, Sun Sue.

You know, you, your partner could be your competitor too, right? It's true. So they're partners, but every time they write 'em a check, they don't think, they don't think to themselves.

Right. Like, that money staying right here. Yeah.

Mm-hmm. Of course. And so, a a, it, it would, it would be ludicrous and naive, who's naive now to think about, to think that they're not gonna put their thumb on the scale and say, hell no.

This is a job for a traum. Mm-hmm. Not a, not, not a, a Blackwell or something like that.

And I, and, and quite frankly, look, Google's gonna do the same thing. Now, interestingly enough, the the thing they're swimming against is that there's a certain amount of, um, good old fashioned marketing mojo behind Nvidia these days. And you go talk to data science teams, and they're like, I absolutely have to have the latest, greatest GPUs.

And frankly, they're not really thinking about like, what are the cost implications of that? They just want the latest greatest. They just want the latest, Greatest.

They're like, my career path is being trained on Cisco. Right? So, but I'm in this case, being trained on Nvidia.

Well, everyone want, you know, it's fomo. It's a little bit of a Boca thing. You all want the, you know, that, that kind of thing.

Again, I, I had this conversation last night with Daniel Newman about this, which is, at least for the foreseeable future, Nvidia will probably stay a generation or maybe two in performance out ahead of the trains and the, you know, the Google chips and the Broadcoms and what have you. But as much as the developer wants to be on that latest and greatest, and the data scientist wants to be on the latest and greatest, if the price gap mm-hmm. Is mm-hmm.

Is wide enough, I'm sorry. Right. And if I can run it on my desktop, I'm even happier and happier.

Exactly. Right. 'cause ultimately, A CFO and A CEO will sit there and look at the IT guy and go, excuse me, you wanna do what for how much?

Yeah. I, I that, and that's where I think I, I think to a certain extent, AWS may be betting on that. And they're not alone.

I think Google is too. I think you're gonna see Microsoft do it. I, you know, I think meta was, was going in that direction though.

Things seem to have gotten a little rocky over there. I I hear the phrase token sticker shock a lot. Yeah.

Mm. Mm-hmm. Mm-hmm.

And that I, and you know, again, what, what's interesting is this is all part of this continuing evolution that is, we're going through with this and we'll, and we'll see how it shakes out, but we're gonna shake out with a break right here. Come back and talk about our third segment from A-W-A-W-S reinvent on tech Strong gang, You've earned it. The spotlight, the responsibility, the weight of teams, companies, and entire industries fall on your shoulders, lives depend on your decisions, your home life included that work you are protected physically and digitally.

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Black cloak, digital executive protection, defending the new attack surface your personal life. All right, folks, we're back with our third and final segment of the day from AWS reinvent. And we're talking about Strands, which is this software development kit that AWS open sourced earlier this year, and now they're adding support for TypeScript to this.

And it's basically an SDK for building AI agents. And it makes that whole process a lot simpler. Now, the other thing that they were talking about was related to AI agents too, was they now have a tool that will help automate the building of the UI for the AI agent, which is, you know, the AI agent still has to have something that looks like a user interface on it.

And that's not something that everybody's very good at. So they're providing some tools to automate that process as well. They were talking, and we discussed a couple of days ago, you know, we're talking about billions and billions of Carl Sagan AI agents that were supposed to be building and deploying right now.

That feels like a fairly manual process, maybe, or so is that whole thing gonna become automated? And what does that look like? Well, you said the word TypeScript, which is code, right?

Mm-hmm. So we're coding agents today, end users aren't, I mean, in our Google workspace, right? We're using Flow that we're not writing code to do that.

We're using a UI to do it. But to do what developers need to do, they write code. And it's just that sophisticated, um, the, the, the strands, SDK, what it does is basically build around models.

So it's all about the prompt and getting the model that you're accessing and making really simple to create an agent to go do that. It's the things that, it's the, it's the, uh, framework that the developers have to go build if they're gonna write it from scratch, right? So you don't have to do that.

If you contrast to like what Microsoft announced, they announced their agent framework, um, about two months ago or so. Mm-hmm. That's about a orchestration.

So companies are tackling different parts of this, right? The, the strands doesn't do orchestration. That's part of Bedrock and, and what, what we're looking for that to do.

So it's, people are working at the pieces of it. But I think think if you back up for a minute and say, oh, there, there's a couple of new SDKs that, that, uh, that AWS came out. Sounds a lot like a development company to me.

Company tailing developers, back to our first conversation. In other words, one more thing saying we're trying to recapture the developer, uh, a persona to come be beyond our platform, use our Tools, and, and they made it open source out of the gate. So they definitely have, so there's no lock this thing as broadly available as possible.

Well, that Addresses the lock-in. You can't claim lock-in then. And, and now they're also talking about using that tool to build AI agents that will be deployed at the edge, including for robotics applications, which, you know, has been the hot topic of AI as a Physical AI you're Talking about.

That's Right. That's the one that's going to, uh, eliminate poverty jobs Among other things. Yeah.

Yeah. And it all Into Star Trek And, and might someday be the, the final killer app for Wasm that we've been waiting for now for about four or five years. Akamai will be happy, right?

Well, that's, that's what's gonna do. Did, did you know, uh, Akamai bought, uh, yeah, yeah, yeah. It was interesting.

They made a bet on Wasm. Yeah. So let me ask you a question, and, and forgive me.

You know, I'm, I, I am not quite as up on this, but are we saying that AI agents will create AI agents? Yes, absolutely. AI is gonna write its own code at some point.

And It, at some point in the short term, it's still gonna, most of 'em are gonna be built by good old fashioned human developers. But eventually an AI agent will be able. So would that be some sort of self-replication?

Uh, it'd be polymorphic code, basically. Yeah. Polymorphic self-replicating code, code writing code.

Could be, could be changing its own code, could be writing other agents. Yeah. In real time.

I mean, Odd, like, to me, it's, Sounds, sounds like a worm to me, but what do I know or About this definitely could do that. I guess it all depends on your outlook. Me, I'm looking for God, they're looking for bacteria and viruses and worm.

It's a spectrum. It's a spectrum. There's the spectrum for you right there.

But, but you know, there is theoretically, you know, something could go wrong where something starts spawning AI agents out of, you know, hundreds of these things, and then you gotta go clean them up somewhere. But I imagine they'll probably be, be an AI agent for cleanup too. Be the mop-up agent, or, you know, well, don't they have those in Zion?

Oh, in, uh, in the, the matrix. Matrix. Matrix, yeah.

What do they call them? I can't remember. Like the octopus looking things that the Sentry agents.

Yeah. And, and the agents may even have a set time life. Right.

And they're just gonna self-destruct after six months and be ought to replace by another AI agent. I gotta tell you, I'm just so damn proud of myself that I was able to get a matrix one in here. Mm-hmm.

Thank you, agent Smith. Well, that's 'cause you think we already in, in matrix, right? Well, well, I, I won't lie to you.

There are days I do red pill, blue pill, guys. It, you know what? Let, let's tie a bow on this.

Um, it's been a really eventful reinvent, right? Look, usually, you know, you come here to reinvent and AWS is just regurgitating out one release and announcement after the next. And it, and it probably takes you three months to digest all of it and kind of make heads or tails.

This isn't that different. They put out a ton of stuff, but it is so focused and it make it so obvious, right? That this is where their, their attention is right now.

Yeah. They probably have a thousand other things going on, but what they chose to highlight here shows a narrative. And there's a thought process as to how all this is playing out.

I'm not sure that, you know, other companies aren't doing the same thing. And in fact, I suspect that they all are. But it's interesting to me that, um, AWS is getting better at articulating the mission and how all the piece parts come together and doing it, frankly, in a way that is better than the other companies that I've seen so far.

There, there are some, like trying to weave it together. What does this mean? How does this work together?

'cause it isn't all being introduced at once, right? It's put a lot of parts. So sometimes it's obvious like, okay, this is clearly what they're doing.

Other times it's, Hmm, not quite sure whether, whether it's AWS, Microsoft, Google, whoever it is. Agreed. Hey, we've got one more, uh, Textron gang that we're gonna record out here for tomorrow.

Mm-hmm. So, very excited by that. We'll continue the conversation, but for now, I think we're going to wrap it up.

We've got, Mitch, I know you've got a ton of briefings to do, dude. Yeah. Mm-hmm.

All day we got stuff happening. Uh, so until later or until tomorrow, that's gonna wrap up our text Drunk Gang Coverage. But hey, we've got a full day of live coverage I'll be doing here at our, uh, text Drunk Studios at AWS Reinvent Hyatt top the tower here at the win.

And, um, stay tuned for that. But until then, on behalf of Mitchell and Mike, and myself, have a great day. Everyone enjoy our coverage.

We're out. Google just announced Gemini three progressing from understanding to thinking to action with their Frontier AI model. This includes Nana Banana image generation, uh, anti-gravity coating assist.

Uh, what should we make of Gemini Three, apart from their use of crazy code names. We also discuss in-row AI and coding assistance, as well as news from Microsoft Ignite, including Fabric IQ, work, IQ, and Foundry IQ on this episode of utilizing ai, welcome to utilizing ai, the weekly podcast focused on practical applications of artificial intelligence from the Futurum group. Each episode brings together diverse perspectives to explore news and new use cases for the ways in which AI is transforming enterprise IT and the industries it serves.

I'm your host, Stephen Foskett, president of the Tech Field Day Business Unit here at the Futurum Group. Before we dive into the discussion, let's meet who's on the panel today. Hi everyone.

I'm Brad Shiman. And Stephen, thanks for having me. I am an analyst with the Futurum Group looking at data intelligence, analytics, and infrastructure, and I'm an unapologetic, uh, believer in all things ai.

So bear that in mind as we go. And I'm Nick Patience on the AI platform's practice lead here at futurum. I've been looking at AI for 25, almost 26 years now.

Um, I kind of, I, I agree with Brad. I am a, um, an un unapologetic supporter of AI as well. And I'm Steven Foskett.

I guess I'm the, uh, designated skeptic here. Uh, now I, I'm unapologetically enjoying and using it. In fact, um, I did some really cool stuff with it this week, but, uh, I am a little skeptical of the business model and, um, and financials of the whole thing.

Uh, but we'll get to that. Uh, not on this episode though, because we've had actually some new announcements this week. Um, key among those, of course, is Gemini three from Google, and I was thrilled to see that, um, you know, a little bit of inside baseball here.

Google actually made this announcement, uh, in a university setting. And, um, the first, uh, official discussion of it was with a bunch of young people who are just getting into the industry. And I love that vibe.

Um, I also really enjoy Gemini. I've been using Gemini, um, as my sort of go-to engine for a while now. Um, just sort of to, to, to get stuff done during the day, uh, along with, of course, apple Intelligence and Gemini three, um, at least so far looks like it works.

It's like Gemini, but better. Um, I guess, uh, uh, Nick, well, let's, let's throw this to you first. Um, what's your initial impression of Gemini three?

Yeah, I agree. I guess it is, it is Gemini, but better you'd expect it to be better after all. Um, that is, that is the kind of, that is the kind of point, the way they've, they pitched it to me.

Um, the, uh, recently was sort of the Gemini one family was about, um, understanding Gemini two family was about, uh, thinking, and the Gemini three family is about action, so taking action. So this is where Gentech and, um, comes in. Um, but there's obviously still, you know, reasoning model and, and, and all that kind of stuff.

And I think the, some of the stuff that was, you know, pretty interesting was the, um, you know, Nana Banana being part of it. Um, and there's been some really interesting images going around, actually interesting images right now from an enterprise AI point of view, not the usual, um, stuff that we see, um, of, you know, astronauts riding bicycles, um, and, and things like that. But actually, um, more, you know, it is ability to, um, you know, create images that are useful in presentations, not necessarily presentations themselves, although it's probably getting, it's getting there.

Um, and also, you know, this kind of advanced planning capabilities and things like that. I've been digging into it a little bit. Um, you know, we're a kind of Google shop here at Futurum, so, um, so we've been using it a lot.

You know, I'm, we all have, I know. Um, but I think it's, you know, it's, it's really interesting. And there's also, um, there's other things around it.

Maybe Brad wanna talk about the, um, uh, anti-gravity and that, that Cain as part of it as well. Yeah, it's, it's a fascinating, uh, release, um, for a lot of reasons, like Nick just mentioned. Um, and it's funny, isn't it, it seems that now we have a, a cadence every year in early December, we get together and talk about all of the new groundbreaking, you know, what do we call those, um, frontier scale models that come out.

And, uh, so I'm just waiting for the next deep seek to, to roll out, uh, before the end of the year. I'm sure it'll happen. Um, but, uh, for Gemini, I, I find it interesting for, for a couple of reasons, um, first to touch on the nano banana facet, uh, best name ever, by the way, I'm, I'm sorry.

Can we just take a moment here and say Yes. Did You have nano banana and anti-gravity on your bingo card this week, Right in the center? Yeah, I mean, what's with that?

Okay. Yeah, go, go ahead, Brad. Yeah.

Yeah. And it, it's like you mentioned Stephen, with rolling it out in, you know, with younger people, uh, and those in research in particular, because Google has really shown in this year that amongst the US-based model makers, that it is, uh, right there with anthropic in terms of, you know, bleeding edge, leading edge, uh, innovation. So, uh, I get that a lot.

And Nana Banana is a good reflection of that, because, like Nick mentioned, it's not just about funny images. It's, it's actually like if you're working in the enterprise and you're working to build software or maintain or manage software, think about what it means to have an image generation model that can actually output, you know, correct syntax, correct grammar, correct English or whatever language, uh, labeling for a very complex, um, diagram of, uh, something let's say all the way down to wiring for, or nano chip design even, let's say, I don't think that's outta the question, but think about what that means. If you can, instead of having to pivot over to a CAD cam design, you can actually just have generative AI create a design that's consumable by software itself, other AI agents or just software.

That's, that's amazing. Think about it, like at a really high level, just for building software, if you can, um, actually create an interface that that is, you know, not, not just a sketch, but an actual, you know, workable interface that could be broken apart by the model and built as software that, that's, that's amazing to me. So I think there's a lot of practicality with Nano banana.

Um, the second thing that I wanna touch on really quick is, um, that, you know, we all need to bear in mind that it's not cheap or easy or or fast to develop these frontier scale models. And that Gemini three is not actually a new model, like Nick mentioned, this is all about tool use and refinements to an existing foundation. 5, the end of 2024.

So it's, it's, you know, not an entirely new model. It's just doing a lot more with what it already had. 5, uh, was that it would run home to mama, uh, every chance it got in terms of, you know, coming up with, uh, a, um, API calls for its own models that were from 2024.

And so you're coding in the middle of 2025 and on, you're like, why are you doing this? And it's because the corpus was trained on that. Well, you know, they've actually, you know, made the model much better at not just using like MP MCP servers to do this, but actually better at, you know, being able to, to sort of see the context of what you're working in so that it uses correct API calls.

So I, I appreciate that about it a lot. And if I could just say one caveat, uh, also that I, I found with working with Gemini three is that, um, it's not very chatty. I, I, I missed that.

5 because it would, it would actually, it did, it did, you know, do some glazing now and again, like, oh gosh, how did you come up with that? Great question. But, you know, you could look beyond that and instead really enjoy the fact that it would share with you some of its thinking and reasoning tokens about how it got arrives at, you know, oh, this is what I meant to do.

This is what I need to do. This was a dead end. I tried this and it worked.

You don't get that with three, it's just all down to business and the business is pretty good. So I, I guess I can take that as a, you know, an acceptable, uh, downgrade. That's pretty, that's pretty interesting.

It's kinda like, um, it's kinda like seeing a band with Lead singer doesn't talk between the songs, it's just, just the songs, you know, that's all you're gonna get. That's right. Um, but I, I was born without the screaming, uh, to, to clap.

Exactly. Exactly. I think, I think it's interesting where we sit at the moment this week, uh, two days ago it was, uh, chat GP t's third birthday, um, and how quickly we, how far we've come and how, also thinking back, um, earlier this year, I can't really put an exact point on it, but somewhere in Q1 where, um, you know, after Deepsea came out in January and, and other things happened and, and open AI carried on its March how Google was apparently doomed.

Um, and the ad search business was, you know, gonna completely fall apart. Um, and here we are, um, as, as Brad said in, in December and, um, of the hyperscalers, I think it's fair to say, um, it's looking like the one that's, you know, currently in the lead in terms of AI from the chips to, to everything to the apps at the up up, you know, the top of the stack and ev and all co of course, everything in between, which is where the, uh, real meat and potatoes is. Um, but, um, I think it's, uh, you know, it's in a, it is in a really, um, really interesting position.

Yeah, you can't count Microsoft out, um, on that, but man, Google is doing everything right, um, in the AI stack it seems. Um, they are, yeah, they have really solid models. Um, you know, we, we should probably also talk about anti-gravity versus cursor.

0 just came out, um, with a lot of the same features and, and anti-gravity is right there. But, um, you know, it, it is really interesting that, uh, Google is able to, uh, to develop, um, really solid, useful tools, uh, get them out there to customers. Um, you know, I, I wonder about the financials of, of it, but I suspect given what Nick just mentioned with Google's own, uh, silicon that they're using here, that they, that those may not be as all that bad either.

Um, and that we may be seeing the emergence of a really solid, um, offering here. One of the things, Brad, that I wanna react to is, um, one of my greatest frustrations with dealing with LLMs is the sort of, um, compounding chaos that you get when you're trying to work with an LLM because that I don't just throw something at it and say, give me a result. I like to try to iterate over that and say, okay, that's good, but now this, and what I found, especially with the open AI models, is that it tends to spiral.

It tends to compound, and you get just absolutely bananas, sorry, Google results, especially, um, when it came to chat GT's image generation, it would really just go off a cliff after four or five images and you'd end up with something that just was completely wrong. And I would have to basically say, forget this. Let's go back to the start and try that all again.

Um, so far I haven't experienced that much as much in Gemini three. And I'm curious if you all, first off, have you experienced this or am I just in inept and second off? No, you were, is this something that's getting better?

Yes, and you're totally right. You're totally totally right that that's spiraling of, of images to the fact that you completely reset and say, this is all completely useless, let's start again. Or often what you end up doing in those situations is going to another, you know, going to clawed or going somewhere else and just trying something and then, and then getting frustrated and then building the slide yourself from scratch.

Um, but I think it's, you know, I think these, yeah, these things are, you know, they are, I mean, I guess the thing is we're always told they're only gonna get better, but they do get better, but they get better when the next version comes out. And so, you know, you, because you're not your, because I always say your stuff is not being used to retrain that model. Um, and you know, obviously as Brad talked about earlier, the cutoff date proves that it's not being retrained.

So yeah, you do kind of have to wait for the new version. 5, um, early today, and it's the first one I found that actually, I mean, I'm mean talking from an analyst building slides a lot. So this is a very kind of narrow point of view.

It's the first one I actually built a slide, a proper one, you know, a PowerPoint slide. I mean, what we use Google Sheets, but in, in PowerPoint format that it was actually good and all the words were correct on it, and yeah. And all those kind of things.

So I think, um, you know, in terms of personal productivity for me, that that's, yeah, that's huge. But yeah, I think, we'll, you know, the images, the image stuff is, um, it's always the first thing to be publicized, isn't it? Because everybody wants to, you know, put out all, put out all the silly stuff.

Uh, but I think it's, it's gonna be really, really useful in all sorts of, you know, the creative industries, but also, you know, everybody else who's not a creative professional, but is a knowledge worker of, of any kind. Yeah, very true. My, the first thing I always use these models for is to, to work on designs, airbrush designs for my 1970s panel van, uh, the ones with the, the little half moon windows in the back.

Um, but how many panel vans do you got now, Brad? Um, yeah, so, so, uh, it's, it's the same for all use, all use cases for these, whether, whatever, if it's a multimodal, uh, or reasoning or, or just a, you know, uh, use trains, um, I'm trying to think of the word now, sorry, uh, a ba a basic chat response model. Um, it doesn't matter.

It's the same because, you know, we're getting better at how we handle context windows, how big they can be, and we're getting better at the attention mechanisms we have that tell the model how to work with the content it sees in those. And so, like you're describing, Steven, you know, it, the, the challenge often when you're working with these, and this is especially the case for ag agentic development, is that the further you get into a session, the the less likely you are to, to end up where you thought you were gonna go. And it's, it's, it's like, you know, I, I can think of it, it's, it's, you know, a human, it's the same as a human being in terms of fatigue sets in confusion, sets in conflicting ideas set in waiting sets in.

Like, well, what's the most important thing? And this huge context window, oh my goodness, how do you know that? And, and so, you know, and I, I say this, Nick's probably sick of hearing me talk about it, uh, because we do internally all the time, and that is that it's not about prompt engineering.

It's, it's all about context engineering. It, you, you really have to be very careful about how you put data in front of a model in the context of what you're asking it to do. Because, you know, the more we work with these, the more I have found that, um, you know, the, the, we, we ask more of them, and therefore they do less until the next iteration, next Christmas comes around.

So you kind of have to, you know, be very cautious about how you use them. For me, like working age agent for software development, um, my best friend is the forward slash compress command to take everything I've been talking, everything that's gone into that conversation and compress it down. Two things that does first is it saves me a ton of money, and the second is it keeps the model from going off the rails, or less likely to go off the rails.

The other is, is simply just cut it off, just clear the cache, clear the memory, and start over because there are limits. The more we ask of these, the less they're able to do, honestly, you know, until we figure out how to do it better. Yeah.

The frustrating thing for me is yeah, when you're in there and you know, you literally will say, okay, keep this part the same and fix this other thing. And this, I've experienced this with coding assistance, I've experienced this with writing and I've experienced this with image generation like crazy with image generation, you'll say like, okay, I like that part. Keep that the same.

Now let's modify this other part. And somehow it will change that part too. And with coating assistance, that can be especially pernicious, because you can find that the function that was working and, and that you got done with before is suddenly off the rails again.

Um, ha. Has anyone tried, uh, coating assistant with Gemini three Anti-Gravity, I guess have, have you? I've spent, yeah, I've spent, I've spent some time with it.

Um, and, you know, I, I, I was, well, I wasn't actually that anxious to use it because I'm not a fan of Electron and I, I really load the VS code, um, because it's, it's really just JavaScript with plugins all day long. You know, I like, I like something like Zed, that's, that's sort of purpose built to, to, to be, you know, a code, an IDE and, um, anyway, but my, you know, preferences aside, I, I was very surprised, um, at, at just how good it was at getting to an outcome, you know, that outcome, if I say a very simple prompt, you know, make, make me a Pomodoro timer, you know, it, it will just set up my environment, get everything in order, like an actual, like good working order, not just half, you know, witted sort of, you know, A POC that you would not wanna share with anyone because they, you'd be too embarrassed to show them, but actually good coding practices to get there. Um, but I, I just don't, you know, I, it's, it's not something that I think is fully baked yet, and it's got a long ways to go.

And I think it also opens up a lot of, of potential issues for us because of, its, its use of what we, you know, have come to know as, as browser use or computer use. Where models are, are given the ability to, to work with your system itself. I mean, with these coding agents, we already give them access to RM slash you know, Tilda Tilda rm, you know, to delete everything.

Um, but, uh, you know, it's, it's like that with, you know, just handing the reins over to the horse and saying, take me home or drag me through trees. Let's go for the former. Okay.

We got a lot of metaphors in there. First off, um, I think you're gonna have to use nano tomato, uh, to design the UI for your pomodoro ti timer. Um, also, uh, baked, uh, fully baked pasta is delicious, especially with Pomodoro.

Um, and, um, and we actually went down to Amish country this weekend here in Ohio, and the horse absolutely can take you home when you're drunk, um, in the middle of The night. That is a, that is a dangerous practice, sir. But I'd let you know.

Um, but Nick, let's, yeah, so let's talk about coding, uh, and browser use. I think browser use is one of those things that a, people are a little shy about. Yeah.

I mean, do you mean shy about admitting they're using it or shy about the, uh, or wanting to, to actually get started with it? Yeah. Like what, like, do I really wanna turn my browser over to Gemini?

Yeah. And I, and I guess they're all, they're all pushing you. Perplexity is pushing you, and they're all pushing you open ais we're all pushing you to do that.

Yeah. There's obviously these kind of, um, core security risks of the, you know, the kind of, um, the, the kind of, what was it in, in indirect prompt injection, um, and, and things like that. I think it's, it's, it's interesting, you know, because we're kind of back to the nineties again, aren't we?

With, with browser walls? I'm, yeah. I think we're all old enough to remember, um, yeah, Netscape and IE and all the others, um, that, that, that were around.

I think it's, you know, I think there's, you know, there's, there's gonna be, I think there's gonna be pushback on the corporate level anyway in corporate it on those kind of things. I mean, there's, there's, there's only so much shadow ai, um, you, you can get away with, um, in a large corporation. I mean, you know, smaller companies then, then, yeah, I think, you know, people will, I mean, they obviously, the, the switching costs then become so high, don't they?

Because if you are investing in, you know, if that becomes your browser, and obviously that, you know, becomes incredibly sticky, that's obviously why the vendors want to do it. Um, so you just don't, like, like we'd been discussing earlier, you don't just go, well, I'm not gonna use that image generator anymore. I'm gonna jump over here and use this one.

Um, but I think at the moment, we're still very, very early in how effective this stuff is, which is we've just talked about for the last 15 minutes, um, that people will not probably want to commit, um, to, you know, I'm only gonna go down this road because, um, as it's pre it's impressive as, as say, you know, Gemini three years, there'll be another state of the art model along in a minute. And so, yeah, you want to, you want to try it, surely. Um, and so I think it's, uh, you know, I think people are gonna be, wanna be more, um, flexible in for, for a while yet.

Yeah. I, I feel like we're entering a new phase, um, with development in particular that, uh, it's not, you know, with age agentic development in particular, you remember how we were talking about vibe coding that Capar came up with last year, and, uh, it's vibe coding is now just a, you know, AI coding or just even coding. We're, we're, we're, we're, we're moving very rapidly toward new, new ways of, of building software in the enterprise.

And, and as we do that, like Nick is saying, you know, you're, you won't got so much tolerance for risk in the enterprise, you know, and you, you don't go beyond that. And if Shadow AI is threatening it, it gets locked down pretty quick. So I, uh, these tools are, are, you know, evolving rapidly to, to really match those requirements.

And I've seen it with some interesting recent, um, you know, agen tech tooling. Like we had, and I just cannot believe this name. It's, I, I'm not gonna say better than Nano Banana, but on par with Nano Banana, and that is Bob from IBM.

Bob is an IDE built free Gentech development. And Bob, Bob, Bob likes literate programming, if you've ever heard of that. And also spec driven developments.

And those two things together, um, those two ideas are, are really informing the, the maturation of a coding with ag agentic tools right now. And I can't believe that Microsoft let the trademark on Bob Laps, so I didn't pick it up That, because it was such a success. Absolutely.

Yeah. But, so tell us more about that, Brad. Yeah, Yeah.

So it's an old idea as, as, uh, you know, um, we've been talking about it, it's old ideas come around, old problems come back around, and I think we'll talk about those in a minute with Microsoft. But, um, you know, back in the seventies, a guy named Knuth with a K came up with this idea of literate programming where you could mix, you know, natural, um, instructions for the program with actual code and sort of treat that as a, a paradigm for developing software that was useful for both humans and machines. And what we kind of came up with from that was what everyone that's into data science knows so fondly is Jupyter Notebooks.

Not, not directly, but indirectly. It sort of led to that idea I'm saying, and that is now evolving and, and maturing in tools like Bob to, to, you know, sort of prioritize the thing that all developers hate the most, which is documentation, explaining their code. And as we have discovered over the last few years, what, what are these tools really good at?

These ag agentic, you know, LMS really good at, they are really good at parsing long strings of data, making sense of it, and documenting it for us. So ideas like literary programming kind of make your agentic, um, experience, like I was talking about with, with Gemini CLI and the non chatty version of, of Gemini. Um, you know, it makes it into a sort of, um, how do, how do I put this sort of like self-documenting software development in which you can have this auditable sort of timeframe time slice, slice, slice of how something was created that can be audited later and used like a black box later, uh, to make software better, more secure, you know, more performant, et cetera.

So those are, those are great ideas. And the spec thing, the spec driven development is simply know what you're doing before you ask an Nagen tool to write your Pomodoro timer for you. And it's just start with good documentation, be clear, you know, make, make this a project, make this less of vibe coding and more of just coding.

Maybe we should talk about, um, the other hyperscaler with the big news Microsoft. Um, yeah, because, uh, it was, uh, recently Microsoft Ignite was held recently in San Francisco. Um, I was there with a couple of other, uh, future analysts.

I thought it was, it was interesting. I, keynotes are not a, not a massive fan of keynotes, um, because, you know, they're, they're very, very well scripted and everything else. Um, but I thought this one was interesting.

It was interesting partly because who was not there, sat Satie and Adela was not there. And obviously this is, goes to part of his, his, uh, the, you know, the announcements I made a few months ago where he's gonna focus more on AI and, and also more on internal issues. And he left it all over to Judd Aloff, the CEO of the commercial business.

Um, and it was, it was, it was interesting how, yeah, Microsoft does have some models, but Microsoft has obviously been leaning on open AI for its frontier models. It's not a frontier model, um, provider, really. Um, it's an enabler and Azure, you know, through Azure.

Um, but so, so many of the announcements and the keynote, for instance, went on for about two and a quarter hours up until about the last 10 minutes. It was all about software, which you probably think, well, yeah, yeah, Nick, Microsoft's software company. Um, but so many things we go to, um, you know, even some elements of companies like Salesforce who've never talked about infrastructure us are talking about infrastructure.

So it's quite revealing. I think they understand where their sweet spot obviously is. Microsoft, this is, um, it's obviously in developers, but also enterprise users, um, of all kinds.

And I think that's, that was, um, that was, that was evident in the, the topics and, and the, uh, things that they, they announced. It wasn't, it wasn't till right at the end. They, they started talking about, um, infrastructure.

The other thing that was, um, unusual about it slightly was the lack of customers. Um, until sort of later on there was a couple of fireside chats, um, one where Mercedes, uh, and a and another, another customer and all the rest of it, they were using a fake company. They said, we have, they were, this is a made up company called Ava, and they were using their own engineers to kind of, you know, play the role of somebody at this company where they could be in, in advertising or a marketing or that cup of that company.

And they're doing demos like that, which if I'm being, you know, nice to and fair to Microsoft, they're obviously doing in public 'cause it's very, very early in terms of customer adoption. Um, and obviously some of the things they're announcing obviously weren't out until, you know, weren't announced until that very day, which has also happened to be the same day. And Google announced Gemini three.

So I think it was, um, it was, it was just, it was an interesting reflection of, of where Microsoft is versus where AWS is, which is reinvent this week. Uh, versus, versus where, where, uh, Google is. Yeah, I, I was lurking from afar.

Um, and, uh, from, from my vantage point, I I, I found it also interesting about, about that in-house, you know, use cases. And I think it, as you mentioned Nick, it really speaks to, um, the fact that Microsoft is looking a bit further forward than, than, um, sometimes we, we get from them. And you could see that really reflected in some of their announcements.

Well, it's always, it's always give and take. The, the give part is they announced this, um, uh, preview of what they call, um, fabric iq, which just sounds like, oh, intelligent quotient, that must be really smart. And and indeed it is.

It's trying to bring, you know, some sort of semantic meaning to your, to your data state, which we could, we could talk about as much as you guys want. But sec the, the balance to that was, you know, they, they came out with their own version of a, um, you know, uh, Postgres SQL database, uh, called Horizon db, which everybody in their, you know, cousin has a version of Postgres. That's why it's the world's most popular relational database.

And the fact that Microsoft announced this as a part of fabric, which is, which they, you know, hinges on their idea of one lake, which is, you know, a, a data lake house, you know, it's like Databricks and somewhat snowflake. It, it's to say that, you know, we, we want a very flexible data platform that separates storage and compute as much as we can, uh, and leans heavily toward what we all love right now about, you know, bringing data to ai. And that's unstructured data, but you kind of need to pay attention to operational data.

So, so I feel like at once, they're, they're trying to leap way ahead with Fabric iq, and they're also trying to, you know, be very grounded in, in what they're doing for existing customers to help bring them forward on this journey. 'cause if you just made fabric, you know, just all about, you know, the, the data lakehouse and don't really try to, you know, bridge that, you know, longstanding gap between, you know, analytics and operational data estates, you know, you're, you're, you're gonna just always end up with in-house customers and you won't have any real ones. Yeah.

Um, and then can I just touch on the other two IQs? They, they announced one called Work iq, one called Foundry iq. The work IQ one was much more of the kind of the M 365 user, um, and copilot.

And this is, this is sort of pulling everything from the graph, the knowledge graph that Microsoft's been building for years, um, plus memory and context. And this is aiming at agents, you know, and if I'm working it, it'll know what documents have opened, who I've been emailing, who I've been chatting to in teams and all that kind of stuff. And it brings it all together.

Uh, the Foundry, um, is, the, is slightly different from the other two. The other two, fabric and work IQ are kind of bounded by the kind of data, um, they are looking at for, for very good reason. Foundry is the one that can kind of go anywhere else, including the web.

Um, and it's based on Azure AI search. Um, so it's kind of, in some ways it's the evolution of enterprise search a the great kind of, um, uh, undelivered promise of, of, of it over the last or 30 or 40 years. Um, but I think, you know, Foundry is a very interesting approach and they also announced, um, agent Factory and Agent 365, the, the latter being their kind of agentic control plane, which is, uh, um, gonna be, I think, you know, very, very important for any kind of agentic or orchestration is gonna be massive over the next couple of years.

So that was the other two things I just want, or three things I just wanted point out. Yeah. Well, thank you for that.

And, um, you know, I honestly, I, I feel like we could have probably talked a lot more about Ignite here. Um, I think for me, the big news was that they didn't use Contoso as their sample company. Um, very disappointed to hear that.

Um, so Microsoft actually has dozens of sample companies they've used in the past. Um, I, I guess, um, you know, Google told us that Kentoso switched to Google Workspace a few years back. Um, How'd it go?

They did make that, uh, press release. So those of you who don't know what we're talking about here, uh, I dunno, Google it or something, you'll find it. Bing it.

Um, yeah, so the, the, and of course, one more thing we, we do have to mention, uh, this episode goes live during, uh, AWS reinvent. So in fact, uh, Brad and Nick, I think both of you are at re reinvent as well, right? So, um, we will be talking about reinvent news, um, probably for a little while because we've, you know, we've given Google quite a lot of love here for Gemini.

Uh, we've given Microsoft quite a lot of love for what they announced at Ignite. Uh, but of course, um, I guess who expects that, uh, AWS is gonna sit on their hands? I mean, um, what, what do you anticipate at this point, uh, to be the big takeaways from reinvent?

Yeah. More, more of what Nick just mentioned, agent orchestration all day, every day, um, breakfast, lunch, and dinner, and with, with, uh, agent Core, I think is what they're branding it from, from AWS and, uh, it's such a critical aspects right now. Um, because as one of the reasons why Microsoft came out with Fabric iq, and it's the same thing we're getting from Salesforce, sorry, with, um, snowflake with Snowflake intelligence and, and from Salesforce with, with their agent force, um, um, is all about how you bring semantic meaning to the data that, to bring the right context to these models.

But it's not just about that. It's about how do you actually bring together these autonomous or semi-autonomous elements, um, in, in a way that are, you know, non-deterministic, but still deterministic enough to, to get the outcome you want in, in a sort of reasonable manner without exposing you to a ton of risk and without costing an arm and a leg. And that's all about orchestration.

So that's what's what we expect to see a lot of from them. And then also, I do expect to see more infrastructure. There will be more, there'll be chip announcements, I assume, um, from AWS, um, so there will be, we're dragging us back down the, um, the stack down, down to that.

Um, the, I'm sure there'll be, there'll be, there'll be a lot. I mean, it's, it's a big, as we were kind of alluding to earlier, you know, if, if you can train your own models on your own silicon, you know, you, there's a big cost advantage. And I think, uh, you know, both A AWS and, and Google are, uh, are seeing that to a greater or a lesser extent.

If they don't have to, um, keep shelling out for, for Nvidia GPUs, then, uh, yeah, that's a big advantage for them. So expect expect more of that. And yeah, we'll be revisiting, um, reinvent for, for quite a while after, uh, is is finished, I'm sure.

Yeah, Nick's right about the hardware thing. We can't forget it, can we? Because a, apparently Oracle was right along with, with Exodata and, and vertical integration being kind of a useful thing.

And we've seen it proven at time and again, with, with Nvidia, um, and that is that, you know, vertical integration leads to optimizations that that lets you do a lot more than if you're just white boxing it all day with just scale out, you know, for trying to solve your problems. And with AI in particular, you know, there are a lot of bottlenecks that show up in places like networking and in places like storage. And, and so that's why we see all of the, you know, networking and hardware, you know, storage server, et cetera, vendors all leaning really hard right now on creating this sort of integration stack of, of optimization.

Optimization. Yep. Absolutely.

And, and, you know, for that matter, um, you know, we just, uh, last week heard about Nokia announcing that they're gonna be the, the, uh, providing networking, uh, connectivity for AI models. Uh, they're investing $5 billion to have made in America networking hardware to support these things. I mean, there's, there's all sorts of, um, investment happening down at the infrastructure layer.

And, um, as Nick points out, I definitely, I know that that's my focus, so I'll be keeping an eye on that. Um, we do have to wrap this week though. Um, so thank you both for joining us.

Uh, before we run, though, since again, it is, uh, reinvent week for our, our listeners when they're listening to this, where can they find your coverage and reactions to reinvent, uh, Brad? com, et cetera. All right, Nick?

Yeah, same. I'll be LinkedIn apnic patients. I, I, I don't use XA lot, but I'm apnic patients on that.

com. We'll be, we'll be writing notes, um, uh, for, uh, for, for our clients and everybody else on what we see at Reinvent. Yeah, absolutely.

And, um, yeah, I'm gonna be watching it as well. You'll find me at s Foskett on most social media networks. Um, I'm, I'm using Blue Sky a lot lately, uh, maybe ahead over there.

Uh, thank you everyone for listening to this episode of the Utilizing AI podcast. If you enjoyed the discussion, please do subscribe. You'll find us on YouTube as well as in your favorite podcast application.

Uh, and also we'd love to hear from you if you wanna give us a rating or review. Uh, this podcast is brought to you by analysts and experts from the Futureum Group, where insights meet AI. For show notes and more episodes, head over to Text Strong ai, uh, the utilizing AI YouTube channel, or the text TV app.

Thanks for listening, and we will catch you next week. We're back here at Cube Con. It's afternoon of day one of the main show yesterday, you know, they had all the satellite conferences, but today's the, the opening of the, uh, show floor, and there's, uh, oh, there's all kinds of things going on, uh, including I think there's a, the Cube crawl or whatever they call it Tonight.

Yeah, yeah. Tonight. Yeah.

I don't know if I'll make it to that, but we'll see. Anyway, let me introduce you to my friend here. He's been on Textron TV before.

His name's Tucker Callaway. Tucker, welcome back. I don't think you've ever done one of these live with us though, right?

I Think I have. I think I did two years ago at Q Con, or No, it was R-S-A-R-S-A. Yes.

Yeah, yeah, Yeah. You're right at Q Con. You're right.

Yeah. I like doing RSA, you know, we just, we'll be at RSA this year. All right.

Going, we just got all my contracts done. Yeah, we're doing, so in addition to doing this there, I put on an event Monday at RSA, and this year it's usually DevSecOps, but this year it's gonna be on AI securing AI native dev. Okay.

So we're gonna talk about AI For that. We're gonna talk about ai, and that's a great segue No. Before we segue into ai.

Seriously, Tucker is the CEO of mes O. Yep. And I don't know if you all know mes o and that's okay if you haven't, don't be ashamed, but Tucker's gonna make you smart about mes o Tucker, go ahead, man.

What's mes o about? Yeah, so Mes o is, uh, you know, we've, we've actually been handling telemetry data in the observability space for a while, and we are all about delivering a new observability experience that's fueled by agent operations. Now, obviously, that's something we've started in the last 12 to 18 months, um, but we think there's a new way to go about doing observability and analyzing data.

Uh, I think AI is going to disrupt this market. Yep. Like crazy, to tell you the truth.

Yeah. com. Okay.

Um, you guys also play, you know, when we look at who does observability, who's using observability data, the s the, uh, SRE segment was a big Yep. A big segment for mes o and lately we've seen the rise of, let's call it ai, SREI don't know if I buy into, I know, it just sounds like a lot of frigging I got opinions, You know, letters put together. Well, let, let's start with defining it.

What does ai AI SRE mean to you? So, I've said this a couple times, I don't really like the term, it's the term that we have. We don't like you either.

Uh, the reason I don't like the term is because an SRE is like a person in a job, in a role, and it's also one that we've given a lot of responsibility to over the years. And so just to suggest that you could AI a person or AI a role like that isn't right to me. So You think it's replacing the SRE with ai?

I, No, I think it's more I, what I like. I think that's what people are referring To. It it, that's Wrong.

Yeah. And, and I think what I think we should be doing is, uh, to me it's AI driven observability, right? Right.

So, how do I take some of these mundane, routine tasks of, of, uh, like tactically managing the system off of the SRE to allow them to go back to designing and scaling systems to do those things that require the big brains to do it. Got it. And not do the routine things.

And that's what really led us to, when I think about ai, SREI think about detection, diagnosis and remediation of incidents. But to me, that's really more AI driven observability. So I think this has a lot to do with what your view towards what role AI plays in all this.

Yeah. Right. I, I just wrote an article a couple days ago about the use of AI in journalism and media.

Mm-hmm. You know, and every media company is using ai. If they're saying they're not, they're full of crime.

They're lying. Yeah. They're lying.

I think it's pretty much like that in observability too. I think a lot of, you know, we see it in developers. 90% of developers are using ai.

Yep. Ops people, everyone PR people. We could replace them all.

No, we can't. But, you know, pr PR people, right. But PR people are using a ai, we're all using ai.

But what I wrote in the article is, you know, from the Billy Jolt, AI didn't start the fire. AI can't start the fire. Right.

It's the human. That's the spark. You, you're out almost anything you're doing with AI today, you must have a human in the loop.

Okay. So to me, when I hear ai, SRE, I'm thinking about how is my SRE becoming better? Here's how I think about it.

And I like the spark analogy. Yeah. You actually just re-branded around.

You got a spark around a spark. Very cool. And you didn't know that that was No, I didn't because it was under there.

That's amazing. What are you having for dinner? No, I'm only kidding.

So, But to me, that spark is actually, the data is the spark. And what's changed a lot is how we can analyze data. So when we think about what observability fundamentally was, it was presenting complex data to humans for them to determine the root cause of a problem and the next steps.

And so, like the, the solutions that are like out there today are basically taking people through an investigatory process and presenting them with the information in a very human consumable way. That is no longer the best way to analyze data. No.

Right Now, that's the best. The problem is AI is a better way to do it. But the problem is, at operational scale, the amount of data that telemetry or observability creates can't just be processed through ai.

So we found that training the models, which is kinda the prevailing approach today and doing better prompts, was an insufficient way to actually derive operational outcomes with AI to do the ai SRE. So we took a different approach, which is to optimize the input. And so we basically apply, it's called context engineering as the discipline versus prompt engineering.

And what we do is we, uh, basically refine the data before it's supplied to the, the model. And that allows us to give better, faster, and cheaper outcomes out of the model. Love it.

So We can do it without training. We can do it without, you know, using your data. We can, we can have you up and running in five minutes to do it.

It happens at like 10% of the cost of what the traditional approaches are. We have benchmarks to prove this too. It's pretty fascinating.

Like, as you know, we've been, we've been focused for a while on the processing of real time data. Well, it turns out that suited us really well in this transformation to an AI work. I, I think, well, I was just gonna say that it, 'cause that's kind of better.

Lucky. You better Better be lucky than good. That best to be both.

Yeah. We're both I less than a 25 years in venture back startups. Um, But, you know, to me, so let, let's put the SRE to the cycle.

Yeah. 'cause that's a human issue. Mm-hmm.

I, I don't think we're replacing humans with AI today. No. I think we're replacing humans with other humans who maybe use AI better.

Right. But the humans gonna be there. But when we look at observability, observability, look, I've watched observability blow up here at CubeCon Yep.

Over the last three, four years. Open telemetry, Prometheus, you know, have become open, tell more than any of 'em Yeah. Giants.

I mean, and almost the whole observability space has open tell kind of under the hood there. And as a, a building block. Yeah.

I really believe that AI and, and ai, whether we're talking agentic ai, which is really, I think where we're going. Yeah. I think generative had its moment in the sun, but it's, it's agentic Agreed.

Um, has the potential to blow it up. Yes. Yet again, it was like observability was a, a fission bomb.

And now we're looking at a hydrogen bomb, potentially. A couple ways that I look at that. One is, I think the consumption of AI is gonna explode observability in a way that's far greater than what the cloud did.

Yeah. Right. That, that's one aspect of it.

Uh, the other aspect is that, um, the way we think about analyzing that data is just gonna change dramatically. Right? So, so an o hotel is a huge part of that.

So it used to be that the incumbent vendors and observability own the creation of data. Well, they don't own that anymore. Oell owns that.

Right. And, and that was this massive disruption without any consequence, because we were still fundamentally dependent upon those incumbents Yes. To analyze and process the data.

Right. Because, Because they were the best way to process the Well, they Were, they were the face. They were the face.

The other stuff was kind of under the hood. Yeah. So now the face is changing.

'cause now, now the best way to, as we were talking about, the best way to analyze that data is actually to do itally. Mm-hmm. So now, if you're an incumbent, you no longer own the creation, nor do you own the analysis of the data.

So now this is just a data management and an experience problem. It's no longer this big giant problem that an incumbent can own. So the problem has, like been shipped away at, but I think, or not, I think, I know AI has taken us to this tipping point where the analysis is now better done, you know, in the model, not by the human.

And what we're proving, or have we believe we have proven is the models today are good enough. They don't need to be trained. You just need to give them the right data.

Right. And if you refine the data in the right way, you will get the outcomes that you expect, that you want and hope for out of AI today. It can be done.

And so to me, this begs the question then, what is the human's role in it? So the human, like, so the human role is going to be, um, like for a period of time gonna be pretty big, right? Because like, 'cause if, again, if you get back into diagnosis, I'm sorry, uh, detection, diagnosis, and remediation, we're really focused on the diagnosis.

'cause I don't believe you can get to a agentic remediation until you have extreme trust in the diagnosis phase. I agree. Right?

Like, no one's gonna, no one's gonna let remediation happen genetically. I mean, you might have a human in the loop trigger it, but that's just a human firing off an automation routine. We've been doing that for a long time, and it would get better with ai, but like, really tightening that loop requires the extreme trust in the diagnosis phase.

That's where we're focused right now. So I think until we have that extreme trust, we're always gonna have the human in the loop. But ultimately, we need to be thinking about designing these systems for, uh, agents to process, not humans to process.

And that doesn't replace humans. That puts humans in control of a agent processing. And it puts humans back into how do I design scalable systems and things like that.

Which is what, which is, you know, not an SREI actually operated as one in a weird way for like, in 99, but I You just stayed in a Holiday Inn Express. Yeah. But You know, like, I, I believe that they got into the, into their discipline and profession because they wanted to build and design systems, not because they wanted to do observability in firefight incidents.

Well, I'm sure none of the people 10 years ago raised their hand and said, I want to grow up to be an SRE. Right. We didn't have SRE until Google wrote the book.

Fair enough. Yeah. Um, but, but I, I do think you're right that I think the SREs come from the ops, what we used to call ops.

Mm-hmm. Right? Yeah.

Some of them are now SREs, other pieces of the cis o you know, platform engineering. I think we're seeing a lot of overlap between SRE and platform engineer Agree engineering. But I think, um, at the end of the day, I'm old, they're all ops people to me.

Right. com Right. I'm a big believer in dev and ops Yeah.

And security and all these disciplines coming together. Um, I do think I'm a half glass, glass half full kind of guy. I do think AI gives us the ability to bring these together and to recognize some of the goals that you just said about why they got into it.

I've been, I was at chef during the DevOps movement. I've been observability. So you go, I've been in observability forever.

We've been trying to do these things Yeah. For a really long time now. And I think we can actually deliver on the promise.

Yeah. Finally, I, I think having better observ abilities and making the SREs job easier, if you will Yes. Is almost a byproduct of all these things.

Right. But in your case, it's the main thing. I mean, it's what mes mo's about to a lot of degrees.

But I, I, look, I think it's a frigging great time to be in the, in the observability SRE business. Right? Now.

Here's An interesting thing to think about. I think I would argue that the only task that an SRE does in a visual ui, uhhuh is observability Most, most, most. But what do they use?

Everything else they do is not Everything else is infrastructure as code. They're all doing, everything is code. Like a UI is like not a, a traditional way scaling, scaling a system.

Now they do it in observability because they're forced to visualize and investigate and complex data. Right. If we take that off their plate, they can now consume observability in their native tools.

Like they can consume it. So what are their native tools, Command lines, slack, uh, cursor, things like that. They don't have to go into this ui.

They can actually operate, not have to switch contexts. They can go do the things that they do in the place that they do it and not have to leave their workspace to go to observability to then go back to where they operate, which is more on the command line. Yeah.

So I'm a Star Trek dude. Yeah. I think with AI we have the capability to bust out from windows and command lines.

The whole human computer interaction Yeah. Is gonna change. Yeah.

And, and so if I'm an SRE, I'm just going to tell the computer what I need to know right now. Yeah. Right.

Right. And it's gonna tell me, and whether it tells me, you know, uh, that I hear or it flashes something on a screen or whatever, the idea of being, having to be able to sit here and type in s**t. Excuse, I know we're live, excuse me.

The idea of being able to sit here and type in on a keyboard Yeah. Is history with like, our kids won't type on keyboards Yeah. To talk to their, or to communicate with their computing devices.

Yeah. Whatever they may be. Yeah.

No, I think it's True. And, and so that's going to create a whole new opportunity about, you know, and I don't know, is it something that, do we get wired in? Is it holographic stuff?

Is it just pure? Because some people don't comprehend what they hear they need to see. Yeah.

And I think, I think to that point though, I think there's obviously a, like, I, you know, I've been on this kick of like an that's kind of anti visualization, anti dashboard. But, but you do need that for trust. And you do need it for verification, and you do need it for audits at times.

But I think you could ask, or not, you could ask the model for it. Just say, gimme a visualization. I don't need a, it Gives it to you on the fly Persistent dashboard that's sitting around spinning, doing I, I agree with you.

I, again, and I think you just ask it Nick, and it it conjures it up. Yeah. Kind of thing.

Right. You don't need to be spending time developing, you know, the UIs And, and I'm sorry for all my friends who are UI developers out there, but I, I do think we are on the verge of, you know what started, I guess in 1970s, right? Park, Xerox Park was the whole window mouse.

Oh yeah. Way of, You know, computer human interaction. We might finally be outgrowing that.

We'll see. Look, I'll settle for, you know, high trust in the diagnosis of mes mo's. SRE agent, agent SRE.

You wanna walk before? I'll settle. I'll settle for that.

I get it. It, I'll settle for that. It, I get it.

Let's Get there. First One step at a time. com.

Did we mention that? We did. Check it out.

MO new brand with the spark and everything. I love it. With the yellow.

Sparks it up. Gimme one of these again, it up perfectly. There you go.

Look at it right there, baby. Yeah. All right.

Hey, enjoy. Q Con. Okay.

Conn, it's a, it's great seeing you again. Thanks for having have that context. Truck TV soon.

Let's hear more. com with a spark here at Q Con. We're gonna take a break.

We got more coming your way. Stay tuned. Hey everyone, we're back here.

We're live at CubeCon. It's, uh, Wednesday day two of the, of the main, main, uh, floor being open and the keynotes and so forth. Let me introduce you to my next guest.

His name is Alexis Richardson. And, um, well, if you've ever used Flux or heard of Flux or GID ops, you have, you know, a little bit. You could thank them.

Alexis. Thank you. Welcome to Textron here.

Thank you, shimmy. I'm really here. Happy to be here.

It's great. It's great to have you. Um, before we get into your company and, and everything else, let's, let's talk a little bit about you.

Thank you. If you wouldn't mind, I, you and I said some things, but share your story with our audience. So I guess I started my career at Goldman Sachs.

I was a trader. Really? Derivatives.

Yep. High stakes. Gambling.

High risk. High high risk. Yeah.

High pressure. High pressure. Not a long lifespan though.

Very much not. No, no. I actually found it boring.

Did You? Yeah. All you do all day, you sit at your desk, you have these boxes squaking at you, and you make a decision whether or not to buy yourself some derivatives.

So for all you day traders out there, and you know who I'm looking at? Boring. Boring.

All right. And I wanted the connection with humanity. Uhhuh.

I wanted to build things that other people would get excited about Uhhuh. So I started a software company and I, that led me to create something called RabbitMQ, which is my first big success. It's still going cool.

As a piece of software stand. A piece of software that was acquired by VMware Uhhuh after a few years. Then I was part of the pivotal spin out.

I led really one of the app teams there, created the whole new generation of Spring Uhhuh product. I'm not an engineer, I'm a product person. Uhhuh.

And then I left to do a company called Weaveworks. Sure. Which is where we invented GI Ops and Flux.

Very Cool. Did the C-N-C-F-I was the chair of the TOC for three, maybe four years. Wow.

Helped get all this stuff going. It was, I went to the first Cube Con. It was a hundred people in San Francisco.

A hundred people. There's nothing going on. No, nothing.

See, I didn't start going, I think till the third one in seat. It was Seattle. Yep.

There you go. So, I mean, that was big by then. Yeah.

Anyhow, so, so exciting to see the change. It's just all incredible. So anyway, um, after doing that, we created GI Ops and every single customer told me, you know, it's a good Alexis, I've got this automation.

I've got all these different tools I can choose. 'cause you did as an open thing. The problem is my ops team really hate me.

'cause you've got it. You're making them all learn this thing called yaml. It has been a scourge that people laugh About, I don't wanna learn yaml, I don't wanna learn Terraform, I don't wanna learn vol.

And if the systems go down, I want to go to the screen and I want to press the on off button until I get it right. And so, you know, we, we, we call this being in configuration hell config hell. And that's what led me to do the next company, which is called Config Hub.

So we're bringing it right up to today. Up to today. Wait, I want to go back a little bit.

Please do. So when you say you, you know GI Ops. Yeah.

First of all, I'm not sure if everyone out here has the same definition of GI ups. Yeah, Yeah. What does GI UPS mean to you?

Right, right. GI UPS is when you run, you operate a system automatically based on a plan which lives in a separate place. We call that plan the desired state that explains everything the system is supposed to do.

It's the what, not the how, but the what that you're expecting to happen. It's like, imagine you're running a Fractory floor and you have a plan of where all the robots are, where all the pipelines are, what everything is supposed to do. And then you have autonomous agents that use the plan to tell all the operating pieces what to do, and continually check that they're doing it correctly.

And they're always checking back to see if the plan has changed or if the, um, or if the operation needs to be corrected. And they'll automatically fix any differences. So it's always doing what we call continuous drift detection and reconciliation and that continuous loop of operations.

That is GI ops. So it's automatic management of systems based on a plan, which we put into Git when we wrote down the rules of GI ops in the GI ops working group in the CNCF, which you can go look at online. It says it doesn't have to actually be Git.

It could be any store that you use as a single source of truth for your plan, your desired state. So long as it has versioning, multiple users, non-repudiation, and all those valuable things that tell us this is a safe place to store your information, your plan about your running systems. But modern GI ops includes people using other stores as well.

People are using GIT with Jfr now. Yes. Uh, OCI stores, I see people using it with Vault from HashiCorp with S3 on Amazon.

It's a little bit more complex than just one physical store, but you have one single view of the truth. And that is your plan. And GID ops is automatically managing systems based on that plan.

It is not putting pull requests in GI No. And, and, but to many people it's Right. And that's why I kind of asked you and that was the money line right there.

Well, I posted a blog post at the beginning 2017, and it was called Operations by Pull Request. And that set off that whole pathway of misunderstanding. I gotcha.

I gotcha. Lemme ask you another question before we jump into config up. You know, the CNCF has Two, I do know the CNCF.

They got over 200 projects in here now. Is it really that many now? Yeah.

Yeah. You could get lost in the pavilion over there. Yeah, you could.

And that's only part of it. But, um, and, and they're, they're good about this, right? They have numerous service mesh projects.

They're managing that. You know, our first blush can be somewhat competitive. They have at least two major GI ups.

Right. Argo CD and Flux. That's right.

As the creator of Flux, just between you and me. Forget all the people watching this. Okay.

I'm just, Does that kind of p**s you off a little bit? Or you think they're handling it well? Having both.

This My fault. It's your fault. Yeah.

Why? Well, I'm not sure how, how I should say this. One of the motivations when we started the CNCF, I was involved in the creation of the C ncf f Craig Mackey from Google was talking to me and he said, I, I wanna put Kubernetes in the foundation.

And I said, well, I've just been a Pivotal and we were creating the Cloud Foundry Foundation with IBM. Yes. I've just had some recent experience of foundations.

I think I can help you with that. So he wrote down some rules and I said, look, it's really important to avoid the mistakes of cloud found past foundations. Yeah.

Cloud found made a couple mistakes. Apache made some mistakes. Eclipse made some mistakes.

They all do. That's life. But The big elephant in the room is OpenStack.

Remember that All of the hope we had on Yes I do. Everybody thought, wow, this is gonna change things. What went wrong?

It's, they've got it kind of back on track now, but for years they got lost. Well, but let, let's be clear, for those of you did don't realize Open Infra, which is now part of C ncf F That's right. Was actually the OpenStack That's right.

Foundation. That's right. That's right.

The bigger thing swallowed the smaller thing with acquisitions. So where OpenStack went wrong is they put, they said it's all gonna be one open product, one stack, and we'll have committees to define how everything fits together. And the more you do that, the, the harder it becomes to actually do any real work.

Yep. You just spend your whole time talking between different committees, meaning That's by meanings. Anyone who's been working at a university will have seen this.

If you've been in Europe and you live the European Union, you'll have seen this Not-for-profits. All of that, you know, and you have these people who love that way of working. Yeah.

And they make it worse. Yeah. We call them bureaucrats.

Mm-hmm. So I said, look, what we're gonna do is we're gonna be project first in the CNCF. We will not assume that we know better about the future than anybody else.

So we will not pick one thing and say that's the winner over another thing when it's clearly far too early. OpenStack did that and they ended up creating a mess around the whole networking layer, for example. And they got held hostage by a couple of projects.

So we said, we are too stupid in the TOC to figure out which ones could be. We're not gonna pick winners. We're gonna let them figure it out in the community.

Let the market, Let the market work it out. I'm such a big believer in that. It's a democratic open model, bit of capitalism, bit of democracy.

And then the ti time will tell over time some things will run ahead, some things will fall behind and we won't make dumb errors like open telemetry. The open telemetry story, I confess, I thought that was the dumbest proposal I'd ever heard. Clearly the team weren't sure what they were doing.

And it was a very, very ill thought outset of standards. But actually we also said as the TOC, but these guys, they have a right to try. Yeah.

Let's let 'em have a go. And they turned into one of the biggest successes in, now we run the C ncf F like OpenStack Open Telemetry would've been shut out the door on day zero, and We'd still be in horseless carriage. Still Be Horseless character.

But you know what? So I have a lot of friends in the VC world. Good.

And one of the favorite things VCs love to do over drinks is talk about the ones they just missed on Uhhuh versus the ones they thought were Sure. Fire. Yep.

Yep. And, and how bad, how badly they miscalculated. Absolutely.

Part of it's part of it, but I, you know, as you sit here in retrospect, you do the best you can. Mm-hmm. And, and at the end of the day, I do believe the market is the great sanitizer.

Right. It doesn't care about your mistakes. It doesn't really, it's Organized around incentives.

Yeah. It's the most important part. Absolutely.

All right, let's pivot over to config hub now. Yes. Yes.

New company. New company. I've never met a founder who wasn't extremely passionate about what they're doing.

Oh yeah. I'm passionate. Where's your, what's your passion on this one?

Um, we think that we can really change how operations works so that it is automated, managed, and safe as, as it's now gonna explode in the world of ai, right? Mm-hmm. What is missing is nobody has a good modern configuration database that tracks what every application is doing.

What what stated is all in keeps it up to date automatically. So you just go look at that database. You have a fresh view.

This is what's happening out there in the, in, in my world, in your, and if something is wrong, you can change it. And if you're about to do something wrong, it says, hello, you're about to do, make a mistake. My, my policy detector has picked that up.

Are you sure you want to do this? 'cause it's gonna blow this up and you can stop it from happening when things do go wrong. The tool will also let you see which particular YAML file was responsible for screwing everything up for you.

You can find it, you can find the right line and fix it. That's a beautiful thing that we don't have today. What we have instead is people talk about yaml, hell yeah.

Configuration. Hell. We see configuration sprawl, lots of systems, lots of tools, lots of files.

Nobody can find anything. Three day outages, data loss, and now bring in ai. You're putting this agent inside your organization.

It could anything could happen. Agreed. Agreed.

We're Gonna get rid of all of that. So given your history, did Fig Hub open source? Nope.

I knew you were gonna say that. All right. Why?

Um, well, one reason is market's much tougher now, 20, 25 than it was. It's different. It's Yeah.

In the past. Yeah. You cannot do two things at once.

If you do, you're gonna fail. Mm-hmm. Remember Docker?

Yes. Vaguely. I think When you run an open source business, you're having to win on two fronts.

You're gonna get the best open source project, or the second best third will not be good enough. And you've gotta succeed commercially as well, which is a whole different thing, different set of features, people, sales, marketing, all of that. And then you've gotta make 'em work together.

And that's a really tough set of challenges. And they've gotta, timing's gotta be right and so on. And also, you've gotta be super patient.

You've gotta spend three years making your open source project successful, then build your first commercial thing, then a bit more open source. It's a 15 year job. Yes, it is.

Okay. And I, I don't know if you've noticed that it's not the most Efficient. Yeah.

It's not the most efficient. Where do you want to be in 15 with Years? Well, on the beach.

Yeah. So we are starting out with the, with our commercial offering first. Really, really highly focused on user success.

That's the big play go to go to the product. It does some things for you and you can tell us if it's working or not. It's a SaaS, SaaS first product.

I've teamed up with some fantastic people who've worked with Brian Grant, who was one of the originators of Kubernetes out of Google. And Yesper Jergenson, who was the lead at Heroku Sure. Rolled out the Twilio platform.

So bringing together a really good set of folks around this problem. It's very exciting. I love it.

We're almost outta time, but for people wanting to get more information on Config Hub, what's the website? com. Easy enough.

Yep. And then how, how would they engage here? Engage, follow me on LinkedIn, get in touch that way.

com or you can, uh, sign up for the product. com. It'll tell you where to sign up.

Those are the ways to engage. I love it. You'll come back.

We we're gonna talk more about the history of GI Ops and other things, but we'll do it on the text. Drunk tv. Okay.

Thank you. We don't have all of this around us. Alexis, thank you very much.

It's been a pleasure. Thank you. Little History right here on Tech Drunk tv.

We're gonna take a break. We'll be back. We've got day two coverage coming at you.

You're watching Tech Drunk tv. Hey everybody, welcome back to Ingram Micro One, and I'm talking with my friend Hans here, who is a solution provider with a specialty in healthcare. How you doing buddy?

Doing well. Been a really, really great conference. There's just been an awesome amount of innovation in healthcare lately, and I, I've just walked people through a little bit.

What's going on in that sector and your role in it and what are the opportunities? Yeah, we're finding, um, there's a lot going on with an interest in ai, you know, trying to find ways to, you know, automate manual processes. And we've delved into a very, very niche aspect of that industry with, uh, organ, uh, procurement organizations and tissue banks, tissue processors.

And, uh, there was a tremendous amount of opportunity, both for AI and just automation across everything they do within their supply chain. Hmm. There's also a lot of regulations in healthcare.

I don't think that's, uh, much of a surprise to anybody, but we're trying to apply AI to that. Haven't we walk that nuances of that where, because concerns about privacy maybe and the data, but we also need to come up with some automation. That seems like a challenge.

Yeah, it is. And uh, you know, some of the things we're finding out, I mean, obviously we've got certain compliance and accreditations and we have to get organizationally SOC two and hipaa, and then you've got the, uh, PII information that they ho hold and that ties into their own processes. So we have to be respectful of the confidentiality of not only the information that they have on the patient records, but also the confidentiality of their own internal processes and how they use it.

'cause in some cases it's, it's a, uh, competitive advantage in some cases. It's, uh, very, very, um, intrinsic to how they operate that they actually don't want it to release. And they're all very sensitive to the aspects of the, the regulations, you know, for that confidential information processes.

And, uh, I mean, right now it's just respect and, and, and, you know, with the, the technology to be able to, to do what they need to do, but at the same time, not allow us to limit us. When I talk to people, there are two challenges, but the first one, everybody seems to know. It's like AI will occasionally hallucinate.

So how do I kind of work around that or, or account for that factor when I'm building out something in a healthcare scenario? Yeah, that is, uh, it's really interesting because the way that we are actually applying AI for our clients in this industry is that, uh, that does exist. And the, is that, uh, the data, um, as much as you need it, as much as it feeds into the AI engine, uh, it's basically the lifeblood of the AI engine as well, because the AI model has to be trained off of that data.

And we've got all the regulations that you just mentioned that prohibits some of, you know, that data in, in terms of how we use it to aggregate it with other clients at a very aggregated, le aggregated level. And we don't have that opportunity. So the, the, the model has to be trained and the more data that we can get from the organization we're working with, you know, we can work around that.

That's number one. Second part is, is that no single AI model will provide the outcome that we need for our clients. So we're having to stack the technology with other types of ML oriented tech, um, code, other types of AI oriented models that do very specific functions to work in tandem to provide an outcome.

So I've kind of got a layered approach where some of the AI models are validating the output of the other AI models to get, make sure that whatever is being generated actually is supposed to be what it is. Correct. Yeah.

A very simplistic equivalent. Yes. The Other side of the coin too is that a lot of the healthcare processes are what we would call deterministic.

They're supposed to be done the same way every time. Mm-hmm. Uh, AI models are probabilistic and hardly ever do anything the same way twice.

So how do I connect something that is probabilistic into a deterministic workflow and kind of meld that together? Yeah. And that, and I, I go back to what I just said earlier, right?

There's, um, um, there's the prompt engineering, there's the AI models, there's the AI model stacked and layered on the other AI models. We got a vector database that's built into there as well. The understanding of their business process and what the outcome is.

Um, so the model in order to to, to train it, to have a predetermined outcome every single time, the more data that we can feed into it, the more scenarios that we actually get from it that we can hone in and refine on, we'll start providing that very, very precise answer. And it's one of those where it's a process of refinement and processes or an iterative process. So the first time you build it out, it's not gonna be perfect.

And so the more data that we can actually feed into it, the more input we can get from the end users. Uh, we actually start honing in on that, that precise answer. Now, everybody watching this is probably having the same question.

Where did you find the people with the skills to go do that? Because most of the folks are saying, I love this AI stuff, but you know, they all wanna work for Nvidia or something. So how do you get those guys to come work for you?

Yeah. Um, trying to answer this the powerful way. So there is a constraint in the marketplace for skilled AI software engineers, and I have to compete with, you know, the Facebooks and the, you know, all the, the big, you know, hyperscalers for that talent.

Uh, in my particular case, I got very, very lucky because the AI engineer that we hired, married, my, my youngest daughter and I provided an opportunity, and there was an opportunity you probably wouldn't get at the other places because he, uh, has an opportunity here to actually kind of define our direction and to be able to be very creative with the AI and in a, in a, in a wide sense. Um, but going beyond, you know, my small team of him and, and a couple of others, um, it, yeah, yeah, it, it naturally is, is very difficult. So one of the way things that we're trying to do to augment that is that there are AI tools that allow us to, uh, do some code development on the front end.

It's not perfect, still has a ways, ways to go, but it does save us time. So we're trying to use automation and code development to be able to close the gap on some of that. And then, like anybody else, you've gotta go out and hire the talent as well.

So we gotta ensure we get the right talent. Of course, we're at an Ingram event. How did you get connected to Ingram, and what does Ingram do for you and as part of the building of this solution?

So the story that, um, you know, most resonates is that we got into this about two years ago. So that's when I hired our, our, uh, senior AI engineer, uh, November 6th of, uh, 2023. And he came on board, and Ingram actually had, and we started out with, uh, IBM's X, they were actually sponsoring a level three workshop with IBM in Chicago.

So his first day on the job was on an airplane heading to Chicago to get his credentials on, on Watson X. So he spent a week there going through the workshop. And that was really brokered by Ingram.

You know, having the foresight to actually, you know, go out and say, how do we actually get our partners enabled? How do we get them engaged? How do we get them them to a point where they can actually start talking ai?

And it was a really good foundation because it allowed us a better understanding of how, uh, the technology was being positioned, you know, in, in terms of a go to market. But we had to learn after that, how do you actually go in and start selling this? So we relied on, on the Ingram team, uh, to, to understand what types of proof of concepts, how do we actually go through a sales cycle, how do we actually engage with prospects who have a need?

And then we had to hone our skills from there. You of course, you also work with a lot of the vendor partners that Ingram represents. Um, I don't know if you can tell me in a lot of detail, but which of those vendor partners are kinda at the core of that solution for you guys right now?

And what is it that you would wish that maybe more of those vendors would remember when dealing with solution providers such as yourself? Yeah. It's right now with, you know, just the, the terminology of ai.

Um, I think the large vendors, you know, the ones that are well known that make the news every day, they're sowing a lot of confusion. Everybody's talking about the art of the possible as a reseller partner, as somebody who actually engages with a client, by the time we engage, they don't want to hear about the art of the possible. They wanna see a solution that actually works.

So there's a big gap between, you know, what, what the, uh, the vendors are providing and what the solution providers actually have to deliver. So we rely on Ingram heavily, not necessarily with the technology partners that we've worked with IBM and Microsoft, and there'll be others in the future, but it's the relationships that we don't have with the other technology partners that Ingram does have. So, for instance, if there is a reason for us to change some of the backend coding with a different LLM or a different AI type model that is, uh, specific to a certain vendor, and we don't have the re relationship, Ingram will probably have that relationship.

And we have to leverage Ingram to help build our credentials and reputation to be able to open the door and get the right resources that we need so we can continue our development to provide that solution to the client. One of the things that I hear a lot about is every CEO has a bad case of fear of missing out and thinks that this AI stuff is all magically happening tomorrow. And then there's their staff and people who are a little more circumspect 'cause they understand what's required to actually implement.

How do you, as the solution provider kind of navigate that relationship and those conversations? 'cause essentially you're a diplomat between these Groups. Yeah, absolutely.

Yeah. And it's, um, that was, I would say situationally, that was probably the case two years ago when we started, um, AI was this concept. And pretty much every executive team says, okay, we've gotta get AI in here.

And our first probably, you know, handful of phone calls or, or overshoots from our, our, our client base, not necessarily our, our prospect base was, Hey, can you help us with AI and my responsibility? Yeah, absolutely. What would you like us to do?

It says, well, that's why we're calling you. And it was this, this panacea that all of a sudden, you, you basically, you install something, you implement it, and everything is gonna work to perfection. It's just like this magic button you press.

In reality, that's not the case on the staff level folks, the operating level folks, they're seeing AI as a threat. So it's basically our executive team wants to bring in AI and it's, it will basically replace my job. And so you have this, uh, uh, diversity of thoughts and understanding of what AI is supposed to do.

So we have to obviously educate the C level folks that it is not that be all end all solution where you push a button, everything magically works, um, and it's trained on your data. And then we also simultaneously have to work with the operations folks and let them know that we're not here trying to put a system in to replace your job. What we are trying to do is that, uh, you know, right now the focus and the benefit of AI is really, you know, time savings and productivity improvement.

So we want the system to be able to do the heavy lifting for them and the process and use everything that AI can do based on the data that's being fed to it, to free up their time to work on the very true value add, you know, needle moving types of, of aspects of their job that's gonna really enhance the company's productivity. One of the funny things about healthcare, at least from my perspective, is it was always perceived as a sluggish kind of business because they were collecting a lot of data, tagging it and organizing it. And yet that may be their secret sauce for ai because they did a lot of that heavy lifting of the data and work already.

Then a lot of other vertical industries have not. They, uh, I would agree that to a certain extent, uh, but there are, um, I would say upstream functions that take place that are still very manual and a lot of that tagging categorization of data, which actually makes our job easier, right? Because all, all well-defined, um, a lot of that has been done, but there's a lot of unstructured data that shows up in forms, in handwritten notes.

Um, it shows up in jpeg images, OCR images that we have to translate in. And not only that, you can have forms that have the same information, but the forms are different and, and the context is missing from that. So when we actually build out these AI solutions, we have to ingest those documents.

We have to understand what is in those documents, we have to understand what the context of those documents are so that we can turn the information on those documents into relevant, very well-defined, categorized information to then let the AI model be able to provide the output that it wants. So to your point, a lot of what they do operationally, you know, for production, for, um, you know, um, patient outcome, yes, that is, but upstream from that, a lot of the information is very unstructured and increase the challenge for them. And there's a huge amount of opportunity as far as productivity gains from that as well.

It almost sounds like, you know, you have a solution and you've kinda landed and now you're looking to expand. So what are you thinking about as the next opportunity? Yeah, so we, we are, we're actually, uh, we, we've got a great client that we're working a, uh, informing a strategic relationship with, and it's a tissue processor, so basically organ donation, and then they actually take, um, uh, tissue, uh, donor tissue and then look at, uh, eligibility requirements.

So they, they be able to look at lifestyle, they be able to look at disease, they look at things of that nature and, uh, qualification criteria for the tissue. They have their own manufacturing process. And, you know, as we spoke earlier, that's very well defined.

The information is very well defined. How they capture the information and move the information through there is very well defined. But on the front end of that, how they actually analyze the, the donor and how the tissue, the suitability for the tissue that they have to process for their, in, you know, inpatients, the hospitals, the doctors, the clinicians.

Um, so we, you know, we've developed an AI application to be able to do that manual process on the front end, and then the automation that follows that, the donor traceability. So now that we've got the components being processed and tracking that all the way through to the final production of that tissue, so that it's either a skin graft or a bone graft or whatever that final product is, so they can inventory that and then push that out to the hospitals. And then there's the entire supply chain and ecosystem where the supply demand match is very inefficient.

So we're looking at the hospitals, the doctors, the clinicians, when they actually need something, how can we actually compress that cycle time so that they can get it from the tissue processors in a shorter period of time? And, and there, there's additional opportunities beyond that as well. So there's a plethora of, of things that we can do.

Technology can help, but you've gotta have a good partnership with folks in the industry to do it. Yeah, I almost think like anywhere there's friction, it becomes an opportunity. Absolutely.

Oh, absolutely. So Last question. As you look into the coming year, what are you excited about?

What are you thinking about and maybe what's keeping you up at night? Well, the geopolitical stuff's keeping me up at night, so not, not a whole lot I can do about that, but there are opportunities. And I think, you know, we've had, uh, two years of maturation, uh, not only within my organization, but I think within, uh, just industry in general and understanding, you know, ai, AI has gone from this concept of AI is, you know, it's just this broad, uh, term that everything's kinda lumped into it.

Now we've got, you know, the, uh, uh, generative ai, we've got agentic ai. Uh, we're still focused on use cases, but I think a lot of the use cases will be addressed by agent AI to a certain extent. And then you have the possibility of that automation where you get the AI agents talking to each other and looking for those opportunities.

But at the end of the day, when you look at it, right, we are still collecting data as part of that process. We're trying to take the friction out of the supply chain. So the question I asked the CEO of our client is that if you were to look at your processes and you were to take all the friction out, what is the shortest, shortest amount of time that it would take to act, you know, to actually process the tissue that, that you work with?

And he thought about this is, I said, that should be our goal, and that's what I'm excited about. 'cause I think the technology can get us closer to that goal because we can automate a lot of the processes in a very smart, intelligent fashion. Not necessarily to replace jobs, but again, take the folks that are very good at what they do and allow them more time to be able to do it better.

Especially this stuff, I don't enjoy doing it in the first place. Yeah. Hey guys, it takes a village to do AI and it kind of starts with the solution providers and includes the distributors and the vendors, and that's how it all comes together.

Buddy, thanks for coming by. Yeah, thanks for having me. And we'll be back in a minute.

Hey everybody, welcome back to Gram Micro one. We're talking with my new friend Sophie here about what's happening in the channel in France. Sophie, welcome to shots.

Good to see you. The partners, I'm sure have lots of challenges. There's a lot happening in France these days.

Yeah. But when you talk to them, what's keeping them up at night? So they have to face a lot of headwinds Right now.

So they think about the, the way they can be innovative in this market, which is slowing down because of the fact that the budget has not been voted yet. So all of the public sector, or most of the sector budget, uh, project have been frozen. Uh, enterprise tends to delay abit DDA investment.

So in front of this, uh, tough market situation, it's important for them, first of all to streamline their opex, uh, see how they can be more efficient, and also to, to, to accelerate their performance. So they're looking at it with us. So it's all about how can we standardize the processes, refocus our teams on the added value task so that we can create mature value.

We've also seen that a lot of the end customers in France are probably navigating all kinds of economic issues themselves. Yes, indeed. And they're probably looking at the partners for some help as to maybe how to streamline things and be more efficient.

Exactly. And, uh, so then the purpose for the resellers is to see how we as distributor, we can help them, uh, meeting their partner's expectations. So it's important for them to be able to leverage our technical resources and our human resources.

So it's all about what we are talking about right now. Mm-hmm. One of the things that we're talking about here at the show a lot is there's a shift a little bit to focus more on business outcomes.

It's not enough just to be a trusted technology advisor. I have to work with the end customer. I have to know something about their business a little bit.

Is that playing out in France as well? Yeah, Of course. So now we are all looking at the way we can, um, create better value for our partners, uh, for their own partners.

And it's based on all of the artificial intelligence we are leveraging through our platform. We have talked about it a lot right now with, uh, and Sanji on stage, talked about it even more this morning. So how can we accelerate the performance?

How can we increase the breadth? How can we leverage the new tools? We have, uh, Sanji talked about the digital assistance, intelligent digital assistance we can leverage to do that.

We can use, uh, and finally what I think it's, we can talk about artificial intelligence, but first of all, it's about human intelligence. How can we have the right purpose, uh, in our discussions? There will always be some human in the middle of that process somewhere, Right?

There will always be, fortunately, I hope so. Um, I guess the question I would have though is things tend to roll downhill in this world. So are the partners leaning more on you for certain expertise and professional services because they can't always find that talent, or they might not wanna invest at something that they once Yeah, yeah.

So the kind of service we are talking about, we were so far talking about professional services, for example, around the cloud, around cybersecurity. But I think that more and more we will talk about new services consultancy around how can we all leverage these new technologies? How can we leverage artificial intelligence, uh, in the way we can all increase our overall efficiency?

And they need also our experience to do that, right? So, uh, Tiffany yesterday talked about the fact that we need to use energy as a service. So I, I kept it in mind, uh, what is the narrative, uh, beyond artificial intelligence?

Because what we see is that we all are using artificial intelligence for our own purpose, but what are the, the true, uh, use cases in the enterprise, right? How can we really streamline the workflows? And I think that most of the partners, they are not very clear on that yet.

So they need us to be trusted advisor and accompany them in this motion. As that occurs in every country that I talk to, there seems to be a skill shortage. And for the partners, it's almost do acute because I need technology people who are customer facing and friendly customers.

So how does Ingram work with them to kind of find that kind of unique unicorn in the IT landscape To, to find what kind of unicorns, Uh, IT people who have, uh, who can talk to customers and explain things to them and are very, uh, have a lot of empathy for the customer? Yeah. So you mean, how do we find this talent, or it's not that easy to find this kind of talent?

Yeah, that's my, that's my question. Yeah, because it, so even in it, the, the, the job is really shifting a lot right now, and we are less relying on the hard skills done on of soft skills. So it's about, so it's a combination of IT skills and, uh, the, the ability to create the right synergies with the salespeople so that we have a common language to the people and to be to the customers.

And to be honest, in France, we didn't find the right profile yet. For, for fortunately, we can rely on the global resources. All of the people from SANJEEP team can really help us.

Then we still have, uh, to deal with the language issues. So we, we are leveraging the local, the global resources from an IT perspective and leveraging our local resources for the sales purpose. So it's a good combination.

France, of course, is part of the eu. Is there more transactions spanning multiple borders, or the partners working with each other in across Europe? Or is they, are they still pretty much focused in their particular country, or?

No, it's Still really focused at local level most of the time. So what we see is the complexity doesn't come so much from the fact that we have to interconnect different countries, even if it can occur. But the complexity, complexity comes more from the, the, the fact that when we deal with project, we have to combine different technologies, different skill sets.

So, and we need sometimes to connect different partners to each other. And for that, the platform advantage is made for that. It's a, it's an ecosystem which is made to create mutual, uh, values between the vendors, the customers, and also to onboard some customers on the same project and be complementary in their skills.

Are the partners becoming more open to that level of alliance with other solution providers? 'cause sometimes, at least historically, they always kind of view each other a little wally because they think they're competitors. Yeah, it's interesting to see during this event, for example, we are here with 10 French partners, and during lunchtime, even during the dinner, they start talking about their, their skills, their experience, and to see how they could, uh, be complimentary and work together to compete with some big players.

Right? And it's true in the mid market, mid-market area, in the SMB area, Of course, we're here in a show. And I don't think you can go very far without running into somebody talking about ai.

And we talked about it earlier, but what's the level of enthusiasm in the, among the partners for ai? I mean, are they kind of studying it or are they, are they all in? No, they are not all in yet.

So it's, it's, uh, they're wondering exactly what it means correctly, right? Because while most of the people, they know what they do with CGPT, for example, for their personal life. Uh, coming back to the enterprise, uh, beyond ai, there are a lot of questions around what, as I mentioned before, what are the concrete use cases, but also from a security perspective, how can we make sure that we have the best use usage of it without compromising our data?

So for that, we need really to explain them through our own AI factory that we can secure their data and that can, we can provide the right added value and, uh, and contribute to their business development without, of course compromising their own security. Mm-hmm. Um, one of the themes of the keynotes here has been this whole notion of using AI to make it easier to do business, not just with the partner in Ingram, but from the partner to the end customer.

Yeah. Do you think that we're gonna see like a significant reduction in the amount of friction that has historically been in those processes over the years? Yeah.

Yeah, I think so. If you take the, what makes the job of a distributor right now, there are still a lot of, let's say, low added value task around the quotation management, for example. And it creates a lot of workload, a lot of burden, uh, even from a financial perspective.

So if we can remove it, remove this kind of friction, if we can, um, accelerate the, the time to market of the quotation, if we can have a really, really, uh, a full digital process from the catalog ingestion to the, to the, to the invoicing, imagine how, how qualitative can be the discussion afterwards. Because now we still have to talk a lot about, do, did you get my quotation? What is the price?

When will it be available to me? Uh, did, uh, do you know when I will be delivered? For example, if all of the information is available on the platform, then we can really start talking about strategic initiative.

How can we build the future together? So I don't think that artificial intelligence will make us less close to the partners. It's exactly the opposite.

The more we'll develop it, the more time we'll have to, to, to develop qualitative discussion and, uh, to, to really, um, build the future together. We might have time for a drink and dinner to discuss something rather than In France. It's very important, you know, to have a very good glass of wine and talk about the business, of course.

Is there something you wish the partners would be focusing more on? And as you kinda look at them and you talk to them, I know they're all different, so it's difficult to generalize, but as you kinda have those discussions, is there something that you kind of, you know, would wish the partners or some piece of advice that you would give them and say, folks, you need to spend a little more time on this. I don't think I'm the right person to give advice to my partners, because we are all on the same boat, all facing the same, of the same kind of difficulties.

You know, the problem with this deep transformation we are going through right now is to deal with the day-to-day operational issues. We have the, with the business pressure we have as well. So we need to deliver outcome while transforming in the longer term.

So the advice I would give is to try to combine both, sorry, and spend enough of time, even one, two hours a day, to keep thinking about how the future will look like, and how can we, uh, foster the right dynamic internally to have the right talent, thinking about the way they can streamline workflows, the way they can create more values for their own partners. And it's easy to say, it's not easy to do because it's about the purpose. We need to reassure all of the people about how the future will look like.

Each time we're talking about artificial intelligence, you know, that there is a fear behind it. We will lose our job. We will be less, uh, intelligent in the future because we fully rely on artificial intelligence.

What will be our added value? What about the, the enablement? So we really need to all think about it and see how we can, first of all, I think really first of all, drive the right purpose.

What is the storyline behind it? What do we want to get out of this artificial intelligence capacity? So we started the conversation with what's happening in France today.

We're kind of at the end of the year. As you look into 2026, you know, what do you think will happen? What is your crystal ball telling you?

So in 26, so, you know, for me, AI is like the, the way we were talking about cloud 10 years ago. You know, we have talked a lot about cloud. And finally, between the time we have started to talk about cloud and the time we have seen concrete, uh, outcome from a business perspective, it took a while.

I think that in 26, we will start, first of all to see two motions. First of all, our partners customers, and even ourself being very much more concrete in the way we can leverage this ai, uh, technologies. And on the other end, start seeing some real business opportunities around it.

Uh, while so far we have seen some very big deals, uh, around ai, uh, were some solutions, uh, embedding Nvidia, for example, but we didn't see in the SMB in the mid-market area, we didn't see really the integration of this AI opportunities from a business perspective. And I hope that in 26, we start seeing it completely. Of course, another big part of this channel equation are the vendors themselves.

There's many of them here. They have boots. Um, is there something that you wish they would understand about the channel in France and the partners in France a little bit more than they do today?

What they need to understand is that it's important for them as well to standardize their processes. Because if they really want to keep benefit of all of this new, um, platform or digital platform opportunities, they need to accompany us as well by providing us the right access to their data, by standardizing their own processes, breaking some silos so that we can really all together make it much more fluid. And, uh, the added value for them will come out of it as well.

All right. Hey, folks, you heard it here, France. It's a funny thing.

They have a different word for everything, but they have the same issues we do. Hey, thanks for being here. Um, so we'll get into a little bit in three parts about what makes the VCF stack a great place for Kubernetes.

Um, first we're gonna go into our Kubernetes service, and I'll cover that here in the following presentation, especially if you're watching us on YouTube. We're gonna, uh, review applications being deployed into our environment. And then the following section, we're gonna talk about how our cloud model, and it best enables your workloads and Kubernetes workloads in our stack here.

And so, yes, we are at KubeCon. So what we are doing today is we are focusing on the VCF cloud and how the VCF Cloud actually delivers Kubernetes. Now, I want to get a few things out of the way here.

First of all, for all things Kubernetes, when you're dealing with VMware by Broadcom for all things Kubernetes, those things all live now in the VCF division, right? And what I mean by that is the way you would deploy Kubernetes clusters, the way you would manage Kubernetes clusters at scale and the way you're gonna operate, those are all a part of our VCF Cloud, right? When we're thinking about VCF Cloud, what we're talking about doing is we're thinking about delivering a cloud in your data center for your, for our end users.

Now, delivering a cloud in your data center has a couple of different changes when you compare, like what's gonna happen, uh, in your environment versus what you do when you go to hyperscaler, right? And really that's because there are a couple different personas in that, in that are playing out here. There's that persona who's in charge of building and managing the cloud, and then they're the actual users, the people who want to use your cloud, right?

And so we have to think about all of those things. Now, luckily, we have a really good head start being VMware. We've got a great bit of networking, a great bit of storage, and our industry standard virtualization platform that make up that base layer of our stack.

Now, we can deploy this in many different areas, including throughout our partners, um, and at the edge, uh, in telco spaces and in your on-prem data centers, right? And functionally, we take that compute, storage and networking and via constructs in our stack, which we'll get into here in a little bit. We'll take those things and we'll actually leverage the underlying VMware infrastructure, the underlying VCF infrastructure to provide these additional services.

So up top, you're gonna see Kubernetes virtual machines. We have a container service that I'm sure Jeremy's gonna talk about in session three today. And we have many other services that actually help you deliver your applications here.

Networking services, storage services, those sorts of things. So let's talk a little bit more about how the cloud admin enables the platform engineer to deliver Kubernetes and what VKS actually is. So, VKS is fully conformant certified Kubernetes delivered in your data center.

And I wanna pause there and, and, and think about what we mean when we say fully conformant certified Kubernetes. Okay? What this means is we're delivering you in your data center the ability to deploy Kubernetes clusters that will functionally equip you to run any application that's suitable for Kubernetes.

Why is that important? Well, that's important because you may have applications that are running in other, in other clouds somewhere else, right? You wanna take that application, you wanna make sure that application will run in your data center.

So by, so by delivering you fully conformant certified Kubernetes, you can be assured that if your app is running in maybe a public cloud provider somewhere, or maybe in other, in some other Kubernetes distribution, that it will run on VKS in your stack. Now, you'll notice in this, in this eye chart here, I've got a couple of Kubernetes clusters here, and that's great. What we actually get the ability to do with a couple of Kubernetes clusters here, right, is we're showing you that it's not just one Kubernetes cluster in your environment.

This is a Kubernetes service. We deliver multiple Kubernetes clusters. When a user requests a Kubernetes cluster, they can get one out of your VCF infrastructure, right?

These are all deeply integrated into the VCF substrate. And what I mean by that is, like all of the CS that usually go into Kubernetes cluster, C-S-I-C-N-I-C-P-I, those sorts of things, those are all covered and integrated deeply within our stack. So that when it comes to maybe a user kicking off an upgrade of their Kubernetes cluster, that the dependencies are just managed for you, right?

You're not worried about, oh, is this version of that applicable with that part of my infrastructure? No, this is just managed for you as part of your VCF, uh, deployment, and we seamlessly integrate with GPU storage and the rest of our compute stack. You'll see that we rapidly deliver vSphere, Kubernetes releases a VKR or vSphere.

Kubernetes release is our way of conceptualizing what might be an operating system for a Kubernetes node and a version of a Kubernetes minor, right? We release vks typically within two months of when the upstream community drops a new version of Kubernetes, right? And so I think, you know, we just had a release of V five, and I think that's right, about two months after the Kubernetes community launched this.

Now, the reason why, yeah, go ahead. Um, I'm my, uh, guy courier Futureum group. My mind is like, like maybe unhelpfully focused on service.

Mm-hmm. Because, um, we got a lot of K's that are p's and here we got a K that's a S. So you have the blank KP and the blank KS, that that's a service versus platform uhhuh, right?

So Nutanix has their NKP. It used to be called NKS used to, before that it was called DKS. Um, why service?

I have an answer in my head that I like, but, but why call it a service instead of a platform, or, Well, this is a Kubernetes service that runs on our cloud, and our cloud has all kinds of things built into it. That might actually be the things that you would consider that make up a platform. Examples in the VCF Cloud, you can run, we, we have the ability to do network isolation.

We have the ability to provide you, uh, coming in a, in a future release here, um, object storage for your, for your, for your use cases. We have the ability to bring you data services, manage databases in our cloud. We have the ability to simply run a container in our cloud as a service, right?

We have the ability to provide you virtual machines. Obviously if we're not doing that, we're not doing well. But we have the ability to provide you virtual machines in your cloud as a service as well.

These are some of the base level components that we feel that you're gonna have to add onto with other things to really build a platform experience. Now, here's the thing about platform. What we have found, and what we think, what we think is happening here is we're, we feel that like when people are using our VKS service, what they've told us is they said, Hey, you know, it would be great if you just had a Kubernetes substrate that my apps can run on.

We have big opinions about the CI part of our process, and in a lot of cases, people have told us we have big opinions about CD in our stack as well, right? And so our goal is to provide a platform that can interact best with those sorts of capabilities. And so when we say service, I want you to think of something that is based on industry standard technology that enables you to leverage that capability.

And then the second part that I want you to understand is that when we're thinking about actually leveraging this service, you can bring different components to bear on that stack, and we will manage that service for you through its lifecycle, both bringing new versions of Kubernetes and helping you upgrade your app, as well as managing dependencies of things running in those Kubernetes, uh, in that Kubernetes infrastructure. So is your view that there's a broad platform engineering paradigm, or there's a broad, uh, operational paradigm? I think VMware used to call it SDDC or something along those lines.

And this is a, uh, an element, maybe a critical element mm-hmm. In that overall infrastructure capability that you are, I I would, that you would reserve for the platform name? Yeah.

Yeah, I would say so. And good. I think we break that up into a couple of key services, maybe like core services.

Let's think of it this way. One is our VM service. You can build on top of that.

Yeah. The other is our container service. Um, we have a, we have this thing called vSphere pods that will just allow you to run a container.

And then we have our Kubernetes service. We feel that most applications can be platformed in one way or another on those things, right? And we may work with partners on each of these areas to platform those applications accordingly.

That's how we're thinking about this. But with regards to the service, we're going to manage the lifecycle of those. We're gonna help you operate those, and we're gonna have, we're gonna help you deploy those at scale across the globe in your VCF deployments.

And that's our view of why we wanna call it a service That is the VCF component. Um, the VCS does VCF only offered as a cloud now As a cloud. Yeah.

'cause you said that the VKS is a service that runs on VCF Cloud. So on the VCF Cloud, VCF is a cloud, it's in your data center. Yep.

So we're looking to be, we, we are looking to be in your data center, helping you where your workloads are in those data centers. Now, we certainly also leverage, uh, partners like right? We have this vast network of, uh, cloud service providers that will help you run VCF as well.

So if you're looking for an experience across multiple areas with the same Kubernetes experience, because that's built into the box, you can leverage, you can leverage your relationships with your, with our CSP partners to do that. I just wanted to be clear. So VCF cloud is just V is VCF, it's, uh, yeah, it's VMware Cloud Foundation.

Uh, and it's, it's, it's the cloud in your data center. This might be A knit, and I'm not sure if it's a smart question or not, but the word conformance came up mm-hmm. With ai.

So when you say conformant, are you talking about CNCF conformance for Kubernetes? We Are Or certified? Yeah.

We are certified Kubernetes. And certified Kubernetes means that we pass the upstream Kubernetes conformance tests. Okay.

Alright. Okay. Yeah.

Got it. And so, like most vendors will tell you, we are certified Kubernetes. We heard that you talked about the AI thing a little bit earlier today.

The new AI conformance, uh, thing that the CCF has brought forward that the community has brought forward, we're happy to be a part of that. We're one of the launch partners for that. Okay.

So like, so again, we want this to be a Kubernetes stack that supports your workloads. We don't necessarily have an opinion about what workloads should run there, but we wanna make sure that this is aligned with what the community is delivering so that you can, so that these workloads are successful on our cloud. So Is, I'm trying to grok this.

So the VCM VMware Cloud Foundation, is that the underpinning for everything that VMware offers? Is it built on Cloud Foundry? Is it VMware Cloud Tan, Zu related Tan?

It, it's unrelated to Tan Zu Tansu is something that could run on top of PCF VMware Cloud Foundation or VCF is, is a set, uh, pre a, a set of previously built components mm-hmm. That we have now put together into one offering and one thing that we can deploy to give you cloud services, right? And that includes the VCF includes our hypervisor, it includes our networking solution, it includes our storage solution.

It includes all of all of the things that you would historically think about leveraging to automate and operate those things at scale. And it, and most importantly, it ties all of those things together in one common life cycle experience. So previously, these were separate components.

And the reason we're starting to talk about this as VCF is because we've put a lot of effort into making it one seamless experience to install everything that you need to get SDN to get your software defined storage, to get your, to get your hypervisor layer, to get all those things so that we can now have the conversation about leveraging services. And those services are the VKS service, our container service, our, our VM service, our, our private AI services that we research. So where would you draw the line for the platform?

Or does it, does it move? I, I mean, I think it depends on, you're Talking about platform engineering and what operations is gonna be focusing on to, to maintain and to orchestrate The Yeah, let's talk, let's talk a little bit about that. I think it, it can kind of move.

And the reason why it can kind of move, it really depends on what you mean by platform. I believe that, yeah. I you mean by platform?

Yes. I know there's a set of our, there's a set of his, there's a set of engineers that are in charge of building the cloud substrate. These people are defining storage, policy, networking, VLANs like, uh, like the design of the compute infrastructure and taking those things and putting them into server cabinets or, uh, procuring that from a third party, right?

Those sorts of people when, or like, could be considered platform operators in some organizations because they may have that extra duty with, with managing Kubernetes infrastructure. In fact, that's what we found. We found that some organizations put the onus of maintaining the infrastructure or would like to put the onus of maintaining the infrastructure on the infrastructure team where they also have platform teams, right?

And so here's where it gets a little bit muddy. We need to build a system that allows platform teams to manage the sets of services that compose their applications, right? So there's a, there is a team that's responsible for building the cloud that ultimately gives you the spot where you're going to deploy your workloads in the cloud, But you're not building a VCF 'cause it comes for you, right?

You're, you're deploying VCF right in your data center, right? And once it's deployed, you can then consume. And at that point, I would say there's a platform team that's responsible for helping you manage your Kubernetes infrastructure, uh, the VMs, the load balancers, the networking that makes up your applications, right?

Um, now that platform team probably only has access to a certain section of your infrastructure, or maybe you have separate platform teams for separate business units. What we've had to do is we've had to build a model that allows people to consume and manage our infrastructure in ways across those separate teams. And so, as you said, Tanzi would sit on top of this as would VKS and Tanzi would sit on top of this.

In fact, what, what Tanzi does, if you're leveraging the, uh, if you're leveraging the Tanzi platform, it's leveraging Bosch to deploy VMs. And those VMs deployed on top of VCF. Um, if you're looking to deploy Kubernetes, our Kubernetes infrastructure, our VKS service deploys Kubernetes.

And that's all built on virtual machines just like any other cloud does, right? And, and that's all deployed on VCF, it's all isolated with our networking constructs. It can be stored using our SDN, uh, our software defined storage, sorry.

Mm-hmm. Uh, vsan n in our stack. Um, and from there, uh, and from there we have the ability to consume those things.

Okay. Uh, let's talk just a little bit more about, uh, the VKS stack specifically. 5, we shipped Kubernetes one point 34, and we did that within two months of the upstream community launching that.

Right? Um, that's important because if you're, look, you're, look, if you're leveraging another cloud provider out there, you're gonna have workloads that may need that version that's available in that cloud. We're launching arou about with the same velocity that you're seeing from these other public cloud vendors out there.

And so this aligns the VCF infrastructure to have that exact same sort of capability, uh, that exact same sort of Kubernetes capability. 34 in our stack will also come with 24 months of support, which is pretty nice. Uh, the Kubernetes community has a more limited support timeframe for a version of, for a version of Kubernetes.

And so what we're doing is we're giving the applications that are running in your enterprise additional leeway on a version of, on a version of Kubernetes. We've been told by our customers that upgrading, uh, is, is, is tricky enough in Kubernetes, and we need a certain amount of time on a, on a version of Kubernetes for our workloads. So we support now version one point 34 with 24 months of support, but it doesn't stop there.

With this release, we're saying that every Kubernetes minor that ships in our stack will have 24 months of support moving forward. So it's, so we'll give you the ability to make sure that your applications can appropriately be life cycled with that 24 month support period. Now, we also integrated Kubernetes multi cluster management directly into VCF in the stack.

5 timeline. 1 release. So if you're deploying a version of VCF, now, you will outta the box have, have Kubernetes enabled for you.

You will outta the box have Kubernetes multi cluster management enabled for you to leverage as well. 5 a unified and on management, uh, system. Uh, you also heard about our Kubernetes AI conformance, uh, AI conformance, uh, news, uh, to at today's keynote, I believe.

Um, and this is where our stack is in, in alignment and now certified for the new AI conformance requirements that, uh, that, that the Kubernetes community has passed. And That includes the DRA. What does that include?

The DA it, it does include DRA. So D-R-A-D-R-A is supported, is supportable in our environment. Okay.

And we're doing, we're doing further work. Yeah. You have a, you have a whole other implementation.

We are you manager, right? Yeah, yeah, yeah. But now you support that as well.

Yes, we support, yes. We can support DRA as. Okay.

The Kubernetes stack. Yep. Alright.

So you mentioned we're rapidly delivering new Kubernetes versions. This is kind of an eye chart, but the thing that I wanna point out for you is that we ship versions of VKS, okay? And every version of VKS is going to at least support n minus two versions of Kubernetes.

Okay? The thing that I want you to take away from this chart though, is that as you think about the version of Kubernetes you're going to run, there are things that need to be life cycled in the background, or there are things that platform administrators may lifecycle on your behalf in the background, right? So a platform administrator might update the version of VKS for you.

You should know as a user of the VKS stack, you'll have 24 months of support on that version of Kubernetes that you're running. Uh, past that, you may need to upgrade the version of Kubernetes that your application's running on. All of this is delivered via our content delivery system in our stack.

Um, and all of this can be upgraded independently of the rest of the VPE stack. The rest of the VCF stack, I bring that up because you do not have to log in as a VMware administrator somewhere in your stack to update vCenter or something like that to get a new version of Kubernetes. We ship the, we ship the VKS versions independently of the VKS stack.

And really the goal of doing this was to quickly deliver this new version of Kubernetes for everybody. Alright? So we have a comprehensive set of packages that can be installed in VKS.

We've got two sets. We've got core packages, and those are at the bottom here. And the way I want you to think about core packages, these are the things that you functionally kind of need for a Kubernetes cluster to exist.

You'll notice the networking stuff's in there, right? Like you've gotta, you've gotta have a networking plugin installed for Kubernetes to be useful. You'll note that I've got some off stuff in there.

You'll note that we've got things, uh, we've got things related to storage in our stack there. These things are gonna be included and pre-installed and preen enabled on all of your, on all of your VKS releases. Okay?

Then above that, we've got a set of standard packages. Now, these are optional things that you could add to a VKS cluster to give you abilities. And you're gonna look through this list and you're gonna see a lot of things that you're like, oh, I recognize the CNCF things, right?

And really, these are things that we feel that you need to add to a cluster to maybe gain additive value. And in many cases here, we've been asked by customers to say, Hey, you know, would you support a service mesh for us? Can you, can you give us a service mesh that you support outta the box?

So we said, sure, you know, as long as you're running VCF, you can run STO and you can run, you run that on our stack and we'll support it, we'll support it down to the networking components in the stack. So your workloads have a service mesh to live, live with. Same.

Can you Run VK VKS without VCF? Can you run, uh, VKS without VCF? It is available in VVF, but there are some, there are, there, there are.

It's, it's lesser. And what I mean by that is you don't get multi cluster management. You don't get, for example, the Istio support in, in that stack.

You get a very basic Kubernetes. So if you have VVF, which is our lower, lower tiered skew, you get access to Kubernetes and you can leverage it, but you miss out on multi cluster management, you miss out on some of these packages and you miss out on what Jeremy's gonna talk about a little bit later, which is that cloud experience. Okay?

So let's talk just a little bit about multi cluster management in our stack. 1, we've bundled in multi cluster management and I think we're gonna actually get a peek at that in our next session. So if you actually like a demo of that, uh, jump over to that YouTube video.

But when we think about the multi cluster management capabilities, we're thinking about fleet wide for the things that you have access to. Now, remember, I'm telling you that we're leveraging VKS in our cloud as a service, which means we give users access to leverage our cloud, and they can ven Kubernetes clusters or virtual machines or containers, networks, load balancers, whatever they need to deliver their application from our infrastructure set. The multi cluster management capability allows those users to manage the Kubernetes clusters that they have access to, right?

So, so it allows folks to ultimately manage and, and, and lifecycle these compo, these clusters. It allows them to manage and, and introspect the workloads running on these clusters. It allows them to manage security and compliance policy within these clusters.

Leveraging open policy agent as a part of this. Uh, it also allows, it also allows you to protect your data. And so we're using Valero under the covers today as a set of APIs for Kubernetes cluster data protection.

And, uh, we are actually, uh, if you actually, if you run by the booth, you can catch a demo of what's coming in a future release, which is actually us backing up our clusters to our own object storage. So Valera supports any object store on the backend. Um, we in a future release have announced that we're shipping, we're going to ship an object store.

We have the ability to natively plug this up and, and give you a full end to end solution for data protection for your Kubernetes clusters. It's really in alignment with, with what the community expects here Is this is Calvin Hendricks. Parker.

Is this a standard part of VCF? If you buy V-C-F-V-K-S comes as part of this package Comes with it, so does the multi cluster management. So does the 2024 month support for each of those versions of Kubernetes.

So does basically all of these things that we've been talking about, these just bundled in to VCF also, those other things that I were talking about that make up the cloud, and Jeremy's gonna talk about this in in session three. If you're watching the stream, you know, these are all components that make up the, uh, that will make up the cloud. So the, you know, the, the VM service, the, uh, vSphere pod service that'll allow you to run containers, the VKS service, the networking service, the load balancing service, this, the volume service, all of those things, they're all just included as a part of VCF.

It's a big difference between a virtualization platform and a self-service cloud platform, which gives you access to all of these sorts of things. And that's really the shift that we've made. We're, we're still relying on that vSphere infrastructure to provide world class best in class virtualization of all the things, your storage, your networking and compute.

But what we're giving you on top of that is this set of services that we've never really provided before in a consumable fashion for your end users. So do you get tan zu with it as well? Tan zu is a different thing.

And, and the reason why I, the reason why I had the disclaimer at the beginning, I'll go back to that. All the Kubernetes stuff is a part of VCF Tan Zu has, tan Zu is about developers and delivering actual code, uh, to production. And they do that using a, a platform that's based on Cloud Foundry today.

And But Doesn't it sit on top of VCF? It can, it can totally sit on top of VCF, but It doesn't have To. It does, it doesn't necessarily have to, right?

We, we of course want it to sit on top of VCF as, as the VCF division, right? Uh, and, and we're happy to, we're happy to run those workloads. Um, but you know that, that is not, uh, something that is included as a part of VCF out of the gates.

Cool. Alright. And then we have some simplified, uh, Kubernetes operations with integrated monitoring.

We have the ability to show you what's running in your Kubernetes clusters, as well as how things are performing as a part of the stack. And we wanna give you the ability to do those exact same things with your VKS clusters. And so we're gonna give you details into how workloads are performing, how control planes are performing, how, uh, how, um, how many, how many objects exist in the state of those objects all available in operations.

Uh, then we have workflows for configuring these, uh, should your end users wanna get to these? Now that said, you might've said, but Timmy, I saw Prometheus and those sorts of thing things on an earlier slide. This is just an opinion of something that comes with our cloud out of the box.

You get this, if you have a different set of tools for logging, for monitoring that are a part of your stack, you know, we're, we're not opinionated. Bring your tools. Feel free to leverage your tools on our stack.

Again, we're, uh, CNCF certified Kubernetes, right? Which means any workload design for Kubernetes is gonna run there. And with that, I'm almost out of time.

Um, Can I get sneak a quick question There? You can, while I get my demo practice. Perfect.

I just wanna show you, so If Tan Zu is really targeted toward developers getting applications into production, what am I getting into production on BKS? Well, so the tan, well, they're targeted for delivering applications on, into production on top of Cloud Foundry, right? Like, and so there's, they're not coming From developers.

Well, I mean, they could be from Yeah, sure they can be from developers. Yeah, but the, but the fact of the matter is there's workloads that are just designed for Kubernetes today. There are developers that have their own sets of tools, right?

Maybe they're running like their own development, their own development tooling. They're leveraging something like harness to deliver things, right? Maybe talking more like something like, uh, if I want Min io deployed with a helm chart, like this is a good, good place to do this If you want.

Yeah. You can absolutely deploy any helm chart on our, on our, on our clusters. We're no limits there.

Yeah. Okay. Or, or if your developer simply has a Kubernetes centric workflow, which many do for delivering their applications and they've already built that framework, we can host that for you here.

And I just wanna give you a quick view of our, of our creation flow. Here's our self-service portal that you're seeing access, and this is the quick way of doing it. You see that I have the services on the left and here I'm clicking create a new VKS cluster.

This is the default configuration flow and functionally what we're getting, we're getting access and provisioning clusters to our project that we've been given access to. You'll see I already have a Kubernetes cluster deployed here, and I just created a new one. 34.

And that's really the standard flow for creating things. We also have a flow that allows you to create workloads in a, in a, in a more custom way. And really that's what we're gonna show here.

And the thing that I want you to take away from this is we can configure just about everything about our clusters under the scenes, under the covers here, we're leveraging upstream cluster, API, and here we're defining a cluster class that cluster API, uh, can leverage. We're also defining what version of Kubernetes we ultimately wanna run. And as this goes along, I just wanna call your attention to the thing on the right hand side, which is Kubernetes Resource yaml.

Every object created in the VCF cloud is, is leveraging our API, which is declarative in nature. We'll get into that in session three a little bit more, but I want you to note that we're actually building that YAML that defines this cluster that we're building here, uh, as we click through this. So if you're someone who does not want use a ui, you are someone you want to check in code to get repo somewhere that defines your Kubernetes cluster, you can do that and you can use Argo cd, which we provide to track that and actually manage your, your infrastructures, code deployments, right?

The Terraform providers to do this too. So we're using Kubernetes custom resources. We absolutely have Terraform providers, but Terraform has a great Kubernetes yeah.

Provider. And as such, and I point that out, I answer the question this way because the point is like just about everything understands how to deal with a Kubernetes custom resource. And everything in the VCF cloud is defined as a Kubernetes custom resource.

More in session three on that. Perfect. Right?

And so, uh, what you'll actually be able to see here, we're creating a node pool here. These are the worker nodes that run in our environment. 04.

Shout out to Canonical, who's our new partner here. Uh, canonical is helping us deliver, uh, you know, the number one cloud operating system as the node OS is a part of our stack here you'll see we're defining details about our node pool, including storage volumes. We're including labels, taints the basic things that you would, uh, leverage for Kubernetes.

Uh, and this is all done via, via the UI for you here. You click finished, you, we look at the node pools here, and we click next, which allows us to review and confirm and we finish. And the deployment goes again.

We can introspect the YAML on the right hand side. Uh, this YA ml's been updated with all of our selections. We click, uh, we click next and it deploys the cluster for you.

When I say deploys the cluster for you, what I mean is it deploys that cluster assuming you have a set of resources that your overall cloud admin has given you access to, right? So we are a user of the cloud and we have access to a set of resources there, and we'll get into that more a little bit later in the session.