Tools

The AI Productivity Paradox: Why Experienced Developers Are Slowing Down

There’s something strange happening in software development right now, and I think we need to talk about it.

Recent research has surfaced a troubling finding: experienced developers working on complex systems are actually 19% slower when using AI coding tools—despite perceiving themselves as working faster. This isn’t a minor discrepancy. It’s a fundamental disconnect between how productive we feel and how productive we actually are.

As someone who’s been experimenting with AI tools extensively (and writing about the results), this finding resonates with my experience. Let me break down what’s happening and what it means for engineering teams.

AI Agents and Google Slides: When Promise Meets Reality

I’ve been experimenting with AI agents to help create Google Slides presentations, and I’ve discovered something interesting: they’re great at the planning and ideation phase, but they completely fall apart when it comes to actually delivering on their promises.

The Promising Start

I’ve had genuinely great success using ChatGPT to help with presentation planning. I’ll start a conversation about my presentation topic, share the core material I want to cover, and ChatGPT does an excellent job of:

When AI Assistants Fail: The Meeting Scheduling Reality Check

I recently tried to use AI assistants to solve what should be a straightforward problem: scheduling a meeting with three other people at my office. We’re all Google Workspace users, so I figured this would be a perfect use case for AI—especially given all the hype about AI assistants being able to handle calendar management and scheduling.

Spoiler alert: both ChatGPT and Gemini failed spectacularly.

The ChatGPT Experience

I started with ChatGPT, thinking it would be able to help coordinate schedules. My request was simple: find a time that works for me and three colleagues for a meeting.