Why AI Will Replace Knowledge Workers

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S1: Hey, what's up? So there's a common narrative right now that you don't need to worry about AI. If you are a knowledge worker, because humans are special and humans are unique in the way they approach work. They are better workers than AI. AI is just a text generator. It's just an LLM. It has no understanding. It has no knowledge. It doesn't really understand anything about what it's trying to do. It can't possibly replace human workers. This is a very popular narrative for multiple reasons. One, it makes us feel better. It makes us feel more safe because people are feeling afraid for good reason right now. And it's just a very common thread right now, basically saying, relax, you don't have to worry, right? AI is actually kind of garbage and humans are basically awesome. You don't need to worry. I think this is wrong. And more importantly, I think it's dangerous. I think it's wrong for a bunch of reasons, which I'm about to talk about. I think it's dangerous because if you adopt this mindset, if you accept this argument, you will not properly prepare to get ready for what is coming, right? So this is a polar opposite argument to the one that's being made. And keep in mind, it's being made by some very smart people. Right? I've got a friend we're going to talk about Marcus Hutchins, very smart security guy, a friend of mine, and we debated this once. Might do it again in the future. He's very smart. He's very technical, he's very knowledgeable. And he has this argument. He basically says, don't worry, it's overinflated. AI doesn't understand anything. And it's not just him. There's, you know, hundreds of people out here saying this very loudly and more importantly to me with a following, right? Marcus has a following. And there's people who have much larger followings than Marcus and me who are saying pretty much the same thing. And so I want to really come at this argument directly, and I want to start by talking about what work looks like inside of companies and sort of just like break down common things that I see all over the place and have seen over the course of my career. All right. So normally in these videos, I don't talk about my background. It's because I just don't care about my background. I don't care about other people's background. Usually. I mostly just want to hear the content. I want to hear the ideas that they're giving me, or if it's a tutorial or whatever, I want the content, right? In this case, I'm making a lot of claims about how businesses function, how they work. And so I think it raises a natural question which should be asked, which is, why should I believe you? Why do I think you understand anything about these companies? And the answer is, I've been working inside of companies for like over 25 years. My background is strictly information security, but I've held senior roles at Apple. I've built a program and a product there which is still running. I've been gone from there for a number of years now. I've worked at Robinhood, HP. I've worked in embedded in very large energy companies. Tons of experience actually, in the fortune ten, let alone like the fortune 100. And that's just the places that have actually been deeply embedded in as a consultant, like basically a full time worker there or working full time consulting though, which I think is even more important for the context here. I've actually worked for hundreds of companies in like the top 10,000 companies in the world, like hundreds over these 25 years. Inside of all these companies, I have seen these problems that I'm about to describe over and over and over. And in fact, when I describe them, I'm sure you have also seen them. If you've worked in multiple companies over the course of your career, all of these are going to be extremely familiar. They're like not even controversial, just a little bit of background. That's where I'm coming from. And I just want to jump into this first part here talking about the companies and their problems. So the first thing to say about working in a, say, a top 10,000 company or whatever, just an average company like medium size, fairly large, we can talk about startups as well. But in general, the natural state of thinking is like, everything seems to be fine. Like, I don't even know why we're talking about bringing AI into these companies. Isn't everything going well? Like we have a good economy. Like, well, we have had a good economy, right? We've built plenty of companies. We've got plenty of market share. The Dow was doing great or whatever for all these years, all these decades. So what actually is the problem? The way I answer that is most companies are actually a giant soup sandwich or a football bat. These are military terms, but essentially there is absolute chaos in most companies. Most companies, it's actually difficult to find out what they're actually trying to do, what they're actually trying to build, especially as like a worker, right? It's very hard to get a clear message of like, what exactly is the mission? This is also the case for like fairly large startups as well. It starts to become a problem fairly early, you know, a few years into the startup, the vision is not super clear. Okay, so the first problem I want to talk about that I see in a lot of these companies and that I'm sure you've seen as well, is the vision isn't always very clear. In fact, sometimes it's non-existent, sometimes it's not conveyed all the way through the company. So people like aren't really sure exactly what they're working on and why. And so that's a huge problem. Another one is it's not clear. And this is kind of like fighting for top here. It's hard to say. These are really big issues. Hard to sort them. But another really big one is there aren't really SOPs. There aren't really standard operating procedures. That's what we call them in the military is SOPs. Most companies just call them processes, but like, how do you actually do things? And the problem isn't that there isn't some document somewhere that says how to do things. The problem is there might be multiple of them and they change all the time, or they decay over time, or people are referring to the wrong one. Okay, so it's like things just keep getting done incorrectly throughout the company. And the more layers of management you have, the larger the company, the longer the company's been around. The more employees you have, the worse this problem gets. Now I want to caveat something. There are certain companies, there are a few companies, very few companies who do this really well. So if you are one of these people who works at these companies or you're a manager at one of these companies and you're like, well, that's not true. I work at Google and it's pretty great. Or I work at so-and-so Giant Bank, which has had decades to figure this out and sort it out. Also, in energy companies, they tend to be extremely risk averse, and they have processes that they follow and they're really good at processes. I'm talking about the other 99.9%. Most companies it's not clear exactly how something is supposed to be done. And what ends up happening when this is the case is you have people doing work and the output is massively inconsistent. Okay? Massively inconsistent, different people doing that same job, even using the same document. They do the job differently. You know, it's common knowledge within the company. Oh, if Sarah does it well, yeah, sure. I love when Sarah produces this report or produces this security assessment or produces this artifact that we're going to send to the customer, she does it really well. Well, guess what, Jim over here, he looked at the same process. He got the same training. He had the same onboarding. His output is absolute garbage. Okay. That's even if there is a process in the company, oftentimes there isn't. It was just like, you know, common onboarding, right? You work at a company or whatever you come in, it's like, hey, you know, here's where you put your keys. We get lunch over here, you know, basically your day to day is going to be, you've got to take these emails, you've got to take it from here. Oh, make sure you don't send it to Mr. Johnson like this. He really hates that. Right. And so these are all being sort of loosely like stored in your mind. Let's say you're some sort of admin or something like that, right? So this admin named Chris suddenly starts learning all these things during onboarding. Yeah. Mr. Johnson doesn't like this. Make sure the format, if you send it to the group, make sure to include these people. Right. You're starting to get all these little things that you're picking up, either from the training or someone complaining about something and it starts accruing knowledge, right? The problem is that's stuck inside that one person's head. And based on how knowledgeable they are, based on how good they are at listening, you know how good they are at following processes like, did we actually write this down or is it stuck in my brain and me? Chris, can I just leave the company? And now that whole process has to start all over again. Very common stuff. You've seen this a million times. Another thing you've seen related to this million times is there are people in the company all throughout the company who just never, ever got good at doing the job. They never got good at following the process. They they don't produce good output. It's really, really hard to hire people in companies. It's also really, really hard to fire people at companies. This person, Chris or Mike or Raj or Sarah or whoever, can just be horrible at doing this for whatever reason. Maybe it's nepotism, maybe it's, you know, they're a nice person, maybe they're good at one little thing. And, you know, it's just you would hate to give that up. So, you know, they can stay on and keep breaking things and messing things up when they execute work other places. The point is this is endemic throughout companies. In a large company, there are dozens or hundreds, maybe even thousands of people who don't follow processes. They aren't good at their job at all. Right. Now, keep in mind there are also a players everywhere, right? There's a players everywhere and they do a fantastic job. They don't even need a process. They're awesome right. No one's disputing that there are awesome humans and that they can do work. That's not what this argument is about. This argument is about overall work. Okay, just a pop quiz here. Guess how much knowledge workers are paid every year? $50 trillion. That is the compensation roughly worldwide for knowledge workers. Okay, that includes the B players and C players and the D players and the F players, right? Forget the A players. We're not going to talk about them. Right. They're awesome. They're going to continue to be awesome with or without AI. Forget that we're talking about most work in most companies. Okay. So you've got these people. They can't follow the process. Okay. They produce bad work. Now you've got the fact that the job isn't that rewarding. They're not exactly sure what they're working on. Their bosses are changing all the time, by the way, their work that they're supposed to be doing, it's also changing all the time, right? Because the the company goals, you know, that they were given a long time ago, they change all the time. Guess what? That has to trickle down. It has to go down to a thing. Guess what that means. Meetings. Lots of meetings. Right? So your developer, you're trying to code, you're trying to do whatever. You're an engineer, you're trying to do a bunch of technical stuff. Nope. You got to get on this call. Why? Because so and so all these people need to have this thing explained to them. You already made a document. You already explained it to them 20 times. You've already been in 40 meetings about this very same topic. Nobody is listening. They keep doing the work. You keep having to correct the work that they did because they didn't follow the document, which you spent three weeks making, right? This is not even special. This is not like a surprise to you. If you've worked in any companies, most companies across your career, this is endemic now. Add on top of this, people are empire building. It's fucking Game of Thrones is what's being played out in real time across many companies for many employees, right? Managers are empire building. They're picking their favorite people also. What about when you have a good idea? How many times have you had a good idea? You submit it up through management because it actually would solve half the stuff that just ruins your soul. It crushes your soul to have to do this stupid work, which doesn't follow this process, which could easily be fixed by these things that you're recommending, right? You're like, look, if we just did this, it would cut four steps out of the process. It would cut four steps out of the process. Everything would be much better. I spent three weeks, I put together this proposal. I wrote all this documentation and they're like, yeah, yeah, I don't think so. Oh, by the way, you need to join this meeting. We're changing our goals again. Oh, by the way, you're going to report to Sarah now instead of Raj. And also we need you to redo all that stuff that you just did. And it's like, okay, great. Sounds awesome. Why do you think people dread Monday so much? Why do you think people on Sunday night are just, like, filled with this pit of despair because they get to maybe get in a car and drive for an hour to commute in, to go to another meeting, to redo their plans, hopefully try to get a bonus, right? Meanwhile, everyone around them is playing Game of Thrones and like breaking everything. Now I'm being a little overdramatic because there are some jobs where it's kind of chill and blah blah blah. It's everything's fine. But in general, it has been known for decades that this is a problem. It has been known that this dysfunction has been happening for decades. People were complaining about the same stuff. Look at office space. We've been making shows about corporate drudgery and stupidity for decade upon decade. We recently had severance soul killing work, right? You're sitting in the queue, blah blah blah. The manager comes by and smiles like nobody's having fun here. That manager's not having fun. You're not having fun. Why? Because your ideas are not getting into the system. You're not even clear exactly what you're working on and why. There's multiple layers separating between you and like whatever's happening at the top, which seems to change all the time, right? So this is not a good state. Okay. Human work is not the ideal state. Workflows, processes. What confidence do you have that if you have a nine step process, each of which has an SOP for it, and you give it to the new people that just came on and got onboarded and got trained, that the people are going to actually read the document and they're going to execute those nine steps. And at the end of it, you're going to have an artifact. You're going to have an outcome, a document project, whatever it is, and it's going to meet the standard. And what confidence do you have that that will also be the same the next time you run it. Because guess what, two of those people are on vacation and they didn't share their knowledge with anyone. Also, they didn't update the document because there was a change. This whole thing is chaos and decay and problems. Meanwhile, people are getting sick. People are changing jobs like every 2 or 3 years or whatever, depending on the industry. Then people are getting so like soul crushed that they're doing the bare minimum, right? I think 23% of total working time in a week is actually spent. Like 40 hours is like 23% of a full week, right? So people aren't working the full week. They're only working this little slot at work. Many people are doing the absolute bare minimum. They're making a fucking sport out of doing the least amount of work possible, right? That was also part of office space. Like this is extremely well known stuff. We are holding this situation up as the standard that AI can never achieve. AI can never, never pierce this because humans are just so innovative, right? It's absolutely insane to me. So another problem here is actually because I've been talking mostly about the worker, right? Mostly about the human doing this job, getting soul crushed. Let me tell you about the other side of it as a leader. Okay? Looking down because I've been here as well. So you're looking down at this company and you're trying to figure out you're looking with the CFO, you're looking at your headcount spend and you're like, okay, well, I'm paying $94 million on headcount across my entire staff, you know, engineers, admins, like all these different people. I'm paying $94 million and that's not even a lot of money. Let's just say that's a total cost of human compensation. Okay, what exactly are we getting? Can I see all of my projects? Can I see all of the work that's being done? Do I have a list of workflows that show all the different processes in my company, what the steps are and how they're actually being performed by the different teams? Do I know how much that costs? Do I know how long it takes to actually execute any of these things? Do you know? I think you do. But rhetorically, do you know what happens when a leader asks this question to their staff, to their company. Hey, I need a list of all work being done. I need a list of what everyone is doing. I need a list of how much this work is costing and how expensive it is at the different stages. Oh, also, is it good work? What's the average quality of an output of one of these processes? Do you know what the answer is? The answer is. Well, we could take a look. That's a good question, ma'am. We've been thinking about that as well. Yeah. If you would like, I could put together a project. What we could do is we could put together a list of all the stuff that we're doing and we could figure out, you know, what exactly our work is and how much it costs, and you know how good of a job we're doing. That is a major endeavor. Oh, by the way, it's separate from their work. This happens constantly inside of companies. Constantly. I'm often brought in to help them do this. I am often there when they are doing it with someone else. So if they spin up a project like this. Okay. Do you think this takes hours. Nope. Days? Nope. Weeks? Nope. Months. This is multi-week, multi-month, or multi-year. This is so work intensive. Keep in mind what has to happen to actually gather all this stuff. You actually have to call a bunch of meetings. Guess what? You have to tell them. Hey, I want you to put together the list of everything you're working on. I want you to note all the different tasks that you do during a day. This is our favorite. This is our absolute favorite thing to do. Yeah. Let me tell my manager exactly what I do. Why don't you look at my output? Why don't you look at my schedule? This is happening constantly while I'm speaking. Some managers, hundreds of them, thousands of them are telling their people, I need a detailed report of what you do and what you work on, and how much it costs and how long it takes, so I can put it up the chain, that's all. So they can get this visibility. And you know what's even crazier? If it's a large company, they don't even ask their people that. They're like, hey, let's bring in McKinsey or KPMG. Let's spend Thousand dollars to have a team of smiling 22 year olds come in here and do the audit for us. This is ridiculous. This is what AI is competing with. Okay, $50 trillion, by the way. It's around $10 trillion just for the US. $50 trillion worldwide is spent. The CEO and the CFO have very little idea what all is happening in the company. They don't know where the money is going. They don't know what the processes are. They don't know what the workflows are. They don't know how well it's going. They just have a rough idea based on, you know, performance of the company or whatever. Like when they hear from their managers and they see the stock doing whatever and obviously they have control, they can say, go do this, go do that. There is no visibility. They can't see shit. This is the state that AI is competing with, which is roughly chaos. Okay, so when you hear the argument, I just want this to crystallize in your mind. When you hear the argument, AI cannot compete with humans inside of a company. This is what they have to compete with. This is a bar that is not low. It is on the floor. It. It burned a hole through the floor. It is descending to the. The core of the earth. This is a low bar. AI can follow instructions. We're not even going to talk about AI yet, but the ability to follow instructions over and over, doing the same thing in a very uncreative, shitty way is better than what is done in most companies most of the time. Like, I cannot over express how bad the situation is and how low this bar is. So the crazy thing about this is everyone knew this before 2022. Everyone knew that this was soul crushing, shitty corporate knowledge work. Everyone knew that this was just like horrible. And I'm not talking about like some people who love their jobs 100%. A lot of people love their jobs. They're lucky. Maybe it lasts for a while, maybe it's short lived. Whatever. There are some people who love their jobs. Some companies are great, they tend to be smaller, but whatever. In general, everyone knew that this was soul crushing before 2022. They knew it before 2022. They knew it in the 90s. They knew it in the 80s. Like, that's why it's part of culture. Everyone knew this was a problem and guess what we were doing? Guess what everyone was doing throughout the culture, they're trying to figure out a solution. What is a better way? Because this is obviously not the way that humans should live. That was the state before 2022 when AI arrived. Okay, so another argument I'm going to continue beating up on us humans here. One last piece. I got positive stuff at the end to say about humans. So don't worry about that. You can hang in for that. This next piece is another negative piece about humans, and it's actually something that a lot of people don't think about. So and it relates to how AI thinks versus how humans think. Another sort of part of this argument is that humans just fundamentally think differently. And we just have like a lot more integrity of thought. We're more cohesive. We have understanding. We know ourselves. You know we can introspect. We can be creative. We have intuition. We have all these advantages over an AI which is like this, you know, dead black box, basically. So I want to sort of like dispel this as well. This is another angle to this argument, which also makes absolutely no sense. The crux of this argument is that humans are better thinking machines. Humans are much better thinking machines than AI. If you've ever studied or read any meditation documentation, or tried to learn it, or tried to study it or whatever, what it reveals about humans is absolutely mind blowing. Okay, so the first thing that it reveals is that humans have no idea what they're thinking at any given moment. Humans are basically like, if you could just zoom in and listen to the minds of people. Okay, sitting in their cars, sitting in their office space or their cube at work or wherever, you're just going to zoom in on these conversations that are happening in their mind. It's like hamsters running on a wheel. You'll just zoom into like Ravi here, and Ravi is like, just can't believe he said that. All that work I put in in the project, like, I hope that guy gets fired. I just hope he gets fired. Like, you know what I would love? Here's what I would love. Just imagine like, okay, so a new boss comes in and the new boss sees my stuff and just absolutely loves it. And then he looks at Chris's stuff. Can't you know, Chris, I can't believe that happened. Yeah, yeah. So looks at Chris's stuff and he's like, oh, this is garbage. You know, the one I really like is I like this one. And then that's me. That's mine. Like that would be so much better. And then, oh, did I feed the dog? I think I need to feed the dog. Hey, let me text someone. I need to feed the dog. Oh, crap. It's almost lunch. I got to get ready for lunch. That is the mind of a person. That randomness is happening constantly. Now check this out. When you try to meditate, what you do is you try to observe thoughts. It is the craziest thing to try to do if you try to observe your thoughts. So you can pause this if you want and try to observe your thoughts for a second. It's a really fun and crazy exercise. It is like this sliding window of chaos. Just scrolling through your brain and it is literally the stuff I was just talking about. Like you go into this, this woman over here, this guy over here, this kid over here, the janitor over here. It's just this rumination, this grinding of like, oh yeah, I should, I'm going to watch that thing. I've got to pick up those things. I don't know why he said that. Yeah. You know, I should get a promotion like these people don't respect me. Blah blah blah. It's just this constant like droning. Where did those thoughts come from? Who is making those thoughts and who is observing the thoughts? The answer is you have no idea. I have no idea what my thoughts are. I have no idea how I'm going to finish this sentence. We'll talk about that one in a second. Nobody has any idea where their thoughts are coming from. Do you know what that reminds me of? That reminds me of an LLM. When you poke an LLM and you say, hey, say smart stuff about logistic workflows and pipelines and you know how to move heavy freight from the East Coast to the West Coast on an 18 Wheeler. It just goes okay, and it starts spewing, starts spewing words, right? And it turns out they're really good and they're awesome. And the words make sense. And it's actually factual unless it's not right. And you could do deep research or whatever and correct that. The point is, you could ping a human to do that and they start spewing words, or you could ping an LLM and start spewing words. Does the LLM have any idea where its thoughts are coming from? No. Do you have any idea where your thoughts are coming from? No. It's a complete black box. Complete black box. We have no idea where our thoughts are coming from, where our dreams are coming from, where our concerns, our feelings, anything. It's bubbling out of us from a black hole. Like actually think about this. If you are in the middle of a sentence like I have been multiple times here already, I have no idea how I'm going to finish the sentence. I'm not planning a sentence. First of all, I didn't come up with the sentence at all. I didn't plan that sentence. I just said, these are spewing out of me just like an LLM. I have no idea how the sentence is going to end. I am just as surprised about anything I say as you are. Who is receiving it right now? Guess what that sounds like also AI. So going back to the work thing and now this, there's this view that we are put together. We are cohesive. We are self understanding. It's complete and utter crap. We have no idea where our thoughts are coming from. We have no idea how we're going to finish sentences. The whole thing is like this random generator. So here's another example. I'm going to ask you a question right now. What are your top seven favorite restaurants in the entire world? You've got 30s to answer. What is your brain doing right now? You're thinking about these. Who are you receiving content from? Where is that content coming from? Now you might say, well, I have memories and those memories are me. Yeah, sure. AI also has memories. AI has weights and AI has context and it goes and retrieves those and brings back stuff, but it brings it back in a different way each time. Right. When it spews an answer to top seven restaurants, it's reviewing content and spewing it in a way that is unpredictable. Right. It's random, hallucinated. Some might say, well, guess what? When I come up with my list of seven items, I have no idea what they're going to be. If you ask me multiple days or weeks apart or whatever, and I'm not like prepared, the list will be different because some I couldn't remember at the time. Actually, some my preference might change in between. This is not a static, cohesive, dependable process. Okay. And that takes us right into the memory one. Okay. One of the most reproduced things in psychology is the fact that memories are not what we think they are in the human mind. When we store memories, we're storing it with like emotion and bias and sort of feelings. And when we recall things, it's extremely well documented all through science, and I'm going to have a whole bunch of science links in the blog that accompanies this. So you can actually review this literature, but you actually don't store. You're not a hard drive, you're not storing unaltered ones and zeros. Okay. First of all, we don't know how we're storing things in the brain. That's step one. But we do know that when we recall events versus what happened, they're massively different and it gets worse over time and it gets worse the more you recall it. So our memory system, even our memory system is completely flawed. So we don't know where our thoughts are coming from. We don't know how we're going to finish sentences. And our memories are not static. They're not ones and zeros. They're actually extremely fluid. And then there's the weird fact that when you go to sleep and wake up, you're actually like a different person. Your entire being, your entire identity, like went offline and came back up, like spun up with like mostly the same memories, like mostly the same feelings, but you're kind of like a different person. So this whole concept of companies are doing great work. They have great workflows. People follow the processes. That's garbage. Then you have the concept of humans are like these, you know, bastions of like perfection. And we are the perfect thinking machines. And AI can't possibly hold a candle to us. Also highly flawed. The whole thing is built on just absolute madness. So once again, as thinking machines, the bar is extremely low. Okay, I want to show you this. I came up with this capability stack, whatever. It's just an image. And by the way, this is not like settled science. Like this is all still up in the air. People are still trying to figure all this out. I read a lot. I've got pretty decent mental models. Just work with me on this. Think of these blue pill boxes here, these blue layers here as fundamentals of a knowledge worker. Okay? Things that make us good at doing knowledge work. So first of all, it's knowledge. So collecting, organizing and accessing facts, right? Knowledge Facts. Easy understanding. Turning knowledge into concepts. Okay, so it's the ability to actually like, let it seep in and actually be able to say, okay, oh, that knowledge is related to that knowledge. Oh, okay. That forms a concept, right? So now we have concepts. So that's, that's what I'm calling understanding here. Again, we could use lots of different definitions. Just work with these knowledge understanding. Now on top of that, in addition to having a whole bunch of facts stored, that's not enough to be a good knowledge worker. Understanding. Okay, now we're getting closer, right? You have to have understanding of all these different concepts and everything and how they relate. Intelligence is the next one above. This is the ability to face obstacles, right? You're navigating obstacles. You're navigating the world around you, and you have goals that you are pursuing. Okay, so you're pursuing goals. So basically, if you're more intelligent, you're able to navigate the world better and better achieve your goals. So that's the definition of intelligence that we're using here. And the final one is creativity. Now this one. I would even argue not to get ahead of myself here, but I would argue that this is truly something that humans have that AI doesn't have right now. And who knows when they're going to get it or how much they're going to get. But even the first three are really solid and most knowledge work, okay? Most knowledge work that people are doing is actually the knowledge, the understanding and the intelligence creativity. You can have some people have some people have a lot of creativity. Some people have a little, some people have none, but they're still really good knowledge workers. I would argue you could even have an A player who crushes knowledge, understanding and intelligence and is still really great at their job. Then you have creativity on top of that. So this is like intuition art, you know, liberal arts thinking, philosophy, you know, logic, like all the different things, the ability to come up with novel things, net new things. That's ultimately what we're talking about here. So taking just the left side, just the blue layers. One of the arguments for why humans cannot be replaced is that they have expertise. This is a thing that my friend Marcus Hutchins is always talking about. He's like, hey, look, there's like, expertise is the thing that AI can't do. Like it can have facts and it can regurgitate them. But like, that's not real useful. That's not what work is. That's ultimately what we're trying to answer here. What is work, right? And how does it relate to human capabilities versus AI capabilities? So where is expertise on this list? And maybe you think I missed a layer. I don't think I did, but I'm challenging you to tell me what I missed. Where is the expertise layer here if it's not one of these four? So my answer to that question is expertise is knowledge, understanding and intelligence combined. Okay, so let's just go through some different work tasks. Okay? You've got engineering, you've got like civil engineering, you've got software developers, you've got site reliability engineer. You've got product manager, you've got project manager, you've got. Head of finance. You've got what do we got lots of other jobs right. Million different jobs. Admin. You're an admin for the company. You you handle you know, meals. You handle emails, you handle events. You handle all that kind of stuff. Maybe there's marketing as well. If I didn't already mention that, tons of different roles, thousands of different roles. Here's the question. When somebody is good at that thing, which of these are they good at? Which of these do they have in combination and which of these are required? Obviously, some require more creativity than others. Some don't require any creativity at all. I would argue they pretty much all require intelligence. So this is really interesting actually. Here's the question. Why hasn't existing automation previous automation, why has it not killed off knowledge workers for all these decades? We've had automation for decades. We've had automation for hundreds of years, and we've had computers for decades and decades, and we've had automation scripts, you know, programming. We've had these things for decades as well with really, really advanced automations, excel, all these different things. Why can't they replace a knowledge worker? Why have they not replaced a knowledge worker? My answer to that is understanding and intelligence. Okay. Previous automation basically just churned it executed it followed a list of steps deterministically, right? What it can't do is the thing that everyone must do as a knowledge worker inside of the chaos that I described earlier. Remember I was describing how everything is chaos. Everything's changing all the time. Oh, the goals are different. This is different when you come into work on an average day and an average company as a knowledge worker, you don't know what you're going to get. They might be like, hey, we're moving offices. Hey, our remit is completely different. All of our jobs are different. We now report to a different person. They have different goals. Maybe the goals are the same, but they like everything to be done differently. They like blue templates. So you need to put all your stuff in brand new templates. Well, if it's base automation, okay, it didn't learn how to use blue templates. It didn't learn how to have new goals. All that stuff needs to be rewritten. Intelligence is the ability to adapt, to change, to navigate, to work around and basically say, okay, well, here are my actual goals, here's what I know and I'm supposed to do. Here's my knowledge, here's my understanding of what is meant by all of this. So here are the changes I'm going to make to the way I do my work based on these new requirements that intelligence piece previous automation didn't have. Okay, now you add creativity. On top of that, the ability to come up with new ideas and innovate and stuff like that, that's way beyond automation. And I would say arguably today, AI, it's also way beyond today's AI. Now, there are some edge cases where it's starting to seep in, but let's just set those aside. So this is what makes a human valuable as a knowledge worker. Expertise is all for. But I would say mostly it's knowledge, understanding and intelligence. So give me an example. Try to think of an example. And this is sort of me arguing with Marcus here. Give me an example of expertise that isn't this. Think about what expertise is. It's lots of experience. It is the knowledge, right? It's the understanding of the concepts. It's the ability to adapt with the intelligence and navigate and change and everything. The combination of those combined with experience is ultimately what gives you your expertise right now. Maybe you would argue, well, what about experience? Maybe experience should be on the list, but experience is memory, right? Experience is recall experiences, accrued iterations on seeing different problems. And ultimately this is why I have the model. This way. It is actually improving your knowledge or your understanding. Okay, your intelligence is not really improving. That's just a thing that you have. It's a tool that you can use, but your knowledge improves with the experience. An experienced person has more knowledge. An experienced person has more understanding. They have better understanding, okay? Which means they have linked more and more facts and more and more situations and more and more patterns to interlock, to connect to each other so they could just see patterns better. And this is related to intelligence as well. But somebody with 25 years experience, 30 years experience, 50 years experience of doing financial audits, they just immediately see things. They see patterns. Okay, here's the question. What part of that does AI not have? First of all, how is AI a knowledge? Okay, insane. Okay. It's read every book. It's a deep expert. I don't want to lead the witness here. It's deeply knowledgeable about many fields, tens of thousands of fields and subdomains. It can rattle off all the details, explain everything to you whatever way you want to receive it. It could write a full book on the topic by itself. It could make flashcards for you about this very specific thing that is like very few people on earth actually know about, but the AI knows about it. It's also read most every book effectively. Okay, so knowledge, forget about it. Okay. Understanding. Yes. Okay. You could argue AI's don't have understanding. There is a type of understanding that AI's don't have and that is experience. Okay. If you argue that understanding requires subjective experience, well, then they don't have it right because they don't experience anything. My definition of understanding is connecting concepts, finding patterns across domains, and AI is unbelievably good at this. It is mind blowingly good at connecting things, cross linking patterns, identifying a pattern in a piece of content. Oh, this relates to the 1930s Russia in the following way. Oh well, how does that relate to chicken feed farming practices from Idaho? It's like, oh, well good question. Here's how it relates. It can cross-reference anything. It holds it all in its mind all at once and can reference this. It can also do that supplemented by tools to go and research anything and add to it. It could also do that by looking at Rag or at a giant composite list of additional knowledge that you give it. Right? So it doesn't have to actually be in the model weights. It could actually be part of a unified system, which includes going to get new information and cross-referencing to make sure it's real. Right. So knowledge and understanding, crushing it, absolutely crushing it. Let's go to intelligence. Can AI navigate the world and achieve goals? Yes, absolutely. And this is fairly modern, right? It's been going on for a couple of years. But the last year, the last six months. Are you kidding me? An agent infrastructure that has access to all these tools and all your company's knowledge, all this context and a really, really smart model and a system of agents that actually can use that context. It actually has a list of rules to follow. And okay, so it's proceeding down a track and you're like, okay, well, now imagine that the customer says, we actually can't do the deal in Japan. We're actually need to do it in the UK. And actually, here's another restraint. So the size of the project has to be larger than this. By the way, you need to employ only people who have residency in Singapore or the UK. Also, we need some people who have understanding of Scotland and also everyone should be over the age of 35, right? And also make sure the report format has to follow this particular template. And also we have to take into account these other 90 things which I'm about to give you. Those are new requirements. That is dynamic changing requirements on the ground. Here's another requirement. Also go and research all things related to this. Find all scientific documents related to this and do thorough research for a day and a half using all your resources. Bring in all the sources to support the document that you're actually going to produce for this customer. That's going to cost $300,000. Those are dynamic, changing situations. Everyone who's used AI recently, who is a decent user, knows that you could throw anything at these eyes. You could throw anything at them. And it's like, oh, well, in that case, you should do this. And you're like, yeah, but here's another requirement. Oh, well, in that case, we should adjust. So can it do intelligence? Do modern AIS have intelligence according to this definition, 100% expertise. Like I said, expertise, knowledge, understanding and intelligence, mostly knowledge and understanding. Okay. Intelligence, I would say definitely. Sure. Let's include that creativity. Again, I'm not sure you need it for expertise. You can have somebody who is just the craziest bookworm and is just like locked in. You give them this task. They just crush it. You give them a different task. They just absolutely crush it. Are they inventing anything new? Not really. Are they coming up with a new way to do it? Nope. The way that they were trained or the way they decided they wanted to do it, that's the way they execute every single time doesn't require creativity. Okay, so now let's slide over here. Let's look at the AI one intelligence, understanding, knowledge creativity. Let's just take it off the list. Let's just pretend it doesn't have it. AI has the expertise. So this argument that a bunch of people are making very smart people, very knowledgeable, very technical, they're saying that you can't possibly replicate human expertise, human thinking, the quality of human thought, the quality of human thinking, the quality of human execution of work, everything that we've been talking about tells us that is just not true. In fact, the polar opposite is actually more true that even decent AI today, given these types of tasks, will just crush an average knowledge worker. Just absolutely crush them on tasks that are there defined. Okay, what are we doing all day? We're accepting emails. We're responding to emails, we're producing reports, we're writing code. These basic commodity tasks make up. I'm making up a number 95% of all knowledge work. It's common stuff. The only thing that stops somebody from walking in off the street who has the requisite, like education or training in this field, which again is just knowledge and understanding, mostly knowledge. Someone walking in brand new to do that in the company, they just don't have the company knowledge. Well guess what? They accrue it over time. They go through onboarding, they read the documentation, they do all that well guess what? AI can do that in a few seconds. Here's a core point about all of this. A big reason that people think there's a gap between what the humans have and what A's have. A big reason they think there's a gap is because the humans have not articulated the stuff. It's not written down. Think about this. The way that AI has gotten so good in the last year is largely because of cloud code. What did cloud Code do? Did it have a dramatically better model than the other models? No. What it did was scaffolding a harness around the model. What it did is it built context and context management relations between documents, allowing it to have knowledge, understanding and intelligence. It linked together everything that the model needs to be able to perform to actually do intelligence tasks, to actually do work. That is what this thing did. That is what cloud code did. Okay. That stuff being written down. Okay, so another thing related to this that's extremely hot right now. It's been hot for a number of months is skills. What are skills? Skills are a combination of resources, tools, Context. Knowledge. It's a whole bunch of stuff written down. This is the center piece. It's essentially the core benefit of a harness versus a model. A model is just like a whole bunch of knowledge, right? Once you realize how powerful these skills are, once you realize how powerful it is to have all this context in one place, that sort of wraps an AI model, you start to realize that the problem is not the AI. The gap between human expertise and AI expertise is not the model, okay? It's not the fact that humans have expertise and AI doesn't. It's the fact that we have not articulated our expertise down into files, into tools, into contexts. I've been doing this for over a year now. I've built a system called Pi. It is a context management system. It is extraordinary. It's open source. You can check it out. Everyone is building with skills now. It is becoming the most powerful concept in all of AI is skills. Why is that? Because it is literally expertise packaged for this very smart model to then be able to use the problem inside companies. That makes people think there's a disconnect between human capability and AI capability. Nobody has any of this written down. All the stuff that is in Robbie's mind and Susie's mind and Kristy's mind and Chris's mind, all the stuff that's in their minds, it's not written down. Their expertise is not captured the way this is supposed to be done, this particular task, this sending of the emails, the creation of the report, the writing of the code. It's not written down anywhere. It's passed from human brain to human brain. And they go on vacation, they go on maternity leave, they retire, they jump companies every couple of years. That knowledge just dies. What's starting to happen now is knowledge is dispersing from brains, from very smart, high expertise, people into skills. This is why there was a giant stock drop when anthropic came out with a whole bunch of legal stuff, right? Those are just processes. Those are expertise captured in documents, which AI can then follow. So the expertise gap between humans and AI is actually the failure so far of us to articulate all the different chaos things that we talked about in the beginning, all that chaos, all those random pieces of knowledge inside of people's brains. Like there's some super old guy who's 62 years old, you know, his name's Cliff. He's the one you call when the things go down and there's no docs, you can't find a thing. You don't know how to get the server back up. People don't even know what servers are anymore. You call Cliff. Cliff has never written the stuff down. Cliff has never created a skill. He's never done a full debrief interview to capture his knowledge. Now you're not going to get all of it. It's not going to be perfect. But if Cliff could document that, and by the way, lots of people are. Lots of people are documenting what they know about Cliff's topic and putting it into skills and their open source. This stuff, this is P that's seeping into the pool and it's not going to come out again, but inside of a given company, if that stuff were to be captured and brought into this context system like I have with Pi, like lots of other people have like are captured in skills that's just absolutely flooding the internet right now. Flooding, especially the AI space. That is what bridges the gap between human expertise and AI expertise. And this is happening fast. It's happening extremely fast. All right. So, so far I've been talking about the problem. I've been talking about what I believe to be the problem with the way people are thinking about this, right? Comparing humans to AI, thinking that work is like easily done by humans and it's like, it's working fine. Like what's even the problem now I want to transition into and we also talked about the whole expertise thing and like what that really means and what I think the gap is there. What I want to pivot to now is essentially talking about what I think the solution is and what I think is inevitable. Okay, so this is kind of getting to why I think this is going to happen and why I think people need to prepare and get ready. So all of that was sort of a setup to what I think is about to happen. And this is an architecture that I've come up with. I call it the lattice architecture. I've been working on this for quite a while. I'm going to release it as an open source project as well. It's the same system that I use with customers to basically build their back end systems to help them solve the stuff that we talked about on the company side. But the system itself, I'm actually going to release, just like I did with Pi, I'm going to release this as kind of a sister project for actually helping companies, actually any type of entity basically solve that chaos mess. So essentially, the concept is addressing this transparency problem, addressing the fact that all these different groups are opaque. The work is opaque. The processes, the SOPs are not clear. So what Latisse does is that it actually has a single unified daemon that everything reports up into. And what you have are the different tiers of organization. You have the company level, you have like a unified context of documents and processes and SOPs and everything, all the different knowledge for a particular company in a unified place. And then you have a set of things for each company. So you have SOPs, you have metrics, you have work, you have goals, you have budget, you have all these different components of a particular entity all the way from the company level. But you also have that same exact thing for the department. You also have it for a team, and you also have it for individual people working inside the company as a worker. So what this does is it takes a system like pie and you can have your own version. People will have their own versions. This is the one that I use. And again, it's open source project. You can go and check out. And what it is, is as an individual, you will have your personal SOPs the way that you like to get things done that you follow. You will have your personal budget of what you spend things on. You will have your own personal metrics, which maybe you got from management or whatever. Actually, you're about to see where you got it from. You have all these different components. You have your knowledge in skills, you have all these capabilities that you have when you learn something new or whatever, you capture it into the system and most importantly, your individual unit. You are broadcasting APIs. You as a person, you, Sarah, you, Ravi, you, Chris, you are broadcasting what your SOPs are. You are broadcasting what your metrics are. You are broadcasting what your budget is. You're broadcasting your work items. They are available for any agent or any other team member or the agent for the team or the agent for the department or the agent for the company can ping these things. Can query you and actually pull the stuff in real time. Most importantly, this allows the CEO and the CFO actually to be able to look down and say, okay, this is the work that we're doing. Okay, so you're going to have workflows also documented in this exact same way. I want to show you some other diagrams here. Okay, so here's another way of looking at it. So again, goals, plans, SOPs, metrics, work cost quality. The thing I want to express to you here, like we talked about already, so you probably already have this point. So I won't belabor it too much. These things goals, plans, SOPs, metrics, work, cost quality, these things, the workflows for the company. This stuff is extremely opaque to the leaders of a company. This is extremely hard to gather and this is going to turn it into seconds. It's going to turn it into less than a second oftentimes, right? It's going to turn it into minutes at the absolute maximum. Like if you have a super large company, right? And it depends on the implementation. Like I want to give actual numbers or anything. That's just ridiculous. It depends on the architecture that you use. But the point is transparency. First of all, and the other core point is that each layer from the worker to the team to the department, to the company is independently authoritative over its stuff. So one of the problems that I see tons with customers is they have no idea what their other departments are doing, right. They have no visibility into how they're interacting with the customer, what they've said to the customer, what's been done with them. You can also have context silos like this for each customer inside of a customer container, right? And you can have other agents because I'm a security person, there's going to be another agent flow here, basically a gateway that basically says, okay, which agents can ask which questions about what context zones, right? So there's going to be permissions. You've got identity stuff, you've got a whole bunch of stuff that's all being worked on. But the core concept of lattice is visibility up and down and across. I could just query, first of all, I'm just I'm not going to be actually running any APIs myself. I just talk to my agent system as an individual worker on the front end team over here. And I say, hey, what are my coworkers working on? I'm thinking about working on this. And they're like, oh, yeah, you know, Julie's already working on that. You should talk to, you know, Modi or whatever. Yeah, hit them up. They're already working on it. Maybe you can do a collab. Do you want me to set up a meeting? Boom. Okay, cool. Well, now you just posted that you're in a meeting now it just posted that you're going to collaborate about this particular feature that could go into your API. It's now available when someone asks, hey, is anyone working on this? Well, now it includes you that you're actually also working on this. It's now available to other teams, other departments, all the way up to the company level. And this unified sort of context layer here, the lattice daemon is where you can query and get it for everyone. Now I want to talk about this whole concept of the SOPs and the goals. Think of what happens right now in a company. I didn't talk about this too much in the sort of negative state. When you go to create goals and change your goals, how many people are triggered by the word OKR? For a good reason. The reason is because they change all the time. People have different systems for doing them. Imagine a giant all hands meeting where the CEO gets together with everyone and says, hey, check it out. These are the new goals. This is what we're doing. Here are our new metrics that go with the goals. By the way, we've got some new SOPs we just published. Boom. Guess what they say. They have now been published. All documentation is now updated, goes all the way down. Updates the documentation, updates the goals. Now inside of the goals of each group. That content is now updated. We're talking about text files here. This is not like magic stuff. This is not difficult stuff. We are doing much more difficult stuff with AI already. This is just not a system that has been built yet and it will take time to roll out. Some companies will be able to adopt this very quickly. Some companies will take, you know, five years or ten years or more, or they'll just die off because they couldn't get there fast enough. And to be clear, this is one architecture. This is the architecture that I'm using. But the concept is actually more important. The functions that this architecture allows is the most important piece. And I think the broadcasting of the demon, the broadcasting of the APIs for each tier level, for each object type within the company. That is what makes this all work, right. So the protocols that we use, who knows? Like it's hard to predict exactly how it's going to play out. Maybe it's going to be something better than HTTP. Maybe it's going to be something better than rest. It doesn't matter. The concept is what I want you to think about here. Okay, so here's another view of this. And just another way to think about it. I've got a whole bunch of these diagrams, by the way, that I put together over quite a decent amount of time, and I'm going to make them all available there in the blog post. They're also in, I think the link is in the description to the video as well. But look at this. All these different queries can go up to the latest statement. You basically post your status. It could be push or pull, right? A combination of the both. But again you have the tiers. You have goals, budget, plans for the department container, the team container, the individual node, right? And they all are speaking to one central place, but they're all able to post to each other and or receive from each other. And the other thing that's part of different diagrams is essentially a big unified context as well. Like kind of like a context data lake, which I've talked about in previous videos. Okay, so here's another view. And this is absolutely critical. This is huge. A big part of the problem that we have is not being able to see the work that we're doing. We don't understand the workflows. We don't understand who's doing which part. So I've got another video where I talked about companies are just graphs of algorithms. Okay? This is a really core concept. The idea that someone's doing like insurance claims or whatever. Well, it's not wizardry. They don't make it up each time. They should be following a set of steps, right? But it's not clear like we talked about in the beginning. It's not clear what those steps are. It's not clear if they're being followed correctly. It's not clear exactly what is happening inside the company. There's not even a list of them. And like I said, it's actually really difficult to go and collect a list. So look at this here. This is the type of view that somebody is going to have. They're going to have the ability, anyone, whoever has access, right, with the security permissions to be able to look at this stuff and they're going to be able to say, okay, well, we still got human components here. We got automated components here. Okay, we got review over here. Okay, we've got audit over here. We've got a quality check over here. This is the type of view that CEOs, CFOs like senior management, really any manager really wants to be able to see because you can't optimize what you don't understand. You can't optimize what you don't see. Right. All right. So this is actually another really cool aspect of once you have those visuals, once you have those workflows, things just fundamentally change. You know how like someone comes up? You know, they take you to a steak dinner, they meet you for some meeting or whatever, and they're like, hey, you know, we have the best image background removal process. I don't know what you're using for that today, but ours is way better. And so it's like, okay, well, I guess we'll do a pilot. You know, I really liked the steak. So I guess we'll do a pilot and I'll tell part of my team, you know, you're going to spend six weeks coming up with test criteria and we're going to test this thing and a B test or whatever. And if they like it, you know, I guess we'll take a look at it that goes away when you actually have a lattice over here of like all the different pieces that make up your company. These are the different processes, these are the different components. This is the tool, this is the decision. This is the human making the decision. This is an AI making decision. This is an AI processing work. This is the graph of algorithms that makes up the company. One particular node, this little node right here, one of hundreds or thousands or tens of thousands or millions of nodes inside your company. Is the process the algorithm for doing image background removal. Today, we don't have metrics on that. Really, most people don't. What do we have? We have like, yeah, it works pretty good. I like it, yeah, it's pretty good. Maybe we could pull some kind of metrics, but it's not like really good what we're going to have, what we're moving towards and most importantly, what people want to get to, what leadership wants to get to is all of these have metrics for them. They're all clearly defined. So now a vendor doesn't come with a steak dinner. They cannot anymore. Okay. Because we're going from wizardry to excel, okay? We show them our metrics. We show them, hey, look, this is how we do things. This is our current lattice structure. This is our current flow structure. This is image background removal. This is how much it costs us. This is how good it is. This is our quality ratings according to so and so standard. What are your ratings? What are your metrics? What are your cost numbers for this? If they can't bring that and all they brought was the steak dinner, that's not a conversation anymore. Once this transition happens, right? This is absolutely essential. Absolutely essential. Essentially, it's turning the company from these sort of black box, sort of vibey things that are happening that hopefully work out pretty well into actual pipelines, actual workflows with metrics and reliability and testing and all this sort of stuff as part of the system. All right. Real quick, this is just a little teaser. I'm not going to talk too much about this. This is a screenshot that I took earlier, I think last week. And this is actually just an example of the lattice system working. And it's actually one agent system reporting up into the lattice daemon basically describing its work. So this is a particular system called the algorithm. And it's running, I guess if you know, you know, but it's part of Pi and it runs work. This is the type of thing I'm just trying to show. This is not a theoretical thing. It is quite easy. And if you're an experienced user of AI, you already know you could do this type of stuff. This is the type of dashboard that line managers want to be able to see, that engineering managers want to be able to see that anybody should be able to drill into and actually see the work that's proceeding through the system. And this is making simple Rest calls and pushing this content up into the system and giving you a dashboard like this. All right. Essentially, what I want you to have up to this point is sort of dispelling some of the myths about how perfect humans are. We are not perfect. I'm not trying to talk crap about anybody. Some of my best friends are actually humans. So like, I'm not trying to throw stones here, right? I'm trying to describe the reality that's actually happening according to my view. Like you could watch all this and still think I'm wrong, but hopefully you won't. So where is this taking us? It seems like this is just horribly bad news. So what I want you to do have had so far is essentially the current state of work. The current state of human work, the current state of companies is not good. The perfect human thinking machine is not correct, right? The way that we actually process information, store information, it is much more like an AI than is actually comfortable to think about. And all that combined basically means the bar is actually extremely low. And then when you add on top of that, this lattice system, which is just one implementation that one person came up with, I'm telling you that is the direction that things are going. This is the architecture that companies are moving to that they are going to have. A lot of them haven't figured it out yet. Like this is still very early stuff, but people will catch on to this very soon and they will start building this type of architecture. And it's going to happen fast. It's going to happen extremely fast. Now, one other point I want to mention about this is a way to think about automation in a completely different way. If you think about it in terms of like, okay, we have the humans. Now let's think about adding AI. Oh my God, that's crazy. I have the same instinct. Okay, it feels icky because they're coming into our processes. Here's another way to think about this. Imagine you have a factory production line that produces iPhones. So it's soldering two nanometer transistors for iPhones. And this thing is amazing. It's producing whatever, $1 million a minute. Who knows what the numbers are, but it's just fully automated. It's a whole bunch of robots and it's just flowing perfectly. Who would say, you know what this thing really needs? It lacks a human touch. It lacks a human touch. We need to get people need jobs. People need jobs. We need to get some humans in here because AI's don't have the right thinking. They're just not as good as humans at doing work. Do you have any idea what would happen if you tried to ask a human to solder two nanometer transistors onto circuit boards? It would not go well. Also known as impossible. Okay. Another one which I think can't remember who mentioned this. Somebody mentioned an example of a amazing Excel sheet. We've all seen one of these before. It's got a million tabs. Looks like an actual application. The thing is just fantastic. Maybe it handles the entire company's finances and just produces the best results. It never messes up. It's just awesome. It's like, you know, 36 months of work goes into this crown jewel of an Excel spreadsheet, and it's just processing millions and millions of lines for the entire enterprise globally. Where on the spreadsheet should we add a human to help the spreadsheet? Where do we add it when automation works, when AI workflows work, when the lattice system that I was showing you works, the answer will not be to add humans to the thing. It becomes extremely obvious that adding humans actually makes it worse. Okay, with an exception, which we're going to talk about. So think about that inversion when you're considering the uncomfortable feeling that you're getting when you're thinking about adding automation, think of the opposite. The default is opposite automation first. And there's another reason to think that one saying that I have that I think is very, very true is the ideal number of human employees for a company is zero. Now, I'm not talking about small boutique startups. I'm not talking about collaborations with your friends. I'm not talking about like sole projects that you just love to do. You want to work with other humans? I do too, that's always going to be there. I'm talking about for large companies, medium sized companies, companies that need to do things at scale. These companies, again, they are paying $50 trillion in knowledge work compensation. They want to do that work internally. Okay. If I had an ice cream truck business and all I had was my ice cream truck, and I had a machine that could actually make the perfect ice cream cone. And maybe I have three trucks and I'm the only employee. And the trucks, they have X number of machines. They can pump out the things. I can hand them off to the kids with a lollipop or whatever, and I'm making X amount of money and I'm happy. I'm just happy. This is a great amount of money. That is the state that companies want to be in. Of course they want to grow, right? So they'll want more and more bigger trucks, you know, more stuff like that. But they won't say, I need more humans to add to this. If I'm sitting here with my ice cream truck and I've got my ice cream cone makers, and I am perfectly happy with the amount of money that I'm making, if a group of people come and they pick at me and they're like, you're against humans. Humans need jobs, humans need jobs. I'm like, what are you talking about? I got my truck. I've got my ice cream cone makers. It's just me. This is literally me. Those machines are me. And this ice cream truck is me. This, as a unit is me by myself doing the work. Well, guess what? That diagram I showed you of the lattice system of the entire company. Let's just say nothing in there was human, which that'll be forever before that happens. Or a long time anyway. Let's just say it's fully automated. Everything in the whole lattice structure, it's all automated, or let's say it's a small company or whatever. Fully automated. Right? Fully automated. It's working great. That is their company. They consider that to be them doing the work. All that infrastructure, all that automation, all the machines, all the tech. It is part of the company now. It's like the walls and the floors and the ceilings and the chairs and the desks. It's literally just part of the core of the company that is the preferred mode. They are currently paying $50 trillion for outside help. They want that help to be inside. They want it to be all internal, okay? That is the desired state. And going back to the whole setup with like the factory and adding humans, one argument I've heard is like, well, yeah, but if you want to scale, right? That's there's no problem. You'll just hire more humans. Like why wouldn't you hire more humans? Because humans can also do the job. Well, no, they can't do the job as good as it's already being done by the automation. Right? So that's where that argument breaks down. You don't add humans to a working system that doesn't have humans in it, because the working system without humans in it is likely to be many times better and many times faster for a lot of tasks, maybe most tasks. So just keep that in mind. All right, so here we are. I promise some good news and this is where we get to it. So we've talked about the bad news. We've talked about what the problems are and what I think the inevitable solution is. And I do believe that is true. Now let's talk about, okay, what are humans supposed to do? What is the good news? This sounds like horribly bad news with no possible upside. All the humans are screwed. What are we going to do? And for that, I want to bring you over to this content here. All right, so for that I want to bring you over to this content here. So remember when we talked about the four layers and we said, okay, knowledge, understanding, intelligence and creativity and we said, look, this is the stuff that humans can do. But there were two layers underneath. And these are the layers that eyes don't have. Okay. They don't have subjective experience. They don't have desires to do anything. Okay, so imagine that whole workflow again, fully automated. It's amazing. And it doesn't need any humans because it's just better and faster and more consistent, higher quality, all of that cheaper way, way cheaper. Fine. Who decides what to make? Who decides what company to make in the first place? Here's what AI doesn't have, and this should be a source of hope. Hopefully what I've done here is show you how bad one part of the situation is, and it should be equally clear. Hopefully, after what I'm about to show you that the opposite is also true. What this unlocks for humans is actually what we should have been doing all along. We should not be the row in the Excel sheet. We should not be the section of the robot army that you know solders the transistor. We should not be the one in the car dreading going into the office Monday morning that all should never have happened. Which, like I said, we knew that before 2022. It's totally understandable to freak out when the jobs are going away and AI is taking them. The natural tendency of someone yanx something out of your arms is to pull it closer, right? Understandable. Like not mad at anyone for that. I'm just trying to help you sort of think through this. That work was garbage. It is garbage. It was garbage for decades. It still is garbage. And AI's are coming to take it. Fine. The place that we're going, the place that we need to get to, is where humans actually have ideas. People are actually broadcasting their own capabilities and their own plans and their own creativity and their own beliefs and their own ideas and their own creations. Nurturing people, helping people grow, creating new art, new experiences for other people. And guess what? Coming up with companies to do this right. How will the company be implemented? How will all the work tasks get done using a system similar to Latisse. It's going to be an all AI system doing the actual crunching of the work. But what can the AI not do? It doesn't have a desire. It doesn't have any goals. It doesn't have ambitions. AI doesn't see a problem in the world and go, yeah, I just don't like that. I just don't like that. You know, that's not the way they should be treated. You know, this is a horrible experience. I don't like this product. We should make a better one. AI is not doing that. Why? Because it doesn't experience anything. It doesn't experience problems and feel bad. It doesn't come up with new ideas. If you ask it to come up with a new idea, it can. But it's not sitting around dormant in like unhappy. We are unhappy. We have these desires and we have this subjective experience for another reason, right? It's because we are powered by evolution. This is kind of a big idea as well. Might be slightly controversial, I think if you're watching the channel, might not be as controversial as it is to most people, but evolution is the thing that gave us our desires. When we sit around ruminating, when that whole freight train of ideas is just scrolling across the things that's in our mind is generated by evolution. The reason we have these desires and goals and ambitions is because evolution is what designed us. We are basically mech suits, we're giant mechs. We're like giant transformer mech suits. Around evolution, which is trying to do things and it's trying to do things through us. Our entire pleasure system, our chemicals, dopamine, serotonin, cortisol, all these things are designed. The whole purpose that they're there is to steer us, to steer us towards desires. Do we have any control over our desires? Can you say I suddenly like broccoli when you didn't a second ago? Can you decide to do that? No you can't. You also can't really control what you desire. You can't really control your ambitions. You kind of have them. You could try to use your intellect and steer them, I'll grant you that. But in general, we are being powered. We are being powered by a very ancient, powerful thing, which is our upbringing, our evolution, our hardware, our mammal minds, right? Our brains, our minds, our identity. It is driving forward, survive, reproduce, become attractive, become powerful, become respected. That drive makes us fundamentally, I would say better, definitely different, but I would say better. It gives us an advantage over eyes. Eyes are dormant. They just sit there. They just execute things that we want. When they act like they feel things, when they act like they have goals and they act like, hey, I'm going to generate ideas that is not coming from their true center of self the way that it does for humans. So what does this mean? What this means is we are still the centerpiece of this whole thing. Everything we've been talking about in this video, everything around why AI is going to replace us doing work is around the execution. It's all around the implementation of someone's idea of a founder's idea of a company builders idea. Well guess what? There's a trillion more ideas out there that need to be made, and AI makes it so that it's easy to go and actually implement them, because you could just implement a system like lattice and stand up your company and you have some humans there, you have your buddies there, you have a bunch of A's and a bunch of agents. You have transparency. You say, okay, I want to execute this. I want to do this. Where are humans in this process of like starting companies, managing companies, doing all this stuff? They're at the top. We are at the top. Do you remember how you saw all those workflows and all those diagrams, all those interconnected pieces? What is the role of the human when the AI does all that work better? The role the human is to say, I don't want you to do that task anymore, okay, I can't improve how you do the task. Your AI, you're great. Do the workflow. You got it faster, cheaper, great. Who determines what the company should be doing, why they're doing it, what problem they're solving that is only human, right? This is incredibly positive. Incredibly optimistic. This is what we do that they can't. Okay. And this is where my recommendation and everything I talk about in all my videos, all my open source projects, my recommendation is to make the switch from focusing on the execution and the implementation, which is all the first part of this video. It is all this debate about can AI do it? Can AI not do it? It's all it's all a distraction. AI is going to crush that stuff. Those jobs are gone. That's why I consider this threat so dangerous. That's why I made this video to address this narrative that our jobs are not in danger from AI. It's because if you don't realize this, then you will keep focusing your energy on trying to maintain this job that is going away. Instead of transitioning all your effort to getting to the other side. This thing I call human 3.0, it is basically becoming your full self, Magnifying your true self, understanding your true self, broadcasting it and interconnecting that with other humans who are doing the same. So we can have value exchange from human to human without the need for this corporate hierarchy, all these corporations, all that kind of stuff. It's all, it's all going away. First of all, but it's not good in the first place. We don't need to hold on to it. We need to get to the other side where we can look down at a lattice like system where all the AI is executing, but we got to decide what to build. We built that company, we started that business. We can have 20 of them, and we have a dashboard that looks down at all 20 of them, and they're all doing awesome stuff. We could drill in and we could say, hey, we're not doing this anymore. We're doing it in a different way. Hey, I want to optimize this. I want to do it that way. And the AI goes and executes. This is why I care so much about this. This is why I counter this argument that AI will not replace knowledge work. It is going to replace it. And we need to get ready. And it's not bad news. It's actually really good news. We'll see you in the next one.