It Has Never Been Easier to Look Like the Perfect Candidate. And Never Harder to Identify the Right One.

Both companies and candidates are using AI.

Yet real hiring quality isn't necessarily improving. In some cases, it may even be declining.

Recently, I've been speaking with several companies trying to understand why they're seeing higher turnover during employees' first six months: sometimes even in hiring processes that have consistently produced great hires in the past.

Two years ago, I would have started by looking at expectation setting, onboarding, or manager support. Those factors still matter.

But I believe something more fundamental is changing.

What's happening on the candidate's side?

Optimizing the application instead of optimizing the fit

Candidates are increasingly using AI to:

• Tailor their résumé for every role

• Craft polished answers to interview questions

• Practice interviews through AI-powered simulations

The result?

Candidates perform exceptionally well throughout the hiring process—even when the actual fit for the role is only partial.

Learning how to pass interviews

AI gives candidates instant access to:

• Large libraries of common interview questions

• Perfectly structured STAR stories

• Insights into what interviewers are likely looking for

Something subtle but important happens here:

We're increasingly evaluating people's ability to perform well in interviews—not necessarily their ability to perform well in the job.

Signal becomes harder to separate from noise

In the past, interviews revealed more authentic differences between candidates.

Today we see:

• Highly polished answers

• Well-constructed career stories

• Less hesitation and more confidence

• Better wording, regardless of underlying capability

The consequence is that distinguishing genuine capability from excellent preparation becomes significantly more difficult.

Expectation gaps become larger

Candidates also use AI to:

• Analyze companies

• Learn what employers want to hear

• Build convincing career narratives

The problem is that these narratives don't always reflect what candidates actually want or what motivates them.

The mismatch often becomes visible only after they start the job.

Higher mobility lowers the switching cost

AI has dramatically reduced the friction involved in changing jobs.

Candidates can now:

• Find relevant opportunities much faster

• Customize applications in minutes

• Manage multiple interview processes simultaneously

The practical outcome is simple:

The threshold for leaving a new role has become much lower.

This is particularly noticeable in fast-moving markets such as Israel.

Why does this lead to higher early turnover?

The new hiring dynamic looks something like this:

• Organizations use AI to make recruiting more efficient.

• Candidates use AI to maximize their chances of getting hired.

• But neither side is necessarily improving the quality of the match.

The result is often:

More "yes" during the hiring process.

More "this isn't the right fit" two to four months after joining.

The deeper issue

AI is becoming increasingly effective at helping people appear to be the right fit.

It is far less effective at determining whether they actually are the right fit.

That's an important distinction.

So how do we close the gap?

Rather than relying even more heavily on traditional interviews, organizations will likely need to rethink how hiring decisions are made.

That includes:

• Designing hiring processes that measure real capability rather than presentation skills.

• Relying less on polished narratives and more on demonstrated performance.

• Understanding which assessment methods still provide strong predictive value in an AI-assisted world.

• Training interviewers to distinguish between preparation and genuine capability.

• Using more work simulations, practical exercises, and job-relevant assessments.

• Investing more time in realistic expectation setting before an offer is accepted.

The hiring challenge is no longer simply identifying the best candidate.

It's identifying the candidate whose performance, motivation, and expectations genuinely match the role.

As AI becomes better at helping everyone look exceptional, the organizations that make the best hiring decisions won't necessarily be those using the most AI.

They'll be the ones designing better decision systems.