Frontier conversational large language models (LLMs) are the closest implementation of the fictional Star Trek “Computer” I’ve yet to experience. I compare my journey with AI to the evolution of CPUs to GPUs.

Before AI, like CPUs, my work was linear and sequential. With AI—like GPUs— I can build and research concurrently while iterating on one idea, processing and triangulating multiple sub-tasks across multiple vectors with very little context-switching cost.

AI can make you faster, but it doesn’t guarantee you’ll be more effective.

But AI does not add hours to the day, eliminate opportunity costs, or change how humans work together. Business principles set out by Drucker, Grove, and other authors focus on finding wedges that grow future results—not “how much code you write.”

The Series

This series is a field report from the front lines: what it looks like to go all-in on AI as an engineering leader, what I’ve shipped, what didn’t work, and what matters for the future.

  1. Evolution of Using AI Every Day
  2. My Setup for Programming
  3. What I’ve Shipped with AI
  4. What Hasn’t Worked
  5. Thinking with AI

Bonus Content: AI Predictions for 2026 and Does Anyone Know A Good Software Engineer

Significant Revisions

  • Jan 21st, 2026 Originally published on https://www.jsrowe.com with uid A81EA5B4-578F-4A0A-A79D-4773DEBC0A51
  • Dec 16th, 2025 Initial rough draft created.