Accountability

A friend messaged me lately: Where do you see IT going? Any new patterns in the post-AI era?

My answer started with two recent jobs, both from my own desk. The first was one afternoon, when I asked an agent to build me a small UI that exports data from my workout tracker and from my Fitbit Air through the Google Health API, and merges it into a training agent I’d built. It works, and it will never need to survive me.

For the second, around the same time, I spent a week updating one outdated JavaScript library in an emergency medical dispatch system (the kind that follows an ambulance from the first call to the hospital doors, with the patient’s status reported along the way). It was already running. An agent helped me debug it and swap the library, and the rest of the week went on reading, tracing, testing, and asking what would break and who would notice first.

Same tools on both, but completely different jobs.

The engagement started with a decommission notice on a system nobody had fully mapped.

The system processed a significant chunk of daily financial data. A risk management platform, wired deep into a legacy mainframe that was still very much alive, and very much a black box. Outside that mainframe sat a single overbloated component doing the work of several, holding the integration layer together through years of accumulated dependencies nobody had ever fully mapped.

There are meetings where everyone in the room knows the truth and nobody says it.

I’d been through the existing codebase. The designs were rough; the code behind them was worse, not catastrophically wrong, just badly built, in the way that happens when a team has been too busy to be honest with itself, and too stubborn to revisit the amateur choices it had made along the way.
I’d written it up. They’d seen my assessment and recommendations, but a new deck was better.

The meeting opened with a presentation: architecture diagrams, process flows, the kind of thing that takes real effort.

I didn’t say much.

The manager called me in.
I already knew.
I’d known for weeks, in the way you know something before you’re willing to say it out loud.
The conversation was ten minutes.
The decision had already been made, by both of us, long before that.

AI was supposed to threaten intelligence.
It exposed how little judgement most systems ever required.

A few months back, I sat in a review where a junior engineer presented an architecture proposal. Clean diagrams, well-structured document, confident annotations. The first questions were answered fluently. Then we pushed into the edge cases: what happens when this service changes, how does it behave under unexpected load, why this particular approach over the alternatives?

“I’d need to check.”

Three times in the same conversation. The work looked senior-grade. The reasoning wasn’t there. When I asked what had informed specific choices, the honest answer eventually came: “Claude suggested it and it seemed right.”

I didn’t blame them. But I kept thinking about what had actually happened in that room.