Source: Forbes

Original author: Sean Lee

Artificial intelligence is rapidly evolving, but the narrative has always been dominated by a few tech giants. While OpenAI, Google, and Meta make headlines, a quieter yet potentially more fundamental transformation is taking place—the rise of decentralized AI (DeAI).

This is not only an innovation of algorithms but also a resistance against centralized control. Users are increasingly wary of black-box systems, hidden data agendas, and power monopolies, but to escape these "walls," it is necessary to reconstruct the infrastructure of AI. Today, multiple projects are directly addressing these challenges, laying the groundwork for redefining the role of AI.

For those building or investing in the decentralized space, understanding this evolution is crucial—because the success or failure of the next wave of AI innovation hinges on whether these alternative infrastructures can be successfully established.

What is the disruption of decentralized AI?

Deploying AI in a trustless decentralized environment fundamentally changes the game: each inference may require cryptographic verification; data calls often need to traverse complex blockchain indexing networks; unlike centralized giants, DeAI projects cannot simply rely on AWS or Google Cloud services for automatic scaling when computational demand surges—unless they abandon their core principles.

Imagine a DeAI model used for community governance: it needs to interact with smart contracts (possibly cross-chain), ensuring privacy through complex cryptography while maintaining operational transparency—this is vastly different from the computational challenges faced by traditional AI analysis.

It is this complexity that has led to the repeated setbacks of early DeAI concepts: projects either sacrifice decentralization for efficiency or are overwhelmed by processing demands. The real turning point came when the development team stopped rigidly applying traditional AI architectures and instead built dedicated systems from scratch, focusing on characteristics such as decentralization, transparency, and user control.

From Blueprint to Mainnet: Application Implementation in Progress

Decentralized AI projects have finally jumped out of the theoretical framework. Multiple teams have deployed practical systems, and these cases not only validate the technical feasibility but also point directly to the inherent flaws of centralized AI.

In the fight against centralized AI black boxes, Kava is becoming a pioneer of the transparency revolution. Its platform deeply integrates decentralized AI components, and co-founder Scott Stuart revealed to us during a talk in Hong Kong that the number of platform users has exceeded 100,000. This genuine demand for an accountable system is shaking the dominance of traditional "black box AI." Through community autonomy and a fully transparent operational mechanism, Kava provides the industry with a tangible alternative.

NEAR Protocol provides scalable infrastructure for high-throughput decentralized applications, greatly enhancing the operational efficiency of DeAI; while Internet Computer (ICP) has pioneered fully on-chain AI applications, ensuring that the entire process from data input to result output meets decentralized security standards.

Battle of Foundations

The special needs of DeAI have exposed key shortcomings in Web3 infrastructure. Akash Network is the first to break through - its DePIN (Decentralized Physical Infrastructure Network) activates idle computing power globally, creating a censorship-resistant, low-cost computing market that provides an alternative to centralized cloud services for AI workloads.

Data availability is another key piece of the puzzle. The Graph optimizes the indexing and querying mechanism of blockchain data, enabling DeAI applications to efficiently access on-chain information, meeting the massive data demands for complex analysis and decision-making while avoiding overload pressure on individual nodes.

These underlying evolutions are reshaping the entire ecosystem. Today's DeAI can tackle more complex tasks—whether optimizing DeFi strategy portfolios or powering decentralized social platforms—without sacrificing the core principles of decentralization.

It is the distributed computing networks built by platforms like Akash that support the actual operation of projects like Kava. This virtuous cycle confirms the chain reaction brought about by breakthroughs in infrastructure: when developers no longer have to choose between "efficiency" and "decentralization," a true paradigm shift becomes possible.

Direction Ahead

The continuous evolution of Web3 infrastructure is opening up unique application scenarios for decentralized AI. Taking DeFi as an example: Kava plans to deploy AI agents later this year that will automate the execution of complex cross-chain strategies or optimize yield farming schemes, using smart encapsulation to alleviate the operational complexity that mainstream users find daunting. This not only requires support from AI algorithms but also relies on seamless interaction across multiple protocols—which is the key value provided by infrastructures like The Graph.

Community governance is another breakthrough. Projects like Dexe are exploring community-driven AI development frameworks that dynamically align model training with user consensus and regulatory requirements. With robust infrastructure support, future AI agents may be able to simulate policy impacts and manage DAO treasuries, achieving true intelligent autonomy.

Beyond Concept Speculation

The success of DeAI cannot solely rely on sophisticated model design or idealistic appeals. Infrastructure providers and application developers still face ongoing challenges such as computing power bottlenecks, cross-chain communication standards, data authenticity verification, and the purity of decentralization.

Many theoretical models will expose their vulnerabilities once they come into contact with the realities of the mainnet. Just ask any DeAI deployment team, and they can list extreme cases that current models struggle to handle—sudden market fluctuations, peak network congestion, governance mechanism loopholes, and so on.

The key to the next phase lies in standardization and interoperability. With the surge in DeAI applications, establishing a unified framework for data, computation, and governance is urgent. Long-term success depends on the ability to build an ecosystem where various components work together seamlessly, rather than a collection of fragmented competitive solutions.

These fundamental elements—robust infrastructure, verifiable data, and flexible governance mechanisms—may not be as eye-catching as breakthroughs in model training. However, they will ultimately determine whether decentralized AI can fulfill its promise of "greater transparency, accountability, and user empowerment," or remain forever trapped in the confines of marginal applications. The teams currently tackling these fundamental challenges are, in fact, shaping the trajectory of AI's future development.

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