Oracle CEO Larry Ellison: The Future of AI Depends on Private Data
In a standout keynote at Oracle AI World 2025, Oracle Executive Chairman and CTO Larry Ellison delivered a clear message that stood out from the usual AI hype. He argued that the next major leap in artificial intelligence will not come primarily from building larger models or adding more computing power. Instead, the real breakthrough will come from securely connecting AI systems to private, high-value data.
The Limitation of Public Data
Today, most AI models are trained on vast amounts of publicly available information scraped from the internet. While this has produced impressive general-purpose tools, Ellison pointed out that this approach has inherent limitations. Public data is the same for everyone; therefore, the real competitive advantage lies in data that is unique to each organization.
According to Ellison, the highest value will come from AI systems that can reason over private enterprise data such as customer records, transaction histories, internal business metrics, and proprietary information.
Oracle’s Vision: Secure Access to Private Databases
Oracle is actively building the infrastructure to make this possible. Through advancements in its database technology, including the recently announced Oracle Database 26ai, the company is enabling AI models to securely analyze and reason over private data while respecting strict governance and compliance rules.
“You can have your cake and eat it too,” Ellison remarked. Companies can now gain powerful, context-aware AI insights without compromising data privacy or security.
The Critical Importance of Sensitive Data: The NHS Example
One of the most compelling examples of why private data matters involves healthcare systems like the UK’s National Health Service (NHS). The NHS holds some of the richest longitudinal health data in the world; spanning decades of patient records, treatment outcomes, genetic information, and population health trends.
This type of data is incredibly valuable for AI development because it allows models to identify patterns, predict disease outbreaks, personalize treatments, and improve clinical decision-making at scale. However, due to strict privacy regulations and ethical concerns, this data has remained largely inaccessible to general AI training.
Ellison’s vision suggests a future where such sensitive datasets can be securely connected to AI systems. This would enable groundbreaking medical research and operational improvements while maintaining patient confidentiality through advanced governance controls. The ability to reason over anonymized or securely governed NHS-style data could accelerate discoveries in precision medicine and public health significantly.
Early Success Stories
Oracle has already started applying these capabilities internally. In one notable project, the company used AI to analyze its own customer database. The models were able to answer targeted questions such as: Which customers are likely to purchase additional products in the next six months, and what specific products might they be interested in?
This demonstrates how AI can move beyond generic responses and deliver highly relevant, business-specific intelligence when given access to private data.
Balancing Innovation and Privacy
A key part of Ellison’s message was the emphasis on responsible implementation. He stressed that this new capability is built with strong security controls, data isolation, and customer oversight. Organizations remain in full control of their data and can define exactly how and when AI systems are allowed to access it.
This approach aims to address growing concerns around data privacy while still unlocking the full potential of artificial intelligence for enterprises.
What This Means for Businesses and the Industry
For companies that have invested years in building rich databases of customer and operational data, Ellison’s vision presents a significant opportunity. Those who can safely integrate their private data with advanced AI systems may gain a lasting competitive edge in the coming years.
As artificial intelligence evolves from broad, general tools to specialized, domain-specific solutions, access to unique private data could become one of the most important differentiators.
The bottom line is clear: Public data helped AI reach its current level of capability. However, private data will drive the next phase of meaningful innovation and business value.
