Amid increasingly intense global technological competition, the U.S. government took a critical step on November 25th. President Trump formally signed an executive order to launch a major initiative named the "Genesis Mission," aimed at comprehensively deploying artificial intelligence technologies to accelerate the enhancement of the nation's overall scientific research capabilities. This plan is not only seen as a significant strategic layout for the U.S. in the realm of technology but was also described by Michael Kratsios, Director of the White House Office of Science and Technology Policy, as "the largest and most integrated systematic mobilization of federal research resources since the Apollo program."

The core objective of the "Genesis Mission" is to promote the deep integration and application of AI technology within the U.S. scientific research system. According to the executive order, the U.S. Department of Energy and its 17 affiliated national laboratories will serve as key implementing bodies for this plan. These institutions have long accumulated vast amounts of scientific research data in critical fields, covering areas such as energy production, materials science, biomedicine, and environmental engineering. At the plan's announcement, Secretary of Energy Wright emphasized the intention to fully leverage AI technologies, particularly advanced algorithms like neural networks, to conduct in-depth analysis and mining of these massive and complex datasets, thereby "significantly accelerating the pace of scientific discovery and innovation."

This strategic deployment is expected to have profound impacts across several key areas. In pharmaceutical R&D, AI technology is anticipated to accelerate drug screening, disease mechanism research, and clinical trial design. In the energy sector, AI can help optimize energy production processes, improve the efficiency of renewable energy, and drive breakthroughs in nuclear and clean technologies. In advanced engineering and materials science, AI-driven research models will substantially shorten the cycle from basic research to practical application. Kratsios further pointed out that the plan not only focuses on technological breakthroughs themselves but also aims to build a new national scientific research ecosystem based on data and supported by intelligent tools.

To achieve this goal, the U.S. government has explicitly stated that relevant agencies must systematically optimize their data management mechanisms to make existing and future scientific research data more suitable for processing by AI tools. This means that multiple departments, including the Department of Energy, will comprehensively upgrade their data storage formats, access interfaces, and standardization processes to support the training and application of advanced models like neural networks.

Simultaneously, the "Genesis Mission" also strives to promote the opening and sharing of scientific research resources at the federal level. According to the executive order, high-quality scientific research data and high-performance computing infrastructure held by the government will gradually be opened to university research institutions, private sector companies, and professionals in national security-related fields. This measure aims to break down data silos and resource barriers that exist in traditional research activities. By building a cross-institutional, cross-disciplinary collaborative network, it seeks to stimulate broader innovation potential and facilitate the rapid translation and application of scientific research成果.

Analysis suggests that the launch of the "Genesis Mission" not only reflects the United States' urgent desire to maintain its leading position in the AI era but also demonstrates the crucial role of national power in guiding major technological directions. By integrating federal resources, overcoming data and computing power bottlenecks, and promoting industry-academia-research collaboration, this plan is expected to lay an important foundation for the U.S. in the next phase of global technological competition. Its actual effectiveness, however, will depend on the scale of subsequent resource investment, the efficiency of cross-departmental collaboration, and the depth of technology implementation. Regardless, it marks a new AI-driven paradigm for scientific research gradually moving from concept to large-scale practice.