Oracle co-founder and Chief Technology Officer Larry Ellison has identified what he believes to be the critical weakness in today's artificial intelligence landscape. During Oracle's fiscal second-quarter earnings call for 2026 held in December, Ellison pointed out that all major AI models—from OpenAI's ChatGPT to Google's Gemini and Meta's Llama—are trained on essentially identical publicly available internet data.
The Commoditization of Cutting-Edge AI
Ellison argued that this shared foundation is rapidly transforming advanced AI into a commodity product with minimal differentiation between competing offerings. "All the large language models—OpenAI, Anthropic, Meta, Google, xAI—they're all trained on the same data. It's all public data from the internet," Ellison stated emphatically. "So they're all basically the same. And that's why they're becoming commoditized so quickly."
This observation comes at a time when AI development has reached fever pitch, with companies investing billions in computational resources and research. Yet according to Ellison, this massive investment in foundational models trained on public data may not yield sustainable competitive advantages in the long term.
The Real Value Lies in Private Enterprise Data
Ellison's proposed solution focuses on what he sees as the next frontier in artificial intelligence. Rather than continuing to build better foundational models on public data, he believes the true opportunity lies in enabling AI systems to work effectively with proprietary enterprise data while maintaining stringent security protocols.
The Oracle executive estimates this second wave of AI development will prove "even larger and more valuable" than the current boom in GPU infrastructure and data center construction. This represents a significant shift in perspective about where the real economic value in artificial intelligence will ultimately reside.
Oracle's Strategic Bet on Enterprise AI
Oracle is placing enormous financial bets on this vision of AI's future. The company has dramatically increased its capital expenditure projections, now estimating approximately $50 billion for the full year—a substantial increase from the $35 billion estimate made just three months earlier.
The technology giant argues it possesses a natural advantage in this emerging enterprise AI space, since most high-value private corporate data already resides within Oracle databases. Their AI Data Platform employs sophisticated techniques like Retrieval-Augmented Generation (RAG) that allow AI models to query private information in real-time without compromising security protocols.
Massive Infrastructure Investments and Partnerships
Oracle announced several significant partnerships during its Oracle AI World event in October, demonstrating the scale of its commitment. These include a massive 50,000-GPU supercluster utilizing AMD MI450 chips scheduled for launch in the third quarter of 2026, along with the OCI Zettascale10 supercomputer that will connect hundreds of thousands of NVIDIA GPUs.
By late 2025, Oracle's cloud backlog had already surpassed $500 billion, with artificial intelligence demand serving as the primary driver of this extraordinary growth. These numbers illustrate the tremendous market appetite for advanced AI capabilities within enterprise environments.
Competitive Challenges and Future Uncertainties
Despite Oracle's ambitious plans and substantial investments, Ellison's thesis faces significant competitive challenges. The development of sophisticated synthetic data generation techniques could potentially reduce reliance on exclusive proprietary datasets, leveling the playing field for competitors.
Meanwhile, cloud computing giants including Amazon Web Services, Microsoft Azure, and Google Cloud are racing to develop similar enterprise AI capabilities. These well-established competitors possess substantial resources and existing customer relationships that could challenge Oracle's position in the market.
The central question remains whether Oracle's existing dominance in enterprise database management will provide a decisive advantage in the AI race, or whether the artificial intelligence landscape will evolve in unexpected directions before the company's massive infrastructure investments can deliver substantial returns.
As the AI industry continues to mature, Ellison's observations about commoditization and the shifting value proposition from public to private data may prove prescient—or may represent just one perspective in a rapidly evolving technological revolution that continues to surprise even its most experienced participants.
