Oracle’s Larry Ellison Just Exposed the Fatal Flaw in ChatGPT, Gemini, and Every Major AI Model

By DailyAIWire Desk | January 27, 2026 | 4 minutes read

 

Oracle cofounder reveals why ChatGPT, Gemini, and all AI models are fundamentally the same—and the multi-trillion dollar solution nobody’s talking about.

You’re using ChatGPT for work. Your colleague swears by Gemini. Another team member just switched to Claude. Here’s the uncomfortable truth Oracle cofounder Larry Ellison just revealed: you’re all using basically the same thing.

During Oracle’s Q2 2026 earnings call, Ellison dropped a bombshell that should make every business leader rethink their AI strategy. “All the large language models—OpenAI, Anthropic, Meta, Google, xAI—they’re all trained on the same public data from the internet,” he said. “So they’re basically the same.”

Why This Actually Matters to You

Think about what that means for your business. ChatGPT doesn’t know your customer orders. Gemini hasn’t seen your sales quotes. Claude can’t access your supplier contracts. These AI models are brilliant at general knowledge but clueless about the specific information that actually runs your company.

That’s not a bug. It’s the entire business model collapsing in real-time.

Ellison isn’t just throwing shade. He’s calling out the $500 billion elephant in the room: AI companies are racing to build slightly better versions of the exact same thing. It’s like having five different grocery stores all selling identical products at identical prices.

The Real Problem Nobody Wants to Talk About

Here’s where it gets interesting. AI models are running out of quality data to train on. Researchers from Oxford and Cambridge published a study in *Nature* warning about “model collapse”—what happens when AI trains on AI-generated content instead of human-created material. After a few generations, these models start producing increasingly bland, homogeneous outputs.

We’re watching the internet become a self-eating ouroboros of synthetic content. And every major AI model is dining at the same mediocre buffet.

Ellison’s Multi-Trillion Dollar Solution

Oracle’s pitch is simple but radical: stop training new models on public data and start connecting existing models to private enterprise data. Your company’s actual information. The stuff that matters.

Oracle built what they call an “AI Database” and “AI Data Platform” that lets multiple AI models—ChatGPT, Grok, Gemini, Llama—securely access proprietary business data through vectorization and RAG (Retrieval-Augmented Generation) technology. You keep your data locked down. The AI gets smarter about your specific needs.

The market potential? Ellison claims AI reasoning on private enterprise data will dwarf even the multi-trillion dollar AI training market.

What This Means for Your Business

If Ellison is right, we’re about to see a massive shift. The companies that win won’t have the best AI models—they’ll have the best data strategies. Oracle’s reported pipeline of $455 billion (up 359% year-over-year) suggests customers are betting he’s onto something.

Think about it: would you rather have an AI that knows everything about ancient Rome or one that knows everything about your top 100 customers?

Oracle’s positioning itself as the “world’s largest custodian of high-value private enterprise data” with 50 years of database expertise. Whether they execute is another question entirely.

The Uncomfortable Questions

Here’s what business leaders should be asking right now:

How secure is our proprietary data? If AI companies are desperate for differentiation, your datasets just became extremely valuable. Who has access? What happens if your competitors get similar capabilities?

Are we prepared for this shift? Most companies haven’t even figured out how to use public AI models effectively. Adding private data integration adds layers of complexity that require serious infrastructure and security protocols.

What Happens Next

Ellison envisions “electronic brains” consuming 1.2 billion watts of power (compared to a 20-watt human brain) solving humanity’s toughest problems. Robot surgeons with microscopic precision. Autonomous drones detecting forest fires. AI sensors that can literally smell cancer.

Whether Oracle delivers on this vision or gets outmaneuvered by AWS, Google Cloud, and Microsoft Azure remains to be seen. But one thing is clear: the AI race just changed lanes.

The question isn’t which AI model you should use anymore. It’s whether you have a data strategy that matters.

The Bottom Line

Every major AI model trained on public internet data faces the same limitation: they’re becoming commodities. The next frontier isn’t building better AI—it’s connecting AI to the private data that actually drives business decisions.

Oracle’s making a massive bet that this shift will create a market even larger than the current AI boom. Whether they’re right won’t depend on the technology. It’ll depend on whether businesses trust them with their most valuable asset: proprietary data.

That’s the real AI revolution nobody saw coming.

Key Takeaways:

• All major AI models (ChatGPT, Gemini, Claude, Grok) are trained on identical public internet data
• This creates rapid commoditization with little meaningful differentiation
• The solution is integrating AI with secure private enterprise data
• Oracle projects this market will exceed the current AI training market
• Companies with strong proprietary datasets will gain competitive advantages
• The shift requires sophisticated infrastructure and security protocols