【💡 Kite: Redefining the 'Data-Model-Market' Closed Loop of AI Value】

In the current situation where data, models, and value flow are disconnected, #Kite has built an efficient closed loop that seamlessly connects data contribution, model training, and market application, reshaping the entire process of AI value creation, evaluation, and distribution.

🚀 Closed Loop Core: Trinity

• Data Assetization: Raw data is transformed into tradable on-chain assets through rights confirmation and privacy protection technologies.

• Model Capitalization: AI models become capital assets that can be owned, invested in, and generate continuous income.

• Market Instantization: Demand and supply are automatically matched through smart contracts, and value is settled in real-time.

🌟 Operational Scenarios

Individual Data Contributors: Securely share data and continuously receive revenue from the use of AI models.

AI Model Developers: Issue models as assets, automatically earn income from each invocation, and continuously optimize using market data.

Businesses/Users: Call the most suitable AI services as needed and based on effectiveness, with transparent and controllable costs.

💎 $KITE : The Driving Force of the Closed Loop

In the @KITE AI ecosystem, $KITE is the core element driving the operation of the closed loop:

✅ General Payment: Payment for data fees, model invocation fees, and service rewards.

✅ Governance Credential: Voting to decide whether ecological resources lean towards the data side or the model side.

✅ Value Carrier: Capturing the growth dividends brought by the expansion of the entire closed loop economy.

The essence of Kite is to build a positive feedback AI economic flywheel: more data generates better models, better models attract more users and revenue, and revenue incentivizes more data and models to join—while $KITE is the lubricant and driver of the entire flywheel.

#KITE #AI Economy #Value Closed Loop #Web3

👇 In this closed loop, which do you think is more critical for initiating the flywheel: incentivizing more high-quality data contributions or cultivating more high-quality AI models? Why?