InfoFi: The New Battleground for Human Attention

The arrival of the digital age has made human attention the most scarce resource. InfoFi, as an emerging field in the Web3 world, seeks to make the production, dissemination, and consumption of information transparent through blockchain, token incentives, and AI technology, returning value to participants. This article will delve into the categorization of the InfoFi project, the challenges it faces, and future development trends.

What is InfoFi?

InfoFi is a combination of Information and Finance, focusing on transforming abstract information that is difficult to quantify into dynamic and quantifiable value carriers. It includes 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.

The main advantages of InfoFi include:

• Value Redistribution Mechanism
• Information monetization capability
• Low barrier to entry
• Innovation in Incentive Mechanisms
• Cross-domain application potential

InfoFi Classification

prediction market

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

• Polymarket: the largest decentralized prediction market
• Kalshi: A prediction market platform regulated by the CFTC in the United States.

Mouth Lick Type InfoFi

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

• Kaito AI
• Cookie.fun
• Virtuals
• Loud
• Wallchain Quacks

Mouth Lick + Task/On-chain Activity/Verification

Combine content contributions with on-chain behaviors or tasks to comprehensively evaluate users' multidimensional contributions:

• Galxe Starboard
• Mirra

Reputation InfoFi

• Ethos: On-chain Reputation Protocol
• GiveRep: A social influence quantification platform based on Sui

Attention Market/Forecast

• Noise: Trend Discovery and Trading Platform Based on MegaETH
• Upside: Social Prediction Market
• YAPYO: Arbitrum Ecosystem Attention Market Infrastructure
• Trends: Tokenized X Post Platform

Token Gated Content Access

• Backroom: Creators launch tokenized spaces offering curated content
• Xeet: A new protocol on the Abstract network

Data Insight InfoFi

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

Challenges Faced by InfoFi

prediction market

• Regulatory and compliance issues
• Insider Trading and Fairness
• Insufficient liquidity and participation
• Oracle Design Dilemma

Mouth Lick

• Information noise intensifies, AI content floods.
• The evaluation criteria for algorithms are not transparent.
• The Matthew Effect of Profit Distribution
• Attention drops off a cliff after the event.
• The attention and market cap ratio are not proportional.

Reputation

• Invitation system restricts user growth
• Malicious Operation Risk
• Cross-platform recognition issues

InfoFi Future Trends

Prediction Market

• The combination of AI and prediction markets
• Social Media Integration
• Applied to decentralized governance
• A content tool for everyone

Mouth Lick and Reputation-based InfoFi

• Introduce social graph and semantic understanding technology
• Incentivize high-quality long-tail creators
• Add penalty mechanism
• Launch Web3-specific InfoFi LLM
• Multi-dimensional evaluation of contributions
• Combined with DeFi
• Abstract asset tokenization
• Expand to more social platforms

Data Insights InfoFi

• Data analysis combined with creator insights
• Introduce AI analysis

Summary

InfoFi aims to address the disconnection between attention creators and value holders. The key lies in establishing a balanced mechanism for information mining, user participation, and value return, forming a better infrastructure for knowledge sharing and collective decision-making. This requires a joint push from both top-down and bottom-up approaches to truly achieve fairness and efficiency in the attention economy. Otherwise, InfoFi may become a gold rush game for a few, deviating from the original intention of making attention value accessible to all.