The data center model has long been defined by the separation between the power company and technology businesses, the power plant and the data center, power generation and power consumption.
For two decades, this seemed logical. After all, power companies were designed to handle generation, transmission, and distribution of power at scale, while data center developers specialized in real estate, cooling, and connectivity, and technology firms were focused on compute density and uptime.
The division allowed each side to optimize within its own domain. The problem is that this architecture was built for a different era, when data loads were predictable, artificial intelligence (AI) was not yet destined to be ubiquitous, and power wasn’t the most critical technology supply chain issue.
Power has become the most critical variable
Today, power has become the most critical variable, from both an availability and a cost perspective. The supply and growth of power cannot keep up with the consumption demand of AI. Additionally, there are many factors that significantly increase the cost associated with power, including transmission losses, cost of distribution, energy price fluctuations due to grid demand, congestion, fuel volatility, and extended timelines for new interconnection permits.
AI has exposed how unsustainable the power supply model of the past has become. Training and running large AI models require a staggering amount of continuous power, far beyond the demand peaks the power grid was designed for. This division between energy producers and data center operators forces a rethink of the entire power supply and AI value chain, with an eye to integrating the two ecosystems across regulatory, geographic, and economic boundaries.
Register for All Access: AI in PEX 2026!
Power at the doorstep of compute
Most megawatts of power consumed by a data center originate somewhere other than the data center location, often from a distant gas-fired power plant or solar field and must travel across a transmission network that loses between 5 percent and 10 percent of its energy along the way. The cost of that journey, compounded by demand charges, grid congestion, and interconnection fees is ultimately borne by the technology businesses and its consumers.
Placing a data center adjacent to a power source, whether solar, other renewables, or a hybrid mix, reduces costs and dependence on the power grid, while providing control over power, now the most critical component of the AI value chain. This electricity generated on-site feeds the data centers and is supported by integrated battery systems that serve multiple purposes, including providing back-up power and reducing costs.
This dynamically controlled microgrid automatically prioritizes between solar, battery, and grid power, allowing data centers to operate continuously while avoiding exposure to volatile peak pricing and grid instability.
At the technical level, these purpose-built on-site power plants are calibrated to respond to the daily curve of power needs. When grid prices spike, highly aware control software supplies stored power from the on-site battery system to the data center. When generation from on-site renewables is abundant or in periods of negative pricing of power on the grid, the batteries recharge.
In addition to providing stable power for the data center operations, this dynamic microgrid helps reduce stress on the regional power grid. The data center, once a large consumer and a strain on the regional power grid, instead now becomes a relief valve that strengthens it. The data center moves from a passive consumer subjected to grid power availability into an active participant in control within the power ecosystem. The power grid now becomes an always available back-up.
In practice, this reduces total power costs up to 30 percent, while allowing data centers to operate on up to 90 percent clean energy on an annualized basis. Microgrids have long been viewed as niche resilience tools for hospitals, campuses, and defense installations. For data centers, they are becoming foundational.
Register for All Access: AI in Business Transformation 2026!
Smarter, distributed, and closer
Just as computing has moved from monolithic mainframes to distributed networks, the same evolution is underway in the infrastructure that powers AI. The next generation of AI data centers will be smarter, distributed, with their own primary power sources, and closer to where the data is used by the consumers.
Instead of a massive, remote campus unsustainably consuming gigawatts of power in a single location while destabilizing the local grid, a specialized constellation of dispersed data centers will reduce data latency to the user, provide efficient energy use, lower costs, and improve redundancy.
These new dispersed data centers are designed for modular scalability and are constructed in a fraction of the time, with a significantly lower environmental impact. When placed strategically near urban load centers or co-located with renewable energy projects, they deliver high-performance AI computing while reducing the dependence on grid power.
Built to outlast subsidies
In areas such as Louden County, Virginia, where land prices near traditional data center corridors have soared to US$2 million per acre and grid power prices are at all-time highs, relocating to nearby regions with on-site power and storage can generate savings in the hundreds of millions of dollars annually for technology businesses from both data center and power costs. The economic argument is as strong as the environmental one while simultaneously solving the power problem, an existential threat to technology.
By structuring projects around direct power sales to the data center, the economics are self-sustaining.
It won’t happen overnight
Co-locating data centers with power sources is not as simple as just redrawing a map. Power transmission and distribution corridors, fiber and water access, zoning rules, and interconnection studies are still drivers of where data center development can occur. Developing and building power sources and data centers at the same site demands a choreography of land, permitting, and local constraints that few developers have mastered.
Solutions, however, are beginning to emerge. Co-developing power plants and data centers on private land shortens permitting timelines. Modular cooling systems that rely on recycled water or air-side economization lessen the need for large reservoirs. Private fiber expansions for the dispersed data centers are now being laid alongside the on-site energy projects.
No more hollow words on sustainability
AI is rapidly becoming the largest consumer of new electricity demand in the US. Meeting this need responsibly requires more than rhetoric about sustainability. It demands structural reform in how energy and data centers collaborate. Integrating power generation and data computation at the same site represents an innovative solution that aligns economic, technological, and environmental objectives.