Cocoon, a Decentralized AI Network on TON, Is Live - What Executives Need to Know
The Cocoon decentralized AI network is now in production on The Open Network (TON), the layer-1 blockchain associated with Telegram. It lets GPU owners rent out compute to process AI requests and get paid in Toncoin (TONUSD). Early workloads have been processed, and node operators are already earning.
Pavel Durov framed the launch as a direct response to cost and privacy pain points: "Centralized compute providers such as Amazon and Microsoft act as expensive intermediaries that drive up prices and reduce privacy. Cocoon solves both the economic and confidentiality issues associated with legacy AI compute providers." He first announced Cocoon at Blockchain Life 2025 in Dubai.
Why this matters for strategy
- Cost dynamics: A decentralized market could compress inference costs, especially for bursty or off-peak workloads.
- Privacy posture: On-chain coordination and distributed compute reduce exposure to single-provider data aggregation.
- Vendor concentration risk: Another path beyond Big Tech clouds for AI inference capacity.
- Elasticity: Access to a global pool of idle GPUs may improve availability during demand spikes.
- Token-based economics: Payments in Toncoin introduce FX and treasury considerations for finance teams.
How Cocoon works at a glance
- Owners of GPUs contribute compute to the network and receive Toncoin for processing jobs.
- Users submit AI queries; the network routes tasks across participating nodes.
- TON provides the settlement layer for payments and coordination. Learn more about TON here.
The bigger picture: decentralized AI and self-sovereignty
Privacy advocates and builders have warned that centralized AI stacks hand too much leverage to a handful of platforms. David Holtzman of Naoris highlighted risks to privacy, cybersecurity, and information integrity, arguing that blockchain can verify sources, keep tamper-proof records, and enable trustless coordination.
In 2024, researchers affiliated with the Dfinity Foundation and executives from Onicai proposed rules for ethical AI, including running AI on permissionless blockchains for transparency and data integrity. You can explore the Internet Computer's approach to on-chain compute here.
Sentiment is shifting, too. A DCG poll reported that 77% of respondents believe decentralized AI would benefit society more than centralized systems.
Where this could fit in your roadmap
- Inference overflow: Burst capacity for LLM and vision workloads when cloud quotas are tight.
- Privacy-sensitive use cases: Scenarios where centralized aggregation is a deal-breaker for legal or brand reasons.
- Global reach: Distributed nodes can reduce latency in regions where your primary cloud footprint is thin.
- Cost experiments: Benchmark decentralized pricing vs. spot instances and reserved capacity.
Risks and due diligence
- Compliance: Clarify data residency, auditability, and industry controls (e.g., SOC 2, HIPAA, GDPR).
- Data handling: Define encryption, redaction, and zero-retention policies for prompts and outputs.
- Model/IP protection: Ensure safeguards for proprietary models and weights if distributed.
- Performance variability: Latency and throughput can fluctuate across heterogeneous nodes; run proofs-of-concept.
- Treasury exposure: Token price volatility and settlement flows require policy and hedging rules.
- Operational risk: Incident response and SLAs in a decentralized environment need clear playbooks.
KPIs to track in pilots
- Cost per 1,000 tokens (or per inference) vs. current providers
- P95/P99 latency and job completion rate
- Data retention/egress policy adherence and audit logs
- Uptime by region and failover success rate
- Effective cost after token conversion and fees
Governance and security questions for your team
- What data classes are allowed on decentralized nodes, and what is strictly barred?
- Which encryption standards and secret management processes are mandatory?
- How will we attest to node integrity and verify computation results?
- What's our incident and takedown protocol if a node misbehaves?
- How do we document and audit model lineage and prompts/results?
First steps
- Run a 4-6 week pilot with non-sensitive inference workloads and publish a cost/performance report.
- Draft procurement and security guardrails for decentralized compute suppliers.
- Map workloads by sensitivity; route only eligible data to distributed nodes.
- Set up treasury rules for Toncoin conversions and accounting.
- Model a multi-provider strategy that blends cloud, on-prem, and decentralized compute.
The signal here is clear: enterprises want lower-cost AI with stronger privacy and less concentration risk. Cocoon's launch on TON puts that option on the table. The smart move is a controlled pilot, data policies locked down, and a clean comparison against your current stack.
If you're building internal capability and need structured upskilling for specific roles, see our curated programs by function: AI courses by job.
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