Adam Tornhill

Adam Tornhill

Founder of CodeScene & Author of "Your Code as a Crime Scene"

Adam Tornhill is a programmer who combines degrees in engineering and psychology. He’s the founder of CodeScene where he designs tools for code analysis. Adam is also the author of multiple technical books, including the best selling Your Code as a Crime Scene and Software Design X-Rays. Adam’s other interests include modern history, music, retro computing, and martial arts.

Presentations

AI-Friendly Code: The Missing Link Between Speed and Quality

7:30 PM MDT

Have you seen early productivity gains from AI, only to watch them disappear under growing complexity and production incidents? You're not alone. There's a common reason: many production systems already struggle with technical debt. When AI agents enter the development loop, that debt becomes a multiplier. Poor-quality code not only increases defects and costs. It dramatically raises AI risk by driving high breakage rates, turning promising AI agents into legacy code generators rather than genuine help.

Fortunately, there's hope on the horizon. In this talk, Adam Tornhill shows how organizations can achieve both speed and quality with AI. Backed by large-scale empirical studies on AI coding and developer productivity, we separate what works from what doesn't in real-world systems. Building on these findings, we then look at a practical framework for driving and sustaining AI-friendly code at scale. The AI revolution is here. Is your code ready?

Your Code Isn’t AI-Ready (Yet): Fixing the Complexity That Breaks AI Agents

5:00 PM MDT

AI agents don’t struggle with syntax. They struggle with missing intent, non-expressive code, and surprising dependencies. Historically, we were supposed to write code for human readers, code that fits our cognitive limits and supports collaboration. In reality, much of our industry has fallen short.

That comes back to bite us.

When AI agents enter the development loop, they amplify those same problems. Where a human developer will ask questions and seek clarification, an AI often proceeds without it, making its best guess from patterns in code that was never designed to be unambiguous.

Code that is hard for humans to understand becomes unreliable for AI.

In this talk, Adam Tornhill shows how to turn that around. You’ll learn the key principles behind AI-friendly code and apply practical AI-assisted refactoring patterns that make those principles concrete. The focus is not on generating more code, but on improving the code you already have so AI becomes reliable instead of risky. All recommendations are grounded in AI research and cognitive psychology.