AI大模型爆发带来的前所未有算力需求增长，已成为能源研究的关键议题。智通财经注意到，北京理 工大学近日发布的2026年能源经济预测与展望研究报告将低碳算力与能源经济形势、全球能源转型、能 源市场、碳市场等主题并列，这是该系列报告连续发布16年来，首次聚焦纯能源和气候议题之外的技术 领域，足见电力算力息息相关——一方面，算力的最终边际成本取决于电力，与此同时，在量级跃升、 密度倍增的同时，双碳目标约束对算力的"含绿量"提出了更高要求。 《低碳算力服务回顾与展望》（2026）报告执笔人、北理工能源与环境政策研究中心副主任从荣刚教授 认为，当前算力供需存在"东紧西松"区域错配、算力类型错配、算力算效水平错配。我国算力产业已迈 入梯度布局、绿色低碳、高效服务的转型关键期。未来，需锚定"低碳+普惠+融合"价值导向，构建东 西对接、类型适配、算电协同、市场多元、业态丰富的低碳算力服务体系与网络，完善可持续的算力服 务生态及市场，助力我国实现从 "算力大国"到"算力强国"的跨越。 比如，东部核心城市群作为数字经济核心承载区，算力需求高度集中，部分热点区域（如上海、北京的 智算枢纽）算力负载率超85%，高峰时段甚至出现算力排 ...

Core Insights - The integration of AI and blockchain technology is reshaping the global tech landscape, with a focus on redefining "computing power" as a core production asset with financial attributes [1] - The company is pioneering a mechanism to NFT-ize computing power, providing a more predictable and efficient resource allocation path for AI developers and institutions [1][2] Group 1: Resource Allocation and Efficiency - The company's computing asset network has transitioned to a global scheduling framework, allowing developers to access remote computing power without concern for physical resource locations [2] - This new mechanism enhances resource utilization efficiency and reshapes incentive structures, enabling resource holders to inject idle resources into a blockchain-based computing pool [2] - The platform automatically allocates resources based on real-time demand and efficiency, allowing for revenue sharing with asset holders post-task completion [2] Group 2: Token Economy and Assetization - The company has developed a dual-token incentive model, where one token is used for resource transactions and the other is tied to platform governance and profit-sharing [3] - Developers are encouraged to encapsulate completed models as NFTs, allowing them to maintain control and earn revenue from each model invocation, thus transforming models into dynamic digital assets [3] Group 3: Security and Compliance - A four-layer security mechanism has been implemented to ensure resource safety, task stability, and data compliance, including identity authentication and hardware encryption [4] - The platform records energy consumption data on-chain and prioritizes high-efficiency nodes, linking token incentives to energy-saving performance, promoting a transition to sustainable energy architectures [4] Group 4: Market Position and Future Directions - The company's system is increasingly viewed as the backbone of next-generation AI infrastructure, attracting a diverse range of developers and institutions [5] - A new derivative product called "computing power on-chain bonds" is being developed, allowing NFT assets to be packaged into income-generating bonds, enhancing liquidity for resource holders [6] - The company aims to build a comprehensive on-chain computing ecosystem by integrating more edge nodes and distributed storage networks, while actively participating in the development of international token standards and scheduling protocols [6]

Group 1 - The founder and CEO of DRW, Don Wilson, suggests that global spending on GPUs may surpass that on oil in the next decade, highlighting the increasing importance of GPUs as a core resource for AI training [2][3] - The demand for GPUs is expected to explode, with the International Energy Agency projecting that electricity demand for AI data centers in the U.S. will reach 123 million kilowatts by 2035, which is 30 times the level in 2024 [3][2] - The supply of GPUs is uncertain due to factors such as TSMC's production capacity, U.S. export controls, and NVIDIA's product release schedule, leading to potential volatility in the market [3][4] Group 2 - The financialization of GPUs could lead to the creation of futures contracts and indices similar to those for oil, copper, and gold, allowing companies to hedge against price fluctuations [3][4] - Historical trends show that financialized commodities often experience bubbles and crashes, raising concerns about the potential for similar outcomes in the GPU market [4][5] - Unlike oil, which can be stored long-term, GPUs have a short lifecycle due to rapid technological advancements, making them more akin to perishable goods [4][5] Group 3 - Long-term investment success in commodities typically comes from companies that hold advantageous positions in the supply chain, such as manufacturers like TSMC and designers like NVIDIA, rather than from speculative trading in GPU futures [5][6] - The concept of "computing power capitalism" suggests a shift in resource perception from tangible materials like coal and oil to intangible assets like data, algorithms, and computing power [5][6] - The market will likely find ways to financialize new demands, but investors should focus on identifying companies and industries that will benefit from the emerging "computing power capitalism" rather than speculating on GPU futures [6]