Published August 2, 2026 · Kiyansh Group
In 2026, a candidate can wear an AI assistant through your entire technical interview. It reads the screen, hears the question, and feeds them the answer in real time. LeetCode screens, take-homes, even live pair-programming — all of it can be quietly narrated by a model. The uncomfortable truth: your interview might be measuring how well someone prompts, not how well they build. We say this as a firm that directs AI agents to ship production software every day — which is exactly why we can tell you where AI makes a weak engineer look strong, and where it can't save them.
Talk to us about staffing →Algorithm puzzles are fully solvable by an AI in the candidate's ear — a clean "Hard" tells you almost nothing now. Take-homes have unverifiable authorship; you're grading an unknown blend of the candidate and a model. And "explain your code" is rehearsable, because the AI writes the explanation too.
Recall and writing code from scratch are commodities now. So test for what AI can't fake.
Judgment under ambiguity. Hand them a vague, underspecified problem and watch how they narrow it — what they ask, what they choose not to build. AI answers questions; it doesn't know which question matters.
Debugging someone else's mess. Give them a real broken repo with a subtle bug and screen-share while they hunt. It's the single most predictive exercise we know and the hardest to fake — the bug isn't a known puzzle with a known answer.
Tradeoff conversations, not right answers. "Why would you not use microservices here?" A prompted answer is confident and generic; a real one is specific to the constraints you just invented. And ask how they use AI out loud — a strong engineer has scar tissue about where it lies; a weak one treats the output as gospel.
Yes, this is more work than a 45-minute LeetCode call. That's the point — the cheap screen is exactly what AI made worthless. The teams getting burned right now kept the cheap screen and are shocked when the hire can't function without a copilot doing the thinking.
At Kiyansh Group, a senior engineer runs every screen, weighted toward the two hardest-to-fake signals: debugging a real broken codebase, and reasoning through tradeoffs on a problem we invent on the spot. We use AI to ship — and we hire people who can tell when it's wrong.
It helps far less. An AI can hand a candidate an answer to a known puzzle; it can't supply the judgment to narrow a vague problem, the instinct to find a subtle bug in unfamiliar code, or a genuine opinion about a tradeoff under constraints you invent live. Those require having actually done the work.
No — invite them, out loud. Ask the candidate to solve a task with their tools while narrating what they trust the model for and what they verify. How someone uses AI tells you more than whether they used it.
It replaces the take-home and the algorithm round, so it usually saves time overall — and it's dramatically more predictive. One 45-minute session watching someone debug real code tells you more than three puzzle rounds an AI could have solved.
If you're filling a contract engineering role and tired of candidates who interview better than they build, that's the gap we close — reach out to Kiyansh Group.
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