If the Modern Data Stack isn't yet dead, it's at least incredibly sleepy. AI is bringing the "long run" closer than ever — here's what might come next for ETL, BI, data warehouses, and the role of data engineers.

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The Modern Data Stack is facing disruption as AI and LLMs rapidly change how data work gets done. Key predictions include: humans will write very little SQL as LLMs take over, context layers will become the heart of the new data stack (replacing structured metrics languages like LookML), data modeling will grow in importance, and data engineers will shift toward managing agents and change rather than writing pipelines. ETL tools and BI vendors face commoditization or irrelevance, while data warehouse vendors will survive but lose margins and data gravity. The MDS swim-lanes (ingestion, transformation, orchestration, BI) will collapse into agent-driven architectures backed by query engines. A text-to-SQL scandal is predicted for 2026 but won't slow adoption.

Future Casting the Modern Data Stack