This is the most fundamental change to computing since the early days of the World Wide Web. Just as companies completely rebuilt their computer systems to accommodate the new commercial internet in the 1990s, they are now rebuilding from the bottom up — from tiny components to the way that computers are housed and powered — to accommodate artificial intelligence.

Big tech companies have constructed computer data centers all over the world for two decades. The centers have been packed with computers to handle the online traffic flooding into the companies’ internet services, including search engines, email applications and e-commerce sites.

But those facilities were lightweights compared with what’s coming. Back in 2006, Google opened its first data center in The Dalles, Ore., spending an estimated $600 million to complete the facility. In January, OpenAI and several partners announced a plan to spend roughly $100 billion on new data centers, beginning with a campus in Texas. They plan to eventually pump an additional $400 billion into this and other facilities across the United States.

The change in computing is reshaping not just technology but also finance, energy and communities. Private equity firms are plowing money into data center companies. Electricians are flocking to areas where the facilities are being erected. And in some places, locals are pushing back against the projects, worried that they will bring more harm than good.

For now, tech companies are asking for more computing power and more electricity than the world can provide. OpenAI hopes to raise hundreds of billions of dollars to construct computer chip factories in the Middle East. Google and Amazon recently struck deals to build and deploy a new generation of nuclear reactors. And they want to do it fast.

The bigger-is-better mantra was challenged in December when a tiny Chinese company, DeepSeek, said it had built one of the world’s most powerful A.I. systems using far fewer computer chips than many experts thought possible. That raised questions about Silicon Valley’s frantic spending.

U.S. tech giants were unfazed. The wildly ambitious goal of many of these companies is to create artificial general intelligence, or A.G.I. — a machine that can do anything the human brain can do — and they still believe that having more computing power is essential to get there.

Amazon, Meta, Microsoft, and Google’s parent company, Alphabet, recently indicated that their capital spending — which is primarily used to build data centers — could top a combined $320 billion this year. That’s more than twice what they spent two years ago.

The New York Times visited five new data center campuses in California, Utah, Texas and Oklahoma and spoke with more than 50 executives, engineers, entrepreneurs and electricians to tell the story of the tech industry’s insatiable hunger for this new kind of computing.

“What was probably going to happen over the next decade has been compressed into a period of just two years,” Sundar Pichai, Google’s chief executive, said in an interview with The Times. “A.I. is the accelerant.”

New computer chips for new A.I.

The giant leap forward in computing for A.I. was driven by a tiny ingredient: the specialized computer chips called graphics processing units, or GPUs.

Companies like the Silicon Valley chipmaker Nvidia originally designed these chips to render graphics for video games. But GPUs had a knack for running the math that powers what are known as neural networks, which can learn skills by analyzing large amounts of data. Neural networks are the basis of chatbots and other leading A.I. technologies.

In the past, computing largely relied on chips called central processing units, or CPUs. These could do many things, including the simple math that powers neural networks.

But GPUs can do this math faster — a lot faster. At any given moment, a traditional chip can do a single calculation. In that same moment, a GPU can do thousands. Computer scientists call this parallel processing. And it means neural networks can analyze more data.

“These are very different from chips used to just serve up a web page,” said Vipul Ved Prakash, the chief executive