WorldLand, the decentralized AI mainnet built for verifiable GPU compute, today announced a $100 million committed investment from Bolts Capital. The investment marks one of the largest protocol-level commitments in the decentralized AI sector. It will directly support global GPU supply expansion, network-level incentives, and the rollout of AI decentralized applications (DApps) on WorldLand’s AI-specialized mainnet.
Bolts Capital’s substantial commitment underscores its strong belief in WorldLand’s long-term vision and technological foundation. The firm noted that WorldLand’s AI-specialized mainnet and verification-driven infrastructure represent a rare combination of technical maturity and regulatory readiness — factors that motivated such a significant investment.
This funding follows a series of recent grants from the Korean government on WorldLand totaling up to $7.2 million, which officially classifies the project as a premier deep-technology initiative. This government backing reinforces its stability as a technological infrastructure provider and positions the project to scale securely across Korea and international markets.
WorldLand’s mainnet is powered by its proprietary Proof-of-Compute (PoC) system, which provides verifiable evidence that GPU training, inference, and storage tasks are executed correctly — transforming idle global GPU capacity into a reliable decentralized AI cloud.
“Bolts Capital’s $100M commitment is a strong endorsement of our mission to build verifiable decentralized AI infrastructure,” said Prof. Heung-No Lee, Founder of WorldLand. “This investment will accelerate GPU expansion and support developers building AI applications on our mainnet.”
With the combined strength of the government grant and Bolts Capital’s investment, WorldLand is now positioned to scale its core protocol, verification layer, and decentralized GPU marketplace on a global level.
Company: Worldland Foundation
Contact: Heung-No Lee
Email: [email protected]
Site : https://worldland.foundation/
Country: Cayman Islands
