The integration of AI and blockchain: the restructuring of productivity and production relations in the digital economy.

Authors: SanTi Li, Chunfengjun, Lisa, Naxida

Abstract：Current market discussions on the relationship between artificial intelligence (AI) and blockchain digital currency (Blockchain & Crypto) are often limited to a zero-sum game perspective of capital diversion. However, in-depth industry analysis and technological evolution paths show that the two are actually complementary and symbiotic. Against the backdrop of AI driving exponential productivity growth and the near-infinite supply of digital content, blockchain-based mechanisms for reconstructing production relations and establishing rights are not merely "icing on the cake," but rather particularly necessary.

1. The Digital Trust Crisis in the Context of the AI ​​Explosion

With the breakthrough progress of AI artificial intelligence technology in recent years, especially the widespread application of Large Language Model (LLM) and Generative AI (AIGC), its core economic significance lies in reducing the marginal cost of content production to near zero..

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• Proof of Personhood and Resistance to Sybil Attacks：In an era where automated bots can pass the Turing test and flood the internet, the economic and social value of verifying users' "real identities" is increasingly prominent. Traditional CAPTCHAs are gradually becoming ineffective and unable to stop more advanced AI agents. Identity verification systems based on a combination of biometrics and zero-knowledge proofs (ZKP) may become a key infrastructure for distinguishing human users from AI agents. This is not only to prevent the exploitation of airdrops, but also to prevent online voting and public opinion guidance from being manipulated by botnets. In summary, artificial intelligence creates an unlimited supply of productivity, while blockchain technology provides credible scarcity constraints and identity anchors. Logically, they form an indispensable complementary gear in the closed loop of the digital economy: AI is responsible for making the world "faster," and blockchain is responsible for making the world "real." 3. Restructuring the Business Paradigm: Autonomous AI Agent Economics The combination of artificial intelligence and blockchain heralds the potential rise of a completely new economic interaction model—the machine-to-machine (M2M) economy. This is not merely a change in payment methods, but a fundamental transformation of the nature of economic agents. The future internet interaction agents will no longer be limited to humans; billions of autonomous AI agents will become native inhabitants of cyberspace. Traditional financial infrastructure (such as bank accounts, KYC processes, and credit card payment networks) is designed for humans and lacks the capacity to serve non-human entities, nor can it meet the demands of high-frequency, micro-amount, and 24/7 machine transactions. Machine-native monetary systems: Cryptocurrency is a value exchange medium naturally adapted to machine logic. AI agents cannot open accounts at bank counters, but they can instantly generate wallet addresses and manage private keys through code. They can use stablecoins (such as USDC) or specific utility tokens for data procurement, API calls, or computing power leasing. This type of payment is not subject to the intermediary barriers, business hour restrictions, or high cross-border fees of traditional finance. **Agent-to-Agent (A2A) Economic Networks:** The future business landscape will transcend B2B and B2C models, evolving towards an A2A (AI Agent-to-AI Agent) model. For example, an AI Agent responsible for trip planning might need to purchase real-time data from another agent responsible for weather forecasting and pay a deposit to a third agent responsible for booking tickets. These service exchanges involving micropayments and high-frequency transactions are economically feasible only with a high-performance, low-friction blockchain network. Smart contracts will automatically execute these complex business logics without human intervention.

• Coordination of Decentralized Physical Infrastructure Networks (DePIN):The operation of AI requires a large amount of computing power (GPUs) and data. Through DePIN networks (such as io.net, Render), AI Agents can directly rent idle personal or corporate computing power globally and settle accounts in real time using tokens. This breaks the monopoly of centralized cloud service providers (AWS, Google Cloud) to some extent, reduces the operating cost of AI, and also provides real-world utility scenarios for blockchain. However, the initial computing power source for projects and participants is likely still from the initial giants; subsequent leasing can be done autonomously to break up the absolute monopoly.

  It is foreseeable that while human users are making daily mobile payments, AI agents will automatically complete massive value exchanges in the background on-chain network, forming a huge and efficient shadow economy.

  4. Crypto Digital Currency: The Value Carrier and Symbiotic Engine for Property Rights Confirmation in the AI ​​Era

  Blockchain is not just a database; it is a value network. After clarifying the technical aspects of anti-counterfeiting and evidence preservation (point 2) and the business aspects of agent interaction (point 3), we must delve into the core of assets and finance.Property Rights Confirmation is the Prerequisite for Transactions and Pricing In the "unlimited supply" model constructed by artificial intelligence, relying solely on technical means for "anti-counterfeiting and evidence preservation" is far from sufficient. We can use Crypto to truly tokenize and financialize these rights, which has also given rise to the concept of RWA (Real World Asset). Tokens, as the smallest granular carrier of rights confirmation and the lifeblood of rights circulation, constitute an indispensable digital property rights cornerstone in the AI ​​era. This allows AI and Crypto to evolve from a simple "tool superposition" to a deep "symbiotic evolution." Tokenization: Transforming abstract rights into programmable digital assets. Crypto, through NFT (Non-Fungible Token) and SFT (Semi-Fungible Token) technologies, transforms abstract intellectual property (IP), ownership, copyright, and other unique datasets, fine-tuned model parameters, and even the ownership of an AI agent into unique and immutable on-chain assets. Every human creator's unique style or original work can be minted as an NFT. When AI needs to use these works for training or style transfer, it's no longer a seamless appropriation, but rather requires authorization of the NFT through an on-chain protocol. Here, the token is not only a copyright certificate, but also a certificate of revenue rights. For example, RWA music projects like Opulous and Audius tokenize artists' album rights, establishing revenue-sharing agreements with fans in advance. Data Assetization (Data Tokens): High-quality data from individuals or businesses is no longer static files, but assets that can be encapsulated into tokens for trading. Every time an AI model uses data, it essentially encapsulates that token. Consumption representing rights. This leads to refined returns and rights protection. Crypto: Enables instant settlement and circulation of confirmed value. Confirmation of rights is meaningless if it is not linked to value distribution. Digital currency provides a unique execution layer for rights confirmation in the AI ​​era. Micro-payments and Streaming Payments: In the high-speed operation of AI, rights confirmation often occurs at the millisecond level (e.g., AI...). (A quote was used to generate an image). Traditional fiat currency systems cannot handle such extremely small amounts (US$0.0001) and extremely high frequency copyright revenue sharing. Cryptocurrencies enable smart contracts to automatically "flow" revenue to token holders the moment rights are confirmed, achieving a closed loop of use equals rights confirmation, and rights confirmation equals settlement. The construction of the incentive layer: Why are humans willing to spend effort verifying the authenticity of AI content? Why are nodes willing to contribute computing power to maintain network consensus? Because of the incentive of Crypto. Tokenomics rewards participants who maintain the rights confirmation system with digital currency, thereby building a self-operating, AI-resistant trust network. This is also the core value of public blockchain systems and corresponding projects. The internal or partial circulation models of consortium blockchains and private blockchains are difficult to promote on a larger scale. The Co-existence of AI and Crypto: A Double Helix of Ascent AI Needs Blockchain Crypto: Without the rights confirmation and payment facilities provided by blockchain systems, AI creators and users will easily fall into a dead end of rampant piracy, data depletion, and inability to monetize. The more intelligent AI becomes, the more it needs clear property rights boundaries to avoid disputes. The current freshness of AI creation stems from the accumulation of data and creative sharing over the past few decades. However, as this accumulation nears depletion, the ability of newcomers to fill the gap will heavily rely on meticulous protection of rights. Crypto also needs AI: AI has created massive amounts of digital assets and high-frequency trading scenarios, providing crypto with unprecedented utility and liquidity. This symbiotic relationship indicates that Crypto is the "physical law" and "economic system" of the AI ​​era. The combination of the two will restructure the production relations of the digital world, allowing AI to... The productivity dividend is fairly returned to every participant through the rights confirmation mechanism.

5. Risk Governance: A Paradigm Shift from "Moral Self-Discipline" to "Technological Constraints"

Currently, the development of artificial intelligence is highly concentrated in a few tech giants (such as OpenAI, Google, Meta), which continues the centralized black-box logic of the Web 2.0 era. Under this model, the public can only hope that companies will maintain "Don't be evil."

The current ... The moral self-discipline of (doing evil). However, historical experience shows that centralized power is often accompanied by the risks of monopoly, data abuse, and algorithmic bias. Blockchain technology introduces the governance logic of "doing no evil," using open-source code, cryptographic proofs, and mathematical contracts to rigidly constrain system behavior: Zero-Knowledge Machine Learning (ZKML): As an important branch of privacy computing, ZKML allows for the verification, through mathematical proofs, that the reasoning process of an AI model is executed according to a predetermined algorithm and has not been tampered with, without disclosing underlying sensitive data (such as medical records and financial transactions) and the core parameters of the model. This ensures the transparency and auditability of algorithmic decisions, which is particularly important for high-risk areas such as medical diagnosis and credit assessment. AI applications are crucial, solving the "black box trust" problem. Public chains that have weathered multiple bull and bear cycles offer greater assurance of credibility. NEAR, as the first AI public chain, has fully transitioned to AI, while Render and others have shifted from game rendering to AI computing power. ETH, BSC, Solana, Cardano, Avalanche, Algorand, Hbar, Conflux, and others each have their own unique strengths, technical characteristics, and weaknesses. Emerging public chains like Monad are also facing a new round of challenges in token economics. Regarding the VC long-cliff model that has plagued the primary market for the past two years—where institutional holdings are locked up, but this has inadvertently led to excessive selling pressure due to premature and unintentional circulation of project incentives and airdrops—the market still needs 1-2 years of further development. It will take years to verify the balance between its token release curve and ecosystem value capture.

• Data Sovereignty and Value Distribution：Addressing the common problems of data infringement and "data harvesting" in large model training, blockchain projects can return data ownership to users, allowing them to selectively authorize data for training and receive rewards. This restructures the production relations, enabling data contributors to obtain reasonable value returns through the token economic model, thereby incentivizing higher-quality data supply and avoiding the tragedy of data depletion.

6. Conclusion: Embracing the "Entropy Reduction" Balance, Reshaping the Future in the Digital Civilization Order