Developer Productivity

Your AI metrics might be measuring the wrong thing. Dr. Catherine Hicks explains how to fix the theory, not the dashboards.

At a CTO Craft Dinner in Toronto, I sat down with engineering leaders from more than a dozen tech companies and asked where AI has actually landed. The free-for-all is over and we need to be realistic.

What economist Charles Goodhart said in 1975 is completely applicable in 2026: When a measure becomes a target, it ceases to be a good measure.

Twenty developers got real about their jobs: the rush of shipping something that actually works, the way a fat paycheck can make you stay longer than you probably should, and the slow death of sitting through another meeting that should've been an email.

At Devoxx UK, I spoke with Trisha Gee - author and one of the most recognized voices in the Java space - about what really happens when teams lean heavily on AI. Her take was far darker than the conference hype.

Production incidents are a context problem. By the time an engineers understand what's happening, they've already bounced across several different tools - and the incident is still ongoing. PagerDuty thinks MCP is the fix.

92% of developers use AI coding tools, but productivity has barely moved - stuck at 10%. Here’s why using AI doesn’t automatically mean getting more done.

At the Pragmatic Summit, I heard firsthand that Uber engineers aren’t just using AI to write code anymore, they’re assigning it work. Let’s see how that plays out.

I was in the room at this year’s Pragmatic Summit when Laura Tacho dropped the numbers: nearly all developers use AI coding assistants, over a quarter of production code is AI-written - and yet productivity gains haven’t budged past 10%.

When teams don’t share information, clash on priorities, or follow unclear processes, even simple tasks can drag on for months.

We’re past just making better tools: machines will do the repetitive coding, and humans will focus on guiding, judging, and architecture.

Ex-Tesla AI director now programs mostly in plain English with AI, calling it the biggest workflow change in 20 years.

Software teams don’t implode because of bad code - they implode because someone really needed to win the argument about it.