Agentic AI market surges from $8B today to $300B by 2035 while memory limits fuel vector database infrastructure for agentic AI and long-context solutions.

According to @DeepLearningAI, researchers from Google and Johns Hopkins show that single-embedding retrievers cannot, in principle, retrieve all relevant document combinations as databases scale, identifying theoretical limits tied to embedding size that set realistic expectations for retrieval augmented generation systems, source: @DeepLearningAI. For traders, these limits are directly relevant when assessing AI infrastructure and data indexing narratives in crypto that depend on RAG and vector databases, source: @DeepLearningAI.