In 2024, the decentralized AI sector saw unprecedented growth. Investors have poured $436 million into the area, up nearly 200% from 2023, according to PitchBook.
This surge coincides with the impressive $214 billion market capitalization of the global AI market this year. The convergence of artificial intelligence and blockchain is changing the way these technologies are developed, accessed, and implemented. But is decentralized AI more than just a speculative trend?
Disrupting Decentralized AI
Decentralized AI integrates artificial intelligence into systems that prioritize distributed ownership, control, and collaboration. Unlike traditional AI models, which are often centralized, decentralized AI operates through trustless structures.
Investors are jumping on the trend now more than ever, with decentralized AI startups raising more money this year than in the previous three years combined.
Projects like SingularityNET exemplify this model, allowing the creation, sharing and monetization of artificial intelligence services. In March 2024, SingularityNET, Fetch.ai and Ocean Protocol announced plans to merge their tokens.
This merger aims to advance joint initiatives in artificial intelligence and democratize access to these technologies. These structures can help reduce dependence on centralized institutions, paving the way for open and fair AI ecosystems.
The rise of decentralized AI is driven by its potential to solve privacy and property issues. These agents can manage wallets, complete transactions, and personalize content while protecting user data.
“Cryptocurrency users already actively own their assets and data, so decentralized AI is ideal, allowing AI agents to work directly for each user. What’s even more interesting is that in cryptocurrency you can collectively own these AI agents. Imagine a DAO collectively owned by an AI that manages its treasury, or a group funding an AI artist to create unique NFTs. It’s about combining the intelligence of artificial intelligence with the transparency and integrity of the blockchain,” said Jawad Ashraf, CEO of Vanar, in an interview with BeInCrypto.
Another key factor is the seamless integration of blockchain and artificial intelligence. Blockchain offers secure data storage, and artificial intelligence processes data and generates analytical information. Community-driven innovation and the attractiveness of shared ownership further drive adoption.
Problems and risks in DeAI
Despite its promises, decentralized AI faces serious challenges. Scalability remains a technical hurdle as current blockchain infrastructure struggles to cope with the resource-intensive demands of AI.
Trust and governance also pose challenges. Transparency and accountability mechanisms are critical to building this trust.
“Scaling large datasets and models on decentralized networks without sacrificing performance is a major hurdle,” said Chi Zhang, CEO of Kite AI, in an interview with BeInCrypto.
Data privacy concerns further complicate implementation. A recent survey by Informatica found that 40% of data leaders consider privacy and data security to be major concerns when implementing generative AI. Frameworks must address these issues to gain widespread user trust.
“Conceptually, one of the most difficult issues is trust. Decentralized AI requires people to trust not only the AI, but the entire network it operates on, meaning that structures need clear and transparent accountability and decision-making mechanisms,” explains Ashraf.
Decentralized AI needs to demonstrate its usefulness to move beyond retail-driven speculation. For example, privacy-preserving AI can securely analyze sensitive medical data without centralizing it.
Financial markets offer another practical use case. Mark Stokich, Head of AI at Oasis Protocol, highlights the role of privacy-enabled AI agents in generating trading signals. These agents protect sensitive data while contributing to the collective intelligence. The key, he says, is to create something that will have value even after the hype dies down.
Moving into the future
Forbes predicts that the global artificial intelligence market will reach $1.339 billion by 2030, a staggering increase from $214 billion this year. This growth highlights the ability of decentralized systems to scale alongside traditional AI.
Stokich envisions these technologies becoming the basis for smart cities, financial instruments and collaboration networks. These use cases have the potential to transform industries by prioritizing user privacy, efficiency, and responsibility.
“It’s not just theoretical. We are seeing real-world applications where decentralized networks provide computing power that would otherwise be impossible to access. In addition, we have finally attracted attention from the crypto world. We see AI PhDs as founders of crypto companies. These aren’t just crypto natives trying to join the AI movement, these are AI experts who recognize the potential of blockchain to solve fundamental problems in the field,” Stokic said in an interview with BeInCrypto.
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 outshines hype.
“Decentralized AI must prioritize equitable development by tokenizing data and modeling contributions to encourage broad participation while reducing dependence on centralized entities. Real-world use cases such as implementing a DeFi strategy, decentralized supply chain management, and privacy-preserving medical diagnostics can demonstrate its practical usefulness. Developing interoperable infrastructures that enable AI to run smoothly across multiple blockchains is essential to promoting scalability and widespread adoption,” said David Pinger, CEO of Warden Protocol, in an interview with BeInCrypto.
Decentralized AI is at a defining moment. Its rapid growth and promising potential must counter serious challenges. This represents both a speculative trend and a transformative technology.
Its growth is driven by privacy, transparency and collaborative innovation. The real test for this sector is whether it can deliver practical and transformative applications.
