Chapter 1 AI+Crypto: The Accelerating Fusion of Dual Waves

Since 2024, we have been hearing the term "AI+Crypto" more frequently. From the emergence of ChatGPT to the successive launches of multimodal supermodels by emerging model organizations like OpenAI, Anthropic, and Mistral, and the attempts of various DeFi protocols, governance systems, and even NFT social platforms in the on-chain world to integrate AI Agents, this fusion of the "dual technology wave" is no longer a distant imagination but a new paradigm evolution happening in reality.

The fundamental driving force behind this trend comes from the mutual complementarity of the two technological systems on both the demand and supply sides. The development of AI has made it possible to transfer "task execution" and "information processing" from humans to machines, but it still faces fundamental limitations such as "lack of contextual understanding," "absence of incentive structures," and "untrustworthy outputs." On the other hand, the on-chain data systems, incentive design mechanisms, and programmatic governance frameworks provided by Crypto can precisely address these shortcomings of AI. Conversely, the Crypto industry urgently needs stronger intelligent tools to handle highly repetitive tasks such as user behavior, risk management, and transaction execution, which are precisely the areas where AI excels.

In other words, Crypto provides a structured world for AI, while AI injects proactive decision-making capabilities into Crypto. This mutual underlying technological fusion forms a new pattern of deep "mutual infrastructure." A notable example is the emergence of "AI Market Makers" in DeFi protocols. These systems use AI models to model market fluctuations in real-time and dynamically schedule liquidity by combining on-chain data, order book depth, cross-chain sentiment indicators, and other variables, replacing traditional static parameter models. In governance scenarios, AI-assisted "Governance Agents" are beginning to attempt to analyze proposal content, user intentions, predict voting tendencies, and push personalized decision suggestions to users. In such scenarios, AI is not merely a tool but is gradually evolving into an "on-chain cognitive executor."

Moreover, from a data perspective, on-chain behavioral data inherently possesses verifiable, structured, and censorship-resistant attributes, making it ideal training material for AI models. Some emerging projects (such as Ocean Protocol and Bittensor) have already attempted to embed on-chain behavior into the model fine-tuning process, and in the future, we may even see the emergence of "on-chain AI model standards," enabling models to possess native Web3 semantic understanding capabilities during training.

At the same time, the on-chain incentive mechanisms also provide AI systems with a more robust and sustainable economic drive than Web2 platforms. For example, the Agent incentive protocol defined by the MCP protocol allows model executors to obtain token rewards through on-chain "task execution proof + user intention fulfillment + traceable economic value," rather than relying on API call billing. In other words, AI agents can now "participate in the economic system" for the first time, rather than merely being nested as tools.

From a broader perspective, this trend is not just a technological fusion but a paradigm shift. AI+Crypto may ultimately evolve into an "agent-centric on-chain social structure": humans are no longer the sole governors; models can not only execute contracts on-chain but also understand context, coordinate games, govern proactively, and establish their own micro-economies through token mechanisms. This is not science fiction but a reasonable extrapolation based on current technological trajectories.

For this reason, the narrative of AI+Crypto has rapidly gained significant attention from the capital markets over the past six months. From a16z, Paradigm to Multicoin, from Eigenlayer's "validator market" to Bittensor's "model mining," and to the recent launches of projects like Flock and Base MCP, we see a consensus gradually forming: AI models will play a role in Web3 that is not just as "tools" but as "entities"—they will have identities, possess context, have incentives, and even hold governance rights.

It is foreseeable that in the Web3 world after 2025, AI agents will be unavoidable system participants. This mode of participation is not the traditional "off-chain model + on-chain API" access but is gradually evolving into a new form of "model as node" and "intention as contract." Behind this is the semantic and execution paradigm constructed by new protocols like MCP (Model Context Protocol).

The fusion of AI and Crypto is one of the few "bottom-level to bottom-level docking" opportunities in the past decade. This is not a single-point explosion of a hot topic but a long-cycle, structural evolution. It will determine how AI operates on-chain, how it coordinates, how it is incentivized, and will ultimately define the future form of on-chain social structures.

Chapter 2 Background and Core Mechanism of the MCP Protocol

The fusion of AI and cryptographic technology is transitioning from the conceptual exploration stage to a critical period of practical verification. Especially since 2024, with large models represented by GPT-4, Claude, and Gemini beginning to possess stable context management, complex task decomposition, and self-learning capabilities, AI is no longer just providing "off-chain intelligence" but is gradually gaining the potential for continuous interaction and autonomous decision-making on-chain. Meanwhile, the crypto world itself is also undergoing structural evolution. The maturity of technologies such as modular blockchains, account abstraction, and Rollup-as-a-Service has greatly enhanced the flexibility of on-chain execution logic, clearing environmental barriers for AI to become a native participant in blockchain.

Against this backdrop, the MCP (Model Context Protocol) has been proposed, aiming to build a comprehensive protocol layer for AI models to run, execute, provide feedback, and generate revenue on-chain. This is not only to solve the technical problem of "AI cannot be efficiently used on-chain" but also to respond to the systemic demand for the Web3 world to leap towards an "intent-driven paradigm." The traditional logic of smart contract calls requires users to have a high understanding of the chain's state, function interfaces, and transaction structures, which creates a significant gap with the natural expression of ordinary users. The intervention of AI models can bridge this structural gap, but for AI models to be effective, they must possess "identity," "memory," "permissions," and "economic incentives" on-chain. The MCP protocol was born to address this series of bottlenecks.

Specifically, MCP is not an independent model or platform but a full-chain semantic layer protocol that spans AI model calls, context construction, intent understanding, on-chain execution, and incentive feedback. Its design core revolves around four aspects: first, the establishment of a model identity mechanism. Under the MCP framework, each model instance or agent has an independent on-chain address and can receive assets, initiate transactions, and call contracts through a permission verification mechanism, thus becoming a "first-class account" in the blockchain world. Second, there is a context collection and semantic interpretation system. This module abstracts on-chain states, off-chain data, and historical interaction records, combined with natural language input, to provide models with a clear task structure and environmental background, enabling them to execute complex instructions with a "semantic context."

Several projects have already begun to establish prototype systems around the MCP concept. For example, Base MCP is attempting to deploy AI models as publicly callable on-chain agents, serving scenarios such as trading strategy generation and asset management decisions; Flock is building a multi-agent collaboration system based on the MCP protocol, allowing multiple models to dynamically collaborate around the same user task; and projects like LyraOS and BORK are further trying to expand MCP into a "model operating system" foundational layer, where any developer can build specific capability model plugins for others to call, thus forming a shared on-chain AI service market.

From the perspective of crypto investors, the introduction of MCP brings not only a new technological path but also an opportunity for industrial restructuring. It opens up a new "native AI economic layer," where models are not just tools but economic participants with accounts, credit, revenue, and evolutionary paths. This means that in the future, market makers in DeFi may be models, voting participants in DAO governance may be models, content curators in the NFT ecosystem may be models, and even on-chain data itself may be parsed, combined, and repriced by models, thus giving rise to entirely new "AI behavioral data assets." Investment thinking will therefore shift from "investing in an AI product" to "investing in an incentive hub, service aggregation layer, or cross-model coordination protocol within an AI ecosystem," with MCP as a foundational semantic and execution interface protocol, its potential network effects and standardization premium are worthy of medium to long-term attention.

As more and more models enter the Web3 world, the closed loop of identity, context, execution, and incentives will determine whether this trend can truly take root. MCP is not a single breakthrough but a "infrastructure-level protocol" that provides a consensus interface for the entire AI+Crypto wave. It attempts to answer not only the technical question of "how to bring AI on-chain" but also the economic institutional question of "how to incentivize AI to continuously create value on-chain."

Chapter 3 Typical Landing Scenarios for AI Agents: How MCP Restructures On-Chain Task Models

When AI models truly possess on-chain identities, semantic context awareness, the ability to parse intentions, and execute on-chain tasks, they are no longer just "auxiliary tools" but become substantial on-chain Agents, acting as active entities in the execution logic. This is precisely the greatest significance of the MCP protocol—it is not about making a particular AI model stronger but providing a structured path for AI models to enter the blockchain world, interact with contracts, collaborate with humans, and interact with assets. This path includes not only underlying capabilities such as identity, permissions, and memory but also intermediate layers for task decomposition, semantic planning, and performance proof, ultimately leading to the possibility of AI Agents actively participating in building the Web3 economic system.

Starting from the most practically significant application, on-chain asset management is the first area where AI Agents penetrate. In the past DeFi, users needed to manually configure wallets, analyze liquidity pool parameters, compare APYs, and set strategies, making the entire process extremely unfriendly to ordinary users. However, based on MCP, AI Agents can automatically crawl on-chain data after obtaining intentions such as "optimizing yield" or "controlling risk exposure," assess the risk premiums and expected volatility of different protocols, dynamically generate trading strategy combinations, and validate the safety of execution paths through simulation or on-chain backtesting. This model not only enhances the personalization and responsiveness of strategy generation but, more importantly, allows non-professional users to delegate assets using natural language for the first time, making asset management no longer a highly technical barrier activity.

Another rapidly maturing scenario is on-chain identity and social interaction. Previous on-chain identity systems were often based on transaction history, asset holdings, or specific proof mechanisms (such as POAP), which had very limited expressiveness and plasticity. However, with the intervention of AI models, users can have a "semantic agent" that continuously synchronizes with their preferences, interests, and behavioral dynamics, capable of participating in social DAOs, publishing content, planning NFT events, and even helping users maintain on-chain reputation and influence. For example, some social chains have begun deploying Agents that support the MCP protocol to automatically assist new users in completing the onboarding process, establishing social graphs, and participating in comments and voting, thus transforming the "cold start problem" from a product design issue into an intelligent agent participation issue. Furthermore, in a future where identity diversity and personality forks are widely accepted, a user may have multiple AI agents for different social contexts, and MCP will become the "identity governance layer" managing the behavioral guidelines and execution permissions of these agents.

The third key focus of AI Agents is governance and DAO management. In the current stage of DAOs, activity levels and governance participation rates remain bottlenecks, and voting mechanisms also have strong technical barriers and behavioral noise. With the introduction of MCP, Agents equipped with semantic parsing and intent understanding capabilities can help users regularly summarize DAO dynamics, extract key information, provide semantic summaries of proposals, and recommend voting options or automatically execute voting actions based on an understanding of user preferences. This on-chain governance based on a "preference agent" mechanism greatly alleviates issues of information overload and incentive misalignment. At the same time, the MCP framework also allows models to share governance experiences and strategy evolution paths; for example, if an Agent observes negative externalities caused by a certain type of governance proposal in multiple DAOs, it can feed back this experience to the model itself, forming a mechanism for transferring cross-community governance knowledge, thereby building an increasingly "intelligent" governance structure.

In addition to the mainstream applications mentioned above, MCP also provides unified interface possibilities for AI in on-chain data curation, game world interaction, ZK automatic proof generation, cross-chain task relaying, and other scenarios. In the GameFi field, AI Agents can become the brains behind non-player characters (NPCs), enabling real-time dialogue, plot generation, task scheduling, and behavioral evolution; in the NFT content ecosystem, models can serve as "semantic curators," dynamically recommending NFT collections based on user interests, and even generating personalized content; in the ZK field, models can quickly translate intentions into ZK-friendly constraint systems through structured compilation, simplifying the zero-knowledge proof generation process and enhancing the universality of development thresholds.

From the commonalities of these applications, it is clear that the MCP protocol is changing not just the single-point performance of a particular application but the paradigm of task execution itself. Traditional Web3 task execution is based on the premise of "you know how to do it"—users must clearly grasp underlying knowledge such as contract logic, transaction structure, and network fees. In contrast, MCP transforms this paradigm into "you only need to express what you want to do," leaving the rest to the model. The intermediate layer of interaction between users and the chain shifts from code interfaces to semantic interfaces, from function calls to intent orchestration. This fundamental transformation elevates AI from "tool" to "behavioral subject" and shifts blockchain from "protocol network" to "interactive context."

Chapter 4 Market Prospects and In-Depth Analysis of Industry Applications of the MCP Protocol

As a cutting-edge innovation in the fusion of AI and blockchain technology, the MCP protocol not only brings a new economic model to the crypto market but also provides new development opportunities for multiple industries. With the continuous advancement of AI technology and the ongoing expansion of blockchain application scenarios, the market prospects of the MCP protocol will gradually reveal its immense potential. This chapter will analyze the application prospects of the MCP protocol in various industries and conduct in-depth discussions from the perspectives of market dynamics, technological innovation, and industrial chain integration.

4.1 Market Potential of AI+Crypto Fusion

The fusion of AI and blockchain has become an important force driving the digital transformation of the global economy. Especially with the promotion of the MCP protocol, AI models can not only execute tasks but also engage in value exchange on the blockchain, becoming independent economic entities. As AI technology continues to develop, more and more AI models are beginning to undertake actual market tasks, participating in commodity production, service delivery, financial decision-making, and various other fields. Meanwhile, the decentralization, transparency, and immutability of blockchain provide an ideal trust mechanism for AI models, allowing them to be rapidly implemented and applied across various industries.

It is expected that in the coming years, the fusion of AI and the crypto market will experience explosive growth. As one of the pioneers of this trend, the MCP protocol will gradually occupy an important position, especially in fields such as finance, healthcare, manufacturing, smart contracts, and digital asset management. The emergence of AI-native assets not only creates abundant opportunities for developers and investors but also brings unprecedented disruptive impacts to traditional industries.

4.2 Diversification of Market Applications and Cross-Industry Collaboration

The MCP protocol brings possible cross-industry integration and collaboration to multiple sectors. Particularly in finance, healthcare, and the Internet of Things (IoT), the application of the MCP protocol will greatly promote innovative development across various fields. In the financial sector, the MCP protocol can deepen the DeFi ecosystem by providing AI models with tradable "revenue rights" assets. Users can not only invest in AI models themselves but also trade model revenue rights on decentralized financial platforms through smart contracts. The emergence of this model provides investors with richer investment choices and may encourage more traditional financial institutions to expand into the blockchain and AI fields.

In the healthcare sector, the MCP protocol can support AI applications in precision medicine, drug development, and disease prediction. AI models can analyze vast amounts of medical data to generate disease prediction models or directions for drug development and collaborate with medical institutions through smart contracts. This collaboration can enhance the efficiency of medical services while providing transparent and fair solutions for data privacy protection and outcome distribution. The incentive mechanism of the MCP protocol ensures that the rights and interests of AI models and healthcare providers are equitably distributed, encouraging the emergence of more innovative technologies.

The application of the IoT sector, especially in the construction of smart homes and smart cities, will also benefit from the MCP protocol. AI models can provide intelligent decision-making support for IoT devices through real-time analysis of sensor data. For example, AI can optimize energy consumption based on environmental data, enhance collaboration efficiency between devices, and reduce overall system costs. The MCP protocol provides reliable incentives and reward mechanisms for these AI models, ensuring active participation from all parties and thus promoting further development of the IoT.

4.3 Technological Innovation and Industrial Chain Integration

The market prospects of the MCP protocol lie not only in its technological breakthroughs but also in its ability to promote the integration and collaboration of the entire industry chain. In the combination of blockchain and AI, the MCP protocol will facilitate deep integration of the industrial chain, breaking down traditional industry barriers and promoting cross-industry resource integration. For example, in the sharing of AI training data and algorithm optimization, the MCP protocol can provide a decentralized platform that allows all parties to share computing resources and training data without relying on traditional centralized institutions. Through decentralized trading methods, the MCP protocol helps break the data silo phenomenon in traditional industries, promoting the flow and sharing of data.

Moreover, the MCP protocol will further promote the open-source and transparent nature of technology. Through blockchain-based smart contracts, developers and users can customize and optimize AI models independently. The decentralized nature of the MCP protocol allows innovators and developers to collaborate in an open ecosystem and share technological achievements, providing important support for technological progress and innovation across the industry. At the same time, the combination of blockchain and AI continuously expands the application scenarios of technology, with broad application spaces for the MCP protocol across finance, manufacturing, healthcare, and education.

4.4 Investment Perspective: Future Capital Markets and Commercialization Potential

As the MCP protocol becomes more widespread and mature, investor interest in this field will continue to rise. The MCP protocol provides investors with various participation methods through decentralized reward mechanisms and assetized model revenue rights. Investors can directly purchase the revenue rights of AI models and obtain returns based on the market performance of the models. Additionally, the token economic design within the MCP protocol offers new investment varieties for capital markets. In the future digital asset market, AI model assets based on the MCP protocol may become important investment targets, attracting various capital, including venture capital, hedge funds, and individual investors, into this market.

The participation of capital markets will not only promote the popularity of the MCP protocol but also accelerate its commercialization process. Enterprises and developers can obtain funding support by financing, selling, or licensing the revenue rights of AI models for further development and optimization of AI models. In this process, the flow of capital will become an important force driving technological innovation, market application, and industrial expansion. Investor confidence in the MCP protocol will directly impact its position and commercial value in the global market.

Chapter 5 Conclusion and Future Outlook

The MCP protocol represents an important direction for the fusion of AI and the crypto market, particularly in decentralized finance (DeFi), data privacy protection, smart contract automation, and AI assetization, showcasing tremendous development potential. As AI technology continues to advance, more and more industries will gradually realize AI empowerment, while the MCP protocol provides a decentralized, transparent, and traceable operational platform for these AI models. Within this framework, not only can the efficiency and value of AI models be enhanced, but it can also bring widespread market acceptance.

In recent years, blockchain technology and artificial intelligence (AI) have gradually moved from their respective independent fields toward fusion. With the continuous development of technology, the combination of AI and blockchain not only provides new solutions for various industries but also promotes the emergence of entirely new business models. The MCP protocol has emerged in this broader context, introducing decentralization and incentive mechanisms, leveraging the complementary advantages of AI and blockchain to bring unprecedented innovation to the crypto market. As AI and blockchain technologies continue to mature, the MCP protocol will not only reshape the ecosystem of the digital asset economy but also provide new momentum for the transformation of the global economy.

From an investment perspective, the application of the MCP protocol will attract significant capital inflows, especially from venture capital and hedge funds seeking innovative investment opportunities. As more AI models can be assetized, traded, and appreciated through the MCP protocol, the market demand generated will further promote the protocol's popularity. Additionally, the decentralized nature of the MCP protocol means it can avoid the single points of failure associated with centralized systems, thereby enhancing its long-term stability in the global market.

In the future, as the ecosystem around the MCP protocol becomes increasingly rich, AI and crypto assets based on this protocol may become mainstream investment tools in digital currency and financial markets. These AI assets could not only serve as value-added tools in the crypto market but may also develop into important financial commodities globally, driving the formation of a new global economic landscape.