AI Outperforms Humans in LeetCode | Generated by AI
Question: How can AI be considered truly advanced when so many experienced engineers still fail LeetCode-style coding interviews?
Answer:
AI (especially large language models like GPT-4, Claude 3.5, or Grok 4) has reached a point where it can solve ~85–95 % of LeetCode “Easy” and “Medium” problems and ~50–80 % of “Hard” problems on the first or second try, often faster and with cleaner than the average human candidate. In 2024–2025 blind benchmarks (e.g., LiveCodeBench, Aider, SWE-bench Verified), the best models now outperform the 90–95th percentile of human competitive programmers on brand-new contest problems they have never seen before.
Yet in real interviews:
- 60–80 % of applicants with 3–10 years of experience still fail to solve a single LeetCode Medium cleanly in 30–45 minutes.
- Even senior engineers at FAANG companies routinely fail mock interviews when put on the spot.
This paradox is easy to explain once you separate the different skills being measured:
| Skill | Top AI models (2025) | Average human candidate in interviews |
|---|---|---|
| Raw problem-solving ability | Extremely high (solves most problems correctly) | Moderate to high |
| Speed on unseen problems | Very high (seconds to minutes) | Low to moderate under pressure |
| Recall of hundreds of patterns | Perfect (trained on every public problem) | Poor (most people remember ~30–50 patterns) |
| Typing speed & syntax perfection | Instant, zero bugs | Slow, many syntax errors under stress |
| Ability to explain thought process | Good but sometimes superficial or verbose | Highly variable |
| Performance under time pressure & anxiety | Unaffected (no nerves) | Severely degraded for most people |
| Interview-specific communication | Can be coached to sound natural | Many freeze or ramble |
| Handling ambiguous requirements | Sometimes over-fits or misreads | Often better at asking clarifying questions |
In short: AI is solving the algorithmic puzzle part much better than almost all humans, but coding interviews are not pure algorithmic puzzles. They are a high-stress performance test that also measures:
- Working memory under pressure
- Verbalizing partial thoughts coherently
- Recovering from mistakes quickly
- Dealing with an interviewer who is silently judging you
These are skills that current AI still struggles with in a real-time interview loop (especially the back-and-forth clarification and the “think aloud” requirement), and they are exactly the main reason humans fail even when they are objectively capable of solving the problem in a quiet room with Google open.
So the conclusion is not “AI isn’t advanced”; it’s “LeetCode interviews are a terrible way to measure pure programming ability, but they accidentally measure something else (stress tolerance and pattern recall) that AI hasn’t fully mastered in the interview setting yet.”
When companies switch to take-home projects, pair-programming sessions, or AI-allowed interviews, the “failure rate” of strong engineers drops dramatically — which proves the bottleneck was never raw intelligence.
References:
- LiveCodeBench leaderboard (2025): https://livecodebench.github.io
- SWE-bench Verified results ( frontier models vs humans): https://www.swebench.com
- 2024–2025 studies on interviewer bias and LeetCode failure rates (e.g., interviewing.io anonymized data)