Shiny new tools won’t fix your problem

Generative AI can help us produce code faster than ever before, but faster code generation does not automatically translate into faster or safer delivery. In practice, AI acts as an amplifier: teams with strong engineering fundamentals improve, while teams with existing bottlenecks and weak software development practices feel those problems more acutely.

Writing code is cheaper. Testing, troubleshooting, building, deploying, understanding and changing it is not.

In this talk, we’ll look past the hype and focus on what actually drives productivity in an AI-accelerated world. We’ll explore why shiny new tools don’t fix broken systems, why optimising individual steps rarely improves end-to-end flow, and why software engineering fundamentals matter more — not less — when code is easy to generate.

We’ll also look at how observability and measurement help teams understand where time is really being spent, and why accelerating feedback is essential if AI is going to help rather than hurt.

You’ll leave with a clear understanding of:

• Which engineering practices consistently improve outcomes in an AI world and provide vital safety nets
• Why good design and readable code still matter
• What to measure to understand whether AI is really making your team more productive

This is not an anti-AI talk. It’s a reminder that the hard parts of software didn’t get easier, and that fundamentals determine whether AI makes us faster, or breaks us faster.