By Dipak Kurmi
The artificial intelligence industry witnessed another seismic tremor in late November as Nvidia, the undisputed titan of AI hardware, saw approximately two hundred fifty billion dollars evaporate from its market capitalization in a single trading session. Shares of the chipmaking behemoth declined by three percent on Tuesday, November twenty-fifth, sending ripples of anxiety through technology markets and reviving uncomfortable memories of an even more dramatic collapse that occurred earlier in the year. While this latest downturn pales in comparison to the catastrophic seventeen percent plunge that erased nearly six hundred billion dollars from Nvidia’s valuation during the DeepSeek frenzy in late January, the underlying implications may prove far more consequential for the future architecture of artificial intelligence computing.
The catalyst for this recent market turbulence was neither a geopolitical crisis nor a fundamental flaw in Nvidia’s business model, but rather the enthusiastic reception of Gemini 3, Google’s latest generation of large language models. Unlike the DeepSeek episode, which centered on fears that more efficient AI models could diminish demand for Nvidia’s expensive graphics processing units, the Gemini 3 development represents a more existential challenge to Nvidia’s dominance. This new model runs entirely on Google’s proprietary tensor processing units, demonstrating that a major technology company has successfully developed cutting-edge AI capabilities without relying on Nvidia’s hardware ecosystem. The significance of this achievement extends far beyond a single product launch, signaling a potential inflection point in the AI industry’s hardware dependency.
Gemini 3 represents Google’s ambitious foray into next-generation artificial intelligence, comprising a suite of models including Gemini 3 Pro, Gemini 3 Pro Image, and Gemini 3 Deep Think reasoning mode. The flagship Gemini 3 Pro is a multimodal reasoning model capable of processing and understanding text, images, audio, and spatial cues across multiple languages. The model offers a one million-input token context window, enabling users to pose longer and considerably more nuanced questions than previous generations allowed. This expanded capacity transforms the nature of human-AI interaction, permitting more sophisticated and contextually rich conversations that more closely approximate human reasoning patterns.
According to Google’s technical documentation, Gemini 3 Pro has been engineered to grasp the intent and context underlying user prompts, theoretically reducing the need for elaborate prompt engineering that has characterized earlier AI interactions. The model employs a technique known as sparse mixture-of-experts, an architectural innovation that enhances computational and cost efficiency by activating only the relevant portions of the neural network for specific tasks. Behaviorally, Google claims the model has been rigorously tested to curb sycophancy, making it less inclined to provide flattering or excessively agreeable responses that have plagued previous AI systems. However, the company acknowledges that hallucinations, the tendency of AI models to confidently generate false information, remain a persistent challenge requiring ongoing attention.
The technical prowess of Gemini 3 has been validated through impressive performance across multiple benchmark assessments. The model has topped the LM Arena leaderboard, a competitive ranking system where AI models are evaluated through blind comparisons, and earned top marks on Humanity’s Last Exam and GPQA Diamond, challenging evaluation frameworks designed to test advanced reasoning capabilities. These quantitative achievements have been complemented by enthusiastic qualitative endorsements from influential technology leaders. Marc Benioff, the founder and chief executive officer of Salesforce, publicly declared that he would not return to using ChatGPT after experiencing Gemini 3’s capabilities. Analysts at prominent investment firms DA Davidson and Bank of America Securities characterized the model as representing the current state of the art and another positive step for Google’s AI ambitions.
The competitive implications of Gemini 3 extend well beyond performance metrics into the fundamental economics of artificial intelligence infrastructure. Major hyperscalers including Google, Microsoft, Amazon, Meta, and Oracle have become extraordinarily dependent on Nvidia’s graphics processing units to train and develop their AI models, while simultaneously renting this computational capacity to AI startups such as OpenAI and Anthropic. This dependency has created a lucrative but potentially vulnerable position for Nvidia, as these technology giants have been quietly investing in developing proprietary, custom-built AI chips designed to reduce reliance on external chipmakers and ultimately decrease operational costs. While these custom chips carry steep upfront development costs that can reach tens of millions of dollars, they promise long-term financial advantages and strategic autonomy.
Google’s tensor processing units represent a critical evolution in this competitive landscape. First launched in two thousand fifteen, TPUs are said to have contributed fundamentally to the invention of the transformer architecture that underlies modern large language models. These specialized processors fall within the category of application-specific integrated circuits, optimized for particular computational tasks rather than the general-purpose flexibility that characterizes graphics processing units. While GPUs have proven effective for training AI models during their development phase, the industry is increasingly focused on optimizing inferencing, the process by which trained models generate outputs on previously unseen data in production environments.
Earlier this month, Google unveiled its seventh generation of TPU chips, codenamed Ironwood, with one million units expected to be deployed by Anthropic to operate its Claude models amid escalating customer demand. According to Gemini 3 Pro’s technical documentation, TPUs are specifically engineered to handle the massive computations involved in training large language models and can accelerate training considerably compared to conventional central processing units. These specialized chips typically incorporate substantial amounts of high-bandwidth memory, enabling the handling of larger models and batch sizes during training, which can ultimately enhance model quality. Currently, Google does not sell its TPUs directly to other companies, instead making their computational power available through Google Cloud services. The company has reportedly been actively pitching firms such as Meta on adopting its specialized AI chips, according to industry reporting from The Information.
The prospect of Meta utilizing Google’s chips to develop AI models not only triggered the recent stock market reaction but also prompted a defensive response from Nvidia. In a post on social media platform X, the chipmaker stated it was delighted by Google’s success while simultaneously asserting that Nvidia remains a generation ahead of the industry as the only platform capable of running every AI model across all computing environments. The company emphasized that its GPUs offer greater performance, versatility, and fungibility compared to application-specific integrated circuits, which are designed for particular AI frameworks or functions. This response, while diplomatically phrased, betrayed underlying anxiety about the competitive threat posed by custom chip development.
Nvidia has historically employed aggressive strategies to maintain customer loyalty, including financing chip purchases through circular deals that have fueled speculation about an artificial intelligence market bubble and its potential collapse. When reports emerged several months ago that OpenAI was considering Google’s chips, Nvidia announced it would invest up to one hundred billion dollars in the company as part of an arrangement ensuring OpenAI would utilize Nvidia’s next-generation hardware. Similarly, Nvidia committed to investing ten billion dollars in Anthropic, reportedly securing an agreement that the startup would use Nvidia’s new hardware alongside Google’s TPU and Amazon’s Trainium chips. Anthropic acknowledged this multi-platform approach in an October blog post, stating it ensures continued advancement of Claude’s capabilities while maintaining strong industry partnerships.
The Gemini 3 episode illuminates a fundamental tension reshaping the AI industry’s technological foundation, as major players seek independence from Nvidia’s hardware ecosystem while the chipmaker fights to preserve its dominance through strategic investments and technological innovation.
(the writer can be reached at dipakkurmiglpltd@gmail.com)
