Андрей Шестаков base.eth

📨 Pavel @Durov , I think Cocoon could become much bigger than a confidential GPU network. Right now, Cocoon is the compute layer: GPU owners provide private AI inference, developers pay for workloads, node operators earn $TON, and Telegram itself could become the first major source of demand. That is already a strong start. But the missing piece is the data layer. The model I would look at is @grass. Not as a token to copy, but as an architecture pattern: ordinary users share unused bandwidth, the network collects public web data, routers and validators verify the work, ZK proofs help establish provenance, and AI developers get fresh datasets. The key point is that Grass is not just a viral DePIN app. It is trying to turn distributed bandwidth into a real data-infrastructure business. That is the part Telegram should care about: not only user rewards, but real enterprise revenue coming from AI data demand. TON could potentially do this even better because Telegram already owns the distribution and onboarding layer. Imagine Cocoon 2.0: 1) Compute Nodes: the current GPU nodes for confidential inference and AI workloads. 2) Data Nodes: lightweight nodes running through Telegram Desktop, a browser extension, a companion app, and eventually Android where background networking is feasible. 3) One TON settlement layer where developers pay for a combined data + compute package. 4) Automatic TON rewards split between bandwidth providers, GPU providers, routers, validators, and the protocol itself. No new token. The asset is TON. This matters for TON because it creates both real resource demand and a new revenue line. Grass has shown that idle bandwidth is not just a community-growth gimmick. It can become a B2B business where AI labs, data companies, search products, and enterprise customers pay for fresh, structured public web data. For @telegram , this could be even more powerful. A Cocoon data layer could generate revenue from developers and AI companies that need both data and compute, while TON becomes the settlement asset underneath the whole system. Part of that revenue could be used to fund Telegram AI features, reward network participants, support TON ecosystem grants, deepen ecosystem liquidity, or even support a transparent TON buyback program if the structure makes sense legally and economically. That is the real difference: not “users farm points and hope for a token,” but a circular economy where customers pay for useful AI infrastructure, users and hardware providers get rewarded in TON, Telegram gets a new infrastructure business, and the TON ecosystem captures more real economic activity. This would close the loop: public web data -> verified datasets -> confidential AI compute -> Telegram AI features -> TON payments -> rewards to real users and hardware providers Telegram could also be the first major customer. Channel summaries, search, translation, content discovery, business bots, AI agents, and Mini App AI features could all benefit from a native data + compute stack that does not depend entirely on Big Tech cloud gatekeepers. $Grass has shown that ordinary users are willing to participate in AI infrastructure if the product is simple enough. Cocoon has already shown that TON can serve as the payment and trust layer for confidential AI compute. Combining the two would make TON more than just a chain with apps. It would make TON the consumer distribution layer for decentralized AI. The biggest unlock is not “another DePIN narrative.” The real unlock is that a billion-user app can turn idle internet, idle GPUs, and privacy-preserving AI into one economic loop. If Cocoon remains only a compute network, it can still be useful. But if Cocoon adds a verified public data layer, TON could own one of the most important AI infrastructure categories: data plus private compute, with distribution already built in. That would be very Telegram. Very TON. And honestly, very Durov.