I've always been curious about one thing: now that everyone is doing AI Coding, what have interviews become like? Do they still ask those outdated interview questions? Let me imagine an interview process. For example, one day Xiao Ming goes to interview for an Android engineer position.

Interviewer: Describe the View drawing process.

Xiao Ming: How low-level can you get? In the AI era, who still cares about that?

Interviewer: Alright then, tell me about any technical highlights in the projects you've worked on.

Xiao Ming: AI wrote it all, I don't know what the highlights are.

Interviewer: Have you solved any particularly memorable problems?

Xiao Ming: ...I don't really remember. I recall one problem consumed a ton of my tokens, hehe~

Interviewer: Seems like you use AI quite a lot. Take a look at this AI-generated code and tell me what's wrong with it and why.

Xiao Ming: ...It doesn't seem to have any problems. I usually just ask AI to find errors in code for me.

Interviewer: You use AI for your daily work. How do you ensure the code AI writes is production-ready? How do you locate issues when something goes wrong?

Xiao Ming: I have AI scan the code again. If there are no problems, it can go live.

Interviewer: Alright, you can go now.

In this interview, all the "AI efficiency" Xiao Ming accumulated in his mind completely failed. He could no longer let AI solve problems for him because interviews don't allow AI; the interviewer wants to test Xiao Ming himself. I'm sure there are many people like Xiao Ming out there. This raises a question: If you're AI Coding every day and you actually go out for an interview, what exactly is your competitive edge?

What Are Interviews Really Testing in the AI Era?

Many people's first reaction is "testing AI skills," right? Although work is now basically inseparable from AI, in the interview setting, AI capability is precisely the last thing they test. Why?

• Everyone can use AI. Interviewers can't differentiate candidates by asking "Do you know how to use AI?" It's a pointless question.
• "Knowing how to use AI" can't be verified in an interview. You can't have someone craft a prompt on the spot; that wouldn't measure anything.

So, in the AI era, interviews never test "whether you can use AI," but rather the following things—things AI can't replace, can't be written on a resume, and can only be tested live.

Judgment: When AI gives you something, do you dare say "that's wrong"?

AI will give you an answer that "looks reasonable," but it's not necessarily correct. What the interviewer is testing is whether you can identify what's wrong, why it's wrong, and what the correct answer should be. This requires you to truly understand, not just "know how to use."

Ownership: Do you take responsibility for the code you write?

AI-generated code is ultimately submitted by you. The interviewer will ask: "This code has a bug. As the person in charge, how do you locate it? How do you troubleshoot?" Embedded in this question is whether you are willing and able to take responsibility for AI's output.

Review Capability: Can you optimize AI's "almost there" into "production-ready"?

In the 2025 Stack Overflow survey, 66% of developers were tormented by the problem of "AI answers being almost right, but just missing that little bit." What the interviewer is testing is precisely your ability to solve that "little bit" and make it production-ready. This requires depth, not AI.

Engineering Judgment: Trade-offs in solutions, performance considerations, architectural boundaries

AI can give you a solution that "runs," but what the interviewer wants is "why this solution is better than that one." Data volume, performance, maintainability, edge cases—these are things AI cannot provide or replace.

A Brief Summary

In today's AI era, what interviews really test is "after AI helps you get things done, what can you still do?" This "remainder" is your true competitive edge.

Competitive Edge Has Never Been About "Knowing How to Use AI"

So, what is the real competitive edge? Summarizing the four points above into three, these are the truly valuable and AI-irreplaceable competitive edges in the AI-era interview.

Depth: To what degree do you "understand"?

This is the core. AI can help you write Kotlin, but do you understand Kotlin's coroutine scheduling, Compose's recomposition mechanism, and the underlying source code implementation? AI cannot give you this. Depth determines whether you can judge if AI's output is correct, and it also determines whether the interviewer trusts you.

Judgment: Can you handle the fact that "AI is unreliable"?

AI makes mistakes, fabricates, and does things that are "almost right but just miss the mark." Your ability to judge that it's wrong and correct it to a production-ready state is your competitive edge. The fact that 46% of developers distrust it and 66% feel frustrated precisely shows how scarce this ability is, because most people don't have it.

Engineering Acumen: You are a "person in charge," not a "generator"

The projects you've been responsible for that went live, the pitfalls you've encountered, your architectural decisions, your performance optimization plans—these things that "you truly took responsibility for and experienced" are things AI cannot generate for you. They are the hard standards that convince an interviewer.

How to "Prove" Your Competitive Edge in an Interview

This is the most practical part. Two job seekers can both write "proficient in AI Coding" on their resumes, but their interview performances are worlds apart. The difference lies in:

• A treats "knowing how to use AI" as a label, writes it on the resume, but can't answer in the interview.
• B treats "knowing how to use AI" as a process, uses AI to work, but proves they have judgment.

How does B do it? In the interview, they show behavioral evidence instead of stacking labels. Specifically, these tactics:

Proactively Show "How You Use AI," Not "That You Used AI"

When the interviewer asks, "Do you use AI daily?" don't just answer "Yes." Answer:

I use AI for code completion and refactoring, but I don't let it directly decide the architecture. For example, last time I was working on a requirement, AI gave a solution. After reviewing it, I found it had performance risks and switched to a different solution.

They are testing "how you use it," not "whether you used it." Your judgment process is the evidence.

Proactively Show "How You Review AI Code"

This is the most direct way to score points. When the interviewer mentions AI code, proactively say:

I review AI-generated code in three passes: can it run, will it bury hidden problems, does it comply with business constraints. For instance, a common mistake it makes is ignoring lifecycles and memory leaks; I focus on those.

You've demonstrated the ability to "manage AI's unreliability," which is exactly what the 66% mentioned above lack.

Proactively Show "How You Take Responsibility"

When the interviewer asks, "How do you locate a bug?" don't just say "I use AI to check." Show your troubleshooting methodology:

AI can help me narrow down the scope, but locating the issue still relies on me. I check logs, look at call chains, reproduce, and use binary search, because the troubleshooting direction AI gives is often wrong.

You've proven "ownership": you are responsible, not the AI.

Proactively Show "Depth" Over "AI"

When you encounter algorithm questions, system design, or source code deep dives in an interview—parts AI can't replace—don't panic. This is precisely your opportunity, because this is your moment to prove "I understand." If the time you saved daily using AI was invested into this depth, you can answer now; if it wasn't, this is where you'll be exposed.

Another Summary

What the interviewer wants is not the label "you can use AI," but the behavioral evidence that you can take responsibility for your AI output. Anyone can write a label; only someone who truly understands can produce the behavior.

Finally

So, this entire article has been talking about one thing: right now, what exactly is your competitive edge?

• If you used the time saved by AI to cultivate judgment, depth, and engineering acumen, then your competitive edge is very strong.
• If you used the time saved by AI only to "paste code faster," your competitive edge is very weak, because you still don't understand what you didn't understand before, and others will see through you in one interview.

The AI era is both fair and cruel. Going to an interview is not like doing a work task; the interviewer won't let you ask AI. At that moment, it's precisely the test of whether you usually use AI to train yourself. AI Coding every day is not the problem. The problem is whether you've used AI to make yourself better than others, and whether the tokens you spend daily are exchanged for anything more than just those few lines of code.