Summary
AI can remove a lot of the need to understand every detail of how code is written. That is not necessarily a problem. Engineers have always worked at higher levels of abstraction as tools improved.
The more important question is where curiosity moves.
As AI takes on more of the implementation work, engineers may need to be less curious about the mechanics of every line of code — but more curious about architecture, system behavior, dependencies, failure modes, and whether the problem has been framed correctly in the first place.
This roundtable will explore what strong engineering judgment looks like when AI handles more of the “how.” What do engineers still need to understand deeply? Where is abstraction helpful, and where does it become dangerous? And how can leaders make sure their teams stay close enough to the system to know what should be built, why it matters, and when the AI-generated answer is not the right one?
Host
Natalia Pyalling
Sr. Director DevOps Engineering @ Intermedia Cloud Communications
Natalia Pyalling is a Senior Director of DevOps Engineering and an engineering executive with experience leading large-scale organizations across infrastructure, platform engineering, reliability, developer experience, software delivery, and AI/ML. Her background combines executive leadership with deep technical foundations, including formal academic training in machine learning and pattern recognition with applications in healthcare.
She focuses on how engineering organizations evolve as AI changes the way software is built — particularly the implications for engineering judgment, systems thinking, developer effectiveness, and leadership. Outside her corporate role, Natalia also maintains hands-on exposure to AI-native product development through an independent project, complementing her enterprise perspective with practical experimentation.
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