Boundless to Replace ZKC Rewards With AI Model Credits
Boundless Foundation plans to launch its tokenised AI inference programme on 2 January 2027. Users who stake ZKC will receive credits for using open-weight AI models, allocated every two days according to their share of total staked ZKC. The initial pool is worth about $1,000. Staked tokens will not be spent or need to be moved or restaked.
Boundless will suspend its zero-knowledge proof service indefinitely from 5 November 2026, ending new proof requests and proof rewards. Its Explorer interface is due to close on 30 November, while staking will remain available through a separate interface. The final staking rewards period ends on 1 January 2027. After rewards are settled, ZKC issuance will stop as the token moves towards a fixed supply. AI usage credits will replace the existing staking and proof rewards.
Neutral
The announcement changes the utility and reward structure of ZKC but does not, by itself, establish a clear directional price catalyst. A fixed supply and the end of new issuance could reduce future sell pressure, which some traders may view as supportive. However, proof rewards are ending, and the initial AI credit pool is only about $1,000, so the near-term economic benefit may be limited. The shift also introduces uncertainty about demand for the new credits and the long-term value of the staking model.
In the short term, traders may focus on the transition dates, staking participation and any changes in token liquidity or exchange flows. The suspension of proof services could weigh on sentiment, while the promise of AI utility could attract interest. Similar token utility changes have often produced volatile, mixed reactions: supply reductions can prompt speculative buying, but price effects tend to depend on whether new demand materialises. Longer term, ZKC’s outlook will hinge on adoption of Boundless AI, the value and distribution of credits, and whether reduced issuance is matched by sustained demand. The article provides no market-price, liquidity or adoption data, so a neutral assessment is most appropriate.