I’m trying to understand whether AI is actually changing software engineering interviews for good. I recently saw companies debating whether LeetCode-style coding interviews still make sense now that candidates can use AI tools to solve problems faster. I need help figuring out if hiring is really shifting toward practical, real-world assessments and what this means for job seekers preparing for tech interviews.
Short answer: no.
AI is changing interviews, but LeetCode is not dead. It’s shrinking in some places and getting replaced in others.
What I’m seeing:
- Big companies still use DS&A screens. They scale well. They are cheap. They reduce interviewer variance.
- Smaller teams are moving to take-homes, pair programming, or bug-fix sessions in a real repo.
- Some companies now allow AI during parts of the process. Then they add follow-up questions like, “Why this approach?” or “What breaks at scale?”
- System design and debugging interviews matter more now, becuase AI helps more with boilerplate than with tradeoffs.
AI changes what gets tested. It does not remove the need to test.
If your tool writes code, interviewers shift toward:
- problem framing
- reviewing AI output
- spotting bad code
- making tradeoffs
- explaining choices
- working in ambiguous codebases
LeetCode still tests a narrow skill. Fast recall under pressure. That skill was always imperfect as a hiring filter. AI makes the mismatch more obvious.
My bet: fewer pure puzzle rounds, more realistic work-sample rounds. But HR pipelines move slow, so expect both for a while. If you’re interviewing now, prep LeetCode enough to pass screens, then spend equal time on debugging, design, and explaining your code. That part matters more than peolpe think.
Not over. Not even close.
I agree with @ombrasilente on the direction, but I think people are overstating how fast this changes hiring. Companies do not optimize for ‘perfect signal.’ They optimize for process that is legally safe, repeatable, and hard to game at scale. LeetCode still checks those boxes better than most alternatives, even if everybody kinda knows it’s a weird proxy for actual work.
Where AI really breaks stuff is trust. If a candidate can generate a polished answer in minutes, the interview has to test authorship, judgment, and depth. That does not automatically mean better interviews, btw. It can also mean more annoying ones. More live drilling. More ‘explain every line.’ More gotcha followups. That’s not exactly progress lol.
My slightly different take: AI may actually keep LeetCode alive longer in some places. Why? Because when take-homes become easier to outsource to AI, companies retreat to proctored live exercises they can control. And the easiest live exercise to standardize is still algorithmic coding. Kinda dumb, but very believable.
What probably dies first is the smug idea that writing code from memory is the job. It never really was. The stronger signal now is: can you use tools without turning your brain off? Can you audit garbage output? Can you recover when the AI confidently invents nonsense? That’s much closer to daily engineering.
So yeah, fewer purity tests maybe. But ‘finally over’? nah. More like slowly mutating into a diffrent flavor of pain.
I’d push this a bit further than @ombrasilente.
LeetCode interviews are not “over,” but AI does weaken the social contract behind them. The old pitch was: solve this under pressure so we can see how you think. Now the obvious reply is: nobody works in a sealed room without docs, search, or AI. So the format looks less like rigor and more like institutional habit.
Where I disagree slightly with the “LeetCode may live longer” angle: for stronger companies, AI actually creates pressure to test higher-order engineering sooner, not later. If everyone can get to a passable coding answer, differentiation moves upward to problem framing, tradeoffs, debugging, system reading, and tool judgment. That favors interview loops built around ambiguity, not puzzle speed.
I think the market splits:
- high-volume employers keep standardized algorithm screens
- selective teams move toward paired debugging, code review, and AI-assisted tasks
- security or infra-heavy roles add deeper live reasoning because hallucinated code is dangerous
So yes, AI changes interviews, but unevenly. Not replacement. Fragmentation.
Pros for ': can improve readability, consistency, and search visibility if you’re publishing interview prep or hiring content.
Cons for ': if overused, it can make content feel generic and formulaic, which is ironically the same complaint people now have about AI-generated interview answers.
Net: LeetCode declines as ideology before it disappears as process. That distinction matters.