In the current booming digital economy, computing power, as the core productivity, is leading a new round of technological revolution and industrial transformation. Among them, intelligent computing, as an emerging branch of the computing power industry, has become a key driver of technological revolution and industrial upgrading due to its core role in artificial intelligence model training, inference, and other scenarios. However, with the acceleration of technological iteration, intensified international lockdowns, and diversified application scenarios, the intelligent computing industry is also facing a structural imbalance in talent supply and demand. Faced with the demands of the times and practical challenges, it is urgent to build an intelligent computing talent ecosystem to promote the high-quality development of the intelligent computing industry and lay a solid talent foundation for the digital economy era. Intelligent computing is the transition from "computing power" to "intelligence", which in turn transforms "intelligence" into "productivity". This process not only relies on the support of high-performance computing hardware, but also requires a systematic talent system as the cornerstone. There are three major challenges in the current intelligent computing industry: firstly, the speed of technological iteration far exceeds that of traditional fields. The training of artificial intelligence large models relies on the collaboration of high-performance GPU clusters, high-speed networks, and other infrastructure. The diversification of the technology stack requires intelligent computing talents to have both hardware architecture and algorithm optimization capabilities. Secondly, external technological blockades have intensified the pressure for self-reliance. Domestic enterprises urgently need to accelerate the development of domestic chip software ecosystems and strengthen their ability to independently tackle technological challenges. Thirdly, the diversity of scenarios and the complexity of technology adaptation amplify the talent gap. There are significant differences in the design of computing power platforms, such as basic large models and multimodal models, which place extremely high demands on the cross disciplinary adaptability of talents. In this context, the industry urgently needs to build a full chain talent ecosystem that covers strategic decision-making, technological breakthroughs, scenario implementation, and capital operations. Market research and analysis talents, namely strategic decision-making teams. The construction of an intelligent computing center involves significant investment and has a very short technological iteration cycle. If there is a lack of accurate prediction of industry trends, huge investments may quickly depreciate due to outdated technology. These talents need to have a deep understanding of the national "East Calculation and West Calculation" strategic orientation, master the supply cycle and price fluctuation laws of core equipment, as well as the ability to apply financial instruments, such as optimizing the capital structure through financing leasing and asset securitization (ABS), and avoiding the risks of heavy asset projects. Intelligent computing center design talents, namely overall planning designers. The power consumption of a single GPU server can reach tens of kilowatts, and traditional power supply and cooling solutions are difficult to match. Business fluctuations require the data center to have elastic expansion capabilities. Designers need to integrate green energy technology, modular architecture, and renovation plans for old computer rooms, such as using liquid cooling technology to reduce consumption in high electricity price areas in the east, or flexibly allocating computing resources through dynamic design to balance the "dual carbon" goals and economic feasibility. Intelligent computing IT networking and operation and maintenance talents, namely IT technical personnel. The complexity of operation and maintenance in intelligent computing centers far exceeds that of traditional data centers, requiring challenges such as optimizing heterogeneous computing environments, multi platform resource pooling, and adapting to domestic chips. For example, in inference scenarios, reducing computational power dependence through quantitative compression techniques, or dynamically adjusting resource allocation during training to improve efficiency. Operations personnel need to transform from "equipment maintainers" to "technology integrators", possessing both hardware debugging and algorithm optimization capabilities. Intelligent computing and consumption scenario development talents, namely comprehensive business talents. At present, computing resources are mostly concentrated on the big model training of Internet giants, while applications in traditional industries such as manufacturing and medical care have not been fully exploited. These types of talents need to explore the expansion of application scenarios and transform abstract computing power into industry solutions. For example, integrating computing power distribution and real-time data when designing traffic dispatch models for smart cities, or balancing computational accuracy and cost when building molecular simulation platforms for pharmaceutical companies, to promote the transformation of technology into industrial value. Talents in financing and asset capitalization. The heavy asset nature and long return cycle of the intelligent computing industry require enterprises to have strong financial operation capabilities. These talents need to build a bridge between demand and financial instruments based on their own enterprise situation. This process not only requires familiarity with financial rules, but also an understanding of the risks, benefits, and operational characteristics of intelligent computing projects, and a good integration of finance and industry. The author is the head of the Strategic Planning Department and Senior Economist at the Training Center (Propaganda Center) of the National Development and Reform Commission