Is the Technical Interview Dying? No, But It's Changing Fast

I have been on both sides of the technical interview for over years. As a candidate, as an interviewer, and sometimes both in the same month. The format never changed much: a few algorithm questions, maybe a system design, and then "do you have any questions for us?"

Looking at that same format today, it feels outdated. The job changed. The interview did not.

The problem with the current system

The traditional technical interview is built on one assumption: if a candidate can write a solution from scratch, alone, under time pressure, they can do the job.

That made sense ten years ago. It does not make much sense now.

AI tools are used in real production environments every day. Engineers open Cursor or Copilot before they open the documentation. But in interviews, candidates are still expected to code alone, without any tools, on a blank screen.

A survey of 400 engineering leaders found that 71% say AI is making it harder to assess technical skills. Two years ago, that number was around 20-30%. Something is shifting, and it is shifting fast.

Take-home projects and automated code tests are losing their value too. A candidate can complete a take-home assignment with ChatGPT. A code test can be solved with an AI assistant in minutes. When you only look at the output, you cannot tell the difference.

So what should we actually measure?

The real question is not whether an engineer can use AI. It is how they use it.

Writing a prompt is one thing. Reading the AI's output and saying "this is wrong" is another. Debugging a bad suggestion, spotting a security issue, asking "why did you structure it this way?" are all different skills. These are the things that matter in production.

This also explains why AI is widening the gap between strong and weaker engineers, not closing it. AI increases productivity by an average of 34%, but not equally for everyone. Strong engineers get disproportionately more value from these tools. That is why 73% of engineering leaders say a strong engineer is worth at least 3x their total compensation.

The questions that matter in an interview are shifting:

• How does this candidate debug a system when something goes wrong?
• Can they read AI-generated code with a critical eye?
• How do they make decisions when there is no clear right answer?

These are not things you can answer from memory. They come from experience and judgment.

What big tech companies are doing

At companies like Google and Meta, the interview format is moving away from LeetCode-style problems. The focus is shifting toward system behavior and engineering judgment.

One story that stuck with me: a candidate expected a dynamic programming problem. Instead, they were given a log output from a failing service. Their job was to find a retry storm caused by a stale cache entry. The candidate started writing a BFS template. A complete mismatch.

That is not just one bad interview. It reflects a real change in what companies want to see. Instead of "do you know this?" the question is becoming "how do you think when things go wrong?"

Some companies are going further. Canva, Shopify, Meta, and Rippling now allow candidates to use AI during technical interviews. In New York, about 25% of companies already allow this, and that number is expected to grow.

The process itself is getting longer

There is also a practical problem that is easy to overlook.

The average software engineer now goes through 4.2 interview stages before getting an offer. In 2021, that number was 3.1. The process is getting longer, candidates get impatient, and the faster offer wins.

This is a loss for companies too. A slow and heavy hiring process pushes good candidates away before it is even finished.

What this means for me

Most of the time I am on the interviewer side. But I have been a candidate recently too.

If the real job is no longer about coding alone without tools, the interview should not be either. Seeing how someone understands a system, questions an AI suggestion, or makes a decision under uncertainty is more useful than watching them recall an algorithm.

The hard part is that this kind of interview takes more preparation. There is no single correct answer. Evaluation becomes more subjective. That is uncomfortable, but it is not a reason to avoid it.

The technical interview is not dying. But it needs to grow up.

Sources

• Karat, Engineering Interview Trends for 2026, karat.com (January 2026)
• IEEE-USA InSight, Three Ways AI is Reshaping Traditional Technical Interviews in 2026 (April 2026)
• Pragmatic Engineer, The Reality of Tech Interviews in 2025 (April 2025)
• Fahim ul Haq, FAANG is Changing: How Big Tech Interviews Are Evolving in 2026, Medium (January 2026)
• SwiftCruit, How AI Will Redefine Technical Interviews in 2026 and Beyond, Medium (December 2025)