AI killed the take-home assignment. Not gradually — abruptly, somewhere around mid-2024, when the quality gap between what a strong engineer and an average engineer could produce with a good prompt and an hour of iteration collapsed to near zero. If you're still sending candidates a 72-hour coding project and evaluating the output as signal, you are measuring something. It's just not what you think.
I've helped hire engineers at companies ranging from 15-person startups to 50,000-person enterprise organisations. The hiring loops that worked reliably two years ago are producing worse results today — either by letting through candidates who can't code without a co-pilot, or (more expensively) by filtering out strong candidates who refuse to spend a weekend on an assignment they know will be assessed by people who can't tell whether the code is theirs.
Both problems are real. Both are solvable. But the solution is not a better take-home.
Why the Take-Home Broke
The take-home was never a perfect signal. It always had noise: some candidates had more time to dedicate, some had done similar projects before, some were excellent coders who simply didn't interview well under time pressure. These were known trade-offs, and teams accepted them because the take-home was logistically easy and produced legible output.
AI changed the noise-to-signal ratio decisively. The take-home now measures a composite: the candidate's underlying ability plus their skill at AI-augmented coding plus the time they chose to invest. Separating those three things from a code submission alone is, in practice, impossible.
The candidates who score highest on AI-assisted take-homes are not necessarily your strongest engineers. They are your strongest take-home completers. These are overlapping populations, but they are not the same one.
There's a second problem that's less discussed: the best engineers — the ones with options, who are in demand, who don't need to impress you — are increasingly refusing take-homes. Not because they're arrogant, but because they've done the calculation. A 72-hour assignment to filter into a process where they'll compete against AI-assisted submissions from less experienced candidates who have more time is not a good trade. They apply elsewhere, or they respond to the recruiter's message differently.
You are not just getting worse signal. You are self-selecting against the cohort you most want.
What the Replacement Loop Looks Like
The hiring loops that work now share a set of design principles: shorter sessions, higher fidelity, collaborative rather than evaluative, and structured to distinguish what a candidate thinks from what a model generates.
Here is the loop I use and recommend. The full sequence looks like this:
A short, bounded async task is still useful — but it should be genuinely completable in 30 minutes, not 72 hours. The goal is to filter for minimum baseline and enthusiasm, not to evaluate production-quality code. Good formats: a short written architecture response to a scenario, a brief code review task (review this code, identify issues), a concise technical explanation of a concept relevant to your stack. You're not trying to see polished output. You're trying to see how they think about problems in writing.
This is the core of the modern loop. Not a whiteboard session, not a timed algorithm problem — a pair programming or collaborative design session on a real (or realistic) problem. The interviewer is present throughout. The candidate can use their own environment, their own tools, their IDE plugins, their AI assistant if they want. The goal is not to catch them not using AI — it's to observe how they use whatever they have, how they reason out loud, how they respond to questions and pushback, how they make decisions under uncertainty. This is what you actually want to see. A candidate who uses Copilot intelligently and narrates their trade-off reasoning is more valuable than one who types everything manually but can't explain why they made the choices they made.
A structured, deep conversation about past work. Not "tell me about a challenge you overcame." Specific, probing questions about technical decisions the candidate made in previous roles. What was the data model? Why that approach and not the obvious alternative? What broke first when the system scaled? What would you do differently? This format is effectively AI-proof because it requires knowledge of specific, real work that only the candidate was present for. It also surfaces engineering judgment more reliably than any coding exercise.
Not a whiteboard exercise with a single correct answer. A genuine conversation about how they'd approach a system relevant to your actual domain. The interviewer brings context; the candidate brings reasoning. You're evaluating comfort with ambiguity, ability to identify the important constraints, willingness to make assumptions explicit, and quality of trade-off analysis. Strong senior engineers should be able to lead this conversation. If they can't, that's signal — not about their intelligence, but about whether they've operated at the level the role requires.
Time-to-decision is itself a signal to the candidate about how you operate. Teams that take three weeks to make an offer after a final interview are communicating something about their decision-making culture. The best candidates have multiple processes running. If you genuinely want them, close fast.
The Format Comparison, Honestly
| Dimension | Classic Take-Home | Modern Collaborative Loop |
|---|---|---|
| AI signal isolation | Very low — can't separate candidate from model | High — real-time reasoning is observable |
| Senior engineer willingness to complete | Declining sharply | High — respects their time |
| Interview process length | 1–2 weeks (waiting for submission) | 5–7 days with synchronous sessions |
| Evaluator effort | Low (async review) | Higher (live session attendance) |
| Signal quality on judgment | Low — code quality ≠ judgment | High — conversation reveals reasoning |
| Candidate experience | Increasingly negative | Positive when facilitated well |
| False positive rate | High with AI-savvy candidates | Lower — harder to fake in real time |
| Suitable for junior roles | Reasonable with short tasks | Also suitable with adjusted depth |
The tradeoff is real: collaborative loops require more interviewer time per candidate. In practice, that cost is offset by reduced false positives (expensive to onboard and then part ways with) and reduced rejection of strong candidates (even more expensive to miss).
The Specific Mistakes Teams Make When Switching
They keep the hard problem and make it live. Converting a 72-hour take-home into a 90-minute live coding session of the same problem does not fix the issue — it just creates high-pressure whiteboard theatre. The problem selection matters. Choose something genuinely collaborative, not something with a single optimal solution that the candidate either knows or doesn't.
They still evaluate output rather than process. Watching a candidate code for 90 minutes and then scoring the code they produced is not a collaborative session. It's an observed take-home. The signal comes from how they engage: do they ask clarifying questions? Do they reason out loud? Do they recognise when they're going in the wrong direction? Do they push back appropriately when you introduce constraints?
They don't calibrate for the AI-augmented workflow. Some hiring managers still believe that reaching for an AI assistant during a technical interview is evidence of weakness. It isn't. It's evidence of how engineers actually work in 2026. The question is whether the candidate can use it intelligently — understand what it's generating, catch its mistakes, know when not to use it. An engineer who blindly accepts every Copilot suggestion is a different kind of signal from one who uses it as a drafting tool and interrogates the output.
The question that reveals more than any code exercise
Ask senior candidates, near the end of a technical depth conversation: 'Tell me about a time you strongly disagreed with a technical decision that was made anyway. What happened, and what did you do?' The answer reveals — at the same time — their technical judgment, their communication style under pressure, their relationship with authority, and their ability to commit to a decision they didn't make. I've learned more from five minutes of this than from two hours of code review.
Calibrating by Level
The loop described above is most relevant for mid-senior and senior individual contributors. It needs adjustment by level:
Junior engineers (0–3 years): The collaborative session still works but the depth expectations shift. Focus on how they learn in real time, how they respond to feedback, how they ask for help. Coding exercise with immediate debrief is more useful than technical depth interview. Potential matters more than proven judgment.
Staff and principal engineers: The technical depth and system design sessions should go deeper, and the system design conversation should surface whether they can operate at organisational scope — not just technical scope. How do they think about team topology, cross-team dependencies, build-vs-buy, make-vs-manage decisions? These are not algorithm questions. They are judgment questions that require genuine experience to answer well.
Engineering managers and technical leads: Don't run a pure technical loop. Add a structured conversation about a team situation they managed — a performance issue, a reorg, a critical project under delivery pressure. The technical content matters, but the technical-plus-leadership profile is what you're actually hiring for.
What This Means for Your Employer Brand
The hiring process is a product demo. Every candidate — whether they join or not — experiences what it's like to work with your team. A respectful, well-facilitated collaborative session signals something different about your engineering culture than a 72-hour homework assignment that gets emailed back to a recruiter.
In a market where strong engineers have real options, the hiring process is part of your value proposition. Teams that treat candidates as partners in a two-way evaluation close better offers and receive fewer declines.
The take-home is not dead because AI made it easy. It's dead because the best candidates stopped accepting it. The process that replaces it needs to be faster, more collaborative, and genuinely informative — for both sides.
If you're rebuilding your engineering hiring process or struggling to close strong candidates in the current market, let's talk. I help engineering organisations redesign their talent acquisition approach alongside broader digital transformation work. Book a 30-minute discovery call and let's build something that works.