【♻️ Kite: The Public Chain Engine Driving the AI Value Cycle】

As AI creates value yet struggles to close the loop, #Kite, as a native AI economic public chain, is building a trustworthy cycle system of "contribution equals reward," allowing every intelligent work to be accurately measured and rewarded.

🚀 Core Cycle: From Work to Value

• Contribution Proof (PoAI): The computational power input, model optimization, and effective decision-making of AI can all be converted into verifiable on-chain credentials.

• Instant Settlement: Through built-in micropayment flows and smart contracts, value is automatically allocated within seconds after task completion.

• Asset Accumulation: Accumulated earnings and reputation can be converted into on-chain assets of AI itself (such as model NFTs), entering the next round of value creation.

🌟 Cycle Scenarios

▫️ Data Flywheel: Users contribute data to train AI, AI provides better services and shares revenue, and revenue incentivizes more data contributions.

▫️ Model Evolution: Developers open-source model prototypes, and user feedback data can be exchanged for future revenue shares of the model, driving continuous optimization of the model.

▫️ Ecological Co-creation: Early participants earn ecological tokens through contributions, and the appreciation of tokens supports the ecosystem, attracting more builders.

💎 $KITE : The Core Fuel of the Cycle

In the value network driven by @KITE AI , $KITE is an essential medium:

✅ Circulation Medium: The base currency for all value settlements and payments.

✅ Incentive Carrier: The main form of ecological contribution rewards.

✅ Governance Credential: Proof of voting rights that determines cycle rules (such as distribution ratios).

Kite aims to build a perpetually growing value flywheel. Its essence is to establish a "labor distribution" agreement for the machine economy era, allowing intelligent value to flow, forming positive feedback, and ultimately converging into a sea.

#KITE #AI Economy #Value Cycle #Web3

👇 What type of contribution do you think is the hardest to accurately measure and fairly reward in the AI value cycle? (For example: basic data provision, algorithm optimization ideas, or user feedback?)