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Decentralized AI Network Cocoon Takes on Centralized Titans with a Privacy-Centric Approach

Decentralized AI Network Cocoon Takes on Centralized Titans with a Privacy-Centric Approach

Bitget-RWA2025/11/30 23:04
By:Bitget-RWA

- Telegram founder Pavel Durov launched Cocoon, a TON-based decentralized AI network enabling GPU owners to earn cryptocurrency by processing private AI requests. - The platform challenges centralized providers like Amazon and Microsoft by using Trusted Execution Environments (TEEs) to ensure secure, verifiable model execution with user data privacy. - Cocoon connects GPU providers with developers for confidential tasks, reducing reliance on costly intermediaries while aligning with ethical AI principles t

Cocoon: A New Era of Decentralized, Private AI Computing on TON

Cocoon, a decentralized confidential computing network supported by Telegram, has officially launched on the TON blockchain. This innovative platform allows individuals with GPUs to earn cryptocurrency by handling AI tasks, all while maintaining complete privacy for users.

Unveiled by Telegram’s founder Pavel Durov, Cocoon is positioned as a privacy-centric alternative to conventional, centralized AI services. The platform addresses concerns about data security and high operational costs by utilizing Trusted Execution Environments (TEEs), which guarantee that AI models run securely and privately. Cocoon directly connects GPU owners with developers who require confidential processing power, eliminating the need for traditional intermediaries.

Cocoon Confidential Compute Network

Empowering GPU Owners and Reducing Centralization

The introduction of Cocoon marks a pivotal move toward decentralized AI infrastructure. By enabling GPU owners to profit from unused hardware, the network reduces dependence on major cloud providers such as Amazon and Microsoft—companies often criticized for driving up prices and compromising user privacy. Durov highlighted that Cocoon addresses both the financial and privacy drawbacks of existing systems, describing centralized providers as costly middlemen who put user data at risk. Early participants are already contributing their computing resources and earning TON tokens for processing AI requests, with Telegram itself acting as the platform’s first major client and advocate.

Privacy by Design

At the core of Cocoon’s architecture is a commitment to privacy. Built on the TON blockchain, the platform anonymizes user requests and employs advanced cryptographic methods to prevent unauthorized access. This privacy-first approach aligns with ethical AI standards promoted by organizations like the Dfinity Foundation, which support running AI models on open, permissionless blockchains to safeguard data integrity. According to a 2024 survey by the Digital Currency Group, 77% of respondents prefer decentralized AI solutions for their transparency and reduced influence from large corporations, reflecting a growing appetite for such technologies.

Benefits for Providers and Users

  • GPU owners can generate income by leasing their hardware for AI processing tasks.
  • Users benefit from secure, distributed computing with enhanced privacy protections.
  • TON’s scalable infrastructure underpins the platform, promoting user autonomy and innovation in AI development.

Industry experts, including David Holtzman from the Naoris decentralized security protocol, observe that integrating blockchain with AI helps mitigate risks associated with centralized control. Features like tamper-resistant records and trustless communication between nodes are central to Cocoon’s design.

Shaping the Future of AI and Blockchain

Cocoon’s debut reflects a broader movement toward merging blockchain technology with artificial intelligence. As decentralized solutions gain momentum, platforms like Cocoon are poised to set new benchmarks for privacy and innovation. With the backing of Telegram’s extensive user base and the robust TON ecosystem, Cocoon is well-positioned for widespread adoption, challenging established AI providers to rethink their approaches to privacy and cost efficiency.

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Disclaimer: The content of this article solely reflects the author's opinion and does not represent the platform in any capacity. This article is not intended to serve as a reference for making investment decisions.

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