XtalPi's science tokens business model
Had an exchange with Mr. Wen from Jingtai $XTALPI(02228.HK) regarding science tokens. The summary is as follows:
The business logic and technical architecture of evolving from "General Tokens" to "Advanced Scientific Research Tokens". The following is a summary of the conversation:
Core Concept: 3.0 Advanced Scientific Research Token
Differentiating from traditional chat or programming tokens, this concept emphasizes that AI not only possesses reasoning capabilities but also the closed-loop ability to solve complex real-world physical problems.
1. Technical Support Architecture
To achieve breakthroughs in drug development and materials science, relying solely on large language models (LLMs) is insufficient. It is necessary to leverage resources from the following three levels:
- Large Model Reasoning Layer: Serves as the central hub, providing logical thinking and strategy generation capabilities.
- Internal Professional Model Library: Calls upon over 200 validated internal professional models (such as first-principles, molecular dynamics models, etc.) to ensure scientific accuracy.
- Physical AI (Physics Lab): Interfaces with automated, robotic laboratories. AI drives the operation of physical entities, transforming "programming code" into real drug samples or material entities.
2. Evolution of Token Value Tiers
- The logic of the content divides the commercial value of tokens into three stages:
| Token Tier | Core Capability | Value Density | Typical Scenarios |
|---|---|---|---|
| Basic Tier | Chat, information exchange | Low | Daily Q&A, customer service |
| Advanced Tier | Programming, logical reasoning | High | Software development, process automation |
| Advanced Scientific Research Tier (3.0) | Scientific Research + Physical Closed Loop | Extremely High | Drug discovery, new materials R&D |
3. Business Model Transformation: From "Pay-per-Use" to "Sales Royalty"
Because 3.0 Tokens can directly produce high-value scientific outcomes, their pricing logic has fundamentally changed:
- Advanced Pricing Strategy: No longer just charging per word count, but pricing based on the scientific value they create.
- **Royalty Sharing Model**:
- Low-Barrier Entry: Partners can use this token for research and development.
- Backend Royalty: Once a drug or new material is successfully developed using this token and goes to market, the company will extract future sales royalties.
Summary
Core Logic: AI reasoning capability + professional scientific models + automated physical labs = 3.0 Token with the ability to deliver physical products. The essence of this token is a "digital scientist," and its commercial endgame is deep participation in the profit-sharing of the industrial chain (sales royalties).
The questions I raised are:
- Is it necessary to define what a science token is? There is currently no clear definition within the industry. Could you attempt to announce what you believe science tokens are? And then define the related business model?
- How to not only make your business partners but also market investors perceive the value of your science tokens? Coding can now be experienced, so the market easily perceives it. Yours currently cannot be perceived for its capabilities. What are some good ways to let investors and the market experience it?
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