Yeah, I think engineering orgs mostly jumped the shark.

Metrics like pr count and commits have always been terrible gauges for success compared to business performance.

But they're easy to measure, and even easier to game now with AI.

So we're seeing an outrageous gain on these metrics, and they've become almost completely divorced from business results.

No one cares how fast you ship prs. They care that you offer a compelling product, that works when they need it work, for a price they're able and willing to pay.

It's like we've decided to measure how far we've traveled in gallons of gas burned, but completely forgotten about measuring miles per gallon.

> It's like we've decided to measure how far we've traveled in gallons of gas burned, but completely forgotten about measuring miles per gallon.

Or forgotten to look at the map to see if we're getting closer to our destination.

I've never seen a consumer bugs that complains about how small our codebase is or how little PRs we have produced this month. It's always about some features not working properly.

Previously the core metrics were reducing consumer complaints and implementing features for the sales team to attract new clients. Then they suddenly got replaced by amount of PRs and token usages.

My organization has noticed that some people's output has gone way up since they started using AI, while some people's has not. I imagine output is measured by LOC and MRs, since we are bad at metrics. There's now an effort to figure out why the laggards aren't using their AI "well" enough. I've seen some of the increased output that was sprayed at my team without consent, it was work that superficially looked good but on closer investigation did not solve the problem it was intended to. All that work has to be redone. The rework is being done quietly, so as not to draw management ire for a lack of "productivity". It's all so tiresome.