待翻譯：The Shape and Feel of the Post-AI Data Stack

AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。

Every step in the evolution of data stacks expanded the blast radius of a data scientist. Here’s what I’ve seen so far, and here’s where we’re going in the post-AI era. Overview The pre-modern data stack (~2013) limited a data scientist’s perspective (data moats, small data, structured data). The transition to cloud data warehouses (~2016) expanded their scope to “ALL company data.” The rise of the Modern Data Stack + Reverse ETL (~2020) turned data scientists from reporters into operators. Each technological shift expanded what a data team could build and accomplish. Today (~2026), we have entered the era of the “Post-AI Data Stack”. AI makes producing analysis cheap. It does not make agreeing on reality cheap. Today’s data teams have two jobs: Enable everyone to build with data and AI: accurately, powerfully, independently. Build and champion the singular reality their company operates on. Coding agents, AI-native vendors, and Slackbot analysts have made it easy for anyone to answer their own questions and generate their own narratives. As data questions get more personalized and dashboard building gets cheaper, the scarce resource becomes company-wide consensus. The post-AI data stack allows data teams to encode expert judgement into the infrastructure that allows agents to produce correct analysis without the data scientist in the room. This shift yet again expands the blast radius of a data scientist: from a “producer of analysis” to the builder of a company’s reality on what’s true, what matters, and why.