Techstrong TV January 8, 2026

Hey, everyone, welcome to another Text Drunk tv, uh, interview. I'm really happy to introduce you to my next, next guest. It's his first time here on Text Drunk tv.

His name is Kevin Aliko. I hope I got that right. Kevin is the, I guess CEO of Emeritus for Syntax.

Is that, is that correct, Kevin? That That is correct. That is correct.

Fantastic. Welcome to Tech Drug tv. It's always good to have another New Yorker arm with me, so I feel I feel comfortable already.

Um, Kevin, before we jump into, we're gonna talk about chief data offices and, and data management and so forth, but let's talk a little bit about Kevin. As I said, you're a, you're a New Yorker, lifelong New Yorker, but give people a sense, kind of, of your journey, Kevin, of how you want to be here, And I appreciate the time. Thank you, Alan.

So, uh, as you said, lifelong New Yorker, um, born and bred. Uh, I started my career in financial services, uh, worked for, uh, chase Matton Bank. And if I talk about the projects I was working on at the time, it was, uh, uh, working with 37 45 cluster controllers in branches.

And the new technology at that time was bringing local area networks into all the branch networks. So, did that for a while and modernized, uh, chase Bank back in the day. And then moved down to, uh, wall Street, working for a real-time market data company.

And at that time it was really taking, uh, I'll say all the, at the time it was all based on tandems, taking tandem newsfeeds and bringing, uh, stock information down to traders floors. And, and we built, uh, an application based on Windows and brought in all those tickers and news feeds, and brought realtime stock information back to, uh, the brokers. And then at that point in time, started to move over to, uh, the vendor side worked from companies, uh, Novell.

It was part of, uh, uh, an acquisition with VMware SpringSource. So it was everything Sure. In terms of, um, building Java based applications.

So at the time, uh, Paul Moritz was the CEO of VMware acquired SpringSource to be able to bring and build applications on type of that virtualization technologies. Uh, stayed there for a little while, then moved on to another company that was really focused on bringing ERP applications to the cloud. We were acquired by, uh, EMC at the time, then Dell, and then that was, brought me to Syntax about five years ago.

So, um, wow. Yeah. You know, that's, uh, that's quite a history.

You've been a lot of, a lot of your history revolves around that EMC, VMware kind of conglomerate, right? That it It was, it was all about it. It, it, it blended between application and infrastructure and really those core mission critical applications and how to provide, uh, the best set of services around those core applications.

So, yeah. Yeah. Very cool.

That's great. Great, great, great. Uh, story, Kevin, I feel like everyone's heard of Syntax, but very few people really understand Sure.

Syntax. So if you wouldn't mind be, be the, uh, the translator here, how would you describe Syntax to our audience? Absolutely.

So, uh, and it, it really attracted me because like you stated, I had heard of Syntax, but never really understood the full history. And it, it really amazed me at the time when I was looking at the organization. They were just shy of about, uh, 50 years, uh, within the industry, which kind of shocked me.

Wow. And when I talked to the founder, and the founder is still here with us, working with us and still leading us, which was great. Um, they started out as a consulting company, and they morphed into actually building their own ERP back in the day during their, during the seventies.

And actually, we still have, it's probably about 35 customers still running that ERP that we wrote almost 50 years ago. And, um, at that point, they started to partner with, uh, companies like JDE, and then that got acquired by Oracle and PeopleSoft, and then they morphed into EBS and then started other acquisitions around, um, SAP. So our lens has always been through the ERP, so that highly structured mission critical, important data that runs the organization.

But as you know, um, the ERP has what I always say is a large gravitational force. It, it ties into all the rest of the business applications. So the lens is always through the ERP, but then we dive out into the rest of the data and the rest of the organization to ensure that you're getting the, the most and most impactful usage of that data, uh, throughout the rest of the business.

Excellent. 50 years. I know.

Wow. Think about that. com.

I, I helped take a company public. We were in early, what they call a SP application service provider. And so we were offering hosted versions of PeopleSoft, hosted versions of Oracle.

Yep. And, uh, Lotus Notes and, and others before this, you know, VMware really hasn't caught on, it isn't the late nineties. Sure.

There is no cloud, so to speak. We're delivering this stuff. So multi-tenant was kind of a fan too.

Yeah. And, uh, even, even bandwidth, you know, you're delivering it over T one at best lines or at worst, hopefully, you know, at best was a T three lie. It was, those were wild, wild West days.

And They were expensive too, back in the day. Oh my God. Talking about about 45 grand.

I think it was like about $40,000 a month for a T three line. But what I was talking about Being down on Wall Street, we were using the modern technology, ATM frame relay and IDN dial back up to be able to support these brokerage f*****g so, Well, you always had to have, in case you stuff went to hell in the hand basket, you had those moments. Um, anyway, so your CEO of Americas, which kinda leads me, we leave that they probably have a separate business unit for the Americas A a.

Absolutely. So we are a global organization, um, significant business within, uh, north America. We have offices that are in South Africa, Germany, France, London, global, uh, within Mexico, within Slovakia.

So it's a little bit over 3000 employees at this point in time. Um, but, uh, I, I run the North American, uh, business for syn pax. Fantastic.

Great, great, great story. So Kevin, you know, the whole ERP business, it, it, it, look in the seventies, you were absolutely pioneers, right? But the ERP business has really undergone some drastic changes, evolutions, especially in the last two, three years with the advent and rise of the, all of this AI and, and so forth.

And, and I would, if I had to characterize it, I'd say it's been a return to the supremacy of data, right? ERPs, whether you hate 'em, love 'em, what whatever you feel, they're only as good as the data you put into them, right? Yeah.

And, and so we've, we've made a lot of strides in automating, getting data in, uh, observability kind of things and, and acting on that data. And at, at the same time, you know, there was, we started off site a couple years ago, Kevin called digital CXO, because all of a sudden everybody had a C level for everything, right? But one of those CXO titles was Chief Data Officer CDO.

Yeah. And I, and I'll be honest with you, a little confusing, what exactly is the CDO? What is their role?

What, what's, what's kind of their, you know, what's their charge? Yeah. What are they supposed to be doing?

And, and as you and I were talking off camera for a lot of these CDOs early on, it was kind of a, a governance risk, you know, GRC kinda role where, hey, we had to make sure we know where our data is, what data we have, and that we are, you know, from privacy regulations and everything else, doing the right thing with it. But, but that's kind of morphed now. I think what, you know, you, you've got a front row seat on the battle front here.

What do you see? Yeah. So, uh, right now, to me, and I, I think that chief Data officer is honestly probably the most exciting role right now.

And as you were saying, originally back in the, when the role first came out, it was strictly about, honestly, more security, governance and compliance, which seemed like a, uh, I would say not to offend anyone, sometimes a boring role because you're, you're, you're within the organization and you're putting handcuffs on everyone. And it was about restriction a lot of times, and right. All the different technologies coming out.

And you see the, the maturation of what's happening with AI and analytics. It's really driving the importance of data within the organization. And now that chief data officer, and in a lot of cases, it becomes Chief Data Officer analytics and ai.

And that person now is not about restriction, but it's about enablement with guardrails. And I, I, I think it's such a critical, and to me, I think 2026 is that inflection point for that individual in that role within the organization to really be a key business, um, enabler and really drive the organization to competitive advantages and using data. 'cause what I look at it historically, data was always an IT asset.

And now this chief data officer really has the opportunity to drive and understand that data's really a business asset and it's gonna drive our, our, the company's future growth. Sure. Is, I mean, data is, is the cornerstone of, of your company's crown jewels of the assets, right?

Because your ERP doesn't work without it, but either does your CRM either does anything you have without your data and, and, you know, the role of the data officer is, is I, I guess to make the company aware of what you actually have in that asset, right? EE, exactly. And it's not a Blow.

Yeah. And, and that's where sometimes I think it could be challenging, dependent upon where that person sits within the organization. I think this individual really has to have a seat, a pie, and be at that table, because, as I said, they're really an enabler with guardrails.

And if it's done properly, because previously, I, I think a lot of the data was really centralized, and that became the bottleneck. And what has to happen today based upon all the technologies and the speed of which the business has to move, it has to become decentralized. And you need that chief data officer to really, um, put that framework in place to allow that decentralization and allow the innovation to happen as close to the business as possible.

And I think this, to me, this is the, the most important thing, because over the years you've seen it and business really becoming closer and closer at this point now with all the different technologies. It's, it's not just close. They're, they're one in the same, and they're intertwined.

And that chief data officer really is the one that's providing that framework, those guardrails to allow that innovation, acceleration, and competitive advantage for the business. Agreed. I, I couldn't say better myself, uh, you know, Kevin, but there's also been, again, with AI and some of these newer technologies, but even before the whole generative end, now, agent ai, I think, burst on the screen, on the scene.

Um, there's been a lot of progress in getting at this data, right? It, it no longer is some AM offers, am mafer blob living somewhere, right? It is, data is dispersed throughout our organization, but of course, the problem was what is that?

What's in that data? What does that data say? What, what actionable intelligence can we gather from that data?

Right? And as you said, how can we allow our users to access that data, contribute to that data, analyze that data in a safe, secure, fast way, right? EE exactly.

Where it, it travels at the speed of business EE Exactly. And I think that that's really the, the problem that the chief data data offer data officer is solving from the perspective of, as, as we were talking about the ERP, that was always very highly structured, well understood, and governed data. So we knew that data was clean.

As you get further and further away from the ERP and closer to the business, you start seeing a lot more of unstructured data. And the problem was, is that, as we talk about AI analytics and all the other data technologies, the, the pace at which that was, um, improving and maturing was significant. The piece of, I would say the, the data cleanliness and the, the data management didn't mature at the same rate that these technologies were.

So what we're seeing right now is that everyone is trying to do something with AI and analytics. And what they're finding is they're, they're stumbling a lot within the pilot phase, and they're having a hard time demonstrating the ultimate business value. Because as they get deeper into it and try to solve the business problems that are presented to them, they realize that, as we were talking about in the very beginning, data is that new currency date, AI and all, and analytics and all these other technologies don't solve data problems.

They, their, their outputs to, and they run on Data E Exactly. So they need the data. They, they, they're realizing right now, they're like, oh, we can't really solve this problem because the data we have isn't accurate.

It's not clean, it's not making sense. It's given us hallucinations in terms of, um, the, the answers that we thought we were gonna get are just completely wrong. So they're having to take a step back.

And to me, that's, um, it's a demonstration that you didn't have a chief data officer and didn't have that, um, process in place. And right now, these new technologies are exposing what the organization hadn't been doing. So that's where, unfortunately, you've seen a lot of people stumble and they're saying, oh, you know what?

AI really isn't gonna work. And it's not about the technology not working, it's about the organization and having the mature process and having the ability to access good, clean data that can represent what the business needs to lead it to a competitive advantage. Gotcha.

Kevin, we're running low on time. I want to bring it home here. Sure.

How did Syntax help with this? So, um, as I said, from from the beginning, we've always looked at organizations through the lens of the ERP, which was really that structured data. Through the years we've had the ability to be able to go on these customer journeys and bring it out closer to the business.

So we've been working for a number of years now from an analytics and even from an AI perspective and helping organizations through this. So we have a, a number of, uh, customers. We have a, a significant team in place to be able to meet the customer where they are within their journey.

It's not only the ERP journey, but what I would say is their data journey, and let's understand where you're at. And a lot of times, times, what we're doing is we take a step back with the customer and provide a level of workshops with them to see where they are within their journey. It's a very honest and transparent conversation to make sure that we're not setting ourselves up for failure.

Let's find the right set of outputs we can provide and the right, um, use cases that if you're not really there with your data, let's not try to fake it. Let's get there. But let's also find the low hanging fruit that we could still provide value back to the business as we're actually going through that data cleansing and, and that data governance process.

So it's really getting all aspects of the business together in one room and having that honest conversation and mapping out what's gonna be, uh, the best move for that organization to move forward on this journey so they can continue to move forward versus sitting and going around in a circle and finding, uh, uh, a number of failures and not being able to move forward. So it's guiding them on this journey. Love it.

Good stuff. Kevin. One last thing.

For people wanting to get more information on Syntax, where do they go? com. S-Y-N-T-A-X-T-A-X.

That's it. All right, man. Hey, Kevin, thanks for coming off here on Text Trunk TV and talking little chief Data officer at Data, uh, challenges with us.

We appreciate it. Keep up the great work at Syntax, come back and visit us. Absolutely.

Always love to t talk to a fellow New Yorker, so really appreciate it. Absolutely, man. Thank you so much.

All right. Forget about it. Ke Kevin, Kevin Aliko, uh, CEO of Americas for Syntax here on Tech Trump tv.

We're gonna take a break. We'll be back. We're kicking off 2026 with a look at the first announcements from Nvidia and a MD at CES.

Vera Rubbin is shipping Helios here and a MD and Intel launched embedded processors with AI capabilities for mobile and Edge. We're also seeing more AI in the physical world with it NVIDIA's, um, all po uh, Osmo and Cosmos. Nvidia is also Aqua hired, uh, grok, which suggests an increased focus on inferencing in 2026.

And we'll look at Accenture's acquisition of faculty AI and predictions for the rest of the year. Welcome to utilizing ai, the podcast focused on practical applications of artificial intelligence from the Futurum group. Each episode brings together diverse perspectives to 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 FoST, president of the Tech Field Day Business Unit here at the Futurum Group. And joining me to kick off 2026 are two fantastic panelists. Let's meet who they are.

Hi everyone. Brad Shiman. I'm VP Practice Lead for Data Intelligence, analytics, and Infrastructure at the Futurum Group.

And, hi, I'm Nick Patience. I'm the AI PLA platforms practice lead at at futurum. And as I said, I'm Steven FoST.

I've been covering AI here on utilizing tech and utilizing AI for a few years. As I said, we are recording this, uh, January 6th, uh, right away here as, as 2026 has started. And also today is the start of CES, which is one of the big industry groups, uh, or industry expos that happens every single year.

Now, CES is not an enterprise tech conference, but there certainly is a lot of AI happening. Uh, so although we can't dive in too, too deep because not all of it's happened yet, uh, what's caught your eye coming out of CES so far, Nick? I guess the, the inevitably the NVIDIA's announcements around Vera Rubbin, although, yeah, not, not necessarily news, talking about, you know, production in volume and shipping later this year.

Um, and then, um, AMD's kind of counter with, its with its Helios, um, you know, rack system. Um, and it's just interesting contrast the way, you know, the two companies, um, you know, are operating in the, in the space. Obviously, Nvidia, um, has a, has a dominant market share as we know in it in GPUs.

And so a lot of what it's talking about is increased vertical integration. Um, so not only hardware, and not only chips, but also ENV Envy link. And then of course, the crucial, the software element, um, including a bunch of new frameworks, um, that it put on GitHub.

And then a AMD obviously has to, you know, uh, reply with a more multi-vendor, um, ecosystem approach, and it has a UA link alternative to MV link. So I just think it's an interesting, um, kind of rail pique of, of, of, of what happens down at that, that chip layer in, in, uh, in AI infrastructure. And I think it's interesting to, to think about the Vera Rubin, um, not as a chip like we did when we thought about, or we heard about Blackwell for the first time, for instance.

Um, but it's more of an architecture. It's more of a platform, like it's a, it's own little supercomputer. Like, like you said, Nick, you know, it in incorporates memory, networking, storage, and compute.

And, uh, you know, that alone makes it something that's, that's going to, I I would say, appeal to a lot of where investment is heading right now within the AI space for data centers, uh, for companies looking to maximize that vertical integration that they, that they see within in, you know, innovations like the Vera Rubin platform. But for me, I wanna say that what struck out, struck my, uh, or tickle my fancy with was cs, uh, was the return of the Dell XPS line. Okay.

Zero ai, but, but a beloved brand. Uh, hopefully everyone listening is old enough to know what that is. Oh, yeah.

Uh, yeah. So these, these are like gorgeous laptops built for gaming, built for, for high performance. And, and I, I applaud them, especially in this time period wherein Ram is so difficult to get in rolling out, you know, this 32 gigs for under two grand with a, with that name as, uh, a backer.

Hmm. Love it. Yeah, absolutely.

And, uh, I, you know, if anything can, uh, should be competing with, uh, with the, uh, the prevalent, uh, MacBook Pro, uh, contingent out there, it should be Dell's XPS. I know, uh, our friend Keith Townsend is a big, uh, fan of the XPS line as well. Uh, oh, is he just a great group overall.

Uh, you know, and it's interesting on that note too, in that we're seeing a little bit more diversity from these companies in terms of these, these platforms. Um, a MD is here with, uh, rise in embedded processors. Intel's Panther Lake is here, which has, uh, AI capabilities as well.

So, um, on the client side, um, you know, if if 2025 was the year of the I pc, I think 2026 is also the year of the AI PC And the Edge too, right? Yeah, Stephen and, and Nick, it's, uh, when you look at Nvidia rolling out their Jetson 7,000, I think that was at CES, and you get basically a Blackwell architecture, uh, if for under, in under 70 watts, uh, power consumption, um, that, that's pretty amazing. For what, what, two, 2000 again, what a price 200 bucks, uh, assuming you buy a lot of them, it's, uh, it's, it's an interesting time for hardware right now, and I, I we're seeing a lot more diversity than I would've expected in this market.

Yeah, It's an interesting kind of, as we, you know, interesting sort of base level for, for the robotics, uh, and the physical ai, um, you know, explosion that we're expecting to happen this year and, and, and next year, I guess. And, uh, obviously those companies we mentioned are, are gonna be at the forefront of all that. Yeah.

Let's talk a little bit about that physical AI concept, because of course, uh, at Nvidia is here with, um, let's call it Alpa Mayo. Uh, I wasn't at CES, so I don't know how to say this. Um, ALPA Mayo, uh, Brad, what is this?

None of us know. Uh, it, it's, it's actually their set of models are family models and supportive, um, you know, frameworks, uh, all geared around robotics. And so, um, you've got specializations for things like Alpha Sim and you have, uh, a bunch of frameworks for designing, um, uh, training runs for robotics.

Like with, you have a visual visual model for a robot that's a, a restaurant robot, let's say that there are very specialized training runs that you must do in order to train the robot to flip an egg properly. And with Al Mayo, um, what we see from, from Nvidia right now is this like massive specialization, uh, of, you know, helping their customers and their partners build out this, the robotic solutions on top of their own, uh, set of models like, uh, the Tron, uh, three family, and which includes gr, which caught my attention in particular because, uh, you know, Nvidia released at the same time a full collection of like thousands and thousands of, of data, uh, data points for training. Sorry, that's the wrong word to use, but you know what I mean.

They, they've actually opened up, uh, training data that's completely open source. It's on GitHub, oh, sorry, not GitHub, but hugging face. You can download it and use it, uh, however you see fit.

And they, and the Osmo, what was it called? Osmo Robotics, um, workflow development cycle thing, um, for orchestrating, you know, synthetic data generation, um, using their, uh, cosmos, uh, world models as, as, uh, Nvidia calls calls them. And so I guess that that's, uh, you know, that's, and then I think they also announced to see us, you know, more cosmos, um, foundation models, um, transfer predict reason, um, and otherwise other ones like that.

So yeah, it's a lot of, um, you know, as, as we know with, with NVIDIA's announcements, uh, although everybody kind of focuses on the chips and love to see Jensen standing there holding something physical, um, yeah. In order for, you know, all these things to actually, uh, make a difference in the world. This, the software is increasingly is so important.

It's absolutely crucial. Um, and, uh, you know, NVIDIA's extremely well placed, um, there, and a lot of it's open source, um, but there's also crucial layers that are not. And so, you know, that's how, uh, that's how they play, play, play Coda.

Yeah. Yeah. Coda in the ultimate.

Absolutely. Yeah. Totally.

Well, uh, you know, I mean, who, who can blame 'em? I mean, they are a, a, a for-profit company, and they're certain, certainly raking in the profit right now. Um, we also heard of an acquisition, or sort of an acquisition by am or by, uh, Nvidia, uh, over the, the holiday.

Um, one of the companies I've been following closely for a long time is Crock with a Q, not the other Crock that also made news this week for a reason that I'd rather not talk about. Uh, again, um, gr with a q they, they were a, an early hardware startup in the AI space. A bunch of Google engineers got together, um, lately they've been, um, using their, uh, inferencing hardware as a, uh, platform, uh, you know, basically developing, uh, enterprise applications around ai.

It's great stuff. Uh, apparently, uh, the company has been acqui hired by Nvidia, um, uh, even though it still kind of exists. Um, we talked about that on the Textron gang.

Uh, what's your take on Nvidia plus GR with the QI mean, I, yeah, it's, it's an, it's a weird, um, well, from what we know, it's a slightly weird deal though, isn't it? It's, it's kind of, what was it, $20 billion to acquire assets and, and IP and, and, and people. Um, but there's also kind of non-exclusive nature to it.

So I think, you know, 'cause CRO was building these, uh, language processing units, LPs, um, you know, and so I think, I guess that's what they were. And they were, and they were extremely, um, performant and in real time, uh, token generation. And I guess that's what they're, uh, you know, looking, looking, looking to get.

Um, so it's, it's, again, I guess one of these, you know, NVIDIA's model or not models have shifted, but its business model has evolved from, you know, training, training and training to inference and grew up with very much an inference shop. Um, and allegedly still is gonna be one. Um, but it's not entirely clear what it's, what it's gonna do with its key people not there.

Um, and, uh, some of it's license, um, it's IP now in, uh, in hands of Nvidia. Yeah, I would, I would imagine that it's gonna play a, a nice, you know, balancing role within NVIDIA's NIMS architecture, for instance. 'cause that's all about containerized, you know, instances where you can stand up a fully working stack and just start inferencing off of it.

And if grok is, is, you know, an alternative to a third party, like open router, uh, for instance, that, you know, Nvidia users would, I'm sure appreciate that. And as you know, Steven mentioned they've been around for a little while, GR has in a little while being less than five years. Um, but, uh, nonetheless, they, um, you know, I, I think we're very early to, to tackle what we see right now as such a huge area of investment in terms of, you know, highly specialized chips for inferencing.

And you just look no further than AWS reinvent we had in early in December last year, where, uh, all, all roads pointed toward tra, uh, and how important that had become to, uh, AWS not for training, but for inferencing. Boy, they named that one wrong. Um, no, I I What's in your name?

I do think that, that you're onto the, onto it there, Brad. Uh, when I thought of, of gr and when I thought of Nvidia, I, I, my first thought was exactly what you mentioned, what we heard at reinvent, what we heard from Google Cloud, uh, what we've been hearing from Microsoft and others about, um, using specialized hardware to do inferencing, to do token generation. Uh, that's really rock's strong point.

That's certainly what Google and Microsoft and AWS have been leaning into, along with the software platform aspect of it, which is something that Grok has been leaning into. And so, I, I do caution listeners that, uh, this is, this is very new. We don't yet know which components are going where and what the angle is gonna be here, but I could certainly see a situation where, you know, Nvidia is part of that conversation as well, just like Google Cloud, AWS and Azure, right?

But they also partnered deeply with, with all of those, it's, of course, they did a highly cooperative competitive marketplace. Yep. Yep.

Now, another acquisition, um, we just heard about is Accenture. So, uh, not that Accenture was purchased, but that Accenture is buying, uh, faculty AI and, um, promoting, uh, the head of an AI company to become the CTO there. Uh, I know that there's been a lot of, uh, fear, uncertainty, doubt, and mischief around, uh, ai, uh, replacing people.

Uh, Accenture is the people company. Uh, what's, uh, your take, Brad on the Accenture, uh, faculty story? Yeah, I, I don't know enough about faculty to say technologically what impact they're going to have.

I think instead, what this points to is, um, you know, a, a sort of raising of a, of a banner at Accenture to say that we are an AI company. We are not a people company anymore. And I guess you could call them accelerationist for that.

Uh, and, and for better or worse, um, but, uh, you know, I recall visiting them in New York City, uh, two years ago, and having them demonstrate their in-house AI that they were building to, to help optimize and, you know, 10 x uh, their employees to, to be better, uh, at their jobs. And, uh, I'm sure nothing has changed since then. I'm sure that that is still what their objective is here, is to become a, a much more efficient company at helping their customers become more efficient companies through the adoption and use of ai.

Yeah. I, I mean, here in London, uh, faculty AI is, is, is quite prominent. And, um, they're quite well known for government work.

Um, mm-hmm. Yeah, I heard, I, I read something this morning trying to make out the, you know, they're the UK's equivalent of Palantir, which is, um, you know, size wise, not really comparable, um, but it did have this kind of, you know, they called it a decision intelligence platform, um, called Faculty Frontier. And again, I think it's kind of these four deployed, um, engineers into, into customers.

But they, they had a lot of government contracts going back many years. I think they're only, well, they're about, uh, 12 years old as a company. Um, but yeah, that CEO Mark Warner, if I, yeah, I go to a lot of, you know, a lot of London AI events, and either he or somebody, somebody else from that, that, that company is often there, they usually keep themselves, um, to themselves.

They're quite quiet about what they do. So it goes, gives you an idea of what some of the work is, um, that, that, that they've done. But yeah, with him becoming Accenture's CTO, it does, uh, it does kind of reinforce that realignment, they said back in September, October, I think it was of last year, um, realign around AI laying off, um, um, up to 11,000 employees even over time.

I mean, not in one go, obviously, um, attrition and all the other kind of usual words that are used, put in, put in perspective. Um, Accenture has 800,000 employees. Um, so it's, it's, it's, it's a, it's a small drop, but the, but faculty does a lot of work with open ai, with anthropic, with the, what was the AI UK AI Safety Institute, ai, UU AI Security Institute, um, and stuff like that.

So there's a lot of kind of AI safety work. While I, I'm very skeptical about some of that emphasis around AI safety. I think it's a little bit, um, um, it's kind of trying to induce some sort of, you know, you know, panic around what AI does.

They, they, they were pretty, um, heavily involved in that. So I think it's, uh, I mean, I, it's, it's an interesting, it's an interesting, um, time and I, I, I sort of wrote a little note about it, um, on very little note on LinkedIn. And my last, my last three words were kind of re-skill or exit.

So it's kind of, it's, it's that kind of thing. Like if you are, if you work for Accenture or you work for any kind of company where, you know, knowledge work is, is, is key, um, you've gotta, you've gotta adopt ai. Um, and I think that that's, that's pretty blindingly obvious statement.

But it was interesting, I noticed when they announced their last results, which were announced in December, um, they put a, they kind of, they have a, a series of charts, you know, QR Q1, I think it was advanced AI bookings, advanced AI revenues and work kind of thing. And then at the bottom, it had a note saying, this would be the last quarter in which we share, um, advanced AI bookings and revenues. We've reached a point, I'm reading it now, where advanced AI is in being embedded in some way across nearly everything we do.

Um, and so, et cetera, et cetera. Um, we're not gonna then spread it out. So, you know, when when does, you know, AI is, uh, the old, the old gag is AI is whatever, it doesn't work yet.

Um, and so, you know, that yeah, they're kind of, they're seeing it so in, so embedded in ev everything they do, um, that, you know, it just, it is business. Um, it's not a, it's not a necessarily a peculiar thing. Um, obviously as the leader of the AI platforms practice, I would, I would, uh, counter, there are some unique things to ai, um, and will be for, for, for many, many years to come.

Um, but for a company like that, to make that kind of statement, uh, and bring in somebody like that, I think it's, uh, you know, it's, it's a, it's an interesting marker for the rest of the industry. Yeah, it does reflect the nature of business, um, that a company of Accenture's status. I mean, as you said, this is a company with, you know, almost 800,000 employees, you know, billions of, of revenue.

Uh, I think it says here, 120 locations. I mean, Accenture is everywhere. They're working with basically every major company.

And to have a company of that status say, effectively, AI is just part of what we do, and part of what everybody does, I guess, that reflects, uh, where we're at in 2026. Frankly, it sounds a little bit like Futurum. I mean, that's what we're doing here as well, right?

Yeah. And I think we kind of move from the, away from the ai ai experimentation years of 2023 and 24, 25 was, uh, a new set of experiments, um, with, with Agen. Um, and I'm not saying in 2026, agen is gonna be completely, you know, um, mainstream, which it isn't.

Um, but other elements of ai, um, you know, may more on the kind of, you know, predictive model side, um, are, are just, um, you know, the meat and potatoes of, uh, of, of, of how, uh, technology gets adopted. I would say, you know, with Accenture, um, in particular, what this makes me think of is that, uh, these companies, futurum included, are right now not endeavoring to, you know, enter, you know, into an opportunity where they're making ai, but instead they're using AI to do their job, to do what they do as a company. 'cause that, that early meeting, I, I mentioned it was all about, you know, we're, we're gonna have our own, you know, chat bot model with its own anthropomorphized name, and it's gonna, you know, be so and so to, to do what you get from, you know, OpenAI and others.

And I think we're seeing a shift where companies, uh, are starting to look at this as just how they do business, not the business they do. And you could see that reflected in how they, you know, talked about and positioned their earning statements. I think that that's very telling.

And, and can I get a hallelujah on that? Um, I, I, I, I think I should remind, uh, listeners that, uh, this is called utilizing ai. I named it utilizing AI in 2020, because I was waiting for the time when we were making practical use of this technology, not when it's a science project, not when it's something cool, not, not when we're doing it for the sake of doing it, which frankly sounds like a lot of AI out there right now, is that we're just sort of playing with it.

Um, you know, I mean, now actually using it for productive purposes. And I think that that's what I want to see in 2026. I want to see this being used for productive purposes that let us do other things, the things we always do, let us do them better.

Um, you know, Brad, I've said that, uh, you know, the, the, the rums use of AI and the, and the, the, the specifically, the, the signal report that I know that you've been deeply involved in, it's not that it's ai, it's that it's, it's, it's this technology that gives you and Nick and the rest of the folks a superpower when it comes to analyzing data. And that's, I think what Accenture is saying is that AI gives their clients, um, extraordinary abilities to do the things they already do. And that's what I'd like to see more of in 2026.

So, let's talk a little bit about what we're looking forward to, Nick. I know that you've had some questions recently about, uh, what are the big momentous things that are gonna come out in 2026. Could you maybe, uh, reiterate that for our audience?

Uh, what, um, you know, what business moves, what IPOs, what actions do you think are gonna happen in 26 that are gonna really shake the foundations here? Sure. So on, yeah, on the financial side of the, rather than the sort of technology side of it, I think the, um, we're reaching that stage of maturity where, um, exits happen.

Um, companies looking, you know, looking for looking for exits either, either through m and a or through IPOs, although the, although the IPO market for enterprise AI is being pretty quiet, um, from what I hear and what I see out there, you know, there's a lot of optimism that it will pick up. I mean, mind you, that that always happens in January. There's a lot of optimism that's gonna pick up, which, you know, you have to see what the state, the actual market is, whether it's ready for it.

But you've got, got open ai, um, and anthropic, um, really as the two, the, as the two kind of, uh, poster children as it were, of, of, uh, not only enterprise ai, but also consumer AI in terms of open a, open ai, a lot of ais. Um, but you know, those, those are both, um, potential IPOs this year. I think they're the, they're interesting contrasts in valuations, uh, to say at least.

So OpenAI, maybe rumor is maybe looking for $1 trillion valuation. Its most recent round was at 500 billion. So there's a, there's a bit of a gap, um, there, which it, which it might.

Um, you know, it's, that's kind of a price for perfection scenario. Um, you know, everything has to work for it to be worth that much money. I mean, not say it won't get it.

Um, but, uh, and then there's also the interest around Microsoft's stake. Obviously Microsoft was incredibly prescient in 2019 with what it did. Um, but also that's, it does create a little bit of a messy, um, kind of scenario from a governance point of view.

Um, so that, that will be interesting. Andro is a more kind of enterprise safety hedge, and the kind of more, you know, it's kind of the, the more sensible looking, you know, enterprise AI company at and at a lower valuation, um, maybe, you know, 3 billion, um, some something like that, who knows. Um, but it's, but it's, you know, so I think it's, if if investors open AI should be the bell, you know, would be the kind of bellwether AI stock.

If it went, if it went, um, but Anthropic could be the kind of more conservative, um, uh, alternative. And then if you think about, you know, what, what's the purpose of IPOs is to raise money and to provide liquidity and all that kind of stuff. So when they're raise money, where does that money go?

What is the use of the proceeds? Now, you would've said maybe if this would, in a fantasy land this was happening 18 months ago, you know, Nvidia would be, you know, vacuuming up all that, that extra new cash. Now it might be interest, it's obviously with, with these companies both working on custom silicon, um, maybe that's not gonna be the case.

So, you know, where do the, where does the proceeds go? Um, so I think, you know, a lot of it will go towards custom silicon development. Um, I also think it will go towards energy infrastructure, um, and obviously data centers and, and, and all that kind of stuff.

So I think it's, um, you know, those, that, those that have the, those stocks or those companies that have the kind of clearest path or the clearest picture of owning a stack and a stack these days doesn't start with a computer. It start, you know, essentially with the power, um, and, and land and things like that. Um, you know, those have the biggest, yeah, the clearest path for that would be, um, probably get the most rewarded and would, in theory at least be, uh, can take on hyperscalers, at least in terms of how the public market sees it.

So yeah, just from a financial point of view, there's many other things we think is gonna happen this year, but then that's, that's gonna be, um, an interesting one to look at. Yeah. And for me, I, I feel like if I were to try to see what's, what's coming for the entirety of the year for the space that I look at, uh, I would say that this is definitely, you know, the morning coffee after the hangover that I think we've, we've been going through a little bit with AI and what it can and can't do.

And, um, I think we're gonna see a, a tremendous focus on, um, you know, the science projects are over. It's all about if it doesn't scale, if it doesn't make money, it gets cut. And this goes to what Nick is talking about with this emphasis on, on ships and vertical integration all the way down to the power coming into your data center.

Um, but it's also about, you know, how that software stack actually works, um, in supporting that optimization. Because as we all know, you know, we, we've gone through a bunch of phases in the industry where we've tried to have these sort of, you know, do everything platforms best of breed that, you know, have a sense of lockin, but also have a sense of performance to them because of that lockin. Um, and what we're trying to understand right now is, well, can we have our cake and eat it too?

Can we have a highly composable stack that still emphasizes speed? So you see, um, companies like, uh, Microsoft with their, their Cosmos db for instance, rolling out, um, you know, uh, basically caching, um, semantic caching within that database to support agentic workflows, uh, directly, you see the sort of death of, you know, ideas like data meshes and even data fabrics to be replaced by a, a more composable, um, landscape or data estate, as we like to say, that's, that's built on top of, uh, a semantic layer, which, uh, is, you know, some something that we all, we all really need to get behind, and if, if we aren't hiring people that understand what the word epistemology means, we're already losing. So I, I think it's, it's gonna be a very interesting, um, you know, vendor community in the hiring system situation in 2026 in terms of what we hire for and how we build our solutions.

It's, uh, not anywhere we've been before, Just to add just a few other things that, that, um, my practice will be looking at this year. And, and I think maybe some things we'll have be future topics of, uh, discussion on this, on this podcast, um, sovereign AI and kind of the corporate con nature of co corporate control. Um, that's, that's gonna be a big, uh, focus this year.

Um, it already was for, you know, towards the back end of last year, but I think, you know, increasingly that will be, um, similarly related is kind of global AI regulation and compliance. The, um, EU AI act really does, does come into force properly in, in, in 2026. Um, and obviously we've seen in the us, although there are various state things going on, um, you know, Trump has made it very clear that, you know, once a federal, um, AI regulation, so there's not as say, 50 different regimes, um, to deal with, um, obviously Ag ai, if not at scale, you know, rolling out you more fully.

Um, and, uh, I guess down at the infrastructure layer, we talked about it just now really, but the energy and cooling as a kind of bottleneck, bottleneck for data center built, build out, bottleneck, bottleneck for all, you know, the stuff that goes in data centers. And I think that will, um, that will become a major, major issue. And as we kind of said earlier, would probably result in some sort of in m and a, uh, we've already seen it to a certain extent, and I expect we'll see more of that.

And then the kind of fragmentation of models, which I, I don't mean breaking up of models. I mean the, the landscape, you know, LLMs are really important. Um, they're not the only game in town.

Small lan, small language models, um, will become more and more, um, prominent. So yeah, those are some of the things that we're looking at. I does, when I'm sort of, when I was drawing up the list, I was thinking this is getting quite infrastructure heavy.

Um, AG agentic is, is, is kind of where the rubber, but where the business value will be created. Um, and, but, but there's a lot of stuff going on underneath, um, that we, that we'll be, uh, spending time in 2026 to say we, we should probably dig into one or two of those, uh, in the coming weeks. Yeah, and another point I'd like to make too is that, uh, the AI infrastructure market is by, by no means dead.

Uh, there's still a lot of money flowing into AI infrastructure, networking hardware, uh, you know, I mean, you look at companies like Cisco, um, you know, broad Broadcom and, and companies like that, that are doing so much, uh, chip work. Uh, and, and for me, one of the, one of the dark horse, um, heroes of AI that I'm looking at is Google Cloud. Um, I'm hearing such good things from enterprise users about, uh, Gemini, uh, as an alternative.

And, you know, you, you talk about, uh, like you did Nick about these, these, you know, vertically integrated stacks, uh, don't count the hyperscalers out, I would say. Um, they, they know how to run data centers. They know how to run platforms, they know how to work with enterprise companies.

Uh, they've, they've been learning it the hard way for 20 years now. And I think that, um, you know, companies like AWS are really going to roar forward, um, Microsoft, Google, uh, in 2026 as, uh, applications become more practical. Uh, that would be my prediction.

Don't count the hyperscalers out. Oh, yeah, I totally agree. Steven.

I just wanna add really quickly to that, that, uh, it is a, it has amazed me as, as both a practitioner and an analyst in looking at, um, Google Cloud in particular in terms of how they're investing in their APIs, uh, around Gem Gemini. And it seems like almost every couple weeks they reinvent what we took for granted before. So we used to like scrape web pages to, to turn them into, you know, to embed them in a rag pipeline.

And they're like, no, we own the search index, so why don't we make it so that you don't have to scrape web pages, you just query the webpage and get all the data from it. And they're, they're doing the same thing again. Uh, and again with different, you know, aspects of what was expensive, uh, and, and time consuming in terms of, you know, optimizing the inferencing cycle.

So it is, the money that they're spending is all about the gravity still has been, always will be about data gravity on their platform. But my goodness, you know, with it, like you said, AWS and Microsoft and Google are seriously investing in, in making the, you know, giving, giving enterprises the tool set they need to, to do this the right way. Final words, Nick?

Yeah, just on that, I'd say, you know, I think of, of the three hyperscalers we're talking about, I would say Google probably had the best year of 2025, um, in terms of improvement of, of its, of its position in the market. Um, but we've reset now we're all back to all back to zero in the kind of analyst ranking game, and, uh, and we go again. And so it'd be really interesting to watch, uh, all three of those and, and many others.

Um, maybe not quite hyperscalers, but other cloud providers around the world. And as we say, the on-premises, um, shift, um, and push because, you know, the, the, the likes of, um, Barcom with VMware or Red Hat with its software stack, um, selling through other cloud providers around the world, there's an awful lot of interest in, in that stuff as well. So, uh, yeah, it's, um, it's not all about infrastructure.

There's a lot of infrastructure stuff, um, to watch, um, to enable all this good AI stuff to happen. Yep, absolutely. And, and I'll just, uh, throw in a little thing.

We're, uh, gonna be holding a, an AI infrastructure field day event, uh, end of January. Uh, we're gonna be hearing from some of the companies that are building, uh, AI infrastructure stacks. Uh, we'll also be doing another AI Field Day event.

Um, and as we joke, every field day event is AI field day. Um, so thank you very much for joining us, both of you. Before we go, um, give us a hint of what you're gonna be working on in the, the coming quarter in terms of research at futurum.

So, uh, why don't we start with Nick this time? Um, so yeah, we're gonna be redoing our, um, our AI platform signal, um, and we're gonna look at some, some other, um, you know, sort of, uh, sub-sectors of, of that, um, potentially around, um, sovereign ai, maybe I haven't quite made my mind up on that. Um, I'm redoing my, um, decision maker survey.

Um, so that's gonna be going into the field this quarter. So that will be a, you know, a, a re-up of that. We've only just published our, our, um, market forecast in, in December, um, our five year market forecast, which is, uh, which is really interesting.

Um, so those are the, those are some of the, some of the things I'm, I'm gonna be working on, Brad, It's similar to me. For me, uh, we're, we're twins obviously. Um, so I'll, I actually have a second forecast that, uh, we're doing for data intelligence, analytics and infrastructure coming out.

That'll be five years as well. And also fielding a, a new survey, uh, for decision maker, uh, we call a decision maker survey. And I also will be updating my, uh, data intelligence platform signal, um, actually really quickly.

So that'll be something we'll publish in early February for that. And, uh, I have a new one that I'll be working on, I'm really excited about. It's gonna be about Semantic bi.

So, uh, for, for all of you guys who love your love a good dashboard, uh, I would invite you to tune in for that one. Well, that, that's really the thing about Signal. And the thing that is exciting about what future and research is doing is the fact that you can refresh this data much, much more frequently than ever before, uh, thanks to the, the tools that we're using on the backend.

And it just means that stuff is more valid because as you said, there, there are things being introduced every single day in this market. And so, you know, you cannot rely on a year old report when making decisions on pretty much anything in enterprise tech these days. So thank you both for joining us.

com. Uh, please do subscribe to the utilizing AI podcast. Uh, you can also catch our other podcasts with the security, uh, Boulevard Podcast, uh, agents of Dev, uh, as well as of course, the Tech Field Day podcast for more.

Um, if you enjoyed this discussion, please do subscribe, uh, where you can find us on YouTube, uh, as well as in your favorite podcast application. Uh, please do give us a rating review and a comment we would love to hear from you. Uh, this podcast is brought to you by the analysts and experts from the Futurum Group, where Insight meets AI.

ai, which is our sister news site, uh, the utilizing AI YouTube channel, or of course, the Textron TV app, where a great many people apparently are watching this according to our, um, our stats. Uh, the majority of people seem to watch this on the, uh, on, uh, TVs and uh, in, in, in formats like that. Thank you for listening and we will catch you next week.

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

Do is, uh, the CEO, I think founder of cid. Yeah, yeah. Co-founder, Co-founder of, of CID db.

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

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

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

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

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

Um, so I'm a technical founder. Uh, I have roots in computer science. And, uh, initially in my career, I went to work for a terabit router company.

The early days tried to take over Cisco's core business in, in, in 2000, uh, the bubble burst, so it didn't work that much, but we did have a fabulous product and a drop in replacement for Cisco CLI, I'll, I'll come later on with more of the importance of, uh, drop in replacements in products. Mm-hmm. Um, and later on I did something with Blade Centers, and then I joined the company, a startup company, where I met my existing co-founder ti and my, uh, existing, uh, chairman who was, uh, the CEO back then.

Uh, that setup had had to pivot three times. This is where I learned how to pivot Uhhuh. The last pivot, we, uh, came up with the KVM hypervisor, so to, uh, renovate around the new hypervisor, a new approach that, that was the KVM.

It worked really well, and Red Hat acquired the company. We, uh, spent their four years, uh, improving KVM and also the Linux kernel, and I'm a big fan of it. And, uh, afterwards, we wanted always to have our own startup.

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

Uh, D os exists, uh, still today. And I met a customer yesterday who runs Sila and knows us because of that os uh, 'cause of that os Yeah. If you don't mind, what os was this?

It's called, uh, OSV. It's, it's a unikernel. Oh, Okay.

Sure. Um, They had their moment in the sun. Yeah.

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

And we managed to accelerate Redis by 70% because we loaded the application into the kernel space was faster. When we did the same with Cassandra, the performance didn't change much. Really.

We realized that the overhead of Cassandra, uh, is itself and, and not, and if you replace it with a fast os it, it doesn't change it. Uh, so we said, oh, that can be a good idea for a pivot, because we didn't get enough traction. And with why, once we rewrite Cassandra from scratch, keeping the compatibility like the Cisco days, uh, also like the KVM days, it's, it's also about compatibility, uh, with, with other things.

Um, and we re rewrote Cassandra from scratch. That's what cila DB does. Uh, it's also, uh, nowadays compatible with DynamoDB.

It's a drop in replacement, and it's a standalone database that can run the biggest, most scalable workloads in the world. I love it. What a great story, huh?

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

I'm a geek person. A lot of the people out here are, we do this. I mean, it's nice to be able to make a living doing it, but we also do it because we love Yeah.

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

Mm-hmm. Uh, now it's, uh, it's more than 10 years. About 10, yeah.

Even, uh, or 11th year, Really. That's, you know, what that, and that's something also, quite frankly, to be proud of, right? Mm-hmm.

Because what do they say the average company, if you make it past three years mm-hmm. It's a big accomplishment. So it's, it's, it's all obviously here.

Um, now talk to me a little bit about how people engage with Cilla, right? There's open source parts of it, there's commercial parts of it for people out there saying, you know, we're always looking for better performance, better bang for the buck. What, how, how do they kind of jump into Cilla?

Um, so, uh, we started, we were big open source fans. Uh, we, we started with open source, actually, uh, a year ago. We changed the license, I remember to source available mm-hmm.

At the time, a year ago. I, I was just sitting here. Um, so it's source available.

We, we do have projects which are, uh, open source, like our What even source available. Let me ask you a question. In the year you did that, how many people have asked for the source?

Um, so PE people do appreciate the, the, that It's available, The source, but It, it, this is, but this is something, look, I've been an open source too for 25 years. The fact of the matter is, 99% of the people never look at the source code or make a change to it. Not, maybe not.

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

We're, uh, now it's source available. It's allows us to, uh, allow people to look at the source and, and also have the, uh, comfortability that the source is, is available for virus cases, uh, for future con continuity. Uh, but, and we have some control to say, okay, up to this, uh, level, it's free and beyond that level, you need to pay because we are here 11 years on the road.

And, and it's a business, Right? Someone's gotta keep the lights on. I, I agree with you.

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

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

Changes. Look, I, you know, one of the nice things that I've seen happen in the open source community over the, as I said, 20, 25 years I'm involved, is that most users recognize that though, open source may be free, someone's working on this. Mm-hmm.

Someone's entitled to get paid for their time and their effort and everything else. They may, they may quibble with how much mm-hmm. But you, you know, it, it's ludicrous to think that people are gonna volunteer this outta the pure love and, and not make a living, you know, not be compensated for it.

So I think that's been a positive development overall in the open source space. Mm-hmm. Right.

It used to be, oh, you know, you're looking, you're in it for the money. Everyone's in it for the money. We have to keep the lights on.

We've gotta feed our families. But, you know, it's just, it's a fact of life. I mean, and if you don't wanna recognize that because you're some sort of, you know, like open source zealot mm-hmm.

Free as in freedom and free as in beer. Don't use the product. What can I tell you?

And, uh, having, uh, paying users allow us to invest back in the product Absolutely. Makes the product better, product Better. And so that's primarily what we do.

And It's a flywheel Is a, is a vendor that, uh, used to, uh, eh, release both open source releases and also, uh, gated product releases. You double the amount of releases. I was just gonna say, what a pain in the Yeah.

You know what that is A hundred percent. I, I agree with you. So there, but there is a freemium version.

You can go check it out, play with it. If you do wanna look at source code, and that's your thing, it's available to you as well. Um, Dora, let's talk reinvent here.

You guys are here. It's been an interesting kinda reinvent because, you know, we, when I, I just finished writing an article when I first got here Monday, and I looked at the keynote, you know, agendas and everything. They gave us a press preview.

It was obvious it was all agentic AI all the time, right? It was all about ai. But over the course of two, three days that I spoke to people and saw things and walked around, see a lot of news about DevOps, cloud native platform engineering databases, hardware, hardware is AI stuff too.

But hardware, um, you know, it, I maybe didn't hear as much as we normally hear about, like things like S3 or serverless or Lambda or these kinds of things. But the geeks are still here. The developers are still here.

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

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

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

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

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

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

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

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

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

Yeah. So explain that to me. What, what's the use case there?

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

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

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

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

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

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

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

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

Yeah. That's fta. So in essence, they could use Stiller as their vector database then.

Mm-hmm. They're creating small language models or, or Yeah. The rag stuff that's gotta be big.

No, Yeah. That's, uh, fantastic. Our, uh, vector search is the most scalable.

We can easily run a model with a billion, uh, objects. Uh, very few, uh, vendors can even get to a billion. And we can do that with hundreds of thousands of requests per second.

So we, we scale, uh, to, to very high numbers. And if, uh, people have a lower medium demand too, like, uh, most will have a model of, uh, 10 million or a hundred million objects, then we can give, uh, the best latency and, and also very low price point. That's fantastic.

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

Um, but certainly, I, I believe there's gonna be a lot of activity in, in the SLM rag kind of space. And, and not only that, because as we develop AI for specific use cases, I don't need the whole world of the internet. I just need, especially if it's my own proprietary information.

Right. And I don't wanna put that out up there. I want it right here.

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

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

Yeah. It's, yeah. Well, no, this, that is, It's not just an opportunity.

It's also a defensive move. Because if we won't do it, then, uh, customers will go elsewhere, uh, to, to be frank. Mm-hmm.

And yeah, the, the fact that, uh, people would expect, uh, all of the ease of use of LLM on the public data set on the internet, they expect to have the same when they come to every vendor. And to ask a free tech search, uh, your questions in, in one liner, and get immediately the best results without diving into a very complicated ui, that's a power of LLM. And sometimes it won't be people, but IT agents, right.

Uh, that come and, and automate and, and get the queries automated. So that begs the question, is there, uh, an MCP server in your future, Uh, in the future? Absolutely, yes.

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

Anyway. You guys have some new announcements that you're previewing here. Mm-hmm.

Share, if you don't mind a little bit. Thank you, uh, for the opportunity. So, um, uh, we'll also move, uh, from beta to general availability.

Our X Cloud, uh, uh, managed platform. Uh, X Cloud is, is, uh, the new generation of our core database with, uh, database as a service management consumption. Uh, the unique thing about it is, uh, our new core architecture, which is called tablets.

It's way, way more elastic than any other database or even infrastructure in the industry. Uh, we, we were okay with regard to, uh, the speed of, uh, increasing the cluster, scaling out, and then scaling in. We were before this technology, we were, we were okay, like, like, uh, an average vendor, but there was a demand to do it much faster.

And frankly, we also compete with DynamoDB. We're a drop in replacement, and DynamoDB, uh, was the first NoSQL database. And, uh, up to this change was the, the best in the industry.

You can easily scale up and down, uh, very easily. And, and if your workload changes throughout the day, uh, then, then you can, uh, instead of paying for the peak consumption all the time, you can just have the workload follow, uh, uh, the work, the workload should follow the usage, right? Dynamically.

So that's exactly what, uh, X cloud is. Uh, we, we have, uh, the technology based on components called tablets. We break the gigantic database of, uh, petabyte of data to five gigabytes chunks, and we can move them around super quickly.

Uh, we, we can also even, uh, it allows us, uh, both to scale super fast. We, we can increase capacity, quadruple it in 10 minutes. Mm-hmm.

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

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

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

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

Sure. Uh, also, it's, it's, it's pretty unique. Uh, we have a sharp per quart, uh, engine.

So let's say if you have, uh, a machine with, uh, 32 cores, we, we'll have 32 independent threads in the server. Wow. Uh, if you have a 64 machine, then we, we'll have 64 threads, uh, in, in engines within that machine.

And it'll perform twice as what? 32. Now, let's say if you have a 64 way machine, uh, but actually you need, uh, um, 66, uh, uh, threads, uh, and you have 64 now, would, would you buy another machine for 64?

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

So it's real Distribution And the starting, we, we can combine the two. Haven't seen any other vendor can do that. No.

And what the user receive is efficiency. Uh, they have exactly what they need. They don't need to buy excessive large ER servers, which are expensive on AWS, uh, They're expensive everywhere.

It's not just AWS but really what we're talking about here is almost like a finops play, right. Because that's, I think that's where we are, especially in cloud usage, right? Look, we're talking about spending $5 trillion on data center AI factories.

But the fact of the matter is, when I talk to people, they say, I wanna get control of my cloud bill. Hmm. I wanna du, I wanna be more efficient in my use of these resources.

And that's why I made the joke with the balloon blowing up. That's pretty much how the cloud is, right? It never seems to go back down.

People, they want that ability to have insight to turn that dial, and they want the ability to say, how can they do this more efficiently? Mm-hmm. Yep.

And our customer success team works with customers. And if we both see, let's say you sometimes utilization people can check their database, how much it, it's loaded on an average basis. Most databases are, are not that loaded.

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

Yeah. It wound up being more of an up thing all the time. But it's good to know that's there.

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

Good stuff. What else from s Um, so it's mostly this. We, we do have, uh, lots of, uh, things that we develop, like tiered storage mm-hmm.

Uh, in, in other technology to, uh, reduce the bill. Uh, normally we use NVME for fast storage, fast performance, and it's also relatively cheap co compared to different alternatives of, uh, of storage. But, uh, SS three is cheaper.

The problem with S3 is that latency is prohibitive big. It's a 50 millisecond, 100 milliseconds. Uh, and with third storage, uh, we can keep the hot data on fast and VME and automatically move the cool data to S3 and come, come up with, uh, a good solution.

'cause sometimes you keep, let's say 30 days of, uh, of history on, on, on Sila in the NVME, but you'd like to keep one year of data and, and access it through the same API and not develop a new access for it. So this allows users to, uh, have one API and, uh, a very cost effective solution. I love it.

Good stuff. You know what, we didn't, we didn't even mention the website, URL for people. Want to go find all this out on their own.

Dig in a little deeper. What's the, what's the best URL to go to Dor? Thanks.

com. com. Just as it says underneath is in his lower third alrightyy.

Dora, it was a pleasure seeing you. Safe travels back home. We are wrapping up now again, you, you're seeing this after we were here at, uh, AWS reinvent, but it's part of our AWS reinvent coverage.

And if you need to find this back on, it'll be listed under the event coverage. But for now, this is Alan Shimmel for Textron tv. Thanks for joining.

Control. This is agent dev. I'm in position.

Copy That. Dev. Stand by for go Standing by.

Hey everybody. Welcome to the Agents of Dev podcast. I'm Mitch Ashley, a lead the software Lifecycle Engineering Practice Analyst practice at the Futurum Group.

Brad, welcome. Always good. Be good to be doing this with you, my co-host.

Yeah, it's, it's great to be doing the show with you today, Mitch. And, uh, I think, I think we have a, a fun topic, uh, on hand. Some something that I, I'm sure a lot of people that are listening in are, are exploring and or dealing with right now, which is the idea of how do you use Ag agentic tools to, to actually build code on day two, not day one.

Mm. Mm-hmm. Listen, when you go beyond the Travel agent book, my book, my Flight, find me a flight example on every developer blog, how do you use this ID to actually do, do your own work?

So, right, we're talking about, well, we're gonna probably touch on three of them. I know we, we've had a lot of the most recent activity with Google Antigravity, with a Ws Kiro, some announcements at, uh, at, at uh, AWS Reinvent. And of course, Bob.

Bob. Uh, what about Bob from I BM and what about Bob? Sorry, I'll never tire of that.

What about Bob? Bob? I'm a, a child of the sixties, seventies.

So I, I definitely know about Bob and we of course, you know, co-pilot know, you know, cursor and Windsurf, and there's all, you know, everybody has an IDE these days, which I can't remember if I said this on a previous podcast, but, you know, I, I thought all those developer jobs were going away. We're sure. Creating a lot of tools for these people that don't, aren't gonna have jobs.

So I don't believe those jobs are going away. They're changing for sure. That's for sure.

Um, uh, actually, you know what? I, I think you're right. Yeah.

I, I I like the notion that, um, all of this tooling is going into, um, helping people who, uh, do the jobs that they lost. Hmm. There you go to basically do, to do the work that you used to do, but do it in a new way.

So you're saying this is a retooling to how to get your next job. Next job. Is that what it is?

Yeah. Just, just to get a raise. 'cause otherwise you have to quit and come back.

That's right. Well, I think that's, that, that's an axiom of the universe. I think that seems to be universally true in about every That's so true.

It's funny when you give people that advice, how do you get a raise? Well, you should probably go, go somewhere else and then come back, go get a lot more money. Tends to happen.

It does to be true. Um, uh, of, of the, of the ides, have you spent more time with one than the other? I know you do a lot of development, Gemini, um, but of course anti-Gravity is pretty new stuff.

Is there anyone you have more familiar with than the other? Yeah, well, I, I, as you say, I use Gemini CLIA lot 'cause I, I got into computers because of Ask Arts. I will stay in computers because of Ask Art.

So you have that Mon Lisa still on still, you know, on Green Bar in your It was, it was, it was Jerry, Jerry Garcia, uh, believe it or not, was, was my first like, mind blowing ask art experience. Mm. Um, and so anyway, yeah.

So I I, and as you know, I, I have a profound hatred of electrons. So I, uh, don't, don't use many of the electron based tools, which are quite a few of them right now, because so many tools are based on vs code. That's great because it has a rich ecosystem and a lot of plugins, et cetera, et cetera.

But I've been, I've been using, if I'm not using Zed, uh, which has a Gentech tooling built in, uh, I for, you know, non Gemini, CLII, I've really been gravitating toward Open Code, which has a degree of what we're gonna talk about today, baked into it. And I've noticed this more and more with these tools that, um, you know, they have two modes and you can just toggle them, you know, from the command line because that's why we use a command line so we don't have to reach and click, um, anyway. So you can basically just toggle to, to thinking mode or planning mode, and then toggle back to coding mode or fixing mode, or whatever you wanna call it.

And it can be anything you want actually. 'cause you can customize those to be whatever you want. All, uh, uh, was it Claude's, um, uh, skills kind of, kind of idea.

Mm-hmm. Yeah. Skills.

Yeah. And, and part of what this, this toggling does to planning mode is to help you to, to set up, you know, a very rigorous or supposedly rigorous, um, methodology and framework for doing something. Be that creating your new find me a flight app or on day two, uh, fixing your flight app because it's throwing a silent error that you can't trace back to, to the source.

Mm-hmm. So mm-hmm. If, if, if you go into planning mode and say, wow, I can't find this error, it will say, okay, he doesn't want me to just jump in and fix it.

He wants me to think about what the problem is to find the problem, to find the steps to resolve the problem, and then we can, can switch back over to execute mode and, and fix it. And that's spec driven development. Mm-hmm.

What we're seeing right now, uh, is sort of a ma maturation of that, where in a lot of these tools are sort of bringing in, uh, purpose-built spectrum tools. Sorry, these IDs are bringing in spectrum tools. So you have, uh, as you mentioned, Bob has it baked in, uh, KIRO from AWS has it baked in?

Um, you can download and use, uh, OpenAI, or is it in, sorry, it's GitHub's spec Kit. Mm-hmm. Which you can actually run in pretty much anything you want.

And these tools basically do a couple of things. They'll, they'll let you sort of initialize your projects if you've never run them in there before. And when they do that, they'll, they'll do, actually, I wrote it down because I did this last night with Conductor, which is this plugin they call it, it's the Google, Google calls it an extension.

An extension. Yes. We need a different term.

We do. Well, because, 'cause it's really different than just using an MCP server. An extension is the entire package built into the surrounding, um, tool itself.

And so it can itself include MCP servers. So it's like a mod in, you know, Minecraft or something. Yeah, totally.

Right? Yes. Yes.

Uh, for those who don't know, Mitch enjoys a bit of the game. I have been known to partake on a few weekends or two counting this last one. And it's, it's, yes.

We must talk about that, by the way, because, uh, I, I, I found that very fascinating because what, what Mitch did this weekend in sort of, uh, I asking to talk to him or to create for him a, a sort of means of saying, what does Mitch like to play? Found that really, really cool. I asked it, this is with chat gt, just as an aside, I asked it.

I said, so I do a lot of work with you, you know, a lot of my preferences, I've explicitly created artifacts with you about a number of them, none of them about gaming. But I have asked a few questions. So analyze for me what kind of game, what, what kind of games do you think I like?

What kind of games don't you think I like? And why? And it went through and it said, and it was very accurate.

And it's, it's because I, I have such short time windows. I can't do games that are, you know, sit down for, for a weekend and really play the whole thing. Like, Mike, no, no.

Skyrim time for you Skyrim. Well, you know, I could, so, so it's possible if it's possible and you don't have to step back in and like, there's a whole, you know, storyline that you're following that you have to remember that you talk to this person way back when, right. All that kind of stuff.

So if you can kind of step in and out of it, it's, uh, much more accept. So, so even like, um, a Civilization C six, I'm not a fan of seven yet, um, but it is com gets complex towards the end of the game, uh, the end game. But I, I can pause, I can save and come back, start over, do whatever.

That's kind of the games that I like. Yeah. And this, so when it, I have to ask, do you have memory turned on in chat GPT?

You have you had it turned on for a while? I have. I've had it turned on for quite a while.

Probably two, three months at least. And I think that that actually speaks to the topic of, uh, hand today. Does it not?

Because spec driven development is, is really nothing more than treating context like a managed artifact. Mm-hmm. That sits next to your coat.

I was thinking exactly the same thing in, in getting ready for this podcast, is you can see the parallels between the, the, the, let's call 'em retail versions of these products. The, uh, Chacha BTS of the world, quad, Claude, et cetera, is very much the same thing as they, they're creating what they call artifacts. And, uh, like you open a canvas that's now an artifact that within some context window within a particular tool, that it's gonna remember those things.

And so if you want it to remember one, the things I did was create a little memory system that one point where I wrote out, uh, remember these things out to chase on? Oh, you didn't, you didn't make a graph database to that b***h. I didn't have that much time that weekend, but it was experimental.

Kind of see how would this work and recall. And so, um, but that, that's what, that's what these kind of spec driven, or intent driven, I guess is what Bob calls. It's interesting to me that we're, weren't we doing spectrum driven development all along kind of Yeah.

We kind of were, but now we have a way actually integrating it into the workflow, you know? Yes. It was, yeah.

I was actually stories and all that before a spectrum driven human in a past life. Uh, I was a business analyst. Um, and so I would work with the developers to define the project, to, to set all of the goals for the project, to define the tech stack we were gonna use, et cetera.

And that's, that's really what these tools do. Mm-hmm. So, to, to jump back to, you know, this conductor extension for Gemini, CLI, I, you know, had a project that was only, you know, 350 lines long.

So very tiny. And it did something. It was, it was basically a research agent.

'cause you know, we're analysts, so always looking for someone to do some work for us. Right. Not lazy at all.

Um, and, and, um, so when I, when I initialized it, it, it did a number of things and it was, it was kind of interesting because it actually opened up and ran within the CLI tool itself, another CLI tool that had a user interface where it, it was basically a, A, B, C, you just would say it would ask you a question and you'd answer a, you know, and then hit return, or you would enter, uh, just a sentence to say, no, this is what I mean. And it would guide you through the steps of trying to initialize this project for spectrum and development. And, and what it got to at the end of that process was it defined the project goals, it defined the tech stack, you know, by looking at what I had, which is something we should come back to actually, because it's an ongoing pet peeve.

And, and spectrum in development doesn't solve the problems that we already have in these tools. Just everyone note that. Um, anyway, it, it would define the code style, uh, for it, the style guide, uh, PEP eight for me.

Uh, and it, it defined the overall project guidelines. And, uh, importantly, and I've found this really fascinating, is that it, it identified and documented known blockers, uh, to the projects, to, to getting things done. So a blocker is a constraint or missing information or whatever, you know, would keep the agent from completing whatever task you'd give it.

So finally found a way to constrain the agent from writing code. That's been the problem with the whole vibe coding, is it loves to write more code for you, more code than you want. Oh, it does?

Yeah. In all the wrong places. Say some more.

In, in, in, in the kero, uh, IDE it very much has phases, right? It has, um, planning, design, and development. You're explicit.

Yeah. Just like you were talking about, you go into planning mode and it sounds like that constraint is, before I move forward, I need these things, because that's part of the spec process. But have you defined that to be yours defined in the tool, which is a good thing?

Sounds like, I mean, you can prototype, you can take it so far, black box some things while you're working on the spec to try out ideas, but still contribute to bat. And Okay, put this in the spec. This goes back in the spec, you know, to, uh, memorialize make that part of the artifact that you're creating memorializing memory is, is like such a difficult part and goes back to context as artifact, which is what these do.

And prior to this kind of of tooling, um, for me anyway, I, I would demand, you know, I would type forward slash I think it's memory. Um, my, my memory's that bad. I can't remember.

Um, but at any rate, you would, you would use an internal command to say, remember to, um, that remember that I'm using this version of this a p mm-hmm. And because, you know, that's kind of critical and is a big problem Yeah. With large language models.

Um, so it would write that to, uh, Gemini MD file in the root of the folder I was working in or for the whole system, you know, for the whole my laptop. And, you know, if you didn't curate that, if you didn't go back in and carefully manage that memory file to, to get rid of things that have been, you know, ob obviated by, you know, know discoveries or changes down the road, you really could get wrapped around an axle because it would be like, wait a minute, Brad said that I wanna use version X, but in the same file, he is telling me to use version YI, it's interesting what memory, what things it will save. Sometimes it's like, that's not one I would've wanted you to save.

Right. That's actually not right. Right.

Which, which goes, you, you were talking about the tech stack earlier. Mm. And I find this for just using the, you know, using uh, uh, Google Gemini or, or chat GT when I'm not doing it inside of an ID or a development tool is, you know, even when you tell it, the tech stack, first of all, you have, you really need to have a running list of what your development environment is, what tech stack you're using for this project is explicitly what versions.

Um, because it will either assume things. I mean, I've had it like say, okay, go download this. Well, that, that doesn't exist anymore.

They canceled that, that they deprecate deprecated that actually, right. Or, or worse, they use a diff totally different package manager than what you're using uv, PEX and pip, for example. It's just like Exactly.

Tos a coins manager. So you gotta be very explicit. I mean, I list everything from mm-hmm.

You know, the full development environment that I have set up through, you know, through home brew or in, in that environment to, you know, what are the, what are the keyboard, um, memory, um, macros that I use on the Mac? Yeah. Because I might do some things from automations that way, like, and, and have to explicitly tell it.

Do not suggest that I use things that have been deprecated, do not use, suggest things. You are not in the product yet, but may have been mentioned in a product release. So Yeah.

It's, um, yeah, like, you know, if you're not using robots txt, you're already in trouble. You, it should already be there. Yes.

Yeah. Ignore it. I'm sorry.

More, more specific to what we're talking about though. Sorry. I was just, I was just thinking about, you know, protection, protecting yourself, but, but, um, requirements, you know, if you don't have a requirements file, you're you asking for, you know, a misunderstandings, like, like you're talking about.

So you gotta start from a good foundation, especially if this is a brownfield, you know, endeavor that you wanna use spec driven development for. Mm-hmm. And, uh, it actually, you know, it, I would add to that, and, and I know we wanna talk about the, um, tech stack a little bit more, but I, I, I felt from my short experience with Kiro and Bob and now Gemini CLI conductor, that, you know, you need to buy into this.

This is not like a, I think I might use a bit of it here or a bit of it there, or, uh, just try it today and not use it tomorrow. You, you know, are basically saying, I want this to be spec driven, and as such, I'm going be using an internal tech. Uh, what do we, what does, uh, conductor call them?

Um, where, where you have a task, they, they call it, uh, hang on, I'll just look it up. What you're describing is opinionated software. It has an opinion about how you have to develop software and you have to buy into that.

It makes it very opinionated, right? Mm-hmm. So they, they call them, um, tracks.

So you have a track, and I, I should note that, um, it's very agentic doing the initialization for, for tools at least like this one in that mm-hmm. It read my code base, it asked me what my objectives were, et cetera, to do all those things we were talking about and setting up the spectrum and development, you know, basically a, a file structure, a directory structure filled with markdown files. And I was shocked to see it come up with the first track.

For me. It's like I, I just finished initializing and it said, oh, I really think that you, you want to do semantic search, um, uh, enhancement, because I, I was trying, um, Gemini's Gemini, uh, has a deep research, uh, which is basically you're not calling a model directly. You're calling a, uh, a, a sort of implementation of a model that that is very opinionated.

And like we were talking about Claude, uh, like skills that that's basically built on skills. Mm-hmm. And this thing, which is, if anyone's interested, it's called deep research.

Sorry, these are all hyphens. When I pause deep research pro preview 12, 20, 25, that is a mouthful. And it, it, uh, it said, oh my gosh, why don't you add semantic search to that?

Because I had a different, I have like a, a different sort of, um, mechanism for doing this outline research, either through semantic search or through the deep research. And it said, why aren't you combining them, Brad, you idiot. Mm-hmm.

Mm-hmm. And, uh, so it's specked out that that track with all the requirements, all of those planning steps that you mentioned to build this, it's really fascinating. Well, I, I'm not an expert in, in any of those tools.

We just don't use them, uh, you know, living inside of them doing, doing work in them every day. But it seems to me the kiro is the one that's bought into the process as much or more than any of 'em. 'cause yes, it has those planning phases and Bob is architecting, et cetera.

They all have kind of some ver version of that. But Kero really, I mean, it includes specs for, uh, acceptance criteria, traceability testing, um, yeah. Yeah.

User guides. Um, it's really, you know, AWS made a big deal about where we're using this, this is our development environment across the company. And you, you could see how that might be because they've put so much into not just doing agent development or using agents as part of development, but making it, I dare I say methodology.

I don't wanna start any methodology wars, but, uh, yeah. Some opinion made stream programming. Programming.

Yes. Yeah. Well, I lived through a few of those.

The age agentic wars are upon us. Mm-hmm. But where whereas, um, like with anti-gravity, you could definitely see the, they've leaned heavily into the agent first.

Uh, ID, you know, using multi-agent orchestration to do work for you. Right. Yeah.

And I don't remember if they explicitly call it spec, but a, a, a version of that. It's kind of doing and screenshots that's like open code and uh, you know, they just have it built in as different modes of work. Mm-hmm.

You know, it's, uh, one of the things I'm curious too is uh, 'cause I think antigravity is supposed to be good at working a much larger code basis, and that's sort of the, one of the, there's lots of challenges and different vendors have taken on parts of it. And when you work with much bigger or multiple code bases Yeah. Keeping that, that in the context window and the memory working with that know where you're, you know, God forbid you say, you know, Hey, put in that search and whoops, that's not where I wanted it.

That was a different project. Right. Um, but that's part of the complexity too, than being able to take on more complex work, not just coding tasks or types of code to write, but the work of writing code and the environment that you're doing it in.

Yeah. That's a big deal, isn't it? And that, and you can see that reflected similarly to Kiro in IBM Bob in that they have built in using their, what would you say, 50, 60 some odd years of experience with cobalt not to Yeah.

Come back to some something called punch cards, I think, wait decks, who switches on a computer? I believe those, those exist in their osi. They, they have fine trained, fine tuned.

Sorry. Wow. They have fine tuned, um, their mo you know, the models that they use for these specific tasks, like you're talking about in managing a large code base mm-hmm.

To, you know, they were early to, to come out with this sort of, let's use LLMs to refactor from one language to another or to transcode and, um, with, I think it was COBAL to Java that they did. And that has grown into Bob and it plays a role in Bob for managing large code bases, not based on what they got off TE stack, sorry. Um, stack overflow or God help us Reddit.

Uh, it, it's actual like, you know, IBM, you know, professional services, you know, use case number two nine x 94. The has a very, you know, distinct solution and pat design pattern that's tested in, you know, some World Bank and now you can enjoy that and use that to solve problems across a complex code base. Love that.

It is, it is interesting. Yeah. The Bob leaned into the modernization path, right?

Helping people modern modernize. And a lot of that is, of course, the LLM that they're using are LLMs, uh, by default, uh, which pushes that button. You know, the, the other we haven't really talked about, I dunno if you considered an IDE cloud code, you know, is something you plug into your IDE or you can use it very much from the, the command line, but essentially you're doing the same thing instead of them issuing their own VS code version.

Um, you do it just like you do with, uh, I guess it's Klein is what it's called. I've used it for a fair amount on a project I was doing a while back. And then, um, same thing for, for, uh, for open, open AI's models that you, you interface to it either through a window, uh, in the command line on a, in your IDE as well as directly on the command line.

So there's sort of these different phases of, seems like so far, anti-gravity is kind of the most opinionated in the user experience of we wanted you to work this way. AWS kiro is the kind of development process. Opinionated.

We want you to work through these design steps, these with these requirements, spec driven steps. Bob, on the re-engineering side, we'll see, but Bob was, is pretty early when we saw it, so we'll see where it goes. But the, the tracks that pe different people are going, and you can see part of it is the customers that they serve.

Right. Lean, heavy, heavy into agent development for Google. That makes sense.

You could see why IBM would do more of a refactoring and, and helping people modernize code. Yeah. Because you know how we used to talk about data gravity, we still do, um mm-hmm.

Data has gravity because it's all designed around locking your data into a platform that you'll pay for, you know, in perpetuity. It's called cement, cement boots. Cement.

Yes. Yes. It's very lucrative cement.

Um, but it, you know, developer gravity, it means a lot. It, it always has and it always will. And what I see evolving right now, um, is this sort of, you know, my walled garden, my semi walled garden is better than your semial garden either because it has better models in the backend or better tooling on the front end, uh, or better services in the middle of the two.

Mm-hmm. And so, you know, these are all I've said for a long time that the, you know, the frontier models were always meant to be platforms, not just models. And they are absolutely evolving into that.

But the tooling, you know, the things like you and I have been talking about today that we decide we like something and, and so we invest time in it. And if you invest time in it, this thing, you know, that, that we all know and love, uh, called not entropy, but, uh, uh, what, what is it where you build momentum and can't stop the momentum from going, um, can't think of it. It's not brownie in motion inertia.

Inert. It's inertia. Inertia, okay.

In, yeah. Need, need to, need to think about the second thermodynamics that inertia carries forward. And you know, the more in an organization that they get inert in these tools, the more likely you are to buy the backend services models that, that are associated with them.

And that nails it. So you see, like, you see like all the, all the investment right now in, in, I think making these enterprise grade is critical and really a, a good step, um, is we, are, we have changed how we think about code, have we not much. Mm-hmm.

Absolutely. It is, this is first time that I can think of that it, we really are buying into, at the very front end of the development tool, the IDE of how we're gonna develop software, right? It's been more of a window into our repository and tools and plug areas.

It's been the Swiss Army knife, right. For development. Um, I call it the, uh, smoking shop.

You know, I I I, they're all brands of cigars, but we'd like you to stay here and smoke them here. That's right. We have a lovely room with leather chairs.

Just sit here. Yes, exactly. This is where you should stay.

Not that everybody out there smokes cigars, but, and I don't that much either. But anyway, it, it, it, it is an opinion way, opinionated way that organizations, when they sign up, they're gonna use this tool as their IDE, then that's the path they've kind of chosen to go down. So yeah, it's, it's, um, you know, how, whether it's cement boots or walled garden or a lighter touch Mm.

It's to keep you in that environment. Of course, they work really well with their technology and make it easy to do that as well as, uh, third party stuff. That's the key with the platform, you know, oriented tools, the hyperscaler oriented tools you see from like Kiro and Bob, like we've been talking about.

Kiro, you know, is steadily surfacing functionality that's been, you know, sitting inside of Bedrock and, um, their broader AI platform, for instance, SageMaker for some time. And that's only going to broaden out, you know, I, I can see all of their analytics tooling, for example, starting to, to be baked in with, you know, headless business intelligence being a part of your application development process. Your spec will have a spec on how you want to visualize data and work with data, for example.

Very good. Well, we've, we've overstayed our welcome here. Got a little long time.

So it's, it's the holidays. It's the holidays. Mitch, I think, you know, I think we're being watched and, you know, we're being trailed here, so we should, uh, get to our last segment.

Last segment is the drop. Okay. It's time for the drop.

Well, you know, it's, it's uh, end of end of the year for, uh, us going into the, the next year. We're, we're obviously deep in our planning cycle, kind of towards the end of our planning cycle. What we're working on, there's kind of two areas of focus that I'm looking at right now.

One is how are we moving observability, uh, security behavior governance into the development process using agents and, you know, companies like New Relic and Dynatrace. And I could go down the list of companies who have been part of announcements with some of the major vendors that they are now part of AWS's security agent or Dev DevOps agent, or pick your vendor. Um, so other parts of the software develop lifecycle are not waiting, um, not every vendor's doing this, but I think the folks that are wanna make sure they're carving out a path for themselves to be part of this, uh, kind of AI SDL or AI DL see, um, very much part more, I'm, I'm, I'm warming up for New Year's Eve, I guess I don't know what I'm doing, but anyway, that, that's definitely on my mind going into next year, uh, as well as many other things.

How about you Brad? Uh, yeah, and I'm sorry, the, the longer the acronym, the better, you know, three letter acronyms are aren't even trying. I can't, that's really put some effort, can't even approach the one you gave the, well that was the name of something, but you know, was, right?

Yeah, yeah. Um, so I, I think for me, and, and uh, it's interesting given what you just said, it, it jives with, um, one of the predictions I have for the coming year, which is that data engineers are, are going to morph a little bit into integration engineers and that companies won't be buying their data platform based on, you know, the specs of the platform as much as they will the meet the way that that platform integrates with their investments that might be, you know, with other vendors on other platforms. So it's definitely, you know, changing how, how these age old, these stable jobs that we've had in the industry for so long.

Um, for me, what's on my mind right now that I'm trying to like wrap my head around a little bit is I, I saw a note from one of my colleagues yesterday talking about how, um, agentic development was going break the database. And I'm thinking, well, I, I don't know, because we, we built databases when we had very little resource to play with and we built them in order to scale massively and be concurrent as possible across that scale. And, um, so I I, I think the problem isn't so much in doing reads and writes, 'cause we, we know how to do that and mm-hmm.

Pick your database structure. It doesn't matter if it's, you know, a blob storage or column or NoSQL or, um, you know, KV cache or wide, you know, field wide table, sorry, um, like Cassandra, it doesn't matter. They, they all are pretty darn performant.

Uh, the problem is agents, you know, need instant context. They need, and I, you know, I think you and I chatted about this a while, a while back mm-hmm. With, um, if you're outta sync, even little bits, um, big, big problems can happen.

And so timely access to accurate information is critical. And how do you do that? Well, you certainly aren't doing a query from a, you know, data warehouse to do that.

You certainly are bringing the data closer to the code, to the agents and, um, you can do that through streaming, uh, which is why we saw IBM pick up confluence, uh mm-hmm. Because that's kind of a big deal. And another way is, thank you.

The developer's favorite pastime, which is caching every you and managing cash almost every, yeah, ev almost every problem can be solved with caching. Um, and, uh, and it already plays a big role with the agentic tools we're talking about, like, every time I hang up from a session with Gemini, CLI, it tells me that I saved, you know, 80% because it had that many hits from cash, that percentage of hits from cash. Wow.

Like, wow, I really am forging new territory, aren't I? To your manager? Yeah.

I gonna say, you know, it's grocery, they already, you save 72. This, this trip to the grocery store, because Brad is so boring in what he does. Everyone's doing the same thing, um, in post post.

So I'm trying, trying to think about, you know, how companies can speed up, you know, access to data for, for agents latency, agent latency, waiting for those, for the data, the context that it needs. Very interesting. Yep.

Well, my friend, it's been fun, um, a lot. I have so many ideas for, uh, things for us to talk to. I know you, you do too, so we'll keep 'em rolling.

com if you'd like to make a suggestion. Uh, if you're interested in making an appearance, maybe being part of this, we've got some folks that are interested in doing that. And, uh, we're, we're kind of getting the, getting things rolling with Brad and I, and then we'll start to have some guests as we do that.

So send us the feedback. Thanks for following us on your favorite podcast platform. And thanks for coming back to listen to, uh, to US Jawbone for a little while about building software in this new AI agent Agentic era.

Uh, behalf of Brad, myself, it's been fun. We'll see you on the next episode. Control.

This is agent dev. I'm in position Copy that Dev. Stand by for Go.

Standing by Marvell and Excon, the final data center Frontier, AWS raises the stakes. We're saying goodbye to Lou Gerstner logging for legal reasons ServiceNow acquires amis, and we should probably talk about A-I-C-E-S in this week's episode of the Tech Field Day Rundown. Hello everyone.

Welcome to the first episode of the Tech Field Day rundown. It is 2026, officially, it is the 7th of January. We're very happy that you are joining us once again, and in the immortal words of Dee Snyder and Twisted Sister, I'm not gonna take it anymore 'cause well, that's what today is.

But what I am gonna take is a moment for my co-host to introduce himself. Al, it's good to see you again. Well, thank you and welcome to 2026.

The, uh, 7th of January is indeed a beautiful day here in New Zealand, where it is not currently International Programmers Day, although maybe it should be International Programmers Day, uh, and of course, national Bobblehead Day over there where you're, uh, Tom. Yeah, you know, the funny thing is, I, I realized that people may not have their own bobblehead. Uh, I actually have a couple of them courtesy of our friends over at SolarWinds many years ago, and it really weirded my kids out that I have my own.

But you know what doesn't weird the kids out, or the adults for that matter, is this wonderful lineup of news. We, we were keeping track of it over the holidays so that we didn't miss any of the big exciting stuff. And we're gonna jump right in because our friends over at Marvell have announced that they are going to acquire Excon Technologies for about $540 million.

This, of course, will strengthen their leadership in AI data center connectivity by adding several advanced PCIE and CXL switching silicon, uh, lines and experienced engineering talent. The deal also expands Marvel's UA links Scale up switch capabilities, which positions the company to support next generation multi rack AI systems that require ultra low latency, high bandwidth interconnections, ex con's production, and next generation switching products broaden Marvel's portfolio, and that's going to expand their addressable market. They're expected to contribute revenues starting in the second half of fiscal year 2027.

The transaction is slated to close early in 2026, pending those pesky approvals. Al, did Marvell make a good move here? Absolutely.

I think this is a, a really significant move for them in terms of getting hung density and in, in data center infrastructure. Um, that's fundamentally what the excon, uh, technology is about, uh, the CXL switch technology with a really high density and for, for PCI five, uh, switching capability in here, uh, if you've been following us for a while, if you've been following Tech Field Day, you know, we're big fans of CXL as a technology for being able to disaggregate, being able to compose together the pieces that make up your, your computer, and particularly your large scale, uh, compute environment. Being able to have a, a whole, uh, collection of GPUs that you can dynamically attach to a whole collection of servers and dynamically attach a whole collection of storage so that you can rebalance this collection of compute that you have based on the workload that you need at the time.

Uh, this kind of technology is absolutely gonna be vital as we're building out bigger and bigger data centers for running ai. And in common with some of the stories we are hearing throughout this news rundown, there'll be a focus on cost and cost efficiency that we maybe didn't see in the rush to get on board with ai. So I think Marvell is absolutely hitting one of the places that's gonna be very important for customers over the next couple of years of being able to build those composable infrastructures for real using CXL.

And, uh, this is gonna be useful for, for in, in businesses. Of course, most of the enterprise customers don't buy directly from Marvell. They'll be buying the package solutions that get shipped out.

And again, this is where having that larger organization in Marvell rather than just Excon, uh, brings that economy of scale that will reach out to deliver these solutions to larger numbers at data centers. I think this is a great thing to see. I'd love to see it happen a little faster, getting more products, uh, shipped out earlier so that we can have, uh, large data center infrastructures for running AI on premises and kind of sitting behind quite a lot of the stories we're also getting is that increased cost in Rand around apocalypse uh, pricing that we're seeing over the last few months.

Uh, that will play into this too. SpaceX is planning to transform some of their star link version three satellites into orbital data centers, maybe as early as well this year. Uh, leveraging the terabit speeds that they have, the links between those satellites, the fact that they've got a heck of a lot of those satellites up there in this mesh network.

Uh, and also using, reusing their low cost launch capacity with Starship, uh, brings back that vision that we had earlier from Jeff Bezos and Eric Schmidt, space based cloud computing to disrupt conventional terrestrial infrastructure and offering global reach sustainability, Ben benefits, uh, but maybe some challenges around regulation security and that also important engineering of making stuff work. Um, Tom, time for your soapbox. Oh, man, you got it.

It's a small soapbox. It's easily liftable into orbit. Um, where do I start?

Uh, Starship hasn't actually launched yet. Uh, I I mean, it's it's gone off the launch pad, but it, it seems to blow up, but you know, who knew? Uh, space is hard, right?

Um, but SpaceX has mostly figured it out. I, I think, except for all the times that it didn't. And, uh, what are we gonna do?

Oh, we're gonna put some data centers in orbit. Well, why could that be a problem? Hmm.

Let's see here. I've talked about this on a number of occasions. We know cooling, cooling is very hard in space.

Um, how are you gonna work on things in space? Do you, do you remember when we had to work on Hubble? Do you remember how much of a pain in the neck that was?

And that was one telescope. Uh, things have to be bulletproof when you send them up. Uh, how Bulletproof Think Voyager two.

Yeah, that thing's still running after doing the math here. 48 years. It's not running well.

I mean, it's mostly down to like two instruments, uh, uh, but they keep it running. Uh, and that was something we built in like 1970 ish with that technology. Uh, this is stuff that breaks every couple of years.

So w why exactly do you wanna launch things into space? Uh, I mean, there's plenty of land around here to build, uh, data centers on, right? Oh, that's right.

You have to ask permission from people to build data centers on that land. You have to repay them for that. And do you think you can just blast some starlink satellites or your, uh, ky uh, satellites into orbit and just use them for whatever?

Uh, you guys seem to think that there's a lot of data center stuff out there that needs to be built. Uh, I'm not necessarily a naysayer on the, uh, the level of zitron, but how about we use what we've got before we start polluting space with junk? Uh, we, we've seen this over and over again.

Like, what's gonna happen if they manage to build out space the way they want it to? Are we gonna be surrounded by some kind of shield of, uh, starlink satellites and, uh, failed, uh, spaceship launch vehicles that that didn't, you know, didn't get it into the right orbit or whatever. Like, there's a lot of reasons to doubt this.

And if you or someone in any form of like, I don't know it, or finance or whatever, you should be asking some very serious questions about why we have to put these things in space. And if the answer doesn't come down to anything other than, well, we can, like, that's the problem. Instead of us saying, wouldn't it be cool to have a data center in space if I was on the board of one of these companies?

Well, my question would be, why do we need to have a data center in space? And if you can't answer that other than, well, it's cool, then don't do it. Uh, but I, I'll have to get off my soapbox yet, because it's about to reenter the atmosphere and land at Point Nemo.

And, uh, Lord knows, I don't wanna get stuck out there long ago. AWS would loudly tell customers that they keep dropping the price of cloud services. But you'll notice that those announcements quietly stopped a few years ago in their place.

We get very quiet announcements of price increases, the latest round of increases. Were about 15% on the hourly costs of everyone's favorite Nvidia, H 200 GPUs. You know, the ones that everybody wants to use for AI training.

Will this drive AI training to other locations? Or is this just the first shoe to drop where everyone is gonna be raising the prices for their AI GPU clusters across the board to kind of drive a little bit more cash out of this market? That's the thing, isn't it?

Uh, are we at the getting to the end of that land grab of, we've gotta prove that this is the right place to, uh, run your ai, which when you're doing a land grab into a new space, you don't mind losing some money because once you've grabbed the land, you can jack up the rentals. Now, to be fair to AWS last year, they did actually reduce the, the cost of these same GPU based, uh, P five E and P five E in, uh, instance types. These are the really large, uh, EC2 instances that that'll run you $43 an hour now 49, 75 an hour after the price increase.

They did drop the prices quite a lot sometimes during the, the time last year. So it's not that they haven't been price drops, it's just that there's also quiet price increases. Uh, as I mentioned in the, the earlier story around Marvell and Excon, the cost of ram, the cost of the, uh, high bandwidth memory is going up.

And so maybe this is simply a reflection that a w S's cost to build new, uh, high performance large instances is going up, or maybe it is that they've got all of these instances, they're in really high demand because they've done a good job of telling people this is the right place to run your ai. And now because demand is exceeding supply, price goes up. It may be that simple.

Uh, there have been some statements from AWS saying, prices go up and down depending on demand and supply. And there was a pre-announcement, there was a warning that prices were going to be adjusted. So this was in, uh, last year, late last year, they, uh, AWS announced that prices on some instances were gonna be adjusted.

They just didn't say whether it was adjusted up or down. This is kind of that we'll be very quiet when we tell you things are gonna go up in price, but we'll try and tell you very loudly when things are going down. Uh, these particular P five instances, P five E and P five E and with the H 200 GPUs, they've been a avail available since, since September, 2024.

And they're really high performance, uh, instance types and buying them from AWS and paying only for the time that you use them is probably a good plan. So long as you have that cloud friendly bursty workload where your peak workload is maybe four or five times your average workload. But as I said, Tom, we might see this driving some return to on-premises.

If your workloads are much more even over time, if you don't have big peaks and troughs in utilization, maybe your AI is gonna be better off on premises. And I think we'll see increasingly the inference component of AI being run on premises, uh, because it's a much more consistent workload, whereas the big bursty stuff is with training and fine tuning. And we may well continue to see that running on cloud platforms, and particularly AWS of course, would like you to have all of your cloud applications on the one true cloud.

If you remember the way AWS talks about the world, some sad news came in over the break as well. September, uh, December 27th, we got the sad news that Lou Gerstner had passed away. Lou had been the IBM chairman and CEO from 1993 to 2002, and that's a pretty significant time of change within IBM and his decisive leadership saved IBM from yeah, being, uh, has been to being, uh, still very relevant to give a company that focused on customers and customer needs rather than just on the, the things that they built themselves and relying on the fact that nobody ever got fired for buying IBM, uh, Lou worked very hard to make sure that that remained true as IBM changed over time.

And so his legacy endures within IBM's values and strategy and the global impact of IBM as an organization. Now, Tom, I believe you, you previously were at IBM. Did you ever get invited up to the boardroom?

No, I did not. Uh, Lou did not sit in the area where I was an intern, uh, but Lou was always there with us. And, and that's one of the things that I think people don't really understand.

In 1993, IBM was effectively dying. John Akers knew that the only way to save the company was to split it up because the only way that IBM could stay relevant was that if you basically took the parts and turned them into their own little organizations and, and tried to get them to, you know, I don't know, fight with everybody else, uh, remember this is when Microsoft was so big that the US federal government was thinking it was time to break them up. Like, like this was the heyday of all of the upstarts that were fighting against IBM.

All you gotta know is that when Lou took over, they were still making Blue Lightning 4 86 processors and OS two. And when Lou stepped in, first thing he did was say, we're not breaking up IBM. There's no way that having IBM in pieces is going to benefit the company.

And so he quietly went about reversing everything John Akers was trying to do, get rid of Blue Lightning, get rid of OS two focus on this new thing called the internet. And this idea that he came up with called E-Business. And when he did that, that marked two points.

One, the turnaround of IBM. That was the moment when they got off of life support and started growing again. And two, and this is something that I have said for a number of years that in my mind was the end of Tom Watson's, IBM.

And that is something that has, was repeated at the company when I was there. And I know for a fact it's still repeated there today. This entity that you see today is not Tom Watson's, IBM.

It is not the group that made the ass 400 or, um, RDA or any of that other stuff. This was the organization that started turning on IBM Global Services. That was the group that I was actually an intern for, even though I worked at the ass 400 manufacturing plant in Rochester, Minnesota, shout out to Mayo, um, I was more, uh, aligned with the IT department.

And Lou's changes were radical at the time because there were still a lot of people that worked at IBM who weren't sure that this upstart knew what he was doing. Fast forward now 24 years after he's gone, I'd say Lou was the reason why we still talk about IBM today in the way that we talk about it and not the way that we talk about something like weighing computers. He knew what had to be done.

He was brave enough to make those changes. And when he finally stepped aside in 2002 and he, he retired, he was not forced out like Akers was, he left the company in better shape than he found it. com bust area.

Like I was there like that quarter when the stock market just started sliding. And, and everyone was like, well, what does this really mean? Lou Gerstner is one of those people that when we put up the Mount Rushmore of CEOs, people tend to put up the, you know, Kokas and the Jack Welchs of the world.

Lou should be on that mountain. And I want the somebody else to keep this in mind. Lou is the fifth person that they asked to be the CEO at IBM.

They were actively trying to unseat acres, and they went through five other people before they got to Lou. He worked at Amex, and they're like, well, we need to bring in an outsider. And I think that it worked.

So Lou rest in peace. You, you did a lot to keep it on track. Um, I, I'm, I'm not saying that I got my internship at IBM and launched my official career because of you specifically, but I did it because of the changes and the environment and the culture that you made at IBM.

And for that I'll ever, I'll be eternally grateful. So here's hoping that the current form of IBM sticks around long into the future, and we can talk about the impact that great men like Lou Gerner and several others have had on the world. There's a group of news organizations who are suing Open AI for copyright infringement, and they just want a judgment that confirms their access to the logs of 20 million.

Sample chat GPT sessions. The group of news organizations wanna show that chat GPT is reproducing their articles without attribution, while OpenAI claims that their operation is fair use of published content. This judgment simply gives the news organizations access to the logs, and it is not proof of whether or not chat GPT is using their content fairly.

With all of the backlash about unscrupulous data harvesting for AI training, is this case the beginning of the end for the current family of LLMs that are being trained on everything that the internet can see? Or is this an opportunity for a group of news organizations to go on a fishing trip? Yeah, it's kind of hard to tell in here.

Certainly there's a threat to the news organizations and, and to most of the ways that consumers access information. Historically, the threat is that they just use chat GPT and ask a, a simple question and get a, what appears to be a conversational, uh, answer that is accurate and, and reasonable may not be either accurate or reasonable, but it's a nice conversational answer. They get answer specifics of this particular, um, situation is that, uh, a magistrate had granted the request to say that, uh, these news organizations, uh, led by New York Times can get access to some sample data.

Now, this is some sample logs of 20 million interactions with chat GPT, and the objective here is to see where the chat GPT is actually using data that was harvested off these news sites and is cleansing out any sign that it came from the news sites and making it appear as if this is information that chat GPT itself knew of without really having to attribute back where it came. Uh, this case, there was a district court, um, Sydney sign who just upheld the previous magistrate's judgment that OpenAI must release a sample 20 million, uh, set of logs and deliver these so they can be analyzed for whether this is fair use, whether there is any direct attempt to hide the source of the information that is being, being presented out in these results to users. Uh, it's fun and interesting to see the sort of back and forth fight here.

Naturally open AI was trying to minimize the amount of information that they release out. Uh, now there's bits of it where OpenAI looks good. It's like we don't wanna have to work too hard on compliance for this case, because while working compliance and discovery for cases is always an expensive and, uh, not very profitable activity, but there's also some behaviors in OpenAI where they immediately started deleting more logs after there was a judgment saying, you need to hold onto your logs.

Um, because, or at least after the litigation begun and they were supposed to have held, held onto their logs. And so a whole lot of logs got deleted very early in this process, uh, that are therefore not available, is that unscrupulous of OpenAI not to comply with the best practice of not deleting things that might be relevant to a call case? Yes.

Yes. That seems to be a bad thing. Uh, and then it's taken a long time for this to, to roll through.

It's been months, it's been around a year since this began. These, uh, news organizations just wanted to find out for sure whether the content that they have, uh, created is being misused and whether maybe they could charge open ai. Just realistically, those news organizations are about generating coverage.

Uh, and either they want somebody to pay for the news that they've, uh, they've generated or they want attribution so that people will come back to their own websites and can be shown the ads that historically have always funded our news organizations. Is this gonna run? Is this, uh, court case gonna be settled quickly?

No, no, this is gonna drag on for a long time. Open AI is gonna continue to follow the same process. They've gone against all of these copyright infringement cases and that other, uh, ai, um, foundation model builders have also done of let's delay this as much as possible.

Let's minimize the information we release. Uh, let's claim that everything is fair use and that if a human had taken exactly the same actions, it would be considered fair use. Ignoring the fact that they've acted at a scale that is so much vastly larger than any human could act.

We'll just have to keep watching this space because the story will continue slowly, slowly unfolding throughout this year. Just before Christmas, ServiceNow announced that they were going to acquire amis for seven and three quarter billion dollars and add cyber physical security and exposure management. Uh, this will expand ServiceNow's existing security and risk business, and it's gonna be a significant focus for ServiceNow as they expand beyond IT service management into managing the IT services that they manage to ServiceNow have a lot of credibility with security teams to replace the existing tools on, I don't dunno that they have it right now, but I think they're hoping they'll have it pretty soon.

Because if there's one thing that stakeholders love, it is a one stop shop, right? You may have heard it as, you know, one group to yell at one throat to do something with. Um, this is ServiceNow looking around at everybody going, well, what do they have that we don't, that people are still buying?

This is very infamously, you know, like Apple's development mantra, right? Is look at what great third party things are going on out there and integrate those pieces into what we're doing. It's the infamous sherlocking if you're old enough to remember when that was a thing.

But I think what ServiceNow is really hoping for here is they want to add some stickiness to things that will allow them to build credibility in the market, but also allow them to expand what they can offer to people to help raise the the pricing, right? Because one of the things that you run into with a s opportunity is that people really want to use it for what they want to pay for it, right? Like, you have to find ways to continually raise the price.

Because if you are working in an organization that is like a restaurant, or if you're working for a company that does a lot of like travel, like raising the cost of something makes a lot of sense, right? Airline tickets aren't getting any cheaper, food costs aren't going down at all. But if you work in a service where people just kind of look at your dashboard and help you figure out your tickets and stuff like that, other than inflation, why do you raise your prices?

Well, in this case, they're raising their prices because they don't have something they can offer you. So if you want to have ServiceNow plus security, then you're gonna pay an extra X number of dollars per seat per month, and that makes the investors happy. But the key there is that you have to be able to integrate what Armas is doing quickly into ServiceNow, because what people don't want is to have one company and one bill, but two separate systems.

And you know, you'll forgive me for invoking the, the, the pane of single pane of glass, but that's what they want, is they wanna log into one system with one dashboard, see one number that gives them a thumbs up or a thumbs down on what's going on and do that. And I think that this is a great opportunity for ServiceNow to expand their offerings. But I also think that it's a good opportunity for people in the market who have a more robust directed security offering to kind of jump out in front and say, Hey, just know that we've been doing this for a while and we're gonna continue to do it while they're catching up.

So you can either go with what's proven or let them kind of shake out the bugs. Not to say that there's any bugs in amis, but any integration is always going to have friction that you're gonna have to watch out for. And for almost $8 billion, you hope that the friction is kept to a minimum because they're gonna have to start recouping the costs on that pretty soon.

And that's not gonna come from laying off the, the accounting department over there. All right, we wanted to take a closer look this week at everyone's favorite kickoff for the year, and that would be the consumer electronic show. And this year, NVIDIA's, CEO and fashion Maven Jensen Wong unveiled a sweeping vision for the future of AI led by their Ruben platform.

The company's first extreme co-design six chip AI architecture designed to dramatically reduce AI computing costs while scaling performance across data centers, enterprises, and devices. Nvidia also introduced a broad portfolio of open models that span healthcare, robotics, climate science, and autonomous driving, which was highlighted by the Alpha Mayo platform delivering AI defined driving in the upcoming Mercedes-Benz CLA, from supercomputers to personal AI agents and physical AI systems. Nvidia positions itself as a full stack AI company driving intelligence into every domain, device and industry.

And as some of the other news sites have already picked up on, you can't even get away from AI at CES. So Al let's kind of dive into this. Nvidia uses this as a launching platform for all of their latest announcements with Reuben and Alpa Mayo and so many other things.

Is Nvidia basically trying to pull away from the rest of the pack and say, if you're using us, you'll always be on the cutting edge, or are they trying to cover for the fact that people are still a little iffy when it comes to ai? I think fundamentally, uh, NVIDIA's been ahead of the pack for a long time. Uh, this is reflected by the, the huge number of Nvidia GPUs, the B two hundreds that we've previously seen.

Now they, uh, Vera Rubin combination that we're seeing as the new product, um, huge numbers that isn't being moved, that most of the frameworks that we use for developing AI applications will very easily target these GPUs using the TOKUDA instructions that get the most performance outta them. So, uh, I think this is absolutely Nvidia saying we, we've moved the goalposts even further out in front of where everybody else is, um, when we're seeing them talking about, uh, when we're seeing Jensen talking about reducing the cost per token by a pretty significant factor. And I don't have the number in front of me.

My recollection was there it is five times higher tokens per second, five times better per perform performance per TCO dollar and five times better power efficiency. This is a, a whole lot of interesting things. This is in the, uh, AI native storage particularly that has announced here.

Uh, this is just a, a whole lead set of hero number announcements at the beginning of the hardware that they're shipping. Because NVIDIA's job and NVIDIA's purpose is to sell you more of their hardware. Uh, it's interesting that alongside it is a whole lot of open models for ai.

So these are models that you can run on your own Nvidia equipment that are being trained on in NVIDIA's own supercomputers, uh, or whatever those super computers mean. Uh, these are models that you can use. This is basically to make it easier for you to start using and buying more Nvidia GPUs, whether they're the high performance ones that are sitting in the data center, soaking up all of that power and probably requiring liquid cooling because there's so much power density going on.

But, uh, it might also be in the DGX spark the little units that people are putting on their desks if they've got a spare, uh, six to 10 grand to spend on, on a desktop computer. But beyond that, NVIDIA's also looking towards getting these things in embedded locations. So there's a lot of dialogue around getting AI out to edge locations, whether those edge locations are autonomous vehicles or manufacturing plants or their retail and commercial locations where Nvidia GPUs are gonna be providing some, uh, some acceleration to those models at the edges.

Uh, I think there's a whole lot of this, which is we've got a lot of technology that that allows you as the end customer to buy more Nvidia GPUs will give away the things that allow us to, to sell you more GPUs. Tom, are you planning to upgrade your desktop to A-G-G-D-G-X spark so you can run AI models locally? Oh yeah, totally.

Why not? I mean, it's, it's only six or seven grand. How, how much can that set me back?

I, I think this is Nvidia realizing that they are going to have to start broadening their market, right? You, you see healthcare on the list and you're like, oh, but healthcare should be using AI already, but not for the things that you are thinking they should be using it for. Autonomous driving is another good example.

There is a lot of work that's been done on using AI for autonomous driving, but maybe not with Nvidia because the people who were working on that project were working on it a long time ago. And I, I wanna use an analogy here because I think it might re it might resonate with people. Um, I have a 3D printer that I backed on Kickstarter, uh, a couple years ago, and it's an okay printer.

Um, I probably paid more for it than I should have, but one of the things that holds it back is that a lot of the really cool stuff that runs on it is only enabled in the custom slicer that the company who made it released for it, now, they promised that they were gonna release open source slicer support, and it's kind of there, but it is like bare bones only. Like it kind of uses the hardware, but not really. And if you wanna do that, why don't you just use our slicer?

The hardware is being driven by the software in the case that we're dealing with here. It's the other way around. The software that Nvidia is releasing works, but it works really well on Nvidia hardware.

It's so much faster, it's so much better. It uses a little less power. I'm sure that's probably in there somewhere and you just need to buy some more GPUs, right?

Just, just buy some more stuff. We got 'em on the truck. We'll, we'll just, we'll, we'll ship 'em out to you today.

And that's really what they want, is they want to continue this model where they are selling more and more and more hardware to everybody. And, and like the, the DGX that you mentioned is a perfectly good example. Why do I need a desktop AI system?

Well, to hear Nvidia tell it, it's because it's the future of computing. And I think it's interesting that the way that we've seen kind of the undercurrent in the market, it's being driven to the point where a lot of people are thinking, well, why do you need to have a desktop system at all? Or a laptop or something?

Why can't you just rent your computers from the cloud? You know, we've, we've seen that a lot, right? Remember when VMware Horizon came out and they're like, why don't you just run your desktop on a, on a cloud system somewhere?

And now what they're saying is, well, why don't you run that in our cloud? And we'll, uh, it'll be AI powered and it'll be wonderful and you'll be able to use it. And that's the end goal, right?

Is they want to have as much consumption of these things, things as possible so that people are forced to use them as much as possible, which means they're always gonna be wanting to use new stuff, which means every couple of years they're gonna have to refresh that new stuff. Because the other thing is the Wiz kids on the r and d side can't stop developing because if they ever stop developing, then that will look like a plateau to people, and that's a bad signal for the stock market. So if you're continually making the systems use more power and have more resources available and do more cool stuff, you constantly have to make them do more cool stuff.

Because if you don't, people might sit up and say, well, wait a minute. The one that I've got now is just as fast as the one that I had last year. I don't need to upgrade now.

And that could be a problem. And that's why I think what you'll see next is Nvidia continuing to offer a lot of these software solutions. And man, they're gonna work great on H two hundreds, but if you had a B two hundreds, they'd work even better.

Um, I know that this sounds like a lot of hating on Nvidia and well, maybe a little bit it is, but one thing you won't hate is all the great stuff that we've got coming up with Tech Field Day, Al your first up in 2026 with your event this month. What have you got going on? Well, in three weeks time, you can catch us at AI Infrastructure Field, day four.

Of course, watch us online in all of your favorite locations, particularly on LinkedIn, where you can find us on the Tech Field Day LinkedIn page. Uh, we've got some great companies coming along. We've got, uh, Cisco, um, uh, good friends, Cisco, uh, other good friends Hammer Space in there as well.

We've got, uh, Ford Networks and exci and Fabrics AI coming in to present. A few of those are companies we've never heard from, from before at Tech Field Day. So I'm looking forward to a pretty packed time at, uh, AI Infrastructure Field Day four.

And it's not too late. There may be a couple more logos added to those companies as well as we get closer. Uh, of course we also have a, a fun Cloud Field Day coming up in March.

That'll be March or 11th and 12th. And, uh, we're still a little earlier on the companies for that one. I've got a good group of people, though.

It's shaping up to be an event that's certainly gonna be fun when you we're not on camera. It should be a lot of fun when we're on camera too and later in March. I'm very jealous of Tom because, um, security conferences, uh, RSA conferences, one of the most interesting conferences that goes on in the industry.

Um, and you're gonna be there. Tom, I'm, this is the very first time that we've ever been at RSA officially as part of Tech Field Day. And, and it's exciting, right?

Because you, like you said, RSA is probably the biggest thing in the security space right now. There's 50, 60, 70,000 people that show up for it, and we are gonna be a part of that. com, you can see what we've got going on.

Uh, we actually have presentations that are gonna be lined up from Veeam and Object First, and there's more there, there is definitely more on the, the schedule. We'll be updating that list as soon as we can, and you'll probably see some familiar faces from the security space joining us as delegates there. And honestly, if you're at RSA, we, we'd love to meet you.

So make sure you reach out to us on social media, uh, let us know. I know it's still a couple months away, but it's never too late to start booking all those meetings right now. Uh, get 'em in before they release your name to all of the, the people on the floor and they book your calendar full of stuff.

But make sure that you leave some time on your calendar for Wednesday, because that, of course, would be the new episodes of the Tech Field Day rundown that we release as a YouTube video or a podcast in your favorite podcast application of choice. And we stream it on Techstrong TV as well. We wanna make sure that you are following along.

So make sure that you are subscribed and you're getting notifications. Leave us a review, leave us a comment. We'd love to hear what you have to say.

And don't, uh, forget that Al and I are also on several other Textron and Future and group programs throughout the week. Uh, we have lots of great podcasts and lots of other things that we take part in. We'll be back next Wednesday to talk about all the stuff that happened when everybody got back into their office and finally got done checking all of their email.

But until then, for myself, Tom Hollingsworth, for Al and everybody else here at Tech Field Day, thank you very much. Enjoy the rest of your week and we'll see you soon. Hey everyone, welcome to another Tech Drunk tv uh, interview.

I'm really happy to introduce you to my next ex guest. It's his first time here on Textron tv. His name is Kevin Dko.

I hope I got that right. Kevin is the, I guess CEO of Emeritus for Syntax. Is that, is that correct, Kevin?

That That is correct. That is Correct. Fantastic.

Welcome to Tech Stroke tv. It's always good to have another New Yorker arm with me, so I feel I feel comfortable already. Um, Kevin, before we jump into, we're gonna talk about chief data offices and, and data management and so forth, but let's talk a little bit about Kevin.

As I said, you're a, you're a New Yorker, lifelong New Yorker, but give people a sense kind of, of your journey, Kevin, of Sure. How you wanna be here, And I appreciate the time. Thank you, Alan.

So, uh, as you said, lifelong New Yorker, um, born and bred. Uh, I started my career in financial services, uh, worked for, uh, chase Matton Bank. And if I talk about the projects I was working on at the time, it was, uh, uh, working with 37 45 cluster controllers in branches.

And the new technology at that time was bringing local area networks into all the branch networks. So did that for a while and modernized a Chase bank back in the day. And then moved down to, uh, wall Street, working for a time market data company.

And at that time it was really taking, uh, I'll say all the, at the time it was all based on tandems, taking tandem news feeds and bringing, uh, stock information down to traders floors. And, and we built, uh, uh, an application based on Windows and brought in all those tickers and news feeds and brought real time stock information back to, uh, the brokers. And then at that point in time, started to move over to, uh, the vendor side, worked from companies.

Uh, no, it was part of, uh, uh, an acquisition with VMware SpringSource. So it was everything Sure. In terms of, um, building Java based applications.

So at the time, uh, Paul Moritz was the CEO of VMware acquired SpringSource to be able to bring and build applications on type of that virtualization technologies. Uh, stayed there for a little while, then moved on to another company that was really focused on bringing ERP applications to the cloud. We were acquired by, uh, EMC at the time, then Dell, and then that was brought me to Syntax about five years ago.

So, um, wow. Yeah, you know, that's, uh, that's quite a history. You've been a lot of, a lot of your history revolves around that EMC, VMware kinda conglomerate, right?

That it, it was, Was all about it. It, it, it blended between application and infrastructure and really those core mission critical applications and how to provide, uh, the best set of services around those core applications. So, yeah.

Yeah. Very cool. That's great.

Great, great, great. Uh, story, Kevin, I feel like everyone's heard of Syntax, but very few people really understand Sure. Syntax.

So if you wouldn't mind be, be the, uh, the translator here, how would you describe Syntax to our audience? Absolutely. So, um, and it, it really attracted me because like you stated, I had heard of Syntax, but never really understood the full history.

And it, it really amazed me at the time when I was looking at the organization. They were just shy of about, uh, 50 years, uh, within the industry, which kind of shocked me. Wow.

And when I talked to the founder, and the founder is still here with us, working with us and still leading us, which was great. Um, they started out as a consulting company and they morphed into actually building their own ERP back in the day during their, during the seventies. And actually we still have, it's probably about 35 customers still running that ERP that we wrote almost 50 years ago.

And, um, at that point, they started to partner with, uh, companies like JDE and then that got acquired by Oracle and PeopleSoft, and then they morphed into EBS and then started other acquisitions around, um, SAP. So our lens has always been through the ERP, so that highly structured mission critical, important data that runs the organization. But as you know, um, the ERP has what I always say is a large gravitational force.

It it ties into all the rest of the business applications. So the lens is always through the ERP, but then we dive out into the rest of the data and the rest of the organization to ensure that you're getting the, the most and most impactful usage of that data, uh, throughout the rest of the business. Excellent.

50 years. I know. Wow.

Think about that. com. I, I helped take a company public.

We were in early, what they call a SP application service provider. And so we were offering hosted versions of PeopleSoft, hosted versions of Oracle. Yep.

And, uh, Lotus Notes and, and others before this, you know, VMware really hasn't caught on it this in the late nineties, sir. There is no cloud, so to speak. We're delivering this stuff.

So multi-tenant was kind of a fan. Yeah. And, uh, even, even bandwidth, you know, you're delivering it over T one at best lines or at wire, hopefully, you know, at best was the T three line.

It was, those were wild, wild West days. And they were expensive too back in the day. Oh My God.

Talking about about 45 grand. I think it was like about $40,000 a month for a T three line. But what I Was talking about being down on Wall Street, we were using the modern technology, ATM frame relay and IDN dial backup to be able to support these brokerage f*****g well, You always had to have in case stuff went to hell in the hand basket, you had those logo.

Um, anyway, so your CEO of Americas, which kinda leads me, we believe that they probably have a separate business unit for the Americas A a. Absolutely. So we are a global organization, um, significant business within, uh, north America.

We have offices that are in South Africa, Germany, France, London, global, uh, within Mexico, within Slovakia. So it's a little bit over 3000 employees at this point in time. Um, but, uh, I, I run the North American, uh, business for syntax.

Fantastic. Great, great, great story. So Kevin, you know, the whole ERP business, it, it, it, look in the seventies, you were absolutely pioneers, right?

But the ERP business has really undergone some drastic changes, evolutions, especially in the last two, three years with the advent and rise of the, all of this AI and, and so forth. And, and I would, if I had to characterize it, I'd say it's been a return to the supremacy of data, right? ERPs, whether you hate 'em, love 'em, what whatever you feel, they're only as good as the data you put into them, right?

Yeah. And, and so we've, we've made a lot of strides in automating, getting data in, uh, observability kind of things and, and acting on that data. And at the same time, you know, there was, we started off site a couple years ago, Kevin called digital CXO, because all of a sudden everybody had a C level for everything, right?

But one of those CXO titles was Chief Data Officer CDO. Yeah. And I, and I'll be honest with you, a little confusing, what exactly is the CDO?

What is their role? What, what's, what's kind of their, you know, what's their charge? Yeah.

What are they supposed to be doing? And, and as you and I were talking off camera for a lot of these CDOs early on, it was kind of a, a governance risk, you know, GRC kinda role where hey, we had to make sure we know where our data is, what data we have, and that we are, you know, from privacy regulations and everything else, doing the right thing with it. But, but that's kind morphed now.

I think what, you know, you, you've got a front row seat on the battle front here. What do you see? Yeah.

So, uh, right now, to me, and I think that chief Data Officer is honestly probably the most exciting role right now. And as you were saying, originally back in the, when the role first came out, it was strictly about honestly, more security, governance and compliance, which seemed like a, uh, I would say not to offend anyone, sometimes a boring role because you're, you're, you're within the organization and you're putting handcuffs on everyone. And it was about restriction a lot of times, and right.

All the different technologies coming out. And you see the, the maturation of what's happening with AI and analytics. It's really driving the importance of data within the organization.

And now that chief data officer, and in a lot of cases it becomes chief data Officer analytics and ai. And that person now is not about restriction, but it's about enablement with guardrails. And I, I, I think it's such a critical, and to me, I think 2026 is that inflection point for that individual in that role within the organization to really be a key business, um, enabler and really drive the organization to competitive advantages and using data.

'cause when I look at it historically, data was always an IT asset. And now this chief data officer really has the opportunity to drive and understand that data is really a business asset and it's gonna drive our, our, the company's future growth. Sure.

Is, I mean, data is is the cornerstone of, of your company's crown jewels of the assets, right? Because your ERP doesn't work without it, but either does your CRM either does anything you have without your data and, and you know, the role of the data officer is, is I, I guess to make the company aware of what you actually have in that asset, right? EE, exactly.

And that's, it's not a Op. Yeah. And, and that's where sometimes I think it could be challenging dependent upon where that person sits within the organization.

I think this individual really has to have a seat, a pie, and be at that table because as I said, they're really an enabler with guardrails and if it's done properly, because previously, I, I think a lot of the data was really centralized and that became the bottleneck. Yeah. And what has to happen today based upon all the technologies and the speed of which the business has to move, it has to become decentralized.

And you need that chief data officer to really, um, put that framework in place to allow that decentralization and allow the innovation to happen as close to the business as possible. And I think this, to me, this is the, the most important thing, because over the years you've seen it and business really becoming closer and closer at this point now with all the different technologies, it's, it's not just close. They're, they're one in the same and they're intertwined.

And that chief data officer really is the one that's providing that framework, those guardrails to allow that innovation, acceleration, and competitive advantage for the business. Agreed. Agree.

I, I couldn't say better myself, uh, you know, Kevin, but there's also been, again, with AI and some of these newer technologies, but even before the whole generative end, Neogen ai, I think burst on the screen, on the scene, um, there's been a lot of progress in getting at this data, right? It, it no longer is some AM offers Amer blob living somewhere, right? It is, data is dispersed throughout our organization.

But of course, the problem was what does that, what's in that data? What does that data say? What, what actionable intelligence can we gather from that data, right?

And as you said, how can we allow our users to access that data, contribute to that data, analyze that data data in a safe, secure, fast way, right? EE Exactly. Where it, it travels at the speed of business EE Exactly.

And I think that that's really the, the problem that the chief data data offer data officer is solving from the perspective of as, as we were talking about the ERP, that was always very highly structured, well understood, and governed data. So we knew that data was clean. As you get further and further away from the ERP and closer to the business, you start seeing a lot more of unstructured data.

And the problem was, is that as we talk about AI analytics and all the other data technologies, the, the pace of which that was, um, improving and maturing was significant. The piece of, I would say the, the data cleanliness and the, the data management didn't mature at the same rate that these technologies were. So what we're seeing right now is that everyone is trying to do something with AI and analytics.

And what they're finding is they're, they're stumbling a lot within the pilot phase, and they're having a hard time demonstrating the ultimate business value. Because as they get deeper into it and try to solve the business problems that are presented to them, they realize that, as we were talking about in the very beginning, data is that new currency date, AI and all, and analytics and all these other technologies don't solve data problems. They, their, their outputs to a and they run on Data E Exactly.

So they need the data. They, they, they're realizing right now they're like, oh, we can't really solve this problem because the data we have isn't accurate. It's not clean, it's not making sense.

It's given us hallucinations in terms of, um, the, the answers that we thought we were gonna get are just completely wrong. So they're having to take a step back. And to me, that's, um, it's a demonstration that you didn't have a chief data officer and didn't have that, um, process in place.

And right now these new technologies are exposing what the organization hadn't been doing. So that's where unfortunately, you've seen a lot of people stumble and they're saying, oh, you know what? AI really isn't gonna work.

And it's not about the technology not working, it's about the organization and having the mature process and having the ability to access good, clean data that can represent what the business needs to lead it to a competitive advantage. Gotcha. Kevin, we're running low on time.

I want to bring it home here. Sure. How did Syntax help with this?

So, um, as I said, from from the beginning, we've always looked at organizations through the lens of the ERP, which was really that structured data. Through the years we've had the ability to be able to go on these customer journeys and bring it out closer to the business. So we've been working for a number of years now from an analytics and even from an AI perspective and helping organizations through this.

So we have a, a number of, uh, customers. We have a, a significant team in place to be able to meet the customer where they are within their journey. It's not only the ERP journey, but what I would say is their data journey and let's understand where you're at.

And a lot of times what we're doing is we take a step back with the customer and provide a level of workshops with them to see where they are within their journey. It's a very honest and transparent conversation to make sure that we're not setting ourselves up for failure. Let's find the right set of outputs we can provide and the right, um, use cases that if you're not really there with your data, let's not try to fake it.

Let's get there. But let's also find the low hanging fruit that we could still provide value back to the business as we're actually going through that data cleansing and, and that data governance process. So it's really getting all aspects of the business together in one room and having that honest conversation and mapping out what's gonna be, uh, the best move for that organization to move forward on this journey so they can continue to move forward versus sitting and going around in a circle and finding, uh, uh, a number of failures and not being able to move forward.

So it's guiding them on this journey. Love it. Good stuff.

Kevin. One last thing. For people wanting to get more information on Syntax, where do they go?

com. S-Y-N-T-A-X-T-A-X. That's it.

All right, man. Hey, Kevin, thanks for coming off here on Text Trunk TV and talking a little chief Data officer at Data, uh, challenges with us. We appreciate it.

Keep up the great work at Syntax, come back and visit us. Absolutely. Always love to talk to a fellow New Yorker, so I really appreciate it.

Absolutely, man. Thank you so much. All right.

Forget about it. Ke Kevin, Kevin dcos, uh, CEO of Americas for Syntax here on Tech Trump tv. We're gonna take a break.

We'll be back. We're kicking off 2026 with a look at the first announcements from Nvidia and a MD at CES. Vera.

Rubbin is shipping, Helios is here, and a MD and Intel launched embedded processors with AI capabilities for mobile and Edge. We're also seeing more AI in the physical world with it NVIDIA's, um, all pyo, uh, Osmo and Cosmos. Nvidia is also Acquihire, uh, grok, which suggests an increased focus on inferencing in 2026.

And we'll look at Accenture's acquisition of faculty AI and predictions for the rest of the year. Welcome to utilizing ai, the podcast focused on practical applications of artificial intelligence from the Futureum group. Each episode brings together diverse perspectives to 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 Foskett, president of the Tech Field Day Business Unit here at the Futurum Group. And joining me to kick off 2026 are two fantastic panelists. Let's meet who they are.

Hi everyone. Brad Shiman. I'm VP Practice Lead for Data Intelligence, analytics, and Infrastructure at the Futurum Group.

And, hi, I'm Nick Patience. I'm the AI plat platform's practice lead at at futurum. And as I said, I'm Steven Foskett.

I've been covering AI here on utilizing tech and utilizing AI for a few years. As I said, we are recording this, uh, January 6th, uh, right away here as, as 2026 has started. And also today is the start of CES, which is one of the big industry groups, uh, or industry expos that happens every single year.

Now, CES is not an enterprise tech conference, but there certainly is a lot of AI happening. Uh, so although we can't dive in too, too deep because not all of it's happened yet, uh, what's caught your eye coming out of CES so far, Nick? I guess the, the inevitably the NVIDIA's announcements around Vera Rubbin, although, yeah, not, not necessarily news, talking about, you know, production in volume and shipping later this year.

Um, and then, um, AMD's kind of counter with, its with its Helios, um, you know, rack system. Um, and it's just interesting contrast the way, you know, the two companies, um, you know, are operating in the, in the space. Obviously, Nvidia, um, has a, has a dominant market share as we know in it in GPUs.

And so a lot of what it's talking about is increased vertical integration. Um, so not only hardware, and not only chips, but also nv, NV link. And then of course, the crucial, the software element, um, including a bunch of new frameworks, um, that it put on GitHub.

And then a AMD obviously has to, you know, uh, reply with a more multi-vendor, um, ecosystem approach, and it has a UA link alternative to MV link. So I think it's an interesting, um, you kind of rail pique of, of, of, of what happens down at that, that chip layer in, in, uh, in AI infrastructure. And I think it's interesting to, to think about the Vera Rubin, um, not as a chip like we did when we thought about, or we heard about Blackwell for the first time, for instance.

Um, but it's more of an architecture. It's more of a platform, like it's a, it's own little supercomputer. Like, like you said Nick, you know, it in incorporates memory, networking, storage, and compute.

And, uh, you know, that alone makes it something that's, that's going to, I I would say, appeal to a lot of where investment is heading right now within the AI space for data centers, uh, for companies looking to maximize that vertical integration that they, that they see within in, you know, innovations like the Vera Rubin platform. But for me, I wanna say that what struck out, struck my, uh, or tickle my fancy with was cs, uh, was the return of the Dell XPS line. Okay.

Zero ai, but, but a beloved brand. Uh, hopefully everyone listening is old enough to know what that is. Oh, yeah.

To, yeah. So these, these are like gorgeous laptops built for gaming, built for, for high performance. And, and I, I applaud them, especially in this time period wherein Ram is so difficult to get in rolling out, you know, this 32 gigs for under two grand with a, with that name as, uh, a backer.

Hmm. Love it. Yeah, absolutely.

And, uh, I, you know, if anything can, uh, should be competing with, uh, with the, uh, the prevalent, uh, MacBook Pro, uh, contingent out there, it should be Dell's XPS. I know, uh, our friend Keith Townsend is a big, uh, fan of the XPS line as well. Uh, oh, is he just a great group overall.

Uh, you know, and it's interesting on that note too, in that we're seeing a little bit more diversity from these companies in terms of these, these platforms. Um, a MD is here with, uh, rise and embedded processors. Intel's Panther Lake is here, which has, uh, AI capabilities as well.

So, um, on the client side, um, you know, if if, if 2025 was the year of the AI pc, I think 2026 is also the year of the AI PC And the Edge too, right? Yeah, Stephen and, and Nick, it's, uh, when you look at Nvidia rolling out their jets, Jetson 7,000, I think that was at CES, and you get basically a Blackwell architecture, uh, if for under, in under 70 watts, uh, power consumption. Um, that, that's pretty amazing.

For what, what, two, 2000 again, what a price 200 bucks, uh, assuming you buy a lot of them. It's, uh, it's, it's an interesting time for hardware right now, and I, I we're seeing a lot more diversity than I would've expected in this market. Yep.

It's interesting kind of as we, you know, interesting sort of base level for, for the robotics, uh, and the physical ai, um, you know, explosion that we're expecting to happen this year and, and, and next year, I guess. And, uh, obviously those companies we've mentioned are, are gonna be at the forefront of all that. Yeah.

Let's talk a little bit about that physical AI concept, because of course, uh, at Nvidia is here with, um, le let's call it Alpa Mayo. Uh, I wasn't at CES, so I don't know how to say this. Um, ALPA Mayo, uh, Brad, what is this?

None of us know. Uh, it, it's, it's actually their set of models are family models and supportive, um, you know, frameworks, uh, all geared around robotics. And so, um, you've got specializations for things like Alpha Sim and you have, uh, a bunch of frameworks for designing, um, uh, training runs for robotics.

Like with, you have a visual, visual model for a robot that's says a restaurant robot, let's say that there are very specialized training runs that you must do in order to train the robot to flip an egg properly. And with Alpa Mayo, um, what we see from, from Nvidia right now is this like massive specialization, uh, of, you know, helping their customers and their partners build out this, the robotics solutions on top of their own, uh, set of models like, uh, the Nitron, uh, three family, and which includes Groot, which caught my attention in particular. 'cause, uh, you know, Nvidia released at the same time a full collection of like thousands and thousands of, of data, uh, data points for training.

Sorry, that's the wrong word to use, but you know what I mean. They, they've actually opened up, uh, training data that's completely open source. It's on GitHub, oh, sorry, not GitHub, but hugging face.

You can download it and use it, uh, however you see fit. And they, and the Osmo, what was it called? Osmo Robotics, um, workflow development cycle thing, um, for orchestrating, you know, synthetic data generation, um, using their, uh, cosmos, uh, world models as, as, uh, Nvidia calls calls them.

And so I guess that that's, uh, you know, that's, and then I think they also announced to see us, you know, more cosmos, um, foundation models, um, transfer, predict reason, um, and otherwise other ones like that. So yeah, it's a lot of, um, you know, as, as we know with, with NVIDIA's announcements, uh, although everybody kind of focuses on the chips and love to see Jen standing there holding something physical, um, yeah. In order for, you know, all these things to actually, uh, make a difference in the world.

This, the software is increasingly is, is so important. It's absolutely crucial. Um, and, uh, you know, NVIDIA's extremely well placed, um, there, and a lot of it's open source, um, but there's also crucial layers that are not.

And so, you know, that's how, uh, that's how they play, play, play. Well, Coda. Yeah.

Yeah. Coda being the ultimate. Absolutely.

Yeah. Totally. Well, uh, you know, I mean, who, who can blame 'em?

I mean, they are a, a for-profit company, and they're certain, certainly raking in the profit right now. Um, we also heard of an acquisition, or sort of an acquisition by am or by, uh, Nvidia, uh, over the, the holiday. Um, one of the companies I've been following closely for a long time is Crock with a Q, not the other Grock that also made news this week for a reason that I'd rather not talking about.

Uh, again, um, gr with a q they, they were a, an early hardware startup in the AI space. A bunch of Google engineers got together, um, lately they've been, um, using their, uh, inferencing hardware as a, uh, platform, uh, you know, basically developing, uh, enterprise applications around ai. It's great stuff.

Uh, apparently, uh, the company has been acqui hired by Nvidia, um, uh, even though it still kind of exists. Um, we talked about that on the Textron gang. Uh, what's your take on Nvidia plus Grok with a q?

I mean, I, yeah, it's, it's an, it's a weird, um, well, from what we know, it's a slightly weird deal though, isn't it? It's, it's kind of, what was it, $20 billion to acquire assets and, and IP and, and, and people. Um, but there's also kind of non-exclusive nature to it.

So I think, you know, 'cause CRO was building these, uh, language processing units, LPs, um, you know, and so I think, I guess that's what they were. And they were, and they were extremely, um, performant in, in real time, uh, token generation. And I guess that's what they're, uh, you know, looking, looking, looking to get.

Um, so it's, it's, again, one of these, you know, NVIDIA's model or not models have shifted, but its business model has evolved from, you know, training, training, training to inference and grew up with very much an inference shop. Um, and allegedly still is gonna be one. Um, but it's not entirely clear what it's, what it's gonna do with its key people not there.

Um, and, uh, some of it's a license, um, it's IP now in, uh, in hands of Nvidia. Yeah, I would, I would imagine that it's gonna play a, a nice, you know, balancing role within NVIDIA's NIMS architecture, for instance. 'cause that's all about containerized, you know, instances where you can stand up a fully working stack and just start inferencing off of it.

If grok is, is, you know, an alternative to a third party, like open router, uh, for instance, that, you know, Nvidia users would, I'm sure appreciate that. And as you know, Steven mentioned they've been around for a little while. Grok has in a little while being less than five years.

Um, but, uh, nonetheless, they, um, you know, I, I think we're very early to, to tackle what we see right now as such a huge area of investment in terms of, you know, highly specialized chips for inferencing. And you just look no further than AWS reinvent we had in early in December last year, where, uh, all, all roads pointed toward tra, uh, and how important that had become to, uh, AWS not for training, but for inferencing. Boy, they named that one wrong.

Um, no, I, I what's in a name, I do think that, that you're onto the, onto it there, Brad. Uh, when I thought of, of Grok and when I thought of Nvidia, I, I, my first thought was exactly what you mentioned, what we heard at reinvent, what we heard from Google Cloud, uh, what we've been hearing from Microsoft and others about, um, using specialized hardware to do inferencing, to do token generation. Uh, that's really rock's strong point.

That's certainly what Google and Microsoft and AWS have been leaning into, along with the software platform aspect of it, which is something that Grok has been leaning into. And so I, I do caution listeners that, uh, this is, this is very new. We don't yet know which components are going where and what the angle is gonna be here, but I could certainly see a situation where, you know, Nvidia is part of that conversation as well, just like Google Cloud, AWS and Azure, right?

Yeah. But they also partnered deeply with, with all of those, it's, uh, of course they did a highly cooperative competitive marketplace. Yep.

Yep. Now, another acquisition, um, we just heard about is Accenture. So, uh, not that Accenture was purchased, but that Accenture is buying, uh, faculty AI and, um, promoting, uh, the head of an AI company to become the CTO there.

Uh, I know that there's been a lot of, uh, fear, uncertainty, doubt, and mischief around, uh, ai, uh, replacing people. Uh, Accenture is the people company. Uh, what's, uh, your take, Brad on the Accenture, uh, faculty story?

Yeah, I, I don't know enough about faculty to say technologically what impact they're going to have. I think instead, what this points to is, um, you know, a a sort of raising of a, of a banner at Accenture to say that we are an AI company. We are not a people company anymore.

And I guess you could call them accelerationist for that. Uh, and, and for better or worse, um, but, uh, you know, I recall visiting them in New York City, uh, two years ago, and having them demonstrate their in-house AI that they were building to, to help optimize and, you know, 10 x uh, their employees to, to be better, uh, at their jobs. And, uh, I'm sure nothing has changed since then.

I'm sure that that is still what their objective is here, is to become a, a much more efficient company at helping their customers become more efficient companies through the adoption and use of ai. Yeah. I, I mean, here in London, uh, faculty AI is, is quite prominent.

And, um, they're quite well known for government work. Um, mm-hmm. Yeah, I heard, I, I read something this morning trying to make out the, you know, they're the UK's equivalent of Palantir, which is, um, you know, size wise, not really comparable, um, but it did have this kind of, you know, they called it a decision intelligence platform, um, called Faculty Frontier.

And again, I think it's kind of these four deployed, um, engineers into, into customers. But they, they had a lot of government contracts going back many years. I think they're only, well, they're about, uh, 12 years old as a company.

Um, but yeah, that CEO Mark Warner, if I, yeah, I go to a lot of, you know, a lot of London AI events, and either he or somebody, somebody else from that, that, that company is often there, they usually keep themselves, um, to themselves. They're quite quiet about what they do, so it gives you an idea of what some of the work is, um, that, that, that they've done. But yeah, with him becoming Accenture CTO, it does, uh, it does kind of reinforce that realignment, they said back in September, October, I think it was of last year, um, realign around AI laying off, um, um, up to 11,000 employees even over time.

Um, not in one go, obviously, um, attrition and all the other kind of usual words that are used, put it, put it in perspective. Um, Accenture has 800,000 employees. Um, so it's, it's, it's, it's a, it's a small drop, but the, but faculty does a lot of work with open ai, with anthropic, with the, what was the AI UK AI Safety Institute, ai, UU AI Security Institute, um, and stuff like that.

So there's a lot of kind of AI safety work. While I, I'm very skeptical about some of that emphasis around AI safety, I think is a little bit, um, um, it's kind of trying to induce some sort of, you know, you know, panic around what AI does. They, they, they were pretty, um, heavily involved in that.

So I think it's, uh, I mean, I, it's, it's an interesting, it's an interesting, um, time and I, I, I sort of wrote a little note about it, um, on very little note on LinkedIn. And my last, my last three words were kind of reskill or exit. So it's kind of, it's, it's that kind of thing.

Like if you are, if you work for Accenture or you work for any kind of company where, you know, knowledge work is, is, is key, um, you've gotta, you've gotta adopt ai. Um, and I think that that's, that's pretty blindingly obvious statement. But it was interesting, I noticed when they announced their last results, which were announced in December, um, they put a, they kind of, they have a, a series of charts, you know, QR Q1, I think it was advanced AI bookings, advanced AI revenues and work kind of thing.

And then at the bottom, it had a note saying, this would be the last quarter in which we share, um, advanced AI bookings and revenues. We've reached a point, I'm reading it now, where advanced AI is in being embedded in some way across nearly everything we do. Um, and so, et cetera, et cetera.

Um, we're not gonna then split it out. So, you know, when, when does, you know, AI is, uh, the old, the old gag is AI is whatever, it doesn't work yet. Um, and so, you know, that yeah, that kind of, they're seeing it so in, so embedded in e everything they do, um, that, you know, it just, it is business.

Um, it's not a, it's not a necessarily a peculiar thing. Um, obviously as the leader of the AI platforms practice, I would, I would, uh, counter, there are some unique things to ai, um, and will be for, for, for many, many years to come. Um, but for a company like that, to make that kind of statement, uh, and bring in somebody like that, I think it's, uh, you know, it's a, it's a, it's an interesting marker for the rest of the industry.

Yeah, it does reflect the nature of business, um, that a company of Accenture's status. I mean, as you said, this is a company with, you know, almost 800,000 employees, you know, billions of, of revenue. Uh, I think it says here, 120 locations.

I mean, Accenture is everywhere. They're working with basically every major company. And to have a company of that status say, effectively, AI is just part of what we do, and part of what everybody does, I guess, that reflects, uh, where we're at in 2026.

Frankly, it sounds a little bit like Futurum. I mean, that's what we're doing here as well, right? Yeah.

And I think we kind move from the, away from the ai ai experimentation years of 2023 and 24, 25 was, uh, a new set of experiments, um, with, with Ag agentic. Um, and I'm not saying in 2026, agen is gonna be completely, you know, um, mainstream, which it isn't. Um, but other elements of ai, um, you know, maybe more on the kind of, you know, predictive model side, um, are, are just, um, you know, the meat and potatoes of, uh, of, of, of how, uh, technology gets adopted.

I would say, you know, with Accenture, um, in particular, what this makes me think of is that, uh, these companies, futureum included, are right now not endeavoring to, you know, enter, you know, into an opportunity where they're making ai, but instead they're using AI to do their job, to do what they do as a company. 'cause that, that early meeting, I, I mentioned it was all about, you know, we're, we are gonna have our own, you know, chat bot model with its own anthropomorphized name, and it's gonna, you know, be so and so to, to do what you get from, you know, OpenAI and others. And I think we're seeing a shift where companies, uh, are starting to look at this as just how they do business, not the business they do.

And you could see that reflected in how they, you know, talked about and positioned their earning statements. I think that that's very telling. And, and can I get a hallelujah on that?

Um, I, I, I, I think I should remind, uh, listeners that, uh, this is called utilizing ai. I named it utilizing AI in 2020, because I was waiting for the time when we were making practical use of this technology, not when it's a science project, not when it's something cool, not, not when we're doing it for the sake of doing it, which frankly sounds like a lot of AI out there right now, is that we're just sort of playing with it. Um, you know, I mean, now we're actually using it for productive purposes.

And I think that that's what I want to see in 2026. I want to see this being used for productive purposes that let us do other things, the things we always do, let us do them better. Um, you know, Brad, I've said that, uh, you know, the, the, the rums use of AI and the, and the, the, the specifically, the, the signal report that I know that you've been deeply involved in it, it's not that it's ai, it's that it's, it's, it's this technology that gives you and Nick and the rest of the folks a superpower when it comes to analyzing data.

And that's, I think what Accenture is saying is that AI gives their clients, um, extraordinary abilities to do the things they already do. And that's what I'd like to see more of in 2026. So, let's talk a little bit about what we're looking forward to, Nick.

I know that you've had some questions recently about, uh, what are the big momentous things that are gonna come out in 2026. Could you maybe, uh, reiterate that for our audience? Uh, what, um, you know, what business moves, what IPOs, what actions do you think are gonna happen in 26 that are gonna really shake the foundations here?

Sure. So on, yeah, on the financial side of the, rather than the sort technology side of it, I think the, um, we're reaching that stage of maturity where, um, exits happen. Um, companies looking, you know, looking for looking for exits either, either through m and a or through IPOs, although the, although the IPO market for enterprise AI is being pretty quiet, um, from what I hear and what I see out there, you know, there's a lot of optimism that it will pick up.

I mean, mind you, that that always happens in January. There's a lot of optimism is gonna pick up, which, you know, you have to see what the state of the actual market is, whether it's ready for it. But you've got open ai, um, and anthropic, um, really as the two, the, as the two kind of, uh, poster children as it were, of, of, uh, not only enterprise ai, but also consumer AI in terms of open a, open ai, a lot of ai.

Um, but you know, those, those are both, um, potential IPOs this year. I think they're the, they're interesting contrasts in valuations, uh, to say at least. So OpenAI, maybe rumor is maybe looking for a $1 trillion valuation.

Its most recent round was at 500 billion. So there's a, there's a bit of a gap, um, there, which it, which it might. Um, you know, it's, that's kind of a price for perfection scenario.

Um, you know, everything has to work for it to be worth that much money. I mean, not say it won't get it, um, but it, and then there's also the interest around Microsoft's stake. Obviously Microsoft was incredibly prescient in 2019 with what it did.

Um, but also that's, it does create a little bit of a messy, um, kind of scenario from a governance point of view. Um, so that, that would be interesting. And thropic is a more kind of enterprise safety hedge, and the kind of more, you know, it's kind of the, the more sensible looking, you know, enterprise AI company at and at a lower valuation, um, maybe, you know, 300 billion, um, some something like that, who knows.

Um, but it's, but it's, you know, so I think it's, if if investors open AI should be the bell, you know, would be the kind of bellwether AI stock. If it went, if it went, um, but Anthropic could be the kind of more conservative, um, uh, alternative. And then if you think about, you know, what, what's the purpose of IPOs is to raise money and to provide liquidity and all that kind of stuff.

So when they raise money, where does that money go? What is the use of the proceeds? Now, you would've said maybe if this would, in a fantasy land this was happening 18 months ago, you know, Nvidia would be, you know, vacuuming up all that, that extra new cash.

Now, it might be interesting, it's obviously with, with these companies both working on custom silicon, um, maybe that's not gonna be the case. So, you know, where do the, where does the proceeds go? Um, so I think, you know, a lot of it will go towards custom silicon development.

Um, I also think it will go towards energy infrastructure, um, and obviously data centers and, and, and all that kind of stuff. So I think it's, um, you know, those, that, those that have the, those stocks or those companies that have the kind of clearest path or the clearest picture of owning a stack and the stack these days doesn't start with a compute. It start, you know, essentially with the power, um, and, and land and things like that.

Um, you know, those have the biggest, you know, the clearest path of that would be, um, probably get the most rewarded and would, in theory at least be, uh, can take on hyperscalers, at least in terms of how the public market sees it. So yeah, just from a financial point of view, there's many other things we think is gonna happen this year, but then that's, that's gonna be, um, an interesting one to look at. Yeah.

And for me, I, I feel like if I were to try to see what's, what's coming for the entirety of the year for the space that I look at, I, I would say that this is definitely, you know, the morning coffee after the hangover that I think we've, we've been going through a little bit with AI and what it can and can't do. And, um, I think we're gonna see a, a tremendous focus on, um, you know, the science projects are over. It's all about if it doesn't scale, if it doesn't make money, it gets cut.

And this goes to what Nick is talking about with this emphasis on, on ships and vertical integration all the way down to the power coming into your data center. Um, but it's also about, you know, how that software stack actually works, um, in supporting that optimization. Because as we all know, you know, we, we've gone through a bunch of phases in the industry where we've tried to have these sort of, you know, do everything platforms best of breed that, you know, have a sense of lockin, but also have a sense of performance to them because of that lockin.

Um, and what we're trying to understand right now is, well, can we have our cake and eat it too? Can we have a highly composable stack that still emphasizes speed? So you see, um, companies like, uh, Microsoft with their, their Cosmos db for instance, rolling out, um, you know, uh, basically caching, um, semantic caching within that database to support agentic workflows, uh, directly, you see the sort of death of, you know, ideas like data meshes and even data fabrics to be replaced by a, a more composable, um, landscape or data estate, as we like to say, that's, that's built on top of, uh, a semantic layer, which, uh, is, you know, some something we all, we all really need to get behind, and if, if we aren't hiring people that understand what the word epistemology means, we're already losing.

So I, I think it's, it's gonna be a very interesting, um, you know, vendor community in the hiring sys situation in 2026 in terms of what we hire for and how we build our solutions. It's, uh, not anywhere we've been before. Just to add to a few other things that, that, um, my practice will be looking at this year.

And, and I think maybe some things we'll have will be future topics of, uh, discussion on this, on this podcast, um, sovereign AI and kind of the corporate con not nature of co corporate control. Um, that's, that's gonna be a big, uh, focus this year. Um, it already was for, you know, towards the back end of last year, but I think, you know, increasingly that will be, um, similarly related is kind of global AI regulation and compliance.

The, um, EU AI act really does, does come into force properly in, in, in 2026. Um, and obviously we've seen in the us, although there are various state things going on, um, you know, Trump has made it very clear that, you know, wants a federal, um, AI regulation. So there's not as, say, 50 different regimes, um, to deal with, um, obviously agen ai, if not at scale, you know, rolling out, you know, more fully.

Um, and, uh, I guess down at the infrastructure layer, we talked about it just now really, but the energy and cooling as a kind of bottleneck, a bottleneck for data center built, build out, bottleneck, bottleneck for all, you know, the stuff that goes in data centers. And I think that will, um, that will become a major, major issue. And as we kind of said earlier, would probably result in some sort of, in MA, um, we've already seen it to a certain extent, and I expect we'll see more of that.

And then the kind of fragmentation of models, which I don't mean breaking of models, I mean the, the landscape, you know, LLMs are really important. Um, they're not the only game in town. Small lan, small language models, um, will become more and more, um, prominent.

So yeah, those are some of the things that we're looking at. I does, when I'm sort of, when I was drawing up the list, I was thinking this is getting quite infrastructure heavy. Um, AG agentic is, is kind of where the rubber, but where the business value will be created.

Um, and, but, but there's a lot of stuff going on underneath, um, that we, that we'll be, uh, spending time in 2026 to say, we, we should probably dig into one or two of those, uh, in the coming weeks. And another point I'd like to make too is that, uh, the AI infrastructure market is by own, by no means dead. Uh, there's still a lot of money flowing into AI infrastructure, networking hardware, uh, you know, I mean, you look at companies like Cisco, um, you know, broad Broadcom and, and companies like that, that are doing so much, uh, chip work.

Uh, and, and for me, one of the, one of the dark horse, um, heroes of AI that I'm looking at is Google Cloud. Um, I'm hearing such good things from enterprise users about, uh, Gemini, uh, as an alternative. And, you know, you, you, you talk about, uh, like you did Nick about these, these, you know, vertically integrated stacks, uh, don't count the hyperscalers out, I would say.

Um, they, they know how to run data centers. They know how to run platforms, they know how to work with enterprise companies. Uh, they've, they've been learning it the hard way for 20 years now.

And I think that, um, you know, companies like AWS are really going to roar forward, um, Microsoft, Google, uh, in 2026 as, uh, applications become more practical. Uh, that would be my prediction. Don't count the hyperscalers out.

Oh, yeah, I totally agree. Steven. I just want to add really quickly to that, that, uh, it is a, has amazed me as, as both a practitioner and an analyst in looking at, um, Google Cloud in particular in terms of how they're investing in their APIs, uh, around g Gemini.

And it seems like almost every couple weeks they reinvent what we took for granted before. So we used to like scrape webpages to, to turn them into, you know, to embed them in a rag pipeline. And they're like, no, we own the search index, so why don't we make it so that you don't have to scrape webpages, you just query the webpage and get all the data from it.

And they're, they're doing the same thing again. Uh, and again with different, you know, aspects of what was expensive, uh, and, and time consuming in terms of, you know, optimizing the inferencing cycle. So it is, the money that they're spending is all about the gravity still has been, always will be about data gravity on their platform.

But my goodness, you know, with, like you said, AWS and Microsoft and Google are seriously investing in, in making the, you know, giving, giving enterprises the tools that they need to, to do this the right way. Final words, Nick? Yeah, just on that, I'd say, you know, I think of, of the three hyperscalers we're talking about, I would say Google probably had the best year of 2025, um, in terms of improvement of, of its, of its position in the market.

Um, but we'd reset. Now we're all back to all back to zero in the kind of analyst ranking game, and, uh, and we go again. And so it'd be really interesting to watch, uh, all three of those and, and many others.

Um, maybe not quite hyperscalers, but other cloud providers around the world. And as we say, the on-premises, um, shift, um, and push because, you know, the, the, the likes of, um, Barcom with VMware or Red Hat with its software stack, um, selling through other cloud providers around the world, there's an awful lot of interest in, in that stuff as well. So, uh, yeah, it's, um, it's not all about infrastructure.

There's a lot of infrastructure stuff, um, to watch, um, to enable all this good AI stuff to happen. Yep, absolutely. And, and I'll just, uh, throw in a little, uh, thing.

We're, uh, gonna be holding a, an AI infrastructure field day event, uh, end of January. Uh, we're gonna be hearing from some of the companies that are building, uh, AI infrastructure stacks. Uh, we'll also be doing another AI Field Day event.

Um, and as we joke, every field day event is AI Field day. Um, so thank you very much for joining us, both of you. Before we go, um, give us a hint of what you're gonna be working on in the, the coming quarter in terms of research at futurum.

So, uh, why don't we start with Nick this time? Um, so yeah, we're gonna be redoing our, um, our AI platform signal, um, and we're gonna look at some, some other, um, you know, sort of, uh, sub-sectors of, of that, um, potentially around, um, sovereign ai, maybe I haven't quite made my mind up on that. Um, I'm redoing my, um, decision maker survey.

Um, so that's gonna be going into the field this quarter. So that will be a, you know, a, a re-up of that. We've only just published our, our, um, market forecast in, in December, um, our five year market forecast, which is, uh, which is really interesting.

Um, so those are the, those are some of the, some of the things I'm, I'm gonna be working on, Brad, It's similar to me. For me, uh, we're, we're twins obviously. Um, so I'll, I actually have a second forecast that, uh, we're doing for data intelligence, analytics and infrastructure coming out.

That'll be five years as well. And also fielding a, a new survey, uh, for decision maker, uh, we call it decision Maker Survey. And I also will be updating my, uh, data intelligence platform signal, um, actually really quickly.

So that'll be something we'll publish in early February for that. And, uh, I have a new one that I'll be working on, I'm really excited about. It's gonna be about Semantic bi.

So, uh, for, for all of you guys who love your love a good dashboard, uh, I would invite you to tune in for that one. Well, that, that's really the thing about Signal. And the thing that is exciting about what Futureum Research is doing is the fact that you can refresh this data much, much more frequently than ever before, uh, thanks to the, the tools that we're using on the backend.

And it just means that stuff is more valid because as you said, there, there are things being introduced every single day in this market. And so, you know, you cannot rely on a year old report when making decisions on pretty much anything in enterprise tech these days. So thank you both for joining us.

com. Uh, please do subscribe to the utilizing AI podcast. Uh, you can also catch our other podcasts with the security, uh, Boulevard Podcast, uh, agents of Dev, uh, as well as of course, the Tech Field Day podcast for more.

Um, if you enjoyed this discussion, please do subscribe, uh, where you can find us on YouTube, uh, as well as in your favorite podcast application. Uh, please do give us a rating review and a comment we would love to hear from you. Uh, this podcast is brought to you by the analysts and experts from the Futurum Group, where Insight meets AI.

ai, which is our sister news site, uh, the utilizing AI YouTube channel, or of course, the Textron TV app, where a great many people apparently are watching this according to our, um, our stats. Uh, the majority of people seem to watch this on the, uh, on, uh, TVs and uh, in, in, in formats like that. Thank you for listening, and we will catch you next week.

Hey, is Intel inside starting to mean Intel Innovates? I don't know. I hope so.

Welcome to Textron Gang. If you haven't caught us since the beginning of the year, we're live now, which means you're live. If you're watching this live comment, whether you're on LinkedIn or X or, or, or uh, YouTube channel or any of our, uh, text drunk channels, if you have a a moment to comment, we'll see it.

We no promise, we'll acknowledge it and talk about it, but we'll try and, uh, we hope you enjoy this live format. It, I think it brings news a little quicker to you, and, uh, we we're liking it here. To tell you the truth.

Let me quickly introduce you to our live gang today, as if we used to have Dead Gang, but no, they were always live. But now we're really live Happy New Year to, uh, first time they've been on this year with us, our gang members, Fred Wilmont and Gina Rosenwald. Fred, Gina, great to see you both.

Hi, good morning. Good morning. And then joining us, I guess he's still in Vegas.

Are you still in Vegas, John? No, you're Home. No, I escaped.

I escaped yesterday. I am home. Good.

I hope you had a good hot shower. Shower. John was out at CES and some other stuff.

John, welcome. And of course, Mike Ard. So gang intel innovating Intel Positive News coming out of Intel.

It's been a while. Mike, what do we got? Well, Intel is saying that it accelerated its schedule for producing these so-called 18 a chips, which are less than two nanometer.

And there's basically at the now leaning edge of semiconductor productions, and they're basically saying at CES, they're back. John, you were there. Um, what are people saying about Intel these days?

You know, as the tenor changed, because I can remember not too long ago in 2025, I think we had an episode where basically everybody was saying, you know, we got some serious doubts about Intel. Yeah, bye-bye, Intel. Yeah, no.

Um, so, you know, when we were talking about Intel last year, we kept talking about Pat Gelsinger and how things weren't working out and how they were in this kind of downward spiral with their manufacturing, and they were falling behind a MD and arm. And much to our surprise, pleasant surprise, Tuesday, they made this announcement that you said, and these are the core Ultra Series three chips. They're gonna be the first products to market using the 18, a manufacturing process.

And it, it was, to me, it was really welcome news. And it, it, you know, we get lost in all the CES or all the shiny objects like robots, ai, innovation, et cetera, auto autonomous vehicles. And here we had this company, which makes the technology that makes a lot of this possible announcing something in a positive nature, which as you said, it's, it's really welcome news because from Intel, I didn't expect to hear anything from them.

I expected to hear a lot, I expected to see too much of, uh, Jensen Wong, which I did. Um, I expected a lot from Samsung, Lenovo. There was a time, Mike, and you know this because you've been to all these shows in Vegas where Intel was one of the anchor companies at these shows, and I did not expect to hear anything from them.

So to my surprise, they actually did make an announcement. And in a sense, they're trying to reverse this manufacturing setbacks with Panther Lake, and, um, perhaps it's part of a road to recovery, which is aided in part, ironically by Nvidia and an investment as well as the federal government. But again, it's good to hear from a venerable company that is so important.

And I am, I'm not allowed to root or I shouldn't root for companies, but in the case of Intel, I would really like to see them become much more relevant than they have in the last couple of years. Alan, what's your take here? Is this, you know, significant, or will this be filed under the heading of maybe a little too little too late?

Well, look, first of all, let's give them their due, right? They, they hit this milestone, congrats to them. Secondly, John, you, me mentioned the Pat Gelsinger situation, and really the crux of him, of his leaving was this, uh, well, it was several reasons, but one of the big ones was, should Intel pursue a dual strategy of making their own chips, their own design chips, the intel inside the Intel core chips, or should they just be a foundry making, you know, chips for hire, or a foundry for hire kind of thing?

Uh, you know, we've, we saw the Nvidia investment and supposedly they'll be able to make maybe some Nvidia chips. I've heard rumors of Apple may use Intel as a foundry to create next Gen M, you know, the Apple chips, not, not necessarily Intel, they're not going back to Intel chips, but that they can manufacture.