InfoFi Depth Research: Attention Financial 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, 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. Nobel laureate Herbert Simon first introduced the concept of "attention economy" in 1971, pointing out that information overload leads to attention scarcity. In the face of overwhelming content from social media, short videos, and news notifications, the cognitive boundaries of humanity are being continuously squeezed, making it increasingly difficult to filter, judge, and assign value.

The scarcity of attention has evolved into a resource competition in the digital age. In the traditional Web2 model, platforms control traffic entry through algorithms, while the users, content creators, and community advocates who truly generate attention resources are often just "free fuel" in the profit logic of the platforms. Leading platforms and capitalists reap the rewards in the attention monetization chain, while ordinary individuals who drive information production and dissemination find it difficult to participate in value sharing.

The rise of InfoFi is occurring against this backdrop. It uses blockchain, token incentives, and AI empowerment as its technological foundation, aiming to "reshape the value of attention." It 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, it enables every user involved in the creation, dissemination, and judgment of information within the ecosystem to share in the value generated.

InfoFi 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, to build a new market structure centered around "cognitive resource financialization." At its core is a complete set of value discovery and redistribution logic revolving around "information → trust → investment → return."

From the land of agricultural society, the capital of the industrial age, to the attention under today's digital civilization, the core means of production in human society are undergoing a profound transfer. InfoFi is the tangible expression of this macro paradigm shift in the on-chain world. It is not only a new windfall in the cryptocurrency market but may also be 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.

2. The Ecological Composition of InfoFi: The Trinary Intersection Market of Information × Finance × AI

The essence of InfoFi is to construct a composite market system that simultaneously nests financial logic, semantic computing, and game theory mechanisms in the contemporary online context where information is highly abundant and its value is difficult to capture. Its ecological architecture is the intersection of the information value discovery mechanism, behavioral incentive system, and intelligent distribution engine, forming a full-stack ecosystem that integrates information trading, attention incentives, reputation ratings, and intelligent forecasting.

From a fundamental logical perspective, InfoFi is an attempt at the "financialization" of information, which means transforming originally unpriced content, opinions, trend judgments, social interactions, and other cognitive activities into measurable and tradable "quasi-assets," thereby assigning them market prices. The involvement of finance makes information no longer a scattered and isolated "content fragment" during the processes of production, circulation, and consumption, but rather a "cognitive product" that possesses game-theoretic properties and value accumulation capabilities.

AI becomes the second pillar of InfoFi, primarily undertaking the roles of semantic filtering and behavior recognition. AI models multi-dimensional data such as user social network behavior, content interaction trajectories, and originality of opinions to achieve precise assessment of information sources. 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 for maintaining ecological stability and credibility.

Information is the foundation of it all, not only the subject of transactions but also the source of market sentiment, social connections, and consensus building. The operational mechanism of the InfoFi market heavily relies on the dynamic ecology constructed by social graphs, semantic networks, and psychological expectations. In this framework, content creators act as the "market makers," users are the "investors," and the platform and AI serve as the "referees + exchanges."

The collaborative operation of this trinary structure has given rise to new species and new mechanisms such as prediction markets, Yap-to-Earn, reputation protocols, attention markets, and token-gated content platforms. Together, they constitute the multi-layered ecology of InfoFi: which includes value discovery tools, value distribution mechanisms, and also embeds a multi-dimensional identity system, participation threshold design, and anti-witch mechanisms.

InfoFi aims to become a "cognitive financial infrastructure" that provides more efficient information discovery and collective decision-making mechanisms for the entire crypto community. However, such a system is bound to be complex, diverse, and fragile. The subjectivity of information determines the impossibility of unified value assessment, the game-like nature of finance increases the risks of manipulation and herd behavior, and the opacity of AI poses challenges to transparency. The InfoFi ecosystem must continuously balance and self-repair in the face of these three tensions, or it risks sliding into the opposite of "covert gambling" or "attention harvesting" under capital drive.

3. Core Game Mechanism: Incentive Innovation vs. Harvest 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-feeding behavior, the construction of reputation assets, the trading of attention, or the mining of on-chain data, they all fundamentally revolve around a core issue: Who puts in the effort? Who shares the profits? Who bears the risks?

InfoFi aims to break the exploitation chain between "platform-creator-user" in traditional content platforms, returning value to the original contributors of information. However, from an internal structural perspective, this value return is not inherently fair, but is based 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 mutual benefit among users; if the mechanisms are imbalanced, it can easily degenerate into a "retail investor harvesting ground" dominated by capital and algorithms.

The essential innovation of InfoFi is to endow the "information"—an intangible asset that has been difficult to measure and financialize in the past—with clear tradability, competitiveness, and settlement. This transformation relies on the traceability of blockchain and the assessability of AI. The prediction market monetizes cognitive consensus through market pricing mechanisms; the mouth-lu ecology turns speech into economic behavior; the reputation system builds inheritable and collateralizable social capital; and the attention market treats trending topics as trading targets. These mechanisms enable information to possess "cash flow" attributes for the first time, making "saying a word, sharing a tweet, or endorsing someone" a genuine productive activity.

However, the stronger the incentives of the system, 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, many projects attract a large number of content creators in the early stages of incentives, only to quickly fall into "information haze" — frequent occurrences of bot matrix account spamming, early participation of influencers in beta testing, and targeted manipulation of interaction weights by project parties. Under the opaque mechanisms of the points system and token expectations, many users become "free laborers": tweeting, interacting, going live, and forming groups, only to ultimately be ineligible for airdrops.

What is even more noteworthy is that the financialization of information does not equate to the consensus of value. In attention markets or reputation markets, those contents, individuals, or trends that are "longed" are not necessarily true signals of long-term value. In the absence of genuine demand and scenario support, once the incentives wane and subsidies cease, these financialized "information assets" often rapidly return to zero, even forming a "short-term speculation narrative, long-term zeroing" Ponzi dynamic.

In addition, in prediction markets, if the oracle mechanism is not transparent enough or is manipulated by large capital players, it is easy to create deviations in information pricing. This reminds us that even prediction mechanisms based on "real-world information" must find a better balance between technology and games.

In summary, InfoFi's incentive mechanism is both its greatest advantage and its largest source of risk. Only when the incentive system is no longer just a game of traffic and airdrops, but becomes a foundational structure that can identify real signals, incentivize quality contributions, and form a self-consistent ecosystem, can InfoFi truly achieve the transition from "hype economy" to "cognitive finance."

IV. Analysis of Typical Projects and Recommended Focus Areas

The InfoFi ecosystem currently presents a pattern of flourishing diversity and rotating hotspots, with different projects evolving differentiated product paradigms and user growth strategies around the core path of "information → incentives → market." The following analysis selects projects from five representative directions:

1. Predict market direction: Polymarket + Upside

Polymarket is one of the most mature and iconic projects in the InfoFi ecosystem. Its core model is to buy and sell contract shares of different outcomes using USDC, enabling collective expectation pricing of real-world events. The win-loss probabilities reflected by Polymarket have repeatedly outperformed traditional polls, sparking heated discussions. With the official partnership with X now in place, its user growth and data visibility have further increased.

Upside focuses on socialized predictions, attempting to monetize content predictions through a mechanism of likes and votes, allowing creators, readers, and voters to share profits. Upside emphasizes a user experience that is characterized by light interaction, low barriers to entry, and de-financialization, exploring a fusion model between InfoFi and content platforms.

2. Yap-to-Earn Direction: Kaito AI + LOUD

Kaito AI is one of the most representative platforms in the Yap-to-Earn model, utilizing AI algorithms to assess the quality, interactivity, and project relevance of user-generated content on X, distributing Yaps (points), and conducting token airdrops or rewards based on leaderboards and project collaborations. However, with the surge in users, it has also faced structural issues such as content signal pollution, bot proliferation, and disputes over points distribution.

LOUD is the first project to conduct an Initial Attention Offering (IAO) using the Yap-to-Earn leaderboard. Although its airdrop strategy created a lot of social buzz in the short term, it has been criticized by the community as a "hot potato scheme" due to the subsequent rapid drop in token prices.

3. Reputation Finance Direction: Ethos + GiveRep

Ethos is currently the most systematic and decentralized attempt in the reputation finance sector. Its core logic is to build an on-chain verifiable "credit score", which is generated not only through interaction records and comment mechanisms but also introduces a "guarantee mechanism". Another major innovation of Ethos is the launch of a reputation speculation market, allowing users to "go long or short" on others' reputations, forming a brand new dimension of financial tools.

GiveRep is lighter and more community-oriented. Its mechanism is to score content creators and commenters by mentioning the official account in comments, with a daily limit on the number of comments, and in conjunction with the active ecology of the X community, it has already achieved a certain scale of dissemination on Sui.

4. Attention Market Direction: Trends + Noise + Backroom

Trends is a platform that explores "content assetization," allowing creators to mint their X posts into tradable "Trends," establish trading curves, and community members can buy in to go long on the post's popularity, while creators earn a commission from the trades.

Noise is an attention futures platform based on MegaETH, where users can bet on the changes in popularity of a certain topic or project, serving as a direct investment venue for attention finance.

Backroom represents an InfoFi product of "paywall + filtering high-value content". Creators can publish high-quality content based on a token threshold, and users can purchase a Key to unlock access. The Key itself also possesses tradability and value volatility, forming a content financial closed loop.

5. Data Insights and AI Agent Platform: Arkham + Xeet + Virtuals

Arkham Intel Exchange has become synonymous with the financialization of on-chain intelligence, allowing users to post bounty rewards to incentivize "on-chain detectives" to disclose address ownership information.

The founder of Xeet stated that they want to be the "noise reducer" of InfoFi by introducing the Ethos reputation system and KO.