Look at the table on this post regarding WATER USAGE.
Computer Chip Innovation: Silicon Binary Chips Vs. my RI13 Carbon Ternary Chip
Our sun and magnetosphere timing frequency is unique to humans. I doubt they know how to crack it. Black ops has been using E.T. for 100 years to try to time hack our timing frequency and failed. You have to COOPERATE with our evolutionary plan or you’re committing suicide. Have at it.
The rapid expansion of artificial intelligence (AI) has triggered an unprecedented surge in global electricity consumption, primarily driven by the massive computational power required to train and run complex models.
Key Consumption Drivers
Data Center Growth: Global electricity demand from data centers is projected to double by 2030, reaching approximately 945–1,000 terawatt-hours (TWh) annually—comparable to the entire current electricity usage of Japan.
Inference vs. Training: While training models like GPT-4 requires enormous upfront energy, “inference” (the energy used every time a user asks a chatbot a question) is expected to account for 75% of AI-related demand by 2030 as adoption scales.
High Power Density: AI-optimized servers consume two to four times more power than traditional servers, leading to individual data center facilities with city-scale energy needs, some exceeding 1 gigawatt (GW).
Environmental and Economic Impact
Grid Strain: In the United States, AI data centers are projected to account for nearly half of all electricity demand growth through 2030. This concentration is already creating local bottlenecks in hubs like Northern Virginia, where data centers consume over 25% of the total electricity supply.
Rising Consumer Costs: To support this demand, utilities are investing heavily in new power plants and grid upgrades. In some regions, these costs are being passed to residents through higher monthly bills, with projected increases of $16 to $70 per month in the coming years.
Water and Emissions: Data centers require millions of liters of water daily for cooling, leading to concerns about water scarcity in drought-prone areas. Despite corporate “green” promises, many facilities rely on fossil fuels like natural gas to ensure a constant, reliable power supply.
Efficiency and Future Outlook
The Jevons Paradox: While AI hardware efficiency is improving rapidly (with performance per watt increasing significantly), history suggests these gains often drive higher total consumption because they make the technology cheaper and more widely used.
Nuclear and Fusion: To meet demand without fossil fuels, tech giants are exploring advanced energy sources, including small modular nuclear reactors and fusion technology. (No. They need new chips-LT)
Synchronicity is the Source of Consciousness 