InfoFi Depth Research: Attention Financial Experiment in the AI Era

1. Introduction: Attention Scarcity Breeds InfoFi

The information revolution has brought about an explosion of knowledge, but it has also triggered the paradox of attention scarcity. In an era of information overload, what is truly scarce is the cognitive resources to process information. Faced with vast amounts of content, the boundaries of human cognition are constantly being compressed, making filtering and judgment increasingly difficult.

This scarcity of attention has evolved into a resource competition in the digital age. In the traditional Web2 model, platforms control the traffic entry through algorithms, while users and creators who truly generate attention resources often find it difficult to share in the value. This structural disconnection has become the core contradiction in the development of digital civilization.

InfoFi was born out of necessity, based on blockchain, token incentives, and AI technologies, aiming to reshape the value of attention. InfoFi attempts to transform users' views, information, reputation, and other unstructured cognitive behaviors into quantifiable and tradable assets, allowing participants to share value through distributed incentives. This is an attempt at redistributing power regarding "who owns attention and who dominates information."

InfoFi connects social networks, content creation, market competition, and AI intelligence to build a new market structure centered around "financialization of cognitive resources". At its core is a value discovery and redistribution logic of "information → trust → investment → return".

From the "land" of agricultural society, to the "capital" of the industrial age, and then to the "attention" of digital civilization, the core resources of human society are undergoing a profound shift. InfoFi is the concrete expression of this macro paradigm transformation in the on-chain world. It is not only a new opportunity in the crypto market but may also reconstruct the governance structure, intellectual property logic, and financial pricing mechanisms of the digital world.

2. InfoFi Ecosystem Composition: The Intersection Market of Information, Finance, and AI

InfoFi essentially builds a composite market system that integrates financial logic, semantic computing, and game mechanisms in an environment of information overload. It is not a simple "content platform" or "financial protocol," but rather a convergence point for information value discovery, behavioral incentives, and intelligent distribution, forming an ecosystem that combines information trading, attention incentives, reputation ratings, and intelligent forecasting.

From a fundamental perspective, InfoFi is an attempt to "financialize" information, transforming cognitive activities such as content, opinions, and trend judgments into measurable and tradable "quasi-assets". The involvement of finance turns information into a "cognitive product" with gaming attributes and value accumulation capabilities. A comment or prediction is not only an expression of individual cognition but may also become a speculative asset with associated risks and returns.

AI is the second pillar of InfoFi, primarily undertaking two roles: semantic filtering and behavior recognition. AI achieves precise evaluation of information sources by modeling user data. It is similar to the market makers and clearing mechanisms in an exchange, maintaining ecological stability and credibility in InfoFi.

Information is the foundation of the entire system. Unlike DeFi, the asset anchors of InfoFi are more fluid and loosely structured "cognitive assets" such as opinions, trust, and discourse power. This determines that the InfoFi market highly relies on the dynamic ecology constructed by social graphs, semantic networks, and psychological expectations.

In this framework, content creators are equivalent to "market makers", providing opinions for market pricing; users are "investors", expressing value judgments through interaction; the platform and AI serve as "referees + exchanges", ensuring market fairness and efficiency.

This ternary structure gives rise to a series of new species: prediction markets provide gaming targets; Yap-to-Earn encourages knowledge mining; reputation protocols transform social behaviors into credit assets; attention markets capture on-chain emotional fluctuations; token-gated content platforms reconstruct payment logic. They form a multi-layered ecosystem of InfoFi, including value discovery tools, distribution mechanisms, identity systems, and participation thresholds.

InfoFi aims to become a "cognitive financial infrastructure" that provides the crypto community with more efficient information discovery and collective decision-making mechanisms. However, this system is destined to be complex, diverse, and fragile. The subjectivity of information, the game nature of finance, and the black-box nature of AI all pose challenges to it. The InfoFi ecosystem must constantly balance among these three tensions; otherwise, it risks sliding into "disguised gambling" or "attention harvesting fields."

The ecological construction of InfoFi is not an isolated project, but a deep attempt by Web3 in the direction of "governance information." It will define the pricing method of information in the next era, building a more open and autonomous cognitive market.

3. Core Game Mechanism: Incentivizing Innovation and Harvesting Traps

Behind the prosperity of the InfoFi ecosystem lies the design game of the incentive mechanism. Whether it is participation in prediction markets, output from mouth-to-mouth production, reputation building, or attention trading, it essentially revolves around the core question of "who contributes, who shares the dividends, and who bears the risks."

InfoFi aims to break the exploitation chain of "platform-creator-user" in traditional content platforms, returning value to the original contributors of information. However, this value return is not inherently fair, but rather a delicate balance built on a series of incentives, validations, and game mechanics. When designed properly, InfoFi can become an innovative space for user win-win; if the mechanism is out of balance, it can easily devolve into a "retail investor harvesting ground" dominated by capital and algorithms.

The core innovation of InfoFi lies in endowing the intangible asset of "information," which is difficult to measure, with clear tradability, competitiveness, and settlement. This transformation relies on the traceability of blockchain and the assessability of AI. Prediction markets will monetize cognitive consensus; the Mouth-Lu ecosystem turns speech into economic behavior; the reputation system builds inheritable social capital; and the attention market treats trending topics as trading targets. These mechanisms enable information to possess "cash flow" properties for the first time and turn social behavior into genuine productive activity.

However, strong incentive systems can easily give rise to "gaming abuse." Taking Yap-to-Earn as an example, it superficially rewards content creation value through AI, but in practice, it often falls into "information haze"—with rampant issues such as bot spam, early participation by influential users, and manipulation of weights by project parties. Under a non-transparent points system, many users become "free laborers," ultimately missing out on airdrops. This kind of "backstabbing" incentive not only damages the platform's reputation but also leads to a collapse of the long-term content ecosystem.

What is more worth noting is that the financialization of information does not equate to the consensus of value. In the attention market, content that is "longed" may not truly have long-term value. When there is a lack of real demand support, once the incentives recede, these "information assets" often quickly drop to zero, forming a "short-term speculation narrative, long-term zeroing" Ponzi dynamic.

In prediction markets, if the oracle mechanism is opaque or subject to manipulation, it is easy to create information pricing biases. This reminds us that even prediction mechanisms that target "real-world information" must find a balance between technology and games.

The ability of the InfoFi incentive mechanism to break free from the "financial capital vs. retail attention" adversarial narrative depends on whether it can construct a triple positive feedback loop: information production behavior is accurately identified → value distribution mechanism is transparently executed → long-tail participants genuinely benefit. This is not only a technical issue but also a test of institutional engineering and product philosophy.

In summary, the incentive mechanism of InfoFi is both its greatest advantage and its biggest source of risk. Each design of the incentive may lead to an information revolution or trigger a collapse of trust. Only when the incentive system becomes the foundational structure for identifying real signals, incentivizing quality contributions, and forming a self-consistent ecosystem can InfoFi truly transition from "hype economy" to "cognitive finance."

4. Analysis of Typical Projects and Recommended Focus Areas

The InfoFi ecosystem presents a flourishing pattern, with different projects evolving differentiated models around the "information → incentive → market" path. Some projects have preliminarily validated their business models and have become key anchors; others are still in the concept validation stage, searching for breakthroughs. We selected projects from five representative directions for analysis and proposed potential camps worth continuous tracking.

1. Predict market direction: Polymarket + Upside

Polymarket is one of the most mature flagship projects in the InfoFi ecosystem. Its core model is to achieve collective expectation pricing of real-world events by buying and selling contract shares of different outcomes using USDC. Polymarket is hailed as the "prototype of information finance" not only for its clear trading logic and robust financial design but also for its demonstration of "media functionality" in the real world. For example, during the 2024 U.S. presidential election, its reflected probability of victory and defeat repeatedly outperformed traditional polls.

With the cooperation with X official landing, Polymarket's user growth and data visibility have further enhanced, and it is expected to become a "super hub platform" for the integration of social opinion and information pricing. However, it still faces challenges such as compliance risks, oracle controversies, and insufficient participation in niche topics.

In contrast, Upside focuses on socialized forecasting, attempting to monetize content predictions through a like-voting mechanism that allows creators, readers, and voters to share in the profits. Upside places greater emphasis on a light interaction, low-threshold, and de-financialized user experience, exploring the integration model of 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 and is currently the project with the largest number of InfoFi users. Its innovation lies in using AI algorithms to assess the quality, interactivity, and relevance of user content on X, distributing Yaps points, and conducting token airdrops or rewards based on leaderboards and project collaborations.

The Kaito model forms a closed loop: projects use tokens to incentivize dissemination, creators compete for attention with content, and the platform controls distribution through data and AI. However, with the surge in users, it also faces structural issues such as content signal pollution, bot proliferation, and points distribution disputes. The Kaito founders are currently iterating algorithms and optimizing mechanisms to address these problems.

LOUD is the first project to conduct an Initial Attention Offering (IAO) through the Yap-to-Earn leaderboard. Although its airdrop strategy created a lot of social buzz in the short term, it has been criticized as a "hot potato style harvesting" due to the rapid price drop of the token. The ups and downs of LOUD show that the Yap-to-Earn track is still in the trial-and-error stage, and the maturity of the mechanism and fairness of incentives need further refinement.

3. Reputation Finance Direction: Ethos + GiveRep

Ethos is the most systematic and decentralized attempt in the reputation finance track. Its core logic is to build an on-chain verifiable "credit score", which is generated not only through interaction records and a comment mechanism but also introduces a "guarantee mechanism": users can stake ETH to endorse others, bear risks, and form a Web3 trust network.

Another innovation from Ethos is the launch of a reputation speculation market, allowing users to "long or short" the reputation of others, creating a new dimension of financial instruments. This opens up imaginative possibilities for the integration of reputation scoring with lending markets, DAO governance, and social identity recognition in the future. However, its invitation-only system has slowed the user expansion rate, and how to lower the threshold and enhance resistance to witch hunts will be key.

GiveRep is lighter and more community-oriented compared to others. Its mechanism involves scoring content creators and commenters through comments that @mention the official account, with a limited number of comments allowed each day. Coupled with the active ecosystem of X community, it has achieved a certain scale of dissemination on Sui. This model is more suitable for projects to conduct lightweight testing for social fission and reputation scoring, and can also serve as a trust foundation for future integration of governance weights, project airdrops, and other mechanisms.

4. Attention Market Direction: Trends + Noise + Backroom

Trends explores "content assetization", allowing creators to mint X posts into tradable "Trends", set trading curves, and community members can buy in and go long on the popularity of the post, with creators receiving a commission from the trades. It creatively transforms "viral posts" into liquid assets, representing a typical attempt at "social financialization".

Noise is an attention futures platform based on MegaETH, where users can bet on the fluctuations in popularity of a certain topic or project, serving as a direct investment field for attention finance. In closed testing, some of its prediction models have already demonstrated early market discovery capabilities. If AI models are subsequently introduced for trend prediction, it could become a "weather vane" tool for the InfoFi ecosystem.

Backroom represents the InfoFi product of "paid unlocking + filtering high-value content." Creators publish high-quality content based on token thresholds, and users purchase Keys to unlock access.