OpenLedger is positioned as an AI-dedicated blockchain (AI Blockchain), focusing on decentralized dedicated small models (SLM) plus on-chain contribution and ownership verification. It belongs to the DeAI (Decentralized AI) track. Its native token is OPEN.
1. Basic overview
1. Founding background
Officially founded in 2024. Founding team: Pryce Adade-Yebesi, Ashtyn Bell, Ram Kumar. First round of funding: USD 8 million. Investors include top crypto funds such as Polychain Capital, Borderless Capital, etc.
The underlying layer is an EVM-compatible L2 network, compatible with Ethereum wallets and contract systems.
2. Core goals
Addressing pain points in the traditional AI industry: data creators are used by major companies without compensation; model contributions cannot be traced; and revenue is highly centralized and monopolized. By leveraging blockchain, it enables on-chain ownership verification, traceability, and monetization for data, models, and AI agents. It proposes the **Payable AI (payable AI)** model: as long as data is called by an AI, contributors continue to earn rewards.
II. Core technologies and architecture
1. PoA — Proof of Attribution (core innovation)
The underlying consensus mechanism of the entire chain; its role is:
- Precisely track the impact weight of every training data point and model parameters on AI output results
- On-chain automatic accounting of contribution share; tokens are distributed automatically according to usage frequency.
- Enables end-to-end AI auditability and explainability, breaking the black-box large-model model
2. Datanets — domain-specific data network
Abandon the route of general-purpose massive datasets; instead, create independent data subnets divided by industry and scenario. The community collectively collects, cleans, and labels vertical-domain data, specifically for training SLM-dedicated small language models. Compared with general-purpose large models, it achieves higher domain accuracy and lower training cost.
3. Model Factory
A visual low-code platform: ordinary developers can fine-tune dedicated SLM models based on the Datanets dataset without deep computational power and algorithm expertise; all training processes are stored and verified on-chain.
4. OpenLoRA lightweight deployment engine
Based on LoRA low-rank adaptation technology, a single GPU can simultaneously support thousands of fine-tuned small models, greatly reducing inference compute costs; at most, it can compress up to 99% of inference costs, facilitating distributed deployment of massive domain-specific models.
III. The token OPEN economic system
Total supply: 1 billion OPEN
Token allocation structure:
- Community: 51.71% (largest share; ecosystem users, nodes, and data contributors)
- Early investors: 18.29%
- Team development: 15%
- Ecosystem incentives: 10%
- Liquidity pool: 5%
Token utility:
1. Pay on-chain Gas, model training, and AI inference fees
2. Reward data providers, node verifiers, and model developers
3. Community governance voting determines network parameters and subnet rules
IV. Core ecosystem roles
1. Data contributors: upload and label vertical data; continuous mining and profit-sharing rewards as long as the data is called.
2. Model developers: build SLMs on the ModelFactory; share of the revenue generated from model usage
3. Node verifiers: run nodes to validate the authenticity of PoA data and obtain staking rewards
4. Application users: call the on-chain SLM services, pay with OPEN tokens, and use low-cost AI capabilities for specific sub-domains
V. Project features and differences
1. Focuses on SLM-dedicated small models: not competing with general-purpose large models like GPT; deep in specific industry scenarios; lightweight, low cost, and easy to confirm ownership.
2. Sustainable revenue: one-time data upload; every subsequent AI call yields a profit share, as opposed to one-time data sales.
3. End-to-end decentralization: data collection, training, inference, and revenue distribution are all executed on-chain, with no centralized platform siphoning profits.
VI. Notice of potential risks
1. The track is an emerging direction for DeAI; large-scale commercial use cases are still in the early stage.
2. The price of the encrypted token is subject to market conditions, project operations, and regulatory policy, resulting in extreme volatility; participating in token trading carries high risk.
3. Data compliance and copyright confirmation in real off-chain scenarios face certain hurdles.