The LeetCode Labyrinth: Why Tech Giants Still Play the Puzzle Game (and Why Some Are Quitting)
If you've ever applied to a tech job, you know the drill. Open LeetCode, pick a problem, stare at it for 20 minutes, solve it, and cry a little. This ritual has become as essential to landing a tech job as having a pulse.
About 80% of interviews at Google, Amazon, and Meta still feature these algorithmic puzzles. Which raises an obvious question: why are we still doing this?
The Brutal Math of Tech Hiring
Here's the thing nobody talks about: tech companies are drowning in applications. A single, entry-level position at Google might get 10,000 applicants. How do you even begin to filter through that?
LeetCode problems are the great equalizer—or the great eliminator, depending on how you look at it. They're harsh, efficient, and scalable. One automated coding test can eliminate up to 90% of candidates before a human ever reviews their resume.
Beyond just filtering, these problems are supposedly designed to test fundamental computer science knowledge. Can you implement a binary search? Do you understand dynamic programming? The theory is that these skills indicate raw problem-solving ability and intelligence.
Companies also love the consistency angle. Every candidate gets the same type of problem, making comparisons fair and reducing interviewer bias. In practice, well...
Why Everyone Hates LeetCode
The complaints write themselves. When was the last time you implemented a graph traversal algorithm at work? Most software engineering involves debugging someone else's code, making architectural decisions, and determining why the deployment pipeline has broken again.
LeetCode tests a particular skill set that rarely translates to day-to-day engineering work. It's like hiring a chef based on their ability to solve crossword puzzles—technically, it requires thinking, but the connection is questionable at best.
Then there's the grinding culture. Thousands of people spend months memorizing problem patterns, turning interviews into endurance tests rather than skill assessments. The person who gets hired might be the one with the most free time to practice, not necessarily the best engineer.
The stress factor is real, too. Coding on a whiteboard while someone watches you is nothing like the actual job. Some brilliant engineers freeze up under pressure, while others perform well in interviews but struggle with real work.
And now we have AI that can solve most LeetCode problems instantly. If ChatGPT can answer these questions accurately, what exactly are we measuring?
The Million-Dollar Question
Does LeetCode predict job performance? The honest answer is that we don't know.
Google's early internal research suggested weak correlations between interview performance and on-the-job success. However, that study only examined individuals who were already employed—a somewhat biased sample.
Supporters argue that algorithmic thinking indicates raw intelligence and learning ability. Critics point out that excellent software engineering requires collaboration, communication, and architectural thinking—none of which LeetCode measures.
It's somewhat akin to judging a musician's ability solely on their skill with scales. Important foundation? Sure. The whole picture? Not even close.
The Quiet Revolution
While Big Tech doubles down on algorithms, smaller companies are experimenting with different approaches.
Take-home projects are becoming popular. Instead of solving contrived puzzles, candidates work on realistic problems using their tools and environment. Companies like GitHub and Stripe have had success with this approach.
System design interviews are replacing algorithms for senior roles. Instead of implementing quicksort, candidates architect scalable systems. It's messy, subjective, and much closer to actual engineering work.
Some companies are emphasizing collaboration through pair programming sessions. Others focus heavily on cultural fit and communication skills. The startup world, in particular, can't afford to miss great talent because they can't invert a binary tree in 20 minutes.
What's Happening Now
The reality is nuanced. Large companies with massive applicant pools will likely continue to use LeetCode because it serves as a filter, even if it's not perfect. The volume problem is real, and no one has figured out a better solution at scale.
But the conversation is shifting. Even within Big Tech, there's growing awareness that the current system has serious flaws. Some teams are experimenting with more practical assessments, especially for experienced hires.
Smaller companies and startups have more flexibility to try different approaches. They're leading the charge toward more holistic hiring practices that look beyond algorithmic puzzle-solving.
The Future of Tech Hiring
LeetCode isn't going anywhere soon, but its monopoly on tech interviews is weakening. The future likely involves a mixed approach—initial screening through coding challenges, followed by more practical assessments that directly relate to the job.
The best hiring processes will test technical skills, problem-solving ability, and collaboration—not just pattern recognition and memorization. It's a slow shift, but it's happening.
What This Means for You
If You're Job Hunting Now
- Keep practicing LeetCode for FAANG interviews
- Research each company's interview style
- Look for companies using alternative assessments
- Build a portfolio of real-world projects
If You're a Hiring Manager
- Consider what skills actually matter for your team
- Look at alternative assessment methods
- Balance efficiency with effectiveness
- Think about what you're really testing for
If You're Leading a Company
- Question traditional practices
- Experiment with different approaches
- Measure the success of your hires
- Consider the talent you might be missing
Until the industry fully evolves, keep grinding those LeetCode problems. But also work on the skills that matter for the job you want—system design, code organization, debugging, and most importantly, working well with others.
Remember: The best engineers aren't always the ones who can solve puzzles the fastest. They're the ones who can build, maintain, and improve complex systems while working effectively with their teams.