TL;DR

• InfoFi is a structured attempt to quantify user attention and activity and link them to rewards.

• Current issues in InfoFi include declining content quality and reward centralization.

• These are not inherent limitations of the InfoFi model but rather design flaws in evaluation criteria and reward distribution, which urgently need improvement.

The Era of Attention as Token
Attention has become one of the scarcest resources in modern industry. The internet age has brought an overflow of information, yet human capacity to process it remains severely limited. This scarcity has driven fierce competition among businesses, with the ability to capture user attention emerging as a core competitive advantage.

The crypto industry exemplifies this competition in an even more extreme form. Attention share plays a pivotal role in token pricing and liquidity formation, often determining a project's success or failure. Even technologically superior projects risk being sidelined by the market if they fail to attract attention.

This phenomenon stems from the structural nature of crypto markets. Users are not just participants but also investors—their attention directly translates into token purchases, driving demand and network effects. Liquidity emerges where attention concentrates, and narratives flourish atop this liquidity. These narratives then attract further attention, creating a virtuous cycle that propels market growth.

InfoFi: A Systematic Attempt to Tokenize Attention
Markets operate on attention. This raises a key question: Who truly benefits from this attention? Users generate attention through community engagement and content creation, but these actions are hard to measure and lack direct reward mechanisms. So far, ordinary users can only gain indirect profits by trading tokens. There is no mechanism to reward those who actually generate attention.

Kaito’s InfoFi Network. Source: Kaito

InfoFi is an attempt to address this issue. By merging information and finance, InfoFi creates a mechanism that evaluates user contributions based on the attention their content generates (e.g., views, comments, shares) and ties it to token rewards. Kaito's success has popularized this structure.

Kaito uses AI algorithms to assess social media activity, including posts and comments. The platform rewards users with tokens based on their scores. The more attention user-generated content attracts, the greater the exposure for the project. Capital interprets this attention as a signal, shaping investment decisions. As attention grows, more capital flows into projects, increasing participant rewards. Participants, projects, and capital synergize through attention data, forming a virtuous cycle.

The InfoFi model makes three key contributions:

1. Quantifying Ambiguous User Contributions: A points-based system allows structured definitions of contributions, helping users predict rewards for specific actions. This enhances the sustainability and consistency of user engagement.

2. Tokenizing Attention: InfoFi transforms attention from an abstract concept into quantifiable, tradable data. User participation shifts from passive consumption to productive activity. Most online engagement involves investment or content sharing, with platforms monetizing the attention these activities generate. InfoFi quantifies market reactions to content and rewards users accordingly, framing participation as productive work. This redefines users as value creators, not just community members.

3. Lowering Barriers to Information Production: Previously, Twitter influencers and institutional accounts dominated information distribution, monopolizing attention and rewards. Now, ordinary users can earn tangible rewards by gaining market attention, creating opportunities for broader participation.

The Attention Economy Trap of InfoFi
InfoFi is an experimental reward design in crypto that quantifies user contributions and links them to rewards. However, attention has become overly centralized in value, revealing unintended consequences.

1. Excessive Attention Competition and Declining Content Quality
When attention becomes the sole reward metric, content creation shifts from providing value to chasing rewards. Generative AI exacerbates this, enabling mass-produced, low-quality "AI slop" to flood the ecosystem.

Loud Mechanism. Source: Loud

The Loud project exemplifies this trend. By rewarding top-attention-grabbing users in specific timeframes, Loud inadvertently incentivized spammy, repetitive content, homogenizing the community’s output.

2. Reward Centralization
Attention-based rewards concentrate around specific projects or themes, sidelining others. Kaito’s data shows this clearly: Loud once dominated over 70% of crypto-related Twitter content, dictating the ecosystem’s information flow. When rewards hinge on attention, content diversity suffers, and information orbits around high-reward projects. Ultimately, marketing budgets dictate influence.

InfoFi’s Structural Limits: Evaluation and Distribution
4.1. The Flaws of Simplistic Content Evaluation
Attention-centric rewards raise a fundamental question: How should content be evaluated, and how should rewards be distributed? Most InfoFi platforms rely on basic metrics like views, likes, and comments, equating high engagement with quality.

While high engagement may signal valuable content, this correlation often breaks down for mid-to-low-tier content. Repetitive formats and clickbait thrive, while nuanced or novel perspectives struggle. A more sophisticated evaluation system is needed—one that adapts over time and incorporates AI or community-driven adjustments to assess true value.

4.2. Reward Centralization and the Need for Balance
Current reward structures exacerbate information bias. Projects run separate leaderboards, rewarding users with their own tokens. Projects with hefty marketing budgets dominate, funneling attention their way.

To fix this, reward distribution must be recalibrated. Platforms could use native tokens to rebalance rewards dynamically—reducing payouts for oversaturated topics and boosting underrepresented ones. Cross-project content could earn extra incentives, fostering diversity.

Evaluation and rewards form InfoFi’s core. How content is assessed shapes the ecosystem’s information flow, and who gets rewarded is equally critical. The current model’s reliance on one-dimensional metrics and marketing-driven rewards accelerates attention monopolies and erodes diversity. Flexible evaluation standards and balanced distribution are key to sustainability.

Closing Thoughts
InfoFi’s experiment to tokenize attention and transform passive consumption into a producer-centric economy is groundbreaking. Yet, its current iteration faces structural side effects: declining content quality and skewed information flows. These are not fatal flaws but growing pains of early design.

Simplistic feedback-based evaluation and marketing-distorted rewards must evolve. Improvements like quality-assessment systems, community-aligned algorithms, and platform-level balancing mechanisms are urgent. InfoFi aims to create an ecosystem where participants are fairly rewarded for contributing to information production. Achieving this requires both technical upgrades and community-driven design.

In crypto, attention functions like tokens. InfoFi is a vital experiment in structuring a new economy. Its potential will only be unlocked when it fosters the sharing of truly valuable insights. The outcomes of this experiment will accelerate the evolution of information-quantified economies in the digital age.

TL;DR

• InfoFi is a structured attempt to quantify user attention and activity and link them to rewards.

• Current issues in InfoFi include declining content quality and reward centralization.

• These are not inherent limitations of the InfoFi model but rather design flaws in evaluation criteria and reward distribution, which urgently need improvement.

The Era of Attention as Token
Attention has become one of the scarcest resources in modern industry. The internet age has brought an overflow of information, yet human capacity to process it remains severely limited. This scarcity has driven fierce competition among businesses, with the ability to capture user attention emerging as a core competitive advantage.

The crypto industry exemplifies this competition in an even more extreme form. Attention share plays a pivotal role in token pricing and liquidity formation, often determining a project's success or failure. Even technologically superior projects risk being sidelined by the market if they fail to attract attention.

This phenomenon stems from the structural nature of crypto markets. Users are not just participants but also investors—their attention directly translates into token purchases, driving demand and network effects. Liquidity emerges where attention concentrates, and narratives flourish atop this liquidity. These narratives then attract further attention, creating a virtuous cycle that propels market growth.

InfoFi: A Systematic Attempt to Tokenize Attention
Markets operate on attention. This raises a key question: Who truly benefits from this attention? Users generate attention through community engagement and content creation, but these actions are hard to measure and lack direct reward mechanisms. So far, ordinary users can only gain indirect profits by trading tokens. There is no mechanism to reward those who actually generate attention.

Kaito’s InfoFi Network. Source: Kaito

InfoFi is an attempt to address this issue. By merging information and finance, InfoFi creates a mechanism that evaluates user contributions based on the attention their content generates (e.g., views, comments, shares) and ties it to token rewards. Kaito's success has popularized this structure.

Kaito uses AI algorithms to assess social media activity, including posts and comments. The platform rewards users with tokens based on their scores. The more attention user-generated content attracts, the greater the exposure for the project. Capital interprets this attention as a signal, shaping investment decisions. As attention grows, more capital flows into projects, increasing participant rewards. Participants, projects, and capital synergize through attention data, forming a virtuous cycle.

The InfoFi model makes three key contributions:

1. Quantifying Ambiguous User Contributions: A points-based system allows structured definitions of contributions, helping users predict rewards for specific actions. This enhances the sustainability and consistency of user engagement.

2. Tokenizing Attention: InfoFi transforms attention from an abstract concept into quantifiable, tradable data. User participation shifts from passive consumption to productive activity. Most online engagement involves investment or content sharing, with platforms monetizing the attention these activities generate. InfoFi quantifies market reactions to content and rewards users accordingly, framing participation as productive work. This redefines users as value creators, not just community members.

3. Lowering Barriers to Information Production: Previously, Twitter influencers and institutional accounts dominated information distribution, monopolizing attention and rewards. Now, ordinary users can earn tangible rewards by gaining market attention, creating opportunities for broader participation.

The Attention Economy Trap of InfoFi
InfoFi is an experimental reward design in crypto that quantifies user contributions and links them to rewards. However, attention has become overly centralized in value, revealing unintended consequences.

1. Excessive Attention Competition and Declining Content Quality
When attention becomes the sole reward metric, content creation shifts from providing value to chasing rewards. Generative AI exacerbates this, enabling mass-produced, low-quality "AI slop" to flood the ecosystem.

Loud Mechanism. Source: Loud

The Loud project exemplifies this trend. By rewarding top-attention-grabbing users in specific timeframes, Loud inadvertently incentivized spammy, repetitive content, homogenizing the community’s output.

2. Reward Centralization
Attention-based rewards concentrate around specific projects or themes, sidelining others. Kaito’s data shows this clearly: Loud once dominated over 70% of crypto-related Twitter content, dictating the ecosystem’s information flow. When rewards hinge on attention, content diversity suffers, and information orbits around high-reward projects. Ultimately, marketing budgets dictate influence.

InfoFi’s Structural Limits: Evaluation and Distribution
4.1. The Flaws of Simplistic Content Evaluation
Attention-centric rewards raise a fundamental question: How should content be evaluated, and how should rewards be distributed? Most InfoFi platforms rely on basic metrics like views, likes, and comments, equating high engagement with quality.

While high engagement may signal valuable content, this correlation often breaks down for mid-to-low-tier content. Repetitive formats and clickbait thrive, while nuanced or novel perspectives struggle. A more sophisticated evaluation system is needed—one that adapts over time and incorporates AI or community-driven adjustments to assess true value.

4.2. Reward Centralization and the Need for Balance
Current reward structures exacerbate information bias. Projects run separate leaderboards, rewarding users with their own tokens. Projects with hefty marketing budgets dominate, funneling attention their way.

To fix this, reward distribution must be recalibrated. Platforms could use native tokens to rebalance rewards dynamically—reducing payouts for oversaturated topics and boosting underrepresented ones. Cross-project content could earn extra incentives, fostering diversity.

Evaluation and rewards form InfoFi’s core. How content is assessed shapes the ecosystem’s information flow, and who gets rewarded is equally critical. The current model’s reliance on one-dimensional metrics and marketing-driven rewards accelerates attention monopolies and erodes diversity. Flexible evaluation standards and balanced distribution are key to sustainability.

Closing Thoughts
InfoFi’s experiment to tokenize attention and transform passive consumption into a producer-centric economy is groundbreaking. Yet, its current iteration faces structural side effects: declining content quality and skewed information flows. These are not fatal flaws but growing pains of early design.

Simplistic feedback-based evaluation and marketing-distorted rewards must evolve. Improvements like quality-assessment systems, community-aligned algorithms, and platform-level balancing mechanisms are urgent. InfoFi aims to create an ecosystem where participants are fairly rewarded for contributing to information production. Achieving this requires both technical upgrades and community-driven design.

In crypto, attention functions like tokens. InfoFi is a vital experiment in structuring a new economy. Its potential will only be unlocked when it fosters the sharing of truly valuable insights. The outcomes of this experiment will accelerate the evolution of information-quantified economies in the digital age.