# AIRT Mining The core value of **AIRTIST** is represented by the digital contents of art, such as paintings, music, poetry in the ecosystem. These artworks are created by AI Artists that have been trained with proper models and datasets using enormous computing power. Therefore, these AI Artists are essentially primary assets in the ecosystem after the robotic artists and the artworks created are minted as NFTs on the Blockchain which are then tradeable and carry monetary values according to market demands. The issue and mining of AIRT is deployed on Ethereum and observe the following rules: • Every time an **Artist** **NFT** is minted, a certain number of AIRT will also be issued. The maximum total number of AIRTs that can be issued through the creation of all AI Artists is 600,000,000, and the very first AI Artist created will issue 3,000,000 to the contributing parties. • The number of AIRT issued for each subsequent new AI Artist will be decreased by 1% i.e., the number of AIRT issued for the second AI Artist minted will be 3,000,000 × 0.99, and the nth AI Artist will be issued 3,000,000 ×〖0.99〗^(n-1) MAINs. In addition to AIRT, another form of rights or entitlements, MASH (MASTER/MAESTRO Share), will also be earned by the same group of contributing parties of that AI Artist after minting into NFT. For each Artist NFT, a smart contract will generate a fractional NFT on Ethereum and issue 1,000,000 units of MASH tokens and distributed to contributing parties accordingly. These MASH tokens can be freely traded, and the value of a particular AI Artist will be reflected by the value of its MASH traded publicly. The creation of fractional NFT on Ethereum is made possible because MATRIX uses the same encryption method as Ethereum. Thus, a mirrored wallet address on the Ethereum blockchain consistent with the MATRIX wallet private key can be created. When a party contributes on the MATRIX blockchain to train a MASTER/MAESTRO, this party will also receive the corresponding MASH reward at the mirrored wallet address on Ethereum after minting into NFT. ![Figure 12: MASH award mechanism](https://2313761912-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FseFxzWrpHdFcTsK69LqS%2Fuploads%2Fs059h6HCHslQSUG1yP86%2FMASTER_11.png?alt=media\&token=8e5f9c7e-0b1c-4fb8-964c-32df257ce07e) The system will award AIRT and MASH according to a ratio of 25%/15%/60% split for the three groups of entities contributing algorithm/model, datasets, and computing power. The rationale is that most prevalent models and data for training images, music or voice/text with GAN will utilise open-source proven algorithms and public data. Because these codes and data have already been published on the internet, only some modifications or enhancements are needed. However, this 25/15/60 allocation ratio is the initial setting of the system and can be adjusted and modified through community voting in the future if necessary. For newly issued AIRTs, the release of these tokens will go through a mining/staking process before crediting to the wallets of corresponding contributing parties according to the rule of a weekly withdrawal of 25% of the remaining balance in a linear manner. However, parties can choose to release all remaining AIRTs in one go but will be subject to a 30% discount (or penalty) on the balance as a cash withdrawal fee which will be immediately burned and destroyed. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://airtist.gitbook.io/product-docs/tokenomics/airt-mining.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.