In the world of Web 2, AI appears more as a "service tool." For example, when you search for hotels in Paris on Booking or Trip.com, AI pushes the most suitable options based on price, ratings, and location; or on Tripadvisor, it recommends attractions and dining based on your browsing behavior. These algorithms greatly enhance user experience, but ultimately, they are just helping the platform facilitate transactions, with the real profits still in the hands of the platform.

However, in Coinsidings 2.0, the role of AI undergoes a qualitative change. It is no longer just an assistant for recommending routes, but a value distribution engine. It can transform users' behaviors such as spending, sharing, voting, and participating in governance into computing power and rights, ultimately reflecting in quantifiable asset returns. In other words, AI here is not helping you "save money," but rather helping you "make money."

This is the biggest difference that Coinsidings has compared to traditional platforms. It transforms tourism from a one-time expense into a long-term asset accumulation through AI-driven revenue distribution, opening up a new wealth logic for users.

1. The Triple Role of AI in Coinsidings

1. User Profiling and Dynamic Revenue Distribution

In Coinsidings, every action of the user is recorded and modeled by AI. It not only counts how many times you booked a room and how much you spent, but also combines frequency, cycle, destination type, social sharing behavior, etc., to construct a dynamic user profile.

With these profiles, AI can design personalized revenue release curves. For example:

• For frequent travelers, AI will accelerate the pace of points release, allowing them to receive more liquidity rewards in a short period;
• For users who travel occasionally but spend larger amounts, AI will design stronger optionized returns, delaying the release of benefits to form long-term asset growth.

This differentiated distribution mechanism avoids a "one-size-fits-all" approach, allowing each user to receive the most reasonable asset returns based on their own behavior.

2. Transparency of Computing Power Incentives

In Coinsidings 2.0, AI is not merely counting user data but is tasked with solving a more complex problem: how to avoid bad behaviors such as order brushing and zombie users while ensuring that genuinely contributing users receive maximized rewards?

This is the significance of "computing power incentives." Users' actions will be converted into computing power values, such as:

• Publishing a high-quality travel guide and receiving community likes can increase computing power;
• Inviting friends to register and genuinely complete purchases can also increase computing power;
• Actively participating in DAO voting and submitting valid proposals will similarly count towards computing power.

AI will dynamically adjust the revenue distribution ratio based on the overall network's computing power distribution, ensuring that rewards truly flow to active and valuable contributors. The advantage of this mechanism is that it makes "active behavior = revenue entry," and through AI models, it avoids cheating behaviors, ensuring ecological health.

3. Risk Hedging and Revenue Optimization

The third role of AI is to help users manage risks and optimize returns. In traditional financial markets, options are tools for hedging risks and amplifying returns, and Coinsidings brings this logic into the tourism scenario.

For example, AI will automatically adjust the exercise conditions of users' options based on the seasonal predictions of a particular destination. When prices rise in peak season, the value of users' options increases; in the off-season, AI will remind or automatically optimize exit strategies to help users reduce losses.

This design allows users, even with just a few hundred dollars in travel spending, to enjoy risk management capabilities similar to Wall Street investors. AI gives "small spending" a "big leverage," enabling ordinary people to feel the power of financial tools in their travel expenditures.

2. Differences from Traditional Platforms: From Recommendation to Distribution

In traditional OTA platforms (such as Booking, Airbnb, Trip.com), the application of AI is more focused on the recommendation level. It analyzes users' search habits, geographical locations, and price preferences through algorithms to recommend suitable hotels, flights, or packages. Undoubtedly, this recommendation improves efficiency and boosts platform revenue, but the core logic always revolves around "the platform making more money," rather than allowing users to share value. For users, the benefits often stop at one-time discounts or point rebates, and once the transaction is completed, the relationship abruptly ends.

Coinsidings' design philosophy is completely different. Its AI does not just help users find cheap hotels but further participates in the revenue distribution mechanism. When users book a night at a hotel in Paris on the platform, they not only enjoy the stay experience but also simultaneously generate points and rights certificates. These rights, under AI modeling, will trigger personalized release and optionized mechanisms, and can even circulate in the secondary market. More importantly, AI will dynamically adjust the release ratio and pace based on users' past behaviors and contributions, gradually elevating users' status to enjoy higher long-term value. It can be said that in traditional platforms, AI is a "service tool"; while in Coinsidings, AI is a "revenue distributor." It transforms users from passive consumers into active investors, participants, and governors. This fundamental role shift also changes tourism from "spending for experience" to "investing while consuming."

3. AI-Driven Ecological Flywheel

The core innovation of the Coinsidings ecosystem lies in its construction of a value distribution flywheel mechanism through AI. Every user's action becomes part of this flywheel, driving the mutual growth of the platform and users.

First, users' spending on the platform will be accumulated as data. This data includes not only order records but also travel frequency, destination choices, social interactions, and sharing behaviors. Next, AI will model and analyze this data to identify the value contributions of different users and allocate points or option returns accordingly. High-frequency users receive immediate releases, while long-term users enjoy delayed dividends, thus forming diverse incentive methods.

When users receive more reasonable returns, they will naturally increase their participation and repurchase rates. They will choose to book again through Coinsidings, travel, or share with friends, leading to a new round of user growth. As participation increases, the overall revenue scale of the platform also rises.

In this process, AI plays a role again, redistributing the newly generated revenue back to users based on their behavioral contributions. This positive cycle ensures that the entire ecosystem is no longer solely enjoyed by the platform but benefits both users and the platform. The role AI plays here is like a transparent and fair "dividend officer," ensuring that value distribution flows to truly contributing users and forms a self-reinforcing flywheel effect.

4. Future Outlook: How AI Upgrades Travel Finance

As the Coinsidings ecosystem expands, the role of AI will not stop at the current distribution mechanism but will continue to evolve, pushing travel finance into a new stage.

Personalized Financial Solutions

AI will customize revenue curves for different user groups. For users who prefer short-term liquidity, AI can accelerate points release to increase immediate returns; for users willing to hold long-term, AI will delay releases to provide more stable long-term returns. This intelligent asset management approach allows every user to find the revenue model that suits them best.

Intelligent Governance Combined with DAO

AI will become an advisory "think tank" for decision-making. Through data modeling and predictive analysis, AI can anticipate the economic impact of a proposal, helping users make more informed choices when voting. This not only improves governance efficiency but also allows the platform to maintain more robust development during global expansion.

Global Fair Distribution

AI will play a larger role. The cross-border nature of travel assets involves multiple currencies and market differences, and AI can monitor and balance revenues between different regions in real-time, reducing unfairness caused by regional and exchange rate fluctuations. This capability will enable Coinsidings to truly become a financial platform shared by global users, rather than just a tourism application in a specific region.

Ultimately, Coinsidings' AI is not just a recommendation engine but an intelligent financial system. It upgrades tourism from a consumption industry to a wealth management industry driven by algorithms.

Conclusion

In traditional travel platforms, the endpoint of AI is "recommendation," and users remain consumers; while in the Coinsidings ecosystem, the endpoint of AI is "distribution," and users have become investors, participants, and shareholders.

AI transforms every user's spending, sharing, and voting into an accumulation of assets; it gives "small spending" a "big leverage," making travel not just a consumption of funds but an entry point to wealth.

This is the true transformation brought by Coinsidings: AI is no longer a tool for the platform to make money, but an engine for users to benefit. In the future, when we look back at the evolution of the travel industry, we will find that Coinsidings has not only changed the travel experience but also altered the logic of wealth.

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