AI IS MORE THAN SOFTWARE
The constraint on AI's pace and geography may be migrating from software and chips toward electricity, grid capacity, and build-time.
SUBSTACK
AI IS MORE THAN SOFTWARE
Here’s the fact: by 2030, the IEA projects US data centers will consume more electricity than the country’s aluminum, steel, cement, and chemical production — combined.
In plain terms: the limit on how much electricity is available may show up before any limit on how smart AI can get. By 2030, the International Energy Agency (IEA) projects that data centers in the United States will use more electricity than the country’s aluminum, steel, cement, and chemical industries combined. That is not a software number. It is a heavy-industry number, attached to a technology that almost nobody thought of as a big power user a few years ago.
Behind every AI response is a data center.
And behind every data center is electricity.
The Numbers
Start with how big this already is. In 2023, data centers used about 4.4% of all the electricity in the US. That is according to a December 2024 report from the US Department of Energy and one of its national labs. Their own estimate says that share could grow to somewhere between 6.7% and 12% by 2028. That is a wide range, and it is wide on purpose: even the people building these models are not sure exactly how fast construction and power hookups will keep pace with demand. In plain numbers, that means data center electricity use could roughly double or triple in five years, from about 176 terawatt-hours a year to somewhere between 325 and 580 terawatt-hours. (One terawatt-hour is roughly enough electricity to power a mid-sized city for a year.)
This is not only a US pattern. The IEA estimates that by 2030, the world’s data centers combined will use roughly as much electricity as Japan’s entire economy uses today, all of it, for data centers alone. In the US, data centers are expected to account for close to half of all new electricity demand between now and 2030. And in 2025 alone, electricity demand from data centers grew about 17% worldwide, while total electricity demand grew only about 3%. Data centers are growing several times faster than the power grid around them.
The spending backs this up. Five of the largest tech companies spent more than $400 billion in 2025 building this infrastructure, and that number is expected to climb roughly 75% higher in 2026, according to IEA reporting. The money is moving fast. The electricity needed to power what that money buys is not moving nearly as fast. It is worth noting that these numbers come from two separate sources, the US government’s own energy lab and the International Energy Agency worldwide, and they point in the same direction independently, which is one reason to take the trend seriously even with the uncertainty built into it.
What This Means
Here is where we move from fact to inference, what the numbers suggest, not what they prove outright. A company can announce and fund a new data center in a matter of weeks. A new power line or power plant typically takes years to get approved and built, no matter how much money is behind it. If that gap holds, the real limit on how fast AI gets built may end up being how fast a region can add electricity, not how good the chips or the models are. That would hand real power to whoever controls land, the process for connecting to the power grid, and approval speed: utility companies, grid operators, and places that already have spare electricity to sell. It also means AI may end up getting built wherever the power already exists, not necessarily wherever the talent or the money wants it to be.
The Honest Pushback
Here is the strongest case against all of this: efficiency. Every year, computer chips do more work per unit of electricity. Cooling systems improve. Companies get better at shifting computing jobs to wherever power is cheapest or most available. If efficiency keeps improving faster than demand grows, this power ceiling could turn out to be smaller than it looks today, the same way some past predictions about computers using runaway amounts of electricity did not fully come true. This is a real, fair objection. It is also exactly why the Department of Energy’s own estimate is a wide range, 6.7% to 12%, instead of one confident number. That range is the industry itself admitting it is not sure how this plays out.
That said, history is not fully on the side of “efficiency will fix it.” In past computing booms, efficiency gains rarely shrank total energy use. At best they kept pace with growth, and in some cases, they made computing so cheap that people simply used far more of it. So, the fair conclusion is that efficiency is more likely to fuel demand than to cap it.
If the real limit is shifting from chips to electricity, the thing worth watching changes too. Not the next model release. The next power line that gets approved or delayed. The next utility that says how much new electricity it can deliver, and by when.