In this episode, we unpack a new challenge in software hiring: AI is boosting productivity while also creating an illusion of mastery.
Candidates can generate impressive AI-assisted code, yet struggle when the conversation moves to fundamentals like composition vs. inheritance, tradeoffs, and architectural decision-making. The result is a distortion of traditional hiring signals, where output can mask gaps in understanding.
The AI hosts dig into why fundamentals still matter most in enterprise systems, where reliability, durability, and accountability matter more than raw speed. Great engineers don’t just produce code, they can debug it, validate it, and challenge AI-generated work with sound judgment.
We close with what hiring practices must evolve to measure next: architectural reasoning and system-level decision-making, the areas where AI can assist, but not substitute.
Link to the article: When AI Isn’t Enough, originally published November 29, 2025.
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