Research comparing AI memory architectures found SQL databases matching or exceeding performance of purpose-built vector stores and graph databases on retrieval and reasoning tasks. The study gained substantial community attention on HackerNews, suggesting practitioners recognize relevance to current production deployments. This challenges the assumption that specialized memory systems are necessary for AI applications. If SQL-based approaches prove sufficient, teams may reduce architectural complexity and consolidate infrastructure around existing database platforms rather than adopting multiple specialized stores. This affects both initial system design and long-term maintenance burden. Builders currently evaluating memory systems should test SQL baselines before committing to vector or graph infrastructure. Teams with existing SQL deployments may defer or eliminate specialized store purchases, reducing operational overhead and licensing costs. The finding implies that architectural differentiation in AI memory may derive from query patterns and data volume rather than fundamental capability gaps between storage types.