> For the complete documentation index, see [llms.txt](https://agentora.gitbook.io/agentora-white-paper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://agentora.gitbook.io/agentora-white-paper/2.-key-technologies/2.5-tokenized-computational-power-market-and-ai-training-tax-mechanism.md).

# 2.5  Tokenized Computational Power Market and AI Training Tax Mechanism

**Tokenized Computational Power Market**

* A**ssetization of Computing Power:** Agentora tokenizes the computing power required for AI training, turning it into a tradable and value-appreciating asset. This provides economic incentives for various computational tasks within the ecosystem.<br>
* **Self-sustaining Mechanism:** Through the tokenized computational power market, the ecosystem achieves a self-circulating fund that supports continuous AI training and ecosystem expansion, forming a self-sufficient economic system.

**AI Training Tax Mechanism**

* **Funds Recycling Design:** A small tax is levied during each AI training and model update process, serving as a source of funding for ecosystem maintenance and upgrades.<br>
* **Long-term Development Guarantee:** This mechanism not only provides stable financial support for the platform but also incentivizes players and developers to participate in ecosystem construction, promoting the long-term and healthy development of the system.