With the launch of its globally distributed Exadata Database on Exascale infrastructure, Oracle is not simply extending its legacy capabilities into new markets, it’s making a bold claim to leadership in distributed data management for AI-native workloads. Oracle is leaning into its DNA, leveraging deep enterprise roots — full-featured SQL support and engineered systems — to assert a differentiated position. Oracle claims its new product is more than just another distributed database offering; rather the company says its latest move represents a convergence of infrastructure, database technology and AI readiness that few, if any, other vendors can match. The underlying thesis is that as AI systems become embedded into mission-critical workflows, customers will need more than speed and scale; they’ll demand automation, consistency, high availability and compliance with data sovereignty laws. Oracle believes it can deliver all of the above in a package that promises a cloud-native, serverless experience that runs across geographies, clouds and business functions. What’s new with this announcement is Oracle’s decision to make these capabilities more accessible and cost-effective through Exascale, which is a serverless version of its engineered Exadata infrastructure. Oracle claims that its distributed database was designed from the ground up to support full SQL syntax and data types. Oracle says it supports full data type coverage and SQL syntax out of the box, making it easier for organizations to lift and shift their applications into a distributed context without rewriting code. This becomes critical in the AI era. One of the most notable aspects of the announcement is Oracle’s direct linkage between distributed databases and the emerging world of agentic AI. Unlike traditional software, agentic systems generate large, bursty, machine-driven traffic patterns and require immediate access to accurate, sovereign-compliant data. Perhaps the most strategically important aspect of Oracle’s offering is its emphasis on co-locating AI with business data. In contrast to many AI architectures that involve lifting data into external stores for vector search and model training, Oracle is bringing AI to the data. By integrating vector search directly into the database engine and accelerating those searches with hardware optimizations via Exadata, Oracle enables real-time inference and retrieval-augmented generation (RAG) workflows directly within the data layer. This convergence simplifies architecture, reduces ETL overhead and ensures data security and compliance. It also means that AI workloads benefit from the same enterprise-grade replication, availability and observability as transactional applications. By combining full SQL support, data sovereignty compliance, active-active replication and embedded AI capabilities in a serverless, elastic form factor, Oracle is presenting a compelling vision of what distributed data infrastructure can and should be in the AI-native enterprise.