Compilation | Wu Talks Blockchain

This issue features content from Alex's personal YouTube channel, focusing on the recent popular social product Kaito, delving into its product strategy, market background, and development logic. Alexon is the CIO of Ferryboat Research. By analyzing Kaito's choices on the Twitter platform and its characteristics in collecting, processing, and applying crypto social data, he explains the reasons for its high pricing and core advantages. Additionally, he compares the directional explorations of similar projects, pointing out how Kaito breaks through the limitations of traditional data services through API optimization, KOL mapping, and social binding mechanisms, successfully completing a strategic transformation and establishing a unique market position. He also shares entrepreneurial experiences and insights from industry practitioners, directly addressing the challenges and opportunities faced in the productization and commercialization of Web3.

Crypto Traffic Acquisition: Differences Between Advertising and Viral Models

Crypto is a high-volatility, high-risk field with strong financial attributes. You may find opportunities within it, but you should also be mentally prepared for the possibility of losing your principal. Next, let's discuss the first part: why Kaito and similar products choose Twitter as their main battleground.

First, from the perspective of the consumer goods industry, traffic structure is generally divided into two categories: public domain traffic and private domain traffic. In terms of traffic acquisition methods, there are two main paths: advertising and viral growth. Public domain traffic typically includes Twitter and YouTube, while in the crypto industry, Telegram and Discord belong to private domain traffic. In contrast, private domain traffic is harder to track and has a more singular structure.

Although platforms like Reddit, Instagram, and TikTok are gradually getting involved in the crypto industry, currently, the concentration of traffic on Twitter and YouTube remains the highest. In a domestic context, it may require leveraging platforms like Xiaohongshu, Douyin, and Kuaishou for promotion, while also needing platforms like Bilibili for recommendations, and finally promoting through direct channels or advertising platforms. Afterward, traffic is directed to WeChat and other private domains for conversion and repurchase.

Overall, the traffic acquisition methods in the crypto industry are relatively simple because the advertising logic cannot support sufficient effectiveness at the current stage of the industry. This leads to a relatively singular method of acquiring traffic, mainly concentrated on viral growth and distribution.

Comparison of User Acquisition Costs and Viral Effects in Different Regions

More than two years ago, when we were developing our own tool product, we tried advertising strategies. I invested tens of thousands of dollars for testing, and while I can't disclose specific data, one obvious result was that the cost of acquiring a user in the U.S. was about ten times that of acquiring a user in Vietnam. However, the viral growth rate of Vietnamese users was significantly higher than that of U.S. users. This indicates that U.S. users are less inclined to actively participate in viral promotion, such as creating and sharing a landing page.

In the entire crypto industry, I believe there are fundamentally only two ways to acquire traffic: distribution and viral growth. Although both methods essentially belong to a form of viral growth, their application logic differs. Distribution relies more on KOLs (Key Opinion Leaders) or KOCs (Key Opinion Consumers) for promotion; you hand over the product for them to endorse, and then they distribute it to retail users.

Viral growth, on the other hand, involves designing an efficient viral mechanism to create activities that attract users to participate actively. For example, Kaito's Yap activity is a typical case. Users share data from their Crypto Twitter (CT) accounts, such as how many "smart followers" they have, creating a gameplay similar to NetEase Cloud's annual playlist or consumption bill. Essentially, these mechanisms aim to achieve viral growth through users' spontaneous sharing, thereby gaining more traffic.

After explaining this background knowledge, it becomes clear why we initially chose Twitter as the main platform rather than a private domain. The biggest problem with private domains is that it is difficult to standardize the acquisition of all content, and it is challenging to effectively weigh and assess the content within private domains. For instance, if a community is entirely focused on discussing Kaito, you cannot accurately assess the true value and influence of that data. Additionally, the decentralization of private domain platforms makes it very difficult to comprehensively acquire relevant data. For this reason, it is not a priority choice.

Why Kaito Chose Twitter as Its Main Platform

On public domain platforms like YouTube, content is usually suitable for long video formats. For example, it could be a monologue video like the one I am recording now, an interview format, or content that focuses more on tutorials and interactions, even some mining operation guides. Such content often requires a long time to produce and watch, suitable for topics that need detailed explanation and learning. Therefore, this type of content medium is inherently not suitable for scenarios driven by immediacy or hot topics.

These long video contents are typically more suitable for handling PoW (Proof of Work) related themes. So even though we also tried to introduce Kaito's monitoring and analysis logic on YouTube and Farcaster, we ultimately found that the projects that could be effectively observed were usually ones like Kaspa and Helium, while performance for certain short-term meme tokens was completely lacking.

In contrast, Twitter is inherently suitable as a data platform, especially in an environment where social data concentration is very high. Almost everyone's marketing budget is concentrated on Twitter, forming a high level of consensus. At the same time, Twitter's social graph is also very transparent; for example, your following list, engagement counts, and other data are presented in an explicit form. On platforms like YouTube, it is difficult to obtain clear fan relationships or interaction details.

Ultimately, the reason for choosing Twitter as the main platform is that it is the optimal solution. Its transparent social graph and centralized traffic structure provide us with clear advantages. In contrast, on platforms like YouTube, acquiring similar relationship data is very difficult, if not impossible. Therefore, both we and Kaito are more inclined to prioritize Twitter as our main battleground.

Two Main Reasons for Kaito's High Pricing: API Costs and Regulatory Restrictions

At that time, we used some "tricks"; Twitter had not yet been acquired by Musk, and there were some gray areas in the system. For example, using educational accounts or other means to obtain data, although not entirely compliant, was common in the early stages. For early projects like Kaito, I suspect they initially adopted similar strategies to acquire data through these informal channels. However, when the product began to commercialize, this approach was clearly no longer viable.

Two years ago, when they completed financing and launched the product, they could only rely on commercial APIs, and after Musk acquired Twitter, many non-compliant channels were blocked. The cost of using commercial APIs is quite high, and as the number of calls increases, this cost grows linearly rather than decreasing.

The second reason for the high pricing is Twitter's regulatory restrictions. Even if a company uses a commercial API, there is a limit on the number of calls per month (I can't recall the specific number). This means that if the product becomes particularly popular, the limitation on call volume will make the ToC (consumer-facing) model unsustainable. Ultimately, both we and Kaito chose the ToB (business-facing) model at similar points in time, which is the best solution to maximize the economic value of limited call volume. For Kaito, there were almost no other options.

Specifically, since the call volume is fixed, the only way to achieve greater economic returns is to increase the value per user, simply put, to raise prices. This is precisely a necessary choice for the product; otherwise, the entire business model cannot be established.

I learned that their delay is about 15 minutes, similar to our delay. It is important to understand that the shorter the delay time, the higher the required cost. This is because it requires scanning historical data at a higher frequency, and this cost increases exponentially. The setting of delay time also directly affects the efficiency and economic feasibility of API calls. In summary, Kaito's high pricing under the constraints of API calling costs and regulatory restrictions is reasonable.

Evolution and Choices of Kaito's Product Direction

Next, let's talk about Kaito's product direction and why they evolved from "trending" type products to the current KOL type features. Here, I will first provide a small conclusion—it's not about teaching others how to start a business, but rather sharing our own experiences. We have tried multiple directions and found three directions that can be derived based on this logic.

The first direction is a purely self-use Alpha tool. Kaito's CEO mentioned in a podcast that they also considered this direction. If the tool is only used for Alpha-type purposes, the more it develops, the more it tends to be for internal use, rather than suitable for large-scale users. We have encountered similar issues—if it is free, users may not value it; if it is charged, why not just use it ourselves? Such issues make Alpha tools generally more suitable for self-use rather than productization.

We once developed a tool using a logic similar to Kaito's. This tool's application allowed us to often discover projects before they became popular. We considered using this logic to create a listing tool for exchanges. For example, I once wanted to collaborate with Binance to provide this tool for free to optimize their listing selection criteria. Because certain projects, like ACT, did not show any noteworthy performance in our "God's eye view" based on Twitter data analysis, yet still got listed on exchanges. Such unreasonable choices could have been avoided with a data-driven tool.

Additionally, we also explored applying Alpha logic to quantitative trading strategies. We tested the top 200 or top 100 projects on Badcase, making trading decisions based on text mining, sentiment analysis, and so on. The test results showed that this strategy was significantly more effective for smaller market cap projects that are easily influenced by sentiment and events, while it had limited effectiveness for larger market cap projects. I believe Kaito has also conducted similar research, as their CEO has a trading background. From this perspective, we and Kaito share many similarities in our early starting points and logic, but the paths we ultimately chose are quite different.

Kaito's Exploration of Community News Tools and Its Industry Potential

In the current model framework, some phenomenal themes, such as memes and NFTs, are very prominent. They can show potential for price increases within this logic. However, such phenomena cannot be completely resolved through standardized programmatic trading, as they still require strong human intervention. This characteristic makes them effective but lacking in standardization. As for whether Kaito has similar product directions internally for its own use, I am not sure.

The second direction worth exploring is news and GPT-related products. What does this mean? For example, a Web3 assistant like Alva (formerly Galxe) can integrate Twitter's real-time data to obtain all tweet corpora and process it using the ChatGPT interface. By adjusting the prompts on the front end, these data can be output in a more intuitive form, generating many timely community news articles.

A simple example: when you see the "elisa" case dispute, you might be confused. At this point, you can directly ask this tool, "What is the reason for the case dispute over 'elisa'? Who initiated it?" In this way, the tool will summarize the answer based on the latest data. The original GPT cannot do this because its data has a fixed cutoff date and usually cannot provide content from the last six months. You can only crawl the relevant corpora yourself and feed it to GPT, then summarize the logic through prompts. The potential of such tools is enormous and is a direction worth exploring in depth.

Currently, Kaito seems to be exploring such products or attempting similar directions. The Alva product I mentioned is a good example. It integrates a large amount of industry data by calling APIs related to the crypto field, such as Rootdata, connecting users with industry information point-to-point. However, Alva has the problem of insufficient data cleaning quality. They spent a lot of time connecting data networks, but there is still room for improvement in data accuracy and the level of detail in cleaning. In contrast, Kaito's advantage lies in its data accuracy, which is beyond doubt.

For a practical case, I recently obtained quick answers regarding the "elisa" case dispute through such tools. The application of these products in the crypto industry can indeed significantly enhance efficiency. More than two years ago, we also developed similar tools, and the test results showed that they could improve work efficiency. However, when we tried to commercialize, the core issue we encountered was the lack of strong willingness to pay from users. Although the tool could enhance efficiency, it did not address a core pain point, which left users without a strong purchasing motivation.

Additionally, due to the high calling costs of such tools (each call to the GPT interface incurs a fee), the product's gross profit margin is relatively low. Therefore, while these tools have certain significance, their commercialization faces considerable challenges. Many calling behaviors are more for activation purposes, and the actual scenarios that generate revenue are limited, all of which become problems that need to be overcome. Overall, while this direction has great potential, more optimization and breakthroughs are still needed in practical implementation.

The Role of Data Accuracy and KOL Mapping in Marketing

When discussing these tools, there is a core question: how do they achieve revenue? If relying solely on a VIP model that allows users to call the API unlimited times, such products are unlikely to have significant profit margins, but their existence is meaningful. They can directly utilize Kaito's logic to read Twitter data for generating and distributing self-media content, such as "Wu Talks" or other forms of community news. These tools not only enhance efficiency but also help project parties distribute content across multiple platforms, such as generating short videos with AI for TikTok or posting directly on Twitter.

I believe this product direction is not something only Kaito or Galxe can attempt; projects like Mask are also very suitable for this. Strangely, Mask does not seem to have deeply ventured into this direction at present. If any team members from Mask hear these suggestions, I hope you can consider trying it out.

For Kaito, its current product direction indicates that they want to move towards a larger market value rather than continue along the Alpha tool route. While Alpha tools can be profitable, they lack productization potential. If focused solely on this, it will ultimately be limited to internal use and unable to form a product aimed at a larger market. By shifting towards KOL mapping, Kaito is clearly aiming to break through this bottleneck.

The early users interested in Kaito's products were almost identical to the user group that was paying attention to our tools at that time. Our tools were also suggested for sale to some trading companies or secondary funds in the early days. Although these trading companies were more focused on profitability, this direction would fall into a cycle of "whether to be profitable." In contrast, KOL mapping provides precise support for marketing placements, enhancing the effectiveness of placements through data accuracy, thereby increasing the marketing value for project parties.

Data accuracy is key. While many companies can collect Twitter data, whether the data is accurate is another matter. In the public market, Kaito and our early tools are among the few that can achieve accuracy. The core of data accuracy lies in "data cleaning," which is the most difficult and critical step. Collecting data is relatively simple, but weighting and cleaning the data requires a lot of repeated testing and logical adjustments, often needing a combination of experience and intuition.

For example, the Chinese community's Crypto Twitter (CT) often has a lot of noise, and the weight needs to be reduced. This noise causes Chinese CT to typically lag behind English CT by 24 to 48 hours. How to effectively clean and adjust the data is a "core skill" and also the company's core competitiveness.

Through precise KOL mapping, Kaito can help project parties optimize their placement strategies and improve the accuracy of placements. This product can not only assist project parties in achieving more efficient marketing but also generate marketing fees, forming a sustainable business model. Choosing this direction is a smart strategy that Kaito has shown in market competition.

The Strategic Logic and Flywheel Effect Behind the Yap Activity

In the entire Crypto field, advertising has always been a relatively vague and inefficient behavior. Current marketing agencies essentially function more like simple tools for maintaining contact lists, with relatively singular means. In this context, the tools provided by Kaito can help project parties determine which KOLs are worth placing ads with and which are not, providing evidence-based references through data analysis. This precision greatly enhances the efficiency of advertising.

Kaito optimizes KOL placements through two key metrics: correctness and core circle. Correctness refers to whether the KOL's judgment is accurate, such as whether they discussed a project before it rose, rather than participating after the project has already increased. Each time a KOL shares or promotes, whether their judgment is correct is recorded and weighted, affecting their weight score. All of this can be repeatedly verified through timestamps and data analysis tools.

The core circle (referred to as "smart followers" in Kaito) measures the depth of a KOL's influence. If an account has more smart followers interacting with it, its weight score will be higher. This helps project parties filter out truly influential KOLs rather than just accounts with a large number of followers.

Kaito's Yap activity demonstrates the success of its strategic transformation. This activity significantly reduced marketing costs by leveraging free KOLs. Traditional marketing requires contacting KOLs one by one and paying high fees, while Kaito directly opened a page to allocate rewards to KOLs through a weight algorithm. This method not only simplifies the process but also enhances credibility through data transparency. This model encourages many KOLs to voluntarily participate in promotion, helping projects spread rapidly.

At the same time, the Yap activity also addresses potential risk issues. Considering that Twitter may change its API rules in the future, Kaito allows all CT users to bind their accounts to its backend through TGE, actively authorizing data usage. This approach enables Kaito to gradually detach from its reliance on Twitter API and begin to master its own data assets. This not only gives Kaito greater independence but also creates a positive cycle between supply and demand: as more CT users bind their accounts, project parties' interest increases, forming a flywheel effect of data matching.

Ultimately, Kaito has created a commercial imagination similar to Alibaba's Alimama or ByteDance's Douyin through this model, becoming a successful marketing ecosystem platform in the crypto industry. Currently, this strategy has been executed quite successfully.

Entrepreneurial Reflection: How Non-Typical Elite Background Practitioners Break Through

If all CT (Crypto Twitter) users bind their accounts to Kaito's backend, then in the future when entering the secondary market, Kaito can clearly tell the outside world: "This data is mine." Whether for project parties or CT users, this binding behavior can form data consensus and trends. This is the core logic behind the Yap activity.

Before concluding the discussion on Kaito, I want to share a little story about ourselves. Before Kaito's financing, we also developed similar products, and it can even be said that we were working on them concurrently. More than two years ago, we simultaneously tried the directions of Alpha tools and GPT-related tools. At that time, the industry was in a downturn, and our team was not very good at socializing, with very few connections in the industry. Although our product was interesting and had potential, there were hardly any friends to introduce us to VCs.

At that time, we approached four VCs, one of whom was willing to co-invest but needed us to find a lead investor. The other three directly ignored us, one reason being that our background did not fit the typical elite entrepreneur image. They did not delve into the logic behind our product, nor did they attempt to imagine its potential value; instead, they simply vetoed us.

It wasn't until later that we gradually gained attention from more industry professionals through platforms like YouTube. Most of these viewers were institutions and practitioners in the industry. Even so, I still did not mention the past to those VCs who had previously contacted us because it felt a bit awkward. Interestingly, I later saw employees from those VCs praising Kaito, which left me quite emotional.

We ultimately chose to pursue the Alpha tool route, a decision related to our limited social circle at that time. We believed that without external help, it would be difficult to successfully commercialize a ToB product. We hoped to gain market expansion through recognition from well-known VCs, rather than relying solely on our own difficult path.

For those entrepreneurs with non-typical elite backgrounds, I have some advice. VCs are more concerned about connections and relationship networks rather than necessarily focusing on your product itself. However, I always believe that a good product can speak for itself. If your product is truly good, do not be afraid to showcase it. Nowadays, I also realize the importance of building social influence. Through social networks, you can not only meet more people but also accumulate a certain level of recognition and trust for future entrepreneurship.

For friends who watch my videos or browse my Twitter, the belief I hope to convey is: regardless of whether you have an elite background, as long as your product is excellent enough, I am willing to help you. Good products and ideas are more important than a glamorous resume. As long as what you present can gain my recognition, I will do my best to help you find resources.

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