InfoFi: A New Paradigm of Attention Market Driven by AI

The theory of attention economy can be traced back to 1971, when psychologist and economist Herbert Simon first pointed out that in a world of information overload, human attention has become the most scarce resource.

Economist Albert Wenger further reveals a fundamental shift: human civilization is undergoing a third leap—from the "capital scarcity" of the industrial age to the "attention scarcity" of the knowledge age.

• Agricultural Revolution: Solved food scarcity, but spurred land disputes.
• Industrial Revolution: Solved land scarcity but shifted towards resource competition and capital accumulation
• Digital Revolution: The Battle for Attention

This transformation stems from two key characteristics of digital technology: the zero marginal cost of information replication and dissemination, and the universality of AI computation (though human attention is not replicable).

Whether it's the booming market for trendy toys or the live streaming sales by top influencers, it fundamentally revolves around the competition for user and audience attention. 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 disrupt this model—by utilizing blockchain, token incentives, and AI technology to make the processes of information production, dissemination, and consumption transparent, returning value to the participants.

What is InfoFi?

InfoFi is a combination of Information + Finance, with the core focus on transforming difficult-to-quantify, abstract information into dynamic, quantifiable value carriers. It encompasses the distribution, speculation, or trading of information or abstract concepts such as prediction markets, attention, reputation, on-chain data or intelligence, personal insights, and narrative activity.

The core advantages of InfoFi:

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

InfoFi Classification

Prediction Market

Prediction markets are a core component of InfoFi, using collective intelligence to forecast the outcomes of future events. Participants express their expectations by buying and selling "shares" linked to specific event outcomes, and the market price reflects the collective expectations of the group regarding the event result. Representative platforms include Polymarket and Kalshi.

Mouth Licking Type InfoFi (Yap-to-Earn)

Earn rewards by sharing insights and content. Major platforms include Kaito AI, Cookie.fun, Virtuals, Loud, Wallchain Quacks, etc.

Muzzle + Task / On-chain Activity / Verification

Combine content contributions with on-chain behaviors or tasks to comprehensively assess users' multi-dimensional contributions. Representative projects include Galxe Starboard and Mirra.

Reputation-based InfoFi

Projects like Ethos and GiveRep generate credibility scores through on-chain activities and social interactions, quantifying users' on-chain trust.

Attention Market / Prediction

Platforms such as Noise, Upside, YAPYO, and Trends allow users to speculate on the attention given to projects or content.

Token gated content access

Projects like Backroom and Xeet provide curated content by tokenizing space and filtering out noise.

Data Insights InfoFi

Platforms like Arkham Intel Exchange provide on-chain data query and intelligence trading functions.

Challenges Faced by InfoFi

Prediction Market

• Regulatory and Compliance Issues
• Insider Trading and Fairness
• Insufficient liquidity and participation
• The security of oracle design

Mouth Lick

• Information noise intensifies, AI content floods.
• Insufficient transparency of content assessment algorithms
• Matthew Effect in Earnings Distribution
• Inattention

reputation

• The invitation system restricts the user scale.
• Malicious operation risk
• Cross-platform recognition issues

InfoFi Development Trends

Prediction Market

• The combination of AI and prediction markets
• The integration of social media and prediction markets
• Decentralized Governance Application
• Content and news tools for everyone

Mouth Licking + Reputation Type InfoFi

• Introduce social graph and semantic understanding technology
• Incentivize high-quality long-tail creators
• Add reduction or penalty mechanisms
• Launch of Web3 dedicated InfoFi LLM
• Multidimensional evaluation of contributions
• Combined with DeFi
• Tokenization of abstract assets and innovation in derivatives
• Cross-platform expansion
• Deep integration with social platforms and news media

Data Insights InfoFi

• The combination of data analysis and creator insights
• The integration of data analysis and AI analysis

Conclusion

The core of InfoFi lies in establishing a "trinity" balance mechanism: information mining, user participation, and value return. This requires not only a technological implementation that pays attention to quantification but also a mechanism design that ensures ordinary participants can receive reasonable returns from information dissemination, avoiding severe imbalances in value distribution. The revolution of InfoFi needs to be jointly promoted from the top down and bottom up, truly realizing fairness and efficiency in the attention economy; otherwise, it may become a gold rush game for a select few.