In the era of the intersection of technological revolution and energy revolution, to solve the contradiction between AI and electricity, it is necessary to find a pivot between technology, policy, and market. The winning or losing hand of the future lies not in the single point breakthrough of "power saving" or "power generation", but in whether a dynamic balance system of "efficiency demand sustainability" can be built. The birth of DeepSeek, an AI company in China, is like a stone thrown into the lake, which has aroused double ripples in the global technology and energy markets. The open-source model it released can be regarded as a model of "low cost, high efficiency", achieving first-class performance with only ultra-low training costs and energy consumption. For a while, the stock prices of European and American chip giants fluctuated, and there was endless debate in the public opinion: Will electricity still be the curse of AI development? The breakthrough of DeepSeek is essentially a 'subtraction revolution'. It significantly reduces training and deployment costs through lightweight model architecture and open source strategy, allowing small and medium-sized enterprises to easily build AI systems. The cost of training an equivalent model for traditional AI giants is 10 times higher, while the energy consumption of DeepSeek models is only a fraction of the industry average. The 'power anxiety' of AI has been torn open by technology for the first time. This efficiency improvement directly shakes the expectations of the energy market. Previously, the industry widely believed that AI would drive explosive growth in electricity demand - the United States predicted that data center electricity consumption would double by 2030, even accounting for 12% of total demand. But with the emergence of DeepSeek, the logic that "AI will inevitably lead to power famine" has been questioned. Some industry insiders believe that the forecast for electricity demand growth may need to be recalculated. However, technological power saving does not necessarily equate to total power saving. There is an economic trap hidden here - the Jevons paradox: In 1865, economist William Stanley Jevons proposed that when technological progress improves efficiency, resource consumption not only does not decrease, but increases sharply. For example, Watt's improved steam engine made coal combustion more efficient, but the result was a surge in coal demand. DeepSeek may also trigger a similar effect, where every kilowatt hour saved could become a spark igniting new demand. Firstly, the threshold has been lowered and demand has surged. Small and medium-sized enterprises, research institutions, and even individual developers are flocking to the AI field, and distributed data centers are blooming everywhere, with total energy consumption possibly increasing instead of decreasing. Secondly, there is an explosion of application scenarios. Cheaper AI will penetrate traditional fields such as healthcare, education, and manufacturing, generating massive new demands. Considering the infinite scalability of AI, energy consumption may increase exponentially. DeepSeek has temporarily eased the pressure of unit energy consumption, but in the long run, it may push up overall electricity demand due to the decrease in technological barriers. If the market is allowed to grow wildly, it may lead to severe power shortages at some point, ultimately limiting the development of related industries. Although it cannot prevent a significant increase in electricity demand, there is no need to be overly discouraged. The emergence of DeepSeek still gives more hope to the energy transition. In the past period, energy companies have shown great enthusiasm for accessing DeepSeek. China Petroleum Kunlun Model has officially completed the privatization deployment of DeepSeek Model, providing a new engine for optimizing the application effect, shortening the research and development cycle, and building a healthy ecosystem for Kunlun Model. The model service cloud platform developed by State Grid Information and Communication Industry Group, a subsidiary of State Grid Corporation of China, has also been fully integrated with the DeepSeek big model. The deep integration of the two will improve the intelligent production capacity of the platform and enhance the efficiency of power grid digitalization project research and development. Energy state-owned enterprises such as Sinopec and CNOOC have also announced the integration of DeepSeek's open-source big model. In the era of the intersection of technological revolution and energy revolution, to solve the contradiction between AI and electricity, it is necessary to find a pivot between technology, policy, and market. On the technical side, the core approach is to reduce the power consumption of chips and algorithms. More advanced production processes can significantly reduce processor energy consumption. According to Kume's law, every 18 months, the energy required for the same amount of computation will be reduced by half. Since the birth of the first electronic computer in 1946, the energy consumption required for the same amount of computing is only one ten thousandth of what it was at that time. Even today, when energy consumption drops again and again, it is still possible to further compress the unit energy consumption through the combination of chip energy efficiency improvement, algorithm optimization and edge computing. On the policy side, it is necessary to guide data centers to use low-carbon renewable energy as much as possible and promote the integrated development of green electricity and computing power. Encourage the aggregation of various computing resources towards national hub nodes to avoid inefficient and redundant investments. Establish AI energy efficiency standards to prevent the proliferation of inefficient models. On the market side, a highly flexible electricity trading mechanism is key. Distributed new energy can be encouraged to participate in green power trading, and data centers can be guided to improve the utilization rate of renewable energy through participating in green power and green certificate trading, in order to optimize resource allocation and reduce electricity costs. AI and electricity are destined to be a marathon of alternating progress. The rise of DeepSeek has opened a new chapter in the relationship between AI and energy, proving that electricity may not necessarily be an absolute bottleneck, but if technology is allowed to flourish, it may also lay new hidden dangers. The winning or losing hand of the future lies not in the single point breakthrough of "power saving" or "power generation", but in whether a dynamic balance system of "efficiency demand sustainability" can be built. (New Society)