1.3 Bottlenecks of Centralized AI Infrastructure

1. High Costs and Monopolistic Pricing:

Centralized cloud providers typically offer computational resources at relatively high prices, with pricing models often favoring large enterprise clients. Small and medium-sized developers lacking bargaining power and economies of scale face significantly higher cost barriers. As AI model capabilities continuously evolve, integrating text, visual, auditory, and other multimodal abilities, the average cost per user is rising substantially. Developers relying on these advanced models consequently bear increasing cost pressure, especially when fully dependent on centralized cloud infrastructure. Furthermore, end-users accustomed to free or low-cost internet services are often unwilling to directly bear these rising costs, further shifting the financial burden onto application developers.

2. Risk of Censorship and Centralized Control:

Concentrating global computational resources in the hands of a few dominant providers introduces significant risks related to censorship and loss of neutrality. These centralized entities have the unilateral authority to impose usage restrictions, monitor user activity, and potentially censor or shut down applications that do not conform to their internal policies. As AI systems continue to develop and integrate more deeply into socio-economic domains, this concentration of power becomes increasingly severe. The control exercised by a few participants could result in the economic benefits from efficiency gains being captured by these entities rather than being broadly distributed among ecosystem participants and society at large. This hinders the reduction of AI technology costs and its democratization. This centralization trend threatens the openness and accessibility of AI technology and ultimately stifles innovation by limiting the diversity of applications and models that can be developed and deployed.