In the eyes of many, the combination of Web3 and AI still remains at the level of conceptual hype, seeming like just a few “buzzwords” added to traditional technologies. However, if we focus on projects that have truly withstood the test of time and the market, we can observe that the interaction between “decentralization” and “smart algorithms” is far more complex than imagined, and indeed exhibits leapfrog innovation potential in specific scenarios. A key premise is that any AI requires real and diversified data in order to grow, and Web3’s token mechanisms and privacy protection methods happen to provide individuals, or even groups, with new agency in the flow and pricing of data.

In a certain sense, the coupling of Web3 and AI is not simply “moving algorithms onto the blockchain.” Instead, it seeks to manage data, computing power, and profit distribution through a completely new production relationship. The following cases are a concentrated embodiment of this “new production relationship.” They are not perfect, but they bring insights from different dimensions.

Numerai

• One of the most frequently mentioned projects is Numerai, in the field of financial hedging. Many may only know that this is a “crypto hedge fund,” but have not carefully dissected its operational logic. Numerai first gains access to a vast amount of real and highly sensitive financial transaction data, which, in the eyes of traditional hedge funds, is considered “core assets” and is never easily shared. However, what Numerai does is encrypt and reduce the dimensions of this data at a high intensity, such that external data scientists can only see the “puzzle” without the “answers.” This processing prevents model trainers from reverse-engineering the real prices of specific stocks or futures, thus reducing the risk of data leakage or misuse. Then, Numerai opens these “puzzle data” to the world, allowing anyone to download and attempt predictions, and then upload their results back to the platform to participate in rankings and evaluations. The real stroke of genius lies in the incentive mechanism: those who excel in the hedging strategies and predictions will receive platform token rewards, and Numerai will incorporate their algorithms into actual trading strategies, thereby gaining returns in the financial market.
• What’s interesting is not just the “crowdsourced algorithm” form, but the underlying trust-based game. On one hand, Numerai gains nearly unlimited talent and algorithmic creativity, overcoming the problem of limited manpower within its internal research team. On the other hand, contributors can earn rewards based on their own abilities, protected under decentralized contracts, without worrying about “whether the platform might default.” However, it is not easy for this model to grow sustainably. First, Numerai was still relatively centralized in its early stages, with the true raw data still managed by the project, and contributors could only “trust” that the encrypted data had no hidden backdoors. Secondly, participants without a certain technical threshold or computational investment would find it difficult to stand out in the global competition. This shows that Web3, in this case, has not completely eliminated the phenomenon of “the strong getting stronger,” but instead opened a door in the previously closed world of financial data, allowing more people to participate. How far it can go still depends on whether the trust and profit distribution among funding parties, data owners, and algorithm contributors can be maintained in the long run.

Alethea AI

• Compared with Numerai, which focuses on financial data, Alethea AI pushes the combination of Web3 and AI in a more imaginative direction from the perspective of digital art. Traditional NFTs are more about “pictures on the chain”, and most of them only show static scarcity. However, Alethea AI proposed the concept of “iNFT”, hoping that NFT would no longer be just an artistic “voucher”, but become something that can interact with users. , and even digital life with the ability to generate independently.
• The specific method is that the artist pre-implants an AI model or training interface when casting the NFT. When the collector buys it, he or she can input specific text, images or other data to trigger the AI ​​to perform secondary or even multiple derivation. creation. Each new creation can be separately minted into NFT, circulated and traded again, and there is a complete set of smart contracts between the original author, secondary creators and collectors to distribute subsequent income. This seems to subvert people’s understanding of the “uniqueness” of artistic creation, but it exactly demonstrates the potential of Web3 and AI to break the boundaries of content and give dynamic attributes to works. In the past, in the traditional art market, creators often only received the proceeds from the initial sale. Subsequent resale and processing often had nothing to do with the artist, and there was no continuous profit sharing.
• Through the programmable features of the blockchain, every derivative and transaction can be traced and recorded, and income can be automatically distributed according to the contract. This model brings a new dimension of “ecological reproduction” to artistic creation. NFT no longer flows one-way from the author to the collector, nor is it limited to the native platform. However, for this mechanism to truly work, it must face disputes at multiple levels such as copyright, regulation, and aesthetics. In terms of copyright, different countries do not have unified regulations on the copyright ownership of “AI-generated objects”. If infringement is suspected, how will the platform and the artist share responsibilities? At the technical level, if Alethea hopes that NFT has higher-level “conversation” or “perception” capabilities, the computing power requirements of the AI ​​model will far exceed what the chain itself can carry, and it will inevitably need to access centralized cloud services. . This leads to a paradox: while talking about a “decentralized art ecology”, on the other hand it still relies on traditional computing infrastructure, so that the real technical and economic structure is more complex than what is advertised. The existence of these contradictions does not mean that the project has no value, but it shows that when the integration of Web3 and AI gradually deepens, “pragmatic hybrid” is likely to go further than “pure decentralization”.

AI + Healthcare

• In the more sensitive and serious field of healthcare, the combination of Web3 and AI is proving its true value. Medical data has always been regarded as “the privacy of privacy,” with any leakage potentially leading to severe legal and ethical consequences. However, it is also one of the most valuable resources needed for AI training. For example, in cancer image recognition technology, breakthroughs require hundreds of thousands, or even millions, of cases and images for training. However, the data from different hospitals, regions, or even countries is confined within their own “information silos,” and patients are often unwilling or unable to easily authorize their medical records to be analyzed on an unknown platform.
• The solution offered by Web3 is to record the ownership and authorization process of data on the distributed ledger of the blockchain, achieving a “give computation rights, not original data” privacy computing model through smart contracts. When an AI model needs to access medical records from a hospital, it must first obtain authorization from the owner (the hospital or the patient). It can only train or infer on de-identified data in a specified environment, and any reading or movement of the original data requires blockchain signatures and documentation. Some even propose a “token incentive” model, where hospitals willing to provide more high-quality data could receive more weight in community governance or share in future revenues. However, when it comes to practical implementation, issues arise one after another: Does the hospital have enough technical capacity to deploy and manage these nodes? To what extent must the data be “de-identified” to meet regulatory requirements in different countries? Can the blockchain’s throughput and storage capabilities handle trillions of medical imaging files? These practical challenges have caused many projects to initially pilot on a small scale, still refining their models and lacking clear business structures like Numerai or Alethea. From another perspective, this also suggests that once the medical community and Web3 ecosystem resolve these key challenges, it may give rise to an AI application revolution with even greater social significance than digital collectibles: the vast, multi-source medical data, once legally and compliantly “aggregated and computed,” could potentially accelerate research on complex diseases like cancer and rare diseases by more than double.

About AI+

• At first glance, these cases seem to be scattered across unrelated fields such as finance, art, and healthcare, but in reality, they are all exploring a “new production relationship.” What Web3 provides is not simply “putting things on the blockchain,” but a mechanism for rebalancing multiple parties’ interests, data, and algorithmic security. For individuals or organizations looking to enter this space, it’s important to first realize that no project can fully detach from centralized resources right from the start. Decentralization and privacy protection are more of a gradual process during the early stages. Second, without a feasible incentive mechanism, data will remain firmly controlled by a few institutions. Therefore, when designing token economies, every invocation or authorization step must be detailed, minimizing unnecessary friction and ensuring that all parties see benefits and find the system easy to use. Third, regulation and compliance are often harder to overcome than the technology itself. Once data involves personal privacy or national sensitive information, it cannot be solved solely with smart contracts on the blockchain—it also requires the cooperation of corresponding laws, regulations, and standards. Lastly, any project seeking to build a completely new ecosystem with Web3 and AI should pragmatically address the current limitations of on-chain performance and computational power. Especially during the model training phase, hybrid solutions—such as distributed computing networks or Trusted Execution Environments (TEE)—are often required to achieve large-scale algorithmic operations.
• Some might argue, if we still rely on centralized computing power and infrastructure, what revolutionary value can Web3 and AI truly bring? The answer often lies in the subtle, gradual transformation of “trust” and “distribution” mechanisms. 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 participate without the capital for an equal bargaining position. Today, through the collaboration 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 are currently only functioning in smaller, niche circles, it is precisely these local successes that provide examples, motivating more people to try building larger-scale, more diverse collaborative networks.
• Perhaps this path will still be bumpy, but as long as someone manages to integrate the advantages of Web3 and AI into the “production chain” in fields like finance, art, healthcare, and other yet-to-be-fully-explored sectors, achieving a better balance of data, algorithms, and revenue structures, it will surely bring new value to the next generation of the internet that transcends mere technological upgrades. Through projects like Numerai and Alethea, we may have already glimpsed this dawn. If given time and the right environment to iterate, we might witness an era of complete evolution in both production methods and trust mechanisms.

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