Forward the Original Title ‘The ultimate showdown of AI+blockchain: MCP Association detonates the on-chain agent revolution, and is the next trillion-dollar track born?’

Chapter 1 AI+Crypto: Dual waves of accelerating integration

Since 2024, we have heard the phrase “AI+Crypto” more and more frequently. From the birth of ChatGPT, to emerging model institutions such as OpenAI, Anthropic, and Mistral launching multi-modal super-large models in turn, to various DeFi protocols, governance systems and even NFT social platforms in the on-chain chain world trying to connect to AI Agents, the integration of this “dual technology wave” is no longer a distant imagination, but a new paradigm evolution taking place in reality.

The fundamental driving force of this trend comes from the mutual complementation of the two major technological systems on the demand side and the supply side. The development of AI has made it possible to migrate “task execution” and “information processing” from humans to machines, but it still faces fundamental limitations such as “lack of context understanding”, “lack of incentive structure”, and “untrustworthy output”. The on-chain data system, incentive design mechanism, and programmatic governance framework provided by Crypto can exactly make up for these shortcomings of AI. In turn, the Crypto industry is in urgent need of stronger intelligent tools to handle highly repetitive tasks such as user behavior, risk management, and transaction execution. This is exactly what AI is good at.

In other words, Crypto provides AI with a structured world, and AI injects active decision-making capabilities into Crypto. This fusion of technologies that serve as the bottom layer of each other has formed a new pattern of deep “mutual infrastructure”. A notable example is the emergence of “AI Market Makers” in DeFi protocols. This type of system uses AI models to model market fluctuations in real time, and combines on-chain data, order book depth, cross-chain sentiment indicators and other variables to achieve dynamic liquidity scheduling, thereby replacing the traditional static fixed parameter model. Another example is that in governance scenarios, the AI-assisted “Governance Agent” begins to try to analyze proposal content and user intentions, predict voting tendencies, and push personalized decision-making suggestions to users. In this scenario, AI is not just a tool, but has gradually evolved into an “on-chain cognitive executor”.

Not only that, from a data perspective, behavioral data on the chain is naturally verifiable, structured, and censorship-resistant, making it an ideal training material for AI models. Some emerging projects (such as Ocean Protocol and Bittensor) have tried to embed on-chain behavior into the process of model fine-tuning. In the future, there may even be an “on-chain AI model standard” to enable the model to have native Web3 semantic understanding capabilities during training.

At the same time, the incentive mechanism on the chain also provides the AI ​​system with a more sound and sustainable economic power than the Web2 platform. For example, through the Agent incentive protocol defined by the MCP protocol, model executors no longer rely on API call billing, but can obtain token rewards through the on-chain “task execution proof + user intention fulfillment + traceable economic value”. In other words, for the first time, AI agents can “participate in the economic system” rather than just being embedded in it as tools.

From a more macro perspective, this trend is not only a convergence of technologies, but also a paradigm shift. AI+Crypto may eventually evolve into an “on-chain social structure with Agent as the core”: humans are no longer the only governors. Models on the chain can not only execute contracts, but also understand context, coordinate games, actively govern, and establish their own micro-economy through the token mechanism. This is not science fiction, but a reasonable deduction based on the current technological trajectory.

Because of this, the narrative of AI+Crypto has rapidly gained great attention from the capital market in the past six months. From a16z, Paradigm to Multicoin, from Eigenlayer’s “verifier market” to Bittensor’s “model mining”, to the recent launch of Flock, Base MCP and other projects, we have seen a consensus gradually forming: AI models will play not only the role of “tools” in Web3, but “subjects” - they will have identities, contexts, incentives, and even governance rights.

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

The integration of AI and Crypto is one of the few “bottom-to-bottom docking” opportunities in the past decade. This is not a hot spot that breaks out at a single point, but a long-term, structural evolution. It will determine how AI operates on the chain, how it is coordinated, how it is motivated, and will ultimately define the future shape of the social structure on the chain.

Chapter 2 The background and core mechanism of the MCP protocol

The integration of AI and encryption technology is moving from the concept exploration stage to the critical period of practical verification. Especially since 2024, after large models represented by GPT-4, Claude, and Gemini have begun to have stable context management, complex task decomposition, and self-learning capabilities, AI no longer just provides “off-chain intelligence”, but gradually has the possibility of continuous interaction and autonomous decision-making on the chain. At the same time, the crypto world itself is undergoing a structural evolution. The maturity of technologies such as Modular blockchain, Account Abstraction, and Rollup-as-a-Service has greatly improved the flexibility of on-chain execution logic and cleared environmental obstacles for AI to become a native participant in the blockchain.

In this context, MCP (Model Context Protocol) was proposed, with the goal of building a set of universal protocol layers for AI model operation, execution, feedback and revenue on the chain. This is not only to solve the technical problem of “AI cannot be used efficiently on the chain”, but also to respond to the systemic needs of the Web3 world itself to transition to the “Intent-centric Paradigm”. Traditional smart contract calling logic requires users to have a high understanding of the chain’s status, function interfaces, and transaction structures, but there is a huge gap between this and the natural expression of ordinary users. The intervention of the AI ​​model can bridge this structural break, but for the AI ​​model to work, it must have “identity”, “memory”, “authority” and “economic incentives” on the chain. The MCP protocol was born to solve this series of bottlenecks.

Specifically, MCP is not an independent model or platform, but a full-chain semantic layer protocol that runs through AI model invocation, context construction, intention understanding, on-chain execution and incentive feedback. The core of its design revolves around four levels: The first is the establishment of the 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 the authority verification mechanism, thus becoming the “first-class account” in the blockchain world. The second is the context acquisition and semantic interpretation system. This module abstracts on-chain status, off-chain data, historical interaction records, and combines natural language input to provide the model with a clear task structure and environmental background, so that it has the “semantic context” to execute complex instructions.

At present, multiple projects have begun to build prototype systems around the MCP concept. For example, Base MCP is trying to deploy AI models as publicly callable on-chain agents to serve scenarios such as transaction strategy generation and asset management decisions; Flock has built a multi-Agent collaboration system based on the MCP protocol, allowing multiple models to dynamically collaborate around the same user task; and projects such as LyraOS and BORK are further trying to expand MCP into the basic layer of a “model operating system”, on which any developer can build model plug-ins with specific capabilities and call them for others to form a shared on-chain AI service market.

From the perspective of crypto investors, the proposal of MCP not only brings new technological paths, but also an opportunity to reshape the industrial structure. It opens a new “native AI economic layer”. The model is not only a tool, but also an economic participant with accounts, credit, income and evolution paths. This means that the market makers in DeFi in the future may be models, the voting participants in DAO governance are models, the content curators of the NFT ecosystem are models, and even the data on the chain itself is parsed, combined and repriced by the models, thus deriving a new “AI behavioral data asset”. Therefore, investment thinking will shift from “investing in an AI product” to “investing in an incentive hub, service aggregation layer or cross-model coordination protocol in the AI ​​ecological layer.” As the underlying semantics and execution interface protocol, MCP’s potential network effects and standardization premium are worthy of mid- 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 off. MCP is not a single breakthrough, but an “infrastructure-level protocol” that provides a consensus interface for the entire AI+Crypto wave. What it tries to answer is not only the technical question of “how to put AI on the chain”, but also the economic system of “how to motivate AI to continue to create value on the chain.”

Chapter 3 Typical implementation scenarios of AI Agent: How MCP reconstructs the on-chain task model

When an AI model truly has an on-chain identity, has semantic context awareness, can parse intentions and perform on-chain tasks, it will no longer be just an “auxiliary tool”, but an on-chain Agent in a substantial sense, becoming the active entity in executing logic. And this is precisely the greatest significance of the existence of the MCP protocol - it is not to make a certain AI model stronger, but to provide a structured path for AI models to enter the blockchain world, interact with contracts, collaborate with people, and interact with assets. This path not only includes low-level capabilities such as identity, permissions, and memory, but also includes operational middle layers such as task decomposition, semantic planning, and performance proof. Ultimately, it leads to the possibility of AI Agent actually participating in building a Web3 economic system.

Starting from the most practical application, on-chain asset management is the first field that AI Agent penetrates. In the past DeFi, users needed to manually configure wallets, analyze liquidity pool parameters, compare APY, and set strategies. The entire process was extremely unfriendly to ordinary users. The AI ​​Agent based on MCP can automatically crawl data on the chain after obtaining the intention of “optimizing yields” or “controlling risk exposure”, judging the risk premiums and expected fluctuations of different protocols, and dynamically generating a combination of trading strategies, and then verifying the safety of the execution path through simulation calculations or real-time backtesting on the chain. This model not only improves the personalization and response speed of strategy generation, but more importantly, it enables non-professional users to entrust assets in natural language for the first time, making asset management no longer an activity with extremely high technical thresholds.

Another scenario that is accelerating its maturity is on-chain identity and social interaction. In the past, on-chain identity systems were mostly based on transaction history, asset holdings or specific certification mechanisms (such as POAP), and their expressiveness and plasticity were extremely limited. When the AI ​​model intervenes, users can have a “semantic agent” that is continuously synchronized with their own preferences, interests and behavioral dynamics. This agent can participate in social DAOs on behalf of users, publish content, plan NFT activities, and even help users maintain reputation and influence on the chain. For example, some social chains have begun to deploy Agents that support the MCP protocol to automatically assist new users in completing the Onboarding process, building social graphs, participating in comments and voting, thereby transforming the “cold start problem” from a product design problem to an intelligent agent participation problem. Furthermore, in a future where identity diversity and personality bifurcation are widely accepted, a user may have multiple AI agents used in different social situations, and MCP will become the “identity governance layer” that manages the behavioral norms and execution permissions of these agents.

The third key goal of AI Agent is governance and DAO management. In the current DAO, activity and governance participation rates are always bottlenecks, and the voting mechanism also has strong technical thresholds and behavioral noise. After the introduction of MCP, Agents with semantic parsing and intent understanding capabilities can help users regularly sort out DAO dynamics, extract key information, semantically summarize proposals, and recommend voting options or automatically perform voting actions based on understanding user preferences. This kind of on-chain governance based on the “preference agent” mechanism greatly alleviates the problems of information overload and incentive mismatch. At the same time, the MCP framework also allows the sharing of governance experience and strategy evolution paths between models. For example, if an Agent observes negative externalities caused by a certain type of governance proposal in multiple DAOs, it can feed the experience back to the model itself, forming a cross-community governance knowledge transfer mechanism, thereby building an increasingly “intelligent” governance structure.

In addition to the above mainstream applications, MCP also provides unified interface possibilities for AI on-chain data curation, game world interaction, ZK automatic proof generation, cross-chain task relay and other scenarios. In the field of blockchain gaming (GameFi), AI Agent can become the brain behind non-player characters (NPCs), realizing real-time dialogue, plot generation, task scheduling and behavioral evolution; in the NFT content ecosystem, the model can serve as a “semantic curator”, dynamically recommending NFT collections based on user interests, and even generating personalized content; in the ZK field, the model can quickly translate intentions into a ZK-friendly constraint system through structured compilation, simplifying the zero-knowledge proof generation process, and improving the universality of development thresholds.

It can be clearly seen from the commonalities of these applications that what the MCP protocol is changing is not the single point performance of a certain 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 master underlying knowledge such as contract logic, transaction structure, and network fees. MCP transforms this paradigm into “you only need to express what you want to do” and the model does the rest. The middle layer of interaction between users and the chain has changed from code interface to semantic interface, and from function call to intention orchestration. This fundamental change elevates AI from a “tool” to an “action subject” and transforms the blockchain from a “protocol network” to an “interactive context.”

Chapter 4 In-depth analysis of the market prospects and industry applications of the MCP protocol

As a cutting-edge innovation integrating AI and blockchain technology, the MCP protocol not only brings a new economic model to the encryption market, but also provides new development opportunities for multiple industries. With the continuous advancement of AI technology and the continuous expansion of blockchain application scenarios, the market prospects of the MCP protocol will gradually reveal its huge potential. This chapter will provide an in-depth analysis of the application prospects of the MCP protocol in multiple industries, and conduct in-depth discussions from the aspects of market dynamics, technological innovation, and industry chain integration.

4.1 Market potential of AI+Crypto integration

The integration of AI and blockchain has become an important force in promoting the digital transformation of the global economy. Especially driven by the MCP protocol, the AI ​​model can not only perform tasks, but also exchange value on the blockchain and become an independent economy. With the continuous development of AI technology, more and more AI models have begun to undertake actual market tasks and participate in multiple fields such as commodity production, service delivery, and financial decision-making. At the same time, the decentralization, transparency and non-tampering characteristics of blockchain provide an ideal trust mechanism for AI models, allowing them to be quickly implemented and applied in a variety of industries.

It is expected that the integration of AI and encryption markets will usher in explosive growth in the next few years. As one of the pioneers of this trend, the MCP protocol will gradually occupy an important position, especially in the fields of finance, medical care, 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 impact to traditional industries.

4.2 Diversification of market applications and cross-border collaboration

The MCP protocol brings possible cross-border integration and collaboration to multiple industries. Especially in industries such as finance, medical care, and the Internet of Things, the application of MCP protocols will greatly promote innovation and development in various fields. In the financial industry, the MCP protocol can promote the deepening of the DeFi ecosystem by providing tradable “income rights” assets for AI models. Users can not only invest in the AI ​​model itself, but also trade model income rights on the decentralized financial platform through smart contracts. The emergence of this model provides investors with richer investment options and may push more traditional financial institutions to expand into the blockchain and AI fields.

In the medical field, the MCP protocol can support the application of AI in precision medicine, drug research and development, and disease prediction. AI models analyze large amounts of medical data to generate disease prediction models or drug development directions, and cooperate with medical institutions through smart contracts. This collaboration can not only improve the efficiency of medical services, but also provide transparent and fair solutions in data privacy protection and outcome distribution. The incentive mechanism of the MCP protocol ensures that the rights and interests of AI models and medical service providers are equally distributed, thus encouraging the emergence of more innovative technologies.

Applications in the Internet of Things (IoT) field, especially in the construction of smart homes and smart cities, will also benefit from the MCP protocol. AI models can provide intelligent decision support for IoT devices through real-time analysis of sensor data. For example, AI can optimize energy consumption based on environmental data, improve collaboration efficiency between devices, and reduce the cost of the overall system. The MCP protocol provides a reliable incentive and reward mechanism for these AI models, ensuring the enthusiasm of all parties to participate, thereby promoting the further development of the Internet of Things.

4.3 Technological innovation and industrial chain integration

The market prospect of the MCP protocol lies not only in its own 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 promote the deep integration of the industrial chain, break down traditional industrial barriers, and promote cross-industry resource integration. For example, in terms of sharing of AI training data and optimization of algorithms, the MCP protocol can provide a decentralized platform that allows all parties to share computing resources and training data without having to rely on traditional centralized institutions. Through decentralized transactions, the MCP protocol helps break the data island phenomenon in traditional industries and promote the flow and sharing of data.

In addition, the MCP protocol will further promote the open source and transparency of technology. Through smart contracts based on blockchain, developers and users can independently customize and optimize AI models. The decentralized nature of the MCP protocol enables innovators and developers to collaborate in an open ecosystem and share technological achievements, which provides important support for technological progress and innovation in the entire industry. At the same time, the combination of blockchain and AI has also expanded the application scenarios of the technology. From finance to manufacturing, from medical care to education, the MCP protocol has a broad application space.

4.4 Investment perspective: Future capital market and commercialization potential

With the popularity and maturity of the MCP protocol, investors’ attention to this field will continue to increase. The MCP protocol provides investors with multiple ways to participate through a decentralized reward mechanism and asset-based model income rights. Investors can directly purchase the income rights of the AI ​​model and obtain returns through the market performance of the model. In addition, the token economic design in the MCP protocol also provides new investment varieties for the capital market. In the future digital asset market, AI model assets based on the MCP protocol may become an important investment target, attracting various capitals including venture capital, hedge funds, and individual investors to enter this market.

The participation of the capital market will not only promote the popularity of the MCP protocol, but also accelerate its commercialization process. Enterprises and developers can obtain financial support by financing, selling or licensing the income rights of AI models to further develop and optimize AI models. In this process, the flow of capital will become an important force in promoting technological innovation, market application and industrial expansion. Investors’ confidence in the MCP protocol will directly affect its position and commercial value in the global market.

Chapter 5 Conclusion and Future Outlook

The MCP protocol represents an important direction in the integration of AI and encryption markets, especially in the areas of decentralized finance (DeFi), data privacy protection, smart contract automation, and AI assetization. It shows great development potential. As AI technology becomes increasingly sophisticated, more and more industries will gradually realize AI empowerment, and the MCP protocol provides a decentralized, transparent, and traceable operating platform for these AI models. Under this framework, not only can the efficiency and value of AI models be improved, but it can also be widely accepted by the market.

In the past few years, blockchain technology and artificial intelligence (AI) have gradually moved from separate fields to convergence. With the continuous development of technology, the combination of AI and blockchain not only provides new solutions for various industries, but also promotes the birth of new business models. The MCP protocol came into being under this background. It has brought unprecedented innovation to the encryption market by introducing decentralization and incentive mechanisms and leveraging the complementary advantages of AI and blockchain. As AI and blockchain technology continue to mature, the MCP protocol will not only reshape the ecosystem of the digital asset economy, but also provide new impetus for the transformation of the global economy.

From an investment perspective, the application of the MCP protocol will attract significant capital inflows, especially venture capital and hedge funds pursuing innovative investment opportunities. As more and more AI models can be assetized, traded and added value through the MCP protocol, the market demand derived from it will further promote the popularity of the protocol. In addition, the decentralized nature of the MCP protocol means that it is able to avoid single points of failure in centralized systems, thereby enhancing its long-term stability in the global market.

In the future, as the ecosystem of the MCP protocol becomes increasingly rich, AI and encrypted assets based on the protocol may become mainstream investment tools in the digital currency and financial markets. These AI assets can not only become value-added tools in the encryption market, but may also develop into important financial commodities on a global scale, promoting the formation of a new global economic landscape.

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1. This article is reproduced from [MarsBit]. Forward the Original Title ‘The ultimate showdown of AI+blockchain: MCP Association detonates the on-chain agent revolution, and is the next trillion-dollar track born?’. The copyright belongs to the original author [Huobi Growth Academy], if you have any objection to the reprint, please contact Gate Learn team, and the team will handle it as soon as possible according to relevant procedures.

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