Original Article Title: "The Comeback Experiment of the Kaito Algorithm: How to Reach the Top of the Chinese Community Ranking with 3 Edgy Images in 24 Hours"

Jesse's InfoFi Field Test Report

Recently, Jesse conducted an experiment on the X (formerly Twitter) platform: posting three pieces of "edgy" crypto content that fell between valuable information and pure fluff to test the boundaries of the Kaito platform's Yap scoring algorithm. Unexpectedly, in less than 24 hours, the user account @jessethecook69 shot up to the ninth position on the global Kaito Yapper leaderboard and claimed the top spot in the Chinese community. This phenomenon, where merely non-high-quality content can quickly rise in the rankings, inevitably raises questions about whether Kaito's AI content scoring algorithm truly lives up to its claim of being fair and rigorous, or if there are vulnerabilities that can be exploited.

Below are the three edgy content tweets released as part of this experiment. These content pieces adopted a casual style, quickly gaining significant engagement through humor and visual impact.

In fact, there have been many similar doubts within the community. An article by Blockworks mentioned that some users were able to unexpectedly earn hundreds of Yap points by repeatedly replying with the same word (such as continuously replying with "reply") to a tweet. While the platform may have quickly patched such loopholes, these cases are enough to spark discussions: Can Kaito's "Information as Capital" (InfoFi) model truly incentivize quality information, or does it sometimes devolve into a new form of traffic game?

To address these questions, it is necessary to delve into Kaito's underlying principles, understand how it leverages the vast metadata provided by the Twitter API, utilizes large-scale language models like OpenAI's ChatGPT for semantic analysis and trend identification, and builds a decentralized information ecosystem through mechanisms like Smart Followers and Yap points for "social incentives." Next, Jesse will analyze this issue from both an industry significance and technical detail perspective.

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

The InfoFi new model advocated by Kaito is not only a technological and product innovation experiment but is also structurally disrupting the information dissemination mechanism and marketing paradigm in the crypto industry. In the past, crypto project marketing mainly relied on traditional methods: hiring PR agencies, collaborating with Key Opinion Leaders (KOLs) in the crypto community to create buzz on social media. Under this model, information was often opaque, dissemination efficiency was low, and it gave rise to a large amount of "advertorials" and hype posts. In contrast, Kaito's algorithm-driven community incentives are changing the rules of the game—shifting the relationship between projects, KOLs, and ordinary users to a competitive environment based on content value and contribution.

Project Marketing Paradigm Shift from "Exposure" to "Engagement"

Under the traditional model, project teams often viewed users' attention as ad space that could be purchased with funds: making contributions to have influencers post promotional content, and then leveraging the influencers' large fan base to spread the information. However, this exposure-based marketing approach has significant drawbacks:

· Difficult to Measure Effectiveness: How many of the KOL's fans are genuinely interested in the project? What is the conversion rate? The project team may spend a high budget, only to obtain inflated "buzz" but minimal actual user conversion.

· Questionable Information Credibility: Nowadays, audiences can easily discern which content is paid promotion. They tend to be wary or even resentful of such hard-sell ad posts.

Kaito has introduced an engagement-driven viral spread paradigm: through "Yap-to-Earn," projects no longer need to concentrate their marketing budget on a few key influencers. Instead, they join Kaito's Yapper leaderboard system, allowing community members to spontaneously promote the project. For example, a new project looking to increase exposure can collaborate with Kaito to launch the project's community leaderboard on the platform—all users who publish original content about the project participate in a points competition.

The actual effect is similar to a nationwide participatory content creation competition. In order to earn Yap points or potential future airdrop rewards, users eagerly research the project, publish in-depth analyses or unique insights, and strive to climb the leaderboard for rewards. The project team, at a relatively low cost (such as promising token airdrops or prizes to leaderboard leaders), harvests a massive amount of high-quality UGC (user-generated content). This content is organically shared by users on public platforms like Twitter, often with greater virality and persuasiveness—after all, this isn't cold advertising but the authentic voice of community members (even with incentivization, the content is user-generated). This model is known as a social version of "Proof-of-Attention": those who rank highly on the leaderboard by posting are seen as providing high-value information and thus receive appropriate rewards.

Whether this approach is labeled as InfoFi or SocialFi, it fundamentally reshapes the organizational approach to project dissemination. Marketing is no longer solely led by a centralized team but transformed into incentive-driven community collaborative creation. The project team's role has shifted from a traditional ad sponsor to a community activity initiator and reward provider.

No Longer a Fan-Only Hero: How Can Small KOLs Successfully Rise with Kaito?

In the InfoFi ecosystem, the role of traditional crypto KOLs has also undergone a transformation. On one hand, top KOLs still hold significant influence: for example, industry giants like Vitalik and jesse.base continue to top the Yapper list, indicating that truly insightful opinion leaders with a large following can still lead the conversation. On the other hand, these KOLs now exist in an openly competitive environment: each time they speak out, their engagement is objectively recorded and scored by algorithms, making their ranking clear for all to see. For genuinely knowledgeable KOLs, this serves as a positive reinforcement; however, those KOLs who relied solely on their popularity but seldom delivered substantial content may see their influence gradually wane under the InfoFi mechanism. If they only post advertisements without earning points and fail to actively participate in discussions, their ranking will drop, and the community may perceive them as "lacking substance." As a result, KOLs are compelled to actively and sincerely engage in community discussions, or risk being overtaken by newcomers.

jesse observed that some mid-tier KOLs have already staged a "comeback" by leveraging Kaito. While they may have fewer followers than top-tier influencers, their diligent production of high-quality content has propelled them to top positions on the leaderboard, granting them exposure on par with the top influencers. This shift poses a challenge to the traditional KOL influence landscape: influence is no longer solely determined by follower count, as content value and reputation now hold equal weight. This can be likened to "influence mining" where KOLs "mine" influence points (Yap) by consistently contributing valuable information. Unlike in the past where influence was solely built on accumulating followers, in this model, influence acquisition is more multifaceted and dynamic.

Simultaneously, KOLs' monetization models are also evolving. While in the past, top influencers mainly relied on project-paid promotions for profit, they now have an additional channel: accruing Yap points for future redemption (e.g., converting them to the platform's token, KAITO). In the short term, Yap points cannot be directly cashed out, but they carry a significant expected value (there already exists a secondary market trading these expectations at a discounted valuation). Due to the scarcity and high acquisition difficulty of Yap, many KOLs invest time in remaining active on Kaito, similar to early participation in "mining" to reap future rewards.

When some projects (such as Berachain) conduct targeted airdrops on the Top Yappers on Kaito, KOLs are incentivized to maintain their lead on the leaderboard to receive these additional rewards. This indirectly reduces the need for project sponsors to directly pay KOLs for advertising: instead of paying a top influencer for a single ad, they prefer allocating a portion of the budget as a community reward to encourage participation in Kaito discussions; KOLs also benefit from this approach. Consequently, the relationship between KOLs and project sponsors transitions from the traditional client-service provider dynamic to that of collaborative partners participating in community operations. KOLs must demonstrate genuine insights into the project to earn community approval, while project sponsors welcome KOLs driving more conversations about their project. Both parties interact on a public platform, increasing the transparency and visibility of information.

The Opportunities and Challenges of KOL Agencies

For KOL Agencies, the Kaito model can be seen as a double-edged sword. On one hand, it has weakened some of the exclusive value previously held by KOL Agencies: project owners can directly use Kaito's provided data and rankings to find truly effective influencers without relying too heavily on the agency's network. Kaito provides a quantified KOL map and performance ranking as reference, allowing project owners to identify the most active influencers in niche areas and which users demonstrate high engagement and loyalty to the project. Such data transparency was previously only held by senior KOL Agencies (based on long-term experience knowing which KOLs are good at driving conversions); now Kaito has made these metrics public and data-driven. An accurate KOL map can enhance marketing effectiveness, increasing the value return for project owners—the construction of this map relies on cleaning and weighting massive amounts of data, which is one of Kaito's core competencies. If KOL Agencies continue to use old models, only providing vague KOL lists and broad promotional strategies, their value will inevitably be questioned.

On the other hand, KOL Agencies still have their place. Agile agency firms can choose to embrace Kaito as a new tool to be utilized. They can subscribe to advanced services like Kaito Pro to gain deep data insights, thereby developing more effective communication strategies for clients. Through the Kaito platform, KOL Agencies can more accurately help project owners achieve their communication goals, such as:

· KOL Selection: Using metrics such as Yapper ranking, Smart Followers (core followers) count, etc., to select the most suitable KOLs for collaboration on projects.

· Topic Planning: Utilizing Kaito's analysis of industry trends to plan topics that integrate projects into hot community discussions, guiding more users to join the conversation.

· Effect Monitoring: Real-time monitoring of promotional effects, measuring volume conversion through Yap point growth and ranking changes, and adjusting strategies at any time.

· Rule Optimization: Guiding project owners to make the most of Kaito's rule benefits, such as how to initiate Launchpad community voting (an activity where the community votes for projects to be listed), and when to incentivize the community to produce more related content. This role is somewhat similar to the SEO consultant of the search engine era—what we see now is an InfoFi consultant, specializing in how to navigate the Kaito ecosystem.

During this process, the value positioning of the KOL Agency will shift from being a "resource intermediary" to a "strategic consultant," requiring a deep understanding of Kaito's algorithmic mechanism and community operation. It can be foreseen that some discerning Agency institutions have already begun studying Kaito's scoring calculation method, seeking the key to triggering high scores in order to better serve clients. Of course, it should be noted that Kaito's algorithm is continuously updated and optimized, making it not easy to opportunistically exploit loopholes for score manipulation through simple tricks. However, there is still a significant opportunity for optimization within compliance boundaries (such as guiding genuine community discussions instead of engaging in spamming and fake engagement). In general, Kaito has presented a challenge to KOL Agencies but has also provided new opportunities for those who can master and effectively utilize the InfoFi tool to continue creating value for clients in the new paradigm.

Enhancing Information Dissemination Quality and Algorithmic Challenges

The improvement in industry communication content quality brought about by Kaito is evident. Through the InfoFi incentive mechanism, the previously prevalent pure advertisements and pumping posts on social platforms have been suppressed, replaced by more in-depth analysis and rational discussions. This undoubtedly has had a positive impact on the information environment of the entire crypto community: investors can see more insightful perspectives, reducing the risk of being misled by meaningless noise; project teams can also receive more genuine feedback and suggestions from the community, rather than just praise or insults. Attention is directed towards genuinely valuable information, significantly enhancing the effectiveness and value of the information flow.

However, all of this also carries a latent concern—the issue of algorithm-driven narrative centralization. As more and more industry discussions shift to platforms like Kaito, the platform's algorithm itself gains significant influence. Just as in the past people worried about Google's search algorithm determining which websites could be seen, today Kaito's algorithm is essentially deciding which voices will be amplified. While InfoFi claims to be fair, the earlier analysis also mentioned that it tends to favor users with existing reputations in its mechanism. This may lead to innovative ideas or dissenting views struggling to gain traction if they do not receive validation from mainstream influencers. Over time, could this lead to another form of "information bubble" forming?

The possibility of Kaito platform making algorithm adjustments for commercial interests is also worth noting—such as the algorithm possibly favoring the promotion of partnership project information (observations suggest that for projects integrated into Kaito, the system apparently significantly encourages users to discuss more). As part of the decentralized ethos of the crypto community, we should be vigilant against algorithmic monopolies, urging Kaito to maintain transparency and fairness in rulemaking. While Kaito has already disclosed some FAQs and basic principles, the specific scoring details remain in a black box. In the future, perhaps a more DAO-like governance structure will be needed to allow the community to participate in supervising the algorithm's evolution, ensuring that the InfoFi model genuinely incentivizes truly high-quality information.

Technical Principle: The Behind-the-Scenes Mechanism from Data Retrieval to AI Parsing

Twitter API Data Retrieval: Fundamentals and Challenges

As a platform focused on crypto information, Kaito first needs to continuously retrieve data from Twitter (X). Through the official API, Kaito automatically fetches the text, posting time, likes, retweets, and other metadata of each tweet, associates them with author information and a list of interacting users, laying the foundation for subsequent algorithmic judgments.

For example, for a tweet discussing Bitcoin, Kaito records its content, posting time, engagement metrics, and the influencer status of the poster. If an industry influencer is involved in the interaction, the algorithm determines that the information holds more significance. The premise for achieving all this is efficient scheduling and utilization of the Twitter API.

Since Elon Musk took over, Twitter significantly raised the API usage fees: the starting price for the enterprise-level API is as high as $42,000 per month (allowing queries of only about 50 million tweets). To track the dynamics of the entire crypto community, the required call volume far exceeds this level, posing a huge cost burden for a startup project. Although Kaito has not elaborated on specific countermeasures, it can be assumed that the team must carefully calculate the cost of each API call. They likely adopted the following strategies to control data acquisition costs:

· Focus on Key Areas: Prioritize fetching data from core accounts and topics in specific crypto areas, rather than indiscriminately crawling platform-wide data to conserve API call quotas.

· Batch Queries and Caching: Utilize techniques such as batch queries and caching to reduce redundant requests and minimize the number of API calls.

· User-Authorized Crowdsourcing: Some analyses speculate that Kaito requires users to link their X account to obtain authorization tokens, outsourcing part of the data retrieval task to users themselves, thereby bypassing official rate limits.

According to jesse, these strategies are aimed at minimizing data costs and risks as much as possible without affecting core functionality, ensuring that the InfoFi model has a stable data source.

ChatGPT Content Parsing: AI Empowering Information Value

Acquiring a vast amount of data is just the beginning; Kaito's more critical tool is leveraging OpenAI's ChatGPT model to perform semantic analysis and quality assessment of the content. In essence, Kaito uses AI as a "taster" and "filter" of information. Whenever a user posts on X, the backend algorithm intelligently analyzes the content, including identifying the topic of the tweet, evaluating the value of the content, and determining if there is any fraudulent activity such as engagement manipulation.

With the help of an advanced large-scale language model, Kaito claims to be able to transcend language barriers, impartially understand and rate multilingual content such as English and Chinese, without showing favoritism. This means that regardless of the language users use to express their views, they should theoretically receive their rightful Yap token rewards.

The ChatGPT model is also used to identify spam and flood content. According to Kaito's official statements and community introductions, they place great importance on the originality and depth of content, not giving high scores based solely on surface-level interaction data, and definitely not rewarding pure spamming or meaningless interaction. For example, even if someone mechanically floods their post with keywords like "cryptocurrency" or "Crypto," they won't fool the AI to earn bonus points because the system prioritizes authentic, meaningful discussions.

Jesse's personal experiment called the above ideal state into question. In the experiment, I posted three posts featuring edgy images with just a few words of text, unexpectedly earning nearly 190 Yap points. The comments section of these three posts was filled with nothing but compliments and pleasantries, with hardly any substantial information.

The fact that such content with high fluffiness can earn such high points inevitably raises doubts: due to cost considerations, Kaito's algorithm may not analyze each post semantically to the letter, or may adopt some form of simplification in the rating process. Perhaps the current system still relies more on basic interaction data to determine scores, with some compromises in semantic understanding. This discovery led Jesse to question the rigor of Kaito's algorithm: to what extent has the supposedly intelligent content rating mechanism truly materialized?

Smart Followers Mechanism: Evaluating Influence Quality over Quantity

While Kaito introduces AI analysis at the content level, it does not overlook the "social network" factor. The platform's innovation lies in the introduction of the "Smart Followers" mechanism, establishing a crypto community social graph that incorporates follower quality into content value assessment. For Kaito, who follows you is more important than mere follower count. Well-known individual accounts that mutually follow each other, forming the core of the crypto circle, are classified by the algorithm as Smart Followers.

If an author's follower list is filled with big names (such as Vitalik Buterin, Binance CZ, etc., following them), then the author's influence is obviously significant, and the maximum points they receive for content will correspondingly be higher.

This social graph model enables Kaito to objectively measure the "circulation within the circle" of each post: whether it is spreading among outsiders or reaching the top figures in the industry. For example, even if a message has 100 retweets, if most of them come from mutual follow-for-follow accounts for entertainment, its actual value may be limited; whereas another message with only 10 retweets but involving heavyweights like Vitalik has higher "quality." In response to these two scenarios, Kaito assigns vastly different Yap points to avoid a mere quantitative comparison based on retweet or like counts.

From the actual results, it can be seen that the accounts at the top of the Yap leaderboard are often not the most popular Internet celebrities in terms of followers, but are more likely deep players recognized by top KOLs. As a research report stated, Kaito does not blindly believe in traditional metrics such as fan count or view count, but rather places the reward emphasis on the reputation weight of "Smart Followers" — even if you have hundreds of thousands of followers, if your content lacks true value, the Yap received may still be meager. This "quality over quantity" evaluation method to some extent corrects the pitfalls of pure traffic competition, injecting a touch of academic "peer review" taste into InfoFi's information distribution: only content endorsed by experts can stand out.

Of course, the specific algorithm details of the Smart Followers system are kept under wraps by the official team, and we can only speculate on its logic from the results. The Kaito team is concerned that if the rules are fully transparent, some may exploit them to gain points unfairly, disrupting the ecosystem's fairness. Currently, introducing a social graph has indeed enhanced the algorithm's ability to resist cheating, but it has also presented new challenges to newcomers: how to earn the attention and interaction of industry insiders has become a key threshold for obtaining high scores. On the one hand, this is a positive incentive for content creators, but on the other hand, there is a subtle concern that it may evolve into a game where a few big shots dominate the discourse — after all, no matter how intelligent the algorithm is, it is ultimately the interpersonal network that gives value.

The Trade-off Between Technical Costs and Multi-layer AI Architecture

After introducing so many "black technology"-enhanced features, it is necessary to calmly examine the real-world cost ledger — supporting this complex system of Kaito incurs significant technical expenses. First is the data acquisition cost. As mentioned earlier, obtaining Twitter data through official channels in large quantities comes at a high price, often running into tens of thousands of dollars per month. According to industry sources, Kaito had previously attempted to obtain data through third-party channels or unofficial APIs, but as Twitter tightened its policies, these gray methods became unsustainable, forcing them to honestly pay for higher-level API permissions. This directly forced Kaito to make trade-offs in its product strategy: if it indiscriminately opened up a large number of queries to regular users, the monthly API call limit would quickly hit a ceiling.

Recently, Kaito has provided relatively limited free query services to regular users, preferring to sell its deep data analysis capabilities to institutions and professional clients. For example, the monthly fee for a hedge fund subscription to the Kaito Pro professional version is $800 or more. By providing deep data analysis services to a few paying "whales" to cover the high data bills, this also explains why Kaito currently chooses a business route mainly focused on B2B (enterprise-oriented).

Another major expense is AI computing power. Kaito officially claims to use AI at the level of GPT-4 to understand content, but behind every ChatGPT-4 interface call is a burn of funds. If really every tweet is real-time analyzed using GPT-4, the costs would be astronomical. Roughly estimated: even using the cheaper ChatGPT-3.5, processing every 50,000 tweet content could cost over a thousand dollars; if using the much more expensive GPT-4 model for full analysis, monthly expenses could even reach tens of thousands of dollars.

Clearly, Kaito wouldn't act so recklessly. It is speculated that the team may have devised a "AI Utilization Rationalization" strategy: using large models only when necessary, employing rule-based filtering or small model pre-judgment in less critical areas, and minimizing ChatGPT calls as much as possible. There are also signs that Kaito is developing its own large models or multi-agent systems, attempting to have some fine-tuned open-source models handle basic semantic scoring tasks. Consequently, the expensive GPT-4 is only invoked for complex problems or when long paragraph summaries need to be generated, significantly reducing the average call cost.

Kaito's founder, Yu Hu, revealed that they are currently using an AutoGPT heterogeneous agent architecture, deploying multiple ChatGPT models in the backend to work together, with ChatGPT-4 as the underlying core model. Meanwhile, they are reducing reliance on third parties by fine-tuning in-house models. This multi-layer model architecture reflects Kaito's delicate balance between effectiveness and cost: on the one hand, ensuring that the algorithmic analysis is sufficiently outstanding and reliable, while on the other hand, carefully managing expenses. This "dilemma" balance is precisely the operational challenge that the current InfoFi business model cannot avoid. It can be said that Kaito is staging a "technological gamble" – burning money to build a technological moat while also hoping to find a more economically feasible alternative in the future.

Conclusion: Reflection and Future of the InfoFi Model

Kaito's platform design is a bold fusion of cutting-edge technology and business model innovation: it quantifies social content into "attention assets" and then uses tokens to incentivize high-quality information output. It sounds great, but the actual implementation is far from straightforward. Kaito's so-called "InfoFi" is, to some extent, more like a disguised SocialFi – whether called Yap points or by any other name, the essence is leveraging social networks to monetize traffic and influence. In this respect, it shares similarities with early SocialFi projects like Friend.tech and Stars Arena.

The difference is that Kaito adds an AI filtering layer and reputation weighting, attempting to raise the "quality threshold" of the game and prevent pure water army traffic from dominating. However, from the current results, this system still struggles to escape the Matthew effect: prominent figures top the list, high scores and top-tier influence closely correlate, and small accounts seeking recognition rely on endorsements from larger accounts. Is this truly breaking the information monopoly, or is it subtly reinforcing existing cliques? This will be one of the core issues that Kaito will need to face in the future.

The more pressing challenge lies in the sustainability of the model. Kaito is currently highly dependent on the Twitter ecosystem – almost all data sources and user interactions are tied to Platform X. How far can this development model, based on others' platforms, go? If Twitter were to raise API prices again or tighten data permissions, can Kaito continue to operate smoothly? The current exorbitant API costs have already forced Kaito to turn to serving paid clients to support its operations. However, if the InfoFi model is to expand to full public participation, eventually, the books will need to be balanced.

On the other hand, there is also uncertainty surrounding the tokenomics that support the Yap incentive. Currently, the value of Yap points largely remains at the expectation level. Once market enthusiasm wanes and the anticipated value drops, will the platform's top KOLs shift their focus elsewhere, putting Kaito at risk of content loss? KOLs who navigate various platforms tend to gravitate towards where the rewards are highest. If Kaito cannot consistently provide sufficient returns or influence rewards, relying solely on sentiment alone will not retain these top users.

Overall, for the InfoFi model to succeed, it ultimately needs to strike a better balance between incentivizing deep content creation and maintaining its own sustainability. Can Kaito carve out a sustainable path forward amidst intense competition and resource constraints? We await eagerly to see.

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