AI is collapsing the storage–compute split and rewiring databases
AI workloads are forcing teams to reduce data movement, bring compute closer to data, and adopt databases that handle agent-scale access patterns and vectors by default. AI pipelines repeatedly touch unstructured data and embeddings, making the classic storage–compute separation a cost center; with data prep consuming up to 80% of effort and 93% of GPUs sitting idle from I/O waits, [InfoWorld](https://www.infoworld.com/article/4138058/why-ai-requires-rethinking-the-storage-compute-divide.html) argues for “smart storage” and near-data processing. At the market layer, databases remain the load-bearing core with high switching costs, but AI agents change access patterns, intensifying the Databricks vs Snowflake platform race, per this [Business Engineer analysis](https://businessengineer.ai/p/databricks-snowflake-and-the-ai-database). On the ground, the FrankenSQLite effort bundles vector search, geospatial, and other extensions into a single precompiled SQLite binary, signaling a shift toward lightweight, compute-local capabilities for server-side and AI use cases ([WebProNews](https://www.webpronews.com/frankensqlite-the-audacious-experiment-stitching-together-sqlite-extensions-into-a-single-monstrous-database-engine/)).