 Decentralized AI: A New Computing Architecture The market for decentralized AI computing is experiencing explosive growth. And it's not just about the hype around Web3 — there are quite pragmatic reasons for this. What's Happening Right Now The main driver is the GPU shortage. Hyperscalers (AWS, Azure, GCP) are overloaded, rental prices for computing power are rising, and the queue for new H100/B200 clusters stretches for months. Data center energy constraints only exacerbate the situation. Key Players to Watch - IO.net — the largest DePIN GPU network, already operating as a distributed computing layer. - Bless Network — raised $8M for the idea of decentralized AI infrastructure. - Planck Network — promises to reduce computing costs by up to 90% through a p2p architecture. - Cocoon by Pavel Durov — a new decentralized AI network with mining, positioned as completely independent from regulators. Why This Matters Centralized data centers are hitting physical limits: electricity, cooling, access to chips. Decentralized networks solve this problem differently — they use idle capacity around the world. Millions of devices that sit idle 70-80% of the time can become part of a global computing pool. What's Next The market is moving toward a hybrid model: heavy tasks (model training) will remain on centralized clusters, while inference and fine-tuning will increasingly shift to decentralized networks. This is not a replacement for clouds — it's a new infrastructure layer. What This Means for Business Lowering the entry barrier: small teams will gain access to computing power that was previously only available to corporations. The market size is estimated at $50-100 billion by 2030, according to analysts. Decentralized AI is not an experiment, but the next logical step in the evolution of computing. The question is not whether this transition will happen, but who will be the first to secure their positions.