We use an AI-powered job description generator. It's supposed to save time by analyzing similar roles, pulling industry-standard requirements, and creating JDs that attract qualified candidates.

It works great—until it doesn't.

Last week, we posted a Senior Recruiter role. The AI generated the job description. Our hiring manager approved it without reading carefully (first mistake). We posted it to LinkedIn, Indeed, and our careers page.

Within 24 hours, we'd received 47 applications.

Every single one was a lie.

The Requirement That Broke Reality

Here's what the AI-generated job description said under "Required Qualifications":

Required:

• 10+ years of experience in full-cycle recruiting
• 7+ years of experience with applicant tracking systems
• 5+ years of experience with AI-powered sourcing tools
• 10+ years of experience with skills-based hiring practices
• Bachelor's degree in HR, Business, or related field

Spot the problem?

Skills-based hiring only became mainstream in 2022. It's 2025. That's 3 years.

Even the most forward-thinking companies didn't start using skills-based hiring at scale until 2021-2022.

Asking for 10 years of experience with a 3-year-old practice is like asking for 15 years of experience with TikTok.

But the AI doesn't know that.

How This Happened

The AI job description generator works by:

1. Analyzing our job title ("Senior Recruiter")
2. Pulling requirements from our "senior role" template
3. Identifying current recruiting trends (like "skills-based hiring")
4. Combining them into a job description

The template says "10+ years of experience" for senior roles.

The trend analysis identified "skills-based hiring" as a key practice.

The AI combined them: "10+ years of experience with skills-based hiring."

Logical, if you're an AI with no concept of time.

Absurd, if you're a human who understands that skills-based hiring wasn't a thing in 2015.

The Applications We Received

We got 47 applications.

Every single applicant claimed to have 10+ years of experience with skills-based hiring.

Here's a breakdown:

The "Pioneers" (12 applicants)

These applicants claimed they'd been "early adopters" of skills-based hiring and had been practicing it since 2015 or earlier.

Sample resume bullet:

"Pioneered skills-based hiring practices at [Company] in 2015, eliminating degree requirements and implementing competency-based assessments before it was an industry trend."

We called references. None of them had done this.

One reference said: "She's a great recruiter, but in 2015 we were absolutely still requiring degrees for everything. I have no idea what she's talking about."

Another said: "We didn't even have an ATS in 2015. We were using Excel spreadsheets. There's no way she was running 'competency-based assessments.'"

The "pioneers" were lying.

The "Consultants" (8 applicants)

These applicants claimed they'd been advising companies on skills-based hiring since 2014-2016, even if they weren't implementing it themselves.

Sample resume bullet:

"Consulted with Fortune 500 companies on transitioning to skills-first talent strategies, 2014-present."

We checked LinkedIn. None of these people had "consultant" in their job titles before 2023.

One of them had been a recruiter at a mid-size company until 2023, then suddenly became a "Skills-Based Hiring Consultant" after the practice went mainstream.

Convenient.

The "Redefining What Counts" Crowd (18 applicants)

These applicants argued that they'd been doing skills-based hiring before it had a name.

Sample cover letter:

"While the term 'skills-based hiring' is new, I've been practicing it for 12 years. I've always focused on what candidates can do, not just their resumes."

This is technically true—good recruiters have always cared about skills.

But "I care about skills" is not the same as "I implemented structured skills assessments, eliminated degree requirements, and used work samples to evaluate candidates."

The practice of formally deprioritizing degrees, using standardized skills tests, and evaluating candidates through work samples is new. Just thinking skills are important is not.

The "I'll Say Whatever You Want" Group (9 applicants)

These applicants just... lied.

No attempt to justify it. No creative interpretation. Just straight-up fabrication.

Sample resume:

"Led skills-based hiring initiative at [Company], 2013-2020, reducing time-to-hire by 40% and improving quality-of-hire by 60%."

We called the company. They'd never heard of this person.

Turns out, they'd worked there for 6 months as a recruiting coordinator in 2019. They did not lead any initiatives. They definitely did not implement skills-based hiring in 2013.

Bold strategy.

What We Did About It

We:

1. Took down the job posting immediately (should have been step one)
2. Reviewed every application to identify anyone who might be qualified despite the nonsense requirement
3. Rewrote the job description with realistic requirements (3+ years of experience with skills-based hiring, or demonstrated ability to learn it quickly)
4. Re-posted the role with a note: "We previously posted this role with an error in the experience requirements. If you applied before, please apply again—we're reviewing all new applications."

We also implemented a new rule: All AI-generated job descriptions must be reviewed by a human who understands what's actually possible.

The Lessons

1. AI doesn't understand time: It can identify trends (skills-based hiring) and requirements (10 years experience), but it can't cross-reference them to ensure they're compatible.

2. Candidates will lie to meet impossible requirements: If you ask for 10 years of experience with a 3-year-old practice, candidates will claim they were "pioneers" or redefine terms to fit.

3. Hiring managers don't always read job descriptions carefully: Our hiring manager approved the JD without noticing the impossible requirement. We now require two people to review all AI-generated content.

4. Impossible requirements signal to candidates that you don't know what you're doing: When candidates see "10 years of experience with skills-based hiring," they assume either (a) you're clueless about the industry, or (b) you don't actually care about the requirements and just want to see who applies anyway.

Neither is a good look.

The Follow-Up Disaster

Here's the kicker: one of the "pioneer" applicants who claimed to have been doing skills-based hiring since 2015 got an interview (our hiring manager didn't read the resume closely).

In the interview, we asked: "Tell us about your experience implementing skills-based hiring in 2015. That was really ahead of the curve."

The candidate paused.

Then said: "Honestly? I have no idea what skills-based hiring even is. I just Googled it when I saw it in your job description and wrote that I'd been doing it for 10 years because that's what you asked for."

At least they were honest in the interview.

We did not make an offer.

Where We Go From Here

We still use the AI job description generator—it saves time and generally works well.

But now we:

• Require human review of all requirements to ensure they're actually possible
• Cross-reference experience requirements with when practices/technologies actually became available
• Ask ourselves: "Could someone actually have this much experience with this thing?"

And we learned an expensive lesson: AI is great at pattern-matching, but terrible at understanding whether those patterns make sense in the real world.

If we'd asked for "10 years of experience with ChatGPT" (released in late 2022) or "15 years of experience with Claude" (released in 2023), the AI would have generated that too.

It doesn't know that skills-based hiring wasn't mainstream until 2022.

But we should have.

From now on, we will.