The Proxy Problem

For years, the industry has used algorithmic puzzles—popularized by platforms like LeetCode—as a “proxy” for engineering competence. The theory was straightforward: the ability to solve a complex puzzle under pressure correlated with the ability to build complex software systems.

With the rise of Generative AI, this proxy has fundamentally broken. If a machine can solve a syntax-heavy puzzle in seconds, the puzzle no longer measures a human’s capacity for engineering—it measures a process that is now largely automated.

We are not seeing a decline in human talent; we are seeing the obsolescence of our measurement tools.

Engineering as Systemic Reasoning

Engineering is not merely the act of writing syntax; it is the act of managing complexity and trade-offs. When AI handles the mechanical implementation (the “how”), the human value shifts to the architectural and contextual (the “why”).

• Syntax is now a commodity: Knowing specific library calls is less of a moat when LLMs can provide them instantly.
• Systems thinking is the requirement: The core skill is now understanding how a specific change impacts the larger machine and predicting long-term maintenance costs.

A Competency-Based Framework

In my work, Reimagining Software Engineering Interviews in the AI Era, I propose moving away from algorithmic puzzles toward real-world competency. We need to stop testing for “machine-like” speed and start testing for the things a machine cannot do: systemic reasoning and contextual judgment.

1. AI-Collaborative Debugging: Instead of banning tools, we should observe how candidates use them. Can they verify AI output? Can they spot subtle hallucinations?
2. Focus on Trade-offs: The interview should be a conversation about architectural choices. Why this database? What are the latency implications for downstream services?
3. The Human-in-the-Loop Skillset: We must evaluate the ability to orchestrate AI tools rather than asking candidates to compete with them.

The Path Forward

If we continue to interview based on 2015 standards, we will miss out on engineers who excel at building resilient, human-centric systems but refuse to spend their time memorizing puzzles that machines now solve better.

We aren’t lowering the bar; we are moving it to where it actually matters.

Status: Completed
Full Paper: DOI: 10.13140/RG.2.2.31005.37601

My deepest gratitude to Mr. Krishna, whose constancy forms the foundation upon which all my work, including this, quietly rests. Salutations to the Goddess who dwells in all beings in the form of intelligence. I bow to her again and again.