I keep seeing “Agile is dead” headlines, now repackaged for the AI era. My take: AI isn’t killing Agile. AI is illuminating constraints that were already in the value stream.

AI can do market research, write documentation, write code fast - it can’t take accountability. As AI compresses execution time, rebundles responsibilities, and enables smaller teams with faster release cycles, the real work shifts to human judgment: decision-making, validation, security, governance, and operating safely in production.

This episode reframes Agile and agility as an enduring capability, and explores what must evolve when software delivery accelerates dramatically with AI.

Link to the article: Agile Isn’t Dead and AI Isn’t Killing It Either, originally published January 24, 2026.

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AI is now table stakes in software engineering hiring, but it is also warping the signals we used to trust.

In this episode, the AI hosts cover my article about a growing pattern I call “AI-fluent, fundamentally lost”: candidates who can produce impressive output with prompts, yet struggle to explain the logic, constraints, and architectural trade-offs behind what they ship.

The result is a new kind of risk: “glass cannons” that look productive fast, but can drive long-term maintenance cost and technical debt when fundamentals and judgment are missing.

They cover the arguments for a more durable hiring approach that evaluates both system-level reasoning and AI-assisted execution, treating AI as a productivity accelerator, not a replacement for critical thinking.

Link to the article: AI Fluent, Fundamentally Lost, originally published December 07, 2025.

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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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