InfoFi Depth Research: Attention Finance Experiment in the AI Era

Introduction: From Information Scarcity to Attention Scarcity, InfoFi Emerges

The information revolution of the 20th century brought about an enormous increase in knowledge, but it also triggered a paradox: when the cost of information acquisition is almost zero, what becomes truly scarce is our cognitive resource for processing information—attention. Nobel laureate Herbert Simon first introduced the concept of "attention economy" in 1971, pointing out that "information overload leads to attention scarcity." Faced with the content bombardment of various social media and information platforms, the cognitive boundaries of humanity are constantly being squeezed, making information filtering and value judgment increasingly difficult.

In the digital age, attention scarcity has evolved into a battle for resources. In the traditional Web2 model, platforms control traffic entry through algorithms, while users, content creators, and community participants who truly create attention value often find it difficult to share in the profits. Leading platforms and capital players continuously harvest from the attention monetization chain, resulting in structural contradictions within the digital ecosystem.

The rise of InfoFi is happening against this backdrop. It is based on blockchain, token incentives, and AI empowerment, attempting to reshape the mechanism of value distribution related to attention. InfoFi aims to transform users' unstructured cognitive behaviors such as opinions, information, reputation, and social interactions into quantifiable and tradable asset forms, allowing every user participating in the creation, dissemination, and judgment of information ecology to share in the value through distributed incentives. This is not only a technological innovation but also an attempt to redistribute the power structure of "who owns attention and who dominates information."

InfoFi connects social networks, content creation, market games, and AI intelligence, inheriting the financial mechanisms 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 around "cognitive resource financialization." Its core is to establish a value discovery and redistribution logic of "information → trust → investment → return."

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

However, any paradigm shift is accompanied by bubbles, hype, and controversy. Whether InfoFi can become a truly user-centric attention revolution depends on whether it can find a balance among incentive mechanism design, value capture logic, and real demand. Otherwise, it may just be another illusion sliding from "inclusive narratives" to "centralized harvesting."

The Ecological Composition of InfoFi: The Ternary Intersection Market of Information, Finance, and AI

The essence of InfoFi is to build a composite market system that integrates financial logic, semantic computing, and game theory mechanisms in an environment where information is overflowing and value is difficult to capture. It is not a single "content platform" or "financial protocol," but rather an intersection of information value discovery mechanisms, behavior incentive systems, and intelligent distribution engines, forming a full-stack ecosystem that encompasses information trading, attention incentives, reputation ratings, and intelligent predictions.

From a bottom-up perspective, InfoFi is an attempt to "financialize" information, transforming cognitive activities such as content, opinions, trend judgments, and social interactions into measurable, tradable "quasi-assets" that have market prices. The involvement of finance makes information no longer a fragmented "content fragment" during its production, circulation, and consumption processes, but rather a "cognitive product" with competitive attributes and value accumulation capabilities. A comment, a prediction, or a trend analysis can be an expression of individual cognition, or it can become a speculative asset with risk exposure and future income rights.

AI has become the second pillar of InfoFi, mainly assuming 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 by analyzing multidimensional data such as users' social network behavior, content interaction trajectories, and originality of opinions. The function of AI in InfoFi is similar to that of market makers and clearing mechanisms in exchanges, maintaining ecological stability and credibility.

Information is the foundation of it all, not just the trading targets, but also the source of market sentiment, social connections, and consensus building. Unlike DeFi, the asset anchors of InfoFi are not on-chain hard assets, but rather "cognitive assets" that are more liquid and timely, such as opinions, trust, topics, trends, and insights. This determines that the operation mechanism of the InfoFi market highly relies on a dynamic ecosystem constructed by social graphs, semantic networks, and psychological expectations.

In this framework, content creators are equivalent to the market's "market makers," providing opinions and insights for the market to assess value; users are the "investors," expressing their valuation of information through interactive behavior, driving it to rise or sink in the network; the platform and AI are the "referees + exchanges," responsible for ensuring market fairness and efficiency.

This trinary structure has spawned a series of new species and mechanisms: prediction markets provide clear targets for speculation; Yap-to-Earn encourages knowledge as mining and interaction as output; reputation protocols transform individual on-chain history and social behavior into credit assets; attention markets attempt to capture the "emotional fluctuations" propagated on-chain; token-gated content platforms reconstruct the logic of information payment through permission economy. Together, they form a multi-layered ecology of InfoFi: which includes value discovery tools, carries value distribution mechanisms, and embeds multi-dimensional identity systems, participation threshold designs, and anti-witch mechanisms.

InfoFi is no longer just a market, but a complex information game system: using information as a trading medium, finance as an incentive engine, and AI as the governance hub, with the intention of building a self-organizing, distributed, and adjustable cognitive collaboration platform. It 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 systems are destined to be complex, diverse, and fragile. The subjectivity of information determines the impossibility of unified value assessment, the game nature of finance increases the risk of manipulation and herd behavior, and the black box nature of AI poses challenges to transparency. The InfoFi ecosystem must constantly balance and self-repair in the tension among these three elements, or it is likely to slide under capital drive into the opposite of "de facto gambling" or "attention harvesting fields."

The ecological construction of InfoFi is not an isolated project of a certain protocol or platform, but a co-performance of a complete socio-technical system. It represents a deep attempt of Web3 in the direction of "governing information" rather than "governing assets." It will define the pricing method of information in the next era and even build a more open and autonomous cognitive market.

Core Game Mechanism: Incentivizing Innovation vs Harvesting Traps

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

From an external perspective, InfoFi appears to be a "production relationship innovation" in the migration from Web2 to Web3: it seeks to break the exploitation chain among "platform-creator-user" in traditional content platforms and return value to the original contributors of information. However, from an internal structure 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 could easily devolve into a "retail investor harvesting ground" dominated by capital and algorithms.

First, we need to examine the positive potential of "incentivizing innovation." The essential innovation of all InfoFi sub-tracks is to give clear tradability, competitiveness, and settleability to "information," an intangible asset that was difficult to measure and financialize in the past. This transformation relies on two key engines: the traceability of blockchain and the assessability of AI.

Prediction markets monetize cognitive consensus through market pricing mechanisms; the mouth-to-mouth ecosystem transforms speaking into economic behavior; reputation systems are building a form of inheritable and collateralizable social capital; attention markets treat trending topics as trading targets, redefining content value through the logic of "information discovery → betting signals → obtaining price differences"; and AI-driven InfoFi applications attempt to construct a data and algorithm-driven information finance network 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, forwarding a tweet, endorsing someone" a real productive activity.

However, the stronger the incentive system, the easier it is to give rise to "game abuse." The biggest systemic risk faced by InfoFi is the distortion of the incentive mechanism and the proliferation of arbitrage chains.

Taking Yap-to-Earn as an example, on the surface, it rewards users for the value of content creation through AI algorithms. However, in actual execution, many projects quickly fall into "information haze" after briefly attracting a large number of content creators during the initial incentive phase—issues such as spam from bot matrix accounts, early participation from big influencers in beta testing, and directional manipulation of interaction weights by project parties frequently occur. A leading KOL stated: "If you don't inflate the numbers, you can't even make the rankings; AI has been trained to specifically recognize keywords and ride the wave of trends." Furthermore, project parties disclosed: "We invested $150,000 for a round of mouth-to-mouth promotion, and as a result, 70% of the traffic is from AI accounts and paid promoters competing with each other; true KOLs do not participate, and it is impossible for me to invest a second time."

Under the opaque mechanisms of the points system and token expectations, many users have become "free laborers": posting, interacting, launching, and building groups, yet ultimately have no qualifications to participate in airdrops. This kind of "backstabbing" incentive design not only damages the platform's reputation but also easily leads to the collapse of the long-term content ecosystem. Some projects have clear distribution mechanisms during the initial phases and provide substantial token value returns; while others, due to imbalanced distribution mechanisms and insufficient transparency, trigger community trust crises and "anti-earning" skepticism. This structural injustice under the Matthew effect significantly reduces the participation enthusiasm of tail creators and ordinary users, even giving rise to the ironic identity of "algorithm sacrifice type free labor players."

What is even more concerning is that the financialization of information does not equate to the consensus of value. In the attention market or reputation market, those contents, individuals, or trends that are "longed" may not necessarily be true signals of long-term value. In the absence of real demand and scenario support, once incentives wane and subsidies cease, these financialized "information assets" often plummet to zero rapidly, even forming a Ponzi dynamic of "short-term speculation, long-term zeroing out." The short lifespan of some projects is a reflection of this logic: on the day of launch, the market value exceeds tens of millions of dollars, but just two weeks later, it falls to less than a million, akin to the InfoFi version of "passing the parcel."

In addition, in prediction markets, if the oracle mechanism is not transparent enough or is subject to manipulation by large investors, it can easily lead to deviations in information pricing. Some platforms have repeatedly sparked user disputes due to "unclear event settlement explanations," and even experienced large-scale compensation storms triggered by oracle voting loopholes. This reminds us that even prediction mechanisms that use "real-world information" as the underlying must find a better balance between technology and game theory.

Ultimately, whether InfoFi's incentive mechanism can break out of the "financial capital vs retail attention" antagonistic narrative depends on whether it can construct a triple positive feedback system: information production behavior can be accurately identified → value distribution mechanism 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 conclusion, InfoFi's incentive mechanism is both its most