The tell showed up in the scheduling emails first. Around the start of 2026, the line “your final round will be onsite at our office” started reappearing in loops that had run fully remote since 2020. Google brought it back for some roles. So did Cisco and McKinsey. Gartner put a number on the pattern: in a recent survey of recruiting leaders, roughly 72% said they were now running at least some interviews in person, and they named fraud as the reason.
This isn’t nostalgia for the whiteboard. Remote technical screening stopped being trustworthy, and the people who run hiring know it.
The number that spooked hiring teams
Fabric analyzed just under 20,000 AI-conducted interviews between July 2025 and January 2026 and flagged 38.5% of candidates for cheating behavior. Filter to software engineering and the rate jumped to 48%. Sales candidates sat at 12%, which tells you something about who has both the tooling and the motive. CodeSignal, looking at its proctored assessments, watched cheating and fraud attempts climb from 16% in 2024 to 35% in 2025. That’s not drift. That’s a doubling in twelve months.
Most engineers running loops felt it before the reports landed. A candidate aces the take-home and the remote screen, then freezes the second a follow-up goes off-script. Answers arrive a beat too smoothly, phrased a beat too completely. Code compiles on the first try and then can’t be explained line by line. Once you’ve seen it a few times, the pattern is hard to unsee.
How remote screens got gamed
The tooling got good and cheap fast. A 21-year-old built an interview overlay that reads the shared screen, sends the question to a model, and prints the answer in a window the interviewer’s screen-share can’t capture. He got suspended from Columbia over it and turned the thing into a company. That was early 2025. By 2026 there were a dozen imitators, several marketed openly as undetectable.
The mechanics vary. A transparent overlay pinned to a second monitor. An earpiece fed by a friend or by a model listening to the call audio. A deepfake avatar for the truly brazen, where the face on camera belongs to someone who won’t be showing up on the first day. The common thread is that a video call gives the interviewer almost no ground truth about what’s happening two feet outside the frame.
Why the proctoring arms race doesn’t close the gap
The obvious counter is detection software, and a whole market grew up to sell it. These tools watch in-browser telemetry, capture the screen, and run behavioral analytics: keystroke cadence, tab switching, eye movement, the tell-tale rhythm of code that was pasted rather than typed. Some of it works. Most of it produces signals, not proof.
The trouble is asymmetry. A false positive means rejecting a strong candidate over a nervous glance off-camera, and most managers won’t stomach that outcome. So the detector gets tuned conservative, the confident cheaters slide through the gap, and you’re back to trusting a feed you can’t verify. Large employers can afford to bolt proctoring onto their stack. The mid-size company doing most of the real hiring still has exactly one detector, and it’s the interviewer’s gut.
In person the whole problem evaporates. You can’t run a hidden overlay or take a feed from an earpiece across a shared table, and there’s no question about whether the person reasoning through the problem is the person who applied. You watch someone think.
What the onsite is actually measuring
The part candidates get wrong: this is not a return to 2015-era whiteboard trivia. Nobody credible is bringing back “invert a binary tree on glass while I stare at you.” The signal teams want has shifted. They want to see how you reason with another engineer in the room, in real time, with no copilot feeding you the next move.
That means talking through a problem before you write anything. Reacting when the interviewer changes a constraint halfway through. Getting stuck, saying so out loud, and working toward the answer instead of going quiet. Reading and fixing code you didn’t write. A model can hand you a finished solution at home; it can’t stand at the board and defend a design while someone pokes holes in it.
A few prompts phrased the way they actually land in a 2026 onsite:
- “This service drops about 1% of writes under load. Here’s the code, find the bug.”
- “Sketch a URL shortener on the board, then make it survive a data-center outage when I ask.”
- “Pair with me on this parser and extend it to handle nested quotes.”
The whiteboard is back as a thinking surface, not a memory test. If you draw a system on it, the interviewer wants the drawing to change as they push.
What a full 2026 loop looks like
Most loops now split cleanly into a remote front half and an in-person back half. The remote rounds still screen for basic competence and, more and more, for whether you can drive AI tools well. The onsite rounds check the reasoning underneath.
| Interview round | Format in 2026 | What it’s really testing | What to practice |
|---|---|---|---|
| Recruiter and hiring-manager screen | Remote video | Role fit and resume accuracy | Clear stories about what you personally built |
| Technical phone screen | Remote, AI sometimes allowed | Baseline coding and tool fluency | Driving an AI assistant and explaining every line it writes |
| Onsite coding | In person, laptop or whiteboard | Reasoning out loud and adapting to new constraints | Narrating your thinking with autocomplete off |
| Onsite debugging | In person, broken repo on a shared machine | Reading unfamiliar code and finding the fault | Tracing open-source bugs from symptom to cause |
| Onsite system design | In person, whiteboard | Driving a design and revising it under pushback | Sketching systems that change as requirements change |
| Team and behavioral fit | In person, conversation | Ownership, conflict, and communication | Concrete stories with real numbers and outcomes |
Prepping for a room again
If your last loop was fully remote, the muscle you’ve let atrophy is thinking out loud in front of another person. Practice it directly. Grab a friend or a mock-interview partner and solve problems while narrating, on paper or a real whiteboard, with autocomplete turned off. The goal isn’t speed. It’s making your reasoning legible to someone watching.
Give the debugging round its own practice time, because most people never train for it. Pull an unfamiliar open-source repo, break something on purpose or find a real open issue, and work the bug from symptom to cause without a model narrating. That skill, tracing code you didn’t write and forming a hypothesis before you touch anything, is exactly what the onsite debugging station tests and what a copilot has quietly done for you for two years.
Then handle the boring logistics, because they’re back too. A full onsite means travel, a day of consecutive interviews, and the plain fact that you’ll be tired by round four. Sleep matters more than one extra practice problem. Eat before you go in.
The AI-enabled round is the other half of this
The same year firms pulled the final round in person, some of them started handing you the AI on purpose. Meta reworked parts of its coding interview to let candidates use an assistant during the session. The skill under test moved with it. The job stopped being about reproducing an algorithm from memory. It became about prompting the tool well, reading what it hands back, and catching the moment it confidently returns something wrong.
That’s why the loop splits the way it does. The AI-allowed remote round measures whether you’re fluent with the tools you’ll actually use on the job. The in-person round measures the raw reasoning that has to be there when the tool isn’t. A candidate who’s strong at one and hollow at the other gets found out, just at different stages.
The people who’ll have the roughest 2026 are the ones who let the tools do the reasoning instead of the typing. Standing at a whiteboard, that gap shows up in about ninety seconds, and there’s no window on a second monitor to close it.
Practice the behavioral round:
