Google’s Gemini 3 Surge with TPU Chips Sparks OpenAI ’Code-Red’ Ahead of GPT-5
Google just fired the loudest shot yet in the AI arms race. Its latest model, Gemini 3, isn't just an upgrade—it's a strategic power play, built and accelerated entirely on Google's custom Tensor Processing Units (TPUs). The move has reportedly sent shockwaves through OpenAI, triggering an internal 'code-red' as the pressure mounts for its next-generation GPT-5 response.
The Hardware Gambit
This isn't just about smarter algorithms. Google's decision to run Gemini 3 on its proprietary TPU chips is a masterstroke in vertical integration. It bypasses the costly, supply-constrained market for third-party AI accelerators, giving Google unprecedented control over its development cycle and cost structure. The performance leap is significant enough to force competitors back to the drawing board.
A Shifting Battlefield
The era of competing solely on model size and training data is over. The new front line is the silicon it runs on. Google's maneuver demonstrates that the future of AI dominance will be won by those who control the entire stack—from the foundational hardware to the final user interface. It's a lesson Big Tech knows well, and one that AI pure-plays are now scrambling to learn.
For the finance crowd watching from the sidelines, it's another reminder that in tech, the real money isn't always in the flashy application—it's in selling the picks and shovels. Or in this case, designing the chips that make the magic happen. OpenAI's 'code-red' isn't just about catching up on software; it's a frantic search for a hardware answer they might not own.
Google scales chips and pushes into outside sales
Google now plans to MOVE beyond using TPUs only inside its own cloud. One recent deal alone sent 1 million TPUs to Anthropic, a move valued in the tens of billions of dollars. That single contract shook Nvidia investors.
The concern is simple. If Google sells more TPUs to outside firms, Nvidia faces direct loss of data‑center demand.
Chip analysts at SemiAnalysis now rank TPUs as “neck and neck with Nvidia” for both training and running advanced AI systems. Morgan Stanley says every 500,000 TPUs sold to outside buyers could generate up to $13 billion in revenue for Google.
The bank also expects TSMC to produce 3.2 million TPUs next year, rising to 5 million in 2027 and 7 million in 2028. Analysts said growth in 2027 now looks stronger than earlier forecasts.
Google builds its processors mainly with Broadcom, with added support from MediaTek. The company says its edge comes from full vertical control over hardware, software, and AI models within one system. Koray Kavukcuoglu, Google’s AI architect and DeepMind CTO, said, “The most important thing is that full stack approach. I think we have a unique approach there.”
He also said Google’s data from billions of users gives it DEEP insight into how Gemini works across products like Search and AI Overviews.
Nvidia shares fell last month after The Information reported that Meta had held talks with Google about buying TPUs. Meta declined to comment. Analysts now say Google could strike similar supply deals with OpenAI, Elon Musk’s xAI, or SAFE Superintelligence, with potential added revenue topping $100 billion over several years.
Nvidia defends while the TPU story cuts deeper
Nvidia pushed back after the selloff. The company said it remains “a generation ahead of the industry” and “the only platform that runs every AI model.” It also said, “We continue to supply to Google.” Nvidia added that its systems offer “greater performance, versatility, and fungibility” than TPUs, which it says target specific frameworks.
At the same time, developers now gain tools that ease the switch away from Nvidia’s Cuda software. AI coding tools now help rewrite workloads for TPU systems faster than before. That removes one of Nvidia’s strongest lock‑in defenses.
The TPU story began long before today’s AI boom. In 2013, Jeff Dean, Google’s chief scientist, gave an internal talk after a breakthrough in deep neural networks for speech systems. Jonathan Ross, then a Google hardware engineer, recalled the moment. “The first slide was good news, machine learning finally works. Slide two said bad news, we can’t afford it.” Dean calculated that if hundreds of millions of users spoke to Google for three minutes a day, data‑center capacity WOULD need to double at a cost of tens of billions of dollars.
Ross began building the first TPU as a side project in 2013 while seated NEAR the speech team. “We built that first chip with about 15 people,” he said in December 2023. Ross now runs AI chip firm Groq.
In 2016, AlphaGo defeated world Go champion Lee Sedol, and that historic match became a major AI milestone. Since then, TPUs have powered Google’s Search, ads, and YouTube systems for years.
Google used to update its TPUs every two years, but that cycle was changed to an annual 2 years ago in 2023.
A Google spokesperson said demand is rising on both fronts. “Google Cloud is seeing growing demand for both our custom TPUs and Nvidia GPUs. We will continue supporting both,” the company said.
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