Hua Er Jie Jian Wen·2026-01-01 12:02Core Insights - The demand for computing power in the AI sector is growing exponentially, leading to a critical mismatch between the rapid expansion of AI data centers and the slow pace of the aging U.S. power grid [1][2][4] - AI companies are increasingly opting to build their own power plants on-site to avoid delays associated with grid connections, with natural gas becoming the primary energy source due to its scalability and quick deployment [5][15][16] - The trend of on-site power generation is expected to become a systemic approach by 2025, as major players like OpenAI and Oracle are already investing in large-scale gas power plants [11][12][22] Group 1: Power Crisis and AI Data Centers - The essence of the power crisis is not a lack of electricity but the slow delivery of power that cannot keep pace with the rapid construction of AI data centers [2][4] - The construction cycle for AI data centers has been compressed to 12-24 months, while the typical cycle for grid expansion and approval remains at 3-5 years, creating a significant risk for companies that wait for grid connections [2][17] Group 2: Economic Implications of Power Generation - The time value of computing power is reshaping decision-making, with potential annual revenues for a 1GW AI data center reaching up to $10 billion, making the cost of electricity a critical factor in project viability [5][20] - Companies are willing to incur higher costs for on-site power generation to ensure timely deployment, as the economic benefits of earlier operation outweigh the additional expenses [5][16] Group 3: BYOG (Bring Your Own Generation) Strategy - The BYOG model has shifted from an unconventional choice to a practical solution, allowing data centers to operate independently of the grid while awaiting connection [6][37] - This strategy enables companies to start operations without waiting for grid upgrades, thus capturing significant revenue opportunities [36][73] Group 4: Industry Trends and Case Studies - xAI has set a precedent by rapidly constructing a 100,000 GPU cluster in Memphis within four months, showcasing the effectiveness of on-site power generation [11][20] - Major companies like Meta, Amazon AWS, and Google are adopting similar strategies, utilizing bridging power solutions to operate AI superclusters before formal grid connections are established [18][20] Group 5: Natural Gas as the Preferred Energy Source - Natural gas has emerged as the dominant choice for on-site power generation due to its ability to meet the demands of AI data centers in terms of scale, stability, and deployment speed [15][16] - The shift towards on-site gas generation is expected to drive significant growth in the market, with numerous suppliers already securing large orders for AI data center projects [13][22] Group 6: Challenges and Considerations - While on-site power generation offers advantages, it also presents challenges such as higher long-term costs compared to grid power and complex permitting processes [26][71] - Companies are exploring innovative solutions to navigate these challenges, including strategic site selection to expedite permitting and deployment [26][37]