Author: Nancy, PANews
Following Kaito's market sweep with the "Yap-to-Earn" model, Cookie DAO recently issued an announcement that further fueled the InfoFi track, with invitation codes flooding various communities, further promoting the dissemination and practical implementation of the InfoFi concept.
Previously, although Kaito was the first to ignite the InfoFi topic and build a new value circulation and incentive mechanism between content creators, community members, and project parties, its platform gradually exposed issues such as content homogenization, inconsistent quality, non-transparent algorithmic mechanisms, and questionable incentive fairness. Some voices even pointed out that this point-oriented incentive structure is trapping creators in an algorithmic cage. Now, with the entry of new players like Cookie, the evolution path of InfoFi is beginning to diverge, from information retrieval to content pricing, from algorithm-driven to community governance, the track is ushering in a new ecological game and paradigm shift.
From Information Retrieval to Content Pricing, InfoFi is Diversifying and Evolving
On May 21, Cookie DAO announced the launch of the first phase of InfoFi, releasing cookie.fun v1.0 alpha and Cookie Snaps, used to analyze crypto projects and KOLs, and obtain high-quality crypto Twitter (CT) content rewards. Additionally, Cookie plans to launch a decentralized, community-driven reward pool in the future. As a pioneer, Kaito has long been providing real-time search, sentiment analysis, trend tracking, and knowledge graph functions, and has dominated the InfoFi field with its Yaps mechanism and AI analysis.
Kaito and Cookie's InfoFi products demonstrate many commonalities, both dedicated to solving the information fragmentation problem in the crypto market through AI technology and incentive mechanisms, constructing an attention economy centered on user participation and quality content. However, their differences in technical architecture, governance mechanisms, and target user groups have led to divergent execution paths and market positioning.
The similarities between Kaito and Cookie lie in building an attention economy through AI and incentive mechanisms. In terms of point incentive mechanisms, both use point systems to incentivize high-quality content creation, effectively enhancing user engagement and building an attention economy system around quality content; in terms of leaderboards and airdrop incentives, both generate leaderboards based on user content contributions and link them to airdrop rewards, forming an incentive closed loop; in terms of data insights, both use AI technology to analyze social media data from platforms like X, generating market sentiment and trend predictions for investors and project parties; in terms of governance, both adopt DAO governance models, allowing token holders to participate in decision-making.
Despite similar concepts, Kaito and Cookie have different execution mechanisms and market positioning. In point and scoring mechanism design, Kaito's Yap points are based on content influence, leaning towards top KOLs, easily causing the Matthew effect, and has previously sparked fairness controversies due to volume manipulation. Snaps, on the other hand, comprehensively considers content quality, user participation sentiment, and loyalty, encouraging users focused on a single project with long-term participation; in terms of technology and data sources, Kaito integrates crypto data sources from Twitter, Discord, on-chain data, and uses AI technology based on large language models (LLM) and knowledge graphs to support multi-language analysis, suitable for professional users requiring multi-dimensional analysis. Cookie's data sources primarily focus on the X platform, with AI concentrating on sentiment analysis and loyalty scoring, covering a narrower range and more suitable for community-driven scenarios; in terms of scoring transparency, Kaito's algorithm scoring is opaque, with community feedback facing black box issues. Cookie has published content scoring rules in its documentation; in terms of data analysis access, Kaito requires paid subscriptions to access advanced analysis features, enhanced search functions, and personalized insights, while all Cookie users can access extensive crypto data for free; in terms of project coverage and incentive scope, Kaito's leaderboard mainly covers projects participating in Launchpad, with more concentrated incentives. Cookie's data covers all crypto-related content, not limited to ongoing activities, offering users more flexible point acquisition; in terms of market performance, Kaito has received support from Sequoia Capital, Dragonfly, and Folius Ventures, with financing exceeding $12 million and a circulating token market cap of $500 million. Cookie has received $5.8 million in financing from SkyVision Capital, Animoca Brands, Spartan Group, with a circulating token market cap of around $100 million.
From this perspective, as representatives of the InfoFi model's innovation, Kaito and Cookie represent professional and community-driven paths, forming complementary competition in strategic thinking and product logic, and driving the continuous evolution of the InfoFi ecosystem, prompting more projects to explore community-driven data and reward models.
Attention Economy Accelerating Evolution, InfoFi Still Faces Multiple Challenges
In the current era of attention economy's comprehensive rise, InfoFi, as an emerging track, is rapidly gaining market attention. Its core concept is to transform traditionally non-asset resources like information, attention, and social data into tradable and priceable dynamic assets through decentralized technology, token incentive mechanisms, and AI-driven data processing capabilities. Unlike traditional Web2 platforms where information value is unilaterally captured, InfoFi redefines information value and promotes structural changes in attention capital, providing new possibilities for user empowerment, data autonomy, and value sharing.
Besides Kaito and Cookie, the InfoFi track is welcoming many newcomers, such as Ethos, Wallchain, GiveRep, and Mirra, who are continuously improving and enriching the InfoFi ecosystem through innovative mechanisms.
Despite the InfoFi ecosystem's innovative potential, it still faces multiple structural challenges in its actual development. First is the risk of data completeness and credibility. InfoFi heavily relies on AI-generated content and user contributions, easily leading to data manipulation, false information, and low-quality content proliferation. With the surge of AI-generated content, information credibility may be weakened, thereby affecting market judgment and decision-making. Without effective noise filtering mechanisms, content verification mechanisms (such as reputation scoring systems), and transparent data governance, user trust could significantly decline, potentially impacting the platform's long-term sustainability.
Secondly, user acquisition thresholds and participation bottlenecks are prominent. Currently, many InfoFi platforms adopt invitation or token threshold mechanisms, which to some extent improve content quality but also limit ordinary users' participation range, weakening the ecosystem's inclusivity and vitality. Overly high entry barriers hinder rapid community expansion and may lead to information power being overly concentrated among a few high-quality users.
Additionally, market fragmentation and intense platform competition are increasingly evident. Numerous new players are emerging in the InfoFi field, with diverse product forms and data dimensions, causing users to frequently switch between platforms with fragmented experiences. Meanwhile, platforms have yet to establish unified information standards and data protocols, creating information silos that obstruct ecosystem integration and scale effects.
Moreover, the design of incentive mechanisms and their sustainability are equally crucial. The InfoFi economic model heavily depends on token incentives, which, if poorly designed, can easily trigger inflation, short-term speculation, and incentive misalignment. Constructing a fair and sustainable incentive system among creators, ordinary users, and institutional brands is key to long-term platform development.
Lastly, the scarcity of long-tail content also limits InfoFi's ecosystem diversity. Although InfoFi performs well in content production and user participation for hot topics, content generation and user participation in niche and professional fields remain insufficient. Excessive concentration of information resources in a few hot topics may weaken the platform's coverage of segmented markets, unfavorable for deep ecosystem construction.
In summary, as a new paradigm of attention economy, InfoFi has enormous potential to reshape information value distribution and empower user data sovereignty. However, at the current stage, it still needs to continuously address core issues such as data quality management, user participation thresholds, platform interconnection and integration, incentive mechanism optimization, and content diversity.
