As artificial intelligence becomes increasingly accessible, industry leaders are shifting their focus from AI models themselves to the data, ecosystems, and business solutions built around them. That perspective gained renewed attention after Oracle CTO Larry Ellison shared his views on the future of AI, which were later endorsed by Zoho founder Sridhar Vembu.
Speaking at a recent event, Ellison argued that most leading AI models, including ChatGPT, Claude, Grok, and Llama, have already been trained on the same pool of publicly available internet data. According to him, the next competitive advantage in AI will come from access to proprietary enterprise data rather than the models alone.
“AI models are trained on publicly available data. All the data on the internet,” Ellison said. “But for these models to reach their peak value, you need to train them not just on publicly-available data, but you need to make private, privately-owned data available.”
Responding to the discussion, Vembu agreed with the broader direction of Ellison’s argument. In a post, he stated, “AI is rapidly commoditising. The value shifts to what is built around it.”
The remarks reflect a growing industry belief that foundational AI models could eventually become standard infrastructure, much like cloud computing or web hosting services. As advanced large language models become more widely available, differentiation may increasingly depend on how businesses apply AI rather than the models themselves.
Companies are now focusing on building enterprise workflows, integrating AI into existing systems, leveraging proprietary datasets, creating industry-specific solutions, and strengthening customer relationships. These layers are emerging as key drivers of long-term value in the AI ecosystem.
Ellison’s comments also highlight why enterprise AI has become one of the most competitive segments in the technology industry. While public internet data enables the development of general-purpose AI systems, private business data allows organizations to create more specialized and practical applications.
Internal documents, customer interactions, operational records, and historical business information can help AI systems generate more accurate insights, automate workflows, and improve decision-making. This is one reason many technology companies are investing heavily in secure enterprise AI infrastructure alongside consumer-facing AI products.
The discussion between Ellison and Vembu points to a broader shift in the AI market. While the early race focused on building the most capable models, the next phase may be defined by the ability to combine AI with proprietary data, trusted ecosystems, and real-world business applications.
For enterprises, success with AI may increasingly depend on how effectively the technology integrates into existing operations rather than simply adopting the latest AI model.
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