The AI Energy Boom: How Growing Demand is Reshaping the Energy Landscape
The rise of artificial intelligence (AI) is transforming industries, economies, and daily life at an unprecedented pace. From public-facing systems like chatbots and virtual assistants to proprietary corporate tools and classified military applications, AI’s computational appetite is driving a surge in energy demand that is both massive and rapidly escalating. As this demand grows—often at a near-exponential rate—energy companies, particularly those in the oil and gas sector, are stepping up to meet the challenge. Chevron, for instance, is leveraging its expertise to power AI-driven data centers with natural gas, while other firms explore a mix of traditional and emerging energy solutions to keep pace with this technological revolution. Looking ahead 10 to 20 years, the energy requirements for AI could push current infrastructure to its limits, prompting a blend of oil and gas adaptations alongside innovative technologies to secure a sustainable future.
The Massive Power Hunger of AI
AI systems, particularly those reliant on deep learning and large language models, require immense computational power. Public-facing applications—think ChatGPT or Google’s AI-driven search enhancements—rely on sprawling data centers housing thousands of high-performance processors. Training a single large language model can consume energy equivalent to the annual usage of hundreds of households, with ongoing operations adding to the tally. A 2024 estimate suggests that data centers, many of which now support AI, account for roughly 3.5% of U.S. electricity consumption, a figure projected to climb past 9% by 2030 due to AI’s growth.
Proprietary AI systems, used by corporations for tasks like supply chain optimization or drug discovery, are no less demanding. These systems often run on dedicated infrastructure, amplifying energy needs. Meanwhile, military applications—ranging from autonomous drones to predictive battlefield analytics—add another layer of complexity. These systems require not only vast power but also reliability and security, often in remote or high-stakes environments. The U.S. Department of Defense, for example, is increasingly integrating AI into its operations, with energy demands rising accordingly.
The growth trajectory is staggering. Computational power for AI doubles roughly every 100 days, a pace far outstripping Moore’s Law. This near-exponential rise translates directly into energy consumption, as more powerful chips and larger datasets push electricity requirements skyward. Without intervention, this trend could strain grids, increase costs, and challenge sustainability goals.
Oil and Gas Giants Step In: Chevron’s Adaptation and Beyond
Energy companies, particularly in the oil and gas sector, are seizing the opportunity to meet this demand. Chevron, the second-largest U.S. oil and gas producer, exemplifies this adaptation. In a January 2025 announcement, Chevron revealed plans to build natural gas-fired power plants specifically designed to supply data centers supporting AI operations. Partnering with Engine No. 1 and GE Vernova, Chevron aims to deliver up to four gigawatts of power, leveraging America’s abundant natural gas reserves. This initiative, dubbed “power foundries,” co-locates power plants with data centers, ensuring a steady, reliable energy supply while potentially feeding surplus power back to the grid.
Chevron’s strategy hinges on natural gas’s reliability and scalability—key advantages over intermittent renewables like wind or solar for AI’s constant energy needs. The company is also exploring carbon capture technologies to mitigate emissions, aligning with broader decarbonization goals. As detailed in a Chevron newsroom release, this approach not only addresses immediate demand but positions the firm as a leader in powering the AI-driven economy.
Other oil and gas giants are following suit. ExxonMobil has signaled interest in supplying natural gas to data centers, while Microsoft is reportedly considering gas with carbon capture as a viable option for its AI infrastructure. These moves reflect a broader industry shift: fossil fuels, long the backbone of global energy, are being repurposed to fuel the digital age. Natural gas, in particular, offers a bridge—abundant, flexible, and capable of rapid deployment—while companies refine lower-carbon technologies.
Adapting to Today, Planning for Tomorrow
Beyond Chevron, energy firms are adopting diverse strategies to tackle AI’s appetite. Some are retrofitting existing gas plants to serve data centers, while others invest in modular power solutions that can scale with demand. For instance, independent power producers are exploring small-scale gas turbines that can be deployed near tech hubs, reducing transmission losses and boosting efficiency.
Planning for future demand is equally critical. Energy companies are modeling scenarios where AI adoption accelerates, particularly in sectors like healthcare, logistics, and defense. Grid operators, too, are bracing for higher peak loads, as AI’s 24/7 operations amplify baseline consumption. To stay ahead, oil and gas firms are partnering with tech giants—think Chevron’s collaboration with GE Vernova or Exxon’s talks with data center operators—to align energy supply with computational growth.
AI’s Energy Future: 10-20 Years Out
Looking a decade or two ahead, AI’s energy requirements could dwarf today’s figures. If computational power continues doubling every 100 days, data centers might consume 15-20% of global electricity by 2045, with AI as a primary driver. Public AI systems could expand into immersive virtual realities or real-time global translations, while proprietary and military applications might include fully autonomous supply chains or planetary-scale simulations. Such scenarios would demand terawatts of power—far beyond current oil and gas output alone.
Oil and gas solutions will likely remain central in the near term. Enhanced extraction techniques, like hydraulic fracturing, could boost supply, while carbon capture and storage (CCS) might temper environmental impacts. Chevron’s early investments in CCS for its AI power plants could scale, potentially capturing millions of tons of CO2 annually by 2040. Methane management, another focus for oil giants, could further lower the carbon footprint of gas-based power.
Yet, reliance on fossil fuels has limits. Enter emerging technologies: nuclear fusion, advanced solar, and quantum computing offer tantalizing possibilities. Fusion, backed by figures like OpenAI’s Sam Altman, promises near-limitless, carbon-free energy—though it remains years from commercial viability. Solar, paired with next-generation batteries, could harness AI’s own predictive capabilities to optimize output, while quantum computing might shrink AI’s energy footprint by making models more efficient. Small modular nuclear reactors (SMRs), already in development, could power remote data centers, offering a compact, reliable alternative to gas.
Balancing Act: Oil, Gas, and Beyond
The AI energy boom is a double-edged sword—unlocking innovation while straining resources. Oil and gas companies like Chevron are adapting swiftly, using natural gas to bridge the gap between today’s grids and tomorrow’s needs. Their focus on reliability and scale makes them indispensable in the short term, especially as AI’s growth shows no signs of slowing.
Yet, the future demands a broader palette. In 10-20 years, a hybrid energy mix—gas with CCS, nuclear fusion, advanced renewables—could power AI sustainably. Energy firms must invest now in these emerging solutions, balancing immediate profits with long-term resilience. For Chevron and its peers, the challenge is clear: fuel the AI revolution today, while paving the way for a cleaner, more efficient tomorrow. The stakes—economic, environmental, and technological—are nothing short of transformative.