I took a look at the $OPEN token economics.

From the perspective of token distribution, OpenLedger's design is quite aggressive: 61.71% is allocated to the community and ecosystem, which is a very high proportion among current AI projects.

However, there are a few questions that I am really concerned about.

1/ Technical Challenges

Although the community allocation accounts for a large portion, it can be broken down into:

Attribution proof rewards

Inference rewards

OpenCircle funding

Data network funding

Airdrops

etc.

The effectiveness of this complex distribution mechanism largely depends on whether OpenLedger's attribution technology can truly track the impact of data on model outputs accurately.

From a technical standpoint, OpenLedger's attribution proof system is its core selling point. By tracking which datasets influence model outputs, it directly rewards high-impact data contributors. Theoretically, this is perfect, but the practical implementation is extremely challenging. The decision-making process of AI models is inherently a black box; how can we accurately quantify the contribution of a single data point?

2/ Ecosystem Development

The token unlock design is relatively reasonable:

21.55% enters circulation at TGE

Both the team and investors have a 12-month lock-up period + 36 months of linear unlocking

Community tokens begin linear unlocking from the first month

This design avoids significant selling pressure at launch.

In terms of use cases, $OPEN supports multiple functions such as gas fees, inference payments, governance, and model deployment. But the core question is: Can OpenLedger truly attract enough developers and data contributors to build the ecosystem?

3/ Future Outlook

The biggest risk is that traditional AI giants hold a vast amount of data and computing resources. The problems that OpenLedger attempts to solve are rooted not only in technology but also in the landscape of interests and legal frameworks.

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