Core Insights - The article discusses the increasing demand for semiconductors driven by the global AI boom and how major tech companies are accelerating their efforts to reduce reliance on NVIDIA for AI chips [1][2][3] Group 1: Microsoft and Custom AI Chips - Microsoft is in talks with Broadcom to co-develop customized AI chips aimed at enhancing cost-effectiveness and control for data centers, marking a strategic shift in its approach [1] - Previously, Microsoft utilized Marvell technology for some AI chips, but the rapid growth of generative AI models has strained existing supply chains [1] Group 2: Other Tech Giants' Initiatives - Alphabet, Google's parent company, launched the Ironwood TPU v7, which is seen as a direct competitor to NVIDIA's Blackwell GPU, expanding its customer base and enhancing its AI chip capabilities [2] - Amazon's AWS has introduced the Trainium3 AI acceleration chip, which is positioned as a low-cost, high-efficiency alternative to NVIDIA's H100 and B100, with claims of superior performance in specific AI training scenarios [2] Group 3: OpenAI's Collaboration - OpenAI is collaborating with Broadcom to develop its own customized AI chips, expected to be deployed in the second half of next year, in response to the soaring demand for GPT models and to reduce costs [3] Group 4: NVIDIA's Position - NVIDIA's CEO Jensen Huang commented on the competition with companies like Google and Amazon, asserting that few teams can match NVIDIA's capabilities in building complex systems [4][6] - Huang emphasized that while Google’s TPU is competitive, NVIDIA remains superior across all AI segments, maintaining an "irreplaceable" status in the industry [6]