InfoFi Depth Research: Attention Finance Experiments in the AI Era

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

The information revolution of the 20th century has brought about an explosive growth of knowledge in human society, but it has also triggered a paradox: when the cost of obtaining information becomes nearly zero, what becomes truly scarce is not the information itself, but our cognitive resources used 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. Faced with the overwhelming influx of information from various social media and content platforms, the cognitive boundaries of humans are constantly being compressed, and it becomes increasingly difficult to sift, judge, and assign value.

The scarcity of attention has evolved into a resource competition in the digital age. In the traditional Web2 model, platforms tightly control the traffic entry points through algorithms, and the true creators of attention resources—whether users, content creators, or community evangelists—often serve merely as "free fuel" in the profit logic of the platform. Leading platforms and capital reap the rewards 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 divide is becoming a core contradiction in the evolution of digital civilization.

The rise of InfoFi( in the context of financialization of information is happening against this backdrop. It is not a sporadic new concept, but rather 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' perspectives, information, reputation, social interactions, trend discovery, and other unstructured cognitive behaviors into quantifiable, tradable asset forms, and through a distributed incentive mechanism, enable every user participating in the creation, dissemination, and judgment within the information ecosystem to share in the value generated. 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 lineage 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, constructing a new market structure centered around "the financialization of cognitive resources." Its core is not simply about content distribution or likes and rewards, but a comprehensive set of value discovery and redistribution logic revolving around "information → trust → investment → return."

From an agricultural society where "land" is the scarce factor, to the industrial era where "capital" is the engine of growth, and now under 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 the concrete expression of this macro paradigm transformation in the blockchain world. It is not only a new opportunity in the crypto market, but also potentially the starting point for a deep reconstruction of the governance structure of the digital world, the logic of intellectual property, and the financial pricing mechanism.

However, any paradigm shift is not linear; it inevitably accompanies bubbles, hype, misunderstandings, and fluctuations. Whether InfoFi can become a true 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 be another illusion sliding from "inclusive narrative" to "centralized harvesting."

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2. The ecological structure of InfoFi: a "Information × Finance × AI" cross-market.

The essence of InfoFi is to build a composite market system that simultaneously nests financial logic, semantic computing, and game mechanisms in the contemporary network context, where information is highly rampant and 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—constituting 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, transforming originally non-quantifiable content, opinions, trend judgments, social interactions, and other cognitive activities into measurable, tradable "quasi-assets" with market prices. The involvement of finance means that information is no longer a scattered, isolated "content fragment" in the processes of production, circulation, and consumption, but a "cognitive product" with game attributes and value accumulation capabilities. This implies that a comment, a prediction, or a trend analysis can be both an expression of individual cognition and a speculative asset with risk exposure and future income rights. The popularity of prediction markets like Polymarket and Kalshi is a prime example of this logic being realized in the realms of public opinion and market expectations.

However, relying solely on financial mechanisms is far from sufficient to address the rampant noise caused by information overload and the dilemma of bad money driving out good. 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 assessment of information sources through modeling multidimensional data such as user social network behavior, content interaction trajectories, and originality of opinions. Platforms like Kaito AI, Mirra, and Wallchain are typical representatives of introducing AI technology into content evaluation and user profiling, playing the role of "algorithmic judges" 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 akin to that of market makers and clearing mechanisms in exchanges, being core to 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 formation. Unlike DeFi, the asset anchors in InfoFi are no longer hard assets like USDC, BTC, etc., but rather "cognitive assets" that are more liquid, structurally looser, but timelier, such as 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. In this framework, content creators act as 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 likes, shares, bets, comments, and other behaviors, driving it to rise or sink throughout the entire network; while the platform and AI serve as "referees + exchanges," responsible for ensuring the fairness and efficiency of the entire market.

The collaborative operation of this trinity structure has spawned a series of new species and mechanisms: prediction markets provide clear targets for wagering; Yap-to-Earn encourages knowledge as mining and interaction as output; reputation protocols like Ethos transform individual on-chain history and social behavior into credit assets; attention markets like Noise and Trends attempt to capture the "emotional fluctuations" propagated on-chain; and token-gated content platforms like Backroom reconstruct the logic of information payment through permission economies. Together, they form a multi-layered ecosystem of InfoFi: containing value discovery tools, bearing value distribution mechanisms, and embedding multi-dimensional identity systems, participation threshold designs, and anti-witch mechanisms.

It is precisely within this cross-structure that InfoFi is no longer just a marketplace, 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 collaborative platform. In a sense, it attempts to become a "cognitive financial infrastructure," not only for content distribution but also to provide a more efficient information discovery and collective decision-making mechanism for the entire crypto society.

However, such a system is destined to be complex, diverse, and fragile. The subjectivity of information determines the inconsistency of value assessment, the game-like nature of finance increases the risk of manipulation and herd behavior, and the black box nature of AI poses challenges to transparency. The InfoFi ecosystem must continually balance and self-repair between these three tensions; otherwise, it is likely to slide under capital-driven forces towards the opposite of "de facto gambling" or "attention harvesting."

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

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3. Core Game Mechanism: Incentivizing Innovation vs Harvesting Trap

In the InfoFi ecosystem, behind all the prosperous appearances lies the design game of incentive mechanisms. Whether it is the participation in prediction markets, the output of mouth-to-mouth actions, the construction of reputation assets, the trading of attention, or the mining of on-chain data, it fundamentally revolves around a core question: Who contributes? 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 rather established on a delicate balance of a series of incentives, verifications, and game mechanisms. If designed properly, InfoFi has the potential 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 + 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", which was previously difficult to measure and impossible to finance, with clear tradability, competitiveness, and cashability. This transformation relies on two key engines: the traceability of blockchain and the assessability of AI.

betting signals

However, the more strongly incentivized the system is, the easier it is to give rise to "game abuse". The biggest 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, but in actual execution, many projects quickly fall into "information smog" after briefly attracting a large number of content creators during the initial incentive phase—issues such as bot matrix accounts flooding, major influencers participating in beta testing early, and project parties manipulating interaction weights occur frequently. A leading KOL bluntly stated: "If you don't inflate your numbers, you can't get on the leaderboard; AI has been trained to specifically recognize keywords and ride trends." Even more, project parties revealed: "I invested 150,000 USD for a round of Kaito mouth-sucking, and as a result, 70% of the traffic is from AI accounts and water armies competing, real KOLs do 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 themselves ineligible for airdrops in the end. This kind of "backstabbing" incentive design not only damages the platform's reputation but also easily leads to the collapse of the long-term content ecosystem. The comparative case of Magic Newton and Humanity is particularly typical.