Jensen Huang's Nvidia Exit: What Happens To The Global AI Boom? (AI-generated) Amid Jensen Huang’s exit from Nvidia, the AI industry has lost one of its most influential figures. The company Huang built has emerged as the world’s most important supplier of AI chips. While the company continues to face the implications of Huang’s alleged multiple affairs, the consequences may extend far beyond Nvidia, potentially affecting tech giants like Anthropic, Google and OpenAI. For years, Nvidia's powerful graphics processing units (GPUs) have powered the development of advanced AI models, including the technology behind ChatGPT and Anthropic's Claude. The company has also invested billions of dollars in AI companies and infrastructure projects, making it an important part of the industry's financial and technological ecosystem. Nvidia announced Huang's resignation on November 12, 2026, and appointed its Chief Financial Officer Colette Kress as interim CEO. The company said it would begin searching for a permanent successor. To understand the possible impact of Huang's departure, it is important to understand how Nvidia became central to the AI revolution. The company's journey into AI computing gained momentum after the 2012 ImageNet competition, when AlexNet demonstrated how GPUs could significantly improve deep learning. Nvidia's GPUs subsequently became important tools for researchers and technology companies working on increasingly complex AI models. Today, Nvidia offers more than just chips. Its CUDA software platform and networking technologies allow developers to build and operate large AI systems using multiple processors. We at Times Now spoke to Jibu Elias, India Lead at the Mozilla Foundation and co-creator of IndiaAI, to understand how Nvidia's technology has become central to the AI revolution. He explained, “Ever since AlexNet’s breakthrough at the 2012 ImageNet competition demonstrated the transformative potential of GPUs for deep learning, Nvidia has become integral to the AI revolution. Today, its advanced GPUs, from the H100 to newer architectures, are central to the computing infrastructure powering companies such as OpenAI and Anthropic. Access to this computing capacity has become a major strategic differentiator in the global AI race, giving the United States a significant advantage while placing countries with restricted access at a disadvantage.” Elias added, “Nvidia’s dominance also extends beyond chips to its CUDA software ecosystem and networking infrastructure, making it extremely difficult to replace. While alternatives are emerging, reducing the American AI industry’s dependence on Nvidia at scale will take considerable time and investment.” This is important because developing advanced AI models requires enormous computing resources. Without access to powerful processors and the infrastructure needed to connect them, companies could find it difficult to train and operate increasingly advanced AI systems. OpenAI and Anthropic are among the companies that could feel the effects of any major change in Nvidia's business strategy. Nvidia is not just a chip supplier for these companies. It has also become a financial backer of the AI industry. The company has announced major investment commitments involving OpenAI and Anthropic, alongside its role in supplying computing infrastructure. According to a TechCrunch report, Nvidia had committed $30 billion to OpenAI and $10 billion to Anthropic. The scale of these financial relationships highlights how closely the chipmaker is connected to the companies developing advanced AI models. A change in leadership could raise questions about whether Nvidia will continue making similar investments and supporting large infrastructure projects at the same pace. However, this does not mean that OpenAI or Anthropic will immediately face a shortage of computing resources. Both companies have relationships with other technology and cloud infrastructure providers. Anthropic, for instance, also uses Google's TPU technology and Amazon's Trainium chips. Google is another important player in this equation. It develops its own AI processors, known as Tensor Processing Units (TPUs), and operates its Gemini AI ecosystem. Its in-house chip technology could become more attractive to companies looking to reduce their dependence on Nvidia. Therefore, while Huang's exit may create uncertainty around Nvidia's future investments, it could also encourage AI companies to diversify their computing infrastructure. Neil Shah, Industry Analyst and Co-Founder at Counterpoint Research, explained how Nvidia's technology has supported different stages of AI development, from training large language models to running generative and agentic AI systems. Shah said, “NVIDIA has been the bedrock of AI era enabling every wave of AI since OpenAI’s ChatGPT in 2023 from first training the LLMs to Generative AI inferencing to Agentic AI inferencing. This enablement has been possible thanks to the