In 2026, the average software engineer uses an AI coding assistant for 73% of their working hours. The average technical interview doesn’t include one.
What engineers actually do now
This is worth being specific about, because the shift has been fast.
A mid-level backend engineer today spends their day reading and debugging AI-generated code, reviewing pull requests that were drafted with Copilot or Cursor, writing prompts that generate architecture scaffolding, and integrating LLM-powered features into production systems. They’re evaluating output quality, catching hallucinations in code suggestions, and making judgment calls about when to trust AI output and when to rewrite it.
This is the job. It has been for about 18 months now.
The technical interview hasn’t caught up. Most teams are still running some variation of the same 2018 playbook: reverse a linked list, find the longest palindrome, implement a binary search tree. Occasionally a system design question that was written before LLM-integrated architectures existed.
Why the mismatch persists
It’s not that engineering managers don’t know the job has changed. Most will tell you exactly what they’re actually hiring for.
The problem is structural:
Question banks age out slowly. Most companies pull from existing interview kits that haven’t been meaningfully updated. Revising them requires buy-in from senior engineers who are already stretched thin.
Algorithmic questions feel “objective.” There’s a right answer to “implement a merge sort.” The rubric is clear. Assessing whether someone can effectively debug AI-generated code, evaluate a model’s output, or make a sound architectural call with LLM constraints requires a more nuanced scoring framework.
Cheating anxiety drove the wrong solution. A lot of teams responded to AI cheating concerns by banning AI tools entirely from assessments. That’s solving for the wrong problem — it creates a test environment that looks nothing like the actual job.
What a 2026-aligned technical interview looks like
HackerRank has been publishing research on this shift, and their argument is hard to argue with: debugging is the most important AI-age skill to assess. Not because it wasn’t important before, but because it’s now the dominant failure mode. Engineers aren’t writing broken code from scratch — they’re shipping AI-generated code they didn’t fully understand.
Some frameworks that leading teams are moving toward:
AI-permitted assessments with evaluation criteria for how candidates use AI. The question becomes: does this person use AI tools intelligently? Can they verify output, catch errors, and make good decisions about when to apply a suggestion vs. override it?
Production scenario questions. Instead of “implement X from scratch,” something closer to “here’s a PR from our codebase — what concerns do you have, what questions would you ask, what would you change?” This is closer to day-one work.
Debugging and code review tasks. Give candidates intentionally flawed code (including AI-generated code with subtle bugs) and evaluate how they find and articulate the problems.
System design with LLM integration constraints. “We’re adding an AI-powered feature. Walk me through the architecture decisions and the tradeoffs.” This surfaces the candidates who actually understand AI systems, not just ones who can talk about them.
The candidate experience problem
There’s a second-order effect that doesn’t get discussed enough: candidates know when the interview doesn’t match the job.
Strong engineers — the ones you’re competing for — have options. When they spend three hours on a LeetCode-style assessment for a role where the actual work is building LLM pipelines, they notice the mismatch. Some will drop out. The ones who don’t are increasingly the ones who practiced for the interview game, not the ones who are best at the actual work.
The interview is a signal in both directions. How you assess candidates says something about how the team thinks and what they value. That matters for offer acceptance rates — which Ashby’s data consistently shows are a leading indicator of hiring quality.
The practical update
You don’t need to overhaul your entire interview process at once. The highest-leverage change is usually upstream: audit what your current technical questions actually predict. Are they correlated with performance in your specific roles? If you don’t know, that’s the starting point.
The second step is introducing at least one scenario-based question that reflects actual 2026 engineering work — code review, debugging AI output, architectural judgment with AI tools in the picture. Run it in parallel with your existing process. See what new signal you get.
Most teams find that the conversation changes significantly. Candidates who performed well on algorithmic questions don’t always stand out on production scenarios, and vice versa. Both data points are worth having.
Casuro runs structured AI interviews that are calibrated to the actual role — so every candidate gets evaluated on the behaviors and judgment that predict performance, not just the ones that have a neat correct answer. Learn more →
