Why Sovereign AI Is Becoming Critical in Europe | Utilizing AI Ep. 11

One of our predictions for AI in 2026 was that sovereign AI would become increasingly important. And this was emphasized by AWS that their European Sovereign Cloud launch in Potsdam. This week, we expect to see an increasing focus on digital sovereignty in the coming year, and many are looking at using specially developed AI models.

But news came out last week of Apple and, uh, Google tying up for next generation Siri based on the Gemini model. So perhaps this signals a trend toward leveraging external foundational models. We'll also consider the push and pull between infrastructure like data center and power versus distributed inferencing.

On this episode of utilizing ai, featuring Nic Patience and Brad Shiman of the futurum Group. Welcome to utilizing ai, the podcast focused on practical applications of artificial intelligence from the Futurum group. Every Wednesday, we explore news and use cases of the ways in which AI is transforming enterprise IT and the industries it serves.

I'm your host Steven fst, president of the Tech Field, a business unit here at the Futurum Group. And joining me today, we have two of our fantastic RUM analysts. Let's meet who's on the panel today, Nick?

Hi. Yeah, I'm Nick Patience. I'm the AI platform's practice lead at at futurum.

Um, and I, I look at everything that's to do, to do with enterprise ai And, uh, hi everyone. Brad Shiman. I also work at futurum, where I am also a practice lead for a different research practice around data intelligence, analytics, and infrastructure.

We kicked off this year of the utilizing AI podcast with a look at some of the predictions for 2026, and we don't wanna revisit those quite yet, but I think that it's worth looking into some in more detail now that, uh, things have started and we're starting to attend events. So, Nick, let me turn it over to you to talk a little bit about what you've learned, uh, this week's, uh, at at events, as well as, uh, what you're thinking in terms of sovereign ai. Sure.

Yeah. Thanks, Steven. So, one of the, one of my predictions, um, for the year and sort of key part of our research agenda for AI platforms, there was, um, I think seven things in all, um, was about sovereign ai.

And, and so I think it's, it's, it's something that was kind of a, I guess a niche geopolitical, um, idea and sort of peculiar in some ways to continental Europe. Uh, and now, as I'm sure many of our viewers and listeners know, it's, it's expanded, uh, in importance to more or less every country, uh, in the world. And there's two ways.

There's, there's the kind of national sovereign ai, uh, issues, um, but which are, which are very important, but, but probably, um, slightly too grandiose in thinking for, for kind of enterprise ai, um, users. But down at the kind of enterprise level, I think it's, it's, it's, uh, it's gonna be a key, um, a key topic and, and this, um, and I was recently at this, uh, AWS launch of this European Sovereign Cloud. Uh, the launch was in Germany.

Uh, the cloud is in, uh, Germany, and it's an entirely separate, separate cloud, um, from, from AWS. It's not a, it's not a region, um, such as it's entirely separate cloud that will be, um, uh, staffed by European Union citizens. Um, and essentially, uh, although it's owned by the parent company, obviously that's, that's unavoidable, uh, is run as a separate, um, unit essentially.

And, you know, the, I the idea is that it can give, uh, companies and organizations, um, you know, absolute control over their data, um, who has access to it, who doesn't have access to it, where it, where it resides, um, and so on and so forth. And I think, so I think it's, and then sorts of specialized security and, and, and all sorts of other things. So I think it's exemp exemplifies one of the things we're gonna be looking at this year.

And I guess the, the, the interesting thing I think from it is that that's a hyperscaler doing that. And when you think of sovereignty, um, you know, an American hyperscaler is, is both in a very, you know, an interesting but challenging position. Um, so there's also a lot of, you know, there's a lot of organizations, uh, and not just in Europe.

This is not just a Germany thing anymore. This is to say this is a, um, to more or less a global thing, but there's lots of organizations that are looking at, um, cloud and thinking, you know, maybe I want to, you know, have, have more control by having more on premises. And so there is repatriation going on, and there's obviously a lot of data that, um, that never, that never actually obviously left OnPrem.

And so it's kind of, it's a little bit of a dance being carried out here by, by the hyperscalers. And they're getting certifications from various countries to say that they meet security requirements and compliance requirements, um, while also trying to grab a bit of that business and say, well, you don't need to have, you don't need to kind of go to an a server server and storage and networking companies and get stuff and, and deploy it in your data centers and go to software companies. You can do it all with us.

And so I think it's gonna be, um, a dominant theme in 2026 and 20 and beyond. Um, and yeah, I just thought the AWS approach was really interesting and they roped out, you know, Matt Garman was there, the CEO, um, various politicians. It was a pretty, pretty big affair, and they, they put a lot of effort into it.

And so it kind of shows you how important, um, AWS thinks it is. And, and, uh, I'm sure the others will, the other hyperscale as well as well. Yeah, and I, I would add to that, Nick, that, um, it's, it's not just about the infrastructure itself, but also about the software that runs on it.

And we're seeing, as you mentioned, uh, and rightfully so, that this is a global concern. And it's not just about legislative compliance, it's, it's about autonomy and being able to anticipate, you know, the fragility and, uh, you know, chaos that sometimes seems to, to make daily headlines for companies right now. And, um, so you're seeing in Europe and other, uh, regions that have some coordination amongst the nations, uh, efforts to, to basically free themselves from, uh, any sort of, uh, obligations they may have to external parties.

And one of the, um, the big sort of unseen bits of fallout from the tariff wars that are going on right now regards software and, um, what sort of obligations a country may have to US-based firms doing business with the United States. And so if for instance, you know, you are running Microsoft Office or Google, um, office workspace, sorry if they keep naming it different things, but, uh, anyway, if you're running that you, you sort of have obligations in terms of how you manage data and what data that company can see for your employees. And so we are starting to see companies not just locked down or take control, I should say, of the infrastructure, but uh, of the further up the stack for the software that they're running on that infrastructure.

Yeah, totally. And then a key part of what, um, AWS are doing at the European Sovereign Cloud level is also all the, all the access and identity management stack is also within, within this cloud. And so it's not because there's, you really have to look at any other way.

It's not just about the data and, and where it resides. It's about, you know, who can have access to, um, you know, the, the systems, the software and the data. Um, and so it's, uh, it's, and the, and the metadata, I mean, it's down at that level of, of granularity of, of kind of control, um, that they're, that they're talking about.

I wonder if I could ask a question about it. Um, what about the models themselves? Is there any, um, thought of creating, um, regional, uh, sovereign models that, uh, are restricted in terms of, uh, training data or, uh, fine tuning, uh, that would avoid sort of, um, regional biases Certainly, Or embrace regional biases?

I think certainly in terms of u using European, you know, in training data, obviously there's the link, the list, there's the language aspect to all this obviously. Um, but yeah, there are, there are definitely, um, you know, there are definitely, you know, specific, um, yeah, specific models. Um, there already has been, you know, there's been, you know, specific ones in, in Greek and stuff, some from Singapore and, and Japan and, and all over.

But, uh, I think it kind of speaks to that general, um, um, you know, interest in, in, in sovereignty, um, all, all over. As I said, it's, I think for, for, for kind of, for organizations, you kind of think about it as a sort strategic autonomy because obviously there, you know, no matter how big you are as a, as a, as an organization, um, you know, you're not, you're not, you know, you're not the nation. And so there's kind of two different ways of looking at it.

There's, there's the, there's the, uh, national way of looking at it, which is, you know, we're gonna control the supply chain. We've do everything ourselves. Um, which is somewhat illusion, you know, a bit of an illusion, um, even for the US and China because obviously they're both interdependent on each other, um, in different ways.

So obviously China, um, is kind of more advanced in the production of energy and has taken open source models that the approach from the US obviously, um, you know, designs, you know, the world's most advanced chips, but they're made in Taiwan. And so there's, you know, all these kind of, the idea that can be completely independent, um, is I think an illusion, but from an enterprise point of view is this kind of strategic autonomy kind of lens. I, I like to think about, um, how, how, how it should we look through And, you know, if you think about models themselves as, you know, a representation of patterns, you know, in the training data that, um, that model itself is literally, you know, a knowledge base, a database, a, you know, map of the institutional knowledge of, uh, an organization or a community or, uh, something even larger.

And, you know, it's, you know, very much about being able to represent the, the way that, uh, not just the language itself as, as Nick you mentioned, but also the nuances of the way that people that speak that language natively think about themselves, think about the rest of the world, you know, from their context. And I, I thought, you know, this, when I heard this, what I'm gonna describe in a second that they, this person was out of their mind, uh, back in 2022, uh, a CEO of a certain company that, that focused on, um, you know, diffusion models and creating images. Uh, said everybody, every country, every city, every person will have their own model that will represent them.

And, you know, what do you think about the data science that goes into training and fine tuning a model, especially at a frontier scale? It's a brute force effort that is very demanding in time and money, uh, and GPUs, uh, and the fact that we're starting to see companies, and I think we talked about it on this podcast a little bit ago with AWS and what they were doing with their, um, Azure Forge capability to, to customize models. We're starting to see even the frontier model makers, uh, give tools to the enterprise to take control of that representation of their own company in a mu in a much friendlier, uh, more accessible manner than having to hire a bunch of data scientists.

You know, it's interesting, Brad, that that brings to mind an announcement that we heard, uh, last week or the week before about, um, well, it wasn't really much of an announcement. Essentially, um, apple and Google have jointly, uh, let it be known to Jim Kramer of all people that, uh, apple is gonna use Gemini to build their next generation Siri. Now it remains to be seen what exactly this means.

And, uh, certainly we'll be watching for that at ww DC this year. But, uh, now, almost two years ago at ww DC Apple, uh, previewed a very expansive vision of what they would do. It sounds like Apple then spent a year plus, uh, sort of spinning their wheels trying to develop their own model before finally throwing in the towel and deciding to use the pretty incredible Gemini from Google instead after, um, sounds like a bake off.

Um, given all of this, what does this say in terms of using in-house models, uh, versus, uh, external foundational models, Brad? Yeah, it's, it's funny, the timing is, is kind of interesting in that, um, as we were just discussing with AWS that, um, you know, frontier model makers are making it easier for companies to take ownership of a foundational model and fine tune it for use internally. And this partnership, uh, is very much, you know, a, an extension of that idea because yes, uh, apple has a longstanding relationship with OpenAI, which is what they announced, you know, with what Steven, you, you, uh, mentioned with our early, we're gonna remake Siri idea.

And, um, you know, that was basically a handoff to open ai, uh, to, to, you know, sort of obfuscate the identity or identifiable information about the user and then pass that on to, to, to open ai. And with what we know about Google Gemini, and its open source rel near relatives, I guess you call them in the Gemma family of models, uh, that you can literally just take the, what's great about the Gemini family and distill it down to a smaller model that still maintains a lot of the capability of the larger model, and then fine tune that thing to meet very specific use cases, not just running in the cloud, but running on the device itself. And we all know that Apple's been spending quite a bit on its system, on a chip design to, to be able to, to run AI effectively on, on their own platforms.

If you look at their mlx, uh, compiler ex, what do you call it, sorry, execution environment for their AI endpoints, it's pretty impressive what the, the size of model that it can run on some, you know, on a laptop, for example. So I think it's at a good time, it's a good timing for Apple to, to make this, uh, joint announcement, not a press release, not not anything of any, any note, uh, in our usual circles because we call it An official leap. Yeah, right.

We, it's, it was called literally an, an official joint statement. My goodness. A, any anyway, you know, if, if, if Apple is serious about preserving user privacy, um, then this is a good partnership to execute on that, to go beyond what they had, you know, like, like you said, Steven tried internally and perhaps didn't succeed with, or ostensibly didn't succeed with.

I think it's, yeah, it's interesting because they, they were, so, apple was so far ahead when they bought the original technology from SRI, didn't they, Stanford Research Institute, wait, I can't, I can't remember when it was the nineties or two thousands or something, I dunno, long time ago. Um, and then, you know, it just sort of, you know, sounded quite impressive when there were no alternatives and then suddenly when the were alternatives didn't, um, to be completely blunt, I mean, I'm a I'm an Apple user, you know, love, love, love the kind of vertically integrated stack and everything and everything like that. Um, but it's, um, I don't use it, um, Siri because it wasn't, it was no particularly functional for me, but, so this should make quite a lot of difference.

But as you say, the privacy aspect, that was, that was the pitch, wasn't it? That was Apple's pitch. That is why we're different.

And it's, I wonder, I think you're right from a technical point of view, this, this will enable 'em to maintain that. I wonder how important that still is for users, um, given the amount of, you remember the kind of early days of, of, of generative AI when like, do not put things, do not upload things into that. Um, well, you know, guess what?

Yeah, I think everybody's doing a lot of that. Um, so I, I, I do wonder how important that is, but, but, but also, Or even, even if it's possible, right? Is there not, did we see, last week there was some legislation put forward in the European Union to, uh, basically, um, scrape data before it's ever encrypted in apps like WhatsApp or, or Signal, Alright.

Yeah, yeah, yeah. I think the the other, in other places too, yeah, the sensitivity or the lack of publicity they want they were seeking is probably got more to do with the, um, the judgment against, uh, Google, um, from last year totally 20, 20, 25, wasn't it? Which as far as I'm, I read, you know, that bar's Google from entering maintaining exclusive agreements that last more than one year.

So this presumably is not an exclusive agreement. So if Apple were to go back to open AI or try anthropic and, and, you know, they could sign another deal, um, so, which is, which is interesting. I mean that's, I the, uh, the judge didn't say, you know, specifically this company with this company and, you know, and these kind of deal, it just said any, any, any exclusive deal that lasts more than one year.

Um, so, so I think it's, um, you know, that that's probably got something to do with, um, some of the, the lack of, uh, shouting this from the, uh, from the rooftop plus the money involved, which I know they didn't announce, but there's numbers out there. Well, Right. What is the money situation?

Because we, we do know that Apple, um, pays goo, or sorry, Google pays Apple quite a bit of money, uh, to make their search engine the default engine in Safari. Mm-hmm. And that was part, that was the reason for the, um, part of the reason for the antitrust, wasn't it?

Exactly. And then now all rumor is Apple is gonna pay Google what billion a year, um, for this. So, Um, I'm sure they're completely separate.

Yes, I'm sure they're totally separate transaction, but the Gemini just shows reinforces the importance of Gemini it, the little folks in, uh, here, in here in London, um, you know, behind all that, you know, it's, uh, now actually, uh, just turning it into absolute real money, um, along with Google Cloud's performance last year, and, um, and, and what we think it'll be doing this year as well. It's, uh, it's a real business, this, uh, these AI models. Yeah.

And I think that's actually the most important thing. In fact, um, depending on what we see about the money changing hands here, we could be looking at the most lucrative AI deal in history, uh, in terms of actual productive, you know, to the point of this podcast, right? You productive use of AI utilizing ai.

Um, if Apple is indeed paying Google billion plus dollars to, to use Gemini, this would be one of the, one of the more lucrative or maybe the most lucrative investments. I do wonder as well, um, what this means in terms of the private cloud compute, which they announced back at, uh, ww DC in 2024 as well. It was a great idea, which was to the point that both of you have made that Apple has developed quite a lot of silicon, uh, compute horsepower, and they are going to seamlessly extend that as a private enclave to a version of their apple silicon running in a private cloud and enable, um, basically cloud bursting of AI processing as needed.

Um, do we want to assume that Gemini is actually running on Apple's private cloud compute, or do we wanna assume that Apple has just sort of waved their hands, and maybe this is running on Google's infrastructure, uh, because of course they've done tremendous things with their TPU hardware and so on. Um, I, I guess there's no hint yet about how that's running. Is that true?

I think you would be foolish to conclude that, um, they're not going to avail themselves of every aspect and avenue that they can to effectively meaning cost effectively serve these Gemini models to their, their somewhat sizable user base. Yeah. Yeah.

Because it could just be the model running on Apple, but Yeah. Yeah. And, and clearly I think that, I think we can assume that it's gonna run natively on Apple local devices, right?

Absolutely. I mean, it's not gonna be completely in the cloud, right? Yeah, no, that's what I'm saying is it's gonna be all of the above.

It's, it's depending on what you're doing, I would imagine you will see a graceful handoff of functionality, um, depending on what you're trying to do. If you're just trying to replicate what Google has with their circle to search capability on, on their phones, that's gonna run locally, why wouldn't it? Um, but maybe the search side of that transaction, not just the extracting the image, but the search of that image is something that you could very well and probably should hand off to a backend service somewhere.

Yeah, and I guess it also just shows from a kind of higher level, um, more abstract point of view in, in ai, the, you know, the, the importance of inference and, you know, know inference is a, is a business, and this is inference obviously, because, you know, Google's gonna handle the, the training of the, of the frontier models, and then Apple is gonna build upon that. Um, but every time, yeah, we're using it, that's obviously inference, and that's what they're, um, essentially, um, not, not what they're paying for because they're paying for the actual model, but you know what I mean, it's kind of, yeah, that's how, that's how this, uh, the value is gonna get realized for the, for the users. So we're doing a lot of, um, sort of guessing here about the specific deal with Apple and Google.

I wonder, uh, if you all can maybe take a step back, what does this mean for the market in 2026 and beyond for ai? Does it look like, uh, this is, uh, what the future is going to be for enterprises trying to deploy AI applications? Are they gonna put themselves in Apple's place and do a bake off and pick a model?

Um, to an extent, yeah. I mean, I always caution that, uh, in most cases, um, the model is not the application, and so, you know, you're not, um, you're, you're, they're not gonna be necessarily, um, talking directly to the model. It'll be layers of abstraction.

Um, but, uh, but yeah, you're gonna end up paying for, um, you're gonna end up paying for this. And this is where, um, you know, the kind of, you know, some of these other sort of trends we expect to, to, to see, um, you know, the new metrics of inference time compute and that, and that, that kind of thing was gonna become really, really important. Um, because obviously it depends what you're doing.

Obviously, if you're trying to create videos, um, you know, the, the expense of that compared to, you know, responding in text to some sort of, you know, text prompt is, is just vastly different. So, so yeah, I think, I think, I don't think you'll see a necessarily major enterprises directly, you know, saying we want to license Gemini. I think, you know, th there's more that there'll be, they'll be buying into a stack, um, or at least Google hopes they'll be, um, and, um, and that will, that will be, um, a key part of it.

And that's kind of, you know, the Gemini enterprise stuff is, is, is like that. It's, um, obviously Google's gonna keep extremely tight control of, of the, of the models. Um, and, and then, then that contrasts with the open source approach of, you know, there's lots of, there's, you know, thousands of open source models out there.

So, so organizations do have a choice of how they go about this. Yeah. And if they can, you know, basically build a, a sort of control plane for their applications that abstracts the exact model underneath, you know, to basically, uh, route the request to the most appropriate model being the model that's best able to answer the question or to carry out the task and to do so with the right, you know, requirement for latency, for privacy, uh, for security, for, um, concurrency, for instance.

All of these little things that, that make up the decisions that drive technology investments around building software are, are very much at play, in play here. And that's why, you know, what we've, and we've said this on this podcast and, and elsewhere that, you know, models are less of just a chat response model and more of a platform that is built on a rich set of capabilities that are exposed through APIs and SDKs. And so if I, as an enterprise builder, uh, am going to use, uh, or if I'm, if I'm gonna run a task, I want to know a, does this model do caching, for example, for just one example of many measures, you know, can it do prompt caching so that I don't have to keep saying the same prompt back and forth?

Does it do speculative decoding to, to optimize, uh, what the transaction is happening inside the model itself? Does it have chain, uh, uh, what am I trying to say? Uh, community of experts.

So what is that called, Nick? Sorry? Um, mixture of experts, MOE model, uh, mixture of experts.

Yeah, sorry. So all of these, these things that, that aren't just a model, but are the surrounding, you know, uh, sort of infrastructure of the model are what are, what are gonna drive a lot of purchasing decisions in the enterprise? Yeah, it does seem as, as was the case with enterprise software as well, that the platform is more important than the underlying, um, you know, uh, infrastructure components.

And I think that that seems likely to continue. But speaking of infrastructure components, um, one more thing, uh, that came up that was interesting. There's been a lot of talk about, um, 2026 marking a transition from the sort of heavy GPU supercomputer data center model or finance model at least to more, uh, focus on inferencing and distributed compute and lighter weight.

And we've just been talking about that a little bit, but at the same time, there are still investments happening in data center power and energy. Um, what are the thoughts, uh, that you have about this sort of, um, one way or the other, uh, direction of the industry, Nick? Yeah, I think that will, um, that, you know, the last thing you said there will continue in, in 2026.

I think the, the, the issue for the data center industry, um, in this year is obviously gonna be, yeah, the, the, the power and calling issue and the, and the energy is the major, you know, energy is a major bottleneck. They understand this very well, the people, the organizations that build that acquire land and build data centers, but it's filtering up to the enterprise as, as this is, this is gonna be a challenge, um, for, for, for organizations overall. So as you kind of, you stuff more and more into a rack, the power cons, the power requirements for that rack goes, you know, from sort of 15 kilowatts to a hundred kilowatts to, to more and more and more.

And, and obviously the idea is obviously you want to stuff, as, you know, cram as many of these into a building. Um, and then of course, what that results in is a lot of heat, um, but a, a lot of power to, to, to, you know, run it and then a lot of heat generated from it. So, um, you know, I think this year we're gonna see, and we've seen a little bit of it in 2025, uh, data center build outs will get delayed due to the lack of, um, energy.

Um, there's obviously an interesting arguments about the kinds of energy and, you know, the kind of in front of the behind the meter, uh, renewables, non-renewable, the contrast between the US approach and the Chinese approach. But always already in the last few years, we know of many smaller, um, you know, data center operators that were kind of gonna, where the power is rather than where the customers are. Um, and so, yeah, that, that's been happening, but I think it was, um, kind of some delays.

And I also think, um, you know, it makes liquid calling mandatory. And so for those organizations, for the data centers that would, would not, not having that, they have to be retrofitted and for the organizations that that sell that equipment, um, you know, I would imagine it's gonna be, you know, it's gonna be a pretty good 20, 26, 27, 28, 29 onwards and onwards and onwards. And obviously a lot of these are the big server companies, um, but also the, you know, the companies like Google that, that build their own data centers or build don't necessarily build themselves, but they build the equipment, um, that that goes within them.

Um, and Amazon, the same kind of thing. And Microsoft the same, same way. So I think it's, it's this, um, you know, air cooling is sort of hitting a physics wall.

Um, there's only so much air you can blow across a rack to keep it cool. Um, so I think, you know, this is, this is something we're gonna be looking at pretty closely in, in, in 2026. Yeah.

Unless you put it in space, then, then it's pretty cool. That's true. Um, clearly, but yeah, man, I, I, I feel like, um, it's not just about the power, but also about the actual infrastructure itself.

And by that I mean the sand that makes up things like RAM and things like NVME drives, uh, in particular that are gonna drive a lot of the economics of the data center. And, uh, I heard earlier last week from a, a vendor that specializes in, in storage and object storage in particular, say that they're already looking at a, a two a, a magnitude of two times the wait, you know, for getting, you know, that those, those basically NBME drives for their customers. So if you're waiting two years to, to get drives, what are you gonna do?

You're, you're gonna optimize the drives you have. And, uh, this vendor, uh, part of their go to market is now going to be saying to their customers, you've already spent X amount of money on your hard drives in the data center. Let's do two things.

First, let's use our management system that gives you, you know, an X increase, fold increase in the amount of data that you can actually store on this thing. And second, let's offload some of the workflows that may normally have sat above that in something like a database, for example. Let's just push it down into the storage layer where it can be run more optimally.

So you're using fewer watts, you require less cooling, and you are, you know, consolidating those workloads in a, in a very effective manner within that existing rack. That's, that's pretty fascinating to me. It's not about let's stand up a new nuclear reactor in a new data center.

It's maybe we should optimize what we have. Yeah. Very pragmatic approach to the, uh, to the, the problem, which I think, think, yeah, many of our, our listeners will, uh, understand and, and, uh, wanna do, Well, certainly The sand must flow.

Yeah, we, we've gotta be pragmatic because, um, shortages of RAM and storage are, uh, just everywhere right now. And so we've gotta be thinking about how we're gonna optimize the, the commodity we have. And of course, that could affect everything we've discussed here.

So, um, this is, uh, getting a little bit long this week. Thank you so much for, uh, weighing in here on these, uh, seemingly disconnected, but actually quite connected topics, uh, of what's happening in the industry from, uh, digital sovereignty to, uh, apple plus Google plus question mark to, uh, the, uh, push and pull between infrastructure and, um, raw materials and, uh, AI applications. Uh, before we go, uh, let me quickly check in with both of you on what your, uh, where your research is taking you this week and, um, what you're going to be doing.

Um, Brad, uh, what's new with you? Well, I'm, I'm actually, um, looking at, uh, a new forecast that, uh, we're finalizing this week, so hopefully I'll have that online, um, within another 10 days or so. And, uh, like Nick has with his, um, survey he is working on, we're, you know, we're always, we're always active building out new research, uh, over here at rum.

com, because you'll be able to gain access to a, a lot of this research. We don't gate everything we do, we, we try to, you know, share our insights as broadly as we can. And for me, I guess I finished my, the survey.

Um, it's, it's going to the field, um, it's in the field, um, by now by the time you listen to this. And so we'll have the results of that, uh, in a few weeks time. And then we'll be publishing, um, reports on it, we'll do, we'll do podcasts on it, we'll publish some of the data, and I'll be doing some presentations to clients, um, as, as to what's going on.

And always with all these surveys, you're always trying to have a, a mixture of longitudinal questions, which kind of gives you show your patterns over time and then trying to ha keep up to date, uh, with what, what's going on, uh, in the enterprise. So that's, that's what I'll be working on. Excellent.

And, um, as for me, um, we're gonna be hosting AI Infrastructure Field day next week, um, tune in live, uh, Wednesday and Thursday and Friday for presentations on a lot of the infrastructure that we've just heard about. Uh, very much looking forward to that one. And of course, we've got another AI Field Day shaping up for, uh, in, uh, Q2 that is already just, uh, bursting at the seams with companies, uh, joining us for that.

So keep an eye on the Tech Field Day socials for that. Thank you for listening to utilizing AI today. Uh, if you enjoyed this discussion, please subscribe on YouTube or your favorite podcast application and consider giving us a rating or a review.

This podcast is brought to you by the analysts and experts at the RUM Group where Insight meets ai. For show notes and more episodes, head over to text ai, the utilizing AI YouTube channel or the text TV app. Thanks for listening, and we'll catch you next week.