What Is TON Based Cocoon and Why Is Telegram Backing It?

Cocoon is a decentralized confidential compute network on the TON blockchain that lets users run AI tasks privately through a global pool of GPUs. It allows anyone with a graphics card to earn Toncoin by processing AI requests for applications that need strong privacy protection. The system is now live, and Telegram is its first major customer.

Introduction to Cocoon

Telegram founder Pavel Durov confirmed that Cocoon, also called the Confidential Compute Open Network, has begun processing real user requests. It connects developers who need private AI inference with individuals who want to lease their GPU power. Every AI task is processed inside a Trusted Execution Environment, often referred to as a TEE, which ensures that data stays encrypted even while being computed. Examples of TEEs include Intel TDX, which is widely used in secure cloud environments.

Cocoon presents itself as a privacy-first alternative to centralized AI platforms that handle user data on their own servers. Systems like these often raise concerns among security researchers and users who do not want sensitive data exposed. Cocoon uses TON, a layer 1 blockchain with deep ties to the Telegram ecosystem, to coordinate tasks and maintain verifiable records of compute activity.

How Cocoon Works

Cocoon connects three groups. Developers submit AI workloads. GPU owners run the workloads. Telegram provides immediate user demand by routing private AI queries into Cocoon’s network. TON acts as the foundation that secures requests and records activity.

Before tasks are processed, the model and data are locked into an encrypted environment. Only the TEE can access the information. Even the GPU owner cannot see what their hardware is computing. This approach keeps the prompt, training data, and outputs private. It also helps guarantee that results are authentic.

What Problems Cocoon Wants to Solve

• High cloud compute prices
• Data visibility during AI processing
• Dependence on centralized infrastructure
• Limited transparency in proprietary AI systems

Examples of these concerns include data leaks from large cloud operators, unauthorized access during model training, and rising compute fees for AI development teams. Cocoon addresses these points by treating privacy as the default setting.

Key features of Cocoon

• Confidential AI execution through TEEs
• Global marketplace of GPU providers
• Payments settled in Toncoin
• Integration with Telegram mini apps
• Support for large AI models such as DeepSeek and Qwen
• End to end encrypted inference

This setup removes the need for a central cloud operator. Users interact with Telegram bots or apps, send an AI request, and the job is passed to Cocoon. GPU owners complete the task and earn TON.

Developer Use Cases for Cocoon

Developers can run models such as DeepSeek, Qwen, and other heavy compute frameworks. Workloads include LLM queries, image tasks, video processing, or specialized AI tools. TON’s sharded design helps the network handle high transaction volumes during peak demand.

Cocoon’s integration with Telegram mini apps also gives developers a direct path to real users. Instead of building separate applications, developers can launch AI tools inside Telegram and let Cocoon handle compute privately.

Why Cocoon Matters for AI Privacy

Centralized AI providers often control infrastructure, decide pricing, and hold sensitive data. Many privacy advocates see this as a risk. They argue that dominant cloud companies could influence behavior, weaken cybersecurity protections, or mishandle personal information.

Cocoon takes a different approach by distributing compute power across many independent GPU owners. Data stays encrypted at all times. Logs are written to TON’s blockchain, providing traceability without exposing user activity. This alignment fits into a broader movement toward decentralized AI, a method that aims to reduce dependence on a few major corporate providers.

Durov said that the two biggest concerns facing AI users are high costs and loss of privacy. Cocoon tries to solve both by creating a competitive compute marketplace and locking all processing inside trusted hardware.

Why Telegram Is the First Customer

Telegram is one of the largest messaging apps with millions of active users. Many people already use Telegram bots for translations, summarization, content generation, and various automated tasks. By running these requests through Cocoon, Telegram provides private AI interactions instead of routing data to external companies. This helps the platform maintain a consistent privacy focused image.

Telegram’s role also helps Cocoon grow faster. Immediate demand encourages GPU owners to join, which strengthens network capacity. In turn, that attracts more developers. This closed loop gives Cocoon a practical starting point instead of waiting for third party adoption.

How GPU Providers Earn TON

Cocoon rewards participants through Toncoin. Whenever a GPU completes a task, the owner receives TON based on workload size. This creates an incentive for people with unused hardware to join. Durov said that several GPU owners are already minting TON by connecting their mining facilities or gaming rigs to the network.

The model is designed to scale. As more GPUs join, Cocoon can process heavier tasks, including larger language models, image generation, video transformation, and machine learning pipelines. Per reports, tech funds supporting TON have pledged large GPU farms to strengthen the network.

How Cocoon Compares to Centralized Providers

Services like Amazon Web Services and Microsoft Azure dominate the AI compute market. They offer convenience, but the tradeoff is central control and visibility into user data. They also set the price. For many developers, these factors raise concerns about long term dependency and data exposure.

Cocoon flips the structure. Instead of a central operator, a decentralized pool of independent GPU owners completes tasks. Prices can adjust based on supply. Data stays encrypted. Records are written to TON. The result is a system built to reduce reliance on trusted intermediaries.

However, some experts say the idea has promise, but it faces challenges. Scaling to the level of cloud giants requires tens of thousands of GPUs. Decentralized networks have struggled with this hurdle before.

Regulators are also paying more attention to AI systems that mix blockchain with encrypted inference. Cross border compute markets raise questions about compliance, jurisdiction, and data handling.

Privacy researchers note that TEEs rely on hardware manufacturers. If a flaw exists in the chip or firmware, confidentiality can be at risk. For this reason, several analysts say that continuous auditing will be necessary.

Conclusion

Cocoon demonstrates a model for decentralized, privacy-preserving AI compute. It enables developers to run AI workloads securely while GPU owners earn Toncoin. By combining TON’s sharded blockchain with Trusted Execution Environments, Cocoon provides a scalable, verifiable, and encrypted network for AI tasks. The system addresses key challenges of cost, privacy, and centralization, creating a functional platform for private AI processing accessible to both developers and hardware providers.

Resources

1. Pavel Durov on X: Post on November 30

2. Cocoon Website: General Information

3. Cocoon Docs: About Cocoon