Cocoon Launches: A Fully Decentralized Confidential Compute Network Goes Live on TON
Cocoon, a new decentralized confidential compute network, has officially gone live — and the first user AI requests are already being processed with full privacy. GPU owners are now earning TON by contributing compute power, marking a major step for on-chain AI infrastructure.
Unlike traditional providers such as Amazon or Microsoft, Cocoon removes centralized intermediaries, enabling private, cost-efficient AI computation secured by cryptography and decentralized GPU supply.
Why Cocoon Matters
Cocoon addresses two of the biggest structural issues in today’s AI compute market:
• centralized pricing power that inflates compute costs
• lack of user privacy, with all computation stored or readable by centralized providers
With Cocoon, all inference is executed inside a confidential environment, ensuring that user data, prompts, and model outputs remain protected end-to-end.
The launch version includes open-source code, documentation, and a live network accessible via cocoon.org.
What Comes Next
The team says scaling begins immediately. More GPU providers will be onboarded in the coming weeks, increasing capacity and reducing costs as supply grows. At the same time, developer demand will ramp up as new apps integrate Cocoon’s confidential compute layer.
Telegram users will see new AI-powered features driven by Cocoon’s private compute engine — a significant move considering Telegram’s massive global audience and emphasis on user control.
BTCUSA original analysis
The launch of Cocoon marks one of the most important steps toward decentralized AI infrastructure in 2025. While many projects claim privacy, very few provide verifiable confidentiality at the compute level. If Cocoon succeeds in scaling both supply and developer adoption, it could become a crucial layer for privacy-first consumer AI.
It also positions TON as one of the most innovative chains in the AI-crypto intersection. The combination of Telegram’s user base, fast blockchain throughput, and now confidential compute could create a powerful feedback loop for ecosystem growth.
Editor’s commentary
Centralized compute providers have long held disproportionate leverage in the AI economy. Cocoon’s architecture challenges that model by giving both users and GPU providers more control. The biggest question is whether decentralized networks can maintain reliability, latency, and cost-competitiveness at scale. If Cocoon solves these challenges, it may become a blueprint for the next generation of AI infrastructure.
