Recently, jesse conducted an experiment on X (formerly Twitter): posting three pieces of "borderline" encrypted content between valuable information and pure spam, to test the boundary of Kaito platform's Yap scoring algorithm. Unexpectedly, in less than 24 hours, his account @jessethecook69 quickly rose to the ninth place on the global Kaito Yapper leaderboard and took the top spot in the Chinese region. This phenomenon of rapidly ranking up with content that is not of high quality inevitably raises doubts about whether Kaito's claimed AI content scoring algorithm is as fair and strict as it claims, or if there are loopholes that can be exploited.

Below are the three borderline content tweets published in this experiment. These contents are close to daily style and quickly gained massive interaction through entertainment and visual impact.

In fact, similar doubts already exist in the community. A Blockworks report mentioned that users could unexpectedly earn hundreds of Yap points by repeatedly replying with the same word (such as continuously replying "reply") in the comments. Although the official may quickly fix such loopholes, these cases are enough to trigger discussions: Can Kaito's "Information as Capital" (InfoFi) model truly incentivize quality information, or does it transform into a new traffic game under certain circumstances?

To answer these questions, it is necessary to delve into Kaito's underlying principles, understand how it utilizes the massive metadata provided by Twitter API, combines large language models like OpenAI's ChatGPT for semantic analysis and trend determination, and builds a decentralized information ecosystem through mechanisms like the Smart Followers system and Yap points "social incentives". Next, jesse will analyze this issue from both industry significance and technical details.

Information as Capital: Kaito's Platform Innovation and Industry Significance

The InfoFi model advocated by Kaito is not only a technological and product innovation experiment but is also bringing structural impact to the information dissemination mechanism and marketing paradigm of the crypto industry. In the past, crypto project marketing mainly relied on traditional methods: hiring PR agencies, collaborating with KOLs (key opinion leaders in the crypto circle) to create momentum on social media. In this model, information was often opaque and had low transmission efficiency, while also breeding numerous soft articles and promotional posts. In comparison, Kaito's algorithm-driven community incentives are changing the game rules - the relationship between project parties, KOLs, and ordinary users is being reset in a competitive environment based on content value and contribution.

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Opportunities and Challenges for KOL Agency

For KOL Agencies, the Kaito model can be seen as a double-edged sword. On one hand, it weakens some of the exclusive values previously held by KOL Agencies: project parties can directly use the data and rankings provided by Kaito to find truly effective communicators, without overly relying on agency connections. Kaito offers a quantified KOL map and performance rankings as a reference, enabling project parties to independently identify the most active communicators in niche areas and users who show high engagement and loyalty to the project. Such data transparency was previously only available to experienced KOL Agencies (who knew through long-term experience which KOLs excel at driving conversions); now Kaito has made these metrics public and data-driven. The precise KOL map can enhance marketing effectiveness and increase project value return - and building this map relies on cleaning and weighting massive data, which is one of Kaito's core competencies. If KOL Agencies continue to use old models, merely providing vague KOL lists and rough distribution strategies, their value will inevitably be questioned.

However, KOL Agencies are not without opportunities. Keen agencies can completely embrace Kaito, viewing it as a new tool to utilize. They can subscribe to advanced services like Kaito Pro, obtain deep data insights, and consequently develop more effective communication strategies for clients. By leveraging the Kaito platform, KOL Agencies can more precisely help project parties achieve communication goals, such as:

· KOL Selection: Reference Yapper rankings, Smart Followers count, and other metrics to choose KOLs most suitable for the project.

· Topic Planning: Use Kaito's industry trend analysis to plan hot topics that integrate the project into community discussions, guiding more users to participate.

· Effect Monitoring: Real-time tracking of promotion effects, measuring volume conversion through Yap score growth and ranking changes, and adjusting strategies accordingly.

· Rule Optimization: Guide project parties to effectively use Kaito's rule dividends, such as how to initiate Launchpad community voting (community-selected project listings) and when to incentivize community content production. This role is somewhat similar to SEO consultants in the search engine era - now emerging as InfoFi consultants specializing in navigating the Kaito ecosystem.

In this process, KOL Agencies' value positioning will shift from "resource intermediaries" to "strategy consultants", requiring deep understanding of Kaito's algorithmic mechanisms and community operations. Some sensitive agencies are already studying Kaito's point calculation methods to find high-scoring secrets and better serve clients. However, it's important to note that Kaito's algorithm is continuously updating, making it difficult to manipulate scores through simple tricks, though optimization within compliance remains possible. Overall, Kaito presents challenges for KOL Agencies but also offers new opportunities: those who master and utilize InfoFi tools can continue creating value for clients in this new paradigm.

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The ChatGPT model is also used to identify spam and irrelevant content. According to Kaito's official sources and community, they highly value content originality and depth, and will not award high scores based on superficial interaction data, let alone reward pure screen flooding or meaningless interactions. For example, mechanically spamming keywords like "cryptocurrency" or "Crypto" in posts cannot trick the AI into gaining bonus points, as the system prioritizes genuine, meaningful discussions.

Jesse's personal experiment challenged this ideal state. In the experiment, I posted three tweets with provocative images and minimal text, unexpectedly earning nearly 190 Yap points. The comment sections for these tweets were filled with generic praise comments with almost no substantive information.

Such low-quality content receiving such high points raises questions: considering costs, Kaito's algorithm might not perform in-depth semantic analysis on every tweet, or may have adopted simplified scoring strategies. Perhaps the current system still relies more on basic interaction data, making compromises in semantic understanding. This discovery made Jesse doubt the rigor of Kaito's algorithm: to what extent has this supposedly intelligent content scoring mechanism truly been implemented?

Smart Followers Mechanism: Influence Assessment Based on Quality Over Quantity

While introducing AI analysis at the content level, Kaito did not ignore the "network" factor. The platform's innovative feature is the "Smart Followers" mechanism, establishing a social graph in the crypto circle and incorporating follower quality into content value assessment. For Kaito, who follows you matters more than the pure number of followers. Those mutually following each other and forming the crypto core circle of notable personal accounts are classified by the algorithm as Smart Followers.

If an author's follower list is populated by big names (such as Vitalik Buterin, Binance CZ following them), that author's influence is extraordinary, and the maximum points for their content will correspondingly be higher.

This social graph model enables Kaito to more objectively measure each tweet's "circle spread": whether it spreads among outsiders or reaches industry top-tier figures. For instance, a message with 100 reposts mostly from mutual small accounts might have limited actual value, while another message with only 10 reposts including heavyweight figures like Vitalik would have significantly higher "gold content". Kaito would assign drastically different Yap points to these scenarios, avoiding pure quantitative judgments based on reposts or likes.

In practice, top-ranked Yap accounts are often not social media celebrities with the most followers, but more likely deep players recognized by top KOLs. As a research report suggests, Kaito does not blindly worship traditional follower or view counts, but shifts reward focus to "smart fans" reputation weights—even with hundreds of thousands of followers, content without genuine value might earn minimal Yap points. This "quality over quantity" assessment approach somewhat corrects pure traffic-driven flaws, injecting an academic peer review flavor into InfoFi information distribution: only content endorsed by experts can stand out.

Of course, the specific algorithm details of the Smart Followers system remain undisclosed by officials, leaving us to speculate its logic from results. The Kaito team fears that completely transparent rules would inevitably invite strategic point farming, disrupting ecosystem fairness. Currently, introducing the social graph indeed increases algorithm resistance to cheating but also presents new challenges for newcomers: how to win attention and interaction from circle big shots becomes a key threshold for high points. On one hand, this provides positive motivation for content creators; on the other, it subtly raises concerns about potential monopolization of discourse by a few elites—after all, no matter how intelligent the algorithm, ultimately value is assigned by human networks.

Technical Costs and Multi-Layer AI Architecture Trade-offs

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The difference is that Kaito added an AI filtering and reputation weighting layer, attempting to raise the "quality threshold" of the game and prevent pure traffic trolls from running rampant. However, from the current effect, this system still cannot escape the Matthew effect: big players dominate the leaderboard, with high scores highly consistent with top influence, and small accounts wanting to break through rely on being supported by big accounts. Is this truly breaking information monopoly or subtly reinforcing existing circles? This will be one of the core issues Kaito needs to face in the future.

A more realistic challenge lies in the sustainability of the model. Kaito is now highly dependent on the Twitter ecosystem - both in terms of data sources and user interactions are almost entirely tied to the X platform. How far can this borrowed development model go? If Twitter raises API prices again or tightens data permissions, can Kaito still operate? The current high API fees have already forced Kaito to turn to paid service customers to support operations. But if the InfoFi model is to expand to mass participation, this account will ultimately need to be figured out.

On the other hand, the token economy supporting Yap also has uncertainties. Currently, the value of Yap points remains more at the expectation level. Once market enthusiasm declines and expected value drops, will the top KOLs on the platform shift elsewhere, thus risking content loss for Kaito? KOLs who navigate between platforms often go where the returns are highest. If Kaito cannot continuously provide sufficient revenue or influence returns, mere sentiment cannot retain these top users.

Overall, for the InfoFi model to work, it ultimately needs to achieve a better balance between incentivizing in-depth content creation and maintaining its own blood-generating capacity. Can Kaito forge a sustainable development path amid fierce competition and resource constraints? We wait and see.

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