SPEAKER_01 0:00

One of those most important things is I want to create the environment in which the best of the people that I work with can come out. That's that's my priority. Totally distinct from tech and even business outcomes. Because I feel like the business outcomes just come from a crew of people. That's our motivation.

SPEAKER_00 0:32

Everyone, welcome to the Before It Was Obvious podcast. But you knew that, or else you wouldn't have clicked on the button. So I always say that every week. This week's guest is uh Tom Richards of Cisco. Uh Tom, welcome to the show. It's very nice to see you and and thanks for coming on. Honestly, many of the guests I've had on so far in this show have been, well, really all my old mates. And although I've known you or know who you are, Tom, and we have actually physically met once or twice over the years, I can't say that we're we don't know each other very well. So this is going to be a voyage of discovery for me as well as our audience.

SPEAKER_01 1:13

So could we could come to the end of this and actually really not like each other, Simon, and and that would make it a really exciting podcast for everybody. Absolutely.

SPEAKER_00 1:22

Let's get into politics and religion. So we'll start with that. There we go. We're off to the race. And we're off. No, let's not start there. Let's start with. I introduce you as Tom Richards of Cisco. I suspect there's 47 other Tom Richards already at Cisco. So we better define what which which one you are and what you do for Cisco at this point.

SPEAKER_01 1:45

Right. Okay. So so yeah. Firstly, what do I do today? Um and I've been in Cisco for 20, let's say 23 years, really. 27 years because when Cisco acquires a company, they backdate your start date to the date you joined the acquisition. So anyway, uh 23 years, let's call it in Cisco proper. And I've done a number of things we'll talk about, I think, uh, as we go through this discussion. Right now, uh I lead a small team focused on, I mean, the the title is Strategy and Planning for Go to Market, specifically focused on the collaboration devices. Primarily, what we concentrate on is the video device portfolio inside Cisco. And what is what is our job? I think that for anyone who's familiar with the video device portfolio, you'll know that there's quite a lot of innovation going on. AI has kind of accelerated that innovation. Uh, the engineering team made some very, very smart decisions now, more than a decade ago, around AI enabling the video devices with NVIDIA chipsets back in 2015. Uh, the joke we all have is why did we not invest in NVIDIA when we decided we were going to actually put NVIDIA chipsets in our devices? None of us did, and we're all still working. Um, but uh that I think laid the foundation now for a really exciting innovation on the product side. What I think I am involved in is how do we change our partner community, our sales community, our customers' understanding of what the art of the possible is based on this speed of innovation. Because I think it's uh I was just having a conversation right before joining this, uh, joining this call with you, Simon, where one of my sort of complaints now is that we talk about collaboration devices, and that is not actually what we're delivering to customers. What we're delivering is workplace devices that drive workplace experiences and a whole sustainability architecture, a real estate and real estate ROI model. And so it's now suddenly all of those, what we're considered to be peripheral and adjacent use cases, are now central. But there's an enormous change that the industry has to go through to be able to actually adopt that thought process, that buying cycle, the data integrations, the AI capabilities around that. And so that's what my team does is really look at how do we drive changes to the industry, the partnership, community, et cetera, based upon some of those innovations and opportunities.

SPEAKER_00 4:27

I'm so itching to dig into that in enormous detail because anyone who reads any of my stuff knows. We promised we wouldn't do that, right? We one, we promised we wouldn't get into all the technical side of it. Um, and two, I have I have lots of thoughts, lots, lots of thoughts on that topic. But we can't do it yet. We're gonna do that a bit later. Now, my I'm going to guess that you're well, I do know that you're not based in the UK anymore, but neither am I, and we both sound sorry, we both sound like we are based in the UK. Um, I'm an Englishman, you're technically British. I'm I'm psycho English, uh Simon.

SPEAKER_01 5:08

Yeah. I'm technically in the UK, let's put it that way.

SPEAKER_00 5:12

There you go. It's so complicated, isn't it? It doesn't make any sense anyway. Um you didn't come out of the egg, let's put it this way, and go, I know I'm gonna be running the strategy for the collaboration business with Cisco. You came out of the egg and thought at some point I'm gonna be a fireman or an astronaut or something. So what what did the beginning of the world for you look like? And and then let's talk through school and how you started making decisions and what you were good at that made you go off and do certain things at university.

SPEAKER_01 5:43

Okay, gosh. Well, firstly, I've got to try and remember that far back, Simon. So give me a moment. And the other, the the other is like um the uh maybe the honest thing is uh, you know, early on at school, it's gonna sound a bit flippant, it wasn't quite this bad, but but I couldn't spell, but I could do a I could do mathematics. It was I was a little bit kind of one-sided, shall we say, uh, you know, singularly focused. I was super science-y mathematics. By the time I got to the end of school, A levels, as you will be familiar, right? The last two years of school, 16 and 17, I was doing maths, further math, step maths, and physics. Those were my courses, right? Anyone who's grown up in the US will be at 16, if that's what you've been doing, you're pretty focused by that point. So uh why did I go there? Um That's interesting. I it I enjoyed the I enjoyed pure maths, was my thing. I enjoyed it because it was clean, it was simple, uh, there was a right, there was a wrong. Uh and and when you got it right, was you know, I I got to the point where I remember I could go through an exam, a mathematics exam, and I would know what mark I was gonna get before the mark came back. Because it was just you just knew it. It was so clear. That was the that was the joy of it, or the horror, depending on, but that was what I loved. And so I just went down that path full bore. Um uh, you know, and it started early on. It really started early on. I mean, like my, you know, when I was, you know, earlier than 10, that was what I was doing, you know, uh, just focused on that. So that was kind of the early path. And then I guess I got to university, I did pure maths undergraduate, I did a pure maths master's degree. And I'm gonna apologize to my professor now because I I remember him asking, hey, got to the end of the master's. I don't know if he was genuine or if he just felt like he needed to ask this, just to be clear. But he said, hey, have you considered doing a PhD? And and I had that kind of gut response, which is absolutely not. And and and so yeah, and why like having just gone down this path, that why wouldn't I? I think what I realized is that I enjoyed people. I enjoy like the these kind of conversations, meeting new people, getting to know new people. I really enjoy that. I I get a lot out of it. I learned so much from it. I realized that if I carried down this path, uh it was like an ever-diminishing community of people that I would be working with that could understand what I was doing, that was getting any value out of what I was doing, etc. And it just it was a tough realization where it was like, uh-oh, I put all I'm all of my eggs in that basket. What am I gonna do? That's not where I'm going. And so it it was interesting. It's like, okay, do I just have to totally restart? And I think uh yeah, by happenstance, I got the opportunity. This is actually through my father, you know, he was a mathematical philosopher, and so he got into AI in the 90s, as did I eventually. But uh, in fact, he probably got into AI well before that. Anyway, I had the opportunity to go into some labs at Imperial College and go and teach myself to program. Because I think that was just like my uh oh moment. How do I make myself useful and employable? I'd better get some just some good basic skills. I discovered Prologue. I don't know how many of your viewers will know Prologue programming language. It's a predicate logic based programming language for somebody who's done mathematics. That was like I could just talk to the computer. I did not have to do C and all of the what I would consider to be real programming. I could just talk to it like a almost conversational type thing.

SPEAKER_00 9:51

Yeah.

SPEAKER_01 9:51

And I got into what's called constraint programming at the time. And uh I was just doing that to teach myself to program in the evenings. Wonderful Yugoslavian guy uh in the days when Yugoslavia existed. He said, he took me under his wing and he said, the only way you're really gonna learn to program is have a project. And I got involved in his project. I started to really learn. It wasn't just prologue, it was a language called Eclipse. Eclipse was designed to be able to do all sorts of constraint reasoning. It was the beginnings of a version, a branch of AI. This was 93, I want to say 93. And that it just led from there. PhD a sponsorship opportunity to came up from British Telecom. I applied for it. I was it was serendipity because I'd been doing this project. The first question was, well, so if we uh if we fund you for this this PhD, what are you gonna go do? I fell into a PhD, I would argue, at that, at that point. So '95. And then through there, it then became the path on which uh, you know, I I ended up. So that's the early history, Simon. Hopefully that's useful.

SPEAKER_00 11:03

Yeah, very uh, by the way, I totally agree with you on the maths. My math isn't at your level, but the thing I love about maths and physics is you you only need to know the basic principles and you can work out the answer. Most of other topics, it's like you either know it or you don't. And I'm like, I can't do anything with that because I've got a terrible memory. So you both I love that. I love the idea of just saying, well, I've got these basic principles and I can build something from there. I think that that's where physics and maths are so wonderful. So, so okay, so perhaps an obvious question, but I don't know the answer to this. Why am I not calling you Dr. Richards?

SPEAKER_01 11:42

Because I it's been so long since I did that work, Simon. What would happen if you said Dr. Richards? Somebody would say, Well, what did you get your doctorate in? And I would say, I AI, and then they would ask me a hard question for which I had no answer.

SPEAKER_00 11:59

So probably best, but we don't okay. So you'd go off and you do a PhD, and I assume it was it wasn't really AI at the time, it was more machine learning, right? So pattern recognition type stuff.

SPEAKER_01 12:10

No, actually, interestingly enough, it wasn't. Uh, we used to uh we used to laugh at the pattern recognition folk down the hallway. They were like, they uh I I'm not sure this is fair, but we used to chuckle. It's like while we were using a laptop to optimize how British Airways was scheduling their entire fleet of aircraft across the globe and were showing them how they could save a 747 from their from their plans and so on and so forth. The guys down the hallway were trying to recognize a teacup. And so we were like, at the time, we were like, well, this is going nowhere, guys. And so, yeah, fast forward 20 years, and it's like, oh, yeah, those guys kind of knew what they were doing. Uh, they were smart. That, you know, that's the AI. That is the AI that that that is driving the the major changes.

SPEAKER_00 13:09

To be fair, it's both. You're the stuff you did just actually came out to be useful earlier and mainly sits under the covers.

SPEAKER_01 13:17

Yeah, no, it's a actually it's a yeah, very valid. I think that that um you know the the the models, what we were doing was solving kind of you know, natural processes, human problem-solving processes, heuristic problem-solving processes, modeling those for these what were arguably, you know, problems that were so large in their search space that they just couldn't be solved effectively. And what we were proving was actually, if you take these, if you can model these natural problem-solving techniques, you can find fabulous solutions to these problems very, very quickly. And so that was the application of what we were doing. But it, yes, yes, I think you're fair to say arguably what we are living in in the sort of AI world is a combination of, I think, you know, machine learning and and and large data models, but also then strong search techniques and algorithmic, uh, you know, algorithmic models as well. I I'm I I guess what I'm trying to say to people is it's not my fault. It's mainly the guys down the corridor.

SPEAKER_00 14:24

Anyway, I love it. We've only been at it 20 minutes, and it's already you're blown in someone else. That's fantastic life. All right, so so you go off and do this PhD. Um then what happens?

SPEAKER_01 14:38

Uh do a PhD surrounded by some very, very, very good programmers of which I was not one. Uh, got involved in some really interesting projects. The, you know, a project with British Airways, you know, working on their sort of uh scheduling models, a number of different problems with them. 90% of our funding for the research group came from industry, from things like British Airways, uh, British Telecom, um Chamberger, a number of other sources. And so the projects were pretty sort of specific. And it was really interesting. Like there, there were some very, very good programmers with whom I worked and learned so much. And it was interesting. Late 90s, we were doing, we would do these projects. They were fast-paced projects. So we would be asked to look at these very complex um optimization problems, huge spaces. We would model them in this declarative programming language, so prologue-based language called Eclipse. Um we would build these models extremely fast, start to get results, and we would meet with the, they were essentially our client customers. We would meet with them monthly and we would update them. Here's what we've done, here's what we've discovered, here's how we've modeled the problem, here's the results that we're getting, and then we would have a discussion. They would give us feedback on well, these are some of the additional things that we need to think about. So with British Airways, you know, we modeled their turnover time between one flight and another, and which crews and how to start thinking about how the crews could apply to different aircraft to make the problem even more complex. So every month we were kind of layering on more things to be able to make the results more realistic, tighter to what was the actual real lived experience of Shenzhen these aircraft. And I realized in hindsight, like years later, what were we doing? Kind of one would argue stream programming, two programmers sitting side by side, one at the keyboard, the other looking. We swept out so many bugs and so many issues that would have just gone past us if it had been one person. We had the two of us, and it was it was far more fun as well. We we had a lot of fun doing it. The monthly cycle was essentially a slightly elongated agile programming process as well, agile development process. It wasn't called agile at the time, but that's what we were doing. And so it was so interesting. We didn't think about it in principles, it's just how we had to work because of the way that these contracts work. But that to me, those were really, really fabulous learning experiences. You know, who knew I'd learn that in an academic environment, but those are some of the really valuable learning experiences I got out of that academic process. And then as time went by, I mean, let's say four years went by, finally getting to the end of the PhD, doing all of these, these, you know, industrial project work as well at the same time. And we realized, look, it, and it has huge value to industry. So we were like, well, you should really spin out a company. And it was a very fraught experience, but you know, by hookah by crook, 13 PhDs, and and you know, we we started a company. And that's how I ended up kind of out of academia and starting down a more industrial commercial path.

SPEAKER_00 18:05

And can I make the can I make a guess here that you were the most um human, you were the human interface guy? As in you interface. I was the one that talked to people while other people got the work done. Yeah, yeah. My my sister's a mathematician. She used to work for a company in Oxford where all the meeting chairs in the rooms would face outwards because all the mathematicians would talk to each other but couldn't look at each other. So I understand the mindset. Um I know in no way do I think that's a bad thing, but these folks aren't building a company, they're building a brilliant idea that someone else is managing. Can I assume that you ended up in that role whether whether you wanted to or not?

SPEAKER_01 18:51

Yeah, yes. I I was gonna look, yeah, I think there is there is absolute validity to what you say. I think again, I you know, enjoying people and enjoying conversing like this. I think, yeah, that that was one of the things that I guess one of one of the the things I could do that perhaps was a little a little bit unique in the community in which I was working. Fair fair point.

SPEAKER_00 19:17

Yeah. Yeah. Not many PhD, math PhDs doing machine learning or artificial intelligence. They're just not their brain has been used up by doing something very clever rather than talking to humans. It's amazing. Um a very good friend of mine I once met him. Uh he was when we first were starting work together, a guy called John Tracy, he uh he came out of the tube station and I watched him at uh Unilever's building in London. He's pouring with rain, everyone else is wearing coats and umbrellas. He turns up in a light grey suit and says to me, as I look at him and says, What's the matter with you idiot? Did you not bring a coat? And he says, Well, it wasn't raining when I left Cambridge. And I said, What kind of IQ have you got? And he says, Well, the army said I've got an IQ of 186 when I did cryptography. And I looked at him and said, Any umbrellas in the IQ tests I made you do? Brilliant what he did didn't really fit planet.

SPEAKER_01 20:16

IQ versus EQ. I think that's the the the emotional quotient. Yes, it's often it's often skewed one way or the other.

SPEAKER_00 20:27

And you need all of them. I mean, I I don't want to suggest that one group is better than the other, but you need a combination. Different people bring different things. So you get when I did this. This is just fascinating. So in the mid-90s, you're offering using AI tools that helped make organizations more efficient in how they're doing things. Yep. You said you did that for four years?

SPEAKER_01 20:49

I think in parallel to that, and to be able to fund the PhD going forward and to fund the research group within which we worked, it was really, as I say, 90% uh industrial projects. And those industrial projects then were very specific, very crisp, very clear. Here's the problem, here's the you know, here's what we're trying to achieve with the problem, and that's how, you know, that's how we really funded ourselves as a team.

SPEAKER_00 21:15

It must have been interesting because if you're a company now selling AI, yeah, okay, there's a thousand other people selling AI, but but you don't have to explain what AI is. Not that many people actually could, but in the mid 90s, you could say AI and no one would have a clue what you're talking about.

SPEAKER_01 21:33

Yeah. Yeah, we used to get the response, oh, that must be really complicated. I mean, the subtext of that is, yeah, I'm really not interested in hearing anymore, right? You know, thanks, but no thanks. Yeah. Uh yeah. Um, yeah, it was it was it was early days for sure. It was like the the Bailing Time and sticky back plastic days of AI for sure. And AI goes back to the you know 70s and and early. But but I think that yeah, we were still very much in the infancy, particularly looking at what's going on now. And you're right, it was not a term that industry used ever. It was an academic, it was an academic term, right? Never I never heard AI mentioned by anybody in industry with whom we worked at that time.

SPEAKER_00 22:21

And and it must have all been um the concept was primarily about pattern recognition. Um I'm guessing because that's what I mean, AI at some level is pattern recognition, and then well, the next legic logical step from where we are now is is here. It's all to do with predictability being very.

SPEAKER_01 22:42

It was interesting. That's a kind of neural network model. I think for us, what we were saying, I mean, to give you an example, what we were saying was imagine you've got a number of finite domain variables. Let's keep it really simple. Imagine I've got a hundred variables, each of which could be a zero or a one, and I've got a bunch of constraints that say this instantiation, this representation of zeros and ones for my hundred variables, these are wrong, these are right, or these are acceptable, or these are not acceptable. And my question is, I've got to now decide on a solution, a set of zeros and ones that doesn't break any of those constraints that say this three markings here for these three variables is not acceptable. And so when you have all of those constraints in place and you look at them and they're how do you read that model? The simple-minded view is to start instantiating the variables until you hit a blocker and then go back and change an instantiation. If you do that, the the search space for just what I described to you, 100 variables, each of which have two values, the size of the search space is two to the 100. Like just that, just that really simple problem. And then think of what we were then modeling in industry. British Airways, you've got flights, they can be ordered in different ways. They could be on different aircraft, up to like, I can't remember what the size of BA's fleet was, 150 aircraft, let's say, give or take. You have, I don't know how many thousands of flights looking at they're typically scheduled across a week and then they repeated the weeks with some variance. But the size of the search space is so much bigger than the problem that I just described. It's yeah, more I we used to say, like if people asked us what we were doing, it's like more than the total number of grains of sand on the planet, possibly more than the total number of atoms in our solar system, right? It's that kind of scale of problem.

SPEAKER_00 24:50

Well, that definitely helps me imagine it when you say more than the total number of atoms in the solar system. I'm like, oh well, that many then.

SPEAKER_01 24:58

Yeah, like it makes your head hurt. And then we were like, well, that's not really. I mean, arguably, when you decide you're gonna go to the office, let's suppose you have a commute of potentially 45 minutes and you have a multitude of different ways. Do I take the car, do I take the bus, do I bicycle? If I do take one of those options, which turns am I gonna take? What if I see traffic? How am I gonna adapt? You do that without doesn't really hurt your head, you just get on with it. And you go do it and you get to the office reasonably effectively. That's the stuff we were doing is taking what you do very naturally and actually helping computers to think and act that way when faced with these ridiculously large search spaces. It's like, don't like if I if I'm if I'm gonna go from London to Edinburgh, I'm not gonna go via Brighton. Okay. So I can immediately take that's a really obvious thing, just take that whole part of the search space out, for example.

SPEAKER_00 25:58

But a good example of that would be uh with IBM with um Deep Blue. They, when they were playing chess, that uh in the 80s, that basically went with, well, there's only a finite number of potential moves. I'm gonna try every single one of them. Yes. By the time they got to Watson, it was it was not going, I'll move every possible random position. Is that is that a reasonable analogy?

SPEAKER_01 26:23

That is exactly right. And and and then we started again, the stuff I then got involved with is like, well, number one, I can do what you just described, which is I can make some sensible choices. But then the other is I can also start to say, well, when I make some of those what I think are sensible choices and then discover that that's not right, I've now learned something. So now I can add that learning to what now guides my future searching. And so this is the beginnings now of a kind of learning algorithm, not just a basic searching algorithm. So those are the that was what we were doing, I would say. And and I think that that for me was sort of, you know, that was sort of the interesting cycle as we started to get into sort of AI, you know, in which we're living today, is like it was evolving, you know, even back now 25, 30 years ago.

SPEAKER_00 27:17

But this is one of the reasons why they couldn't brute false go, right? Because the number of variables was so enormous.

SPEAKER_01 27:25

Search space is way too big. Way too big, exactly right. I mean, yeah, where deep blue required whatever, a a cray, this was like, yeah, you know, my Apple Watch has got more compute than a cray in it. I know you're so right. Well, I'm showing my age, but at the time, uh, but yeah, you know, that that's exactly right. I think exactly right. It was just these these problem spaces just too large, and so what do you do? And we started to say, well, there's there's actually an awful lot that you can do, which is kind of available to us through all these different modeling techniques.

SPEAKER_00 28:00

All right. So um did Cisco come and buy you, or was there something in between?

SPEAKER_01 28:05

Yeah, so so I guess so so um PhD finished that um late 90s, year, year year or two as a I guess of just an academic, but doing these industrial projects. And then uh we we kind of collectively decided as a research team that we would spin out a company. That was uh such a learning experience in and of itself. Some of us said, yeah, we want to go into the company, and some of the team said we want to remain in academia, but we want to keep those two things together. So the company would actually continue and focus on building the IPR, the intellectual property rights, develop what we're doing, but look to monetize the stuff that we were doing and find the next really big industrial problems that we could go solve. But that we would also have a strong continued linkage with the research team to fund ongoing research, which would not necessarily be specific to an industrial problem. We wanted to make sure we just maintained that continuity for the for the academic group. And that was very challenging, uh, a naive and worthy goal, uh, which I would say, you know, limp sort of mixed success. But that company, I went into that company, and I think that maybe Simon is what we were talking about before. It's like, you know, was not something that I I was like consciously planning. Oh, I'm gonna get away from this whole academia stuff and start getting more, you know, into sort of developing some of the sort of people skills, et cetera. But I think that was unconsciously maybe part of the path. I was just fascinated as well. It's like, I would love to go learn about product. And and so when we started this small company, we spun it out, we got first round of investment from you know venture capital, for good or for bad, can talk about that ad nauseum. But we we ended up uh we ended up kind of at this point sitting down and going, right, with 13 PhDs coming out of this AI group, we've got to run a company. What are we gonna go do? So, you know, it was like some of us, some of us, some of us who were really good programmers, etc., you know, could could lead programming teams, went into that. I said, you know what, I'm really interested in getting into product management. I was kind of interested in that strategy side of things. And so that's what I took on within the team. I led the the product management within the team. And that was the beginnings of me kind of set going down quite a different path, I guess, uh than I than I had been on. Think of that like from pure mathematics. Now I'm going into a company and doing product management, right? It was quite a quite a different model over, let's say, a decade. Um, absolutely no regrets, but that was kind of a big change for me. And I think that was sort of I think the attitude I had was, yeah, that looks interesting, let's give it a shot. It was not a very deeply educated choice. It was kind of interesting, and that was it.

SPEAKER_00 31:12

My own experience of that is I think a lot of us think the the the engineering, the technical, the math, the physics, as the kind of intellectually clever piece, and the and the commercial stuff as adding up, and some of the numbers are in uh parentheses, right? I mean it's it's business, at least on a surface level, looks not very intellectually stimulating. But my personal experience is that actually the exciting thing about physics is you can work from basic principles and build something without having to know it before you start. But the human angle is just as interesting, it's just differently interesting and probably more complicated because it's not you can't build human reactions up from a base set of equations. How did you navigate that? Did you just sort of fall into it?

SPEAKER_01 32:15

What a super question. Because I I it's firstly like now where I am in Korea, which is like now, you know, manage teams and you know, been through product management and started to look at go to market and the journey from a mutty from truly a commercial perspective, right? You know, how do we go to market? What is our route to market? What is the partner our ecosystem? What is the sales architecture? How do we price? How do we license? So on and so forth. All of those kind of questions. And I think you're, I think we would say you're you're spot on. It's a very different thought process to some extent. It's like give you back to what I was saying, the joy I had in mathematics of like there's the right answer and there's the wrong answer, you kind of don't get that. So it makes it pretty complicated. But yeah, you know, it it is really interesting. I I think I yeah, I can't agree with you more that that's sort of been one of my big learnings, maybe in in the sort of the journey, is that it's yeah, there's an awful lot of complexity. And maybe what I would say also the other thing in taking this sort of now, getting more into this go-to-market and so on and so forth, what what I see an awful lot of is that we we live in kind of well, product. I've got a product strategy. This is what I'm gonna go build. Yeah. And then I've got sales, and I've got a sales team, and there's my sales opportunity, and I'm gonna go after the that business. I've got partners, and partners are like, well, I've got these partners and we want to grow them by X percent. And it's like, where's the joined up thinking across all of those? Like, is the product well designed and clearly differentiated for the target customers to whom the sales team wants to get after? And do we have a partner community that can actually deliver the results and outcomes that we're selling to the customers? Right? That joined, oh, and by the way, is there an adoption process? And have we got a pricing and licensing model that reflects that? And how do we refresh and so on and so forth? All of that, it's like the joined up thinking is the piece that I maybe that's been my biggest discovery out of all of this.

SPEAKER_00 34:34

And there's there's the other element that the product guys always think that the product is what matters, and the salespeople always think it's the commercial relationship is what matters, and the customer might decide that actually what matters to them is the thing that's not gonna get them fired, um, or some other set of criteria. And who do you ask? Because the person you ask is gonna give you a uh either, I mean, as Henry Ford said it, if I'd asked my customers what they wanted, they would have said a faster horse. Um you've got to be careful with asking the horses, the the horses, the customers, because if you ask the customers, they see the world as it is today and just want a faster version of today. The product people may go off and build a product that is technically brilliant, but does anyone actually want to buy it? I was talking to uh one of our old engineers the other day about the you end up with a cope hanger cigar light. So, you know, technically genius, but do I actually need a cope hanger I can light my cigar with?

SPEAKER_01 35:36

Yes. And and I we were we were very guilty of that, I think, as our AI startup. We were out there, we've had this wonderful, highly differentiated, totally unique technology. Nobody else was doing this across the globe. We've been solving these industrial problems. One is it, wasn't it wonderful? And then there's the reality, which is we're coming with these solutions, and it's like totally upending the operational structure of the companies with which we're working. And in most cases, they're like, yeah, this is great. I don't know how, I don't know how to implement this because you're you're now your solution is cutting across functional organizations within my within British Airways. Who who does the fleet scheduling? Well, it's a combination of the crew team and the aircraft operations and the ground staff and the airport, you know, the airport controllers. And it's like that was the stuff that I think maybe back to what you're saying, it's like those are the things that you know I had to go and learn. I I I I can't say I really knew those. And to some extent, when I think of where we got to with the startup, we were very, very lucky that Cisco came along and said, hey, we're trying to do voice over IP. We want to figure out how we can do it without building a kind of replica IP network to protect the voice services in any failure condition. How do we do it? And when we modeled it, it was like the mathematical model of the problem was identical to another problem that we'd solved. So it was just like it just fell in our laps. So we were extremely lucky because Cisco wanted this solution. It was specifically related to what they were doing. That was like if Cisco hadn't come along, we'd be battling with the stuff that you're talking about right now, which is like if you don't understand the environment into which you're actually putting this technology, your likelihood of success is extremely low. Right. And I think that, you know, we I I can't say we learned that as a startup, we just were darn lucky. That's the way I would put it.

SPEAKER_00 37:46

But also, you once you're part of Cisco, I mean, Cisco, and I I hope no one feels that what I'm about to say is in any way uh denigrating to Cisco. It's not. I think it's actually the genius of Cisco, and I've talked before about it. How as an organization it's got what 25,000 salespeople, and every couple of months it goes off and finds another thing for that team to sell and it buys it it looks at all the companies that are in a space that's emerging, it picks the winner that it that it's decided it likes, it plugs that into the thing and now 20 into the machine and now 25,000 salespeople have got the opportunity to sell it.

SPEAKER_02 38:26

Yeah.

SPEAKER_00 38:26

I mean that you can't one cannot push back on that as being a wildly successful business model for for Cisco. Um but it's interesting to be part of that sausage machine because now you're an element within a bigger sausage. So how do you we're getting a bit out of sync, but this is fascinating. So I want to explore this. You know, your your team, right, who's only tiny, goes into the Cisco beast, which was probably 50,000 people even in the year 2000. It must have been that sort of scale. Yeah, 50, 60. Yep. Um so there's 20 of you. How do you how do you kind of get bought and then sort of bump into one of your old colleagues three years along in the hallway? Go, oh hello, Bob. I haven't seen you in three years. How do you keep that together?

SPEAKER_01 39:17

Well, in our case, we didn't, truth be told. In our case, we kept it together for a period of time, but the the the team relatively quickly within a few years had dispersed into all sorts of different parts of Cisco. So I think I think and I think remember, we were very small, Cisco's done very small acquisitions, it's done very, very large acquisitions. Um and yeah, it's so interesting to see how you know how how those have evolved. Like now, spent 23 years in Cisco and seeing how these acquisitions, having lived it ourselves, but also seeing how these other acquisitions evolved. It's a yeah, it's it's interesting, it's not always successful. Um it's very different for every single acquisition, is maybe what I would say. It's not like there is, I think, uh kind of there is certainly a very strong structure around any acquisition that is made, how to support the teams and integrate the teams and and and get them connected with the right organizations within within Cisco, etc. But every single acquisition I've seen has been fundamentally unique. Right?

SPEAKER_00 40:31

And I think Which is remarkable considering I remember in 2009 when you when Cisco bought you, Cisco bought Tamburg. For 3.2, 3.4 billion dollars. Probably the biggest acquisition in the company's history to that date. May even be up there as the biggest they've done even now. Um eclipsed that. Splunk, Splunk, mate, yeah, fair enough. Okay, but it's it's up there, isn't it? In the top couple. Um that was the hundred and thirty-second company Cisco have bought. So you're probably over 200 by now. Oh yeah, easily. Easily. So each one is unique? That's reminding.

SPEAKER_01 41:17

I would say each one is yeah, each one is unique. Um I mean, there is for sure, as I say, a process, but I think it's like maybe this is the other thing I would say, just the realization, every group, every company, every team, whatever it is, has its own unique DNA, just as we do as individuals. And I think that like what you don't want to do is if you make an acquisition, you don't want to like ignore the DNA of the acquisition that you're bringing into the company. I think that's that would be a desperate error because that's part of what you're acquiring, I would argue. Sometimes you're just acquiring assets, but generally, I don't think that's Cisco's model. Cisco's much more about like, hey, we've got, you know, a company who is number one or two in a critical industry that is part of Cisco's overall strategy. When integrating them, it's just, you know, there's there's a much broader thought process. It's not, it's not a world I'm heavily involved with at all. I I I've worked with now, like having come in, I think there were a few projects that I got involved in relatively early on with companies that we were OEMing their technology into Cisco to help, you know, to build out the architecture for various solutions. Um so I worked with them, usually small, you know, small entities of the scale that I was involved with and and you know, and got involved with some MA work as well. Uh, but that wasn't central to what I was doing. So my knowledge of it is limited. It's more what I see just being part of the sort of the working group of Cisco and the touch points as I see the acquisitions happening. Tanberg, by the way, I'd been in Cisco for uh five years before Tanberg was acquired. And actually, the point at which Tanberg was acquired was the point at which I moved away from the technology Cisco had bought my startup for, and I moved into telepresence. It was virtually the day that the announcement was made, and it it was that was another very interesting experience. But yes, so so you know, I I didn't come out of Tamburg, that's not not my history.

SPEAKER_00 43:35

No, embark, but don't underestimate Cisco's ability to do what you did. You say, Well, of course, you retain the DNA. Yeah, HP, uh, I'm looking at you. Uh, is there any poly DNA in HP? No. Yeah. Was there ever any uh life size DNA in Logitech? No. So I would argue, and the uh the list could go on and on and on. Cisco is not. unique but it's not common. You speak of the well, of course we retain the DNA and why else will we have bought them? Sure, that seems logical, but believe me, a lot of companies don't do it and they don't understand why what they did do then fails. Yeah, it's a great point. But yeah let's get back to let's get back to telepresence. So so you're the bugger who was responsible for the another reinvention of telepresence. Is it your fault?

SPEAKER_01 44:28

God no uh hell no uh there there was some much smarter people than me one of whom was in I we were talking about some interesting meetings that that that you know stand out in our careers one of the folk who was really part of I think realizing that we could deliver true HD real-time video conferencing experiences globally over thousands of miles like these are the people who who are really changing that industry my involvement came in I I guess I came in in 2009 and my comment to the telepresence team in Cisco right you know and in now we're just in the process of like getting the you know the Tamburg portfolio and team onboarded into Cisco but my comment was why are these screens being used for video only by the time you've had a good half hour conversation with people everybody wants to look at the content the product the thing that we're trying to develop and do evolve solve whatever it is we've moved away from the people and into the into the info into the data you've got all the screen real estate available why isn't it doing that stuff and why isn't it able to evolve to that stuff and uh I I think at the time I had sworn in church when I said that right because it was like real you can't break the scale of telepresence it's all that's the thing so anyway yeah I you know like what is it read the room I didn't read the room very well at that point but anyway you know that was kind of that was what drove me that was what got me excited was like this this wonderful opportunity I think Tamburg coming in and providing this massive shakeup I think there was a lot of talk at the time about we really want to democratize this reshir this very kind of um uh exec level experience that was considered like oh only the executives get telepresence you'll remember this right it was that was the special thing and then everybody else could have some CRT based kind of you know thing on weeks you know and it was a kind of you know most people were on easyjet telepresence was the Learjet. Yeah exactly and I think that that I think that the mantra was democratize those experiences which is great. I think that was a really really good way to do it democratize those experiences we can do this for everybody and here we are you know look at us like you know we're having you know HD experiences now and all sorts of different platforms so I think that was the that was the excitement at the time that was the the the change that was coming within that I guess I like a typical academic I sort of was thinking like too far out for for the industrial folk but I was like I really want to get into this I want to get content integrated into this whole experience how do we do that and took took many years hence but that was kind of where and where I guess I was going at the time it's really interesting so I have a question for you which well you don't have to answer it right and I can cut this whole bit out of the interview but I would love an answer.

SPEAKER_00 47:58

So one of the things that your CEO at the time um uh your CEO at the time was um John Chambers John Chambers thank you sorry my brain's turned a mush so Mr Chambers said two things he said for every dollar spent on telepresence or video collab it drives seven dollars worth of network infrastructure the other thing he I don't think he said this but the rumor was the other reason he wanted telepresence was because it allowed him to talk to any one of his top thousand customers anytime he liked and I've heard from many people that that uh Mr Chambers who I've never met but have a huge amount of respect for uh was very customer focused. Would you like to make comments on either of those two topics?

SPEAKER_01 48:47

So I think uh I think there's validity to seeing the telepresence and HD video as something that was a driver to a kind of new level of network investment across industry as a whole so sure it fit very neatly with Cisco's core business Cisco's core Rev switch business I think root switch business we can say that our root switch business that that was a good fit so I don't think that is inaccurate Simon at all I think that that's fair I mean it's a good marketing I just push back slightly on that brilliant marketing story because no one could refute it was there any actual evidence to support the $7 to one yeah you know what I guess one thing I've learned you know in in the years of now being in you know more on the sort of commercial side and you know thinking about the sort of the whole journey for our customers the one thing I've realized is attributing pull-through revenue like it's game uh or or a genius move depending on whether you or a genius move I I think that the problem is you can argue the data either way all day long uh you know I think the more you try to get into the data the the the less the less what is it the more desperate you seem so stick with the top level story yes if I'm gonna do HD video I need somewhere between a meg and God knows how many megs six ten twenty you know depending on what decade you want to talk about it that is that fundamentally is true. And so I think take from it what you will is it $7 50 cents who knows there was clearly it was the best story the industry's ever generated so it's whether it was let's go with directionally true. Directionally accurate and then and then I think you had another question which was around customer focus and John Chambers did you actually use it to to talk to to the clients oh that I don't know that I don't know I mean I mean I I can say internally of course we all lived by telepresence as it was then and I loved it because I here I am on the east coast the primary development and management team were on the west coast for me and I could just drop in I didn't have to knock on the door I literally made the made the call on the system and our policy was auto answer.

SPEAKER_00 51:14

So I it was it was fabulous because then I'm like I just want to talk to my leadership I'm in if you were bold enough to just make the call then you could just get in touch so that certainly the technology was designed that way whether Mr Chambers used it to get into to to talk to his you know CEO compatriots I have no idea that I can't comment but it would certainly work that way yeah yeah so um Cisco had a very interesting we're meant to be talking about you but I have to ask about this one because this is clearly right in my wheelhouse right is what I've spent the last since 1992 in um you you took the Cisco sorry you took the Tambo gear the the ignoring telepresence the normal video conferencing gear but then you did something really interesting to me at least which was you completely transformed the platform that you put all this stuff on and you you did something that no one else I don't know if anyone else was even allowed to do maybe you uh had a relationship with NVIDIA that said no one else can do this but you reinvented yourself into a into an NVIDIA based platform. I've always felt uh you know having worked for many of your competitors over the years or some of your competitors over the years and sin them all that the folks who try and compare Cisco products at the endpoint level for the last 10 years and all the other things is like comparing a Toyota Camry to a helicopter they are fundamentally different things. I mean sure helicopters cost more money than than Toyota Camries do but they they are capable of fundamentally different things.

SPEAKER_01 53:02

Were you involved in that decision to move to the Nvidia chipset and or did you at the time you know as part of our before obvious do you think that the people who did make those decisions if it wasn't you understood what the long-term ramifications of that were going to be so look that I I was not a key player in the decision at all but I was there when the decision was made and I remember making the trip to Nvidia's HQ in 2015 and seeing what they were doing and how that aligned with what our strategy was. And so I think I think that I mean I I would say our engineering leaders I mean that is one of the for me one of the one of the critical things that Cisco kind of I guess Cisco's asset when it comes to video and video collaboration devices. I as I say I hate calling them collab devices because they're really they're room devices that do amongst other things support collaboration in a number of different form factors and models and so on and so forth, but they're part of the the workplace now and they deliver to a workplace solution as much as they do to just those collaboration capabilities. That said um at the time I think the engineering team the real secret source is that the engineering team who works on these devices is an engineering team that are again back to what we're saying about DNA DNA of groups the DNA I think of that group is very much like we're gonna be here for years. So if I'm gonna build this stuff I just need to be thinking about what I want to be doing in five or 10 years time. So at the time we were looking at multicam just beginning kind of to really look at multicamera solutions and how to start letting the device make the decisions about what to show the far end participants rather than having to kind of somebody in the room going, well that's the view I want to give the far end participants we all know that's absurd. No users in their right minds worry about what the far end participants are seeing and then adjust the viewpoint just doesn't happen right they're like how do you make this thing work? Not interested need to get on with my job it's like it's really not yeah yeah we were just starting down that path but I think there was a realization that this was going to be a the the the aperture was going to widen extremely quickly in terms of the number of different requirements and use cases and so on and so forth.

SPEAKER_00 55:40

And I think that it's interesting that that you as Cisco you made that decision and everyone else at that point in 2015 made an entirely different one. Polycom were making uh H323 and SIP based video bars or sorry video devices not bars and then had to move to Zoom and Teams and Google.

SPEAKER_01 56:04

Logitech we decided as a part of the team at Logitech that did this we did what we did with uh Qualcomm based basically mobile phone type technologies um and they all had we all knew it from the day we started they all had a pretty short life expectancy because they're all based on uh ever shrinking versions of Android which meant that they were going to run out of computer and I think they were going into products that had that kind of a life cycle as well right they were going into predominantly they were you know mobile devices as an example those mobile devices don't have the kind of longevity and life cycle that a room device has. So it was kind of I think those were some of the thought processes as I you know I think we said look these devices are going to be in these rooms for four five six years in some of the public sector use cases even further it's like you know you have plenty of gear out there that's 10 years old. The average according to Frost out of only seven years my own research says five to six even now with that yeah with that I think we're uh and you know what is it it's like uh you know damned if you do damned if you don't I think I I I I really like that decision process because it was very much about well what are the outcomes? We want people to be able to deploy this stuff and then be able to leverage it for many years and not just leverage it for the fixed set of use cases that were dreamt up and understood at the time of deployment but all the new stuff that's come along in the years hence. And I think that was those were the decision processes that really drove it, which I I just really I really applaud. That's the right that feels like the right way to think about it. That's a little bit maybe of the academic in me it's like academically that's the right thing to think about. To some extent back to what you're saying one of our challenges is that these devices just keep going and keep going and keep going and so we we we get asked to continue to maintain them and it just becomes impossible for us. Some of these things that are out there in the market we don't even have components to go and keep them running and service them you know so I think you know that you know we've end of life these products but some people still keep them going you know that's just the way of that it's a pretty high class problem.

SPEAKER_00 58:26

I wouldn't worry too much about that one. You can fix that problem for the client if you had to just simply replace the box with something else and put some of them in it exactly right yeah exactly right you are still running the product the you're now still responsible for that whole product portfolio no uh I have a counterpart that's responsible the product portfolio I do go to market.

SPEAKER_01 58:52

So it's probably I want to say six years ago I shifted out of product and was an interesting it was that was maybe an another interesting let's go back to sort of career paths and career you know yeah yeah trajectory. So I'd been doing product management at that point for nil 20 years and was responsible for a large part of the portfolio the management architecture and so on and so forth. I think that I was based in Northeast US all the engineering goes on in Oslo and I think there's rightly or wrongly I think there's a view that that the product management and the engineering needs to be closer together optimize the decision process etc so that to me is like okay I think there's there's rationale to that but in doing so what does that mean that you don't have the outreach with the product management team for the what's going on in the world right and so yeah yeah kind of I was like okay that's uh that was what I I I was doing maybe back to what you're saying about the sort of the people side I enjoyed that I really liked I kind of want to go where the problems are like I want I always wanted to talk to the customers that were really angry and upset because it's like but we've got great product why are they angry and upset and it's like well I just got to go talk to them and find out and so I just you know I I I I was learning an awful lot and that's that evolution at that point was a really interesting step change because that was where I was like okay now I'm really starting to understand all the things that as you described are actually the difficult pieces of this which is the product either works or it doesn't and you know for the defined use cases. The real questions that get interesting are why are the users not loving this? Why are they not lapping it up and doing more and so on and so forth? The issue may not be product at all. The issue may be that historically they didn't go into a particular room because that particular room was reserved for telepresence, let's say I don't go in there or maybe the issue is because the way that people were scheduling those rooms I might need a room this is his you're you'll know this model Simon I might need a room so I'm gonna book a room and then what happens is the um the the room is therefore no longer available for anyone else and 90% of the time wasn't being used because it was only I might need the room. Nothing to do with the product. And so these are the things well now we go and we actually start solving some of those problems we can actually start addressing those those needs and issues. That's the stuff I started to get more and more involved with understanding as we go down an AV over IP strategy which is simplifying deployments into more complex rooms leveraging AI to be able to go and make it much much easier for the users, for the deployers now we can start scaling so we can start getting to many, many more rooms much, much more efficiently and oh hey presto I just completely upended the A V community how do the partners make money how do we make sure that they continue to survive we have to bring them with us they're still going to be vital to the ongoing growth of this whole market but we need to bring them with us and I think those are the things that I started to get then at that point much much more involved with that and that to be honest with you was I think here's the here's a confession as a product manager I really thought I understood my customers no I learned an awful lot post that that I did not know. So that may be the other you know the other sort of inflection point career wise Simon that that yeah was interesting and unplanned let's say but but hugely valuable. And also interesting it looks very logical in hindsight but clearly it wasn't at the time no no it was more just organizationally we're shifting around and I think I think at that point maybe the here's the interesting at that point I think his you know up to then it had been hey here's a really interesting opportunity we're starting a new company who wants to do product management I'll do that that sounds really interesting let's go in there like hey telepresence is now growing massively you've been doing all sorts of really interesting voiceover IP management solutions why don't you come and actually start doing the endpoints and get involved with this new HD video uh conferencing uh market yeah I'll go do that these are opportunities that came this last opportunity was one where it was like we don't really know what we don't know and so can you go figure it out it was completely it was like the blank sheet of paper. But it's the best that's the most interesting work. And I think you're right. I think in hindsight I think initially I was a bit like boy you know it I was like what what what's going on you know you know what what do I do with this and I had you know it was a bit of soul searching to say you know do I really want to get into this and then I had exactly the the the the the point you you brought up which is like oh I get to invent this I get to go and decide I can I can do what I what I think is right. And I think that it was almost coming full circle back to our startup where we just had total control of what we were doing. That was the model I think that yeah that that was a really interesting cycle in and of itself I think over the last few years.

SPEAKER_00 1:04:53

Yeah I think that's it's interesting I had no idea when we started this conversation how many parallels there would be at life size I got product management to run because no one else had ever met a client before. Which may or probably will not actually satisfy an end user's requirement andor bring the channel along that that they actually want to sell your product and recommend it to their clients. A lot of vendors forget about that. They go well it's easy and there's always that there's always that tension between the resellers who will tell you well it needs to be easy to install but equally uh it you don't make it too easy to install because I can't add any value and therefore I'm not going to recommend it. It's like and then you end up with your your smart car with Formula One tires on it because the reseller decided they had to make money and therefore they made lots of money selling Formula One tires but you only need you don't need it on your Toyota Corolla. Interesting set of dynamic great fun go to market stuff.

SPEAKER_01 1:06:04

Yeah, and maybe maybe the other thing I the the other thing I I I guess I had realized already, but as I started to get into this go-to-market model, it's like part of Agile, if you go back to the original definitions of large-scale scrum and agile, one of the tenants is remove indirection. And I I I remind myself of that as often as I can because it's like in large companies, indirection is kind of endemic in the organization, right? And what I mean by indirection is like there is a person or people, a team that is going to actually execute, build, and do something. And there is a person, group, team who are going to be the people who will actually use what is built. And the task you have is remove all the people in between those two groups so that they build the right thing, they get the right feedback. And so I think that maybe has been one of the things. I think we can always, always, always get better. But that's one of the it's one of those things I learned God knows how many years ago. Good friend who who also was, I think, very keen on Chrome and Agile and how that process worked, but at the at its core principles. And I think um, yeah, I really took that to heart. I think that's one of the things you're talking about with the engineers. I think the engineers, they really want that. I think it's the task of being able to say, right, I don't want to expose all those engineers to every single possible crazy discussion, you know, an idea and everything else, but I need to find which customers, which groups, which teams within those customers are strategically aligned and will be the exemplar of to whom we are delivering this technology and then create that connection. That I think is that's something that I just think we we collectively, I think as an industry, we can we need to get better and better at, you know, to to really to really build good product and do it at speed.

SPEAKER_00 1:08:09

I think you're right. One of my other guests, uh Bo Shami, has been on, and he he taught he was an engineer with me at Life Size, and we worked together at Logitech. And he would say to me that the engineers really want to meet customers and really want to understand their problems because they can solve them better if you do. Uh but organizations don't really know how to put engineers in front of clients in a or most don't, in any kind of scaling or useful way. Yeah. If you only expose them to three important clients, then you end up with thing that's the perfect corner case for that for those three clients.

SPEAKER_01 1:08:48

And that I think is the critical job of like a strategic leader. Like strategic leaders, I I think we get we get way too involved in the details of the technology. Maybe that's been my realization, right? I'm I'm I'm late in my career now. I am not the AI expert anymore. There are some very smart people now coming through who are much more up to speed and up to date with all this tech and capabilities and what's possible and so on and so forth. What I I think what our jobs are, as as folk who've been doing this for years, are to be able to say, this is this is now exemplifying strategic direction for the industry, for the market, et cetera. And so these are the people that we should connect the engineering teams with. Right. So it's it's targeted, it's specific, it's clear, it's not huge and large scale, uh, you know, but it is but it is exemplifying where the market is going. That's the art, I think, of where when you have that industry experience, you can really start to, I think, add value. Stay out of the tech weeds. Let the smart folk, you you know, you got people coming out of like grad hires who are coming in with fabulous knowledge, experience, ideas. You let them loose, let them go do their stuff, you know. I think that's a bit really crucial.

SPEAKER_00 1:10:17

And zoom out. You you can't see the bigger picture if you're right in the middle of the weeds. Yeah. Um fascinating. So, what's the next couple of years look like for you?

SPEAKER_01 1:10:33

Wow. Uh um uh I don't know. Here's my prediction, Simon. Here's my prediction. Change. Change is a coming, don't know what that change is gonna be. I've learned to really like change. Right? That that's maybe the other thing. Like I was not very comfortable. It took me so long to make the decision to switch from the kind of voiceover IP and the management stuff and all those things I was doing post the acquisition of our startup. It took me ages to come to the conclusion that I should go to the telepresence, to the video conferencing, to the endpoints, you know, uh area. And here I am nearly 20 years later. It's been great. It's been absolutely fabulous, right? Boy, I was I was slow to do that because I was just nervous. It's like, oh, I've got all of this, what is it? I've got all of this credit in the bank with the people with whom I've been working for the last five, seven years. And, you know, they know my worth and they can understand my career aspirations. And, you know, I should, I should really stick with that, you know, and and thank God I I I I sort of finally was like, you know what, this is too interesting, too exciting not to do it. Just get on with it and stop, stop being nervous. And in hindsight, it's like I just sort of gradually through that, and then you know, having all the opportunities that then followed, it's like, love the change, love the change. It can bring all sorts. I mean, you have to kind of love it in its very, very broadest sense because God knows where you may end up.

SPEAKER_00 1:12:15

Yeah.

SPEAKER_01 1:12:16

But all of it is opportunity, right? Especially in big companies where there's opportunities. Often a large company has a very fixed and clear architecture and model, and it can be hard to sort of find your career path within it. You can constantly feel like you're somewhat sort of locked into a particular path. When change is afoot, that's the time in which you get an opportunity to sort of see what you might like to do next and where you might like to go. And things just start to happen. So I think, you know, I I don't have a really very crisp answer of what's next, but with AI coming in, you know, now into every single aspect of our lives, with you know what Cisco is doing, you know, in our in our in our small fiefdom, let's say, within within the devices organization, there's change in there. Cisco is very focused on the workplace and realizing that our technology is no longer, well, there's a networking thing over here, and there's a, you know, there's a video devices thing over there, and there's a Wi-Fi architecture over here, and there's a security architecture over there. I think there's a realization that there is a clear set of outcomes that our customers are fundamentally struggling with. They're making massive investments in the carpeted real estate to be able to then glue back together a workforce that lost its cultural center of gravity post-COVID. Um, our technology is a really fundamental part of that, but it's our technology as a whole. So there's all of this change now afoot, and it's like exciting. God knows what my piece of the pie is gonna be. Uh, we shall see.

SPEAKER_00 1:14:09

With that, uh Tom, this has been an absolute joy. Seriously, I've really thoroughly enjoyed this. We could have I could have picked into everything. Could have gone on for a while. Yeah, oh yeah. But we've we've been going on for a while, which is fantastic. So, but we have to let the ladies and gentlemen go. So, ladies and gentlemen, thank you very much for uh getting involved. Love your comments on this. Um, Tom, again, thank you very much for your time, and we will see the same.