Nvidia fired back at mounting competition concerns Tuesday, declaring its GPUs remain "a generation ahead" of Google's tensor processing units as Wall Street weighs whether the search giant's AI chips could finally crack the semiconductor giant's iron grip on AI infrastructure. The defensive stance comes as Nvidia shares tumbled 3% following reports that Meta might ditch some Nvidia hardware for Google's TPUs.
Nvidia just blinked. For the first time since the AI boom began, the chip giant that commands over 90% of the artificial intelligence processor market felt compelled to publicly defend its technological superiority. The trigger? Growing whispers that Google's in-house tensor processing units might actually pose a credible threat to Nvidia's seemingly unshakeable dominance.
"We're delighted by Google's success — they've made great advances in AI and we continue to supply to Google," Nvidia posted on X Tuesday. But the diplomatic tone quickly sharpened: "NVIDIA is a generation ahead of the industry — it's the only platform that runs every AI model and does it everywhere computing is done."
The defensive posture marks a notable shift for a company that's been riding high on AI infrastructure demand. Nvidia shares dropped 3% Tuesday after The Information reported that Meta, one of Nvidia's biggest customers, could strike a deal with Google Cloud to use TPUs for its data centers instead of buying more expensive Blackwell GPUs.
The timing couldn't be more pointed. Earlier this month, Google proved TPUs weren't just theoretical competition when it released Gemini 3, a state-of-the-art AI model trained entirely on the company's custom chips rather than Nvidia hardware. The model's impressive performance sent a clear message: you don't need Nvidia to build cutting-edge AI.
"NVIDIA offers greater performance, versatility, and fungibility than ASICs," the company insisted in its Tuesday statement, referring to application-specific integrated circuits like Google's TPUs. It's a technical argument that highlights the core difference between the two approaches - Nvidia's GPUs can run any AI workload, while Google's TPUs are optimized specifically for certain tasks.
But that specialization might be exactly what large cloud customers want. Unlike Nvidia, which sells individual chips at premium prices, Google doesn't sell TPUs directly. Instead, it uses them internally and lets companies rent access through Google Cloud - potentially offering a more cost-effective path to AI infrastructure.
