In this episode, we step back from the hype and take an honest look at why LLMs and coding copilots haven’t lived up to the claims of productivity revolutions and autonomous engineering.

From hallucinated logic and brittle architecture to context loss and duplicated code, we unpack real-world failures surfaced in AI-generated production code — and explain why today’s tools still lack the judgment, systems thinking, and trade-off awareness that real software engineering demands.

We also explore the marketing narratives shaping expectations, and why benchmarks and demos obscure the true cost of using these tools at scale.

This episode is not a rejection of AI — it’s a reframing. Because while these tools can accelerate parts of development, they are not engineers. They are assistants. And treating them as such is where their real value begins.