The AI Interview Paradox: Why Programmers Defend Interview Coder

What non-programmers need to understand about coding interviews

To appreciate why so many developers sympathize with Interview Coder, you need to understand what modern technical interviews actually test – and what they don't.

The disconnect is so severe that an entire industry exists to help programmers prepare for interviews, with platforms like LeetCode offering thousands of practice problems that rarely apply to day-to-day work.

The value and limitations of algorithm knowledge

To be clear — I'm not opposed to engineers practicing algorithms on platforms like LeetCode or studying computer science fundamentals. These skills have genuine academic value and do provide important mental frameworks. Certain specialized roles, particularly at companies building high-performance systems or working on complex computational problems, legitimately require deep algorithmic knowledge.

I studied these subjects along with more theoretical computer science concepts in college, and I'm glad I did. This foundation helps engineers reason about efficiency and understand system constraints.

That said, for the vast majority of software engineering positions, making these skills a primary filter creates an unnecessary hurdle that doesn't predict job success. The problem isn't the knowledge itself, but using it as a universal gatekeeper regardless of its relevance to the actual work.

The scale problem: 100 lines vs. 50,000 lines

The most glaring disconnect in technical interviews is scale. LeetCode problems typically involve 50-100 lines of code in a perfect vacuum. Real engineering involves navigating codebases with 50,000+ lines, multiple frameworks, undocumented dependencies, and years of technical debt.

This isn't just a quantitative difference—it's qualitative. Skills that matter at scale:

• Mental mapping: Understanding how components connect in a system too large to comprehend all at once
• Technical archaeology: Determining why decisions were made years ago with incomplete documentation
• Judgment: Knowing when to refactor versus when to work around existing patterns
• Pragmatism: Balancing theoretical purity with business needs and deadlines

None of these skills are tested in a LeetCode interview. In fact, the algorithms-focused mindset that excels in interviews often creates problems in production code, where readability and maintainability trump clever optimizations.

The chef analogy

Imagine applying for a chef position where instead of cooking a meal, you're tested on:

• Reciting the chemical composition of baking powder
• Solving physics equations about heat transfer
• Drawing perfect circles freehand in under 60 seconds

Would you consider it cheating if chefs used AI to answer food chemistry trivia? Or would you prefer they demonstrate their ability to actually cook delicious food?

The AI irony

Here's the greatest irony: While headlines warn that "AI will replace programmers," companies still:

1. Need to hire human programmers (AI isn't replacing them yet)
2. Test these humans with puzzles that AI can solve in seconds
3. Reject capable engineers who don't memorize algorithms AI has mastered

If coding interviews truly measured essential human skills, they would test what AI can't do well: understanding business contexts, collaborating with teammates, and designing intuitive systems for human users.

The interview prep industry: A symptom of dysfunction

What's perhaps most telling about the disconnect between technical interviews and actual engineering work is the massive industry that has emerged solely to help programmers pass these tests:

• LeetCode, HackerRank and other platforms charge monthly subscriptions for practice problems
• "Cracking the Coding Interview" has sold millions of copies and spawned countless imitators
• Boot camps charging thousands of dollars specifically for interview preparation
• Programmers gathering in weekly study groups to practice problems they'll never use in real jobs

Engineers often spend 3-6 months studying full-time just to pass these interviews. That's half a year learning skills they'll immediately discard once hired. This isn't professional development – it's an elaborate initiation ritual with no connection to job performance.

The talent we're filtering out

The most concerning aspect of algorithm-focused hiring is who it excludes:

• Veteran engineers with 10+ years of experience building complex systems who haven't implemented a red-black tree since college
• Self-taught developers who've launched successful products but never formally studied algorithmic complexity
• Engineering leaders whose strengths lie in system design, architecture, and code organization

These are precisely the engineers most companies need – experienced problem-solvers who can navigate complex systems, make sound technical decisions, and build maintainable solutions. Yet many would fail standard technical screens focused on algorithmic puzzles.

I've watched brilliant engineers with decades of experience fail interviews at companies whose systems they could dramatically improve, all because they couldn't solve a contrived binary tree problem under pressure. Meanwhile, new graduates who've memorized LeetCode patterns but never worked with real-world constraints sail through.

When hiring becomes disconnected from job performance, we don't just waste time – we systematically exclude valuable talent. We create artificial barriers that filter for interview skills rather than engineering capability.

The ethical perspectives

This isn't just about cheating – it's about what we value in software development. Using AI to misrepresent core skills is problematic. But requiring engineers to perform like algorithms instead of demonstrating human judgment, creativity, and technical decision-making is equally troubling.

The way forward

Instead of asking whether tools like Interview Coder are ethical, perhaps we should ask:

1. Why do we still use interview methods that AI can easily defeat?
2. What would interviews that test relevant skills look like?
3. How can we make hiring both rigorous and representative of actual work?

Some companies are already finding better approaches:

These approaches are harder to "cheat" because they test skills that require genuine human judgment and experience.