The rise of artificial intelligence is often framed in terms of software and data, but what powers that software is becoming a bigger part of the story. AI systems depend on physical infrastructure that isn’t always visible but is critical behind the scenes, and that infrastructure lives in data centers. These centers run 24/7 and require substantial electricity to train and operate large-scale models. In much of the U.S., that energy still comes from hydrocarbons.
For U.S. oil and gas producers, particularly those supplying natural gas, this growing infrastructure may represent a new and durable source of demand. The buildout of AI data centers is quietly creating a new category of energy consumer. These customers are not only large but increasingly permanent. As AI becomes embedded in daily processes, its power demands will not shrink.
The data center boom and AI workloads
AI workloads are fundamentally energy-intensive. Training large language models, computer vision systems, and inference pipelines requires massive compute clusters. These systems, in turn, need a stable, around-the-clock power supply.
According to the International Energy Agency (IEA), data centers consumed roughly 460 terawatt-hours of electricity in 2022. That number is projected to double by 2026, driven largely by AI.
In the U.S., data centers tend to be located in regions with affordable energy, fiber infrastructure, and dependable power sources. Natural gas is increasingly becoming the energy source of choice for this kind of demand.
Why natural gas remains critical
Renewables such as wind and solar play an essential role in the evolving power mix. However, their intermittency presently makes them less suited to the continuous loads required by AI systems. By contrast, natural gas provides a dependable and responsive baseload option.
In some regions, developers are building “behind-the-meter” power solutions — private generation plants using contracted or even owned gas supply. Others rely on mobile generation units powered by diesel or natural gas liquids (NGLs). These use cases introduce new opportunities for upstream gas producers, especially those near data center hubs or transmission corridors.
The upstream sector faces new questions
Traditionally, power planning sat with utilities and midstream companies, but the rise of AI-driven infrastructure has changed that. Now, upstream producers are being asked to engage directly with energy-intensive buyers who operate on different timelines and expectations.
This evolution presents challenges:
- Is there sufficient takeaway capacity in the region?
- Are compression and processing facilities configured for 24/7 load profiles?
- Can producers commit to long-term volumes with unconventional offtakers?
These are engineering and logistical questions, but they also require new modes of coordination. Producers, tech firms, and grid operators must now plan together.
Infrastructure pressure and regional implications
This coordination is most urgent in regions already near capacity. Areas like Northern Virginia and Central Texas, where data center density is highest, are examples of this dynamic. In such zones, natural gas already supplies over 40% of the power mix — often more.
As power capacity tightens, expectations for reliability and delivery certainty will increase. Long-term contracts may need to evolve. Producers that can navigate these shifts stand to benefit not just from selling gas but from gaining new industrial partners.
Not just digital but deeply physical
Though AI feels intangible and abstract, its infrastructure is grounded in physical energy systems. Every server rack requires real-world energy to operate, cool, and maintain. These systems exist in the same energy markets as industrial customers.
That overlap is accelerating. Data centers are increasingly sited near energy infrastructure. In some cases, they’re even investing in generation assets, making them quasi-utilities in their own right.
A call for integrated thinking
To be clear, the oil and gas sector is not being asked to carry the AI economy on its own. Renewables, nuclear, storage, and transmission modernization are all part of the solution. But for the foreseeable future, hydrocarbons are necessary to support the scale and speed of AI growth.
This issue is not just limited to the U.S. Globally, AI-linked data center demand is influencing how countries think about energy resilience, grid diversification, and fuel sourcing. In Europe and parts of Asia, similar dynamics are emerging, though shaped by local market constraints and regulatory frameworks.
Global data centers may consume more than 800 terawatt-hours annually by 2026 — more than Japan’s entire electricity usage. That figure underscores the urgency of integrated energy planning.
Final thoughts: Opportunity for proactive producers
Energy producers should embrace this shift not as a threat but as a signal. A new class of customers is emerging: technologically advanced, capital-rich, and energy-dependent. They bring different needs but also new opportunities.
Producers who can align with these customers through direct supply, private power projects, or co-location strategies will be well-positioned. The convergence of AI and energy is no longer theoretical; it is happening now.
The AI revolution may run on code, but molecules power it. Oil and gas producers have a central role to play in this transformation.
Disclaimer: The views and opinions expressed in this article are solely those of the author and do not necessarily reflect the views of the author’s employer or any affiliated organizations, including Phoenix Energy One, LLC.
