InfoFi Depth Research: Attention Finance Experiment in the AI Era

I. Introduction: From Information Scarcity to Attention Scarcity, InfoFi Emerges

The information revolution of the 20th century has brought about an explosive growth in knowledge, but it has also triggered a paradox: when the cost of obtaining information is almost zero, what is truly scarce is our cognitive resources 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 poverty." In the face of the overwhelming content from social media and various platforms, the cognitive boundaries of humanity are constantly being squeezed, making filtering, judging, and valuing increasingly difficult.

In the digital age, the scarcity of attention has evolved into a battle for resources. In the traditional Web2 model, platforms control traffic entry through algorithm distribution, while the users, content creators, and community evangelists who truly create attention resources often serve merely as "free fuel" in the profit logic of the platform. Leading platforms and capital parties continuously reap profits in the attention monetization chain, while ordinary individuals who drive information production and dissemination find it difficult to participate in value sharing. This structural disconnection has become the core contradiction in the evolution of digital civilization.

The rise of Information Financialization (InfoFi) is happening against this backdrop. It is based on blockchain, token incentives, and AI empowerment as its technological foundation, aiming to "reshape the value of attention." It attempts to transform users' viewpoints, information, reputation, social interactions, trend discovery, and other unstructured cognitive behaviors into quantifiable and tradable asset forms. Through a distributed incentive mechanism, it allows every user participating in the creation, dissemination, and judgment of the information ecosystem to share in the value generated. This is not only a technological innovation but also an attempt at redistributing power regarding "who owns attention and who dominates information."

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

The essence of InfoFi is to build a composite market system that simultaneously integrates financial logic, semantic computing, and game mechanisms. Its ecological architecture is the intersection of the information value discovery mechanism, behavior incentive system, and intelligent distribution engine, forming a full-stack ecosystem that combines information trading, attention incentives, reputation ratings, and intelligent forecasting.

From a fundamental perspective, InfoFi is an attempt at the "financialization" of information, which means converting cognitive activities such as content, opinions, trend judgments, and social interactions into measurable and tradable "quasi-assets" that have market prices. This means that a comment, a prediction, or a trend analysis can not only express individual cognition but also become a speculative asset that carries risk exposure and future rights to income.

AI plays a role in InfoFi as a semantic filter and behavior recognition, acting as the "first line of defense" against information signals and noise. At the same time, it achieves precise evaluation of information sources through multi-dimensional data modeling of user social network behavior, content interaction trajectories, originality of opinions, and more. The function of AI in InfoFi is equivalent to that of market makers and clearing mechanisms in exchanges, serving as the core for maintaining ecological stability and credibility.

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

The collaborative operation of this ternary structure has spawned a series of new species and new mechanisms: prediction markets, Yap-to-Earn, reputation protocols, attention markets, token-gated content platforms, and so on. Together, they form the multilayered ecosystem of InfoFi: which includes value discovery tools, carries value distribution mechanisms, and embeds multidimensional identity systems, participation threshold designs, and anti-witch mechanisms.

3. Core Game Mechanism: Incentivizing Innovation vs. Harvesting Traps

In the InfoFi ecosystem, the prosperity behind everything is the design game of incentive mechanisms. The core questions are: Who puts in the effort? Who receives dividends? Who bears the risks?

From an external perspective, InfoFi seems to be a "production relationship innovation" in the migration from Web2 to Web3: attempting to break the exploitation chain between "platform-creator-user" in traditional content platforms and return value to the original contributors of information. However, from an internal structural standpoint, this value return is not inherently fair, but rather a subtle balance built on a series of incentives, verification, and game mechanisms.

The incentive innovation potential of InfoFi lies in endowing "information," an intangible asset that has been difficult to measure and financialize in the past, with clear tradability, competitiveness, and settlement. Prediction markets realize cognitive consensus through market pricing mechanisms; the mouth-licking ecosystem turns speaking into economic behavior; the reputation system builds inheritable and mortgagable social capital; the attention market treats hot trends as trading targets. These mechanisms enable information to possess "cash flow" attributes for the first time, transforming "saying a word, sharing a tweet, endorsing someone" into genuine productive activities.

However, the more incentivized the system is, 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, many projects attract a large number of content creators in the early stages of incentives, only to quickly fall into "information haze"—frequent occurrences of issues such as bot matrix accounts flooding, early participation of influencers in beta testing, and project parties manipulating interaction weights.

Under the opaque mechanisms of the points system and token expectations, many users have become "free laborers": tweeting, interacting, launching, and building groups, yet they end up having no eligibility to participate in airdrops. This kind of "backstabbing" incentive design not only damages the platform's reputation but also risks leading to a collapse of the long-term content ecosystem.

What's more concerning is that the financialization of information does not equate to the consensus of value. In the absence of real demand and scenario support, once the incentives recede and subsidies stop, these financialized "information assets" often rapidly drop to zero, even creating a "short-term speculation narrative, long-term zeroing" Ponzi dynamic.

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

4. Analysis of Typical Projects and Recommended Focus Areas

The InfoFi ecosystem currently presents a flourishing and rotating pattern of hotspots. The following is an analysis of selected projects from five representative directions:

1. Predict market direction: Polymarket + Upside

Polymarket is one of the most mature projects in the InfoFi ecosystem, allowing users to buy and sell contract shares on different outcomes using USDC, thereby achieving collective expectation pricing for real-world events. Its prediction accuracy during the 2024 U.S. elections has garnered widespread attention. However, it currently faces challenges such as compliance risks and oracle controversies.

Upside focuses on socialized predictions, attempting to market content prediction through a like-voting mechanism that allows creators, readers, and voters to share profits. Its exploration of the integration model of InfoFi and content platforms is worth paying attention to.

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

Kaito AI is currently the project with the most InfoFi users, utilizing AI algorithms to assess the quality of user content on social platforms, distribute points, and conduct token airdrops. However, with the surge in users, it also faces issues such as content signal pollution and the proliferation of bots.

LOUD is the first project to conduct an Initial Attention Offering (IAO) using the Yap-to-Earn leaderboard, but it has been criticized for its token price plummeting rapidly, being labeled as a "musical chairs harvesting". Its ups and downs indicate that the Yap-to-Earn track is still in the trial-and-error stage.

3. Reputation Finance Direction: Ethos + GiveRep

Ethos builds an on-chain verifiable "credit score" and introduces a guarantee mechanism to form a Web3 trust network. It launches a reputation speculation market, allowing users to "go long or short" on others' reputations, opening up imaginative space for future integration with lending markets, DAO governance, and more.

GiveRep is more lightweight, scoring content creators through comments, suitable for projects to perform social virality and lightweight reputation scoring tests.

4. Attention Market Direction: Trends + Noise + Backroom

Trends allows creators to mint social posts into tradable "Trends", where community members can buy in to long the post's popularity. Noise is an attention futures platform based on MegaETH, where users can bet on the popularity changes of a certain topic or project. Backroom represents the product model of "paywall unlocking + filtering high-value content".

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

Arkham Intel Exchange enables the financialization of on-chain intelligence, allowing users to post bounties to incentivize "on-chain detectives" to disclose address ownership information. Xeet plans to create a "noise-reducing" signal market by introducing mechanisms such as a reputation system and KOL recommendations. Virtuals will incorporate AI agents as new InfoFi participants, injecting "non-human productivity" into the ecosystem.

V. Future Trends and Risk Outlook

The three major trends in the future development of InfoFi:

1. The deep integration of AI and prediction markets will usher in a new era of "inference capital", enhancing the credibility of prediction markets in governance, news verification, trading strategies, and more.

2. The convergence of reputation, attention, and financial properties will trigger a massive explosion of decentralized credit systems. In the future, reputation points are expected to become the foundation for DAO voting rights, DeFi collateral, content distribution priorities, and more.

3. The tokenization and derivatives of attention assets is the ultimate form of InfoFi, which will create a new financial market, from narrative-based Meme Tokens to attention dynamic-based derivative assets.

At the same time, InfoFi faces three major structural risks:

1. The inadequate design of the mechanism has led to the proliferation of the "mouth-lifting trap," resulting in the fate of "airdrop is the peak."

2. The "Matthew Effect" exacerbates ecological fragmentation, with most rewards concentrated in the hands of top users, making it difficult for long-tail users to benefit.

3. The dual dilemma of regulatory risks and information manipulation poses compliance challenges for emerging products such as prediction markets and reputation trading.

Six, Conclusion

InfoFi attempts to answer what is truly scarce in the age of information overload—human attention, real signals, and credible subjective judgment. It is a "reverse power revolution" against the traditional attention economy system, aiming to redistribute the value of attention to the true creators, disseminators, and identifiers through blockchain, tokenization, and AI protocols.

However, potential does not equate to reality. The future of InfoFi is not defined by a single platform or track, but shaped collectively by all creators, observers, and recognizers of attention. On the long-term path of de-platforming and de-intermediation, we should maintain calm judgment and prudent participation, while also not overlooking its potential to possibly grow a new narrative forest on the soil of the next generation Web3.