Most conversations about generative artificial intelligence in schools eventually zoom in on using AI in the classroom. Before districts redesign teaching and learning around AI, they may need to answer a more fundamental question: Can schools afford an AI-first future?
The question sounds strange because generative AI is often presented as software with free and low cost tiers to individual users. Teachers open a browser window, type a prompt, and receive a response in seconds. The experience feels almost weightless and as simple as a Google search. The infrastructure behind that interaction is much more complicated.
A useful way to think about generative AI is to remember the large desktop computers that once sat in school computer labs. Students interacted with a monitor and keyboard, but much of the important work happened elsewhere inside a massive tower packed with hardware.
Today’s AI systems operate similarly, except the tower has been replaced by massive data centers located hundreds or thousands of miles away — and increasingly in some cases, just a few miles away.
Cost of Compute
An explanation is in order. How do chatbots and the hardware behind them work? Think of the chatbot prompt as the remote control. The hardware stored at the data center is the wiring within a television, and the chatbot’s output is what appears on screen as you watch and flick through channels.
Every student prompt, teacher-generated lesson plan or AI-assisted feedback comment depends on specialized processors, networking infrastructure, electricity, water, and increasingly scarce computing capacity.
Most discussions about AI in education begin after those systems are already in place. However, a growing body of research suggests schools should pay closer attention to the infrastructure itself.
Researchers studying AI adoption in education have largely focused on classroom implementation, AI literacy and governance. Stanford’s review of the evidence base for AI in K-12 education found that adoption continues to outpace rigorous evidence about educational outcomes. At the same time, UNESCO and other organizations have increasingly emphasized governance, transparency and human oversight as schools experiment with AI tools.
A separate body of research examines the infrastructure that makes those tools possible. Urban planners, computer engineers and environmental researchers have begun documenting the physical footprint of artificial intelligence. Their work points to a reality that is largely invisible to educators: generative AI is both software and hardware that requires robust infrastructure to support and scale.
Research by Xiaofan Liang, PhD on data centers describes how AI expansion increasingly shapes land use, energy systems, local planning decisions and community development. Research by Shaolei Ren, PhD on power and water demand demonstrates that large-scale AI deployment carries substantial resource requirements that extend well beyond the technology sector. Researchers and policymakers are now examining how data center growth affects electricity demand, water consumption, electrical grid capacity, and environmental sustainability.
According to estimates cited by the Congressional Research Service, U.S. data centers consumed about 176 terawatt-hours of electricity in 2023, roughly 4.4% of all U.S. electricity consumption. Using average residential electricity consumption estimates from the U.S. Energy Information Administration, that’s enough electricity to power nearly 17 million American homes for a year. The map below shows where the United States sits in the world’s energy picture and why AI’s growing appetite for power matters.
Attribution: Hannah Ritchie, Pablo Rosado, and Max Roser (2020) – “Energy Production and Consumption” Published online at OurWorldinData.org. (archived on May 18, 2026).
Traditionally, districts purchase educational technology such as learning management systems, assessment platforms and instructional software through licensing agreements that can often be forecast years into the future. But generative AI operates differently.
Unlike traditional software, which becomes cheaper to distribute as it scales, generative AI continues generating costs each time users engage with the system. Industry observers increasingly point to what’s called “inference costs,” which are the computing resources required to generate responses. These are some of the major costs of LLMs for consumers and one of the central economic challenges facing AI companies.
For schools, how can a district plan for these costs, and what happens when the costs far exceed expectations? Put another way, it’s unclear whether generative AI is financially feasible for schools.
Many districts are currently experimenting with AI through pilot programs, limited licenses or AI features embedded within existing products. There are few examples of what universal access would actually cost.
What would it mean for every student and their teachers to have access to generative AI every day? Before we address this question, there is another cost variable to consider: data privacy.
Many educators and parents have expressed concerns about student information flowing into commercial AI systems. One response has been to advocate for private deployments, district-controlled systems or locally hosted models that offer greater oversight and protection.
Those approaches may provide stronger governance, but they also require additional investment. That makes student data privacy a matter of policy and infrastructure. The more control schools want over data, the more likely they are to encounter costs related to storage, cybersecurity, hardware, networking and technical expertise.
Understanding the Generative AI Market
Meanwhile, the broader market continues to evolve.
OpenAI, Anthropic and other major AI companies are still competing to define the commercial landscape. Product offerings change frequently. Pricing models continue to evolve. Infrastructure investments remain enormous.
The result is a technology ecosystem with long-term economics that remains uncertain at precisely the moment schools are being encouraged to integrate it more deeply into teaching and learning. This uncertainty arrives during a challenging financial period for many districts.
Federal ESSER funding has expired. States continue debating educational technology spending priorities. District leaders face growing pressure to justify technology investments while responding to staffing shortages, student mental health concerns, and academic recovery efforts post-COVID-19 school shutdowns.
Against that backdrop, AI presents a different kind of procurement question: Do districts understand the long-term commitments they may be making when AI becomes embedded in curriculum, assessment and daily operations?
There is still one more cost factor to consider: community impact around data centers. Data centers are expanding rapidly across the United States. Local governments and residents are increasingly debating the benefits and tradeoffs associated with new facilities. Questions about energy demand, water consumption, environmental exposure and land use have become common features of public meetings and planning discussions.
For educators, these debates may seem distant from classroom practice. But every discussion about AI in schools ultimately depends on the infrastructure being built in communities across the country.
Schools are currently debating how to integrate AI into teaching and learning while the infrastructure, economics and governance systems required to support large-scale adoption are still taking shape.
Before schools decide how deeply AI belongs in classrooms, they may need a clearer understanding of how much it costs and if it’s feasible to maintain the systems that make an AI-ready classroom possible.
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