‘AI’s utility is being fluffed up to justify its progression,’ a policy and safety expert said.
Major technology firms are investing billions of dollars in new data center facilities, power supplies, and computing resources. Industry insiders, however, are divided over whether artificial intelligence is worth the cost.
The five biggest cloud and AI infrastructure providers in the United States—Alphabet, Amazon, Meta, Microsoft, and Oracle—have committed to spending almost $700 billion combined on AI capital expenditures this year, Futurum reports.
AI’s computational resources, or “compute,” are a primary driver of the technology’s growing resource footprint. This includes things such as memory, processing power, and specialized hardware. Many AI models run on cloud computing, most of which is provided by one of three tech giants: Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
According to Usage AI, those three companies alone represent around 68 percent of total global cloud compute spending, giving rise to the term “hyperscalers.”
Hyperscale facilities come with significantly higher resource demands, and there are an estimated 670 of these facilities planned in 2026, iRecruit.co reports.
A growing number of Americans are using AI every day, with nearly half using some kind of AI chatbot regularly. Roughly one in four U.S. adults use these tools on a daily basis, according to a 2026 Pew Research Center study. The technology’s footprint has prompted some insiders working on the front lines to pause and consider the true cost of its rapid development.
“The engineers and architects actually building these systems are the ones asking the hard questions about compute, model redundancy, and whether the infrastructure footprint is proportional to the value being delivered,” Elvin Aghammadzada, an AI architect at NVIDIA whose job gives him “a ground-level view of what serious AI compute actually demands,” told The Epoch Times.
“The infrastructure required to run production agentic applications at enterprise scale—the GPUs [graphics processing units], the cooling, the redundancy—is substantial, and it compounds fast as adoption accelerates.”
