"In an AI-infused application, the database stops being a passive store of record and becomes the active boundary between a probabilistic model and your system of record. The difference between a cool demo and a mission-critical system is not usually the large language model (LLM). It is the context you can retrieve, the consistency of that context, and the speed at which you can assemble it."
"We abstracted it behind object-relational mappers (ORM). We wrapped it in APIs. We stuffed semi-structured objects into columns and told ourselves it was flexible. We told ourselves that persistence was a solved problem and began to decouple everything. If you needed search, you bolted on a search system. Ditto for caching (grab a cache), documents (use a document store), relationships (add a graph database), etc."
Developers treated databases as an implementation detail, abstracting them with ORMs, APIs, and semi-structured columns while bolting on search, caching, document, and graph systems. That pattern shifted complexity from the database engine into glue code, pipelines, and operational overhead. AI turns databases into the active boundary between probabilistic models and systems of record, making context retrieval, consistency, and assembly speed central to reliability. AI memory becomes a database problem; the ability to assemble accurate, consistent context at low latency distinguishes experiments from mission-critical systems. The prior proliferation of caches and search clusters exposed architectural fragility now amplified by AI.
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