InfoFi Depth Research: Attention Finance Experiment in the AI Era

1. Introduction: From Information Scarcity to Attention Scarcity, InfoFi Emerges

The information revolution of the 20th century brought about an explosive growth of knowledge for human society, but it also triggered a paradox: when the cost of obtaining information is almost negligible, what becomes truly scarce is not the information itself, but the cognitive resources we use to process that information—attention. As Nobel laureate Herbert Simon first proposed the concept of "attention economy" in 1971, "information overload leads to attention scarcity," and modern society is deeply entrenched in this issue. Faced with the overwhelming content inundated by social media, short videos, and news push notifications, the cognitive boundaries of humanity are continuously squeezed, making filtering, judgment, and value assignment increasingly difficult.

The scarcity of attention has evolved into a battle for resources in the digital age. In traditional internet models, platforms tightly control the flow of traffic through algorithmic distribution, and the true creators of attention resources—whether users, content creators, or community evangelists—are often reduced to "free fuel" in the profit logic of the platform. Leading platforms and capitalists reap the rewards at every level of the attention monetization chain, while ordinary individuals who truly drive information production and dissemination find it difficult to participate in value sharing. This structural disconnection is becoming a core contradiction in the evolution of digital civilization.

The rise of Information Financialization (InfoFi) is happening against this backdrop. It is not an incidental new concept, but a fundamental paradigm shift based on blockchain, token incentives, and AI empowerment, with the goal of "reshaping the value of attention." InfoFi attempts to transform users' unstructured cognitive behaviors, such as opinions, information, reputation, social interactions, and trend discovery, into quantifiable and tradable asset forms. Through a distributed incentive mechanism, every user who participates in the creation, dissemination, and judgment within the information ecosystem can share the value generated. This is not only a technological innovation but also an attempt at redistributing power regarding "who owns attention and who dominates information."

In the narrative lineage of Web3, InfoFi serves as an important bridge connecting social networks, content creation, market games, and AI intelligence. It inherits the financial mechanism design of DeFi, the social drive of SocialFi, and the incentive structure of GameFi, while introducing AI capabilities in semantic analysis, signal recognition, and trend forecasting, creating a new market structure centered around "cognitive resource financialization." Its core is not merely about content distribution or likes and rewards, but a complete set of value discovery and redistribution logic revolving around "information → trust → investment → return."

From an agricultural society where "land" is a scarce factor, to an industrial era where "capital" serves as the engine of growth, and now to today's digital civilization where "attention" has become the core means of production, the focus of resources in human society is undergoing a profound shift. InfoFi is a tangible expression of this macro paradigm shift in the blockchain world. It is not only a new opportunity in the crypto market but may also be the starting point for a deep restructuring of the governance structure of the digital world, the logic of intellectual property, and financial pricing mechanisms.

However, no paradigm shift is linear; it is inevitably accompanied by bubbles, hype, misunderstandings, and fluctuations. Whether InfoFi can become a true user-centered attention revolution depends on whether it can find a dynamic balance between incentive mechanism design, value capture logic, and real demand. Otherwise, it will merely be another illusion sliding from "inclusive narrative" to "centralized harvesting."

2. The ecological structure of InfoFi: A "Information × Finance × AI" trinary intersection market

The essence of InfoFi is to build a composite market system that simultaneously embeds financial logic, semantic computing, and game mechanics in the contemporary online context where information is highly proliferated and value is hard to capture. Its ecological architecture is not a singular dimension of a "content platform" or "financial protocol," but rather the intersection of information value discovery mechanisms, behavior incentive systems, and intelligent distribution engines—constituting a full-stack ecosystem that integrates information trading, attention incentives, reputation rating, and intelligent prediction.

From a fundamental perspective, InfoFi is an attempt to "financialize" information, transforming cognitive activities such as content, opinions, trend judgments, and social interactions that cannot originally be priced into measurable and tradable "quasi-assets," thereby endowing them with market prices. The involvement of finance means that information is no longer fragmented and isolated "content fragments" during production, circulation, and consumption processes, but rather "cognitive products" with competitive attributes and value accumulation capabilities. This implies that a comment, a prediction, or a trend analysis may not only express individual cognition but can also become a speculative asset with risk exposure and future income rights. The popularity of certain prediction markets is a prime example of this logic taking root at the levels of public opinion and market expectations.

However, relying solely on financial mechanisms is far from enough to solve the rampant noise and the problem of bad money driving out good in the face of information overload. Therefore, AI has become the second pillar of InfoFi. AI primarily takes on two roles: first, semantic filtering, serving as the "first line of defense" against information signals and noise; second, behavior recognition, achieving precise evaluation of information sources through multi-dimensional data modeling of users' social network behaviors, content interaction trajectories, and originality of opinions. Some platforms are typical representatives of introducing AI technology into content evaluation and user profiling, playing the role of "algorithmic referee" in the Yap-to-Earn model, deciding who should receive token rewards and who should be blocked or downgraded. In a sense, the function of AI in InfoFi is equivalent to market makers and clearing mechanisms in exchanges, being the core of maintaining ecological stability and credibility.

Information is the foundation of all this. It is not only the subject of transactions but also the source of market sentiment, social connections, and consensus building. Unlike DeFi, the asset anchors of InfoFi are no longer on-chain hard assets like USDC or BTC, but rather "cognitive assets" that are more liquid, structurally looser, but more timely, including opinions, trust, topics, trends, and insights. This also determines that the operational mechanism of the InfoFi market is not a linear stacking but a dynamic ecology that highly relies on social graphs, semantic networks, and psychological expectations. In this framework, content creators act as "market makers"; they provide opinions and insights for the market to judge their "price". Users are the "investors" who express their value judgments on specific information through likes, retweets, bets, comments, and other behaviors, pushing it up or sinking it in the entire network. Meanwhile, the platform and AI serve as the "referee + exchange", responsible for ensuring the fairness and efficiency of the entire market.

The synergy of this trinary structure has given rise to a series of new species and mechanisms: prediction markets provide clear targets for speculation; Yap-to-Earn encourages knowledge as mining and interaction as output; reputation protocols transform individuals' on-chain histories and social behaviors into credit assets; attention markets attempt to capture the "emotional fluctuations" propagated on-chain; while some token-gated content platforms reconstruct the logic of information payment through permissioned economies. Together, they form a multi-layered ecosystem of InfoFi: consisting of value discovery tools, value distribution mechanisms, as well as embedded multi-dimensional identity systems, participation threshold designs, and anti-witch-hunt mechanisms.

It is precisely within this cross-structure that InfoFi is no longer just a market, but a complex information game system: it uses information as the medium of exchange, finance as the incentive engine, and AI as the governance hub, ultimately aiming to construct a self-organizing, distributed, and adjustable cognitive collaboration platform. In a sense, it attempts to become a "cognitive financial infrastructure," not merely for content distribution, but to provide the entire crypto community with a more efficient information discovery and collective decision-making mechanism.

However, such a system is also destined to be complex, diverse, and fragile. The subjectivity of information determines the inability to unify value assessment, the competitive nature of finance increases the risks of manipulation and herd effects, and the opacity of AI challenges transparency. The InfoFi ecosystem must continuously balance and self-repair between these three tensions; otherwise, it is likely to slide towards the opposite of "de facto gambling" or "attention harvesting" under the drive of capital.

The ecological construction of InfoFi is not an isolated project of a certain protocol or platform, but a co-performance of a whole set of socio-technical systems. It is a profound attempt of Web3 in the direction of "governing information" rather than "governing assets." It will define the pricing mechanism of information in the next era and even build a more open and autonomous cognitive market.

3. Core Game Mechanism: Incentivizing Innovation vs Harvesting Traps

In the InfoFi ecosystem, behind every prosperous appearance is ultimately the design game of incentive mechanisms. Whether it is the participation in prediction markets, the output of mouth-to-mouth behavior, the construction of reputation assets, the trading of attention, or the mining of on-chain data, it all revolves around a core question: Who puts in the effort? Who shares the profits? Who bears the risks?

From an external perspective, InfoFi seems to be a "production relationship innovation" in the migration from Web2 to Web3: it attempts to break the exploitation chain among "platform-creator-user" in traditional content platforms, returning value to the original contributors of information. However, from an internal structural standpoint, this value return is not inherently fair but is built on a delicate balance of a series of incentives, validations, and game mechanisms. If designed properly, InfoFi is expected to become an innovative experimental field for user win-win; if the mechanism is unbalanced, it can easily degenerate into a "retail investor harvesting ground" dominated by capital and algorithms.

The first thing to examine is the positive potential of "incentivizing innovation." The essential innovation across all sub-tracks of InfoFi is to endow "information," an intangible asset that has been difficult to measure and financialize in the past, with clear transactionality, competitiveness, and settlement. This transformation relies on two key engines: the traceability of blockchain and the assessability of AI.

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However, the more strongly incentivized the system is, the more likely it is to give rise to "game abuse." The greatest systemic risk faced by InfoFi is the alienation of the incentive mechanism and the proliferation of arbitrage chains.

Taking Yap-to-Earn as an example, on the surface, it rewards users for the value of content creation through AI algorithms. However, in actual execution, many projects quickly fall into "information haze" after briefly attracting a large number of content creators during the initial incentive phase—issues such as spam from bot matrix accounts, early participation by influential users, and the project's directional manipulation of interaction weights frequently occur. A leading KOL bluntly stated: "If you don't boost your numbers, you can't even make the rankings; AI has been trained specifically to recognize keywords and ride trends." Furthermore, a project party revealed: "We invested $150,000 for a round of publicity, but 70% of the traffic was from AI accounts and fake accounts competing with each other, and the real KOLs did not participate. It’s impossible for me to invest a second time."

Under the opaque mechanisms of the points system and token expectations, many users have become "free laborers": tweeting, interacting, launching, and building communities, only to find they are not qualified to participate in airdrops. This kind of "backstabbing" incentive design not only damages the platform's reputation but also risks the collapse of the long-term content ecosystem. Some comparative cases of projects are particularly typical: the former has a clear allocation mechanism during the initial phase, with substantial token value returns; the latter, due to an imbalanced allocation mechanism and lack of transparency, has triggered social...