S1: Hey. What's up? So I'm going to try to encapsulate
S1: a whole bunch of stuff that's going on right now
S1: and try to wrap it into a single container. It's
S1: actually very difficult to do because there's so much change,
S1: as everyone knows, and things are just getting crazier, like
S1: every single week. Every single day almost. And I've noticed
S1: like a whole bunch of different transitions happening at the
S1: same time. So I call it the Great Transition. It's
S1: really a whole bunch of smaller transitions, but I think
S1: they kind of have a theme and they have a direction,
S1: and I think I know roughly where they're going. So
S1: what I want to try to give you is something
S1: where if you watch this whole thing and you think
S1: about all these ideas and you just let them stew,
S1: I think the news that comes out over the next weeks, months,
S1: even years is just going to make a lot more
S1: sense because you can kind of put it into this container,
S1: this mental model of thinking about things. So I'm just
S1: going to jump through these and uh, they're going to
S1: jump around quite a bit because there are different topics
S1: we got like personal we got like corporate and stuff
S1: like that. So let's just jump into it. All right.
S1: So the first one is this concept of knowledge going
S1: from private to public okay. This is really, really important.
S1: So there are a few different things that are making
S1: this happen. One is just LMS in general AI in general.
S1: Right now the concept is that it consumes all the
S1: stuff from the internet or whatever, right? All the books,
S1: all the blogs, whatever forum conversations, like all this training
S1: that's been done on these models going back to 2022 actually,
S1: before that. But anyway, all that sort of condenses into
S1: a model. Right. So you have this model that's kind
S1: of representative of like all this knowledge. Right? Everybody knows that.
S1: That's kind of understood What's not so much understood. Is
S1: that what this is actually doing to knowledge work? So
S1: in the past, right. You know, going back ten, 20, 30,
S1: 50 years, if you were an expert in something, you
S1: have knowledge that no one else has, right? You have
S1: knowledge that no one else has. If you are a
S1: specialist consultant at McKinsey, or you are a heart doctor
S1: or whatever you are, you have special knowledge and you
S1: haven't captured even a 10th of it. Let's say you've
S1: written two books. You still haven't captured a 10th of
S1: your knowledge, right? You just know things that other people don't, right?
S1: If you're a security professional who's been doing this for
S1: 20 years, you just understand things. If you're a CISO
S1: that's done this multiple times. You you just understand things
S1: and and get things that nobody else has. And importantly,
S1: it's not in a book somewhere. Okay. Even if you've
S1: written books, your knowledge is still not fully in the books, right?
S1: So that is a powerful thing. It is always protected
S1: smart people. It's always protected the intellectual people who have
S1: all this experience. So it's a combination of smarts plus experience.
S1: And that magical combination has made those people very special.
S1: What is happening now is completely changing that. And this
S1: is one of the major transitions from private knowledge to
S1: public knowledge. What is happening now, especially with skills, this
S1: whole concept that anthropic came up with with skills, it
S1: is scary. We're talking about a folder full of markdown
S1: files that can encapsulate a decent amount of your knowledge, right?
S1: You still have the capture problem where they don't know
S1: exactly what to say, How to capture it. But, but
S1: here's the situation. Many, many smart people are producing skills,
S1: and many, many other smart people are going to collect
S1: knowledge and specialized knowledge from all over the internet, anywhere
S1: it's been written down, and bring that into a skill. Plus,
S1: all these specialist people, they're writing books, right? They have
S1: been writing books. They've been doing presentations, they've been writing blogs,
S1: they've been doing interviews. They've been doing podcasts. Well, in
S1: the past, we'd never had a system that could basically say, well,
S1: go get all of that, right? Go get everything. So
S1: and so Doctor Huberman has ever said about health or
S1: morning routines or whatever, bring it all together and turn
S1: that into a skill. I mean, this is one prompt.
S1: This is one prompt, you know, find everything Huberman has
S1: said about morning routines from every podcast he's ever done,
S1: every blog he's ever done, whatever he's ever put out,
S1: every article, every interview and put that into a skill.
S1: That new thing combined with the models just getting better
S1: and they're absorbing it and everything. I mean, that's a
S1: crazy thing. This just feeds on itself, right? The model
S1: then can consume all those skills, right. Which of course
S1: is going to happen. So ultimately you have this transition
S1: that is really accelerated. But it's just going to continue
S1: to accelerate. And what it means is the gap between
S1: special privatized knowledge that's inside of someone's mind, some specialist doctor,
S1: some specialist psychiatrist who's been doing this work for 40
S1: years or 50 years. The delta between what they know
S1: and no one else knows is getting smaller. And that
S1: that is massively, massively impactful for like, humanity in general. Okay.
S1: Then there's another sort of layer on this which is
S1: which is crazy. So let's just assume all that is
S1: happening and all of that is being consumed by these
S1: labs who are spending billions of dollars bringing that knowledge
S1: into the models. Fine. Okay. That's happening. But what we
S1: just saw from anthropic, and this is happening all over
S1: the place, anthropic just called it out because a bunch
S1: of these Chinese labs are doing it in mass, like,
S1: very organized. I'm so surprised. Like, everyone's, like, surprised by this.
S1: And they're like, hey, what's the big deal? You know,
S1: maybe they're lying or whatever. Why would they be lying?
S1: China is known for doing this. China is, like, famous
S1: for stealing things. Like, they're famous for a lot of
S1: good things, too, right? But they are famous for stealing ideas,
S1: stealing content. And, um, they're also massively going all in
S1: on open source models. So I believe that they have
S1: a very clear strategy. It is you don't have to
S1: compete to be a pinnacle lab, right? They don't have
S1: an anthropic. They don't have a Google DeepMind. They don't
S1: have an OpenAI, but they do have deep seq. And
S1: deep seq has been, you know, called out for doing
S1: this for a very long time. They are capturing the
S1: knowledge of all the billions of dollars of work and
S1: bringing it into open source. And then what they are
S1: doing as a Chinese strategy for AI, I believe, is
S1: they're just saying distill it, not distill it. Well, they
S1: are distilling, but release it, diffuse it, absorb it into
S1: the pool. It's like you've heard the the metaphor. Peeing
S1: in the pool. That's that's what happens. Our specialized knowledge
S1: of what Specialized humans could do that no one else
S1: could do that is the P that's going into the pool.
S1: You can't pull it out, can't pull it out. It's
S1: just going to be in there. And China is basically
S1: making this happen at a mass scale because yes, the
S1: large billion dollar labs will have it first. It'll go
S1: into those models first actually. First it'll go into skills. Right. First.
S1: It's going into this whole knowledge sharing concept of skills.
S1: So it's in the markdown file. So it's on GitHub
S1: and stuff like that. So that takes a little more effort.
S1: But then it goes up into these labs. It gets
S1: consumed however long that takes a period of months or whatever.
S1: But then the open source models are pulling from it.
S1: And the other thing it's pulling is not just the data,
S1: the techniques that make those Premiere Labs better and better,
S1: or have an advantage over another lab, or have an
S1: advantage over open source. Those techniques are also being diffused
S1: somehow when the major labs have a major advantage and
S1: they jump ahead somehow. The Chinese models seem to get it.
S1: A few months later. So all of these things are
S1: actually contributing to the same thing. The specialized knowledge is
S1: being diffused into public domain. That's just a transition that's happening. Okay.
S1: So that's the first one. Knowledge is going from private
S1: to public. Okay. The second one is products are going
S1: from standalone software to APIs. So I talked about this
S1: in my stupid little book from 2016. Basically said that
S1: businesses become APIs. And we're finally now starting to see this.
S1: So if you've seen like all these people releasing tools, right. Um,
S1: they're releasing, you know, so and so model for this
S1: so and so model for that. Or not just models
S1: but like so and so functionality. Um, like uh there's
S1: a company that does remove background. Oh I'll give you
S1: a great example. Um, Excalibur. Excalibur just came out with
S1: a new piece of functionality where you could just describe
S1: what you want to make, and it will build all
S1: the different, uh, objects for you in your favorite fonts
S1: and your favorite aesthetic. Like, it'll just build you diagrams
S1: like the perfect, like, really cool looking Excalibur diagrams. And
S1: my first question when I saw this was, hold on,
S1: because I went and looked at the documentation and it
S1: basically said, yeah, you just go into the interface and
S1: you type into Excalibur, draw what you want to say.
S1: And I'm like, what? What are you talking about? Like,
S1: do you, do you honestly think in like early 2026,
S1: I'm going to open up a drawer and type in
S1: a prompt? Are you kidding me? So I posted, I
S1: was like, hey guys, this looks amazing. This looks amazing,
S1: looks fantastic. There's no way I'm going to use it.
S1: Can you make this available as an MCP? Can you
S1: make this available as an API? I'm not going to
S1: do any of this ever. Like if I have to
S1: open an app, I have already lost, right? I've already lost.
S1: Like this is not a good thing. It means my
S1: tooling is horribly broken. My AI should be doing all
S1: of this for me, so I'm not sure if they
S1: made that adjustment. But when I posted that on Twitter,
S1: like a whole bunch of people showed up and they're like, yeah, 100%,
S1: I need, you know, I need an MCP for this.
S1: Otherwise it's not useful. That is the way everything is going.
S1: If you notice most of the releases coming out for
S1: products now, they're like, here's the MCP for it. Here's
S1: how your agents can do this automatically, right? This is
S1: just becoming the new way to release software. And this
S1: is heading in the exact direction that I put in
S1: that stupid book in 2016. Businesses become APIs. Now why
S1: is this important? It's important because the consumer is not
S1: so much making the choice anymore. The consumer is not
S1: going to be like, hmm, yeah, there's 47,938 different options
S1: for removing backgrounds from images. Let me pull up GitHub
S1: and Google and let me spend 2.5 hours sampling and
S1: trying different ones. No, no, no, that there's too many apps.
S1: And because of AI, there's too many apps being made
S1: right on top of that. So So there will be
S1: hundreds more of these things coming out all the time.
S1: The only way this resolves, which is what I was
S1: talking about in that text, was there are directories. Okay.
S1: If you have a background remover tool, by the way,
S1: my favorite is remove BG, I think is the one
S1: I use. Um, and they do have an MCP or
S1: they have an API at least. And that's what my
S1: system Cai actually calls. So if you do have one
S1: of these agents, um, there will basically be orchestration layers,
S1: directories of services labeled and categorized, you know, taxonomy, folksonomy, whatever.
S1: Basically saying if you want to, um, remove backgrounds from images,
S1: here's your list of 27,000. But they will be rated right.
S1: You'll have different services with different ratings. And my system
S1: Chi will know which services it prefers, right? He prefers
S1: to go to this rating service or whatever, which is
S1: like IMDb or Rotten Tomatoes, right? But it's for software
S1: and it says, okay, find me the highest rated one
S1: with the most ratings, the least negative ratings, whatever algorithm
S1: Chi wants to use. And he's going to select okay,
S1: it's remove background boom, pulls that in, drops it into
S1: our workflow inside of our skill boom. That's it. From
S1: now on, when I want to remove or when Chi
S1: wants to remove a background for me for an image,
S1: that's what he uses. Okay, where is the website? Where's
S1: the website for remove background? Who needs a website? This
S1: is a service. A directory service like the old days
S1: like Yahoo directories or whatever. Right? This is a directory
S1: service of the best thing, right? It's already been rated.
S1: My agents are going to go check those ratings and
S1: it's going to find the API. And it's going to
S1: integrate it. This old way of making the software, packaging it. Oh,
S1: it's got to have a nice UI. It's got to
S1: have a nice website. You know, when the person comes
S1: to the website, they've got to really like it. Then
S1: they go click the buy button. Then they do all this.
S1: It's all going away. Right. And this is tightly coupled
S1: with another related one I have here in the list
S1: which is interface. Interface is going away SEO is going
S1: away okay. So interface used to be for humans. We
S1: make software. We have services whatever software service, whatever it
S1: is we have to have an interface for that. Right.
S1: It's the interface you use actually day to day to
S1: interact with it. But you also have to have an
S1: interface for the marketing and describing how to use it
S1: and the documentation interfaces in general. Front end in general
S1: is going away. It's not that the content won't be there,
S1: it's that it will be designed to be consumed by agents.
S1: Write your agents are the ones who will be the
S1: main consumers. AI will be the main consumers. And this
S1: also relates to another one, which is everyone has their
S1: own digital assistant. Right? This this was the number one
S1: sort of prediction. And call out, uh, in uh, the
S1: real Internet of Things in 2016. Everyone gets a digital assistant.
S1: Everything gets an API. Most importantly, businesses, people eventually as well.
S1: But objects, businesses, services, they all have an API. And
S1: then our AI, because it knows us so well. When
S1: we make a request, it goes and gets the thing
S1: from the API, brings it back to us. And then
S1: the third piece is interface. When we want an interface,
S1: when we want to look at I'm buying shoes, I
S1: want to see what they look like. Um, I'm buying
S1: a house. I want to see what it looks like.
S1: Our AI will be presenting the interface to us. Okay,
S1: this is already starting to happen. People are building bespoke software.
S1: Custom software is the direction that it's going, right? So
S1: software goes from being everyone has the same packages to
S1: everyone gets bespoke software, everyone has custom software. Interfaces go
S1: from being tied to the application itself. It's inherently part
S1: of the application to all of that being separated. The
S1: core part of your business, the core part of your
S1: product is the API, which will be used by the
S1: agents of the user, of the consumer, and the interface
S1: will be between their agent that user's agent and them.
S1: That's the interface. Right. And this will take a little
S1: more time. But it's already starting to happen a little
S1: bit with everyone having custom software and custom interfaces. So
S1: that that's a number of sort of things all wrapped
S1: into one software goes from standardized and consolidated and integrated
S1: to being separated between its interface and its functionality. And
S1: the user of most software becomes AI as opposed to
S1: the user itself. Right. So those are kind of the
S1: major transitions there. Uh, so SEO again, SEO used to
S1: be about being attractive to the user. SEO goes from
S1: trying to attract the user to trying to attract the
S1: user's AI I again. So when I say, hey, I
S1: need a new bed mattress, I'm not saying that to
S1: the internet. I'm not saying that to Google. I'm not
S1: saying that to the World Wide Web, a browser. No,
S1: I'm saying that to my agent. I am saying that
S1: to my Da. I need a new mattress. Well guess what?
S1: My Da knows my sleeping habits, knows my routine, knows
S1: if I like a firm mattress or a soft one. Knows.
S1: You know my girls. She likes a softer bed. I
S1: like a harder one on my side. So it's like
S1: this is where the customization comes in, right? Your agent
S1: knows you, therefore it can do smarter queries for you.
S1: But the point is, it's the one doing the queries.
S1: It's the one who, if it's going to be tricked
S1: into picking one mattress versus another, the tricking needs to
S1: happen at the AI layer, because I'm just going to
S1: do whatever my agent tells me, right? The agent's going
S1: to be like, yeah, I found the best one. It's
S1: not even a question. You know, it's this much. Do
S1: you want me to get it for you? You know,
S1: I'll have it here tomorrow. And that's the end of that. Okay,
S1: so that was a few things that were based around
S1: the consumer. Now I want to bring it a little
S1: bit over to the enterprise side. All right. So all
S1: of that is sort of on the consumer side. Let's
S1: switch over to talk a little bit about the enterprise side.
S1: So much the same sort of stuff that we're talking
S1: about on the consumer side is also going to be
S1: happening on the enterprise side. The changes that are happening
S1: on the enterprise side, they're so massive, like absolutely massive.
S1: One of the big things that's going to be happening
S1: on the enterprise side is the transition from humans creating
S1: processes and sort of following them to AI, kind of
S1: running the business based on SOPs And basically building out
S1: a lattice structure, like a graph structure of all the
S1: work that needs to be done. I did a post,
S1: I think, in 2024, maybe talking about companies or just
S1: a graph of APIs, a graph of operations. And I
S1: was talking about like somebody who just does a task, right.
S1: Let's say it's a threat intelligence task or no, let's
S1: take the insurance one. Right. You have to look at
S1: the photos. You have to look at their account. You know,
S1: they're making a claim. For example, you know, I need
S1: to be paid for this accident I just got into.
S1: And you need to filter for fraud, right? So I'm Sarah,
S1: I'm looking at these things. I'm trying to figure out if, uh,
S1: this is fraud. I'm looking at the picture. Does it
S1: look real? Do they have a real account? Are they
S1: making lots of claims just recently? Like, does their account
S1: look compromised? All these different things. And if it looks legit, okay,
S1: here's how much we're going to pay you out. You know,
S1: some of that's automated, but you still have people that
S1: are their job. Their actual physical job is to do
S1: this task right. So that's the type of thing that
S1: currently in the enterprise, if you look at any major company,
S1: there is not a map, there is not a graph
S1: that basically the CEO can look down and say, this
S1: is my entire business. This is every task happening in
S1: my company and the process of how it's done. The
S1: SOP right, also a process kind of the same word.
S1: Uh unmilitary. So that's the way I say it. There's
S1: an SOP standard operating procedure for how this thing is done. Right.
S1: And here are the people that do it. Here are
S1: the workflows. Here are the multiple steps involved. AI is
S1: going to have this for every company. This is the
S1: major transition that's going to be happening. This is just
S1: now starting. This is very slow. This is much slower
S1: than all this consumer adoption that we've seen over the
S1: last couple of years. This is going to take time.
S1: And this is what companies are still figuring out. They're like,
S1: what exactly am I going to do with AI? Well,
S1: this is what they're going to do with AI. And
S1: I'm sure a million different companies, this is a thing
S1: I do for companies. But people like McKinsey, lots of
S1: different companies like this. They're already bringing in, you know,
S1: an army of 22 year old smiling kids to interview
S1: everyone and produce this map. Here is the work that
S1: takes place. Here's where decisions happen. Here's where tools happen.
S1: Here's where all these things take place, right? So bringing
S1: you back to the consumer side, when you have a
S1: map like that, the whole conversation around software changes once
S1: again before you would have people. The people are basically
S1: the company. The people are doing the work. Yes, they
S1: have documents. Yes, they have processes. But it's the people
S1: doing the work. They're supposed to follow this policy, but
S1: it's just a doc. It's just a word doc used
S1: to be. Right. Um, now it's like a Google doc
S1: or whatever, but do they follow it? Not really. Sometimes.
S1: Often it depends on the company, but it's it's not required. Right?
S1: A company can have people who mostly do the work.
S1: They're barely referring to the policy. By the way, there
S1: might be 3 or 4 of those docs and the
S1: main person who maintain them. You know, she went on
S1: maternity leave. She never came back. Right. Whatever it is like, oh,
S1: that was Scott. Scott left the company, you know, he retired.
S1: He's not he's not coming back. So those docs get old.
S1: Nobody's following the policy. That is completely different than an
S1: AI saying, okay, I now own all these SOPs. Here
S1: is the map of all work that's being done. And
S1: the humans humans are still there. There's going to be
S1: some humans left in the company. Humans are the ones
S1: responsible for improving this AI, for telling the AI, hey, look,
S1: we need to change this SOP. Like, that's not how
S1: this should be done anymore. And you have a conversation
S1: with AI or whatever. And the AI is like, okay,
S1: are you sure about this? Boom. It makes the change.
S1: All the documentation is updated, all the SOPs are updated,
S1: the cross references are updated. That is the new model
S1: for for business, right? It's not started yet. It's barely started.
S1: Massively different for different companies, right? Some people have been
S1: doing this for a year or two already. People like
S1: Google probably, but most companies, they're barely even realizing that
S1: this is a thing right now. They're just like, oh,
S1: AI is a tool. Everyone's telling me I have to
S1: use it. Tell all your employees you must use AI.
S1: What does that actually mean? This is the thing that's
S1: actually going to happen. Sop's are established, goals are established.
S1: What are we actually trying to do as a company?
S1: We're going to talk about that one later. This is
S1: a massively important one. But all of that gets canonicalized captured,
S1: turned into SOPs, turned into goals, turned into texts, turned
S1: into actual things you could look at. But the important
S1: part for this particular point is the the map, this
S1: graph of all the different work, they're okay. Now a
S1: software vendor comes in and they're like, hey, look, we got,
S1: you know, the best. We'll just say this company needs background, uh,
S1: image removal or whatever. We have the best one out there.
S1: It's the best. Now, in the past, what would happen
S1: is they would bring their salesperson, take someone out to
S1: a steak dinner and be like, yeah, yeah, it's just
S1: it does background removal way better than anything else. Like
S1: you just have to have this. And if they convince
S1: this person and the manager and, you know, the purchasing
S1: authority or whatever procurement, whatever, they buy it, once again,
S1: it's a human doing the work. They have this image tool. Okay,
S1: maybe there's an API call if there's some automation, if
S1: it's relatively new, but ultimately it's humans buying software. Software
S1: is a package. It's sitting on a shelf and they've
S1: purchased a bunch of software. Maybe they use it, maybe
S1: they don't. In this new model, this lattice, this graph system,
S1: it includes all the tools. The work is laid out
S1: as a grid, as a graph. Here's here's the stuff.
S1: So watch this. This is a completely different conversation you
S1: have with the software vendor. Um, okay. Cool. Here is
S1: my map. Here's all the processes, all the work that
S1: I do. Every single workflow being done. What are you replacing?
S1: What are you doing better. Click on this note. Boom.
S1: You click on the note. Background image removal. Let's look
S1: at the metrics for it. Here's how fast it is.
S1: Here's how cheap it is. Here's how many times it
S1: failed here. Here's how many times it succeeded. Here's the
S1: ratings for how that's been done. Now what are yours?
S1: Now what are yours. And they have they're going to
S1: have to produce metrics that say I can do that
S1: function better. So now it's not about a software package
S1: that some human is buying. And maybe they use it.
S1: Maybe they don't. Now it's about an AI saying, here
S1: are my metrics for this function which fits into this graph.
S1: Can you prove that your metrics for doing this function
S1: are better? That's a completely different way of thinking about software.
S1: Once again, going back to for the consumer for the
S1: same service. Okay. Let's just say it's the same company.
S1: Remove background. Before they were marketing to the consumer with
S1: a pretty website and with SEO from Google, which is
S1: I'm sure, how I found this company in the first place.
S1: Remove background. Boom. It's a number one hit on Google
S1: that was targeted at me and it worked. Now that
S1: marketing goes to my agent, Chi Chi finds that for
S1: me in a directory, the same thing is going to
S1: happen inside the enterprise. The enterprise is going to be like, look,
S1: find all of our software, find out where it is
S1: on this function map. And if it's not, first of all,
S1: if it's not on the map, they're all fired. Cancel
S1: all contracts. Right. If it's not in this map or
S1: it's not in something, we're about to add a node
S1: because we have a new function, a new business line
S1: or new product or service or whatever. If it's not
S1: part of our structure, why do we have it? So
S1: it's just gone, right? But the conversation becomes improving upon
S1: this matrix. That's all that matters. So all these incoming
S1: like pitches and the steak dinners and like sales and marketing, blah, blah, blah,
S1: it becomes a whole lot less important once we're actually
S1: doing an objective measurement of what is the function that
S1: you're performing and how well do you do it. Right.
S1: So that's that's absolutely huge. Okay. So the transition here
S1: is going from corporate product marketing being a human to
S1: human targeted operation that results in a software package being
S1: purchased by a human, which is then used by a
S1: human or not. That's the old world. Two. The enterprise
S1: has a grid, a graph of all operations, and all
S1: functions inside of its company, and software is evaluated based
S1: on the function and the metrics that it provides. And
S1: those functions and metrics need to be superior or they
S1: don't get swapped out. So it's a that's the transition.
S1: It's a fundamental shift there. Okay. Next topic is work itself. Okay.
S1: So automation inside of companies. So the transition here is
S1: automation going from a thing that helps humans do their
S1: jobs better and basically improves productivity and efficiency and stuff
S1: like that, to being a way for companies to get
S1: to their ideal state of being able to do all
S1: the work themselves. This is colossal. This is economy changing. Okay.
S1: Because this really is the end of labor, right? So
S1: there's labor and there's capital. And these have always been
S1: in balance. This is how this is going to get disrupted.
S1: It gets disrupted because companies have always wished that they
S1: could do all the work themselves without employees. If you
S1: are only one founder and you don't have much work
S1: to do, you do all the work yourself. You get
S1: all the profits yourself. Now, if you hire a washing
S1: machine and your your business is washing clothes and you
S1: don't want to expand, you're not trying to scale. You're
S1: just trying to make the money that you make people
S1: bring you dirty clothes. You give them back clean clothes.
S1: That's a cool business. And you make lots of money
S1: and you are able to feed your family and you just.
S1: You have a good life, right? If you hire a
S1: washing machine before you were doing it on, on the
S1: board right in the river and you were making money,
S1: but you weren't making that much money, you weren't making
S1: as much as you could. So you decided to save
S1: up for a year and buy a washing machine. Now
S1: your washing machine is doing way more. You're able to
S1: do way more clothes and make way more money. If
S1: a whole bunch of people come to you and say, hey,
S1: I can also wash clothes in the river. Think about this.
S1: You need to hire me as a clothes washer in
S1: the river, because that's what's fair. And, um, there needs
S1: to be a balance between labor and capital if you're
S1: going to be like, are you kidding me? I can
S1: do all this work myself. I am literally doing all
S1: the work myself. They're going to be like, no, you're
S1: not doing all the work yourself. You have a washing machine.
S1: That is me. Okay. If I have a clothes washing
S1: business and I have ten washing machines behind me, that
S1: is me doing all the work myself. That is the
S1: transition that is happening. That is what AI is. And
S1: just to be clear, the total amount of compensation that
S1: knowledge workers receive is somewhere around $50 trillion per year,
S1: $50 trillion per year globally. I think it's somewhere around
S1: 10 trillion for the US. That is how much money
S1: companies are spending to pay humans. And the major transition
S1: here is they don't want to be paying those humans.
S1: They actually never did want to be paying those humans.
S1: My favorite way of capturing this is the ideal number
S1: of human employees inside of any company is zero. That
S1: is the number that they're trying to get to. Now,
S1: there are exceptions to this, right? If I'm a small,
S1: spunky founder and you know, I want to work with
S1: my friends, um, you know, I want to do a
S1: project with my friends. I get five of my friends involved.
S1: We build a small startup or whatever. It's kind of
S1: like we're all owners at that point, right? So you
S1: still will have, like, elite employees, cadre co-founders, stuff like
S1: that inside of these companies. Like, that's that's not going
S1: to go away. Like hardly any companies are going to
S1: have like zero employees, especially large or medium sized companies.
S1: But we're talking about going from tens of thousands of
S1: employees to a few hundred, maybe eventually a few dozen,
S1: but let's just say a few hundred. We're talking about massive,
S1: massive reduction because of this different way of thinking about automation.
S1: It's not a thing that helps a human do a task,
S1: which is what it's always been. It is a way
S1: to get to the state of the company does the
S1: work itself, which is a natural, clean, happy state for
S1: any company. They prefer to be doing the work themselves. Again,
S1: go back to the single person with a washing machine business.
S1: The washing machine business is part of them. It is
S1: part of their business. So they don't have a reason
S1: to hire employees. So that is the major transition, their
S1: automation going from a thing that helps employees to being
S1: a way for the company to do all the work themselves. Okay,
S1: so that brings us to the next transition, which is okay, fine.
S1: That makes sense. What are we supposed to do? What
S1: are we supposed to do? We have jobs, right? How do. Okay.
S1: If everyone gets fired, who's going to buy all the stuff? Right?
S1: Who's going to buy all the stuff? So that's a
S1: UBI conversation. Not really going to go into that. Like
S1: there's going to be money. People are going to receive
S1: money to pay their bills and stuff like that. Otherwise
S1: you just don't have a society. It's all bad. So
S1: that will get solved one way or another. Hopefully, uh,
S1: more gently and faster and easier, uh, rather than the alternative. Okay.
S1: So what does this look like for actual people? So
S1: instead of people en masse working for companies, you know,
S1: medium large sized companies or whatever, that goes away, that
S1: goes away, right? Because those companies like we talked about,
S1: they're trying to get rid of everyone. Instead of that
S1: being the way that we make our money. We're going
S1: to make money by producing value ourselves, by articulating the
S1: skills that we have, the capabilities that we have, the
S1: products that we provide, the services that we provide, broadcasting
S1: that out. And that is going to go up into
S1: one of these directories like I talked about before, with
S1: products that AI can look at, right? AI can find
S1: products in one of these directories, but this will also
S1: be the substrate for all work to be done. So
S1: humans will broadcast their capabilities and say, look, you know,
S1: I'm a systems engineer. I've got eight years of experience,
S1: two years experience, 25 years experience, whatever. Here's all the
S1: different stuff I can do. Here's my portfolio, blah, blah, blah.
S1: By the way, I like to mountain bike, blah blah blah.
S1: So this is your demon. This is your broadcast system describing,
S1: you know, the people you've worked with, like your reputation score.
S1: People give you upvotes kind of similar to what LinkedIn
S1: was doing. They kind of went away from this. It's
S1: now kind of like a social network. So this is
S1: the play though. There's a substrate that connects all these
S1: different people, right. When I need a cat sitter because
S1: I'm going on vacation, I'm going to broadcast out, hey,
S1: I need someone to watch my cat. They need to
S1: be fed. You know, they're crazy. They might scratch you. Whatever.
S1: Broadcast that out. Keep in mind, uh, my AI is
S1: broadcasting that out for me. Okay, everyone around AI is
S1: watching all the substrate, whatever this thing is called, and
S1: they're like, hey, I'm a cat person. You know, I'm
S1: a cat lady, I love cats, I can take care
S1: of cats. Um, you know, I don't get scratched or whatever,
S1: or I don't get sick when I get scratched, whatever
S1: it is. And I also live, you know, two blocks away.
S1: Being in her ear, her AI tells her, hey, there's
S1: a cat sitting job over here. You know it's going
S1: to pay $84. Um. Do you accept? Boom. Yes. Same thing.
S1: Like someone. There's a crash on the on the corner.
S1: You know, someone injured themselves. Hey, does anyone have any medical, uh,
S1: you know, professional training or whatever? Does anyone have EMT skills? Boom.
S1: It's going to beacon for people all around nearby who
S1: have a reputation score above so and so. They get beacon.
S1: Someone takes the job, they go help the person. Okay,
S1: same for gardening, same for engineering services, same for hey,
S1: I need a shoulder to cry on. Hey, I need nurturing. Hey,
S1: I need tutor for my kids. Hey, I need meal prep. Um,
S1: I need to gain a lot of muscle mass in
S1: my legs. Uh, I need a personal trainer. Everyone who
S1: has services, capabilities, value to offer, they are beaconing out
S1: onto this system. It is out there on the substrate,
S1: and then everyone's AI is looking at that system. And
S1: that's how you find work. Okay. Another version of this
S1: is like Fiverr. Okay. It never it's not automated, but
S1: it's the same kind of vibe. Uh, also as LinkedIn,
S1: it's like, here are my services. Here are the jobs. Right.
S1: So you you've got consumer and producer, right? You've got like,
S1: here's the work I need done. And then you've got
S1: the other group saying, here's all the stuff I can do.
S1: And then it's a matter of, okay, we're going to
S1: join together. It's going to be five people on this project.
S1: It's going to be a six month long project. Here's
S1: the money that I'm offering. Boom, boom, boom. Everyone agrees. Boom,
S1: the work starts. That is what I'm calling human 3.0.
S1: It's what I'm calling the state that we're going to
S1: get to do. I know this is going to happen
S1: for sure. I believe the answer is yes. Um, only
S1: because I'm an optimist and I believe we are not
S1: just going to die off and, you know, devolve into
S1: chaos after the corporate system breaks down. Uh, I can
S1: tell you that the corporate system is going to break
S1: down because the corporate system is going to pull all
S1: the work internally and do it with automation and AI
S1: and robotics. That seems obviously inevitable. Again, not everyone, but
S1: most people will be laid off as a result of this.
S1: Over time, it'll happen different speeds and different industries, but
S1: that is pretty much inevitable. I think that this better solution,
S1: this new solution, is also inevitable. The reason I love this,
S1: and this is a little bit of an aside, but
S1: I love this because it's more human focused. It's humans
S1: connecting with humans. And there'll still be companies, right? It's
S1: humans connecting with companies. But it's not in this hierarchical,
S1: you know, you know what corp corporate. That's that's military.
S1: This idea of. I work for Sarah. Sarah works for Joe.
S1: Joe works for Raj. And. Oh, I'm having a meeting
S1: with Raj. Oh my God. You know, he's three skip
S1: levels above me. Like this whole military structure, this whole, like,
S1: dreading Monday. And just like, this whole thing is, like,
S1: toxic and poisonous. And it has been for decades. People
S1: have been so unhappy with this. And now that it's
S1: actually under threat, people are like, well, don't get rid
S1: of my job. Don't fire me. And which is justified, right?
S1: Because they're worried about losing their livelihood and they're worried
S1: about being able to pay the rent and a mortgage and,
S1: you know, school and groceries. So it's understandable that they're
S1: clinging to that. But remember, we shouldn't be clinging to
S1: a thing we hate and we have hated. Right. All
S1: this to say, I think the way that it's going,
S1: the way that I'm pushing for, I'm actively building to
S1: make this happen is to have this new human based
S1: substrate where producer, consumer producer, consumer things are a lot
S1: more equal. You get hired based on your skills, and
S1: that's a relationship you can get out of any time.
S1: And here's what's cool about it. You can be in
S1: multiple of these, right. You can have ongoing like retainer
S1: type things going on with like 20 different customers. And
S1: you're on this big project for a full six months
S1: and you're getting paid from that. Plus you're doing the
S1: cat sitting. Plus you're a part time EMT in the
S1: in the nights just in case someone falls off their
S1: bike or whatever, you know what I mean? So it's
S1: like it's just a more aligned thing, right? Because now
S1: you're actually broadcasting everything that you you are right. You're like, yeah,
S1: I play violin. I will come to your son's birthday
S1: party and play violin if they like classical music. So
S1: it's like you're broadcasting not just, oh, you know, here's
S1: my resume. I'm a tech engineer, level three, blah blah, blah.
S1: I worked at Intuit. That is not you. You. What
S1: this is going to allow us to do is broadcast
S1: our full selves and monetize ourselves in the best way possible.
S1: Not in a gross way. We're going to say we
S1: are going to be compensated and rewarded for being ourselves.
S1: If we are the best nurturers in the world, forget
S1: tech skills. Forget any tech, right? You're the best nurturer.
S1: You're the best listener. You're the best mother, you're the
S1: best parent, you're the best tutor. You're the best trainer.
S1: You're the best boxing coach. Okay? They're not producing products.
S1: They're not releasing startups. They're helping other people become the
S1: best versions of themselves. In many ways, they're the most
S1: important people. Well, guess what? That's in there. Demon that
S1: is broadcasted. They become world famous for that. So they
S1: got everyone trying to come in and use them, right?
S1: And they make money from it, which they should. This
S1: is how humanity should work, right? So anyway, this is
S1: the tech layer I think is going to replace the
S1: whole system. This is the transition that I think is happening.
S1: I just want to cover that because a lot of
S1: people have questions. What am I supposed to do after
S1: we lose all these jobs? Okay, next topic I want
S1: to talk about is cybersecurity. So similar to a lot
S1: of the other things we've already talked about. Cybersecurity has
S1: been human based, right. Um, it's you hire a human team,
S1: you have human staff. They are good security engineers or whatever.
S1: They're doing pen tests, they're doing security assessments, vulnerability assessments.
S1: They are manually looking at all the different vendors and
S1: trying to figure out, is this one dangerous? Um, should
S1: we allow it through procurement? And they're just being bombarded
S1: by all these requests. And, you know, if you have
S1: a really good, you know, third party service person, um,
S1: auditor person, they could look at x number of, of
S1: those vendors per day. Maybe some of them get escalated
S1: because the CEO really wants that, uh, piece of software.
S1: So you have to do a security assessment. You have
S1: to find some way to make it secure because it's
S1: coming in the company anyway. That's the life of a
S1: security person. That's the life of a security program. It's
S1: humans grappling with all this stuff in a very gross
S1: sort of in the weeds, like sewer type of thing.
S1: It's not pretty, it's not clean, but it's got the
S1: job done, right. We're still alive. The inner still mostly works.
S1: You could still go to the ATM and get cash out.
S1: So security has been largely successful. Keep in mind, we're
S1: still losing billions to fraud all the time, but it's
S1: a messy process. What security becomes now with all this
S1: AI stuff is it becomes your AI stack. As a
S1: defender against the AI stack of the attacker. And unfortunately,
S1: you're not facing one attacker, you're facing all the attackers.
S1: So the attacker is trying to understand your company extremely well.
S1: It's making a list of all your employees. It's creating
S1: a personality profiles on all your employees. It's coming up
S1: with the best spear phishing campaigns to find the ones
S1: who probably have the most access based on their job title.
S1: And it's sending out these spear phishing attacks. It's constantly pulling.
S1: Your DNS is trying to see if you're doing a
S1: merger and acquisition with a company that doesn't have good security,
S1: so that's going to be a weakness. And they're launching
S1: a malware. They're sending you a spear phishing emails. They're
S1: trying to compromise all your websites. They're trying to pivot internally.
S1: They're trying to sell the access that they have. And
S1: they're doing this at like machine speed, right? They've just
S1: got so many agents working on this constantly, constantly hitting you.
S1: You can't tell Chris and Raj and Sarah. Hey, uh,
S1: great job last year. Um, I need you to do
S1: 895 times as much work because, uh, that's how many
S1: more attacks were being hit with. That doesn't work. Also,
S1: you can't be like, uh. Hey, great news. We got
S1: three more head count. That also won't make a dent.
S1: Your only chance is to have the same AI or
S1: better as the attacker. And this goes back to what
S1: happens to all companies. What happens to all companies also
S1: happens to security programs. It's no longer about here's our
S1: security team hears roughly the things we need to do.
S1: And yeah, there's some documentation, but ultimately it's like who's
S1: on call, who's doing the work, blah, blah, blah. And
S1: it's like a very human focused thing that's all out
S1: the window. SOPs, SOPs, everything is a process and workflows
S1: which you could visually look at and see, look, this
S1: is the queue for processing incoming things. Uh, here's a
S1: queue for, you know, here are the constant workflows that
S1: are happening, all the cron jobs or whatever it is
S1: for finding insider threats. Here's all the processes for managing
S1: the CI, CD. Here's every single tool, every single decision point,
S1: every single approval point that needs to happen as part
S1: of CI CD before something goes live. We're doing static analysis.
S1: We're doing dynamic. We're doing, uh, you know, we're looking
S1: at some other aspect of the application, how it impacts
S1: the business. Each one of those turns into a little node.
S1: That is where a tool drops into. This is where
S1: a human drops into. This is where the human is
S1: replaced because a very smart AI model just took over
S1: that role. Everything becomes transparent, visible with discrete actions and
S1: activities and decision points at each area. And the question
S1: is then how well are we doing this? How good
S1: are our information sources feeding our context? This concept of
S1: unified context that I put out a year or so ago,
S1: maybe 2024, actually unified entity context. Everyone like I was
S1: talking about with the corporate side, also the security side,
S1: we're all working off a unified context of the company.
S1: What are its goals? What are its challenges? What's the
S1: risk register. Look like they're all working off the same
S1: sort of thing. Right here are our attackers, here are
S1: sensitive things. Here's our crown jewels over here. All of
S1: this is unified. We have this map of all the
S1: workflows that are happening, all the different tools. And now
S1: the question is, are we getting that context updated? Okay.
S1: New AWS account, which just stood up. We just launched
S1: a new application, a new service in Asia and also
S1: Iceland and also in Seattle. And it's got all these
S1: new services on it. How quickly are we as a
S1: security team learning that they did that? Because that was
S1: some crazy marketing group over there or whatever. Let's just
S1: assume we're going to have shadow it still for quite
S1: a while. This transition I'm talking about with like everything
S1: being mapped out and SOPs. That's not going to happen overnight.
S1: This is going to take a long time. Some companies
S1: this is going to take 15 years and they'll probably
S1: be dead, but some will survive and it will take
S1: them 15 years because they're a soup sandwich. It will
S1: take them forever. Somehow they survived. Many companies, they'll do
S1: this over the course of two or 3 or 5 years. Right?
S1: Some companies have already started this. Some companies won't start
S1: this for another three years. It's massive, massive variance in
S1: how fast it's going to happen across various industries. Okay.
S1: So I don't want you to think like, oh, I
S1: think this is happening right now to everyone. And it's
S1: just like it's going great. No, this is barely starting. Okay.
S1: So the point is this structure is what attackers are
S1: going to use to attack you. They're going to have
S1: a world model of your company which has all these
S1: pieces right there. Hopefully they're not as filled in as
S1: your version, right. Um, when you launch that AWS stuff,
S1: when you launch those new services, you have access to AWS. Hopefully.
S1: Hopefully your attacker doesn't, so you should be able to
S1: pull that like really quickly. Okay. You should have access
S1: to all your endpoint visibility. You should have access to
S1: all your different tools, all your different APIs, all your
S1: different agent infrastructure constantly pulling asset management. Asset management is
S1: about to get elevated to top top tier. Finally thank goodness.
S1: But you should have this data way faster than your attacker. Unfortunately,
S1: all of this is very, very ancient inside of most companies.
S1: I have been doing consulting for over 20 years. Most
S1: companies I see they do not have asset management. They
S1: do not have unified documentation. They do not have most
S1: of this stuff. Okay, we're in a bad state. We're
S1: in a bad state because as AI starts spinning this
S1: stuff up for the attackers, they're going to build a
S1: world model of these companies faster than the company has it,
S1: because the company has to go slow. They have to
S1: have 19 meetings to prepare for the meeting. Attackers are
S1: just going to like YOLO it, you know, submit the
S1: single prompt, make no mistakes and start attacking. So all
S1: this to say, transition is humans doing security work to
S1: a unified workflow model with SOPs of the work that
S1: is being executed largely by agents, largely by AI, with
S1: humans there to tweak and improve and guide and instruct
S1: and steer and validate the AI automation. And the game
S1: here is for your orchestration system to be better than
S1: the attackers. All right. So this next one is a
S1: way of thinking about enterprise AI in like a completely
S1: different way. And I put this out about a year
S1: ago or so, and I think it's really powerful as
S1: an inversion. So the idea is that, um, currently it
S1: definitely a year ago, two years ago. But even still
S1: now everyone is thinking, okay, we have security, we need
S1: to put AI on it. Okay, we have finance, we
S1: need to put AI on it. Okay. We have um,
S1: HR we need to put AI on that, which means
S1: we need AI agents. Um, for finance we need finance AI,
S1: we need, um, security, AI, security, AI agents. Right. So
S1: the idea is you have the discipline, you have the topic,
S1: and then AI gets sprinkled on top, and it's supposedly
S1: going to do something that benefits you related to that topic.
S1: This is I don't think, the way to think about it. Okay.
S1: I think the way to think about this is actually
S1: you have a company and you have the company's work
S1: and all its workflows and the graph of all the
S1: services and the tools and the operations, SOPs, goals, everything
S1: that is actually the system. The system is the graph
S1: of operations, okay? It is the graph of algorithms that
S1: take place to make this business function okay. Maybe it
S1: has humans there, maybe it doesn't, maybe has lots of humans,
S1: maybe it's mostly AI and a few humans. Doesn't really matter.
S1: This graph of functions, this graph of algorithms is the company.
S1: Now think of AI as a system for running this
S1: graph of algorithms. That is what AI is. Then you
S1: have the question of okay, what are you doing for finance?
S1: What does procurement look like? Show me procurement. You're looking
S1: at this graph. And this one little, um, line lights up.
S1: It's actually like 19 different lines. They all light up. Oh,
S1: these are the procurement workflows. Cool. So we can drill
S1: into those. We can inspect. Okay, here's the tools. Here's
S1: the human involved. Here's the decisions. Here's the sign offs.
S1: Here's the exceptions. Here's the risk register. Whatever it is,
S1: the overall system is the graph of algorithms largely run
S1: by AI. What ends up happening is all the different
S1: things that used to be industries. They become use cases
S1: inside of AI. Okay. So the before is you have
S1: industries using AI. And now what you have is a
S1: graph of algorithms run by AI that has use cases
S1: for different things. Right. You you've got some of these
S1: things are security. Some of these things are HR. Some
S1: of these things are engineering. Some of these things are marketing.
S1: AI is the container. AI is the thing. And it
S1: just has functions that happen to be affiliated with what
S1: we used to call industries, that that is a fundamental transition.
S1: Now it's not absolute. This is a mental model, but
S1: I think this mental model is a lot more descriptive
S1: of what's actually happening. Then thinking about it in terms
S1: of like, well, let's do everything the way we used
S1: to do it before. Okay, we've got an HR database,
S1: we're going to have an HR interface. We're going to
S1: have HR tools and an HR dashboard. No that's going away. Okay.
S1: If you abstract everything to questions and everything to algorithms,
S1: the question is something like how happy are our employees?
S1: How much money are we spending on compensating employees? Should
S1: we pay more for bonuses? Those are questions. Those questions
S1: are not in industry. Those are questions that just happen
S1: to be associated with what we used to call an industry.
S1: Those are all hitting unified entity contexts. The results are
S1: coming out and you have the answer. But ultimately, it
S1: is all feeding off this underlying unified context and graph
S1: of algorithms and understanding of what the business actually is.
S1: And all of that is powered and enhanced and managed
S1: and orchestrated by AI. Okay, here's the next one. Custom everything.
S1: So in the past and this one crosses consumer and enterprise.
S1: So in the past you basically had very few people,
S1: very few organizations, very few companies producing software, right? You've
S1: got Adobe, right? There aren't many competitors to Adobe. When
S1: Adobe was on top, it was on top. When Microsoft
S1: was on top, it was on top. You have a
S1: few competitors, but you go into any enterprise. They're mostly
S1: running Microsoft Shop, they're mostly running Google Shop, whatever. I
S1: think that might start to go away. Um, it's not
S1: that I think that these major platforms won't still have
S1: a stronghold or whatever. It's just that I don't think
S1: the implementation of their software is going to look the
S1: same inside of all these different companies, and it could
S1: be for a much smaller companies that aren't legacy. Their
S1: software stack could look completely different from another startup, maybe
S1: even doing the same sort of stuff just because the founders,
S1: which they'll probably be very few, the founders will just
S1: be like, yeah, I like this style. I like terminal style.
S1: I don't like UIs, you know? I want everything to
S1: be API based. You know, I really like the color purple.
S1: I need purple and blue. I need the software to
S1: look a certain way. I need it to act a
S1: certain way. I want all the auditing and logging to
S1: go a certain way. You know, I want to be
S1: stood up only in these regions, whatever, whatever their preferences are,
S1: and the preferences for the interface for all this stuff. Custom.
S1: We're already seeing this with like custom replacements of tons
S1: of different SaaS software. I mean, this is just all
S1: over the news everywhere. It's really easy to create a
S1: version of something now. It's a big difference between being
S1: able to have that thing roll out, you know, enterprise wide,
S1: and it's stable and it's secure and all of that. Right?
S1: There's some work to be done there that'll take, you know,
S1: a couple years to sort of work its way through.
S1: But I think there's a very high chance that companies
S1: and consumers will be making their own software even if
S1: they're not making their own software. Let's be clear about that.
S1: There's 8 billion people on the planet. Not everyone's making
S1: their own software, but just just consider in 2019 or 2022,
S1: the number of people who made software products was rounded
S1: down to zero. Okay, that's the way to think about this.
S1: Rounded down to zero. Everything in the App Store, every
S1: software company rounded down to zero. If you multiply that
S1: by 1000 or 10,000 or 1 million, that is a
S1: lot more software that is. Plus, we're talking about the
S1: ability for someone to speak to their AI agent, which
S1: we've already talked about. Everyone's going to have one and say, hey,
S1: I really wish I had a workout app. You know,
S1: I've been using this one. You know, it's $19 a month.
S1: It's pretty good, but I just wish it did this.
S1: I wish it did this. I wish it did this.
S1: And boom, it's now installed on your phone. It's on
S1: your thing. The other one's uninstalled. The subscription is cancelled.
S1: That same exact thing is going to happen inside the enterprise.
S1: Now in the enterprise, it's going to take a little
S1: bit longer because that thing needs to be integrated with
S1: everything else. It's got to have the tie ins, it's
S1: got to have procurement, it's got to have legal sign off.
S1: Auditors have to sign off. There's a lot more workflow
S1: involved in being able to do that. But I can
S1: tell you this every CFO is looking at their software
S1: list and saying, how can I cancel all of this?
S1: It's just like the employee thing, how can I get
S1: to zero employees? That's what the CFO is thinking as well.
S1: How can I not pay any other company for software?
S1: How can it all be ours? Everyone wants this. The
S1: question is, you know, we couldn't do it before. We
S1: couldn't do it before because it's hard to make software.
S1: You got to maintain it. You got to do all
S1: this stuff. Well, the better AI gets, the easier that gets. Okay,
S1: So the tendency here is from common shared large few
S1: companies producing software to everyone has their own and right.
S1: And it's not going to go to the full extreme right.
S1: It's going to I'm going to say it's going to
S1: go to like a 90% extreme or an 80% extreme,
S1: highly specialized software, everyone running their own personal AI stack
S1: as a person, as a human, your AI system is
S1: going to look, feel, act, behave differently than someone else.
S1: They might even be using similar products, but your interfaces
S1: are going to be different. The way it speaks to
S1: you is going to be different. The news sources that
S1: you're filtering is going to be different. And that takes
S1: me to my next one, which Robert Putman talked about
S1: in his book Bowling Alone in, I think, 2000. When
S1: you have fragmentation like this across companies, but mostly across people.
S1: When you have fragmentation like this, that's going to be profound.
S1: So one of the things Putman talks about in his
S1: book is the reason the country was so unified before
S1: is that we were all watching the same TV shows.
S1: We're all watching the same news. We all drove the
S1: same cars, we all had the same watches. We all
S1: went to the same churches. We all lived on the
S1: same block. Maybe someone had a slightly bigger house, but
S1: we're all living in the same sort of community and
S1: everyone's consuming the same sources. Everyone's thinking similar things because
S1: we're reading the same newspapers, we're learning the same things
S1: in schools. Well, when you take what I just talked about,
S1: everyone gets custom software, everyone has custom AI, and in fact,
S1: you're not even viewing the sources. All these APIs, all
S1: these services that make up the world, you're not viewing him.
S1: There's millions of them. There's billions of them. Your AI's
S1: job is to consume all of that. Its job is
S1: to understand you and understand your needs. It consumes all
S1: those services, and then it gives you what you want. Well,
S1: what does that open up? It opens up the possibility
S1: that all of us will be having a different world experience.
S1: We believe the reality that we see. Okay. So when
S1: you watch a particular news source, if you watch particular
S1: YouTube channels, you watch particular podcasts or YouTube channels or
S1: TV shows or, you know, news channels or whatever, you
S1: think the world works that way. That is your world.
S1: That is reality to you. And if you watch this
S1: other set, which this person might live next door, they
S1: are in a completely different reality than you. They don't
S1: even know that this new, uh, dancing team from, uh,
S1: Korea is the most popular thing. And they got, uh,
S1: 20 billion views on the video that just came out yesterday.
S1: And to all of your friends, this is the most
S1: important thing that's ever happened. And you just can't believe it.
S1: You've watched it over and over. You cry every time
S1: you watch it. It's the most amazing thing ever. The
S1: person next door doesn't know it exists. In fact, they're
S1: not sure if Korea is a real country based on
S1: the media they're watching. They're like, yeah, that could just
S1: be made up. This fragmentation, because of custom. Everything is
S1: going to be massive right inside of companies. The dynamic
S1: here is very strange. How do you audit software. How
S1: do you do security scans. How do you do auditing. Um,
S1: how do you do legal compliance? It's a lot easier
S1: when everyone has SAP and everyone has Microsoft three. 65.
S1: And everyone has, you know, CrowdStrike and everyone has Palo
S1: Alto firewalls and checkpoint firewalls. Right. That's a whole lot
S1: easier because people have skill sets around those things. What
S1: happens when everything is custom, right. So the transition here
S1: roughly is few things that are packaged that most everyone
S1: uses to millions of options with infinite customization. And everyone
S1: is kind of using different stuff customized just for them,
S1: and they're experiencing different realities as a result. All right,
S1: final one. I've saved this one for last. So I
S1: think the kind of ultimate use case for AI is
S1: what I'm calling ideal state management or state management or
S1: state orchestration. Not really sure on the term. Doesn't really matter.
S1: The real term will be created probably in the next
S1: year or so. The idea is. What we've been doing
S1: inside of companies, what we've been doing as people, what
S1: we've been doing as a society is just kind of yoloing.
S1: This is what we've always done. This is what humanity does.
S1: We're like, hey, you know, I need to do better
S1: next time. I need to do you know, less of
S1: this thing that's really hurting me? Companies are like, hey,
S1: we should have goals. We should have OKRs. Hey, we
S1: should have a meeting. We should plan the next year
S1: or whatever. Those documents go somewhere. Maybe they get revised,
S1: maybe they don't. But what you do see, and again,
S1: I've been doing this for so long. If you track
S1: a company's goals, um, for an average company, like a
S1: medium sized company, which there are just, you know, hundreds
S1: of thousands of these things all over the world If
S1: you look at their goals and plans and metrics over
S1: the course of a year or five years or ten years,
S1: they're just making shit up like it changes constantly. The
S1: management changes like they, uh, they come out with a
S1: set of metrics. They don't hit them, they come out
S1: right after that and they're like, uh, yeah, guess what?
S1: We got a new plan. Uh, you know, we hired
S1: this person. It's going to be amazing. Here's new metrics,
S1: here's the new plan. And it's just kind of like
S1: constant reinvention, but not because of innovation. More so just
S1: because we're winging it. And this is no fault of anybody's.
S1: Everyone's doing it very, very smart. People are doing it.
S1: It's just the reality that we live in. Okay. This
S1: is one of the fundamental changes that AI is going
S1: to bring. I think what we're about to move into
S1: is a situation that moves away from this ad hoc
S1: YOLO sort of situation to the ultimate use case for AI,
S1: which is state management. And state management starts with defining
S1: what ideal state is. This is a thing that most
S1: companies do not have. They they do not have an
S1: articulated statement, kind of like a PRD document which has
S1: multiple SOPs revolving around it that says, here is our
S1: actual mission, here are our actual goals, here are the
S1: problems we are trying to solve in the world, which
S1: is why this is our mission. Here are our goals.
S1: Here are our challenges. These are the things that are,
S1: you know, we can't hire fast enough. You know, we
S1: can't ship product fast enough. We keep getting beaten by
S1: our competitors. We need to move into Asia. We're not
S1: there yet. Like you put all your challenges, you got
S1: your risk register, you have your projects, you have your budget.
S1: You have your people and this is your unified document.
S1: This is your your grail. This is your system. This
S1: is your algorithm for what? What you're chasing. Okay? This
S1: being locked into the core DNA of the company and
S1: everything revolving around that. So I talked about all this infrastructure, this, um,
S1: graph of algorithms, that graph of algorithms needs to be
S1: feeding this system, this ideal state, this perfectly articulated and
S1: not perfectly. Nothing's gonna be perfect. This very well articulated
S1: ideal state is about to become the most important thing
S1: for companies, but also for anything, for organizations, for entities,
S1: for people. This is a big thing that I work
S1: on in my private work. If the Pi system with
S1: my Telos system, like all the stuff that we're doing
S1: in my community, we think about ideal state from the
S1: very beginning, ideal state. This concept is extraordinary. It's extraordinarily powerful. Okay.
S1: I've been using it for a long time. Not this
S1: new version that I have now, but roughly I've been
S1: using a system like this for probably ten years and
S1: there wasn't any AI. You don't need AI to sort
S1: of think about, articulate this and start moving towards it.
S1: It's very powerful. Forget any tech, just note cards, index cards,
S1: a space pen, you're off and you're going to have
S1: massive benefits from doing this. Here's where the AI comes in.
S1: And this this is like the sickest, most powerful, most
S1: exciting thing in all of AI across all AI ideas
S1: that I've ever heard about or come up with or whatever. Okay,
S1: so check this out. This is the universal game. When
S1: we meet aliens flying around the galaxy or whatever, and
S1: we tell them this thing I'm about to tell you,
S1: they're going to be like, yeah, that's what everyone does.
S1: How do you think we got here? How do you
S1: think we have all these spaceships? How do you think
S1: we have all these planets? How do you think we
S1: have Dyson spheres around all these stars? Like that's obvious.
S1: That is the algorithm, right? So this is what I
S1: call it. I call it the algorithm. The algorithm is
S1: we have ideal state. What is our current state okay.
S1: Step two what is our current state. So remember what
S1: I was talking about before about you have the graph
S1: of algorithms. You have asset management gets elevated to the top.
S1: This current state. It's the job of your AI and
S1: all the different agents and all the different systems. It's
S1: not all AI. This is deterministic code as well, right?
S1: What is the current snapshot of the ORC? What is
S1: the current state of our problems and our solutions and
S1: our products and our services? How happy are our people?
S1: How happy are our customers? What is the current churn number?
S1: What is the current products we're about to release? What
S1: are our competitors about to release? What are the market conditions? Snapshot. Snapshot. Snapshot.
S1: Ideal state. Ideal state is here. We're in Asia. We
S1: have 30% more employees, actually more likely 30% less employees.
S1: But separate topic. Um, you know, we want to be
S1: in these markets. We need to have more services. We
S1: need to have more private luncheon learns, uh, in our
S1: local community, whatever it is, all that's going to be
S1: hundreds of thousands of things or hundreds of thousands or
S1: millions of things. Whatever it is, our current state also
S1: has millions of things in it, right? Because it's going
S1: to go down to like eventually at some, some level.
S1: Here's the current open ports for all of our Kubernetes pods, right.
S1: All of our AWS infrastructure. Current state, current state over here,
S1: they're all locked down, nothing publicly available, strong authentication on them, etc..
S1: Now here it is. Watch this. The role of AI
S1: is continuous migration continuous gap closing between current and ideal state.
S1: This works as an individual trying to lose 20 kilos
S1: which I'm trying to do. An individual trying to lose
S1: 20 kilos. Trying to increase their Vo two Max trying
S1: to find a wife trying to get their art exhibited
S1: in the local gallery and eventually at MoMA in San Francisco,
S1: trying to come up with, uh, new EDM tracks so
S1: they can go to EDC and actually play live on
S1: stage trying to run a federation of planets. Okay. If
S1: we're talking about, you know, 9000 years in the future
S1: or whatever, anything you're trying to do at any scale
S1: can be managed by ideal state and current state and
S1: the migration. This is extremely, extremely powerful. I think we're
S1: about to apply this to almost everything. I think prds engineering.
S1: So I have this thing inside of the Pi platform.
S1: It's called the algorithm. I literally start the algorithm by
S1: decomposing the prompt that comes in and turning it into
S1: reverse engineered ideal state components. I break out the pieces,
S1: I look at the context from the user, and I
S1: do deep analysis, and we do research as well. This
S1: is if you have a lot of time, do research
S1: and you figure out, okay, what do they actually mean
S1: by this? Okay, I might have sent in a single
S1: one sentence prompt, but I do all this research to
S1: break it into its pieces. What is he explicitly say
S1: he wants? What is he explicitly say he doesn't want? Okay.
S1: What is he maybe mean by that? What are some
S1: common gotchas for someone trying to build a system like this?
S1: All that gets decomposed, reverse engineered and I start creating
S1: ideal state criteria. These actually go into cloud code tasks, right?
S1: The task system from cloud code, which is extraordinary. I
S1: put them all in there. Then they go into the
S1: PRD and the PRD is what we work off of,
S1: but it's all working off of ideal state so engineering
S1: can be run this way. Companies can be run this way,
S1: you managing your life, you managing your entire family's happiness. Okay.
S1: My husband, you know he needs to be more healthy.
S1: My kids, they need to have better education. They need
S1: more tutoring in this. Hey, I'm looking at the dashboard.
S1: Looks like they haven't had enough vegetables. Okay, I'm going
S1: to tell my AI we're going to order more vegetables. Hey,
S1: we haven't gone on a vacation together in a while. Hey, um,
S1: I noticed, uh, we were looking at our phones a
S1: little bit too much at the dinner table last few days.
S1: My AI notified me about that, so. Yeah, let's make
S1: sure we're all looking in each other's faces and talking about, uh,
S1: physical art. Let's make sure we do a number of
S1: vacations during the year where there's no tech involved whatsoever.
S1: We're focusing on human things and all of this. Again,
S1: the AI is managing this stuff for you because you
S1: put it in your ideal state. Okay, so if you're
S1: running an ice cream truck business, you're running a federation
S1: of planets, or you're just trying to like, find a
S1: girlfriend or a boyfriend. All of this can be managed
S1: the same way. Same way as if you're trying to
S1: launch a new SaaS replacement app, or a new workout app,
S1: or whatever you're trying to do with AI. Everything can
S1: be managed by thinking about it in this way. Now
S1: there's a lot of steps in between, but those steps
S1: can be scaffolded. Those steps can be engineered, those steps
S1: can be filled in. And the smarter the AI gets,
S1: the more it's going to be able to just know
S1: how to help you transition and build plans to help
S1: you transition. So I think this is potentially the biggest
S1: idea in AI. I don't think many people are talking
S1: about it yet, but I think this concept of ideal
S1: state is is is about to be huge. And in fact,
S1: I think Agentic platforms are about to start building it
S1: in to like everything they do. Wouldn't be surprised to
S1: see this cloud code Codex Gemini start to move towards
S1: AGI generality by doing this reverse engineering of requests into
S1: an ideal state that you then pursue. Because here's the trick.
S1: And I got this from Andrej Karpathy you can't hill climb.
S1: You can't progress towards something if you don't have failures,
S1: if you don't have a thing to hill climb against.
S1: And this is why I reverse engineer everything into ideal
S1: state criteria, because they are also my verification criteria. So
S1: what Karpathy said was previous software was you can make
S1: anything the next generation of software will be you can
S1: verify anything and I think it's very powerful. So my
S1: ideal state criteria in the algorithm within, uh, personal AI infrastructure,
S1: the open source project, it turns the ideal state into
S1: the verification criteria. And they're all discrete and they're all verifiable,
S1: yes or no. And that's what gets us to this
S1: verifiability thing that Andre was talking about. And that's what
S1: ultimately allows us to go from current state to ideal state.
S1: So really excited about this. I know this was tons
S1: of content and I just want you to think about it.
S1: It's a set of transitions. It's not just one transition.
S1: So I consider all of this to be part of
S1: the great transition. Hopefully it's a mental model. I know
S1: it includes lots of subset things, but I think all
S1: of it combined does produce a single mental model. So
S1: hopefully you can now see when all this news comes out, right?
S1: New models, new products, new services. You know, um, Co-work
S1: can now do this. Oh, it's it's it's going after
S1: the lawyers. It's going after whatever. What my goal is
S1: here is for you to be able to say, well,
S1: hold on, you know, based on this great transition model,
S1: that thing that's happening is not something, something new. It's
S1: not going to produce anxiety. Right? I'm trying to reduce
S1: your anxiety of thinking about all this change, because I
S1: believe this great transition sort of model, with all the
S1: little submodels and transitions that I was talking about, it's
S1: kind of like a container. It's a container. It's a
S1: mental model container that can reduce your anxiety if you
S1: understand this, in my opinion, if you understand this, then
S1: you just won't be surprised by things, right? And I
S1: can be surprised. Like I could have missed something, right?
S1: Obviously I could be wrong about any one of these
S1: or more, but in general, I don't think I'm going
S1: to be surprised by this. I put the stuff into
S1: the book in 2016. It is coming true. I think
S1: it's a natural progression for the stuff to be happening.
S1: Like who knows how it's going to happen, when it's
S1: going to happen, which company is going to do it.
S1: You can't predict that stuff. It's impossible to predict, but
S1: the direction I think is possible to to see where
S1: it's potentially going. And that gives you a container, a
S1: mental model to be like, hmm, okay, this all sort
S1: of makes sense inside of this framework. So you're not
S1: spooked out by like all these new models coming out,
S1: new products coming out. It's like, oh, this person's going
S1: to lose their job, that person. These industries are being disrupted. No.
S1: If you have this mental model, I think you're more
S1: likely to just be like, ah, yeah, that seems to
S1: be the way it's going. And, uh, you can buckle
S1: down and just move forward. So hopefully this has been
S1: helpful and, uh, we'll see you in the next one.