Decentralized storage: The key to AI’s next evolution | Opinion

AI has rapidly evolved from a futuristic concept to an essential part of modern life, with a projected market value of $1,278 billion by 2028. However, this growth comes with significant challenges, especially in how AI data is stored, managed, and accessed across networks. Decentralized storage systems offer a promising solution, providing enhanced scalability, efficiency, and security to support AI’s expanding needs, but they can still be hindered by scalability, efficiency, and security issues.

The surge in AI’s influence also drives an exponential increase in data and power consumption, with data center energy use expected to rise by 160% by 2030. Decentralized storage systems must evolve to meet these rising demands, ensuring AI’s continued success and sustainability.

Challenges with current decentralized storage

As AI grows at an annual rate of 28%, it puts significant pressure on decentralized storage networks. The challenge lies not only in managing present data needs but also in anticipating future demands. AI applications require vast, real-time data access, and existing systems often struggle to scale effectively.

Current decentralized systems also face difficulties in ensuring data integrity. For AI to function accurately, it must rely on high-quality, unbiased data. Without proper verification mechanisms, the risk of data manipulation or errors becomes a serious concern, potentially undermining the outcomes of AI models.

Key requirements for decentralized storage to support AI

Traditional centralized storage systems are becoming increasingly inadequate. They are prone to censorship, slower data retrieval, and security vulnerabilities. Decentralized storage alternatives, in contrast, offer greater security and censorship resistance but still need to address three critical factors: scalability, speed, and security.

Scalability 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.

Speed is another vital consideration. AI applications, such as machine learning and real-time data processing, demand ultra-fast data access. Many decentralized systems are not optimized for these high-volume, low-latency requirements. Enhancements in storage retrieval times and network throughput will be necessary to keep pace with AI.

Security is non-negotiable. With AI’s reliance on accurate data, any compromise in security can lead to faulty or manipulated outputs. Decentralized storage must ensure data integrity, leveraging encryption, data validation, and blockchain technology to ensure tamper-proof storage. Advanced security protocols will be critical in protecting AI’s underlying datasets.

The path forward for decentralized storage

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.

In this rapidly changing landscape, the role of decentralized storage will become more integral than ever. By evolving alongside AI, these systems can become the backbone of innovation, ensuring AI operates with the highest levels of reliability, speed, and security. With the right infrastructure in place, decentralized storage will not just support AI—it will enable its full potential, empowering industries to innovate and thrive in an AI-driven world.

Ryan Levy

Ryan Levy 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.