Techstrong TV November 20, 2025

Microsoft's on Fire. Again, you're watching Textron. Hey everybody.

Welcome back to the Textron Gang. Today we have our, some of our usual gang members. We got John Schwartz, Terry Robinson, guy Courier, who will be giving us a report from St.

Louis in the supercomputer show shortly. But first, let's get started with the Microsoft Ignite Conference that John Schwartz attended this week. And it seems like Microsoft's partnering with everybody and anybody one more time.

They're, I guess they telegraphed this one. They said they were gonna, you know, not be exclusive with open ai, so they partnered up with Anthropic and Nvidia. But, um, John is this kind of par for the course for Microsoft, because like, sometimes I feel like maybe they built software that's kind of cool, and the rest of it is kind of like, well, they're a distribution outfit for everything else.

Yeah, it seems like that. I mean, you know, actually this, this deal, this partnership, which kind of distanced themselves a little bit more from mop ai, it's like this major cloud infrastructure partnership with philanthropic and Nvidia. It, it was so important to them that they actually announced this before the Ignite Show, which is their big conference out here in San Francisco.

So they thought it was much more important than actually the products they, that, the products that Microsoft announced. So that gives you an idea of the scope and the kind of the magnitude of this announcement. And just really quickly, um, anthropic is pledging to purchase or buy $30 billion in computing capacity from Azure Cloud platform.

NVIDIA's gonna invest 10 billion in Anthropic, and Microsoft's gonna contribute up to $5 billion in Anthropic. So basically what they're trying to do is address this, uh, enormous computational demands of advanced AI systems. So that was first what they did.

Um, and then later in the day, during a three hour, seemed like it was five hour marathon keynote speech and presentation, which, uh, just went on and on and on. They, Microsoft unveils some AI tools to weave AI capabilities into corporate workflows rather than treating technology as an optional enhancement. So they showcase a number of things bearing this IQ branding, which is short for intelligence quo, and it extends from individual workstations to enterprise data centers.

So in a sense, what they're doing is they're pushing AI beyond the experimental phase in their words, and, and making a stronger case than measurable ROIs. Now within Reach, and as you said, Mike, it was, I think in a incredible extraordinary week for tech news. They probably won the week, but I mean, just wait until next week when Google wins the week, or who, whomever.

This is just, I, I've never seen a series of news announcements, uh, and partnerships where they're shifting alliances, uh, people we don't know who's in whose corner. Uh, it's just a free for all. And I, I think it kind of underscores this land rush that's been going on for ai, and it's just only gonna accelerate Guy.

Do you perceive that any of these alliances are strategic? And I'm asking this question because well, pretty sure that Microsoft somewhere is probably building its own GPU chip somewhere. And it's probably gonna say, you know, well, we're gonna offer that alongside Nvidia.

And then they're also probably saying, well, we're offering anthropic alongside, uh, open AI because well, they're gonna consume cloud resources. And, you know, as far as we're concerned, everybody's all good with us. Anybody who wants to pay us money for hardware processing is great.

So, you know, from your perspective, I mean, how significant are these alliances? I don't think they're particularly significant except in the, in the general sense of this is how, you know, this is how AI is evolving, this is what we're getting moving towards. We're moving at.

I mean, if, if you may, you may remember, uh, earlier this year and last year, I, I went on a little thing about how there's no such thing as an AI application. There's an application that uses ai. Even a chat is, you know, uh, you know, the, the fact that there's an AI powering their responses to you is, is it's a chat application.

Um, so Microsoft, uh, is doing its best to control the entire stack. Um, they did, they couldn't buy, uh, DeepMind like, uh, Google did. So instead they, um, funded OpenAI that got them started.

Um, they've been using the the GPT um, models, um, as they've come out. But Microsoft most famously owns the user end of all of this, um, more thoroughly than probably any other player in the game. And I'm including Oracle and Google in that, um, you know, Google has a full stack, lots of folks have a full stack.

But the point is, um, that, uh, Microsoft had to, uh, decouple itself, and this has been developing for a long time from open AI as its sole source. Uh, and so this is, uh, one example of it. Um, it's just chump change for them.

What is it, 15 bill or something like that, uh, with Anthropic, right? That's, uh, you know, they probably have, uh, more in Satya Nadel is a bank account to, to work with if they want to. Um, so I think just in general, this is where it's going.

Uh, someone asked me yesterday actually, uh, if, uh, I thought that, uh, we were heading towards some kind of, um, you know, sort of Microsoft, similar to Microsoft, some sort of dominant, you know, platform slash operating, the AI AI source or whatever, the World computer. Yeah, Yeah, yeah. And, and I said, I, I just, I laughed.

Um, now that, that might indicate, um, especially your reaction just now, Mike, that I'm just completely wrong about this. But from my standpoint, this is worse than the cloud MCP from MCP to API to whatever. Like, um, the, the, when I say worse than the cloud, the, the cloud birthed the universal, you know, uh, use of APIs everywhere.

The so-called API economy, which meant that you could start doing mix and match best of breed stuff. Um, AI makes it even worse when you think about how MCP and about how agents work, where in a sense, any agent can kind of go anywhere and use anything and do whatever it wants to. And, uh, it can last for 20 minutes.

The agent can last for 20 minutes for you. So the idea of somehow getting a monopoly or a dominant model or producer of a model or whatever is absurd. And I think as usual, Satya is a little bit ahead of the game here and just say, screw this, we need to make sure, like tomorrow it might be somebody else.

Tomorrow it might be, um, cohere, it might be somebody brand new. So that's the significance of it as a strategic move. It's not about anthropic, it's about ensuring that you are delivering the right AI experience to your business and consumer users.

However you're going to do that. To that point that the guy was mentioning, I think there's an IDC report that says businesses are gonna deploy. 3 billion AI agents and automate workflows by 2028.

So in a sense, what Microsoft is doing is logical. This is what they do. You know, they, they cooperate, they work with companies until they compete with them.

So they're just hedging their bets across the board. Yeah, I think the word you're looking for is promiscuous, but Mike, They're that, and that's sort of Microsoft, isn't it? Pro promiscuous Is the right word.

Uh, technically speaking, Mike, but other reasons might not be the word you wanna use. And you might be right. I do wanna mention, I do wanna say one other thing.

'cause you talked about Microsoft building A GPU, Microsoft's not gonna build a GPU, the action has gone to XP as far as that goes. And I know that also sounds silly, given that 80%, 80% of processing spending is on GPUs right now. Um, granted, but the XP meaning TPUs, tensor processing units, NPUs neural processing units, um, just a sort of a heterogeneous chips set of pro, not a chip set, a heterogeneous set of processing units, um, is rapidly going to develop in these systems, especially, and initially in the hyperscalers like Azure, that's Microsoft, literally has a, uh, I believe it's an NPU that they've developed themselves.

Maybe it's TPU, um, I should be better up on that. And that's really where, what, what they're gonna come out with that's well known. I don't disagree, but Terry, you know, so to John's point, thousands and maybe hundreds of thousands, even millions of AI agents and wow, all this talk couple of days long and, you know, this whole thing about like maybe how we're gonna orchestrate, govern and secure these things, footnotes if we're lucky.

What do you say? Yeah, that again, you know, it's like security gets the short shrift, I believe in this discussion. I, it's not saying that they, they don't have some plans, but I mean, they certainly didn't, uh, to my knowledge share anything that was, that was concrete about how they're gonna protect all of this stuff.

I mean, there are huge governance questions. There's, that's, and, and it just, it started, it's like, mind, it's mind boggling, you know? Um, and I was thinking, didn't anthropic just put out a report on, uh, uh, a hack, maybe, you know, and it's Chinese hackers are using Claude.

Yes, yes. Yeah. Using Claude.

And, and so I just, I guess I don't understand. I mean, even why not, why they're not just at least paying some serious lip service to security when they come out with an announcement like this. And, You know, Terry, I, I, I, so I'm so glad you said that because to me, there's like this classic lag between all these announcements.

They just bombard us with all these things they're gonna promise and then pie in the sky ideas. You know, they're, they're probably applicable, but there's, but it is never, we never hear from the customer or from anyone with a real concrete example, because they're trying to digest all this, and they're trying to figure out how do we cover our asses? And when you press the companies on this topic, they, they, they switch the topic.

Yeah. And I mean, and then you have to pity the customers. They, they are being, That's what I mean, yeah.

The customers, yeah. And they're really having, uh, I think issues sorting through, but they're also having to make decisions and move on this stuff. They, uh, there's a sense that they don't wanna be like left behind.

And yeah, you might be right, guy. There's not gonna, you know, Microsoft is not gonna be able to dominate. Doesn't mean that they don't want to, you know, but it also doesn't mean that customers aren't gonna feel, you know, some sort of pressure to, you know, jump on board before the security stuff is sorted out.

So why, Why would I possibly wanna bring up anything that might make you think twice about building and deploying an AI agent tomorrow morning? Come on up. Just, just build it and it'll break and then Yeah.

Build it and it'll don't worry about it. And they'll come, well, you know, Whatever. Honestly, Mike, when you told us that, that, that, that we would start off the show with, uh, this, uh, this announcement, which of course I saw, um, about, uh, Microsoft's investment with Anthropic, my initial thought was, you know, of course you're always thinking, what are you gonna say when Mike calls on you when you're asked about it?

And what I was gonna, what my first thought was to say, what the hell is even going on anymore? It's Just, you just cannot. So, so, John, you know, to, to your point or my thoughts about what, what you, you're, you're, you know, what you said a minute ago is, I, I feel like a 10th of 1% of what is being done in AI right now is what's being talked about.

9% of what's being done in AI is a bunch of people like you, I like the folks on this call and other people we know messing around in, uh, chat GPT mostly, but also in other places, just doing stuff in chat. And when we are hearing about how companies are doing this and organizations are doing that, and now when you can do this, you can have this 12 stage, uh, super agent, blah, blah, blah, that's gonna search all your email and give you recommendations as to, you know, the nearest water closet for when you get out of the train in Shanghai or whatever. Right?

That, that is, that is really, that's, that's the cool sparkly bits that we do wanna talk about the possibilities and everything. But for those who are watching this, you know, uh, podcast are listening in, they're still at, at stage, yeah. 5.

Yeah. Even if you Press, if you press, for the most part, Yes. If you press the companies who are announcing like Agent Force or what have you, I'm not gonna pick on Salesforce, but any of these companies, ServiceNow, you really press them and ask them for a customer.

If you reach the customer, they give you the blandest, lowest level safest application that they're using it for. And it's usually some sort of customer service or, uh, HR internally, and it's very controlled, and it's usually overseen by humans. So they're not practicing what they're preaching.

Yeah. I mean, I'm with you guys. I'm experimenting with this stuff on a regular basis, and I'm getting annoyed because all my experiments are kind of, eventually within about a day or so, I hit some sort of brick wall and I'm like, you know, this isn't quite gonna work, and I'm hoping that somebody will go build an agent that will do the thing that I want to do.

But right now I'm trying to navigate my way through prompt engineering and context engineering to create some sort of automated workflow that, by the way, is not very repeatable, because well, uh, uh, the LLM has no memory. So it's like basically, you know, talking to my, uh, father-in-law who doesn't remember what I told them yesterday. So, So I, so, so mine being two takeaways here from this story are, uh, well, three thanks to you guys, but number one is that there is a distinction to be made between the, the application and the use of AI versus the AI model and training and stuff itself.

And right now it feels a whole lot like a, like, it's weird. Another weird thing to say, it's like a commodity market. Like in perplexity, for example, you just go and you pick the model you wanna use and do that Firefox as well, whatever.

Um, the second one is the really excellent point that we keep hurtling off into the stratosphere without, you know, uh, uh, keeping an eye or keeping our thoughts on the incredible, uh, uh, security, uh, implications. Um, and the third, and the third is, what is it with these keynotes now, John? I mean, like three hours.

They're just, they're so long. And then you got, you got one every day. Also, by the way, like, it's no longer a keynote.

One of the great things about the super commute con conference that I'm at right now, they do one keynote and it's got two speakers, and then they're done. So the, so that's, sorry, the keynote. So sorry, uh, guy, the keynote yesterday reminded me of the Irishman movie.

I almost, you know, like, I couldn't, I couldn't watch it. I know, decade. And my wife and I stopped watching that after an hour.

She was like, I can't do it anymore. I would watch It for half an hour, then I leave the next day, watch half an hour. It took me like a week to watch it.

I felt if only I could have done that yesterday, I would've felt as if I didn't l my soul was crushed. It was, it was brutal. Sorry.

Wait, sorry, Microsoft. Wait, I don't even think that these things rise to the level of the word keynote, because they're basically 20 minutes of dialogue and two hours of videos. Yeah.

Right. And then you have the, you have the, the third party come on and, and bow to the master. They, they do their presentation, you know, the audience, they lose the audience by that point.

It's like a series of eight or nine guests. I mean, apple started this Mons monstrosity, um, but at least theirs was somewhat streamlined, although theirs is pretty tired at this point. Yeah.

Well, John, you need, you need post apple traumatic syndrome therapy or something. There you go. I would just love it if somebody somewhere, maybe stands up and gives a keynote note for 45 minutes, no slides, no nothing, and just straight up, here's where we are and what's going on.

And this is what it's all about. But I would say Microsoft is not the only one of these vendors that has trouble, much like Bill Clinton parsing the word is in a lot of things that they're talking about are gonna be true in about three years. But as of today, not so much.

But anyway, that's just my take on it, and we'll see how it goes. But folks, we're gonna move on to our next topic, which has something to do with this one, but it's gonna be more of a Google take. We'll be back in a minute.

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Hmm. Little suspicion that's about, and basically, yeah, and basically this too was telegraphed. They said that this was coming forever in a day, and now they're finally announcing that they have this thing, and there's also some interesting vibe coding tools, and they are calling it the most powerful AI model yet.

And John, as you roll your eyes, um, the question, the question I ever, you is, it seems like, correct me if I'm wrong, but like every other, what, 45 days, somebody stands up and says they have the most powerful AI model yet. Absolutely. So look, think about the timeline.

5. Okay, let's see. 5.

And so what they, the companies do is they've been to prove their, their superiority. They, they demonstrate, or they, they come out with these independent benchmarks. 4, which I, in context, I know, I have no idea what that means on human's.

Last exam, uh, the previous record was GPT five Pro Pro. So, um, you know, it's, it's, it's all part of the, the hype. It's, it's all part of the one upsmanship, I think you said it well.

And, and on the show notes is L-L-L-L-M, leapfrog game. Um, and again, you know, in addition to this, I'm gonna point out that, um, the, uh, Google, CEO Sundar Paka gave an interview to BBC, I think a day before, and he kind of dropped this bomb about the AI bubble and how no one's immune, including Google. So he got attention for that.

Of course, he also said Google was better positioned than anyone, so they're gonna be fine. Um, so you're, you're right. It's just one ship.

They all try to undercut one another. Um, you know, so be it. I mean, there's, there's a lot at stake.

And, um, you know, ab we'll see more. Yeah, I believe, I believe he said there may be some irrational exuberance. I was like, maybe, Yes, as we see these, these investments that are just outrageous from company to company, you know, they're just switching alliances, switching partners.

It's all, it's as, as guy said earlier, it's just like head spinning is, I can't keep track of this. All I said was, that's probably as intended. I mean, everybody's trying to, you know, get a piece of this and, and declare superiority, and it, it, it's kind of, in my estimation, my professional opinion.

Stupid. So I, I'm sorry. So guy that was, you know, I'm sorry to have interrupted you for that brilliant insight.

9% of my commentary. So, Well, But guy to, you may have some insights on the following. So it's the most powerful, yet, according to a quote unquote series of benchmarks that somebody created and tracked that last time I checked, I've yet to run into a benchmark that actually reflected any real world activity.

So what is the definition of, you know, most powerful? What, according to whom when? Uh, so the benchmarks, um, are more or less, um, things like, you know, you know, tokens per second and, you know, query depth and length of, or, you know, size of context, window, and like that sort of thing.

Um, actually that's not a last, that one, last one's more like a claimed feature than a benchmark. Um, all of your points are correct, uh, that, um, real world, um, real world experience with a new model, um, that scores really, really well. Um, it does reflect that score.

But one thing that is not commonly understood is, um, this sort of recursive mechanism by which, uh, well, I maybe it's more commonly understood than, than, you know, you realize, like, remember the application, um, uses the model, the model itself. Um, they, they started out as sort of what you call feed forward models, where, um, there's input and then output, um, and very quickly became recursive in how they operated, so that not only could they take larger and larger, larger and larger context windows, meaning they took a lot more input. And by the way, a lot of that input, you don't even see you put in a prompt.

And then whatever application or chat you're using, uh, adds, it's not just rag so-called rag. Like, there could be all kinds of additional context added to help you with. That's one of the way perplexity works.

Um, most of 'em work this way to, to make the output look better. So the model doesn't see any of that. It doesn't know which is yours and which is supplied by the application.

Anyway, long, long way of saying that, um, there, there is a lot more, uh, complexity in how these models work than there used to be. And they, they, as, as indicated in Google's release, um, all of these benchmark scores and everything, including the intelligence score and everything else, are ways to, um, reflect that. It's not just simply doing a single pass, it's doing what you might think of as several passes of the same thing, breaking down the prompt, organizing it a little bit, running the different parts of the prompt through different stages, um, before providing an output even to the application, let alone to you.

Um, I actually, unlike what we were talking about earlier with Microsoft announcement, where I was going, what the heck is even going on here anymore, Mike, I got a, a, a, a funny feeling of deja vu that maybe you had as well, seeing this announcement, which is like, Hey, I know what this is. Like, this is like the old chip wars. Now we're gonna have all the models coming out and saying, you know, I've hit 61 giga flops, I've hit 61 to 62, uh, gigabits, giga, whatevers like, and so on and so on.

And that just feels nicely familiar. We can maybe settle in a little bit to just watching these folks going and say, no, mine is bigger. No, no, no, no, no, mine is bigger.

No, mine is taller, mine is deeper. And that, that actually is a welcome development. Because if the different models are just starting to, uh, and the companies that produce 'em are starting to just compare themselves by size and speed and that sort of thing, then that helps us take them as a little bit more of a commodity play.

I mean, which one really is better at coding? Is it Gemini or is it clawed, or is it whatever? Who knows?

We never get to use these models directly anyway. We use the applications that use the models, and there's a lot in that experience that's due to the application. I have heard a new phrase, and the new phrase is called token fatigue.

And it comes in two forms. One is, I created something that was so complex that I ran it up against the LLM, and it takes that LLM forever to do it. So then I gotta take my next thing and move it up to the next model.

But by the time I run my thing up against the next model, it's so expensive, I can't afford to do it. So people are like, I'm kind of, you know, lost coming and going here. But I think we're gonna get to a point soon.

I hope maybe that, uh, I'm gonna create some sort of prompt that will have some form of automated context engineering, and then we'll be routed to the most efficient LLM that's fit for purpose. And I won't have to think twice about it, because it'll figure that out for me. What do you say, guy?

Can we get to that? Um, there's a lot of fatigue building up in the system. Um, I think so.

I think, I think, I think you're right. I think that, um, I think that that, you know, we've talked a little bit here about how there's all this focus on the model, but what about the data? What about the quality of the data?

It's not just a, it's not, you know, there, there's a lot that can, that, I'm talking about training data now to create the model. I'm not talking about the data added to, you know, the inference in the model. Um, and then on the other side, I was just talking about the application.

Well, listen to Gentech, AI is an application that uses ai. And so there's a lot of work to do there. And what I have, I, I was just talking to one of the neo clouds here at the Super compute show the other day, and I told him that this moment, this AI moment we're in feels a little starting to feel a little weird to me.

And that I don't know how to qualify it at any better than that. But I think talking about concepts like token fatigue, um, or recursive use of AI data to further train ai, these are all things that can build friction into the system and help, help generate the inevitable backlash that occurs in every wave. AI is, it's just gonna have faster in ai, um, out of which we'll emerge with something better.

But in the meantime, yeah, I think that that's the sort of thing that, uh, contributes to that. I'll go a step further. I think that given the amount of money invested in all this stuff, and how upside down that is that soon, that context routing engine that I'm gonna put in the middle of this is gonna be pinged by various LLMs that say, oh, pick me, pick me.

I'll do it cheaper. Come on, pick me, pick me. Wow.

It's like a Kubernetes based structure for, for, for, for a, oh man, Uhhuh, just think about it. And, and, and I, and I, Now I have fatigue and I, and I only bring, and I only bring this up because there are indeed open source context routing projects underway that are gonna have these kinds of capabilities. So stay tuned.

You are so right, Mike. We will see. All right, my friends, we'll be back in a minute with our third segment Discover Techron Group, the epicenter of tech innovation.

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Guy Courier is in St. Louis at the SC Show, formerly known as Supercomputing, and he's been there for the last couple of days. And Guy, we talked a little bit about what Nvidia was up to at that show earlier this week, but there's a whole lot of other folks there.

And, you know, walk us through some of your impressions. 'cause man, there's nothing like having feet on the ground. Uh, yeah, thanks.

It's, uh, this is my favorite show, um, because its Roots are scientific and, uh, research based. Um, and it was all about high performance computing, uh, year after year after year. Um, starting, uh, had AI related to it for a very long time, but that wouldn't have been generative ai that would've been predictive or, or recommendation engines.

Um, I'll get to that in a second. Um, but now it is, uh, a very large also commercial trade show along the lines of like a Q con or whatever, where there's, you know, uh, pursuit of development and inquiry and all that. Um, in parallel with, uh, technology and announcements and all those other sort of things.

It's also one of my favorite shows. 'cause it only has one keynote. Just wanna repeat that.

Um, last year, um, I reported also from, from, uh, on this, uh, program from, um, super Compute. And the big story was about cooling. Cooling was, it was sort of, you know, AI of course was a big story last year.

Again, I'll get to that again here. Um, but, uh, uh, as far as the sorts of things that are really pervasive, uh, outside of AI cooling was the big story. Um, now what we're seeing is what you might call, um, a rapid, uh, um, a rapid emergence into the market of those cooling technologies.

Uh, you, you had advanced, uh, di direct liquid cooling systems showcased from, from some of the system vendors like, uh, HPE, Dell Super Micro, um, Dell and Super Supermicro actually have open rack conforming, uh, or compliant, um, systems on display. Um, but they aren't, they weren't the only ones from smaller vendors as well. Um, I didn't check out if Lenovo had one, but the, the now we're seeing, um, real, um, and the data center market incidentally, uh, most data center providers are, are, are now enabling direct liquid cooling a lot, even more easily.

So that's hitting ahead now that we saw emerging last year. Um, one thing that seems relatively new this year for this show, um, is, uh, the neo clouds are here. Um, core weave is here.

Neas is here. It's probably a few more that I didn't see. Um, neo clouds in case requires explanation.

Um, are, um, cloud providers, they tend to be global, but they are built recently from the ground up, so to speak, get to that in a second. But they're built from the ground up to help host AI in particular. And the reason why I say so to speak is that a lot of them started out as crypto mining farms, um, that got repurposed, uh, to, to help host, um, ai.

Um, so the question is, why are neo clouds here at this show where you have a lot of tinkerers and builders and that sort of thing, people building high performance, uh, systems, uh, uh, mostly for research purposes, but also now for ai. So why would, um, a provider be here? Um, it's because these folks are also their customers.

Um, which leads me to the third thing. I'm sort of, I've sort of picked up here at Super Compute, which, um, uh, what's emerging now. So I said like cloud is, uh, sorry, cooling was sort of emerging.

Obviously cooling and liquid cooling's been around for a long time, but it was sort of emerging as this major force a year ago. Um, one of the big things emerging this year is, um, integration of distinct AI systems. Now, I mentioned that AI has been around at HPC for quite a while.

Um, mostly for in, in in predictive or recommendation engines for things like, uh, AI based job configuration and scheduling. In other words, when you're running a high performance computing job to try and simulate, I don't know, um, hurricanes on Mars or whatever they're doing to help, you know, uh, cure the world's ills. Um, when you're doing that, the faster you can run your jobs, the faster you can.

So you run a job in a high performance computing environment, and then maybe you need to run it again. You need to tinker with something and run it again in order to tune it and then get the kinda research you want. The faster you can do that, the better.

The less human intervention you can have, the better. So that was a natural place for AI as a sort of a controlling system. But now what they wanna do is they want to use generative AI in various forms in order to, um, help boost simulation effectiveness, invent or create jobs or test schemes, all kinds of analytic related stuff that can work in conjunction with HPC.

So that is real buzz at this show. It's on the main stage, just lots of sessions about how to integrate, build and integrate distinct ai. So there you're thinking about the neo clouds, again, that that's a possible source for that sort of thing.

That leads you to another theme, which is smaller, but like one flash session here from WWT was very well attended on workflow, workflow in HPC workflow out of AI into HPC and back, which leads me to the last bit of emerging, um, uh, uh, emerging trends. Um, which is, uh, has to do with quantum, quantum computing really looks like it's on the verge of becoming real in a number of respects. They have, um, 150 plus qubit systems, uh, going into production right around now.

150 is a pretty considerable number, but to, to really make it work, um, you need all kinds of other systems, which I won't go into here. Mostly the most of 'em have to do with error correction that leads you to ultimately a 150 qubit system, giving you five, um, five effecting, you'd even call it sort of virtual, actual reliable cubits. Five cubits is actually a lot.

Doesn't sound like a lot, it's a lot the way quantum computing works. What you're doing is running sometimes multiple problems that would take months on even modern hyper computing, uh, uh, uh, um, high performance computing systems to run. You can run them in a matter of seconds.

Um, it's not, it, it, it becomes a part of an overall high performance computing system. And so the incorporation of quantum into HPC is a hot architectural topic here right now because these systems are starting to really go into production. So, guy, are people talking about making these platforms more efficient?

And I'm asking the question 'cause it seems like the number of research projects that wanna be run on a supercomputer and eventually a quantum computer far exceed the amount of available capacity and, you know, for lack of a technical term, but the conversation seems to always be about, Hey, stop Bogart in that supercomputer and let me get my project on there. Yes. Um, that is a general theme that as, like you said, it's been a relatively perennial theme at this show.

'cause there always seems to be more, uh, there's always seem to be war work, uh, demanded of these systems that are available. Um, but, um, one of the, so the, the, um, at the keynote, um, uh, there was one of these sort of vision talks was the main part of the keynote. Um, and, um, the speaker, um, it pointed out the way that the ways that AI can ultimately help with just what you're talking about.

Um, the analogy made was to, so bear with me just a second, was to electric vehicles. That the initial, the current model for, uh, like personal transport with vehicles is you have your car, you own your car, and rideshare services are a way to make that a little bit more efficient. But you still wanna own your car.

But when it moves ultimately to just being a service and you can use any car, then you need fewer cars. Even as the amount of energy use goes up, the actual number of units needed goes down. Um, and so by, by analogy, Mike, the idea is that, uh, these systems can be built and connected together and used a lot more efficiently because they could be shared in a lot more efficiently.

I thanks to ai, there's, there's a lot of work to do there. Um, it, one of the sources of this workflow discussion though, has to do with just what you're talking about, Mike, is it's not so much about idle time. It's the fact that instead of it just being one of the top 500 supercomputers that you can use, maybe there are ways that you can use one of the 5,000 or so supercomputers available right now around the world to do your work.

Mm-hmm. I'm not sure I can get that metaphor, because when I drive, I notice that there's more Uber and Lyft cars out there than ever. And I've concluded that basically people who previously would not have gone anywhere are now getting in cars, going somewhere.

And so now there's more cars on the road than less. That's the initial stage of it. It's when it's when people start giving up, um, uh, their personal cars.

And so the analogy here is when institutions start giving up their personal systems, um, not that they wouldn't build the personal systems anymore, but it's almost the distinction we were talking about earlier between the service or not, sorry, the, the, um, the, let's call it the backend and the business end or something like that, of this overall industry and market. And when you are building, uh, in this case, the instead of foundational AI models, what you're building are foundational supercomputer systems for your own use for things, but also you're allowing other people in to use them. Or in fact, you're allowing AI agents to schedule secure jobs in those.

So Like A GPU share. Yeah. It's a, it's an Uber for super compute there.

That is, I just coined a new wonderful phrase for someone, right? Uber for super Last question and The next keynote you go to, Right? And, and you were in CubeCon last week, but we didn't get a chance to talk.

But one of the things we were talking about in re relates to AI at CubeCon, and I'm curious if it plays out at sc, is that, you know, there's folks who are saying you should run those AI workloads over on Kubernetes clusters because, well, the IT folks know how to manage those. And yet, when I go talk to the AI community, they have a thing called slum, and that's their favorite job schedule. And they're like, you know what?

We don't need no stinking Kubernetes 'cause it's too hard and we don't understand it. S SLM is here in effect. S SLM is dominant, you're right, it's dominant for workflow.

Um, it's dominant for job scheduling. It's dominant for all of this kind of, uh, sort of application, uh, orchestration in HPC and in ai. Um, but, you know, mostly ai.

Um, there's only one neo cloud I know of. Um, and I've mentioned the neo clouds because, you know, they are at the forefront of, of these kind of buildouts right now. There's only one neo cloud I know of that uses Kubernetes, that's neas.

Um, so it's not, um, it's not, uh, particularly common. Um, there isn't really a lot of, um, support behind that sort of thing. But you have to remember, like they're using storage systems that are really, you know, um, probably showing their age, like luster, um, that's really still dominant.

Um, there's lots of storage, you know, related stuff around here, um, that is, uh, more advanced. Um, so I think that moment just has not come Mike. Um, and I don't know if I'm, I'm kind of, you know, I'm a little skeptical about Kubernetes as the ring to rule them all, but maybe that's just, you know, overall hostility to anybody saying anything is the ring to rule them all.

Um, Kubernetes is definitely coming for HPC and ai, though there's no question. It's just whether or not it's gonna have, uh, the, the world, um, you know, the world dominant effect it seems to have everywhere else. Alright, well, folks, you heard it here.

The one thing for sure about that show that guy goes through every year, it's kind of where the cutting edge of research and AI and physics and science all comes together. So if you do get a chance to go, you should absolutely take advantage of it. Hey folks, thanks for sharing your thoughts and insights as usual, and thank you all for watching the latest episode of The Gang.

Please stay tuned for the rest of the lineup of Techstrong tv 'cause it too will be equally awesome. And we'll see you again tomorrow. All right.

Hey, we're live back here in CubeCon. It's, uh, Tuesday night, which is cube curl night on the floor. So when I look out there, I see popcorn machines getting ready to set up.

I think I saw some hummus, but they'll usually have a lot of stuff here. Um, I think that starts at five 30 or something though. I don't know if I'm gonna make it to five 30 here, Patrick.

I might make it to the, to the first part, but, uh, yeah. Yeah, it's been a and it's been a day. I get you.

But let me introduce you. This next gentleman here. Well, he's gonna tell you the company he's with now, but I've, over the last 20 years, 20 would be 2005.

Yeah. Maybe 20 years. Yeah, probably, maybe more, maybe more.

Um, my friend Patrick McBride has held a variety of roles and had a very interesting career, and I think we'll start right there. Patrick, welcome to Tech Drunk tv. You know, I, I gave you the buildup.

Tell, tell him share your story. Yeah, thanks. Having me in.

Um, yeah, I, a long, long time ago in a, in a, in a place far away that was too far Away. Yeah. I, uh, I actually started off as a software engineer, so I wrote code outta school.

I was a finance and, and, uh, CS major, so I could go one of two ways onto Wall Street. I could either write code for financial services company or, or go trading. And I, I, uh, I took the former You ever regret that?

No, not at all. Not at all. Uh, I've had a, had a great, you know, great time in in tech as you know, since we've known each other for so long.

Yeah. But yeah, I, I, I graduated, I, I worked at, you know, one of the large insurance companies and wrote a lot of code. Um, I would say I was a a, a mediocre coder.

I was prolific, but, you know, but I was really good at figuring out what to build. So that kind let me down more of a product path, helping, you know, companies do that, which got me, you know, into, uh, one of the early research firms. I worked with our, our buddy, uh, Mike Rothman, back at a company called Meta Group.

Yep. And, um, you know, so yeah, came in there as an analyst and, uh, had had a lot of fun. But then I, you know, with my product roots, I ended up advising a bunch of companies and ended up taking, uh, a marketing job, a CMO job, which I had never done before, an early cybersecurity company, and learned the ropes from, uh, Mo Rosen and, and some other folks, Other people I know.

Yeah. Good People. Yeah, Mo Mo Mo's taught a lot of us.

And so, yeah, it's, um, yeah, had a good time doing that. So now I'm, I guess, a seven time startup offender. I just love early stage companies.

I love the, the excitement. I like when, you know, people are just wanna run through walls Once in your blood. It's, it's hard to get out of It is I couldn't go back to, uh, uh, to an easy job.

And, you know, I do like getting dunked in the kind of the deep end of the pool every once in a while and having to learn stuff, Swim, sink. Exactly. That.

That's kind of where I'm at now with, uh, with Antithesis, uh, where I'm at now Into that. Yeah. So you mentioned two names.

I just wanna shout out, of course. I don't know if you talked to Mike recently. I did the candy man.

He's The candy man. Yeah. Mike's doing candy and Mo uh, Mo sold his last company, but he's already CEO of another company.

Mo Yeah, he's, he's a multi-time offender Too. Cereal. Yeah, he's a serial.

Yeah, no doubt about it. It's, Uh, he owes me a glass of wine over at our famous restaurant, our, our favorite restaurant in Reston Town Center. So Absolutely he, but both stellar people.

So shout out to both of them. So antithesis, look, I know you a long time, you were very big in cyber. We didn't call it cyber, of course then, I know it Was security infosecurity network security.

Yeah. But you were big in security. Tell us the anti, the antithesis story.

Yeah, so the, um, you know, the quick thing that they do is they test stateful distributed systems. Um, so really complex disturbia systems that are particularly hard to test. And kind of the backstory is really interesting.

Um, the founding team at Antithesis, antithesis, uh, did a company that, that you may not know, but you use it every day. You get an iPhone in your pocket, right? Sure.

Um, it was a company called Foundation db, and uh, it was a, Actually I know them. Yeah. So the, the foundation guys, you know, built this really cool, um, distributed database that had some really nice acid properties, um, that people couldn't, you know, said, Hey, you really can't do that.

You can't put all that stuff together. They're, they said, hold my beer and went off, off and did it. Well, in building that, you also have to figure out how to test it.

You know, they, they had property and guarantees that they wanted to make, so they spent a whole lot of time as they were building the product. They were building the whole testing methodology, and they were trying some new things, you know, the traditional, you know, write your unit test, write your integration test, and, you know, and see what it, you know, brings up was just not gonna work in an environment like that. So they all, they all ended up at, you know, apple and, and then migrated from there to different places.

And I think the, you know, around the Bay, and one of the things that they all realized was testing was pretty crappy, you know, even for some really notable companies with big systems. And they said, Hey, let's, let's go and, you know, take what we started at, at, uh, at Foundation and take, carry it forward. You know, testing has always been, I I, I had this conversation once with two guys.

One was sort of a, a classically trained, you know, went to school testing guy and one was a maverick dude Yeah. Who was self-taught, but the, you know, where they met was for too many organizations. And, and the, actually in the market in general, everyone had this vision that testers were for like junior programmers or failed programmers, people who weren't good enough, right.

Less than, right. Yeah. And that of course, therefore testing wasn't gonna be great because the people we had working on it weren't, weren't the, weren't Professional.

They were a little bit of the redheaded s trial. They were of vi they, it was always Yeah. They, you know, but the fact of the matter is most testing is done by a cadre of professional testers.

Yeah. And what we run into all the time these days is the, the actual app development teams that are building this distributed applications are highly involved. You know, even if they weren't using something like antithesis, you know, they, they, they have to figure it out, you know?

Yep. They're, they're highly involved in the testing because when the pager goes off at 2:00 AM it's, They're the one who has there, They're highly involved in the answer. And, you know, depending on what kind of an error is, if it's a, an outage, it may be a bad, you know, couple of hours, a couple of days in the office, if it's a, a correctness issue, the database is, you know, saving the wrong type of, of data in the database or incorrect data in the database.

That's a, that's a rough month or a couple of months in the office. And so yeah, they're, they're, they're far more engaged than, than they were in my history back when I was writing. Oh, absolutely.

There was separate groups and they're much more integrated. I mean, look, there are developers who say, look, I'm, I just want to code, I don't want to test. But also the, the, in the inter intervening time, we've seen this whole automated testing where even today, the professional testers don't necessarily test, they write the coverage, right?

Right. They decide what to test, how to test, when to test, and then a lot of the testing today is automated. It is, you know, yeah.

We, And, and we, we really carried that to the nth degree. We kind of flip testing a little bit on its head. So the, with traditional testing, you have a couple of issues.

First of all, typically the guys that are writing the test are the engineers, right. That are writing the original test. A, they don't like it, and b, if they're writing a test, they're trying to figure out, you know, what, what they're testing for.

So they've typically thought a little bit about what could go wrong. So they're writing try test coverage to try to figure out those things. Well, if they're already thinking about, they were probably pretty conscious when they were coding uhhuh to try to not make those things go wrong too.

So you end up kind of in a testing for the things that, that you actually built tended to build Well, for, it's the stuff that you, you know, forget to test. That is the stuff that, you know, makes the pager go off at 3:00 AM So one of the things that we've adopted is, is something that we call, it's, it's called, you know, formerly property based testing. But what you end up doing is instead of writing individual tests, you write a set of, uh, instructions about what the system should do or shouldn't do.

You know, it, what, what state things. If, you know, it should never give the user a 4 0 4 error. Uh, it, you know, if we're doing a debit, you know, of some amount, it'll always do a credit of some amount, you know, up in an application there.

I got it. You can get weighed down in it. And then what we did was take, you know, take a lot of the burden off of the engineers.

You still have to think, I mean, you still have to work through the properties. I get it. And, and, and, you know, it's a mind shift for folks.

And then we take, uh, that it basically put what is effectively their production environment. We take that their containers and run it on a, a, a hypervisor that we built, um, that's, we call it, it's a perfectly determined deterministic hypervisor, which I'll come back to the imports of. But what we end up doing is we put load under that and we'll run it massively parallel, you know, on a pull request or overnight even.

And since we're running it in such a, a massively parallel thing, we can test months worth of production wall clock time in a couple of hours over overnight. But in that time, we're not just running it in happy path. We're throwing all kinds of faults at it.

A network fault, a race condition, a service drops a disc fails. We'll, we'll throw all those kinds of things at the system so that we can get all those edge case types of issues and, you know, types of faults that you don't really typically see in your happy path coding. You know, that's, that's a problem.

You, the, the, the other problem is you're, you know, the engineers write tests for the things that they think about and they get tested in a, in an environment that's much more pristine, not at all like production. So, right. Those two things together make it, you know, hard.

So it's a one part property based testing, one part chaos testing, and one part massively parallel inspection, you know, of, of the, of the code base. And, and so the, you know, the hypervisor pieces, you know, it was, took them a long time to build. Um, the more interesting piece of technology was the piece of technology that decides, you know, what pieces of, of, you know, where it should branch and, you know, how do we get through the whole code base and where should we inject faults?

That's, that's really where a lot of the math and, and science came in. And, but the hypervisor being perfectly deterministic is important too. 'cause finding, finding a bug is only half the problem.

That's right. You got it. Then you gotta figure out what the hell happened and, and fix It.

And how, well, how do you want to fix it too, because Yeah, you know, there might be multiple paths to a fix. It's not always just a patch. This determinism element is, is important.

So we, you know, we, we, we actually, I have the engineering team named this. I loved it. It was, um, uh, you know, we've, in our hypervisor, you've got a debugging tool that's, we call the multiverse debugger.

You can actually Multiverse. Yeah, multiverse. Debugger.

You can, so it goes through quantum, you can literally rewind time. Right. It's quantum.

Yeah. It's, it's totally quantum. See, so you rewind time to, you know, a minute, minute or a couple minutes, you know, before testing and, and we can show you graphs of, you know, you're expecting a particular fault.

We can show you literally graphs that when the, it was a zero probability that would happen, and then it stepped up, oh wait, something interesting happened there. Usually it'll, you know, be another step up. And then it was, you know, a hundred percent going to happen.

We know, you know, you know, Since it's Perfect. Got it. So we rewind and then you can inspect exactly the state of the whole system, all the logs right.

Where right where it was, and really figure out what the hell Happened. It's like good time machine there. That's f yeah, It's, it's a debugged time machine.

Exactly. And, and you know, with perfectly preserved and state, so you can repro, uh, a bug. Exactly.

And then you can literally, you can go into a, a, a terminal session and, and throw stuff at it and see if that changed it, then rewind it, try it again, try different things. So it's, uh, love it, you know, debugging that. I've never, you know, when I, when I saw it and saw what they were doing, I just had to join the company.

We haven't done that. So, you know, speaking of which, Patrick, we, we didn't, what's your role at antithesis? See, you know, this is where I get in trouble.

Since I'm running marketing now, my, my credibility went down like 30% or, or whatever. He's Not a marketing guy. I'm, yeah, I'm a, I'm an, I'm an old, old engineer.

You know, a strain, strain or stress on the old, but, uh, running On the business side of things. Yeah. I'm, I'm, I'm working with our, our go-to market team and, and, you know, helping make sure that we get to things, Nothing to be ashamed of.

I do it too. The More important part of it, the cool part of it and, and contextually for the conference is I get to work with some of the, the cool projects here. Right.

Um, one of the things that was a lot of fun that, that we had earlier was, um, uh, one of our, one of the maintainers for the ET CD project, which is a critical component of Kubernetes. Uh, we helped, we, we reached out to those guys and worked with them to put that under test. Oh, that's Cool.

And, Um, and they did, so Merrick, their, the, the maintainer who was, was working on that, um, you know, joined us on stage in the beginning of the morning and then did a, a talk, uh, to, you know, really take, take them through how you use the deterministic simulation environment, you know, to test CD. Um, and we had, you know, the other thing I, I got a chance to work out, uh, was, you know, working out an actual partnership with the CNCF team, so that we're offering now, uh, to put all the, all the folks that are all the projects that are incubated, uh, or further along and graduated, uh, under tests now, we're not gonna take 'em all on at once. We're taking, you know, we're out here searching for our first two, and we've had a lot of people come by the booth and ask us about it.

So we're, you know, we'll just probably do it two at a time for the, the next bunch of quarters. But those projects, yeah. We, we, we feel we use a lot of open source, so it really matters to us.

It matters to a lot of our end customers. And so being able to help the projects, It's also being just a good community member. Yeah.

We, we wanna be a good citizen in the community. Absolutely. And It's, it's in our best interest in lots of ways, and it's in lots of companies best Interests.

So I, I think that's why you see all these people here that you do. Did, man. Yeah.

Patrick, you guys did announce some news here, though. Yeah, that was the big one. That was the, was the big one.

We were putting all the, putting all the projects under test. Um, you know, we've got, we've got some other, you know, other fun stuff up, up our sleeve over the next couple of quarters that, uh, I don't want get you in trouble. They'll love to put you back to coding.

No, they're, they're, they're, yeah. They, they, they, no, nobody lets me touch the code base anymore. I promise.

They're, I'll let you do that anyway, man. Did we mention the website? Ah, www dot antithesis.

com or ai. You can do or do AI as well. We, we, we own that domain as well, so, absolutely.

Either way, you're, you're, you're coming to the right spot, Patrick, thanks for coming on, man. Alan, great to see you. I appreciate it.

com. You know, I, I think that I'm going to give me credit on this one a time machine, right? Yeah.

For, for your testing and your, and your states. It's, it's an interesting way of thinking about a very complex kind of, of, uh, process. Yeah.

Very cool. We're gonna take a break. Hey, everyone.

Welcome back to day two coverage of, uh, cube Con here in, well, not quite as frigid as yesterday, Atlanta, but it, it, look for this Florida boy. It's still pretty cold here. Um, let me introduce you.

Well, I've got a double here. If you couldn't, well, you could probably see that looking in. We've got three people here.

Let me introduce you to my two guests. First of all, the man in the middle from Broadcom is Rit Bja. I hope I got it right.

You got it. Reet, I just found out, grew up in the same town as me in Queens, New York, and, um, there aren't a lot of us in the tech industry from Rosedale, my friend. Yeah.

But, uh, he's a Molloy boy though, so he, yeah. You know, most of my friends went to Cross, but anyway, um, joining Del Fried and I, on the far end, if you don't know him already, is our friend Alistair Cook. Alistair is a host extraordinaire and roving correspondent with Tech Field Day, and we had a tech field day here yesterday.

Yep. And Dre was in the room, and Dre Dre was in the room. So we continued the conversation and, and, and just we're winging it.

This is not scripted or anything like that. So I'll, we'll see how it works. I, I think it'll work out pretty well.

Gentlemen, welcome. First of all, thank you. Thank you for having me.

So, we're gonna get to the point where I'm gonna ask you how did the tech field day go yesterday for you? But before we do that, you know, I interviewed my friend Chu from, from, uh, VMware. Broadcom, VMware yesterday.

And, and he said something that stuck with me, which is, when you look at the code contribution to Kubernetes, forget who writes checks, forget who loud, who's loud. Forget who does the, the keynotes. When you look at people who are contributing code, VMware is the number three contributor to code here at CNCF.

Yeah. I mean, the, the interesting part is like we, when we really got going with Kubernetes, uh, cluster API wasn't a thing. It just started out.

And, uh, you know, we latched onto that technology and we said we need a, a good cluster management solution, and we pushed cluster API to where it needed to be. You know, when we did Project Pacific, we had to do certain things downstream, uh, for time purposes. And then, but we spent a lot of time pushing it back upstream so that the, the communicative benefit from it that not only that, you know, like projects like yes, et CD we've done significant things in, we've recently just, uh, uh, upstreamed a bunch of tools for et CD to be able to support them better in the field.

We, we, we have a lot of learnings with our customers and some of the issues they run into. And so, uh, these debugging tools were created internally. We were like, Hey, let's get them out into the community.

It's the right thing. Yeah. Like a number of different projects like, uh, Harbor Entre, you know, so on, so forth.

Like, there's a, a ton of contribution from our control. So, and, and look, you don't get to number three on a, on a one shot deal. No.

You know what I mean? This has been a consistent, consistent pattern of being a good community member Yes. Over a longer period of time.

And, and I, I, I recall a, probably 10 years ago when VMware first, uh, first put out a job posting for the head of open source at VMware. Yep. And there was a, it was greeted with a, a massive amount of cynicism at the time.

Yep, yep. But the proof is, and the actual contributions, that's the projects that have come out. It's, I remember when Harbour was brand new and being surprised that it was primarily developed by VMware, yet here it was open source.

It's freely available to anybody to use. And, and the interesting thing is, it's like, kind of like the best kept secret. It, it, we, we've been contributing like to PostGrest for a number of years.

Yes. It's, it's, it's just not something that we do a very good job of, of talking about and taking the necessary, well, some would say it's not something you should do a good job at. Yeah.

You do it because it's the right thing to do, not to blow your horn. Yeah. Plus it, it makes a ton of business sense.

Absolutely. It, it, we, we are not doing this just for the sake of doing open source. It is because these particular investments drive a customer outcome, and we really, really care about that customer outcome.

I, I agree with you a hundred percent. And, and, and I think, so the fact that you haven't blown your horn real loud about it speaks louder than had you blown your horn. Right.

Because you're doing it for the right reasons. Yes. If you don't mind, I, I know you presented yesterday on, on VCF.

Right? And again, after speaking to Homan yesterday, I have a much better picture of it. I don't know if our audience does, but, you know, under Broadcom's leadership, I, I think rightfully so, the decision was made that VMware needs to be focused.

Yes. We need to be focused. Yes.

We can't, we gotta put our wood behind an arrow. Yes. Not 15 arrows.

Yes. And so the future of VMware clearly rests with VCF. That is, that's VMware.

Yes. Absolutely. Like again, we had talked about, even while we were VMware without, uh, prior to the acquisition, we talked about building a private cloud experience for our customers.

And, uh, while we attempted to do a number of things, all of our business units were focused on individual achievements rather than that aggregate achievement. Sure. And as Broadcom, uh, acquired us, and thanks to the leadership of ah, Hawkin and Chris, we were able to then coalesce the set of things that were required to be able to deliver against that vision.

And now, when with V-V-V-C-F nine, we have, we've delivered on the set of like, uh, capabilities required to be able to manage the underlying infrastructure in a streamlined way. But not only that, like we did the all this work to be able to deliver the, and this is what my team directly focuses on, is, is the cloud consumption experience. Mm-hmm.

Which is something that, you know, VMware had done poorly in the past, but we took, uh, two of our, uh, product offerings, VCD and VRA, and we brought them together along with some of the foundational Kubernetes work that we had done to be able to deliver a end-to-end Kubernetes consumption experience. Meaning like the, the actual foundations of our IAS is based on Kubernetes and Kubernetes APIs. So when you're, uh, provisioning a VM or provisioning a Kubernetes cluster, or provisioning any of the various objects that we support, VPCs, uh, you know, so on so forth, you, you are dealing with a, a, a desired state Kubernetes API.

Yeah. Right. And we, we leverage all the CRD mechanisms that Kubernetes offers to be able to deliver it.

Now, the, you know, the power of this is now realize when you look at, like, we, we did some, an implementation of Argo cd. Mm-hmm. Now we could take Argo and put it into each of our VKS clusters, but we, we went a little bit further and we said, Hey, let's put it into our foundation supervisor.

Now you can use a GI op style way of provisioning all of the objects we support in our eyes. Love it. So you can use that for VMs, Kubernetes clusters, pods, any number of different objects that we support in our core, you can just provision them using this GI ops style.

One of the challenges for the existing vSphere administrators Yes. Is that this is all very foreign and new. Uh, one of the things I really liked in the UI in VCF nine is that you can do click ops to de to design this thing the way you always used to, and then pull out the manifest that you're gonna use in the future and transition to that methodology.

Absolutely. Like what we, we want all users to transition to this cloud consumption experience, including the traditional VI admins that have been doing ticketing based mechanisms. And so they, they, they can't navigate navigate Kubernetes, uh, API really well, but as they work through the UI and pick various options, the, the YAML is being created for them so they can pull it out and then use it on APIs or check it into a GI op style repo, uh, to be able to do all of their provisioning if they wanted to.

So it, it's pretty, pretty killer if you look at the UI and the experience. I, I'm really proud of it. Uh, and we're doubling down on it as we go along.

Absolutely. Well, I, I think we hit on the subject of what is the involvement or what, what's the connection between Kubernetes and VCF, right? Yes.

It's, it's, it's deep and it's, and it's rich. I wanna bring up something, again, something I spoke about with Haman yesterday was, you know, I used to think of, let's say like Tan Zu had the cloud native piece of this. Yes.

But again, under Hawk's leadership and the new team, or the new vision, we've actually taken some of that Kubernetes cloud native functionality and made it native. If, if that's not a punt, uh, native to the VCF stack, if you will. Yes.

Yes. So you, you know, our view, uh, and this is, this is why we've done this, is that there is no cloud experience without a Kubernetes service. No.

Right. And, and fundamentally, if that's the case, then you need to have the end-to-end Kubernetes service tied with your, your private cloud. Absolutely.

And so, as part of that, as, as we walked Hawk through these, uh, this, uh, decision process, he was like, yeah, absolutely. You're right. Uh, you're right.

And not only that, he believes in Kubernetes Deeply. And so he, he moved all of the Kubernetes assets, uh, into VCF so that we could formulate it as part of our private cloud offering. I love that includes not only the, the, the, the service and the Kubernetes service itself, but all of the management capabilities of Kubernetes, the multi cluster management capabilities of Kubernetes as well.

That was, uh, TMC now is now built deeply into VCFA. Absolutely. Absolutely.

I wonder, if you don't mind, I'm going to ask you both, not everyone watching this live had a chance to watch Cloud Field Day or to Field Day. Yes. Tech Field Day yesterday.

It is available, of course, on the YouTube channel for, uh, cloud, uh, tech Field Day. But let, let's talk about, you know, how some of the things we've discussed here came out at at Field Day, and if there's anything else that we missed, Alistair. Yeah.

I think for me, one of the standout moments from yesterday's Tech Field Day was one of my delegates, John Willis. So when Tech Field Day, we get presenters from the, the, uh, sponsoring company, in this case VMware by Broadcom. And we get subject matter experts who are independent to be in the room together and ask them the difficult questions.

And I was just amazed that John Willis, my, my most deeply DevOps cloud native guys said, you, you guys provide the most reliable platform to underpin everything else. Uh, and lots of the other companies here at the show have pixie dust and promises. Uh, and I think that was probably one of the most telling things around this.

This is the, the way to talk about what VMware has brought to Kubernetes. And now Kubernetes is sitting on top of that foundation. Uh, I think I sort, I think, Joe, I mean, let's say when, when it comes to the world of virtualization, VMware is the mountain that a lot of competitors have died on or frozen out on, or whatever.

Yep. Yeah. Uh, stability was always, uh, stability, performance, uh, being able to utilize the hardware, uh, to its fullest extent.

Right. So that you don't have, again, VMware's, uh, um, you know, mainstay value proposition is still true. Meaning if you, uh, your bare metal capacity that was historically we converted, was used maximum, like 30% back in the days.

Right. Now it's being used at like way, way higher, Hopefully. No, no higher than 60% due to queuing theory.

Right. But the, the other element was the absolutely ironclad separation between workloads. Yes.

That, that, that level of isolation, um, that really enables multi-tenancy on top of that limited set of hardware to a far greater extent than, than lighter types of isolation. Yeah. No, and, and, and not only that isolation, but there's a clear boundary between the IT administrator and the, the actual usage of the infrastructure, meaning that the IT admin is free to do the operations they need to do on the underlying infrastructure.

Mm-hmm. Without disturbing the applications that the end users, it is fundamentally the o you know, we're the only private cloud that can, or any cloud actually that can do it quite that way. Right?

Yeah. Given our, given our investments in vMotion in maintenance mode, so on and so forth, DRS, that, that allows us that flexibility. But I, you know, well, one of the, the, the interesting things is like, we've done a number of different, like, evaluations of our Kubernetes performance.

How well do we compare against Kubernetes virtualized? How well do we perform, uh, like compare against Kubernetes, that's bare metal. And the, the underpinnings of the platform is what shines when we do that.

We are not doing anything special in our Kubernetes to be able to get, make, make the, uh, hardware work better. Uh, but like the, the scheduler inside of ESX that can actually give us the density we're looking for shines when we look at these performance evaluations, the, the performance of vsan, oh, it, it, it, it, it, when we did some evaluations, it shines through when, uh, Kubernetes is running on top of it. And so that's what, uh, really drives some of the value prop is like, not only the reliability and so on, but like, you're getting, uh, the full utilization of your hardware and you're getting the performance that you rely, rely on.

So, um, I, we were, uh, frankly, in some of these cases, we were kind of shocked, like, really, like we we're, we're that much better, uh, uh, you know, on storage performance against bare metal. Right. And so, like, uh, you know, it's been, it's been cool.

Absolutely. Now you pair that with all the operational values of virtualization. You know, we like to say this, uh, to, to folks is like, um, 90% of Kubernetes is running on virtual virtualization.

Right? Uh, all of the clouds run it on top of virtualized, uh, uh, uh, infrastructure. We run it on, on top of virtualized infrastructures.

Some of our competitors all run it on top of virtualized infrastructure. There's a small percentage of folks that are doing bare metal. That's because the operational winds of, of Kubernetes on top of virtualization is so, so much better than doing it, uh, on bare metal compute.

You know, I, I, I had this conversation with someone yesterday, not, not, not from VMware. Look, there's always been, I don't wanna call 'em an outlier, but there's been a small minority of people who ran, uh, Cobe on bare metal. Yep.

I remember there was a startup that sold like a server that you could run in your database. It was really just a regular server, but I think they were charging about 60 grand for it, but it was optimized for Kubernetes on bare metal. They're not here anymore.

And I think that's probably speaks volumes. But, um, the, the, it's, it's always been that way. And, you know, you could run coup anywhere on the edge and, and, and everything else.

But I remember when this first came out, right, there was the big debate over server density and, and data density. And, you know, performance of, hey, virtualization gives you this kind of density, containerization gives you that much more density, but containerization on top of virtualization gives you that much more density. And it was a lot more dense.

I think that's still true today. That hasn't fun. It's like physics.

It hasn't fundamentally changed, at least until quantum or something. Mm-hmm. Since it could be true and false.

But, um, I got another area, 'cause I know we're short on time real quick. I mentioned before how the, the, the makeup of the community of the people here has changed over the years. It was, it used to be very developer heavy.

Yes. It's, it's focused now on observability and on operations and on platform engineering and, and all of these, you know, internal development platforms and all these other things besides just Pure Cube. In the meantime, VMware is very different.

Broadcom, you know, has kind of changed the Yes. Parameters. How are these two things kind of playing off each other?

What do you notice different, you know, uh, one, uh, frankly, Kubernetes has grown up, right? No doubt. It's matured.

Yeah. It's matured. And you can see that from the contributions overall is like the, the amount of big changes inside of Kubernetes has, has, has not, it's not a bad thing.

It's slowed down a bit. But, uh, the other, the, the other thing is that, uh, here you see a lot more focus on how do I get the best value from my Kubernetes or, and the operational wins that I'm looking for. So what we're doing here is we really, really want one to make sure that people understand the value that VMware brings to the table.

But I, we want them to touch it, feel it, understand it from the demos that we're doing at our booth, and in other conversations to be able to understand how much different, uh, our offering is compared to other offerings. Right. And, um, you know, so I, I think that that level of conversation also, the feedback, Hey, have you thought about this?

Well, we are using this particular package. How does that work here? Um, we, we use this ecosystem.

One of our customers, uh, who is moving wholesale to our Kubernetes, um, from another cloud Kubernetes is, is like, well, I don't want my developers to know what they're running on. We, they, they, they use Harness GI and, uh, Artifactory we're like, perfect. You just drop and replace us into, into that ecosystem.

And that's, and developers have to don't have to know. Right? Yeah.

No, wait. Well, when you have a standardized compute stack, which is what this really is now. Yep.

Right. It you Exactly. Just, you know, it runs on top of it.

Yes. That's what you need to know. Yes.

Um, Alistair, we're about outta time, but I wanna make sure, did we miss any big highlights from the field day presentation that, You know, it was a 90 minute present? Well, it was a four hour presentation put into 90 minutes. Uh, so doing it just as here probably is really hard.

Uh, we hit the, the, some of the core things that I think were important, but there's a lot more in that presentation. And it's on the YouTube channel. It, it'll be on actually on PS TV too.

Yeah. I replayed on tv. Yeah.

And you'll find it on the text a, uh, tick field day LinkedIn at the moment, and then by about next week it'll be up on YouTube. Okay. Awesome.

We'll try to get it up faster. Very nice meeting you. My fellow Rose Deion.

Yes. Whatever the word is, we gotta catch up. We'll catch up.

You know, if you ever get a chance to speak with Mike Ard, our Chief Content Officer, you gotta give him a hard time. Okay. He went to Cardinal Spelman in the Bronx.

Oh, whoa. Okay. Not, not, not anywhere near Malloy.

But anyway. Hey man. A pleasure having you, Alistair.

Thank you. We're live at, uh, we're live here at Q Con. We'll be back with more in just a second.

Thank you. Hey everybody. Welcome back to Ingram Micro One.

We're here with my new friend Eric, talking about the relationship between Proofpoint and Ingram Micro and how that's kind of changing the way we transact business in the cybersecurity space. Buddy, welcome to the show. Thank you.

Appreciate it. Well, one of the things that we do know about cybersecurity is it changes all the time, right? I mean, just when we think we've got one mole whack the next one comes up, how does the relationship between Ingram Micro and Proofpoint kind of help the providers and, and and their customers at the end of that day kind of stay current Yeah.

And protect them from the threats we face? Yeah. Well, first I'll say that we celebrated our 10 year anniversary with our Proofpoint partnership this year.

Um, and it's been a very exciting partnership. I've been involved with it since we signed the contract 10 years ago. And we've certainly seen the strength of this partnership grow and grow.

And so it's been a really exciting relationship for us. But, you know, early last year, mid last year, we sat down together and just started having conversations about what can we do to free up the channel to respond faster to the threats that their clients are seeing? And this, you know, where we are today is a result of just asking those simple questions.

It seems like what you guys are really doing is taking as much friction out of that process as we possibly can. And some of that may be using AI and other technologies. But what is the mission and what are some of the things you're targeting to kind of make it easier to do business?

Yeah. Well, you know, one of the things was for the commercial market segment, again, we wanted to empower the channel to act faster, right? So today, a channel partner as a net new opportunity, they can go to the X vantage platform, you know, new products, new solution bundles.

They can select, you know, the product that suits the needs to address the risk that that client is facing. And instead of waiting three days or as long as it takes to fly to the moon, right? They can submit this information in there and have it approved, quote back in minutes.

Right? And that's a game changer, you know, for the partner community. And that's critical.

'cause the end customer doesn't have a lot of tolerance for that process, right? Right. 'cause they're sitting there saying, I can't really say to the end customer, lemme get back to you in three days with that.

And yeah, you hopefully nothing bad happens. Exactly right. Yeah.

It, it is all about, you know, speed to execution and, and helping them to respond to those threats faster than ever. Um, what went into the, uh, measurement side of this equation? So how are you tracking success?

How are you looking at working with the partners? What's on the back end of this thing? Well, you know, I would say that there, there's been a lot of new, right?

It's a new process. It's a new way to go to market with their channel organization. It's a new way to go to market with their commercial sellers.

And this whole journey, this whole 10 year journey we've been on, our role has been to be an extension of their channel. And so for many, many years we would talk about, I can do everything that a Proofpoint channel account manager can do, but I can't approve special pricing. Well, now we can.

And so there's been, like I said, new solution bundles like prime threat protection and others. And um, you know, for us to be able to bring that to market faster than ever, for know, for, for a solution provider to sit down with a healthcare system and say, Hey, I can turn a quote around for you while we're sitting here having a conversation. You know, those are the types of things and pipeline continues to build for these commercial ready, ready to go SKUs Outta curiosity, I noticed that you guys have invested in AI and around the sales agent and you're taking historical data and market opportunities, correlating that and giving some advice to the partners.

I think the partners are also building their own AI agents, or will eventually into the purchasing cycle. Right? How do you think this whole ecosystem will evolve in time?

Well, that's what Sanje is here for, right? We got to see it firsthand and uh, you know, I think we're just in the infancy, right? When I think about, I've been at Ingram a very long time.

I've been in the cyber practice for roughly 25 years. And I think where cyber was 20 so years ago was where we are with the maturity of AI right now. Meaning, you know, you've got thousands and thousands and thousands of partners that are capable of selling cybersecurity.

And our role there is to help them to go to market faster, to be more profitable, how they show up differently as it relates to kind of all these things. Ai, we're in the very, very early stages of partners really formulating their AI strategy. And certainly as you think about cybersecurity, I mean, the game is changing right in front of our eyes with AI too, right?

Yeah, absolutely. Where it's not just the good guys using it, the bad guys are using it just as much. Which Brings us to our next question.

Are you starting to hear from the partners saying, Hey, we think there's gonna be a cybersecurity opportunity around ai, and what can you guys do to help us get them? Yeah, and again, it goes back to being the extension of that channel, right? And I know as an example, you know, pretty much every cyber vendor out there, Proofpoint included, is, is using AI right?

To, to reduce the burden of, you know, day-to-day security practitioners, um, and ultimately respond to these human-centric critical threats faster than ever. And so we'll continue to see that, right? So, yeah.

What, uh, metrics are you guys actually tracking? I mean, how do you measure success? How do you know what success is?

Yeah, Well, I would just say, you know, look, we work very closely together and uh, we work very closely with our commercial sales leader and, uh, Sherry Rhodes who was here yesterday on stage. And, uh, we, we hold each other accountable for some very specific metrics and uh, and I think we are definitely going in the right direction. Of course, security requires a bunch of tools and solutions, right?

There's no one size fits all. So yeah. How do you kinda work that with Proofpoint when you're trying to build a solution where there's multiple right things involved, and how does that whole process get smoother?

Because we're taking out the friction. Yeah. Um, you know, Microsoft is a great example.

They've got a good, you know, integration and partnership with Microsoft and we wanna make sure that we're bringing the Proofpoint message to the Microsoft community and vice versa. And I think you'll see us continue to build out these things within the X Vantage platform as well to make those recommendations. Hey, if you're picking up Office 365, are you thinking about Proofpoint threat protection to augment that as an example?

So I think there's probably more to come there. We of course, you know, have had the notion of customer relationship management forever and a day, but there's also vendor relationship management, right? Yeah.

Yeah. Is that becoming a discipline in its own right and a, and a thing that you and the partners are kinda working through, but it seems like this too can be, uh, a set of best practices. Absolutely.

And um, you know, there are other vendors that each have their own specific channel initiatives that they're trying to accomplish. And, you know, just thinking back, you know, the, the, how this went with Proofpoint was, it was a design thinking session. What are you trying to accomplish?

And how do we go build something unique together? And that's what we've done. Now every vendor, you know, has a different strategy or slightly different, but I think this is just the starting point for, you know, how to capitalize on this platform to go drive meaningful results and, and ultimately to empower the biggest sales force there is, which is the channel.

True that. So Proofpoint partners have a lot of choices when they want to go get something. So we were talking to them right now.

What would you tell them about why Ingram to work with Proofpoint versus anywhere else? Yeah, Well it's, you know, the platform is important, but it's also the people. And I think we've done a very good job with our people, again, acting as an extension of that Proofpoint team better than anybody.

And the, the relationships that we have with the solution providers and those partnerships are super important. And at the end of the day, you know, people buy from people they like, the platform makes it easier to spend more time having discussions about the right threat protection. And so, you know, we continue to get feedback and learn, right?

There's more enhancements coming, you know, as you start on a journey like this, you don't know all the answers. And we listen to our partners and they say, Hey, have you thought about this? So we're launching deal registration as an example, that will be all there and instantly available on the X Advantage platform.

I think from there we start thinking about, hey, what lifecycle services do we start doing? How do we help educate the channel on what new solutions should we be upselling as we go down this journey together? So I think the sky's the limit.

I think one of the other things I've seen you do is invest more in your own cybersecurity professional services team that has the partner's back. Yep. Um, is that too gonna become something that I can maybe call on a, on, on a moment's notice, just like the transaction?

Absolutely. Yeah. And we're already doing some of those services specific with Proofpoint and others, but you know, I've always said that, you know, there are thousands of cybersecurity partners in the United States, right?

Our role is to help the partners buying from Ingram to be more profitable and proactive. And one way we can do that is by injecting services capability to make them look like they're the top security practitioner in the country. And they can do that with Ingram Micro.

So we used to have this line between what we call resellers and managed service providers. But if everybody's leaning on you a little bit for the services, eventually all the partners kind of evolve in the service providers. Yeah.

Set another way, right? I mean services, you're gonna build it, you're gonna buy it or you're gonna partner, right? And so we want to be that partner that, you know, even if we're not the one delivering some of those services, although we've built it out for Proofpoint, they can tap another partner on the shoulder to deliver, you know, whatever services they need.

Or perhaps it's in a adjacent part of the cybersecurity landscape. Mm-hmm. Yeah.

One of the challenges that we hear is, and there's been a lot of talk here at the show about this notion of focusing more on business outcomes, but in security you're kind of trying to prove a negative that something didn't happen. So therefore how do I kind of measure that or Yeah. Or convince somebody the value of that.

So how are the partners kind of measuring the value of their services and how are you helping them figure that Out? Well, I mean, there's only one right answer, right? You're gonna get breached or you're not.

But, um, but I think, you know, it has to be more of an outcome discussion and not about a point product, right? What are the critical risks that you need to solve for today? Are they using assessments to truly understand what those risks are?

And then you kind of map it back to what outcome a technology or a a multi solution technology stack can go deliver. Somebody once told me being in security is kinda like being in the army. It's long moments of sheer boredom followed by a few seconds of sheer terror.

Uh, so when there is a crisis and there is an incident, um, how do you kinda work with the partners to ring that alarm bell and kind of get all hands on deck and 'cause they're gonna call you? Well, um, I don't know about that because I don't know, hopefully they're not calling us because we've provided them with the right tools. But you know, uh, it's a fascinating industry.

It's been one that's fun to watch, you know, it makes the headline news pretty much every day with another mass scale breach. You just hope that it's not your day, right? So, but yeah.

Yeah. Well to a certain degree we've probably all been breached. It's just a question when we figure it out.

Exactly right. Exactly right. And to that point, um, it does feel like the nature of the game has changed a little bit.

It's not just like, how many breaches did I stop? But it's on the assumption that we all know that we have been breached somewhere. And when it goes active, I think the measurement that people are looking at from the end customer side to the partner is saying, how long does it take you to respond to that and close that gap?

Exactly. Right. And for that, I need to have this whole ecosystem in place.

Right? Right, right. Exactly.

Right. And I mean even like if you think about the cybersecurity insurance industry, right? We're not an insurance broker, but you know, we have partners that can go and help a client to understand not only when you need cybersecurity insurance, but what kind of remediation can you have on retainer, right?

And ultimately what solutions do you need to buy to reduce the amount of that premium that you're paying, right? So you have to follow the NIST framework as an example and make sure you've got a well architected security defense and then you know, when the catastrophe happens, how quickly can you respond? You mentioned the insurance industry and it seems like they are shifting more in custom words towards consuming managed security services as a result.

So have you seen the partners kinda shift the balance of their portfolio? I think they have to, right? And um, ultimately if you're having a comprehensive discussion with a client, cyber insurance has to be part of it, right?

And um, so you've gotta have the right partners to make that happen, but you also have to help them reduce their premiums. And so yeah, that's all part of the whole integrated ecosystem. Is there something that you see the partners doing today that you would advise them to maybe tweak or change to be more efficient?

Is there something on their side that you look at and you go, Hey guys, you know, if there's one little thing would change the world would be a little bit better place? Well, I don't know if I'm answering this directly, but I've been at Ingram a long time and we talked to partners about all the services and all the things that we can do to help them. And without going through a laundry list of 9,000 things, I think it's just simply put, which is if you have an area of need and you don't know where to go, go to Ingram first.

There was a guy on main stage with Paul yesterday and his quote was up there. I wish I remembered what it said, but it was like, if I don't know what to do, I Ingram it, you know, which means I'm gonna go to Ingram and I'm gonna find the right answer, uh, to help, you know, whatever I'm trying to solve for, for my client. To your point about that, one of the issues I hear a lot about is there's just a lot of skews to navigate.

Yeah. And they get confused and it becomes overwhelming. Yeah.

So are you guys, we're thinking about anything, maybe we Proofpoint or somebody else to reduce those numbers That's ex exactly what was done, which is creating these solution bundles that are designed to stop human-centric threats. You know, whether you need, you know, obviously there's email security and threat prevention and all these things, but you know, you choose the package that you feel is gonna best suit that client. And so that's what we've done is create very priced to the market and ready to go and ready to solve those critical risks.

Alright, we are close to the end of the year. What are you most excited about going into 2026 Thanksgiving? Oh no.

Um, uh, I love turning the calendar. I love building the plans for next year and um, I think we've got some exciting growth opportunities, you know, here in our future with Proofpoint and others. And so it's an exciting time.

You gotta close the year strong, but I really enjoy the planning season and getting ready for next year and setting lofty goals that we're gonna go hit. Alright. Hey folks, you're heard in here.

A good mantra for the coming year. If you're a Proofpoint partner, eliminate the friction. Hey buddy, thanks for being Here.

Thanks. Appreciate it. And we'll be back in a minute.

Hey everybody, we're at Ingram Micro one and we're talking about innovation in the channel. My new friend Hope here. Hope.

Welcome to United States hopes from Australia. It's a very competitive marketplace in Australia. What are the challenges that you're dealing with?

I think many of our MSPs are facing a number of challenges in the Australian market, which is highly competitive as you've mentioned. I think one of the areas that a lot of our MSPs are, are really struggling with is profitability. A recent study suggested that 95% of MSPs in Australia have listed profitability as their number one concern moving into 2026.

I think one of the other areas of concern is the skill shortage that we face in Australia, particularly in the areas of cybersecurity, cloud and ai. So our partners are having to invest in upskilling their existing teams or invest in capability, which is very expensive and difficult to find. And I think the third area where a lot of our partners are really facing challenges is our market is highly complex.

Our ecosystem, which was once linear, is now evolving rapidly and it's a 360 ecosystem. So we're seeing far more complexity around licensing models, multi-cloud, multiple vendor solutions and end customers really expecting higher return on investment and strategic advisory services from our partner community. So given all that, what distinguishes the MSPs who are succeeding in that marketplace?

We've got a number of MSPs that are also thriving despite the competitive challenges and the headwinds that they're facing in the market where we see our MSPs with a very clear value proposition that they've defined and they can execute against. We're seeing them have a great deal of success where we're seeing MSPs aligned to business outcomes and solving business challenges. We're also seeing a great deal of success with our MSPs.

On the other side of that coin is the ones who are maybe struggling a little bit. What do you see them doing that maybe they should be thinking about fixing? Yeah, absolutely.

So I think the lack of clarity on the value proposition is, is hard. I think customers, MSPs that are struggling with that complexity and not really able to navigate that are finding the market difficult to, to navigate and, and the high degree of competition. So really being able to understand where you add value, where you play, whether that's niche or more, more broadly, but really sticking to your capabilities and then expanding upon them within your accounts, I think is a, is a big challenge for many of our partners.

Given those issues, one of the things we talk about at the show a lot is business outcomes and focusing on those business outcomes. And that's a lot more than just providing an IT service per se. Um, what are customers looking for from you?

What are, is that changing? Are the expectations changing? Absolutely.

I think as the market becomes even more complex, I think our role as distribution needs to evolve with the changing needs of our partner and vendor community. My team and I recently this year traveled all around the country, uh, and our exec connect series, we spoke to 500 C levels within our partner community to really understand what the challenges are that they're facing, where they see the opportunity, and to really lean into areas where we feel Ingram Micro can help support their growth. I think that where we're focused is really around making sure that we're investing in the areas of opportunity to support our partners.

I mentioned the skill shortage issue. We've really invested heavily in some of those areas to ensure that our partners can leverage our skillset and our capabilities to not have to invest in that headcount themselves and to really leverage our expertise. So I think that's one area where we're really supporting our partners to be successful.

Another area of huge focus for us is on enablement. So making sure that our partners and their teams are enabled around what's currently moving the solutions and stacks that are really accelerating, but also future-proofing them around ai. We recently launched in Australia enable AI where we're able to provide assessments for our partners development tracks for our partners to help them capitalize on the AI opportunity that's currently accelerating in market, although a little bit slower than the rest of the world in Australia, but certainly a big mover in the next two years.

How are the partners themselves gonna evolve along that part of the conversation? Are they changing, changing their business models a little bit? Are they getting a little more nuanced about how they deliver something in terms of cost structure?

I mean, you know, walk that through the final equation. I think we're all looking for operation operational efficiencies. We're all looking for productivity gains.

I think our sitting from micro ourselves through our advantage platform is really leading the way there. It's all about how do we remove operational friction and how do we free up ourselves and our partners and our vendors to do more and sell more and grow more. So I think we're, we are playing a pivotal role in helping our partners evolve their own operating models to be more platform orientated, to be more digital in nature so that we can do more for less essentially.

Mm-hmm. And following up on the skills conversation a little bit, in your professional services, do the partners need to get savvier about who they hire full-time versus what they're leaning on from a distributor partner like yourself? And it seems like the model's a little more fluid.

I would say it is a little more fluid and I don't think it's a one size fits all. I think that you could have partners, for example, who have got a huge amount of PS capability, professional services capability whose talent bench may be fully utilized. And so they need capacity and that's where they can lean on distribution to do that.

We have other instances where we have partners who maybe haven't got that skillset right now from a PS perspective around ai and they can leverage our skillset and our capability not just locally, but also globally to help augment their capability and give them access to opportunities that perhaps they haven't got the skillset to access. Today We started talking about how competitive the marketplace is, but are the partners working with each other? Are they trying to leverage up a little bit, not just Ingram, but each other and there's, that's being facilitated through you guys?

Absolutely. So Trust X Alliance is a, is a global program where we essentially bring together uh, you know, 150 plus partners into a shared community or ecosystem where knowledge is shared, ideas are are born and ideated and, and that shared knowledge is what makes our ecosystem stronger and we facilitate that. I'm really proud that actually at Ingram one this year we've launched our own Australian Trust X.

So we're part of the global community now and it's really born for the reason, allowing our partners to share ideas, cross pollinate, uh, leverage each other's skill sets and strengths to do more together. And I think that's a really powerful role that distribution can play. We talked about the skills gap a little bit, but are there particular skills in areas where you're seeing partners need more expertise than others?

Are there a seems to me there's a lot of emerging technologies these days. I might not have anybody who knows anything about those things, but um, where's the pressure? Yeah, I think, um, beside profitability, there was a canal study that was recently done that indicated that the skill shortage was also one or two top priority of concern for partners in the Australian APAC and even global ecosystem today.

So I think where we're seeing partners struggle is around those technology stacks that are really accelerating. So cybersecurity, cloud modernization, um, ai, they're really the areas where partners are struggling to fill some of those skills. The shortage of talent, particularly in Australia is, is exceptionally high.

The cost to get that resource into the business is extremely high and they're all looking to do more with less. So it's a little bit of a conundrum that I think many of our partners are facing today. And the type of people you're looking for if you're a partner are kind of a little bit different than if I'm just an internal IT organization where I'm looking to hire somebody 'cause they have to have some nice ability to have a good experience with a customer.

Right, right. That's requires a lot of patience is usually the first attribute. But, um, what kind of folks are you guys looking for and are there, are there things that you and Ingram are doing to kind of coach and train people that become more suitable for being in a professional services role in a partner organization?

Yeah, So I often say we're looking for unicorns Because The skillset is actually really hard to find. So you're looking for higher levels of technical competence, but you're also looking for people that have an element of charisma and, and a sales bent to, uh, how they engage with customers as well. So being able to be technically competent but also be able to communicate with partners around business outcomes and partners, customers around business outcomes, I think is critically important to the success of people in those roles.

We do a lot of work with the technical community at Ingram Micro around enablement, uh, sales enablement and technical enablement to really ensure that our partners resources are equipped to be able to go out and successfully manage end user environments. Mm-hmm. Are you doing anything with the local universities or colleges that, uh, help maybe expand the bench a little bit?

We are actually, so we have a relationship with a university in Queensland that we work closely with to really help develop nurture and foster talent coming out of their cybersecurity, um, university courses. And we incubate that talent into our own organization but also expand that into our partner base so that our partners can identify talent early and bring them into their own organizations as well. Now we're here at the show, so I've had the benefit of walking around, but people are talking about, um, X Advantage and Growth Tracks.

What is that exactly? Yeah, so we launched Enable AI in Australia in July, August of this year, and it's really about enabling our partners to assess their own skills, understand where they are from a skills perspective on the, on their AI journey, benchmark themselves against other similar partners to understand their readiness. And then for us to build growth tracks bespoke to those partners that help them enable and accelerate their AI capabilities so that they can capture that market opportunity.

So we're well on the way there. We have 25 partners in Australia already that have started that journey. I think really interestingly also with enable AI is the number of use cases by vertical that partners can pull down and learn from, but also use to position to position with their end users to capitalize on AI opportunity as well.

Mm-hmm. How do you make sure that the self-assessments that people are taking are, shall I say valid? Because, I mean, I know I have a very grandiose opinion of my own capabilities, but they're probably not realistic.

So how do you make sure that partners kinda, you know, answer those questions in a way that are Truthful in Australia? I think culturally we're very hard markers and we're very critical of ourselves and others. So I feel as though that the partners probably do a fairly good job of assessing themselves.

I mean, at the end of the day you need to be honest in that process in order to maximize the opportunity and, and the enablement tracks that we have. So I do think that the partners are, are fairly good critics of themselves and they know that to really make sure they can leverage that program, uh, they have to do an honest self-assessment so that we can help them appropriately. Talent is everything in this business.

What's your advice to the partners to make their organization like the most attractive place where the best talent wants to work? I think it's really important that we nurture our people. Um, I think being a people first organization is super important.

You hear Paul Bay talk about it all the time, uh, and we live it. It's in our DNA at Ingram that, you know, we're, we're customer and people obsessed, um, and we put our people and our customers first. And I think if you can provide that in an organization, I think you become quite sticky and I think you get, you know, a lot of loyalty.

Your attrition drops and it's somewhere people want to stay. I think providing people with the opportunity to learn and to grow and to adapt to what's happening in our market, um, but enable them to be ready for what's next as well, I think is critically important. So I think investing in people, being a culture that people are in inspired to, to, to be in each and every day and and to feel purposeful about their work.

I think if you can do those things, it's easier said than done. I think you're able to retain top talent and attract top talent, which is super important for us in this industry. Do you think that it's important for the folks that run the organization to exhibit this one attribute that I think is important, but um, they seem to be always have an appetite for continuously learning.

They're constantly curious and one of the things, you walk around the show floor, everybody here is kind of checking out the latest and the greatest. And I cannot help but wonder if that's like one of the, the core attributes that is overlooked about success in the channel is that everybody in it seems to be fundamentally curious and always learning. Absolutely.

And I think that's what makes our industry amazing. That's why I've been in the industry 25 years and I can't imagine being anywhere else. I mean the transformation in this industry from the, you know, the birth of the internet to, to cloud, to ai, um, to, you know, cyber it, it's constantly evolving and you have to remain curious and you have to be willing to challenge the status quo and you have to be willing to expand your mind beyond what you ever thought was possible.

And I think that's the beauty of working in this industry. And I think I'm very lucky to be surrounded by partners and vendors who are always curious, always evolving, always ideating and innovating. And I think that's what keeps us young.

Maybe not biologically young, but mentally young and and really, you know, keeps us all super interested and committed to the ecosystem that we operate in. Keeps us young and second part stressed. So it's an east missing combination.

Young And gray. There you go. So as we've mentioned, you're from Australia, but we're here in DC or just outside of DC and there's a lot of international cooperation.

The globalization is still a trend. I mean there's a lot of issues in the world, but still people are doing business across countries. How is that playing out in Australia?

Are you partnering more with people from overseas? Are you seeing more of that and what does that look like? Yeah, absolutely.

So globalizations absolutely impact in the Australian market and I think you can look at that positively or negatively depending on how you frame that challenge. So I think for many of our partners, it means that the competitive landscape has become heightened because we're now not just competing locally, we're competing globally. I think on the flip side, what that presents is an opportunity.

However, for partners who are looking to expand into other geographies and locations. And I think Ingram Micro Australia is very, very well placed to be able to support our partners to tap into the globalization opportunity, um, where a, a local distributor with massive global presence, uh, and the backing of a very large global organization, we can scale into 57 countries with our partners out of Australia. So I think for us, it's more of an opportunity collectively to harness that opport, that growth that we see in other parts of the world and in emerging markets.

We're a very mature market in Australia. Organic growth is hard to find, and I think geographic expansion is critical for our success and our partner's success. All right, folks.

Hey, if you are looking to partner with folks in Australia, find hope. Yeah. She knows everybody.

Yeah. We'll be back in a minute. Thanks for Thank you.

Thank you so much. Thank you. Hey everyone.

Welcome back here to Text Drunk tv. Yeah. I got a new company that we haven't talked to before and a first time, uh, guest on Tech Drunk TV today.

Let me introduce you to Kurt Mu Mill. I hope I got that right. And Kurt is the head of AI strategy at DataIQ.

I, we got, I think I got that one too. Kurt, how are you? Welcome.

I'm, uh, I'm doing really well, Alan, too. Uh, thanks so much for, for having me on. It's a, yeah, it's an honor to be here first from DataIQ.

Uh, so happy to, uh, uh, happy to be here today. It's great to have you here with us. So, Kurt, let's start with you and then we'll jump into DataIQ head of AI strategy.

Right. We're seeing these kinds of titles more and more, but you know, when they asked you at your high school career day, what do you want to be when you grow up? You probably didn't say head of AI strategy.

Um, how, how'd you get here? Yeah, That's a funny story. Uh, I definitely was not saying that, um, you know, so if we rewind back, you know, I, uh, I did my undergraduate studies in philosophy and environmental sciences.

Two things. I was passionate, very cool. Um, but, you know, I think if you asked me, like at the end of my freshman year, uh, in college, what I wanted to do, my answer was gonna be a wildland firefighter.

I wanted to go out to like Montana and fight forest fires. Really Very cool Against my build here fully. I'm not built for that.

It would've been a terrible choice. Uh, so instead, thankfully, I, uh, I'm a, an incredible person, a beautiful woman, uh, who's become my wife. Uh, but she, uh, she was from France.

And so after I finished college, uh, I followed her to France, and from there started working in a number of different things. And in 2013, roughly, I joined this funny little startup, uh, called Dataiku that was 15, 20 people at the time doing stuff in, you know, data science. You know, the, the term AI back then was, uh, you know, uh, you know, a little bit, uh, looked down upon just as a marketing term.

Uh, and so we were, you know, kind of in the hardcore data science, uh, realm. And I said, you know, I, I don't have a background in data science, but I like to read. I like to learn stuff.

Uh, and so I, uh, I got on board with the company back then, so it's been 11 years, uh, since, uh, since I joined, really? 2015. Yeah.

Um, and yeah, I've done a number of different things over the course of the years for this company. Um, everything from, uh, from selling the software very early on to, uh, to, you know, managing different teams. I was our chief customer officer for a, for a couple of years, you know, managing our customer success and services teams.

Um, and then, yeah, for the past, I guess it's going on three years now, um, I've been in this head of AI strategy role, which, uh, allows me to really look outward at what the market is doing, where the technology landscape is evolving, and most importantly, what our customers are doing and what they need so that we can make sure that our product strategy, our marketing strategy, is aligned with all of that. What a great story and a testament to philosophy majors everywhere, right? I some of the best philosophy minor here, I was a political science major with a philosophy, history minors, and, um, you know, a lot of people like to try to crap on, uh, liberal arts degrees and everything, but it, you know, it, it teaches you how to think.

It ignites your curiosity and makes you infinitely adaptable. So, absolutely. Yeah.

I, I love it. Good for you. Let me ask you a question, though.

How did you learn about ai? I self-taught, I mean, you know, yeah. You didn't go back to school for it.

No, no, no. You know, so it's, uh, you know, I've had the benefit of, you know, sitting and working with some of the greatest minds in ai, uh, here at Dataiku. Um mm-hmm.

You, so, uh, a lot of the engineers, a lot of, you know, the founders of course, uh, all out of France, but, you know, incredible schools that, uh, that just pump out these, uh, these engineers, computer science engineers. Uh, and a lot of that has then, uh, evolved into AI as well. So it's been one from my colleagues, uh, two from the customers, right?

When, when I go and I speak to them and talk to them about their challenge challenges, right? It's, uh, you know, it's, it's really the applied angle of ai, right? You're talking about, you know, political science that's like applied philosophy, right?

So not very, very much always focused on the applied angle of ai. Um, and then of course, yeah, just a ton of reading and, you know, what the best, uh, resource has been in the last couple of years, of course, AI itself, right? Whenever I have a, yeah, a question, um, I just open it up.

I ask about it. I was listening to a podcast earlier today, um, you know, and there was some, it was more technical, a little bit outta my, uh, my reach. I took the transcript, dropped it into my chat bot of the day, and just started asking it questions about that transcript, right?

And here I am, you Know, it's so funny you say that. I, I actually wrote an article, it's published in Textron AI today. It's, it's a riff off of an article that was in the New York Times that some studies suggest that, you know, using AI and social media are rotting our brains.

And I think we've all suspected that a little bit, right? I mean, we, we see it. And, but what they compared, it was, you know, using AI versus let's say Google search, that at least in Google search, there was a little bit more work involved in ferreting out the information you were looking for and everything else.

And I'm looking at it saying, Google search my, my butt. You know, when Google Search first came out, I thought that was dumbing us down, because I'm, I'm from going into the, the library and opening up the, the Dewey Decimal catalog, you know, and looking at a book or, or, you know, when we were little, I, my mom, the greatest thing she ever bought my brothers, and I was the World Book encyclopedia. And, and yet remember those, I don't know if you remember those things.

I have, I have de to do a report, you had to, you know, go through the encyclopedia and then while you were there, you learned other things. So, you know, yeah, we've seen this. But the, the flip side of it is AI used correctly, like what you just spoke about, right?

Allows us, we have more information at our fingertips, and we could, we could challenge it to challenge us to dig deeper. Listening to a podcast is not going on the chat bot and say, feed me. Right?

Listen. But it, it ignites the, the, the chat bot ignited sparked your curiosity enough to then listen to this podcast, which as you said was deeper, and then you had to go back to help explain it. That's the uses, that's the uses where it's, it's expanding our brain, not rotting our brain.

Exactly. Yeah. Right?

Yeah. I, I totally agree. And, you know, I feel some of the brain rots sometimes as well, right?

Where it's like, you know, looking at something, it's like, oh, I should get the, you know, the chat bot to write that response to that email. For me, it's like, no, just take the two seconds to sit down and think to do it. Just write the email, right?

It's The whole thing. It's the doom scrolling all of it, right? I, I, it's up on LinkedIn.

It's up on Techstrong ai. You could check out my thoughts on it. You do.

Kurt, we, we got off on a left turn there, but let's come back to DataIQ. So you joined, you said, was it in 2013? 2014?

The company was founded in 2013. I joined early 2015, like just about two years after it started. Back then, it was more data science, maybe more machine learning type of AI and stuff based in France, you mentioned.

Yeah. Bring us up. What's DataIQ today?

Yeah, absolutely. So, so since then, you know, uh, we're now a US incorporated company. We're something like, I think, you know, 1200, 1300 employees worldwide, $350 million in annual recurring revenue.

Um, you know, what do we say, one out of four of the 500 biggest companies in the world are our customers. Um, and that's across industries, right? Who we tend to sell to are typically larger enterprises, but it could be, you know, banks, insurance, financial services, broadly manufacturers, retailers, companies like Michelin, like LDMH, uh, like ge, uh, these are all big dataiku customers.

And what is Dataiku now today, right? From a, from a platform perspective? Well, what we like to think of is that we're the, you know, kind of the piece in the puzzle that goes, uh, that, that sits between where that company's data is already running.

And, you know, most of these companies have great data platforms that they've already deployed, right? And they've, you know, they're working on consolidating their data, and that's great. Uh, they've also got like great cloud computing resources, uh, you know, through one of their cloud providers.

And now they have access to these incredible AI models. So what's the challenge, right? What, what's missing in that picture?

Uh, what's missing is the piece that brings together the trust, brings together the different sources of data, and then brings together the different types of intelligence, which can be like the machine learning intelligence, uh, can be the AI intelligence coming from those models, from open AI or Anthropic, uh, or, or in most importantly, I would say is the human intelligence of those experts who know their business better than anyone else in the world. So that they, and create for themselves the AI solutions that actually augment their processes, you know, grow their revenue, automate, uh, you know, different parts of their business, increasing efficiency so that they themselves have the ability to, to build these capabilities on top of that kind of core infrastructure across data computing. And now ai.

I love it. com? ai?

What's the website? com. com.

Excellent. People can go on there, reach out, contact, absolutely. Get smart about what you have.

All of the above. Yeah. Yeah, we've got plenty of content up there.

The, the blog is great. Uh, people say we sometimes have too much content, uh, but it's also great that, uh, it's hard to, uh, it's hard to not produce it. Um, but yeah, plenty to learn, uh, uh, about, I would recommend going straight to the customer stories.

Uh, that's where you really get a good sense of what, uh, what these companies can do at dataiku. Excellent. So you guys recently released a report called the Global AI Confessions, like true confessions.

I, you know, the name caught my, caught my attention. What exactly is it? Tell, tell us about it.

Yeah, so this is actually the, the second in a series. Uh, first we did a CEO, uh, confessions, and now we've done the CIO confessions. Um, and it's a, you know, we go out and we work with a, you know, a, a research firm that, uh, that conducts these studies, uh, to go out and just kind of take the pulse of what, you know, previously, the CEO.

And now the CIO is really thinking, and we frame it as confessions because there's, you know, kind of the, the, the public face that, uh, that everyone is putting up, especially leaders of these larger companies, right? They need to say that everything is successful, they're moving so fast, everything is great. But we know that that's not the most interesting story.

The most interesting story is when, you know, they pull you aside and say, let me tell you how it's really going. And so we orient the questions in that direction, uh, to make sure that we're getting the real story. Obviously, it's anonymous, you know, we, we work with the, uh, the study firm to make sure that an anonymity is protected while at the same time we're actually getting the, uh, uh, the real people from these companies.

And what comes back is often, uh, both really surprising and not surprising at all sometimes, right? Uh, and so here in the CIO Confessions report, you know, we start to see, I think what we all suspected is true is that there's great ambition, uh, for ai and especially agents. Uh, this is a big focus, right?

For all of these companies these days. There's this big push for it, but really this, this huge gap in the trust that we can have for these systems. Uh, and I think that what that is pointing to is the fact that, you know, it's, it's not like, you know, we we're lacking the technical capabilities, you know, the, these AI models that, uh, that we have from the Frontier Labs, they're, they're incredible.

They know they're, they're super powerful. All these companies have so much data to work from, right? Uh, the challenge is how do you actually build something that you can trust put into production, and then of course, have a process where you can start repeating that and scaling out these AI solutions.

Absolutely. You know, it's, um, it's funny, I, I recently saw, I, I was at a conference somewhere and someone presented a study of CIOs. I forgot if the number was 40% or 60% of CIOs were increasing their budget, ask for next year for ai.

Not because they really understood what they were gonna spend it on, right? Or what they were gonna do with that money. But because their CEO and their board, their board of directors, was telling them they needed to spend more money on ai, which to me just sounds like a recipe for waste and fat.

But I mean, I, I think that it's telling of, of kind of the times we live in. I wonder if you guys saw anything like this? Yeah, I think, uh, you know, we all saw this kind of bumper crop of, uh, you know, new budgets that just emerged, uh, for, for ai.

And I think what that led to pretty directly is that study that said that 95% of pilots aren't making it into production Or the MIT study. Exactly. Well, there's MIT and then there's the Wharton study that says 70% find value in it.

Right? You know, it depends, I guess if you're an M mi T or a Warton fan, actually, I wrote an article on that too. I think in quantum fashion they're both right.

In some level. Exactly. I think it Depends what you're looking for.

Exactly. Right. Because, uh, you know, what we saw is a lot of testing.

And ultimately, I don't think that's a bad thing. I, I think it's a very good thing. Um, but what's critical is for those enterprises to then find a path out of that testing phase, that experimentation phase to start, you know, getting down to real use cases, uh, that actually deliver value.

And again, I'll, I'll bring it back more to the process, uh, but really a, a process and engineering method internally where they can start producing these things reliably, right? And by these things, I mean, you know, these agents, you know, built into applications that are actually moving the business forward in a, uh, in an important way. You know, doing it once or just buying one off the shelf, that's great.

Doesn't hurt. But what really is going to transform these companies where, where AI is actually gonna get to the potential that, you know, everyone is claiming that, uh, that it will have, is when they can actually, let's say, industrialize the production of these capabilities for themselves when they can start pumping out. The agents basically have their own internal, uh, factory where they are, you know, designing engineering, producing testing, and then maintaining these, uh, you know, a growing number of agents, uh, that they're running throughout their business.

Absolutely. You know, I, to me, look, I, I've been through several technology waves in my career. I think AI has the potential by far, to be the biggest.

And then when you combine AI with quantum, with robotics, physical ai, whatever you wanna call it, it, it, to me, that's an industrial revolution in the making, right? Yeah. Um, but even though time is crunched more than it ever was now, AI time, whatever you wanna call it, you, these, this still needs a chance.

You know, like you put wine in a decanter to let it breathe. This still needs a chance to breathe a little bit. This still needs a chance.

We need to go through that experimentation stage, that testing stage. Yeah. What works, what doesn't work?

What could we, how do we tweak that, improve that, not do this? Um, and I think sometimes we lose sight of that in our instant gratification center of our brain that we wanted to do everything we think it could do right now. Um, and I'm sure that's borne out in, in, in this confessions report.

Yeah, absolutely. Right. Uh, you know, I think that where we, where we see the market today is in a phase, which is honestly similar to where enterprises were with machine learning 10 years ago, which is mm-hmm.

You know, kind of testing out what are the right use cases? How do we get some of this, you know, this new technology, this new software, this new like, you know, cluster of, uh, uh, uh, of computing that we have available to us. How do we actually get this to do anything at all that's useful?

And it took several years for that to, to to happen. And so I think it's totally reasonable that we would expect that to happen in AI as well. Um, just because we're not seeing, you know, total business transformation in a matter of, you know, a couple of years since the first chatbots came out.

Uh, I'm not surprised at all. And, you know, one of the reasons is, sure the technology is moving fast, but we know that organizations, especially large organizations can't and should not move that it's A lot of inertia should, you know, turning those ships. For sure.

Kurt, let me ask you another question. The, the, the CIOs and the CEOs for that matter, in your confessions reports, talk about the distinction between like chatbot generator of AI versus agen ai. Yeah.

So, uh, what we saw, uh, in there is that the, the real enthusiasm is to move beyond chatbots. Chatbots are, are useful, right? They're great for those one-off tasks, you know, where you can go have a quick conversation, get it to summarize something for you, et cetera.

But especially when we talk to the CIOs, and so of course that's a role that's focusing on, you know, what, what are our processes? What are, uh, what are the, uh, the systems that we have in place? That's where they're eager to get AI working almost on the backend, right?

Not just as a, a, a one-off interaction with every single user. Uh, sure, that's great, that's productivity software, but more in a way which is automating processes, uh, such that, you know, the com, uh, the, the end user can go and kick off something that the, the, the human being would've had to have spent hours on so that they, they can go and do something else and then come back and have the result at a level that, uh, that is, uh, you know, as good, uh, maybe even better than what they would've done themselves. And, you know, we, we see this within our customers, right?

We, we have customers across, uh, different industries who, who are experimenting and succeeding with this building things that are saving them in some cases, you know, thousands of hours per, uh, uh, per year, uh, uh, thanks to the fact that they were able to design something that actually matched what they needed. Absolutely. Kurt, I always ask people when, you know, when I'm doing interviews around surveys and reports and stuff, research what came up that you didn't ha you didn't see it on, you didn't have that on your bingo card.

Hmm. You know what I mean? Like, just came outta left field for you and we're like, wow, I didn't see that coming.

Yeah. You know, there, uh, uh, there's always something, right? And, and like I said at the outset, there was a lot that, you know, kind of confirmed a lot of hypotheses, right?

That, uh, you know, trust is kind of the barrier here and so on. Uh, but there was one number that jumped out at me that, you know, I didn't realize it was that bad. Uh, and that was when, uh, they came back, the CIOs came back and said that they could, that only 5% responded that they could, uh, answer or trace an AI decision for regulat regulators 100% of the time.

Uh, you know, most other people were saying, you know, some of the time I can trace a decision, but only 5% said that they could do that systematically. And I read that. I'm like, you know, I know that, you know, we, we can't eliminate all risk.

Uh, but if you're a large enterprise and you are running AI powered systems and you're not able to explain that to a regulator when they come knocking, you know, in the event, uh, that, uh, that some something goes wrong, that sounds like a pretty hefty roll of the dice. Um, and so I was surprised that, uh, uh, that it was that low, that number. Um, and, you know, I think that that reinforces the, you know, the, the conclusion that we're drawing, which is, yeah, I mean, organizations need to get their hands around the way that AI is coming to the decisions that it's making.

They need to get their hands around the way that AI is being applied to, to reach these outcomes. Um, because at some point, you know, somebody's gonna ask, well, how come you, how come your business made this decision? Right?

And it might be just for internal purposes just to understand why we made the right decision or the wrong decision. But in some cases it might be a regulator who's coming, uh, in coming, especially in certain industries and says, well, how is it, you know, why is it that you denied this loan? Or why did you charge this insurance premium for, uh, for this customer?

If you put your hands up and say, well, I don't know, AI did it. I don't think that regulator is going to, uh, to be fully set. You know, you know, you can't, you can't abdicate your responsibility.

No, no. And I think that that's, uh, you know, very important in all of this is making sure that, you know, everyone understands the saying, just because AI came up with the, that solution that's no defense, doesn't Mean you don't own it. Exactly.

You're, You're still gonna be liable. Yep. Kurt, we're about outta time.

For people who maybe want to take a look at both the CIO and the CEO AI confessions report, go to the DataIQ website. Yeah, absolutely. It's, uh, it's right up there on the, uh, uh, on the homepage right now.

Um, and if not, just type in DataIQ Confessions to your favorite, uh, search engine. It'll be the top hit. Or ask your favorite chat Chat out.

Absolutely. That's right. 35% of people are doing that.

Kurt, thank you for joining us here on Tech Trunk tv. It is great continued success at DataIQ. Come back and keep us posted.

Yeah, I'll be, uh, I'll be glad to. Thanks so much, Alan. Alrighty.

Kurt, let me, sure. I get this Kurt Muell right head of, uh, AI strategy at DataIQ here on techstrong tv. We're gonna take a break, we'll be right back.

Hello and welcome to the latest edition of the Techstrong AI Leadership Insights series. I'm your host, Mike Bazar. Today we're with Manoj Chadri, and he is the CTO for Chitter bit.

And we're having a little chat about AI agents accountability and guardrails and all that good stuff, because well, we're building it, but I think once again, maybe we're over our skis a bit. Manoj, welcome to share. Thank you very much, Mike, and it's pleasure to be on your show.

All right. Just about everybody you talk to is building some type of AI agent. At the very least, they have a prototype, if not a few running in production.

But I kind of feel like we're all rushing to go do this without think, are we gonna put in place for guardrails and who's gonna be accountable for what? Because I think a lot of these AI agents are not just autonomous, but they're kind of, you know, if left unsupervised, they'll do all kinds of things we weren't planning on them doing. Yep.

So you're you, you're absolutely right. Right. So let's, let's look at how, how AI's been evolving, right?

Ai, as we know, as you just said, AI is evolving faster than the regulation and governance framework. You know, the architecture people implemented few months ago is now kind of getting outdated. It's, it's at that past, the AI innovation is happening.

With my personal experience, when we first wrote our first AI assistant, three months later, we rewrote the entire AI assistant. Why? Because the evolution in this phase is much faster.

Just look at how many protocols have come in in reality by various different companies. We have MCP from philanthropic, we have agent to agent in from Google, we have a CP from, uh, which is, uh, agent communication protocol from IBM. All of these has come in life in last six to nine months.

So that's the pace at which it's changing. In fact, Google last week launched a P two, which is agent payment protocol. If the things that evolving at that pace, what's happening is all the vendors and organization wants to have some kind of AI associated with them, whether it is AI agent, whether it is AI assistant, whether it is agent ai.

And in that hype, the security and governance is left behind. If you look from the governance and security regulatory standpoint, there is only one ISO standard available for ai, which is ISO 40 2001. And that also has launched in last six months only.

And what it promises, it, it comes and makes sure the companies are designing, developing and deploying AI technology with the transparency, data quality, security ethics, trust is what it's doing. So that's what is happening, essentially. And I feel like there's two conversations that are closely related, but one is security and the other is governance.

When I think about the security conversation, it almost seems like maybe we don't understand the real level of risk that's going on here because these agents are autonomous, and if they get compromised, won't the bad guys just take over an entire workflow rather than just kind of compromising a a particular endpoint? Yes, you, you, you're right. Like the, the, when AI agent comes into play, let's talk about that and I'll, I'll talk about MCP from same angle, right?

So if you look, as you said, AI agent is autonomous, if the security is not in play, there can be so many things which can go wrong. Not only bias, not only basically prompt injection, people can inject prompts into the AI agents and cause it to do something really bad for that business. And guess what?

Not many companies are putting human in loop to verify. In fact, I believe that verifying the output of an AI agent is going to become in itself a profession. So people are even right now with current, uh, innovation and early stages of ai, in my opinion, human in loop is a very important aspect.

And, and many AI agent is basically taking human out of the question equation, right? So these security things needs to be implemented. If you look at MCP, in fact, I put, I posted a LinkedIn, um, uh, last week because many people asked this question to me, what about security when it comes to these new protocol, you know, MCP model context protocol, which is delivered by en anthropic, it is a protocol to build, make the development of AI agent easier, but the security is ignored in this.

There is no clear standard for authentication. There is no clear standard for sandboxing the AI agents. There is no clear standards for making sure that tools are secure and connecting to the database, the data sources underneath it securely.

And there is no clear guidelines on how to protect the prompt injection, for example. And then on the other side of it, with the governance, it seems like a lot of these AI agents or leveraging various large language models, all of which seem to be very, um, aggressive about hunting down data sources. And if I don't restrict them, they'll incorporate that data whether I intended to or not.

And won't that just one day result in, I don't know, some embarrassing data showing up in an output in some way that we didn't plan for? Yeah, so look, I, I think that that's the key thing that mo the moment that data leave and go into LLM, you are at risk of that data getting exposed to the author world, period. That that's how I see the world.

And that's how the reality is that that's where if you look at the short-term memory, long-term memory is in place for these AI agent. If you look at one of the AI assistant, which is we all use and which basically is all this gen AI evolution from them is chat GPT by default, anything you send to chat GT is used for training their model. You have to go explicitly uncheck that setting.

And still, we don't know what they're gonna do. Not many people go read all the fine prints. So what we need to do and what organization has to do is when they build AI agent, they should make sure that AI accountability is in place.

What does AI accountability means? Any input going into LLM, any output coming from LLM? Any input going from LLM to AI agent to invoke tools and make decision needs to be checked against the guardrails and make sure that they are valid and doesn't gonna cause the leakage of the data doesn't cause any compromise of the data sources.

To your earlier point, I don't think that a lot of folks are actually reading any of these end user license agreements any more now than they did then. And but today you have more at risk because somewhere in there you're supposed to opt out and check a box that says, I don't want my data to be used to train the AI model. But you gotta go find that box, right?

Yes, absolutely. And those box by design are hidden. And if you look at most of these LLM providers or a are where you are giving your data by default, use your data, your prom, your document, your data sources to accumulate and train the future models, which is gonna come.

'cause that's how the models are becoming intelligent. That's how models are becoming smart. And not only about becoming intelligent and smart, that's how they can give you a personalized experience on the task you are asking these models to do for you, right?

That's basically is why they need this data and that's why they're basically having these boxes and opting out hidden from the users To that end, who's in charge of all this? 'cause I think that we see a lot of data science tiger teams that people have spun up and they're out creating agents, but it's not clear to me that anybody from security or the GRC team is invited to that conversation. So, uh, are we just waiting for some sort of cataclysmic event before we get serious about this?

Or what's gonna happen? You know, there are a bunch of events already happened as we all, as majority of people might know, or your audience, Samsung IP got compromised because one of the employees of Samsung literally take some confidential document and put it into the chat GPT, right? And that is now, and that is available on the internet, right?

So this is happening again. The point is the people want, the organizations don't want to left behind and the innovation is move on. AI is moving at much faster pace, whereas that security team and governance is lagging behind and not able to catch up with the innovation on that development of ai.

So yes, I think there will be going to be some catastrophic events before people are gonna get serious. Although I would say there is a positive news where majority of analysts and majority of big enterprises are now asking the diff various organizations who are building AI agent that, Hey, please take AI accountability. Please take AI guardrail, please take AI security into account.

Now how much is gonna happen in next six months is still to be seen. But I'm happy to see that now people are talking about these, whereas six months ago nobody was even discussing about the guardrails and accountability. So what's your best advice to folks then about how to go and get into the middle of this conversation?

'cause I think a lot of it seems to be happening beyond the realm of the governance and security people. So do I gotta go scan for these projects and insert myself into them, or how do I kind of get myself into this conversation? Yeah, I, I think basically there are one few open source tools now built.

You know, there are vendors like us who basically are building AI accountability and governance into the product pay. Get yourself educated with that. If the cycles permit, go look at the control of ISO 40 2001, which allow users to get educated with how to design, develop, and deploy, um, AI ethically and not have problems.

That's what I would suggest people to do. And make AI security and governance not as a second thought for ai. Make security and governance as a first class citizen while building an AI agent.

Build your team and train your team around AI controls. When I say your team, your design team as well as your development team, along with the security on the controls you need in ai, Might we one day have AI agents that are gonna manage the security and the governance of other AI agents? Is that where we're headed?

Absolutely. You know, what's gonna happen is AI agents are like digital workforce like US humans. There are people who are developing and there are people who are doing security and governance on the things developed by the development team in the AI agent world.

There will be AI agents which are responsible for doing the work, the task autonomously, and there will be AI agent, which are watching them for the security and governance and making sure that they are doing the things ethically. There is no breach, there is no violation of anything. Absolutely.

That's what is the future and that's where we are gonna go. We've also been struggling to manage both human and non-human identities for a long time. Now, is an AI agent essentially a new type of non-human identity or is it an extension of our human identities and we'll track it that way?

So right now it is where we are and at what stage we are, I would say it's an extension of human identity, but pretty soon it'll become a standalone identity of itself, which as I said will be a digital workforce. There will be, uh, agents which are basically onboarding these AI agents and there will be like, call them the HR agents, which will be responsible for onboarding the agent into the IT system. There will be AI agent as we discussed, to make sure they are operating and performing securely according to the policies of the company.

So in pretty soon, not in a distant future, they will be the digital entity of their own. Are you at all worried that we might get too comfortable with these AI agents and not think all this through and we're just just gonna have people kinda executing things just because Well, the AI said it was okay. I I, I honestly don't think so because you know, a lot of, there is still a lot of hype around AI agent.

If you look at the companies who are building true AI agents are very handful. A lot of companies are building AI chart board and AI assisted, which are basically sitting next to humans and basically helping humans to do the task at much higher productivity level. There are very few companies are building AI agent, which are doing, making decision autonomously and operating autonomously.

That's basically our very few companies. And once, by the time this become a commodity, the true AI agent become commodity, I hope. And I think the standards will get caught up and it will basically have governance around it.

Do you think the auditors out there are already tracking this and will soon come knocking and asking people about these issues? Absolutely. Like that's where ISO 40 2001 is come.

I think there are more standard and compliance, which is happening. It's just matter of time. It's these, these are regulatory authorities will come and start putting them in place.

All right, well folks, you heard it here. I guess there's two things to remember about AI agents. I mean, they're awesome, they're powerful, but they won't pay the fines for you and they certainly won't do any jail time.

So be careful. Manu, thanks for being on the show. Thank you very much, very much, Mike.

It's pleasure. All right. And thank you all for watching the latest episode of the Techstrong AI Leadership Inside series.

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

ai Leadership Insights series. I'm your host, Mike Baar. Today we're with Santiago Suarez Ordonez, who's the CEO of Momentum do io.

And it goes by the name Santi for short. But we're gonna have a little chat about where are we with the state of AI and the enterprise. 'cause I think we're entering maybe the what they call the trough of disillusionment.

But we'll see. Santi, welcome to sha. Hi Mike.

Thanks for having me. Excited to chat about this. What's going on here?

It seems like early on every executive on the planet was like, oh, we gotta invest in this tomorrow. We gotta drive all this AI stuff and we're gonna get these incredible returns. And there's magic in the air.

And now it looks like people are starting to figure out that, well, it takes work to get this thing to happen. And there's a lot of data management issues and a lot of things that we have ignored for decades are now raising their ugly head. But what's your assessment of what's going on here?

Um, personally, based on my slice of the market and the day, day-to day we live in, I feel like we may today be on the early innings where interest is continuing to build up and, um, buyers continue to come into the scene with excitement and strong mandates to understand and spend and explore in ai. I do agree with you that there are some indications of burn where buyers have, uh, gone ahead of themselves and spend money on things that are not really delivering. But I do think the wave is still forming there.

Of course, we can, we can dive in and and debate weather. We think it's gonna crash, uh, and we think there's something on the other side that's positive. But in general, I would say today I see energy building up, uh, not quite yet deflating.

I'm not entirely sure that it's a crash as much as maybe a more realistic set of expectations. And to that end, everybody I talk to has multiple, if not tens, maybe even a hundred or more experimentations going on. But I wonder if we're experimenting too much and maybe we should just pick three or four things that we're actually gonna get done and put into a production environment.

I think you're right. I think one of the interesting qualities of this new, you know, disrupting function that is ai, this new type of technology that is driving such transformation throughout all industries and all solutions, is this, um, density to make every single demo work every single first take on a new solution. An AI forward attempt to whatever problem that may be, you know, affecting a buyer tends to work pretty well, at least for a demo on first try.

So I think that's created an explosion of cool demos and superficial solutions that buyers can go spend their money and time on. Um, I think one of the biggest challenges that buyers have today is to really be thoughtful and uh, double click to understand what is their beyond that first sexy demo. How of operationalized a workflow is for the long run, how scalable a solution can be for a big team like an enterprise company.

Uh, I think that's really what's at the, at the center of not getting burned, spending a ton of exploratory money on AI and then having to turn up a bunch of vendors, uh, 12 months down the line. Mm-hmm. Are there any patterns you're seeing in use cases that are maybe the first low hanging fruit that people can really operationalize?

And maybe everybody should because everybody soon will, but at least I'll have something to show for my efforts. Yes. I think ulti, at least of course, this is a talk in my book, right?

I do think, um, data extraction, using AI, driving a data set that is inherently unstructured and requires a bunch of manual admin work to have the business be able to leverage at scale and having LLMs be the conduit through which that becomes structured, um, is a really good, is a really good use case. It may not be the sexiest one, right? You know, what is sexy today in the industry?

Well, what's sexy to it in the industry is to come and pitch you that I can replace 2000 people when you know, an LLM for you. And that's kind of the easiest thing to go try and sell. 'cause everybody would be in the market to dramatically lower cost.

Um, but as it turns out, that is a, a really hard thing to deliver on in the long run. There's a lot more nuance to solving those types of problems. Uh, something that may not sound as sexy, uh, but it's more concrete and more contained of a value prop to say, Hey, I'm gonna grab something that is very time consuming and gives you low quality data for the business and turn it into more reliable, higher quality.

I'm not going to clean a bunch of people's jobs, but I'm going to dramatically reduce the amount of admin they have to do so they can focus on something better. Uh, that's a little bit of what we sell ourselves every day. Yeah.

It seems like to your point, we got a little obsessed about the labor arbitrage equation when maybe we should just be thinking about, well, people are spending a third of their day on tasks that are either manual or intensive and toil that doesn't really add a whole lot of value into the business. And if we get rid of that, well then we'll get more value out of the people we have. Correct.

That is a thing. What's going on? Um, usually what you see is, um, founders and startups built on the idea of only replacing labor.

And those tend to be younger founders who have not really lived in the enterprise, who don't understand the complexities of a bigger organization and the new ones of having humans as the glue between complex processes and communication, uh, chains. Uh, so they come and they idea idealistically say, well, if AI can write an email, then I can replace a team of 1500 SCRs with a bunch of ai. Uh, but then when you put those in practice, uh, they crash and burn when they hit the hard reality of, you know, the real world.

Um, what you're seeing is more, um, senior, um, uh, veterans from the field will come and put very concrete solutions, they can slot it into a big enterprise for a very simple problem, and those deliver quite well. Uh, so that's what I would kind of nudge towards every time I would talk about, okay, how does these get operationalized? How does it, how is it integrated into my tools?

How is it blended into my processes? Uh, that's the real questions to be asking in, in 2025 when there's so many vendors, there's so many submissions around Mm-hmm. To your point, it's not clear to me that organizations have a big appetite for large scale business process re-engineering, and they kind of want some of this stuff to slide into their existing workflows and processes because otherwise it would just be too disruptive.

But it does seem maybe on an evolutionary scale we will re-engineer these processes. It's just gonna take a while. Exactly.

Exactly. It's the same, you know, I have an analogy too on companies that today are rebuilding the CRM from the ground up and say, Hey, look, we are a, we're a brand new AI forward CRM now who's gonna go dump their, you know, Salesforce or Microsoft Dynamics since this overnight, when you have a team of two sales reps who have been using it for, you know, 11 years to capture all their data? Well, not too many companies, they'll find a lot of small startups that may, you know, take a spin on a new approach.

But the majority of the industry will not mitigate impact. And the companies that are, you know, leading the transformation are the ones that are making the current situation incrementally better and may end up leading to a destination that is the same, but you know, may take a couple of years to get there rather than doing it overnight. Mm-hmm.

One of the things I do hear people struggling with is they'll take a notion and they'll be like, wow, we can use AI to create this, and they'll even maybe get as far as creating an AI agent and then they'll wake up the next morning and one of their vendors already did the same thing, and we will give it to them as part of the application that they're already using. So where do I find that line between when am I gonna bill versus buy something that, um, I can add some unique value around versus reinventing a wheel that somebody else is gonna do for me? I mean, I'm pro buy versus build every time.

I just feel like if you are in a business that is thriving, that is growing, you have a market that is hungry for your solution, it is rarely the case that you should be spending your time building some workflow optimization for operationalizing your company. Uh, there's somebody who's, you know, there's some vendor out there whose only focus is to do that, right? And you will never be able to do it at the degree of refinement they do it.

Um, it usually tends to cost a lot less than it takes to build, uh, even in the age of AI with agents around, you know, upkeep, maintenance, um, continue to be considerations. So in the build versus buy, uh, kind of spectrum, I'm a big fan of, you know, buy while you're growing. Sure.

If your company is, you know, growing 10% or year over year and you've kind of tapped to the bulk of your total adjustable market and now it's about optimizing cost, sure. Then go consolidate, then go build your own business from the ground up. Uh, but if you're thriving and your business is doubling year over year or tripling year over year, I wanna get my ass off the ball.

I would just focus on my market, uh, with every piece of technical resource I have at my disposal. Mm-hmm. We're humans though, and we all get obsessed with quote unquote making things better.

And I think if I buy, I wanna customize that 'cause um, we're all convinced that we have unique business processes and that may be debatable, but, uh, people are people and that's what they want to do. So do I also need to figure out how to choose platforms that give me something that I can buy, but I can extend Completely. I think in the world of ai, what you're referring to is the idea of prompting what makes AI custom, what makes AI behave the way you really need is to be able to highly refine the prompt that is ultimately given to the model.

Even having the selection of what model is being used gives you a, a degree of customization and control on the end result that I think is really deal breaker between something that's kind of cool for a demo, but not really practical in the real world and something that really works the way a business wants it. So in my book, it's all about integration automation and prompting that will be at the core of a solution that feels your own versus something that is vanilla and doesn't really get you to level of, uh, replacement of labor that you expected in the first place. Mm-hmm.

One of the challenges I hear also is that, um, most of the business processes today that we're trying to do are very deterministic in that sense that they're supposed to be done the same way every time. And the one thing AI never does is the same thing the same way twice. Um, so we have a probabilistic set of technologies that we're trying to insert sometimes into deterministic workflows.

So how do we do that and, and how do we strike that balance because, well, you know, it, it's neither a hundred percent of one way or the other. Yeah, this is a really good point. I mean, I think the oversimplification of some of these patterns and tools into simple categories and words, things like agents, uh, can lead to issues like the one you described ultimately, you know, if you really were to go to the very core of what an agent really should be, you know, the word agency comes up, right?

An agent needs to have agency of what it's doing, and therefore it becomes a very probabilistic, non-deterministic, um, type of execution you would get out of this technology. Um, and I don't think most enterprises out there are looking for something like that in reality where you're, they're looking for is basically a codifiable deterministic workflow that has a certain level of intelligence when it really matters. You want something to be 90% programmed and always behave the exact same way and then be 10% intelligent in the parts where, you know, simple if conditionals won't cut it.

Uh, that to me is really what most people talk about when they say they're buying agents or they're selling agents, uh, real, real agent behavior that is fully non programmed and provide ballistic. I'm rarely seeing it in the field and I rarely see buyers interested in bringing that in. Mm-hmm.

So what's your best advice to folks about how to get started with all this? I think we have some on the one end irrational exuberance. And on the other end we've got people who are probably overall and too terrified to do much anything at this point.

So between those two extremes, how do I get to something that feels like a reasonable meal? So I will say everybody should be bold and everybody should be hang hungry for this. You should not be waiting this one out.

If you are, your competitor is gonna be exploring it, and if they do, they're gonna be ahead just the delta in results from the people who get leverage from Gen AI today versus the ones who don't. It's just too big. Uh, so I do wanna drive urgency on, on companies out there no matter what you're selling or what you're building to go use it every day.

Um, you know, we put together a book and, you know, regardless of whether buyers, uh, whether you're listeners get it or not, the book is called Ignite, uh, go to market with ai. I got to interview a lot of really interesting people to put together the book, and I'm gonna steal this one from Kyle, or she's the CO owner. com is a thriving company, uh, hundreds of reps, uh, performing really, really well utilizing ai.

And he gave me one of the most interesting insights and approaches to this question. He said, look, Santi, the companies that are gonna thrive the most are the ones who have their leadership be AI forward. I think an anti-pattern that is developing today is to have AI adoption come from the bottom up.

Let the front lines explore ai, understand AI and bubble it up to leadership for leadership to decide what gets bought and what gets used. Uh, I do agree with Kyle that the right pattern here is to have your CEO, your CRO, your CTO, be the ones hungry for innovation, be the ones understanding the differences between Claude and Chad GPT, understand what an embedding says, what a vector database is, and if they do, then they're gonna be the ones making the most bold decisions that they wanna be. They're gonna be the ones buying the most disruptive technology.

Um, so, so that's my advice. I'm copying Kyle and saying, you better have a CEO who understands what this tech can do. You better have A-C-R-O-A-C-T-O-A-C-P-O that are looking into this technology and are spending an hour or two a week dabbing in into the latest and greatest 'cause that will drive the best results at the enterprise.

Ultimately. Is there something that you are seeing organizations do that just makes you shake your head a little bit and go, folks, we could be a little bit smarter than that? Um, I think organizations are quite exploratory today and they're very boldly bringing in vendors and trying to stack, and I don't think that's a bad idea.

Um, I think where it really can be, uh, a bad approach is when you're being careless about data this vendors handle for you and how they use it. I think buyers should be looking into their MSAs into terms of service to understand if data is being used to refine models, if data is being used to train models, uh, you wanna be in business. If you're a business, you wanna be in business with vendors who's only interest is to charge you money to provide you a service.

And the only reason they're gonna use your data is to provide that service. And whenever the engagement is over, your data is yours and it gets deleted on a, I read the phone, I'm, um, I'm commitment. So that would be my one component is don't be too careless about your own business data these days.

Data is a commodity. It's, it's something that the companies are looking for, it's a currency. Uh, so you should protect it and be responsible about it.

All right, folks. You heard in here one way to think about ai. It's an undiscovered country, and the only wrong decision is to stand still and do nothing.

Hey, Santi, thanks for being on the show. You bad, Mike, thank you for having me. And thank you all for watching the latest episode of the Techstrong AI Leadership Insight series.

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

Agents for cloud migration, Commvault Unity has arrived hero for Code. Arista and Palo Alto are gonna team up. We're gonna be talking more about Microsoft and Anthropic.

There's some AI driven espionage out there. And, uh, wait a minute, we're gonna see if cloud flares back online in the closer look in this episode of the Tech Field Day rundown. Hello everyone.

Welcome to the Tech Field Day rundown for November the 19th. Uh, today is a day that I absolutely cannot endorse it's national play Monopoly Day. You're probably thinking to yourself, oh yeah, my family's gotten into some fights before.

I once played Monopoly with lawyers, and they made me sign contracts, so never again. Thankfully, I've pivoted that business into talking all about the tech news that's been going on this week. And, uh, joining me is my somewhat sleepy co-host, Mr.

Alistair Cook. Al, it was great to see you last week in person, but I think that Jet lags starting to catch up with you. You know, it's one of the amazing things is, uh, being in the states after working with the team, I gotta go to Ohio with the team and, uh, be in person with people.

But I also gotta experience it's Sunday in the, in the US while it's Monday in New Zealand. And I'm usually on the other side of that. It was a fun experience, but yeah, it's a long way home.

It's a long way home. But thankfully a lot happened in the time that you were gone on that plane. And we're gonna be talking all about it because, eh, well, you know, how often do you break the internet?

We're gonna start off with a story about the last people that broke the internet, and that's our friends over at Amazon Web Services. Because AWS introduced a new AI powered agent that dramatically accelerates cloud migration projects by automating tasks that used to take us weeks. The professional services delivery agent can generate proposals from diagrams or notes, deploy sub-agents for coding and testing, and leverage AWS transform to modernize legacy systems.

It's all backed by insights from thousands of prior migration. These tools position AWS alongside other heavyweights like Google and Microsoft, and using AI to streamline large scale cloud modernization. Al, can Amazon finally turn the corner and make it easy to move things onto AWS?

I Hope so, because historically we've picked up what we had on premises shoved into, into AWS or another cloud platform, and been terrified at the costs that we got after a little while. So going through a good consulting process to do that migration, to, to get the most value out of getting our applications onto the public cloud, that's, that's a really vital part of cloud adoption. And the aim with this particular component, the AWS Professional Services delivery agent, is to shorten the cycle for that instead of taking six months to a year to go through that planning and discovery and specification to have some AI agents deal with the huge volumes of data that you get as you're going through that, that process and to make sense of things through here.

So, doing some of the, the design and iteration a around what does my infrastructure look like on AWS that's different from how it looks on premises. What things should I rewrite in order to use new platforms? You know, the, how many s are we up to 7, 8, 9 hours of migration that's, uh, that we get with AWS.

Having some of those guidance and decisions is, is absolutely vital. And I've seen this previously that it looks very simple to move to a cloud platform. You just take what you got and you shift it across.

But there's so many interdependencies in discovering and analyzing those interdependencies, working out your cycles to, to move. That's actually really hard work. And it does seem like it's a combinations of choices, uh, process that really does suit an AI agent.

Will this lead to more successful and and faster migrations from on-premises to AWS remains to be seen. And it's a, it's a complex kind of process to go through. And one of my concerns is that as you go through that process, you need to build knowledge within your organization about how to, how to actually enact the transition from on-premises up to AWS and the differences.

My concern is that these agents might prevent some of that knowledge gain. That, um, internal awareness that's a vital to speed up a, a normal migration. Of course, it may remove the need for that because the AI agents might be that smart.

Commvault, our good friends at Commvault have just launched Cloud Unity and AI powered platform that unifies data protection, recovery, and identity security across cloud and hybrid environments, tackles AI driven data sprawl and rising identity based attacks, uh, with integrated governance tools and all of the nice advanced threat detection and recovery testing. Any access is available now, but full releases coming early next year. Tom, have you had access to the early, I have not had early access, but I was in the audience for last week's keynote when Sanjay Meti came out and kind of started talking about what Unity entails.

Uh, we're gonna be, uh, following along with the live stream that's actually happening today on the 19th. com for more details on that. Here were some of my big takeaways from what I got during the Commvault Shift keynote.

The first thing is res ops. I hope you're ready for a new ops, uh, designation. Uh, in this case, you know, with Commvault, it's resilience ops, right?

It is not enough to be available. You have to be resilient. You have to make sure your data never goes down.

Uh, that is part and parcel of what Commvault has always been about. We know that for a fact because we've seen that over the years. Convault's come the gold standard for backup and recovery, but that's not the market that we're in anymore.

We are in the data protection market. And one of the things that they announced that, it took me a minute to kind of understand the totality of it, is something called synthetic restores. And you're probably thinking to yourself, well, how hard is it to restore data?

But I want you to think about this because this is the way that the market has been going. So it used to be that if something blew up, we had to restore it, right? Well, we had to go back and figure out where we had good data from.

And in the case of a security incident, we have to make sure that the data is still clean, and that means restoring back to a, uh, certain point in time, right? But how do we know that the point in time is far enough back that we're getting the right data to restore? And what happened to all the data that's been created since that restore point?

Can't all be bad, right? So what Convault is proposing with this synthetic restorer capability is that they're gonna take a look at the backup. So we're not just going to LA yesterday or last week or last month.

We're gonna take all of the backups and we're gonna look for the critical files that are infected, and we're gonna roll those back to the point where they were not infected. But for everything else that's not destroyed, we are going to roll it back to the last known good of that file. So if that last known good file was yesterday, then we could restore part of the system to a month ago and another part to last night.

That means minimal data loss, that means resilience all over the place. And that is huge for people who are uncomfortable with this idea of like, you know, we gotta start taking big full backups and full snapshots all the time, because you never know when we're gonna miss something and we're gonna have to roll back. The other thing that I thought was big deal was the fact that Convault is integrating identity protection into their platform.

'cause one of the things I think that a lot of people are missing out on is that you can get infected and you can clean up all of the servers, but if the attackers penetrated into active directory, they've got a foothold, they can just keep coming back. Like, what if they changed the password on backup operators and added themselves as a unknown user, or worse yet at one that looks like a system account, they could just keep coding back in and doing everything they want to do, and you're just gonna have to keep rolling back. Only now they're watching you roll back in real time and they're combating all of those rollback points.

So eventually you're either gonna be forced to pay or you're going to be extorted for whatever else and have your data dumped. So I like where Convault's headed with this. Uh, the, the name change, of course, is important because we're unifying resilience into the platform.

And, and I'll admit some of the commercials in the keynote were kind of cute. I kinda liked it. Uh, so make sure you stay tuned for more of that.

And also, we got some great content coming out from the people that attended, not my just myself, but Jack Poller and, uh, Jay Coutre and More Great Field Day delegates. So make sure you're tuned in for that. We're going back to AWS because they also have something else they came out with.

It's their Kero AI coding tool that has new features aimed at producing more reliable, secure, and testable code, including everybody's favorite, A CLI version. It also has proprietary based testing to validate behavior against specifications and the ability to rewind to earlier checkpoints. The update also enables Kero to work across multiple project roots and support custom AI agents while qualifying startups can get a free year of Kero Plus Pro Kero.

Pro plus AWS engineers say that these enhancements promote a more disciplined, specification driven workflow that reduces debugging stress and scales AI power development with quality rather than speed. Al I kind of like the vibes on this one. Is Kero gonna turn the corner from letting AI just kinda write whatever looks good?

Well, I think that's fundamentally what Kiros all about is not having those vibes, um, moving to a, a position where you write the specification of how your application should work and the AI implements what's written in that spec. Uh, I haven't gone hands on with this. The preview was, was launched in July and, uh, has has got a bit of buzz around.

I've, I've heard a little of of people using it. And I say, I really like the idea that there is a more declarative way of working here. This is define at the beginning how the software should work and then validate that it does work.

Uh, in some ways it's, it follows a little bit of, of what I liked in the test driven design methodology where you first test for the functionality that you're going to build and make sure it fails 'cause you haven't yet built the functionality. And then you build functionality until you, uh, pass the test. Uh, that is one of the ways of going around.

Let's design how things should work and, and make sure they do work that way. Uh, I definitely, this, this testing by properties has a really good functionality in here as well. This is the ability to say, this particular function should have this, this result.

Now build a set of tests that, uh, uh, uh, somebody walks into a bar and orders a beer, orders 99 beers, orders minus one beer, uh, orders a car. Uh, does the software do what it's supposed to do? That, that kind of building those use cases, uh, those tests.

So unlike test, different development where you build those first, the property based test approach builds those from your specification of how things should work. I like that whole declarative behavior. Uh, and then the ability to work with larger projects, integrating Kero into your pipelines by using that CLI rather than having to do everything through a, uh, a, a click ops.

Absolutely. This, this kind of stuff is where we're going to see value in actually building real applications that are supportable and maintainable in the long term. Yet don't require the vast number of developers hacking on little bits of code and particularly repeated bits of code over time, uh, remains to be seen how the pricing works versus value on this.

And whether AWS can continue to deliver this, uh, age agent ai, almost age ai, um, for, for the long term. Uh, there's, there's a lot still to be written about the cost of delivering these things versus what you can charge people for them. So we'll keep an eye on it.

Over time, Arista and Palo Alto Networks are expanding their partnership to address the rising hybrid data center complexity. And of course, AI driven cyber threats. Uh, integrating Arista's, uh, collection of product eos Ava MSS with Palo Alto's own NextGen Fire Rule.

And, uh, Prisma Airs. Uh, they offer a unified zero trust, uh, security and real time threat. Quarantining centralized policy management, always my favorite.

And DevOps friendly automation, delivering scalable, consistent protection across multiple data center and multi-cloud platform, uh, environments. Uh, this sounds like there's a partnership between uneasy friends and the, hopefully this is gonna be magically wonderful for customers. It should be wonderful for customers because when you look at what people are doing, especially in the cloud environment, they're leveraging a lot of Arista switches.

That's where Arista has made a lot of their money over the last few years. But one of the things that Arista's having trouble getting off the ground is a security practice. They've purchased a number of security firms over the last few years.

I don't necessarily know that they've really taken off as much as we might like. And so what do you do when you can't really build it? Well, you gotta buy something.

And in this case, the buying is involved in a partnership, but they picked the best one on the block, right? Palo Alto Networks is one of the, if not the premier security firms in the business, right? They have next gen firewall.

They have a lot of cloud security offerings. What they don't have is an inroad into those clouds through the networking team. Because as more and more people are starting to recognize the fact that networking security are two sides of the same coin, how do you displace existing installations?

Well, in a lot of cases, you have to partner up with a company who's displacing other things out of the network. Arista made their money displacing Cisco. So how do you get into that network now that Arista has displaced them?

Well, you partner up with Arista and Arista says, well, our stuff works really well with Palo Alto. If you've got a refresh coming up, now's an opportunity to take a look at it. I will say though, that when this announcement was made, there were a lot of eyebrows that got raised because historically these kinds of partnerships lead to more engagement down the road.

I'm not going to say the a word, because that would be speculative at this point, but if there was a transaction to be had, this would not be a bad place for it to occur because both of these companies are very well known in their individual spaces. And I think wrapping them up would be a very unique opportunity for some people. I don't think that that's gonna happen anytime soon though, because while Palo Alto is sitting on a lot of money, a wrist is worth a lot.

And I don't think that this is an acquisition that they could really swallow in whole yet, but it would be very complimentary. And, uh, quite honestly, I, I'd, I'd love to see, uh, someone like a Ken do to doing some Palo Alto presentations. 'cause that could be real fun.

Microsoft announced yet another major new AI partnership with Anthropic and nvidia, but this is signaling a move beyond the once exclusive ties that it had to open. Ai Anthropic will buy $30 billion in Azure compute while invest Nvidia invests up to $10 billion and Microsoft up to $5 billion to help scale Anthropics Claw AI models. This deal gives anthropic massive Nvidia powered capacity and strengthens Microsoft's AI infrastructure strategy as its relationship with the open AI involves.

At Microsoft Ignite, which is happening this week, they also introduced a new IQ lineup of AI tools, work iq, fabric IQ, and Foundry iq, as well as Microsoft Agent 365, designed to automatically support business workflows as companies prepare for future of billions of AI agents. Al, I think it's interesting that we've always heard so much about the partnership that Microsoft has with OpenAI, and now they're going out and seeking out Anthropic. Do you think Microsoft's playing the long game by betting on different horses, or is there something else going on here?

I think this is definitely a, a hedging strategy. So we covered previously on the rundown that OpenAI pre had had an exclusive deal with Microsoft that OpenAI would run on Azure, and that Microsoft would use OpenAI as its standard AI and it powers things like copilot. We covered that This tight deal had come to an end and there was a much lucid deal where OpenAI could use other vendors and we're contracting to use other vendors to provide that infrastructure, and that Microsoft is free to work with other vendors.

So Microsoft has always been the king of partnerships partner with everybody. Uh, I see this relationship with, uh, Andro has being part of that. They recognizing that there is no longer an exclusive relationship.

They need to have a real, real relationship with a competitor to open ai in this case anthropic. And it's all part of the same partner with everybody and make sure that nobody can hold you to ransom. So OpenAI can't say to Microsoft, well the, this is the only feature set you get because this is what we're, we're doing.

Uh, Microsoft has then no leverage to say, well, we want this other feature we need for copilot as they make a commitment into Anthropic. Yeah, it absolutely gives them some more choices in there, uh, naturally enough, this is the AI money go round, and so Nvidia is in here as well. And Nvidia investment in anthropic means that Anthropic will hand back some of that money to buy Nvidia hardware.

So when we talk about this as a money go round, it really is money changing hands back and forth in some of these places. Um, Microsoft continues to, uh, put money into this, uh, $5 billion, uh, invested here. So it's not a trivial amount, but it's not the 30 billion, $60 billion we've seen in in other deals along the way.

Um, Microsoft has $13 billion in open AI investment. Uh, it really is, I think, a, a protection against, uh, open AI choosing their own path. We've seen some interesting challenges with open AI as they wanna change their, their governance and structure around there.

Uh, if that represents a risk to Microsoft, connecting up with Anthropic seems like a great way to mitigate that risk. Uh, we'll see just over time as, uh, whether this continues to grow, we see more and more money being put into, uh, anthropic and maybe a, a dial off on open ai. I don't think so.

I think this is really a protection of the relationship with open ai. Newly uncovered cyber attack shows the first large scale espionage campaign carried out mostly by ai. A apparently Chinese stake backed group used AI agents to handle up to 90% of the operation.

So including scouting potential, uh, victims and exploiting vulnerabilities and stealing data. Uh, the incident highlights the rapid escalation of AI driven threats and the need for stronger safeguards around these AI attacks, better detection and also industry-wide cooperation in as near real time as possible to protect against these attacks. Tom, uh, this is another arms race, isn't it?

It's AI for attack and AI for for defense, Yeah. And that's exactly what we talked about on this week's episode of Security Boulevard, because it really did feel like this is kind of turning the weapons back on the creators a little bit. I also thought it was a really fascinating way that they were able to kind of slice this attack up so that multiple quad agents were not actually knowing what was going on.

It was almost like a operating in a compartmentalized cell structure so that each of the agents were returning work that was then being tied together by other agents to do the actual exploiting so that it evaded all of the models jailbreak capabilities of saying, oh yeah, we're not gonna do bad things. You know, theoretically it'd be like, you know, the difference of me asking how do I rob a bank versus describe what good bank security looks like. Uh, one of those things would probably set off a trigger while the other one when used improperly would probably get me the answers that I need to know how to avoid security cameras and guards and things like that.

Uh, also, uh, the, the press release from Claude, uh, I'm sorry, from philanthropic about Claude was rather interesting in the fact that they said, well, we detected it and we stopped almost all of it, but almost all of it ain't all of it. And it, some of the attacks did go out, and of course now they're probably going behind the scenes to try to figure out exactly what was used to bypass the filters and the protections and how they're going to evade it in the future. And, uh, I I think that we're just, we're starting to see the tip of the iceberg here, because this is probably not the first one that we've seen, but it is definitely the first one that somebody is reporting about.

So, bravo to philanthropic for at least admitting that this was going on. But I think one of the things that we're gonna see as this goes on more and more is that people are gonna refine their prompt engineering. They're gonna be able to break these tasks down into finer and finer detail so that it really is going to become impossible or worse, yet they're gonna take the outputs from one group of AI and feed it to a separate set to actually leverage the attack.

So that attribution is gonna be very difficult to trace down. And, and I, I don't know if there's a clear cut answer here, because the real thing that people are saying we need to do is lock it all down so that it can't be used for attack. That's like saying, oh, well, we should get rid of all VCRs because they're only ever used to record, you know, copyrighted programs when that's actually not the case.

Uh, we just, we have to find a better way to figure out how to use them. All right, we wanted to take a closer look at a story today. Uh, it took us a little bit while to write it, of course, because there were some internet issues.

Everyone's favorite web infrastructure provider, CloudFlare had a massive global disruption that caused error 500 messages and took down major platforms, including X Twitter and chat GPT, along with most other AI platforms. The outage, uh, triggered by a sudden spike in unusual networking traffic impacted thousands of websites early on Tuesday morning, and highlighted the fragility of internet architecture. 20% of global web traffic flows directly through CloudFlare.

The firm is actively investigating the root cause and working to restore full service while underscoring the need for greater resilience and digital infrastructure. And I think it's kind of funny that we've run into this problem, uh, recently, al where DNS update and AWS took down one half of the internet, and CloudFlare getting knocked offline by a massive traffic spike, uh, took out part of the rest of it this week. Uh, you know, they, they're still attributing what exactly went on.

We know that CloudFlare is gonna give us a great postmortem when they figure out exactly what happened, but I want to turn it over to you and maybe ask, are we putting too many eggs in one basket by relying on CloudFlare to protect us? Because when CloudFlare goes down, we can't get anywhere. So here's the thing.

CloudFlare is very popular because it does a great job of a very specific task of delivering applications globally, uh, to your users in a way that gives them really good performance anywhere in the world. CloudFlare has built an amazing network to do this. And then let's, let's put this in context.

Although we've centralized everything on CloudFlare, we don't often see CloudFlare outages. We certainly don't often see CloudFlare outages of this scale. So yeah, the outage is extremely visible the same way any of these centralization outages are extremely visible, but they're very rare.

And so when you look at what is the impact on the total uptime of whatever system you're using, you'll still find that even with the, this particular failure or this type of failure, uh, you're still getting higher availability and better performance for your users across the, the last year than you would've if you ran this yourself or you tried to do some equivalent of this. So, yeah, centralization means it's very visible when things go wrong, but centralization means you can spend a lot of engineering on making sure they don't go wrong often. One of the things I've read on this, and, and it came across Reuters, is that the, the sort of triggering event here is an automatically generated config file that got too large and then caused a service to crash.

Um, this feels a little bit like what we saw with CrowdStrike where a config file was deployed out, and that caused the massive outage for, for, um, CrowdStrike. Uh, it seems like these config file changes need to run through a ci cd pipeline to validate they're not gonna cause problems, yet we need to deploy them out fast. You're the eternal optimist, al, and that's what I love about you because I, I took a, a slightly different, uh, tack from this and it come courtesy of our friends at down detector, you know, the website that everybody goes to, to figure out when you're having problems with stuff.

Oh, wait, I couldn't check that one either. 'cause it turns out it runs on CloudFlare. Yes.

I, the world is better off because CloudFlare is front-end sites and preventing massive DDoS attacks. And their engineering has done a really great job of helping us extend and expand the way that that web works. And, and a lot of other things too.

They're effectively the Internet's proxy layer at this point. 1 from the trash heap of history, uh, because of bad configuration stuff. All that being said, you cannot put all of your eggs in one basket.

And, and we've learned that from a lot of things. I mean, all you gotta do is just look up the infographic from the XKCD of, you know, the entire modern internet. And then there's a little thing down there, a little Jenga piece, and it's insert name there, AWS CloudFlare, uh, NGINX, whatever.

What we're starting to see is that when these outages do happen, they happen at a a, a level that is difficult to contain. And you mentioned CrowdStrike. We've talked about AWS before.

Um, there are a lot of challenges that happen when we've built something that is effectively too big to fail, and more than any other company, I think CloudFlare is pretty strict in the way that they deploy things, in the way that they, uh, leverage stuff. I can't fault them for this. How can you figure out, oh, the config file got too big.

When do we test for that? How do we understand that The problem is not the outage itself, it's the rolling effects from that outage. If, you know, it went down for 10 minutes, everything that checks on CloudFlare kept hanging, it kept going offline.

You know, little things that you didn't think were reliant on that. I was having a meeting first thing this morning and went to go check the calendar on somebody's website. It's offline, it's hosted on CloudFlare.

Like, oh, well, that's fascinating. And so you've got to figure out how to prevent this from happening. Yeah, in some cases, you, you probably do need to have your, your load balanced infrastructure running on it, but I don't know, maybe put your status page somewhere else, host it somewhere that nobody goes like Oracle Cloud Cloud.

So you're, you're suggesting to avoid the, the problem that a WS had along with outages when they had the big S3 outage. And the, uh, the website at a WS that reports on the status of AWS services is dependent on the S3 service. Uh, yes, circular dependencies like that are really problematic, but, uh, yeah, I, I still view using a, a well-engineered system that is designed to continue to operate at large scale absolutely as, as way better than any of the other solutions for this.

So yeah, I, I still comfortable with putting our websites through CloudFlare, having even trivial things go, go through CloudFlare. But yeah, the tool that tells me whether things are working or not can't tell me if they're not working, if it's dependent on the thing that's not working. Yeah, mindful of circular dependencies.

The other thing that strikes me on this is that there's, there do seem to be a lot of circular dependencies that go through CloudFlare, because what we didn't see is when the res the issue was resolved, everything miraculously started working again straight away. It took a little while for it to filter through the various layers that are dependent on CloudFlare before all of the layers caught up with one another. You know, this eventually consistent, uh, systems that we usually see at internet scales, uh, would be interesting to see if you had centralized everything into a single place, rather than having these eventually consistent distributed systems, whether they time to get back into operation would've been longer or shorter.

'cause of course, in a centralized place, you have to replay everything in a single stream. Whereas when you're doing eventually consistent, you're replaying in different locations and your total timeout might be lower. Something that's not going to be affected, I hope, by any cloud outages, any security outages.

Um, now I'm gonna have to make some sacrifices To all of the good luck Gods, something that I expect not to be a affected is AI infrastructure field day four. That will be my next return to the United States January 28th and 29th. The event is already filling up.

We already have, uh, four companies confirm to be there, and we're expecting a few more to roll in as well. Uh, really looking forward to kicking off 2026 for Tech Field Day with AI infrastructure Field Day. Uh, and then of course, Tom, you're going a long way afield as well.

That's right. We're looking at the possibility of heading over to Cisco live in Maya, which is gonna be an Amsterdam once again this year. You know, I can't get enough of those little teeny tiny pancakes.

com is gonna be, uh, your home for that. So when Al flies halfway across the world, I fly the other halfway across the world. But then Al you're, you're coming back in March?

Absolutely. I can't stay away. Uh, I'll be back for Cloud Field day 25 in, uh, the middle of March.

Uh, and those tiny, uh, pancakes, the puffs, uh, my Dutch, uh, sister-in-law has, uh, gave gifted us the pan to make them. So maybe come down, visit me and we'll make you some tiny pancakes. Uh, what isn't tiny, of course, is the tech Field Day rundown.

Do join us, uh, continue to join us for the Tech Field Day Rundown. You can catch new episodes every Wednesday, either as a YouTube video or in your favorite podcast application. Rundown streamed on Techstrong tv, of course, that's part of the Futurum Group.

And you can find us on both Techstrong and RUM Group, uh, locations. We'll be back next Wednesday to talk about all of the IT news for the week. That was, and until then, for myself and for Tom Hollingsworth, and for, and all of us here at the team, we're wishing you and yours a great Wednesday.

Microsoft's on Fire. Again, you're watching Textron. Hey everybody.

Welcome back to the Textron Gang. Today we have our, some of our usual gang members. We got John Schwartz, Terry Robinson, guy Courier, who will be giving us a report from St.

Louis in the supercomputer show shortly. But first, let's get started with the Microsoft Ignite Conference that John Schwartz attended this week. And it seems like Microsoft's partnering with everybody and anybody one more time.

They're, I guess they telegraphed this one. They said they were gonna, you know, not be exclusive with open ai, so they partnered up with Anthropic and Nvidia. But, um, John is this kind of par for the course for Microsoft, because like, sometimes I feel like maybe they built software that's kind of cool, and the rest of it is kind of like, well, they're a distribution outfit for everything else.

Yeah, it seems like that. I mean, you know, actually this, this deal, this partnership, which kind of distanced themselves a little bit more from mop ai, it's like this major cloud infrastructure partnership with philanthropic and Nvidia. It, it was so important to them that they actually announced this before the Ignite Show, which is their big conference out here in San Francisco.

So they thought it was much more important than actually the products they, that, the products that Microsoft announced. So that gives you an idea of the scope and the kind of the magnitude of this announcement. And just really quickly, um, anthropic is pledging to purchase or buy $30 billion in computing capacity from Azure Cloud platform.

NVIDIA's gonna invest 10 billion in Anthropic, and Microsoft's gonna contribute up to $5 billion in Anthropic. So basically what they're trying to do is address this, uh, enormous computational demands of advanced AI systems. So that was first what they did.

Uh, and then later in the day, during a three hour, seemed like it was five hour marathon keynote speech and presentation, which, uh, just went on and on and on. They, Microsoft unveils some AI tools to weave AI capabilities into corporate workflows rather than treating technology as an optional enhancement. So they showcased a number of things bearing this IQ branding, which is short for intelligence quo, and it extends from individual workstations to enterprise data centers.

So in a sense, what they're doing is they're pushing AI beyond the experimental phase in their words, and, and making a stronger case than measurable ROIs. Now within Reach, and as you said, Mike, it was, I think in a incredible extraordinary week for tech news. They probably won the week, but I mean, just wait until next week when Google wins the week, or who, whomever.

This is just, I, I've never seen a series of news announcements, um, and partnerships where they're shifting alliances, uh, people we don't know who's in whose corner. Uh, it's just a free for all. And I, I think it kind of underscores this land rush that's been going on for ai, and it's just only gonna accelerate Guy.

Do you perceive that any of these alliances are strategic? And I'm asking this question because well, pretty sure that Microsoft somewhere is probably building its own GPU chip somewhere. And it's probably gonna say, you know, well, we're gonna offer that alongside Nvidia.

And then they're also probably saying, well, we're offering anthropic alongside, uh, open AI because well, they're gonna consume cloud resources. And, you know, as far as we're concerned, everybody's all good with us. Anybody who wants to pay us money for hardware processing is great.

So, you know, from your perspective, I mean, how significant are these alliances? I don't think they're particularly significant except in the, in the general sense of this is how, you know, this is how AI is evolving, this is what we're getting moving towards. We're moving at.

I mean, if, if you may, you may remember, uh, earlier this year and last year, I, I went on a little thing about how there's no such thing as an AI application. There's an application that uses ai. Even a chat is, you know, uh, you know, the, the fact that there's an AI powering the responses to you is, is it's a chat application.

Um, so Microsoft, uh, is doing its best to control the entire stack. Um, they did, they couldn't buy, uh, DeepMind like, uh, Google did. So instead they, um, funded open AI that got them started.

Um, they've been using the, the GPT um, models, um, as they've come out. But Microsoft most famously owns the user end of all of this, um, more thoroughly than probably any other player in the game. And I'm including Oracle and Google in that, um, you know, Google has a full stack, lots of folks have a full stack.

But the point is, um, that, uh, Microsoft had to, uh, decouple itself and that this has been developing for a long time from open AI as its sole source. Uh, and so this is, uh, one example of it. Um, it's just chump change for them.

What is it, 15 bill or something like that, uh, with Anthropic, right? That's, uh, you know, they probably have, uh, more in Satya Mandela's, uh, bank account to, to work with if they want to. Um, so I think just in general, this is where it's going.

Uh, someone asked me yesterday, actually, uh, if, uh, I thought that, uh, we were heading towards some kind of, um, you know, sort of Microsoft, similar to Microsoft, some sort of dominant, you know, platform slash operating, the AI AI source or whatever, the world Computer. Yeah, Yeah, yeah. And, and I said, I, I just, I laughed.

Um, now that, that might indicate, um, especially your reaction just now, Mike, that I'm just completely wrong about this. But from my standpoint, this is worse than the cloud MCP from MCP to API to whatever. Like, um, the, the, when I say worse than the cloud, the, the cloud birthed the universal, you know, uh, use of APIs everywhere, the so-called API economy, which meant that you could start doing mix and match best of breed stuff.

Um, AI makes it even worse when you think about how MCP and about how agents work, where in a sense, any agent can kind of go anywhere and use anything and do whatever it wants to. And, uh, it can last for 20 minutes. The agent can last for 20 minutes for you.

So the idea of somehow getting a monopoly or a dominant model or producer of a model or whatever is absurd. And I think as usual, Satya is a little bit ahead of the game here and just saying, screw this, we need to make sure, like tomorrow it might be somebody else. Tomorrow it might be, um, coherent.

It might be somebody brand new. So that's the significance of it as a strategic move. It's not about anthropic, it's about ensuring that you are delivering the right AI experience to your business and consumer users, however you're going to do that.

3 billion AI agents and automate workflows by 2028. So in a sense, what Microsoft is doing is logical. This is what they do.

You know, they, they cooperate, they work with companies until they compete with them. So they're just hedging their bets across the board. Yeah, I think the word you're looking for is promiscuous, but Mike, They're that, and that's sort Microsoft, isn't it?

Promiscuous is the right word. Uh, technically speaking, Mike, but other reasons might not be the word you wanna use. And you might, I do wanna mention, I do wanna say one other thing.

'cause you talked about Microsoft building A GPU. Microsoft's not gonna build a GPU. The action has gone to XUS as far as that goes.

And I know that also sounds silly, given that 80%, 80% of processing spending is on GPUs right now. Um, granted, but the X ps meaning TPUs, tensor processing units, NPUs neural processing units, um, just a sort of a heterogeneous chips, a set of pro, not a chip set, a heterogeneous set of processing units, um, is rapidly going to develop in these systems, especially. And initially in the hyperscalers like Azure, that's Microsoft, literally has a, uh, I believe it's an MPU that they've developed themselves.

Maybe it's TPU, um, I should be better up on that. And that's really where, what, what they're gonna come out with that's well known. I don't disagree, but Terry, you know, so to John's point, thousands and maybe hundreds of thousands, even millions of AI agents and wow, all this talk, couple of days long and, you know, this whole thing about like maybe how we're gonna orchestrate, govern and secure these things, footnotes if we're lucky.

What do you say? Yeah, that again, you know, it's like security gets the short shrift, I believe in this discussion. I'm not saying that they, they don't have some plans, but I mean, they certainly didn't, uh, to my knowledge share anything that was, that was concrete about how they're gonna protect all of this stuff.

I mean, there are huge governance questions. There's, that's, and, and it just, it started, it's like my, it's mind boggling, you know? Um, and I was thinking, didn't anthropic just put out a report on, uh, uh, a hack maybe, you know, and it's Chinese hackers are using Claude.

Yes, yes. Yeah. Using Claude.

And, and so I just, I guess I don't understand. I mean, even why not, why they're not just at least paying some serious lip service to security when they come out with an announcement like this. And, You know, Terry, I, I, I, so I'm so glad you said that because to me, there's like this classic lag between all these announcements.

They just bombard us with all these things they're gonna promise and then pie in the sky ideas. You know, they're, they're probably applicable, but there's, but it is never, we never hear from the customer or from anyone with a real concrete example because they're trying to digest all this, and they're trying to figure out how do we cover our asses? And when, when you press the companies on this topic, they, they, they switch the topic.

Yeah. And I mean, and then you have to pity the customers. I they are being Bumped.

That's what I need. Yeah. The customers, yeah.

And they're really having, uh, I think issues sorting through, but they're also having to make decisions and move on this stuff. They, there's a sense that they don't wanna be like left behind. And yeah, you might be right, guy.

There's not gonna, you know, Microsoft is not gonna be able to dominate. Doesn't mean that they don't want to, you know, but it also doesn't mean that customers aren't gonna feel, you know, s some sort of pressure to, you know, jump on board before the security stuff is sorted out. So Why, why would I possibly wanna bring up anything that might make you think twice about building and deploying an AI agent tomorrow morning?

Come on. Just, just build it and it'll break and then Yeah, build it and it'll don't worry about it. And they'll come, well, you know, whatever.

Honestly, Mike, when you told us that, that, that we would start off the show with, uh, this, uh, this announcement, which of course I saw, um, about, uh, Microsoft's investment with Anthropic, my initial thought was, you know, of course you're always thinking, what are you gonna say when Mike calls on you when you're asked about it? And what I was gonna, my first thought was to say, what the hell is even going on anymore? It's ju You just cannot.

So, so John, you know, to, to your point or my thoughts about what, what you, you're, you're, you know, what you said a minute ago is, I, I feel like a 10th of 1% of what is being done in AI right now is what's being talked about. 9% of what's being done in AI is a bunch of people like you, I like the folks on this call and other people we know messing around in, uh, chat GPT mostly, but also in other places, just doing stuff in chat. And when we are hearing about how companies are doing this and organizations are doing that, and now you can do this.

You can have this 12 stage, uh, super agent, blah, blah, blah, that's gonna search all your email and give you recommendations as to, you know, the nearest water closet for when you get out of the train in Shanghai or whatever. Right? That, that is, that is really, that's, that's the cool sparkly bits that we do wanna talk about the possibilities and everything.

But for those who are watching this, you know, uh, podcast or listening in, they're still at, at stage. Yeah. Zero point.

Yeah. Even if you press, If you press, for the most part, Yes. If you press the companies who are announcing like Asian force or what have you, I'm not gonna pick on Salesforce, but any of these companies, ServiceNow, you really press them and ask them for a customer.

If you reach the customer, they give you the blandest, lowest level safest application that they're using it for. And it's usually some sort of customer service or, uh, HR internally, and it's very controlled, and it's usually overseen by humans. So they're not practicing what they're preaching.

Yeah, I mean, I'm with you guys. I'm experimenting with this stuff on a regular basis, and I'm getting annoyed because all my experiments are kind of, eventually within about a day or so, I hit some sort of brick wall and I'm like, you know, this isn't quite gonna work, and I'm hoping that somebody will go build an agent that will do the thing that I want to do. But right now, trying to navigate my way through prompt engineering and context engineering to create some sort of automated workflow that by the way, is not very repeatable because well, uh, uh, the LLM has no memory.

So it's like basically, you know, talking to my, uh, father-in-law who doesn't remember what I told him yesterday from, So I, so, so mine being two takeaways here from this story are, uh, well, three thanks to you guys, but number one is that there is a distinction to be made between the, the application and the use of AI versus the AI model and training and stuff itself. And right now it feels a whole lot like, like, it's weird. Another weird thing to say, it's like a commodity market.

Like in perplexity, for example, you just go and you pick the model you wanna use and do that Firefox as well, whatever. Um, the second one is the really excellent point that we keep hurtling off into the stratosphere without, you know, uh, uh, keeping an eye or keeping our thoughts on the incredible, uh, uh, security, uh, implications. Um, and the third, and the third is, what is it with these keynotes now, John?

I mean, like three hours. They're just, they're so long. And then you got, you got one every day.

Also, by the way, like it's no longer a keynote. One of the great things about the super commute con conference that I'm at right now, they do one keynote and it's got two speakers, and then they're done. So the, The keynote, so, sorry, uh, guy, the keynote yesterday reminded me of the Irishman movie.

I almost, you know, like, I couldn't, I couldn't watch it. I know decade. My wife and I Stopped watching that after an hour.

She was like, I can't do it anymore. I was watch it for half an hour, then I leave the next day, watch half an hour. It took me like a week to watch it.

I felt it only I could have done that yesterday. I would've felt as if I didn't. My soul was crushed.

It was, it was brutal. Sorry. Wait, sorry, Microsoft.

Wait, I don't even think that these things rise to the level of the word keynote, because they're basically 20 minutes of dialogue and two hours of videos, Right? And then you have the, you have the, the third party come on and, and bow to the master. They, they do their presentation, you know, the audience, they lose the audience by that point.

It's like a series of eight or nine guests. I mean, apple started this Mons monstrosity, um, but at least theirs was somewhat streamlined, although theirs is pretty tired at this point. Yeah.

Well, John, you need, you need post apple traumatic syndrome therapy or something. There you go. I would just love it if somebody somewhere maybe stands up and gives a keynote for 45 minutes, no slides, no nothing, and just straight up, here's where we are and what's going on.

And this is what it's all about. But I would say Microsoft is not the only one of these vendors that has trouble, much like Bill Clinton parsing the word is, and a lot of things that they're talking about are gonna be true in about three years, but as of today, not so much. But anyway, that's just my take on it and we'll see how it goes.

But folks, we're gonna move on to our next topic, which has something to do with this one, but it's gonna be more of a Google take. We'll be back in a minute. You've Earned it.

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Little suspicious what that's about. And basically, yeah, and basically this too was telegraphed. They said that this was coming forever in a day, and now they're finally announcing that they have this thing, and there's also some interesting vibe coding tools, and they are calling it the most powerful AI model yet.

And John, as you roll your eyes, um, the question, the question I ever used, it seems like, correct me if I'm wrong, but like every other, what, 45 days, somebody stands up and says they have the most powerful AI model yet. Absolutely. So look, think about the timeline.

5. Okay, let's see. 1 was about a week ago, two months ago.

5. And so what they, the companies do is they been to prove their, their superiority. They, they demonstrate, or they, they come out with these independent benchmarks.

4, which I, in context, I know, I have no idea what that means. On humanity's last exam, uh, the previous record was GPT five pro. So, um, you know, it's, it's, it's all part of the, the hype.

It's, it's all part of the one upsmanship, uh, I think you said it well. And, and on the show notes is L-L-L-L-M, leapfrog game. Um, and again, you know, in addition to this, I'm gonna point out that, um, the, uh, Google, CEO Sundar Paka gave an interview to BBC, I think a day before, and he kind of dropped this bomb about the AI bubble and how no one's immune, including Google.

So he got attention for that. Of course, he also said Google is better positioned than anyone, so they're gonna be fine. Um, so you're, you're right.

It's just one ship. They all try to undercut one another. Um, you know, so be it.

I mean, there's, there's a lot at stake and, um, you know, ab we'll see more. Yeah, I believe, I believe he said there may be some irrational exuberance. I was like, Maybe yes, as we see these, these investments that are just outrageous from company to company, you know, they're just switching alliances, switching partners.

It's all, it's as, as guy said earlier, it's just like head spinning. It is, I can't keep track of this. All I said was, that's probably as intended.

I mean, everybody's trying to, you know, get a piece of this and, and declare superiority, and it, it, it's kind of in my estimation, my professional opinion. Stupid. So I, I'm sorry.

So guy that was, you know, I'm sorry to have interrupted you for that brilliant insight. 9% of my commentary. So, Well, But Guy, you may have some insights on the following.

So it's the most powerful, yet, according to a quote unquote series of benchmarks that somebody created and tracked that last time I checked, I've yet to run into a benchmark that actually reflected any real world activity. So what is the definition of, you know, most powerful? What, according to whom when?

Uh, so the benchmarks, um, are more or less, uh, things like, you know, you know, tokens per second and, you know, query depth and length of, or, you know, size of context, window, and like that sort of thing. Um, actually that's not a last, that one, last one's more like a claimed feature than a benchmark. Um, all of your points are correct, uh, that, um, real world, um, real world experience with a new model, uh, that scores really, really well.

Um, it does reflect that score. But one thing that is not commonly understood is, um, this sort of recursive mechanism by which, uh, well, I maybe it's more commonly understood than, than, you know, you realize, like, remember the application, um, uses the model, the model itself. Um, they, they started out as sort of what you call feed forward models, where, um, there's input and then output, um, and very quickly became recursive in how they operated, so that not only could they take larger and larger, larger and larger context windows, meaning they took a lot more input.

And by the way, a lot of that input, you don't even see you put in a prompt. And then whatever application or chat you're using, uh, ads, it's not just rag so-called rag, like there could be all kinds of additional context added to help you with. That's one of the way perplexity works.

Um, most of 'em work this way to, to make the output look better. So the model doesn't see any of that. It doesn't know which is yours and which is supplied by the application.

Anyway, long, long way of saying that, um, there, there is a lot more, uh, complexity in how these models work than there used to be. And they, they, as, as indicated in Google's release, um, all of these benchmark scores and everything, including the intelligence score and everything else are ways to, um, reflect that. It's not just simply doing a single pass, it's doing what you might think of as several passes of the same thing, breaking down the prompt, organizing it a little bit, running the different parts of the prompt through different stages, um, before providing an output even to the application, let alone to you.

Um, I actually, unlike what we were talking about earlier with Microsoft announcement, where I was going, what the heck is even going on here anymore, Mike, I got a, a, a, a funny feeling of deja vu that maybe you had as well, seeing this announcement, which is like, Hey, I know what this is. Like, this is like the old chip wars. Now we're gonna have all the models coming out and saying, you know, I've hit 61 giga flops.

I've hit 61 to 62, uh, gigabits, giga, whatevers like, and so on and so on. And that just feels nicely familiar. We can maybe settle in a little bit to just watching these folks go in and say, no, mine is bigger.

No, no, no, no, no. Mine is bigger. No, mine is taller, mine is deeper.

And that, that actually is a welcome development because if the different models are just starting to, uh, and the companies that produce 'em are starting to just compare themselves by size and speed and that sort of thing, then that helps us take them as a little bit more of a commodity play. I mean, which one really is better at coding? Is it Gemini or is it clawed, or is it whatever?

Who knows? We never get to use these models directly anyway. We use the applications that use the models, and there's a lot in that experience that's due to the application.

I have heard a new phrase and a new phrase is called token fatigue. And it comes in two forms. One is, I created something that was so complex that I ran it up against the LLM and it takes that LLM forever to do it.

So then I gotta take my next thing and move it up to the next model. But by the time I run my thing up against the next model, it's so expensive, I can't afford to do it. So people are like, I'm kind of, you know, lost coming and going here.

But I think we're gonna get to a point soon. I hope maybe that, uh, I'm gonna create some sort of prompt that will have some form of automated context engineering, and then we'll be routed to the most efficient LLM that's fit for purpose. And I won't have to think twice about it because it'll figure that out for me.

What do you say, guy? Can we get to that? Um, there's a lot of fatigue building up in the system.

Um, I think so. I think, I think, I think you're right. I think that, um, I think that that, you know, we've talked a little bit here about how there's all this focus on the model, but what about the data?

What about the quality of the data? It's not just a, it's not, you know, there's a lot that can, that, I'm talking about training data now to create the model. I'm not talking about the data added to, you know, the inference in the model.

Um, and then on the other side, I was just talking about the application. Well, listen to Gentech AI as an application that uses ai. And so there's a lot of work to do there.

And what I have, I, I was just talking to one of the neo clouds here at the Super Compute show the other day, and I told him that this moment, this AI moment we're in feels a little starting to feel a little weird to me. And that I don't know how to qualify it any better than that. But I think talking about concepts like token fatigue, um, or recursive use of AI data to further train ai, these are all things that can build friction into the system and help, help generate the inevitable backlash that occurs in every wave.

AI is, it's just gonna happen faster in ai, um, out of which we'll emerge with something better. But in the meantime, yeah, I think that that's the sort of thing that, uh, contributes to that. I'll go a step further.

I think that given the amount of money invested in all this stuff and how upside down that is that soon, that context routing engine that I'm gonna put in the middle of this is gonna be pinged by various LLMs that say, oh, pick me, pick me. I'll do it cheaper. Come on, pick me, pick me.

Wow. It's like a Kubernetes based structure for, for, for, for a, oh man, Uhhuh, just think about it. And, and, and I, and I, Now I have fatigue and I, And I only bring, and I only bring this up because there are indeed open source context routing projects underway that are gonna have these kinds of capabilities.

So stay tuned. You are so right, Mike. We will see.

All right, my friends, we'll be back in a minute with our third segment Discover Techron Group, the epicenter of tech innovation. We are your go-to for reaching IT leaders and practitioners worldwide. Our secret impactful content that sparks awareness, engagement, and top quality leads with us.

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Contact us today and tell your story to the world in the most powerful way with Textron Group. Hey folks, we're back and we're in for a little bit of a treat. Guy Courier is in St.

Louis at the SC Show, formerly known as Supercomputing, and he's been there for the last couple of days. And Guy, we talked a little bit about what Nvidia was up to at that show earlier this week, but there's a whole lot of other folks there. And, you know, walk us through some of your impressions.

'cause man, there's nothing like having feet on the ground. Uh, yeah, thanks. It's, uh, this is my favorite show, um, because it's Roots are scientific and, uh, research based.

Um, and it was all about high performance computing, uh, year after year after year. Um, starting, uh, had AI related to it for a very long time, but that wouldn't have been generative ai that would've been predictive or, or recommendation engines. Um, I'll get to that in a second.

Um, but now it is, uh, a very large also commercial trade show along the lines of like acute con or whatever, where there's, you know, uh, pursuit of development and inquiry and all that. Um, in parallel with, uh, technology and announcements and all those other sort of things. It's also one of my favorite shows.

'cause it only has one keynote. Just wanna repeat that. Um, last year, um, I reported also from, from, uh, on this, uh, program from, um, super Compute.

And the big story was about cooling. Cooling was, it was sort of, you know, AI of course was a big story last year. Again, I'll get to that again here.

Um, but, uh, uh, as far as the sorts of things that are really pervasive, uh, outside of AI cooling was the big story. Um, now what we're seeing is what you might call, um, a rapid, uh, um, a rapid emergence into the market. Uh, those cooling technologies.

Uh, you, you had advanced, uh, di direct liquid cooling systems showcased from, from some of the system vendors like, uh, HPE, Dell Super Micro, um, Dell and Super Micro actually have open rack conforming, uh, or compliant, um, systems on display. Um, but they are, they weren't the only ones from smaller vendors as well. Um, I didn't check out if Lenovo had one, but now we're seeing, um, real, um, and the data center market incidentally, uh, most data center providers are, are, are now enabling directly cooling a lot more easily.

So that's hitting ahead now that we saw ING last year. Um, one thing that seems relatively new this year for this show, um, is, uh, the neo clouds are here. Um, core Weave is here.

Neas is here. There's probably a few more that I didn't see. Um, neo clouds in case requires explanation.

Um, are, um, cloud providers, they tend to be global, but they are built recently from the ground up, you know, so to speak. I'll get to that in a second. But they're built from the ground up to help host AI in particular.

And the reason why I say so to speak is that a lot of them started out as crypto mining farms, um, that got repurposed, uh, to, to help host, um, ai. Um, so the question is, why are neo clouds here at this show where you have a lot of tinkerers and builders and that sort of thing. People building high performance, uh, systems, uh, uh, mostly for research purposes, but also now for ai.

So why would, um, a provider be here? Um, it's because these folks are also their customers. Um, which leads me to the third thing.

I'm sort of, I've sort of picked up here at Super Compute, which, um, uh, what's emerging now. So I said like cloud is, uh, sorry, cooling was sort of emerging. Obviously cooling and liquid cooling's been around for a long time, but it was sort of emerging as this major force a year ago.

Um, one of the big things emerging this year is, um, integration of distinct AI systems. Now, I mentioned that AI has been around at HPC for quite a while. Um, mostly for in, in, in predictive or recommendation engines for things like, uh, AI based job configuration and scheduling.

In other words, when you're running a high performance computing job to try and simulate, I don't know, um, hurricanes on Mars or whatever they're doing to help, you know, uh, cure the world's ills. Um, when you're doing that, the faster you can run your jobs, the faster you can. So you run a job in a high performance computing environment, and then maybe you need to run it again.

You need to tinker with something and run it again in order to tune it and then get the kinda research you want. The faster you can do that, the better. The less human intervention you can have, the better.

So that was a natural place for AI as a sort of a controlling system. But now what they wanna do is they want to use generative AI in various forms in order to, um, help boost simulation effectiveness, invent or create jobs or test schemes, all kinds of analytic related stuff that can work in conjunction with HPC. So that is real buzz at this show.

So on the main stage, just lots of sessions about how to integrate, build and integrate distinct ai. So there you're thinking about the neo clouds, again, that that's a possible source for that sort of thing. That leads you to another theme, which is smaller, but like, one flash session here from WW t was very well attended on workflow, workflow in HPC workflow out of AI into HPC and back, which leads me to the last bit of emerging, um, uh, uh, emerging trends.

Um, which is, uh, it has to do with quantum, quantum computing really looks like it's on the verge of becoming real in a number of respects. They have, um, 150 plus qubit systems, uh, going into production right around now. 150 is a pretty considerable number, but to, to really make it work, um, you need all kinds of other systems, which I won't go into here.

Mostly the most of 'em have to do with error correction that leads you to ultimately a 150 qubit system, giving you five, um, five effecting, you'd even call it sort of virtual, actual reliable qubits. Five qubits is actually a lot. Doesn't sound like a lot.

It's a lot the way quantum computing works. What you're doing is running sometimes multiple problems that would take months on even modern hyper computing, uh, uh, uh, um, high performance computing systems to run. You can run them in a matter of seconds.

Um, it's not, it, it becomes a part of an overall high performance computing system. And so the incorporation of quantum into HPC is a hot architectural topic here right now, because these systems are starting to really go into production. So, guy, are people talking about making these platforms more efficient?

And I'm asking the question 'cause it seems like the number of research projects that wanna be run on a supercomputer and eventually a quantum computer far exceed the amount of available capacity and, you know, for lack of a technical term, but the conversation seems to always be about, Hey, stop Bogart in that supercomputer and let me get my project on there. Yes. Um, that is a general theme that as, like you said, it's been a relatively perennial theme at this show.

'cause there always seems to be more, uh, there's always seem to be war work, uh, demanded of these systems that are available. Um, but, um, one of the, so the, the, um, at the keynote, um, uh, there was one of these sort of vision talks was the main part of the keynote. Um, and, um, the speaker, um, it pointed out the way that the ways that AI can ultimately help with just what you're talking about.

Um, the analogy made was to, so, bear with me just a second, was to electric vehicles. That the initial, the current model for, uh, like personal transport with vehicles is you have your car, you own your car, and rideshare services are a way to make that a little bit more efficient. But you still wanna own your car.

But when it moves ultimately to just being a service and you can use any car, then you need fewer cars. Even as the amount of energy use goes up, the actual number of units needed goes down. Um, and so by, by analogy, Mike, the idea is that, uh, these systems can be built and connected together and used a lot more efficiently because they could be shared in a lot more efficiently.

I thanks to ai, there's, there's I a lot of work to do there. Um, it, one of the sources of this workflow discussion though, has to do with just what you're talking about, Mike, is it's not so much about idle time as the fact that instead of it just being one of the top 500 supercomputers that you can use, maybe there are ways that you can use one of the 5,000 or so supercomputers available right now around the world to do your work. Mm-hmm.

I'm not sure I can get that metaphor, because when I drive, I notice that there's more Uber and Lyft cars out there than ever. And I've concluded that basically people who previously would not have gone anywhere are now getting in cars, going somewhere. And so now there's more cars on the road than less.

That's the initial stage of it. It's when it's when people start giving up, um, uh, their personal cars. And so the analogy here is when institutions start giving up their personal systems, um, not that they wouldn't build the personal systems anymore, but it's almost the distinction we were talking about earlier between the service or not, sorry, the, the, um, the, let's call it the backend and the business end or something like that, of this overall industry and market.

And when you are building, uh, what, in this case, the instead of foundational AI models, what you're building are foundational supercomputer systems for your own use for things, but also you're allowing other people in to use them. Or in fact, you're allowing AI agents to schedule secure jobs in those. So like A GPU share.

Yeah. It's a, it's an Uber for super computing there. That is, I just coin a new wonderful phrase for someone Uber Last question and be In the next keynote you go to.

Right? And, and you were in CubeCon last week, but we didn't get a chance to talk. But one of the things we were talking about and as relates to AI at CubeCon, and I'm curious if it plays out at sc, is that, you know, there's folks who are saying you should run those AI workloads over on Kubernetes clusters because, well, the IT folks know how to manage those.

And yet, when I go talk to the AI community, they have a thing called S slm and that's their favorite job schedule. And they're like, you know what? We don't need no stinking Kubernetes 'cause it's too hard and we don't understand it.

S SLM is here in effect. S SLM is dominant, you're right, it's dominant for workflow. Um, it's dominant for job scheduling.

It's dominant for all of this kind of, uh, sort of application, uh, orchestration in HPC and in ai. Um, but, you know, mostly ai. Um, there's only one neo cloud I know of.

Um, and I've mentioned the neo clouds because, you know, they are at the forefront of, of these kind of build outs. Right now there's only one neo cloud I know of that uses Kubernetes and that's nebulus. Um, so it's not, um, it's not, uh, particularly common.

Um, there isn't really a lot of, um, support behind that sort of thing, but you have to remember, like they're using storage systems that are really, you know, um, probably showing their age, like luster, um, that's really still dominant. Um, there's lots of storage, you know, related stuff around here, um, that is, uh, more advanced. Um, so I think that moment just has not come Mike.

Um, and I don't know if I'm, I'm kind of, you know, I'm a little skeptical about Kubernetes as the ring to rule them all, but maybe that's just, you know, overall hostility to anybody saying anything is the rink to rule them all. Um, Kubernetes is definitely coming for HPC and ai, though there's no question. It's just whether or not it's gonna have, uh, the, the world, um, you know, the world dominant effect it seems to have everywhere else.

Alright, well, folks, you heard it here. The one thing for sure about that show that guy goes through every year, it's kind of where the cutting edge of research and AI and physics and science all comes together. So if you do get a chance to go, you should absolutely take advantage of it.

Hey folks, thanks for sharing your thoughts and insights as usual, and thank you all for watching the latest episode of The Gang. Please stay tuned for the rest of the lineup of Techstrong tv 'cause it too will be equally awesome. And we'll see you again tomorrow.

All right. Hey, we're live back here in Cube Card. It's, uh, Tuesday night, which is cube curl night on the floor.

So when I look out there, I see popcorn machines getting ready to set up. I think I saw some hummus, but they'll usually have a lot of stuff here. Um, I think that starts at five 30 or something though.

I don't know if I'm gonna make it to five 30 here, Patrick. I might make it to the, to the first part, but, uh, yeah. Yeah, it's been a, it's been a day.

I get you. But let me introduce you. This next gentleman here.

Well, he's gonna tell you the company he's with now, but I've, over the last 20 years, 20 would be 2005. Yeah. Maybe 20 years.

Yeah, probably, maybe more, maybe more. Um, my friend Patrick McBride has held a variety of roles and had a very interesting career. And I think we'll start right there.

Patrick. Welcome to Text Drunk tv. Yeah, I, I gave you the buildup.

Tell, tell 'em share your story. Yeah, thanks. Having me in.

Um, yeah, I, a long, long time ago in a, in a, in a place far away. Yeah. Was too far Away.

Yeah. I, uh, I actually started off as a software engineer, so I wrote code outta school. I was a finance and, and, uh, CS major, so I could go one of two ways onto Wall Street.

I could either write code for financial services company or, or go trading. And I, I, uh, I took the former And You ever regret that? No, not at all.

Well, not at all. Uh, I, I've had a, had a great, you know, great time in in tech as you know, since we've known each other for so long. Yeah.

But yeah, I, I graduate, I, I worked at, you know, one of the large insurance companies and wrote a lot of code. Um, I would say I was a, a, a mediocre coder. I was prolific, but, you know, but I was really good at figuring out what to build.

Uh, so that kinda led me down more of a product path, helping, you know, companies do that, which got me, you know, into, uh, one of the early research firms. I worked with our, our buddy, uh, Mike Rothman, back at a company called Meta Group. Yep.

And, um, you know, so yeah. Came in there as an analyst and, uh, had had a lot of fun. But then I, you know, with my product roots, I, I ended up advising a bunch of companies and ended up taking, uh, a marketing job, a CMO job, which I had never done before at an early cybersecurity company, and learned the ropes from, uh, Mo Rosen and, and some other folks, Other people I know.

Yeah. Good People. Yeah, Mo Mo Mo's taught a lot of us.

And so, yeah, it's, um, yeah, had a good time doing that. So now I'm, I guess, a seven time startup offender. I just love early stage companies.

I love the, the excitement. I like when, you know, people are just wanna run through walls. It gets once in your blood.

It's, it's hard to get out of It is I couldn't go back to, uh, uh, to an easy job. And, you know, I do like getting dunked in the kind of the deep end of the pool every once in a while and having to learn stuff, swim, Sink. Exactly.

That. That's kind of where I'm at now with, uh, with Antithesis, uh, where I'm at Now into that. Yeah.

So you mentioned two names. I just wanna shout out, of course. I don't know if you talked to Mike recently.

I did the candy man. He's the candy man. Yeah.

Mike's doing candy and Mo uh, Mo sold his last company, but he's already CEO of another company. Mo Yeah, he's, he's a multi-time offender Too. Cereal.

Yeah, he's a serial. Yeah, no doubt about it. It's, uh, he owes me a glass of wine over at our famous restaurant, our, our favorite restaurant in rest and town center.

So Absolutely he, but, but both stellar people, so shout out, shout to both of them. So, antithesis, look, I know you a long time, you were very big in cyber. We didn't call it cyber, of course then, I know It was security infosecurity network security.

Yeah. But you were big in security. Tell us the antithesis the antithesis story.

Yeah, so the, um, you know, the quick thing that they do is they test stateful distributed systems. Um, so really complex distributed systems that are particularly hard to test. And kind of the backstory is really interesting.

Um, the founding team at Antithesis, antithesis, uh, did a company that, that you may not know, but you use it every day. You got an iPhone in your pocket, right? Sure.

Um, it was a company called Foundation db, and, uh, it was A, actually I know them. Yeah. So the, the foundation guys, you know, built this really cool, um, distributed database that had some really nice acid properties, um, that people couldn't, you know, said, Hey, you really can't do that.

We can't put all that stuff together. They're, they said, hold my beer and went off, off and did it. Well, in building that, you also have to figure out how to test it.

You know, they, they had property and guarantees that they wanted to make, so they spent a whole lot of time as they were building the product. They were building the whole testing methodology, and they were trying some new things, you know, the traditional, you know, write your unit test, write your integration test, and, you know, and see what it, you know, brings up was just not gonna work in an environment like that. So they all, they all ended up at, you know, apple and, and then migrated from there to different places.

And I think the, you know, around the Bay, and one of the things that they all realized was testing was pretty crappy, you know, even for some really notable companies with big systems. And they said, Hey, let's, let's go and, you know, take what we started at, at, uh, at Foundation and take, carry it forward. You know, testing has always been, I I, I had this conversation once with two guys.

One was sort of a, a classically trained, you know, went to school testing guy and one was a maverick dude Yeah. Who was self-taught. But the, you know, where they met was for too many organizations.

And the, actually in the market in general, general, everyone had this vision that testers were for like junior programmers or failed programmers, people who weren't good enough, right. Less than, Right. Yeah.

And that of course, therefore testing wasn't gonna be great because the people we had working on it weren't, weren't the, weren't For professional. They were a little bit of the redheaded sub trial they were of Viking Was always Yeah. They're, you know, but the fact of the matter is most testing is done by a cadre of professional testers.

Yeah. And what we run into all the time these days is the, the actual app development teams that are building these distributed applications are highly involved. You know, even if they weren't using something like antithesis, you know, they, they, they have to figure it out, you know?

Yep. They're, they're highly involved in the testing because when the pager goes off at 2:00 AM It's, they're the one who has there, They're highly involved in the answer. And, you know, depending on what kind of an error is, if it's a, an outage, it may be a bad, you know, couple of hours, a couple of days in the office, if it's a, a correctness issue, the database is, you know, saving the wrong type of, of data in the database or incorrect data in the database.

That's a, that's a rough month or a couple of months in the office. And so yeah. They're, they're, they're far more engaged than, than they were in my history back when I was writing code.

Absolutely. There were separate groups and they're much more integrated. I mean, look, there are developers who say, look, I'm, I just want to code.

I don't want to test. But also the, the, in the inter intervening time, we've seen this whole automated testing where even today, the professional testers don't necessarily test, they write the coverage, right? Right.

They decide what to test, how to test, when to test, and then a lot of the testing today is automated. It is. Yeah.

We, you know, yeah. We, and, and we, we really carried that to the nth degree. We kind of flipped testing a little bit on its head.

So the, with traditional testing, you have a couple of issues. First of all, typically the guys that are writing the test are the engineers, right. That are writing the original test.

A, they don't like it. And b, if they're writing a test, they're trying to figure out, you know, what, what they're testing for. So they've typically thought a little bit about what could go wrong.

So they're writing try test coverage to try to figure out those things. Well, if they're already thinking about, they were probably pretty conscious when they were coding uhhuh to try to not make those things go wrong too. So you end up kind of in a testing for the things that, that you actually built tended to build Well, for, it's the stuff that you, you know, forget to test.

That is the stuff that, you know, makes the pager go off at 3:00 AM. So one of the things that we've adopted is, is something that we call, it's, it's called, you know, formally property based testing. But what you end up doing is, instead of writing individual tests, you write a set of, uh, instructions about what the system should do or shouldn't do.

You know, it, what, what state things. If, you know, it should never give the user a 4 0 4 error. Uh, it, you know, if we're doing a debit, you know, of some amount, it'll always do a credit of some amount, you know, up in an application.

There. I, then you can get weighed down in it. And then what we did was take, you know, take a lot of the burden off of the engineers.

You still have to think, I mean, you still have to work through the properties. I get it. And, and, and, you know, it's a mind shift for folks.

And then we take, uh, that it basically put what is effectively their production environment. We take that, their containers and run it on a, a, a hypervisor that we built, um, that's, we call it, it's a perfectly determinative deterministic hypervisor, which I'll come back to the imports of. But what we end up doing is we put load under that and we'll run it massively parallel, you know, on a pull request or overnight even.

And since we're running it in such a, a massively parallel thing, we can test months worth of production wall clock time in a couple of hours over overnight. But in that time, we're not just running it in happy path. We're throwing all kinds of faults at it.

A network fault, a race condition, a service drops a disc fails. We will throw all those kinds of things at the system so that we can get all those edge case types of issues and, you know, types of faults that you don't really typically see in your happy path coding. You know, that's, that's a problem.

You, the, the, the other problem is you're, you know, the engineers write tests for the things that they think about, and they get tested in a, in an environment that's much more pristine, not at all like production. So, right. Those two things together make it, you know, hard.

So it's a one part property based testing, one part chaos testing, and one part massively parallel inspection, you know, of, of the, of the code base. And, and so the, you know, the hypervisor pieces, you know, it was, took them a long time to build. Um, the more interesting piece of technology was the piece of technology that decides, you know, what pieces of, of, you know, where it should branch.

And, you know, how do we get through the whole code base and where should we inject faults? That's, that's really where a lot of the math and, and science came in. And, but the hypervisor being perfectly deterministic is important too.

'cause finding, finding a bug is only half the problem. Okay. Got it.

Then you gotta figure out what the hell happened and, and fix It. And how Well, how do you want to fix it too? 'cause Yeah.

You know, there might be multiple paths to affix. Not always just a patch. This determinism element is, is important.

So we, you know, we, we, we actually have a, the engineering team named this. I loved it. It was, um, uh, you know, we've, in our hypervisor, you've got a debugging tool that's, we call the multiverse debugger.

You can actually multiverse. Yeah. Multiverse, debug Goes through, you can literally Wine Talk quantum, right?

It's quantum, yeah. It's, it's totally quantum. See, so you rewind time to, you know, a minute or a couple minutes, you know, before testing and, and we can show you graphs of, you know, you're expecting a particular fault.

We can show you literally graphs that when the, it was a zero probability that would happen, then it stepped up, oh wait, something interesting happened there. Usually it'll, you know, be another step up. And then it was, you know, a hundred percent going to happen.

We know, you know, you know, since it's perfect. Got it. So we rewind and then you can inspect exactly the state of the whole system, all the logs Right.

Where right where it was, and really figure out what the hell happened. It's Like a time machine there. That's fun.

Yeah. It's, it's a debugged time machine. Exactly.

And, and, you know, with perfectly preserved and state, so you can repro, uh, a bug. Exactly. And then you can literally, you can go into a, a, a terminal session and, and throw stuff at it and see if that changed it, then rewind it, try it again, try different things.

So it's love, you know, debugging that I've never, you know, when I, when I saw it and saw what they were doing, I just had to join the company. We Haven't done that. So, you know, speaking of which, Patrick, we, we didn't, what's your role at antithesis?

See, you know, this is where I get in trouble. Since I'm running marketing now, my, my credibility went down like 30% or, or whatever. He's Not a marketing guy.

I'm, yeah. I'm a, I'm an, I'm an old, old engineer, you know, a strain, strain or stress on the old. But, uh, running On the business side of things.

Yeah. I'm, I'm, I'm working with our, our GoTo market team and, and, you know, helping make sure that we get to things, nothing To be ashamed of. I do it too.

The More important part of it, the cool part of it and, and contextually for the conference is I get to work with some of the, the cool projects here. Right. Um, one of the things that was a lot of fun that, that we had earlier was, um, uh, one of our, one of the maintainers for the ET CD project, which is a critical component of Kubernetes.

Uh, we helped, we, we reached out to those guys and worked with them to put that under test. Oh, that's cool. And, um, and they did, so Merrick, their, the, the maintainer who was, was working on that, um, you know, joined us on stage in the beginning of the morning and then did a, a talk, uh, to, you know, really take, take them through how you use the deterministic simulation environment, you know, to death cd very, um, and, and we had, you know, the other thing I, I got a chance to work out, uh, was, you know, working out an actual partnership with the CNCF team, so that we're offering now, uh, to put all the, all the folks that are all the projects that are incubated, uh, or further along and graduated, uh, under Tess now, we're not gonna take 'em all on at once.

We're taking, you know, we're out here searching for our first two, and we've had a lot of people come by the booth and ask us about it. So we're, you know, we'll just probably do it two at a time for the, the next bunch of quarters. But those projects, you know, we, we, we feel we use a lot of open source.

So it really matters to us. It matters to a lot of our end customers. And so being able to help the projects, And it's also being just a good community member too.

Yeah. We, we wanna be a good citizen in the community. Absolutely.

And It's, it's in our best interest in lots of ways, and it's in lots of companies best interest. So I, I think that's why you see all these people here that you do, man. Yeah.

Patrick, you guys did announce some news here though. Yeah, that was the big one. Oh, that was the, that was the big one.

We were just putting all the, putting all the projects under test. Um, you know, we've got, we've got some other, you know, other fun stuff up, up our sleeve over the next couple of quarters that, uh, I, I don't wanna get you in trouble. They'll allow me to put you back to coding.

No, they're, they're, yeah. They, they, they, no, nobody lets me touch the code base anymore. I promise they, I'll let you do that anyway, man.

Did we mention the website? Ah, www dot antithesis. It's antithesis.

ai as well. We, We, we own that domain as well. So Absolutely.

Either way, you're, you're, you're coming to the right spot, Patrick, thanks for coming on, man. Alan, great to see you. I appreciate it.

com. You know, I, I think that I'm going to give me credit on this one a time machine, right? Yeah.

For, for your testing and your, and your states. It's, it's an interesting way of thinking about a very complex kind of, of, uh, process. Yeah.

Very cool. We're gonna take a break. Hey, everyone.

Welcome back to day two coverage of, uh, cube Con here in, well, not quite as frigid as yesterday, Atlanta, but it, it, look for this Florida boy. It's still pretty cold here. Um, let me introduce you.

Well, I've got a double here. If you couldn't, well, you could probably see that looking in. We've got three people here.

Let me introduce you to my two guests. First of all, the man in the middle from Broadcom is Rit Bja. I hope I got it right.

You got it. Reet, I just found out, grew up in the same town as me in Queens, New York. And, uh, there aren't a lot of us in the tech industry from Rosedale.

That's my friend. Yeah. But, uh, he's a Malloy boy though, so you, yeah.

You know, most of my friends went to Cross. But anyway, um, joining Frit and I, on the far end, if you don't know him already, is our friend Alistair Cook. Alistair is a host extraordinaire and roving correspondent with Tech Field Day, and we had a tech field day here yesterday.

Yep. And Dre was in the room, and Dre Dre was in the room. So we would continue the conversation and, and, and just, we're winging it.

This is not scripted or anything like that. So I'll, we'll see how it works. I, I think it'll work out pretty well.

Gentlemen, welcome. First of all, thank you. Thank you for having me.

So, we're gonna get to the point where I'm gonna ask you how did the tech field day go yesterday for you? But before we do that, you know, I interviewed my friend Hemanchu from, from, uh, VMware, but Broadcom, VMware yesterday. And, and he said something that stuck with me, which is, when you look at the code contribution to Kubernetes, forget who writes checks, forget who loud, who's loud.

Forget who does the, the keynotes. When you're look at people who are contributing code, VMware is the number three contributor to code here at CNCF. Yeah.

I mean, the, the interesting part is like we, when we really got going with Kubernetes, uh, cluster API wasn't a thing. It just started out. And, uh, you know, we latched onto that technology and we said we need a, a good cluster management solution, and we pushed cluster API to where it needed to be.

You know, when we did Project Pacific, we had to do certain things downstream, uh, for time purposes. And then, but we spent a lot of time pushing it back upstream so that the, the community could benefit from it. That not only that, you know, like projects like yes, et CD we've done significant things.

And we recently just, uh, uh, upstreamed a bunch of tools for ET CD to be able to support them better in the field. We, we, we have a lot of learnings with our customers and some of the issues they run into. And so, uh, these debugging tools were created internally.

We were like, Hey, let's get them out into the community. It's the right thing. Yeah.

Like, and number of different projects like, uh, Harbor Entre, you know, so on and so forth. Like, there's a, a ton of contribution from our control. So, and, and look, you don't get to number three on a, on a one shot deal.

No. You know what I mean? This has been a consistent, consistent pattern of being a good community member.

Yes. Over a longer period of time. And, and I, I recall, uh, probably 10 years ago when VMware first, uh, first put out a job posting for the head of open source at VMware.

Yep. And there was a, it was greeted with a, a massive amount of cynicism at the time. Yep, yep.

But the proof is, and the actual contributions, it's the projects that have come out. It's, I remember when Harbo was brand new and being surprised that it was primarily developed by VMware, yet here it was open source. It's freely available to anybody to use.

And, and the interesting thing is, it's like, kind of like the best kept secret. It, it, we, we've been contributing like to Postgres for a number of years. Yes.

It, it's, it's, it's just not something that we do a very good job of, of talking about and taking the necessary, some would say it's not something you should do a good, good job at. Yeah. You do it because it's the right thing to do, not to blow your horn.

Yeah. Plus it, it makes a ton of business sense. Absolutely.

It, it, we, we are not doing this just for the sake of doing open source. It is because these particular investments drive a customer outcome, and we really, really care about that customer outcome. I, I agree with you a hundred percent.

And, and, and I think, so the fact that you haven't blown your horn real loud about it speaks louder than had you blown your horn. Right. Because you're doing it for the right reasons.

Yes. If you don't mind, I, I know you presented yesterday on, on VCF. Right?

And again, after speaking to Haman yesterday, I have a much better picture of it. I don't know if our audience does, but, you know, under Broadcom's leadership, I, I think rightfully so, the decision was made that VMware needs to be focused. Yes.

We need to be focused. Yes. We can't, we gotta put our wood behind an arrow.

Yes. Not 15 arrows. Yes.

And so the future of VMware clearly rests with VCF. That is, that's VMware. Yes.

Absolutely. Like again, we had talked about, even while we were VMware without, uh, prior to the acquisition, we talked about building a private cloud experience for our customers. And, uh, while we attempted to do a number of things, all of our business units were focused on individual achievements rather than that aggregate achievement.

Sure. And as Broadcom, uh, acquired us, and thanks to the leadership of a Hawkin and Chris, we were able to then coalesce the set of things that were required to be able to deliver against that vision. And now with, with VV VCF nine, we have, we've delivered on the set of like, uh, capabilities required to be able to manage the underlying infrastructure in a streamlined way.

But not only that, like we did the all this work to be able to deliver the, and this is what my team directly focuses on, is, is the cloud consumption experience. Mm-hmm. Which is something that, you know, VMware had done poorly in the past, but we took, uh, two of our, uh, product offerings, VCD and VRA, and we brought them together along with some of the foundational Kubernetes work that we had done to be able to deliver a end-to-end Kubernetes consumption experience.

Meaning like the, the actual foundations of our IAS is based on Kubernetes and Kubernetes APIs. So when you're, uh, provisioning a VM or provisioning a Kubernetes cluster, or provisioning any of the various objects that we support, VPCs, uh, you know, so on, so forth, you are, you are dealing with a, a, a desired state, Kubernetes API. Right.

And we, we leverage all the CRD mechanisms that Kubernetes offers to be able to deliver it. Now, the, you know, the power of this is now realize when you look at, like, we, we did some, an implementation of Argo cd. Mm-hmm.

Now we could take Argo and put it into each of our VKS clusters, but we, we went a little bit further and we said, Hey, let's put it into our foundation supervisor. Now you can use a GI op style way of provisioning all of the objects we support in our eyes. Love it.

So you can use that for VMs, Kubernetes clusters, pods, any number of different objects that we support in our core, you can just provision them using this GI op style. One of the challenges for the existing vSphere administrators Yes. Is that this is all very foreign and new.

Uh, one of the things I really liked in the UI in VCF nine is that you can do click ops to de to design this thing the way you always used to, and then pull out the manifest that you're gonna use in the future and transition to that methodology. Absolutely. Like what we, what we want all users to transition to this cloud consumption experience, including the traditional VI admins that have been doing ticketing based mechanisms.

And so they, they, they can't n navigate navigate Kubernetes, uh, API really well, but as they work through the UI and pick various options, the, the YAML is being created for them so they can pull it out and then use it on APIs or check it into a GI style repo, uh, to be able to do all of their provisioning if they wanted to. So it, it's pretty, pretty killer if you look at the UI and the experience. I, I'm really proud of it.

Uh, and we're doubling down on it as we go along. Absolutely. Well, I think we hit on the subject of what is the involvement or what, what's the connection between Kubernetes and VCF?

Right? Yes. It's, it's deep and it's, and it's rich.

I wanna bring up something, again, something I spoke about with Homan yesterday was, you know, I used to think of, let's say like Tan Zu had the cloud native piece of this, but again, under HO'S leadership and the new team or the new vision, we've actually taken some of that Kubernetes cloud native functionality and made it native, if, if that's not a pun, uh, native to the VCF stack, if you will. Yes. Yes.

So you, you know, our view, uh, and this is, this is why we've done this, is that there is no cloud experience without a Kubernetes service. No. Right.

And, and fundamentally, if that's the case, then you need to have the end-to-end Kubernetes service tied with your, your private cloud. Absolutely. And so, as part of that, as, as we walked Hawk through these, uh, this, uh, decision process, he was like, yeah, absolutely.

You're right. Uh, you're right. And not only that, he believes in Kubernetes Deeply.

And so he, he moved all of the Kubernetes assets, uh, into VCF so that we could formulate it as part of our private cloud offering. I love that includes not only the, the, the, the service and the Kubernetes service itself, but all of the management capabilities of Kubernetes, the multi cluster management capabilities of Kubernetes as well. That was, uh, TMC now is now built deeply into VCFA.

Absolutely. Absolutely. I wonder, if you don't mind, I'm going to ask you both.

Not everyone watching this live had a chance to watch Cloud Field Day or to Field Day y Tech Field Day yesterday. It is available, of course, on the YouTube channel for, uh, cloud, uh, tech Field Day. But let, let's talk about, you know, how some of the things we've discussed here came out at at Field day, and if there's anything else that we missed, Alistair.

Yeah, I think for me, one of the standout moments from yesterday's Tech Field Day was one of my delegates, John Willis. So when Tech Field Day, we get presenters from the, the, uh, sponsoring company, in this case VMware by Broadcom. And we get subject matter experts who are independent to be in the room together and ask them the difficult questions.

And I was just amazed that John Willis, my, my most deeply DevOps cloud native guy said, you, you guys provide the most reliable platform to underpin everything else. And, and lots of the other companies here at the show have pixie dust and promises. Uh, and I think that was probably one of the most telling things around this.

This is the, the way to talk about what VMware has brought to Kubernetes. And now Kubernetes is sitting on top of that foundation. Uh, I think I, I think thought Joe, I mean, let's say when, when it comes to the world of virtualization, VMware is the mountain that a lot of competitors have died on or frozen out on, or whatever.

Yep. Yeah. Uh, stability was always, uh, stability, performance, uh, being able to utilize the hardware, uh, to its fullest extent, right.

So that you don't have, again, VMware's, uh, um, you know, mainstay value proposition is still true. Meaning if you, uh, your bare metal capacity that was historically we converted was used maximum, like 30% back in the days. Right.

Now it's being used at like way, way higher, Hopefully. No, no higher than 60% due to queuing theory. Right.

But the, the other element was the absolutely ironclad separation between workloads. Yes. That, that, that level of isolation, um, that really enables multi-tenancy on top of that limited set of hardware to a far greater extent than, than lighter types of isolation.

Yeah. No, and, and, and not only that isolation, but there's a clear boundary between the IT administrator and the, the actual usage of the infrastructure, meaning that the IT admin is free to do the operations they need to do on the underlying infrastructure. Mm-hmm.

Without disturbing the applications that the end users, it is fundamentally the o you know, we're the only private cloud that can, or any cloud actually that can do it quite that way. Right? Yeah.

Given our, given our investments in vMotion in maintenance mode, so on so forth, DRS, that, that allows us that flexibility. But, you know, one of the, the, the interesting things is like, we've done a number of different, like, evaluations of our Kubernetes performance. How well do we compare against Kubernetes virtualized?

How well do we perform, uh, like compare against Kubernetes, that's bare metal. And the, the underpinnings of the platform is what shines when we do that. We are not doing anything special in our Kubernetes to be able to get, make, make the, uh, hardware work better.

Uh, but like the, the scheduler inside of ESX that can actually give us the density we're looking for shines when we look at these performance evaluations, the, the performance of vsan, oh, it, it, it, it, it, when we did some evaluations, it shines through when, uh, Kubernetes is running on top of it. And so that's what, uh, really drives some of the value prop is like, not only the reliability and so on, but like you're getting, uh, the full utilization of your hardware and you're getting the performance that you rely, rely on. So, um, I, we were, uh, frankly, in some of these cases, we were kinda shocked, like, really, like we we're, we're that much better, uh, uh, you know, on storage performance against bare metal.

Right. And so, like, you know, it's been, it's been cool. Absolutely.

Now you pair that with all the operational values of virtualization. You know, we like to say this, uh, to, to folks is like, um, 90% of Kubernetes is running on virtualization. Virtualization, right.

Uh, all of the clouds run it on top of virtualized, uh, uh, uh, infrastructure. We run it on, on top of virtualized infrastructures. Some of our competitors all run it on top of virtualized in infrastructure.

There's a small percentage of folks that are doing bare metal. That's because the operational winds of, of Kubernetes on top of virtualization is so, so much better than doing it, uh, on bare metal compute. You know, I, I had this conversation with someone yesterday, not, not, not from VMware.

Look, there's always been, I don't wanna call 'em an outlier, but there's been a small minority of people who ran, uh, Cobe on bare metal. Yep. I remember there was a startup that sold like a server that you could run in your database.

It was really just a regular server, but I think they were charging about 60 grand for it, but it was optimized for Kubernetes on bare metal. They're not here anymore. And I think that's probably speaks volumes.

But, um, the, the, it's, it's always been that way. And, you know, you could run Cobe anywhere on the edge and, and, and everything else. But I remember when this first came out, right, there was the big debate over server density and, and data density.

And, you know, performance of, hey, virtualization gives you this kind of density, containerization gives you that much more density, but containerization on top of virtualization gives you that much more density. And it was a lot more dense. I think that's still true today.

That hasn't fun. It's like physics. It hasn't fundamentally changed, at least until Quantum or something.

Mm-hmm. Says it could be true and false. But, um, I got another area, 'cause I know we're stored on time real quick.

I mentioned before how the, the, the makeup of the community of the people here has changed over the years. Yes. It was, it used to be very developer heavy.

Yes. It's, it's focused now on observability and on operations and on platform engineering and, and all of these, you know, internal development platforms and all these other things besides just Pure Cube. In the meantime, VMware's very different.

Broadcom, you know, has kind of changed the Yes. Parameters. How are these two things kind of playing off each other?

What do you notice different? You know, uh, one, uh, frankly, Kubernetes has grown up, right? Uh, no doubt it's matured.

Uh, it's matured. And you can see that from the contributions overall is like the, the amount of big changes inside of Kubernetes has, has, has not, it's not a bad thing. It's slowed down a bit.

But, uh, the other, the, the other thing is that, uh, here you see a lot more focus on how do I get the best value from my Kubernetes or, uh, and the operational wins that I'm looking for. So what we're doing here is we really, really want one to make sure that people understand the value that VMware brings to the table. But I, we want them to touch it, feel it, understand it from the demos that we're doing at our booth, and in other conversations to be able to understand how much different, uh, our offering is compared to other offerings.

Right. And, um, you know, so I, I think that that level of conversation also the feedback, Hey, have you thought about this? Oh, we are using this particular package.

How does that work here? Um, we, we use this ecosystem. One of our customers, uh, who is moving wholesale to our Kubernetes, um, from another cloud Kubernetes is, is like, well, I don't want my developers to know what they're running on.

We, they, they, they use Harness GI and uh, Artifactory we're like, perfect. You just drop and replace us into, into that ecosystem. And that's, and the developers have to don't have to know.

Right? Yeah. No, wait.

Well, when you have a standardized compute stack, which is what this really is now. Yep. Right.

It you Exactly. Just, you know, it runs on top of it. That's all you need to know.

Yes. Um, Alistair, we're about outta time, but I wanna make sure, did we miss any big highlights from the field day presentation that, You know, it was a 90 minute present? Well, it was a four hour presentation put into 90 minutes.

Uh, so doing it just as here probably is really hard. Uh, we hit the, the, some of the core things that I think were important, but there's a lot more in that presentation. And It's on the YouTube channel.

It, it'll actually on tv, YouTube actually on TST V too. Yeah. I replayed on tv.

Yeah. And you'll find it on the text a, uh, tick field day, LinkedIn at the moment, and then by about next week it'll be up on YouTube. Okay.

Awesome. We'll try to get it up faster. Very nice meeting you, my fellow Rian.

Yes, yes. Whatever the word is, we gotta catch up on. We'll catch up.

You know, if you ever get a chance to speak with Mike Ard, our chief Content Officer, you gotta give him a hard time. Okay. He went to Cardinal Spelman in the Bronx.

Oh, whoa. Okay. Not, not, not anywhere near Malloy.

But anyway. Hey man. A pleasure having you on Alistair.

Thank you. We're live at, uh, we're live here at Q Con. We'll be back with more in just a second.

Thank you. Hey everybody. Welcome back to Ingram Micro One.

We're here with my new friend Eric, talking about the relationship between Proofpoint and Ingram Micro and how that's kind of changing the way we transact business in the cybersecurity space. Buddy, welcome to Show. Thank you.

Appreciate it. Well, one of the things that we do know about cybersecurity is it changes all the time, right? I mean, just when we think we've got one more whack the next one comes up.

How does the relationship between Ingram Micro and Proofpoint kinda help the providers and and their customers at the end of that day kind of stay current Yeah. And protect them from the threats we face? Yeah.

Well, first I'll say that we celebrated our 10 year anniversary with our Proofpoint partnership this year. Um, and it's been a very exciting partnership. I've been involved with it since we signed the contract 10 years ago.

And we've certainly seen the strength of this partnership grow and grow. And so it's been a really exciting relationship for us. But, you know, early last year, mid last year, we sat down together and just started having conversations about what can we do to free up the channel to respond faster to the threats that their clients are seeing?

And this, you know, where we are today is a result of just asking those simple questions. It seems like what you guys are really doing is taking as much friction out of that process as we possibly can. And some of that may be using AI and other technologies.

But what is the mission and what are some of the things you're targeting to kind of make it easier to do business? Yeah. Well, you know, one of the things was for the commercial market segment, again, we wanted to empower the channel to act faster, right?

So today, a channel partner as a net new opportunity, they can go to the X vantage platform, you know, new products, new solution bundles. They can select, you know, the product that suits the needs to address the risk that that client is facing. And instead of waiting three days or as long as it takes to fly to the moon, right?

They can submit this information in there and have it approved, quote back in minutes. Right? And that's a game changer, you know, for the partner community.

And that's critical. 'cause the end customer doesn't have a lot of tolerance for that process, right? Right.

'cause they're sitting there saying, I can't really say to the end customer, lemme get back to you in three days with that. And yeah, you hopefully nothing bad happens. Exactly right.

Yeah. It, it is all about, you know, speed to execution and, and helping them to respond to those threats faster than ever. Um, what went into the, uh, measurement side of this equation?

So how are you tracking success? How are you looking at working with the partners? What's on the back end of this thing?

Well, you know, I would say that there, there's been a lot of new, right? It's a new process. It's a new way to go to market with their channel organization.

It's a new way to go to market with their commercial sellers. And this whole journey, this whole 10 year journey we've been on, our role has been to be an extension of their channel. And so for many, many years we would talk about, I can do everything that a Proofpoint channel account manager can do, but I can't to prove special pricing.

Well, now we can. And so there's been, like I said, new solution bundles like prime threat protection and others. And, um, you know, for us to be able to bring that to market faster than ever, you know, for, for a solution provider to sit down with a healthcare system and say, Hey, I can turn a quote around for you while we're sitting here having a conversation.

You know, those are the types of things and pipeline continues to build for these commercial ready, ready to go SKUs Outta curiosity, I noticed that you guys have invested in AI and around the sales agent and you're taking historical data and market opportunities, correlating that and giving some advice to the partners. I think the partners are also building their own AI agents, or will eventually into the purchasing cycle, right? How do you think this whole ecosystem will evolve in time?

Well, that's what Sanjeev is here for, right? We got to see it firsthand and uh, you know, I think we're just in the infancy, right? When I think about, I've been at Ingram a very long time.

I've been in the cyber practice for roughly 25 years. And I think where cyber was 20 so years ago was where we are with the maturity of AI right now. Meaning, you know, you've got thousands and thousands and thousands of partners that are capable of selling cybersecurity.

And our role there is to help them to go to market faster, to be more profitable, how they show up differently as it relates to kind of all these things. Ai, we're in the very, very early stages of partners really formulating their AI strategy. And certainly as you think about cybersecurity, I mean, the game is changing right in front of our eyes with AI too, right?

Yeah, absolutely. Where it's not just the good guys using it, the bad guys are using it just as much. Which brings us to our next question.

Are you starting to hear from the partners saying, Hey, we think there's gonna be a cybersecurity opportunity around ai, and what can you guys do to help us get them? Yeah, and again, it goes back to being the extension of that channel, right? And I know as an example, you know, pretty much every cyber vendor out there, Proofpoint included, is, is using AI right?

To, to reduce the burden of, you know, day-to-day security practitioners, um, and ultimately respond to these human-centric critical threats faster than ever. And so we'll continue to see that, right? So, yeah.

What, uh, metrics are you guys actually tracking? I mean, how do you measure success? How do you know what success is?

Yeah, well, I would just say, you know, look, we work very closely together and, uh, we work very closely with our commercial sales leader and, uh, Sherry Rhodes who was here yesterday on stage. And, uh, we, we hold each other accountable for some very specific metrics and, uh, and I think we are definitely going in the right direction. Of course, security requires a bunch of tools and solutions.

There's no one size fits all. So how do you kinda work that with Proofpoint when you're trying to build a solution where there's multiple right things involved? And how does that whole process get smoother?

Because we're taking out the friction. Yeah. Um, you know, Microsoft is a great example.

They've got a good, you know, integration and partnership with Microsoft, and we wanna make sure that we're bringing the Proofpoint message to the Microsoft community and vice versa. And I think you'll see us continue to build out these things within the X Vantage platform as well to make those recommendations. Hey, if you're picking up Office 365, are you thinking about Proofpoint threat protection to augment that as an example?

So I think there's probably more to come there. We of course, you know, have had the notion of customer relationship management forever and a day, but there's also vendor relationship management, right? Yeah.

Yeah. Is that becoming a discipline in its own right? And a, and a thing that you and the partners are kind of working through, but it seems like this too can be, uh, a set of best practices.

Absolutely. And, um, you know, there are other vendors that each have their own specific channel initiatives that they're trying to accomplish. And, you know, just thinking back, you know, the, the, how this went with Proofpoint was, it was a design thinking session.

What are you trying to accomplish? And how do we go build something unique together? And that's what we've done.

Now every vendor, you know, has a different strategy or slightly different, but I think this is just the starting point for, you know, how to capitalize on this platform to go drive meaningful results and, and ultimately to empower the biggest Salesforce there is, which is the channel. True that. So Proofpoint partners have a lot of choices when they wanna go get something.

So we were talking to them right now. What would you tell them about why Ingram to work with Proofpoint versus anywhere else? Yeah, well it's, you know, the platform is important, but it's also the people.

And I think we've done a very good job with our people, again, acting as an extension of that Proofpoint team better than anybody. And the, the relationships that we have with the solution providers and those partnerships are super important. And at the end of the day, you know, people buy from people they like, the platform makes it easier to spend more time having discussions about the right threat protection.

And so, you know, we continue to get feedback and learn, right? There's more enhancements coming, you know, as you start on a journey like this, you don't know all the answers. And we listen to our partners and they say, Hey, have you thought about this?

So we're launching deal registration as an example, that will be all there and instantly available on the X Advantage platform. I think from there we start thinking about, hey, what lifecycle services do we start doing? How do we help educate the channel on what new solutions should we be upselling as we go down this journey together?

So I think the sky's the limit. I think one of the other things I've seen you do is invest more in your own cybersecurity professional services team that has the partner's back. Yep.

Um, is that too gonna be become something that I can maybe call on on, on a moment's notice, just like the transaction? Absolutely. Yeah.

And we're already doing some of those services specific with Proofpoint and others, but you know, I've always said that, you know, there are thousands of cybersecurity partners in the United States, right? Our role is to help the partners buying from Ingram to be more profitable and proactive. And one way we can do that is by injecting services capability to make them look like they're the top security practitioner in the country.

And they can do that with Ingram Micro. So we used to have this line between what we call resellers and managed service providers, but if everybody's leaning on you a little bit for the services, eventually all the partners kind of evolve in the service providers. Yeah.

Said another way, right? I mean, services, you're gonna build it, you're gonna buy it or you're gonna partner, right? And so we want to be that partner that, you know, even if we're not the one delivering some of those services, although we've built it out for Proofpoint, they can tap another partner on the shoulder to deliver, you know, whatever services they need.

Or perhaps it's in a adjacent part of the cybersecurity landscape. Mm-hmm. Yeah.

One of the challenges that we hear is, and there's been a lot of talk here at the show about this notion of focusing more on business outcomes, but in security you're kind of trying to prove a negative that something didn't happen. So therefore how do I kind of measure that or Yeah. Or convince somebody the value of that.

So how are the partners kind of measuring the value of their services and how are you helping them figure that out? Well, I mean, there's only one right answer, right? You're gonna get breached or you're not.

But, um, but I think, you know, it has to be more of an outcome discussion and not about a point product, right? What are the critical risks that you need to solve for today? Are they using assessments to truly understand what those risks are?

And then you kind of map it back to what outcome a technology or a, a multi solution technology stack can go deliver. Somebody once told me being insecurity is kinda like being in the army. It's long moments of sheer boredom followed by a few seconds of sheer terror.

Yeah. So when there is a crisis and there is an incident, um, how do you kinda work with the partners to ring that alarm bell and kind of get all hands on deck and 'cause they're gonna call you? Well, um, I don't know about that because I don't know that hopefully they're not calling us because we've provided them with the right tools.

But, you know, uh, it's a fascinating industry. It's been one that's fun to watch, you know, it makes the headline news pretty much every day with another mass scale breach. You just hope that it's not your day, right.

So, but yeah. Yeah. Well To a certain degree we've probably all been breached.

It's just a question when we figure it out. Exactly right. Exactly right.

And to that point, um, it does feel like the nature of the game has changed a little bit. It's not just like, how many breaches did I stop? But it's on the assumption that we all know that we have been breached somewhere.

And then when it goes active, I think the measurement that people are looking at from the end customer side to the partners say, how long does it take you to respond to that and close gap? Exactly. And for that I need to have this whole ecosystem in place.

Right. Right, right. Exactly.

Right. And I mean even like if you think about the cybersecurity insurance industry, right? We're not an insurance broker, but you know, we have partners that can go and help a client to understand not only when you need cybersecurity insurance, but what kind of remediation can you have on retainer, right?

And ultimately, what solutions do you need to buy to reduce the amount of that premium that you're paying, right? So you have to follow the NIST framework as an example and make sure you've got a well architected security defense and then you know, when the catastrophe happens, how quickly can you respond? You mentioned the insurance industry and it seems like they are shifting more in custom words towards consuming managed security services as a result.

So have you seen the partners kinda shift the balance of their portfolio? I think they have to, right? And um, ultimately if you're having a comprehensive discussion with a client, cyber insurance has to be part of it, right?

And um, so you've gotta have the right partners to make that happen, but you also have to help them reduce their premiums. And so yeah, that's all part of the whole integrated ecosystem. Is there something that you see the partners doing today that you would advise them to maybe tweak or change to be more efficient?

Is there something on their side that you look at and you go, Hey guys, you know, if this one little thing would change the world would be a little bit better place? Well, I don't know if I'm answering this directly, but I've been at Ingram a long time and we talked to partners about all the services and all the things that we can do to help them. And without going through a laundry list of 9,000 things, I think it's just simply put, which is if you have an area of need and you don't know where to go, go to Ingram first.

There was a guy on main stage with Paul yesterday and his quote was up there. I wish I remembered what it said, but it was like, if I don't know what to do, I Ingram it, you know, which means I'm gonna go to Ingram and I'm gonna find the right answer, um, to help, you know, whatever I'm trying to solve for, for my client. To your point about that, one of the issues I hear a lot about is there's just a lot of skews to navigate and they get confused and it becomes overwhelming.

Yeah. So are you guys, we're thinking about anything maybe with Proofpoint or somebody else to reduce those numbers That's ex exactly what was done, which is creating these solution bundles that are designed to stop human centric threats. You know, whether you need, you know, obviously there's email security and threat prevention and all these things, but you know, you choose the package that you feel is gonna best suit that client.

And so that's what we've done is create very priced to the market and ready to go and ready to solve those critical risks. Alright, We are close to the end of the year. What are you most excited about going into 20 20 26 Thanksgiving?

Oh no. Um, uh, I love turning the calendar. I love building the plans for next year and, um, I think we've got some exciting growth opportunities, you know, here in our future with Proofpoint and others.

And so it's an exciting time. You gotta close the year strong, but I really enjoy the planning season and getting ready for next year and setting lofty goals that we're gonna go ahead. Alright.

Hey folks, you're heard in here. A good mantra for the coming year. If you're a Proofpoint partner, eliminate the friction.

Hey buddy, Thanks for being here. Thanks. Appreciate it.

And we'll be back in a minute. Hey everybody, we're at Ingram Micro one and we're talking about innovation in the channel. My new friend Hope here.

Hope. Welcome to United States hopes from Australia. It's a very competitive marketplace in Australia.

What are the challenges that you're dealing with? I think many of our MSPs are facing a number of challenges in the Australian market, which is highly competitive, as you've mentioned. I think one of the areas that a lot of our MSPs are, are really struggling with is profitability.

A recent study suggested that 95% of MSPs in Australia have listed profitability as their number one concern moving into 2026. I think one of the other areas of concern is the skill shortage that we face in Australia, particularly in the areas of cybersecurity, cloud and ai. So our partners are having to invest in upskilling their existing teams or invest in capability, which is very expensive and difficult to find.

And I think the third area where a lot of our partners are really facing challenges is our market is highly complex. Our ecosystem, which was once linear, is now evolving rapidly, and it's a 360 ecosystem. So we're seeing far more complexity around licensing models, multi-cloud, multiple vendor solutions, and end customers really expecting higher return on investment and strategic advisory services from our partner community.

So given all that, what distinguishes the MSPs who are succeeding in that marketplace? We've got a number of MSPs that are also thriving despite the competitive challenges and the headwinds that they're facing in the market, where we see our MSPs with a very clear value proposition that they've defined and they can execute against. We're seeing them have a great deal of success where we're seeing MSPs aligned to business outcomes and solving business challenges.

We're also seeing a great deal of success with our MSPs. On the other side of that coin is the ones who are maybe struggling a little bit. What do you see them doing that maybe they should be thinking about fixing?

Yeah, absolutely. So I think the lack of clarity on the value proposition is, is hard. I think customers, MSPs that are struggling with that complexity and not really able to navigate that are finding the market difficult to, to navigate and, and the high degree of competition.

So really being able to understand where you add value, where you play, whether that's niche or more broad, more broadly, but really sticking to your capabilities and then expanding upon them within your accounts, I think is a, is a big challenge for many of our partners. Given those issues, one of the things we talk about at the show a lot is business outcomes and focusing on those business outcomes. And that's a lot more than just providing an IT service per se.

Um, what are customers looking for from you? What are, is that changing? Are the expectations changing?

Absolutely. I think as the market becomes even more complex, I think our role is distribution needs to evolve with the changing needs of our partner and vendor community. My team and I recently this year traveled all around the country, uh, and our exec connect series, we spoke to 500 C levels within our partner community to really understand what the challenges are that they're facing, where they see the opportunity, and to really lean into areas where we feel Ingram Micro can help support their growth.

I think that where we are focused is really around making sure that we're investing in the areas of opportunity to support our partners. I mentioned the skill shortage issue. We've really invested heavily in some of those areas to ensure that our partners can leverage our skillset and our capabilities to not have to invest in that head count themselves and to really leverage our expertise.

So I think that's one area where we're really supporting our partners to be successful. Another area of huge focus for us is on enablement. So making sure that our partners and their teams are enabled around what's currently moving the solutions and stacks that are really accelerating, but also future-proofing them around ai.

We recently launched in Australia enable ai, where we're able to provide assessments for our partners development tracks for our partners to help them capitalize on the AI opportunity that's currently accelerating in market, although a little bit slower than the rest of the world in Australia, but certainly a big mover in the next two years. No. How are the partners themselves gonna evolve along that part of the conversation?

Are they changing their business models a little bit? Are they getting a little more nuanced about how they deliver something in terms of cost structure? I mean, you know, walk that through the final equation.

I think we're all looking for operation operational efficiencies. We're all looking for productivity gains. I think us at Ingram Micro ourselves through our expand platform is really leading the way there.

It's all about how do we remove operational friction and how do we free up ourselves and our partners and our vendors to do more and sell more and grow more. So I think we're, we are playing a pivotal role in helping our partners evolve their own operating models to be more platform orientated, to be more digital in nature so that we can do more for less essentially. Mm-hmm.

And following up on the skills conversation a little bit, in your professional services, do the partners need to get savvier about who they hire full-time versus what they're leaning on from a distributor partner like yourself? And it seems like the model's a little more fluid. I would say it is a little more fluid, and I don't think it's a one size fits all.

I think that you could have partners, for example, who have got a huge amount of PS capability, professional services capability, whose talent bench may be fully utilized. And so they need capacity and that's where they can lean on distribution to do that. We have other instances where we have partners who maybe haven't got that skillset right now from a PS perspective around ai, and they can leverage our skillset, our capability, not just locally, but also globally to help augment their capability and give them access to opportunities that perhaps they haven't got the skillset to access.

Today We started talking about how competitive the marketplace is, but are the partners working with each other? Are they trying to leverage up a little bit, not just Ingram, but each other and there's, that's being facilitated through you guys? Absolutely.

So Trust X Alliance is a, is a global program where we essentially bring together, uh, you know, 150 plus partners into a shared community or ecosystem where knowledge is shared, ideas, uh, are born and ideated and, and that shared knowledge is what makes our ecosystems stronger and we facilitate that. I'm really proud that actually at Ingram one this year we've launched our own Australian Trust X. So we're part of the global community now, and it's really born for the reason, allowing our partners to share ideas, crosspollinate, uh, leverage each other's skillsets and strengths to do more together.

And I think that's a really powerful role that distribution can play. We talked about the skills gap a little bit, but are there particular skills in areas where you're seeing partners need more expertise than others? Are there?

It seems to me there's a lot of emerging technologies these days. I might not have anybody who knows anything about those things, but, um, where's the pressure? Yeah, I think, um, beside profitability, there was a canal study that was recently done that indicated that the skill shortage was also one or two top priority of concern for partners in the Australian APAC and even global ecosystem today.

So I think where we're seeing partners struggle is around those technology stacks that are really accelerating. So cybersecurity, cloud modernization, um, ai, they're really the areas where partners are struggling to fill some of those skills. The shortage of talent, particularly in Australia is, is exceptionally high.

The cost to get that resource into the business is extremely high and they're all looking to do more with less. So it's a little bit of a conundrum that I think many of our partners are facing today. Mm-hmm.

And the type of people you're looking for if you're a partner are kind of a little bit different than if I'm just an internal IT organization where I'm looking to hire somebody. 'cause they have to have some nice ability to have a good experience with a customer, right, right. That's requires a lot of patience is usually the first attribute.

But, um, what kind of folks are you guys looking for and are there, are there things that you and Ingram are doing to kind of coach and train people that become more suitable for being in a professional services role in a partner organization? Yeah, So often say we're looking for unicorns Because The skillset is actually really hard to find. So you're looking for higher levels of technical competence, but you're also looking for people that have an element of charisma and, and a sales bent to, uh, how they engage with customers as well.

So being able to be technically competent, but also be able to communicate with partners around business outcomes and partners, customers around business outcomes, I think is critically important to the success of people in those roles. We do a lot of work with the technical community at Ingram Micro around enablement, uh, sales enablement and technical enablement to really ensure that our partners resources are equipped to be able to go out and successfully manage end user environments. Mm-hmm.

Are you doing anything with the local universities or colleges that, uh, help maybe expand the bench a little bit? We Are actually, so we have a relationship with the university in Queensland that we work closely with to really help develop nurture and foster talent coming out of their cybersecurity, um, university courses. And we incubate that talent into our own organization, but also expand that into our partner base so that our partners can identify talent early and bring them into their own organizations as well.

Now we're here at the show, so I've had the benefit of walking around, but people are talking about, um, X Advantage and Growth Tracks. What is that exactly? Yeah, so we launched Enable AI in Australia in July, August of this year, and it's really about enabling our partners to assess their own skills under stem, where they are from a skills perspective on the, on their AI journey, benchmark themselves against other similar partners to understand their readiness.

And then for us to build growth tracks bespoke to those partners that help them enable and accelerate their AI capabilities so that they can capture that market opportunity. So we're well on the way there. We have 25 partners in Australia already that have started that journey.

I think really interestingly also with enable AI is the number of use cases by vertical that partners can pull down and learn from, but also use to position, to position with their end users to capitalize on AI opportunity as well. Mm-hmm. How do you make sure that the self-assessments that people are taking, or shall I say valid?

Because, I mean, I know I have a very grandiose opinion of my own capabilities, but they're probably not realistic. So how do you make sure that partners kinda, you know, answer those questions in a way that are Truthful in Australia? I think culturally we're very hard markers and we're very critical of ourselves and others.

So I feel as though that the partners probably do a fairly good job of assessing themselves. I mean, at the end of the day, you need to be honest in that process in order to maximize the opportunity and, and the enablement tracks that we have. So I do think that the partners are, are fairly good critics of themselves and they know that to really make sure they can leverage that program, uh, they have to do an honest assessment so that we can help them appropriately.

Talent is everything in this business. What's your advice to the partners to make their organization like the most attractive place where the best talent wants to work? I think it's really important that we nurture our people.

Um, I think being a people first organization is super important. You hear Paul Bay talk about it all the time, uh, and we live it. It's in our DNA at Ingram that, you know, we're, we're customer and people obsessed, um, and we put our people and our customers first.

And I think if you can provide that in an organization, I think you become quite sticky and I think you get, uh, you know, a lot of loyalty, your attrition drops and it's somewhere people want to stay. I think providing people with the opportunity to learn and to grow and to adapt to what's happening in our market, um, but enable them to be ready for what's next as well, I think is critically important. So I think investing in people, being a culture that people are in inspired to, to, to be in each and every day and and to feel purposeful about their work.

I think if you can do those things, it's easier said than done. I think you're able to retain top talent and attract top talent, which is super important for us in this industry. Do you think that it's important for the folks that run the organization to exhibit this one attribute that I think is important, but, um, they seem to be always have an appetite for continuously learning.

They're constantly curious and one of the things, you walk around the show floor, everybody here is kind of checking out the latest and the greatest. And I cannot help but wonder if that's like, one of the, the core attributes that is overlooked about success in the channel is that everybody in it seems to be fundamentally curious and always learning. Absolutely.

And I think that's what makes our industry amazing. That's why I've been in the industry 25 years and I can't imagine being anywhere else. I mean, the transformation in this industry from the, you know, the birth of the internet to, to cloud, to ai, um, just, you know, cyber it, it's constantly evolving and you have to remain curious and you have to be willing to challenge the status quo.

And you have to be willing to expand your mind beyond what you ever thought was possible. And I think that's the beauty of working in this industry. And I think I'm very lucky to be surrounded by partners and vendors who are always curious, always evolving, always ideating and innovating.

And I think that's what keeps us young. Maybe not biologically young, but mentally young and, and really, you know, keeps us all super interested and committed to the ecosystem that we operate in. It keeps us young and second part stressed.

Yeah. So it's an easy combination, young And gray. There you go.

So as we've mentioned, you're from Australia, but we're here in DC or just outside of DC and there's a lot of international cooperation. The globalization is still a trend. I mean, there's a lot of issues in the world, but still people are doing business across countries.

How is that playing out in Australia? Are you partnering more with people from overseas? Are you seeing more of that and what does that look Like?

Yeah, absolutely. So globalizations absolutely impact in the Australian market, and I think you can look at that positively or negatively depending on how you frame that challenge. So I think for many of our partners, it means that the competitive landscape has become heightened because we're now not just competing locally, we're competing globally.

I think on the flip side, what that presents is an opportunity, however, for partners who are looking to expand into other geographies and locations. And I think Ingram Micro Australia is very, very well placed to be able to support our partners to tap into the globalization opportunity, um, where, uh, a local distributor with massive global presence, uh, and the backing of a very large global organization, we can scale into 57 countries with our partners out of Australia. So I think for us, it's more of an opportunity collectively to harness that opport, that growth that we see in other parts of the world and in emerging markets.

We're a very mature market in Australia. Organic growth is hard to find, and I think geographic expansion is critical for our success and our partner success. All right, folks.

Hey, if you are looking to partner with folks in Australia, find hope. Yeah. She knows everybody.

Yeah. We'll be back in a minute. Thanks.

Thank you. Thank you so much. Thank you.

Hey everyone. Welcome back here to Text Drunk tv. Yeah, I got a new company that we haven't talked to before and a first time, uh, guest on Text Drunk TV today.

Let me introduce you to Kurt Mu Mill. I hope I got that right. And Kurt is the head of AI strategy at DataIQ.

I, we got, I think I got that one too. Kurt, how are you? Welcome.

I'm, uh, I'm doing really well, Ellen. Uh, thanks so much for, for having me on. It's a, yeah, it's an honor to be your first from DataIQ.

Uh, so happy to, uh, uh, happy to be here today. It's great to have you here with us. So Kurt, let's start with you and then we'll jump into DataIQ head of AI strategy.

Right? We're seeing these kinds of titles more and more, but you know, when they asked you at your high school career day, what do you want to be when you grow up? You probably didn't say head of AI strategy.

Um, how how'd you get here? Yeah, That's a funny story. Uh, I definitely was not saying that, um, you know, so if we wind back, you know, I, uh, I did my undergraduate studies in philosophy and environmental sciences.

Two things I was passionate about. Very Cool. Um, but, you know, I think if you asked me, like at the end of my freshman year, uh, in college, what I wanted to do, my answer was gonna be a wildland firefighter.

I wanted to go out to like Montana and fight forest fires. Really? That's cool.

Against my build here fully. I'm not built for that. It would've been a terrible choice.

Uh, so instead mm-hmm. Thankfully, I, uh, I met a, an incredible person, a beautiful woman, uh, who's become my wife. Uh, but she, uh, she was from France.

And so after I finished college, uh, I followed her to France and from there started working in a number of different things. And in 2013, roughly, I joined this funny little startup, uh, called Dataiku that was 15, 20 people at the time doing stuff in, you know, data science. You know, the, the term AI back then was, uh, you know, uh, you know, a little bit, uh, looked down upon just as a marketing term.

Uh, and so we were, you know, kind of in the hardcore data science, uh, realm. And I said, you know, I, I don't have a background in data science, but I like to read. I like to learn stuff.

Uh, and so I, uh, I got on board with the company back then, so it's been 11 years, uh, since, uh, since I joined really? 2015. Yeah.

Um, and yeah, I've done a number of different things over the course of the years for this company. Um, everything from, uh, from selling the software very early on to, uh, to, you know, managing different teams. I was our chief customer officer for, uh, for a couple of years, you know, managing our customer success and services teams.

Um, and then, yeah, for the past, I guess it's going on three years now, um, I've been in this head of AI strategy role, which, uh, allows me to really look outward at what the market is doing, where the technology landscape is evolving, and most importantly, what our customers are doing and what they need so that we can make sure that our product strategy, our marketing strategy, is aligned with all of that. What a great story and a testament to philosophy majors everywhere, right? I some of the best philosophy minor here, I was a political science major with a philosophy, history minors, and, um, you know, a lot of people like to try to crap on, uh, liberal arts degrees and everything, but it, you know, it, it teaches you how to think.

It ignites your curiosity and makes you infinitely adaptable. So, absolutely. Yeah.

I, I love it. Good for you. Let me ask you a question, though.

How did you learn about ai? I self-taught, I mean, you know, you didn't go back to school for it. No, No, no.

You, so it's, uh, you know, I've had the benefit of, you know, sitting and working with some of the greatest minds in ai, uh, here at Dataiku. Um, mm-hmm. You know, so, uh, a lot of the engineers, a lot of, you know, the founders of course, uh, all out of France, but, you know, incredible schools that, uh, that just pump out these, uh, these engineers, computer science engineers.

Uh, and a lot of that has then, uh, evolved into AI as well. So it's been one from my colleagues, uh, two from the customers, right? When, when I go and I speak to them and talk to them about their challenge challenges, right?

It's, uh, you know, it's, it's really the applied angle of ai, right? You're talking about, you know, political science that's like applied philosophy, right? So not very, very much always focused on the applied angle of ai.

Um, and then of course, yeah, just a ton of reading and, you know, what the best, uh, resource has been in the last couple of years, of course, AI itself, right? And whenever I have a, yeah, a question, um, I just open it up, I ask about it. I was listening to a podcast earlier today, um, you know, and there was some, it was more technical, a little bit outta my, uh, my reach.

I took the transcript, dropped it into my chat bot of the day, and just started asking it questions about that transcript, right? And here I am, you Know, it's so funny you say that. I, I actually wrote an article, it's published in Textron AI today.

It's, it's a riff off of an article that was in the New York Times that some studies suggest that, you know, using AI and social media are rotting our brains. And I think we've all suspected that a little bit, right? I mean, we, we see it.

And, but what they compared, it was, you know, using AI versus let's say Google search, that at least in Google search, there was a little bit more work involved in ferreting out the information you were looking for and everything else. And I'm looking at it saying, Google search my, my butt. You know, when Google Search first came out, I thought that was dumbing us down because I'm, I'm from going into the, the library and opening up the, the Dewey Decimal catalog, you know, and looking at a book or, or, you know, when we were little, I, my mom, the greatest thing she ever bought my brothers and I was the World Book encyclopedia, right?

And, and yet remember those, I don't know if you remember those things. I have, I have definitely. But to do a report, you had to, you know, go through the encyclopedia, and then while you were there, you learned other things.

So, you know, yeah, we've seen this. But the, the flip side of it is AI used correctly, like what you just spoke about, right? Allows us, we have more information at our fingertips, and we could, we could challenge it to challenge us to dig deeper.

Listening to a podcast is not going on the chat bot and say, feed me. Right? Listen.

But it, it ignites the, the, the chat bot ignited sparked your curiosity enough to then listen to this podcast, which as you said was deeper, and then you had to go back to help explain it. That's the uses, that's the uses where it's, it's expanding our brain, not rotting our brain. Exactly.

Right? Yeah. Yeah.

I, I totally agree. And, you know, I feel some of the brain rots sometimes as well, right? Where it's like, you know, looking at something, it's like, oh, I should get the, you know, the chat bot to write that response to that email.

For me. It's like, no, just take the two seconds to sit down and think to do it. Just write the email, right?

Yeah. It's the whole thing. It's the doom scrolling all of it, right?

I, I, it's up on LinkedIn. It's up on Techstrong ai. You could check out my thoughts on it.

Kurt, we, we got off on the left, turn there, but let's come back to DataIQ. So you joined, you said, was it in 2013? 2014?

The company was founded in 2013. I joined early 2015, like just about two years after it started. Back then, it was more data science, maybe more machine learning type of, uh, AI and stuff based in France.

You mentioned, bring this up. What's DataIQ today? Yeah, absolutely.

So, so since then, you know, uh, we're now a US incorporated company. We're something like, I think, you know, 1200, 1300 employees worldwide, $350 million in annual recurring revenue. Um, you know, what do we say, one out of four of the 500 biggest companies in the world are our customers.

Um, and that's across industries, right? Who we tend to sell to are typically larger enterprises, but it could be, you know, banks, insurance, financial services, broadly manufacturers, retailers, companies like Michelin, like LDMH, uh, like ge, uh, these are all big dataiku customers. And what is Dataiku now today, right?

From a, from a platform perspective? Well, what we like to think of is that we're the, you know, kind of the piece in the puzzle puzzle that goes, uh, that, that sits between where that company's data is already running. And, you know, most of these companies have great data platforms that they've already deployed, right?

And they've, you know, they're working on consolidating their data, and that's great. Uh, they've also got like great cloud computing resources, uh, you know, through one of their cloud providers. And now they have access to these incredible AI models.

So what's the challenge, right? What, what's missing in that picture? Uh, what's missing is the piece that brings together the trust, brings together the different sources of data, and then brings together the different types of intelligence, which can be like the machine learning intelligence, uh, can be the AI intelligence coming from those models, from open AI or Anthropic, uh, or, or in most importantly, I would say is the human intelligence of those experts who know their business better than anyone else in the world, so that they can create for themselves the AI solutions that actually augment their processes, you know, grow their revenue, automate, uh, you know, different parts of their business, increasing efficiency so that they themselves have the ability to, to build these capabilities on top of that kind of core infrastructure across data computing.

And now ai. I love it. com?

ai? What's the website? com.

com. Excellent. People can go on there, reach out, contact, absolutely.

Get smart about what you have. All of the above. Yeah.

Yeah, We've got plenty of content up there. The, the blog is great. Uh, people say we sometimes have too much content, uh, but it's also great that, uh, it's hard to, uh, it's hard to not produce it.

Um, but yeah, plenty to learn, uh, uh, about, I would recommend going straight to the customer stories. Uh, that's where you really get a good sense of what, uh, what these companies can do at dataiku. Excellent.

So you guys recently released a report called the Global AI Confessions, like true confessions. I, you know, the name caught my, caught my attention. What exactly is it?

Tell, tell us about it. Yeah, so this is actually the, the second in a series. Uh, first we did a CEO, uh, confessions, and now we've done the CIO confessions.

Um, and it's a, you know, we go out and we work with a, you know, a, a research firm that, uh, that conducts these studies, uh, to go out and just kind of take the pulse of what, you know, previously, the CEO. And now the CIO is really thinking, and we frame it as confessions because there's, you know, kind of the, the, the public face that, uh, that everyone is putting up, especially leaders of these larger companies, right? They need to say that everything is successful, they're moving so fast, everything is great.

But we know that that's not the most interesting story. The most interesting story is when, you know, they pull you aside and say, let me tell you how it's really going. And so we orient the questions in that direction, uh, to make sure that we're getting the real story.

Obviously, it's anonymous, you know, we, we work with the, uh, the study firm to make sure that anon anonymity is protected, while at the same time we're actually getting the, uh, uh, the real people from these companies. And what comes back is often, uh, both really surprising and not surprising at all sometimes, right? Uh, and so here in the CIO Confessions report, you know, we start to see, I think what we all suspected is true is that there's great ambition, uh, for ai and especially agents.

Uh, this is a big focus, right? For all of these companies these days. There's this big push for it, but really this, this huge gap in the trust that we can have for these systems.

Uh, and I think that what that is pointing to is the fact that, you know, it's, it's not like, you know, we we're lacking the technical capabilities, you know, the, these AI models that, uh, that we have from the Frontier Labs, they're, they're incredible. You know, they're, they're super powerful. All these companies have so much data to work from, right?

Uh, the challenge is how do you actually build something that you can trust put into production, and then of course, have a process where you can start repeating that and scaling out these AI solutions. Absolutely. You know, it's, um, it's funny, I, I recently saw, I, I, I was at a conference somewhere and someone presented a study of CIOs.

I forgot if the number was 40% or 60% of CIOs were increasing their budget, ask for next year for ai. Not because they really understood what they were gonna spend it on, right? Or what they were gonna do with that money.

But because their CEO and their board, the board of directors, was telling them they needed to spend more money on ai, which to me just sounds like a recipe for waste and fat. But I mean, I, I think that it's telling of, of kind of the times we live in. I wonder if you guys saw anything like this?

Yeah, I think, uh, you know, we all saw this kind of bumper crop of, uh, you know, new budgets that just emerged, uh, for, for ai. And I think what that led to pretty directly is that study that said that 95% of pilots aren't making it into production or The MIT study. Exactly.

Right. Well, there's MIT and then there's the Wharton study that says 70% find value in it. Right.

Exactly. You know, it depends, I guess if you're an MIT or a Wharton fan, actually, I wrote an article on that too. I think in quantum fashion they're both right.

In some level. Exactly. I think What you're looking for Exactly right, because, uh, you know, what we saw is a lot of testing.

And ultimately, I don't think that's a bad thing. I, I think it's a very good thing. Um, but what's critical is for those enterprises to then find a path out of that testing phase, that experimentation phase to start, you know, getting down to real use cases, uh, that actually deliver value.

And again, I'll, I'll bring it back more to the process, uh, but really a process, an engineering method internally where they can start producing these things reliably, right? And by these things, I mean, you know, these agents, you know, built into applications that are actually moving the business forward in a, uh, in an important way. You doing it once or just buying one off the shelf, that's great.

It doesn't hurt. But what really is going to transform these companies where, where AI is actually gonna get to the potential that, you know, everyone is claiming that, uh, that it will have, is when they can actually, let's say, industrialize the production of these capabilities for themselves when they can start pumping out. The agents basically have their own internal, uh, factory where they are, you know, designing engineering, producing testing, and then maintaining these, uh, you know, a growing number of agents, uh, that they're running throughout their business.

Absolutely. You know, I, to me, look, I, I've been through several technology waves in my career. I think AI has the potential by far, to be the biggest.

And then when you combine AI with quantum, with robotics, physical ai, whatever you wanna call it, to me, that's an industrial revolution in the making, right? Yeah. Um, but even though time is crunched more than it ever was now, AI time, whatever you want to call it, you, these, this still needs a chance.

You know, like you put wine in a decanter to let it breathe. This still needs a chance to breathe a little bit. This still needs a chance.

We need to go through that experimentation stage, that testing stage. Yeah. What works, what doesn't work?

What could we, how do we tweak that, improve that, not do this? Um, I, and I think sometimes we lose sight of that in our instant gratification center of our brain that we wanted to do everything we think it could do right now. Um, and I'm sure that's borne out in, in, in this confessions report.

Yeah, absolutely. Right. Uh, you know, I think that where we, where we see the market today is in a phase, which is honestly similar to where enterprises were with machine learning 10 years ago, which is mm-hmm.

You know, kind of testing out what are the right use cases? How do we get some of this, you know, this new technology, this new software, this new like, you know, cluster of, uh, uh, uh, of computing that we have available to us. How do we actually get this to do anything at all that's useful?

And it took several years for that to, to to happen. And so I think it's totally reasonable that we would expect that to happen in AI as well. Um, just because we're not seeing, you know, total business transformation in a matter of, you know, a couple of years since the first chat bots came out.

Uh, I'm not surprised at all. And, you know, one of the reasons is, sure the technology is moving fast, but we know that organizations, especially large organizations can't and should not move that it's A lot of inertia, you know, turning those ships. For sure.

Kurt, let me ask you another question. Did, did the CIOs and the CEOs for that matter in your confessions reports, talk about the distinction between like chat bot generator of AI versus agent ai? Yeah.

So, uh, what we saw, uh, in there is that the, the real enthusiasm is to move beyond chatbots. Chatbots are, are useful, right? They're great for those one-off tasks, you know, where you can go have a quick conversation, get it to summarize something for you, et cetera.

But especially when we talk to the CIOs, and so of course that's a role that's focusing on, you know, what, what are our processes? What are, uh, what are the, uh, the systems that we have in place? That's where they're eager to get AI working almost on the backend, right?

Not just as a, a, a one-off interaction with every single user. Uh, sure, that's great, that's productivity software, but more in a way which is automating processes, uh, such that, you know, the com, uh, the, the end user can go and kick off something that the, the, the human being would've had to have spent hours on so that they, they can go and do something else and then come back and have the result at a level that, uh, that is, uh, you know, as good, uh, maybe even better than what they would've done themselves. And, you know, we, we see this within our customers, right?

We, we have customers across, uh, different industries who, who are experimenting and succeeding with this building things that are saving them in some cases, you know, thousands of hours per, uh, uh, per year, uh, uh, thanks to the fact that they were able to design something that actually matched what they needed. Absolutely. Kurt, I always ask people when, you know, I'm doing interviews around surveys and reports and stuff, research what came up that you didn't ha you didn't see it on, you didn't have that on your bingo card.

Hmm. You know what I mean? Like, it just came outta left field for you and we're like, wow, I didn't see that coming.

Yeah. You know, there, uh, uh, there's always something, right? And, and like I said at the outset, there was a lot that, you know, kind of confirmed a lot of hypotheses, right?

That, uh, you know, trust is kind of the barrier here and so on. Uh, but there was one number that jumped out at me that, you know, I didn't realize it was that bad. Uh, and that was when, uh, they came back, the CIOs came back and said that they could, that only 5% responded that they could, uh, answer or trace an AI decision for regulat regulators 100% of the time.

Uh, you know, most other people were saying, you know, some of the time I can trace a decision, but only 5% said that they could do that systematically. And I read that. I'm like, you know, I know that, you know, we, we can't eliminate all risk.

Uh, but if you're a large enterprise and you are running AI powered systems and you're not able to explain that to a regulator when they come knocking, you know, in the event, uh, that, uh, that some something goes wrong, that sounds like a pretty hefty rule of the dice. Um, and so I was surprised that, uh, uh, that it was that low, that number. Um, and, you know, I think that that reinforces the, you know, the, the conclusion that we're drawing, which is, is, yeah, I mean, organizations need to get their hands around the way that AI is coming to the decisions that it's making.

They need to get their hands around the way that AI is being applied to, to reach these outcomes. Um, because at some point, you know, somebody's gonna ask, well, how come you, how come your business made this decision? Right?

And it might be just for internal purposes just to understand why we made the right decision or the wrong decision. But in some cases it might be a regulator who's coming, uh, incoming, especially in certain industries and says, well, how is it, you know, why is it that you denied this loan? Or why did you charge this insurance premium for, uh, for this customer?

If you put your hands up and say, well, I don't know, AI did it. I don't think that regulator is going to, uh, to be fully set. You know, you know, you can't, you can't abdicate your responsibility.

No, no. And I think that that's, uh, you know, very important in all of this is making sure that, you know, everyone understands the saying, just because AI came up with the, that solution that's no defense, doesn't Mean you don't own it. Exactly.

You're, you're still gonna be liable. Yep. Kurt, we're about outta time.

For people who maybe want to take a look at both the CIO and the CEO AI Confessions report, go to the Data IQ website. Yeah, Absolutely. It's, uh, it's right up there on the, uh, uh, on the homepage right now.

Um, and if not, just type in dataiku Confessions to your favorite, uh, search engine. It'll be the top hit. Or ask your favorite chat Chat out.

Absolutely. That's right. 35% of people are doing that.

Kurt, thank you for joining us here on Tech Trunk tv. Is great. Continued success at DataIQ.

Come back and keep us posted. Yeah, I'll be, uh, I'll be glad to. Thanks so much, Alan.

Alrighty. Kurt, let me, sure. I get this Kurt Muell head of, uh, AI strategy at DataIQ here on techstrong tv.

We're gonna take a break. We'll be right back. ai Leadership Insight series.

I'm your host, Mike Bazar. Today we're with Manoj Chad Ri, and he is the CTO for Chitter bit. And we're having a little chat about AI agents accountability and guardrails and all that good stuff because, well, we're building it, but I think once again, maybe we're over our skis a bit.

Manoj, welcome to share. Thank you very much, Mike, and it's pleasure to be on your show. All right.

Just about everybody you talk to is building some type of AI agent. At the very least, they have a prototype, if not a few running in production. But I kind of feel like we're all rushing to go do this without, are we gonna put in place for guardrails and who's gonna be accountable for what?

Because I think a lot of these AI agents are not just autonomous, but they're kind of, you know, if left unsupervised, they'll do all kinds of things we weren't planning on them doing. Yep. So you're, you're, you're absolutely right.

Right. So let's, let's look at how, how AI's been evolving, right? Ai, as we know, as you just said, AI is evolving faster than the regulation and governance framework.

You know, the architecture people implemented few months ago is now kind of getting outdated. It's, it's at that past, the AI innovation is happening. With my personal experience, when we first wrote our first AI assistant, three months later, we rewrote the entire AI assistant.

Why? Because the evolution in this phase is much faster. Just look at how many protocols have come in in reality by various different companies.

We have MCP from philanthropic, we have agent to agent in from Google. We have a CP from, uh, which is, uh, agent communication protocol from IBM. All of these has come in life in last six to nine months.

So that's the pace at which it's changing. In fact, Google last week launched a P two, which is agent payment protocol. If the things are evolving at that pace, what's happening is all the vendors and organization wants to have some kind of AI associated with them, whether it is AI agent, whether it is AI assistant, whether it is agent ai.

And in that hype, the security and governance is left behind. If you look from the governance and security regulatory standpoint, there is only one ISO standard available for ai, which is ISO 40 2001. And that also has launched in last six months only.

And what it promises, it, it comes and makes sure the companies are designing, developing, and deploying AI technology with the transparency, data quality, security ethics, trust is what it's doing. So that's what is happening, essentially. And I feel like there's two conversations that are closely related, but one is security and the other is governance.

When I think about the security conversation, it almost seems like maybe we don't understand the real level of risk that's going on here because these agents are autonomous, and if they get compromised, won't the bad guys just take over an entire workflow rather than just kind of compromising a, a particular endpoint? Yes, you, you, you're right. Like the, the, when AI agent comes into play, let's talk about that and I'll, I'll talk about MCP from same angle, right?

So if you look, as you said, AI agent is autonomous. If the security is not in play, there can be so many things which can go wrong. Not only bias, not only basically prompt injection, people can inject prompts into the AI agents and cause it to do something really bad for that business.

And guess what? Not many companies are putting human in loop to verify. In fact, I believe that verifying the output of an AI agent is going to become in itself a profession.

So people are even right now with current co uh, innovation and early stages of ai, in my opinion, human in loop is a very important aspect. And, and many AI agent is basically taking human out of the question equation, right? So these security things needs to be implemented.

If you look at MCP, in fact, I put, I posted a LinkedIn, um, uh, last week because many people asked this question to me, what about security when it comes to these new protocol? You know, MCP model context protocol, which is delivered by en anthropic, it is a protocol to build, make the development of AI agent easier, but the security is ignored in this. There is no clear standard for authentication.

There is no clear standard for sandboxing the AI agents. There is no clear standards for making sure that tools are secure and connecting to the database, the data sources underneath it securely. And there is no clear guidelines on how to protect the prompt injection, for example.

And then on the other side of it, with the governance, it seems like a lot of these AI agents are leveraging various large language models, all of which seem to be very, um, aggressive about hunting down data sources. And if I don't restrict them, they'll incorporate that data whether I intended to or not. And won't that just one day result in, I don't know, some embarrassing data showing up in an output in some way that we didn't plan for?

Yeah, so look, I, I think that that's the key thing that mo the moment that data leave and go into LLM, you are at risk of that data getting exposed to the world, period. That, that's how I see the world. And that's how the reality is that that's where, if you look at the short-term memory, long-term memory is in place for these AI agent.

If you look at one of the AI assistant, which is we all use, and which basically is all this gen AI evolution happened from them is chat GPT by default, anything you send to chat GTS used for training their model, you have to go explicitly uncheck that setting. And still, we don't know what they're gonna do, who, uh, uh, not many people go read all the fine prints. So what we need to do, and what organization has to do is when they build AI agent, they should make sure that AI accountability is in place.

What does AI accountability means? Any input going into LLM, any output coming from LLM? Any input going from LLM to AI agent to invoke tools and make decision needs to be checked against the guardrails and make sure that they are valid and doesn't gonna cause the leakage of the data doesn't cause any compromise of the data sources.

To your earlier point, I don't think that a lot of folks are actually reading any of these end user license agreements any more now than they did then. And, but today you have more at risk because somewhere in there you're supposed to opt out and check a box that says, I don't want my data to be used to train the AI model. But you gotta go find that box, right?

Yes, absolutely. And those bots by design are hidden. And if you look at most of these LLM providers or a, or where you are giving your data by default, use your data, your prom, your document, your data sources to accumulate and train the future models, which is gonna come.

'cause that's how the models are becoming intelligent. That's how models are becoming smart. And not only about becoming intelligent and smart, that's how they can give you a personalized experience on the tasks you are asking these models to do for you.

Right? That's basically is why they need this data and that's why they're basically having these boxes and opting out hidden from the users To that end, who's in charge of all this? 'cause I think that we see a lot of data science tiger teams that people have spun up and they're out creating agents, but it's not clear to me that anybody from security or the GRC team is invited to that conversation.

So, uh, are we just waiting for some sort of cataclysmic event before we get serious about this? Or what's gonna happen? You know, there are a bunch of events already happened as we all, as majority of people might know, or your audience, Samsung IP got compromised because one of the employees of Samsung literally take some confidential document and put it into the chat GPT, right?

And that is now, and that is available on the internet, right? So this is happening again. The point is the people want, the organizations don't want to left behind and the innovation is move on, on AI is moving at much faster pace, whereas the security team and governance is lagging behind and not able to catch up with the innovation on that development of ai.

So yes, I think there will be going to be some catastrophic events before people are gonna get serious. Although I would say there is a positive news where majority of analysts and majority of big enterprises are now asking the diff various organizations who are building AI agent that, Hey, please take AI accountability. Please take AI guardrail, please take AI security into account.

Now how much is gonna happen in next six months is still to be seen. But I'm happy to see that now people are talking about these, whereas six months ago nobody was even discussing about the guardrails and accountability. So what's your best advice to folks then about how to go and get into the middle of this conversation?

'cause I think a lot of it seems to be happening beyond the realm of the governance and security people. So do I gotta go scan for these projects and insert myself into them, or how do I kind of get myself into this conversation? Yeah, I, I think basically there are one few open source tools now built.

You know, there are vendors like us who basically are building AI accountability and governance into the product. BA get yourself educated with that. If the cycles permit, go look at the control of ISO 40 2001, which allow users to get educated with how to design, develop, and deploy, um, AI ethically and not have problems.

That's what I would suggest people to do. And make AI security and governance not as a second thought for ai. Make security and governance as a first class citizen while building an AI agent.

Build your team and train your team around AI controls. When I say your team, your design team as well as your development team, along with the security on the controls you need in Ai, might we one day have AI agents that are gonna manage the security and the governance of other AI agents? Is that where we're headed?

Absolutely. You know, what's gonna happen is AI agents are like digital workforce like US humans. There are people who are developing and there are people who are doing security and governance on the things developed by the development team in the AI agent world.

There will be AI agents which are responsible for doing the work, the task autonomously, and there will be AI agent, which are watching them for the security and governance and making sure that they are doing the things ethically. There is no breach, there is no violation of anything. Absolutely.

That's what is the future and that's where we are gonna go. We've also been struggling to manage both human and non-human identities for a long time. Now, is an AI agent essentially a new type of non-human ident, or is it an extension of our human identities and we'll track it that way?

So right now it is where we are and at what stage we are, I would say it's an extension of human identity, but pretty soon it'll become a standalone identity of itself, which as I said, will be a digital workforce. There will be, uh, agents which are basically onboarding these AI agents and there will be like, call them the HR agents, which will be responsible for onboarding the agent into the IT system. There will be AI agent, as we discussed, to make sure they are operating and performing securely according to the policies of the company.

So in pretty soon, not in a distant future, they will be the digital entity of their own. Are you at all worried that we might get too comfortable with these AI agents and not think all this through and we're just gonna have people kinda executing things just because Well, the AI said it was okay. I I, I honestly don't think so because you know, a lot of, there is still a lot of hype around AI agent.

If you look at the companies who are building true AI agents are very handful. A lot of companies are building AI chart work and AI assisted, which are basically sitting next to humans and basically helping humans to do the task at much higher productivity level. There are very few companies are building AI agent, which are doing, making decision autonomously and operating autonomously.

That's basically our very few companies. And once, by the time this become a commodity, the true AI agent become commodity, I hope. And I think the standards will get caught up and it will basically have governance around it.

Do you think the auditors out there are already tracking this and will soon come knocking and asking people about these issues? Absolutely. Like that's where ISO 40 2001 has come.

I think there are more standard and compliance, which is happening. It's just matter of time. It's these, these are regulatory authorities will come and start putting them in place.

All right. Well folks, you're heard it here. I guess there's two things to remember about AI agents.

I mean, they're awesome, they're powerful, but they won't pay the fines for you and they certainly won't do any jail time. So be careful. Manu, thanks for being on the shelf.

Thank you very much, very much, Mike. It's pleasure. All right.

And thank you all for watching the latest episode of the Techstrong AI Leadership Inside series. You can find this episode and others on our website. We invite you to check those all out.

Until then, we'll see you next time. Hello and welcome to the latest edition of the Techstrong AI Leadership Insights series. I'm your host, Mike Bazar.

io, and it goes by the name Santi for short. But we're gonna have a little chat about where are we with the state of AI and the enterprise. 'cause I think we're entering maybe the what they call the TR of disillusionment.

But we'll see. Santi, welcome to Shah. Hi Mike.

Thanks for having me. Excited to chat about this. What's going on here?

It seems like early on every executive on the planet was like, oh, we gotta invest in this tomorrow. We gotta drive all this AI stuff and we're gonna get these incredible returns. And there's magic in the air.

And now it looks like people are starting to figure out that, well, it takes work to get this thing to happen. And there's a lot of data management issues and a lot of things that we have ignored for decades are now raising their ugly head. But what's your assessment of what's going on here?

Um, personally, based on my slice of the market and the day to day we live in, I feel like we may today be on the early innings where interest is continuing to build up and, um, buyers continue to come into the scene with excitement and strong mandates to understand and spend and explore in ai. I do agree with you that there are some indications of burn where buyers have, uh, gone ahead of themselves and spend money on things that are not really delivering. But I do think the wave is still forming there.

Of course, we can, we can dive in and and debate whether we think it's gonna crash, uh, and we think there's something on the other side that's positive. But in general, I would say today I see energy building up, uh, not quite yet deflating. I'm not entirely sure that it's a crash as much as maybe a more realistic set of expectations.

And to that end, everybody I talk to has multiple, if not tens, maybe even a hundred or more experimentations going on. But I wonder if we're experimenting too much and maybe we should just pick three or four things that we're actually gonna get done and put into a production environment. I think you're right.

I think one of the interesting qualities of this new, you know, disrupting function that is ai, this new type of technology that is driving such transformation throughout all industries and all solutions, is this, um, density to make every single demo work every single first take on a new solution. An AI forward attempt to whatever problem that maybe, you know, affecting a buyer, tends to work pretty well, at least for a demo on first try. So I think that's created an explosion of cool demos and superficial solutions that buyers can go spend their money and time on.

Um, I think one of the biggest challenges that buyers have today is to really be thoughtful and, uh, double click to understand what is there beyond that first sexy demo. How operationalized a workflow is for the long run, how scalable a solution can be for a big team like an enterprise company. Uh, I think that's really what's at the, at the center of not getting burned, spending a ton of exploratory money on AI and then having to turn up a bunch of vendors, uh, to a month down the line.

Mm-hmm. Are there any patterns you're seeing in use cases that are maybe the first low hanging fruit that people can really operationalize? And maybe everybody should because everybody soon will, but at least I'll have something to show for my efforts.

Uh, yes. I think ulti, Elise, of course, this is a talk in my book, right? I do think, um, data extraction, using AI, driving a data set that is inherently unstructured and requires a bunch of manual admin work to have the business be able to leverage at scale and having LLMs be the conduit through which that becomes structured, um, is really good.

It's a really good use case. It may not be the sexiest one, right? You know, what is sexy today in the industry?

Well, what's sexy to it in the industry is to come and pitch you that I can replace 2000 people with, you know, an LLM for you. And that's kind of the easiest thing to go try and sell 'cause everybody would be in, in the market to dramatically lower cost. Um, but as it turns out, that is a, a really hard thing to deliver on in the long run.

There's a lot more nuance to solving those types of problems. Uh, something that may not sound as sexy, uh, but is more concrete and more contained of a value prop is to say, Hey, I'm gonna grab something that is very time consuming and gives you low quality data for the business and turn it into more reliable, higher quality. I'm not going to clean a bunch of people's jobs, but I'm going to dramatically reduce the amount of admin they have to do so they can focus on something better.

Uh, that's a little bit of what we sell ourselves every day. Yeah. It seems like to your point, we got a little obsessed about the labor arbitrage equation when maybe we should just be thinking about, well, people are spending a third of their day on task that are either manual or intensive and toil that doesn't really add a whole lot of value into the business.

And if we get rid of that, well then we'll get more value out of the people we have. Correct. That is a thing.

What's going on? Um, usually what you see is, uh, founders and startups built on the idea of only replacing labor. And those tend to be younger founders who have not really lived in the enterprise, who don't understand the complexities of a big organization and the new ones of having humans as the glue between complex processes and communication at pains.

Uh, so they come and they idea idealistically say, well, if AI can write an email, then I can replace a team of 1500 SCRs with a bunch of ai. Uh, but then when you put those in practice, uh, they crash and burn when they hit the hard reality of, you know, the real world. Um, what you're seeing is more, um, senior, um, uh, veterans from the field will come and put very concrete solutions.

They can slot it into a big enterprise for a very simple problem, and those deliver quite well. Uh, so that's what I would kind of nudge towards every time I would talk about, okay, how does these get operationalized? How does it, how is it integrated into my tools?

How is it blended into my processes? Uh, that's the real questions to be asking in, in 2025 when there's so many vendors, there's so many submissions around Mm-hmm. To your point, it's not clear to me that organizations have a big appetite for large scale business process re-engineering, and they kind of want some of this stuff to slide into their existing workflows and processes, because otherwise it would just be too disruptive.

But it does seem maybe on an evolutionary scale, we will re-engineer these processes. It's just gonna take a while. Exactly.

Exactly. It's the same, you know, I have an analogy too on companies that today are rebuilding the CRM from the ground up and say, Hey, look, we are, uh, we're new a forward CRM now, who is gonna go dump their, you know, Salesforce or Microsoft Dynamics instance overnight when you have a team of 2000 sales reps who have been using it for, you know, 11 years to capture all their data? Well, not too many companies, they'll find a lot of small startups that may, you know, take a spin on a new approach.

But the majority of the industry will not mitigate impact. And the companies that are, you know, leading the transformation are the ones that are making the current situation incrementally better and may end up leading to a destination that is the same, but, you know, may take a couple of years to get there rather than doing it overnight. Mm-hmm.

One of the things I do hear people struggling with is they'll take a notion and they'll be like, wow, we can use AI to create this, and they'll even maybe get as far as creating an AI agent, and then they'll wake up the next morning and one of their vendors already did the same thing, and we will give it to them as part of the application that they're already using. So where do I find that line between when am I gonna build versus buy something that, um, I can add some unique value around versus reinventing a wheel that somebody else is gonna do for me? I mean, I'm pro buy versus build every time.

I just feel like if you are in a business that is thriving, that is growing, you have a market that is hungry for your solution, it is rarely the case that you should be spending your time building some workflow optimization for operationalizing your company. Uh, there's somebody who's, you know, there's some vendor out there whose only focus is to do that, right? And you will never be able to do it at the degree of refinement they do it.

Um, it usually tends to costs a lot less than it takes to build, uh, even in the age of AI with agents around, you know, upkeep, maintenance, um, continue to be considerations. So in the build versus buy, uh, kind of spectrum, I'm a big fan of, you know, buy while you're growing. Sure.

If your company is, you know, growing 10% or year over year and you've kind of tapped to the bulk of your total adjustable market, and now it's about optimizing cost, sure. Then go consolidate, then go build your own business from the ground up. Uh, but if you're thriving and your business is doubling year over year, or tripling year over year, I wanna get my ass off the ball.

I would just focus on my market, uh, with every piece of technical resource I have at my disposal. Mm-hmm. We're humans though, and we all get obsessed with quote unquote making things better.

And I think if I buy, I wanna customize that 'cause, um, we're all convinced that we have unique business processes, and that may be debatable, but, uh, people are people and that's what they wanna do. So do I also need to figure out how to choose platforms that give me something that I can buy, but I can extend Completely. I think in the world of ai, what you're referring to is the idea of prompting what makes AI custom, what makes AI behave the way you really need is to be able to highly refine the prompt that is ultimately given to the model.

Even having the selection of what model is being used gives you a, a degree of customization and control on the end result. That I think is really real breaker between something that's kind of cool for a demo, but not really practical in the real world, is something that really works the way a business wants it. So in my book, it's all about integration automation and prompting that will be at the core of a solution that feels your own versus something that is vanilla and doesn't really get you the level of, uh, replacement of labor that you expected in the first place.

Mm-hmm. One of the challenges I hear also is that, um, most of the business processes today that we're trying to do are very deterministic, and that the sense that they're supposed to be done the same way every time. And the one thing AI never does is the same thing the same way twice.

Um, so we have a probabilistic set of technologies that we're trying to insert sometimes into deterministic workflows. So how do we do that and, and how do we strength that balance because, well, you know, it's, it's neither a hundred percent of one way or the other. Yeah, this is a really good point.

I mean, I think the oversimplification of some of these patterns and tools into simple categories and words, things like agents, uh, can lead to issues like the one you described ultimately, you know, if you really were to go to the very core of what an agent really should be, you know, the word agency comes up, right? An agent needs to have agency of what it's doing, and therefore it becomes a very probabilistic, non-deterministic, uh, type of execution you would get out of this technology. Um, and I don't think most enterprises out there are looking for something like that.

In reality, what you're, they're looking for is basically a codifiable deterministic workflow that has a certain level of intelligence. When it really matters. You want something to be 90% programed and always behave the exact same way, and then be 10% intelligent in the parts where, you know, simple if conditionals won't cut it.

Uh, that to me is really what most people talk about when they say they're buying agents or they selling agents, uh, real, real agent behavior that is fully non programmed and probabilistic. I'm rarely seeing it in the field, and I rarely see buyers interested in bringing that in. Mm-hmm.

So what's your best advice to folks about how to get started with all this? I think we have some on the one end irrational exuberance. And on the other end we've got people who are probably overall and too terrified to do much anything at this point.

So between those two extremes, how do I get to something that feels like a reasonable mill? So I will say, everybody should be bold, and everybody should be hang hungry for this. You should not be waiting this one out.

If you are, your competitor is gonna be exploring it, and if they do, they're gonna be ahead just the delta in results from the people who get leverage from Gen AI today versus the ones who don't. It's just too big. Uh, so I do wanna drive urgency on, on companies out there, no matter what you're selling or what you're building to go use it every day.

Um, you know, we put together a book and, you know, regardless of whether buyers, uh, whether you're listeners get it or not, the book is called Ignite, uh, go Market with ai. I got to interview a lot of it interesting people to put together the book. And I'm gonna steal this one from Kyle, or she's the CO of owner.

com is a thriving company, uh, hundreds of reps, uh, performing really, really well utilizing ai. And he gave me one of the most interesting insights and approaches to this question. He said, look, Santi, the companies that are gonna thrive the most are the ones who have their leadership be AI forward.

I think an anti-pattern that is developing today is to have AI adoption come from the bottom up. Let the front lines explore ai, understand AI and bubble it up to leadership from leadership to decide what gets bought, what gets used. Uh, I do agree with Kyle that the right pattern here is to have your CEO, your CRO, your CTO, be the ones hungry for innovation, be the ones understanding the differences between Quad and Chad, GPT, understand what an embedding says, what a vector database is, and if they do, then they're gonna be the ones making the most bold decisions they wanna be, they're gonna be the ones buying the most disruptive technology.

Um, so, so that's my advice. I'm copying calls and saying, you better have a CEO who understands what this tech can do. You better have A-C-R-O-A-C-T-O-A-C-P-O better are looking into this technology and are spending an hour or two a week diving in into the latest and greatest 'cause that will drive the best results at the enterprise.

Ultimately. Is there something that you are seeing organizations do that just makes you shake your head a little bit and go, folks, we could be a little bit smarter than that? Um, I think organizations are quite exploratory today, and they're very boldly bringing in vendors and trying the stack, and I don't think that's a bad idea.

Um, I think where it really can be, uh, a bad approach is when you're being careless about data this vendors handle for you and how they use it. I think buyers should be looking into their MSAs into terms of service to understand if data is being used to refine models, if data is being used to train models, uh, you wanna be in business. If you're a business, you wanna be in business with vendors whose only interest is to charge you money to provide you a service.

And the only reason they're gonna use your data is to provide that service. And whenever the engagement is over, your data is yours and it gets deleted on a, I agreed upon, I'm, um, I'm commitment. So that would be my one component is don't be too careless about your own business data these days.

Data is a commodity. It's, it's something that the companies are looking for, it's a currency. Uh, so you should protect it and be responsible about it.

All right, folks, you heard it here. One way to think about ai, it's an undiscovered country and the only wrong decision was to stand still and do nothing. Hey, San, thanks for being on the show.

You bet. Mike, thank you for having me. And thank you all for watching the latest episode of the Textron AI Leadership Insight series.

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

Agents for cloud migration, Commvault Unity has arrived hero for Code Arista and Palo Alto are gonna team up. We're gonna be talking more about Microsoft and Anthropic. There's some AI driven espionage out there.

And, uh, wait a minute, we're gonna see if cloud flares back online in the closer look in this episode of the tech field. A rundown. Hello everyone.

Welcome to the tech field, a rundown for November the 19th. Uh, today is a day that I absolutely cannot endorse it's national play Monopoly day. You're probably thinking to yourself, oh yeah, my family's gotten into some fights before.

I once played Monopoly with lawyers and they made me sign contracts. So never again. Thankfully I've pivoted that business into talking all about the tech news that's been going on this week.

And, uh, joining me is my somewhat sleepy co-host, Mr. Alistair Cook. Al it was great to see you last week in person, but I think that jet lags starting to catch up with you.

Catch you know, it's one of the amazing things is, uh, being in the states after working with the team, I got to go to Ohio with the team and, uh, be in person with people, but I also gotta experience it's Sunday in the, in the US while it's Monday in New Zealand, and I'm usually on the other side of that. It was a fun experience, but yeah, it's a long way home. It is a long way home, but thankfully a lot happened in the time that you were gone on that plane.

And we're gonna be talking all about it because Yeah, well, you know, how often do you break the internet? We're gonna start off with a story about the last people that broke the internet, and that's our friends over at Amazon Web Services. Because AWS introduced a new AI powered agent that dramatically accelerates cloud migration projects by automating tasks that used to take us weeks.

The professional services delivery agent can generate proposals from diagrams or notes, deploy sub-agents for coding and testing and leverage AWS transform to modernize legacy systems. It's all backed by insights from thousands of prior migration. These tools position AWS alongside other heavyweights like Google and Microsoft, and using AI to streamline large scale cloud modernization.

Al, can Amazon finally turn the corner and make it easy to move things onto AWS? I hope so, because historically we've picked up what we had on premises shoved into, into AWS or another cloud platform and been terrified at the costs that we got after a little while. So going through a good consulting process to do that migration to, to get the most value out of getting your applications onto the public cloud, that's, that's a really vital part of cloud adoption.

And the aim with this particular component, the AWS professional Services delivery agent, is to shorten the cycle for that instead of taking six months to a year to go through that planning and discovery and specification to have some AI agents deal with the huge volumes of data that you get as you're going through that, that process and to make sense of things through here. So, doing some of the, the design and iteration ar around what does my infrastructure look like on AWS that's different from how it looks on premises. What things should I rewrite in order to use new platforms?

You know, the, how many Rs are we up to 7, 8, 9 Rs of migration that's, uh, that we get with AWS. Having some of those guidance and decisions is, is absolutely vital. And I've seen this previously that it looks very simple to move to a cloud platform.

You just take what you got and you shift it across. But there's so many interdependencies and discovering and analyzing those interdependencies, walking out your cycles to, to move. That's absolutely really hard work.

And it does seem like it's a combinations of choices, uh, process that really does suit AI agent will this lead to more successful and and faster migrations from on-premises to AWS remains to be seen. And it's a, it's a complex kind of process to go through. And one of my concerns is that as you go through that process, you need to build knowledge within your organization about how to, how to actually enact the transition from on-premises up to AWS and the differences.

My concern is that these agents might prevent some of that knowledge gain, that, um, internal awareness that's vital to speed up a Commvault. Our good friends at Commvault have just launched Cloud Unity and AI powered platform that unifies data protection, recovery, and identity security across cloud and hybrid environments, tackles AI driven data sprawl and rising identity based attacks, uh, with integrated governance tools and all of the nice advanced threat detection and recovery testing. Early access is available now, but full release is coming early next year.

Tom, have you had access to the earliest? I have not had early access, but I was in the audience for last week's keynote when Sanjay Meti came out and kind of start talking about what Unity entails. Uh, we are gonna be, uh, following along with a live stream that's actually happening today on the 19th.

com for more details on that. Here were some of my big takeaways from what I got during the Commvault Shift keynote. The first thing is res ops.

I hope you're ready for a new ops de uh, designation. Uh, in this case. You know, with Commvault, it's resilience ops, right?

It is not enough to be available. You have to be resilient, you have to make sure your data never goes down. Uh, that is part and parcel of what Commvault has always been about.

We know that for a fact because we've seen that over the years. Commvault's kinda the gold standard for backup and recovery, but that's not the market that we're in anymore. We are in the data protection market.

And one of the things that they announced that, it took me a minute to kind of understand the totality of it, is something called synthetic restores. And you're probably thinking to yourself, well, how hard is it to restore data? But I want you to think about this because this is the way that the market has been going.

So it used to be that if something blew up, we had to restore it, right? Well, we had to go back and figure out where we had good data from. And in the case of a security incident, we have to make sure that the data is still clean, and that means restoring back to, um, a certain point in time, right?

But how do we know that the point in time is far enough back that we're getting the right data to restore? And what happened to all the data that's been created since that restore point? Can't all be bad, right?

So what Convault is proposing with this synthetic Restore capability is that they're gonna take a look at the backup. So we're not just going to LA yesterday or last week or last month. We're gonna take all of the backups and we're gonna look for the critical files that are infected, and we're gonna roll those back to the point where they were not infected.

But for everything else that's not destroyed, we are going to roll it back to the last known good of that file. So if that last known good file was yesterday, then we could restore part of the system to a month ago and another part to last night. That means minimal data loss, that means resilience all over the place.

And that is huge for people who are uncomfortable with this idea of like, you know, we gotta start taking big full backups and full snapshots all the time because you never know when we're gonna miss something and we're gonna have to roll back. The other thing that I thought was big deal was the fact that Convault is integrating identity protection into their platform. 'cause one of the things I think that a lot of people are missing out on is that you can get infected and you can clean up all of the servers, but if the attackers penetrated into active directory, they've got a foothold, they can just keep coming back.

Like, what if they changed the password on backup operators and added themselves as a unknown user, or worse yet at one that looks like a system account, they can just keep coding back in and doing everything they want to do, and you're just gonna have to keep rolling back. Only now they're watching you roll back in real time and they're combating all of those rollback points. So eventually you're either gonna be forced to pay or you're going to be extorted for whatever else and have your data dumped.

So I like where Commvault's headed with this. Uh, the, the name change of course is important because we're unifying resilience into the platform. And, and I'll admit some of the commercials in the keynote were kind of cute.

I kinda liked it. Uh, so make sure you stay tuned for more of that. And also we've got some great content coming out from the people that attended, not my just myself, but Jack Poller and, uh, Jay Krell and More Great Field Day delegates.

So make sure you're tuned in for that. We're going back to AWS because they also have something else they came out with. It's their Kero AI coding tool that has new features aimed at producing more reliable, secure, and testable code, including everybody's favorite, A CLI version.

It also has proprietary based testing to validate behavior against specifications in the ability to rewind to earlier checkpoints. The update also enables Kero to work across multiple project routes and support custom AI agents while qualifying startups can get a free year of Kero Plus Pro Kero. Pro plus AWS engineers say that these enhancements promote a more disciplined, specification driven workflow that reduces debugging stress and scales AI power development with quality rather than speed.

Al I kind of like the vibes on this one. Is kiro gonna turn the corner from letting AI just kinda write whatever looks good? Well, I think that's fundamentally what Kiro ISS all about is not having those vibes, um, moving to a, a position where you write the specification of how your application should work and the AI implements what's written in that spec.

Uh, I haven't gone hands on with this. The preview was, was launched in July and, uh, has has got a bit of buzz around. I've, I've heard a little of of people using it and I say, I really like the idea that there is a more declarative way of working here.

This is define at the beginning how the software should work and then validate that it does work. Uh, in some ways it's, it follows a little bit of, of what I liked in the test driven design methodology where you first test for the functionality that you're going to build and make sure it fails 'cause you haven't yet built the functionality and then you build functionality until you, uh, pass the test. Uh, that is one of the ways of going around.

Let's design how things should work and, and make sure they do work that way. Uh, I definitely, this, this testing by properties is a really good functionality in here as well. This is the ability to say this particular function should have this, this result.

Now build a set of tests that, uh, uh, somebody walks into a bar and orders a beer, orders 99 beers, orders minus one beer, uh, orders a car. Uh, does the software do what it's supposed to do? That that kind of building those use cases are those tests.

So unlike test, different development where you build those first, the property-based test approach builds those from your specification of how things should work. I like that whole declarative behavior. Uh, and then the ability to work with larger projects, integrating Kero into your pipelines by using that CLI rather than having to do everything through a, uh, a, a click ops.

Absolutely. This, this kind of stuff is where we're gonna see value in actually building real applications that are supportable and maintainable in the long term. Yet don't require the vast number of developers hacking on little bits of code and particularly repeated bits of code over time, uh, remains to be seen how the pricing works versus value on this.

And whether AWS can continue to deliver this, uh, a agent ai, almost a ai, um, for the long term. Uh, there's, there's a lot still to be written about the cost of delivering these things versus what you can charge people for them. So we'll keep an eye on it.

Over time, Arista and Palo Alto Networks are expanding their partnership to address the rising hybrid data center complexity. And of course, AI driven cyber threats. Uh, integrating Arista's, uh, collection of product di Ava MSS with Palo Alto's own Next Gen Firewall and, uh, Prisma Airs.

Uh, they offer a unified zero trust, uh, security and real-time threat. Quarantining centralized policy management, always my favorite. And DevOps friendly automation, delivering scalable, consistent protection across multiple data center and multi-cloud platform, uh, environments.

Uh, this sounds like there's a partnership between uneasy friends and that hopefully this is gonna be magically wonderful for customers. It should be wonderful for customers because when you look at what people are doing, especially in the cloud environment, they're leveraging a lot of Arista switches. That's where Arista has made a lot of their money over the last few years.

But one of the things that Arista's having trouble getting off the ground is a security practice. They've purchased a number of security firms over the last few years. I don't necessarily know that they've really taken off as much as we might like.

And so what do you do when you can't really build it? Well, you gotta buy something. And in this case, the buying is involved in a partnership, but they picked the best one on the block, right?

Palo Alto Networks is one of the, if not the premier security firms in the business, right? They have next gen firewall. They have a lot of cloud security offerings.

What they don't have is an inroad into those clouds through the networking team. Because as more and more people are starting to recognize the fact that networking security are two sides of the same coin, how do you displace existing installations? Well, in a lot of cases you have to partner up with a company who's displacing other things out of the network.

Arista made their money displacing Cisco. So how do you get into that network now that Arista has displaced them? Well, you partner up with Arista and Arista says, well, our stuff works really well with Palo Alto.

If you got a refresh coming up, now's an opportunity to take a look at it. I will say though, when this announcement was made, there were a lot of eyebrows that got raised because historically these kinds of partnerships lead to more engagement down the road. I'm not going to say the a word because that would be speculative at this point, but if there was a transaction to be had, this would not be a bad place for it to occur because both of these companies are very well known in their individual spaces, and I think wrapping them up would be a very unique opportunity for some people.

I don't think that that's gonna happen anytime soon though, because while Palo Alto is sitting on a lot of money, a wrist is worth a lot. And I don't think that this is an acquisition that they could really swallow in whole yet, but it would be very complimentary. And, uh, quite honestly, I, I'd I'd love to see, uh, someone like Akin do to doing some Palo Alto presentations.

'cause that could be real fun. Microsoft announced yet another major new AI partnership with Anthropic and Nvidia, but this is signaling a move beyond the once exclusive ties that had to open ai. Andro will buy $30 billion in Azure compute while invest Nvidia invests up to $10 billion and Microsoft up to $5 billion to help scale Anthropics Claw AI models.

This deal gives anthropic massive Nvidia powered capacity and strengthens Microsoft's AI infrastructure strategy as its relationship with open AI involves at Microsoft Ignite, which is happening this week, they also introduced a new IQ lineup of AI tools, work iq, fabric IQ, and Foundry iq, as well as Microsoft Agent 365, designed to automatically support business workflows as companies prepare for future of billions of AI agents. Al I think it's interesting that we've always heard so much about the partnership that Microsoft has with open ai and now they're going out and seeking out anthropic. Do you think Microsoft's playing the long game by betting on different horses or is there something else going on here?

I think this is definitely a, a hedging strategy. So we covered previously on the rundown that OpenAI pre head head an exclusive deal with Microsoft that OpenAI would run on Azure and that Microsoft would use OpenAI as its standard AI and it powers things like copilot. We covered that this type deal had come to an end and there was a much lucid deal where OpenAI could use other vendors and were contracting to use other vendors to provide that infrastructure and that Microsoft is free to work with other vendors.

So Microsoft has always been the king of partnerships partner with everybody. Uh, I see this relationship with, uh, anthropic as being part of that. They recognizing that there is no longer an exclusive relationship.

They need to have a re real relationship with a competitor to open ai in this case anthropic, and it's all part of the same partner with everybody and make sure that nobody can hold you to ransom. So open AI can't say to Microsoft, well the, this is the only feature set you get because this is what we're, we're doing. Uh, Microsoft has then no leverage to say, well, we want this other feature we need for copilot as they make a commitment into Anthropic.

Yeah, it absolutely gives them some more choices in there, uh, naturally enough, this is the AI money go round, and so Nvidia is in here as well. And Nvidia investment in anthropic means that Anthropic will hand back some of that money to buy Nvidia hardware. So when we talk about this as a money go round, it really is money changing hands back and forth in some of these places.

Uh, Microsoft continues to, uh, put money into this, uh, $5 billion, uh, invested here. So it's not a trivial amount, but it's not the $30 billion, $60 billion we've seen in in other deals along the way. Um, and Microsoft has $13 billion in open AI investment.

Uh, it really is, I think, a, a protection against, uh, open AI choosing their own path. We've seen some interesting challenges with open AI as they wanna change their, their governance and structure around there. Uh, if that represents a risk to Microsoft, connecting up with Anthropic seems like a great way to mitigate that risk.

Uh, we'll see just over time as, uh, whether this continues to grow, we see more and more money being put into, uh, anthropic and maybe a, a dial off on OpenAI. I don't think so. I think this is really a protection of the relationship with OpenAI.

Newly uncovered cyber attack shows the first large scale espionage campaign carried out mostly by ai. A apparently Chinese state backed group used AI agents to handle up to 90% of the operation, so including scouting potential, uh, victims and exploiting vulnerabilities in stealing data. Uh, the incident highlights the rapid escalation of AI driven threats and the need for strongest safeguards around these AI attacks, better detection and also industry wide cooperation in as near real time as possible to protect against these attacks.

Tom, uh, this is another arms race, isn't it? It's AI for attack and AI for for defense, Yeah. And that's exactly what we talked about on this week's episode of Security Boulevard because it really did feel like this is kind of turning the weapons back on the creators a little bit.

I also thought it was a really fascinating way that they were able to kind of slice this attack up so that multiple Claude agents were not actually knowing what was going on. It was almost like a operating in a compartmentalized cell structure so that each of the agents were returning work. It was then being tied together by other agents to do the actual exploiting so that it evaded all of the models jailbreak capabilities of saying, oh yeah, we're not gonna do bad things.

You know, theoretically it'd be like, you know, the difference of me asking how do I rob a bank versus describe what good bank security looks like. Uh, one of those things would probably set off a trigger while the other one when used improperly would probably get me the answers that I need to know how to avoid security cameras and guards and things like that. Uh, also, uh, the, the press release from Claude, uh, I'm sorry, from philanthropic about Claude was rather interesting in the fact that they said, well, we detected it and we stopped almost all of it, but almost all of it ain't all of it.

And it, some of the attacks did go out, and of course now they're probably going behind the scenes to try to figure out exactly what was used to bypass the filters and the protections and how they're going to evade it in the future. And, uh, I I think that we're just, we're starting to see the tip of the iceberg here because this is probably not the first one that we've seen, but it is definitely the first one that somebody is reporting about. So, bravo to philanthropic for at least admitting that this was going on.

But I think one of the things that we're gonna see as this goes on more and more is that people are gonna refine their prompt engineering. They're gonna be able to break these tasks down into finer and finer detail so that it really is going to become impossible or worse, yet they're gonna take the outputs from one group of AI and feed it to a separate set to actually leverage the attack. So that attribution is gonna be very difficult to trace down.

And I, I don't know if there's a clear cut answer here, because the real thing that people are saying we need to do is lock it all down so that it can't be used for attack. That's like saying, oh, well, we should get rid of all BCRs because they're only ever used to record, you know, copyrighted programs when that's actually not the case. Uh, we just, we have to find a better way to figure out how to use them.

All right. We wanted to take a closer look at a story today. Uh, it took us a little bit while to write it, of course, because there were some internet issues.

Everyone's favorite web infrastructure provider, CloudFlare had a massive global disruption that caused error 500 messages and took down major platforms including x Twitter and chat GPT along with most other AI platforms. The outage, uh, triggered by a sudden spike in unusual networking traffic impacted thousands of websites early on Tuesday morning, and highlighted the fragility of internet architecture. 20% of global web traffic flows directly through CloudFlare.

The firm is actively investigating the root cause and working to restore full service while underscoring the need for greater resilience and digital infrastructure. And I think it's kind of funny that we've run into this problem, uh, recently, al where DNS update and AWS took down one half of the internet and CloudFlare getting knocked offline by a massive traffic spike, uh, took out part of the rest of it this week. Uh, you know, they, they're still attributing what exactly went on, but we know that Cloudflare's gonna give us a great postmortem when they figure out exactly what happened.

But I want to turn it over to you and maybe ask, are we putting too many eggs in one basket by relying on CloudFlare to protect us? Because when CloudFlare goes down, we can't get anywhere. So here's the thing.

CloudFlare is very popular because it does a great job of a very specific task of delivering applications globally, uh, to your users in a way that gives them really good performance anywhere in the world. CloudFlare has built an amazing network to do this. And then let's, let's put this in context.

Although we've centralized everything on CloudFlare, we don't often see CloudFlare outages. We certainly don't often see CloudFlare outages of this scale. So yeah, the outage is extremely visible the same way any of these centralization outages are, are extremely visible, but they're very rare.

And so when you look at what is the impact on the total uptime of whatever system you're using, you'll still find that even with the, this particular failure or this type of failure, uh, you're still getting higher availability and better performance for your users across the, the last year than you would've if you ran this yourself or you tried to do some equivalent of this. So, yeah, centralization means it's very visible when things go wrong, but centralization means you can spend a lot of engineering on making sure they don't go wrong often. One of the things I've read on this, and, and it came across Reuters, is that the, the sort of triggering event here is an automatically generated config file that got too large and then caused a service to crash.

Um, this feels a little bit like what we saw with CrowdStrike where a config file was deployed out and that caused the massive outage for, for, um, CrowdStrike. Um, it seems like these config file changes need to run through A-C-I-C-D pipeline to validate they're not gonna cause problems, yet we need to deploy them out fast. You're the eternal optimist, Al, and that's what I love about you because I, I took a, a slightly different, uh, tack from this and it come courtesy of our friends at down detector, you know, the website that everybody goes to, to figure out when you're having problems with stuff.

Oh, wait, I couldn't check that one either. 'cause it turns out it runs on CloudFlare. Yes.

I, the world is better off because CloudFlare is front-end sites and preventing massive DDoS attacks. And their engineering has done a really great job of helping us extend and expand the way that that web works. And, and a lot of other things too.

They're effectively the Internet's proxy layer at this point. 1 from the trash heap of history, uh, because of bad configuration stuff. All that being said, you cannot put all of your eggs in one basket.

And, and we've learned that from a lot of things. I mean, all you gotta do is just look up the infographic from the XKCD of, you know, the entire modern internet. And then there's a little thing down there, a little Jenga piece, and it's insert name there, AWS cloud flare, uh, NGINX, whatever.

What we're starting to see is that when these outages do happen, they happen at a a, a level that is difficult to contain. And you mentioned CrowdStrike. We've talked about AWS before.

Um, there are a lot of challenges that happen when we've built something that is effectively too big to fail and more than any other company, I think CloudFlare is pretty strict in the way that they deploy things and the way that they, uh, leverage stuff. I can't fault them for this. How can you figure out, oh, the config file got too big.

When do we test for that? How do we understand that The problem is not the outage itself, it's the rolling effects from that outage. If, you know, it went down for 10 minutes, everything that checks on CloudFlare kept pinging, it kept going offline.

You know, little things that you didn't think were reliant on that. I was having a meeting first thing this morning and went to go check the calendar on somebody's website. It's offline, it's hosted on CloudFlare.

Like, oh, well that's fascinating. And so you've got to figure out how to prevent this from happening. Yeah, in some cases, you, you probably do need to have your, your load balanced infrastructure running on it, but I don't know, maybe put your status page somewhere else, host it somewhere that nobody goes like Oracle Cloud.

So you're, you're suggesting to avoid the, the problem that AWS had along with outages when they had the big S3 outage and the, uh, the website at AWS that reports on the status of a WS services is dependent on the S3 service. Uh, yes, circular dependencies like that are really problematic, but, uh, yeah, I, I still view using a a well-engineered system that is designed to continue to operate at large scale absolutely is, is way better than any of the other solutions for this. So yeah, I, I'm still comfortable with putting our websites through CloudFlare, having even trivial things go, go through CloudFlare.

But yeah, the tool that tells me whether things are working or not can't tell me if they're not working, if it's dependent on the thing that's not working. Yeah, mindful of circular dependencies. The other thing that strikes me on this is that there's, there do seem to be a lot of circular dependencies that go through Cloud Flat because what we didn't see is when the res the issue was resolved, everything miraculously started working again straight away.

It took a little while for it to filter through the various layers that are dependent on CloudFlare before all of the layers caught up with one another. You know, this eventually consistent, uh, systems that we usually see at internet scales, uh, would be interesting to see if you had centralized everything into a single place, rather than having these eventually consistent distributed systems, whether the time to get back into operation would've been longer or shorter, because of course, in a centralized place, you have to replay everything in a single stream. Whereas when you're doing eventually consistent, you're replaying in different locations and your total timeout might be lower.

Something that's not gonna be affected, I hope by any cloud outages, any security outages. Um, now I'm gonna have to make some sacrifices To all of the good luck Gods, something that I expect not to be, uh, affected is AI infrastructure field day four. That will be my next return to the United States January 28th and 29th.

The event is already filling up. We already have, uh, four companies confirmed to be there, and we're expecting a few more to roll in as well. Uh, really looking forward to kicking off 2026 for Tech Field Day with AI infrastructure Field day.

Uh, and then of course, Tom, you're going a long way afield as well. That's right. We're looking at the possibility of heading over to Cisco Live and Maya, which is gonna be in Amsterdam once again this year.

You know, I can't get enough of those little teeny tiny pancakes. com is gonna be, uh, your home for that. So when Al flies halfway across the world, I fly the other halfway across the world.

But then Al you're, you're coming back in March. Absolutely. I can't stay away.

Uh, I'll be back for Cloud Field day 25 in, uh, the middle of March. Uh, and those tiny, uh, pancakes, the puffs, uh, my Dutch, uh, sister-in-law has, uh, gave gifted us the pan to make them. So maybe come down, visit me and we'll make you some tiny pancakes.

Uh, what isn't tiny, of course, is the tech Field Day rundown. Do join us, uh, continue to join us for the Tech Field Day Rundown. You can catch new episodes every Wednesday, either as a YouTube video or in your favorite podcast application.

Rundown streamed on Techstrong tv, of course, that's part of the Futurum Group. And you can find us on both Techstrong and RUM Group, uh, locations. We'll be back next Wednesday to talk about all of the IT news for the week.

That was, and until then, for myself and for Tom Hollingsworth and for and all of us here at the Field Day team, we're wishing you and yours a great day. Great. And we you on Wednesday.