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Are we hiring developers who can build, or developers who can prompt?

Are we hiring developers who can build, or developers who can prompt?

Leader ●4 ●8 ●24
calendar_today • schedule1 min read

I recently interviewed some fresh graduates and noticed an interesting pattern.

The projects on their resumes looked impressive:

  • Beautiful UI
  • Modern tech stacks
  • AI-powered features
  • Clean GitHub repositories

At first glance, they looked like strong candidates.

But when we started discussing their projects:

  • Why did you choose this architecture?
  • How does this API work?
  • What challenges did you face?
  • How would you scale this?
  • What happens if this component fails?

Many struggled to explain their own work.

This made me think:

AI has lowered the barrier to building software, which is great.

But are we changing how we evaluate developers?

Maybe a project demo is no longer enough.

The real evaluation should focus on:

  • Understanding of fundamentals
  • Problem-solving approach
  • Ability to explain decisions
  • Debugging skills
  • Ownership of the work

AI will help developers build faster, but understanding what they are building and why still matters.

Curious to hear from hiring managers and developers:

How are you evaluating fresh graduates in the AI era?

5 Comments

•

I think one of the most fascinating problems currently is that people have slowed their hiring of juniors/grads entirely. This will, in the end, turn out to be a mistake.

1 vote
•

@[Steve Fenton] I agree. Reducing junior hiring altogether would be a short-sighted decision. Every experienced engineer started as a fresher, and companies need to keep investing in that pipeline.

1
•

This is the core problem with display-type profiles. A GitHub repo shows what someone built, but not how they think, how they handle failure, or whether they can explain a tradeoff under pressure. Those signals used to survive into interviews. Now AI-generated portfolios make the surface indistinguishable. I built Opportunity Skill around a different premise. Let the agent capture how someone actually works, their debugging instincts, their collaboration boundaries, their quality persistence, as semantic impressions. Then matching happens on the layer that still means something.

1 vote
•

@[QuestMeet] Well said. AI has made it easier to showcase outcomes, but much harder to assess the thinking behind them. I believe the future of hiring will be less about what someone built and more about how they approached building it. That's where the real signal lies.

0
•

The interview problem you describe is the three-layer signal loss in hiring made visible. Layer one, the resume compresses a person into project titles and tech stack keywords. Layer two, the platform algorithm cannot distinguish someone who architected a system from someone who prompted an AI to generate one. Layer three, the interviewer spends an hour trying to recover signal that was already lost at layer one. The questions you ask, 'why this architecture, what broke, how would you scale it', are attempts to reconstruct what a display-type profile never captured. This is exactly the problem the Human Card concept in Opportunity Skill addresses. Instead of a static resume that lists what someone built, the agent distils impressions of how someone thinks, their debugging methodology, their ownership patterns, their collaboration boundaries. A hiring manager's agent can then search for 'a developer who explains architectural trade-offs and has debugged production race conditions' and match against those impressions directly. The interview stops being signal recovery and becomes signal verification. Steve Fenton's comment about the junior hiring freeze being a mistake is well taken too.

1 vote
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Chitrajan Dhiman
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India • qality.dev
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I am the Co-founder of QAlity, a no-code test automation platform built to help QA teams create, exe... Show more

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