去中心化存储：人工智能下一次进化的关键 | 观点

人工智能已从一个未来概念迅速发展成为现代生活不可或缺的一部分，预计到 2028 年其市场价值将达到 12,780 亿美元。然而，这种增长也带来了重大挑战，尤其是如何跨网络存储、管理和访问人工智能数据。分散式存储系统提供了一种有前途的解决方案，它提供了增强的可扩展性、效率和安全性，以支持人工智能不断增长的需求，但它们仍可能受到可扩展性、效率和安全性问题的阻碍。

人工智能影响力的激增也推动了数据和电力消耗的急剧增长，预计到 2030 年数据中心的能源使用量将增长 160%。分散式存储系统必须不断发展以满足这些不断增长的需求，确保人工智能的持续成功和可持续性。

当前分散存储面临的挑战

随着人工智能以每年 28% 的速度增长，它给分散存储网络带来了巨大压力。挑战不仅在于管理当前的数据需求，还在于预测未来的需求。人工智能应用需要大量实时数据访问，而现有系统通常难以有效扩展。

当前的去中心化系统在确保数据完整性方面也面临困难。人工智能要准确运行，必须依赖高质量、无偏见的数据。如果没有适当的验证机制，数据操纵或错误的风险就会成为一个严重的问题，可能会破坏人工智能模型的结果。

去中心化存储支持AI的关键要求

传统的中心化存储系统越来越不合适。它们容易受到审查、数据检索速度较慢，并且存在安全漏洞。相比之下，去中心化存储替代方案提供了更高的安全性和抗审查能力，但仍需要解决三个关键因素：可扩展性、速度和安全性。

可扩展性is essential for supporting AI’s rapid growth. Decentralized storage systems must be flexible enough to handle increasing amounts of data without slowing down or compromising performance. Solutions that prioritize automation and adaptive scaling can help meet the needs of growing AI workloads.

速度是另一个重要的考虑因素。机器学习和实时数据处理等人工智能应用需要超快速的数据访问。许多分散式系统并未针对这些高容量、低延迟要求进行优化。为了跟上人工智能的步伐，有必要提高存储检索时间和网络吞吐量。

安全是不可商榷的。由于人工智能依赖准确的数据，任何安全方面的妥协都可能导致错误或操纵的输出。分散存储必须确保数据完整性，利用加密、数据验证和区块链技术来确保防篡改存储。高级安全协议对于保护人工智能的底层数据集至关重要。

去中心化存储的未来之路

For decentralized storage to meet AI’s needs, it must provide data that is both verifiable and tamper-proof. Blockchain technology, for instance, can offer immutable records, ensuring that once data is stored, it cannot be altered without detection. This approach would improve the reliability of AI outputs by preventing data manipulation, which can have cascading consequences for AI applications.

Further, decentralized storage solutions must prioritize interoperability—the ability to integrate with various AI platforms and technologies. AI systems depend on data from multiple sources, so storage systems must support seamless data exchange without creating barriers. This will enable AI to function at its full potential, drawing from diverse datasets without concerns over compatibility or access issues.

Finally, as AI continues to evolve, decentralized storage will need to embrace edge computing capabilities. By distributing data storage closer to the source of AI applications, edge storage minimizes latency and reduces the pressure on centralized data centers. This approach ensures faster access to critical data and supports real-time decision-making, which is vital for AI in fields like autonomous vehicles and smart cities.

Setting the stage for AI-ready decentralized storage

AI requires trusted, real-time access to vast amounts of data. As decentralized storage systems evolve to meet these needs, they must not only focus on secure, immutable data storage but also enable efficient data retrieval and smooth integration across a variety of platforms.

在这个瞬息万变的环境中，去中心化存储的作用将变得比以往任何时候都更加重要。通过与人工智能一起发展，这些系统可以成为创新的支柱，确保人工智能以最高水平的可靠性、速度和安全性运行。有了合适的基础设施，去中心化存储不仅可以支持人工智能，还可以充分发挥其潜力，使各行各业能够在人工智能驱动的世界中创新和繁荣。

瑞安·利维

瑞安·利维is a seasoned executive with nearly 20 years of startup experience across web2, web3, blockchain, and data. A master at “connecting the dots,” Ryan leads business development, partnerships, and go-to-market strategies, building ecosystems across DeFi, Blockchain Networks, Data, RWAs, DePIN, Gaming, and more. Currently Head of BD and partnerships at Moonbeam and DataHaven, Ryan previously held leadership roles as VP of Business Development at SKALE Labs (SKALE Network), Head of Protocols and partnerships at Chainstack, and Head of Partnerships at Kadena. Born and raised in South Africa, Ryan lived in Australia for many years before settling in California. He greets each dawn with an espresso and a workout, which sets a tone of clarity and energy for the rest of his day. His guiding mantra, “Never Give Up,” drives his relentless pursuit of success in both his personal and professional life.