On July 24, OpenGradient announced a $9.5 million funding round led by a16z Crypto, with participation from Coinbase Ventures, SV Angel, Foresight Ventures, Pragma, SALT, Symbolic Capital, Canonical Crypto, Black Dragon, NEAR, Celestia, Thanefield Capital, and several renowned angels including Balaji Srinivasan, Illia Polosukhin, Sandeep Nailwal, Bruno Faviero, Daniel Cheung, Ryan Watkins, and Ekram Ahmed.
What Verifiable AI Actually Means
OpenGradient is building a compute layer for "verifiable AI" — AI outputs that can be cryptographically proven. The network uses GPU nodes and TEE (Trusted Execution Environment) nodes to run inference and verify model, input, and output. This transparency matters for apps, blockchains, and AI agents that rely on trust, especially when most AI tools remain black boxes.
Network Stats: 2M+ Inferences, 500K Proofs, 2,000+ Models
The project reports that its network has already processed over 2 million inferences, generated over 500,000 verifiable proofs, and hosts more than 2,000 models (some sources cite over 4,000). These metrics give context to the token launch and the protocol's growth trajectory.
OPG Tokenomics: Fixed 1B Supply, 4% Airdrop
The $OPG token will have a fixed supply of 1 billion tokens, allocated as follows: ecosystem 40%, foundation 15%, contributors 15%, investors & advisors 10%, staking 10%, liquidity 6%, and airdrop 4%. The token will launch as an ERC-20 on Base, with functions for inference payments, model rewards, staking, premium app access, and governance. No TGE date or initial price has been set, though MiCAR and ESMA filings suggest progress toward launch.
Early 2026 Price Speculation
With no live trading yet, any price is speculative. Based on the project's traction, fixed supply, and current market appetite for AI-linked crypto, early price discovery in 2026 could fall in the $0.03–$0.08 range. Actual trading may differ sharply. The bigger takeaway: investors are backing infrastructure that makes AI outputs auditable. If OpenGradient scales, its model hub, proof system, and token design will be a closely watched test case for verifiable AI networks.