Briefing
Telegram has officially launched Cocoon, a decentralized privacy computing network designed to execute AI inference tasks within Trusted Execution Environments (TEEs). This new infrastructure layer immediately addresses the critical market demand for verifiable and private AI execution, which is essential for scaling sophisticated dApps that handle sensitive user data or proprietary models. The network architecture uses $TON as the native payment rail for compute resources, creating a direct economic flywheel between the network’s utility and the underlying Layer 1. Early-stage traction validates the model, with the network already securing an approximate Total Value Locked (TVL) of 4,487 TON and onboarding 30 work nodes.
Context
The application landscape has been constrained by a fundamental trade-off between AI utility and data privacy. Prior to the launch of Cocoon, decentralized AI models often struggled with the ‘verifiability problem,’ making it difficult to prove that an off-chain computation was executed correctly without exposing the underlying data. Centralized AI services, while performant, create an unacceptable single point of failure and control over user data and model weights. This created a significant product gap → a need for a decentralized, secure, and verifiably private computation layer capable of handling the high-frequency, low-latency demands of AI inference, particularly for consumer-facing platforms like Telegram Mini-Apps.
Analysis
Cocoon fundamentally alters the Web3 application layer by introducing a new primitive for private, verifiable compute. The system is architected with three distinct roles → Clients, Proxies, and Work Nodes. Work Nodes, powered by GPU servers with TEEs, execute AI inference requests in a shielded environment, guaranteeing confidentiality. Proxies route client requests to the optimal Work Node based on reputation and load, acting as the network’s decentralized scheduler.
This TEE-protected execution model ensures that the AI operation is both private and transparently verifiable on-chain, eliminating the need for complex zero-knowledge proofs for every transaction. The use of $TON as the payment mechanism for work completion aligns the economic incentives of compute providers (Work Nodes) with the growth of the TON ecosystem. This framework creates a defensible network effect → as demand from Telegram’s massive user base increases, the demand for $TON-paid compute power rises, attracting more Work Nodes and increasing the network’s capacity and resilience. This strategic integration positions the TON ecosystem as a foundational provider in the decentralized AI vertical.
Parameters
- Total Value Locked → 4,487 TON. This is the approximate value of the token collateral currently staked within the network’s early-stage contracts.
- Work Node Count → 30. This represents the number of TEE-enabled GPU servers actively contributing compute power to the network.
- Core Technology → Trusted Execution Environments (TEEs). Hardware-based secure areas used to guarantee the confidentiality and integrity of AI inference execution.
- Ecosystem Integration → Telegram Mini-Programs. Expected to be the primary driver of demand for Cocoon’s AI inference services.
Outlook
The immediate next phase involves scaling the Work Node network to handle the anticipated demand from integrated Telegram features, such as voice-to-text and summarization, and the broader Mini-App developer ecosystem. The TEE-based approach is highly susceptible to being copied by competing Layer 1 and Layer 2 ecosystems that prioritize decentralized AI, particularly those with strong GPU compute communities. Cocoon’s primary competitive moat is its direct integration with the massive Telegram user base, which provides a ready-made demand side that competitors lack. This private compute primitive is now a foundational building block for any dApp on TON that requires verifiable, confidential data processing, from identity solutions to complex gaming logic.
Verdict
Cocoon’s launch establishes a critical, high-utility infrastructure primitive, setting the benchmark for how decentralized ecosystems will capture the strategic and economic value of the emerging AI computation market.
