Курс MyShell Token

AI Centralization vs Decentralization: What’s Worth Playing? Imagine two arenas: one is dominated by tech giants running massive data centers, training frontier models, and setting the rules. The other distributes compute, data, and decision-making across millions of miners, edge devices, and open communities. Where you choose to build or invest depends on which arena you believe will capture the next wave of value, or whether the true opportunity lies in bridging both. --- What Centralization and Decentralization Mean in AI Centralized AI is primarily found in hyperscale cloud platforms like AWS, Azure, and Google Cloud, which control the majority of GPU clusters and hold a 68% share of the global cloud market. These providers train large models, keep weights closed or under restrictive licenses (as seen with OpenAI and Anthropic), and use proprietary datasets and exclusive data partnerships. Governance is typically corporate, steered by boards, shareholders, and national regulators. On the other hand, Decentralized AI distributes computation through peer-to-peer GPU markets, such as @akashnet_ and @rendernetwork, as well as on-chain inference networks like @bittensor_. These networks aim to decentralize both training and inference. --- Why Centralization Still Dominates There are structural reasons why centralized AI continues to lead. Training a frontier model, say, a 2-trillion parameter multilingual model, requires over $500M in hardware, electricity, and human capital. Very few entities can fund and execute such undertakings. Additionally, regulatory obligations such as the US Executive Order on AI and the EU AI Act impose strict requirements around red-teaming, safety reports, and transparency. Meeting these demands creates a compliance moat that favors well-resourced incumbents. Centralization also allows for tighter safety monitoring and lifecycle management across training and deployment phases. --- Centralized Model Cracks Yet this dominance has vulnerabilities. There’s increasing concern over concentration risk. In Europe, executives from 44 major companies have warned regulators that the EU AI Act could unintentionally reinforce US cloud monopolies and constrain regional AI development. Export controls, particularly US-led GPU restrictions, limit who can access high-end compute, encouraging countries and developers to look toward decentralized or open alternatives. Additionally, API pricing for proprietary models has seen multiple increases since 2024. These monopoly rents are motivating developers to consider lower-cost, open-weight, or decentralized solutions. --- Decentralized AI We have on-chain compute markets such as Akash, Render, and @ionet that enable GPU owners to rent out unused capacity to AI workloads. These platforms are now expanding to support AMD GPUs and are working on workload-level proofs to guarantee performance. Bittensor incentivizes validators and model runners through $TAO token. Federated learning is gaining adoption, mostly in healthcare and finance, by enabling collaborative training without moving sensitive raw data. Proof-of-inference and zero-knowledge machine learning enable verifiable model outputs even when running on untrusted hardware. These are foundational steps for decentralized, trustless AI APIs. --- Where the Economic Opportunity Lies In the short term (today to 18 months), the focus is on application-layer infrastructure. Tools that allow enterprises to easily switch between OpenAI, Anthropic, Mistral, or local open-weight models will be valuable. Similarly, fine-tuned studios offering regulatory-compliant versions of open models under enterprise SLAs are gaining traction. In the medium term (18 months to 5 years), decentralized GPU networks would spiral in as their token prices reflect actual usage. Meanwhile, Bittensor-style subnetworks focused on specialized tasks, like risk scoring or protein folding, will scale efficiently through network effects. In the long term (5+ years), edge AI is likely to dominate. Phones, cars, and IoT devices will run local LLMs trained through federated learning, cutting latency and cloud dependence. Data-ownership protocols will also emerge, allowing users to earn micro-royalties as their devices contribute gradients to global model updates. --- How to Identify the Winners Projects likely to succeed will have a strong technical moat, solving problems around bandwidth, verification, or privacy in a way that delivers orders of magnitude improvements. Economic flywheels must be well-designed. Higher usage should fund better infrastructure and contributors, not just subsidize free riders. Governance is essential. Token voting alone is fragile, look instead for multi-stakeholder councils, progressive decentralization paths, or dual-class token models. Finally, ecosystem pull matters. Protocols that integrate early with developer toolchains will compound adoption faster. --- Strategic Plays For investors, it may be wise to hedge, holding exposure to both centralized APIs (for stable returns) and decentralized tokens (for asymmetric upside). For builders, abstraction layers that allow real-time switching between centralized and decentralized endpoints, based on latency, cost, or compliance, is a high-leverage opportunity. The most valuable opportunities may lie not at the poles but in the connective tissue: protocols, orchestration layers, and cryptographic proofs that allow workloads to route freely within both centralized and decentralized systems. Thanks for reading!