Utilizing Tech - Season 7: AI Data Infrastructure Presented by Solidigm - 06x11: Considering the Promise of Quantum Computing in AI with Dr Bob Sutor of The Futurum Group

Episode Date: April 29, 2024

Both AI and quantum computing seemed entirely theoretical just a few years ago, yet generative AI is everywhere today. This episode of Utilizing Tech considers the promise of quantum computing generally and the applicability of this technology in AI with Dr. Bob Sutor of The Futurum Group, Alastair Cooke, and Stephen Foskett. One big challenge for quantum computing is the difficulty of storing data for calculations, a severe limitation for using the technology in AI. But there is the possibility of pairing classical computers with quantum processors to bring the best of both concepts. Both AI and quantum computing deal with linear algebra, so there is an affinity between the technologies, but it is likely that each will find use cases in different areas. Ultimately there is still a lot of development to do but quantum technology shows great promise in AI and beyond. Hosts: Stephen Foskett: https://www.linkedin.com/in/sfoskett/ Alastair Cooke: https://www.linkedin.com/in/alastaircooke/ Guest: Bob Sutor, Vice President and Practice Lead od Emerging Technologies at The Futurum Group: https://www.linkedin.com/in/bobsutor/ Follow Utilizing Tech Website: ⁠⁠⁠https://www.UtilizingTech.com/⁠⁠⁠ X/Twitter: ⁠⁠⁠https://www.twitter.com/UtilizingTech ⁠⁠⁠ Tech Field Day Website: ⁠⁠⁠https://www.TechFieldDay.com⁠⁠⁠ LinkedIn: ⁠⁠⁠https://www.LinkedIn.com/company/Tech-Field-Day ⁠⁠⁠ X/Twitter: ⁠⁠https://www.Twitter.com/TechFieldDay Tags: @UtilizingTech, @GestaltIT, @TechFieldDay, @SFoskett, @DemitasseNZ, @TheFuturumGroup, #UtilizingAI, #AI,

Both AI and quantum seemed entirely theoretical just a few years ago, yet generative AI is everywhere today. This episode of Utilizing Tech considers the promise of quantum computing generally and the applicability of this technology in AI with Dr. Bob Souter of the Futurum Group. Join myself and Alistair Cooke as we discuss the integration of quantum and AI with Dr. Souter. Welcome to Utilizing Tech, the podcast about emerging technology from Tech Fielding, part of the Futurum Group. This season of Utilizing Tech is returning to the topic of artificial intelligence, and we're exploring the practical applications, the impact, and the technology needed to support artificial intelligence in enterprise IT.

I'm your host, Stephen Foskett, organizer of the Tech Field Day event series. And joining me today as my co-host, you may recognize from previous seasons of Utilizing Tech, is Mr. Alistair Cook. Welcome. Thanks, Stephen. It's always a pleasure to be here with you. I'm a longtime friend and delegate at Tech Field Day and lately have become a CTO Advisor at the Futurum Group, so joining the wider family. AI Field Day was really interesting a couple of months ago and it's great to be continuing that conversation. Thank you so much for joining us. It's good to have you. When we talk about AI, it used to seem a little bit theoretical, but of course now, your mother's using AI probably. I mean, it's everywhere because of

LLMs. And the thing that jumped immediately to mind when we started seeing AI getting real was the question of quantum. And I know that quantum computing is still pretty theoretical, but there's a lot of practical work being done. There's a lot of stuff happening there. Al, do you think that quantum computing could revolutionize AI? Well, I think quantum computing has a lot of potential that we aren't yet able to deliver. We can't build machines yet. We're in the very early days where it feels like we're building quantum computers in the way early computers were built with the vacant tubes. And we need to get towards the point where we've got integrated circuits and

get massive scale production in order to do something useful. So potential, it's an interesting thought. Quantum is really good where you start with, here's a question. I need to work out a huge amount of possible answers and then centralize on the correct answer. And that sounds like what large language models do. So maybe sometime. But quantum has been promising for a very long time. Yeah, it is an interesting point. And I agree with you that it's one of those

things that smells right. You kind of look at it and you're saying, this technology is pretty equivalent or pretty analogous or pretty applicable. I don't know what the right way to say it is, but it makes a lot of sense. So you and I have a recent coworker joining us at the Futurum on the analyst side, Dr. Bob Suter, who is, in fact, probably one of the most knowledgeable people I've ever talked to about quantum and AI. So that's why we've decided to invite Mr. Suter, Dr. Suter here to join us for Utilizing Tech and talk about how quantum computing could revolutionize AI. Welcome to the show, Bob. Thank you very much. Very glad to be here. I joined Futurum on March 1st, so I'm a new guy here. This was after, however, 39 years at IBM, where I did many different things, a lot in IBM research.

The last six years, I was in the IBM quantum program. And then I spent two years looking at some other quantum applications. You know, it's not just computing. It's sensing, it's atomic clocks, it's quantum communications. And then I decided, since I love talking and writing and advising so much, I'd come over here and join you guys. Well, it's good to have you, and especially for the purposes of this conversation. So you heard what Al and I just said. Are we off base? Does quantum technology have promise related to AI?

Well, promise is a great word. And in fact, I've heard many sentences which say, quantum computing promises this for chemistry. Quantum computing promises this for financial services optimization. And it's a lot safer word saying it will do this. It definitely will do that. There's a chance with AI, but there are a few issues. And let's start with the quantum computers themselves. So first of all, every quantum computer today we have is very small, very, very small. And one way of measuring the scale of this is in the number of qubits. A qubit is like a bit classically zero and one, but it's much more sophisticated, a lot more math to it. And most of the machines you see out there

have double digit, maybe low triple digit number of computers. IBM last year had something, an experimental chip with over 1,100. But depending on who's counting, we need 100,000, a million, 20 million qubits. That's not just manufacturing more qubits and throwing them in the box. It's not like adding more memory to a classical computer. So that's the fundamental problem, which is the computing power of the systems that are too small

today. But the other is called the input problem. And when we talk about AI, we talk about data. And this is why every data company has become an AI company in the last year or two, at least, right? It's very, very slow and expensive today to load classical information, classical data into a quantum computer. And so today, you can't use much data. And I don't know of many interesting AI problems that don't use much data. And if I did, I'd just use a classical computer anyway. So we have to scale these quantum computers up significantly. And that's going to take a few years. And then there's a lot that we'll have to go into that. It reminds me a bit, though, of the transfer learning and discussion that we've had, as well as the RAG, retrieval augmented generation question, in that essentially with large language models, you create this model and then you give the model the tools needed to go look for data. I'm wondering if there's maybe a possibility of a

hybrid approach where maybe the quantum processor is responsible for doing that difficult matrix math that you use in training and doesn't have access to as much data. Maybe it's sort of a sidecar or an accelerator. Is that realistic or is that just off the wall? Well, for this beginning part of the problem where you're focused on the data, it will remain a problem for quite some time. However, there may be situations where you already have quantum encoded data. For example, a quantum sensor, something that's generating a time series set of information that's already quantum encoded. We might be able to do machine learning of that already. Now, interestingly enough, the current approaches to QML, quantum machine learning, do have a classical component because you're doing

some heavy-duty quantum computation, but at the end, you have to do a little bit of optimization. So if you're familiar with AI, you might be doing something like gradient descent to optimize your weights through back propagation, right? Well, you do something similar right now. However, I think we're going to throw all that out by the time we get to these really big quantum computers. And it's going to look less like a hybrid and more like a pure computing model where, frankly, we use classical computing for what it's best for

and quantum computing for what it's best for. I think it'll be a few years down the line. Bob, we're looking backwards to see the future, sort of following Paul Nashawaty's idea of looking at the past, the present and the future. About five years ago, I was interviewing somebody who at the time was working in quantum and was talking about having single digit to low double digit qubit counts and now you're talking about maybe having three to four digit is this a linear growth rate that we're seeing or is the need to get to that millions

of qubits going to require something that is a completely different approach because although you talk about these things as being small in the number of qubits, they're not physically small. These things are usually pretty huge and require super cooling and those kinds of elements in order to make them work. Is this technology something that just needs to be revolutionized before it can get to the scale, or are we actually on a track towards a usable quantum computer at scale? The qubits themselves are actually fairly small. They could be Josephson junctions, right, from the semiconductor world. They could be individual

ions. They could be photons. They could be electrons and things like this. It's all the extra packaging of, you know, if you're using chips, if you're using lasers or microwaves or cooling and things like that, that take up so much room. I remember the first IBM quantum computer that we put up at the time. It had this big thing that looked like an inverted garbage can. They hate that characteristic, but someone called it that. Had all this cooling, but the actual it that. It had all this cooling, but the actual chip was about the size of a quarter, right? So the thing almost fills a room, but the actual quantum chip, which gets us back to the idea is we're not going to get to the thousands, the hundreds of thousands of qubits by having individual chips that are that big. So if you think of, you know, we use the CPU, right?

Central processing unit. We can think of a quantum processing unit, the QPU. Or even more correctly, the notion of a core. So this desktop machine I have right here is 12 cores. And certainly supercomputers have a lot more cores like that. So we're going to have multiple quantum processing units that need to be networked together. And it is in that way that we will ultimately get the scale. But there's a gotcha.

It's not just like semiconductors. We don't just run a little line between them. We have to transmit quantum information, which is very different. So we may need optical interconnects, right? We may need to do something else entirely different. We may need to take quantum information that's somehow inside your QPU and move it over to photons so we can send it optically someplace. So this is why so many of these computers are so small, because they're just worrying about one QPU, one core. But the future is going to be dozens, probably hundreds, maybe thousands of cores. So, you know, as you know, for regular computing, dual cores, which were so exciting, didn't come along until 2005.

I mean, we had computers for a long time, but it took decades upon decades. There's not enough investment in that today now. A lot of the companies will die because they can't get to that point. Sorry, that's a little depressing. It does seem a little bit depressing to look at the quantum market, because as you point out, I mean, obviously, IBM is huge and has spent a lot of money and a lot of work and developed some amazing stuff here. Companies like Google as well, of course, are very deep in quantum computing. But for the most part, it's a lot of small companies with researchers trying to make something work. But it's important to remember that that's exactly what the AI market looked like not too long ago, too. There were a few big companies working on it.

There were a lot of small research-led companies. And then, boom, we developed deep learning. And we figured out how to do this in a way that seemed realistic. And then suddenly we're there. And I know that there is a lot of research being done here. My feeling is that there's a breakthrough waiting to happen. And when that breakthrough happens, suddenly this thing gets big and gets real. Again, you know the market better than me. Am I am I off base there? Is that something I should be looking for? Well, I can do AI. You know, I mentioned than me. Am I off base there? Is that something I should be looking for? Well, I can do AI.

You know, I mentioned my desktop machine here. It's got a GPU, you know, the video controller, a few cores. I can do AI. I can write AI software. Now, it's not ideal. Obviously, I could put it on a much more powerful server with lots of GPUs. So that is, I can create a very large AI software market for relatively little cost, to jump into it. What most of these quantum companies are, though, is they're creating hardware, right?

So, you know, I don't know what the right number is, but what's the ratio of ai software companies to ai hardware companies it's probably several hundred or several thousand to one right i mean in that sense whereas in quantum i would easily estimate there are probably 50 or let me say 25 hardware companies for every one software company so you see it's flipped. And so the investment's very different. And if I need a million dollars for the hardware, well, that's hard to get, whereas a million dollars could go a much longer way for a software startup. So it is a little bit different in that way. But a lot of the work in quantum is being done in simulation and in software right now, or in theory and simulation, right? I mean, yes, there are all these hardware companies,

but it seems to me that the hardware companies are trying to find the right way to do the thing, whereas the software companies, I guess, are finding the right way to ask the question. Is that right? Well, there are many different ways of doing quantum computing to focus on that. There are things which we call modalities. I don't know who invented that word. It's things like superconducting. That's the approach that IBM, Google takes. Smaller companies like Alice and Bob, and they do variations of this. IonQ, Continuum do trapped ions. My old company, Inflection, companies like Pascal do neutral atoms. So that is, you know, quantum is part of nature. So we're harnessing in different ways nature to do the computation. And by the way, that's why we think it's so great,

because nature is the biggest computer, literally, in the universe. So it solves some pretty good problems, you know, day to day and things like this. So I think every one of these companies, these little companies, they think that they have some twist. Many of them come from academic departments, as do AI companies, right? And things like this. And they somehow feel that they're just a little bit smarter than somebody else. That their method will allow them to somehow magically have far fewer errors, for example, when they do this. In terms of their exit strategy, well, you know, I think many of them hope that the big companies will buy them. That does happen in the AI world. It doesn't always happen in the hardware

world, right? There's a lot of not invented here in the hardware world. So I do think that they will try out different methods. I think some will pan out, maybe not for computing per se, maybe quantum memory, quantum communications. So I think we'll see a bit of a shakeout in the next decade. And then there's always the, you know, who's going to come in from left field? What's the technology no one thought about yet? And in 50 years, I won't be here, but I think we'll be using some pretty cool things we haven't seen yet. And I think that's what we see, isn't it?

That the revolution comes from somewhere we never even looked. We were too busy worrying about the thing we understood and something completely different comes in from another angle. But it does feel like what you're describing is a series of startups who are all saying we understand quantum better than anybody else, and so our way is going to be the best way. And Stephen will remember that the all-flash vendors with the same approach. And so it continues to feel very early in that something will be a huge change to enable the sort of shift in scale, shift in capability that we need. If quantum is going to take over and become the future of AI and solve the problems that AI is trying to solve. Of course, quantum may solve these problems without the use of AI. It

may solve these problems through its own quantum behavior, the actual way quantum works different to our conventional compute. So I think something coming out of a weird left field is the most likely way we'll see this revolution. Regarding AI and quantum, there's a lot of similarity in the math when you get down deep. It's an area of math called linear algebra, which deals with great big matrices and things like this. So quantum computers in some way are huge linear algebra calculators, if you have the data. And so that's a good thing. So there's an affinity to certain types of problems like those in AI. But oh, by the way, in things like computational fluid dynamics, if you happen to be building an airplane and you're worrying about air flowing over the wing or future cars

and things like that. The other really open thing that we will not see for a little while, but I think will be the killer app with quantum and AI, is it's a completely different programming model. It's not just a little different. It's not like the newest version of a programming language. It's completely different. The representation of information is different. You can't even copy information in quantum. You want to have a quantum database? Great. Every time you pull something of finding patterns, detecting patterns that are beyond our capabilities classically, that's where quantum will really win. So it's not just throwing a lot of brute strength, more GPUs and more data and things like this. It's actually doing things fundamentally different. And that's where it will or will not pan out.

Yeah, that does seem very likely. I think that if we had tried to build computers that were more like the way that we did math in the 1920s and 1930s, well, they wouldn't look anything like they would. And they would also not have been very practical. In fact, it would have taken a lot of challenge, I think, not to use binary logic. And yet, once we adapted ourselves to that, well, then we opened up a whole new world. And we were able to basically formulate almost any possibility in simple binary math. And I think that the same is gonna happen with quantum. I think that we will certainly figure out ways of reformulating, re-asking the question

in a way that the computer can understand. And it really does remind me, as Alistair said, of the early days of integrated circuits. I mean, I'm a student of the history of integrated circuits, and it is really interesting to see how in the 1950s, there were all these different approaches, all these different ways of trying to put a transistor together that would work. And then through the 1960s, we sort of figured it out. And then suddenly, boom, here's the answer. This works. And we went from nowhere to somewhere from 1965 to 1975 in a way that is really mind bending. It reminds me a lot of artificial intelligence and the development of large language models in that, you know, in 1965, people were still literally almost every store around the world for practical uses, you know, as watches and calculators and things like that. And then, you know, 10 years after that, we have the Macintosh on our desk. You know, it's just incredible to see the acceleration of this

technology. And I think that if we look at the history of technology, there's a good chance, if quantum is possible at all, that could be a similar timeline where we would go from, we're trying to figure out how to make, you know, a quantum annealer practical to there's one on my desk. Maybe I'm crazy thinking that, but I think that that's kind of the path that technology takes, assuming that it's possible. Yeah, you might not have an annealer, but something else. Well, the other advantage is that we've been through the last 70 years of classical computation. So it's not like we have to go back and be ignorant and say, let's forget all the computer science and hardware technology we've developed, right? We can build on that. And so that's why you see, in part, the variety.

There are several semiconductor approaches or things embedded in semiconductors. But the other approach you mentioned, integrated circuits, we'll stick another word in front of that, photonic integrated circuits, right? Silicon photonics. Often people use that phrase. You know, if you talk to vendors today out there that for just regular classical computers for data centers, they talk about these subjects. They talk about optical communications. Once they jump in this game and say, hey, we can use our technology, right?

With adaptation for quantum, it's really going to start to increase things too. Once they jump in this game and say, hey, we can use our technology, right, with adaptation for quantum, it's really going to start to increase things too. So yes, once those things do drop in, I think that'll be interesting. But we need to organize these little companies slightly differently. And again, going back to this business about so many of them are just hardware. It's so expensive. They've got to partner, they've got to be acquired. going back to this business about so many of them are just hardware. It's so expensive. You know, they've got to partner, they've got to be acquired. They have to become subcontractors,

they have to become part of a supply chain. They just will not exist all by themselves. And I'm working on this presentation, which is why quantum needs an Apollo program. And basically, if you look at all the effort in only eight years of JFK saying, we need to go to the moon and our landing on the moon, right? I mean, there are so many politics, there are so many arguments, there are so many false starts in this, but they got it done. They got it organized. We need that for quantum computing too. We can't just let all these different activities just keep happening randomly in these different

startups and stuff. So I think ultimately it's either going to be the big companies like the IBMs, the Googles, the Microsoft, the Amazons who just do it themselves. Or something or some country will step in and say, we will have a long-term view. We'll do the proper investment. We'll stop duplicating. And we'll start working on what we need in two years and four years and six years. And if that happens, we'll see this whole thing speed up. And therefore, maybe we'll find out the interesting AI applications sooner than later. So it seems like you're advocating a move away from venture capital-based startups and

making quick money towards long-term strategic investment, which that tends to go under the radar in the popular press of the IT industry. Well, there's not much quick money in quantum. There were three startups that went public. One did very well, the other two somewhat less well, relatively speaking. There are a lot of early quantum companies who have pivoted, strangely enough, into AI. Gee, there's going to be a little more money there, a little more VC there. So yeah, there's enough to get going. And I think how many get Series C? I think that's a way of measuring, right? So Series A, yeah, we see a lot of those, Angel Series A. Series B, it really drops off a lot fewer. And Series C, now you're talking about a handful or less. And that's how to judge it. So I don't think there is a lot of quick money. But if you

can stay a little bit longer, maybe you're not a traditional Silicon Valley VC, but you're a family office or something like that has a longer term view. It could be there. Just have patience. Jim Grant 00,00,00 I love your metaphor of the Apollo program too, because that's actually another great example of a technology that required a lot of basic science to be done before it could be practical or profitable. And we spent a lot of time and spent a lot of money and did a lot of experiments and learned a lot of science, materials science, aerospace science in the 1960s and 1970s and 1980s. And then now we have this explosion of companies, some of which are practical and some of which are, not, but they couldn't exist if we hadn't put, we humanity hadn't put the basic work in to develop the technology that they use.

And I think that, you know, I think it's safe to say that, you know, a successful company like SpaceX or somebody would not be possible if it had to be a venture funded startup figuring out like the basics of rocketry. It just would never have happened in a million years. But by having all of this, you know, existing work that had been done by NASA and the military and the European Space Agency and the Russians and everybody. It all worked together. And then that basic foundation came together and worked. I want to leave this episode and actually this whole season of Utilizing Tech focused on AI.

I want to ask you a question. I want you to kind of put on your visionary. We imagine a future where that moment has happened. We figured out how to do this in a practical way. What, you know, what would the unique nature of quantum bring to artificial intelligence that we can't have with conventional approaches? And how would that be transformative? And what would that look like? So fundamentally, a quantum computer is a computer. So we might say, are there certain tasks

that it can do much faster or much more accurately? And so I do think within AI, there'll be certain classes of computation where quantum can help. And maybe people don't even notice that so much. It's just buried in maybe a black box, go and compute this in some way. Compute a great kernel function or something like that for doing classification. Not everything is Gen AI, by the way, even if we go beyond this. So I think it will be broadly used across all the different areas AI is used,

not just Gen AI in particular. And to return to the one thing I said, I believe quantum will allow us to detect patterns because of entanglement, right? Something which is very weird and does not exist classically. So we'll be able to detect new types of patterns in data that we just can't see classically. Or maybe we could see them, it would just take a million years if you've got that much time, or so much longer to do that.

And that's the surprise that's going to much longer to do that and that's the surprise that's going to be in the box and that's what i think people hope for ultimately these systems will be integrated classical systems hpc quantum great big servers they may even be so-called serverless right where you just give a task to the right type of computer to do whatever it is. So it will fit in that way ultimately. You know, just just to kind of conclude, I I ran the software and Linux business in IBM years ago, and I had a boss executive who first 100 days he's going to visit 100 clients, right?

And he would ask them, he said, well, have you used Linux today? And we're talking about 20 years ago. And they say, oh, no, no, no, I'm a Windows. I'm a Linux person, right? They said, have you used the web today? Have you used Google today? Have you do things like that? So we will know quantum is a success when we don't even know we're

using it. And whether it's an AI or it's helping to better optimize our pension funds or developing new products, you know, it may say quantum inside, it probably won't. But that's when we know it's really made it. I can see a future where quantum is just another type of accelerator that's attached to a conventional system for some use cases. And so in the same way that we use GPUs to do particular classes of math, we'll use quantum acceleration. And that quite possibly is the use case where a relatively small amount of quantum compute can make a very big difference

because you use it for what it's good at, but you use other technologies for what they're good at but I'm really intrigued by the idea of quantum sensors and having a quantum data generation at the beginning rather than classical data generation and having that feeds through a quantum analysis so really changing the way we're generating data as well. It doesn't help us deal with the old, we need to find information about all this data we previously had, but it will help us for more real-time decision-making. I can see that future where the speed at which a quantum computer can deal with a large amount of quantum data and produce an answer is going to be hugely beneficial for real-time processing.

And then we'll transition that data out into more conventional forms for longer-term analysis. And I definitely see that this is one of the things we discussed in the Edge series is that separation of the immediate decision-making locally and using a different technology for more strategic long-term analysis. We may see little quantum computers in aircraft, right? You know, all these quantum sensors, inertial sensors, gyroscopes, things like this. They generate quantum data. Maybe there'll be a little companion quantum computer up there too, or in your ship or in your submarine or whatever. So here I'm hinting that maybe defense and military is going to do a lot of the early investment. Well, of course, you know, we already have quantum-based systems out there in every big box store in the world with the quantum dot televisions, which actually I think do use

quantum effects, right, Bob? There are some, and one that people are more aware of are MRIs. So, you know, you have a health problem, your shoulder hurts, whatever, again, MRI, that's been around for about 50 years now. Right? And why? Because it provides better resolution, and it's safer. Right? So those two things, you know, better resolution and being safer, pretty good things to aim for. So faster, more precise calculations that are, are less expensive, right? Use less energy, all these types of comparisons, I think we'll see for quantum computing. Well, it certainly does seem as though this technology could be very practical.

I know that there's a lot of, you know, concern or FUD or worries about AI. There's also a lot of worries about what quantum computing is going to mean for the future. But as you point out, when we really look at it, when we really look at how it's implemented, what it really is, it's not the tabloid worry. It's something a little bit different and a little bit more nuanced. And I appreciate your feedback. I'll also, of course, be watching and listening to everything you've got to say on this subject because it's a fascinating subject. Before we go, Bob, where can we continue to hear what you have to say about quantum and

about AI? Well, I am now with Futurum Group. As we said, I'm within the research group. I write regular research notes on both AI and quantum, as well as some analyst insights. The research notes are just something happens in the industry. There's some news. And I put it in context. What does this really mean? So two dimensions really. Broadly, what does this mean for the industry today?

And then in the long term, as we've talked about, such as with the Apollo program. I've also started advising clients. So if you would like a little bit more private discussion about this and what it means for your strategy, if you'd like to know what's really going on, we can do that. I regularly keynote conferences and so forth and whatever. So connect with me on LinkedIn. I'd like to talk to you about what Futurum Group is doing, Futurum Research, as well as Futurum Intelligence. I think you have something to say about Futurum Intelligence and AI, right?

That's right. I'm very, very happy to say that we demonstrated the Futurum Intelligence platform at our AI Field Day earlier this year, and we have revamped it and are currently updating it with a whole bunch more data. So if you're interested in learning more about the whole world of AI products and specifically how customers are approaching the questions of AI, there's a intelligence platform for that. And you can learn more about that at Futurgroup.com. Alistair, as well, you're working with me now, too. We're finally working together. It's been a long time in coming. If I lived a little more geographically convenient, that might have taken less time. But eventually, a mutual friend, Keith Townsend, pulls me into the Futurum Group. And like Bob, I write research notes and analyst insights, probably rather fewer than he does, as well as creating other content that ends up coming out through the CTO advisor side.

So there's lots of really interesting content coming out of the Futurum group, and the group just keeps expanding. Well, thank you so much for joining us here for Utilizing AI, part of the Utilizing Tech podcast series. You can find this podcast in your favorite podcast applications. Just search for Utilizing Tech. You'll also find us on YouTube if you prefer a video version. If you enjoyed this discussion, we would love it if you would leave a rating and leave us a review. We would also love to hear from you. Just reach out. This podcast is brought to you by Tech Field Day, home of IT experts from across the enterprise, which is now part of Futurum Group, as you've heard a couple times during this episode. For show notes and more episodes, though, head over to our dedicated website, utilizingtech.com. You can also find us on X Twitter and Mastodon at Utilizing

Tech. Thanks for listening, and we will see you next week.