Nvidia vs Google: How Gemini Ultra and TPUs Threaten the GPU King

Описание: Nvidia’s stock drop was a direct challenge to the core assumption driving the entire AI market.

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Google’s Gemini Ultra the flipped the narrative that every major AI breakthrough must run on Nvidia GPUs by proving frontier AI models can be trained on Google’s own TPU chips, without relying on Nvidia hardware. When the world’s largest AI buyers — Google, Amazon, Meta, Microsoft, Apple, Tesla — begin shifting toward custom silicon, the AI hardware landscape changes. And if Nvidia loses its role as the mandatory supplier of AI compute, the company’s two-trillion-dollar valuation faces real structural pressure.
In this breakdown, we explore why Nvidia’s dominance is suddenly vulnerable, how hyperscalers are using in-house chips to escape GPU bottlenecks, and why this moment mirrors patterns seen with Intel, Cisco, and IBM when their once-inevitable empires hit technological turning points. If you're trying to understand whether Nvidia’s fall marks a long-term trend, a temporary correction, or the beginning of a new AI arms race, this deep dive will give you the historical and economic context you need.

In this video, we break down:
• Why Google’s Gemini Ultra + TPUs shook Wall Street
• How hyperscalers quietly prepared to reduce reliance on Nvidia
• Why custom AI chips (Trainium, Inferentia, MTIA, Athena, Dojo) matter
• What happens when a chokepoint supplier stops being a chokepoint
• The historical pattern: Intel → Cisco → IBM → Nvidia?
• How Nvidia’s valuation gets fragile when customer dependence shifts
If you’ve been feeling like the AI market is changing faster than the headlines admit — you’re not wrong.
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⚠️ Disclaimer
This content is for education and entertainment only and is not financial, legal, or tax advice.
Do your own research and consider speaking with a licensed professional before making major financial decisions.