The Great Transition

same time. So I call it the Great Transition. It's really a whole bunch of smaller transitions, but I think they kind of have a theme and they have a direction,

and I think I know roughly where they're going. So what I want to try to give you is something where if you watch this whole thing and you think about all these ideas and you just let them stew, I think the news that comes out over the next weeks, months, even years is just going to make a lot more sense because you can kind of put it into this container,

this mental model of thinking about things. So I'm just going to jump through these and uh, they're going to jump around quite a bit because there are different topics we got like personal we got like corporate and stuff like that. So let's just jump into it. All right. So the first one is this concept of knowledge going from private to public okay. This is really, really important. So there are a few different things that are making this happen. One is just LMS in general AI in general.

Right now the concept is that it consumes all the stuff from the internet or whatever, right? All the books, all the blogs, whatever forum conversations, like all this training that's been done on these models going back to 2022 actually, before that. But anyway, all that sort of condenses into a model. Right. So you have this model that's kind of representative of like all this knowledge. Right? Everybody knows that. That's kind of understood What's not so much understood. Is

that what this is actually doing to knowledge work? So in the past, right. You know, going back ten, 20, 30, 50 years, if you were an expert in something, you have knowledge that no one else has, right? You have knowledge that no one else has. If you are a specialist consultant at McKinsey, or you are a heart doctor or whatever you are, you have special knowledge and you haven't captured even a 10th of it. Let's say you've written two books. You still haven't captured a 10th of

your knowledge, right? You just know things that other people don't, right? If you're a security professional who's been doing this for 20 years, you just understand things. If you're a CISO that's done this multiple times. You you just understand things and and get things that nobody else has. And importantly, it's not in a book somewhere. Okay. Even if you've written books, your knowledge is still not fully in the books, right? So that is a powerful thing. It is always protected

smart people. It's always protected the intellectual people who have all this experience. So it's a combination of smarts plus experience. And that magical combination has made those people very special. What is happening now is completely changing that. And this is one of the major transitions from private knowledge to public knowledge. What is happening now, especially with skills, this whole concept that anthropic came up with with skills, it

is scary. We're talking about a folder full of markdown files that can encapsulate a decent amount of your knowledge, right? You still have the capture problem where they don't know exactly what to say, How to capture it. But, but here's the situation. Many, many smart people are producing skills, and many, many other smart people are going to collect knowledge and specialized knowledge from all over the internet, anywhere it's been written down, and bring that into a skill. Plus,

all these specialist people, they're writing books, right? They have been writing books. They've been doing presentations, they've been writing blogs, they've been doing interviews. They've been doing podcasts. Well, in the past, we'd never had a system that could basically say, well, go get all of that, right? Go get everything. So and so Doctor Huberman has ever said about health or morning routines or whatever, bring it all together and turn that into a skill. I mean, this is one prompt.

This is one prompt, you know, find everything Huberman has said about morning routines from every podcast he's ever done, every blog he's ever done, whatever he's ever put out, every article, every interview and put that into a skill. That new thing combined with the models just getting better and they're absorbing it and everything. I mean, that's a crazy thing. This just feeds on itself, right? The model then can consume all those skills, right. Which of course

is going to happen. So ultimately you have this transition that is really accelerated. But it's just going to continue to accelerate. And what it means is the gap between special privatized knowledge that's inside of someone's mind, some specialist doctor, some specialist psychiatrist who's been doing this work for 40 years or 50 years. The delta between what they know and no one else knows is getting smaller. And that that is massively, massively impactful for like, humanity in general. Okay.

Then there's another sort of layer on this which is which is crazy. So let's just assume all that is happening and all of that is being consumed by these labs who are spending billions of dollars bringing that knowledge into the models. Fine. Okay. That's happening. But what we just saw from anthropic, and this is happening all over the place, anthropic just called it out because a bunch of these Chinese labs are doing it in mass, like,

very organized. I'm so surprised. Like, everyone's, like, surprised by this. And they're like, hey, what's the big deal? You know, maybe they're lying or whatever. Why would they be lying? China is known for doing this. China is, like, famous for stealing things. Like, they're famous for a lot of good things, too, right? But they are famous for stealing ideas, stealing content. And, um, they're also massively going all in on open source models. So I believe that they have

a very clear strategy. It is you don't have to compete to be a pinnacle lab, right? They don't have an anthropic. They don't have a Google DeepMind. They don't have an OpenAI, but they do have deep seq. And deep seq has been, you know, called out for doing this for a very long time. They are capturing the knowledge of all the billions of dollars of work and bringing it into open source. And then what they are doing as a Chinese strategy for AI, I believe, is

they're just saying distill it, not distill it. Well, they are distilling, but release it, diffuse it, absorb it into the pool. It's like you've heard the the metaphor. Peeing in the pool. That's that's what happens. Our specialized knowledge of what Specialized humans could do that no one else could do that is the P that's going into the pool. You can't pull it out, can't pull it out. It's just going to be in there. And China is basically making this happen at a mass scale because yes, the

large billion dollar labs will have it first. It'll go into those models first actually. First it'll go into skills. Right. First. It's going into this whole knowledge sharing concept of skills. So it's in the markdown file. So it's on GitHub and stuff like that. So that takes a little more effort. But then it goes up into these labs. It gets consumed however long that takes a period of months or whatever. But then the open source models are pulling from it.

And the other thing it's pulling is not just the data, the techniques that make those Premiere Labs better and better, or have an advantage over another lab, or have an advantage over open source. Those techniques are also being diffused somehow when the major labs have a major advantage and they jump ahead somehow. The Chinese models seem to get it. A few months later. So all of these things are actually contributing to the same thing. The specialized knowledge is

being diffused into public domain. That's just a transition that's happening. Okay. So that's the first one. Knowledge is going from private to public. Okay. The second one is products are going from standalone software to APIs. So I talked about this in my stupid little book from 2016. Basically said that businesses become APIs. And we're finally now starting to see this. So if you've seen like all these people releasing tools, right. Um, they're releasing, you know, so and so model for this

so and so model for that. Or not just models but like so and so functionality. Um, like uh there's a company that does remove background. Oh I'll give you a great example. Um, Excalibur. Excalibur just came out with a new piece of functionality where you could just describe what you want to make, and it will build all the different, uh, objects for you in your favorite fonts and your favorite aesthetic. Like, it'll just build you diagrams

like the perfect, like, really cool looking Excalibur diagrams. And my first question when I saw this was, hold on, because I went and looked at the documentation and it basically said, yeah, you just go into the interface and you type into Excalibur, draw what you want to say. And I'm like, what? What are you talking about? Like, do you, do you honestly think in like early 2026, I'm going to open up a drawer and type in a prompt? Are you kidding me? So I posted, I

was like, hey guys, this looks amazing. This looks amazing, looks fantastic. There's no way I'm going to use it. Can you make this available as an MCP? Can you make this available as an API? I'm not going to do any of this ever. Like if I have to open an app, I have already lost, right? I've already lost. Like this is not a good thing. It means my tooling is horribly broken. My AI should be doing all of this for me, so I'm not sure if they

made that adjustment. But when I posted that on Twitter, like a whole bunch of people showed up and they're like, yeah, 100%, I need, you know, I need an MCP for this. Otherwise it's not useful. That is the way everything is going. If you notice most of the releases coming out for products now, they're like, here's the MCP for it. Here's how your agents can do this automatically, right? This is

just becoming the new way to release software. And this is heading in the exact direction that I put in that stupid book in 2016. Businesses become APIs. Now why is this important? It's important because the consumer is not so much making the choice anymore. The consumer is not going to be like, hmm, yeah, there's 47,938 different options for removing backgrounds from images. Let me pull up GitHub and Google and let me spend 2.5 hours sampling and

trying different ones. No, no, no, that there's too many apps. And because of AI, there's too many apps being made right on top of that. So So there will be hundreds more of these things coming out all the time. The only way this resolves, which is what I was talking about in that text, was there are directories. Okay. If you have a background remover tool, by the way, my favorite is remove BG, I think is the one I use. Um, and they do have an MCP or they have an API at least. And that's what my

system Cai actually calls. So if you do have one of these agents, um, there will basically be orchestration layers, directories of services labeled and categorized, you know, taxonomy, folksonomy, whatever. Basically saying if you want to, um, remove backgrounds from images, here's your list of 27,000. But they will be rated right. You'll have different services with different ratings. And my system

Chi will know which services it prefers, right? He prefers to go to this rating service or whatever, which is like IMDb or Rotten Tomatoes, right? But it's for software and it says, okay, find me the highest rated one with the most ratings, the least negative ratings, whatever algorithm Chi wants to use. And he's going to select okay, it's remove background boom, pulls that in, drops it into

our workflow inside of our skill boom. That's it. From now on, when I want to remove or when Chi wants to remove a background for me for an image, that's what he uses. Okay, where is the website? Where's the website for remove background? Who needs a website? This is a service. A directory service like the old days like Yahoo directories or whatever. Right? This is a directory service of the best thing, right? It's already been rated. My agents are going to go check those ratings and

it's going to find the API. And it's going to integrate it. This old way of making the software, packaging it. Oh, it's got to have a nice UI. It's got to have a nice website. You know, when the person comes to the website, they've got to really like it. Then they go click the buy button. Then they do all this. It's all going away. Right. And this is tightly coupled with another related one I have here in the list which is interface. Interface is going away SEO is going

away okay. So interface used to be for humans. We make software. We have services whatever software service, whatever it is we have to have an interface for that. Right. It's the interface you use actually day to day to interact with it. But you also have to have an interface for the marketing and describing how to use it and the documentation interfaces in general. Front end in general

is going away. It's not that the content won't be there, it's that it will be designed to be consumed by agents. Write your agents are the ones who will be the main consumers. AI will be the main consumers. And this also relates to another one, which is everyone has their own digital assistant. Right? This this was the number one sort of prediction. And call out, uh, in uh, the real Internet of Things in 2016. Everyone gets a digital assistant.

Everything gets an API. Most importantly, businesses, people eventually as well. But objects, businesses, services, they all have an API. And then our AI, because it knows us so well. When we make a request, it goes and gets the thing from the API, brings it back to us. And then the third piece is interface. When we want an interface, when we want to look at I'm buying shoes, I want to see what they look like. Um, I'm buying a house. I want to see what it looks like.

Our AI will be presenting the interface to us. Okay, this is already starting to happen. People are building bespoke software. Custom software is the direction that it's going, right? So software goes from being everyone has the same packages to everyone gets bespoke software, everyone has custom software. Interfaces go from being tied to the application itself. It's inherently part

of the application to all of that being separated. The core part of your business, the core part of your product is the API, which will be used by the agents of the user, of the consumer, and the interface will be between their agent that user's agent and them. That's the interface. Right. And this will take a little more time. But it's already starting to happen a little

bit with everyone having custom software and custom interfaces. So that that's a number of sort of things all wrapped into one software goes from standardized and consolidated and integrated to being separated between its interface and its functionality. And the user of most software becomes AI as opposed to the user itself. Right. So those are kind of the major transitions there. Uh, so SEO again, SEO used to

be about being attractive to the user. SEO goes from trying to attract the user to trying to attract the user's AI I again. So when I say, hey, I need a new bed mattress, I'm not saying that to the internet. I'm not saying that to Google. I'm not saying that to the World Wide Web, a browser. No, I'm saying that to my agent. I am saying that to my Da. I need a new mattress. Well guess what? My Da knows my sleeping habits, knows my routine, knows if I like a firm mattress or a soft one. Knows.

You know my girls. She likes a softer bed. I like a harder one on my side. So it's like this is where the customization comes in, right? Your agent knows you, therefore it can do smarter queries for you. But the point is, it's the one doing the queries. It's the one who, if it's going to be tricked into picking one mattress versus another, the tricking needs to happen at the AI layer, because I'm just going to do whatever my agent tells me, right? The agent's going

to be like, yeah, I found the best one. It's not even a question. You know, it's this much. Do you want me to get it for you? You know, I'll have it here tomorrow. And that's the end of that. Okay, so that was a few things that were based around the consumer. Now I want to bring it a little bit over to the enterprise side. All right. So all of that is sort of on the consumer side. Let's switch over to talk a little bit about the enterprise side.

So much the same sort of stuff that we're talking about on the consumer side is also going to be happening on the enterprise side. The changes that are happening on the enterprise side, they're so massive, like absolutely massive.

One of the big things that's going to be happening on the enterprise side is the transition from humans creating processes and sort of following them to AI, kind of running the business based on SOPs And basically building out a lattice structure, like a graph structure of all the work that needs to be done. I did a post, I think, in 2024, maybe talking about companies or just a graph of APIs, a graph of operations. And I was talking about like somebody who just does a task, right.

Let's say it's a threat intelligence task or no, let's take the insurance one. Right. You have to look at the photos. You have to look at their account. You know, they're making a claim. For example, you know, I need to be paid for this accident I just got into. And you need to filter for fraud, right? So I'm Sarah, I'm looking at these things. I'm trying to figure out if, uh, this is fraud. I'm looking at the picture. Does it look real? Do they have a real account? Are they

making lots of claims just recently? Like, does their account look compromised? All these different things. And if it looks legit, okay, here's how much we're going to pay you out. You know, some of that's automated, but you still have people that are their job. Their actual physical job is to do

this task right. So that's the type of thing that currently in the enterprise, if you look at any major company, there is not a map, there is not a graph that basically the CEO can look down and say, this is my entire business. This is every task happening in my company and the process of how it's done. The SOP right, also a process kind of the same word. Uh unmilitary. So that's the way I say it. There's an SOP standard operating procedure for how this thing is done. Right.

And here are the people that do it. Here are the workflows. Here are the multiple steps involved. AI is going to have this for every company. This is the major transition that's going to be happening. This is just now starting. This is very slow. This is much slower than all this consumer adoption that we've seen over the last couple of years. This is going to take time. And this is what companies are still figuring out. They're like, what exactly am I going to do with AI? Well,

this is what they're going to do with AI. And I'm sure a million different companies, this is a thing I do for companies. But people like McKinsey, lots of different companies like this. They're already bringing in, you know, an army of 22 year old smiling kids to interview everyone and produce this map. Here is the work that takes place. Here's where decisions happen. Here's where tools happen.

Here's where all these things take place, right? So bringing you back to the consumer side, when you have a map like that, the whole conversation around software changes once again before you would have people. The people are basically the company. The people are doing the work. Yes, they have documents. Yes, they have processes. But it's the people doing the work. They're supposed to follow this policy, but it's just a doc. It's just a word doc used to be. Right. Um, now it's like a Google doc

or whatever, but do they follow it? Not really. Sometimes. Often it depends on the company, but it's it's not required. Right? A company can have people who mostly do the work. They're barely referring to the policy. By the way, there might be 3 or 4 of those docs and the main person who maintain them. You know, she went on maternity leave. She never came back. Right. Whatever it is like, oh, that was Scott. Scott left the company, you know, he retired.

He's not he's not coming back. So those docs get old. Nobody's following the policy. That is completely different than an AI saying, okay, I now own all these SOPs. Here is the map of all work that's being done. And the humans humans are still there. There's going to be some humans left in the company. Humans are the ones responsible for improving this AI, for telling the AI, hey, look, we need to change this SOP. Like, that's not how this should be done anymore. And you have a conversation

with AI or whatever. And the AI is like, okay, are you sure about this? Boom. It makes the change. All the documentation is updated, all the SOPs are updated, the cross references are updated. That is the new model for for business, right? It's not started yet. It's barely started. Massively different for different companies, right? Some people have been doing this for a year or two already. People like Google probably, but most companies, they're barely even realizing that

this is a thing right now. They're just like, oh, AI is a tool. Everyone's telling me I have to use it. Tell all your employees you must use AI. What does that actually mean? This is the thing that's actually going to happen. Sop's are established, goals are established. What are we actually trying to do as a company? We're going to talk about that one later. This is

a massively important one. But all of that gets canonicalized captured, turned into SOPs, turned into goals, turned into texts, turned into actual things you could look at. But the important part for this particular point is the the map, this graph of all the different work, they're okay. Now a software vendor comes in and they're like, hey, look, we got, you know, the best. We'll just say this company needs background, uh, image removal or whatever. We have the best one out there.

It's the best. Now, in the past, what would happen is they would bring their salesperson, take someone out to a steak dinner and be like, yeah, yeah, it's just it does background removal way better than anything else. Like you just have to have this. And if they convince this person and the manager and, you know, the purchasing authority or whatever procurement, whatever, they buy it, once again,

it's a human doing the work. They have this image tool. Okay, maybe there's an API call if there's some automation, if it's relatively new, but ultimately it's humans buying software. Software is a package. It's sitting on a shelf and they've purchased a bunch of software. Maybe they use it, maybe they don't. In this new model, this lattice, this graph system, it includes all the tools. The work is laid out as a grid, as a graph. Here's here's the stuff.

So watch this. This is a completely different conversation you have with the software vendor. Um, okay. Cool. Here is my map. Here's all the processes, all the work that I do. Every single workflow being done. What are you replacing? What are you doing better. Click on this note. Boom. You click on the note. Background image removal. Let's look at the metrics for it. Here's how fast it is. Here's how cheap it is. Here's how many times it

failed here. Here's how many times it succeeded. Here's the ratings for how that's been done. Now what are yours? Now what are yours. And they have they're going to have to produce metrics that say I can do that function better. So now it's not about a software package that some human is buying. And maybe they use it. Maybe they don't. Now it's about an AI saying, here are my metrics for this function which fits into this graph. Can you prove that your metrics for doing this function

are better? That's a completely different way of thinking about software. Once again, going back to for the consumer for the same service. Okay. Let's just say it's the same company. Remove background. Before they were marketing to the consumer with a pretty website and with SEO from Google, which is I'm sure, how I found this company in the first place. Remove background. Boom. It's a number one hit on Google

that was targeted at me and it worked. Now that marketing goes to my agent, Chi Chi finds that for me in a directory, the same thing is going to happen inside the enterprise. The enterprise is going to be like, look, find all of our software, find out where it is on this function map. And if it's not, first of all, if it's not on the map, they're all fired. Cancel

all contracts. Right. If it's not in this map or it's not in something, we're about to add a node because we have a new function, a new business line or new product or service or whatever. If it's not part of our structure, why do we have it? So it's just gone, right? But the conversation becomes improving upon

this matrix. That's all that matters. So all these incoming like pitches and the steak dinners and like sales and marketing, blah, blah, blah, it becomes a whole lot less important once we're actually doing an objective measurement of what is the function that you're performing and how well do you do it. Right.

So that's that's absolutely huge. Okay. So the transition here is going from corporate product marketing being a human to human targeted operation that results in a software package being purchased by a human, which is then used by a human or not. That's the old world. Two. The enterprise has a grid, a graph of all operations, and all functions inside of its company, and software is evaluated based

on the function and the metrics that it provides. And those functions and metrics need to be superior or they don't get swapped out. So it's a that's the transition. It's a fundamental shift there. Okay. Next topic is work itself. Okay.

So automation inside of companies. So the transition here is automation going from a thing that helps humans do their jobs better and basically improves productivity and efficiency and stuff like that, to being a way for companies to get to their ideal state of being able to do all the work themselves. This is colossal. This is economy changing. Okay. Because this really is the end of labor, right? So there's labor and there's capital. And these have always been

in balance. This is how this is going to get disrupted. It gets disrupted because companies have always wished that they could do all the work themselves without employees. If you are only one founder and you don't have much work to do, you do all the work yourself. You get all the profits yourself. Now, if you hire a washing machine and your your business is washing clothes and you don't want to expand, you're not trying to scale. You're just trying to make the money that you make people

bring you dirty clothes. You give them back clean clothes. That's a cool business. And you make lots of money and you are able to feed your family and you just. You have a good life, right? If you hire a washing machine before you were doing it on, on the board right in the river and you were making money, but you weren't making that much money, you weren't making as much as you could. So you decided to save up for a year and buy a washing machine. Now

your washing machine is doing way more. You're able to do way more clothes and make way more money. If a whole bunch of people come to you and say, hey, I can also wash clothes in the river. Think about this. You need to hire me as a clothes washer in the river, because that's what's fair. And, um, there needs to be a balance between labor and capital if you're going to be like, are you kidding me? I can do all this work myself. I am literally doing all

the work myself. They're going to be like, no, you're not doing all the work yourself. You have a washing machine. That is me. Okay. If I have a clothes washing business and I have ten washing machines behind me, that is me doing all the work myself. That is the transition that is happening. That is what AI is. And just to be clear, the total amount of compensation that knowledge workers receive is somewhere around $50 trillion per year,

$50 trillion per year globally. I think it's somewhere around 10 trillion for the US. That is how much money companies are spending to pay humans. And the major transition here is they don't want to be paying those humans. They actually never did want to be paying those humans. My favorite way of capturing this is the ideal number of human employees inside of any company is zero. That is the number that they're trying to get to. Now,

there are exceptions to this, right? If I'm a small, spunky founder and you know, I want to work with my friends, um, you know, I want to do a project with my friends. I get five of my friends involved. We build a small startup or whatever. It's kind of like we're all owners at that point, right? So you still will have, like, elite employees, cadre co-founders, stuff like that inside of these companies. Like, that's that's not going

to go away. Like hardly any companies are going to have like zero employees, especially large or medium sized companies. But we're talking about going from tens of thousands of employees to a few hundred, maybe eventually a few dozen, but let's just say a few hundred. We're talking about massive, massive reduction because of this different way of thinking about automation. It's not a thing that helps a human do a task,

which is what it's always been. It is a way to get to the state of the company does the work itself, which is a natural, clean, happy state for any company. They prefer to be doing the work themselves. Again, go back to the single person with a washing machine business. The washing machine business is part of them. It is part of their business. So they don't have a reason to hire employees. So that is the major transition, their automation going from a thing that helps employees to being

a way for the company to do all the work themselves. Okay, so that brings us to the next transition, which is okay, fine. That makes sense. What are we supposed to do? What are we supposed to do? We have jobs, right? How do. Okay. If everyone gets fired, who's going to buy all the stuff? Right? Who's going to buy all the stuff? So that's a UBI conversation. Not really going to go into that. Like there's going to be money. People are going to receive

money to pay their bills and stuff like that. Otherwise you just don't have a society. It's all bad. So that will get solved one way or another. Hopefully, uh, more gently and faster and easier, uh, rather than the alternative. Okay. So what does this look like for actual people? So instead of people en masse working for companies, you know, medium large sized companies or whatever, that goes away, that goes away, right? Because those companies like we talked about,

they're trying to get rid of everyone. Instead of that being the way that we make our money. We're going to make money by producing value ourselves, by articulating the skills that we have, the capabilities that we have, the products that we provide, the services that we provide, broadcasting that out. And that is going to go up into one of these directories like I talked about before, with products that AI can look at, right? AI can find products in one of these directories, but this will also

be the substrate for all work to be done. So humans will broadcast their capabilities and say, look, you know, I'm a systems engineer. I've got eight years of experience, two years experience, 25 years experience, whatever. Here's all the different stuff I can do. Here's my portfolio, blah, blah, blah. By the way, I like to mountain bike, blah blah blah. So this is your demon. This is your broadcast system describing, you know, the people you've worked with, like your reputation score.

People give you upvotes kind of similar to what LinkedIn was doing. They kind of went away from this. It's now kind of like a social network. So this is the play though. There's a substrate that connects all these different people, right. When I need a cat sitter because I'm going on vacation, I'm going to broadcast out, hey, I need someone to watch my cat. They need to be fed. You know, they're crazy. They might scratch you. Whatever. Broadcast that out. Keep in mind, uh, my AI is

broadcasting that out for me. Okay, everyone around AI is watching all the substrate, whatever this thing is called, and they're like, hey, I'm a cat person. You know, I'm a cat lady, I love cats, I can take care of cats. Um, you know, I don't get scratched or whatever, or I don't get sick when I get scratched, whatever it is. And I also live, you know, two blocks away. Being in her ear, her AI tells her, hey, there's a cat sitting job over here. You know it's going

to pay $84. Um. Do you accept? Boom. Yes. Same thing. Like someone. There's a crash on the on the corner. You know, someone injured themselves. Hey, does anyone have any medical, uh, you know, professional training or whatever? Does anyone have EMT skills? Boom. It's going to beacon for people all around nearby who have a reputation score above so and so. They get beacon. Someone takes the job, they go help the person. Okay, same for gardening, same for engineering services, same for hey,

I need a shoulder to cry on. Hey, I need nurturing. Hey, I need tutor for my kids. Hey, I need meal prep. Um, I need to gain a lot of muscle mass in my legs. Uh, I need a personal trainer. Everyone who has services, capabilities, value to offer, they are beaconing out onto this system. It is out there on the substrate, and then everyone's AI is looking at that system. And that's how you find work. Okay. Another version of this is like Fiverr. Okay. It never it's not automated, but

it's the same kind of vibe. Uh, also as LinkedIn, it's like, here are my services. Here are the jobs. Right. So you you've got consumer and producer, right? You've got like, here's the work I need done. And then you've got the other group saying, here's all the stuff I can do. And then it's a matter of, okay, we're going to join together. It's going to be five people on this project. It's going to be a six month long project. Here's

the money that I'm offering. Boom, boom, boom. Everyone agrees. Boom, the work starts. That is what I'm calling human 3.0. It's what I'm calling the state that we're going to get to do. I know this is going to happen for sure. I believe the answer is yes. Um, only because I'm an optimist and I believe we are not just going to die off and, you know, devolve into

chaos after the corporate system breaks down. Uh, I can tell you that the corporate system is going to break down because the corporate system is going to pull all the work internally and do it with automation and AI and robotics. That seems obviously inevitable. Again, not everyone, but most people will be laid off as a result of this. Over time, it'll happen different speeds and different industries, but that is pretty much inevitable. I think that this better solution,

this new solution, is also inevitable. The reason I love this, and this is a little bit of an aside, but I love this because it's more human focused. It's humans connecting with humans. And there'll still be companies, right? It's humans connecting with companies. But it's not in this hierarchical, you know, you know what corp corporate. That's that's military. This idea of. I work for Sarah. Sarah works for Joe. Joe works for Raj. And. Oh, I'm having a meeting

with Raj. Oh my God. You know, he's three skip levels above me. Like this whole military structure, this whole, like, dreading Monday. And just like, this whole thing is, like, toxic and poisonous. And it has been for decades. People have been so unhappy with this. And now that it's actually under threat, people are like, well, don't get rid of my job. Don't fire me. And which is justified, right?

Because they're worried about losing their livelihood and they're worried about being able to pay the rent and a mortgage and, you know, school and groceries. So it's understandable that they're clinging to that. But remember, we shouldn't be clinging to

a thing we hate and we have hated. Right. All this to say, I think the way that it's going, the way that I'm pushing for, I'm actively building to make this happen is to have this new human based substrate where producer, consumer producer, consumer things are a lot more equal. You get hired based on your skills, and that's a relationship you can get out of any time. And here's what's cool about it. You can be in multiple of these, right. You can have ongoing like retainer

type things going on with like 20 different customers. And you're on this big project for a full six months and you're getting paid from that. Plus you're doing the cat sitting. Plus you're a part time EMT in the in the nights just in case someone falls off their bike or whatever, you know what I mean? So it's like it's just a more aligned thing, right? Because now you're actually broadcasting everything that you you are right. You're like, yeah, I play violin. I will come to your son's birthday

party and play violin if they like classical music. So it's like you're broadcasting not just, oh, you know, here's my resume. I'm a tech engineer, level three, blah blah, blah. I worked at Intuit. That is not you. You. What this is going to allow us to do is broadcast our full selves and monetize ourselves in the best way possible. Not in a gross way. We're going to say we are going to be compensated and rewarded for being ourselves. If we are the best nurturers in the world, forget

tech skills. Forget any tech, right? You're the best nurturer. You're the best listener. You're the best mother, you're the best parent, you're the best tutor. You're the best trainer. You're the best boxing coach. Okay? They're not producing products. They're not releasing startups. They're helping other people become the best versions of themselves. In many ways, they're the most important people. Well, guess what? That's in there. Demon that

is broadcasted. They become world famous for that. So they got everyone trying to come in and use them, right? And they make money from it, which they should. This is how humanity should work, right? So anyway, this is the tech layer I think is going to replace the whole system. This is the transition that I think is happening. I just want to cover that because a lot of people have questions. What am I supposed to do after we lose all these jobs? Okay, next topic I want

to talk about is cybersecurity. So similar to a lot of the other things we've already talked about. Cybersecurity has been human based, right. Um, it's you hire a human team, you have human staff. They are good security engineers or whatever. They're doing pen tests, they're doing security assessments, vulnerability assessments. They are manually looking at all the different vendors and trying to figure out, is this one dangerous? Um, should

we allow it through procurement? And they're just being bombarded by all these requests. And, you know, if you have a really good, you know, third party service person, um, auditor person, they could look at x number of, of those vendors per day. Maybe some of them get escalated because the CEO really wants that, uh, piece of software. So you have to do a security assessment. You have to find some way to make it secure because it's coming in the company anyway. That's the life of a

security person. That's the life of a security program. It's humans grappling with all this stuff in a very gross sort of in the weeds, like sewer type of thing. It's not pretty, it's not clean, but it's got the job done, right. We're still alive. The inner still mostly works. You could still go to the ATM and get cash out. So security has been largely successful. Keep in mind, we're still losing billions to fraud all the time, but it's a messy process. What security becomes now with all this

AI stuff is it becomes your AI stack. As a defender against the AI stack of the attacker. And unfortunately, you're not facing one attacker, you're facing all the attackers. So the attacker is trying to understand your company extremely well. It's making a list of all your employees. It's creating a personality profiles on all your employees. It's coming up with the best spear phishing campaigns to find the ones who probably have the most access based on their job title.

And it's sending out these spear phishing attacks. It's constantly pulling. Your DNS is trying to see if you're doing a merger and acquisition with a company that doesn't have good security, so that's going to be a weakness. And they're launching a malware. They're sending you a spear phishing emails. They're trying to compromise all your websites. They're trying to pivot internally. They're trying to sell the access that they have. And

they're doing this at like machine speed, right? They've just got so many agents working on this constantly, constantly hitting you. You can't tell Chris and Raj and Sarah. Hey, uh, great job last year. Um, I need you to do 895 times as much work because, uh, that's how many more attacks were being hit with. That doesn't work. Also, you can't be like, uh. Hey, great news. We got three more head count. That also won't make a dent. Your only chance is to have the same AI or

better as the attacker. And this goes back to what happens to all companies. What happens to all companies also happens to security programs. It's no longer about here's our security team hears roughly the things we need to do. And yeah, there's some documentation, but ultimately it's like who's on call, who's doing the work, blah, blah, blah. And it's like a very human focused thing that's all out

the window. SOPs, SOPs, everything is a process and workflows which you could visually look at and see, look, this is the queue for processing incoming things. Uh, here's a queue for, you know, here are the constant workflows that are happening, all the cron jobs or whatever it is for finding insider threats. Here's all the processes for managing the CI, CD. Here's every single tool, every single decision point, every single approval point that needs to happen as part

of CI CD before something goes live. We're doing static analysis. We're doing dynamic. We're doing, uh, you know, we're looking at some other aspect of the application, how it impacts the business. Each one of those turns into a little node. That is where a tool drops into. This is where a human drops into. This is where the human is replaced because a very smart AI model just took over that role. Everything becomes transparent, visible with discrete actions and

activities and decision points at each area. And the question is then how well are we doing this? How good are our information sources feeding our context? This concept of unified context that I put out a year or so ago, maybe 2024, actually unified entity context. Everyone like I was talking about with the corporate side, also the security side, we're all working off a unified context of the company. What are its goals? What are its challenges? What's the

risk register. Look like they're all working off the same sort of thing. Right here are our attackers, here are sensitive things. Here's our crown jewels over here. All of this is unified. We have this map of all the workflows that are happening, all the different tools. And now the question is, are we getting that context updated? Okay. New AWS account, which just stood up. We just launched a new application, a new service in Asia and also Iceland and also in Seattle. And it's got all these

new services on it. How quickly are we as a security team learning that they did that? Because that was some crazy marketing group over there or whatever. Let's just assume we're going to have shadow it still for quite a while. This transition I'm talking about with like everything being mapped out and SOPs. That's not going to happen overnight. This is going to take a long time. Some companies this is going to take 15 years and they'll probably be dead, but some will survive and it will take

them 15 years because they're a soup sandwich. It will take them forever. Somehow they survived. Many companies, they'll do this over the course of two or 3 or 5 years. Right? Some companies have already started this. Some companies won't start this for another three years. It's massive, massive variance in how fast it's going to happen across various industries. Okay. So I don't want you to think like, oh, I think this is happening right now to everyone. And it's

just like it's going great. No, this is barely starting. Okay. So the point is this structure is what attackers are going to use to attack you. They're going to have a world model of your company which has all these pieces right there. Hopefully they're not as filled in as your version, right. Um, when you launch that AWS stuff, when you launch those new services, you have access to AWS. Hopefully. Hopefully your attacker doesn't, so you should be able to

pull that like really quickly. Okay. You should have access to all your endpoint visibility. You should have access to all your different tools, all your different APIs, all your different agent infrastructure constantly pulling asset management. Asset management is about to get elevated to top top tier. Finally thank goodness. But you should have this data way faster than your attacker. Unfortunately, all of this is very, very ancient inside of most companies.

I have been doing consulting for over 20 years. Most companies I see they do not have asset management. They do not have unified documentation. They do not have most of this stuff. Okay, we're in a bad state. We're in a bad state because as AI starts spinning this stuff up for the attackers, they're going to build a world model of these companies faster than the company has it, because the company has to go slow. They have to

have 19 meetings to prepare for the meeting. Attackers are just going to like YOLO it, you know, submit the single prompt, make no mistakes and start attacking. So all this to say, transition is humans doing security work to a unified workflow model with SOPs of the work that is being executed largely by agents, largely by AI, with humans there to tweak and improve and guide and instruct

and steer and validate the AI automation. And the game here is for your orchestration system to be better than the attackers. All right. So this next one is a way of thinking about enterprise AI in like a completely different way. And I put this out about a year ago or so, and I think it's really powerful as an inversion. So the idea is that, um, currently it definitely a year ago, two years ago. But even still now everyone is thinking, okay, we have security, we need

to put AI on it. Okay, we have finance, we need to put AI on it. Okay. We have um, HR we need to put AI on that, which means we need AI agents. Um, for finance we need finance AI, we need, um, security, AI, security, AI agents. Right. So the idea is you have the discipline, you have the topic, and then AI gets sprinkled on top, and it's supposedly going to do something that benefits you related to that topic. This is I don't think, the way to think about it. Okay.

I think the way to think about this is actually you have a company and you have the company's work and all its workflows and the graph of all the services and the tools and the operations, SOPs, goals, everything that is actually the system. The system is the graph of operations, okay? It is the graph of algorithms that take place to make this business function okay. Maybe it has humans there, maybe it doesn't, maybe has lots of humans,

maybe it's mostly AI and a few humans. Doesn't really matter. This graph of functions, this graph of algorithms is the company. Now think of AI as a system for running this graph of algorithms. That is what AI is. Then you have the question of okay, what are you doing for finance? What does procurement look like? Show me procurement. You're looking at this graph. And this one little, um, line lights up. It's actually like 19 different lines. They all light up. Oh,

these are the procurement workflows. Cool. So we can drill into those. We can inspect. Okay, here's the tools. Here's the human involved. Here's the decisions. Here's the sign offs. Here's the exceptions. Here's the risk register. Whatever it is, the overall system is the graph of algorithms largely run by AI. What ends up happening is all the different things that used to be industries. They become use cases inside of AI. Okay. So the before is you have

industries using AI. And now what you have is a graph of algorithms run by AI that has use cases for different things. Right. You you've got some of these things are security. Some of these things are HR. Some of these things are engineering. Some of these things are marketing. AI is the container. AI is the thing. And it just has functions that happen to be affiliated with what we used to call industries, that that is a fundamental transition.

Now it's not absolute. This is a mental model, but I think this mental model is a lot more descriptive of what's actually happening. Then thinking about it in terms of like, well, let's do everything the way we used to do it before. Okay, we've got an HR database, we're going to have an HR interface. We're going to have HR tools and an HR dashboard. No that's going away. Okay.

If you abstract everything to questions and everything to algorithms, the question is something like how happy are our employees? How much money are we spending on compensating employees? Should we pay more for bonuses? Those are questions. Those questions are not in industry. Those are questions that just happen to be associated with what we used to call an industry. Those are all hitting unified entity contexts. The results are

coming out and you have the answer. But ultimately, it is all feeding off this underlying unified context and graph of algorithms and understanding of what the business actually is. And all of that is powered and enhanced and managed and orchestrated by AI. Okay, here's the next one. Custom everything. So in the past and this one crosses consumer and enterprise. So in the past you basically had very few people, very few organizations, very few companies producing software, right? You've

got Adobe, right? There aren't many competitors to Adobe. When Adobe was on top, it was on top. When Microsoft was on top, it was on top. You have a few competitors, but you go into any enterprise. They're mostly running Microsoft Shop, they're mostly running Google Shop, whatever. I think that might start to go away. Um, it's not that I think that these major platforms won't still have

a stronghold or whatever. It's just that I don't think the implementation of their software is going to look the same inside of all these different companies, and it could be for a much smaller companies that aren't legacy. Their software stack could look completely different from another startup, maybe even doing the same sort of stuff just because the founders, which they'll probably be very few, the founders will just be like, yeah, I like this style. I like terminal style.

I don't like UIs, you know? I want everything to be API based. You know, I really like the color purple. I need purple and blue. I need the software to look a certain way. I need it to act a certain way. I want all the auditing and logging to go a certain way. You know, I want to be stood up only in these regions, whatever, whatever their preferences are, and the preferences for the interface for all this stuff. Custom. We're already seeing this with like custom replacements of tons

of different SaaS software. I mean, this is just all over the news everywhere. It's really easy to create a version of something now. It's a big difference between being able to have that thing roll out, you know, enterprise wide, and it's stable and it's secure and all of that. Right? There's some work to be done there that'll take, you know, a couple years to sort of work its way through.

But I think there's a very high chance that companies and consumers will be making their own software even if they're not making their own software. Let's be clear about that. There's 8 billion people on the planet. Not everyone's making their own software, but just just consider in 2019 or 2022, the number of people who made software products was rounded down to zero. Okay, that's the way to think about this. Rounded down to zero. Everything in the App Store, every

software company rounded down to zero. If you multiply that by 1000 or 10,000 or 1 million, that is a lot more software that is. Plus, we're talking about the ability for someone to speak to their AI agent, which we've already talked about. Everyone's going to have one and say, hey, I really wish I had a workout app. You know, I've been using this one. You know, it's $19 a month. It's pretty good, but I just wish it did this. I wish it did this. I wish it did this.

And boom, it's now installed on your phone. It's on your thing. The other one's uninstalled. The subscription is cancelled. That same exact thing is going to happen inside the enterprise. Now in the enterprise, it's going to take a little bit longer because that thing needs to be integrated with everything else. It's got to have the tie ins, it's got to have procurement, it's got to have legal sign off. Auditors have to sign off. There's a lot more workflow

involved in being able to do that. But I can tell you this every CFO is looking at their software list and saying, how can I cancel all of this? It's just like the employee thing, how can I get to zero employees? That's what the CFO is thinking as well. How can I not pay any other company for software? How can it all be ours? Everyone wants this. The question is, you know, we couldn't do it before. We couldn't do it before because it's hard to make software.

You got to maintain it. You got to do all this stuff. Well, the better AI gets, the easier that gets. Okay, So the tendency here is from common shared large few companies producing software to everyone has their own and right. And it's not going to go to the full extreme right.

It's going to I'm going to say it's going to go to like a 90% extreme or an 80% extreme, highly specialized software, everyone running their own personal AI stack as a person, as a human, your AI system is going to look, feel, act, behave differently than someone else. They might even be using similar products, but your interfaces are going to be different. The way it speaks to you is going to be different. The news sources that

you're filtering is going to be different. And that takes me to my next one, which Robert Putman talked about in his book Bowling Alone in, I think, 2000. When you have fragmentation like this across companies, but mostly across people. When you have fragmentation like this, that's going to be profound. So one of the things Putman talks about in his book is the reason the country was so unified before is that we were all watching the same TV shows.

We're all watching the same news. We all drove the same cars, we all had the same watches. We all went to the same churches. We all lived on the same block. Maybe someone had a slightly bigger house, but we're all living in the same sort of community and everyone's consuming the same sources. Everyone's thinking similar things because we're reading the same newspapers, we're learning the same things

in schools. Well, when you take what I just talked about, everyone gets custom software, everyone has custom AI, and in fact, you're not even viewing the sources. All these APIs, all these services that make up the world, you're not viewing him. There's millions of them. There's billions of them. Your AI's job is to consume all of that. Its job is to understand you and understand your needs. It consumes all those services, and then it gives you what you want. Well,

what does that open up? It opens up the possibility that all of us will be having a different world experience. We believe the reality that we see. Okay. So when you watch a particular news source, if you watch particular YouTube channels, you watch particular podcasts or YouTube channels or TV shows or, you know, news channels or whatever, you think the world works that way. That is your world. That is reality to you. And if you watch this other set, which this person might live next door, they

are in a completely different reality than you. They don't even know that this new, uh, dancing team from, uh, Korea is the most popular thing. And they got, uh, 20 billion views on the video that just came out yesterday. And to all of your friends, this is the most important thing that's ever happened. And you just can't believe it. You've watched it over and over. You cry every time you watch it. It's the most amazing thing ever. The

person next door doesn't know it exists. In fact, they're not sure if Korea is a real country based on the media they're watching. They're like, yeah, that could just be made up. This fragmentation, because of custom. Everything is going to be massive right inside of companies. The dynamic here is very strange. How do you audit software. How do you do security scans. How do you do auditing. Um, how do you do legal compliance? It's a lot easier

when everyone has SAP and everyone has Microsoft three. 65. And everyone has, you know, CrowdStrike and everyone has Palo Alto firewalls and checkpoint firewalls. Right. That's a whole lot easier because people have skill sets around those things. What happens when everything is custom, right. So the transition here roughly is few things that are packaged that most everyone

uses to millions of options with infinite customization. And everyone is kind of using different stuff customized just for them, and they're experiencing different realities as a result. All right, final one. I've saved this one for last. So I think the kind of ultimate use case for AI is what I'm calling ideal state management or state management or state orchestration. Not really sure on the term. Doesn't really matter. The real term will be created probably in the next

year or so. The idea is. What we've been doing inside of companies, what we've been doing as people, what we've been doing as a society is just kind of yoloing. This is what we've always done. This is what humanity does. We're like, hey, you know, I need to do better next time. I need to do you know, less of this thing that's really hurting me? Companies are like, hey, we should have goals. We should have OKRs. Hey, we should have a meeting. We should plan the next year

or whatever. Those documents go somewhere. Maybe they get revised, maybe they don't. But what you do see, and again, I've been doing this for so long. If you track a company's goals, um, for an average company, like a medium sized company, which there are just, you know, hundreds of thousands of these things all over the world If you look at their goals and plans and metrics over the course of a year or five years or ten years,

they're just making shit up like it changes constantly. The management changes like they, uh, they come out with a set of metrics. They don't hit them, they come out right after that and they're like, uh, yeah, guess what? We got a new plan. Uh, you know, we hired this person. It's going to be amazing. Here's new metrics, here's the new plan. And it's just kind of like constant reinvention, but not because of innovation. More so just because we're winging it. And this is no fault of anybody's.

Everyone's doing it very, very smart. People are doing it. It's just the reality that we live in. Okay. This is one of the fundamental changes that AI is going to bring. I think what we're about to move into is a situation that moves away from this ad hoc YOLO sort of situation to the ultimate use case for AI, which is state management. And state management starts with defining what ideal state is. This is a thing that most

companies do not have. They they do not have an articulated statement, kind of like a PRD document which has multiple SOPs revolving around it that says, here is our actual mission, here are our actual goals, here are the problems we are trying to solve in the world, which is why this is our mission. Here are our goals. Here are our challenges. These are the things that are, you know, we can't hire fast enough. You know, we can't ship product fast enough. We keep getting beaten by

our competitors. We need to move into Asia. We're not there yet. Like you put all your challenges, you got your risk register, you have your projects, you have your budget. You have your people and this is your unified document. This is your your grail. This is your system. This is your algorithm for what? What you're chasing. Okay? This being locked into the core DNA of the company and

everything revolving around that. So I talked about all this infrastructure, this, um, graph of algorithms, that graph of algorithms needs to be feeding this system, this ideal state, this perfectly articulated and not perfectly. Nothing's gonna be perfect. This very well articulated ideal state is about to become the most important thing for companies, but also for anything, for organizations, for entities, for people. This is a big thing that I work

on in my private work. If the Pi system with my Telos system, like all the stuff that we're doing in my community, we think about ideal state from the very beginning, ideal state. This concept is extraordinary. It's extraordinarily powerful. Okay. I've been using it for a long time. Not this new version that I have now, but roughly I've been using a system like this for probably ten years and

there wasn't any AI. You don't need AI to sort of think about, articulate this and start moving towards it. It's very powerful. Forget any tech, just note cards, index cards, a space pen, you're off and you're going to have massive benefits from doing this. Here's where the AI comes in. And this this is like the sickest, most powerful, most exciting thing in all of AI across all AI ideas that I've ever heard about or come up with or whatever. Okay,

so check this out. This is the universal game. When we meet aliens flying around the galaxy or whatever, and we tell them this thing I'm about to tell you, they're going to be like, yeah, that's what everyone does. How do you think we got here? How do you think we have all these spaceships? How do you think we have all these planets? How do you think we have Dyson spheres around all these stars? Like that's obvious. That is the algorithm, right? So this is what I

call it. I call it the algorithm. The algorithm is we have ideal state. What is our current state okay. Step two what is our current state. So remember what I was talking about before about you have the graph of algorithms. You have asset management gets elevated to the top. This current state. It's the job of your AI and all the different agents and all the different systems. It's not all AI. This is deterministic code as well, right?

What is the current snapshot of the ORC? What is the current state of our problems and our solutions and our products and our services? How happy are our people? How happy are our customers? What is the current churn number? What is the current products we're about to release? What are our competitors about to release? What are the market conditions? Snapshot. Snapshot. Snapshot. Ideal state. Ideal state is here. We're in Asia. We have 30% more employees, actually more likely 30% less employees.

But separate topic. Um, you know, we want to be in these markets. We need to have more services. We need to have more private luncheon learns, uh, in our local community, whatever it is, all that's going to be hundreds of thousands of things or hundreds of thousands or millions of things. Whatever it is, our current state also has millions of things in it, right? Because it's going to go down to like eventually at some, some level.

Here's the current open ports for all of our Kubernetes pods, right. All of our AWS infrastructure. Current state, current state over here, they're all locked down, nothing publicly available, strong authentication on them, etc.. Now here it is. Watch this. The role of AI is continuous migration continuous gap closing between current and ideal state. This works as an individual trying to lose 20 kilos which I'm trying to do. An individual trying to lose

20 kilos. Trying to increase their Vo two Max trying to find a wife trying to get their art exhibited in the local gallery and eventually at MoMA in San Francisco, trying to come up with, uh, new EDM tracks so they can go to EDC and actually play live on stage trying to run a federation of planets. Okay. If we're talking about, you know, 9000 years in the future or whatever, anything you're trying to do at any scale can be managed by ideal state and current state and

the migration. This is extremely, extremely powerful. I think we're about to apply this to almost everything. I think prds engineering. So I have this thing inside of the Pi platform. It's called the algorithm. I literally start the algorithm by decomposing the prompt that comes in and turning it into reverse engineered ideal state components. I break out the pieces, I look at the context from the user, and I

do deep analysis, and we do research as well. This is if you have a lot of time, do research and you figure out, okay, what do they actually mean by this? Okay, I might have sent in a single one sentence prompt, but I do all this research to break it into its pieces. What is he explicitly say he wants? What is he explicitly say he doesn't want? Okay. What is he maybe mean by that? What are some common gotchas for someone trying to build a system like this?

All that gets decomposed, reverse engineered and I start creating ideal state criteria. These actually go into cloud code tasks, right? The task system from cloud code, which is extraordinary. I put them all in there. Then they go into the PRD and the PRD is what we work off of, but it's all working off of ideal state so engineering can be run this way. Companies can be run this way, you managing your life, you managing your entire family's happiness. Okay.

My husband, you know he needs to be more healthy. My kids, they need to have better education. They need more tutoring in this. Hey, I'm looking at the dashboard. Looks like they haven't had enough vegetables. Okay, I'm going to tell my AI we're going to order more vegetables. Hey, we haven't gone on a vacation together in a while. Hey, um, I noticed, uh, we were looking at our phones a little bit too much at the dinner table last few days.

My AI notified me about that, so. Yeah, let's make sure we're all looking in each other's faces and talking about, uh, physical art. Let's make sure we do a number of vacations during the year where there's no tech involved whatsoever. We're focusing on human things and all of this. Again, the AI is managing this stuff for you because you

put it in your ideal state. Okay, so if you're running an ice cream truck business, you're running a federation of planets, or you're just trying to like, find a girlfriend or a boyfriend. All of this can be managed the same way. Same way as if you're trying to launch a new SaaS replacement app, or a new workout app, or whatever you're trying to do with AI. Everything can be managed by thinking about it in this way. Now there's a lot of steps in between, but those steps

can be scaffolded. Those steps can be engineered, those steps can be filled in. And the smarter the AI gets, the more it's going to be able to just know how to help you transition and build plans to help you transition. So I think this is potentially the biggest idea in AI. I don't think many people are talking about it yet, but I think this concept of ideal state is is is about to be huge. And in fact, I think Agentic platforms are about to start building it

in to like everything they do. Wouldn't be surprised to see this cloud code Codex Gemini start to move towards AGI generality by doing this reverse engineering of requests into an ideal state that you then pursue. Because here's the trick. And I got this from Andrej Karpathy you can't hill climb. You can't progress towards something if you don't have failures, if you don't have a thing to hill climb against. And this is why I reverse engineer everything into ideal

state criteria, because they are also my verification criteria. So what Karpathy said was previous software was you can make anything the next generation of software will be you can verify anything and I think it's very powerful. So my ideal state criteria in the algorithm within, uh, personal AI infrastructure, the open source project, it turns the ideal state into the verification criteria. And they're all discrete and they're all verifiable, yes or no. And that's what gets us to this

verifiability thing that Andre was talking about. And that's what ultimately allows us to go from current state to ideal state. So really excited about this. I know this was tons of content and I just want you to think about it. It's a set of transitions. It's not just one transition. So I consider all of this to be part of the great transition. Hopefully it's a mental model. I know it includes lots of subset things, but I think all

of it combined does produce a single mental model. So hopefully you can now see when all this news comes out, right? New models, new products, new services. You know, um, Co-work can now do this. Oh, it's it's it's going after the lawyers. It's going after whatever. What my goal is here is for you to be able to say, well, hold on, you know, based on this great transition model, that thing that's happening is not something, something new. It's

not going to produce anxiety. Right? I'm trying to reduce your anxiety of thinking about all this change, because I believe this great transition sort of model, with all the little submodels and transitions that I was talking about, it's kind of like a container. It's a container. It's a mental model container that can reduce your anxiety if you understand this, in my opinion, if you understand this, then you just won't be surprised by things, right? And I

can be surprised. Like I could have missed something, right? Obviously I could be wrong about any one of these or more, but in general, I don't think I'm going to be surprised by this. I put the stuff into the book in 2016. It is coming true. I think it's a natural progression for the stuff to be happening. Like who knows how it's going to happen, when it's going to happen, which company is going to do it.

You can't predict that stuff. It's impossible to predict, but the direction I think is possible to to see where it's potentially going. And that gives you a container, a mental model to be like, hmm, okay, this all sort of makes sense inside of this framework. So you're not spooked out by like all these new models coming out, new products coming out. It's like, oh, this person's going

to lose their job, that person. These industries are being disrupted. No. If you have this mental model, I think you're more likely to just be like, ah, yeah, that seems to be the way it's going. And, uh, you can buckle down and just move forward. So hopefully this has been helpful and, uh, we'll see you in the next one.