Web3 and AI coupling attempts to coordinate data, computing power, and profit distribution through a brand new production relationship.
Author: @Nicholas030412
In the eyes of many, the combination of Web3 and AI remains at the level of conceptual hype, seemingly just adding a few "buzzwords" to traditional technology. However, if we focus on projects that have truly withstood the test of time and market, we can find that the interaction between so-called "decentralization" and "intelligent algorithms" is far more complex than imagined, and indeed shows leapfrog innovative potential in specific scenarios. A key premise is that any AI needs real and diverse data to grow, and the token mechanism and privacy protection methods of Web3 can empower individuals and even groups with new discourse power in the circulation and pricing of data.
In a sense, the coupling of Web3 and AI is not as simple as "moving algorithms onto the chain," but rather attempts to coordinate data, computing power, and profit distribution through a brand new production relationship.
The following cases are concentrated embodiments of this "new production relationship." They are not perfect, but they can provide insights from different dimensions.
Numerai
One of the most frequently mentioned projects is Numerai in the financial hedge fund sector. Many people may only know that this is a "crypto hedge fund," but have not carefully dissected its operational logic. Numerai first masters a large amount of real and highly sensitive financial transaction data, which is considered "core assets" in traditional hedge funds and is never easily leaked. However, what Numerai does is to encrypt and reduce the dimensionality of this data at a high intensity, allowing any external data scientist to only see "puzzles" and not "answers." This processing makes it impossible for model trainers to reverse-engineer the real prices of specific stocks or futures, thus reducing the risk of data leakage or misuse. Next, Numerai opens these "puzzle data" to the world, allowing anyone to download and attempt to predict, then upload their predictions back to the platform for ranking and evaluation. The real highlight lies in the incentive mechanism: those who excel in hedge strategy predictions will receive platform token rewards, and Numerai will incorporate their algorithms into actual trading strategies to earn profits in the financial market.
What is interesting is not just this form of "crowdsourced algorithms," but the underlying trust game it harbors. On one hand, Numerai gains nearly boundless talent and algorithmic creativity, overcoming the challenge of limited manpower in internal research teams; on the other hand, contributors can obtain returns under the protection of decentralized contracts based on their own strength, without worrying about whether "the platform will default." However, sustaining and growing this model is not easy. First, Numerai was relatively centralized in its early stages, with the original data still held by the project party, and contributors could only "trust" that the encrypted data had no backdoors. Second, participants without a certain technical threshold and computing power investment find it difficult to stand out in global competition. This indicates that Web3 has not completely eliminated the phenomenon of "the strong getting stronger," but rather opened a door to a previously closed financial data world, allowing more people to participate, though how far they can go still depends on whether the trust and profit distribution among funders, data providers, and algorithm developers can be maintained in the long run.
Alethea AI
Compared to Numerai, which focuses on financial data, Alethea AI pushes the combination of Web3 and AI towards a more imaginative direction from the perspective of digital art. Traditional NFTs are more about "images on the chain," mostly presenting static scarcity, while Alethea AI proposes the concept of "iNFT," aiming to make NFTs not just art "certificates," but digital lives that can interact with users and even have autonomous generation capabilities.
The specific approach is that artists embed an AI model or training interface when minting NFTs, allowing collectors to input specific text, images, or other data after purchasing, triggering the AI to create secondary or even multiple derivative works. Each new creation can be minted as a separate NFT, traded again, and there is a complete set of smart contracts to distribute subsequent profits among the original author, secondary creators, and collectors. This seems to subvert people's understanding of the "uniqueness" of artistic creation, but precisely demonstrates the potential of Web3 and AI to break content boundaries and endow works with dynamic attributes. In the traditional art market, creators often only receive profits from the initial sale, while subsequent resales and modifications are often unrelated to the artist, with no ongoing profit sharing.
Through the programmable features of blockchain, each derivative and transaction can be traced and recorded, and profits can be automatically distributed according to contracts. This model brings a new dimension of "ecological reproduction" to artistic creation, where NFTs no longer flow unidirectionally from authors to collectors, nor are they limited to the original platform. However, for this mechanism to truly work, it must confront controversies across multiple levels, including copyright, regulation, and aesthetics. Regarding copyright, different countries do not have unified regulations on the ownership of "AI-generated works." If questioned for infringement, how will the platform and artists share responsibility? On the technical level, if Alethea wants NFTs to possess higher-level "dialogue" or "perception" capabilities, the computational demands of AI models will far exceed what can be supported on-chain, necessitating access to centralized cloud services. This leads to a paradox: on one hand, it speaks of a "decentralized art ecosystem," while on the other, it still relies on traditional computational infrastructure, making the actual technical and economic architecture more complex than advertised. The existence of these contradictions does not mean the project lacks value; rather, it indicates that as the integration of Web3 and AI deepens, a "pragmatic mix" may be more sustainable than "pure decentralization."
AI + Healthcare
In the more sensitive and serious field of healthcare, the combination of Web3 and AI shows its true value. Medical data has always been regarded as "privacy within privacy," and any leakage can lead to serious legal and ethical consequences, yet it is also the high-value resource most needed for AI training. For example, cancer imaging recognition technology requires hundreds of thousands or even millions of cases and images for training, but data from different hospitals, regions, and even countries are locked in their own "information silos," and patients are often unwilling or afraid to authorize their medical records for analysis on unknown platforms.
The solution provided by Web3 is to record the ownership and authorization process of data on a distributed ledger on the blockchain, achieving a privacy computing model of "only granting computing rights, not sharing raw data" through smart contracts. When an AI model needs to access a hospital's medical records, it must first obtain permission from the authorizing party (hospital or patient), and can only train or infer on de-identified data in a designated environment. Any reading or moving of raw data requires on-chain signatures and documentation. Some have even proposed a "token incentive" scheme, where hospitals willing to provide more quality data can gain more weight in community governance or subsequent profits. However, when it comes to operational implementation, problems arise: do hospitals have sufficient technical capability to deploy and manage these nodes? What level of "de-identification" is sufficient to meet regulatory requirements in different countries? Is the blockchain's throughput and storage capacity sufficient to handle trillions of medical imaging files? These practical challenges mean that many projects can only refine their models in small-scale pilots, without presenting a clear business model like Numerai or Alethea. However, from another perspective, this also suggests that once the medical community and the Web3 community resolve these issues one by one, it may trigger an AI application revolution with more social significance than digital collectibles: once vast multi-source medical data can be compliantly "aggregated and computed," research on complex diseases like cancer and rare diseases may accelerate by more than double.
About AI+
At first glance, these cases seem scattered across unrelated fields such as finance, art, and healthcare, but they are all exploring a "new production relationship." What Web3 provides is not simply "on-chain," but a rebalancing mechanism for multi-party interests, data, and algorithm security. For individuals or organizations looking to enter this track, the first thing to recognize is that no project can completely detach from centralized resources at the outset; decentralization and privacy protection are more of a gradual process in the early stages. Secondly, without a viable incentive mechanism, data will still be firmly held by a few institutions, so when designing token economies, it is essential to detail every invocation or authorization step, minimizing unnecessary friction and ensuring that all parties can see benefits and find it convenient to use. Thirdly, regulation and compliance are often more challenging to overcome than the technology itself, because once data involves personal privacy or national sensitive information, it cannot be resolved solely by on-chain smart contracts; corresponding laws, regulations, and standards must also be coordinated. Finally, any project hoping to establish a new ecosystem with Web3 and AI should pragmatically view the current state of on-chain performance and computing power, especially in the model training phase, where in many cases, hybrid solutions—such as distributed computing networks or trusted execution environments (TEE)—must be leveraged to achieve scalable algorithm operations.
Some may ask, since we cannot do without centralized computing power and supporting facilities, what revolutionary value can Web3 and AI bring? The answer often lies in the subtle changes in "trust" and "distribution." In the past, platforms and giants were the absolute center of the data world, and individual users and small to medium enterprises could only passively invest, without equal bargaining power; now, through the collaborative design of smart contracts and token economies, data contributors, model developers, and ecosystem governors can participate in cooperation on the same network with clear agreements. Although these "new" relationships currently operate only in relatively niche circles, it is precisely these localized successes that provide examples, encouraging more people to attempt to build larger-scale, more scenario-based collaborative networks.
Perhaps this path will be bumpy, but as long as someone can truly integrate the advantages of Web3 and AI into the "production chain" in finance, art, healthcare, or even other yet-to-be-fully-explored fields, achieving a better balance of data, algorithms, and profit structures, it will undoubtedly bring new value to the next generation of the internet that transcends mere technological upgrades. Through the steps of projects like Numerai and Alethea, we may have already seen this glimmer of hope. If given time and the right environment to iterate, we may witness an era of complete evolution in production methods and trust mechanisms.
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。
