I spend most of my week reading model release notes, changelogs, and competitor feature launches so I don't have to explain to my team why we shipped the wrong thing. A few patterns keep showing up:

1. Bolting "AI chat" onto a product that doesn't need conversation.

If your users' problem is "find X faster" or "fill this form correctly," a chat interface adds latency and ambiguity. Structured input + a model call under the hood usually beats a chatbot. Chat is a UI pattern, not a feature.

2. Ignoring inference cost until it's a pricing crisis.

Teams prototype with the biggest, most expensive model, ship it, then get a shock when usage scales. Decide your cost-per-action ceiling before you pick a model, not after.

3. Treating every new model release as a roadmap event.

Most releases are incremental. The ones that actually change your roadmap are rare — bigger context windows that kill a RAG pipeline you built, or reasoning improvements that make a "human review" step unnecessary. Most weeks, nothing changes. Don't let hype cycles set your sprint priorities.

If you're a PM trying to filter signal from noise on this stuff, that's exactly what I built StackShift for — a weekly briefing on what actually shifts the roadmap, skipping what doesn't.