1. Introduction: From information scarcity to attention scarcity, InfoFi came into being
The information revolution of the 20th century brought explosive knowledge growth to human society, but it also triggered a paradox: when information acquisition is almost free, what is truly scarce is no longer the information itself, but the cognitive resources we use to process information - attention. As Nobel Prize winner Herbert Simon first proposed the concept of attention economy in 1971, information overload leads to attention poverty, and modern society is deeply trapped in it. Faced with the overwhelming content instilled by Weibo, X, YouTube, short videos, and news push, the cognitive boundaries of human beings are being continuously squeezed, and screening, judgment, and assignment are becoming increasingly difficult.
This scarcity of attention has evolved into a battle for resources in the digital age. In the traditional Web2 model, platforms firmly control traffic entrances through algorithmic distribution, and the real creators of attention resources - whether users, content creators or community evangelists - are often just free fuel in the platforms profit logic. Head platforms and capital parties harvest layer by layer in the chain of attention monetization, while ordinary individuals who truly promote information production and dissemination find it difficult to participate in value sharing. This structural split is becoming the core contradiction in the evolution of digital civilization.
The rise of information financialization (InfoFi) is happening in this context. It is not an occasional new concept, but an underlying paradigm shift with blockchain, token incentives and AI empowerment as the technical foundation and the goal of reshaping the value of attention. InfoFi attempts to transform users unstructured cognitive behaviors such as opinions, information, reputation, social interaction, trend discovery, etc. into quantifiable and tradable asset forms, and through a distributed incentive mechanism, every user who participates in the creation, dissemination, and judgment of the information ecosystem can share the resulting value. This is not just a technological innovation, but also an attempt to redistribute power about who has attention and who dominates information.
In the narrative genealogy of Web3, InfoFi is 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 AIs capabilities in semantic analysis, signal recognition and trend prediction to build a new market structure around financialization of cognitive resources. Its core is not simple content distribution or likes and rewards, but a set of value discovery and redistribution logic around information → trust → investment → return.
From the agricultural society with land as a scarce factor, to the industrial era with capital as the growth engine, to todays digital civilization with attention as 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 shift in the on-chain world. It is not only a new outlet for the crypto market, but also the starting point for the deep reconstruction of the digital worlds governance structure, intellectual property logic and financial pricing mechanism.
But any paradigm shift is not linear, it is inevitably accompanied by bubbles, hype, misunderstandings and vacillations. Whether InfoFi can become a truly user-centric attention revolution depends on whether it can find a dynamic balance between incentive mechanism design, value capture logic and real needs. Otherwise, it will just be another dream of sliding from inclusive narrative to centralized harvesting.
2. InfoFi’s Ecosystem: A Triple Crossover Market of “Information × Finance × AI”
The essence of InfoFi is to build a composite market system that embeds financial logic, semantic computing, and game mechanism in the contemporary network context where information is highly flooded and value is difficult to capture. Its ecological architecture is not a single-dimensional content platform or financial protocol, but the intersection of information value discovery mechanism, behavior incentive system, and intelligent distribution engine - forming a full-stack ecosystem that integrates information trading, attention incentives, reputation rating, and intelligent prediction.
From the underlying logic, InfoFi is an attempt to financialize information, that is, to transform cognitive activities such as content, opinions, trend judgments, social interactions, etc. that were originally unpriced into measurable and tradable quasi-assets and give them market prices. The intervention of finance makes information no longer scattered and isolated content fragments in the process of production, circulation, and consumption, but a cognitive product with gaming attributes and value accumulation capabilities. This means that a comment, a prediction, and a trend analysis can be an expression of individual cognition, or a speculative asset with risk exposure and future income rights. The popularity of prediction markets such as Polymarket and Kalshi is an example of the implementation of this logic at the level of public opinion and market expectations.
However, financial mechanisms alone are far from enough to solve the problem of noise flooding and bad money driving out good money caused by information explosion. Therefore, AI has become the second pillar of InfoFi. AI mainly plays two roles: one is semantic screening, as the first line of defense between information signals and noise; the other is behavior recognition, through modeling of multi-dimensional data such as user social network behavior, content interaction trajectory, and originality of opinions, to achieve accurate evaluation of information sources. Platforms such as Kaito AI, Mirra, and Wallchain are typical representatives of introducing AI technology into content evaluation and user portraits. In the Yap-to-Earn model, they play the role of algorithmic referee for incentive distribution, deciding who should get token rewards and who should be blocked or demoted. In a sense, the function of AI in InfoFi is equivalent to the market maker and clearing mechanism in the exchange, and is the core of 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. Unlike DeFi, InfoFis asset anchors are no longer on-chain hard assets such as USDC and BTC, but cognitive assets such as opinions, trust, topics, trends, and insights that are more liquid, looser in structure, but more timely. This also determines that the operating mechanism of the InfoFi market is not a linear stack, but a dynamic ecology that is highly dependent on social graphs, semantic networks, and psychological expectations. In this framework, content creators are equivalent to the market makers of the market. They provide opinions and insights for the market to judge their prices; users are investors who express their value judgments on certain information through behaviors such as likes, forwarding, betting, and comments, driving it to rise or sink in the entire network; and platforms and AI are referees + exchanges responsible for ensuring the fairness and efficiency of the entire market.
The coordinated operation of this ternary structure has spawned a series of new species and mechanisms: prediction markets provide clear targets for gaming; Yap-to-Earn encourages knowledge as mining and interaction as output; reputation protocols such as Ethos transform personal on-chain history and social behavior into credit assets; attention markets such as Noise and Trends attempt to capture the emotional fluctuations spread on the chain; and token-gated content platforms such as Backroom rebuild the information payment logic through the permission economy. Together, they constitute the multi-layered ecology of InfoFi: it contains value discovery tools, carries value distribution mechanisms, and also embeds a multi-dimensional identity system, participation threshold design, and anti-witch mechanisms.
It is in this cross-structure that InfoFi is no longer just a market, but a complex information game system: it uses information as a transaction medium, finance as an incentive engine, and AI as a governance center, and ultimately intends to build a self-organizing, distributable, 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 also destined to be complex, diverse and fragile. The subjectivity of information determines the inconsistency of value assessment, the gaming nature of finance increases the risk of manipulation and herd effect, and the black box nature of AI also challenges transparency. The InfoFi ecosystem must constantly balance and self-repair between the three tensions, otherwise it is very easy to slide into the opposite of disguised gambling or attention harvesting field driven by capital.
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 social-technical systems. It is a deep attempt of Web3 in the direction of governing information rather than governing assets. It will define the information pricing method of the next era and even build a more open and autonomous cognitive market.
3. Core Game Mechanism: Incentive Innovation vs. Harvesting Traps
In the InfoFi ecosystem, behind all the prosperous appearances, it all comes down to the design game of incentive mechanisms. Whether it is the participation in the prediction market, the output of verbal behavior, the construction of reputation assets, the transaction of attention, or the mining of on-chain data, it is essentially inseparable from a core question: Who works? Who gets dividends? Who bears the risks?
From an external perspective, InfoFi seems to be a production relationship innovation for the migration from Web2 to Web3: it attempts to break the exploitation chain between platform-creator-user in traditional content platforms and return value to the original contributors of information. However, from the internal structure, this value return is not inherently fair, but a delicate balance based on a series of incentives, verification and game mechanisms. If designed properly, InfoFi is expected to become an innovative experimental field for win-win users; if the mechanism is unbalanced, it will easily become a retail investor harvesting field dominated by capital + 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 information, an intangible asset that was difficult to measure and financialize in the past, clear transaction, competitiveness and settlement. This transformation relies on two key engines: the traceability of blockchain and the assessability of AI.
The prediction market monetizes cognitive consensus through market pricing mechanisms; the chat ecosystem turns speech into economic behavior; the reputation system is building a kind of inheritable and mortgageable social capital; the attention market uses hot trends as trading targets and redefines the value of content through the logic of information discovery-> betting on signals-> obtaining price differences; and the AI-driven InfoFi application attempts to build an information financial network driven by data and algorithms through large-scale semantic modeling, signal recognition, and on-chain interaction analysis. These mechanisms give information the cash flow attribute for the first time, and also make saying a word, forwarding a tweet, and endorsing someone a real production activity.
However, the stronger the incentives, the more likely it is to give rise to game abuse. The biggest systemic risk faced by InfoFi is the alienation of incentive mechanisms and the proliferation of arbitrage chains.
Take Yap-to-Earn as an example. On the surface, it rewards the value of user content creation through AI algorithms. However, in actual implementation, many projects attracted a large number of content creators in the initial stage of incentives, but quickly fell into information haze - robot matrix accounts flooded, big Vs participated in internal testing in advance, and project parties manipulated interaction weights. A top KOL said bluntly: Now you cant get on the list without brushing. AI has been trained to identify keywords and take advantage of popularity. A project owner also revealed: I invested 150,000 US dollars in a round of Kaitos mouth-to-mouth, but 70% of the traffic was AI accounts and water army rolling up. Real KOLs didnt participate. Its impossible for me to invest a second time.
Under the opaque mechanism of the points system and token expectations, many users have become free workers: posting tweets, interacting, going online, and creating groups, but in the end they are not eligible to participate in airdrops. This type of backstab incentive design not only damages the reputation of the platform, but also easily leads to the collapse of the long-term content ecology. The comparison case of Magic Newton and Humanity is particularly typical: the former has a clear distribution mechanism in the Kaito mouth-pushing stage and rich token value returns; the latter has an unbalanced distribution mechanism and lack of transparency, which has caused a community trust crisis and anti-pushing doubts. This structural injustice under the Matthew effect has greatly reduced the enthusiasm of tail creators and ordinary users to participate, and even spawned the ironic identity of algorithm-sacrificing mouth-pushing players.
What is more noteworthy is that the financialization of information does not mean the consensus of value. In the attention market or reputation market, those contents, people or trends that are long may not be signals of real long-term value. In the absence of real demand and scenario support, once the incentives ebb and subsidies stop, these financialized information assets often quickly return to zero, and even form a Ponzi dynamic of short-term speculation narrative, long-term return to zero. The short life of the LOUD project is a microcosm of this logic: the market value exceeded 30 million US dollars on the day of its launch, and fell to less than 600,000 just two weeks later, which can be called the InfoFi version of pass the parcel.
In addition, in the prediction market, if the oracle mechanism is not transparent enough or is manipulated by large investors, it is very easy to form information pricing deviations. Polymarket has repeatedly caused user disputes due to unclear explanation of event settlement, and in 2025, there was even a large-scale compensation storm caused by a oracle voting vulnerability. This reminds us that even if the prediction mechanism is based on real world information, it must find a better balance between technology and game theory.
Ultimately, whether InfoFis incentive mechanism can break away from the confrontational narrative of financial capital vs. retail investor attention depends on whether it can build a triple positive feedback system: information production behavior can be accurately identified -> value distribution mechanism can be transparently executed -> long-tail participants can be truly motivated. This is not only a technical issue, but also a test of institutional engineering and product philosophy.
In summary, InfoFis incentive mechanism is both its biggest advantage and its biggest source of risk. In this market, every design of incentives may 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 an infrastructure that can identify real signals, incentivize high-quality contributions, and form a self-consistent ecosystem, can InfoFi truly achieve the transition from gimmick economy to cognitive finance.
IV. Analysis of typical projects and recommended areas of focus
InfoFis ecosystem is currently flourishing and hot topics are rotating. Different projects have evolved differentiated product paradigms and user growth strategies around the core path of information → incentives → market. Some projects have initially verified the business model and become the key anchor of InfoFis narrative; while others are in the concept verification stage and are still looking for breakthroughs in the process of user education and mechanism optimization. In the complex track, we try to select projects from five representative directions for analysis and propose potential camps worthy of continued tracking.
1. Predicting the market direction: Polymarket + Upside
Polymarket is one of the most mature and iconic projects in the InfoFi ecosystem. Its core model is to achieve collective expected pricing of real events by buying and selling contract shares with different outcomes through USDC. The reason why it is called the prototype of information finance by Vitalik is not only because its trading logic is clear enough and its financial design is robust enough, but also because it has begun to have media functions in the real world - for example, during the 2024 US election, the probability of winning or losing reflected by Polymarket was better than traditional polls many times, which triggered heated discussions and reposts including Musk.
With the official cooperation between Polymarket and X, its user growth and data visibility will be further enhanced, and it is expected to become a super hub platform integrating social opinion and information pricing. However, the challenges currently faced by Polymarket still include compliance risks (CFTC has repeatedly challenged it), oracle disputes, and insufficient participation in niche topics.
In contrast, Upside focuses on social prediction and is an emerging project invested by well-known capital such as Arthur Hayes. It attempts to commercialize content prediction through the mechanism of likes and voting, so that creators, readers, and voters can share the benefits. Upside emphasizes light interaction, low threshold, and de-financialized user experience, exploring the integration model between InfoFi and content platforms. It is worth paying attention to its subsequent performance in user retention and content quality maintenance.
2. Yap-to-Earn direction: Kaito AI + LOUD
Kaito AI is one of the most representative platforms in the Yap-to-Earn model and the project with the largest number of users in InfoFi. It has attracted more than 1 million registered users and more than 200,000 active Yappers. Its innovation lies in the use of AI algorithms to evaluate the quality, interactivity, and project relevance of user content posted on X (formerly Twitter), thereby distributing Yaps (points) and cooperating with projects based on the rankings to conduct token airdrops or rewards.
The Kaito model forms a closed loop: projects use tokens to incentivize community dissemination, creators use content to compete for attention, and the platform uses data and AI models to control distribution and order. However, with the surge in users, it has also encountered structural problems such as content signal pollution, robot proliferation, and point distribution disputes. The founder of Kaito has recently begun to iterate algorithms and optimize community mechanisms to address these issues.
LOUD is the first project to conduct IAO (initial attention offering) with the help of Yap-to-Earn score list. It monopolized 70% of the attention on the Kaito list through word-of-mouth activities before going online. Although its airdrop strategy created a lot of social volume in the short term, it was criticized by the community as harvesting by passing the parcel due to the rapid drop in token prices. 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 the fairness of incentives still need to be polished.
3. Reputation Finance: Ethos + GiveRep
Ethos is the most systematic and decentralized attempt in the current reputation finance track. Its core logic is to build a verifiable credit score on the chain. It not only generates scores through interactive records and comment mechanisms, but also introduces a guarantee mechanism: users can pledge ETH to endorse others and bear certain risks, thus forming a Web3-like trust network.
Another major innovation of Ethos is the launch of a reputation speculation market, which allows users to go long or short on the reputation of others, forming a new dimension of financial instruments - trust monetization. This mechanism opens up imagination space for the integration of reputation scoring with the lending market, DAO governance, and social identity recognition in the future. However, its invitation-based mechanism also slows down the expansion of users. How to lower the threshold and improve the ability to resist witches in the future is the key to the development of the platform.
Compared with Ethos, GiveRep is more lightweight and community-oriented. Its mechanism is to rate content creators and commenters by commenting @ official accounts, with a limited number of comments per day. With the active ecology of the X community, it has achieved a certain scale of dissemination on Sui. This model is more suitable for projects to conduct lightweight tests of social fission and reputation scoring, and can also serve as a trust basis for future integration of governance weights, project airdrops and other mechanisms.
4. Attention market direction: Trends + Noise + Backroom
Trends is a platform that explores content assetization, allowing creators to cast their X posts into tradable Trends and set up trading curves. Community members can buy and go long on the popularity of the post, and creators receive commissions from the transactions. It creatively transforms hot posts into liquid assets, which is a typical attempt at social financialization.
Noise is an attention futures platform based on MegaETH. Users can bet on the popularity changes of a topic or project. It is a direct investment platform for attention finance. In the closed beta test that requires an invitation code, some of its prediction models have demonstrated early market discovery capabilities. If AI models are introduced to predict popularity trends in the future, it may become a barometer tool in the InfoFi ecosystem.
Backroom represents an InfoFi product that pays to unlock + screens high-value content. Creators can publish high-quality content based on token thresholds, and users can unlock access by purchasing keys. At the same time, the keys themselves are tradable and have value volatility, forming a closed loop of content finance. Against the backdrop of the popularity of NoiseFi, this model focuses on reducing noise and screening signals and is becoming a new tool for knowledge creators.
5. Data Insight and AI Agent Platform: Arkham + Xeet + Virtuals
Arkham Intel Exchange has become synonymous with the financialization of on-chain intelligence, allowing users to issue bounties and incentivize on-chain detectives to disclose address ownership information. Its logic is similar to that of the traditional intelligence market, but for the first time it has achieved decentralization and tradability. Although there are constant controversies (such as privacy violations and witch hunts), it has established the basic paradigm of data insight InfoFi.
Although Xeet has not yet been fully released, its founder Pons has publicly stated that he wants to be the noise reducer of InfoFi. By introducing mechanisms such as the Ethos reputation system, KOL recommendations, and private content recommendations, he will create a more authentic and spam-free signal market, which is a direct counterattack to the noise problem of Yap-to-Earn.
Virtuals innovation is to use AI agents as new InfoFi participants, injecting non-human productivity into the InfoFi ecosystem by launching tasks, completing evaluations, and generating interactive data. The Yap-to-Earn stage in its Genesis Launch mode is linked with Kaito, which also shows the trend of ecological linkage between InfoFi projects.
5. 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 credible attention are becoming increasingly valuable. Against this backdrop, InfoFi is called the next narrative engine or even a potential asset of new gold by many industry insiders. The logic behind this is that with the increasing proliferation of AI computing power and the approaching zero content cost, what is scarce is not content, but the signal that can accurately guide action, and the real attention itself that focuses on this signal. Whether InfoFi in the future can move from concept to assetization, and from short-term mouth-to-mouth incentives to long-term on-chain influence standards, the key lies in the struggle and evolution of the three major trends and the three major risks.
First, the deep integration of AI and prediction markets will usher in a new era of inference capital. Currently, the combination of Polymarket, X, and Grok has taken the lead in implementing this model: real-time public opinion + AI analysis + real money game results, building a flywheel between effectiveness, authenticity, and market feedback. If future InfoFi projects can use AI to provide event modeling, signal extraction, and dynamic pricing, it will greatly enhance the credibility of prediction markets in governance, news verification, and trading strategies. For example, the governance DAO under the Futarchy model may use a combination of AI and prediction markets to formulate policies in the future.
Second, the intersection of reputation, attention and financial attributes will trigger a major explosion in the decentralized credit system. The current exploration of reputation InfoFi projects (such as Ethos and GiveRep) is building a set of on-chain reputation points that do not require third-party credit intermediaries. In the future, reputation points are expected to become the basis for DAO voting rights, DeFi collateral, content distribution priorities, etc., and become a true on-chain social capital. If cross-platform mutual recognition, anti-sybil attack and traceable credit trajectory can be achieved, the attention reputation system will rise from an auxiliary indicator to a core asset.
Third, the tokenization and derivativeization of attention assets is 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 valuable content, a KOLs attention bond, and a set of on-chain signals into tradable assets, and allow users to go long, go short and even construct ETFs. This will also form a brand new financial market: from narrative-based Meme Tokens to derivative assets based on attention dynamics.
But at the same time, InfoFi still faces three major structural risks if it wants to become truly sustainable.
The first is that the imperfect mechanism design leads to the proliferation of mouth traps. If the incentive is too biased towards quantity rather than quality, the platform algorithm is not transparent, and the airdrop expectations are unreasonable, it will lead to extremely high enthusiasm in the early stage of the project, but the attention will drop sharply in the later stage, forming the SocialFi-style fate of airdrop is the peak. For example, LOUD initially attracted users with Yap ranking incentives, but after the token was launched, the market value plunged and the participation rate dropped sharply, reflecting the lack of long-term mechanisms in the ecosystem.
The second is that the Matthew effect has intensified, leading to ecological fragmentation. The data of most current platforms have revealed that more than 90% of the rewards are concentrated in the hands of the top 1% of users. Long-tail users can neither benefit from the interaction nor break through the KOL class, and eventually choose to exit. Once this structure cannot be broken through mechanisms such as reputation weighting and credit flow, it will weaken users willingness to participate and turn InfoFi into another platform oligopoly system.
The third is the dual dilemma of regulatory risk and information manipulation. For emerging products such as prediction markets, reputation trading, and attention speculation, major jurisdictions around the world have not yet formed a unified regulatory framework. Once a platform involves gambling, insider trading, false propaganda, or market manipulation, it is very easy to trigger high regulatory pressure. For example, Polymarket has encountered dual scrutiny from the CFTC and FBI in the United States, and Kalshi has also taken a differentiated path due to its compliance advantages. All this means that the InfoFi project must consider the regulatory-friendly path from the Day One design to avoid going to the edge of illegality.
In summary, InfoFi is not just a next-generation content distribution protocol, but also a new attempt to financialize attention, information, and influence. It is a challenge to the traditional platform value possession model, and a collective experiment of everyone is the discoverer of Alpha. Whether InfoFi can become the new gold in the Web3 world in the future depends on whether it can find the optimal solution among the fair mechanism, incentive design and regulatory framework, and truly turn the attention dividend from the prey of a few people into the assets of the majority.
6. Conclusion: The revolution is not yet complete, InfoFi still needs to be cautiously optimistic
The emergence of InfoFi is another cognitive evolution of the Web3 world after going through multiple cycles such as DeFi, NFT, and GameFi. It attempts to answer a core question that has been ignored for a long time - in an era of information overload, free content, and algorithm proliferation, what is truly scarce? The answer is: human attention, real signals, and credible subjective judgment. This is exactly what InfoFi is trying to give value, incentive mechanisms, and market structures.
In a sense, InfoFi is a reverse power revolution against the traditional attention economy system - instead of allowing platforms, giants, and advertisers to monopolize data and traffic dividends, it attempts to redistribute the value of attention to real creators, communicators, and identifiers through blockchain, tokenization, and AI protocols. This structural redistribution of value gives InfoFi the potential to change the content industry, platform governance, knowledge collaboration, and even social public opinion mechanisms.
However, potential does not mean reality. We still need to be cautiously optimistic.
The revolution is not yet complete, but it has begun. The future of InfoFi is not defined by a certain platform or track, but is jointly shaped 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 way value is perceived and distributed. On the long-term path of de-platforming and de-intermediation, we should maintain calm judgment and prudent participation, but we should not ignore its potential as the soil for the next generation of Web3, which may grow a new narrative forest.
