Larry Ellison Says Shared Data Limits Differentiation in AI Models

Shared Training Data Limits AI Differentiation

Oracle cofounder and chief technology officer Larry Ellison has said that the biggest challenge facing today’s artificial intelligence models is their reliance on the same public data sources. According to Larry Ellison, this issue affects nearly all major AI systems, including ChatGPT, Gemini, Grok and LLaMA.

Speaking during an Oracle earnings call, Larry Ellison noted that most large language models are trained on publicly available internet data. As a result, he argued, their core capabilities are increasingly similar, making it difficult for any one model to stand out in the market.

Larry Ellison described shared training data in AI as a structural problem that leads to commoditization. When models are built on overlapping datasets, improvements tend to be incremental rather than transformative, he said.

This approach may accelerate early development, but it also reduces long-term differentiation. Larry Ellison suggested that many AI products now deliver comparable results because their underlying knowledge sources are largely the same.

Why Public Data Creates a Ceiling

Industry observers say public internet data has been essential to the rapid growth of generative AI. However, dependence on open data can create a performance ceiling, particularly as high-quality content becomes saturated across models.

Larry Ellison emphasized that without access to exclusive information, AI developers may struggle to offer unique value. This dynamic, he warned, could limit both innovation and monetization across the AI sector.

Proprietary Data as the Next Competitive Edge

Larry Ellison pointed to proprietary and enterprise data as the next major opportunity for AI advancement. He said models that can securely analyze private business data could deliver more meaningful insights and practical use cases.

Oracle’s strategy, he explained, focuses on enabling AI systems to work with protected datasets while maintaining security and compliance. This, Ellison believes, will be critical to overcoming the limitations created by shared training data in AI.

Implications for the AI Industry

Larry Ellison’s remarks reflect a broader debate within the technology industry about how AI models will evolve. While competition among developers remains intense, the challenge of differentiation is becoming more pronounced.

As companies invest in infrastructure, partnerships and data strategies, the ability to move beyond shared training data in AI may determine which platforms achieve sustained leadership in the next phase of artificial intelligence development.