AI didn't kill the take-home interview. It revealed what we were actually measuring.
Engineering hiring has been forced into a reckoning. The formats that survive are closer to actual engineering work.
The take-home coding assignment is a fixture of engineering hiring. A real-world problem, a time estimate of three to five hours, a private Git repo, and a rubric the candidate never sees. The goal, as stated, is to assess whether someone can write working code. That goal is no longer being assessed.
A 3-hour assignment, done in eight minutes
Data from evaluating more than 50,000 candidates across interview platforms shows AI-assisted completion of take-home assignments doubled from roughly 15% in June 2025 to 35% by December 2025. The trajectory is continuing upward.
The code that arrives now is often cleaner than the candidate's on-site performance. The README is more thorough than anything they write once hired. The architecture section reads like a blog post, not working notes. Interviewers are noticing the gap between the submitted project and how the candidate talks about it when asked to extend or defend it.
The instinct is to treat this as a cheating problem. The more useful frame: if a take-home can be completed in eight minutes with tools every engineer on your team uses daily, the assignment stopped measuring what you think it measured.
What the take-home was actually measuring
The stated purpose is something like: can this person write code that solves a well-defined problem? That sounds reasonable. But the operational reality is different.
A take-home measures whether a candidate can produce polished code, asynchronously, unsupervised, with unlimited time, no constraints on tools, no ambiguity in requirements, no deadline pressure, and no existing codebase to integrate with. That is not what most engineering jobs look like. It describes a very specific kind of task that most engineers do rarely.
The majority of engineering work is: debugging code written by someone who has left, navigating architectural tradeoffs with real constraints, making system decisions with incomplete information, reading code far more than writing it, and explaining reasoning to non-engineers under time pressure. A take-home is a canvas assignment for a job that is mostly renovation.
| What a take-home measures | What the job actually requires |
|---|---|
| Writing new code from a blank slate | Extending and debugging existing code |
| Solo, asynchronous work with no interruption | Collaboration, context-switching, real-time decisions |
| Clean output with unlimited time | Reasonable decisions under deadline pressure |
| A self-contained problem with clear spec | Ambiguous requirements, competing constraints |
| Written presentation and documentation quality | Communication in code review and standups |
Three failure modes that were already there
Before AI made the problem obvious, the take-home had structural problems worth naming. These predate AI coding tools.
The senior candidate refusal rate. Strong engineers with jobs do not do four-hour take-homes. This is consistent enough to be a reliable signal in the wrong direction. Sending a take-home to senior candidates pre-filters for people who are earlier in their career, currently between roles, or specifically motivated by your company. Competence and willingness to do a take-home do not correlate the way most hiring managers assume.
The polished-presentation bias. A candidate who builds a correct but sparsely documented solution scores below a candidate who builds the same solution with a polished README and inline comments on obvious things. These skills correlate with some job performance. But they correlate more with how much time the candidate had and how comfortable they are with performative polish. One of these things is more useful to measure than the other.
The context collapse. No existing codebase. No constraints from other systems. No product manager with a deadline. No other engineers to coordinate with. The artificial cleanness of a take-home strips out most of the context that real engineering decisions require. What looks like good architecture in isolation may be inappropriate for the actual system. You cannot assess fit from a blank-canvas exercise.
What survives AI coding tools
Three formats are gaining adoption among companies that have directly confronted this problem.
Live coding with AI permitted. When you allow candidates to use Cursor, Claude, or Copilot during a live session, what you are evaluating shifts from whether the code is correct to how the candidate directs the tool. Do they know when to trust the output? Can they spot errors? Can they extend the solution when asked? Can they explain the tradeoffs in the approach they chose? These questions survive AI assistance precisely because AI does the mechanical work. What remains visible is judgment, not output.
Project deep-dive with no prep time. Ask a candidate to walk you through a system they built, then dig technically for 45 to 60 minutes. What happens when this queue backs up? How would you handle it if the upstream service started returning 5xx? What did you try that did not work, and why? This format cannot be AI-assisted in the moment because it is about the candidate's actual experience. There is no way to generate authentic engineering decisions about a system you did not build.
Brief async plus live defence. If you still want an asynchronous component — useful for roles where written clarity matters — cap it at 90 minutes and immediately follow with a 30-minute live session where the candidate explains every decision. The async component is not the assessment; the defence is. If someone used AI heavily in the async portion, the live defence surfaces it quickly. And if someone used AI thoughtfully, that is worth knowing too.
“The project deep-dive reveals more about technical depth in 60 minutes than any take-home does. You cannot fake experience with a system you did not build.”
What to actually measure
Being forced to redesign the interview is useful because it requires naming what you are trying to assess. The honest list, for most engineering roles:
- How they approach ambiguous problems when the spec is incomplete
- Whether their mental model of systems matches the complexity in your codebase
- How they handle being wrong or corrected mid-session
- Whether their instincts about tradeoffs are calibrated, not just whether they can name the patterns
- How they communicate under mild pressure, with incomplete information
None of these are well-measured by a take-home. All of them are measurable in a structured live session, if the interviewer is trained to probe for them rather than evaluate output quality.
Where this leaves hiring in 2026
The practical problem is that live interviews do not scale as easily as take-homes. They require trained interviewers, allocated senior time, and structured rubrics that most companies do not have. A take-home could be reviewed asynchronously by one person on a Tuesday afternoon. A live system design session requires two people, advance preparation, and a rubric that does not vary between interviewers.
That is a real cost. But it is worth naming what the take-home was costing: senior candidates who declined, good engineers filtered out by written presentation skills, and a false sense of signal from a format that was measuring the wrong thing cleanly. Clean signal on a proxy is still a proxy.
The companies currently building better processes are those that stopped treating the interview as a test to be passed and started treating it as a two-way signal. A candidate who goes through a well-run live interview comes away with a better read on the team and the codebase than any take-home ever provided. That is not incidental. It is part of how you close candidates who have options.
AI making the take-home trivially completable has pushed engineering hiring toward formats that are closer to what the job actually looks like. That is an accidental improvement worth keeping.
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