Original Title: "Kaito Algorithm's Comeback Experiment: How to Reach the Top of the Chinese Region Chart in 24 Hours with 3 Borderline Posts"
Original Author: yunfeng, BlockBeatsJesse's InfoFi Practical Report
Recently, Jesse conducted an experiment on the X (formerly Twitter) platform: he published three posts that straddled the line between valuable information and pure spam, to test the boundaries of Kaito platform's Yap points algorithm. Surprisingly, in less than 24 hours, his account @jessethecook69 reached the global ninth place on the Kaito Yapper leaderboard and took the top spot in the Chinese region. This phenomenon of quickly climbing the charts with content that is not high quality raises questions about whether Kaito's claimed AI content scoring algorithm is as fair and strict as it purports to be, or if there are exploitable loopholes within it.
Below are the three borderline content tweets released during this experiment. These posts are styled to be relatable, quickly gaining a lot of interaction through their fun and visually striking nature.
In fact, there have been many similar doubts within the community. A report from Blockworks mentioned that some users managed to rack up hundreds of Yap points by repeatedly replying with the same word (for example, continuously replying "reply"). Although the official team may quickly fix such loopholes, these cases are enough to spark discussions: does Kaito's "Information is Capital" (InfoFi) model truly deliver incentives for quality information, or does it sometimes devolve into a new traffic game?
To answer these questions, it is necessary to delve into the underlying principles of Kaito, understanding how it utilizes the vast metadata provided by the Twitter API, combines it with large language models like OpenAI's ChatGPT for semantic analysis and trend judgment, and constructs a decentralized information ecosystem through mechanisms like Smart Followers and Yap points as "social incentives." Next, Jesse will analyze this issue from both industry significance and technical details.
Information is Capital: Kaito's Platform Innovation and Industry Significance
The new InfoFi model advocated by Kaito is not only a technical and product innovation experiment but is also bringing structural shocks to the information dissemination mechanisms and marketing paradigms in the crypto industry. In the past, marketing for crypto projects primarily relied on traditional methods: hiring PR agencies and collaborating with KOLs (Key Opinion Leaders in the crypto space) to create buzz on social media. In this model, information is often opaque, dissemination efficiency is low, and it has spawned a large number of "soft articles" and promotional posts. In contrast, Kaito's algorithm-driven community incentives are changing the rules of the game—relationships between project parties, KOLs, and ordinary users are being re-established in a competitive environment based on content value and contribution.
Project Marketing Paradigm Shifts from "Placement" to "Participation"
In the traditional model, project parties often view user attention as ad space that can be purchased with funds: paying big influencers to post promotional content, then leveraging their large fan base to spread the information. However, this placement-based marketing has obvious pitfalls:
· Difficult to Measure Effectiveness: How many of the KOL's followers genuinely care about the project? What is the conversion rate? Project parties may spend a hefty budget only to receive inflated "volume" with very few actual user conversions.
· Credibility of Information is Questionable: Nowadays, audiences can easily discern which content is paid promotion, and they tend to be wary or even resentful of such hard-sell ads.
Kaito's emergence has led to a participatory viral dissemination paradigm: through "Yap-to-Earn," projects no longer need to concentrate their marketing budgets on a few big influencers but can connect to Kaito's Yapper leaderboard system, allowing community members to spontaneously voice their support for the project. For example, if a new project wants to amplify its voice, it can collaborate with Kaito to launch a community leaderboard for that project on the platform—original content published by all users around this project will compete for points.
The actual effect resembles a nationwide content creation competition. Users, eager to win Yap points or potential future airdrop rewards, rush to research the project, publish in-depth analyses or unique insights, striving to climb the leaderboard for rewards; project parties, at a relatively low cost (for example, promising token airdrops or prizes to users at the top of the leaderboard), gain a wealth of high-quality UGC (User Generated Content). This content is actively shared by users on public platforms like Twitter, often possessing greater dissemination power and persuasiveness—after all, this is not a cold advertisement but the genuine voice of community members (even if there are incentive factors, the content is still user-generated). This model is referred to as a social version of "Proof-of-Attention": those who rank high on the leaderboard are seen as providing high-value information and thus receive their due rewards.
Whether this approach is labeled as InfoFi or SocialFi, it fundamentally reshapes the organizational method of project dissemination. Marketing is no longer entirely dominated by centralized teams but has shifted to incentive-driven community collective creation. The role of project parties has also transformed from traditional advertisers to initiators of community activities and providers of rewards.
No Longer Just Fans as Heroes: How Small KOLs Can Successfully Make a Comeback with Kaito
In the InfoFi ecosystem, the role of traditional crypto KOLs has also changed. On one hand, top KOLs still hold significant sway: for instance, industry heavyweights like Vitalik and jesse.base continue to rank high on the Yapper leaderboard, indicating that true thought leaders with a large following can still steer the direction of topics. On the other hand, these KOLs are now in a publicly competitive environment: every time they speak, their contributions are objectively recorded and scored by the algorithm, with point totals visible. For KOLs who genuinely have valuable insights, this is a positive incentive; however, those who previously relied solely on their fame but produced little substantive content may find their influence gradually diminished under the InfoFi mechanism. If they only post promotional content without earning points and do not actively engage in discussions, their leaderboard rankings will drop, and they will be viewed by the community as "lacking substance." Consequently, KOLs are compelled to participate more actively and sincerely in community discussions, or risk being surpassed by newcomers.
Jesse has observed that some mid-tier KOLs have already achieved a "comeback" through Kaito. They may not have as many followers as top influencers, but due to their diligent production of high-quality content, they have managed to rank higher on the leaderboard, gaining exposure comparable to that of top influencers. This is a disruption to the traditional KOL influence landscape: influence is no longer solely determined by follower count; content value and reputation also play a crucial role. This can be likened to a form of "influence mining"—KOLs "mine" influence points (Yap) by continuously contributing valuable information. Compared to the past reliance on long-term follower accumulation, influence acquisition in this model is more multifaceted and dynamic.
At the same time, the monetization model for KOLs is also transforming. Previously, top influencers primarily profited from paid promotions by project parties; now, they have an additional channel: accumulating Yap points to await future redemption (for example, exchanging them for platform tokens KAITO). In the short term, Yap points themselves cannot be directly monetized, but they are assigned a significant expected value (there are already secondary markets trading this expectation at discounted valuations). Due to the scarcity of Yap and the difficulty of acquisition, many KOLs are investing time in Kaito to remain active, similar to early participants in "mining" for future gains.
When some projects (like Berachain) target airdrop rewards to the Top Yappers on Kaito, KOLs are even more motivated to maintain their leading positions on the leaderboard to gain these additional benefits. This, in turn, reduces the need for project parties to directly pay KOLs for advertising: rather than spending money to have a top influencer post an ad, it is more effective to allocate part of the budget as community rewards, incentivizing everyone to participate in discussions on Kaito; KOLs can also benefit from this. Thus, the relationship between KOLs and project parties shifts from a traditional client-vendor dynamic to a partnership in community operational activities. KOLs must demonstrate their genuine insights about the project to earn community recognition, while project parties welcome KOLs to engage more people in discussions about themselves. Both sides interact on a public platform, making information more transparent and visible.
Opportunities and Challenges for KOL Agencies
For KOL Agencies, the Kaito model is a double-edged sword. On one hand, it undermines some of the exclusive value previously held by KOL Agencies: project parties can directly leverage the data and leaderboard provided by Kaito to find truly effective communicators without overly relying on the agency's network resources. Kaito offers a quantifiable KOL map and performance leaderboard as references, allowing project parties to identify the most active communicators in niche areas and which users show high engagement and loyalty to the project. Such data transparency was previously only possessed by experienced KOL Agencies (who knew from long-term experience which KOLs excelled at driving conversions); now Kaito has made these metrics public and data-driven. An accurate KOL map can enhance marketing effectiveness and increase project parties' value returns—building this map relies on the cleaning and weighting of vast amounts of data, which is one of Kaito's core competitive advantages. If KOL Agencies continue to use outdated models, providing vague KOL lists and broad strategies, their value is bound to be questioned.
On the other hand, KOL Agencies are not without their place. Astute agencies can choose to embrace Kaito, viewing it as a new tool to utilize. They can subscribe to advanced services like Kaito Pro to gain in-depth data insights, thereby formulating more effective communication strategies for their clients. With the Kaito platform, KOL Agencies can more accurately help project parties achieve their communication goals, such as:
· Selecting KOLs: Using Yapper rankings, Smart Followers (core followers) counts, and other metrics to choose the KOLs that best fit the project for collaboration.
· Planning Topics: Utilizing Kaito's analysis of industry trends to plan hot topics that integrate the project into community discussions, guiding more users to participate.
· Monitoring Effectiveness: Real-time monitoring of promotional effectiveness, measuring volume conversion through Yap point growth and leaderboard changes, and adjusting strategies as needed.
· Rule Optimization: Guide project parties to make good use 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 relevant content. This role is somewhat similar to SEO consultants in the search engine era—now emerging are InfoFi consultants, specializing in how to navigate the Kaito ecosystem.
In this process, the value positioning of KOL Agencies will shift from "resource intermediaries" to "strategic consultants," which requires a deep understanding of Kaito's algorithmic mechanisms and community operation strategies. It is foreseeable that some astute agencies have already begun to study Kaito's point calculation methods, seeking the keys to trigger high scores in order to better serve their clients. Of course, it should be noted that Kaito's algorithm is continuously updated and optimized, making it difficult to rely on simple tricks to game the system for points, but there is still significant room for optimization within compliance (such as guiding genuine community discussions rather than spamming). Overall, Kaito presents challenges to KOL Agencies but also offers new opportunities for those who can master and effectively utilize InfoFi tools to continue creating value for clients under the new paradigm.
Improving Information Dissemination Quality and Algorithm Challenges
Kaito's enhancement of the quality of industry communication content is evident. Through the InfoFi incentive mechanism, the pure advertisements and promotional posts that once flooded social platforms have been suppressed, replaced by more detailed analyses and rational discussions. This undoubtedly has a positive effect on the information environment of the entire crypto community: investors can see more insightful viewpoints, reducing the risk of being misled by meaningless noise; project parties can also receive more genuine feedback and suggestions from the community, rather than just flattery or insults. Attention is directed towards truly valuable information, significantly improving the effectiveness and quality of the information flow.
However, this also conceals a cautionary concern—the concentration of discourse power under algorithmic dominance. As more industry exchanges migrate to platforms like Kaito, the platform's algorithm itself gains immense influence. Just as people once worried that Google's search algorithm determined which websites could be seen, Kaito's algorithm is now effectively deciding which voices will be amplified. Although InfoFi claims to be fair, the previous analysis also pointed out that it is mechanism-wise biased towards users with existing reputations. This could lead to innovative ideas or contrarian viewpoints struggling to gain traction if they do not receive recognition from mainstream influencers; over time, could this create another "information cocoon"?
The possibility that Kaito may fine-tune its algorithm for commercial interests is also worth noting—for example, the algorithm may favor promoting information about partnered projects (observations suggest that the system seems to encourage users to discuss projects integrated with Kaito). As a crypto community that values decentralization, we should remain vigilant against algorithmic monopolies and urge Kaito to maintain transparency and fairness in rule-making. Kaito has currently made some FAQs and basic principles public, but specific scoring details remain a black box. In the future, a more DAO-like governance structure may be needed to allow the community to participate in supervising the evolution of the algorithm, ensuring that the InfoFi model genuinely provides fair incentives for truly high-quality information.
Technical Principles: From Data Acquisition to AI Analysis Behind the Scenes
Twitter API Data Acquisition: Content Foundation and Challenges
As a platform focused on crypto information, Kaito first needs to continuously acquire data from Twitter (X). Through the official API interface, Kaito automatically captures metadata for each tweet, including text, publication time, likes, and retweets, linking it to author information and interaction user lists to lay the groundwork for subsequent algorithmic judgments.
For example, for a tweet discussing Bitcoin, Kaito will record its content, publication time, interaction heat, and the influence of the poster; if an industry heavyweight interacts with it, the algorithm will determine that this information has higher value. The prerequisite for achieving all this is the efficient scheduling and utilization of the Twitter API.
Since Elon Musk took over, Twitter has significantly increased API usage fees: the starting price for enterprise-level interfaces is as high as $42,000 per month (allowing access to only about 50 million tweets). To track the dynamics of the entire crypto circle, the required call volume far exceeds this level, placing a huge cost pressure on startup projects. Although Kaito has not detailed specific countermeasures, it can be imagined that the team must be very frugal with each API call. They likely adopted the following strategies to control data acquisition costs:
· Focus on Key Areas: Prioritize capturing core accounts and topics in specific crypto fields rather than indiscriminately crawling data across the entire platform to save on call quotas.
· Batch Queries and Caching: Use batch queries, caching, and other technical means to reduce duplicate requests and minimize API call frequency.
· User Authorization Crowdsourcing: Some analyses speculate that Kaito requires users to bind their X accounts to obtain authorization tokens, "crowdsourcing" part of the data capture tasks to the users themselves, thereby bypassing official frequency limits.
In Jesse's view, these strategies aim to minimize data costs and risks without affecting core functionality, ensuring that the InfoFi model has a stable data source.
ChatGPT Content Analysis: AI Empowering Information Value
Acquiring vast amounts of data is just the starting point; Kaito's more important tool is utilizing OpenAI's ChatGPT model for semantic analysis and quality assessment of the content. Simply put, Kaito lets AI act as an "appraiser" and "filter" for information. Whenever a user posts on X, the backend algorithm performs intelligent analysis of the content, including identifying the topic discussed in the tweet, assessing whether the content is valuable, and determining if there are any spammy behaviors.
With the help of advanced large language models, Kaito claims to be able to understand and score multilingual content, such as English and Chinese, equally without bias. This means that regardless of the language users use to express their views, they should theoretically receive the appropriate Yap point rewards.
The ChatGPT model is also used to identify spam and low-quality content. According to Kaito's official statements and community feedback, they place great importance on the originality and depth of content, and will not award high scores simply based on superficial interaction data, nor will they reward pure spamming or meaningless interactions. For example, even if someone mechanically floods a post with keywords like "cryptocurrency" or "Crypto," they cannot deceive the AI to gain point bonuses, as the system prioritizes genuine, meaningful discussions.
Jesse's personal experiment raises questions about this ideal state. In the experiment, he posted three borderline content tweets with flashy images and only a few words, unexpectedly earning nearly 190 Yap points. The comments on these tweets were all filled with flattering remarks, with almost no substantive information.
Such highly inflated content being able to earn such high points raises doubts: based on cost considerations, Kaito's algorithm may not conduct a word-for-word deep semantic analysis of each tweet, or it may have adopted some simplified strategy in the scoring process. Perhaps the current system still primarily relies on basic interaction data to determine points, making some compromises in semantic understanding. This discovery has led Jesse to question the rigor of Kaito's algorithm: to what extent does the so-called intelligent content scoring mechanism truly materialize?
Smart Followers Mechanism: Quality Over Quantity in Influence Assessment
While Kaito introduces AI analysis at the content level, it does not overlook the "network" factor. The platform's innovation lies in the introduction of the "Smart Followers" mechanism, which establishes a social graph in the crypto circle, incorporating the quality of followers into content value assessment. For Kaito, who follows you is more important than simply how many followers you have. Well-known accounts that mutually follow each other and form a core circle in crypto will be classified by the algorithm as Smart Followers (core followers).
If a certain author's follower list is filled with big names (for example, if Vitalik Buterin, Binance's CZ, etc., are following him), then this author's influence is clearly exceptional, and the upper limit of points they can earn for their content will be correspondingly higher.
This social graph model allows Kaito to more objectively measure the "intra-circle diffusion" of each tweet: whether it is spreading among outside bystanders or reaching the eyes of top industry figures. For instance, a message may have 100 retweets, but if most come from mutual-following small accounts just for fun, its actual value may be limited; whereas another message may have only 10 retweets but includes participation from heavyweight figures like Vitalik, making the latter's "value" clearly higher. In these two scenarios, Kaito would assign vastly different Yap points, avoiding a simplistic ranking based solely on retweet or like counts.
From actual results, accounts that rank high on the Yap leaderboard are often not the most followed influencers but rather deep players recognized by top KOLs. As a research report states, Kaito does not blindly trust traditional metrics like follower count or view count; instead, it focuses on the reputation weight of "smart followers" for rewards—regardless of whether you have hundreds of thousands of followers, if your content lacks real value, the Yap you receive may still be minimal. This "quality over quantity" assessment method corrects some of the pitfalls of purely traffic-based metrics, injecting a touch of academic "peer review" flavor into InfoFi's information distribution: only content that receives approval from experts can stand out.
Of course, the specific algorithmic details of the Smart Followers system remain undisclosed by the official team, and we can only infer its general logic from the results. The Kaito team is concerned that if the rules are completely transparent, it is inevitable that some will exploit the system to game the points, undermining ecological fairness. Currently, the introduction of the social graph has indeed increased the difficulty for the algorithm to resist cheating, but it also presents new challenges for newcomers: how to win the attention and interaction of industry bigwigs has become a key threshold for obtaining high points. On one hand, this serves as a positive incentive for content creators; on the other hand, there is a subtle concern that it may evolve into a game where a few big players monopolize discourse power—after all, no matter how intelligent the algorithm, the value ultimately assigned still comes from interpersonal networks.
Balancing Technical Costs and Multi-layer AI Architecture
Introducing so many features powered by "black technology," we also need to calmly examine the reality of the cost ledger—supporting Kaito's complex system comes with significant technical expenses. First is the cost of data acquisition. As mentioned earlier, obtaining Twitter data through legitimate channels in bulk is expensive, often costing tens of thousands of dollars per month. According to industry sources, Kaito initially attempted to acquire data through third-party channels or non-public interfaces, but as Twitter tightened its policies, these gray methods became unsustainable, forcing them to honestly pay for higher-level API access. This directly compelled Kaito to make trade-offs in its product strategy: if they opened up extensive queries to ordinary users, the monthly API call limits would quickly hit the ceiling.
Recently, Kaito has provided relatively limited free query services to ordinary users, preferring to sell its deep data analysis capabilities to institutions and professional clients. For example, some hedge funds subscribe to Kaito Pro, with monthly fees exceeding $800. By serving a small number of paying "big clients," Kaito can cover its high data bills, which also explains its current choice to primarily pursue a B2B (business-to-business) commercial route.
Another significant expense is AI computing power. Kaito claims to use GPT-4 level AI to understand content, but each call to the ChatGPT-4 interface incurs substantial costs. If they were to call GPT-4 in real-time for every tweet, the expenses would be astronomical. A rough estimate suggests that even using the cheaper ChatGPT-3.5, processing 50,000 tweets could cost over a thousand dollars; if they switched to the much more expensive GPT-4 model for full analysis, monthly expenses could even reach tens of thousands of dollars.
Clearly, Kaito would not operate recklessly. It is speculated that the team may have developed a strategy for "rationalizing AI usage": using large models only when necessary, while employing rule filtering or smaller models for less critical tasks to minimize the frequency of ChatGPT calls. There are also indications that Kaito is developing its own large model or multi-agent system, attempting to have some fine-tuned open-source models handle basic semantic scoring tasks. This way, they would only call the expensive GPT-4 when faced with complex issues or needing to generate long summaries, significantly reducing the average call cost.
Kaito's founder, Yu Hu, revealed that they currently use an AutoGPT heterogeneous agent architecture, deploying multiple ChatGPT models to work collaboratively, with ChatGPT-4 as the underlying core model, while also fine-tuning self-developed models to reduce reliance on third parties. This multi-layer model architecture reflects Kaito's difficult balance between effectiveness and cost: on one hand, ensuring that algorithmic analysis is sufficiently excellent and reliable, and on the other hand, being frugal to cut expenses. This "double-edged" balance is an operational challenge that the current InfoFi business model cannot avoid. It can be said that Kaito is engaged in a "technological gamble"—burning money to build a technological moat while hoping to find more economically viable alternatives in the future.
Conclusion: Reflection on the InfoFi Model and Its Future
Kaito's platform design is a bold integration of cutting-edge technology and business models: it quantifies social content into "attention assets" and uses tokens to incentivize the production of high-quality information. It sounds great, but implementing it is not without challenges. Kaito's so-called "InfoFi" is, to some extent, more like a rebranded SocialFi—whether called Yap points or something else, the essence is still a monetization game played through social networks to manipulate traffic and influence. In this regard, it shares similarities with early SocialFi projects like Friend.tech and Stars Arena.
The difference is that Kaito has added a layer of AI filtering and reputation weighting, attempting to raise the "quality threshold" of the game, preventing purely bot-driven traffic from running rampant. However, from the current results, this system still struggles to escape the Matthew effect: big names dominate the leaderboard, high scores closely align with top influence, and smaller accounts seeking recognition rely on support from larger accounts. Is this truly breaking the information monopoly, or is it indirectly reinforcing existing circles? This will be one of the core issues Kaito needs to confront in the future.
A more realistic challenge lies in the sustainability of the model. Kaito is currently highly dependent on the Twitter ecosystem—both data sources and user interactions are almost tied to the X platform. How far can this dependent development model go? If Twitter raises API prices again or tightens data access, can Kaito still manage? The currently high API costs have already forced Kaito to shift towards serving paying clients to sustain operations. However, if the InfoFi model is to expand to widespread participation, this cost will ultimately need to be distributed somehow.
On the other hand, the token economy supporting Yap incentives also holds uncertainties. Currently, the value of Yap points remains largely at the expectation level; if market enthusiasm wanes and expected value declines, will the top KOLs on the platform turn to other places, putting Kaito at risk of content loss? KOLs who navigate various platforms often go where the returns are highest. If Kaito cannot continuously provide sufficient returns or influence rewards, mere sentiment will not retain these top users.
Overall, for the InfoFi model to work, it ultimately needs to achieve a better balance between incentivizing deep content creation and maintaining its own revenue-generating capabilities. Can Kaito carve out a sustainable development path amid fierce competition and resource constraints? We shall see.
This article is from a submission and does not represent the views of BlockBeats.
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