So You Learned Claude, Now What?

So, you've learned everything you can about Claude. You can build agents,

about Claude. You can build agents, automations, and complex systems, but what do you do now? Do you start an AI agency? Do you sell automations, or do

agency? Do you sell automations, or do you build software for work companies?

There are so many different options out there, but 90% of those options aren't relevant to the average viewer of this channel. And I understand that most of

channel. And I understand that most of you guys work normal jobs, you have corporate careers, and much prefer the security of being employed than, you know, the ups and [music] downs of being self-employed. But with the AI space

self-employed. But with the AI space changing like every single day, that security that most of you are used to is disappearing. So, in this video, I'll

disappearing. So, in this video, I'll show you the best thing that you can do right now to make money with your Claude skills inside of your preferred career and the exact roadmap to do so. So,

let's get into it. Before I give you the actual roadmap, there is one thing that you have to understand first, which is the AI space never stops moving. It does

not sit still for a second. So, getting

really good at Claude right now on its own means almost nothing long-term because the tools are going to change.

So, what actually matters are the skills underneath the tool and learning how to take those skills and apply them to every new phase of AI as it shows up.

And the reason that matters so much is because the AI space has never really had one fixed best job or one best business model. It keeps swapping them

business model. It keeps swapping them out every year or so. And every single time it does, a brand new window opens up for whoever is paying [music] attention. So, let me walk you through

attention. So, let me walk you through what I mean real quick. So, if you rewind about a year back when AI first really started blowing up, the first real paid gigs were pretty simple. You

could be the person who set up one automation or one chatbot for a small business or [music] a team, and that alone was enough to get you paid pretty well. But obviously that shifted pretty

well. But obviously that shifted pretty quick. It became the whole AI systems

quick. It became the whole AI systems phase, or what most people now are calling the AI automation agency phase.

Everybody's, you know, trying to productize services, spinning up agencies, and selling done-for-you systems [music] left and right. But then

it sort of shifted again, you know, over to the AI agent builder era. And this is where people stopped building those simple little automations, and they started building agents that can actually think and execute a ton of these repetitive tasks [music] that we

all do every single day. And then we get to the newest phase, the agentic one.

Gartner is projecting around $202 billion in spending on agentic AI in 2026 alone. So, if you just think about

2026 alone. So, if you just think about that for a second, companies are pouring that kind of money into AI [music] that doesn't just answer your questions, but it actually goes and does the work for you. And that seems to be the phase that

you. And that seems to be the phase that we're currently sitting in right now.

But the pattern that I really want you to catch there is that every single time one of these phases changed, the people who moved early, you know, were able to catch and ride the wave. And the people who stayed glued to the old phase ended

up fighting just to survive in the new super crowded sort of race to the bottom market. It was never really about

market. It was never really about learning the specific tool, it was about understanding what the tools could actually do and what the value of that was to actual human people. And the

crazy part is that the building itself is getting easier every single month.

The barrier to entry keeps lowering.

McKinsey found that around 88% of organizations are now using AI somewhere in their business, but only about a third of them have actually turned that into real projects. So, just going to repeat that real quick. Almost everyone

is using AI, but almost nobody is good at AI. And that gap right there is the

at AI. And that gap right there is the entire opportunity. So, the next phase

entire opportunity. So, the next phase isn't some new flavor of builder. The

value is shifting to the person who decides what to build in the first place, why you're even building it, and whether the thing actually worked. And

that person is an AI consultant. Now, a

consultant is really just the person who figures out what's actually wrong and then figures out how to fix it. So,

instead of just sitting there and doing whatever they're told to do, think about it like a doctor versus a pharmacist. A

pharmacist will basically just hand you exactly what you're asking for, but a doctor has to figure out what you actually need. So, builders are kind of

actually need. So, builders are kind of like the pharmacists and consultants are the doctors. And the doctor is the one

the doctors. And the doctor is the one who gets paid the real money because clients never actually know what they need. They just know what hurts. So,

need. They just know what hurts. So,

your job isn't being the fastest person at the build, your job is naming the real problem in the first place. And of

course, the money backs all of this up.

AI consulting market is expected to grow past $64 billion by 2028, and there's a giant gap to fill here. Roughly 30% of company AI projects just get abandoned, and only about 6% of companies using AI

are actually good at it. So, almost

every business out there is pretty bad at this right now. And the important part is that they know they're bad at it. And this is exactly where the

it. And this is exactly where the consultant walks right in to help. So,

the best move that you can make right now is to become that consultant. But

there are actually two completely different roads into that role. And the

one that you take really comes down to the type of person that you are. So,

road number one is the independent AI consultant. This is where you go into

consultant. This is where you go into other businesses, you find their problems, and then you prescribe and build the AI solutions that fixes them.

It's really just AI agency idea, but you're just framing yourself way more as a long-term partner rather than a team of scrappy devs who can build AI automations. Because instead of selling

automations. Because instead of selling those automations, you're selling the actual solution to a specific problem.

And full disclosure, this is pretty much the road that I went down myself, so it pretty well. But, road number two is

pretty well. But, road number two is more of the in-house AI consultant. And

this is the door that I want to start talking about more on my channel, as I've realized not everyone wants to start their own consulting practice. So,

instead of consulting a bunch of different companies from the outside, you basically just become the go-to AI person inside of one single company. And

that company could even be the one that you're already working at right now. And

of course, companies are starting to take this very seriously. They're

starting to hire in-house AI leaders with real titles, things like a chief AI officer or a director of AI. And a lot of these roles are paying into the low to mid six figures. IBM actually just put out their 2026 CEO [music] study,

and they found that 76% of organizations now have someone in a chief AI officer type of role. And that's up from just 26% 2 years ago. So, that nearly tripled in a record amount of time for a C-suite role. Now, I do want to be fair about

role. Now, I do want to be fair about that number for a second. That study

only surveyed about 2,000 CEOs, and these were pretty massive companies.

We're talking a median revenue of around $5.8 billion, and almost four out of five of them were publicly traded. So,

that 76% number is really just, you know, giant enterprises racing to fill the seat first. and mid-size businesses, which is where most of you guys will try to be working and consulting for, I'm assuming, that market is still very much

wide open. So, if anything, that tells

wide open. So, if anything, that tells me there is a ton of room left for the rest of us to just walk right in. And

the cool part is that both of these roads are basically the same idea, meaning you diagnose the problem, you prescribe the [music] AI solution, and then you prove that it actually worked.

The only real difference is whether you're doing that for a bunch of companies or just one. And it doesn't matter what tool you end up using as long as you can bring real results, which is why the skills matter so much more. The independent road is going to

more. The independent road is going to fit you if you want full ownership of your time, you like variety, and you don't mind doing things like sales calls and going out to find your own clients.

The in-house road fits you better if you'd rather have stability, you want one place where you can go really deep, and you like the idea of a steady paycheck while you do it, and not having to like pivot out of your job and try to start your own business. So, I'm not sitting here telling you that one path

is better than the other. It really

comes down to your goals, your experience, what you want out of life, and you know, what you want out of this whole AI opportunity in the first place.

And look, I get it. There's a million people out there telling you what to do with AI right now. So, before we keep going, I want to give you a little bit of context on why I'm even the one standing here telling you all [music] this, because I've kind of been on both sides of what we just talked about. I

actually started full-time out of college at Goldman Sachs. And when AI first really started taking off, I was the guy on the team who was researching it on the side and who was obsessed with it, playing around with it in my free time, and even trying to like show my team and pitch it to my team. I

genuinely wanted to be like the in-house AI person. I was kind of hoping I could

AI person. I was kind of hoping I could like, you know, spearhead a new initiative or work on AI projects at Goldman. But, it's obviously a massive

Goldman. But, it's obviously a massive firm with a ton of regulation and a ton of change management issues, and the whole thing [music] just felt way too slow for what I wanted to do. So, that's

when I ended up making a bet on myself and pivoting [music] out of that role. A

little while after that, I was doing a lot of freelance work. I started my own AI agency called True Horizon with a couple of business partners, and we scaled that thing past $100,000 a month in under a year, and I ended up exiting it because [music] I just realized that I was way more passionate about

educating and getting in front of people and helping as many people as I could figure out how to use this stuff for themselves, which basically led me to where I am now, building what is the largest AI automation community in the world. We've got over 400,000 members

world. We've got over 400,000 members and all this happened in just under 2 years. Now, the reason I'm telling you

years. Now, the reason I'm telling you guys this isn't to flex. It's basically

just to say that I have a big community and I'm able to see what's actually happening in the market, and I still get to see what business owners are trying, what employers are trying, what aspiring entrepreneurs are trying. And I've

noticed that there's a ton of content out there about how to start an AI business, but most of you guys aren't trying to do that or want to do that, or you think that's the only option. But,

most of you might just want to advance in your career as an employee or maybe get a better job or promotion or more security. And almost nobody is putting

security. And almost nobody is putting out the stuff that actually helps you guys do that. Now, the other important thing I want you to hear is that I don't have a technical background. Like I came from marketing and analytics. I'm not an engineer and I never have been. And only

reason this matters is because the barrier to entry on actually learning this stuff has basically dropped to zero. Like anyone can do this now. So,

zero. Like anyone can do this now. So,

if you're sitting there wondering, can I even learn this? That's just not even a question you should be asking anymore.

The answer is yes. The real question that matters is this. When someone asks you, why should I hire you over the person who watched the exact same amount [music] of YouTube tutorials and knows the exact same terminology and has the same experience as you. You have to

think about how you answer that question. Because the people who win in

question. Because the people who win in this market aren't the ones who learned the most, they're the ones who can actually prove they can deliver real business results. So, that right there

business results. So, that right there is the core question. It's also a big part of why we've been building something silently on our end to help you guys out with credibility, but more on that later. For now, just sit with that question for a sec because [music]

no matter which of these roads you go down, that question is exactly what your entire path comes down to. And you will get asked that question. Now, the great thing is that no matter which of those two roads sounds more like you, becoming an AI consultant is probably the next

natural step in your journey. And I want to break that down for a second because there are basically, in my mind, four types of people who are probably watching this right now. And this move makes sense for every single one of these four buckets. The first type is

the person who's just building with Claude as a passion project. And hey, if that's you, there's nothing wrong with that. It's totally fine. But if you can

that. It's totally fine. But if you can actually monetize what you're doing in some way, it does two big things for you. First, it validates that you're

you. First, it validates that you're actually good at this because it's probably the cheapest way to find out if your skills are good enough where someone's willing to pay for them. And

second, it funds the hobby. You know,

there's so many new AI tools and, you know, tokens are expensive and subscriptions are expensive. So, if you can have a little bit of extra cash coming in on the side, then you can just keep playing with this stuff and maybe keep experimenting with even more tools.

Now, the second type of person is the aspiring entrepreneur. The person who

aspiring entrepreneur. The person who actually wants to make real money with this and wants to build a business. And

if that's you, you've built a skill in a market with massive demand and almost nobody who can deliver on it. So, just

look at the numbers. The World Economic Forum projects that AI is going to create around 170 million new jobs by 2030. And even if you subtract, you

2030. And even if you subtract, you know, all the roles that it's going to replace or change, that's still a net gain of about 80 million jobs. On top of that, roughly half of all tech job postings in the US already ask for AI

skills, and that number doubled in just 1 year. So, you've got this giant wave

1 year. So, you've got this giant wave of demand, and you've got barely anybody who can actually ride it. And what that means for you is that you don't have to be the best in the world, you just have to be the best one in the room. Now, the

third type of person is the employee who just wants to level up at work. And this

is the newest bucket of the bunch, and it's where that in-house road turns into an absolute cheat code. Because becoming

your company's AI person protects your job. And on top of that, it basically

job. And on top of that, it basically works like a raise. PwC went through close to a billion job postings, and they found the pay premium for AI skills more than doubled in a single year, jumping from 21.5% all the way up to

56%. And the pay is only half of it.

56%. And the pay is only half of it.

Another study put out a report where nearly two-thirds of employees admitted they had passed over someone for a promotion because their skills were outdated. So, the skills you already

outdated. So, the skills you already have are quietly becoming the thing that decides who moves up and who gets left behind. And the fourth type of person is

behind. And the fourth type of person is someone who already owns a business. And

for you, this one's almost too easy. You

don't need to go hire some expensive consultant, and you don't need to go become one for hire either. You just

consult your own business, you know, one process at a time. You already have the perfect first client, and it is you.

There is one thing I want to call out that's pretty interesting because it changes how urgent all of this really is. And that's basically the label AI

is. And that's basically the label AI consultant. I think that it has an

consultant. I think that it has an expiration date on it. Just like every other wave that we've talked about earlier, this one's also temporary. AI

is going to seep into every single industry. It's going to fill the cracks,

industry. It's going to fill the cracks, and it's going to seep into every single role out there. So, in a few years, nobody's probably going to be calling themselves an AI consultant anymore.

It's just going to be consultant because every consultant is going to be using AI and has to be completely native with it.

Because if they don't speak AI, they're really just not going to get business.

So, just think about it like this.

Somebody today, if they walked up to you and called themselves an Excel accountant, wouldn't that sound completely ridiculous? Because it's

completely ridiculous? Because it's assumed that every accountant uses Excel. But if you rewind to when Excel

Excel. But if you rewind to when Excel maybe first came out, you might have a bunch of accountants that use Excel, but a bunch of them who still stick to the old way. So, maybe calling yourself, you

old way. So, maybe calling yourself, you know, an Excel accountant or listing that as one of your skills was important. It's a similar story with

important. It's a similar story with people who called themselves an internet marketer back when the internet first showed up. But now, that is just

showed up. But now, that is just marketing. It's how everybody does it.

marketing. It's how everybody does it.

So, that's exactly where we're sitting with AI right now. The label is the part that's temporary, but the edge is real, the skills are real, and the window to grab it is wide open right now. It just

won't stay open this wide forever. Now,

I know what some of you are probably thinking. To go become an AI consultant

thinking. To go become an AI consultant or the AI in-house person at your job, you have to go quit your job tomorrow, go build some massive personal brand, start making YouTube videos, and bet your entire life on it. But you really don't. The whole point of this video is

don't. The whole point of this video is that there is a much smarter, much lower risk way to do this. So, let me set up the operating principle that makes the whole road map work, and then walk you guys through the four steps that you can start tomorrow. Because most people are

start tomorrow. Because most people are approaching this completely wrong, and the difference is exactly why most builders never become consultants. The

wrong move here is just looking at your job, finding the repetitive stuff, and automating it. And don't get me wrong,

automating it. And don't get me wrong, that's obviously not a bad thing to do.

It just isn't what makes you super valuable. Because if you automate

valuable. Because if you automate something that isn't actually constraining the [music] business, you've just spent a week saving maybe, you know, 20 minutes on a task that nobody was even waiting on. Nobody cares

as much. So, the real move is two things. First, every single project that

things. First, every single project that you build has to target [music] an actual constraint of the business.

Something that if you fix it, the business gets faster or makes more money or stops bleeding somewhere that it shouldn't be [music] bleeding. And

second, every single project needs a clear KPI tied to it before you start building it. A specific number that

building it. A specific number that you're actually trying to move, and that's your North Star for that specific project. So, constraint comes first and

project. So, constraint comes first and KPI comes second. And what's important about that is it's how you actually communicate the value in a way that nobody else in this market is currently communicating it. Because anyone can

communicating it. Because anyone can say, "Hey, you know, I built this AI agent." Almost nobody can say, "I built

agent." Almost nobody can say, "I built an AI agent that moved this specific number by this much for this specific business, which resulted in X percent more profit or, you know, X more customers, or whatever the metric might be." And that right there is what sets

be." And that right there is what sets you apart. The full operating model

you apart. The full operating model behind this stuff lives in a book that I'm currently writing. But for the purposes of this road map, the rule is very simple. Constraint first, KPI

very simple. Constraint first, KPI second, build third. So, step one is to audit your own role. Sit down with that constraint lens that we just talked about and walk through your job. Instead

of listing every repetitive thing you do, ask which one is actually slowing the team down or gating revenue or causing real pain somewhere downstream that nobody's currently solving. And

that's your real list. Then for each item, write down the specific number you'd be trying to move if you fixed it, the specific metric. And that right there is your first project list, not the easy stuff, but the stuff that actually matters. Step two is to

actually matters. Step two is to actually take on small projects. So, you

pick one item off that list and you go scope out and build the solution for it.

Then you go ask if you can implement it in just one area of the company, just one little corner of the business where you can actually test something, prove that it works, and show real results.

Because those results, whatever they end up being, are basically your first case study. You can show it to your boss, you

study. You can show it to your boss, you can show it to your team, you can put it on your portfolio, you can throw it up on LinkedIn, just so people start to notice. Step three is then to become an

notice. Step three is then to become an expert at solving problems, not just building. This is actually the most

building. This is actually the most important step in the road map, because once you've done enough of those small builds, you start to see the patterns underneath them. You start to notice

underneath them. You start to notice that businesses keep running into the same handful of problems over and over again. And that right there is the last

again. And that right there is the last skill that you need to become a real consultant. You stop being the person

consultant. You stop being the person who just builds whatever they're handed, you're now the person who can walk into a situation, find the actual problem, design the system, and solve the problem. And the moment that happens,

problem. And the moment that happens, your coworkers start coming to you instead of the other way around. Then

step four is to formalize it. Once

you've shipped enough proof that the company actually needs this kind of person on the payroll, that's when you formalize it. You go to your boss or

formalize it. You go to your boss or your leadership team with the evidence and you propose the role yourself. Most

of you guys aren't going to find this job posted on LinkedIn, you're going to maybe create it from the inside out. And

the only reason you can pull that off is because you spent steps one through three quietly building the case for it.

And even if the organization isn't ready to have that role yet, when they are ready, inevitably, you want to be the first name that pops into their heads.

So, that's the whole road map. Audit,

build, pattern recognize, formalize.

Four steps. And remember how I talked about AI seeping into everything? You

don't have to go learn a completely new industry. Just think about what you

industry. Just think about what you already do on the day-to-day, what are you good at, and how can AI help you move faster and be smarter and better outputs at what you already do. And the

cool part here is that it's not theoretical. I actually sat down with

theoretical. I actually sat down with someone in my community who ran this exact play in real time. Her name is Alan. About a year ago, she had spent 15

Alan. About a year ago, she had spent 15 years as an email developer, so not super technical, no AI background. But

her whole team was then let go and she had to figure out something new. So, she

started learning an NN, she pivoted into cloud code, and while she was learning, she did the one thing that people skip, which is she built in public. She

started two YouTube channels just showing off the stuff she was making.

She posted her builds on LinkedIn. She

recorded demos of every single thing that she had been working on. And the

cool thing is she doesn't have thousands and thousands of subscribers and followers. She just has proof. So, when

followers. She just has proof. So, when

she applied for a head of AI role at a 15-person company called Young, the recruiter sent her an email with one question on it, which was, "What have you built?" And remember that is the

you built?" And remember that is the exact question I told you decides who wins in this market. So, instead of her having to type up some long vague explanation, she just sent links. She

just sent her case study. She sent real proof. And she was able to skip HR

proof. And she was able to skip HR entirely, go straight to the CEO, and she got the job. [music] So, in a year from basically no AI to head of AI is really cool. If you guys want to see the

really cool. If you guys want to see the full interview I did with her, I will tag that right up here so you guys can check that out. So, let me sum up this entire video in one breath. You don't

have to quit your job. You don't have to build some massive personal brand. You

don't even have to be super technical.

The real move is to pick an actual constraint in your role or your company, tie to a KPI, build a solution, and then build a few more so you have a real track record. Then put that track record

track record. Then put that track record somewhere that people can actually see it, even if that's just a personal website where you build your own portfolio. Just something that you can

portfolio. Just something that you can send to people. So, if you want help running this whole playbook, the link in the description goes to my free school community. Everything we talked about in

community. Everything we talked about in this video I've broken down into a free resource guide that you can access in there, as well as other courses, GitHub repos, skills, frameworks, and hundreds of thousands of people who are looking to stay ahead with AI. It's completely

free. It is the first link in the description. But anyways, that's going

description. But anyways, that's going to do it for today. If you guys enjoyed the video or you learned something new, please give it a like. It helps me out a ton. And as always, I appreciate you

ton. And as always, I appreciate you guys making it to the end of the video, and I'll see you on the next one. Peace

out.