This post accompanies the presentation: Some Thoughts on the Impact of AI on Software Engineering.

What’s in the presentation?

The aim of the presentation is to inspire and encourage engineering (leadership) to think about some of the long term and big picture effects of the AI coding craze. The presentation ends with some very practical questions that teams need to start finding answers to if they decide to go “all in” with 1 or 2 fast moving AI teams.

The slides are organised into four sections:

Key Takeaways — AI coding is a magnifier, not a magic wand. If your codebase, feedback culture, or company culture has problems, AI will amplify them. And when you go faster, you need to look further ahead.

Organisational — Pace layering, the simplicity cycle, cognitive debt, and why the handovers we lost were actually hidden quality gates. Includes a technique for unwinding fidelity when reviewing AI-generated prototypes.

Engineering Management — Where does the rigor go when AI writes code? Testing as survival, security as non-optional, LLM-as-judge, and why you shouldn’t ship slop.

Workshop Time — Fifteen questions to discuss with your team, from pace layering your technology to protecting your master branch.

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