InfoFi Depth Research: Attention Finance Experiment in the AI Era

Introduction: From Information Scarcity to Attention Scarcity, InfoFi Emerges

The information revolution of the 20th century brought explosive growth in knowledge to human society, but it also triggered a paradox: when the cost of obtaining information is almost zero, 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 introduced 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 content inundated by various social media and short video platforms, the cognitive boundaries of humanity are being continuously squeezed, making it increasingly difficult to filter, judge, and assign value.

This scarcity of attention has evolved into a resource competition in the digital age. In the traditional Web2 model, platforms firmly control traffic entry through algorithms, and the true creators of attention resources—whether users, content creators, or community evangelists—often serve merely as "free fuel" in the platforms' profit logic. Leading platforms and capitalists continuously reap profits from the chain of attention monetization, while ordinary individuals who genuinely 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 occurring against this backdrop. It is not a sporadic new concept, but rather a fundamental paradigm shift based on technologies such as 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, and through distributed incentive mechanisms, allows every user participating in the creation, dissemination, and judgment within the information ecosystem to share in the value generated. This is not just technological innovation, but also an attempt to redistribute 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 drivers of SocialFi, and the incentive structures of GameFi, while introducing AI capabilities in semantic analysis, signal recognition, and trend prediction, thereby constructing a new market structure centered around "cognitive resource financialization." Its core is not merely simple content distribution or likes and tips, but a complete set of 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 productive material, the resource focus of human society is undergoing a profound shift. InfoFi is the concrete expression of this macro paradigm shift in the on-chain world. It is not only a new trend in the crypto market, but it may also be the starting point for a deep restructuring of the governance structure of the digital world, the logic of intellectual property, and the financial pricing mechanism.

But no paradigm shift is linear; it necessarily comes with 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."

The Ecological Composition of InfoFi: A "Information × Finance × AI" Ternary Intersection Market

The essence of InfoFi is to construct a composite market system that simultaneously embeds financial logic, semantic computing, and game mechanics in today's context of information overload and difficulty in capturing value. Its ecological architecture is not a single-dimensional "content platform" or "financial protocol," but rather the intersection of information value discovery mechanisms, behavior incentive systems, and intelligent distribution engines—forming a full-stack ecosystem that integrates information trading, attention incentives, reputation rating, and intelligent forecasting.

From the perspective of underlying logic, InfoFi is an attempt at the "financialization" of information, which means transforming cognitive activities such as content, opinions, trend judgments, and social interactions that cannot originally be priced into measurable and tradable "quasi-assets," thereby assigning them a market price. The intervention of finance means that information is no longer fragmented and isolated "content fragments" during the processes of production, circulation, and consumption, but rather "cognitive products" that possess gaming attributes and value accumulation capabilities. This implies that a comment, a prediction, or a trend analysis can not only be an expression of 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 materializing in public opinion and market expectations.

However, relying solely on financial mechanisms is far from sufficient to solve the noise overflow and the problem of bad money driving out good money caused by information explosion. Therefore, AI has become the second pillar of InfoFi. AI mainly assumes two roles: first, semantic filtering, acting 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. Some platforms are typical representatives of introducing AI technology into content evaluation and user profiling, playing the role of "algorithmic judge" in the Yap-to-Earn model, determining 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, the asset anchors of InfoFi are no longer on-chain hard assets like USDC and BTC, but rather "cognitive assets" that are more fluid, loosely structured, and more timely, 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 highly dependent on social graphs, semantic networks, and psychological expectations. In this framework, content creators serve as the "market makers" of the market, providing opinions and insights for the market to assess their "prices"; users act as "investors," expressing their value judgment on certain information through actions like liking, sharing, betting, and commenting, thereby driving its rise or fall within the entire network; while the platform and AI serve as "referees + exchanges," responsible for ensuring the fairness and efficiency of the entire market.

The synergistic operation of this trinity structure gives 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 convert individual on-chain history and social behavior into credit assets; attention markets attempt to capture the "emotional fluctuations" propagated on-chain; and token-gated content platforms reconstruct the logic of information payment through permissioned economies. Together, they form a multi-layered ecosystem of InfoFi: encompassing not only value discovery tools but also value distribution mechanisms, while embedding a multi-dimensional identity system, participation threshold designs, and anti-witchcraft mechanisms.

It is precisely within this intersecting structure that InfoFi is no longer just a market, but a complex information game system: it uses information as the medium of transaction, finance as the incentive engine, and AI as the governance hub, ultimately intending to build a self-organizing, distributed, and adjustable cognitive collaboration 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 society.

However, such a system is destined to be complex, diverse, and fragile. The subjectivity of information determines the non-uniformity of value assessment, the game 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 among the three-dimensional tensions; otherwise, it is likely to slide under capital drive into the opposite of "disguised gambling" or "attention harvesting field."

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, representing a profound attempt in the direction of "governing information" rather than "governing assets" in Web3. It will define the pricing mechanism of information in the next era and even build a more open and autonomous cognitive market.

Core Game Mechanism: Incentivizing Innovation vs Harvesting Traps

In the InfoFi ecosystem, behind all the prosperous appearances lies the strategic game of incentive mechanism design. Whether it is the participation in prediction markets, the output of speculative behavior, the construction of reputation assets, the trading of attention, or the mining of on-chain data, it fundamentally revolves around a core question: Who puts in the effort? Who shares the profits? Who bears the risks?

From an external perspective, InfoFi appears to be a "production relationship innovation" in the migration from Web2 to Web3: it attempts to break the exploitative chain between "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 relies on a subtle balance of a series of incentives, validations, and game mechanisms. If designed properly, InfoFi has the potential to become an innovative experimental field for user win-win; if the mechanisms are unbalanced, it can easily devolve 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 of all sub-tracks of InfoFi is to give clear tradability, competitiveness, and settlement to "information," an intangible asset that was 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 strongly incentivized a system is, the more likely 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. 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 robot matrix accounts spamming, top influencers participating in beta testing in advance, and project parties manipulating interaction weights frequently occur. A leading KOL candidly stated: "If you don't inflate the numbers, you basically can't get on the leaderboard. AI has been trained to specifically recognize keywords and ride the wave of popularity." Even more shocking, project parties revealed: "We invested $150,000 for a round of mouth marketing, but 70% of the traffic was from AI accounts and bots competing, and real KOLs are not participating. It’s impossible for me to invest a second time."

Under the opaque mechanism of the points system and token expectations, many users have become "free laborers": tweeting, interacting, launching, and forming groups, only to find themselves ineligible for airdrops in the end. This kind of "backstabbing" incentive design not only undermines the platform's reputation but also easily leads to the collapse of the long-term content ecosystem. The comparative cases of different projects are particularly typical: some projects have clear allocation mechanisms during the early stages, with substantial token value returns; while others suffer from a lack of allocation mechanism.