InfoFi is an important experiment in designing and operating a new economic structure. Its potential can only be fully realized when it evolves into a structure where valuable information and insights can be shared.
Written by: Jay Jo, Tiger Research
Compiled by: AididiaoJP, Foresight News
TL;DR
InfoFi is a structured attempt to quantify user attention and activity and link it to rewards.
InfoFi currently has some structural issues, including a decline in content quality and the centralization of rewards.
These are not limitations of the InfoFi model itself, but rather design issues related to the evaluation criteria and reward distribution methods, which urgently need improvement.
The Era of Attention as a Token
Attention has become one of the scarcest resources in modern industries. In the age of the Internet, information is flooding, while human ability to process information is extremely limited. This scarcity has prompted many companies to engage in fierce competition, and the ability to capture users' attention has become a core competitive advantage for businesses.
The crypto industry has shown the level of competition for attention in a more extreme form. Attention share plays an important role in token pricing and liquidity formation, which also becomes a key factor in determining the success or failure of a project. Even technologically advanced projects are often eliminated by the market if they fail to attract market attention.
This phenomenon stems from the structural characteristics of the cryptocurrency market. Users are not only participants but also investors, and their attention directly leads to actual purchasing behavior of tokens, thereby creating greater demand and network effects. Liquidity is generated where attention is concentrated, and narratives develop on this basis of liquidity. These established narratives subsequently attract new attention and form a virtuous cycle that drives market development.
InfoFi: A Systematic Attempt to Tokenize Attention
The market operates based on attention. This structure raises a key question: who can truly benefit from this attention? Users generate attention through community activities and content creation, but these actions are difficult to measure and lack a clear direct reward mechanism. So far, ordinary users can only gain indirect benefits by buying and selling tokens. There is currently no reward mechanism for contributors who actually create attention.
Kaito's InfoFi network, source: Kaito
InfoFi is an attempt to address this issue. InfoFi combines information with finance, creating a mechanism that assesses user contributions based on engagement generated by user content (such as views, comments, and shares) and links it to token rewards. Kaito's success has allowed this structure to be widely disseminated.
Kaito evaluates social media activities, including posts and comments, through AI algorithms. The platform provides token rewards based on scores. The more attention user-generated content attracts, the greater the exposure the project can achieve. Capital views this attention as a signal and makes investment decisions accordingly. As attention grows, more capital flows into the project, and the rewards for participants increase. Participants, projects, and capital collaborate through attention data as a medium, thereby creating a virtuous cycle.
The InfoFi model has made outstanding contributions in three key areas.
First, it quantifies user contribution activities where evaluation criteria are unclear. The points system allows individuals to structurally define contributions and helps users predict which rewards they can earn through specific behaviors, thereby enhancing the sustainability and consistency of user engagement.
Secondly, InfoFi transforms attention from an abstract concept into quantifiable and tradable data, shifting user participation from simple consumption to productive activities. Most existing online participation involves investment or content sharing, and the platform profits from the attention generated by these activities. InfoFi quantifies users' market reactions to this content and issues rewards based on this data, thereby leading participants' actions to be regarded as productive work. This shift empowers users with the role of value creators in the network, rather than merely being community members.
Thirdly, InfoFi has lowered the threshold for information production. In the past, Twitter's big influencers and institutional accounts dominated information distribution, occupying most of the attention and rewards. Now, ordinary users can also receive tangible rewards after gaining a certain level of market attention, creating more opportunities for users from different backgrounds to participate.
The Attention Economy Trap Triggered by InfoFi
The InfoFi model is a new reward design experiment in the cryptocurrency industry that quantifies user contributions and ties them to rewards. However, attention has become an overly centralized value, and its side effects are gradually becoming apparent.
The first issue is the excessive competition for attention and the decline in content quality. When attention becomes the standard for rewards, the purpose of creating content has shifted from providing information or encouraging meaningful engagement to merely seeking rewards. Additionally, generative AI has made content creation easier, leading to the rapid spread of bulk content that lacks real information or insights. This so-called "AI Slop" content is proliferating throughout the ecosystem, raising concerns.
Loud Mechanism, Source: Loud
The Loud project clearly illustrates this trend. Loud tries to tokenize attention, and the platform chooses to distribute rewards to the top users who get the most attention in a specific period of time. This structure is experimentally interesting, but attention becomes the only criterion for rewards, which leads to overheated competition among users and triggers the generation of a large number of duplicate low-quality content, which ultimately leads to the homogenization of content in the entire community.
Source: Kaito Mindshare
The second issue is the centralization of rewards. Attention-based rewards begin to focus on specific projects or themes, while the content of other projects actually passively disappears or diminishes from the market, as clearly shown by Kaito's shared data. Loud once occupied more than 70% of the crypto content on Twitter and dominated the information flow within the ecosystem. When rewards focus on attention, the diversity of content declines, and information gradually revolves around projects that offer high token rewards. Ultimately, the scale of the marketing budget determines the influence within the ecosystem.
Structural Limitations of InfoFi: Evaluation and Distribution
4.1. Limitations of Simple Methods for Content Evaluation
The attention-centered reward structure raises a fundamental question: how should content be evaluated, and how should rewards be distributed? Currently, most InfoFi platforms judge content value based on simple metrics (such as views, likes, and comments). This structure assumes that "high engagement equals good content."
Content with high engagement may indeed possess better information quality or communication effectiveness; however, this structure primarily applies to very high-quality content. For most mid- to low-tier content, the relationship between feedback quantity and quality remains unclear, leading to a phenomenon where repetitive formats and overly positive content receive high ratings. Meanwhile, content that presents diverse perspectives or explores new themes struggles to gain the recognition it deserves.
To solve these problems, a more comprehensive content quality assessment system is required. The evaluation criteria based solely on participation are fixed, while the value of content can change over time or with environmental shifts. For example, AI can recognize meaningful content, and it can also introduce community-based algorithm adjustment methods. The latter can involve allowing the algorithm to adjust the evaluation criteria based on regularly provided user feedback data, thereby helping the assessment system flexibly respond to changes.
4.2. Concentration of Reward Structure and Balance Demand
The limitations of content evaluation coexist with issues in the reward structure, and the reward structure exacerbates information flow bias. Currently, the InfoFi ecosystem typically has separate leaderboards for each project, which use their own tokens for rewards. In this structure, projects with large marketing budgets can attract more content, and users' attention often focuses on specific projects.
To address these issues, adjustments to the reward distribution structure are necessary. Each project can retain its own rewards, while the platform can monitor content concentration in real-time and make adjustments using platform tokens. For example, when content is overly concentrated on a specific project, the platform token rewards can be temporarily reduced, while topics with relatively low coverage can receive additional platform tokens. Content that covers multiple projects can also receive extra rewards. This will create an environment of diverse themes and viewpoints.
Evaluation and rewards form the core of the InfoFi structure. How content is evaluated determines the flow of information within the ecosystem, and who receives what type of reward is also crucial. The current structure relies on a single standard evaluation system combined with a marketing-centered reward structure, accelerating the dominance of attention while also undermining the diversity of information. The flexibility of evaluation criteria is essential for sustainable operation, and the balanced adjustment of the distribution structure is also a key challenge faced by the InfoFi ecosystem.
Conclusion
The structured experiment of InfoFi aims to quantify attention and convert it into economic value, transforming the existing one-way content consumption structure into a producer-centered participatory economy, which is of great significance. However, the current InfoFi ecosystem faces structural side effects during the attention tokenization process, including a decline in content quality and distortion of information flow. These side effects are more of a dilemma that is inevitable in the initial design phase than limitations of the model.
The evaluation model based on simple feedback has exposed its limitations, and the reward structure influenced by marketing resources has also revealed issues. There is an urgent need to improve systems that can accurately assess content quality, as well as community-based algorithm adjustment mechanisms and platform-level balancing mechanisms. InfoFi aims to create an ecosystem that allows members to earn fair rewards through participation in information production and dissemination. Achieving this goal requires technological improvements and also encourages community involvement in design.
In the crypto ecosystem, attention operates like a token. InfoFi is an important experiment in designing and operating a new economic structure. Its potential can only be fully realized when it evolves into a structure where valuable information and insights can be shared. The results of this experiment will accelerate the development of the quantification economy of information in the digital age.
The content is for reference only, not a solicitation or offer. No investment, tax, or legal advice provided. See Disclaimer for more risks disclosure.
The InfoFi Dilemma in the Attention Economy
Written by: Jay Jo, Tiger Research
Compiled by: AididiaoJP, Foresight News
TL;DR
The Era of Attention as a Token
Attention has become one of the scarcest resources in modern industries. In the age of the Internet, information is flooding, while human ability to process information is extremely limited. This scarcity has prompted many companies to engage in fierce competition, and the ability to capture users' attention has become a core competitive advantage for businesses.
The crypto industry has shown the level of competition for attention in a more extreme form. Attention share plays an important role in token pricing and liquidity formation, which also becomes a key factor in determining the success or failure of a project. Even technologically advanced projects are often eliminated by the market if they fail to attract market attention.
This phenomenon stems from the structural characteristics of the cryptocurrency market. Users are not only participants but also investors, and their attention directly leads to actual purchasing behavior of tokens, thereby creating greater demand and network effects. Liquidity is generated where attention is concentrated, and narratives develop on this basis of liquidity. These established narratives subsequently attract new attention and form a virtuous cycle that drives market development.
InfoFi: A Systematic Attempt to Tokenize Attention
The market operates based on attention. This structure raises a key question: who can truly benefit from this attention? Users generate attention through community activities and content creation, but these actions are difficult to measure and lack a clear direct reward mechanism. So far, ordinary users can only gain indirect benefits by buying and selling tokens. There is currently no reward mechanism for contributors who actually create attention.
Kaito's InfoFi network, source: Kaito
InfoFi is an attempt to address this issue. InfoFi combines information with finance, creating a mechanism that assesses user contributions based on engagement generated by user content (such as views, comments, and shares) and links it to token rewards. Kaito's success has allowed this structure to be widely disseminated.
Kaito evaluates social media activities, including posts and comments, through AI algorithms. The platform provides token rewards based on scores. The more attention user-generated content attracts, the greater the exposure the project can achieve. Capital views this attention as a signal and makes investment decisions accordingly. As attention grows, more capital flows into the project, and the rewards for participants increase. Participants, projects, and capital collaborate through attention data as a medium, thereby creating a virtuous cycle.
The InfoFi model has made outstanding contributions in three key areas.
First, it quantifies user contribution activities where evaluation criteria are unclear. The points system allows individuals to structurally define contributions and helps users predict which rewards they can earn through specific behaviors, thereby enhancing the sustainability and consistency of user engagement.
Secondly, InfoFi transforms attention from an abstract concept into quantifiable and tradable data, shifting user participation from simple consumption to productive activities. Most existing online participation involves investment or content sharing, and the platform profits from the attention generated by these activities. InfoFi quantifies users' market reactions to this content and issues rewards based on this data, thereby leading participants' actions to be regarded as productive work. This shift empowers users with the role of value creators in the network, rather than merely being community members.
Thirdly, InfoFi has lowered the threshold for information production. In the past, Twitter's big influencers and institutional accounts dominated information distribution, occupying most of the attention and rewards. Now, ordinary users can also receive tangible rewards after gaining a certain level of market attention, creating more opportunities for users from different backgrounds to participate.
The Attention Economy Trap Triggered by InfoFi
The InfoFi model is a new reward design experiment in the cryptocurrency industry that quantifies user contributions and ties them to rewards. However, attention has become an overly centralized value, and its side effects are gradually becoming apparent.
The first issue is the excessive competition for attention and the decline in content quality. When attention becomes the standard for rewards, the purpose of creating content has shifted from providing information or encouraging meaningful engagement to merely seeking rewards. Additionally, generative AI has made content creation easier, leading to the rapid spread of bulk content that lacks real information or insights. This so-called "AI Slop" content is proliferating throughout the ecosystem, raising concerns.
Loud Mechanism, Source: Loud
The Loud project clearly illustrates this trend. Loud tries to tokenize attention, and the platform chooses to distribute rewards to the top users who get the most attention in a specific period of time. This structure is experimentally interesting, but attention becomes the only criterion for rewards, which leads to overheated competition among users and triggers the generation of a large number of duplicate low-quality content, which ultimately leads to the homogenization of content in the entire community.
Source: Kaito Mindshare
The second issue is the centralization of rewards. Attention-based rewards begin to focus on specific projects or themes, while the content of other projects actually passively disappears or diminishes from the market, as clearly shown by Kaito's shared data. Loud once occupied more than 70% of the crypto content on Twitter and dominated the information flow within the ecosystem. When rewards focus on attention, the diversity of content declines, and information gradually revolves around projects that offer high token rewards. Ultimately, the scale of the marketing budget determines the influence within the ecosystem.
Structural Limitations of InfoFi: Evaluation and Distribution
4.1. Limitations of Simple Methods for Content Evaluation
The attention-centered reward structure raises a fundamental question: how should content be evaluated, and how should rewards be distributed? Currently, most InfoFi platforms judge content value based on simple metrics (such as views, likes, and comments). This structure assumes that "high engagement equals good content."
Content with high engagement may indeed possess better information quality or communication effectiveness; however, this structure primarily applies to very high-quality content. For most mid- to low-tier content, the relationship between feedback quantity and quality remains unclear, leading to a phenomenon where repetitive formats and overly positive content receive high ratings. Meanwhile, content that presents diverse perspectives or explores new themes struggles to gain the recognition it deserves.
To solve these problems, a more comprehensive content quality assessment system is required. The evaluation criteria based solely on participation are fixed, while the value of content can change over time or with environmental shifts. For example, AI can recognize meaningful content, and it can also introduce community-based algorithm adjustment methods. The latter can involve allowing the algorithm to adjust the evaluation criteria based on regularly provided user feedback data, thereby helping the assessment system flexibly respond to changes.
4.2. Concentration of Reward Structure and Balance Demand
The limitations of content evaluation coexist with issues in the reward structure, and the reward structure exacerbates information flow bias. Currently, the InfoFi ecosystem typically has separate leaderboards for each project, which use their own tokens for rewards. In this structure, projects with large marketing budgets can attract more content, and users' attention often focuses on specific projects.
To address these issues, adjustments to the reward distribution structure are necessary. Each project can retain its own rewards, while the platform can monitor content concentration in real-time and make adjustments using platform tokens. For example, when content is overly concentrated on a specific project, the platform token rewards can be temporarily reduced, while topics with relatively low coverage can receive additional platform tokens. Content that covers multiple projects can also receive extra rewards. This will create an environment of diverse themes and viewpoints.
Evaluation and rewards form the core of the InfoFi structure. How content is evaluated determines the flow of information within the ecosystem, and who receives what type of reward is also crucial. The current structure relies on a single standard evaluation system combined with a marketing-centered reward structure, accelerating the dominance of attention while also undermining the diversity of information. The flexibility of evaluation criteria is essential for sustainable operation, and the balanced adjustment of the distribution structure is also a key challenge faced by the InfoFi ecosystem.
Conclusion
The structured experiment of InfoFi aims to quantify attention and convert it into economic value, transforming the existing one-way content consumption structure into a producer-centered participatory economy, which is of great significance. However, the current InfoFi ecosystem faces structural side effects during the attention tokenization process, including a decline in content quality and distortion of information flow. These side effects are more of a dilemma that is inevitable in the initial design phase than limitations of the model.
The evaluation model based on simple feedback has exposed its limitations, and the reward structure influenced by marketing resources has also revealed issues. There is an urgent need to improve systems that can accurately assess content quality, as well as community-based algorithm adjustment mechanisms and platform-level balancing mechanisms. InfoFi aims to create an ecosystem that allows members to earn fair rewards through participation in information production and dissemination. Achieving this goal requires technological improvements and also encourages community involvement in design.
In the crypto ecosystem, attention operates like a token. InfoFi is an important experiment in designing and operating a new economic structure. Its potential can only be fully realized when it evolves into a structure where valuable information and insights can be shared. The results of this experiment will accelerate the development of the quantification economy of information in the digital age.