InfoFi Depth Research: Attention Finance Experiments in the AI Era

Introduction: From Information Scarcity to Attention Scarcity

The information revolution of the 20th century brought about an explosion of knowledge, but it also triggered a paradox: when the cost of acquiring information is almost zero, what becomes 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 scarcity." In today's society, faced with an overwhelming amount of content, the cognitive boundaries of humanity are continuously being squeezed, making filtering and judgment increasingly difficult.

In the digital age, the scarcity of attention has evolved into a battle for resources. In the traditional Web2 model, platforms control the flow of traffic through algorithmic distribution, while the users, creators, and community advocates who truly create attention resources often become the "free fuel" for the platform's profits. Leading platforms and capital continually harvest in the monetization chain of attention, while ordinary individuals who drive information production and dissemination struggle to share in the value. This structural disconnection is becoming the core contradiction in the development of digital civilization.

The emergence of Information Financialization (InfoFi) has occurred against this backdrop. It is based on technologies such as blockchain, token incentives, and AI, with the goal of "reshaping the value of attention." It attempts to transform users' viewpoints, information, reputation, and other unstructured cognitive behaviors into quantifiable and tradable asset forms, allowing each user participating in the creation, dissemination, and judgment of the information ecosystem to share value through a distributed incentive mechanism. This is not only a technological innovation but also an attempt to redistribute power concerning "who owns attention and who dominates information."

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

The essence of InfoFi is to build a composite market system that simultaneously integrates financial logic, semantic computation, 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 predictions.

From a fundamental perspective, InfoFi is an attempt to "financialize" information, transforming cognitive activities such as content, opinions, and trend judgments that were originally difficult to price into measurable and tradable "quasi-assets". The involvement of finance makes information no longer a scattered "content fragment" in the processes of production, circulation, and consumption, but rather a "cognitive product" with attributes of game theory and value accumulation.

AI becomes the second pillar of InfoFi, mainly undertaking two roles: semantic filtering and behavior recognition. AI achieves precise evaluation of information sources by modeling multidimensional data such as user social network behavior, content interaction trajectories, and originality of opinions. In InfoFi, the function of AI is similar 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 it all. Unlike DeFi, the asset anchors of InfoFi are "cognitive assets" that are more liquid but time-sensitive, such as opinions, trust, and discourse power. This determines that the operating mechanism of the InfoFi market relies heavily on a dynamic ecosystem constructed from social graphs, semantic networks, and psychological expectations.

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 personal on-chain history and social behavior into credit assets; attention markets attempt to capture the "emotional fluctuations" disseminated on the chain; token-gated content platforms reconstruct the logic of information payment through permissioned economies. Together, they form a multi-layered ecosystem of InfoFi.

Core Game Mechanism: Incentivizing Innovation vs Harvesting Trap

Behind the prosperous facade of the InfoFi ecosystem lies, at its core, the design game of incentive mechanisms. Whether it's participation in prediction markets, outputs from speculative behaviors, building reputation assets, or attention trading, it fundamentally involves the core issues of "Who exerts effort? Who shares the dividends? Who bears the risks?"

The innovative potential of InfoFi's incentives lies in endowing the previously hard-to-measure intangible asset of "information" with clear tradability, competitiveness, and settlement. This transformation relies on the traceability of blockchain and the assessability of AI. Prediction markets monetize cognitive consensus through market pricing mechanisms; the mouth-to-mouth ecology turns speech into economic behavior; reputation systems build inheritable and pledgeable social capital; attention markets treat trending topics as trading targets. These mechanisms enable information to possess "cash flow" attributes for the first time.

However, strong incentives can also easily give rise to "game abuse." Taking Yap-to-Earn as an example, many projects attract a large number of content creators in the initial incentive phase, only to quickly fall into "information haze"—frequent occurrences of bot matrix accounts flooding, major influencers participating in beta tests ahead of time, and project teams manipulating interaction weights. Under the opaque mechanisms of point systems and token expectations, many users become "free laborers" and ultimately do not qualify for airdrops. This "backstabbing" incentive design not only damages the platform's reputation but also easily leads to the collapse of the long-term content ecosystem.

What is more noteworthy is that the financialization of information does not equate to the consensus of value. In the attention market or reputation market, those contents, figures, or trends that are "longed" are not necessarily true signals of long-term value. In the absence of genuine demand and scenario support, once the incentives recede and subsidies stop, these financialized "information assets" often quickly return to zero, even forming a "short-term speculation narrative, long-term return to zero" Ponzi dynamic.

Analysis of Typical Projects and Recommended Areas of Focus

1. Predict market direction: Polymarket and Upside

Polymarket is one of the most mature projects in the InfoFi ecosystem, allowing users to buy and sell contract shares of different outcomes using USDC, thus realizing collective expectation pricing of real-world events. Its performance during the 2024 U.S. presidential election, which frequently outperformed traditional polls, has sparked heated discussions. Upside, on the other hand, focuses on social prediction, marketizing content predictions through a voting mechanism based on likes, exploring the fusion model between InfoFi and content platforms.

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

Kaito AI is currently the project with the highest number of InfoFi users, utilizing AI algorithms to assess the quality and relevance of user-published content, distributing points and conducting airdrop rewards. LOUD is the first project to conduct an Initial Attention Offering (IAO) using the Yap-to-Earn leaderboard, but its token price has quickly plummeted, sparking controversy and reflecting that the Yap-to-Earn track is still in a trial-and-error phase.

3. Reputation Financial Direction: Ethos and GiveRep

Ethos builds an on-chain verifiable "credit score", introducing a collateral mechanism to form a Web3 trust network, and launching a reputation speculation market. GiveRep adopts a more lightweight community scoring model, suitable for projects to conduct social virality and reputation scoring tests.

4. Market Direction of Attention: Trends, Noise, and Backroom

Trends allows creators to mint X posts into tradable "Trends", exploring content assetization. Noise is an attention futures platform based on MegaETH, where users can bet on changes in topic popularity. Backroom represents the InfoFi product of "paid unlock + filtering high-value content", focusing on reducing noise and filtering signals.

5. Data Insights and AI Agent Platforms: Arkham, Xeet, and Virtuals

Arkham Intel Exchange realizes the financialization of on-chain intelligence, allowing users to publish bounties to disclose address ownership information. Xeet plans to create a denoised 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.

Future Trends and Risk Outlook

The future development of InfoFi mainly revolves around three major trends:

1. The deep integration of AI and prediction markets will usher in a new era of "reasoning capital".
2. The intersection of reputation, attention, and financial attributes will trigger a massive explosion of decentralized credit systems.
3. The tokenization and derivation of attention assets is the ultimate form of InfoFi.

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

1. The inadequate design of the mechanism has led to the proliferation of "mouth-lifting traps".
2. The "Matthew Effect" exacerbates ecological fragmentation.
3. The dual dilemma of regulatory risks and information manipulation.

Conclusion

The emergence of InfoFi is another cognitive evolution in the Web3 world, attempting to redefine the value of attention, real signals, and trustworthy judgments in an era of information overload. It is a "reverse power revolution" against the traditional attention economy system, trying to redistribute the value of attention to the true creators, disseminators, and identifiers.

However, potential does not mean reality. The future of InfoFi still requires cautious optimism. It is not only a revolution about value recognition and distribution methods, but it may also become an important part of the next generation Web3 ecosystem. In this process, we should maintain calm judgment and prudent participation, while also not overlooking its potential as a fertile ground for new narrative forests.