InfoFi Depth Research: Attention Finance Experiment in the AI Era

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

The information revolution of the 20th century brought explosive knowledge growth to human society, but it also triggered a paradox: when the cost of obtaining information is almost zero, what becomes truly scarce is no longer the information itself, but the cognitive resources we use to process that information—attention. As Nobel laureate Herbert Simon first proposed in 1971 with the concept of "attention economy," "information overload leads to attention scarcity," and modern society is deeply entrenched in this. Faced with the overwhelming content inundated by social media, short videos, and news push notifications, the cognitive boundaries of humans are continuously being squeezed, making it increasingly difficult to filter, judge, and assign value.

In the digital age, the scarcity of attention has evolved into a battle for resources. In the traditional Web2 model, platforms firmly control the flow entry through algorithms, and the true creators of attention resources—be it users, content creators, or community evangelists—often serve merely as "free fuel" in the profit logic of the platforms. Leading platforms and capitalists harvest at various levels in the chain of attention monetization, 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 a sporadic new concept, but rather a fundamental paradigm shift aimed at "reshaping the value of attention," built on the technological foundation of blockchain, token incentives, and AI empowerment. InfoFi attempts to transform users' non-structured cognitive behaviors such as viewpoints, information, reputation, social interactions, and trend discovery into quantifiable, tradable asset forms, allowing every user participating in the creation, dissemination, and judgment within the information ecosystem to share in the value generated through a distributed incentive mechanism. This is not just a technological innovation, but also an attempt at redistributing power regarding "who owns attention and who dominates information."

In the narrative framework of Web3, InfoFi serves as an important bridge connecting social networks, content creation, market dynamics, 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, thereby constructing a new market structure centered around "cognitive resource financialization." Its core is not merely about content distribution or likes and rewards, but a comprehensive value discovery and redistribution logic revolving around "information → trust → investment → return."

From an agricultural society where "land" is the scarce factor, to an industrial era where "capital" is the growth engine, and now to today’s digital civilization where "attention" has become the core means of production, the resource focus of human society is undergoing a profound shift. InfoFi is a concrete expression of this macro paradigm shift in the on-chain world. It is not only a new trend in the crypto market but may also serve as the starting point for a deep reconstruction of the governance structure of the digital world, the logic of intellectual property, and financial pricing mechanisms.

However, no paradigm shift is linear; it inevitably comes with bubbles, hype, misunderstandings, and fluctuations. Whether InfoFi can become a truly user-centric 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 slide again from the "inclusive narrative" into the illusion of "centralized harvesting."

2. The Ecological Structure of InfoFi: A Triadic Intersection Market of "Information × Finance × AI"

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

From a fundamental perspective, InfoFi is an attempt at the "financialization" of information, which means transforming originally unpriceable content, opinions, trend judgments, social interactions, and other cognitive activities into measurable and tradable "quasi-assets" that have market prices. The involvement of finance makes information no longer fragmented and isolated "content fragments" during the processes of production, circulation, and consumption, but rather "cognitive products" with game attributes and value accumulation capabilities. This means that a comment, a prediction, or a trend analysis can be an expression of individual cognition and 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 being implemented at the level of public opinion and market expectations.

However, relying solely on financial mechanisms is far from sufficient to address the noise overflow and the Gresham's law dilemma brought about by information explosion. Therefore, AI has become the second pillar of InfoFi. AI mainly takes on two roles: first, semantic filtering, serving as the "first line of defense" against information signals and noise; second, behavior recognition, which models multidimensional data such as user social network behavior, content interaction trajectories, and originality of opinions to achieve precise assessment of information sources. Some platforms are typical representatives that incorporate 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 that of market makers and clearing mechanisms in exchanges, serving as the core to maintain 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 formation. Unlike DeFi, where the assets are anchored to on-chain hard assets such as USDC and BTC, InfoFi's assets are anchored to "cognitive assets" that are more fluid, loosely structured, and time-sensitive, 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 ecosystem that highly relies on social graphs, semantic networks, and psychological expectations. Within this framework, content creators act as the "market makers," providing opinions and insights for the market to judge their "price"; users are the "investors," expressing their value judgments on certain information through actions like liking, sharing, betting, and commenting, thereby pushing it up or down across the entire network; while the platform and AI serve as the "referee + exchange," responsible for ensuring the fairness and efficiency of the entire market.

The synergistic operation of this trinary structure has given rise to a series of new species and new mechanisms: prediction markets provide clear targets for speculation; Yap-to-Earn encourages knowledge as mining and interaction as output; reputation protocols convert individual on-chain history and social behavior into credit assets; attention markets attempt to capture the "emotional fluctuations" propagated on-chain; while token-gated content platforms reconstruct information payment logic through permission economies. Together, they form a multi-layered ecosystem of InfoFi: which includes value discovery tools, carries value distribution mechanisms, and embeds multi-dimensional identity systems, participation threshold designs, and anti-witch mechanisms.

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

However, such a system is bound to be complex, diverse, and fragile. The subjectivity of information determines the inability to unify value assessments, the game-like nature of finance increases the risks of manipulation and herd behavior, and the black-box nature of AI poses challenges to transparency. The InfoFi ecosystem must constantly balance and self-repair between these three tensions, otherwise it is likely to slip under capital drive into the opposite of "disguised gambling" or "attention harvesting fields."

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

3. Core Game Mechanism: Incentivizing Innovation vs Harvesting Traps

Behind the prosperous facade of the InfoFi ecosystem lies the fundamental game of incentive mechanism design. Whether it is participation in prediction markets, the output of mouth-to-mouth behavior, the construction of reputation assets, trading of attention, or the mining of on-chain data, it ultimately revolves around a core question: Who contributes? Who gets dividends? 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 between "platform-creators-users" 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 rather built on a subtle 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 mechanisms are unbalanced, it could easily degenerate into a "retail investor harvesting ground" under the dominance of capital and algorithms.

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

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However, the more incentive-driven the system is, the easier it is to give rise to "game abuse." The biggest systemic risk faced by InfoFi is the distortion 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, but in practice, many projects quickly fall into "information haze" after briefly attracting a large number of content creators during the initial incentive phase—issues like bot matrix accounts flooding the platform, prominent influencers participating in internal testing early, and project teams manipulating interaction weights frequently occur. A leading KOL frankly stated: "Now, if you don't increase the volume, you can't make the rankings; AI has been specifically trained to recognize keywords and ride the wave of popularity." Moreover, some project teams revealed: "We invested 150,000 USD for a round of mouth marketing, but 70% of the traffic was from AI accounts and paid promoters competing with each other; real KOLs did not participate, and it is 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, going online, and building groups, 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 projects serve as particularly typical comparative cases: the former has a clear distribution mechanism during the "mouth-to-hold" phase, with substantial token value returns; the latter, however, suffers from distribution issues.