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| Oracle CEO: We're Entering The MOST Dangerous Phase Of AI |
Larry Ellison outlines how AI models, having consumed all publicly available internet data, must now integrate private data for peak performance. Oracle is positioning its databases as the secure bridge, enabling reasoning over proprietary information while preserving privacy — a shift with profound implications for business, power, and the future of intelligent systems.
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Hey dear, I'm Rahul Sanaudwala, News Analyst, Founder & CEO of Tap2Call and OyeTools.
These AI models are trained on publicly available data — all the data on the internet. ChatGPT, Anthropic, Grok, Llama, and others have all been trained on that public corpus. But for these models to reach their peak value, they need access not just to public data but to privately owned data as well.
That is where Oracle plays a particularly important role. Most of the world’s high-value data already sits in Oracle databases. The task now is to enable those databases to make that data available to AI models for reasoning, so the models can work across both public and private information.
What Actually Happened
Larry Ellison, founder of Oracle and one of the richest men alive, recently made these points explicitly. He described the current state: companies are spending vast fortunes training multimodal AI models on publicly available internet data. This training phase represents the largest, fastest-growing business in human history, surpassing the railroads or the industrial revolution.
Ellison noted that the real transformation will come when these remarkable electronic brains are applied to humanity’s most difficult problems using private data. AI models reason through deductions, inferencing, calculations, strategy, and rules — faster and on more complex problems than humans.
The limitation so far has been that models lack training on private data because people want to keep it private. Yet users also want these powerful tools to reason over their private information. Oracle is addressing this with its new AI database featuring RAG capabilities. It vectorizes data in Oracle databases, OCI object stores, Amazon cloud storage, or other sources, creating vector indexes so models can reason over it securely without sharing the raw private data.
What Most Coverage Misses
Mainstream discussions often focus on model scale or public data training. The deeper signal is the shift to private data as the next frontier. Ellison stated it directly: the well of public data has run dry. The largest opportunity lies in connecting models to proprietary datasets while maintaining strict privacy.
Oracle’s approach solves the “have your cake and eat it too” problem. The database handles vectorization and indexing, allowing chosen models to reason across public and private sources without exposing the underlying data. This is not a simple integration. It requires secure, high-performance handling of sensitive information across clouds and storage systems.
The real signal here is a deeper shift in where value will concentrate. Control over the infrastructure that safely unlocks private data for AI reasoning becomes a strategic moat. Oracle’s existing position with enterprise databases positions it centrally in this transition.
Why This Really Matters
Ellison highlighted practical applications. Oracle’s first project vectorized its own customer data and used RAG to make it available to models. The system then predicted which customers were likely to buy additional products in the next six months and identified the specific products. An AI agent generated customized outreach emails using the three best references tailored to each prospect’s industry, location, and relationships.
This demonstrates how private data integration enables autonomous, highly contextual action. In robotics and physical tasks, models can learn from internet videos at high speed — playing piano in seconds or performing surgery with microscopic vision and superior hand-eye coordination that outperforms human surgeons like the renowned Dr. Mohs in precision and tissue preservation.
The compute scale is staggering. Ellison described building a 1.2 billion watt AI brain, eventually with half a million Nvidia GPUs — enough power for one million four-bedroom homes in the United States. Oracle is training multimodal models, including the first version of Grok for Elon Musk, and more than any other company in its cloud. This involves not just GPUs but full infrastructure: power generation, natural gas pipelines, transmission, data centers, networks, and advanced identity systems.
Biometrics replace passwords for better security and privacy. Drones with infrared cameras can detect forest fires immediately and even identify arsonists. These systems perceive, reason, and act across domains.
Scenario Analysis
Best case: Secure private data integration accelerates solutions to complex problems in medicine, business optimization, scientific research, and daily life. Enterprises gain massive productivity from reasoning over their proprietary information. Oracle’s AI database becomes the standard bridge, driving adoption while privacy is preserved through technical controls. Compute infrastructure scales responsibly, delivering breakthroughs without compromising data sovereignty.
Likely case: Private data access unlocks significant value in enterprise settings first, with predictive sales, personalized automation, and domain-specific reasoning. Adoption grows among organizations already using Oracle databases. Challenges around implementation security and integration across hybrid environments persist but are managed incrementally. The business of AI training and inference continues its explosive growth, with Oracle playing a central infrastructure role.
Worst case: Technical or governance gaps in private data handling lead to breaches or unintended sharing, eroding trust. Concentration of capability among a few infrastructure providers raises concerns about data power imbalances. Massive energy demands strain resources, and the drive for ever-larger models outpaces safety considerations around autonomous agents acting on sensitive data.
What Happens Next
Key triggers to watch include broader rollout of Oracle’s AI database features, enterprise adoption rates of private data reasoning capabilities, and how competitors respond with their own secure integration solutions. Timelines will be shaped by compute infrastructure buildout — power plants, pipelines, and GPU clusters — and regulatory responses to biometric identification and large-scale data platforms.
Decision points center on how organizations balance the value of AI reasoning on private data against privacy and sovereignty concerns. Ellison’s vision suggests rapid progress in agentic applications and multimodal systems, with Oracle enabling the connection to existing enterprise data stores.
This is part of a broader trend I’ve been tracking: after exhausting public data, the AI race is turning inward toward the vast reserves of private information that power real-world decisions and operations.
Conclusion
Larry Ellison has articulated the next phase clearly. Public data training was the appetizer. Private data, securely vectorized and made available for reasoning while remaining private, represents the main course. Oracle’s AI database, RAG capabilities, and infrastructure scale position the company to facilitate this transition.
The implications stretch from enterprise productivity and robotic capabilities to fundamental questions of data control and energy infrastructure. As these electronic brains grow to city-scale power consumption and integrate deeply with private records, the systems we build today will determine how intelligence is applied — and to whose benefit.
I’ll continue tracking this closely.
5 FAQs
- What is the core limitation of current AI models according to Ellison? They have been trained primarily on publicly available internet data. To reach peak value, they need access to privately owned data for reasoning.
- How does Oracle enable AI to use private data securely? Its new AI database uses RAG capabilities to vectorize data from Oracle databases, OCI object stores, Amazon cloud storage, or other sources, creating accessible indexes while keeping the raw private data secure and unshared.
- What was Oracle’s first internal project with this technology? They vectorized customer data, predicted which customers would buy additional products in the next six months and which specific products, then had an AI agent generate customized sales emails with tailored references.
- What scale of compute is Oracle building for these models? A 1.2 billion watt AI brain with up to half a million Nvidia GPUs, requiring full infrastructure including power generation, natural gas pipelines, transmission, data centers, and networks. They are training multiple multimodal models, including Grok.
- What other applications did Ellison highlight? Superior AI surgeons with microscopic vision and precision, robots learning skills rapidly from videos, biometric identification replacing passwords for better privacy, and drones for immediate forest fire detection and arson identification.
Thanks for reading. The public internet was only ever the appetizer. I’d value your thoughts on what private data integration means for the future of AI. I’ll be watching how this develops.