Writing on AI
Two series: real-world AI case studies from the field, and plain-language guides to understanding AI.
Practical stories from the field: how organizations are actually using (and struggling with) AI today. No hype. Just honest lessons.
Should You Still Learn to Code in 2026? Yes, But Not for the Reason You Think
AI has made it easy to produce something that looks finished. That's a much harder problem than a bug. Why technical foundations still matter, even if you never write production code.
The People Pushing Back on AI Aren't the Ones You Should Worry About
Everyone focuses on getting the skeptics on board, but the real risk is the people who already think AI can do everything. The strongest developers are usually the most resistant, and that's exactly right.
The AI Panic Is Not About AI
Every week someone quits an AI company and the headlines say the world is ending. But most of that noise has nothing to do with how the technology actually works. Knowing the difference matters.
AI Won't Fix Your Process
Everyone's optimizing their AI prompts, but they should be optimizing their process. The quality of what AI gives you has almost nothing to do with the tool. It has everything to do with the process your team already has.
AI in the Real World: Why Legacy Codebases Are the True Test of AI Adoption
Everyone's talking about building with AI. Almost nobody's talking about what happens when your codebase is older than the AI tools themselves.
From 7 Degrees to AI: A Nonlinear Career Path
How studying criminology, graphic design, and operations research prepared me for AI better than any bootcamp could. The story of a career that looked scattered until it didn't.
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