AutoGPT: The Rise of Digital Agents and Non-Human Platforms & Business Models (Tech Strategy – Podcast 163)


This week’s podcast is about AutoGPT / AgentGPT and how it is leading to the rise of digital agents. This will lead to the emergence of non-human platforms and business models.

You can listen to this podcast here, which has the slides and graphics mentioned. Also available at iTunes and Google Podcasts.

Here is the link to the TechMoat Consulting.

Here is the link to the China Tech Tour.

Here are the 9 digital business models I see often.


Related articles:

From the Concept Library, concepts for this article are:

  • GPT and Generative AI: AutoGPT
  • Digital Marathon: Zero human operations
  • Digital vs. Human Agents
  • Platform Business Models: Non-Human Platforms and Business Models

From the Company Library, companies for this article are:

  • OpenAI / ChatGPT
  • AgentGPT

Photo by Andrew Neel on Unsplash

——–Transcription Below


Episode 163 – Digital Agents.1.transcribe

Fri, May 05, 2023 10:00AM • 43:16


agent, digital, gpt, business models, human, companies, business, nonhuman, platform, customers, operations, bigger, create, pizza, auto, scale, interaction, website, selling, advantages


Jeffrey Towson


Jeffrey Towson 

Welcome, welcome everybody. My name is Jeff Towson, and this is the tech strategy podcast where we analyze the best digital businesses of the US, China and Asia. And the topic for today, auto GPT, from digital agents to non human platforms and business models. Now, that’s a little bit wordy. Let me explain this. This is basically a continuation of last week’s podcast, which was 162, which was titled auto GPT. And other tech that I’m super excited about. Basically, this idea of, you know, large language models GPT being the most famous, that can prompt themselves. And you go from giving a prompt a create a photo of this or create a article about that, to giving it a goal, create a website that accomplishes x and it basically lays out multiple tasks for itself, it writes its own prompts to itself. And it can revise and really what we’re talking about when we talk about auto GPT is a digital agent. And the primary user interface people are using for this is called agent GPT, which is a company or at least a service at this point. We’re talking about digital agents that can accomplish goals, not unlike how human agents can’t accomplish goals. And it’s starting to look a lot like that. So this the point of today, which I’ll go into is what happens when you go from auto GPT that can create a digital agent that does something for you write a code for this, go get me a pizza and have it delivered. Create a website, what happens when you go from the idea of a digital agent, to a platform or other type of business model not built on humans, but built on digital agents instead? So think of all the businesses you know of how they’re all you know, the business has operations that are internal and lots of staff and org charts. And they have customers who are humans, whether they’re businesses or people? What if that’s not humans? What if it’s digital agents selling to each other? What if it’s businesses organized completely of digital agents. This is kind of what I’ve been working on or thinking about a lot, because there’s not a lot of companies to study at this point. But I think it makes a lot of sense. And I’m going to kind of lay out my thinking of how auto GPT agent GPT is going from digital agent to non human platform and other non human business models. And that is going to be the topic for today. Housekeeping, nothing really going on to the standard disclaimer, nothing in this podcast or in my reading a website is investment advice and numbers information for me and any guests may be incorrect. The views and opinions expressed may no longer be relevant or accurate. Overall, investing is risky. This is not investment, legal or tax advice, do your own research. And with that, let me get into the content. Now, as always, they’re sort of concepts for this talk. And there’s actually several big ones for today. And I think, pretty important. I actually think we’re kind of ahead of the curve on this. Keep in mind auto GPT is about three weeks old, as far as I can tell. I mean, GPT chat, GBT. Okay, that’s what six months old. Now, auto GP is a couple of weeks. So we’re really on the frontier. And I’m sort of trying to take apart the business model and the place we can start I’ll give you a couple concepts for today. These are in the show notes, as always, one obviously GPT and generative AI. That’s not really a concept in auto GPD not really a concept more like an idea or a system or something. But one big concept, which I’ve talked about before is zero human operations. For those of you who are subscribers, I’ve talked about ant financial mean for years in terms of what I call a zero human operation. That’s not really my phrase. This is also sometimes called an AI factory. I’ll talk a little bit about that. But that’s kind of concept number one for today’s zero human operations. Concept number two digital agents. And then concept number three, non human plants platforms, and other business models. So three big concepts for today. And I’ll put that in the show notes. I’ll also put in the link to podcast 162, which is the previous one about auto GPT. Okay, so let’s talk about zero, I’m sorry, zero human operations. And I’ve been talking about this for I don’t know, a couple of years and, and financial was sort of my standard explanation or example for this But really, we could look at YouTube, we could look at tick tock, those are also zero human operations. And the the point is, okay, it’s a normal business in the sense that you have customers, you have suppliers, you have external parties you deal with, which are companies or people, customers, whatever seller, and then you have the internal operations of the company, which, again, is made up of a combination of business and technology and people. And so the question is, what happens when you remove the last human being from the internal core operations, what and we call that a zero human operation. And that’s a little bit in my opinion, the holy grail of scale. Now, if we look at sort of scale is the default business strategy, you’re always trying to get bigger. Now, people argue that faster is also important, okay, probably is, but bigger is, you want to be bigger. And there’s a lot of advantages to having scale, relative to others. And just as a standalone entity, you get specialization, you can start to take your people, and some of them are specialized in one activity. In summary, this is a contrast to if we’re thinking in terms of factories, a large factory has a massive scale advantage over a traditional workshop, which is hence the Industrial Revolution, you get specialization, that’s a big deal, you get more stability, you’re not as exposed to changes in demand, you’re not going to get put out of business as easily, you get greater access to capital, you typically get better management menu, you get a lot of benefits from just being big, as well as from being bigger than a competitor. And typically, when we talk about sort of Industrial Economics, factories, power plants, retail all of it, as you get bigger, one of the other benefits you get relative to a customer is you generally get a decreasing per unit cost. And there’s a standard slide, which I’ll put in the notes of just look per unit cost generally decreased with volume. Now that can follow from the fixed costs. Right, if you have fixed costs and greater volume, your factory is bigger than someone else’s factor, your per unit costs are going to be lower. You also get usually economies of scale based on purchasing, your bigger you can get a better price, you can get learning advantages, which is a type of accumulated production advantages, a lot of advantages that come with scale. And usually people look at it in terms of cost. Okay, that’s generally true. But as Charlie Munger often talks about, there are also disadvantages to scale. As you get bigger, you get increased complexity. Now, it could be bigger, we are just a bigger company that does one product, we make tables. As you get bigger and bigger and bigger, you do get certain sorts of problems in terms of managing people. You get bureaucracy, you tend to get politics, human beings don’t really work together, when you put them in groups that are beyond a certain size, it becomes very difficult. It also makes you vulnerable to specialists as you’re this, you know, gigantic, I know desk maker, well, you you open yourself up to specialty place that can be more niche focused. Generally just sort of becomes unwieldy for various reasons. And you can see that when you look at companies, you can see a company like GE get bigger and bigger and bigger. And there’s a lot of advantage to that and more products, more services, more technology, but also, they start to struggle beyond a certain point. And usually companies don’t get that big. They sort of max out. We don’t see companies with you know, 10 million employees, they don’t exist. We don’t see companies that sell 10,000 different products don’t exist, it just gets to be too much. Okay, so the point is when you move to a zero human operation, which means you remove all the people from the core ops, you have people that oversee the system, people that design the system and manage that the internal functioning operationally, there’s no humans involved. And the argument is, when that happens, all the disadvantages that we know of, of scale appear to disappear. Does a let’s say YouTube, does YouTube really struggle? With 10 million people watching versus 1 million, well, you got to add more servers and stuff and connectivity, but not really, what about 100? million? What about a billion? How many people watch you two, 3 billion out of a billion humans, I mean, we are seeing companies scale up to something we’ve never seen before. Facebook, 3 billion users, Instagram, same range. We’ve never seen companies do this before. And it’s because core operationally it’s all software. And you can also look at in terms of volume, but you can also look at it in terms of complexity, there is no bank in the world that offers 100,000 Different checking accounts. It’s too complicated. But YouTube has no trouble offering you every single type of video content you could ever dream of every little sub niches they’re. So complexity, the disadvantages of scale, bureaucracy, politics, all of that don’t appear to be a problem. So there’s this idea that if you remove the last human from the core ops, you get all the advantages of scale, and none of the disadvantages and maybe not only do you not have disadvantages, as you get bigger and bigger, your data learning might get better and better, because you have more data maybe. So that was why I would always talk about ant financial this way, because they were building a zero human operation bank, with no people in the core operations. And if you actually look at ant financial, then it became called ant group. And now I think it’s just called ant. Like when I visited them, you know, they would have all over, you know, their standard slogan was 3103, stood for three minutes to do an application for a loan or a credit card or bank account or whatever, one second to approve it, that’s three in one. And zero stands for zero humans involved. I mean, they basically put it in the title. So in theory, you could have a international bank, with billions of customers offering hundreds of 1000s of types of checkings accounts. We’ve never seen anything like that. So that’s kind of the first idea. For today I want to talk about which is, you know, once you start removing humans, a lot of the way we view business changes. Okay, so the example I gave you zero human operations, that’s an example of sort of extreme digital transformation. It’s still people buying and selling stuff, it’s still humans getting bank accounts. It’s still I don’t know, wealth management companies and companies are basically groups of humans or managers, you know, you know, selling wealth management products through there to humans, so the buyers and the sellers are still people. That’s why we talk about businesses as b2c b2b BTG. business to government. That’s kind of the basic idea of an extreme example of changing the internal operations to be all digital digital, which will be a combination of software and digital agents. Okay, fine. That’s kind of idea number one, for today. Now, here’s the next idea, which is an extension of that, what happens when you start to introduce auto GPT, and digital agents to that picture? Now, the examples I gave you last week, were for those of you who aren’t familiar auto GPT, as I kind of explained is it’s real simple. You just go on to agent GPT. You can do it right now. type in something like right, give me five ideas for a, let’s say, viral video on China. Based on my writing, Jeffrey, Towson, that will get a lot of traction and then create the outline. So you give it a goal, which is different than standard GBT, where you give it a prompt to create a picture for me that looks like this. I’m not telling it what to create, I’m giving it a goal to accomplish. And it will then break that into several tasks. You know, study all of Jeff housings, writings, which it did, look at the all the characteristics of viral videos, you know, prioritize them, and then prioritize a list of what Jeff should write about and come up with headlines and basically breaks it into four tasks accomplishes each and comes back and gives me the answer. Okay, and auto GPT is, you know, it can self prompt. Basically. It’s good at things like that. It’s good at creating software. In particular, write me a code that accomplishes x and it will just figure out how to write the code for create a website that sells shoes and make so much money, it’ll create the website, it’ll analyze the product categories, it will decide on the product to sell, maybe its shoes, it will set it up, it will take credit card information, it will process it so on It can create a website or a mobile app for you. Here’s $1,000 invest this money for me and turn it into more money, it can do that. Now it doesn’t do it necessarily well, but it’s getting better. And you will start to study markets and what to invest and put in places and whatever. And the example I gave last week was, buy me a pizza. Buy me a pizza and have it delivered right now. That’s the goal. So what does it do? It studies all the restaurants nearby, it looks at all the reviews, it looks at the prices, it kind of makes an assessment of what what’s a good restaurant to do, it makes a decision, we’re going to order a medium pepperoni from this one. Let’s say Domino’s, task number two, it goes to the Domino’s website, it starts putting in the information in the webpage, my name, my address, my credit cards, it clicks it to go, the pizza gets ordered. And then as the pizzas ordered and delivered, it will oversee the process and handle any issues. So three tasks. And this has already been done. People have tried this and it works. Now you can’t do all of this on audit GPT at agent GPT. But it’s pretty close. And it’s only been a couple of weeks. Okay, those were the examples. But think about the last example I just gave you, what am I really doing? I am deploying a digital agent to act on my behalf, go get me a pizza, the digital agent is then going out into the marketplace and accomplishing it not me. The business it decides on so it engages in Google, maybe it searches it might engage on I don’t know Matewan or grab and it studies all the restaurants and it reads all the reviews, it engages with that. And then ultimately, it engages with the Domino’s Pizza Company through its web page, and it places the order. So that is a digital agent effectively operating as the customer. So that’s different than the zero human operation example I just gave. We’re not just changing the internal operations to be digital. This is no longer a business transaction between two humans. This is a business transaction between a digital agent and a company staffed by humans. That’s a very different type of interaction. And you can take that forward. So this we would call this a digital agent interacting doing a transaction with a business and its web page. Now, we take it one step further, let’s say I was dealing with Expedia the other day because I had an airline ticket problem, which was a nightmare of so of course, I had to get on the phone and I I dealt with the chat bot on my phone for like an hour. And that was a nightmare. And then they eventually put me through to a real agent. But I had to talk to the digital agent because they have you know, they have chat bots total nightmare, right. And we’re all used to this, right? We call up the phone that whatever company we’re dealing with, and they route us to deal with a digital agent of some sort. Now maybe it’s primitive, it’s just a phone tree. But they’re getting more sophisticated, you know, press one if you would like to, but that is a human me dealing with either a digital system or increasingly digital agents that will try and solve my problem. So they’re deploying a digital agent to deal with customers. Well, what I can do now is I can deploy my own digital agent, using agent GPT, to deal with their digital agent. So I would tell my agent, go change this flight on Expedia, it would go to the website, and it would start to interact with their digital agent. So the interaction, which is not a transaction in this case, but it’s an interaction is digital agent to digital agent. There’s no human in that interaction. That’s pretty interesting. So what we’re starting to see is and we could see that and I’ll give you some more examples we could see merchants that are entirely digital agents going out into the world to sell could be a robo taxi, the robo taxi just cruises the street. It’s got some degree of its own, you know, digital goals, and it’s looking to sell so it is a digital agent that selling but I could also deploy a digital agent, that’s the buyer. Go get me a ride across town to the airport. So we’re seeing these interesting interactions with the deployment of these digital age since. And there’s a couple of concepts that we keep in mind here, like digital transformation has largely been internal to companies, we replace some of the activities and workflow with software, or usually a combination of people and software. We increasingly connect with other parts of the ecosystem. The supply chain, merchants, things like that. And that could be people purchasing offices, or more likely, it’s people Plus software. But the users of all these systems have always assumed to be humans, if we’re on a marketplace platform, there’s buyers and sellers, we assume their people, either people as companies, or people individually, content creators on YouTube. That’s one user group, the other user group on YouTube is viewers, again, humans, developers writing software that is then put on, let’s say, the Google Play Store, again, developers, we think user groups that I’ve been talking about for years, have all been human agents. What happens? And I’ve just given you two examples of what happens when users are no longer just human agents, but they can be digital agents as well. Maybe they’re representing someone like the customer, maybe they’re representing the business. Maybe they are autonomous entities that just operate on their own. Isn’t that an interesting idea? So that’s kind of the point for today, which I’ll get to in but next is, what does it mean when business models are no longer about human agents alone? buyers, sellers, content creators, developers? What if digital agents are their own types of users now? Just like us, are the business models going to be different? And the answer is yes. And that’s kind of what I’m trying to take apart, what are those business models look like? And I’m going to give you a couple examples. A couple of conclusions on what I just said. And then I’ll get to that. Next point. We are seeing somewhat of a based on what I just said, one of the conclusions me buying a pizza at Domino’s, with a digital agent and not myself is we’re seeing a break in the relationship between businesses and customers, they’re no longer able to reach me, they have to deal with my agent, just the same way I have to deal with their phone tree, they’ve got to deal with my agent. So we’re seeing a sort of a break in the relationship. At a time when everyone is trying to have a direct relationship with customers. These agents are kind of becoming intermediaries. The other thing we’re seeing is how many digital agents are you really going to have? Are you gonna have hundreds of them? No, it’s um, I’ve heard it referred to as the race to be your best friend. Certain digital agents services are going to try and become your best friend as soon as possible. Because truth is, we’re probably going to rely on a couple each. And that’s it. So they’re trying to be that person right now. So that’s kind of a funny thing I read about the race to be your best friend by various digital agents. So let’s talk about specific business models. Now. Now I have laid out for those of you who are subscribers, I have laid out nine digital business models that I just see pretty frequently. And my argument has been that business models are usually a combination of some sort of linear production, a pipeline, and something that enables interaction. Now the purest form of a traditional pipeline, business product or service, could be a factory goods come in one side, we have a lot of value in a concentrated way, as it moves down the conveyor belt, it comes out the other side. It’s a finished product pipeline, business model, pure form, pure breed. The other example might be a platform business model, say Amazon, well, not Amazon, say YouTube. They are purely in the interactions business. They enable content creators to interact with content viewers, but they don’t create it than anything themselves. So that’s sort of a pure interactions business. Well, it turns out most businesses are a mix of those two things. And I’ve laid out nine types, which basically go in order from pure linear to total interactions. So linear business model, fine production, ecosystems, and then consumption ecosystems. I’m not going to go through these but for those of you who are subs, you can go to the website. I’ve written a lot about these. Then we get to something like a pipeline plus a platform, Walgreens, we can get to company ecosystems, which is like five 610 company these things connect together, we can get to digital platforms, we can get to platform protocol hybrids, which are a web to web three amalgam Protocol networks. And then finally, protocol ecosystems. I’m not going to go through all those, but they’re all in the webpage, you can look them up. But if those are sort of the nine digital business models I see most often. How does injecting a digital agent change those? That’s the question I’ve been kind of thinking about a lot. And we’ll just talk about platforms, because I think those are the easiest. And I’ve talked about them like forever. Okay, so this is concept food to for today, which is what I’m calling nonhuman platforms. These are platform business models, where the users are all digital agents. They’re not humans. So let’s say we’re talking about a marketplace platform, I’ve given you five types of platform business models, marketplaces, the most common one, not the most common, the easiest, understand the two user groups, buyers and sellers. The Interaction Type is a transaction that’s getting a ride on Uber, that’s buying something on Amazon, and so on. Okay, what happens if one or both of those user groups is now digital agents, not human? So well, the the example I just gave you of me ordering a Domino’s Pizza, that digital agent on my behalf could have just gone to grab, or Uber Eats, or whatever, and just interface to there. And on that platform, the buyer would have been a digital agent, not a person. Okay, kind of interesting, not terribly thrilling. But what if we were to redesign Matewan grab Uber Eats not to be based on humans, but to be based purely on digital agents. Well, as I kind of teed up before, when you get rid of people. So we’ll call this like, let’s say a zero human platform. Once you get rid of people, certain things become possible that weren’t, complexity becomes much easier to manage, you’re not going to go to a local platform like grab or Uber Eats and look at 150,000 different restaurants, you’re not going to do that that’s too complicated. For humans. It’s not too complicated for a digital agent. Just like the 10,000 checking accounts was not a problem. If I have a digital agent going on grab Uber Eats and that is a nonhuman platform, it can look at 150,000 Different pizza restaurants as easily as I look at 10. So we can move to a much greater scale of complexity. It will also do it much, much faster. It won’t take 10 minutes, five minutes of looking at reviews, it’ll take seconds at most, it’ll be faster, we should see dramatically more connections. And we should see millions of merchants, not 50. Now pizzas are a little bit dependent because it’s a very local business. Let’s say we take that up to the next level of something like Alibaba, or Amazon, something where we are selling internationally. Now in that case, you could have 10s of millions of merchants all selling tables on a nonhuman version of Amazon. And the digital agents could search that quite easily in a way that we never could. So a marketplace, a nonhuman marketplace, a marketplace for digital agents on both sides won’t have any of the limitations that we have. Okay, so there’s an example. And I think, a nonhuman version of Amazon could be pretty unbelievable. What about, let’s say getting transportation, I mean, the first version I gave you first example was surfaces, local surfaces ordering food. Okay, let’s let’s look at another local service, let’s say a version of Uber or grab to get a ride to the airport. Now again, I could deploy my digital agent as the buyer, it could go, you know, we could go on the website, it would be a nonhuman version of Uber or grab and you could look at all the cars running around and in theory could handle hundreds of 1000s of cars. But that’s assuming human drivers. What if it’s not human drivers on the supply side? What if it’s digital agents, Robo taxis, providing services On the supply side, would there be any problem, let’s say in Bangkok or Shanghai, of having 100,000 Robo taxis on the streets of Shanghai, all interacting on the non human version of DD, with all the digital agents, why not? doable, you could flood the seats with Robo taxis and they could all go on the platform on the supply side and find their writers and digital agents could deal on the other side. So when you start to look at sort of, let’s call it zero human platform, I don’t really have a great name for it. I’ve been calling it non human platform business models. Definitely complexity and scale on both sides change dramatically. Okay, think about that for logistics. for shipping, transoceanic shipping, rail, cars, trucks, Robo trucks. Very, very interesting. So I think that’s going to be very compelling. Now, let’s say we move out of physical products, Amazon and physical services, trucks, pizza, what if we move to digital content? What if we go to YouTube? And okay, I’m going to be the viewer, human. But the other side, the content creators are human agents, but they’re also digital agents. And we already know AI is very good at creating content. So what if the content creators creating these massive libraries of videos I watch? Or you can watch? What if those are all digital agents, I mean, they could make youtube today look like nothing. You know, one of the biggest barriers to entry YouTube has is its existing content library, because it would take 10 years to recreate it. But that’s assuming that humans are recreating it and not digital agents as content creators, they could probably recreate that content library relatively quickly. So it’s a big disruption to their their barrier to entry, maybe. Now, I’ve been thinking about YouTube quite a bit. And here’s my sort of working model for what YouTube may look like. I think we will see, obviously, human beings are going to use all these great digital tools, and the amount of human created content with digital tools is going to absolutely explode on the content creator side. It has to I mean, it’s so easy to make videos. Now. Anyone can make movies, anyone can make anything now music, whatever. So the supply side of something like YouTube, or Spotify is going to just explode. Humans using digital tools, but we’re also going to see purely digital agents, also creating content. And you would just give the digital agent a tool, I want you to create songs for Spotify, that get as many views as possible, create five songs per day, and it will just do it. And it’ll just keep doing it. And maybe they’ll suck and you know, maybe they’ll get better, and I suspect they’re gonna get better. So you could see digital agents operating as full time content creators almost immediately. Now whether YouTube will let them on? I don’t know, maybe they’ll stop them. Okay, fine. And then I’ve already said in a previous podcast, I think what will also happen is a company like YouTube will have to offer a personalized content creation service, as a complement to the core library. If you liked the Avengers, here’s lots and lots of movies and videos about the Avengers cartoons whatever. Oh, and by the way, we also offer a service where we will create personalized Avengers cartoons for you, Jeff, because we know what kind of stories you’re like. We know you like The Avengers, we know you’d like revenge stories, we know the 20 videos we’ve already shown you we’re gonna make the number 21. So that’s what people are starting to call personalized dreaming. So YouTube looks like a combination of a platform business model with a massive library, where the content creators are human agents and digital agents and a complimentary AI powered service for content creation. That to me looks like the future. And it may well end up being specialized. Like do I think one large language model or let’s say jpg, stable diffusion GPU? Is one model going to do everything? Are we going to see people start to build models that are very Very good at, let’s say, creating Avengers. And that’s all this model does. And all day long, all it does is create Avengers TV shows and movies and it creates a big library and then it feeds that library into its inputs, and it starts getting better and better and better and better. I don’t know, maybe, maybe? And how long is it going to be? Until the library of content on something like YouTube goes from being 98% created by humans with digital tools, to 95% content created by AI without humans involved? Seems like that’s going to happen pretty fast. Maybe they can just know that bility to create content is far more. So it may be that YouTube in the future is 90 plus percent created by AI as a library, plus the personalized content creation service. Plus, there’s some human created videos in there as well. I don’t know we’ll see. Okay, I think if sort of that sort of concept number two for today is non human platform business models. And I think audience builder platforms YouTube, absolutely going to happen. I think marketplace platforms, absolutely going to happen. The other ones innovation platforms, collaboration platforms. I’m not totally sure yet, but I think those two are going to happen immediately. That’s a prediction. Okay, last concept for today. What about other non human business models that aren’t platforms? So I gave you nine digital business models that I look for? I didn’t really explain them. But I’ve gone through them before. I’ve given you how digital agents I think will change platform business models. But what about the others? That’s kind of what I’m struggling with? And all sort of I don’t have an answer to that. But I think that’s a big important question. And it really comes back, there’s a big idea at the center of this, which is there’s something called perpetual GPT that people are talking about, which is, if Auto GPT is about giving it a goal to accomplish, go get me a pizza. Perpetual gfpt is about creating a system. Basically a company, this is a digital agent, this is a company that exists to go forward in the world perpetually, to make money to accomplish a service, to survive, and to perpetuate, made up entirely of digital agents, not humans. So it’s almost like a business. But instead of having a bunch of people in a building, you have hundreds of digital agents within an entity. That’s a new type of business model. That’s a new type of organizational structure. And its goal is to go out in the world and just make money. And it makes, I don’t know, $10,000 a day, and it has to spend $5,000, a day on the compute power required to do all the AI because you have to pay for all the compute. But it makes another 5000 of profit. And it uses that to launch more digital agents. And, you know, your average business is a group of people organized together a group of human agents, what happens when a business becomes a group of digital agents that all work together? That’s something totally new. You don’t need offices, you don’t need roads, you don’t all need to live in the same city. That’s a really interesting idea. So I’ve been playing around with the idea of b2c b2b, B to G, B to D, B to C business to customer, usually consumer, what they mean by b2c b2b business to business, big business to government, what if it’s B to D, you’re in the business of selling to a digital entity that doesn’t have people, that would be a very different type of sales process. Anyways, that’s kind of what I wanted to go through for today. But that what I’m trying to walk down the path of going from zero human operations, to the role of digital agents within companies within marketplaces within the world, and finally, to the emergence of businesses and other entities that are entirely digital agent organizations. And I have no I’m not sure what that looks like, but I’m thinking about a lot. It’s hard to it’s hard to go too far down theoretical path if there aren’t companies to study. So I kind of max out out about halfway through that sequence, I can see the other I can see the marketplaces, I can see the YouTube changes that next step, it’s theory at this point, but why not? Why not? Okay. I think that is it for content for today. Hopefully you thought that was pretty cool. I think it’s pretty cool. The three concepts for today, which are all going to be in the concept library, zero human operations, digital agents, and non human platforms and business model. And I think that is it for the content. Let’s see, as for me, normal week, just plugging away here having a lot of fun, heading back into China shortly, which is, that’s always great fun on, I’m trying to get back to Japan, right, right before COVID hit, I just sort of do as a little routine is, every January and every July, I moved my life somewhere else for a month. And you know, just for keep life interesting, I suppose. And I kind of copied that from my old boss, Prince AlWaleed. Because he would always spend all of August traveling. Now it’s easier if you’re traveling with the big entourage and a 767, and all that. But the principle was the same, I thought those are really good ideas. He just sort of, you know, moved his life somewhere else, he would usually go to France, south of France, somewhere like that. But you just learn to work that way. So I’ve been doing that for several years. And it’s really great. I mean, I really do enjoy it. And it was right before COVID, I was planning to go to Japan for a month, and sort of spend the month there. And then I was going to spend the summer or the winter in Italy. That was kind of one of the reasons I like to do this is it kind of means I always have something I’m looking forward to. Like as if it’s March, no matter what I’m doing. I’m like, ooh, and another couple months, I’m gonna go live in, I don’t know, Greece for a month. And then in October, it’s like, ooh, and so it’s always like right on the horizon. So I always look forward to it, right? Anyways, but that all got shut down because of COVID. And so now that things are opening up, I’m trying to figure out how to sneak back to Japan. That’s the so what of that is, I’m heading to China, I guess in a week or so. And I’m going to try and sneak into Japan and sort of make up for my because I was really looking forward to it. I really like spending time in Japan. So I get camping. I’ve had that a little adventure on hold for two years. So I know it’s that’s kind of what I’m thinking about. If you have any suggestions, please let me know. I’m always looking for fun places to sort of move camp to for about a month. Next on the list was Japan and Italy. But I’ve already been to both I’m kind of looking for something new, I suppose. So if you have any suggestions, yeah, let me know. It appreciate it. I was thinking for Japan, I’d be in Tokyo, because I tend to like big cities. I don’t seem to be able to last a month. Somewhere like a beach community. I get too restless. Like I kind of need a big city. So that’s where I tend to go anyways. That’s what I’m thinking about this week. kind of exciting. But that’s it for me. I hope everyone is doing well and I will talk to you next week. Bye bye.

I write, speak and consult about how to win (and not lose) in digital strategy and transformation.

I am the founder of TechMoat Consulting, a boutique consulting firm that helps retailers, brands, and technology companies exploit digital change to grow faster, innovate better and build digital moats. Get in touch here.

My book series Moats and Marathons is one-of-a-kind framework for building and measuring competitive advantages in digital businesses.

Note: This content (articles, podcasts, website info) is not investment advice. The information and opinions from me and any guests may be incorrect. The numbers and information may be wrong. The views expressed may no longer be relevant or accurate. Investing is risky. Do your own research.


Leave a Reply