Is the windfall a false proposition? What is the value of the AI + Web3 track?

The rise of AI Crypto is not just talk, but a bottom-up system reconstruction.

Written by: TinTinLand

Entering 2025, the narrative of "AI + Web3" remains as hot as ever. According to the latest report released by Grayscale in May 2025, the overall market value of the AI Crypto sector has reached $21 billion, achieving nearly five times growth compared to $4.5 billion in the first quarter of 2023.

Behind this wave, is it a true integration of technology, or yet another concept packaging?

From a macro perspective, the traditional AI ecosystem has revealed an increasing number of structural issues: high barriers to model training, lack of data privacy protection, highly monopolized computing power, black-box inference processes, and imbalanced incentive mechanisms... These pain points align perfectly with the native advantages of Web3: decentralization, open market mechanisms, on-chain verifiability, and user data sovereignty.

The combination of AI and Web3 is not just a mere overlap of two popular terms, but a structural technological complement. Let’s start from the current core pain points faced by AI and delve into those Web3 projects that are effectively addressing these issues, helping you to understand the value and direction of the AI Crypto track.

🤖 The threshold for accessing AI services is too high, and the costs are expensive.

Currently, AI services are often expensive, and acquiring training resources is difficult, creating a high barrier for small and medium-sized enterprises and individual developers. Additionally, these services are often technically complex and require a professional background to get started. The AI service market is highly concentrated, with users lacking diverse choices, opaque calling costs, unpredictable budgets, and even facing issues of computing power monopolization.

The solution of Web3 is to break down platform barriers through a decentralized approach, build an open GPU market and model service network, support flexible scheduling of idle resources, and incentivize more participants to contribute computing power and models through on-chain task scheduling and a transparent economic mechanism, thereby reducing overall costs and improving the accessibility of services.

represents the project

• Render Network: Focused on decentralized GPU rendering, it also supports AI inference and training, adopting a "pay-per-use" model to help developers access image generation and AI services at a low cost.
• Gensyn: Building a decentralized deep learning training network that uses the Proof-of-Compute mechanism to verify training results, promoting the shift of AI training from platform centralization to open collaboration.
• Akash Network: A decentralized cloud computing platform based on blockchain technology, where developers can rent GPU resources on demand for deploying and running AI applications, is the "decentralized version of cloud computing."
• 0G Labs: A decentralized AI native Layer-1 that significantly reduces the cost and complexity of running AI models on-chain through an innovative storage and computing separation architecture.

🤖 Lack of Incentives for Data Contributors

High-quality data is the core fuel of AI models, but under traditional models, data contributors find it difficult to receive compensation. The lack of transparency in data sources, strong redundancy, and the absence of feedback on usage methods have led to a long-term inefficiency in the data ecosystem.

Web3 offers a new solution paradigm: through cryptographic signatures, on-chain rights confirmation, and composable economic mechanisms, it creates a clear collaboration and incentive loop among data contributors, model developers, and users.

represents the project

• OpenLedger: Innovatively proposes the concept of "Payable AI", combining data contribution, model invocation, and economic incentives to promote the formation of an AI on-chain collaborative data economy network.
• Bittensor: A complete incentive system centered around TAO rewards, Yuma consensus mechanism, precise subnet incentives, and knowledge collaboration, directly linking data contributions to model performance outcomes, thereby enhancing overall value contribution.
• Grass: An AI data network that collects user browsing behavior data through plugins, contributing to the training of on-chain search engines. Users are rewarded based on data quality, creating a community-driven data sharing mechanism.

🤖 Model black-boxing, AI reasoning cannot be verified

The inference process of current mainstream AI models is highly opaque, making it difficult for users to verify the correctness and credibility of the results, especially in high-risk fields such as finance and healthcare. In addition, models may be subject to tampering, poisoning, and other attacks, making it challenging to trace or audit.

To this end, Web3 projects are trying to introduce Zero-Knowledge Proofs (ZK), Fully Homomorphic Encryption (FHE), and Trusted Execution Environments (TEE) to make the model inference process verifiable and auditable, enhancing the interpretability and trust foundation of AI systems.

represents the project

• Sentient: Ensure that invocation behavior is traceable through innovative model fingerprint recognition technology, enhancing model usage transparency and tamper resistance.
• Modulus Labs: Utilizing ZK technology for encrypted verification of the model inference process, achieving a new paradigm of "trusted AI".
• Giza: Utilizing zero-knowledge cryptography to put machine learning inference calculations on-chain, thereby enhancing the transparency and trust of AI model deployment.

🤖 Privacy and Security Risks

The AI training process often involves a large amount of sensitive data, facing risks such as privacy breaches, model misuse or attacks, and a lack of decision transparency. At the same time, the ownership of data and models is vaguely defined, further exacerbating security risks.

By leveraging the immutability of blockchain, cryptographic computing technologies (such as ZK, FHE), trusted execution environments, and other means, ensure the security and controllability of AI system data and models throughout the entire process of training, storage, and invocation.

represents the project

• Phala Network: Provides support for Trusted Execution Environments (TEE), encapsulating critical computations within secure hardware to prevent data leakage and model theft.
• ZAMA: Focused on fully homomorphic encryption (FHE) technology, enabling model training and inference to be conducted in an encrypted state, achieving "calculations without needing plaintext."
• Mind Network: A decentralized AI data sharing and inference platform that supports privacy protection, achieving secure data sharing and privacy computing through cutting-edge cryptographic technologies (such as homomorphic encryption, zero-knowledge proofs, etc.).
• Vana: an AI identity generation application designed to allow users to regain ownership and control over their own data, ensuring the privacy and security of that data.

🤖 AI Model Copyright and Intellectual Property Disputes

Currently, AI model training relies heavily on internet data, but often uses copyrighted content without authorization, leading to frequent legal disputes. At the same time, the copyright ownership of AI-generated content is unclear, and the distribution of rights among original creators, model developers, and users lacks a transparent mechanism. Cases of models being maliciously copied or misappropriated are also common, making intellectual property protection difficult.

Web3 utilizes on-chain verification mechanisms to store evidence of the model's creation time, sources of training data, contributor information, and more, while using tools such as NFTs and smart contracts to identify the copyright ownership of the model or content.

represents the project

• Story Protocol: Build an on-chain intellectual property protocol that allows AI content, code, models, etc. to be modularly certified, combined, and authorized, realizing the mechanism of "creation is certification, invocation is payment."
• Alethea AI: A generative AI model (such as characters, voices, etc.) bound to on-chain identities and NFTs, where each AI character has clear creator and copyright information to prevent abuse and plagiarism.

🤖 Lack of Decentralized AI Governance

The development and evolution of current AI models heavily rely on large tech companies or closed teams, with an opaque model update rhythm and difficult-to-correct value biases, which can easily lead to algorithmic bias, abuse, and a trend of "technological feudalism." Communities and users often cannot intervene in the model's update path, parameter adjustments, or behavioral boundaries, lacking mechanisms for effective supervision and correction of AI systems.

The advantages of Web3 lie in programmable governance and open collaboration mechanisms. With on-chain governance, DAO mechanisms, and incentive structures, key aspects such as the design, training objectives, and parameter updates of AI models can gradually incorporate community consensus, enhancing the democracy, transparency, and diversity of model development.

represents the project

• Fetch.ai: Introduces Autonomous Economic Agents (AEA) and an open governance mechanism, allowing the behavior of AI agents to be constrained by community rules, and coordinating cooperation among agents through economic incentives.
• SingularityNET: Packages AI services into composable on-chain modules, allowing users to choose or replace models in an open marketplace, with the platform governance mechanism supporting consensus evaluation and improvement proposals for model quality and services.

🤖 Cross-chain AI Collaboration Issues

In a multi-chain environment, AI agents and models may be distributed across different blockchains, making it difficult to unify state, context, or invocation logic, resulting in a fragmented user experience, complex development, and difficulty in data synchronization.

Some projects are exploring the "Multi-Chain AI Protocol," attempting to promote the continuity and consistency of AI agents running across chains through shared context, cross-chain communication, and state synchronization mechanisms.

represents the project

• OpenPond: Uses the MCP cross-chain protocol to connect AI models and agents across different chains, enabling synchronization of call states and context sharing, simplifying multi-chain collaboration scenarios.
• Lava Network: Provides cross-chain RPC and data bridge services, enabling underlying communication channels for multi-chain AI systems, supporting agent data synchronization and unified task execution.
• Virtuals Protocol: Supports cross-agent requests, negotiations, execution, and settlement processes through the ACP (Agent Commerce Protocol) smart collaboration protocol. Its "Parallel Hypersynchronicity" technology enables AI agents to run in parallel across platforms, synchronizing behaviors and memories in real-time.

🎯 Conclusion

The rise of AI Crypto is not mere talk, but a bottom-up systemic reconstruction: it breaks the centralized shackles of the era of large models, gradually building a new paradigm of AI that is participatory, transparent, trustworthy, and collaboration-driven across dimensions such as computing power, data, incentives, security, and governance.

The field has now transitioned from the conceptual stage to the substantial product implementation phase. It is believed that AI Crypto projects that can genuinely create real value and solve core pain points will have the opportunity to lead the next wave of development in the AI era, promoting the advancement of artificial intelligence technology towards a more open, fair, and trustworthy direction.