AI and autonomy
Everything we have written on this subject, newest first.
The AI Hype Is Colliding with Reality
MIT studied 300 public AI deployments and found that 95% of enterprise AI pilots deliver no measurable P&L impact. The pattern behind the 5% that work is not better models or bigger budgets. It is specificity: solving one painful operational problem instead of bolting generic AI onto existing workflows.
Read moreCalling AI Code Slop Is Lazy
The backlash against AI-generated code follows a pattern that has played out before: a new tool lowers the barrier to entry, incumbents panic about quality, and the ceiling for what skilled practitioners can accomplish rises anyway. People made the same arguments about Rails, and the companies that ignored the naysayers built GitHub, Shopify and Airbnb.
Read moreAI isn't a bubble. It's a correction.
The current AI investment cycle looks different from previous tech bubbles. A look at why the infrastructure layer is where the real value compounds — and what that means for operations.
Read moreYou can't jump from manual database ops to full AI autonomy
The trust problem in database automation isn't technical — it's human. A response to Percona's vision for the future of database management, and why progressive trust matters more than raw capability.
Read moreAsk not what AI can do for you
Treating AI as a teammate rather than just a tool changes the way you build with it. Thoughts on collaboration over delegation, and what it means for operations teams.
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