How Notion AI Is Reshaping data cleanup for Modern Teams
Inside the quiet rewiring of onboarding at Notion.
For most of the last year, the conversation around evals-first development has been louder than the evidence. That is starting to change.
Inside Figma, 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 tool-first agents tend to share a habit: they write the evals before they write the prompts. Everything else follows from that.
Linear has been quietly running pricing analysis through Raycast AI for months. The results are unglamorous and, for that reason, more interesting than another benchmark chart.
Inside Stripe, the rollout looked less like a moonshot and more like a slow migration. A pilot, a champion, a quiet expansion, a budget line.
The cost curve matters here. Qwen 3 is roughly an order of magnitude cheaper per token than the equivalent model 18 months ago, and that changes which problems are worth automating at all.
Teams that win with evals-first development 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. Meta FAIR 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.