Original author: Jay Jo, Tiger Research
Original translation: 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 declining content quality and reward centralization.
These are not limitations of the InfoFi model itself, but rather design issues in the evaluation criteria and reward distribution methods, which urgently need to be improved.
The era of attention as token
Attention has become one of the most scarce resources in modern industry. In the Internet age, information is flooding, but human beings have extremely limited ability to process information. This scarcity has prompted many companies to compete fiercely, and the ability to compete for user attention has become the core competitive advantage of companies.
The crypto industry has demonstrated the degree of competition for attention in a more extreme form. Attention share plays an important role in token pricing and liquidity formation, which has become a key factor in determining the success or failure of a project. Even technologically advanced projects will often be eliminated by the market if they fail to attract market attention.
This phenomenon stems from the structural characteristics of the crypto market. Users are not only participants, but also investors, and their attention directly leads to actual purchases of tokens, creating greater demand and network effects. Where attention is focused, liquidity is created, and narratives develop on this liquidity. These established narratives then attract new attention and form a virtuous circle that drives market development.
InfoFi: A systematic attempt to tokenize attention
The market operates based on attention. This structure raises a key question: who actually benefits from this attention? Users generate attention through community activities and content creation, but these behaviors are difficult to measure and there is no 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 solve this problem. InfoFi combines information with finance, creating a mechanism that evaluates user contributions based on the attention generated by their content (such as views, comments, and shares) and links them to token rewards. Kaito's success has made this structure widely spread.
Kaito uses AI algorithms to evaluate social media activities, including posts and comments. The platform provides token rewards based on the scores. The more attention user-generated content attracts, the greater exposure the project can get. Capital regards this attention as a signal and uses it to make investment decisions. As attention grows, more capital flows into the project, and the rewards for participants also increase. Participants, projects, and capital work together through attention data as a medium, forming a virtuous circle.
The InfoFi model makes outstanding contributions in three key areas.
First, it quantifies user contribution activities where the evaluation criteria are unclear. The point-based system allows people to define contributions in a structured way and helps users predict what rewards they can get for specific behaviors, thereby improving the sustainability and consistency of user participation.
Secondly, InfoFi transforms attention from an abstract concept into quantifiable and tradable data, and user participation from simple consumption to productive activities. Most existing online participation involves investment or content sharing, and platforms make money from the attention generated by these activities. InfoFi quantifies the market response of users to these contents and issues rewards based on this data, resulting in the behavior of participants being regarded as productive work. This transformation gives users the role of network value creators, rather than just community members.
Third, InfoFi lowers the threshold for information production. In the past, Twitter influencers and institutional accounts dominated information distribution and took up most of the attention and rewards, but now ordinary users can also get tangible rewards after gaining a certain degree of market attention, creating more opportunities for users from different backgrounds to participate.
The attention economy trap caused by InfoFi
The InfoFi model is a new reward design experiment in the crypto industry that quantifies user contributions and links them to rewards. However, attention has become an overly centralized value, and its side effects are gradually emerging.
The first problem is excessive competition for attention and a decline in content quality. When attention becomes the criterion for reward, the purpose of creating content now shifts from providing information or encouraging meaningful engagement to simply for rewards. While generative AI makes content creation easier, batches of content that lack real information or insights spread rapidly. These so-called "AI Slop" contents are spreading throughout the ecosystem, causing concern.
Loud Mechanism, source: Loud
The Loud project clearly demonstrates this trend. Loud attempts to tokenize attention, and the platform chooses to distribute rewards to the top users who receive the most attention in a specific time period. This structure is interesting experimentally, but attention becomes the only criterion for rewards, which leads to excessive competition between users and triggers the generation of a large amount of duplicate low-quality content, ultimately leading to the homogenization of content throughout the community.
Source: Kaito Mindshare
The second problem is reward centralization. Attention-based rewards begin to focus on specific projects or topics, and content from other projects actually passively disappears or decreases from the market, which is clearly shown by Kaito's sharing data. Loud once occupied more than 70% of the crypto content on Twitter, dominating the information flow within the ecosystem. When rewards are focused on attention, content diversity decreases, and information gradually revolves around projects that offer high token rewards. Ultimately, the size of the marketing budget determines the influence within the ecosystem.
Structural limitations of InfoFi: evaluation and distribution
Limitations of Simple Approaches to 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 have better information quality or delivery, but this structure mainly applies to very high-quality content. For most mid- and low-end content, the relationship between feedback quantity and quality is not clear, resulting in repetitive formats and overly positive content receiving high scores. At the same time, content that presents diverse perspectives or explores new topics is unlikely to receive the recognition it deserves.
Solving these problems requires a more complete content quality evaluation system. The evaluation criteria based solely on engagement are fixed, while the value of content changes over time or in different environments. For example, AI can identify meaningful content, and community-based algorithm adjustment methods can also be introduced. The latter can help the evaluation system to flexibly respond to changes by allowing the algorithm to adjust the evaluation criteria based on regularly provided user feedback data.
Reward structure concentration and the need for balance
The limitations of content evaluation coexist with the problem of reward structure, which also exacerbates the information flow bias. The current InfoFi ecosystem usually runs separate leaderboards for each project, which use their own tokens for rewards. Under this structure, projects with large marketing budgets can attract more content, and users' attention tends to be focused on specific projects.
To address these issues, adjustments to the reward distribution structure are needed. Each project can keep its own rewards, and the platform can monitor content concentration in real time and use platform tokens to make adjustments. For example, when content may be too concentrated on a specific project, platform token rewards can be temporarily reduced, and topics with relatively low coverage can receive additional platform tokens. Content covering multiple projects can also receive additional rewards. This will create an environment with diverse topics and perspectives.
Evaluation and rewards form the core of InfoFi's structure. How content is evaluated determines the information flow of the ecosystem, and who gets what kind of rewards is also crucial. The current structure relies on a single-standard evaluation system combined with a marketing-centric reward structure, which accelerates the dominance of attention while also weakening the diversity of information. Flexibility in evaluation criteria is essential for sustainable operations, and balanced adjustment of the distribution structure is also a key challenge facing the InfoFi ecosystem.
Conclusion
InfoFi’s structured experiment aims to quantify attention and convert it into economic value, transforming the existing one-way content consumption structure into a producer-centric participatory economy, which is of great significance. However, the current InfoFi ecosystem faces structural side effects in the process of attention tokenization, including the decline of content quality and the deviation of information flow. These side effects are more of a dilemma in the initial design stage than a limitation of the model.
The evaluation model based on simple feedback has exposed its limitations, and the reward structure affected by marketing resources has also exposed problems. There is an urgent need to improve the system that can correctly evaluate the quality of content, as well as a community-based algorithm adjustment mechanism and a platform-level balance adjustment mechanism. InfoFi aims to create an ecosystem where members can receive fair rewards by participating in information production and dissemination. To achieve this goal, technical improvements are needed, and it is also necessary to encourage community participation in design.
In the crypto ecosystem, attention works 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 develops into a structure where valuable information and insights can be shared. The results of this experiment will accelerate the development of the information quantitative economy in the digital age.
