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Key Takeaways

• Nvidia’s Jensen Huang insists the company still leads AI chips despite Meta potentially pouring billions into Google TPU purchases.
• Meta’s ballooning AI capex raises questions about long-term returns, even as Google Cloud monetizes AI rapidly.
• Nvidia shares briefly dipped on the Meta-Google reports, but it recovered as the company defended its tech advantage.
• Industry analysts warn that PyTorch teams may face new migration hurdles if TPU adoption accelerates globally.

Nvidia CEO Jensen Huang recently moved to calm investors after reports surfaced that Meta could shift a substantial portion of its AI workloads to Google’s tensor processing units (TPUs).

Speaking at a recent event, Huang emphasized that Nvidia continues to occupy a “unique position” in the global AI semiconductor market, even as competitive pressure intensifies. Huang described the AI compute landscape as “extremely large” and complex, suggesting that even if Meta were to purchase billions of dollars’ worth of TPUs, the opportunity left for Nvidia remains vast.

“We have to keep running very fast,” he said, underscoring both the opportunity and the pace of competition in the sector.

His remarks followed a turbulent week for Nvidia’s stock, which briefly fell after reports that Meta and Google were in advanced discussions over a potential deal involving Google’s in-house AI accelerators. Nvidia’s shares have since rebounded.

Meta’s Massive AI Bet Raises Questions

Meta’s role in the intensifying chip rivalry is hard to ignore. The company recently raised its 2025 capital expenditure estimate to $70-$72 billion, signaling even higher investment in 2026. Much of this spending is tied to compute infrastructure for AI training and inference at scale.

Yet the market responded cautiously despite Meta’s strong earnings. Shares fell more than 12% after the spending announcement, with analysts questioning whether such enormous investment will generate proportionate returns.

Google, meanwhile, has been quietly emerging as a major beneficiary of Meta’s chip ambitions. Alphabet reported a striking 34% year-over-year jump in Google Cloud revenue, hitting $15.15 billion in Q3 2025, growth largely tied to AI-driven services. If Meta begins buying TPUs in bulk, Google’s cloud infrastructure business could receive an even greater boost.

Developers Brace for TPU Migration Challenges

A major shift toward Google hardware would carry implications far beyond Meta. The broader machine-learning community relies heavily on Nvidia’s CUDA ecosystem, which powers the vast majority of today’s AI workloads.

Open-source tools like PyTorch/XLA have made progress enabling model training on TPUs with minimal code modifications. However, engineers note that moving away from CUDA is not as simple as flipping a switch. TPU workflows require careful device reassignment and new approaches to lazy tensor execution, which queues operations differently from traditional GPU pipelines.

Meanwhile, a wave of cross-hardware efforts is rising. The PyTorch/XLA team is transitioning more operations to code-generated kernels, while high-throughput inference engines such as vLLM are beginning to offer experimental TPU support. This could open the door for cloud providers to offer new integration tools and support packages, reducing friction for developers willing to diversify away from Nvidia GPUs.

Nvidia Pushes Back Against Valuation Criticism

This week’s Meta-Google rumors hit Nvidia just as the company was defending its $4.5 trillion valuation amid public scrutiny. A memo circulated to stock analysts and later shared by Bernstein addressed claims from prominent critics, including investor Michael Burry, regarding inventory build-ups and customer payment risks.

Nvidia disputed these characterizations, saying that publicly available data does not support comparisons to historical accounting scandals. The company acknowledged, however, that its new Blackwell chips come with lower gross margins and higher warranty costs.

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