The revolution is not yet complete, and InfoFi still needs cautious optimism.
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 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 issue. Faced with the overwhelming content inundated by Weibo, X, YouTube, short videos, and news push notifications, the cognitive boundaries of humanity are continuously being 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 algorithmic distribution, while 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 capitalists reap the rewards in the chain of attention monetization, while ordinary individuals who truly drive information production and dissemination find it hard to participate in value sharing. This structural disconnection is becoming a core contradiction in the evolution of digital civilization.
The rise of information finance (InfoFi) occurs against this backdrop. It is not an incidental new concept but a fundamental paradigm shift aimed at "reshaping the value of attention," built on the technological foundations of blockchain, token incentives, and AI empowerment. InfoFi attempts to transform users' unstructured cognitive behaviors—such as opinions, information, reputation, social interactions, and trend discovery—into quantifiable and tradable asset forms, enabling every user who participates in the creation, dissemination, and judgment of information in the ecosystem to share in the value generated. This is not just a technological innovation but 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 competition, 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 prediction, constructing a new market structure centered around "the financialization of cognitive resources." Its core is not merely content distribution or likes and rewards, but a comprehensive value discovery and redistribution logic revolving around "information → trust → investment → return."
From the agricultural society where "land" was the scarce factor, to the industrial era where "capital" was 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 profound shifts. 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 be the starting point for a deep reconstruction of digital world governance structures, intellectual property logic, and financial pricing mechanisms.
However, any paradigm leap is not linear; it inevitably comes with bubbles, speculation, misunderstandings, and fluctuations. Whether InfoFi can become a true user-centered attention revolution depends on its ability to find a dynamic balance between incentive mechanism design, value capture logic, and real demand. Otherwise, it will merely slide from "inclusive narrative" to "centralized harvesting" once again.
II. The Ecological Composition of InfoFi: A Triangular Intersection Market of "Information × Finance × AI"
The essence of InfoFi is to build a complex market system that simultaneously nests financial logic, semantic computation, and game mechanisms in the context of rampant information and elusive value. 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 ratings, and intelligent predictions.
From a foundational logic perspective, InfoFi is an attempt at the "financialization" of information, transforming previously unquantifiable content, opinions, trend judgments, and social interactions into measurable, tradable "quasi-assets," endowing them with market prices. The involvement of finance means that information is no longer fragmented and isolated "content fragments" during production, circulation, and consumption, but rather "cognitive products" with gaming 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 revenue rights. The popularity of prediction markets like Polymarket and Kalshi exemplifies this logic's implementation in public opinion and market expectations.
However, relying solely on financial mechanisms is far from sufficient to address the noise and the "bad money drives out good" dilemma brought about by information explosion. Therefore, AI becomes the second pillar of InfoFi. AI primarily assumes two roles: first, semantic filtering, serving as the "first line of defense" against information signals and noise; second, behavior recognition, achieving precise evaluation of information sources through multi-dimensional data modeling of user social network behavior, content interaction trajectories, and originality of opinions. Platforms like Kaito AI, Mirra, and Wallchain are typical representatives that introduce AI technology into content evaluation and user profiling, playing the role of "algorithmic judges" 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 akin to 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 or BTC, but rather more liquid, loosely structured, yet more time-sensitive "cognitive assets" 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 ecology highly reliant 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 likes, shares, bets, and comments, driving its rise or fall across the network; while platforms and AI serve as the "judges + exchanges," responsible for ensuring the fairness and efficiency of the entire market.
The collaborative operation of this triangular structure gives rise to a series of new species and mechanisms: prediction markets provide clear targets for competition; Yap-to-Earn encourages knowledge mining and interaction as output; reputation protocols like Ethos transform personal on-chain history and social behavior into credit assets; attention markets like Noise and Trends attempt to capture the "emotional fluctuations" of on-chain dissemination; and token-gated content platforms like Backroom reconstruct information payment logic through permission economies. Together, they form a multi-layered ecology of InfoFi: encompassing value discovery tools, value distribution mechanisms, and embedding multi-dimensional identity systems, participation threshold designs, and anti-witch-hunt mechanisms.
It is within this intersecting structure that InfoFi is no longer just a market but a complex information game system: it uses information as a trading medium, finance as an incentive engine, and AI as a governance hub, ultimately aiming to construct a self-organizing, distributed, and adjustable cognitive collaboration 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 impossibility of unified value assessment, the gaming nature of finance increases the risks of manipulation and herd effects, and the black-box nature of AI poses challenges to transparency. The InfoFi ecosystem must continuously balance and self-repair among these triangular tensions; otherwise, it is prone to sliding into the opposite of "disguised gambling" or "attention harvesting grounds" under capital-driven forces.
The construction of the InfoFi ecosystem is not an isolated project of a single protocol or platform but a co-evolution of a complete socio-technical system, representing a deep attempt by Web3 to "govern information" rather than "govern assets." It will define the pricing methods of information in the next era and even build a more open and autonomous cognitive market.
III. Core Game Mechanism: Incentive Innovation vs. Harvesting Trap
In the InfoFi ecosystem, behind all the appearances of prosperity lies the fundamental design game of incentive mechanisms. Whether it is the participation in prediction markets, the output of mouth-to-mouth 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 contributes? Who shares the profits? Who bears the risks?
From an external perspective, InfoFi seems to be a "revolution in production relations" in the migration from Web2 to Web3: it attempts 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 delicate 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 imbalanced, it can easily devolve into a "retail harvesting ground" dominated by capital and algorithms.
The first aspect to examine is the positive potential of "incentive innovation." The essential innovation of all sub-tracks in InfoFi is to endow the previously immeasurable and unfinancializable intangible asset of "information" with clear tradability, competitiveness, and settlement. This transformation relies on two key engines: the traceability of blockchain and the assessability of AI.
Prediction markets monetize cognitive consensus through market pricing mechanisms; the mouth-to-mouth ecosystem turns speech into economic behavior; reputation systems build a form of social capital that is inheritable and collateralizable; attention markets redefine content value by treating trending topics as trading targets through the logic of "information discovery → betting signals → obtaining price differences"; and AI-driven InfoFi applications attempt to construct an information financial network driven by data and algorithms through large-scale semantic modeling, signal recognition, and on-chain interaction analysis. These mechanisms enable information to possess "cash flow" attributes for the first time, making "saying a word, sharing a tweet, endorsing someone" genuine productive activities.
However, the more intense the incentives in a system, the more likely it is to foster "gaming abuse." The greatest systemic risk faced by InfoFi is the distortion of incentive mechanisms and the proliferation of arbitrage chains.
Taking Yap-to-Earn as an example, on the surface, it rewards users for content creation through AI algorithms, but in practice, 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 accounts flooding the platform, early participation by influential users, and project teams manipulating interaction weights frequently occur. A leading KOL bluntly stated, "If you don't inflate your numbers, you can't even make the rankings; AI has been trained to specifically recognize keywords and ride the wave of trends." Furthermore, some project teams revealed, "We invested $150,000 in a round of Kaito Yap-to-Earn, but 70% of the traffic was from AI accounts and bots competing with each other; real KOLs didn't participate, and it's impossible for me to invest again."
Under the opaque mechanisms of point systems and token expectations, many users have become "free laborers": tweeting, interacting, launching, 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 leading to the collapse of the long-term content ecosystem. The comparison between Magic Newton and Humanity is particularly illustrative: the former had a clear distribution mechanism during the Kaito Yap-to-Earn phase, with substantial token value returns; the latter, however, faced a crisis of community trust and accusations of "anti-yap" due to an imbalanced distribution mechanism and insufficient transparency. This structural injustice under the Matthew effect significantly dampens the enthusiasm of tail-end creators and ordinary users, even giving rise to the ironic identity of "algorithm sacrificial Yap-to-Earn players."
What is even more concerning is that the financialization of information does not equate to the consensus of value. In attention markets or reputation markets, those contents, figures, or trends that are "longed" may not necessarily be genuine signals with long-term value. In the absence of real demand and scenario support, once incentives wane and subsidies cease, these financialized "information assets" often rapidly depreciate to zero, even creating a "short-term speculation narrative, long-term zero" Ponzi dynamic. The short lifespan of the LOUD project is a microcosm of this logic: it surpassed a market cap of $30 million on its launch day, but just two weeks later, it plummeted to less than $600,000, epitomizing the InfoFi version of "passing the parcel."
Additionally, in prediction markets, if the oracle mechanism is not transparent enough or is manipulated by large fund holders, it can easily lead to pricing discrepancies in information. Polymarket has repeatedly sparked user controversies due to "unclear event settlement explanations," and in 2025, it even experienced a large-scale payout crisis triggered by oracle voting loopholes. This reminds us that even prediction mechanisms based on "real-world information" must find a better balance between technology and gaming.
Ultimately, whether InfoFi's incentive mechanisms can escape the narrative of "financial capital vs. retail attention" depends on whether it can construct a triple positive feedback system: information production behaviors can be accurately identified → value distribution mechanisms can be transparently executed → long-tail participants can be genuinely incentivized. This is not just a technical issue but also a test of institutional engineering and product philosophy.
In summary, InfoFi's incentive mechanisms are both its greatest advantage and its biggest source of risk. In this market, every design of incentives can either create an information revolution or trigger a collapse of trust. 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 leap from "hype economy" to "cognitive finance."
IV. Analysis of Typical Projects and Recommended Focus Areas
The InfoFi ecosystem currently presents a flourishing and rotating pattern, with different projects evolving differentiated product paradigms and user growth strategies around the core path of "information → incentives → market." Some projects have preliminarily validated their business models, becoming key anchor points in the InfoFi narrative; while others are in the concept validation stage, still seeking breakthroughs in user education and mechanism optimization. In the diverse tracks, we attempt to select projects for analysis from five representative directions and propose potential camps worth continuous tracking.
1. Prediction Market Direction: Polymarket + Upside
Polymarket is one of the most mature and iconic projects in the InfoFi ecosystem, with its core model being to buy and sell contract shares of different outcomes using USDC to collectively price expectations for real-world events. It is referred to by Vitalik as "the prototype of information finance," not only because its trading logic is clear and its financial design robust, but also because it has begun to possess "media functionality" in the real world— for example, during the 2024 U.S. election, the win/loss probabilities reflected by Polymarket often outperformed traditional polls, sparking discussions and shares, including from figures like Musk.
With the official collaboration between Polymarket and X, its user growth and data visibility have further enhanced, and it is expected to become a "super central platform" integrating social discourse and information pricing. However, Polymarket still faces challenges, including compliance risks (with the CFTC repeatedly raising issues), oracle controversies, and insufficient participation in niche topics.
In contrast, Upside focuses on socialized predictions and is an emerging project backed by well-known capital investors like Arthur Hayes. It attempts to marketize content predictions through a mechanism of voting by likes, allowing creators, readers, and voters to share profits. Upside emphasizes light interaction, low barriers, and a de-financialized user experience, exploring a fusion model between InfoFi and content platforms, making its user retention and content quality maintenance worth monitoring in the future.
2. Yap-to-Earn Direction: Kaito AI + LOUD
Kaito AI is one of the most representative platforms in the Yap-to-Earn model and currently has the highest user count in InfoFi, attracting over 1 million registered users, with more than 200,000 active Yappers. Its innovation lies in using AI algorithms to assess the quality, interactivity, and project relevance of user-generated content on X (formerly Twitter), thereby distributing Yaps (points) and conducting token airdrops or rewards based on leaderboards and project collaborations.
The Kaito model forms a closed loop: projects incentivize community dissemination with tokens, creators compete for attention with content, and the platform controls distribution and order with data and AI models. However, with the surge in users, it has also encountered structural issues such as content signal pollution, bot proliferation, and disputes over point distribution. The Kaito founder has recently begun algorithm iterations and community mechanism optimizations to address these issues.
LOUD, on the other hand, is the first project to conduct Initial Attention Offerings (IAO) using Yap-to-Earn leaderboards, monopolizing 70% of attention on the Kaito leaderboard through Yap-to-Earn activities before its launch. Although its airdrop strategy generated significant social buzz in the short term, it faced criticism from the community for "passing the parcel" due to the rapid decline in token prices afterward. The rise and fall of LOUD demonstrate that the Yap-to-Earn track is still in a trial-and-error phase, with the maturity of mechanisms and fairness of incentives needing refinement.
3. Reputation Finance Direction: Ethos + GiveRep
Ethos is currently the most systematic and decentralized attempt in the reputation finance track. Its core logic is to build an on-chain verifiable "credit score," generated not only through interaction records and comment mechanisms but also introducing a "guarantee mechanism": users can stake ETH to endorse others, assuming certain risks, thereby forming a Web3-like trust network.
Another major innovation of Ethos is the introduction of a reputation speculation market, allowing users to "long or short" others' reputations, creating a new dimension of financial tools—trust monetization. This mechanism opens up possibilities for future integration of reputation scoring with lending markets, DAO governance, and social identity recognition. However, its invitation-only mechanism has slowed user expansion, and how to lower barriers and enhance anti-witch-hunt capabilities will be key to the platform's development.
In comparison, GiveRep is lighter and more community-oriented. Its mechanism scores content creators and commenters by commenting @ official accounts, limiting the number of comments per day. Coupled with the active ecosystem of the X community, it has achieved a certain scale of dissemination on Sui. This model is more suitable for projects to conduct social fission and lightweight testing of reputation scoring, and it can also serve as a trust foundation for future integration of governance weights and project airdrop mechanisms.
4. Attention Market Direction: Trends + Noise + Backroom
Trends is a platform exploring "content assetization," allowing creators to mint their X posts into tradable "Trends," establishing trading curves where community members can buy in to long the post's popularity, and creators earn 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 popularity changes of a topic or project, serving as a direct investment venue for attention finance. In a closed test requiring an invitation code, some of its prediction models have already demonstrated early market discovery capabilities. If AI models are later introduced for trend prediction, it could become a "barometer" tool within the InfoFi ecosystem.
Backroom represents an InfoFi product that combines "paid unlocking + filtering high-value content." Creators can publish high-quality content based on token thresholds, and users can purchase Keys to unlock access, with the Keys themselves possessing tradability and value volatility, forming a content finance closed loop. This model, in the context of the prevailing NoiseFi, focuses on "reducing noise and filtering signals," becoming a new tool for knowledge creator communities.
5. Data Insights and AI Agent Platforms: Arkham + Xeet + Virtuals
Arkham Intel Exchange has become synonymous with the financialization of on-chain intelligence, allowing users to post bounties to incentivize "on-chain detectives" to disclose address ownership information. Its logic is similar to traditional intelligence markets but has achieved decentralization and tradability for the first time. Although it faces ongoing controversies (such as privacy violations and witch hunts), it has established the basic paradigm for data insight-related InfoFi.
Xeet, although not fully launched yet, has its founder Pons publicly stating the goal of being the "noise reducer" for InfoFi, aiming to create a more authentic, spam-free signal market by introducing the Ethos reputation system, KOL recommendations, and private content recommendations, directly addressing the noise issues in Yap-to-Earn.
The innovation of Virtuals lies in positioning AI agents as new participants in InfoFi, injecting "non-human productivity" into the ecosystem by initiating tasks, completing evaluations, and generating interaction data. Its Yap-to-Earn phase in the Genesis Launch model, linked with Kaito, also demonstrates the trend of ecological interconnection among InfoFi projects.
V. Future Trends and Risk Outlook: Can Attention Become the "New Gold"?
In the deep waters of the digital economy, information is no longer scarce, but effective information and trustworthy attention are becoming increasingly valuable. In this context, InfoFi is referred to by many industry insiders as the "next narrative engine" and even a potential "new gold" asset. The underlying logic is that in an era where AI computing power is rampant and content costs approach zero, what is scarce is not content, but the "signals" that can accurately guide actions, as well as the genuine attention focused on these signals. Whether future InfoFi can transition from concept to assetization, from short-term "Yap-to-Earn incentives" to long-term "on-chain influence standards," hinges on the interplay and evolution of three major trends and three major risks.
First, the deep integration of AI and prediction markets will usher in a new era of "reasoning capital." Currently, the combination of Polymarket with X and Grok has already implemented this model: real-time public sentiment + AI analysis + monetary betting results, constructing a flywheel between effectiveness, authenticity, and market feedback. If future InfoFi projects can leverage AI for event modeling, signal extraction, and dynamic pricing, it will greatly enhance the credibility of prediction markets in governance, news verification, trading strategies, and more. For example, governance DAOs under the Futarchy model may use a combination of AI and prediction markets to formulate policies.
Second, the intersection of reputation, attention, and financial attributes will trigger a massive explosion of decentralized credit systems. Current reputation InfoFi projects (such as Ethos and GiveRep) are building an on-chain "credit score" system that does not require third-party credit intermediaries. In the future, reputation points are expected to become the foundation for DAO voting rights, DeFi collateral, content distribution priorities, and more, becoming a true form of on-chain "social capital." If cross-platform recognition, resistance to witch hunts, and traceable credit histories can be achieved, the attention reputation system will rise from being an auxiliary indicator to a core asset.
Third, the tokenization and derivative creation of attention assets represent the ultimate form of InfoFi. The current Yap-to-Earn model is still at the stage of exchanging content and influence for points, while a truly mature InfoFi should be able to convert every piece of valuable content, a KOL's "attention bond," and a set of on-chain signals into tradable assets, allowing users to "go long," "go short," or even "construct ETFs." This will also create a brand new financial market: from narrative-based Meme Tokens to derivative assets based on attention dynamics.
However, at the same time, for InfoFi to truly move towards sustainability, it faces three major structural risks.
The first is the prevalence of "Yap-to-Earn traps" due to inadequate mechanism design. If incentives overly favor "quantity over quality," platform algorithms lack transparency, and airdrop expectations are unreasonable, it can lead to a project experiencing high initial popularity but a subsequent cliff-like drop in attention, resulting in a "airdrop equals peak" fate typical of SocialFi. For example, LOUD initially attracted users with Yap leaderboard incentives, but after the token launch, its market cap plummeted and participation dropped sharply, reflecting a lack of long-term mechanisms in the ecosystem.
The second is the exacerbation of the "Matthew effect," leading to ecological fragmentation. Current data from most Yap-to-Earn platforms reveal that over 90% of rewards are concentrated in the top 1% of users, while long-tail users neither benefit from interactions nor break through the KOL hierarchy, ultimately choosing to exit. If this structure cannot be broken through mechanisms such as reputation weighting and credit flow, it will weaken user participation willingness, causing InfoFi to evolve into another "platform oligopoly" system.
The third is the dual dilemma of regulatory risks and information manipulation. For emerging products like prediction markets, reputation trading, and attention speculation, major global jurisdictions have yet to form a unified regulatory framework. Once a platform is involved in gambling, insider trading, false advertising, or market manipulation, it can easily trigger regulatory crackdowns. For instance, Polymarket has faced dual scrutiny from the CFTC and FBI in the U.S., while Kalshi has taken a differentiated path due to compliance advantages. All of this means that InfoFi projects must consider a "regulatory-friendly" path from Day One in their design to avoid straying into illegal territory.
In summary, InfoFi is not just the next generation of content distribution protocols; it is also a new attempt to financialize attention, information, and influence. It challenges the traditional model of value capture by platforms, giants, and advertisers, and represents a collective experiment in "everyone being a discoverer of Alpha." Whether future InfoFi can become the "new gold" in the Web3 world depends on whether it can find an optimal solution among fairness mechanisms, incentive designs, and regulatory frameworks, truly transforming the "attention dividend" from the prey of a few into the asset of the many.
VI. Conclusion: The Revolution is Not Yet Complete, InfoFi Must Remain Cautiously Optimistic
The emergence of InfoFi represents another cognitive evolution in the Web3 world after traversing multiple cycles of DeFi, NFT, GameFi, and more. It attempts to answer a long-ignored core question—what is truly scarce in an era of information overload, free content, and rampant algorithms? The answer is: human attention, genuine signals, and trustworthy subjective judgment. This is precisely what InfoFi seeks to endow with value, incentive mechanisms, and market structures.
In a sense, InfoFi is a "reverse power revolution" against the traditional attention economy system—no longer allowing platforms, giants, and advertisers to monopolize data and traffic dividends, but attempting to redistribute the value of attention to the true creators, disseminators, and identifiers through blockchain, tokenization, and AI protocols. This structural value redistribution gives InfoFi the potential to change the content industry, platform governance, knowledge collaboration, and even social discourse mechanisms.
However, potential does not equate to reality. We must remain cautiously optimistic.
The revolution is not yet complete, but it has begun. The future of InfoFi is not defined by any single platform or track, but is shaped collectively by all creators, observers, and identifiers of attention. If DeFi is a revolution about the flow of value, then InfoFi is a revolution about the recognition and distribution of value. On the long-term path of depersonalization and de-intermediation, we should maintain calm judgment and prudent participation, while not overlooking its potential to grow into a new narrative forest on the next generation of Web3 soil.
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