The TON ecosystem is expanding again, and this time, it’s entering the race to build private, censorship-resistant AI infrastructure.
Telegram founder Pavel Durov has announced the launch of Cocoon, a decentralized confidential compute network that runs AI workloads directly on The Open Network (TON). The system went live this week, and it is already processing real user requests with full privacy while GPU providers earn TON in real time.
Durov confirmed the launch in a public announcement on X, writing that “the first AI requests from users are now being processed by Cocoon with 100% confidentiality. GPU owners are already earning TON.”
His statement marks a major milestone for TON’s ambitions to build decentralized digital infrastructure that rivals traditional cloud services, but with privacy embedded at the protocol level.
According to the project’s website, Cocoon is designed as a decentralized platform for running AI on TON. It works by “securely connecting GPU owners” who provide compute power with applications that need to run private AI models.
Cocoon focuses on a core idea: AI should be able to run without exposing data to node operators, cloud providers, or intermediaries. And users should be free to deploy models without relying on Big Tech platforms or centralized gatekeepers.
The network is already live. It handles encrypted AI requests on-chain and distributes TON rewards instantly to GPU contributors.
Cocoon is structured around three components, each playing a role in its decentralized workflow:
The Client is the user. They pay for AI requests and send them to the Cocoon proxy. Once a request enters the system, the network handles all routing and security.
The Proxy acts as the routing engine. It selects a suitable Worker node based on the GPU required, availability, and performance, then forwards the request securely.
The Worker is where the AI task is executed. It runs the GPU hardware, processes encrypted jobs inside a secure environment, and returns the results without ever seeing the input data.
This structure enables a simple user experience on the surface, with a complex cryptographic and hardware-secured network underneath.
The centerpiece of Cocoon’s technology is its use of hardware Trusted Execution Environments (TEEs). These environments isolate workloads so that:
Cocoon uses remote attestation to prove that each workload runs inside a secure enclave. This approach mirrors the security model used by major confidential computing providers, but without the centralized infrastructure.
Cocoon positions itself as a privacy-first AI platform suitable for startups, developers, enterprise workloads, and end users who need confidentiality as a default feature, not an add-on.
Beyond confidentiality, Cocoon introduces a decentralized GPU marketplace. It allows anyone with capable hardware to connect, contribute compute cycles, and earn $TON automatically.
For GPU owners, the pitch is direct:
For developers, Cocoon provides a cheaper alternative to cloud giants:
Cocoon’s decentralized model avoids the overhead and pricing structures of traditional cloud compute, and early testing indicates that the cost for AI inference could be significantly lower.
Cocoon’s launch is more than a technical deployment, it signals TON’s push into decentralized AI infrastructure. The impact spans several categories:
Reduced Reliance on Centralized Cloud Providers
Cocoon allows developers to run AI workloads without AWS, Azure, or GCP. It challenges cloud monopolies with open, peer-to-peer compute supply.
Lower-Cost GPU Access
The decentralized marketplace structure brings competitive pricing. GPU owners worldwide can supply compute power, removing traditional barriers.
Privacy-Preserving and Censorship-Resistant AI
Because all jobs run inside encrypted enclaves, no provider can intercept or censor user workloads.
Real Infrastructure for TON’s Growing Ecosystem
Cocoon adds a new layer to TON’s architecture, one that supports both AI applications and broader decentralized compute services.
The arrival of confidential compute on a major blockchain positions TON as a serious competitor in the global AI-crypto intersection.
Cocoon is not a testnet or pre-release. It is fully live.
According to project updates:
Most notably, Telegram has been confirmed as Cocoon’s first major client, immediately putting the network into production for millions of active users.
This gives Cocoon a powerful early advantage: real demand from day one.
Developers are also beginning to integrate the network into their own pipelines, testing encrypted inference for analytics, bots, private assistants, and data-sensitive AI tools.
One of Cocoon’s core principles is openness:
There are no allowlists. No approval processes. No KYC requirements. The system is structured to scale globally as long as GPU owners continue connecting hardware.
The project’s team highlights that Cocoon is built as a permissionless infrastructure layer, one that stays aligned with TON’s broader vision of decentralized digital ownership and freedom.
Cocoon’s arrival signals a shift in how AI can be deployed and managed. Instead of relying on centralized cloud giants or exposing sensitive data to third-party servers, users can now run workloads in a verifiable, private, decentralized environment.
The combination of:
makes Cocoon one of the most ambitious decentralized AI launches this year.
And for TON, it pushes the blockchain into a new category, one where AI, privacy, and crypto infrastructure converge.
Disclosure: This is not trading or investment advice. Always do your research before buying any cryptocurrency or investing in any services.
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