Author: Zhang Feng
The current field of artificial intelligence, especially generative AI, is experiencing an unprecedented boom. Financing amounts are reaching new highs, product iterations are changing rapidly, and giants and newcomers are competing on the same stage, creating a vibrant atmosphere. However, the fluctuating market sentiment seems to be telling us that beneath this prosperity, a ghost is lurking—the ghost of the AI bubble. History is never short of lessons from technological bubbles, from the early internet's ".com bubble" to the recent "metaverse craze." When the tide recedes, what often remains is a mess.
So, how should we penetrate the noise and rationally judge whether there is a bubble in the current AI wave? We believe that a crucial measure is to examine the degree and distance of the integration between AI and trusted digital assets. The value of AI should not only be reflected in impressive model parameters and massive computational power consumption but should also be evident in whether it can generate value carriers with real demand, liquidity, and trustworthiness in the real world, especially at the asset level, which is the core of the digital economy.
I. AI Empowerment as the "Value Engine" and "Intelligent Core" of Trusted Digital Assets
Traditional digital assets derive their "trustworthiness" primarily from blockchain technology, which ensures the uniqueness, immutability, and traceability of assets through distributed ledgers, cryptography, and consensus mechanisms. However, this trust is more static and rule-driven. The integration of AI injects a new dimension of dynamic, cognition-driven trustworthiness into digital assets.
Integrating Dynamic Trust, Surpassing Static Rights Confirmation: Blockchain can prove "who owns a digital item at a certain point in time," but it struggles to assess the intrinsic value, authenticity, or status of that item in complex environments. AI, especially its branches like IoT AI and predictive analytics, can continuously monitor asset status, evaluate wear and tear, predict future returns, and even identify potential fraud. For example, combining AI with IoT sensors allows for dynamic traceability and quality control throughout the lifecycle of physical goods (such as high-end artworks and rare collectibles) that are mapped as digital assets, upgrading "trustworthiness" from static ownership records to dynamic value and status assurance.
Unlocking Data Value, Catalyzing Asset Derivatives: Data is the oil of the new era, but unrefined crude oil has limited value. AI is the top-tier "refinery" for data. It can extract insights, generate knowledge, and create content from vast, chaotic datasets. This process itself is giving rise to new asset classes: AI models can be traded and licensed as assets; AI-generated content (AIGC), such as high-quality text, images, videos, and code, can become digital assets with unique value. More importantly, AI can transform traditionally hard-to-asset data resources (such as user behavior data and industrial operation data) into priceable, tradable data assets through analysis and modeling, greatly expanding the boundaries and depth of digital assets.
Achieving Intelligent Governance, Ensuring Ecological Health: In the complex ecosystems of decentralized finance (DeFi) or DAOs (Decentralized Autonomous Organizations), governance activities such as risk management, compliance review, and protocol upgrades are becoming increasingly burdensome. AI can be embedded in governance processes to achieve intelligent risk control (real-time monitoring and early warning of liquidity risks and contract vulnerabilities), automated compliance (ensuring transactions meet regulatory requirements), and data-driven proposal analysis and decision support. This enables ecosystems centered around trusted digital assets to operate more safely, efficiently, and sustainably.
The path for AI to empower trusted digital assets is already clear:
Intelligent DeFi (DeAI), which introduces AI into decentralized finance, giving rise to smarter liquidity management, personalized investment strategies, dynamically adjusted lending rates, and fraud-resistant credit scoring, allowing DeFi protocols to evolve from "code is law" to "intelligent code is better law," with the protocol itself and the rights it generates being core trusted digital assets.
AI Native Assetization, which includes AI models themselves (represented by specific tokens indicating usage or ownership), AI-generated content (NFTs to ensure uniqueness and provenance), and AI computing resources (tokenization of computing power), forming a new type of asset native to AI technology in the digital economy era.
AI-Driven Compliance and Governance: Utilizing AI to automate KYC (Know Your Customer), AML (Anti-Money Laundering), and transaction monitoring, clearing obstacles for trusted digital assets to integrate into mainstream financial systems while enhancing the intelligence level of decentralized governance.
II. The Integration of AI and Blockchain Builds a Trusted Digital Future
AI and blockchain are not in a replacement relationship but are foundational technologies that complement each other in the digital economy. Blockchain provides a trusted "skeleton" and "ledger": it ensures the immutability of data, the transparency and traceability of transactions, and the unique confirmation of assets, addressing the "hard foundation" of trust. AI, on the other hand, provides the intelligent "brain" and "engine": it processes complex information, makes optimal decisions, and creates new forms of value, addressing the "soft core" of efficiency and value.
Only by combining the two can we build a digital economic infrastructure that is both trustworthy and intelligent. Blockchain ensures the authenticity and auditability of the data used by AI, avoiding the risk of "garbage in, garbage out" in AI training data; AI, in turn, endows blockchain systems with intelligent processing and value creation capabilities that go beyond simple bookkeeping. It is under the joint support of these two foundational technologies that trusted digital assets can mature from concept to reality, moving from the margins to the mainstream.
III. Measuring AI Bubble with the Distance of Trusted Digital Assets
Whether a bubble exists hinges on the authenticity and sustainability of value. For AI, its value ultimately needs to be carried and measured through commercialization and assetization. Therefore, the development of trusted digital assets with real demand and application scenarios becomes the core indicator for judging the AI bubble. We can examine this from several levels and dimensions:
(1) Overall Grasp of the Balance Between Technological Prosperity and Value Accumulation
Signs of a Bubble: Capital market frenzy, with valuations severely deviating from actual revenue (especially story-based valuations based solely on future potential); severe homogenization of AI projects, with a large concentration of resources in the inward competition of model training rather than solving specific industry problems; many discussing the vision of "General Artificial Intelligence (AGI)," but few practically working on vertical applications.
Signs of De-Bubbling: A clear, scalable market for trusted digital assets driven or enhanced by AI emerges. For example, physical assets being dynamically assessed and put on-chain (RWA) form a massive scale; the AIGC digital collectibles market establishes stable supply-demand relationships and value assessment systems; AI-optimized DeFi protocols see healthy growth in total value locked (TVL) and effectively serve the real economy. When the value of AI is "packaged" into various trusted digital assets and achieves efficient circulation, the bubble is squeezed out, and a solid value foundation is established.
(2) Examining Assetization Capability in Sub-Sectors
Computer Vision: If its value is limited to security or beauty filters, its potential is limited. However, if it can empower the assetization of high-precision industrial quality inspection data or ensure the dynamic trustworthiness of luxury goods traceability digital twins, its integration with trusted digital assets becomes closer, and its value foundation stronger.
Natural Language Processing and Large Models: If used merely for chatbots or content generation tools, its business model may fall into red ocean competition. But if it spawns high-quality, vertically specialized models (such as legal or medical diagnostic models) that can be traded as assets, or builds an economic ecosystem centered around AIGC digital asset creation and trading, its value becomes more unique and defensible.
Reinforcement Learning and AI Agents: If achievements in "playing games" in virtual environments cannot be translated into real value, they may be seen as castles in the air. However, if they can optimize real-world logistics and energy networks and share the cost savings or efficiency gains with participants through tokenization, or if AI agents can autonomously execute value exchanges on the blockchain, their integration with trusted digital assets brings real value creation.
(3) From "Useful" to "Valuable" in Product Assetization Direction
When evaluating an AI product, we should not only ask, "Is it useful?" but also, "Can it generate or integrate into trusted digital assets?" For example, an AI painting tool that charges per use as a SaaS service has a clear value ceiling. But if it can generate unique, verifiable, collectible, and tradable digital artworks (NFTs) and establish a sustainable value-sharing mechanism with creators, it becomes not just a tool but a creator of a new asset class, with its ecological value growing exponentially. Similarly, an AI-driven prediction market that cannot link its predictions to on-chain assets or real-world rights is akin to a gambling game. However, if its predictions can directly trigger insurance payouts in DeFi protocols or guide the pricing and trading of RWAs, it becomes an indispensable value discovery tool in the world of trusted digital assets.
(4) From Technical Demonstration to Development Roadmap of Asset Ecosystem
A healthy AI project should clearly demonstrate its evolution from technology validation to product implementation and then to building a digital asset economic ecosystem centered around it. Investors and observers should focus on whether the project team consciously combines AI capabilities with blockchain's assetization capabilities. Is its token economic model (if applicable) reasonably designed to effectively capture the value created by its AI? Is its ecosystem attracting developers, creators, and users to collaboratively build a thriving market around its trusted digital assets?
IV. Grasping the Integration Direction, Sailing Towards a New Blue Ocean of Value
AI is undoubtedly a transformative technology, but its true greatness lies not in how "intelligent" it is, but in whether it can make the entire economy and society operate more intelligently, efficiently, and fairly. Trusted digital assets, as the value carriers of the digital economy, are the perfect stage for AI to exert its transformative power.
Therefore, in the face of the current AI frenzy, we should maintain a clear insight. Using "the distance of integration with trusted digital assets" as a key measure to assess the true value behind each AI story. Projects and companies that can stay grounded and are committed to transforming AI capabilities into real, trustworthy, and tradable digital assets are more likely to navigate through cycles and become the winners of the future.
The integration of AI and blockchain is not a simple technological overlay but the beginning of a profound evolution of production relationships and productivity. By grasping this direction and actively promoting AI's empowerment of digital assets in dynamic trust, data value release, and intelligent governance, while solidifying the trust foundation of blockchain, we can break through the bubble and collectively sail towards a new blue ocean of trusted digital worlds filled with opportunities and value.
免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。
