The AI Decision Models for PE-Backed Tech CEOs to Predict Strategic Growth with Kam Star

Welcome to the Future Finance show where we talk about treasury management on bars.

This world just flipped upside down. It's 99% inspiration and 1% perspiration because the time it takes for you to build the damn thing is almost instant. What's really interesting to me is Cam.

Star is a seasoned product and technology executive with over 25 years of experience scaling private equity backed companies from founding ventures like Play Gen to leading Blue Prism's growth from $50 million to $250 million ARR before its $1.6 billion exit. The prophetic nature of combining computer science and psychology, how did the combination of those two come about?

I've always loved computers, but at the same time I love understanding how people think, what makes them tick, why they make decisions. We are very, very predictable, right? What we do, how we think, those are quite creepy.

It is interesting how much better these models are getting.

Technology can be used both make it's.

Never going to gain in the context of financial services. How could we use gamification in areas like that?

Very carefully. Games fundamentally are competitive. One of the fundamentals of what makes games quite interesting is this idea of.

Future finance is brought to you by qflow AI the strategic finance platform solving the toughest part of Planning and analysis. B2B Revenue align Sales, marketing and finance seamlessly speed up decision making and lock in accountability with qflow AI. Welcome to Future Finance. I have my co host Glenn here with me and today we're going to be interviewing Cam Star. We're going to have a chat with him. So welcome to the show Cam.

Hey, thanks for having me.

Yeah, really excited to have you. So let me give a little bit of his background before we jump into the questions. Dr. Cam star is a strategic product and technology leader specializing in driving exponential growth and value creation for private equity backed companies. With over 25 years of experience, Cam has a proven track record of scaling businesses including growing Blue Prism's product portfolio from 50 million to over 250 million ARR leading to its $1.6 billion acquisition. As the founder of Play Gen, Cam has firsthand experience building and scaling ventures exceeding £100 million. He's also founded a digital shoreditch, growing the festival to over 40,000 participants, establishing it as a major event in London. Cam holds a PhD in Computer Science and psychology, a Master's in architecture and advanced studies in Artificial Intelligence from MIT Sloan and London School of Economics. Passionate about harting emerging technologies, Cam thrives on creating products and strategies that fundamentally transform how we work and live Quite the background cam, I have to say, impressive.

Thank you. I'll try, I try. I'm very curious about the world. So any opportunities to get any letters before or after my name or try something new, I'll be there.

I love it.

I have to add here too, the, the, the prophetic nature of combining computer science and psychology, obviously now, I mean, with AI and the way we interact, that, that's perfect. I mean, I guess the, your studies looking at, at both of those. Was that your reason for picking. Look, Paul, I'm just jumping in straight.

To a question, but I knew you would. You get excited with these things, so go for it.

Glenn, was, I mean, was your thinking at the time that when we, if we do move towards AI, that you're. That psychology is going to be an important component of it? Or how did, how did the combination of those two come about?

So my, my life philosophy is, is not a dualist. I'm a monist, so I don't really see a difference between a beautiful painting or a beautiful mathematical equation. I'm a monoist. It's all one thing. And what's really important that yes, it's all one thing. Whether it's physical, you know, something physical or something emotional, we do have to experience it. And experiencing things is, is a, is a big part and component of the reality that we experience. So I've always loved computers, but at the same time I love understanding how people think, what makes them tick, why they make decisions. And so that's also perhaps the reason I did architecture. I just couldn't make a choice between art or the sciences. So I had to go into something where I could have the physics and the chemistry of the war and then color theory and form and shape and history and just. It's something that's been with me since, since I was very little. I think I grew up with, back in those days we had physical books for a lot of knowledge. So we had encyclopedias, a couple of full lengths of encyclopedias at a height where my sort of seven, eight year old hand could reach them. And I sort of one day just started reading it from one end to the other and then when I finished I did it again. So I really got spoiled myself in being able to just pick one thing. So computer science and psychology. Absolutely. History and culture and music and art with quantum physics. Yes, please. It's very interesting.

We got a learner here. I love it. A lot of, a lot of different background there. That's great. And speaking of that. Yeah. I was reading this story about how you developed your first product and sold it to a construction company when you were 12. Can you tell our audience that story? I love that story. Give us a little bit of background.

Yeah, of course. So perhaps some of the listeners would be old enough to remember. We had a computer called Sinclair ZX Spectrum. This is in the very early 80s, and I got one for Christmas when I was 11. And back in those days you used to get magazines that had basic, that was the programming language. And you could play and make things and type things out. In any case, I was making little games and playing with, with, with my ZX Spectrum. I went to my dad's company. He was part of a large construction company. And they were rows of engineers sitting at their tables with big, big sheets of paper with these, what we call slide rules, these rulers that you can move that have particular calculations on them. And they were just sitting there moving these things back and forth and writing numbers down and moving them again. It just looked very, very odd to me. So I got very curious. What were these guys doing? I asked my dad, he said, well, why don't you find out? So I sat with one of the engineers and he very kindly showed me what he was doing, which was calculating the steel depth of these steel beams in this really large construction site for Siemens factory. Absolutely enormous thing had hundreds of thousands of these steel beams and they were all slightly different, so they all needed to be calculated. He showed me how he does it. And as he was showing me, I was sort of storing in my head his process. Oh, okay. Da, da, da. So if then, then this, if that, and then calculate these things. So I took that away. I think I made some notes or I think I went back and made some notes. In any case, I decided to make a computer program that would just ask you, okay, what's the span and what's the load? And then it would tell you based on the Young's modulus and the set of a table that could be just transformed into a set of if, then statements. What the steel beam, the I beam steeling needed to be. I went and presented this back. Well, first I showed my dad and he was like, hey, does it work? And I'm like, I think so. So I got invited to go in with. We had to take a TV in and a tape deck in the computer set it up. And one of the chief engineers, he brought over this whole table and we started to kind of go through the first four, five, six that we tried. It was all actually correct because, well, guess what? It's just a set of predetermined calculations. I think the crunch came was. And I think it was maybe five or six in. We put in one of the beams, and the software gave a different answer. My little program gave a different answer. And I was convinced, well, I must have made a mistake. Something's gone wrong. And I was quite disappointed. But actually, probably credit to the chief engineer. He decided to rerun the calculations by hand on that specific beam. And what turned out was that the original calculation that the engineers had done was wrong. Someone had made a mistake. And he immediately, he was like, we're going to buy this. This is going to save our bacon. I can't have this thing. So. So that was a really good accident for me. And I, you know, I never really looked back in terms of building stuff, but, you know, it's kind of really just started by spending the time with the user, understand what they're doing, kind of get into it, and then create something that solves their problem in a better way.

I love that story. Glenn, what were you doing at 12?

At 12, I was actually, I'll tell you, I. On my Commodore Vic 20 because we couldn't afford the Commodore 64. I had the data set, too, and I was trying in BASIC to program my version of AI which was centered around a Dungeons and Dragons game. But nobody was buying my game. I never got very far in it. I was using.

I just played the games. I had a Commodore 64. We had the Magnavox Odyssey. We had all of it. But unlike you two, that we're using it to. To learn. I was just like, shoot them up. Let's have fun.

Hey, that was fun too, right?

It had its. Had its moments, I won't lie. All right, so I love that story. And, you know, I want to talk a little bit, obviously, AI on our show, we talk a lot about AI and technology and where we're going. You published a master's thesis back in 97. You did it in 95, 96, about artificial intelligence in the built environment. Talk a little bit about that. What made you so interested back in that, you know, kind of period on AI and what was the. Tell us a little bit about the paper. Just a little background.

Sure, absolutely. I don't know if you remember Lawnmower Man. It was a movie that came out in the very early 90s. You know, it's all about virtual reality and how are we going to live in this virtual space? I was, you know, I was doing architecture or studying architecture at the time, and you know, I've always been quite tacky, quite geeky, and it's just sort of. I got really engaged with that and there's an AI in that and of course 2001 Space Odyssey, where we all know about that. But in any case, I was always really interested in systems. Back in those days, artificial intelligence was really seen as expert systems. And the promise of it was that we could use it. I mean, later on I would just call it artificial stupidity because these are just heuristic rule based systems. There is no intelligence, there's no theory thinking if it's just a set of rules. But there were machines and really the idea was there will come a time soon where we can not only design much better buildings using these systems, using these expert systems, but we can use them, leverage them in the built environment to control everything from our heating, our cooling, when things need to be made. How, you know, the kind of homes for the future, that kind of a thought. And you know, I think as some of those things have come and gone, expert systems are really kind of not, not the thing anymore. And I think what we mean today by artificial intelligence is probably much more recent phenomena around deep learning and these emergent behaviors that we're seeing out of very large neural networks that. Well, to be frank, at the time neural networks were really still in their infancy, you know, and what we consider to be artificial intelligence is probably as sophisticated as what, you know, a Model 4T is compared to the latest hydrogen run car that you can buy. So, you know, things in common, but really fundamentally different.

Sure. I was going to say I might have to go watch Lawnmower Man. I briefly remember seeing ads for it. Did you watch that one, Glenn?

Yeah, it was, it was an adaptation of a Stephen King short story, I think too, from Skeleton Crew. I can't remember which book it was. It was one. Yeah, but it was, yeah, it was a good movie. It was, it's, it's funny everything you're talking about. I'm just picturing it all building to where we are with AI right now. Talking about going from expert systems and then I guess the Perceptron and Hinton's, you know, his, his approach to it and all that and then. But looking at your, at your PhD research and gamification, it's making me think of, you know, reinforcement learning and, and learn like the alphago kind of stuff. But I. The research that you did for gamification's impact on team performance, I thought with your work in finance also, I mean this would be for teams or even maybe for training, training models in it. But in the context of financial services, how could game gamification strategies be employed to improve customer engagement, promote better financial decision making? I mean, what are, how could we use gamification in areas like that?

Very carefully. Yeah, you have to be very careful not to make things worse. So the games fundamentally are competitive, right? So most, most games are competitive in nature in some ways. And, and, but that, there's a lot of good things in games and in gaming and in that, that whole space which you can leverage to engage folks. So you know, when we think about, I think maybe the two different things, the customer engagement, probably the simplest things is whenever you, you collect coins or tokens, when you go shopping or something like that, right. It's a very, very simple thing. It's a kind of a reward system, a very basic reward system. It's actually not that exciting, but, you know, and it's hard to kind of keep it exciting. But the framing that goes around gamification, like, oh, you're playing a game or there's some kind of progression, they can be quite useful, the idea of progression in that, you know, you're leading towards something. I mean, you know, your question around kind of financial decision making. One of the fundamentals of what makes games quite interesting, apart from their competitive and social nature, oftentimes is this idea of mastery, autonomy and purpose. So if you can somehow create a system which seems to have a purpose that would lead you towards mastery, towards something that gives you some sense of autonomy around the decisions that you're making, puts decisions in front of you that perhaps has, you know, not just a celebratory aspect, but an aspect of how you are social signaling in, in your own ability to become better at something. I think those can be made in a way to make things engaging. Now, as with anything specifically gamification, a lot of the times if it's just use of the blunt instrument, it may work for a while and it may work in certain contexts. I don't think it's a, it's a, it's an, it's a rule for. In, in all contexts. What I was very interested in is given how competitive nearly all games are, but given that our social structure calls for deep cooperation, we don't compete with each other generally on a day to day. What we do is we cooperate with each other. We do get into little groups and we compete against other groups, whether those are sports teams or companies or something else. But generally people are working together in a cooperative sense. How do you frame something and does it matter what kind of a person you are, what personality you have as to how you frame it so that the contributions that you make towards something is celebrated, or that you are competing in a group against the other groups, or you're cooperating the group and competing in other groups because that turns out to be the most efficient way to get people motivated. And that, that solving the motivation issue, I think is. Is oftentimes the biggest question. You know, engagement is around motivation. If you are motivated, you will engage. So how do we build. How do we build motivation? And, and these are some of the tools. And I think so long as you apply them carefully, so long as you keep them fresh and there is a, you know, there should be a sense of fun and celebration. It's hard to do sometimes, but as long as. And brands, I think that do that, that bring a sense of magic to what it means to engage with them. And everyone's like, oh my God, this is so much fun. This is interesting. This is a pleasure to use. And people are sometimes surprised that it's working. It's like, well, compare it to nothing. And I did a randomized control trial for my PhD where I set up an experiment where we had three groups. One set of folks were in a competitive setting, one set winner in a cooperative setting, and another set they just had to do the job without any kind of framing. And it turns out any framing is better than no framing at all. So that's helpful. But it also turned out that if you're a highly extroverted individual, then you probably would prefer it to be in a competitive setting. Whereas if you're a highly agreeable, conscientious person, then you would probably much prefer being a cooperative framing. So there isn't one thing that is going to work better for everyone. And that personalized kind of approach, we haven't got to the point where all software is entirely personalized to our usage. I think that day might come. Just like our search results are very personalized. I think having interfaces that are personalized to us is. And that we don't have to necessarily craft that could be something that would be on the horizon the next five years or so, maybe sooner. So yeah, I think there is a lot in there. But approach with care because you can make things worse. Yeah.

And your research just all seems so prophetic because I'm thinking of Homo ludens, I guess is the book on basically argues that game playing is a fundamental part of being human. And, and I think about recently Demis Asabis was on Reid Hoffman's podcast. I can't remember the name of the podcast, but they were talking about Masters.

Of Scale, I believe. Is Reid Hoffman right? Or Scale Matters or something like that?

Yeah, thanks. Yeah, I don't know, it came up in my playlist.

I.

But it was, it was fascinating like that, that move 37 in AlphaGo where it's just all the reinforcement learning and the gamification. I just, you've just been, you've been right on the, on the line with kind of where the technology is going and where it's going now. And I'm, I'm curious with, with all that. I mean, and as you're seeing what's going on with these LLMs and, and how far the, the neural networks have gone, what are you seeing? You know, where, where do we go from here? Kind of where is your focus right now?

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My focus at the moment is around prediction and predicting the outcome of very difficult decisions, especially strategic decisions of enormous consequence where there's nobody, no single individual that can hold all of the different variables in their mind in their head. You know, a company that needs to make a strategic decisions about something that's happening in it, you know, they have high ch. What do you do? Do you hire more staff here? You know, maybe the CRO is saying, well we just need more support folks. The, the product person is saying we need to improve the, the, the, the quality of the product so that, you know, people don't need support or the customer success person is like we need to do proactive customer success so that, you know, folks are onboarded and they, they, they don't check well who's right because they're probably all right in some way. How do you make that Decision. And I think one of the interesting things that the scale and AI can offer us is actually being able to build these prediction machines not just to predict the next word, but to predict far more complex situation or at least support people in being able to understand what the parameters are and be able to then explain what the thinking around a particular decision might be. You're probably right. Ten years ago I was talking about how we were going to have neuromorphic computers and neuromorphic chips in all of our, in our computers within a few years. It took 10 years, okay? Now it's on our phones and it's all on our laptops. I don't know it's been used or not. But you know, and I do like being on the very much on the frontier side of things. But I think in, at least for me, for the next this, this, this chapter, I mean right now what's really interesting to me is how do we apply some of the very latest techniques around causality, around being able to figure out what the consequences, cause and effect creating what if scenarios, particularly around decisions that are of consequence for large organizations where one person really doesn't have all the depth of knowledge and even a group of people may end up in a group think or worse still might ask ChatGPT. You know, once upon a time, if we needed a new idea, Glenn, we would just probably, we'd go in a room, maybe the three of us would go in a room with some post it notes and we'd play some games. Let's do the five whys. Let's keep asking why, why, why until we get to something and after, you know, and maybe we'd use the six hats of thinking and then we'd come out several hours later with maybe two or three ideas that we thought, you know, quite good, sounded quite good, we could pursue one of these. And now, you know, my 11 year old can go onto ChatGPT and say, hey ChatGPT, give me 1 million ideas of how to improve the EBITDA margin on this business and make it sound entirely convincing. And it will. But then how do you choose when the stories are so convincing in the absence of data, in the absence of some analytical thinking and even the analytical thinking that was fine for the ages that have gone the world that was when you're facing into a new world of fundamentally very different possibilities, particularly around the ability to compute in an intelligent way, or at least seem to compute in an intelligent way. What does that mean? I think it probably brings back to focus this need to do More analytics, be more data driven rather than just, well, it sounded really good because once upon a time it sounded good because someone had experience and they were convincing. Now the machine can sound like it has experience. Whether or not it does, it's hard to know. So those kinds of things. And we've seen things like digital twins or synthetic controls. I've done several projects in the past around creating large agent based models where you create synthetic populations that then you can use some mathematical techniques and Markov techniques to be able to decipher what are the main building blocks, what are the causal points. I'll go back to the business case. Look, sales went up when we did X. That's what people see. That's what you see on a dashboard. Well, that's. No, that's, that's. That doesn't tell you why. And it doesn't say that, you know, doing X caused the sales to increase by this Y percent. And here is Y. And I think that why aspect is one that's really interesting for me because when we know the why of something, we can really double down on it and make better decisions. Yeah.

And, you know, there's so much focus now in AI on the generative part of it, but there is still that sort of classical, you know, just machine learning and taking this data and really analyzing data that way. But it is interesting how much better these models are getting. And then I keep going back to generative AI because that's what everybody's talking about right now, which could even be a gateway to, you know, more classical algorithms. It could be, you know, the interface that lets people interact with the models when they can't write Python or, or, you know, whatever they were doing previously.

So, oh, 100%. You know what you can do today with. And I, by the way, I love my vibe coding or whatever it is we call it these days. I love all of this is I have them all. Someone asked me the other day, oh, which one do you have? I'm like, I think I have them all. Because they all have, you know, you know, each like Cursor does these things with these models. And you know, Windbrush does that thing and Lovable will give you this and Gemini will give you that and Dive Deepseek will give you this and Claude will give you that. And you know, each of the models have got their own kind of nuances and peculiarities and it's always good to get a second opinion as far as I'm concerned. And the models goes, right. Ask one model to check the other One, you know, but what's really interesting is what it allows you to do is it allows you to, you know, once upon a time. And I think it's Einstein that's been credited with this, with this quote, which is, you know, it's. It's 1% inspiration and 99% perspiration. Well, guess what? This world just flipped upside down. It's 99% inspiration and 1% perspiration. Because the time it takes for you to build the damn thing is almost. And it will be instant. It will be near instant. If I was. If I had enough money to buy myself like a. A couple of H1 hundreds or whatever they call the GX200, you know, that can do, I don't know, several thousand million tokens per second. The moment you write the prompt for the app, it's finished. It's done. Like you press Enter, it's done. Right. So then it's about, well, what's the inspiration behind it? Where are you getting your ideas from? Are you just trying everything and seeing what works, or are you really thinking about what it is and how, you know, how it feels to someone? And I think we've seen it in art, in digital art and generative art in. So far as it initially is a big rush and everyone's creating images, and after a while, those who are really thinking about the images that they're making, they're telling a story, they're really thinking through what is interesting. So it just becomes a tool to be able to communicate. Well, those are the ones that are really valuable, right? And I think maybe we'll find the same way for software in that we'll just have this enormous proliferation of apps and software in the same way as YouTube. Right now, for every second, there's more than a lifetime worth of video being going online and being shared, and yet even there, there are people who know how to tell a story, who are. Who people want to follow and watch, maybe sometimes for hours. You know, this idea that we've got really short attention spans. Well, do we? You know, maybe. But, you know, people still create long videos and they still watch them in the millions because it's interesting, right? And so I think we'll have. We'll find the same thing. So, yes, there will be a lot more dross, there'll be a lot more terrible videos, much more terrible writing, far more terrible apps. But the ones that really resonate with us for whatever reason, whether they solve out the problem in a better way, but that the experience is one that we really covet and prefer, I think, you know, those will rise to the top. And yeah, I completely agree with you. We, whilst we are still, we're today sitting here with a mouse and a keyboard, you know, there would potentially come a time where maybe that is really no longer efficient. I don't know, I don't know whether I could do everything that I can do just by telling the computer what I want to do because I think sometimes it's easier to jab at something but if, if it could preempt what I'm thinking and you know, if I was, if I, if I'd like to, you know, if I had a crystal ball, I'd like to get to a point where my computer is. And I don't mean this in a creepy way, I mean this in a, in a privacy sensitive way, but it's watching me. It's watching me because it wants to, you know, actually do what I want so it can see where I, what I'm looking at. It can detect from my tone what I mean with, oh, can you just, just like make that a little bit better? Because it knows what I mean. Right. I can't wait for something like that. You know, a tool that is so hand in glove with my thinking and has learned my ways that I really can just flow and create without feeling encumbered by a tool that is kind of getting in the way and slowing me down.

Yeah. And you know, that's, that's Sam Altman's vision of the kind of the infinite context window where in, you know, in OpenAI just turned on memory across all your chats and everything. So, and as they expanded their own context window. So I think, you know, that's the way that it, something would be truly helpful. It knows it's, it's like targeted ads on steroids. It's because it's. Yeah, but for every aspect of your life because.

And you're concerned about privacy and something knowing everything about you now just wait as long as it's a good way. I mean there are scary things around that, but it can be incredibly helpful at the same time.

And you know the problem, My problem is my younger daughter insists on using my, my OpenAI chatgpt for all kinds of making up stories and talking about capybaras and all kinds of stuff. So whenever I talk, that's my daughter too. Right. Okay. So if you say create an image of my life based on what you know about me, there's like little toys, there's all kinds of strange things. It's like, oh, you know, based on what. What you know about me. Right.

You didn't assume you were a female. That's what it did for me when I asked.

This is it. Right. So, you know, I think, I think it's good. I think it's fun. But yeah, I think that these machines, we are very, very predictable. Right. What we do, how we think, you know, as long as, as much as any of us, me included, would like to think, oh, no, I am the master of my own destiny, I am making new decisions today. Oh, no, I'm not. I really am not. I'm a highly predictable individual with the same sort of. Ah, he's reading the New Scientist again. Yep. No, he's going to be reading. He's going to be on the slash science Reddit now. He's going to be reading into that thing that. It's just, it's going to be the same thing. Right. And I think that's. Yeah. That being able to. To your point, Paul, around advertising to us, I think, you know, we saw that with, with WhatsApp and Facebook, listening to people's conversation and then shaping the adverts that are sent to you or the stories we hear about people, you know, preempting someone's pregnancy without them even knowing it because of the changes in their habits. I think those are quite creepy. But I think probably we've seen nothing yet. Right. It's going to become so personalized that we'll probably feel. And if it can be done in a way where we don't feel threatened, but it feels supportive, I think that's the real. Can we do that? I don't know. But that's, that would be the, the, I think the next, the playground for something like a Google if, if it could indeed be Google, but you know, the kind of a. The advertising world would, would want that so that the adverts that we see are totally personalized to us, not just because of the cookies that we have of the webpage that we went to, but because of a much deeper understanding of the factors that motivate us to make decisions. And so I think that's quite interesting whether it could be misused. Yeah, I think it probably can.

Right. The Internet's been an issue. Technology can be used both positively and negatively. That's never going to change. I mean, right. It's like hackers. You build something to keep them out, they find a way in. You build again, they find a way in. You got really smart people on both sides. And I don't see, at least in the short term, maybe in some distant future we might have some solution. But I don't see it anytime soon that you can have something just be used for good.

Absolutely. I think that would be nice. A kind of a society of not just universal basic income, but universal generous income where everybody gets to have all the things that they want is a generous income. You know, that kind of the Star wars. That would be a lovely thing. I've read far too much about human behavior, the struggles around power and dominance to fool myself into thinking that's going to be an easy one. But we can try.

Yeah, I mean it would be great if everybody had what they needed and we lived in a world where we were all content, but we all know human nature and I don't think any of us plan on that anytime soon. So I want to ask a question first about your company that you recently built. So you're working on a company called Causalpha and you talked a lot about this earlier about decisions and how you're using AI to help big organizations. So causal, it feels like that's kind of what this company's doing, right? Data driven value creation for PE backed temp companies. You're really helping that make better decisions with predictive analytics. Is that kind of the purpose there, what you talked about earlier?

Absolutely, that's, that's entirely, you know, private equity is an, is incredibly effective there. You know, there's some very, very smart folks who, who bill. Who buy and scale businesses in a way that that business was never going to be able to probably do by itself. But even the best firms, they have this fundamental challenge of certainty. You know, how do they know how are they sure about a particular value creation is actually going to move the needle for that specific company in that tight timeline. And I think that's well, and that's where I'm building right now to create those causal discovery algorithms that help you and you know, adapt it to the business context so that you can do scenario modeling so that you can actually have a tool that starts with ingesting some of the information and some of these and this is quite frictionless and some of these algorithms are quite data sparse. You don't need to have a huge number amount of information right. In order to create hypothesis and then run simulations on top of and then be able to with go a little bit deeper in terms of the causal inference. So I mentioned the digital twin, but a digital twin of the company and perform and seeing how it would perform with or without a particular intervention. What that might look like in order to move away from just a pure gut feel. But I think I need to stress here that this isn't about replacing all those many years of experience and expertise that folks who are in this space have. Is actually the vision is to support better decision making. Having more predictable, more efficient, data driven decisions around things that are not easy, around product decisions, around particular trade offs that you might have. And here I'm not talking about purely financial engineering. I think the financial engineering time space is very well understood. It's almost commoditized. You know, of course you're going to run a model, of course. Right. But I don't mean that, I actually mean anything. Everything outside of that that is to do with actively building or refining particular capabilities of a business, applying data to that and sort of data insights to that. And to that end I'm pretty convinced that within a few years we're going to see this causal inference. Why something happens to strategic validation across the board. It's not going to be a novel thing. I know right now the top tier funds already do this but they can afford to have armies of data people. What I'm really interested in is bringing this to, democratizing this, actually bringing these solutions to if you like the lower mid market where you don't have half a million, two million to spend to figure some of this stuff out you actually want to have, but you want to have access to this kind of insight. And I think that's a, this is an interesting, I find it really interesting because it's really challenging.

Love it. Thank you for sharing that. That's great. I think we're going to move on to a fun section we have. This is one we kind of surprise our guests with. So he's like what am I in for? So I'm going to let Glenn do the first question on this one because I've been hogging it lately and been going first. So I'll let him explain it and then I'll ask my question after him.

Yeah, so I actually wasn't set up for the, for doing my question. But I'll tell, I'll tell you what what we do. So we used to do like a bake off every week where we would have, we would try chat GPT vs Gemini vs Bing back in the day and all that. But then we just, we kind of settled on and they're, they're so similar right now, all of them for all this. But we, we take your, your profile and your, whatever information bio we have on you we load it in there and we say come up with some fun questions about this. And so Paul does his a little bit different but then I, I then take it and feed it into which is what I'm doing right now. I normally do this while Paul is talking, but I, but I go into another GPT and I say I can only ask one of these questions. Typing this out live here.

He asked which one should it ask?

So I'm going to do that now.

And which tool are you using?

So I'm using Grok again. I've been playing around with this more. I paid for the upgrade. I'm trying like, like you were saying earlier, Cam, I feel like between replet, between Manus I'm now on the paid version of Manus I'm on. I mean, I don't know, I'm, I'm, I'm sort of just blissfully ignorant and willfully ignorant of how much I'm spending every month on these subscriptions.

I bet between the three of us we can get to over 500amonth on these things.

Well, no, I, I'll tell you alone, I'm paying for the $200 chat GPT so I've got us almost halfway there.

So yeah, I think you two are both going to have me beat by quite a bit. I think I'm around 60amonth, maybe a little more. You know, I have about three different tools I pay for right now.

All right, I'm going to tell you before I tell you the question. These and these responses are getting better. It says to get the most engaging and re. Revealing response I recommend asking. Well, let me tell you what it says. This question stands out because it's imaginative, open ended and invites Cam to share a mix of creativity, personality and personal interests. And it goes on like that for a whole other paragraph. But, but I'll skip all that and I'll tell you what the question is. If you could build a secret room in your house, what would you put in it?

If I could. Okay. I mean, I would make the holodeck.

Sure.

Because like, you know, why not instantly I have every room that imaginable in my room. But assuming we can't manipulate, you know, the quantum fabric of the universe with that assumption in mind, if I could build a secret room, and this is going to sound very boring to most people who don't live in central London, but I would put in, and this is very personal to me, I would deck it with just, I don't know, as many layers of soundproofing and I would turn it into an anechoic chamber so that I could for maybe 10 minutes a day, just go and sit in it in complete and utter peaceful, peaceful silence with no noise from the many millions of inhabitants who seem to just be outside my window most of the time. That's what I would do. I'm sorry if it's a bit.

My daughter would love that room. Some peace.

I was going to say someone who lives also in the heart of downtown in my city. Not quite as many people as London, but with the ongoing God knows what kind of vehicles and people fights outside, just drunks yelling around outside. It's like if I could just go. And we, you know, get glass walls everywhere. So it's. If I could just go shut myself in somewhere, I'm right there with you. So great answer. And I think Grok. Grok picked a good question.

All right. So I take a different approach. I'm not quite as sophisticated as Glenn is what it boils down to. So you got two options here. I use a number generator and you can pick it and I can randomly pick a number, or you can pick a number and it's between 2 and 25 because Glenn used question one. So one's off the board. You could pick the number or the random number generator.

Let's pick 25.

25. No idea what question.

Find out the alpha and the omega.

All right, I like this one. What's one ambitious goal you still have on your bucket list? And then it adds this. It adds a little bit more that might surprise our listeners.

Well, let's see. It may or may not surprise some of the listeners. My one ambitious goal is to list the business on the stock exchange. That's what I would like to do. I have had the pleasure of ringing the bell, but it was not my company. I would like to do it when it is my company and for no other reason than it's just fun. No, there is no than that.

If that happens, when that happens, we'll bring you back on the show to talk all about it.

Yes, please.

We already have a future date set up.

I thought you were going to invite yourself to accompany him, Paul.

All righty. Well, thank you so much, Cam, for joining us. It's been a real pleasure. I've enjoyed it and, you know, love all the learning you've done around AI and the knowledge you're able to share. So thanks for joining us.

Wonderful. Glenn, Paul, thank you so much for having me. It's been a real pleasure talking to you. That's a. You've made my Friday afternoon, so thank you.

You're welcome.

Likewise.

Thank you.

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SA.