Google is making a powerful comeback in the AI race, sending shockwaves through the tech industry and challenging the dominance of NVIDIA. In late November, the company received a series of major developments that collectively pushed its parent company, Alphabet, to a market valuation approaching $4 trillion. Just one day, Alphabet’s market cap surged by hundreds of billions of dollars, with the momentum continuing in the following days. Since mid-October, the company’s value has increased by nearly $1 trillion. A key catalyst was a report that Meta is considering a massive purchase of Google’s TPU (Tensor Processing Unit) chips, potentially worth tens of billions of dollars. The news sent NVIDIA’s stock tumbling nearly 7%, while Google’s own shares rose sharply. This move signals a potential shift in the AI hardware landscape, as Google’s self-designed chips begin to directly compete with NVIDIA’s GPU dominance. The renewed attention comes on the heels of the launch of Gemini 3, a new AI model that has performed strongly across major benchmarks like LMArena, excelling in reasoning, coding, and language understanding. For a company that was widely seen as lagging behind after the 2022 launch of ChatGPT, this performance marks a significant turnaround. Google’s Bard, once dismissed as a rushed response, is now being reevaluated as part of a broader resurgence. “Google has been the sleeper giant in the AI race,” said Neil Shah, an analyst at Counterpoint Research. “Now it’s fully awake.” CEO Sundar Pichai has consistently emphasized Google’s “full-stack” AI strategy—developing everything from foundational models and applications to cloud infrastructure and custom silicon. This vertical integration gives Google a unique edge. While OpenAI relies on Microsoft’s cloud and NVIDIA’s chips, Google controls the entire stack, from TPU design to model training and deployment. The TPU, first developed in 2015 for internal use in search, has evolved into a powerful AI accelerator. The latest version, the seventh-generation TPU “Ironwood,” is designed to handle both large-scale training and high-speed inference. Gemini models are trained entirely on TPUs, proving their capability at scale. Until recently, TPUs were used almost exclusively within Google. But with the success of Gemini and rising demand, Google is now aggressively pushing TPU adoption beyond its own walls. In October, AI company Anthropic announced plans to use up to 1 million TPUs, with a deal potentially worth hundreds of billions. Other major players, including Safe Superintelligence (founded by Ilya Sutskever) and Salesforce, have also become TPU customers. The potential Meta deal could be the largest external TPU order to date. If realized, it would mark a major milestone in Google’s effort to break into the AI hardware market. For Google Cloud, this is a game-changer. The unit reported $15.2 billion in revenue in Q3, a 34% year-on-year increase, but still trails behind AWS and Microsoft Azure. TPU sales could help close the gap, with some Google Cloud executives reportedly aiming to capture 10% of NVIDIA’s $49 billion in AI-related revenue by 2025—roughly $50 billion. Google’s data advantage is another key asset. Its vast ecosystem—including Search, Android, and YouTube—generates immense volumes of real-world data, which can be used to train and refine AI models. Unlike some competitors, Google often uses this data internally, avoiding the high costs of data acquisition. However, success in the market is not guaranteed. In consumer AI, Gemini still lags behind ChatGPT. Google claims 650 million users for its Gemini app, but OpenAI reports 800 million weekly users. App download data from Sensor Tower shows ChatGPT outperformed Gemini in October, with 93 million vs. 73 million monthly downloads. In the enterprise space, Google faces strong competition. Microsoft’s deep integration with OpenAI gives it a powerful edge, while Anthropic’s Claude model is already a top choice for many businesses. TPU commercialization also faces hurdles. While Google offers TPU@Premises—allowing customers to deploy TPUs in their own data centers—it’s still a new program. Most developers still rely on Google Cloud, and the TPU software stack remains less mature than NVIDIA’s CUDA, which has become the de facto standard for AI development. Migrating from GPU to TPU requires significant engineering effort, including code rewrites and performance tuning. For companies like Meta, which already has a massive GPU infrastructure, switching to TPU is not a simple or cost-effective move. TPU’s unique ring-based interconnect and optical circuit switching differ fundamentally from traditional GPU clusters, creating technical and operational barriers. Even if Meta does move forward, it’s more likely to use TPUs for inference optimization in specific workloads rather than a full replacement of GPUs. Meta is also developing its own MTIA chip, a more strategic long-term path to reduce dependency on third-party hardware. NVIDIA, far from being passive, is fighting back. When news of Google’s TPU deals emerged, NVIDIA quickly announced a $10 billion investment in OpenAI, securing future access to its next-gen chips. After Anthropic’s TPU deal, NVIDIA offered a similar investment, ensuring continued use of its GPUs. In November, it also announced a $30 billion Azure compute deal with Microsoft, further cementing its position. NVIDIA’s public stance is clear: “We’re happy for Google’s success—but we remain the only platform that can run all AI models, anywhere, with the most performance, flexibility, and portability. Unlike specialized chips, our GPUs are the universal engine of AI.” This confidence reflects a reality: the AI hardware market is still fluid. No single player has a monopoly. Anthropic uses TPU, GPU, and Amazon’s Trainium. Google uses both TPU and NVIDIA chips. Amazon develops its own chips but still offers NVIDIA in the cloud. This “multi-path” approach shows that the market is far from settled. For Google, the TPU push is just one part of a larger story. The real transformation lies in its full-stack strategy—combining data, models, cloud, and hardware into a cohesive, competitive system. After nearly two years of repositioning, the once-sleeping giant is now firmly in the game. The race for AI supremacy is intensifying, and no one can afford to underestimate Google’s comeback.