5. Economic Model

Token Design

5. Economic Model

5.1 Token Design

cAIToken (cAI) is the native token of the cAI decentralized computing network and the core credential measuring computing contributions, task execution quality, and node verification value in the AI training economy. Unlike traditional token issuance models, cAI's design centers entirely on "proof of computing," rejecting any manual issuance or centralized control.

cAI issuance logic follows four principles: fixed supply, non-mintable; zero permissions, immutable; unique pool, unified output; contribution-driven, no pre-mine.

Token Name: cAIToken (cAI)

Total Supply: 80,000,000 (fixed cap, non-mintable)

Issuance Method: 100% released from pool

Pre-mine / Private Sale / Team Allocation: 0

Contract Status: ownership permanently renounced; pool contract immutable

All 80M cAI are pre-minted and locked in the pool contract. The pool has renounced all permissions, meaning no team, individual, or external entity can adjust issuance strategy, modify reward rules, or access additional tokens.

cAI release occurs in two ways:

PoW Computing Contribution Output (Compute-PoW): nodes complete basic computing or simple verification tasks via GPU/CPU and submit verifiable proofs, receiving rewards proportionally from the pool.

Staked Node Reward (Stake-to-Validate): nodes stake cAI to gain task acceptance eligibility, participate in high-level task verification, training result checks, and state arbitration, and earn pool incentives by completing ecological verification tasks.

This economic system forms a purely contribution-driven incentive model, deeply linking token value with real network productivity.

5.2 cAI Value Attributes

cAIToken's value derives from its fundamental role in the decentralized computing network. It is not a speculative trading token but an on-chain asset certificate representing computing contributions and task execution. Its value comes from real computation demand and task execution, making it an intrinsic "computing behavior" resource rather than relying on hype.

Within the network, all computing access, node task acceptance, model training submission, inference service calls, and cross-chain computing requests must use cAI as payment, incentive, or authorization, making it the irreplaceable computing fuel and proof of computing credit. As more enterprises, developers, and AI training needs enter the network, the scarcity of cAI as a resource access credential increases.

Nodes staking cAI gain eligibility for higher-value tasks such as verification, arbitration, and gradient validation, giving cAI a role as a "power credential." Staking does not directly produce yield but grants access to higher-value production stages, creating a dual-track "contribution + staked reputation" mechanism. As the network scales, cAI also becomes a foundational asset for governance, including proposal voting, network parameter adjustment, and ecological fund governance, forming a stable positive cycle between long-term holding and governance participation.

At the ecosystem level, cAI value extends to computing rentals, data collaboration, model training marketplaces, and cross-chain computing bridges, handling resource billing, access control, verification incentives, and cross-ecosystem coordination. As external AI training projects, federated learning platforms, and multi-chain DApps join, cAI gradually evolves from a purely incentive asset to the core value carrier of the decentralized computing economy, with long-term stability tied to computing demand and ecosystem expansion.

5.3 cAI Circulation Logic

cAI circulation relies entirely on a bidirectional structure of "real computing contribution production and real task demand consumption." The token system does not depend on market speculation or manually designed vesting curves but on network productivity and utilization demand. All tokens are released from the unique pool, fully dependent on nodes' contributed workloads and task execution, with no team rewards, private allocations, or manual intervention, making circulation naturally transparent, fair, and unmanipulable.

On the production side, nodes execute base Compute-PoW tasks, AI-PoW training fragments, or participate in validation tasks after staking. These actions generate verifiable on-chain work, and the pool contract releases cAI based on contribution value and task quality. Different task types and node tiers determine output rates, keeping cAI circulation aligned with network computing power, avoiding inflation risks seen in traditional token models ("high issuance, low utilization").

On the usage side, developers, model trainers, AI inference service providers, cross-chain task initiators, and computing rental users consume cAI to pay for tasks, rewards, resource fees, and cross-chain calls. Nodes staking for high-tier validation also lock cAI as reputation credentials, generating continuous lock-up demand. As more ecosystem projects join, cAI forms a stable circulation loop: contributors earn cAI → demand-side consumes cAI → nodes stake cAI for governance → ecosystem recycles cAI through fees and tasks, continuously strengthening the "produce → consume → reclaim" value loop, keeping cAI tied to real computing resources.

5.4 Future Value Mapping

As the network scales and AI training tasks increasingly move on-chain, cAI's long-term value will evolve from an incentive token to the core standard of "on-chain computing assets." All future training tasks, inference services, data collaboration, model validation, and cross-chain computing actions will use cAI as a value unit, establishing it as the "computing currency" of the AI training era.

With expansion into multiple public chains and L2 networks via cross-chain computing bridges, cAI will circulate across Ethereum, BSC, Arbitrum, Solana, AI-specialized Rollups, and more, serving as a unified credential for cross-chain computing resource access, enabling true cross-ecosystem liquidity of computing power.

Once the cAI public chain launches, cAI will also serve as gas fees, staked node assets, task execution proof, cross-chain verification rewards, and ecosystem subsidies, becoming the underlying energy of the AI computing network. As training tasks multiply and model inference calls accelerate, cAI demand will grow geometrically, providing strong support. Concurrently, DAO proposals will implement deflationary mechanisms such as fee burns, task-sharing buybacks, computing market revenue recycling, and cross-chain bridge fee burns, giving cAI a controllable long-term deflationary trend, enhancing scarcity and asset properties.

Ultimately, cAI will evolve beyond an incentive token into a composite asset representing global computing market efficiency, real AI training demand, cross-chain computing scale, and ecological governance power, with value positively correlated to decentralized computing network depth, AI industry growth, and cross-chain collaboration, providing long-term, structural, and sustainable value appreciation.