InfoFi: A New Paradigm of Attention Economy Driven by AI

In today's age of information overload, human attention has become the most scarce resource. The InfoFi projects emerging in the Web3 world are attempting to make the processes of information production, dissemination, and consumption transparent through blockchain, token incentives, and AI technology, returning value to the participants. This article will delve into the classification of InfoFi projects, the challenges they face, and future development trends.

InfoFi Overview

InfoFi is a combination of Information and Finance, with the core focus on transforming abstract information into quantifiable value carriers. It not only includes traditional prediction markets but also encompasses the distribution, speculation, and trading of information or concepts such as attention, reputation, and on-chain data.

The main advantages of InfoFi include:

• Redistribute value so that information producers, distributors, and consumers share the profits.
• Transform abstract attention, insights, etc. into tradable assets
• Lower the participation threshold, allowing users to participate in value distribution through content creation.
• Innovative incentive mechanism to reward content creation, dissemination, interaction, and other aspects.
• Potential for cross-domain applications, such as AI-assisted content quality assessment, etc.

InfoFi Project Classification

Prediction Market

Prediction markets are a core component of InfoFi, predicting future events through collective intelligence. Representative platforms include:

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

Mouth Licking InfoFi

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

• Kaito AI: Collaborating with multiple projects to assess users' cryptocurrency-related content on social platforms.
• Cookie.fun: Tracking the mind share and interaction of AI agents
• Virtuals: AI agent launch platform, the launch mechanism includes mouth stroking elements
• Loud: Attention Value Experiment in the Kaito Ecosystem
• Wallchain Quacks: A programmatic AttentionFi project based on Solana

Multidimensional Contribution Value

Projects that combine content contribution with on-chain behavior:

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

Reputation-based InfoFi

• Ethos: On-chain reputation protocol, generating credibility scores
• GiveRep: Transforming social influence into quantifiable on-chain reputation

Attention Market/Forecast

• Noise: Trend Discovery and Trading Platform based on MegaETH
• Upside: Social Prediction Market, rewards for discovering and predicting valuable content
• YAPYO: Attention market infrastructure of the Arbitrum ecosystem
• Trends: Tokenizable social platform posts

Token Gate Content Access

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

Data Insight InfoFi

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

Challenges Faced by InfoFi

Prediction Market

• Regulatory and compliance pressures
• Insider trading and fairness issues
• Insufficient liquidity and participation
• The security of oracle design

Mouth Licking Project

• Information noise intensifies, AI content floods.
• The algorithm evaluation criteria are not transparent.
• The Matthew Effect of Profit Distribution
• Insufficient sustained attention
• The disconnection between attention and actual value

Reputation System

• Invite system limits user growth
• Malicious operation risk
• Difficulties in cross-platform recognition

InfoFi Development Trends

Prediction Market

• Deep integration of AI technology
• Integrated with social media platforms
• Applied to decentralized governance
• Develop into a content tool for the general public

Mouth Licking and Reputation-based InfoFi

• Introduce advanced technology to enhance content evaluation accuracy
• Optimize incentive mechanism
• Develop dedicated LLM models
• Multidimensional evaluation of user contributions
• Combined with fields such as DeFi
• Expand to more social platforms

Data Insights InfoFi

• Combine creator insights with incentive mechanisms
• Integrate AI analytical capabilities

Conclusion

InfoFi aims to address the contradiction between attention creators and value holders in the digital age. Its key lies in establishing a balanced mechanism for information mining, user participation, and value return, creating a better infrastructure for knowledge sharing and collective decision-making. However, InfoFi still needs to overcome the challenges of uneven distribution of benefits to avoid becoming a gold rush game for a few, and truly realize fairness and efficiency in the attention economy.