Hey Reader,

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Everyone tells junior engineers to use AI.

“Be more productive.”

“Let ChatGPT write your code.”

“Work smarter, not harder.”

This advice is creating an entire generation of helpless engineers.

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I’ve been mentoring DevOps engineers for years now.

I’ve helped 800+ people land six-figure tech jobs.​

And I’m watching AI ruin careers before they even start.

Here’s what nobody wants to tell you:

If you can’t function without AI, you’re not an engineer.

You’re a prompt typist.

And companies are starting to figure this out.

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The Craft Cannot Be Shortcut

The place where I help people land $200K DevOps & Kubernetes jobs is called KubeCraft.

I gave it that name KubeCraft for a reason:

Kube for Kubernetes. Craft for craftsmanship.

This is not a bootcamp.

Bootcamps churn out superficial factory workers who know where buttons are in a GUI.

I take a different approach.

The craft of the Kubernetes engineer starts long before Kubernetes.

It starts with Linux.

It starts with the command line becoming your natural interface with computers.

It starts with text editing in Vim until it feels like breathing.

When I was making my career change from nursing, I had mentors who told me this.

“Learn Linux properly.”

“Don’t jump into Kubernetes.”

“Master the fundamentals first.”

I didn’t think of this myself.

I was taught by people who knew what actually matters.

Now I’m passing those teachings forward.

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The 10% Problem

Here’s what happens when you rely on AI:

It works 90% of the time.

You paste an error log into ChatGPT.

You get an answer.

It works.

You feel productive.

Then you hit the 10%.

The AI gives you an answer that doesn’t work.

You try again.

Still doesn’t work.

You rephrase the question.

Circular answers.

You’re stuck.

You don’t know how to debug because you never learned.

You don’t know where to look because you’ve never had to look.

The AI ran you in circles for 3 hours and you have nothing to show for it.

Now imagine this scenario:

It’s 2 AM.

Production is down.

You’re SSHed into a server.

No GUI.

No internet access.

No ChatGPT.

Just you, a terminal, and a broken system.

Can you debug it?

Can you read man pages?

Do you know ps, grep, find, journalctl?

Can you navigate the filesystem and understand what’s running?

If you’ve only ever pasted errors into AI, you’re useless.

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Reverse Engineer the Interview

I’ve interviewed DevOps candidates.

When I ask “How did you solve this problem?” I’m listening carefully.

Now imagine you're sitting in that interview. And all you have to say is:

“I pasted it into ChatGPT and got the answer”.

It may sound obvious and stupid right now.

But most people don't think this far ahead.

It tells me this person can’t function independently.

It tells me when things break, they’ll be stuck.

It tells me they don’t have real skills. They have AI access.

Here’s what you need to understand:

Reverse engineer the interview situation.

When you’re sitting across from a hiring manager, you need to demonstrate that you can think.

That you can debug.

That you understand the systems you’re working with.

“AI helped me” is not a compelling story.

“I checked the Kubernetes events, traced the error through the logs, realized the volume mount was misconfigured, and fixed it” that’s a story.

That’s a hire.

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My Recommendation: No AI For Your First Year

This will be controversial.

In your first year of learning DevOps, do not use AI to generate code or solve problems.

Use it as a sparring partner only.

“Am I on the right track with this approach?”

“What should I research next?”

“Can you point me in a direction?”

Never: “Give me the answer.”

Never: “Write this code for me.”

Never: “Fix this error.”

You need to develop the skill of finding answers without AI.

Searching documentation.

Reading man pages.

Debugging logs line by line.

Googling error messages and reading obscure blog posts.

These are the skills that make you a real engineer.

When the AI fails (and it will), you need to be able to fall back on fundamentals.

If you never built those fundamentals, you have nothing to fall back on.

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AI Amplifies Skills You Don’t Have Yet

Here’s the principle:

AI is an amplifier.

It multiplies your existing abilities.

But you need abilities first.

Zero multiplied by anything is still zero.

If you don’t know how to write a Python function, AI-generated Python is useless to you.

You won’t know if it’s correct.

You won’t be able to modify it.

You won’t be able to debug it when it breaks.

If you don’t understand Linux processes, you can’t verify what the AI tells you about debugging them.

You’re just trusting blindly.

And blind trust in AI is a career death sentence.

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The Long Route Wins

I’m not going to promise you a job in 30 days.

Some KubeCraft students have landed jobs 2 weeks after joining.

But they already had experience.

If you’re a complete beginner, your path is longer.

That’s the truth.

Anyone who tells you otherwise is a scammer.

But here’s what the longer route gives you:

Deep skills that stretch beyond DevOps.

Thinking patterns that transfer to any technical role.

The ability to debug any Linux system, anywhere, without help.

A foundation that AI cannot replace.

When I was a nurse making $50K a year, I had no CS degree, no bootcamp, no connections.

I walked the long path.

I learned Linux properly.

I built homelabs and broke things.

I struggled through problems manually.

Now I’m a Microsoft MVP pulling multiple six figures.

The long route works.

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Shallow Knowledge Gets Commoditized

Here’s my prediction:

In 5 years, shallow cloud engineering knowledge will be worthless.

If your only skill is clicking around in AWS consoles or writing basic Terraform configs, AI will replace you.

Not because AI is magical.

Because those skills are shallow enough to automate.

The engineers who survive will be the ones who understand systems deeply.

Linux internals.

Networking fundamentals.

Kubernetes architecture.

Container runtimes.

This knowledge cannot be commoditized.

Because debugging production systems during outages requires understanding, not prompts.

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The Choice Is Yours

You have two paths:

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Path 1: Use AI for everything. Get quick answers. Skip the struggle. Land a mediocre job faster (maybe).

Then discover you can’t function when AI fails. Get exposed in interviews. Watch your career stall because you have no real skills.

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Path 2: Take the long route. Learn Linux properly. Build homelabs. Debug problems manually. Struggle through the fundamentals.

Then become the engineer everyone calls when things break. Pass interviews because you actually understand systems. Build a career that AI cannot threaten.

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KubeCraft students are landing jobs every single week.

Not because I give them shortcuts.

Because I teach them the craft.

The deep skills.

The fundamentals that make everything else easy.

If you’re ready to do this the right way:

​>>> Click here to apply for KubeCraft <<<​

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I’m opening 15 spots this month for motivated people.

The longer route is harder.

But it’s the only route that actually works.

Honor thy craft,

Mischa

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P.S. I spent a year learning DevOps the wrong way before my mentors set me straight. I can help you skip my mistakes and build real skills in 2-6 months. CLICK HERE if you’re serious about a career that lasts.

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Recent quote from a student: "Anyway, wanted to share that as an apprentice I’m learning from a master. And with all the fast paced internet gurus out there teaching clickops this feels awesome."

This student is 25 years older than me and has 20 years of experience in the industry, but wanted to upskill. Teaching is a craft too.