Interviewing in the Age of AI

One thought I’ve been wanting to talk about for a while is how hiring should change now that AI is changing everything around us—including how people apply and how companies hire.

A Quick Look Back

A few years back, things were simpler. There was a quick screening call, maybe an onsite interview, and that was enough to decide if the person is a good fit or not. Then companies started using online tools to make the process easier: assessments, take-home tasks, Zoom interviews, and systems to auto-filter CVs (ATS). It made scheduling and logistics easier, and although there were some concerns about cheating, it was still manageable. Tools could catch it, and honestly, cheating wasn’t as common or as easy as it is now.

The AI-Driven Landscape

Today, we need to look again at every step in this process. Most CVs now are optimized using AI just to pass the ATS and make the candidate seem like a perfect fit—even if they’re not. The tasks that used to take a day or two can be done in a few minutes. The online assessments can be solved instantly with AI. All of this makes the modern hiring process harder than ever. Personally, I’ve seen cases where I was sure the person was cheating, and suddenly I’m in a full Detective Conan episode trying to prove it.

At the same time, HR teams and hiring managers are depending more and more on AI because it saves time. And it does help in some ways—it can give feedback on a candidate’s fit, suggest improvements, and even guide people on what they should learn. But the downsides are becoming a real issue.

The Paradox of AI in Hiring

For example, AI-generated CVs often include exaggerated or fake qualifications. I’m pretty sure many people just generate them, don’t even read the final version, and then submit directly. That makes ranking or evaluating real candidates harder than it should be. On top of that, the new online assessment tools track behavior and movement to detect AI usage or cheating, but sometimes they just make people more anxious. I remember before, I used to pause for a couple of minutes just to think about the best way to approach a question. Now, some systems flag that as suspicious behavior—like maybe I’m using a second device. It’s frustrating.

Online interviews aren’t better. Instead of focusing on the actual content, I often find myself trying to figure out if someone is using AI behind the scenes. It’s no longer just a technical interview—it’s like a game of mental chess.

As for take-home tasks, if you say it’s okay to use AI, then fine—it’s fair for everyone. But if you clearly say “don’t use AI,” then you’re basically punishing the honest people. The ones who play fair (and these are usually the kind of people you want to hire) end up at a disadvantage. So ironically, you end up filtering out the candidates with integrity.

Are We Hiring the Right People?

I don’t have exact data on how much AI affects hiring results, but I liked this post that shows how weird the current state is for both candidates and companies:
https://www.linkedin.com/posts/nasserjr_recruitment-process-activity-7267260107128778753-_mnF

And another meme from the same person that really hits home:
https://www.linkedin.com/posts/nasserjr_well-thanks-activity-7292851244702834689-QW0M

The real problem is that while AI saves the company time, we need to ask: Are we actually hiring the right people? And even if we find a great match, what’s the guarantee they’ll even accept the offer?

Final Thoughts

I’m not against using AI in hiring, but we need to adapt. We can’t keep applying old-school interview methods in a world where AI is involved on both sides. The system needs to evolve, or we’ll keep making the wrong calls, for both candidates and companies.

Off-Topic in Software Engineering — Does It Really Not Matter?

Although I’ll focus mainly on software engineers in this post, what I’m saying applies just as much to data scientists, data engineers, QA, DevOps, and honestly anyone working in tech.

While talking with fresh or junior engineers, I’ve noticed something: many topics I encountered during my learning journey are now often seen as “unnecessary for work.” Things like how the internet works, how data flows through systems, or how computers handle memory — they’re often brushed off as irrelevant or “too low-level.”

Perhaps you don’t need them in your daily tasks for now. But that doesn't mean they won’t be useful later.

For example, I’ve had conversations where I mention (in very abstract terms) how browsers work, only to realize the other person doesn't know how data even travels over the internet. Or when I bring up data storage or database internals and hear something like: "I’m a full-stack developer" or "I'm doing ML models — this is outside my scope."

But the truth is: understanding these things — even at a high level — makes you better at what you do. Whether you're debugging, optimizing, scaling, or building something new, having that foundational context helps.

You don’t need to dive deep into every topic. But knowing just enough about what's behind the scenes helps you make smarter decisions. A QA engineer who understands backend behavior can write better test strategies. A data scientist who knows how pipelines are built can spot issues earlier. A software engineer who understands how memory works will write more efficient code.

Think of it like this: doctors study the entire human body before they specialize, not because they’ll use all of it every day, but because it helps them see the full picture. The same goes here.

So if you're early in your career — don’t dismiss things as “off-topic” too quickly. The things you skip today might be the exact things you need a few years down the line. Knowledge compounds — and it always pays off.