The interaction of artificial intelligence (AI) and blockchain emerges. (Photo illustration by … More
An increasing number of blockchains is actively Seeking to integrate AI Abilities, but this growth of “AI on blockchain” is accompanied by important challenges, in particular the question of “chain silos”, which fragment the sector and can retain the full achievement and the usefulness of the decentralized AI (Deai) potential. After all, if there is no widely available use case scenario, how do we continue the story and innovation in the Deai sector currently already testing?
Take Autonomous native blockchain of AI agents For example, while a specific census of AI agents on the blockchain is elusive, the available data strongly suggest a rapid and dynamic landscape. The number is probably in hundreds to thousands of thousands of thousands when you are considering individual deployed agents on various platforms and projects. All these AI agents reside in a dispersed landscape of chains. It is like when computers could not communicate with other computers before the invention of the World Wide Web, consequently, the full potential of computers could not be unleashed.
While Centralized Ai Suffering from silos of data controlled by companies, the risks of AI creating new silos in the blockchain if interoperability is not prioritized, blocking the full potential of Deai.
This fragmentation does not simply concern data residing on different books. It extends to unique protocols, intelligent contract languages, virtual machinery environments, consensus mechanisms and the overall operational logic of each distinct blockchain.
For example, a Deai application built to take advantage of the specific characteristics of Ethereum and its EVM May not be able to interact natively with or use AI models deployed on a non -EVM channel like Solana without using complex and potentially unsecured bridging solutions. Likewise, AI agents formed in the environment of a chain may have trouble functioning effectively elsewhere. This leads to scenarios where separate databases or non -communicating tools on different chains become isolated islands of Deai activity.
Fragmentation problems, similar to those observed in decentralized identity systems or electronic health health files due to platform incompatibilities, can limit the scalability and impact of Deai solutions.
Super cross -chain applications building
The vision of the Deai community extends beyond isolated applications on unique blockchains. The construction of “super ia applications” becomes a key mission for many.
Imagine it as a global platform or a network of integrated services that take care of various AI features – such as sophisticated data analysis, distributed model training, autonomous deployment of agents and complex decision -making – in different, often varied and disappeared blockchain environments. Such an application would not be limited to the resources or limits of a single chain.
On the one hand, specialized blockchains of layer 1 such as Bittensor and humann are designed from zero with specific Deai requirements in mind. These platforms aim to provide optimized environments for tasks such as processing of high volume data, intensive calculation or mechanisms for incentive for unique AI models, depending on the premise that L1 for general use may not be ideally adapted to the distinct requests of Deai.
On the other hand, many prominent Deai applications and protocols, such as Ocean Protocol and Singelaritynet, were initially launched on L1 for general use established like Ethereum and are now looking for multi -hole strategies.
Then, a key debate appeared: engaging in a specialized L1 for potentially higher tailor -made performance but a smaller initial ecosystem, or building on / through the L1 / L2 established to exploit more widely but with possible limits in the specific IA optimizations?
Inevitably, successful Deai platforms will be relying more and more on reliable and functional cross-chain capacities to access larger markets, liquidity and data sources, whatever their fundamental architecture, thus avoiding the “even” silo-zoking “that they aim to overcome.
AI challenges on the blockchain
The realization of the vision of Super AI applications is however responsible for significant challenges.
1. Technical hedges
- Incompatibility of the protocol and fragmentation of data
- Vulnerabilities of safety of transversal bridges and limitations of scalability and transaction speed
2. Challenges in the governance and standardization of data
- Establishing consistent and effective data governance on several autonomous blockchain networks presents a formidable challenge for transversal AI
3. challenges in the AI model and the interoperability of agents through blockchains
- The unique environments of individual blockchains also create specific challenges for the interoperability of AI models and autonomous agents
Far for AI on the blockchain, but promising
Despite these challenges, the players in the industry proactively explore solutions and standardization for super applications with the cross channel, including leaders like BSC and Solana, although it is still at an infantile stage. In the meantime, innovations in protocols, platforms and conceptual frames also take shape to build a more interconnected Deai ecosystem which can potentially become real use for novice internet users. This trend is inevitable, driven by the enormous potential in the synergistic advantages of AI and blockchain. The inherent characteristics of blockchain can take up some of the most urgent challenges of AI, while AI can unlock new features and efficiency for decentralized systems, such as network optimization and intelligent resources, or automated safety audit, etc. For the advantages and advantages of AI on the blockchain on the centralized AI, I discussed in my previous articles:
How to resolve data collection challenges for AI needs of your business
Deepseek lesson: the future of AI is decentralized and open-source
Top 5 decentralized data collection suppliers in 2025 for the IA company
