Crypto and AI analysts are increasingly pointing to a Bitcoin-inspired framework as a potential solution to structural limitations in large-scale artificial intelligence systems. The proposal centres on transforming AI models into open, decentralized computation networks, drawing parallels with Bitcoin and its proof-of-work (PoW) mechanism.
Current AI development, analysts argue, remains highly centralized. Data is largely sourced from users, while value accrues to platform operators. At the same time, model training relies on closed datasets and static processes, limiting adaptability and restricting broader participation in value creation.
# 大模型的下一步：走向类 Bitcoin 的开放计算系统
当前大模型的发展正面临两个根本性瓶颈：其一是商业结构不闭环，其二是训练范式的中心化。今天的 AI… https://t.co/IjIB7M0SOS
— Lux(λ) ｜光灵｜GEB (@gguoss) March 18, 2026
Bitcoin runs on a decentralized network of nodes, where each computer maintains a copy of the blockchain without central control. It uses a UTXO model, meaning transactions spend specific units of Bitcoin, unlike Ethereum’s account-based system that tracks balances. Meanwhile, AI is increasingly used in crypto trading and risk modeling, helping analyze large amounts of market data, detect patterns, and improve decision-making in real time.
From centralized training to open compute networks
The proposed model introduces a PoW-like structure where data contribution and model training become verifiable computational tasks. In this system, participants supplying high-quality data or compute resources would be rewarded, similar to mining in blockchain networks.
This approach aims to create a closed-loop economic system where users act as contributors, consumers, and beneficiaries. By aligning incentives, the framework could address the disconnect between data ownership and value capture that defines current AI platforms.
Dynamic AI evolution through blockchain principles
Analysts also highlight a shift from static to continuous training. Instead of periodic updates, AI systems would evolve in real time, with data continuously entering the network and models dynamically adjusting. This mirrors blockchain processes where transactions are continuously validated and added to the chain.
Under this structure, AI systems could function as self-organizing networks, with model improvements emerging from ongoing competition and collaboration among participants. The result would be a more adaptive and decentralized intelligence system, capable of evolving beyond predefined training cycles.
Meanwhile, Bitcoin may be approaching the final phase of its current market correction, as several technical and on-chain indicators begin to converge near levels historically associated with market bottoms.
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