Technical interviewing in software engineering has always been messy.
For decades, companies cycled through different approaches, from ridiculous brainteasers about moving Mount Fuji to the LeetCode grind that dominates today.
None of these methods were perfect, but they got the job done. Candidates who prepared could get through. Companies could make reasonably informed hiring decisions. The system was flawed but functional.
Then AI tools arrived and completely demolished the foundation that technical interviews were built on.
When the Old System Actually Worked
Before diving into the chaos, it's worth understanding what made the previous interview process tolerable. Sure, asking candidates to solve algorithmic puzzles in 45 minutes didn't perfectly mirror real engineering work.
Most developers rarely implement binary search trees from scratch or optimize dynamic programming solutions on the job. When they do encounter these concepts, they have days to think through the problem, not minutes.
But here's the thing: these interviews compressed evaluation time. They gave hiring managers a standardized way to compare candidates quickly. The questions were artificial, the pressure was manufactured, but the process was predictable.
If someone practiced enough, they could demonstrate competence. If they didn't prepare, they probably wouldn't make it through. A stable system, even if imperfect.
The critical assumption holding everything together was simple: the person sitting in the interview was actually doing the thinking. That person's brain was generating the solutions, making the trade-offs, and explaining the reasoning. Whatever came out of their mouth represented their actual capabilities.
That assumption is now gone.
The Collapse Happened Fast
AI didn't gradually influence technical interviews. It detonated them. The change was immediate and brutal.
Candidates suddenly started producing suspiciously perfect solutions with zero visible thought process. They'd deliver final code as if reading from a script, with no false starts or corrections. Behavioral answers began sounding polished in a way no human naturally speaks under pressure.
Before AI, cheating had natural limits. Getting help from a friend required coordination, time, and someone skilled enough to assist. Even then, human helpers were slow and made mistakes.
They couldn't instantly generate optimal solutions. AI removed all those barriers. Now anyone with a second monitor or even just clever camera angles has access to expert-level output on demand.
The really insidious part isn't just that people cheat. It's that the line between genuine skill and AI assistance has blurred into nothing. A strong candidate working independently and a mediocre candidate with AI support can look identical during a remote interview.
When you see someone deliver a flawless solution, you can't tell if you're evaluating them or their prompting skills.
The New Red Flags Nobody Talks About
Interviewers started noticing strange patterns that didn't exist a year ago.
Candidates jump straight to final solutions, skipping the messy exploration real engineers go through. There are no deleted lines, no "wait, let me reconsider" moments, no visible debugging of their own logic. Natural problem-solving has texture and roughness. AI-generated answers are suspiciously smooth.
The pacing feels off too. Humans pause to think. AI-assisted candidates pause to wait for responses. Their eyes drift slightly off-screen. They repeat questions back, apparently buying time.
Ask them to adjust the problem by ten percent, and suddenly the fluency vanishes. Ask why they chose a specific approach, and you get circular reasoning or generic definitions that don't connect to the code they just wrote.
Even behavioral interviews aren't safe. Some candidates recite perfectly structured stories with ideal lessons learned but zero personality. It sounds like listening to someone's carefully crafted LinkedIn post rather than hearing about their actual experience.
One interviewer described a candidate who turned off their camera and answered every question without a single pause or filler word. No "um," no thinking sounds, nothing. Just polished responses delivered like a TED talk.
None of these signals alone proves anything. But together, they create a pattern that forces interviewers to run two separate evaluations simultaneously. First, can this person solve the problem? Second, is this person actually the one solving it?
That second question never used to exist, and it completely changes interview dynamics.
Why Companies Are Going Back to Conference Rooms
The shift back to in-person interviews isn't about nostalgia.
Nobody suddenly decided whiteboards were magical or missed those barely-working dry-erase markers. This is damage control. Companies realized they were no longer interviewing candidates. They were interviewing candidates plus whatever AI models those candidates were quietly consulting.
Google recently announced a return to in-person interviews specifically because too many candidates were using AI. Other tech companies are following suit, adding physical rounds even to otherwise remote hiring processes.
The logic is straightforward: remote interviewing depends on transparency and authenticity, two things AI quietly erases.
Physical rooms create constraints that force authenticity. Second monitors disappear. Silent prompting becomes impossible. Overly polished behavioral answers can't be read from a screen.
When someone explains an idea while drawing it, debating it, and reworking it in real time, you see the actual shape of their thinking. You see hesitations, corrections, the messy mechanics of real problem-solving. AI can generate perfect answers, but it can't fake that human process.
There's also a practical element: reducing noise in the hiring pipeline. Remote interviewing made it cheap to interview massive numbers of people. That inflated standards, which ironically rewarded candidates who used AI to hit those inflated bars. Bringing interviews back in-person naturally reduces volume while raising signal quality.
The Ethical Trap Candidates Face
It's easy for hiring managers to say "don't cheat." But consider the candidate's perspective.
They're not just competing against other engineers anymore. They're competing against people using AI during interviews, people with rehearsed AI-written behavioral stories, and people who bought specialized interview cheating tools.
You might be brilliant and competent, but you could still lose to someone who found that shady Chrome extension. This creates an awful paradox. If you cheat and get caught, you're done.
If you don't cheat and underperform compared to AI-assisted peers, you're also done. The system's incentives have completely collapsed.
Some candidates rationalize it by asking: is it even cheating if the job itself lets me use AI every day? Why should I handicap myself when the system isn't fair to begin with? The pressure pushes otherwise principled people to compromise their ethics just to compete.
The truth is, even if someone wins by cheating, that victory is hollow. They enter a role based on someone else's performance. Companies eventually notice. Teams eventually feel it.
The person feels it most when expected to deliver independently. Getting performance-improvement-planned out is just a matter of time.
What Actually Needs to Change
If AI can outperform most humans on promptable tasks, then interviews need to stop measuring flawless output. Perfect answers aren't impressive anymore. They're suspicious. The metric has to shift from what you produce to how you think.
Future-proof interviews should measure reasoning under uncertainty, because engineering is rarely a clean algorithmic exercise. It's a series of half-informed decisions made with incomplete information.
Watching someone work through not knowing reveals far more than watching them implement a memorized pattern.
Interviews should test adaptability when constraints change. Real work is dynamic. A candidate who collapses when the problem shifts slightly is someone who can't function in actual engineering environments.
AI-assisted answers crumble instantly under changed assumptions. Humans generally adapt, even if they need hints.
The focus should be on actual engineering judgment: the ability to balance business needs, technical debt, scalability, trade-offs, team constraints, and the cost of being wrong.
This is the real craft of engineering, and it's exactly what AI can't replicate convincingly in live conversation.
The Hybrid Future Taking Shape
The future of interviewing won't be fully remote, but it won't be 2015 either. The industry is converging toward a hybrid model. Remote rounds will serve as lightweight screens: basic technical checks, behavioral looks, sanity filters. They'll weed out noise, not determine competence.
The serious evaluation will happen on-site, forcing candidates into a mode AI can't easily assist: whiteboard collaboration, debugging in front of someone, architectural debates, live problem-solving, actual conversation. The hard stuff moves back into the room.
Companies are also realizing that interviewing absurd numbers of people creates more problems than it solves. The volume game rewards cheaters, prompt optimizers, and test-takers over actual engineers.
The trend is shifting toward fewer candidates who get deeper attention. Quality over quantity. Human signal over machine signal.
Future interviews will look less like gladiator matches and more like working sessions. "Let's debug this together" or "Walk me through how you'd approach this if the deadline was tomorrow" creates space for genuine reasoning to emerge.
Less theater, more thinking.
When AI Gets Used Honestly
AI won't be banned from interviews. That would be pointless and unrealistic. Instead, it'll be integrated transparently.
Imagine being told: "Here's a problem. Use AI if you want, but walk me through why you trusted its output. Explain what the model got wrong and how you'd correct it."
This is where the industry will eventually settle, because the future engineer isn't the one who avoids AI. It's the one who uses it competently and honestly. The competitive advantage shifts from having access to AI to having judgment about when and how to use it.
For years, technical interviewing limped along on memorized patterns and predictable formats. It wasn't great, but it was stable. AI ended that era almost overnight by erasing the fundamental trust that the person answering was the person thinking.
What's emerging now is potentially better: interviews that measure reasoning over recall, conversation over performance, judgment over pattern-matching. Companies are learning to test for things only real engineers can do, not things models can generate on demand.
This transition won't be smooth or perfect. It won't satisfy everyone. But for the first time in years, interviewing has a chance to evolve into something that actually measures what matters: judgment, clarity, ownership, collaboration, adaptability, and real problem-solving.
AI broke the old system.
Maybe it needed breaking. What comes next might finally reflect how engineering actually works: as a conversation between people who want to build something together, not as a battle against an unfair system.
