Why analysts Are Suddenly Standardizing on Notion AI

The interesting question is not whether Cursor works. It does. The interesting question is what teams do with it once the novelty wears off.

What Meta FAIR actually shipped with Gemini 3 Pro is less a single capability and more a cluster of small, compounding improvements — the kind that only show up when you put a real workflow on top.

Eval harnesses, once an afterthought, are becoming the most important piece of code in many AI projects. Linear's team treats theirs the way an SRE team treats a runbook.

Teams that win with structured outputs tend to share a habit: they write the evals before they write the prompts. Everything else follows from that.

Inside Databricks, the rollout looked less like a moonshot and more like a slow migration. A pilot, a champion, a quiet expansion, a budget line.

Inside Linear, the rollout looked less like a moonshot and more like a slow migration. A pilot, a champion, a quiet expansion, a budget line.

Teams that win with fine-tuned distillation tend to share a habit: they write the evals before they write the prompts. Everything else follows from that.

None of this guarantees a clean story. Alibaba Qwen could ship a model next month that rearranges the assumptions in this piece. But the direction of travel, for now, is clear enough to plan around.