Challenges Confronting AI

The demand for computing power in artificial intelligence development is surging, leading to increased cost pressures. As AI technology rapidly advances, the demand for computing resources has skyrocketed, but these resources are concentrated in a few large data centers, resulting in uneven resource allocation and waste. Small businesses and individuals struggle to access sufficient computing power, which limits innovation, while some resources remain idle during off-peak times, causing further waste. Additionally, centralized management leads to high costs for using computing power, making it difficult for small and medium-sized enterprises and individual developers to afford, thereby stifling innovation and research and development.

The formation of technological monopolies allows a small number of companies to dominate the AI field, hindering fair competition. At the same time, centralized computing power relies on infrastructure with centralized management, increasing the risk of single points of failure, which can lead to service disruptions and affect system reliability. Moreover, substantial investment is required to ensure effective redundancy and disaster recovery mechanisms.

In the current AI ecosystem, users' personal data is often collected and stored in a centralized manner, leading to an increased risk of privacy breaches and data misuse. Centralized management makes sensitive information vulnerable to attacks, and users' data may be used for commercial purposes without their consent, harming their interests. Additionally, the personalized data users contribute while interacting with AI systems is not fairly compensated, with economic benefits primarily accruing to platform owners. This leads to a disregard for user contributions and decreased engagement, thereby hindering data sharing and technological development.

Furthermore, centralized AI systems deprive users of sovereignty over their personal data, increasing distrust towards the system. Users worry about data misuse or unauthorized sharing, which ultimately reduces their participation and affects data acquisition and model optimization.

Current centralized AI systems limit scalability and flexibility, making it difficult to adapt to ever-changing application demands, which leads to inefficient resource allocation and stifled innovation. At the same time, centralized systems lack self-iteration and optimization capabilities, preventing continuous evolution and resulting in rigidity, decreased competitiveness, and adversely affecting the overall vitality and progress of the ecosystem. Furthermore, centralized architectures pose risks of single points of failure, impacting system reliability and availability, making it challenging to meet the demands of distributed AI, particularly in the fields of edge computing and the Internet of Things, thus restricting the widespread adoption and development of AI applications.