EP 549 The $30K AI Mistake | How AI Entrepreneurs Learn Independence

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Cursor AI just saved me $90,000,
but that is not actually the

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interesting part of this story.

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Look, for some of you, you're about to
spend $120,000 on someone else's hands

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while yours are sitting there idle.

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That's what I thought three months ago
when I hired, let's call him Marcus.

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Senior developer, portfolio
like a resume from Google.

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He quoted me 120K annually, and I
said yes without even negotiating.

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Not because I don't know how
to negotiate or 'cause I can't

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negotiate, because I was scared.

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Scared that without him, the product
I had in mind would just sit in limbo.

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Scared that building it myself would
take five years instead of five months.

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Scared that I would be the bottleneck.

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Marcus started in January.

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By March, I fired him.

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Not because he was bad, because
let's be honest, he was exceptional.

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The code was clean.

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The architecture was smart.

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The problem wasn't Marcus.

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The problem was that I handed him the most
valuable resource I had, my own evolution.

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Every day he worked, I got
dumber about my product.

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Every feature he shipped
meant I understood it less.

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By month two, Marcus got
sick, and I was paralyzed.

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By month three, I realized I
had paid 30 grand to become

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incompetent in my own business.

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What is up, what is up, what the hell
is up, my Dark Horse friends and family?

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Welcome back to another episode of The
Dark Horse Entrepreneur AI Escape Plan.

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We break free from the grind.

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We share some AI-powered tips and side
hustle strategies, all with the mission

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of supporting my parents out there chasing
freedom like modern-day trailblazers.

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So if you know someone who dreams of
ditching the grind but still makes

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time for those beautiful little
nuggets in their life like bedtime

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stories, et cetera, go ahead and
forward this episode to them, would ya?

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So as you can imagine from the story I
started off with, I did something stupid.

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I decided to rebuild the entire back end.

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Yeah, I'm gonna rebuild three months
of Marcus' work, and I'm gonna do it

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alone In four weeks using nothing but
Cursor AI, Claude, and a completely

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different approach to prompting
than most people know exists.

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And here's what happened.

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The first thing you need to
understand is that most people, I

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think, are out there using AI like
it's Google with a personality.

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They ask it a question, they
get an answer, they paste it in.

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Boom.

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This is about the slowest way to work.

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There's actually a name for what I
was trying to do, and Andrej Karpathy,

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the former AI Tesla director, coined
it as vibe coding previously, and it

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just became Collins Dictionary Word
of the Year for twenty twenty-five.

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The idea being that you're
directing the AI through intent

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rather than just in-instruction.

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But here's where most people are gonna
get vibe coding completely wrong.

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They treat it like dictation.

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What I learned from Marcus-- actually,
what I actually extracted from watching

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Marcus was that great developers
don't-- do not work in isolation.

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They work in conversation with
constraints, with requirements,

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with feedback loops that get tighter
and tighter and tighter each time.

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So I rebuilt my prompting system
around that singular principle.

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I looked at the obvious
alternatives first, right?

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GitHub Copilot, Claude Code
as a standalone product.

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But I kept coming back to Cursor,
specifically Cursor Composer, which

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lets you maintain persistent context
across your entire code base.

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Cursor is built by Anysphere, and
it's a fork of the Visual Studio

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Code, which matters because it
means the mental model transfers.

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The tools feel similar, the shortcuts
work, and you're not learning a new IDE

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while you're learning a new way to think.

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Okay?

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So instead of asking Cursor
something like, "Build me a user

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authentication system," I started
to treat it like a junior developer

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who needed constant context.

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I would paste my entire database
schema, the post-GRE SQL tables, foreign

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key relationship, the whole thing
into the conversation, and I'd show

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previous solutions from my code base.

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I'd explain why certain approaches
failed in previous examples.

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I'd even specify the exact model, Claude
Sonnet for architecture decisions,

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faster models for boilerplate.

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The AI at that point
Stopped hallucinating.

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It stopped generating
code that did not fit.

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It stopped wasting my time with
these beautiful solutions to

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the problems I didn't even have.

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By week one, I'd rebuilt the
authentication layer, the JWT

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tokens, refreshed logic, and the
full session management stack.

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By week two, the REST API endpoints.

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By week three, the payment
processor integration.

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By week four, I had something
Marcus would've recognized from

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his previous three months of work.

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Better in some ways, maybe worse in
others, but it was entirely mine.

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The real shift began to happen at day 10.

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I realized I wasn't rebuilding
code, I was learning the

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architecture for the first time.

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Every line I reviewed, because
I had to review every line now,

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became embedded in my memory.

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When a bug appeared on day 15, I didn't
wait for the developer to find it.

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I found it myself at 11:00 PM in 20
minutes because it was my system, my

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architecture, my code, even though
I hadn't written a single character

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with these little fingers right here.

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This is the part that nobody talks about.

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The financial spreadsheet said
I'd saved $90,000 by month three.

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The SaaS product was still live, still
generating monthly revenue income, and

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I still owned every single layer of it,
but the actual return was different.

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I could now build features myself.

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I could now maintain the
system without outside help.

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I could now make mistakes about the
product architecture without having

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to explain them to somebody else.

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The hourly rate for a developer
might be 150 bucks, but the

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cost of dependency is infinite.

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Here's the actual mechanism of how
I did it because this is where I

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think a lot of people get lost.

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Standard prompting might say
something like, "Build me a

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user dashboard." Okay, not bad.

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It's a little plain.

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Better prompt, "I have users with these
fields," and you give them the schema.

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"They need to see this
data," gives an example.

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"The design matches this
pattern-" Give them a screenshot.

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Build a dashboard component.

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Here's what we tried before and why
it failed, and give them examples of

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the previous attempt if you have that.

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And make sure it handles
loading states and error cases.

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Here, don't get me wrong here.

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The difference isn't
creativity, it's constraint.

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That second prompt, it has walls.

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The AI doesn't have enough
freedom to hallucinate.

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It can only solve the specific problem in
the specific context that you have given

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it, or that in that case I had given it.

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It sounds slower, but
it's actually faster.

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You will get usable code on the first
pass maybe 70% of the time, right?

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The other 30% might need an iteration,
like one iteration, maybe two, but

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it won't need seven or nine or 10
iterations from the previous example.

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But there's a cost here, and I,
I need to be honest about it.

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The 30 hours I save per
week came from somewhere.

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They came from Marcus's paycheck.

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Yes, I get that, but they also
come from somewhere else entirely

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It came from the fact that I was
no longer learning by osmosis.

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I was no longer sitting next to someone
excellent and unconsciously absorbing

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their problem-solving patterns.

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I was no longer getting pushback on bad
ideas from someone with institutional

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knowledge of building products at scale.

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Marcus had built three exits.

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He knew where the bodies were buried.

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He knew the scaling problems that you
won't hit until month six, and he knew

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which architectural decisions felt clean
now but become technical debt later on.

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When I fired him, I didn't just
fire an employee, I fired my

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own access to that knowledge.

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The question that kept me up wasn't
whether I could rebuild the product.

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I knew I could eventually.

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The question was whether the product
I was building was actually good,

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and I found my answer in a really
weird place, the bug reports.

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Three months in, users started
reporting issues, very specific ones,

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and there were scaling problems.

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Race conditions in payment processor,
database query patterns that worked

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at a hundred users but just broke
consistently at a thousand users.

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They weren't the kind of bugs you'd
notice during development normally.

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They would be the kind of bugs that you'd
only notice when you've built something

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at that scale multiple times before.

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The kind of issues that live at the
microservices boundary or surface

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when you haven't thought through
horizontal scaling from the start,

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and I had no Marcus to fix them.

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So I did something differently.

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I took a look at those bugs, the
payment race condition, and I

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treated it like a teaching tool.

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I went back to the code I'd written
with Cursor, and I asked myself,

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"Where did I choose the wrong
solution?" Not wrong as in broken,

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but wrong as in this isn't optimal.

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And then I went to, uh, Cursor-- to
the Cursor Composer with that context

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in mind, and I typed in, "This payment
integration has a race condition at scale.

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Show me three ways this could fail.

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For each failure mode, show me
the right architectural pattern."

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And here's what Cursor showed me.

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Not because Cursor had read Marcus'
code, but because I had given it

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the constraint of the problem, the
failure mode, and the context that

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there was a better way to do it.

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The next bug, well, it took less time.

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And the one after that, less time still.

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By months five, I wasn't just
maintaining the code anymore.

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I wasn't even debugging it.

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I was improving it,
proactively redesigning it.

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Yeah.

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Yeah, see, look, if you're a parent
entrepreneur, and maybe you're

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juggling family time and you're
still trying to build something real,

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you know the very dependency trap
I, I've been talking about here.

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You hire experts because you
think it'll free up your time, but

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sometimes it actually makes you a
little more dependent than less.

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That's why I created the A- AI
Escape Plan newsletter, 'cause it's

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designed specifically for parents
like you who want to break free

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from the grind without sacrificing
all their precious family time.

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Each week, I share practical
AI-powered strategies to start, grow,

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and streamline side hustles that
actually work around your family life.

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You could call it your roadmap
to more money, more freedom,

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and more of what truly matters.

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Because here's the truth: the goal
isn't just to make money online.

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That's a goal, but it's not the only goal.

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It's to build systems that you own, that
you understand, and that you can control.

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The systems that work for
your family Not against it.

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Okay?

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So some of you I can hear in my magic
headphones here, why does this matter b-

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besides just saving money on developers?

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Well, I'm, I'm glad you asked.

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We're living through one of
the biggest shifts in how work

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gets done since, I don't know,
maybe the Industrial Revolution.

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Maybe there's another
time previous to that.

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Maybe since the introduction
of the internet, okay?

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That was a big shift there.

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AI isn't just changing what's possible,
it's changing what's necessary.

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That's a big one.

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The old model was hire experts,
delegate complexity, and

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focus on your zone of genius.

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I think the new model is use AI to expand
your zone of genius until it includes

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the things that you used to outsource.

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This isn't about replacing
human experience, it's about

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reclaiming your own evolution.

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Every time you hand off a critical
piece of your business to someone else,

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you're making a bet that their knowledge
will always be available to you.

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But what happens when it's not?

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What happens when they leave or when
they get sick or they just disagree

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with your vision or your version?

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The parents who thrive in the next
decade, I don't think they're gonna be

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the ones who can afford the best experts.

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I think they're gonna be the ones
that use AI to become their own

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expert in their own business.

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They'll be the ones who understand
their systems deeply enough to

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improve them, perhaps even debug
them, and scale them without having

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to ask permission from anyone else.

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So all that said, here's your
whiskered wisdom for this episode.

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Dependency is expensive,
but ownership is priceless.

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The specific action I would like you
to take after listening to this episode

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is pick one thing in your business or
your side hustle that you have or are

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dreaming of that you currently have
outsourced or that you're avoiding

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entirely, there's another one to think
about, because it seems too technical.

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Maybe it's website updates, maybe
it's email automation, maybe it's

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social media scheduling or just
your simple analytics, right?

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Choose one, just one, and then this
week spend 30 minutes learning how to

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do that one thing, maybe even one step
in that one thing, all by yourself

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using AI as a teaching assistant.

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Do not try to become the expert.

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That's gonna take some time.

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Just try to understand it well enough
that you're not completely dependent on

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whomever you have doing it for you, right?

235
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Start small, start simple, but start
owning your own business again.

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The goal isn't to do
everything by yourself.

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The goal is to understand everything well
enough that you can make smart decisions

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about what to keep and what to delegate.

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AI gives you that understanding
without that 10,000-hour expert

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commitment we've all heard about.

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Your family deserves a business that
you can actually own, not the one

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that owns you through dependency.

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All right?

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Think successfully and take action.