InfoFi: A New Paradigm of Attention Market Empowered by AI

In 1971, psychologist and economist Herbert Simon first proposed the theory of attention economics, pointing out that in a world of information overload, human attention has become the scarcest resource.

Economist Albert Wenger further reveals a fundamental shift in "The World After Capital": human civilization is undergoing a third leap - from the "scarcity of capital" in the industrial age to the "scarcity of attention" in the knowledge age.

• Agricultural Revolution: Solved the problem of food scarcity, but gave rise to land disputes.
• Industrial Revolution: Addressing land scarcity issues, shifting towards resource competition and capital accumulation.
• Digital Revolution: The Battle for Attention

This transformation stems from two major characteristics of digital technology: the zero marginal cost of information replication and dissemination, and the universality of AI computation (but human attention cannot be replicated).

Whether it's the booming toy market or the live-streaming sales by top influencers, it is essentially a competition for user and audience attention. However, in the traditional attention economy, users and fans contribute their attention as "data fuel," while the excess profits are monopolized by the platforms. The InfoFi in the Web3 world attempts to subvert this model—by utilizing blockchain, token incentives, and AI technology to make the processes of information production, dissemination, and consumption transparent, thus returning value to the participants.

InfoFi Overview

InfoFi is a combination of Information and Finance, with the core focus on transforming difficult-to-quantify abstract information into dynamic quantifiable value carriers. This encompasses not only traditional prediction markets but also the distribution, speculation, and trading of information or concepts such as attention, reputation, on-chain data, personal insights, and narrative activity.

Core advantages of InfoFi:

• Value Redistribution: Returning the value monopolized by platforms in the traditional attention economy to the real contributors.
• Information Valorization: Transforming abstract concepts into tradable digital assets, creating a market for information value trading.
• Low threshold for participation: Users can participate in value distribution through content creation simply by using their social media accounts.
• Innovative incentive mechanism: Reward various stages of content creation, dissemination, interaction, etc., allowing niche content and long-tail users to also receive returns.
• Cross-domain application: The introduction of AI provides advantages such as content quality assessment and market optimization prediction for InfoFi.

InfoFi Classification

Prediction Market

Prediction markets are a core component of InfoFi, predicting future event outcomes through collective intelligence. Participants buy and sell "shares" linked to specific event outcomes to express their expectations, and the market price reflects the collective expectations of the crowd regarding the outcomes.

Representative platform:

• Polymarket: The largest decentralized prediction market, based on the Polygon network.
• Kalshi: A U.S. prediction market platform regulated by the CFTC, focusing on event contract trading.

Mouth Lick Type InfoFi (Yap-to-Earn)

Earn rewards by sharing insights and content. AI algorithms assess the quantity, quality, engagement, and depth of the content to allocate points or token rewards.

Representing the project:

• Kaito AI: Collaborating with multiple projects to assess users' crypto-related content posted on X.
• Cookie.fun: Track AI agent mindshare, interaction status, and on-chain data
• Virtuals: AI agent launch platform, Genesis Launch mechanism includes Yap-to-Earn elements.
• Loud: "Attention Value Experiment" in the Kaito AI ecosystem
• Wallchain Quacks: A programmatic AttentionFi project based on Solana

Mouth Lick + Task / On-chain Activity / Verification

Combine content contributions with on-chain behavior to comprehensively evaluate users' multidimensional contributions.

Representative Project:

• Galxe Starboard: Rewards for real contributions in off-chain and on-chain actions.
• Mirra: A decentralized AI model trained on community-selected data.

Reputation-based InfoFi

• Ethos: On-chain reputation protocol that generates credibility scores
• GiveRep: Built on Sui, it transforms social influence into quantifiable on-chain reputation.

Attention Market/Prediction

• Noise: Trend discovery and trading platform based on MegaETH
• Upside: Social prediction market that rewards discovering, sharing, and predicting valuable content.
• YAPYO: The attention market infrastructure of the Arbitrum ecosystem
• Trends: Tokenizable X posts, becoming a trend on the joint curve.

Token-gated content access

• Backroom: Creators launch tokenized spaces, providing curated content
• Xeet: A new protocol on the Abstract network designed to reduce noise and enhance signal.

Data Insight InfoFi

• Arkham Intel Exchange: On-chain data query tool and intelligence trading platform

Challenges Faced by InfoFi

Prediction Market

• Regulation and Compliance: May be viewed as a quasi-gambling market, facing regulatory pressure.
• Insider Trading and Fairness: May be affected by insider information, large funds may distort prices in the short term.
• Liquidity and Participation: Niche topics face the "long tail liquidity shortage problem"
• Oracle Design: Needs to guard against manipulation attacks and ensure the fairness of results.

Mouth Licking Type InfoFi

• Information noise intensifies: The proliferation of AI content and advertisements obscures the real signals.
• Algorithm Transparency: Lack of public explanation for content quality assessment standards
• The Matthew effect in profit distribution: tail-end creators and retail investors face low returns and fierce competition.
• Sustained attention: After the reward is distributed, user engagement drops sharply.
• Attention is not equivalent to market capitalization ratio.

Reputation-based InfoFi

• Invitation system restricts new users from joining, making it difficult to form a wide network effect.
• There is a risk of malicious operations.
• Cross-platform reputation score recognition issues

Future Trends of InfoFi

prediction market

• Combining AI with prediction markets to improve prediction accuracy
• The integration of social media and prediction markets has become the core infrastructure of the information economy.
• Applied to decentralized governance (Futarchy)
• Developing into a content and news tool for everyone

Zui Lu and Reputation-type InfoFi

• Introduce social graph and semantic understanding technology to enhance the accuracy of content value assessment.
• Optimize the incentive mechanism to encourage high-quality long-tail creation.
• Develop a Web3-specific InfoFi language model
• Multi-dimensional assessment of user contributions
• Reputation scoring combined with DeFi, serving as a basis for lending and staking credit.
• Tokenization of abstract assets, giving rise to more derivatives.
• Expand to more social platforms and news media

Data Insight InfoFi

• Combining data analysis with creator insights
• Integrate AI analysis capabilities

Conclusion

InfoFi aims to address the contradiction between attention creators and value holders in the digital age. The key lies in establishing a balanced mechanism for information mining, user participation, and value return, to create better infrastructure for knowledge sharing and collective decision-making. This requires the joint promotion of technological innovation and mechanism design to achieve fairness and efficiency in the attention economy. It is crucial to avoid reducing it to a gold mining game for a few and to truly realize the original intention of "inclusive attention value."