The $200K AI Job That Didn't Exist Last Year

So, there's a new opportunity for the So, there's a new opportunity for the people who know how to use AI that people who know how to use AI that people who know how to use AI that nobody's really talking about. nobody's really talking about. nobody's really talking about. Specifically, if you have a corporate Specifically, if you have a corporate Specifically, if you have a corporate career or work a job, 12 months ago, career or work a job, 12 months ago, career or work a job, 12 months ago, this didn't really exist, but it's now this didn't really exist, but it's now this didn't really exist, but it's now turning into the next AI gold rush. This turning into the next AI gold rush. This turning into the next AI gold rush. This opportunity could genuinely make many of opportunity could genuinely make many of opportunity could genuinely make many of you career millionaires over the next you career millionaires over the next you career millionaires over the next few years, and I've seen a lot of the few years, and I've seen a lot of the few years, and I've seen a lot of the members in my community making this members in my community making this members in my community making this shift and getting great results with it. shift and getting great results with it. shift and getting great results with it. But like in every shift in the AI space, But like in every shift in the AI space, But like in every shift in the AI space, it's the ones that get in early that'll it's the ones that get in early that'll it's the ones that get in early that'll make the most out of it. So, in this make the most out of it. So, in this make the most out of it. So, in this video, I'll break down exactly what this video, I'll break down exactly what this video, I'll break down exactly what this opportunity is and the four-step road opportunity is and the four-step road opportunity is and the four-step road map to take advantage of it. So, let's map to take advantage of it. So, let's map to take advantage of it. So, let's dive in. Okay, so this is a new dive in. Okay, so this is a new dive in. Okay, so this is a new opportunity in the AI space, but it's opportunity in the AI space, but it's opportunity in the AI space, but it's not some random thing that came out of not some random thing that came out of not some random thing that came out of nowhere. It's really just a product of nowhere. It's really just a product of nowhere. It's really just a product of the same pattern that we've been seeing the same pattern that we've been seeing the same pattern that we've been seeing ever since AI went mainstream. So, let ever since AI went mainstream. So, let ever since AI went mainstream. So, let me tell you about a company called CHEG. me tell you about a company called CHEG. me tell you about a company called CHEG. And if you guys haven't heard of them, And if you guys haven't heard of them, And if you guys haven't heard of them, for years, CE basically sold homework for years, CE basically sold homework for years, CE basically sold homework help to students. It was the service help to students. It was the service help to students. It was the service where you could go ask a question and an where you could go ask a question and an where you could go ask a question and an actual expert on their end give you an actual expert on their end give you an actual expert on their end give you an answer. And it also had loaded in, you answer. And it also had loaded in, you answer. And it also had loaded in, you know, like study guides and answer keys, know, like study guides and answer keys, know, like study guides and answer keys, stuff like that. And millions of stuff like that. And millions of stuff like that. And millions of students were paying for this every students were paying for this every students were paying for this every single month. It was a super stable, single month. It was a super stable, single month. It was a super stable, super profitable business. I personally super profitable business. I personally super profitable business. I personally used Cheg all the time back in college.

used Cheg all the time back in college. used Cheg all the time back in college. And boy, did I get my money's worth. But And boy, did I get my money's worth. But And boy, did I get my money's worth. But then late 2022 happens and JGBT comes then late 2022 happens and JGBT comes then late 2022 happens and JGBT comes around and pretty much overnight, every around and pretty much overnight, every around and pretty much overnight, every single student could get the exact same single student could get the exact same single student could get the exact same homework help in just a few seconds for homework help in just a few seconds for homework help in just a few seconds for much cheaper, sometimes even free. And much cheaper, sometimes even free. And much cheaper, sometimes even free. And as soon as that happened, I canceled my as soon as that happened, I canceled my as soon as that happened, I canceled my CH subscription as well. In 2023, CHEG's CH subscription as well. In 2023, CHEG's CH subscription as well. In 2023, CHEG's stock crashed almost 50% in a single stock crashed almost 50% in a single stock crashed almost 50% in a single [music] day. And they basically came out [music] day. And they basically came out [music] day. And they basically came out and admitted that CatchBeT was killing and admitted that CatchBeT was killing and admitted that CatchBeT was killing their business. So the point I'm trying their business. So the point I'm trying their business. So the point I'm trying to make, CHEG is just the most famous to make, CHEG is just the most famous to make, CHEG is just the most famous example, but over the last couple of example, but over the last couple of example, but over the last couple of years, we've watched company after years, we've watched company after years, we've watched company after company after company announce layoffs company after company announce layoffs company after company announce layoffs and point at AI as part of the reason and point at AI as part of the reason and point at AI as part of the reason why. But I do think that most people why. But I do think that most people why. But I do think that most people read this the wrong way. Those layoffs read this the wrong way. Those layoffs read this the wrong way. Those layoffs didn't happen because AI could just didn't happen because AI could just didn't happen because AI could just replace people's jobs. They happened replace people's jobs. They happened replace people's jobs. They happened because companies realize that one because companies realize that one because companies realize that one person using AI can now do the work that person using AI can now do the work that person using AI can now do the work that used to take three to five people to do used to take three to five people to do used to take three to five people to do so. So when you actually look at all of so. So when you actually look at all of so. So when you actually look at all of that, it's always coming back to the that, it's always coming back to the that, it's always coming back to the same thing. the people who know how to same thing. the people who know how to same thing. the people who know how to use AI get ahead of the people who use AI get ahead of the people who use AI get ahead of the people who don't. And that's exactly how the first don't. And that's exactly how the first don't. And that's exactly how the first AI related jobs started changing the way AI related jobs started changing the way AI related jobs started changing the way corporate work actually worked. corporate work actually worked. corporate work actually worked. Companies started going out and hiring Companies started going out and hiring Companies started going out and hiring AI experts and AI agencies and AI experts and AI agencies and AI experts and AI agencies and consultants to come in and to diagnose consultants to come in and to diagnose consultants to come in and to diagnose the problems, build automations, solve the problems, build automations, solve the problems, build automations, solve the problems. And over the past couple the problems. And over the past couple the problems. And over the past couple of years, this whole AI automation of years, this whole AI automation of years, this whole AI automation market went from being this brand new market went from being this brand new market went from being this brand new thing to being worth around $130 thing to being worth around $130 thing to being worth around $130 billion. But the exact same thing that billion. But the exact same thing that billion. But the exact same thing that made these AI agencies a ton of money is made these AI agencies a ton of money is made these AI agencies a ton of money is the same thing that's about to replace the same thing that's about to replace the same thing that's about to replace them. So for the last couple of years, them. So for the last couple of years, them. So for the last couple of years, companies were in kind of this weird companies were in kind of this weird companies were in kind of this weird spot. They knew the problems they had.

spot. They knew the problems they had. spot. They knew the problems they had. They just had no idea how to actually They just had no idea how to actually They just had no idea how to actually solve them. They knew that their, you solve them. They knew that their, you solve them. They knew that their, you know, support inbox was a mess. They know, support inbox was a mess. They know, support inbox was a mess. They just didn't know what to actually do just didn't know what to actually do just didn't know what to actually do about it. And that gap right there about it. And that gap right there about it. And that gap right there between knowing the problem and knowing between knowing the problem and knowing between knowing the problem and knowing the solution, that's the reason that AI the solution, that's the reason that AI the solution, that's the reason that AI agencies could charge such premium agencies could charge such premium agencies could charge such premium prices. And of course, because AI was a prices. And of course, because AI was a prices. And of course, because AI was a big buzzword and businesses were feeling big buzzword and businesses were feeling big buzzword and businesses were feeling pressure both from, you know, their pressure both from, you know, their pressure both from, you know, their boards and their competitors to start boards and their competitors to start boards and their competitors to start using AI. And of course, the AI agencies using AI. And of course, the AI agencies using AI. And of course, the AI agencies were the ones who could fill that gap. were the ones who could fill that gap. were the ones who could fill that gap. But for a while they were basically the But for a while they were basically the But for a while they were basically the only ones who could come in and actually only ones who could come in and actually only ones who could come in and actually build a solution. And that's just not build a solution. And that's just not build a solution. And that's just not the case anymore because AI has gotten the case anymore because AI has gotten the case anymore because AI has gotten so accessible and so easy to use that at so accessible and so easy to use that at so accessible and so easy to use that at this point even the busiest CEO has this point even the busiest CEO has this point even the busiest CEO has opened up Chad GBT or Claude and used it opened up Chad GBT or Claude and used it opened up Chad GBT or Claude and used it in some way to solve a problem that they in some way to solve a problem that they in some way to solve a problem that they were having. So it's just completely were having. So it's just completely were having. So it's just completely flipped now. The cost and the value of flipped now. The cost and the value of flipped now. The cost and the value of development is dropping. Companies still development is dropping. Companies still development is dropping. Companies still know exactly what problems they have, know exactly what problems they have, know exactly what problems they have, but instead of going out and paying but instead of going out and paying but instead of going out and paying another company to come and solve them, another company to come and solve them, another company to come and solve them, they're looking for ways to solve these they're looking for ways to solve these they're looking for ways to solve these problems themselves. And they want to do problems themselves. And they want to do problems themselves. And they want to do it in house. And this brings me to the it in house. And this brings me to the it in house. And this brings me to the actual opportunity, the one that I think actual opportunity, the one that I think actual opportunity, the one that I think is going to create more AI careers than is going to create more AI careers than is going to create more AI careers than anything else over the next few years. anything else over the next few years. anything else over the next few years. So, a lot of people are going to assume So, a lot of people are going to assume So, a lot of people are going to assume that the safe AI career here is just to that the safe AI career here is just to that the safe AI career here is just to become the best builder for these become the best builder for these become the best builder for these companies. Like, you know, the AI companies. Like, you know, the AI companies. Like, you know, the AI engineer, the person who can actually go engineer, the person who can actually go engineer, the person who can actually go in and build all the automations. And in and build all the automations. And in and build all the automations. And yeah, I think that that's part of it yeah, I think that that's part of it yeah, I think that that's part of it because then you get a really good because then you get a really good because then you get a really good understanding of how it works and how, understanding of how it works and how, understanding of how it works and how, you know, like what success looks like.

you know, like what success looks like. you know, like what success looks like. Just knowing how to build this stuff is Just knowing how to build this stuff is Just knowing how to build this stuff is honestly a very small piece of the honestly a very small piece of the honestly a very small piece of the puzzle because what really matters right puzzle because what really matters right puzzle because what really matters right now is not about knowing how to build now is not about knowing how to build now is not about knowing how to build something. It's more about knowing what something. It's more about knowing what something. It's more about knowing what to build. These big AI labs like OpenAI to build. These big AI labs like OpenAI to build. These big AI labs like OpenAI and Anthropic have even come out and Anthropic have even come out and Anthropic have even come out themselves and said, "Hey, these models themselves and said, "Hey, these models themselves and said, "Hey, these models are getting so good, like scary good to are getting so good, like scary good to are getting so good, like scary good to the point where we might need to slow the point where we might need to slow the point where we might need to slow this down." But that's a topic for this down." But that's a topic for this down." But that's a topic for another video. But what they've all another video. But what they've all another video. But what they've all agreed on is where is the value in a agreed on is where is the value in a agreed on is where is the value in a human? The value is in judgment, taste, human? The value is in judgment, taste, human? The value is in judgment, taste, you know, solving ambiguity, deciding you know, solving ambiguity, deciding you know, solving ambiguity, deciding what problems to point AI at and what what problems to point AI at and what what problems to point AI at and what problems to not point AI at. So that problems to not point AI at. So that problems to not point AI at. So that human judgment so so important. And it's human judgment so so important. And it's human judgment so so important. And it's not a skill that everyone is just not a skill that everyone is just not a skill that everyone is just naturally good at because like I said naturally good at because like I said naturally good at because like I said earlier, AI is getting better. The cost earlier, AI is getting better. The cost earlier, AI is getting better. The cost of development and value of development of development and value of development of development and value of development is dropping. It's getting easier and is dropping. It's getting easier and is dropping. It's getting easier and easier. You don't need a computer easier. You don't need a computer easier. You don't need a computer science degree anymore. You barely even science degree anymore. You barely even science degree anymore. You barely even need a tech background at all. And so, need a tech background at all. And so, need a tech background at all. And so, as all of this is happening, like I as all of this is happening, like I as all of this is happening, like I said, all the real value is shifting said, all the real value is shifting said, all the real value is shifting over to judgment. Sam Alman said that over to judgment. Sam Alman said that over to judgment. Sam Alman said that the idea guys are about to have their the idea guys are about to have their the idea guys are about to have their day in the sun. So, think about it like day in the sun. So, think about it like day in the sun. So, think about it like a doctor and a pharmacist. A pharmacist a doctor and a pharmacist. A pharmacist a doctor and a pharmacist. A pharmacist basically just hands you exactly what basically just hands you exactly what basically just hands you exactly what you asked for, but a doctor is the one you asked for, but a doctor is the one you asked for, but a doctor is the one who actually figures out what you need who actually figures out what you need who actually figures out what you need in the first place. The builder, that's in the first place. The builder, that's in the first place. The builder, that's kind of more of the pharmacist, and the kind of more of the pharmacist, and the kind of more of the pharmacist, and the in-house AI consultant, that's the in-house AI consultant, that's the in-house AI consultant, that's the doctor. And the doctor is the one who doctor. And the doctor is the one who doctor. And the doctor is the one who gets paid the real money. So day-to-day, gets paid the real money. So day-to-day, gets paid the real money. So day-to-day, what that role actually looks like is what that role actually looks like is what that role actually looks like is auditing how your team currently works, auditing how your team currently works, auditing how your team currently works, finding the tasks that are eating up the finding the tasks that are eating up the finding the tasks that are eating up the most hours, and building the automations most hours, and building the automations most hours, and building the automations to handle those tasks, and then training to handle those tasks, and then training to handle those tasks, and then training everyone else on how to use what you everyone else on how to use what you everyone else on how to use what you just built because adoption is another just built because adoption is another just built because adoption is another huge problem. Change management, huge problem. Change management, huge problem. Change management, stakeholder communication. Every company stakeholder communication. Every company stakeholder communication. Every company already has like that one IT person, already has like that one IT person, already has like that one IT person, right? The one that everybody runs to right? The one that everybody runs to right? The one that everybody runs to the second their laptop breaks or the the second their laptop breaks or the the second their laptop breaks or the Wi-Fi goes down. Now, the in-house AI Wi-Fi goes down. Now, the in-house AI Wi-Fi goes down. Now, the in-house AI consultant role might not actually like consultant role might not actually like consultant role might not actually like exist yet, at least not as a formal exist yet, at least not as a formal exist yet, at least not as a formal position with a standardized name and

position with a standardized name and position with a standardized name and standardized, you know, roles and standardized, you know, roles and standardized, you know, roles and responsibilities. And that's exactly why responsibilities. And that's exactly why responsibilities. And that's exactly why this is such a good opportunity because this is such a good opportunity because this is such a good opportunity because most people won't even realize that the most people won't even realize that the most people won't even realize that the job exists until the market's already job exists until the market's already job exists until the market's already kind of saturated, [music] which is the kind of saturated, [music] which is the kind of saturated, [music] which is the exact same thing we just watched out exact same thing we just watched out exact same thing we just watched out play with AI automation services. Now, play with AI automation services. Now, play with AI automation services. Now, I'm not saying that AI automation I'm not saying that AI automation I'm not saying that AI automation services are completely saturated, but services are completely saturated, but services are completely saturated, but I'm just saying it's kind of the trend I'm just saying it's kind of the trend I'm just saying it's kind of the trend in the market. But I know what some of in the market. But I know what some of in the market. But I know what some of you are probably thinking right now, you are probably thinking right now, you are probably thinking right now, which is, Nate, if this position doesn't which is, Nate, if this position doesn't which is, Nate, if this position doesn't even exist yet, how am I actually even exist yet, how am I actually even exist yet, how am I actually supposed to get there? And it is a fair supposed to get there? And it is a fair supposed to get there? And it is a fair question. So here's the exact road map question. So here's the exact road map question. So here's the exact road map that I'd use to basically create this that I'd use to basically create this that I'd use to basically create this position inside the company you already position inside the company you already position inside the company you already work for. And it's four steps. All work for. And it's four steps. All work for. And it's four steps. All right. So step one is to audit your own right. So step one is to audit your own right. So step one is to audit your own job. So I mean like literally just sit job. So I mean like literally just sit job. So I mean like literally just sit down and think about what you do on the down and think about what you do on the down and think about what you do on the dayto-day and on the week to week and dayto-day and on the week to week and dayto-day and on the week to week and just write down those things. Now most just write down those things. Now most just write down those things. Now most people will just go and automate people will just go and automate people will just go and automate whatever annoys them the most. But the whatever annoys them the most. But the whatever annoys them the most. But the most annoying task isn't always the most annoying task isn't always the most annoying task isn't always the right one to start with. So instead go right one to start with. So instead go right one to start with. So instead go down your list and find the tasks that down your list and find the tasks that down your list and find the tasks that check two boxes. One, it eats up real check two boxes. One, it eats up real check two boxes. One, it eats up real hours every single week. And two, if the hours every single week. And two, if the hours every single week. And two, if the AI gets it a little wrong, nobody gets AI gets it a little wrong, nobody gets AI gets it a little wrong, nobody gets hurt. So you can stay in the loop and hurt. So you can stay in the loop and hurt. So you can stay in the loop and you can fix it and you can move on. So you can fix it and you can move on. So you can fix it and you can move on. So stuff like maybe a weekly status report stuff like maybe a weekly status report stuff like maybe a weekly status report or meeting notes or sorting through your or meeting notes or sorting through your or meeting notes or sorting through your inbox, cleaning up some data, doing some inbox, cleaning up some data, doing some inbox, cleaning up some data, doing some basic research. So then you take the basic research. So then you take the basic research. So then you take the tasks that check both boxes and that is tasks that check both boxes and that is tasks that check both boxes and that is your starting list. And don't try to go your starting list. And don't try to go your starting list. And don't try to go fix the entire company on day one. You fix the entire company on day one. You fix the entire company on day one. You are just getting your feet wet. You're are just getting your feet wet. You're are just getting your feet wet. You're starting with your own work. And then starting with your own work. And then starting with your own work. And then step two is you take those top tasks on step two is you take those top tasks on step two is you take those top tasks on the list and you actually start to the list and you actually start to the list and you actually start to automate them. And this is when you're automate them. And this is when you're automate them. And this is when you're able to basically just prove that it able to basically just prove that it able to basically just prove that it works. When I say that, I mean actually works. When I say that, I mean actually works. When I say that, I mean actually writing down the numbers. So, you know, writing down the numbers. So, you know, writing down the numbers. So, you know, this report used to take me 2 hours this report used to take me 2 hours this report used to take me 2 hours every week, but now it takes me 10 every week, but now it takes me 10 every week, but now it takes me 10 minutes a week because those hours you minutes a week because those hours you minutes a week because those hours you saved, that's your proof. And it's kind saved, that's your proof. And it's kind saved, that's your proof. And it's kind of like how a personal trainer gets of like how a personal trainer gets of like how a personal trainer gets their own body in shape first before their own body in shape first before their own body in shape first before they go try to get everyone else in they go try to get everyone else in they go try to get everyone else in shape. Nobody's going to hire a trainer

shape. Nobody's going to hire a trainer shape. Nobody's going to hire a trainer who's just like horribly out of shape. who's just like horribly out of shape. who's just like horribly out of shape. You want proof that you can actually get You want proof that you can actually get You want proof that you can actually get the results before you try to sell the results before you try to sell the results before you try to sell anyone on the idea. And once the first anyone on the idea. And once the first anyone on the idea. And once the first one works, you don't stop there. You go one works, you don't stop there. You go one works, you don't stop there. You go right back to your list and you knock right back to your list and you knock right back to your list and you knock out the next one and then the next one. out the next one and then the next one. out the next one and then the next one. And then we move on to step three, which And then we move on to step three, which And then we move on to step three, which is you start making all of this proof is you start making all of this proof is you start making all of this proof more visible. So, you demo your wins in more visible. So, you demo your wins in more visible. So, you demo your wins in your team meetings. You offer to go fix your team meetings. You offer to go fix your team meetings. You offer to go fix your co-worker's most annoying task, your co-worker's most annoying task, your co-worker's most annoying task, too. And you document every single thing too. And you document every single thing too. And you document every single thing that you're doing along the way. But the that you're doing along the way. But the that you're doing along the way. But the way that you talk about it really way that you talk about it really way that you talk about it really matters as well. Don't go in saying, matters as well. Don't go in saying, matters as well. Don't go in saying, "Hey, you know, I used Chat GBT for "Hey, you know, I used Chat GBT for "Hey, you know, I used Chat GBT for this." Tie it back to the business. This this." Tie it back to the business. This this." Tie it back to the business. This saved us 8 hours before the quarterly saved us 8 hours before the quarterly saved us 8 hours before the quarterly report went out. That's the version that report went out. That's the version that report went out. That's the version that your boss actually cares about and your boss actually cares about and your boss actually cares about and actually will remember. And if you put actually will remember. And if you put actually will remember. And if you put together a little internal doc with your together a little internal doc with your together a little internal doc with your best prompts and workflows that everyone best prompts and workflows that everyone best prompts and workflows that everyone else can just start to use, now your else can just start to use, now your else can just start to use, now your name is on the whole thing that the name is on the whole thing that the name is on the whole thing that the whole team relies on. Now, once you've whole team relies on. Now, once you've whole team relies on. Now, once you've got a few wins stacked up and people got a few wins stacked up and people got a few wins stacked up and people start coming to you, you're ready to start coming to you, you're ready to start coming to you, you're ready to graduate from annoyances to constraints graduate from annoyances to constraints graduate from annoyances to constraints because all of that kind of like lowrisk because all of that kind of like lowrisk because all of that kind of like lowrisk stuff that we just talked about is where stuff that we just talked about is where stuff that we just talked about is where you start and that's definitely the you start and that's definitely the you start and that's definitely the right move because it let you experiment right move because it let you experiment right move because it let you experiment somewhere safe while you were still somewhere safe while you were still somewhere safe while you were still proving the value and still learning. proving the value and still learning. proving the value and still learning. Automating annoyances doesn't really Automating annoyances doesn't really Automating annoyances doesn't really grow a business. What does attacking grow a business. What does attacking grow a business. What does attacking [music] the constraints and that's what [music] the constraints and that's what [music] the constraints and that's what these, you know, AI consultants are these, you know, AI consultants are these, you know, AI consultants are getting paid the big bucks for is to getting paid the big bucks for is to getting paid the big bucks for is to grow the business by attacking grow the business by attacking grow the business by attacking constraints. So, this is the point where constraints. So, this is the point where constraints. So, this is the point where you run the same audit from step one you run the same audit from step one you run the same audit from step one except now on basically the entire except now on basically the entire except now on basically the entire business. And the question completely business. And the question completely business. And the question completely changes. You're not asking, you know, changes. You're not asking, you know, changes. You're not asking, you know, what's annoying or what eats up a few what's annoying or what eats up a few what's annoying or what eats up a few hours of my day. You're asking what's hours of my day. You're asking what's hours of my day. You're asking what's actually holding the business back. If actually holding the business back. If actually holding the business back. If we doubled our customers tomorrow, what we doubled our customers tomorrow, what we doubled our customers tomorrow, what process or what thing would break first?

process or what thing would break first? process or what thing would break first? And that's where your first constraint And that's where your first constraint And that's where your first constraint is and that's your project. Because is and that's your project. Because is and that's your project. Because saving your team a few hours every week saving your team a few hours every week saving your team a few hours every week makes you helpful, but removing the makes you helpful, but removing the makes you helpful, but removing the bottleneck the whole business is stuck bottleneck the whole business is stuck bottleneck the whole business is stuck behind is how you make the company real behind is how you make the company real behind is how you make the company real money. And that is a completely money. And that is a completely money. And that is a completely different conversation with your different conversation with your different conversation with your managers or your bosses. And then step managers or your bosses. And then step managers or your bosses. And then step four, you formalize the whole thing into four, you formalize the whole thing into four, you formalize the whole thing into an actual position. So you add up all an actual position. So you add up all an actual position. So you add up all the hours and all the money your the hours and all the money your the hours and all the money your automations have saved and you turn it automations have saved and you turn it automations have saved and you turn it into one single number. Something like, into one single number. Something like, into one single number. Something like, you know, across these five automations, you know, across these five automations, you know, across these five automations, I'm giving the team back the equivalent I'm giving the team back the equivalent I'm giving the team back the equivalent of a full-time hire every single year. of a full-time hire every single year. of a full-time hire every single year. That's the kind of math that will allow That's the kind of math that will allow That's the kind of math that will allow the company to have a real budget to put the company to have a real budget to put the company to have a real budget to put towards AI projects. And then you take towards AI projects. And then you take towards AI projects. And then you take that to your manager and you're not that to your manager and you're not that to your manager and you're not asking them for a favor. You're asking them for a favor. You're asking them for a favor. You're proposing the actual role and the title. proposing the actual role and the title. proposing the actual role and the title. Because most of you guys are not going Because most of you guys are not going Because most of you guys are not going to find this job posted on LinkedIn. to find this job posted on LinkedIn. to find this job posted on LinkedIn. We're going to slowly start to see that We're going to slowly start to see that We're going to slowly start to see that over the next few months and years. A over the next few months and years. A over the next few months and years. A lot of you might just kind of create it lot of you might just kind of create it lot of you might just kind of create it from the inside out. And this isn't some from the inside out. And this isn't some from the inside out. And this isn't some hypothetical thing that I'm making up. hypothetical thing that I'm making up. hypothetical thing that I'm making up. Right now about 76% of companies in this Right now about 76% of companies in this Right now about 76% of companies in this IBM survey, so it was 2,000 CEOs from IBM survey, so it was 2,000 CEOs from IBM survey, so it was 2,000 CEOs from pretty big companies. 76% of them say pretty big companies. 76% of them say pretty big companies. 76% of them say that they already have some kind of that they already have some kind of that they already have some kind of chief AI officer. And a year ago, that chief AI officer. And a year ago, that chief AI officer. And a year ago, that number was 26%. So that seat went from number was 26%. So that seat went from number was 26%. So that seat went from basically non-existing to being almost basically non-existing to being almost basically non-existing to being almost everywhere in about 2 years. And the everywhere in about 2 years. And the everywhere in about 2 years. And the people stepping into these roles are the people stepping into these roles are the people stepping into these roles are the ones who saw it coming early. On top of ones who saw it coming early. On top of ones who saw it coming early. On top of that, people with AI skills are also that, people with AI skills are also that, people with AI skills are also getting paid around 56% more than the getting paid around 56% more than the getting paid around 56% more than the co-orker sitting right next to them co-orker sitting right next to them co-orker sitting right next to them doing the exact same job without them.

doing the exact same job without them. doing the exact same job without them. Because at that point, you're not asking Because at that point, you're not asking Because at that point, you're not asking for the job. You've already built it. for the job. You've already built it. for the job. You've already built it. And for a group of you that might be And for a group of you that might be And for a group of you that might be saying, "Okay, well, I work in a saying, "Okay, well, I work in a saying, "Okay, well, I work in a regulated industry and we have no AI at regulated industry and we have no AI at regulated industry and we have no AI at work and stuff like that." Be smart. work and stuff like that." Be smart. work and stuff like that." Be smart. Don't obviously go throw AI at sensitive Don't obviously go throw AI at sensitive Don't obviously go throw AI at sensitive data. Don't automate stuff without data. Don't automate stuff without data. Don't automate stuff without permission if you can't. Be smart about permission if you can't. Be smart about permission if you can't. Be smart about it. But that doesn't mean that you can't it. But that doesn't mean that you can't it. But that doesn't mean that you can't be the AI person in your team and in be the AI person in your team and in be the AI person in your team and in your company. You can be the person your company. You can be the person your company. You can be the person that's experimenting with stuff on the that's experimenting with stuff on the that's experimenting with stuff on the side. You can be the person who's side. You can be the person who's side. You can be the person who's building out projects that are basically building out projects that are basically building out projects that are basically exactly what you do for work, but with exactly what you do for work, but with exactly what you do for work, but with dummy data. Those four steps are dummy data. Those four steps are dummy data. Those four steps are basically still the same. It's proving basically still the same. It's proving basically still the same. It's proving that you understand how to think about that you understand how to think about that you understand how to think about it and that you can deliver real value it and that you can deliver real value it and that you can deliver real value with it. Anyways, now you've got the with it. Anyways, now you've got the with it. Anyways, now you've got the full road map to becoming your company's full road map to becoming your company's full road map to becoming your company's in-house AI consultant, but you can in-house AI consultant, but you can in-house AI consultant, but you can follow every single step in that road follow every single step in that road follow every single step in that road map. And if you can't actually build the map. And if you can't actually build the map. And if you can't actually build the solutions with AI, then nothing else solutions with AI, then nothing else solutions with AI, then nothing else matters. The good news is you can learn matters. The good news is you can learn matters. The good news is you can learn how to do that completely for free how to do that completely for free how to do that completely for free inside of my free community. There are inside of my free community. There are inside of my free community. There are full courses in there walking you full courses in there walking you full courses in there walking you through how to use AI to solve problems through how to use AI to solve problems through how to use AI to solve problems and the tools you need and every single and the tools you need and every single and the tools you need and every single resource that I've ever given out for resource that I've ever given out for resource that I've ever given out for free. You know, agent skills, GitHub free. You know, agent skills, GitHub free. You know, agent skills, GitHub repos, resource guides, all that kind of repos, resource guides, all that kind of repos, resource guides, all that kind of stuff. And it's all in there for free. stuff. And it's all in there for free. stuff. And it's all in there for free. Also, there is a complete resource guide Also, there is a complete resource guide Also, there is a complete resource guide from everything that we just talked from everything that we just talked from everything that we just talked about in this video that you can about in this video that you can about in this video that you can download. But that is going to do it for download. But that is going to do it for download. But that is going to do it for this one. As always, I appreciate you this one. As always, I appreciate you this one. As always, I appreciate you guys making it to the end of the video guys making it to the end of the video guys making it to the end of the video and I'll see you on the next one.