AI can write a résumé in seconds. It can turn a job description into polished bullet points, find keywords, fix awkward sentences, and make almost anyone sound like a “results-driven strategic thinker.”
That’s exactly the problem.
When everyone has access to the same tools, polished language stops being a differentiator. Specific experience does. A good résumé isn’t supposed to sound impressive — it’s supposed to make a recruiter think: this person actually did this.
Recruiters are noticing
AI-generated résumés often become generic, keyword-heavy, and light on specific details. After reading thousands of résumés, recruiters are notcing that AI-written resumes tend to have similar formatting and writing styles.
The issue isn’t that recruiters hate AI. It’s that they hate résumés that don’t tell them anything about the person behind them.
The problem isn’t AI — it’s generic writing
AI tools asked to “tailor my résumé for a research scientist position” tend to produce things like:
- Conducted innovative research
- Leveraged advanced analytical techniques
- Delivered data-driven insights
None of these are necessarily false. They’re just not useful.
Compare that with:
Built a transformer-based model for low-resource language glossing, evaluated it against baseline approaches, and analysed failure cases across multiple datasets.
Now there’s something to talk about. A recruiter can ask what model you used. An interviewer can ask what went wrong. The specificity creates a path into the actual work — which is exactly what a good résumé should do.
Matching keywords isn’t the same as getting hired
AI-generated résumés are good at matching job description language, which matters for automated screening. But candidates who use AI to write their résumé can appear to have exactly the right skills on paper, only for their claims to fall apart in the interview.
The résumé got the keywords right. The candidate didn’t have the story to back them up. That’s a much bigger problem.
The human parts are the valuable parts
Think about what makes your experience different from someone else’s. It probably isn’t that you know Python. It might be that you used Python to solve a particular problem. It might be that you tried three approaches and discovered why two of them failed.
Those details are difficult for AI to invent because they’re supposed to come from you. They’re also the details that make a recruiter remember you.
A résumé doesn’t need more personality. It needs more evidence of a real person having done real things.
So should you stop using AI?
No. AI is useful when you treat it like an editor rather than a ghostwriter.
Use it to catch awkward sentences, find repetition, and identify which experience is relevant. Then put the specifics back in. Your starting material should be your experience — not the job description.
AI can help you say it better. It shouldn’t decide what happened.
This is where Takeoff fits
A résumé is deliberately compressed. That’s useful when someone has hundreds of applications to review. But compression has a cost — “developed machine learning model” leaves a lot out.
With Takeoff, that project becomes a page of its own. You can explain the problem, show the work, describe the decisions you made, and link to code or research. Your résumé gets them interested. Your portfolio gives them evidence.
Don’t let AI make your résumé sound like everyone else’s. Use it to polish your words — keep the experience, evidence, and story human.
Build the part that AI can’t write with Takeoff.