This AI Technology Will Replace Millions (Here's How to Prepare)
A YouTube creator argues the real AI job threat isn't robots — it's coworkers who learned to direct agentic AI — then live-demos three tasks it can already do and a three-step plan for learning to manage it yourself.
Posted
2 days ago
Duration
Format
Demo
educational
Views
34.4K
1K likes
Big Idea
The argument in one line.
Agentic AI already performs a meaningful share of office work, and the skill that determines who keeps their job isn't coding — it's learning to manage AI the way you'd onboard a new hire.
Who This Is For
Read if. Skip if.
READ IF YOU ARE…
A solo founder, freelancer, or employee with zero coding background who wants a concrete, repeatable way to start delegating real tasks to an AI agent.
Someone who has used a web chat AI tool but hasn't tried an agentic coding tool and isn't sure what changes once it can act on its own.
A small business owner who wants working examples — data analysis, a simple internal app, lead generation — before committing time to learning a new tool.
SKIP IF…
You already run agentic workflows daily and want technical depth on tool-calling, MCP servers, or automation architecture — this stays at the conceptual/onboarding level.
You're looking for a coding tutorial — the video's entire point is that no code was written by the presenter.
TL;DR
The full version, fast.
Agentic AI already performs an estimated 60% of tasks inside companies, and the video argues the coming job losses come less from robots replacing people than from AI-equipped coworkers doing two jobs at once. The presenter demonstrates three natural-language tasks handled by an AI coding agent with no code written: a full quarterly YouTube analytics review built into a formatted Excel dashboard in about ten minutes, a working business-tracking app built from a plain description, and fifty enriched, personalized cold-outreach leads generated in about twenty minutes. The actionable takeaway is a three-step onboarding plan — start talking to the AI like a new hire, hand it one real recurring task to build trust, then stack tasks and connect it to your actual tools.
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Cold open claims agentic AI already handles roughly 60% of company tasks and reframes the threat as losing your job to a coworker who uses AI, not to a robot.
00:31 – 00:43
02 · Not a robot
Clarifies that AI job loss isn't robots replacing people physically — it's a human who learned AI doing two roles at once.
00:43 – 01:06
03 · CEO's warning
A licensed interview clip reinforces the message: you lose your job to someone who uses AI, and companies lose to competitors who use AI.
01:06 – 01:34
04 · Why now?
Explains why this is happening now: agentic AI takes action toward a stated goal instead of only executing fixed steps, unlike older automation.
01:34 – 01:55
05 · Which side?
Frames the choice as binary — operate the AI or be displaced by it — and stresses no coding knowledge is required.
01:55 – 03:34
06 · Three real jobs
Introduces chat-style AI vs. an agentic coding tool, shows the agent's built-in knowledge of the presenter's business, and sets up three live demo examples.
03:34 – 06:22
07 · Example 1: Quarterly YouTube analytics
A single goal-prompt has the agent pull YouTube data, build a formatted Excel dashboard with an executive summary and video scorecard, and surface strategic insights — in about ten minutes.
06:22 – 07:44
08 · Example 2: Business tracking app
From a plain-English description, the agent builds a working job-tracking web app with persistent memory, with no code written by the presenter.
07:44 – 10:41
09 · Example 3: Lead generation
The agent finds and enriches 50 leads via Clay, then writes 50 personalized cold-outreach emails referencing each business's specific pain points, in about twenty minutes.
10:41 – 14:03
10 · The One Skill to Learn
Names the transferable skill as being an 'AI manager' and lays out a three-step onboarding plan: start talking to it, hand it one real task, then stack tasks and connect tools.
Atomic Insights
Lines worth screenshotting.
Agentic AI is estimated to already handle around 60% of the tasks inside companies, and hundreds of thousands of people have already lost jobs to it.
AI doesn't take your job directly — it takes your job by making the coworker next to you twice as productive, so the company no longer needs both of you.
The shift from 'old automation' to 'agentic AI' is that agentic systems take action toward a stated goal instead of only executing fixed steps you specify.
You don't need to know how to code to use agentic AI tools — natural-language instructions are the entire interface.
A single natural-language goal prompt can drive an AI agent through a multi-step task autonomously until a stated completion condition is met, without step-by-step supervision.
A quarterly YouTube analytics review — pulling data, building a formatted Excel dashboard, and writing strategic insights — took an AI agent about ten minutes versus roughly half a day of manual work.
An AI coding agent can build a working, persistent-memory business tracking app from a plain-English description, with no HTML or coding knowledge required from the requester.
AI-driven lead generation can enrich 50 leads with business data, pain points, and personalized cold-email copy in about twenty minutes — work that traditionally consumes a full-time role.
The most transferable skill for the AI era isn't a technical one — it's learning to manage AI the way you'd onboard and manage a new hire.
Treat AI onboarding like managing a new employee: explain the goal, define what good and bad output look like, and don't dump everything on it in week one.
The fastest way to build trust in an AI tool is to hand it one real recurring task from your own life or work, not just watch demos of other people's use cases.
Stacking AI tasks together and connecting it to tools you already use compounds its usefulness because it can act on live data without manual export or import steps.
Measuring AI's impact requires setting a concrete goal, like saving a specific number of hours or generating a target number of leads, and tracking the number before and after adoption.
Takeaway
Manage AI Like You'd Onboard a New Hire
THE MANAGER SKILL
The transferable skill isn't coding — it's learning to onboard, supervise, and gradually hand more responsibility to an AI agent, the same way you'd bring a new hire up to speed.
02Not a robot
The threat isn't a physical robot taking your role — it's a human coworker who learned to direct AI and can now do your job and theirs at once.
A company doesn't fail because a competitor bought AI; it fails because a competitor's team became twice as productive using the same headcount.
03CEO's warning
Being told you'll lose your job to someone who uses AI, not to AI itself, reframes the risk as a skills gap you can close, not a layoff you can't.
04Why now?
Older automation only executed the exact steps you specified and broke if anything changed; agentic AI is told an outcome and figures out the steps itself.
05Which side?
You can't stop agentic AI from spreading through your industry, but you can choose to be the person operating it instead of the person displaced by it.
07Example 1: Quarterly YouTube analytics
A single natural-language goal prompt can drive an AI agent through data-gathering, analysis, and a polished deliverable without step-by-step instructions.
Setting a goal condition lets the agent keep working and verify its own output — down to checking spreadsheet formatting — before handing it back.
Work that would take a person roughly half a day, like pulling analytics and building a dashboard, took the agent about ten minutes.
08Example 2: Business tracking app
An AI agent can build a working internal tool from a plain-English description, with zero code written by the person requesting it.
Once a first version exists, refining it further is just a matter of asking for the change in plain language, no different from giving feedback to a person.
09Example 3: Lead generation
Lead generation and enrichment — finding prospects, researching pain points, and writing personalized outreach — is a full-time role an agent completed in about twenty minutes.
Personalization that references a specific detail, like a bad review or a real gap in the business, reads as more credible outreach than generic templated copy.
10The One Skill to Learn
The transferable skill for this era isn't technical — it's learning to manage AI, which means onboarding it gradually the way you'd bring a new hire up to speed.
Trust in an AI tool is built by handing it one real, recurring task from your own life or work, not by watching someone else's demo.
Correcting the agent's output and explaining what changed, rather than accepting or discarding it outright, is what actually improves its performance over time.
Once a task feels easy, connecting the agent to tools you already use compounds its usefulness because it can act on live data directly.
Glossary
Terms worth knowing.
Agentic AI
AI that can take actions toward a stated goal on its own, working through a multi-step plan instead of only responding to a single prompt at a time.
Goal prompt
A command-style instruction that sets a target condition and lets an AI agent keep working autonomously, taking and reasoning through actions, until that condition is met.
Clay
A lead-enrichment platform that can be directed by an AI agent to find, research, and qualify prospective sales leads.
“You're not gonna lose your job to AI. You're gonna lose your job to somebody who uses AI.”
tight, quotable reframe of the AI-job-loss fear→ TikTok hook↗ Tweet quote
00:52
“Your company is not going to go out of business because of AI. Your company is gonna go out of business because another company used AI.”
parallel-structure line that lands as a warning without hype→ IG reel cold open↗ Tweet quote
01:37
“If you can't beat them, join them.”
short, familiar idiom repositioned as the video's whole thesis→ newsletter pull-quote↗ Tweet quote
10:25
“Using Claude to me feels like I have a cofounder rather than just, you know, hiring a virtual assistant.”
reframes the AI-as-virtual-assistant comparison people default to→ IG reel cold open↗ Tweet quote
10:55
“You're not the engineer or the operator anymore, you are now the manager.”
crisp statement of the video's core skill claim→ TikTok hook↗ Tweet quote
The Script
Word for word.
Read-along
Don't just watch it. Burn it in.
See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.
17px
metaphor
So Jentic AI is on its way to replace around 50% of all jobs. Tools like Cloud Code are being used to do around 60% of the tasks in companies. And if you don't know how to use it, you might end up like the hundreds of thousands who have lost jobs because of AI.
But this is simpler than you think. You don't need to learn how to code in order to stay ahead of this. You need to learn one skill, and it's a skill that anyone can pick up.
I have thousands of members in my community who have mastered it in just a few weeks, and none of them had any technical background. So in this video, I'm gonna show you what's really happening with these jobs, why this AI is so powerful, and the simple steps that you can follow to learn how to use it. So let's get into it.
Now, when you hear AI is going to take jobs, you probably picture a robot rolling in and replacing you. But that's not really how this works. It's actually replacing you is just a human.
And I'll let the CEO of the biggest tech company explain this to you.
Actively and aggressively, you're doing it wrong. You're not gonna lose your job to AI.
You're gonna lose your job to somebody who uses AI.
Your company is not going to go out of business because of AI. Your company is gonna go out of business because another company used AI. Basically, happens is that the person next to you learns to use AI, and they start doing your job and their job at the same time.
So the company doesn't need both of you anymore. Now, why is this happening now and not five years ago? Because the AI we have today is different.
Old automation only ever did exactly what you told it. And if something went wrong, you'd have to fix it. But the new AI is what people are calling agentic.
Agentic just means the AI can take action on its own towards a goal instead of only answering your questions. So instead of you doing every step, you just tell it the outcome you want, and it will go figure out all the steps and it will do them for you. And it's not just me saying that this is a big deal.
Agents are coming for the kind of work people do at a desk all day. So here's how to prepare this. And it's actually really simple.
If you can't beat them, join them. You're not gonna stop this from happening. There's just no way.
But you do get to choose which side of it that you're gonna be on. Either the person holding the controls or the person who might lose their job to AI. And once again, the good news is you don't need to know how to code or be a super technical person in order to do this.
I'm gonna show you how to use it in less than fifteen minutes, but I'm actually gonna show you what you'll be able to do. So let me show you three jobs that it can already do today. So let me show you guys a few practical examples here.
The first thing I wanna call out is I'm using the Clawd desktop And if earlier when I said Claude code, you got a little scared, there's no reason to be. This is Claude chat. Right?
You can come in here. You can talk to Claude, and it's basically just using your natural language to get what you want, except for in the Claude chat, we're missing the whole agentic capabilities. We're missing the ability for Cloud to really feel like an officer at your company and a full employee rather than just a helpful little tool.
So all we have to do is switch over from Cloud Chat to Cloud Code. And this is the interface. You know, we've got our ability down here to talk.
The only difference is now we're working out of local folders and files. So if I pull up my file explorer, and you can see right here I've got my camera roll open. You can see I can go to my desktop or my downloads.
This is all we're doing, is we're working with Claude, instead of on the web, we're working inside of our actual local files. So that means it can look through my pictures, it can look through Excel sheets and Word docs that it creates for me, and it can help me organize those, move them, create more, all that kind of stuff.
So it's just way more powerful. And it has a much better memory than just using Claude Chat. Even if you're using chat and you're organizing stuff by projects and you've got, you know, a few connectors plugged in, this is way different because, like I said, it's looking at all of our files.
So real quick, if I just said, hey. Can you quickly tell me who I am, what my business does, and what are some of our goals for this year? Because my Herc two project, which is kind of like my AI operating system is what I call it, it can read all my emails.
It can see all of my communication. It can look through all of my meeting transcripts. It knows everything about me and my business, as you can see right here with what it is currently spitting out.
It knows my YouTube subs. It knows, you know, our two different communities in school. It knows our certification program we're working on.
It knows my book. It knows my team. It knows all of this kind of stuff.
Anyways, the three examples. This first one, take a look at this. So I came in here, and I did something called a slash goal, which means I am able to set a goal, and Claude will keep working until the condition is met, which is super cool.
I mean, how much more argentic do you get than that? So this is the prompt I I shot off. I'm not gonna read this whole thing, but feel free to take a look at that if you want.
I basically wanted to pull in all my videos from quarter two of twenty twenty six, analyze it, comments, click through rate, all the stats, and help me look at those insights and tell me what to do about it. So as you start to go through here, what you'll notice is this is not technical at all. This is basically just Claude thinking about what to do and looking through sources.
So it said, I'll start by exploring what YouTube data and tooling is already available, then I'll figure out how to pull q two analytics. So it read a markdown file that I have called YouTube channel dot m d. So it reads the goal, and it needs to understand exactly what it can pull.
So it starts looking through things. Right? It uses this Python script to actually be able to get the data from my YouTube channel.
It has the right token. It continues to take action and then reason, and take action and then reason. And it just goes all the way through until everything's done.
You can even see here, once it's actually created an Excel sheet, it screenshots it because it's supposed to verify it. Right? It's gonna make sure the colors appear.
It's gonna make sure the spacing's right, and it's not gonna hand me something until it actually feels confident that I'm gonna like what I got. So let me pull up the full Excel sheet so I can show you guys what it just did. And just again, to prove my point, inside of my Herc two project, it created all of this stuff.
And here is the actual Excel sheet that it made for me locally. So I opened this up, and this is what we got right here. If we go to the first tab, what we can see is we have an assessment.
We can see the different tabs, like the executive dashboard, the video scorecard. We can see the views numbers. We can see the different data sources.
We can see all this kind of stuff. I can go to my executive dashboard, which shows me from April through June. I've got these amount of views, this much watch time, these these many new subscribers, and we can see some of the most important things that matter.
Cloud Code is the engine, massive subscriber quarter, and some bad things. Impressions and CTR were missing, so for some reason, it wasn't able to pull those, so we would be able to work that back in. Topic concentration is a risk.
So as you can see, not only is it pulling data, but it's helping me analyze it a little bit. As we keep going through, we can see a video scorecard. So I can see some of the videos that I've uploaded this quarter, and it's gonna tell me based on the views and the length and the viewer duration and all this kind of stuff, which ones are good.
Also putting them in different pillars. So we've got Cloud Code and Agenetic. We've got Voice AI.
We've got other. We've got selling, all this kind of stuff. I can go through my monthly trends.
I can go through content pillars, format and length. I can get into the audience and traffic. So all of this stuff is gonna help me every single quarter do a review like this, analyze the insights, analyze the data, and then help me work on my strategy for the next quarter of YouTube videos.
And I want you just to real quick think about how much data that was, and how long would that have taken me manually to go to YouTube, extract all of that, put all of that in an Excel sheet, and then analyze all of it? As you can see, I uploaded 75 videos in this quarter. So that would have taken me at least half of the day to just write all this down, to just pull all the data in here, get this spreadsheet looking nice, and analyze it.
Whereas Claude was able to do this in about ten minutes for me. Okay. Let's look at example number two.
I once again utilized a slash goal prompt so that I could set the condition and it would keep working. This time, I wanted to pretend that I had a small cleaning business, and I wanted to build myself an app so that I could track my jobs and how much I'm getting paid all and that kind of stuff. Once again, same thing as last time, it reasons through.
Reads the instructions, it decides what to do, it makes a plan, and then it starts reading files, it starts executing. And every time it executes, it reasons, and then it executes. And then it reasons, and then it executes.
And that's basically the loop. You can see here, this time what it created for me was an HTML file. So let me go ahead and open up this HTML, and here's what we have.
Cleaning jobs, we have today's date, we have how much we made this month, how much we're still owed. You can see, let's say, Sarah Lynn paid me. I could go ahead and mark as paid and then mark done.
If Mike goes ahead and pays me, I could mark paid, and then mark that as done. Let's say for some reason Amy Brooks cancels, I can just x that out and delete that job. And I can also come in here and add jobs.
And now the cool thing is we asked for this app to have a memory. So basically, that means if I was to refresh this, all of that stuff that we've done is going to save. It's not just gonna reset every time.
Now think about this. Do you know how to build this? Could you manually write this HTML?
Nope. I couldn't either. But I know how to explain to Claude what I want.
If I want this to be different colors, I just say that. If I want there to be more functionality, I just say that. If I wanna turn this from an HTML into an actual URL, an actual website that I could pull up on my phone, or if I wanted to turn this into an iOS app or a desktop app, I would just ask Claude to do that for me.
And once again, this took minutes. And finally, this third example, lead generation. How many people sit there and not only struggle with lead generation, but some people's full time job is lead generation and outreach?
So look at this. So for this goal, I had it look in Clay to find me 50 leads that are my exact avatar. Not only find those leads, but enrich them.
So find out what their business does, their business pain points, Google reviews, things like that, and then help me, based on the data it knows about me and my business, create outreach messages for all 50 of those leads. Once again, it reasons, it executes. It reasons, it executes.
It reasons, it executes. And what it did for me here is it created this Excel sheet right here. Leads 07/07/2026, which is today's current date at the time of filming.
So I went ahead and opened up this Excel sheet, and what do I get? This Excel sheet. I've got 50 leads here.
If I scroll through, you can see that we have 50 columns of actual leads, or sorry, rows. And the columns are business, decision maker, title, email, email verified, prior email status, phone, website, location, Google rating, review count, business pain points, recent current signal, notable achievements, personalization hook, hook source, email subject, email body, and needs review.
So all of this, think about how long that would take you as a human to go find, enrich, and write outreach messages for 50 leads, whereas this took me about twenty minutes by just asking Claude Code with my natural language. And I'm not a master, you know, cold email copywriter, but just take a look at this. It's not very bad.
Right? So the business pain point is that a recent one star review calls out chaotic communication. And so if we go over here to the email subject, your one bad review is about callbacks.
And then the body says, hi, Ante. Saw your review about a customer still waiting on a callback a month later. A few different people reaching out with no one following through.
It's a rare miss for a shop at 4.7 stars across 41,000 reviews. We recently helped a local business turn calls that it was missing into booked jobs without adding anyone to the phones.
So honest offer, I'm still getting this off the ground. No case study yet, but I'll set it up on your line, run it for free for thirty days. And if it doesn't book you jobs, then you owe nothing.
Now here's something to think about. Claude Chat could connect to Clay and could find you leads and could enrich those leads, but Claude Code knows more about my business. It knows our case studies.
It knows our avatar. And because I'm building my AI operating system with its own second brain, using Claude to me feels like I have a cofounder rather than just, you know, hiring a virtual assistant, which is what people used to compare AI to a lot, was like hiring a VA. But now you can literally have a founder at your business, or you can have an officer at your company.
And let's say you don't own a business. That's completely fine. If you're an employee, you now are able to manage a bunch of different super high level employees, super high functioning and high performing employees, which makes you look 10 times better.
Because once again, you can do 10 times more work and keep the same level of quality than other people who are in your class or, you know, have the same job as you. So if you've never opened up Cloud Code because you were scared it might technical or because you thought it didn't relate to you and your position, this is your wake up call.
Please, please give it a shot. Alright. Now here's the thing.
All three of those examples had in common. Every single one of them was a job that a real person gets paid to do today or at least used to get paid to do. And now, any nontechnical person can do all of that with Claude.
Now, I'm going to show you how to use it in three simple steps, and we're gonna do all of this with Claude code because right now, I think it's the best one to learn. But real quick, remember at the beginning of the video I said you only had to learn one skill? That skill is being an AI manager.
Think about how you might manage a new hire. You would onboard them. You'd let them get to know you, and get to know the business, and get to know their role.
And you wouldn't dump everything on them in week one. You'd slowly phase them in. You'd explain exactly what they should do, and you'd explain exactly what they shouldn't do.
And then you'd watch them until they prove that they can work without mistakes. And when they start handing you work back, you don't just accept it blindly, you review it, you give feedback, and you help them get better. That's exactly the mindset you should have when you're working with AI.
You're not the engineer or the operator anymore, you are now the manager. Your whole job is to make this thing as good as possible. And the three steps that I'm about to give you are basically just that onboarding plan.
Okay. So step one, just start talking to it. Open up Cloud Code and treat it like a really smart person that you're handing a task to.
Explain what the goal is, what a good result looks like, what a bad result looks like, and what it should avoid. And practice that on small stuff. And this is the onboarding part.
Let it get to know you. Tell it what you do, how you like things done, what your week actually looks like. The more it knows, the better every single answer is going to be.
Okay. Step two. Pick one real thing that you already do and try to do it with Claude.
I mean, grab something from your week that you actually do over and over. And it doesn't even have to be something that's associated with your work. Maybe you just want to have AI help you plan your gym schedule and your meal prep or figure out your groceries.
The most important thing is that you pick something that you will actually use because there's a huge difference between feeling the ROI yourself and just watching demos of other people talking about all this stuff that AI can do. You're only gonna get hooked on it when it actually saves you time on something that you really care about.
So truly, the goal of step two is to convince yourself as fast as possible that this thing, AI, is actually helpful. And when it gives you something back, don't just accept it, correct it. Tell it what you changed and keep going until the output is something that you would actually use.
And this is the whole trust building part because you're watching it, you're reviewing it, and then you're teaching it, and every single correction, every iteration makes it better. And step three, once that feels easy, start stacking bigger tasks together and connect Claude to the tools you already use. Whatever you use every day, your Gmail, Slack, Calendar, etcetera, there's probably a way to connect Claude to it or to connect any sort of AI agent to it.
You guys saw in that first example when Claude pulled my YouTube analytics, I didn't have to go in there and export the data or copy and paste anything or upload some spreadsheet. I just said to Claude, hey, go get this data from YouTube. And it just did it.
And because I have that connection set up and all of my other connections set up to all the other tools that I use every day, AI can actually see what's going on in my business and everything from there gets easier and better. You just wanna do it in a way where it's safe and you always stay in control. Claude asks before it touches anything and you decide what it's allowed to reach and you give it certain permissions.
And once you're automating real work, this is where you start keeping score. So set a goal. Like, maybe this week, I wanna save myself three hours, or I want a 100 new leads per month.
And then track where you're at right now before you start using AI, and then check the number after, and the next month, and the next month. Because if you didn't hit it, you can improve the system and make it better. And that's really the only way that you actually improve is if you're keeping the data.
So that's the path. You talk to it, you hand it one real thing, and then you just stack. Those are the first steps to start using a Jentic AI.
But this space is moving really fast, and every week AI takes more and more jobs. But it's also creating a ton of new opportunities. So if you wanna start mastering how you can use Agenetic AI, you can join my free school community.
I've got a full free course in there around building your own AI operating system, which is a great place to start. The link for that is in the description. But anyways, that's gonna do it for today.
I appreciate you guys making it to the end of the video, and I'll see you on the next one. Thanks, guys.
The Hook
The bait, then the rug-pull.
Nate Herk opens with a claim built to unsettle: agentic AI is already doing roughly 60% of the tasks inside companies, and the people losing jobs aren't being replaced by robots — they're being replaced by a coworker who learned to use AI. What follows is a low-drama case for why coding was never the actual bottleneck.
Frameworks
Named ideas worth stealing.
11:00list
The Three-Step AI Onboarding Plan
Start talking to it
Pick one real task
Stack tasks and connect your tools
A three-step plan for building working fluency with an agentic AI tool, modeled on how you'd onboard a new hire rather than configure a piece of software.
Steal forany onboarding doc or first-time-user guide for an AI agent product
03:34list
Three Jobs an AI Agent Can Already Do
Quarterly data analysis and reporting
Build a simple internal app from a plain description
Lead generation, enrichment, and personalized outreach
Three concrete categories of paid work demonstrated live, each completed via a single natural-language goal rather than manual execution.
Steal forpicking a first real task to test an AI agent on
CTA Breakdown
How they asked for the click.
VERBAL ASK
13:42product
“you can join my free school community. I've got a full free course in there around building your own AI operating system”
Single soft CTA placed only at the very end after the demos and framework were delivered; no mid-video pitch interruptions.
An 18-minute walkthrough of wiring Claude Code into Clay's data platform to source, enrich, and write cold email copy for 50 leads from one natural-language prompt.
Nate Herk breaks down the four ways an AI operating system's context quietly goes wrong, then walks through the five habits that keep a growing second brain accurate instead of confidently wrong.
A single founder makes the case that Claude Code has erased the cost of building software, using a three-person team's state government contract as proof.
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