What's slowing down the AI buildout

What's really slowing down the AI buildout

America has the electricity to power its data centers; the problem is getting it where it's needed.

Works in Progress and Chris Gillett

Jun 25, 2026

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One of the most expensive projects in history is under construction in Abilene, Texas. This joint venture, Stargate, is the flagship of a bigger project by the same name led by OpenAI and Softbank, and is expected to cost well over $40 billion for a high-performance computing campus that will train new generations of AI models.

Stargate is just one major project in one of the biggest investment booms in history, driven by the belief that increasingly powerful AI models can deliver explosive economic growth. But it will require enormous amounts of electricity to work: Stargate is expected to draw 1.2 gigawatts, as much as 313,000 median American family homes, at peak load. A report by EpochAI and an energy research institute projected that total AI computing power would reach 100 gigawatts worldwide in 2030 if the 2025 growth rate stays steady. And data centers aren’t the only energy-hungry element of the AI revolution. The biggest battery manufacturing plants in the US draw energy at a rate of 115 megawatts, and the first phase of TSMC’s Arizona semiconductor plant will draw 200 megawatts.

The primary bottleneck to this growth is the availability of electricity. But this doesn’t mean there is an energy shortage. Instead, the constraint is connecting the flood of new data centers and the plants to power them to the electric grid. Before any new piece of infrastructure can be connected, grid operators must study how it will change power flows around the grid and determine whether upgrades to the system are required. That process is significantly backlogged. Though the median power plant in 2005 waited less than 20 months for interconnection, this had jumped to 55 months by 2023.

The interconnection process wasn’t created for today’s world. Grids use an inflexible first-come, first-served queue that leaves some of the most valuable projects stuck behind less important ones. They also evaluate according to rigid conditions that don’t reward plants for being willing to cover their own power needs for short periods. To prepare for the AI age, grid processes need to change.

A power-hungry future

Estimates vary for how much power will be needed by the data centers and chip manufacturers of the future, but the heads of every major AI company agree that they will need more than they are currently able to get. Jensen Huang, CEO of Nvidia, has said that ‘every data center in the future will be power-limited’. Mark Zuckerberg said Meta ‘would build … bigger [AI training] clusters … if we could get the energy to do it’. And OpenAI CEO Sam Altman told Congress that ‘the abundance of [AI] will be limited by the abundance of energy’.

Regardless of how the data center boom plays out, there is a long-term shift towards electrification across the economy. Electricity can be converted into work instantly, unlike fuels, and with little energy loss. Electricity creates motion directly, while fuels must first be combusted in an engine. This is why electric vehicles can cost half as much to fuel even though electricity is more expensive than gasoline. The simplicity of electric motors also means that electric vehicles have half the lifetime maintenance costs of gas-powered ones.

Electricity also transmits information. Transistors switch on or off depending on the voltage applied to their gates, which allows circuits to perform logical operations. Radios, screens, and computers cannot run on gasoline alone.

Between 1990 and 2024, the price of electric motors declined by 97.5 percent. Power electronics fell 99.5 percent in price, processors built into devices by nearly 99.9 percent, and batteries 98.8 percent. As prices have declined, performance has improved. For example, the amount of energy a battery can store per kilogram has increased five-fold over the same period.

These trends allowed the progression from the Walkman to the iPod and then the iPhone, as well as enabling battery-powered cars, delivery vans, and bicycles. At the same time, the increased computing power built into devices is giving autonomy to newly electrified cars and trucks, robotic vacuum cleaners and mowers, and drones. The commercialization of humanoid robots may also be on the horizon. The future will be largely defined by technologies that run on electricity.

The grid bottleneck

This growth in demand is already straining power grids. Operators are increasingly forced to use expensive local plants because of what grid operators call congestion: cheaper plants are on the other side of transmission bottlenecks and there aren’t enough cables to get the electricity through. In the United States, the additional costs incurred because of grid inefficiencies like congestion ran to $11.5 billion in 2023, an increase of 45 percent from the year before.

ERCOT, the grid that provides 90 percent of Texas’s electricity (including for the plant being built in Abilene), forecasts that it won’t have enough power to meet demand in summer 2028. PJM is the US’s largest grid, both by the amount of electricity it provides and the population in its coverage area, serving an area between Chicago, New Jersey and North Carolina. In 2025, PJM was not able to buy enough future generating capacity to meet projected demand. MISO, another large US grid operating between Louisiana and Minnesota, concluded in one study that ‘resource adequacy risks could grow… absent increased new capacity additions’. PJM’s CEO put it more plainly: ‘We need capacity – a lot of capacity.’

Discussions about expanding electricity supply to power the future often become debates about which source is most suitable: gas, nuclear, solar, or something else. But these are a distraction. Far more fundamental is ensuring power can be efficiently delivered where needed.

When different generation technologies coexist, they can average out to a grid that is cheaper, more reliable, and less polluting than any single type alone. The question isn’t which technology to use, but what balance. But this question isn’t best answered in the marketplace of ideas. Instead, it should be addressed in the marketplace for electricity.

The market for electricity

In the US and Europe, power grids are largely liberalized: provided they can get the necessary permits, independent developers build power plants, and the local utility is required to connect those plants to the grid. In the United States, 88 percent of large-scale power projects currently in development are privately organized and funded.

The power market is run by the grid operator’s economic dispatch software. Each power plant tells the operator its costs, and the operator commissions the cheapest plants, accounting for transmission constraints. As load increases during the day, the operator keeps commissioning the next cheapest plant. The price of power is set at the marginal cost of the next cheapest plant, and that’s the price every power plant is paid.

Market prices signal to power plant developers about levels of supply and demand. In the same way, prices balance different energy sources based on the strengths and weaknesses of each. For instance, as more solar panels are built, the value (and therefore price) of power during the middle of the day, when the sun is shining most, adjusts downward. From December 2020 to September 2025, maximum solar output in ERCOT increased from 4 to 29.8 gigawatts. And from 2020 to 2025, the value of power at 1pm relative to the highest-priced hour decreased from 92.9 percent to 38.7 percent. As one technology type becomes overbuilt, prices reflect that and developers react accordingly.

The evolving daily price shape in response to the abundance of solar energy was a signal that the grid needed storage capacity, and power plant developers responded. From 2020 to October 2025, ERCOT went from having almost no battery storage to a combined battery discharge of 8.6 gigawatts. The same process has played out in California and many European markets.

One might assume that the price of electricity for consumers is dictated by market forces, like those that regulate supply and demand across different power plants. But to a large extent, it is not. Grid infrastructure, like large power lines, is generally planned by the grid operator, and the cost is passed on to consumers at a price approved by state and federal regulators. In one typical utility territory within ERCOT, the portion of regulated costs on the average residential customer’s bill has grown from 28 percent in 2002 to 40 percent in 2025.

Regulators of all major grids have set caps on wholesale prices in response to public outrage at price volatility. As a result, the generators needed to keep the grid reliable are sometimes unprofitable. Under normal market circumstances, generators would stop running when it wasn’t profitable to do so. Instead, regulators put further policies in place to prevent this. Markets for capacity mean that generators are paid not for energy itself but for committing to be available if there is extra demand. Must-run agreements pay unprofitable generators to keep running if they keep the grid reliable. These are negotiated bilaterally between generators and the grid operator, outside of any wider competitive process.

Policy choices also shift the equilibrium. For example, America’s Inflation Reduction Act gives a $30 tax credit for every megawatt-hour produced by qualifying renewables. Power plants that opt in, typically wind, paradoxically often offer their power at negative prices, making money from the tax credit even when they literally pay consumers to use them. At times, a large enough share of the market offers electricity at a negative price that electricity overall (not just from one supplier) can have a negative price. A similar dynamic is playing out in some European countries.

Despite these interventions, which make them less efficient, markets still find an equilibrium. The interaction of supply and demand creates prices that power plant developers use as indicators for what the market does or doesn’t need more of. If prices are high, new power plants enter. That’s why arguing about the best power generation method is overrated. Well-designed energy markets answer this question automatically. The real bottleneck is connecting to the grid at all.

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Power struggles

xAI’s Memphis data center operated partially off-grid for months. When it first came online in 2024, it could reportedly draw only eight megawatts from the grid (enough to power a few thousand electric toasters). Rather than wait for its grid connection to be upgraded, xAI installed 422 megawatts of on-site gas turbines. Once transmission upgrades were completed, the project would shift to consuming grid power and the on-site generators would be used only for emergency backup.

Such off-grid generation is a temporary solution. Grid power is more reliable and, on average, cheaper. But thanks to the long queue of projects waiting for connection, 62 percent of data centers are considering off-grid solutions, either to get up and running faster or to improve reliability. Google is even exploring the possibility of a data center in space powered by solar panels. Grids connect to new generators and energy-hungry infrastructure only after studying how to do so using an engineering model that simulates power flows during peak load scenarios. If the new infrastructure would cause overloads on any parts of the system, then those elements need to be upgraded. The utility then needs to estimate the cost of those upgrades and build them.

The grid operato

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