AI Writes the CV. AI Reads the CV. Who Sees the Person?

By Gavin Traylor, Founder of Hike

AI is writing the CVs. AI is reading them. I keep wondering how much of the actual person makes it through.

After more than twenty years in headhunting, I’ve read plenty of CVs that told me almost nothing about the human behind them. A conversation could change that completely. Someone would explain a difficult decision, a relationship they had repaired or a problem they had worked through, and suddenly their experience became much more interesting than the document suggested.

That gap has always existed. But hiring managers I speak with today describe another frustration: applications that are beautifully written, closely matched to the role and remarkably difficult to distinguish from one another. The language is polished, yet they still struggle to understand what each person actually brings.

The growing pile of polished applications

In June 2025, The New York Times reported that LinkedIn was receiving around 11,000 applications a minute, an increase of 45% over the previous year. Recruiters interviewed for the article described applications with increasingly similar wording, making it harder to distinguish between candidates.

Those numbers give a sense of the pressure behind the hiring managers’ frustration. More applications create more material to assess, while familiar language can make that material less useful. A pile of apparently relevant CVs still leaves an employer with the job of understanding whose experience fits the work.

I completely understand why candidates turn to AI. If you’ve spent weeks applying and heard little back, using a tool to explain your experience is a sensible response. Writing about yourself is difficult, especially when you are trying to translate years of work into a few pages. Knowing how to write a brilliant CV shouldn’t be a prerequisite for getting a job.

AI can help someone organise their thoughts, make a sentence clearer or recognise experience they have struggled to articulate. The difficulty comes when tailoring an application removes the details that would help an employer understand the person. Different career journeys can end up expressed through the same familiar phrases: strategic leadership, stakeholder management, cross-functional collaboration.

An application becomes easier to read, while the experience behind it remains hard to see.

AI judging what AI has written

Employers facing that volume have understandable reasons to use AI too. Reviewing applications takes time, and technology can help organise information and identify points worth exploring. But if the material going into the process says little about each person’s contribution, faster processing leaves that underlying gap unresolved.

This is the loop that concerns me: software helps candidates produce increasingly similar descriptions of themselves, and more software helps employers compare those descriptions.

A close match between a CV and a job description can be a useful starting point. It leaves plenty of questions unanswered. How much of the work did this person own? What made it difficult? What judgement did they exercise? What would they need to learn in this particular role?

The quality of the information underneath the wording matters. An assessment can sound precise while relying on an incomplete account of someone’s experience.

Trust and experience need different questions

There is also a serious issue of trust. In July 2025, Gartner predicted that by 2028, one in four candidate profiles worldwide would be fake. The forecast adds urgency to the question of whose information an employer can trust.

Technology is responding. In June 2026, Zoom and BrightHire announced fraud detection within live interviews, including deepfake detection, initially available through early access. Checking that someone is who they claim to be is an important part of hiring.

Genuine candidates using AI to explain genuine experience deserve a fair hearing. Familiar phrasing alone cannot establish whether someone is honest or capable. Employers need to examine the claims and understand the work behind them.

Identity checks and an understanding of someone’s contribution answer different questions. Both matter, and a hiring process needs room for both.

What three words can leave out

Consider the phrase “managed key accounts”. Imagine one candidate looking after established customer relationships and another stepping into an account where the customer was preparing to leave. Both might reasonably use those three words. The circumstances, decisions and work involved could be quite different.

For the second candidate, the useful conversation might explore why the relationship had deteriorated, what they could influence and how they decided where to begin. Did they listen to a frustrated customer, negotiate changes internally or reset expectations that the business could no longer meet? What happened afterwards, and how much of that outcome can reasonably be attributed to their actions?

For the first candidate, there may be equally valuable experience in spotting problems early, maintaining trust through change or developing an account over several years. The point is to understand what each person’s description contains.

Those details give an employer something concrete to explore. They also help the candidate recognise and explain their own contribution. Experience that felt routine at the time can become much more meaningful when someone asks what it took.

Seeing where experience could lead next

That understanding matters particularly when someone is changing roles or industries. A job title gives us a label for their previous work. The decisions they made and the problems they handled help us explore where that experience could be useful next.

Someone who rebuilt a customer relationship may have experience relevant to a role involving trust, negotiation or competing expectations in another sector. The new role will also have its own demands. A clearer account makes it easier to identify both the transferable experience and the gaps worth discussing.

That is the thinking behind Hike. We are building around questions that help people explain the experience a polished CV can leave out, and help employers understand why it matters to the work they need done.

Over time, I want that understanding to grow with the person: what they have done, what they have learnt and how their experience develops. It could give their next career decision a more useful starting point than rewriting the same few pages again.

AI has a valuable role here. It can help us ask thoughtful follow-up questions, organise specific examples and make relevant details easier to find. The opportunity is to give it a richer account of someone’s working life to work with.

A polished application can introduce a person. Hiring still requires us to understand them.

If you’re hiring today, what helps you see the person when the applications all sound alike?