Is there a bubble in AI? I'm afraid this logic can clarify.

Author: Zhang Feng

The field of artificial intelligence, especially generative AI, is currently experiencing an unprecedented boom. Financing amounts are reaching new highs, product iterations are changing rapidly, and giants are competing alongside newcomers, creating a vibrant scene. 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 never lacks lessons from technological bubbles, from the early internet's '.com bubble' to the recent 'metaverse craze'; when the tide goes out, 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 benchmark 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 the flashy model parameters and massive computing power consumption, but more importantly, in whether it can generate value carriers with real demand, circulation, and trustworthiness in the real world, especially at the asset level, which is the core of the digital economy.

1. AI** empowerment is 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 largely static and rule-driven. The integration of AI introduces a dynamic, cognition-driven dimension to trustworthy digital assets.

Integrating Dynamic Trust and Surpassing Static Rights Confirmation: Blockchain can prove “who owns a certain digital item at a certain point in time,” but it is difficult to determine 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 the status of assets, assess their wear and tear, predict their future returns, and even identify potential fraud. For example, combining AI with IoT sensors can enable dynamic tracing and quality control of physical goods (such as high-end artworks and rare collectibles) mapped as digital assets throughout their entire lifecycle, upgrading “trustworthy” from a static ownership record to a dynamic guarantee of value and status.

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 is capable of transforming traditionally hard-to-asset data resources (like user behavior data and industrial operation data) into priceable and tradable data assets through analysis and modeling, greatly expanding the boundaries and depth of digital assets.

Achieving Smart Governance and Ensuring Ecological Health: In the complex ecosystem of decentralized finance (DeFi) or DAO (Decentralized Autonomous Organization), governance activities such as risk management, compliance review, and protocol upgrades are becoming increasingly burdensome. AI can be embedded in governance processes to enable intelligent risk control (real-time monitoring and early warning of liquidity risks and contract vulnerabilities), automated compliance (ensuring transactions meet regulatory requirements in various regions), and data-driven proposal analysis and decision support. This allows ecosystems centered around trusted digital assets to operate more safely, efficiently, and sustainably.

The path to empowering trustworthy digital assets with AI is already clear:

Smart DeFi (DeAI) refers to the integration of AI into decentralized finance, giving rise to smarter liquidity management, personalized investment strategies, dynamically adjusted borrowing rates, and fraud-resistant credit scoring. This evolution allows DeFi protocols to transition from “code is law” to “smart code is better law,” with the protocol itself and the rights it generates serving as core trusted digital assets.

AI****native assetization refers to including the AI model itself (represented by specific tokens for its usage rights or ownership), AI-generated content (tokenized as NFTs to ensure its uniqueness and origin), and AI computing resources (tokenization of computing power). These constitute a new type of asset in the digital economy era, native to AI technology.

AI-Driven Compliance and Governance: Utilizing AI to automate KYC (Know Your Customer), AML (Anti-Money Laundering), and transaction monitoring, clearing obstacles for the integration of trusted digital assets into mainstream financial systems, while enhancing the intelligence level of decentralized governance.

2. The Integration of AI and Blockchain Builds a Trusted Digital Future

AI and blockchain are not in a relationship of replacement, but rather are cornerstone technologies of the digital economy that are two sides of the same coin, complementing each other. Blockchain provides a reliable “framework” and “ledger”: it ensures data immutability, transaction transparency, traceability, and unique asset rights, 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.

The combination of the two is essential to 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 the blockchain system with capabilities for intelligent processing and value creation that go beyond simple bookkeeping. It is under the support of these two cornerstones that trustworthy digital assets can mature from concept to reality, moving from the periphery to the mainstream.

Three, Measuring the Distance of Trusted Digital Assets from the AI Bubble

Whether a bubble exists depends on the authenticity and sustainability of value. For AI, its value ultimately needs to be carried and measured through commercialization and assetization. Therefore, whether credible digital assets with real demand and application scenarios are developed becomes the core indicator for judging the AI bubble. We can examine this in specific layers and dimensions as follows:

(1) Overall grasp of the balance between technological prosperity and value accumulation

Signs of a bubble: The capital market is frenzied, with valuations severely deviating from actual revenue (especially story valuations based solely on future potential); AI projects are highly homogeneous, with a large amount of resources concentrated on the inward competition of model training rather than solving specific industry problems; there are many visionaries discussing the prospects of “Artificial General Intelligence (AGI),” but few are practically working on vertical applications.

Manifestations of De-leveraging: The emergence of a clear, large-scale, AI-driven or enhanced trustworthy digital asset market. For example, the on-chain (RWA) of physical assets based on AI dynamic assessments forms a massive scale; the AIGC digital collectibles market establishes a stable supply-demand relationship and value assessment system; AI-optimized DeFi protocols see healthy growth in total locked value (TVL) and effectively serve the real economy. When the value of AI is “packaged” into various trustworthy digital assets and achieves efficient circulation, the bubble is squeezed out while a solid value highland is established.

(2) Sub-segment the track to examine its assetization capability

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 credibility of digital twins for luxury goods traceability, then its combination with trusted digital assets is closer and its value foundation is more solid.

Natural Language Processing and Large Models: If they are only used for chatbots or content generation tools, their business model may fall into fierce competition. However, if they give rise to high-quality, vertically specialized models that can be traded as assets (such as legal and medical diagnosis models), or if an economic ecosystem centered on AIGC digital asset creation and trading is established, their value will be more unique and defensible.

Reinforcement Learning and AI Agents: If their achievements in “playing games” within virtual environments cannot be translated into actual value, they are easily seen as castles in the air. However, if they can be used to optimize real-world logistics and energy networks, and share the costs saved or efficiencies gained with participants through tokenization, or if AI agents can autonomously execute value exchanges on the blockchain, then their integration with trusted digital assets brings about real value creation.

(3) From “Useful” to “Valuable”: The Assetization Direction of Product

When evaluating an AI product, it is not enough to ask “Is it useful?”; one must also ask, “What credible digital assets can it generate or integrate into?” For example, an AI painting tool that charges per use as a SaaS service has a clear ceiling on its value. However, if it can create unique, rights-assertable, collectible, and tradable digital artworks (NFTs) while establishing a sustainable value-sharing mechanism with creators, then it becomes more than just a tool; it is the creator of a new asset class, and its ecological value will grow exponentially. Similarly, an AI-driven prediction market is akin to a gambling game if its predictions cannot be linked to on-chain assets or real-world rights proofs. Yet, if its predictions can directly trigger insurance payouts in DeFi protocols or guide the pricing and trading of RWA, then it becomes an indispensable value discovery tool in the world of credible digital assets.

(4) From Technical Demonstration to the Development Roadmap of Asset Ecology

A healthy AI project should clearly demonstrate its evolution path from technical 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 the assetization capabilities of blockchain. Is its token economic model (if applicable) well-designed to effectively capture the value created by its AI? Is its ecosystem attracting developers, creators, and users to participate in building a prosperous market around its trusted digital assets?

IV. Grasp the Overall Direction of Integration and Sail Towards a New Blue Ocean of Value

AI is undoubtedly a transformative technology, but its true greatness lies not in how “intelligent” it is in itself, but in whether it can enable the entire economy and society to operate more intelligently, efficiently, and fairly. Trustworthy digital assets, as the value carriers of the digital economy, are indeed an excellent stage for AI to exert its transformative power.

Therefore, in the face of the current craze for AI, we should maintain a clear insight. Using “the distance to the integration with credible digital assets” as a key metric to measure the real value behind each AI story. Projects and companies that are down-to-earth and committed to transforming AI capabilities into real, credible, 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 in production relationships and productivity collaboration. By grasping this major direction and actively promoting the empowerment of AI in dynamic trust, data value release, and intelligent governance for digital assets, while also solidifying the trust foundation of blockchain, we can collectively navigate towards a trustworthy digital world new blue ocean filled with opportunities and value while bursting the bubble.