In an article published by Grayscale in May this year, it was mentioned that currently, in the entire cryptocurrency industry, there are only a few AI protocols capable of generating sustainable income, namely:

Virtuals: Annual revenue of approximately $30 million, mostly from transaction fees of AI agent tokens on the platform.

Grass: By using distributed residential IPs to bypass traditional web scraping restrictions, it has achieved annual revenue of tens of millions of dollars by selling the collected web data.

"The data is the oil of the new era," and the value of this statement is still rising.

Tonight, the TGE and the launch on Binance Alpha of @codatta_io is also a project deeply engaged in the data sector. Unlike Grass, Codatta provides data labeling services and previously secured $3 million in seed funding led by OKX Ventures, along with significant support from the Avalanche Foundation and EigenLayer.

Codatta, as an open data labeling protocol, combines blockchain-based data infrastructure with a collaborative network of human contributors and professional AI agents. There are several noteworthy points:

First, the team.

The Codatta team has a strong "Alibaba flavor." The CEO was previously a senior engineering manager at Alipay and holds a PhD in AI from the University of Cincinnati; the Chief Product Officer was a former deputy director at Alibaba. The CTO has served as the Chief Technology Officer at VipKid and Douban.

Second, revenue.

According to official disclosures, Codatta has completed over 560 million labels and is nearing break-even.

Third, airdrop.

0.3% of the tokens will be airdropped to ecosystem users, including Codatta point holders, Codatta NFT holders, and participants in significant ecosystem activities. The link for airdrop queries and claims will be announced on July 24.

Codatta will expand into other fields in the future. Although there are no details, I can speculate that it may include:

AI pre-labeling: Utilizing large language models and visual models as pre-labeling tools, with human labelers shifting more towards roles as "curators" and "auditors."

Deepening vertical domain data: For example, in robotics, medical imaging, legal texts, financial data, etc.

More complex incentive and governance models: For instance, implementing dynamic task pricing through smart contracts based on factors such as task difficulty, urgency, and labeler reputation.

Lastly, I want to share some news: the Codatta Booster Campaign Season 2 will launch tomorrow, with a weekly prize pool of 50 million $XNY. Get ready to open the book.

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