In the development of artificial intelligence (AI) chips, technology giants such as Amazon, Apple and Tesla have tried to create their own dedicated AI chips that replace NVIDIA, but the results did not meet expectations.
Tesla has abandoned its self-developed Dojo supercomputer chip and turned to Nvidia and AMD solutions, indicating that its own chip design has encountered major challenges. These companies are also in a bad position in the competition in the generative AI market, partly because they are lagging behind Nvidia's CUDA platform in terms of core development tools and ecological systems.
Nvidia's CUDA platform has become the industry standard for training and deployment of large language models (LLMs), with high-performance and complete developer tools, which other manufacturers cannot match. Although Amazon AWS has invested a lot of money to develop its own AI chips Trainium and Inferentia, its software is not as convenient as CUDA, resulting in the market adoption rate being lower than expected, and the market share of generative AI is lower than its overall cloud service market.
In terms of Apple, although it continues to strengthen its semiconductor strength and its own AI platform Apple Intelligence, its progress is relatively slow. Apple Intelligence launched in 2024 has brought a variety of AI features such as writing tools, image generation (Image Playground and Genmoji) and Siri optimization, but the most anticipated Siri upgrade supported by a complete large language model is expected to be launched until 2026. Apple still emphasizes privacy protection, and uses AI technology to focus on computing on the device side, which makes competitors who mainly compete on the cloud-oriented ecosystem more difficult.
In addition, OpenAI is based on Nvidia, quickly expanding the market and creating annual income of up to US$12 billion, with more than 7 million weekly active users, highlighting the competitive advantages of starting with Nvidia's mature platform.
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