The argument in one line.
As AI makes building automations cheap and easy for anyone, the scarce and valuable skill shifts from knowing how to build something to knowing what to build, which is why the next AI career boom is an internal role, not an agency.
Read if. Skip if.
READ IF YOU ARE…
- You have a corporate job or career and want to use AI to create more value, security, or income inside it.
- You're comfortable identifying repetitive tasks in your own workflow and are willing to spend a few months quietly proving results before asking for anything.
- You want a structured way to turn scattered ChatGPT/Claude usage into something your manager can point to and fund.
SKIP IF…
- You're looking for a done-for-you list of AI job postings to apply to — this is about creating a role, not finding one.
- You want a technical build tutorial — this video is a positioning and career-strategy roadmap, not a how-to-automate walkthrough.
The full version, fast.
AI agencies got rich for a couple of years by being the only ones who could translate a company's known problems into working automations. That gap is closing fast because AI is now accessible enough that any employee, not just a hired consultant, can build the solution themselves. The video argues this creates a new internal role, the in-house AI consultant, which barely exists yet as a formal title even though IBM survey data shows Chief AI Officer roles jumped from 26% to 76% of surveyed companies in a year. The four-step roadmap: audit your own job and automate the low-risk, high-hour tasks first; measure and prove the time saved; make that proof visible to your team and manager; then formalize it into a named position with a budget, once you can show the equivalent of a full-time hire's worth of saved hours.
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00:00 – 00:34
01 · Next AI goldrush
Cold open stating the thesis: a new, not-yet-named AI career opportunity for people with corporate jobs, plus a preview of the four-step roadmap to come.
00:34 – 02:52
02 · Chegg died
Uses Chegg's ChatGPT-driven collapse as the template: AI-pointed disruption isn't AI replacing people directly, it's one AI-equipped person doing what used to take three to five — the same pattern that built the ~$130B AI automation agency market.
02:52 – 03:20
03 · Everything flipped
The accessibility that made AI agencies rich is now available to anyone, so companies are choosing to solve problems in-house instead of paying an agency.
03:20 – 04:21
04 · Builder trap
Warns against assuming the safe AI career is 'best builder' — building is a small piece of the puzzle; the scarce, valuable skill is judgment about what to build at all.
04:21 – 05:32
05 · Doctor vs pharmacist
Introduces the central metaphor: the builder is the pharmacist, the in-house AI consultant is the diagnosing doctor who gets paid more, and explains why this role barely exists yet as a formal title.
05:32 – 08:41
06 · The 4 steps
The full roadmap: (1) audit your own job for high-hour, low-risk tasks, (2) automate and prove the time saved, (3) make the proof visible to your team and manager, (4) formalize it into a funded, titled role.
08:41 – 10:14
07 · 26% to 76%
Backs the opportunity with IBM survey data on Chief AI Officer adoption and an AI-skills pay premium stat, addresses regulated-industry viewers, and closes with a CTA to the creator's free community.
Lines worth screenshotting.
- AI agencies got rich by being the only ones who could bridge the gap between a company knowing its problem and knowing the solution — and that gap is now closing because AI is accessible to everyone.
- Companies didn't cut jobs because AI replaced people outright; they cut jobs because one person with AI could now do the work that used to take three to five people.
- The AI automation services market went from nonexistent to roughly $130 billion in a few years, the same pattern now repeating one level down inside individual companies.
- Knowing how to build an automation is a small piece of the puzzle; the scarce skill is judgment — deciding what problems are worth pointing AI at in the first place.
- A pharmacist executes exactly what's asked; a doctor diagnoses what's actually needed. The AI 'builder' is the pharmacist, the in-house AI consultant is the doctor, and the doctor gets paid more.
- Most people won't realize the in-house AI consultant role exists until the market for it is already saturated, the same blind spot that let early AI automation agencies get rich uncontested.
- The right first automation target isn't the most annoying task, it's the task that eats real hours every week and where a wrong AI answer causes no real damage.
- Proof of value means writing down the before/after numbers, e.g. a report that used to take two hours now takes ten minutes, the same way a personal trainer gets their own body in shape before selling training.
- Automating annoyances doesn't grow a business; attacking constraints does — the real money is in finding what would break first if the company doubled its customers overnight.
- The pitch that lands with a manager isn't 'I used ChatGPT for this,' it's a business result: 'this saved us eight hours before the quarterly report went out.'
- 76% of companies in a 2026 IBM survey of 2,000 CEOs now report having some kind of Chief AI Officer role, up from 26% a year earlier.
- Employees with AI skills are getting paid around 56% more than a coworker doing the identical job without those skills.
- The formalization move is presenting a manager with one number — total hours/money saved across all your automations — and proposing an actual title and role, not asking for a favor.
AI is making building cheap, which makes judgment the whole job.
WHAT TO LEARN
The argument isn't that AI takes jobs — it's that as building an automation gets easy for anyone, the money moves to whoever decides what's worth automating in the first place.
02Chegg died
- AI agencies got rich by being the only ones who could bridge the gap between a company knowing its problem and knowing the solution — and that gap is now closing because AI is accessible to everyone.
- Companies didn't cut jobs because AI replaced people outright; they cut jobs because one person with AI could now do the work that used to take three to five people.
- The AI automation services market went from nonexistent to roughly $130 billion in a few years, the same pattern now repeating one level down inside individual companies.
03Everything flipped
- The same accessibility that made AI agencies rich for a while is now available to anyone, so companies increasingly solve problems themselves instead of paying an outside agency.
04Builder trap
- Knowing how to build an automation is a small piece of the puzzle; the scarce skill is judgment — deciding what problems are worth pointing AI at in the first place.
- As AI gets easier to use, the value of pure development/build skill keeps dropping while judgment and taste become the differentiator.
05Doctor vs pharmacist
- A pharmacist executes exactly what's asked; a doctor diagnoses what's actually needed — the builder is the pharmacist, the in-house AI consultant is the doctor, and the doctor gets paid more.
- Most people won't realize the in-house AI consultant role exists until the market for it is already saturated, the same blind spot that let early AI automation agencies get rich uncontested.
06The 4 steps
- Step 1 — audit your own job for tasks that eat real hours weekly AND are low-risk if AI gets them slightly wrong; don't start with whatever's most annoying.
- Step 2 — automate those top tasks and write down the before/after numbers as proof, the same way a trainer gets their own body in shape before selling training.
- Step 3 — make the proof visible: demo wins in meetings, fix a coworker's task, document prompts/workflows, and frame results in business terms, not tool names.
- Step 4 — attack constraints (what would break the business first if it doubled overnight) rather than annoyances, then total the savings into one number and propose an actual titled role.
0726% to 76%
- 76% of companies in a 2026 IBM survey of 2,000 CEOs now report having some kind of Chief AI Officer, up from 26% a year earlier.
- Employees with AI skills are getting paid around 56% more than a coworker doing the identical job without them.
- Even in regulated industries where you can't automate real work, you can still build practice projects on dummy data to develop the same judgment.
Terms worth knowing.
- In-house AI consultant
- An informal, not-yet-standardized internal role where an employee audits how their company or team works, builds automations for the highest-value problems, and trains others to use them — as opposed to an outside AI agency doing the same work on contract.
- Chief AI Officer (CAIO)
- A C-suite role responsible for a company's AI strategy; per the IBM survey referenced in the video, the share of companies reporting one jumped from 26% to 76% in about a year.
- AI automation agency
- An outside company or consultant hired to diagnose a business's problems and build custom AI-driven automations for it, typically charging premium prices because the business itself doesn't know how to build the solution in-house.
- Constraint (vs. annoyance)
- In the video's framing, an annoyance is a task that wastes an individual's time; a constraint is the specific bottleneck that would break first if the business scaled — fixing constraints, not annoyances, is what actually grows a company.
Things they pointed at.
00:45companyChegg
03:36linkOpenAI and Anthropic public statements on slowing AI development
03:36linkQuartz — "Anthropic is urging leading AI labs to build a shared brake pedal for AI development"
07:40toolClaude Code
08:45linkIBM Study: CEOs are Reshaping C-suite Roles for the AI Era
Lines you could clip.
00:15
“This opportunity could genuinely make many of you career millionaires over the next few years.”
02:20
“Those layoffs didn't happen because AI could just replace people's jobs. They happened because companies realized that one person using AI can now do the work that used to take three to five people to do.”
04:21
“Think about it like a doctor and a pharmacist. A pharmacist basically just hands you exactly what you asked for, but a doctor is the one who actually figures out what you need.”
05:40
“Find the tasks that check two boxes: it eats up real hours every single week, and if the AI gets it a little wrong, nobody gets hurt.”
06:45
“It's kind of like how a personal trainer gets their own body in shape first before they go try to get everyone else in shape. Nobody's gonna hire a trainer who's just horribly out of shape.”
08:45
“76% of companies in this IBM survey already have some kind of chief AI officer. And a year ago, that number was 26%.”
09:20
“People with AI skills are also getting paid around 56% more than the coworker sitting right next to them doing the exact same job without them.”
Word for word.
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analogystory
00:00So there's a new opportunity for the people who know how to use AI that nobody's really talking about, specifically, if you have a corporate career or work a job. Twelve months ago, this didn't really exist, but it's now turning into the next AI gold rush. This opportunity could genuinely make many of you career millionaires over the next few years, and I've seen a lot of the members in my community making this shift and getting great results with it.
00:20But like in every shift in the AI space, it's the ones that get in early that'll make the most out of it. So in this video, I'll break down exactly what this opportunity is and the four step road map to take advantage of it. So let's dive in.
00:31Okay. So this is a new opportunity in the AI space, but it's not some random thing that came out of nowhere. It's really just a product of the same pattern that we've been seeing ever since AI went mainstream.
00:40So let me tell you about a company called Chegg. And if you guys haven't heard of them, for years, Chegg basically sold homework help to students. It was the service where you could go ask a question and an actual expert on their end give you an answer, and it also had loaded in, you know, like study guides and answer keys, stuff like that.
00:54And millions of students were paying for this every single month. It was a super stable, super profitable business. I personally used Chegg all the back in college, and boy, I get my money's worth.
01:03But then late twenty twenty two happens and ChatGPT comes around, and pretty much overnight, every single student could get the exact same homework help in just a few seconds for much cheaper, sometimes even free. As soon as that happened, I canceled my Chegg subscription as well. In 2023, Chegg's stock crashed almost 50% in a single day, and they basically came out and admitted that ChatGPT was killing their business.
01:23The point I'm trying to make, Chegg is just the most famous example, but over the last couple of years, we've watched company after company after company announce layoffs and point at AI as part of the reason why. But I do think that most people read this the wrong way. Those layoffs didn't happen because AI could just replace people's jobs.
01:39They happened because companies realized that one person using AI can now do the work that used to take three to five people to do so. So when you actually look at all of that, it's always coming back to the same thing. The people who know how to use AI get ahead of the people who don't.
01:53And that's exactly how the first AI related jobs started changing the way corporate work actually worked. Companies started going out and hiring AI experts and AI agencies and consultants to come in and to diagnose the problems, build automation, solve the problems. And over the past couple of years, this whole AI automation market went from being this brand new thing to being worth around a $130,000,000,000.
02:12But the exact same thing that made these AI agencies a ton of money is the same thing that's about to replace them. So for the last couple of years, companies were in kind of this weird spot. They knew the problems they had.
02:22They just had no idea how to actually solve them. They knew that their, you know, support inbox was a mess. They just didn't know what to actually do about it.
02:29And that gap right there between knowing the problem and knowing the solution, that's the reason that AI agencies could charge such premium prices. And, of course, because AI was a big buzzword and businesses were feeling pressure both from, you know, their boards and their competitors to start using AI. And, of course, the AI agencies were the ones who could fill that gap.
02:45But for a while, they were basically the only ones who could come in and actually build a solution, and that's just not the case anymore because AI has gotten so accessible and so easy to use that at this point, even the busiest CEO has opened up Chad, GBT, or Claude and used it in some way to solve a problem they were having.
03:00So it's just completely flipped now. The cost and the value of development is dropping. Companies still know exactly what problems they have, but instead of going out and paying another company to come and solve them, they're looking for ways to solve these problems themselves, and they wanna do it in house.
03:12And this brings me to the actual opportunity. The one that I think is going to create more AI careers than anything else over the next few years. So a lot of people are going to assume that the safe AI career here is just to become the best builder for these companies.
03:23Like, know, the AI engineer, the person who can actually go in and build all the automations. And, yeah, I think that that's part of it because then you get a really good understanding of how it works and how, you know, like, what success looks like. Just knowing how to build this stuff is honestly a very small piece of the puzzle Because what really matters right now is not about knowing how to build something, it's more about knowing what to build.
03:41These big AI labs like OpenAI and Anthropic have even come out themselves and said, hey. These models are getting so good, like scary good, to the point where we might need to slow this down, but that's a topic for another video. But what they've all agreed on is where is the value in a human?
03:53The value is in judgment, taste, you know, solving ambiguity, deciding what problems to point AI at and what problems to not point AI at. So that human judgment, so so important. And it's not a skill that everyone is just naturally good at.
04:04Because like I said earlier, AI is getting better, the cost of development, and value of development is dropping. It's getting easier and easier. You don't need a computer science degree anymore.
04:12You barely even need a tech background at all. And so as all of this is happening, like I said, all the real value is shifting over to judgment. Sam Altman said that the idea guys are about to have their day in the sun.
04:21So think about it like a doctor and a pharmacist. A pharmacist basically just hands you exactly what you asked for, but a doctor is the one who actually figures out what you need in the place. The builder, that's kinda more of the pharmacist.
04:31And the in house AI consultant, that's the doctor. And the doctor is the one who gets paid the real money. So day to day, what that role actually looks like is auditing how your team currently works, finding the tasks that are eating up the most hours, and building the automations to handle those tasks, and then training everyone else on how to use what you just built because adoption is another huge problem.
04:49Change management, stakeholder communication. Every company already has, like, that one IT person. Right?
04:54The one that everybody runs to the second their laptop breaks the Wi Fi goes down. Now the in house AI consultant role might not actually, like, exist yet, at least not as a formal position with a standardized name and standardized, you know, roles and responsibilities, and that's exactly why this is such a good opportunity.
05:08Because most people won't even realize that the job exists until the market's already kinda saturated, which is the exact same thing we just watched outplay with AI automation services. Now I'm not saying that AI automation services are completely saturated, but I'm just saying it's kinda the trend in the market. But I know what some of you are probably thinking right now, which is, Nate, if this position doesn't even exist yet, how am I actually supposed get there?
05:26And it is a fair question. So here's the exact road map that I'd use to basically create this position inside the company you already work for, and it's four steps. Alright.
05:34So step one is to audit your own job. So I mean, like, literally just sit down and think about what you do on the day to day and on the week to week and just write down those things. Now most people would just go and automate whatever annoys them the most, but the most annoying task isn't always the right one to start with.
05:48So instead, go down your list and find the tasks that check two boxes. One, it eats up real hours every single week, and two, if the AI gets it a little wrong, nobody gets hurt. So you can stay in the loop and you can fix it and you can move on.
05:59So stuff like maybe a weekly status report or meeting notes or sorting through your inbox, cleaning up some data, doing some basic research. So then you take the tasks that check both boxes and that is your starting list. And don't try to go fix the entire company on day one.
06:11You are just getting your feet wet. You're starting with your own work. And then step two is you take those top tasks on the list, and you actually start to automate them.
06:18And this is when you're able to basically just prove that it works. When I say that, mean actually writing down the numbers. So, you know, this report used to take me two hours every week, but now it takes me ten minutes a week.
06:27Because those hours you saved, that's your proof. And it's kind of like how a personal trainer gets their own body in shape first before they go try to get everyone else in shape. Nobody's gonna hire a trainer who's just, like, horribly out of shape.
06:36You want proof that you can actually get the results before you try to sell anyone on the idea. And once the first one works, you don't stop there. You go right back to your list and you knock out the next one and then the next one.
06:46And then we move on to step three, which is you start making all of this proof more visible. So you demo your wins in your team meetings. You offer to go fix your coworker's most annoying task too, and you document every single thing that you're doing along the way.
06:56But the way that you talk about it really matters as well. Don't go in saying, hey. You know, I used ChatGPT for this.
07:00Tie it back to the business. This saved us eight hours before the quarterly report went out. That's the version that your boss actually cares about and actually will remember.
07:08And if you put together a little internal doc with your best prompts and workflows that everyone else can just start to use, now your name is on the whole thing that the whole team relies on. Now once you've got a few wins stacked up and people start coming to you, you're ready to graduate from annoyances to constraints because all of that kind of, like, low risk stuff that we just talked about is where you start, and that's definitely the right move because it let you experiment somewhere safe while you were still proving the value and still learning.
07:30Automating annoyances doesn't really grow a business. What does is attacking the constraints, And that's what these, you know, AI consultants are getting paid the big bucks for is to grow the business by attacking constraints. So this is the point where you run the same audit from step one, except now on basically the entire business, and the question completely changes.
07:46You're not asking, you know, what's annoying or what eats up a few hours of my day. You're asking, what's actually holding the business back? If we doubled our customers tomorrow, what process or what thing would break first?
07:56And that's where your first constraint is, and that's your project. Because saving your team a few hours every week makes you helpful, but removing the bottleneck the whole business is stuck behind is how you make the company real money. And that is a completely different conversation with your managers or your bosses.
08:08And then step four, you formalize the whole thing into an actual position. So you add up all the hours and all the money your automations have saved, and you turn it into one single number. Something like, you know, across these five automations, I'm giving the team back the equivalent of a full time hire every single year.
08:22That's the kind of math that will allow the company to have a real budget to put towards AI projects. And then you take that to your manager, and you're not asking them for a favor. You're proposing the actual role and the title.
08:32Because most of you guys are not gonna find this job posted on LinkedIn. We're gonna slowly start to see that over the next few months and years. A lot of you might just kind of create it from the inside out, and this isn't some hypothetical thing that I'm making up.
08:42Right now, about 76% of companies in this IBM survey, so it was 2,000 CEOs from pretty big companies, 76 of them say that they already have some kind of chief AI officer. And a year ago, that number was 26%.
08:53So that seat went from basically nonexisting to being almost everywhere in about two years. And the people stepping into these roles are the ones who saw it coming early. On top of that, people with AI skills are also getting paid around 56% more than the coworker sitting right next to them doing the exact same job without them.
09:07Because at that point, you're not asking for the job. You've already built it. And for a group of you that might be saying, okay.
09:12Well, I work in a regulated industry, and we have no AI at work and stuff like that. Be smart. Don't obviously go throw AI at sensitive data.
09:18Don't automate stuff without permission if you can't. Be smart about it. But that doesn't mean that you can't be the AI person in your team and in your company.
09:25You can be the person that's experimenting with stuff on the side. You can be the person who's building out projects that are basically exactly what you do for work, but with dummy data. Those four steps are basically still the same.
09:34It's proving that you understand how to think about it and that you can deliver real value with it. Anyways, now you've got the full roadmap to becoming your company's in house AI consultant. But you can follow every single step in that road map, and if you can't actually build the solutions with AI, then nothing else matters.
09:49The good news is you can learn how to do that completely for free inside of my free community. There are full courses in there walking you through how to use AI to solve problems and the tools you need and every single resource that I've ever given out for free, you know, agent skills, GitHub repos, resource guides, that kind of stuff, and it's all in there for free.
10:04Also, there is a complete resource guide from everything that we just talked about in this video that you can download. But that is gonna do it for this one. As always, I appreciate you guys making it to the of the video, and I'll see you on the next one.
The Hook
The bait, then the rug-pull.
Twelve months ago, the creator says, this job didn't exist. Now he claims it's turning into the next AI gold rush — and unlike the AI-agency boom that came before it, this one gets built from the inside of a company you already work for, not sold to it from outside.
Named ideas worth stealing.
04:21model
Doctor vs. Pharmacist
- Pharmacist (the builder) — hands you exactly what you asked for
- Doctor (the in-house AI consultant) — figures out what you actually need and gets paid the real money
A two-role model used to argue that execution (building automations) is commoditizing while diagnosis (deciding what to automate and why) is where the pay is concentrating.
Steal forpositioning any technical hire or service around judgment/diagnosis rather than raw build capability
05:32list
The 4-Step Roadmap to Becoming an In-House AI Consultant
- 1. Audit your own job — list tasks that eat real hours weekly AND are low-risk if AI gets them slightly wrong
- 2. Automate and prove it — build the fix, then write down the before/after numbers as proof
- 3. Make the proof visible — demo wins in team meetings, fix a coworker's task, document prompts/workflows, frame results in business terms
- 4. Formalize the position — total up hours/money saved into one number, then propose an actual titled role to your manager
A sequential playbook for creating a new internal AI role from scratch, starting with personal low-risk automation and ending with a formal pitch for a funded position.
Steal forany employee trying to build a case for a new internal role or budget line around a skill they've been using informally
How they asked for the click.
VERBAL ASK
10:00product
“The good news is you can learn how to do that completely for free inside of my free community... there is a complete resource guide from everything that we just talked about in this video that you can download.”
Single soft CTA placed at the very end, after the full roadmap has already been delivered for free — positions the community/resource guide as the implementation layer on top of value already given, not a paywall on the idea itself.
MENTIONED ON CAMERA
FROM THE DESCRIPTION
PRIMARY CTAWhere the creator wants you to go next.
AFFILIATECommission earned if you click.
OTHER LINKSAlso linked in the description.
Visual structure at a glance.
