Last week, data reported that meta is in the process of buying billions of AIs by 2027
Google officially launched its Ironwood TPU in early November. TPU, or TESONOR EXPTION UPTIT, is a regional integrated application program designed for mathematical models of mathematical learning. Unlike the CPUS that handle everyday computing tasks or the GPUS that process graphics and now power machine learning, the TPUS is purpose-built to run AI programs smoothly.
Ironwood’s withdrawal reflects the industry-wide shift: Workloads are evolving towards greater training, more focused on running critical tasks, with greater personality, supporting everything from agentic systems. The transition to that change also examined the Economics of AI, favoring hardware like Ironwood designed for responsiveness and efficiency of Brute-Force Training.
The TPU Ecosystem is gaining momentum, though Real-world adoption remains limited. Korean semiconductor giants Samsung and SK Hynix are reportedly expanding their roles as component manufacturers and chip packaging partners for Google. In October, anthropic announced plans to access a million TPUs from Google Cloud (not to say, but effectively hire them) by 2026 to train and run future generations of its dirty models. The company will use it internally as part of its distributed computing strategy alongside Amazon’s Trainium Asics and Nvidia GPUS.
Analysts describe this moment as “AI is coming back.” “Nvidia can’t satisfy the demand for AI, and other hyperScalers companies like Google and semiconductor companies like their AI infrastructures,” Alvin Nguyen, ONE ONE TORETRETE analyst, “It’s something that builds the AI infrastructure.
These shifts reflect a broader push across major technologies to reduce reliance on nvidia, whose GPU prices and limited availability have limited cloud providers and AI labs. Nvidia still offers Google with Blackwell Ultra GPUS – like the GB300-with its cloud and Iron Center loads, but Ironwood now offers us one of the first reliable paths to greater independence.
Google started developing TPUS in 2013 to manage AI workloads within data centers more effectively than GPUS. The first chips went live in the year 2015 with renovation works before going to expand training with TPU V2 in the year 2017.
Ironwood now powers Google’s Gemini 3 model, which sits at the top of the Benchmark leaderboards in multimodal thinking, text generation and image editing. At X, Salesforce CEO Marc Beniof Beniof called the Gemini 3’s Leap “crazy,” while OpenAi CEO Sam Altman said it “looks like a great model.” Nvidia also praised Google’s progress, noting “that it is happy with Google’s success” and will continue to supply chips to the company, although it added that its GPUS are still “more flexible and self-inflicting than Asics” made by Google.
Nvidia’s dominance under pressure
Nvidia still controls more than 90 percent of the AI Chip market, but the pressure is mounting. Ngiyen said Nvidia is likely to lead the next phase of competition in the near Term, but long-term leadership is likely to be more distributed.
“Nvidia has ‘Golden Handcuffs’: They see the face of AI, but they are forced to continue to suppress creativity according to the Law,” he said. “Semiconductor processes need to continue to improve, software development needs to continue to happen, etc. This keeps them moving products/markets higher. This will give competitors the ability to increase their shares.”
Meanwhile, AMD continues to gain ground. The company is already well equipped with the workloads of softening, updating its hardware in Nvidia, and delivering the existing or higher performance with the same nvidia products. AI’s new AI CHIPS also claim performance advantages and advantages over Nvidia’s current ones, although slower release cycles can change the balance over time.
Google may dethrone nvidia anytime soon, but it has forced the industry to think about the future of TPU-Gemini where TPU-Gemini Stack where TPU-Geminict Stack is combined.
