This document mainly focuses on the development of Chinese AI application companies going global in 2025. In simple terms, it mainly covers the following topics:
First, it clarifies what AI application companies going global are—Chinese companies that promote their self-developed AI products and services to overseas markets. It also introduces the key elements supporting their operations, such as computing power infrastructure, marketing services, cross-border payments, etc., as well as the research subjects and scope of this report.
Then, it analyzes the main driving forces for Chinese AI application companies to go global. Technologically, Chinese AI companies have made breakthroughs in core algorithms and large model performance, lead in the number of patents, and the application costs are also decreasing; in the market, global demand for AI applications is growing rapidly, different regions have different market opportunities, and mature AI application models in China have the potential to be replicated overseas; in terms of policy, many countries and regions around the world have introduced relevant policies to promote the development of artificial intelligence, also emphasizing compliance and other aspects.
Next, it explains the current development status of Chinese AI application companies going global. This includes the industry landscape of going global, with fields such as AI productivity tools and AI audio & video being key areas; the regional distribution of going global, mainly concentrated in Asia-Pacific, Europe, and North America, with the Middle East and South America having great potential; as well as the types of companies going global, business models, commercial data growth, and technology types, etc.
After that, it provides in-depth insights into the development needs of these companies. They face challenges such as insufficient global computing power layout, high and lengthy cross-border payment costs, and single marketing channels. Accordingly, they have core needs for computing power infrastructure, localized marketing services, cross-border payment and financial compliance solutions, etc. It also analyzes in detail the different needs for computing power infrastructure during the training and inference stages, their dependence on GPU clouds, and the key considerations when choosing computing power service providers.
Furthermore, focusing on seven key areas—AI productivity tools, emotional companionship, AI audio & video, education, gaming, AI terminals, and embodied intelligence—it analyzes their differentiated needs for computing power infrastructure, including latency, storage, computing, elastic scaling, and other aspects.
Finally, it looks ahead to future trends for AI application companies going global, such as the deep integration of infrastructure with AI application scenarios, the explosive growth of AI infrastructure software value, the construction of localized operations and compliance systems becoming important competitive advantages, and the integration of multimodal technologies broadening the boundaries of AI application scenarios, etc.
