InfoFi: AI-Driven Attention Monetization New Paradigm

The theory of attention economy was first proposed in 1971 by psychologist and economist Herbert Simon, pointing out that in a world of information overload, human attention has become the most scarce resource. Economist Albert Wenger further revealed that human civilization is undergoing a fundamental shift from the "capital scarcity" of the industrial era to the "attention scarcity" of the knowledge era.

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. However, in the traditional attention economy, users contribute their attention as "data fuel," while the excess profits are monopolized by the platforms. The InfoFi in the Web3 world attempts to overturn this model by utilizing blockchain, token incentives, and AI technology, making the production, dissemination, and consumption of information transparent and returning value to the participants.

What is InfoFi?

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

The core advantages of InfoFi are reflected in:

• Value redistribution mechanism: returning the value monopolized by platforms in the traditional attention economy to the true contributors.
• Information monetization capability: transforming abstract attention, insights, reputation, etc. into tradable digital assets.
• Low threshold participation: Users can participate in value distribution through content creation with just a social media account.
• Innovation of incentive mechanisms: rewarding content creation, dissemination, interaction, verification, and other aspects.
• Cross-domain application potential: The introduction of AI provides InfoFi with advantages such as content quality assessment and predictive market optimization.

InfoFi Classification

prediction market

Prediction markets are a core component of InfoFi, serving as a mechanism to forecast future event outcomes through collective intelligence. Representative platforms include:

• Polymarket: the largest decentralized prediction market built on the Polygon network.
• Kalshi: A prediction market platform in the US regulated by the CFTC, supporting cryptocurrency deposits.

Mouth Lick Type InfoFi ( Yap-to-Earn )

Earn rewards by sharing insights and content. Mainstream projects include:

• Kaito AI: Evaluates user-generated crypto-related content published on X using AI algorithms.
• Cookie.fun: Track the mind share, interaction, and on-chain data of AI agents.
• Virtuals: AI Agent Launch Platform, supports Yap-to-Earn.
• Loud: "Attention Value Experiment" in the Kaito AI ecosystem.
• Wallchain Quacks: A programmatic AttentionFi project based on Solana.

Mouth Lick + Tasks/On-chain Activities/Verification

Combine content contributions with on-chain behaviors or tasks to comprehensively assess users' multi-dimensional contributions.

• Galxe Starboard: Reward 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: Converts user activity on the X platform into quantifiable on-chain reputation.

Attention Market/Forecast

• Noise: A trend discovery and trading platform based on MegaETH.
• Upside: Social prediction market that rewards discovering, sharing, and predicting valuable content.
• YAPYO: Infrastructure for the attention market in the Arbitrum ecosystem.
• Trends: Tokenization X Posts, becoming a trend on the joint curve.

Token Gate Content Access

• Backroom: Creators can launch tokenized spaces to offer curated content.
• Xeet: A new protocol on the Abstract network aimed at reducing noise and enhancing signals.

Data Insight InfoFi

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

Challenges Faced by InfoFi

Prediction Market

• Regulation and Compliance: It may be viewed as a market similar to binary options and gambling.
• Insider trading and fairness: may be affected by insider information.
• Liquidity and Participation: Niche topics face the "long tail liquidity shortage problem".
• Oracle Design: Need to guard against operational attacks.

Mouth Lick

• The information noise is intensifying, and AI content advertisements are rampant.
• The criteria for algorithm evaluation are not transparent, raising questions about fairness.
• The Matthew effect of income distribution: tail creators face the dilemma of low income and fierce competition.
• User participation lacks sustainability.
• Attention does not equal market capitalization ratio.

reputation

• The invitation system restricts new users from joining.
• There is a risk of malicious operations.
• Cross-platform recognition issues.

InfoFi Development Trends

prediction market

• The combination of AI and prediction markets.
• The combination of social media and prediction markets.
• Decentralized governance application.
• Develop into a content and news tool for everyone.

Mouth Lure + Reputation Type InfoFi

• Introduce social graph and semantic understanding technologies to improve AI assessment accuracy.
• Encourage high-quality long-tail creators.
• Add reduction or penalty mechanisms.
• Release of InfoFi LLM specifically for Web3.
• Multi-dimensional evaluation of contributions.
• Combined with DeFi, reputation score serves as a basis for credit.
• The tokenization of abstract assets gives rise to more derivatives.
• Expand to more social platforms.
• Combine with social platforms and news media to form attention and Alpha discovery tools.

Data Insight InfoFi

• The combination of data analysis charts with creator insights and AI analysis.

Summary

The core of InfoFi lies in establishing a "trinity" balancing mechanism: information mining, user participation, and value return. This requires the technical level to realize the quantification of attention and the mechanism design to ensure that ordinary participants receive reasonable returns. The revolution of InfoFi needs a joint push from both top-down and bottom-up approaches to truly achieve fairness and efficiency in the attention economy, avoiding becoming a gold rush game for a few.