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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This episode breaks down the Product Operating Model and what it really takes to succeed in a modern software organization.

The AI hosts explore why POM is not a plug-and-play framework, but a system that only works when architecture, funding, and team design actually support long-lived product ownership. We clarify the most common misconceptions, from the belief that POM replaces DevOps to the myth that it calls for larger teams, reframing the model around small, empowered groups owning a complete slice of value.

They also discuss why shifting from project funding to product funding is essential, and how Value Stream Management provides the visibility needed to understand how work truly flows across the organization. If you’re trying to implement POM or make sense of the friction around it, this episode gives you a clear, practical view of what the model demands and how to make it work in your unique context.

Link to the article: When the System Fits, the Product Operating Model Works, originally published November 27, 2025.

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