What Cocoon Is and How It Works: The TON-Powered Confidential Compute Network

Cocoon, a new confidential compute network built on the TON blockchain, is reshaping how AI workloads are processed and monetized. Developed with support from Telegram founder Pavel Durov, the platform connects GPU owners, developers, and Telegram’s ecosystem through a decentralized, privacy-preserving compute layer. Its launch marks a major shift toward low-cost, private, and verifiable AI execution outside traditional cloud providers.

Cocoon, short for Confidential Compute Open Network, is a decentralized AI compute platform designed to deliver private, attested, and low-cost GPU processing across a distributed network of machines. Built on The Open Network (TON), it introduces a new model for accessing and monetizing AI compute, challenging big cloud providers that currently dominate the market.

At its core, Cocoon allows powerful GPUs owned by individuals or data centers to execute AI tasks within confidential, isolated environments. This ensures that neither operators nor third parties have access to users’ data, making it suitable for sensitive workloads such as LLM inference, translation, and private model execution.

What Is the Cocoon Confidential Compute Network?

Cocoon uses Trusted Execution Environments (TEEs) such as Intel TDX to run computations inside protected, tamper-proof zones. These enclaves provide three guarantees:

Private Execution – All AI tasks run in a sealed, encrypted environment where data cannot be accessed, even by GPU rig owners.
Verifiable Output – Every result is cryptographically attested, proving that the model executed correctly.
Isolated Workloads -Apps run independently, protecting both user data and model integrity. Cocoon’s architecture, documentation, and codebase are open source, reinforcing transparency and auditability across the network.

How Cocoon Creates a New AI Compute Economy

Cocoon brings together three groups: GPU owners, developers, and end users, each receiving a distinct benefit.

GPU Owners: The Era of AI Compute Mining
Instead of mining blocks or generating hashes, GPU owners earn rewards by performing real AI computation.

Key features:

Earn TON tokens: GPU operators run a Cocoon node and receive TON for each completed AI task.
Professional hardware required: Recommended setups include an NVIDIA H100 PCIe 80GB with a TDX-enabled CPU like Intel Xeon.
Estimated cost: Approximately $30,000–$40,000 to build a compliant rig.

This model transforms mining into something productive, lowering barriers for compute-hungry AI tasks while decentralizing the supply of GPU power.

Developers: Low-Cost, On-Demand Compute With Full Privacy
Developers can use Cocoon to run AI workloads at prices significantly lower than AWS or Azure, without sacrificing privacy.

Benefits include:

Pay-per-compute: No need to rent cloud machines or buy GPUs.
Confidential execution: All tasks run in private TEEs, ensuring 100% data isolation.
Instant scaling: The network expands elastically as GPU operators join.

This makes Cocoon attractive for startups, AI builders, and enterprises needing secure inference environments.

Telegram Users: Private, Fast AI Features
Telegram is Cocoon’s first major customer and the initial engine for demand.

Telegram users receive:

Privacy-focused AI features
Fast inference speeds
Built-in translation and AI tools are already routed through Cocoon
End-to-end confidentiality for their requests

Telegram’s involvement accelerates adoption and ensures early volume for GPU operators.

Why Telegram and TON Matter for Cocoon’s Growth

Telegram will actively promote Cocoon and route initial AI workloads through it, making it both a business customer and a distribution channel.

Key impacts:

TON becomes the economic layer powering AI compute payments.
More GPU operators join to earn TON.
Developers adopt TON for payments and application logic.
Telegram drives mainstream demand for decentralized AI tools.

This creates a circular economy where TON fuels compute, compute powers AI apps, and AI apps attract users and developers to the ecosystem.

The Bigger Picture: Why Cocoon Matters for AI Infrastructure

Cocoon addresses several challenges in AI and cloud computing:

Breaking Centralization – Cloud giants dominate GPU supply, pricing, and availability. Cocoon decentralizes access.
Lowering AI Compute Costs – Distributed computing reduces cost pressure and opens AI development to more teams.
Ensuring Data Privacy – Confidential compute models allow sensitive workloads to run securely without trusting operators.
Building a Transparent, Verifiable AI Layer – Cryptographic attestation ensures correctness and trust. As AI models become more powerful and resource-intensive, decentralized compute networks like Cocoon may become essential infrastructure.