In 2013, data analysis at Wayfair was trapped on on-premise SQL Server boxes that couldn't even join across databases. Over the following decade, cloud data warehouses and the modern data stack tore down constraint after constraint, turning data scientists from reporters into operational decision-makers. 📈 Now AI has made producing an analysis nearly free. But in "The Shape and Feel of the Post-AI Data Stack," Ian Macomber argues that agreeing on reality never gets cheaper. The more dashboards proliferate, the more likely different interfaces return different answers to the same question — a growing "consensus divergence" problem. 🤖 The post-AI data stack Macomber describes puts an agent harness between infrastructure and people: agents parse data products and reassemble insights for humans, not the other way around. That demands four things — artifacts agents can read, tools agents can operate via API, context that stays agent-agnostic across vendors, and consensus that can be systematically tested. Ramp, he notes, fires identical questions through every interface and measures how often the answers diverge, aiming for zero. ✍️ Macomber's conclusion: a data scientist's worth will be measured not by any single analysis, but by how well their judgment gets encoded into infrastructure so every future agent, decision, and employee inherits what's true and why. URL: #DataStack# #AIAgents#