AI × options × computing power Coinsidings brings "travel demand side" into the era of algorithms

[TFExchange]

The digital transformation of the tourism industry: from "transactional platforms" to "algorithmic ecosystems".
In the past 20 years, the digital revolution of the global tourism industry has always revolved around "supply-side efficiency". Traditional OTAs such as Booking, Expedia, and Airbnb make it more convenient for consumers to complete orders by aggregating listings, reducing costs, and matching transactions. However, their essence is still a "one-way matching" transaction network. The value of user behavior is monopolized by the platform, the profits of merchants are eroded by high commissions, data cannot circulate, and trust lacks transparency.
The emergence of Web3 has made "value" no longer a traffic asset exclusive to the platform, but an economic right jointly created and held by users and merchants. Coinsidings unfolds on this logic: it uses a triple model of AI × options × computing power to redefine the value structure of the "travel demand side", making every trip, sharing, and interaction quantifiable, profitable, and investable.
In Coinsidings' system, AI is no longer a subsidiary of the recommendation engine, but a dynamic value scheduler - it understands demand, distributes revenue, optimizes liquidity, and truly enters the era of "algorithmic autonomy" for the tourism economy.
AI Reconstruction Demand Side: From Big Data to "Travel Personality"
The recommendation logic of traditional travel platforms is often based on "popularity ranking" or "price priority". The algorithm focuses on "what sells well" rather than "what you really need". This model ignores the complexity of travel behavior - emotions, time, budget, preferences, peer relationships, cultural background, etc., all of which are key dimensions that shape decision-making.
Coinsidings' AI system uses MultiModal Machine Learning data modeling to build a core asset called "Travel Identity Graph".
The system generates dynamic portraits by recognizing multiple signals such as the user's browsing path, search frequency, payment habits, Time Stay, and interactive behavior. It not only knows that you like island vacations or city roaming, but also infers that you prefer "quiet experiential accommodation" or "cost-effective short-distance self-driving", and can even predict the time window for your next trip.
The significance of this model is that for the first time, AI can calculate "potential travel demand" and intelligently match it with the merchant's supply before the user clearly expresses their intention. When supply and demand matching is led by AI, the resource allocation efficiency of the tourism industry will be multiplied, and Coinsidings has also built its own differentiated Competitive Edge - Algorithms understand human nature, and data feeds back to the economy.
III. Computing Power: Quantifying and Valuing Behavior
Another revolutionary design of Coinsidings is to abstract user behavior into "computing power".
In the traditional Web2 model, user behavior (clicking, browsing, ordering, sharing) is only regarded as input signals for algorithms; but in the Coinsidings system, these behaviors themselves are the source of value generation.
Every consumption, invitation, interaction, and evaluation will be converted into corresponding computing power values in the system. The AI algorithm adjusts the computing power weight in real time based on behavior type, frequency, relevance, and ecosystem contribution. For example:

• Consumer computing power reflects the real purchasing power of users in the ecosystem;
• Invite computing power to represent social influence and communication contribution;
• Community computing power measures engagement and content contribution;
• Merchant computing power is based on revenue, ratings, re-purchase rate, and activity participation.

The accumulation of computing power is not just a "reward" in the form of points, but an economic weight that can participate in dividends, option allocation, and governance. The AI system continuously analyzes the distribution of computing power across the entire network to ensure the dynamic fairness of the incentive structure and keep the energy flow of the ecosystem in a balanced state. This means that Coinsidings turns the behavioral data that was previously "freely obtained" by the platform into users' own economic chips. Behavior is an asset, interaction is a benefit.
IV. Option mechanism: linking returns to contributions
In traditional financial logic, options are a tool for balancing risk and return, while in the Coinsidings ecosystem, options have become the core currency for distributing value. AI issues corresponding option types to each participant based on parameters such as computing power contribution, holding time, user level, and ecosystem active level - consumption options, contribution options, asset options, and airdrop options .
Each type of option has an independent yield coefficient and release period. For example:

• Consumer options are generated based on the user's travel consumption and are directly related to the platform's transaction income.
• Contribution options reward active users and promoters, reflecting the dividends of network growth.
• Asset options are linked to RWA, representing the share of real-world tourism assets held by users.
• Airdrop options are aimed at early participants and are used to incentivize ecosystem diffusion.

These options are not static vouchers, but dynamic circulating assets. AI will automatically adjust the release rhythm based on market fluctuations, profit scale, and user activity. For example, when the overall revenue of the platform increases and the user engagement rate increases, the system can accelerate the release of options; otherwise, it will automatically tighten circulation to maintain market stability. This algorithmic governance of returns makes wealth distribution no longer dependent on human rules, but self-driven by data logic.
AI-driven liquidity and market autonomy
In the market layer of RWA asset and option trading, Coinsidings' AI plays the role of "Market Maker".
It adjusts the allocation of funds in the liquidity pool in real time through intelligent analysis of order depth, trading volume, and price range. AI can identify which assets have increased demand and automatically increase liquidity allocation; when price fluctuations exceed the threshold, it triggers repurchase and destruction mechanisms to maintain price stability.
This is different from the "passive liquidity" of traditional DeFi. Coinsidings' AI market-making model is closer to a "dynamic hedging system" - it can provide price stability during high volatility periods and improve fund utilization during low liquidity periods. More importantly, all market-making logic is executed by smart contracts, and anyone can view parameters and execution records in real time, forming a new type of financial bottom layer that is open, transparent, and data traceable .
This mechanism not only enhances the market depth of CSS tokens and options, but also makes platform assets more risk-resistant.
Traditional Web3 projects often collapse due to "liquidity exhaustion", while Coinsidings' AI dynamic market-making mechanism constructs a self-balancing structure, enabling the entire ecosystem to maintain resilience in different market cycles.
VI. AI Virtual Tour Guide and Intelligent Experience: How Algorithms Define Travel Fun
Coinsidings' AI system is not only a financial engine, but also opens up new space in the User Experience layer.
When users enter their destination, budget, and time preferences, AI can automatically generate personalized itineraries - hotels, transportation, dining, and one-click packaging for activities; at the same time, it can dynamically adjust recommendations based on user portraits, such as:

You have stayed at a boutique hotel by the lake in Zurich. This time, we can give priority to recommending the Como Lake area in Italy, which is of the same level of experience.