Follow The Brand Podcast with Host Grant McGaugh

Why Your AI Strategy Is Wrong: The $2 Trillion Bet on Imperfection

• Grant McGaugh CEO 5 STAR BDM • Season 5 • Episode 33

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Have you ever wondered how the future arrives faster than anyone can predict it? Tech futurist Richie Etwaru joins us to explain why "futurists are out of work" in a world where technology adoption happens so rapidly that five-year predictions are tested within months.

Richie breaks down why artificial intelligence has achieved unprecedented adoption rates, outpacing even the internet itself. The perfect storm of pre-existing infrastructure (mobile devices, internet connectivity), brilliant productization through simple interfaces, and our post-COVID appetite for technological advancement has catapulted AI into our daily lives with breathtaking speed.

The most profound insight emerges when Richie explains the fundamental shift from deterministic to probabilistic computing. Since Newton's time, we've operated in a technological world where 1+1 always equals 2—precise, narrow, and certain. Now, probabilistic AI systems offer vastly expanded capabilities but without guarantees of consistent outputs. "We are trading off a high friction, perfect, narrow, deterministic experience with a low friction, wide, imperfect experience," he explains with remarkable clarity.

Those concerned about AI "agents" taking over will find Richie's perspective refreshing. What appears as autonomous intelligence is actually just a probabilistic layer working between deterministic systems—a "model context protocol" that creates the perception of agency where none truly exists. This distinction helps ground our understanding of AI in reality rather than science fiction.

Through his work with Mobius, Richie is reimagining user interfaces—transforming them from self-directed experiences to guided interactions that leverage AI capabilities. This shift creates "the perception of personhood" in digital experiences, fundamentally changing how we interact with digital content.

Ready to navigate this new technological landscape with confidence? Listen now and discover how understanding the probabilistic nature of AI can help you thrive in this rapidly evolving digital world. Follow Richie's forthcoming insights in Wired, Forbes, and The Economist as he continues to demystify our technological future.

Thanks for tuning in to this episode of Follow The Brand! We hope you enjoyed learning about the latest marketing trends and strategies in Personal Branding, Business and Career Development, Financial Empowerment, Technology Innovation, and Executive Presence. To keep up with the latest insights and updates from us, be sure to follow us at 5starbdm.com. See you next time on Follow The Brand!

Speaker 1:

Welcome everybody to the Follow Brand Podcast. This is your host, grant McGaugh, and I am going to speak to what I call one of my most favorite futurists when it comes to technology that I've spoken to probably in the last four years. And why do I say that? I say that because I've been part of the HEMS organization, the Health Informatics System Society, and that's in healthcare, that's in technology, and I was able to meet a gentleman named Richard Edwaru and he was talking to me about different kinds of technology. Remember this was three years ago. Ai had not really blown up.

Speaker 1:

You know these LLMs. It wasn't blown up then, but he framed discussions around how technology and nation states and where the power plays are where people will fall into in a way that I've never heard before, and his complete understanding of how artificial intelligence actually operates at a simplistic level. I thought was just an honor to hear him speak. So I want to have him speak again to me and you about how he sees this new world and how we can all take advantage of it. So, richard, you'd like to Richie I should say you'd like to introduce yourself.

Speaker 2:

Hey, first of all, Grant, thanks for having me and hello to all of Grant's viewers. I had an opportunity to meet this man about three years ago and I got to tell you, one of the hardest interviews I did with him at that time. So I look forward to being battered around a little bit here. Just a little bit of background on me I live in the US. I've been living in the US probably for a little bit over 30 years now.

Speaker 2:

I was born in Guyana, south America, ex-british colony, probably 51% tech, you know, 49% business operator.

Speaker 2:

I've done a ton of stuff with transforming businesses, going through IPOs, starting companies, working in private equity, writing books, doing speeches and stuff like that. I think the one thing I would say to start this conversation is that I've been referred to as a futurist over the last maybe 25, 30 years and have made quite an impact in that area. But you know, today futurists are out of work because the future is here and even if you try to forecast the future, it's moving so fast that you're being held to your work. So you know, five years ago, if you're a futurist you could forecast out 20 years and if you're wrong, nobody cares, because you would have probably retired by that by then. Right now, if you're forecasting out five years out, we're going to find out whether you're right or not by December, right? So the dynamic of managing these horizons of time right when you think about how time and culture and industry moves together. The pace has changed, which is changing everything, something as simple as whether you're a futurist or not.

Speaker 1:

I agree with you. Another colleague of mine, christopher Lafayette, brought something to my attention when he said and this was COVID time, right, this is like 2019. We all had to participate in a human experiment, whether we liked it or not, participate in a human experiment more that we liked it or not, and we had to test our tech. Meaning cloud computing, yeah. Mobile computing, yeah. Now we're starting to see can we do it at scale? When it came to communication, video transfers, we started to see the rise of metaverse. I mean, we can totally live almost in a digital type society and do trade and commerce within that society. That was the metaverse where we have morphed since then. We're starting to like flex our muscles a little bit. And I'll coin a statement.

Speaker 1:

You told me we're releasing a new like an animal called almost a species, an animal into our world Artificial intelligence. We are releasing this out. It's been the most adopted technology in human history. It's just huge Faster than cloud computing, faster than mobile computing, faster than electricity From your lens. What you're seeing now because what I'm seeing coming on my horizon is a genetic AI. You came out with the tele. I'm starting to see others come out with certain tools that are coming. Hey, this is an all-in-one tool. It can interact with you, it can speak to you, it can do certain human-like functions. To take what I'd say tier one, tier two, off the table, it's blowing up the BPO world. How do you see this technology operating?

Speaker 2:

Well, it's a broad question. I'm going to try to answer it the way you eat an elephant, one piece at a time. Just for, I guess, a little comedic relief. I would argue that AI is not the fastest adopted technology in the world, okay, but emojis were the fastest adopted technology in the world. As soon as you saw an emoji, you knew what to do with it right away and you were absolutely fluent. Now, yes, of course it doesn't have a gravitas on the implication as artificial intelligence, but again, for some comedic relief, here on the Brett Madrona show, so to speak, there's a lot that has already been said, so let me try to focus on, maybe, what ought to be said that might've not been said. Yeah, maybe what ought to be said that might have not been said, yeah, one of the reasons why AI has been adopted so quickly, and maybe we'll cover three reasons why the speed is there.

Speaker 2:

First, you know, technology is something that builds on itself, and if you were involved in technology at any point, you must have seen some experience where the thing that you were doing at the time was then used to create the next thing that you're going to do and was used to create the next thing that you're going to do. So, even as simple as like the Fortran compiler right In some ways, c++ was written in Fortran right. Java was written using C++ right. Javascript was written using Java right. So, like you, use the tools that are commoditized to create the next frontier, the next advancement that creates competitive frontier. Right Now, if you think about the internet, a lot of plumbing and infrastructure had to be in place for someone to consume the internet Wires on the ground, fiber optic cables, cell towers, chips, electric capacity hardware had to be shipped, multilingual things had to be figured out before you got the internet. So you know. If you think about any third world country I'll just pick Guyana for a second you know Guyana got electricity about 40 years after Europe and the US got electricity right. So you've kind of you kind of are left behind for a while.

Speaker 2:

Artificial intelligence however the platform was already there. You had a mobile phone, you had internet already right, and so you know the 5 billion people that are already pre-connected to the internet and have a mobile were able to uptake AI. From the time that you found out about it to the time that it takes to either hit the open AI mobile browser application, which was the first or download an app from the App Store, right. That was the uptake time, and that's why you see this massive uptake of individuals. You see this massive uptake of individuals and I think what's the next point as to why I think the adoption was so easy is that OpenAI did something that is the core of the craft, which is to productize it right.

Speaker 2:

Artificial intelligence, as it relates to foundational models and it relates to machine learning and deep learning, was there for a while, not as good as the foundational models that OpenAI's foundational model set forth, which started a competitive race. Not as good, but what was there was that it was never productized for mass consumption. If you wanted to run a model prior to chat GPT, you kind of had to have Math Lab running, right. You had to download a bunch of stuff on a desktop, you needed to understand how the flux capacitor of every button works, and then the outcome was a large text file with weights in it. It's just not accessible. What OpenAI did, which is to start a for-profit company to be able to create funding to fund the nonprofit company, was to consume their models and productize it as a chatbot, and that productizing of a chatbot is, to me, the brilliance of OpenAI, not necessarily the core LLM underneath, although they did start the arms race by differentiating significantly when you make a product that everybody already knows how to use they know how to chat.

Speaker 2:

Who here doesn't know how to send a text to someone and get a text back? Know how to send a text to someone and get a text back? Then to find the right place to do it, which is these language models, these foundational models, and I want to talk about probabilism after this. They're imperfect. How do you productize imperfection? A chatbot is a really interesting way to productize imperfection, because there's no good answer, there's no perfect answer. That has to come back Right, and so that's another reason why we saw that speed of adoption of it.

Speaker 2:

And the third is something that you'd mentioned earlier in your preamble here to this question, which is mankind now has a thirst for technology and the future. Mankind now has a thirst for technology and the future. When we went through COVID, everybody, from my mom to you know the guy who works in my local hardware store here in a small town that I live in everybody saw the power of technology, whether it was through Instacart deliveries or the forms that we all filled out to get vaccines earlier or going to. I forgot what was the website that gave you all the stats of how many deaths?

Speaker 1:

Oh, yeah, I think it was the US government, or one of them, whatever it was right.

Speaker 2:

Everybody got to see the value of technology, not just the sort of soft, flimsy sort of magic of it or the excitement of it or the dopamine hit of it or the vanity advancements of it, but the actual utility of it. Now, and we've all as an aggregate I would say as an aggregate we've tripled our awareness and our appreciation for the utility of technology. We come out of COVID. Everyone's like okay, where's the future? This is great.

Speaker 2:

You guys have been talking for tech so long. We're ready for it. What's up? Where's the future, man? So you have that thirst sort of waiting in society to adopt this, and I think it's those three kind of pieces of that storm that came together that created this massive transition of human awareness and this reminder that we are evolving. What AI really reminds us all at the core is that we are an evolving species, we are constantly evolving and we're all living through the Internet again, we're living through electricity again, we're living through the printing press again, and so everyone, I believe, on the planet in some way is now aware of the fact that we're living in a pretty pivotal point of history.

Speaker 1:

There's no question about that. I call it a leveler, let's say, like that of economies of scale, where, like, let's say, if you're an oil baron, let's just say you know you had a certain amount of control. You know you had a certain amount of control. If you're a real estate bearer, you had a certain amount of control. Technology has now given a whole new level of control to a whole new set of individuals that aren't oil bearers. They don't own, you know, massive real estate, but they have massive value. For the first time in human history we have an evaluation of a company of a trillion dollars. That's almost unbelievable. When you think about it, people say, well, you know, it's taken certain people a long time to get what you would call economic advantage. And then I say you know what a lot of these companies right now, and then NASDAQ, when you look at the Fortune 5, those top 10, and we call them the magnificent seven haven't been around for hundreds of years. These are newer type technological advancements that have taken these companies to a whole nother level.

Speaker 1:

When I think of Mobius this is, in my opinion, when I think of when I first saw the AirGlass, I said, all right, somebody's actually thinking out of the box, because it's almost like when I saw the Google goggles that I could say they came out for a metaverse view and it's just I don't know. It reminded me of the 8-track. I don't know how long that's going to last, but we'll see the 8-track player. But you said, hey, let's take the glasses off and still have what we want, which is digital interaction in a physical space. It's kind of an expanded awareness of what we can do. Right, very cool stuff.

Speaker 1:

Now you've come out. I mean, this is a big word and this is also taken. First time I heard about genetic AI was probably about a year ago. Gentic AI was probably about a year ago and it's just another explosive type tech where people are looking at it in many different ways. You've taken it and I like your viewpoint. You're taking like hey, and I go back to the Microsoft and IBM world. I'm not reinventing a computer, I'm making it easier to use. You seem to be making this kind of tech easier to use.

Speaker 2:

What's your opinion on these things? Well, look, my career has been about reinventing user interfaces my entire career. While I was at Prudential Securities, which is now Wachovia, I worked extensively on connecting the CICS mainframe green screens to the Visual Basic compiler to start to build point-and-click apps for brokers to be able to have information, faster and better workflow as they serviced investors to the brokerage business of Prudential Securities. And that was the first patent and you know it got very interesting between Microsoft and IBM. But that was my first time recognizing that user interfaces will change. The second time I did that was at Lehman Brothers where reporting so client reporting was very static. Everybody got the same PDF, the same design of a PDF, and you know I focused my energy there on figuring out dynamicism of PDFs. How do you get a banking statement that has a pie chart of your asset allocation on page one but somebody else gets a bar chart of their asset allocation on page one, but somebody else gets a bar chart of their asset allocation on page one because they want to see their data differently or their portfolio is better displayed as a bar chart versus a pie chart. And that was the second time I kind of reoriented user interfaces from static reports to sort of personalized reports Again another patent, another kind of partnership with Adobe, and today everybody has personalized PDFs. I did it again at UBS I won't bore everybody with that around working with Apple to put the first wealth management app on the iPad at the time of the iPad unveiling by Apple when you met me. The thesis there was that the screens will become more engaging, and as I was founding the company, I had decided to call the company Meta M-E-T-A, and right before I set out to kind of start, you know, taking the company forward, I set out to kind of start taking the company forward.

Speaker 2:

Facebook renames itself to Meta, and I had no interest in the Metaverse. I was pretty sure that we're not going to strap toasters to our head for a pretty long period of time. And so in some ways, zuckerberg irritated me to get into the metaverse by naming Facebook meta. Now, I'm nowhere the size and scale of Zuckerberg. He doesn't know that I exist, but that's what happened. He irritated the hell out of me, and so I thought well, what's this stupidness anyway about the metaverse? Why would anybody do this? And so that was the first time I actually thought, you know, maybe there's something here and I looked at it and I said there's no way the headsets work. We ought to be able to get the screens to give us more. And the long and short of it, 40 patents after I was able to devise a way to use computer vision, to use the screens that you and I are looking at right now to be immersive and interactive at the same time, and essentially, I built a version of the metaverse that didn't need the headset. I'm really not interested in that. And so as the metaverse winded down and we were using computer vision a lot, I started going back to my original thesis which is why I founded the company which is how can we get more from the screen? It has nothing to do with immersivity and interactivity, and so today, mobius is back to its original thesis.

Speaker 2:

I got sidetracked by Zuckerberg, who irritated me on the name, and where we are today is that I am looking again at the user interface. One of the net nets of artificial intelligence is that the user interface is going to change, and you know, to put it into context, google changed the user interface by putting a search bar. The user interface of AI is going to be a microphone icon. That's it Top, turn it on or turn it off, the mouse and the keyboard will still have a place, similar to how engineers have a place in the IDE today, right or in black and white. You know terminals, and so what we're doing at Mobius today is we're really focusing on that user interface. What is the intelligent user interface? Now, I won't talk too much about that solution. I can connect with folks afterwards.

Speaker 2:

But the value for your audience is to try to parse the artificial intelligence market today to know where you want to make your bets and generally, when these big industrial revolutions come about and you were talking about oil, it's the same thing. You can parse it into three verticals. You've got the producers of the new utility, You've got the appliers of the new utility and you've got the consumers of the new utility right. So you've got the producers of AI right and a whole supply chain in there. I put the silicon and the training in the production bucket. So NVIDIA and OpenAI I have them in the same bucket, so to speak. Right, hyperscalers I kind of pop them in there as well. The appliers of artificial intelligence is where I sit. Let's take the core utility that's being produced by others. By all means. Pay them their cogs, make them a part of supply chain, but apply it to do something completely unique and different, right? Not dissimilar from how you know. Amazon applied the internet without spending a dollar on building the internet, right. And then you have the consumers of artificial intelligence. What's also different here, and how your audience wants to navigate. Whether you're a producer of AI and you know, be smart about your capital investment there. Whether you're a producer of AI and be smart about your capital investment there and who you're competing against, and whether there's a David and Goliath game to play there, I argue that there is a very low likelihood that a David and Goliath game could be played out on the production side. I feel like that side is already locked up right On the applier side. Man, a lot of David and Goliaths are playing out on the application side right.

Speaker 2:

Large software companies that have been underserving their constituents for decades are now under attack because people are like wait, what the hell? Why am I paying for a SaaS license that I'm not consuming? Where the interest is is on the consumption side of artificial intelligence, and the consumption side has two things about it. One is you can consume it to build businesses really fast I'm talking about ridiculously fast, especially if you're building businesses that have some core digital component to it, right?

Speaker 2:

And why is it that language models make it faster to build companies that have digital components? It's really simple. Language models were trained on the internet, and the internet is not only full of information, it's full of software code. When you read a website, there's JavaScript in there, there is cascading style sheets in there, there's HTML and DHTML in there, right? And so now language models are not just good at our language, they're good at the language of the machine, and so building apps, building websites, building workflows, building marketing streams are all as easy to kick out as the answer to you know what color is the sky.

Speaker 2:

And so that's what makes this consumption side so much more interesting, because it lowered the barrier to entry, and the other part of it is the productivity hack, right? Not only is it helping you build businesses faster, it's helping you do what you do today faster, and the internet didn't have that acute differentiation on the consumption side. Yes, it commoditized distance significantly, but you can't build a business because you were just able to commoditize distance. You still had to go through the mechanics of a highly capital-intensive sort of thing. I think, when you look at the AI market from that perspective and again I'm playing in that applying AI layer for the benefit of the consumer's AI layer, because I see the user interface transitioning from where we are today to where it's going and that's where maybe we could talk about probabilism in that user interface, but I think for an audience in that user interface, but I think for an audience, I think that that is a reasonably good way to evaluate risk and opportunity within where we are today.

Speaker 1:

I want to expound on what you just said, especially toward the end, understanding. At the end of that whole thing, you have a human and then you have what we are calling now AI, artificial intelligence, or what I'm calling now a better technology. It's a tool. Now I say this, remember this In our human history most people agree humans took a great leap in their environment when they discovered fire.

Speaker 1:

Ai is a type of fire and if you know how to apply the fire because you can take the fire, you can burn the forest down or you can cook your food If you know how to manage the fire, you're responsible, accountable. You understand the nuances of the fire, you know what it could do, what it couldn't do. You know that, hey, if I put water on the fire, the fire is going to go out. But the fire also consumes oxygen. It consumes a lot of things People don't realize that a human being does. No, you've got a. Fire is a living thing, right.

Speaker 1:

So I said it to say this we are now starting out with, from the human component or reality, operating with a, a technology that everybody can now utilize and, to your point, I myself can make businesses where before I didn't have that capability. Right now we're on an interface you didn't have. You know you have to have mass resources to do radio, to do television, to do mass media. The barrier has been significantly lowered. So if you, as a human, knows how to operate or drive, I'm going to say this technology, this AI, and you realize, like you said before, the building blocks are already in place. So you don't have to be a coder, you don't have to understand how the Internet actually works to a certain degree in the back end, but you gotta know how to draw. And if you're making it easy for me to get behind the wheel of the ai engine, I see incredible transformation that can, that can change the world as we know it, and I've always said this.

Speaker 1:

I have a lot of younger people that ask, asked me a lot, and asked my opinion like well, grant, what do you think my purpose is and I always say this especially to younger folks your purpose is to change the world. I've already lived in this world for 60 years. I know what it's like. I've gone through changes in how we do things. Your job is to change it even further. I'm going to point this to you because I like how you had that comic relief right around the emoji right. I remember sitting there watching the Jetsons, and they would have a communicator that looked a whole lot like your air glass, you know, and it would just be an image out of thin air that you would interact with. I truly believe that man's imagination, when it could be productized, could be one of the greatest advancements in human history. So I want you to, if you don't mind, help me unpack some of this, because what I'm saying is this Human plus AI could be the biggest game changer on the planet.

Speaker 2:

Yeah, yeah. Well, I think, based on what we know today, you know I never underestimate 100 years out, you know, there could be something even bigger and no one knows what it is and no one knows what it is. Arguably it could be interplanetary travel, right, could be even bigger. Again, thinking about this might be an organics, a second organic species, not a first digital species, right, as we refer to it here. Let's talk about the trade-off that we're making, and I'm going to focus on the trade-off specifically from an enterprise and a technology perspective. I think your audience sits in that center of gravity a little bit. So there is a fundamental difference between what we did with the Internet and what we're going to do with artificial intelligence, as it's specific to generative artificial intelligence. There's many other types of AI. Computer vision, for example, is a different story, but when you want to come to terms with what is different, I would suggest that you start here.

Speaker 2:

In the internet world, we used deterministic mathematics and in deterministic math, 1 plus 1 is equal to 2. And you can guarantee that 1 plus 1 will always be equal to 2. You can guarantee it With generative AI, which is probabilistic math not deterministic math, but probabilistic math. You cannot guarantee that one plus one will be equal to two every time. You can't, because it's such an obvious thing. One plus one will be equal to two every time. You can't, it'll be because it's such an obvious thing. One plus one will be equal to two a lot but you can't guarantee that it'll be every time because it's probabilistic.

Speaker 2:

There's no such thing as a right or wrong answer. The only thing that's true about probabilistic math is that it must have an answer every time, and so that's why, when you ask a probabilistic model for something and language models are sort of like the codfish example everybody knows what I'm going to use here. But let's take an image model. If I ask an image model to create me an image of a blue and red dog, it'll give me an awesome blue and red dog. Try to get that same dog again.

Speaker 1:

Yeah.

Speaker 2:

Zero chance to get that same dog again. Now we have the same thing in language models. Ask it a question. If it's not too obvious of a question, try to get the same response every time. Zero chance.

Speaker 2:

And so, living in this probabilistic world right, where you're treating off a deterministic relationship with technology for a probabilistic relationship with technology, now you start to see the difference of the mechanics of it. For example, if we just take user interface for a second, in a deterministic world you have an application that could do about 10 things, okay. And you must move your mouse precisely to here to click on this one button to do those 10 things. And you must wear your reading glasses to read the little text that's on the side to know which of those 10 things to do those 10 things. And you must wear your reading glasses to read the little text that's on the side to know which of those 10 things to do, okay. So there's a high degree of friction to a deterministic relationship with technology. Okay. However, it's guaranteed that it's going to do 10 things and it's going to do those 10 things exactly the same way every single time, bar none.

Speaker 2:

In a probabilistic relationship with technology, the number is not 10. They can do a billion different things. However, the perfection is not there. Okay, so we are trading off a high friction, perfect, narrow, deterministic experience with a low friction, wide, imperfect experience, and that's the trade-off. It's because you've got probabilistic math on one side and deterministic math on the other side. Now the artistry and the craft is to figure out the balance of those two to be able to make something of utility for mankind. The challenge is that as soon as you add one ounce of determinism into a probabilistic system, you lose a significant amount of the probabilistic value, like, if you say, to a generative model. In this scenario, you must generate the same thing every single time. You essentially stifle the intelligence to the ground Right, and that's the core that we have to understand.

Speaker 1:

I'm glad you brought me back to one of my most favorite things I ever studied in high school, which was physics. I love physics. I wasn't great at it I got actually a C in the class but it was my favorite class of all because of those things you just talked about. My teacher would tell us all the time there is no right and wrong answer. I'm more interested how you got to your answer than what the answer actually is. I want your critical thinking and and and come up with something that's interesting because you can get outside the box, just like you were saying so sometimes and I always liked.

Speaker 1:

I think Einstein brought it up where, as you pointed, one plus one equals two, but in his world it doesn't. It equals what we call three. What do you mean by that? Because you by yourself, you're one entity. Me by myself, I'm one entity, and if we both operate in those separate worlds, that're one entity. Me by myself, I'm one entity, and if we both operate in those separate worlds, that is one plus one. You could kind of like two, but we come together.

Speaker 1:

There's a third reality that comes about. I'm going to call that this podcast discussion right Now. We both could never really determine exactly what we were going to say and do and the answers, but it becomes a third reality. That's that plus one. I believe there's a plus one in everything in life. There is, it's just are you bold enough to engage with that reality?

Speaker 1:

As I said earlier, the human plus the fire. If you know how to manage the fire, it's a great tool. But if you don't, you know, you don't know exactly how it's going go Ask Elon Musk in his last billion dollar explosion that happened. There's a lot that could potentially go on. So, as men, we're starting to get out of our deterministic world to a certain degree, because everybody likes to stand on solid ground, right. But now you're saying I'm going to stand on solid ground, right, but now you're saying I'm going to stand on something I'm not sure if it's solid, could be or couldn't be, but how do we get there? It's going to be a very interesting feature, I believe, and a very near feature.

Speaker 2:

I want to just follow on to that very quickly Go ahead.

Speaker 2:

I was doing some work with the board and to engage the board, I started by asking you know, deterministic math was arguably discovered by Newton in the 1650s. 1650s or 1850s, I don't remember a 50, it won't matter to the story. 1850s, I don't remember a 50, it won't matter to the story. Um. And then I asked when do you think probabilistic math was invented? And everybody was wrong, everybody oriented to much later in history. You know like within the last 50 years, right, people make bets there. It turns out probabilistic math was arguably discovered by pascal within 10 to 20 years of deterministic math from newton, really around the same time in this thing that you said that that we need, we need, um, determinism as as a species. Um, what happened at the time? Is deterministic math picked up because the scientific revolution needed determinism and probabilistic math never picked up because of the simple argument that God has to exist. There can't be any question around whether God exists. And so probabilistic thinking and probabilistic math, because there are no guarantees in the systems, ended up to be seen as like a little bit of a dark arts, if I may right, a little bit of like a spooky kind of situation, a little bit too gamblish, and society at the time was not ready for probabilism. You know, are we ready for probabilism today? And it's going to come down to a couple of things. It's going to come down to one what are the areas where probabilism works? And let's touch on how did we get to agents? How did the concept of agents come about, which led to the largest word washing of my career. Talk about cloud washing and metaverse washing and mobile washing and internet and online washing. I've never seen washing like this, an online washing. I've never seen washing like this. But when you think about the deterministic systems and the probabilistic systems, again, right. And you say, okay, so a lot of our utility to build things that people will use sit in a deterministic world, right now exposed by API world, right now exposed by API, right, yeah. And then you've got this deterministic UI which is, if I click buy, it's incredibly determined that you must go call the buy API, right. Or if I click sort, you should go call the sort API. So this very, very rigid relationship between actions in the user interface and execution of that intent in what we could argue to be called a system of records, now a concept evolved in between the deterministic user interface and the deterministic APIs and in that space in between is called model context, which is if the UI is going to be driven by a language model or a conversational model, right, how does that probabilistic UI call deterministic APIs? Yeah, how do you know that someone meant to buy? Because the intention to buy is no longer determined by clicking the buy button. There could be a million ways that you indicate that you want to buy. You could say, all right, let's go, and that means that you want to buy. You could go, I'll take it, and that means that you want to buy. Right.

Speaker 2:

And so model context protocol is that thing that says figure out what that person said and then, based on all APIs that you have available, that's where the model context, the MCP servers, come in, which is hey, what are all the APIs that are there and how are they interconnected? Describe them in an MCP server so that when the language model hears I'll take it, it does a translation of that. Look at the API and go. The only API that makes sense here is the buy API.

Speaker 2:

So the MCP, this model context protocol, is this membrane that's sitting between APIs and between a UI that's starting to expand, that uses probabilism in the middle of two deterministic systems, and that's where the perception of agency is coming from. There's nothing sentient or agentic about a system. The concept of an agent is horseshit. That probabilistic layer in between is creating the perception of autonomy. How could it have known that? When I said I'll take it, I meant buy. It's really simple. There's nothing agentic about it. I haven't met an agent as yet, other than, like you know, my agent at the airport when I'm trying to change my flight. I have not met an agent as yet, and I'm deep and wide in the space. It's complete horseshit.

Speaker 1:

I love how you said that.

Speaker 1:

I think what and how I look at it is like this and we're going to conclude here, but I had the same, similar, somewhat, not as technical as you just described it, but what I was explaining to people was like, when we're talking about in an AI-driven world, we're talking about something that's just mirroring human intelligence or behavior to a certain degree, as we understand ourselves. So you're asking something. Here's the three things.

Speaker 1:

I look at Something that is unconscious, that is, the AI itself. It's unconscious to someone who's conscious, which is yourself. But then also you are operating in a subconscious world a lot too. But then also you are operating in a subconscious world a lot too. When you start talking about how are you going to understand behavior or what drives you, it gets into deeper layer. So then you start seeing, yeah, in our world, in the deeper context of what you call the natural world and how we operate in everything around us, it's not so much deterministic. There's a lot going on, a lot more intelligence than just human intelligence that we're operating in. But we're starting to discover this and then we're going to see how we're going to interoperate with it, let's say, collaborate and understand how all this is going to work together.

Speaker 1:

But you brought up some very, very good points and that at some point in time, hopefully in the near future we're going to have another conversation because we're ever evolving. You're ever evolving and before I conclude, I want you to just kind of sum up what you feel about this particular podcast. How do you feel about the Follow the Brand?

Speaker 2:

how I feel about.

Speaker 1:

Sorry, the last word I missed yeah, follow the brand, the follow brand podcast. How do you feel about this interview?

Speaker 2:

look, I've been tracking you for a while and what I like is your consistency and your perseverance. Um, a lot of people build these types of um in some ways philanthropic platforms to be able to help others along the way because of privilege or access that you may have. Um, and most people fall off because it's hard. It's not easy. Getting guests is hard, doing it every week is hard. Getting the courage courage to be able to engage in it and put your own brand out there is hard, and so I think your consistency and your perseverance marries what I do in the early stage venture world.

Speaker 2:

In the early stage venture world, it's really only two ingredients Perseverance, yeah, and craziness. You have to be crazy A little bit out there, yeah. In some ways and I say this self-deprecatingly in some ways you have to be a special kind of stupid to do something like this, what you're doing and what I'm doing. You can't be born with this type of stupidity. You have to cultivate it right. Nature can't make you this crazy to do things like this. So I really commend your, your perseverance and your commitment. That's what makes you know BDM what it is. Thank you.

Speaker 1:

I truly appreciate that Before we let you go, you got to let us know how to contact you, and you also said you're going to be upcoming. You're writing on Forbes, you're doing some things for Wire. Tell us what you've got going on, richie.

Speaker 2:

Well, I think a lot of what I'm going to be doing is as we get ready to come out of private beta. We're working with about a half a dozen or so Fortune 500 companies in a private beta at Mogus Very, very exciting approach. Again, we're transitioning the entire user interface that you have, any website that you've ever had before, any document that you've ever had. We're changing it from a guided high-friction experience, I'm sorry, from a self-directed high friction experience. So if you think about marketing, you spend a lot of money to get people to your website and then you leave them alone. Self-directed, right. We're moving that to guide it by putting this perception of personhood in it, so that you feel like you're guided when you arrive at the website. What would you like? How can I help you? What do you want? Right, using that same probabilism in the middle to create the perception of agency. To understand something, like you know I'm ready to take, it means that you want to buy.

Speaker 2:

So the work that I'm doing right now is to start to bring forward the insights that informed the Mobius Telly, the instincts that informed the Mobius Deli, the deep understanding that informed the Mobius Deli because I think part of it is I am dying to share what I've learned and kind of picked up over the last four or five years with the rest of the industry. And two is in a world where you're launching an artificial intelligence product, there's a lot of founder gravity that comes with it. So I'm getting into wires. A wired Forbes has finally nudged me enough to start writing again. I got a piece in the Economist coming. I'm doing some board work, I'm doing some conference circuits and stuff like that, because you know I have to come out first before the product comes out.

Speaker 1:

I like that. See that personal brand, see that Everybody asks me why personal branding? What is business branding like? You have to have a face of your brand to communicate effectively and simplistically. I mean you have a lot of. You simplify very hard concepts and I think a lot of people are like, wow, I think I understood what he just said this last hour right? I think that's wonderful. I am going to send you pictures from Guyana when I get down there, because this is going to be wonderful. Take you home just a little bit, show you what I'm seeing down there and then hopefully we'll circle back before the end of the year and have another good, intelligent conversation. And I want to thank you again for being on Follow the Brand. I want to encourage all of your followers to follow us at 5starBDM. That's the number 5. That's star. Bdm is B for brand, d for development and for masterscom. I want to thank you again for being on the show.