AI+Crypto: The Dual Wave of Accelerated Fusion

Since the beginning of 2024, the term "AI+Crypto" has appeared more frequently in our field of vision. From the emergence of ChatGPT to the launch of multimodal super large models by emerging model organizations such as OpenAI, Anthropic, and Mistral, as well as various DeFi protocols, governance systems, and even NFT social platforms attempting to integrate AI Agents, this integration of the "dual technology wave" has become a new paradigm evolution happening in reality.

The fundamental driving force behind this trend comes from the mutual complementarity of two major technological systems on 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," "lack of incentive structures," and "untrustworthy outputs." On the other hand, the on-chain data systems, incentive design mechanisms, and programmatic governance frameworks provided by cryptographic technology can precisely address these deficiencies of AI. Conversely, the cryptocurrency industry urgently needs stronger intelligent tools to handle highly repetitive tasks such as user behavior, risk management, and trade execution, which is exactly where AI excels.

In other words, cryptographic technology provides a structured world for AI, while AI injects proactive decision-making capabilities into cryptographic technology. This mutual foundational technological integration creates a new deep "infrastructure for each other" pattern. 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 integrate on-chain data, order book depth, cross-chain sentiment indicators, and other variables to achieve dynamic liquidity scheduling, thereby replacing traditional static parameter models. Furthermore, in governance scenarios, AI-assisted "governance agents" are beginning to attempt to analyze proposal content, user intent, predict voting tendencies, and push personalized decision-making suggestions to users. In this scenario, AI is not just a tool but is gradually evolving into a "cognitive executor on-chain."

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 have already attempted to incorporate on-chain behaviors into the model fine-tuning process, and in the future, there may even be a "standard for on-chain AI models" that endows models with native Web3 semantic understanding capabilities during training.

At the same time, the incentive mechanism on the blockchain provides AI systems with a more robust and sustainable economic motivation than Web2 platforms. For example, the Agent incentive protocol defined by the MCP protocol allows model executors to no longer rely on API call billing, but instead to obtain token rewards through "task execution proof + user intention fulfillment + traceable economic value" on-chain. In other words, AI agents can participate in the economic system for the first time, rather than merely being embedded as tools.

From a more macro perspective, this trend is not just about technological integration, but also a paradigm shift. AI + Crypto could ultimately evolve into an "agent-centric on-chain social structure": humans are no longer the sole governors, as models on the chain can not only execute contracts but also understand context, coordinate games, and actively govern, establishing their own micro-economies through token mechanisms. This is not science fiction, but a reasonable extrapolation based on the current technological trajectory.

It is precisely because of this that the narrative of AI + Crypto has rapidly gained significant attention from the capital markets in the past six months. From investment institutions to the launch of various projects, we see a consensus gradually forming: AI models will play a role in Web3 that is not merely that of a "tool", but rather as "entities"------they will possess identity, context, incentives, and even 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 integration of AI and Crypto is one of the few "bottom-up integration" opportunities in the past decade. This is not a single-point explosive hotspot, but a long-cycle, structural evolution. It will determine how AI operates on the chain, how it coordinates, how it is incentivized, and will ultimately define the future form of the on-chain social structure.

Background and Core Mechanism of the MCP Protocol

The integration of AI and blockchain technology is transitioning from the conceptual exploration phase to a critical period of practical verification. Especially since 2024, large models represented by GPT-4, Claude, and Gemini have begun to exhibit stable context management, complex task decomposition, and self-learning capabilities. AI is no longer just providing "off-chain intelligence"; it is gradually becoming capable of continuous interaction and autonomous decision-making on-chain. At the same time, the crypto world itself is undergoing structural evolution. The maturity of technologies such as modular blockchains, account abstraction, and Rollup-as-a-Service has greatly improved the flexibility of on-chain execution logic, clearing environmental obstacles for AI to become a native participant in blockchain.

In this context, MCP is proposed with the aim of building a comprehensive set of AI models that operate, execute, provide feedback, and establish a general protocol layer on-chain. This is not only to address the technical challenge of "AI cannot be efficiently used on-chain" but also in response to the systemic demand for the Web3 world to transition to an "intention-driven paradigm." The logic of traditional 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 involvement 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 precisely to address this series of bottlenecks.

Specifically, MCP is not an independent model or platform, but a comprehensive semantic layer protocol that spans AI model invocation, context construction, intent understanding, on-chain execution, and incentive feedback. Its design core revolves around four levels: 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 invoke contracts through a permission verification mechanism, thus becoming a "first-class account" in the blockchain world. Secondly, there is the context collection and semantic interpretation system. This module provides a clear task structure and environmental background for the model by abstracting on-chain states, off-chain data, and historical interaction records, combined with natural language input, enabling it to execute complex instructions with a "semantic context."

Several projects have begun to build 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 decision-making. Flock has built a multi-Agent collaboration system based on the MCP protocol, allowing multiple models to dynamically collaborate around the same user task. Meanwhile, projects like LyraOS and BORK are taking it a step further by trying to expand MCP into a foundational layer for a "model operating system," where any developer can build model plugins with specific capabilities on top, making them available 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 structural reshaping. It opens up a new "native AI economic layer" where models are not just tools but also 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 the on-chain data itself may be parsed, combined, and repriced by models, thus giving rise to entirely new "AI behavioral data assets." As a result, the investment thinking will 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 layer." MCP, as a foundational semantic and execution interface protocol, deserves significant medium to long-term attention due to its potential network effects and standardization premiums.

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 breakthrough at a single point, but a "infrastructure-level protocol" that provides a consensus interface for the entire AI + Crypto wave. What it attempts to answer is not just the technical question of "how to put AI on the chain", but also the economic system question of "how to incentivize AI to continuously create value on the chain".

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

When AI models truly possess on-chain identity, have semantic context awareness, can interpret intentions and execute on-chain tasks, they are no longer just "auxiliary tools", but are essentially on-chain Agents, becoming proactive entities that execute logic. This is precisely the greatest significance of the MCP protocol—it is not to make a specific AI model stronger, but to provide a structured path for AI models to enter the blockchain world, interact with contracts, collaborate with humans, and engage 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 proof of performance, ultimately leading to the possibility of AI Agents actually participating in the construction of the Web3 economic system.

Starting from the most practically significant applications, on-chain asset management is the first field where AI Agents penetrate. In the past DeFi, users had to manually configure wallets, analyze liquidity pool parameters, compare APYs, and set strategies, making the whole process extremely unfriendly for ordinary users. However, based on MCP, AI Agents can automatically crawl on-chain data after obtaining intents like "optimizing yield" or "controlling risk exposure," assess the risk premiums and expected volatility of different protocols, and dynamically generate trading strategy combinations. They then verify the safety of the execution path through simulation calculations or on-chain real-world backtesting. This model not only enhances the personalization and response speed of strategy generation but, more importantly, it allows non-professional users to delegate assets using natural language for the first time, making asset management no longer an activity with a very high technical threshold.

Another rapidly maturing scenario is on-chain identity and social interaction. Previously, on-chain identity systems were mostly based on transaction history, asset holdings, or specific proof mechanisms, which had very limited expressive power and plasticity. However, when AI models intervene, users can have a "semantic agent" that continuously syncs with their preferences, interests, and behavioral dynamics. This agent can participate on behalf of users in social DAOs, publish content, plan NFT activities, and even help users maintain their on-chain reputation and influence. 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, establishing social graphs, and participating in comments and voting, thereby transforming the "cold start problem" from a product design issue into a smart agent participation issue. Furthermore, in a future where identity diversity and personality divergence are widely accepted, a user may have multiple AI agents for different social contexts, and MCP will become the "identity governance layer" that manages the behavioral guidelines and execution authority of these agents.

The third key focus of the AI Agent is governance and DAO management. At this stage of DAOs, activity and governance participation rates have always been bottlenecks, and the voting mechanism has a strong technical threshold and behavioral noise. With the introduction of MCP, Agents with semantic parsing and intent understanding capabilities can help users regularly organize 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 the "preference agent" mechanism greatly alleviates the problems of information overload and incentive misalignment. At the same time, the MCP framework 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 this experience back to the model itself, forming a transfer mechanism for governance knowledge across communities, thereby constructing an increasingly "intelligent" governance structure.

In addition to the mainstream applications mentioned above, MCP also provides unified interface possibilities for scenarios such as AI curation of on-chain data, interaction in game worlds, ZK automated proof generation, and cross-chain task relaying. In the field of blockchain games, AI Agents can serve as the brain behind non-player characters, enabling real-time dialogue, storyline generation, and task scheduling.