Efficiency vs Effectiveness: What Hiring Filters Actually Optimise For

I’ve been thinking about this for a while, and the more I look at it, the more it feels like product designers slowly handed over control of the story about what we actually do. Seriously. Not because we’re weak. Not because we don’t care. But because we adapted to the wrong thing. We tried to be “hireable.” We followed the rules. And now the rules are being written almost entirely by people who don’t actually do product design.

Write your case study like this. Make it scannable. Use these keywords. Show impact in three bullet points. Make sure you’ve worked in this exact industry, ideally at a company that looks suspiciously similar to ours. And if you haven’t? That’s a risk. Oh, and go through five to seven rounds of interviews. Really?

Now before anyone jumps, I do understand why companies build filters. Filtering sounds smart. More filters mean less noise. Recruiters have hundreds of applicants. Systems need criteria. Humans need shortcuts. On paper, it’s logical.

But here’s where I think it quietly flips.

The more specific the filters become — industry background, exact tool stacks, keyword matches, perfectly structured case studies — the smaller the matching pool becomes. That’s just logic. If you filter for five narrow criteria, the number of people who pass all five is automatically tiny. You end up with a very small group that looks aligned on paper.

Aligned to what, though?

Because what those filters actually measure is familiarity, not thinking ability. Surface similarity, not depth. You’re selecting for people who resemble what you already know, not necessarily the ones who would challenge it.

Take the industry obsession. If you’ve worked in fintech before, suddenly you’re “low risk.” If you haven’t, eyebrows go up. As if working at one fintech company means you now carry the sacred scrolls of the entire industry. But that experience is always tied to a specific product, a specific architecture, specific trade-offs, specific politics. Move to another company in the same industry and half of that context evaporates anyway. You’re still learning. You’re still adapting.

And actually, coming from the same industry doesn’t automatically make you sharper. It often just makes you comfortable. You inherit assumptions. You normalise patterns. You stop questioning things because they feel familiar. That’s not mastery. That’s inertia carried over from your previous role. Sure, industry experience can be an advantage. It’s just not the guarantee people treat it as.

Meanwhile, the designer from outside the industry might ask the “stupid” question that exposes a blind spot everyone else stopped seeing years ago.

So yes, filtering reduces noise. But if you filter aggressively on the wrong variables, you don’t just reduce noise, you reduce variance. You shrink the pool to people who look safe. And safe doesn’t always mean effective.

What actually predicts whether someone will create impact in product design isn’t whether they’ve memorised your industry’s vocabulary. It’s how they think. How fast they learn. Whether they understand systems and trade-offs. Whether they can sit in ambiguity without flinching. Whether their fundamentals travel.

Those things don’t jump off a CV in ten seconds. They don’t fit neatly into keyword searches. They’re harder to measure. Which is exactly why they get sidelined.

If hiring processes optimise for familiarity over thinking ability, companies shouldn’t be surprised when they hire people who maintain the current trajectory instead of changing it.

You can’t say you want innovation and then filter for pattern-matched comfort. If your filters are built around looking safe, don’t be shocked when your product stays safe.

Maybe I’m wrong. But from where I’m standing, it feels like we’ve confused efficiency in hiring with effectiveness in outcomes.

And that confusion has consequences.