So, you've learned everything you can 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 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 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 changing like every single day, that security that most of you are used to is 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 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 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 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 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 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 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 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 like the pharmacists and consultants are 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, 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 on

Most AI agents are simply just toys, and it's not because they are dumb. It's because they cannot do two very simple things. They cannot so easily run on their own without you sitting there and hitting go, and they cannot safely use a real password or API key. So, they end up just being cool demos and idealistic things that never actually do the work. However, Claude just changed both of those completely. So, about a week ago, they added two things to Claude managed agents that quietly moved these from demos to something that you would actually leave running. So, I want to actually answer the question today, are these things production ready now? So, I'm going to be running Claude against a real production checklist box by box, build one with you, and then you can decide for yourself. Now, really quick on what this even is because the name isn't telling you much. So, normally, if you want an AI agent doing real work for a business, you're going to have to rent a server, you're going to have to store your API keys somewhere safe, babysit the code, and hope that nothing breaks at 2:00 in the morning. Claude managed agents, it's basically just hiring Anthropic to be the landlord for that worker. So, they run it on all of their own machines in their own locked sandbox, and you get a dashboard to watch it, and you get a bill at the end. And the thing to get is this isn't a chatbot that you just sit and you talk to. It's more like an employee in the sense that it sits there quietly. It's going to run when you tell it to. It uses real tools like your inbox or your Slack or whatever else is in your stack, and then hands you back a finished result. And if you've ever used an N or make.com, this will feel very similar. So, it's the same idea where you have a task that runs on a schedule across all of your applications. So, the difference with this is you do not build the workflow. You just describe in plain English, and the agent, it works out all of the steps itself, even the judgment calls that a rigid workflow cannot normally make. So, under the hood, it's really just three things, and these are the only three words that you're going to be needing. So, the agent, that is effectively going to be the job description plus the tools it is allowed to touch. We have the environment, that is the safe little computer that it is running on, and the session, and that's just one actual run of the the So, we have the job, we have the machine, we have the run, that is it. So, now, what actually separates just a toy from something that you would put real work on, something that you would deploy in production for your business? It's a short list. So, it has to run on its own on a schedule, it has to hold secrets safely, so a tool can actually use your key without leaking anything and exposing any vulnerabilities, it needs the right tools for the job, logs that you can actually read when something looks off, you need retries, so when a hiccup isn't going to be killing the whole run. We need permissions, so it cannot go off and do something that it shouldn't, and we need a way for you to approve the risky stuff before it actually happens. So, that's going to be the production checklist, and for most of this year, two of those boxes were empty on Claude, and that's exactly why people called these just toys. So, it could not run on its own, and secrets were very clunky, and those two were just fixed. So, look here, this is what it gets you. We have our whole inbox summarized and waiting inside of Slack. So, this does look very simple, and the only question that really matters now is whether you can trust it to do that on its own every day without you. So, let's run Claude against the checklist, and let's just check. So, box one, runs on a schedule. So, you can now put an agent on a recurring schedule, run it daily, hourly, whatever actually fits, and Anthropic, they're going to be running it for you on their side. So, every time it does fire, it kicks off a very fresh run. The one thing I will be very specific about is the finest that you can go is once a minute. So, it's not going to be down to the second, and there's actually a few seconds of wiggle room inside of the timing. So, for morning report, who cares? And for something that has to fire at an exact second, that is not this. So, for that matter, I haven't really had any other tools or platforms that have really offered this. So, not a big deal. Box two is the secrets. So, Claude, they actually already had a vault for the logins that it connects to, so things like your CRM and so on, and it used those without ever actually seeing them. But, what's new is you can now hand a regular tool inside of the sandbox its own secret. You can to it an API key, store it as an environment variable, and the important part is the agent's never going to be actually seeing that key. It just gets swapped in on the way out only to the addresses that you allow for. So, your worker, they just use

Before you integrate AI into your business, run it through this checklist. I've deployed AI into over [clears throat] a hundred service businesses and the owners who skipped these five steps wasted their time and their money on systems that their team will not use. So, here's exactly what to check before you automate anything. [music] Step number one, map. Before you touch any AI tool, list every workflow that your team repeats and score each one one to five on how many hours that it eats, how much it drives revenue, and how doable it is right now to automate. Your top three are what you're going to be building first. Step two is foundation. So, don't build anything yet. First, give the AI real context on your business, your tools, how you talk to clients, your standards, and then connect it to where your team already works. If you skip this, every single output is going to stay generic. Now, step three is building three of them. So, take your top three and automate just those ones, picking the ones that your team will feel the results in the first week. Step four is scale up. So, turn those wins into reusable skills and don't roll it out to everyone all at once. Hand it to your one most curious person and then let them spread it. Step five is to compound. So, you keep stacking because around week three, it stops feeling like setup and it starts running itself and every model upgrade makes everything that you built better automatically. Now, most people quit before they ever get any results. I put together a full playbook with the scoring matrix that we use on every client in one document. Just comment need and I'll send it over to you.

여러분 이제 클로드 안 써도 됩니다. 착오해서 단돈 이원 없이 시작해 지금은 구글 도그 하나로 전 세계의 콘텐츠 공장을 돌리는 폴 제임스. 그가 공개한 유료 구독 한 푼 안 쓰고 유튜브로 돈 복사하는 다섯 가지 비밀 공식 저장하고 끝까지 보세요. 첫째, 여러 도구를 버리고 단닐 소스 스택을 구축하세요. 리서치부터 제작까지 여기저기 옮겨 다닐 필요 없이 노트북 하나만 중앙으로 사용하면 됩니다. 모든 정보를 한 곳에 몰아놓고 구글의 강력한 지능을 활용해 당신만의 완벽한 콘텐츠 기획시를 공짜로 만드세요. 둘째, 구글의 실시간 데이터로 돈대는 주제만 골라내세요. 당신의 감이 아니라 실제 웹 데이터를 통해 2026년 가장 광고 단가가 높은 황금 니치를 찾아야 됩니다. 이미 20만 조이스가 터진 성공 사례를 분석해 실패할 수 없는 시장에만 당신의 깃발을 꽂으세요. 셋째, 데이터에 기반한 패널 설계도 먼저 프로그래밍 하세요. 무작정 영상부터 만들지 말고 AI가 당신의 운영 전략과 파겟을 완벽히 학습하게 만들어야 합니다. 채널 이름부터 영상 스타일까지 데이터가 가르치는 방향으로 설정하면 초보자도 전문가처럼 채널을 운영할 수 있습니다. 잘 터진 영상의 DNA를 그대로 흡수하세요. 이미 성공한 영상의 주소만 넣으면 AI가 대본 전체를 추출해 그 성공 패턴을 당신의 것으로 재탄생시켜 줍니다. 단순히 베기는 것이 아니라 검증된 구조 위에 당신만의 독창성을 더해 퀄리티 높은 스크립트를 순식간에 완성할 수 있습니다. 다섯째, 편집기 없이 스튜디오 패널에서 영상을 바로 뽑아내세요. 비싼 편집 프로그램을 배우느라 시간을 낭비하지 말고 AI가 직접 영상과 목소리를 입히게 만들어야 합니다. 한 몇 분의 렌더링만으로도 즉시 업로드 가능한 완성본이 탄생하며 당신의 제작피는 영원히 영원을 유지합니다.이 내용을 더 자세히 정리했습니다. 요약번호 받아보실 분들은 구독 본문의 프로필 링크를 확인해 주세요.