December 23, 2024
The decentralized AI sector saw unprecedented growth in 2024. According to PitchBook: Investors invested $436 million in the space, representing an increase of nearly 200% compared to 2023.
This surge also coincides with the global AI market’s staggering market cap of $214 billion this year. The convergence of AI and blockchain is reshaping how these technologies are developed, accessed, and deployed. But is decentralized AI more than just a speculative trend?
Decentralized AI Analysis
Decentralized AI integrates AI into systems that focus on distributed ownership, governance, and collaboration. Unlike traditional AI models, which are often centralized, decentralized AI operates through trustless frameworks.
Investors are investing in this trend now more than ever, with decentralized AI startups raising more money this year than the previous three years combined.
Projects like SingularityNET also demonstrate this model by enabling the creation, sharing, and monetization of AI services. In March 2024, SingularityNET, Fetch.ai, and Ocean Protocol announced plans to merge their tokens.
“Crypto users are already interested in owning their assets and data, so decentralized AI fits perfectly by enabling AI agents that work directly for each user,” Vanar CEO Jawad Ashraf told BeInCrypto. “Even more exciting, in crypto, you can have shared ownership of these agents. Imagine a DAO that collectively owns an AI that manages its vault, or a group that funds an AI artist to create unique NFTs. It’s about combining the intelligence of AI with the transparency and fairness of blockchain.”
The seamless integration of blockchain and AI is another key driver. Blockchain provides secure data storage, while AI processes data and generates insights. Community-driven innovation and the appeal of shared ownership drive its adoption.
Challenges and Risks in Decentralized AI
Despite its promise, decentralized AI faces significant challenges. Scalability remains a technical hurdle as current blockchain infrastructure struggles to efficiently handle the resource-intensive demands of AI.
Trust and governance also pose challenges. Transparency and accountability mechanisms are critical to fostering this trust.
“Scaling large datasets and models across decentralized networks without compromising performance is a major hurdle,” Chi Zhang, CEO of Kite AI, said in an interview with BeInCrypto.
Data privacy concerns complicate AI adoption. A recent study by Informatica found that 40% of data leaders identified data privacy and protection as major challenges in adopting generative AI. Frameworks must address these issues to gain widespread user trust.
“Conceptually, one of the most difficult issues is trust,” Ashraf explains. “Decentralized AI requires people to trust not just the AI but the entire network that runs it, which means frameworks need clear and transparent mechanisms for accountability and decision-making.”
Decentralized AI must demonstrate its utility to move beyond retail-driven speculation. For example, privacy-preserving AI could securely analyze sensitive medical data without centralizing it.
Financial markets offer another practical use case. Mark Stockic, head of AI at Oasis Protocol, emphasizes the role of privacy-enabled AI agents in generating trading signals. These agents protect sensitive data while contributing to collective intelligence. According to him, the key is to build something that remains valuable once the noise subsides.
Moving towards the future
Forbes predicts that the global AI market will reach $1,339 billion by 2030, a staggering increase from $214 billion this year. This growth highlights the opportunity for decentralized systems to expand alongside traditional AI.
Stockic envisions these technologies powering smart cities, financial instruments, and collaborative networks. These use cases could transform industries by prioritizing privacy, efficiency, and user ownership.
“This is not just theoretical,” Stockic said in an interview with BeInCrypto. “We are seeing real-world applications where decentralized networks provide computing power that would otherwise be unattainable. Also, we are finally getting some interest from outside the crypto world. We are seeing AI PhDs as founders of crypto companies. These are not just crypto natives trying to jump on the AI bandwagon, they are AI experts who recognize the potential of blockchain to solve fundamental problems in the field.”
To realize its potential, decentralized AI must prioritize real-world applications and sustainable infrastructure. Projects like OG Labs and Warden Protocol are paving the way, showing what’s possible when utility outweighs hype.
“Decentralized AI must prioritize equitable development by tokenizing data and model contributions to incentivize broad participation while reducing reliance on centralized entities,” said David Binger, CEO of Warden Protocol, in an interview with BeInCrypto. “Real-world use cases, such as implementing DeFi strategies, decentralized supply chain management, and privacy-preserving health diagnostics, can demonstrate their practical utility. Developing interoperable frameworks that enable seamless AI operations across multiple blockchains is essential to fostering scalability and broad adoption.”
Decentralized AI is at a critical moment. Its rapid growth and promising potential must face significant challenges. It represents both a speculative trend and a transformative technology.
Its growth is driven by privacy, transparency, and collaborative innovation. The real test of this sector is whether it can deliver practical, transformative applications.