Problem Statement

As AI applications rapidly expand, AI projects face growing demands for robust computing power and vast data storage. However, achieving this in a decentralized environment presents challenges. Decentralized systems encounter high requirements in data processing, computation, and storage, whereas traditional centralized solutions lack the flexibility to meet users’ needs for privacy, security, and data sovereignty in a decentralized context.

Mainstream centralized AI platforms (such as Google Cloud AI, AWS, OpenAI) provide efficient computation and data processing but have notable privacy and ownership drawbacks. These platforms typically require users to upload data to centralized servers, relinquishing full control over their data, which may be accessed, exploited, or misused by third parties. Additionally, since data is stored on single servers or data centers, risks of data breaches and privacy violations increase significantly.

Conversely, decentralized AI solutions can effectively address these issues. StratrosAI’s decentralized storage and Data Availability (DA) layer allow users to store data on a distributed network, preserving ownership and control over their data while ensuring privacy and security during processing. Furthermore, through StratrosAI’s restaking feature, users can contribute their assets to strengthen the network's security and stability, achieving long-term yield optimization. This innovative decentralized architecture has garnered significant interest from developers and users alike, laying a foundation for the long-term growth of decentralized AI projects.