There are evaluations pointing out that Nvidia (NVDA) and OpenAI (OpenAI) still hold the core dominance in the artificial intelligence (AI) market. Although the market currently has high expectations for Google's (GOOGL) Gemini model and its self-developed Tensor Processing Unit (TPU), the real limitations within the AI industry, such as cost and revenue structure, architectural constraints, and supply chain bottlenecks, are expected to restrict its breakthrough capabilities.
According to the analysis proposed by SiliconANGEL, Nvidia's GB300 chip and its subsequent product Rubin are capable of redefining the entire AI economics, diagnosed as optimized for high-performance and high-scalability AI factories. Particularly, as the training and inference of large-scale language models evolve towards centralized communication and bandwidth orientation, GPU-based systems are likely to continue maintaining a technological advantage over TPU. This is also the reason why Google, even while attempting to promote TPU, ultimately had to adopt a parallel strategy alongside GPU-based products.
Moreover, the study also noted that the current excessive expectations in the TPU market are less about substantial market disruptions and more about temporary responses due to supply shortages. The analysis pointed out that TSMC's CoWoS packaging capacity, which is core to the AI accelerator supply chain, is inherently insufficient, while NVIDIA is expected to account for over 60% of that output before 2027. Consequently, NVIDIA has ensured both cost advantages and supply chain advantages within the GPU-centered AI ecosystem.
In terms of software and platforms, OpenAI's performance is also quite impressive. Notably, in the core metric of 'user engagement time,' which is more critical than the growth of monthly active users, ChatGPT still maintains an advantage over Gemini, and the adoption rate by enterprises is rapidly expanding to 40%. OpenAI excels in APIs, application ecosystems, and enterprise support, and based on its close relationship with NVIDIA, it may gain a strategic advantage in priority allocation of GPU resources in the future.
On the other hand, Google faces the challenge of fundamentally changing its business model, which is centered around search advertising, in order to enter the AI era. The original search was an ultra-efficient structure that could generate high returns at a low cost per click, but the AI-assisted interface requires more than ten times the computational resources for each interaction, leading to a sharp increase in cost structure. Furthermore, if it cannot quickly adjust the revenue conversion model based on the new user expectation of providing trustworthy results, the user churn in its core revenue areas (such as high-profit product searches) may accelerate.
Ultimately, the direction of the AI ecosystem is expected to be influenced by platform integration capabilities, economics, and trust-based revenue models, rather than purely technical strength. The analysis assessed that NVIDIA has already achieved the unique learning curve effect of the semiconductor business in terms of scale and cost, while OpenAI is the leading company in extending this effect to the software and service sectors. The conclusion is that the comprehensive cooperation between the two companies is likely to form a strong virtuous cycle structure in the reconstruction of the next-generation platform around AI factories.