My Lil' Mule

A friend of mine—let’s call him Kevin—was interviewing a developer candidate over Zoom.

The candidate joined the call and opened with something unexpected:

“Before we begin, I need to tell you—I’m not the person on the resume.”

He explained that he’d been hired by a placement company to impersonate someone else. The resume was fake. The identity was fake. The entire process was engineered. His job was simple: show up to interviews, get hired, and collect a cut while the agency pocketed placement fees.

And here’s the part that should concern all of us:

He was getting through interviews.

Not because he was qualified.
Because he had help.

---

The New Interview Assistant

This wasn’t someone memorizing answers or bluffing their way through.

He was using AI.

Tools now exist that can:

• Listen to interview questions in real time
• Analyze the question against a resume
• Generate polished, conversational answers instantly

Not after the interview. During it.

And it’s working.

Roughly 39% of candidates are already using AI in the application process, and 6% admit to outright interview fraud—impersonation or substitution

What used to be preparation has quietly become augmentation.

And in some cases, replacement.

---

From Optimization to Exploitation

For years, we’ve encouraged candidates to “use the tools available.”

• Use AI to refine your resume
• Use AI to prep for interviews
• Use AI to practice coding problems

That was the shift.

But now we’re seeing the next phase.

AI isn’t just helping candidates present themselves better—it’s helping them be someone else entirely.

• AI-generated resumes tailored to beat screening systems
• Deepfake video and voice to pass interviews
• Synthetic identities built in minutes
• Real people acting as proxies for fake ones

What used to require a coordinated fraud operation now requires… a browser and a prompt.

---

This Isn’t Edge Case Behavior

It’s becoming systemic.

• Experts predict 1 in 4 candidates could be fake by 2028
• AI-powered hiring fraud is now described as “scalable” and industrialized
• Organizations are already reporting fake applicants successfully landing jobs

And this isn’t just about bad hires.

In some cases, fake candidates are being used to:

• Access internal systems
• Steal intellectual property
• Deliver malware during technical interviews

Let that sink in.

The interview process itself is becoming an attack vector.

---

The Real Problem: Trust Is Breaking

For decades, hiring has operated on a set of assumptions:

• The person on the call is the person on the resume
• The answers reflect the candidate’s knowledge
• The interview evaluates the individual

Those assumptions are no longer safe.

AI has quietly eroded each one.

We didn’t just introduce a new tool into hiring.
We introduced a way to bypass its core premise.

---

The Tension No One Talks About

Here’s the uncomfortable reality:

We want candidates to use AI.

We expect it, even.

But we don’t want:

• AI answering instead of the candidate
• AI masking a lack of competence
• AI enabling identity fraud

So where is the line?

Because right now—it’s blurry.

---

So What Do We Do?

This isn’t a call to panic. It’s a call to adapt.

Some shifts are already becoming obvious:

1. Interviews need to evolve
Less trivia. More reasoning. More “show me how you think.”

2. Identity matters again
Basic verification—something we took for granted—is now critical.

3. AI use should be surfaced, not hidden
The best candidates won’t pretend they aren’t using AI. They’ll explain how.

4. Trust needs to be rebuilt intentionally
Because it’s no longer implicit.

---

The Part That Stuck With Me

Back to Kevin’s story.

The candidate—“Steve”—had a choice.

He could have continued playing the game. He could have taken the job, collected the check, and moved on.

Instead, he told the truth.

And he said he’d started doing that in every interview.

Because even he realized something was off.

---

Final Thought

We’ve spent years optimizing hiring for speed, scale, and efficiency.

AI just optimized it for deception.

The question isn’t whether candidates are using AI anymore.

It’s whether you’re still interviewing a human.

There’s been a lot of conversation lately about vibe coding—the idea that you can describe what you want, let an LLM do the heavy lifting, and ship software without really getting into the weeds.

To be clear, generative AI has gotten much better. Tools like GitHub Copilot are genuinely impressive. But better tools don’t remove the need for understanding—they just raise the stakes when you don’t have it.

A recent study making the rounds claims Gen Z may be the first generation “less capable” than their parents. I’m not interested in dunking on an entire generation, but I do think the underlying concern is worth talking about: what happens when we outsource too much thinking to our tools?

I Grew Up Fixing Things

I grew up in a household where things got fixed, not replaced.

My dad grew up on a farm. If something broke, you didn’t run to the store—you figured it out. That mindset carried forward. I learned how to change a tire, replace brakes, use power tools, and make things work again.

Today, I have a machine shop at home. I fix broken things. I make new ones. I know how stuff works because I’ve had to take it apart and put it back together—sometimes more than once.

That mindset didn’t stop when I became a software engineer. Code is just another system. You build it. You fix it. You own it.

AI as a Mechanic, Not a Chauffeur

Relying on AI to write all your code for you is a lot like dropping your car off at a random mechanic and coming back later hoping everything went well.

Maybe it did.
Maybe it didn’t.
And when something goes wrong… then what?

That’s why vibe coding, as a philosophy, doesn’t sit right with me.

I don’t expect every developer to understand every line of code at all times—but you do need to understand what matters, especially in critical paths, security-sensitive areas, or systems you’ll be responsible for long-term.

How I Actually Use AI

I use AI the same way I use my mechanic—the one I’ve trusted for over 25 years.

When I bring my car in:

• I already have a pretty good idea what might be wrong
• We talk about what he’s going to look at
• When I pick it up, he explains what he did
• And I ask questions if something doesn’t make sense

That’s exactly how I use GitHub Copilot.

I already understand the problem.
I usually know how I would fix it.
Copilot is like a supercharged IntelliSense that saves time and keystrokes.

But—and this part matters—I read the code it writes. I question it. I adjust it. If something feels off, I know what to look for because I understand the system.

That’s not outsourcing thinking. That’s using a power tool.

Where Vibe Coding Might Be Fine

Are there cases where pure vibe coding is acceptable? Sure.

• One-off scripts
• Exploratory analysis
• Throwaway log parsing
• “Let’s see what the data says” type work

If it breaks, no one’s hurt and nothing catches fire.

But for production systems? For software I’m accountable for? For code that impacts customers or revenue?

Not a chance.

Tools Don’t Replace Craft

Power tools didn’t eliminate the need to understand carpentry.
CNC machines didn’t make machinists obsolete.
And AI doesn’t remove the need to understand software engineering.

If anything, it makes understanding more important, not less.

Because when something breaks—and it will—you don’t get to tell your boss or your customers:

“Sorry, the AI wrote it.”

If you don’t read the code your AI generates, you’re not engineering—you’re just hoping. And hope has never been a great production strategy.