Boring Wins: Quiet Automation Reshapes expense reporting

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

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

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.

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.

What xAI actually shipped with Grok 4 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.

HubSpot has been quietly running QBR prep through Hex Magic for months. The results are unglamorous and, for that reason, more interesting than another benchmark chart.

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

The cost curve matters here. Phi-4 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.

None of this guarantees a clean story. Google DeepMind 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.