Can Schools Afford an AI-First Future?

As generative AI moves from demos into everyday classroom use, a tougher question is coming into focus: can schools truly pay for an “AI-first” future when every prompt depends on costly infrastructure, uncertain pricing, tight district budgets, and rising loc

When teachers open a browser and type a prompt, it feels almost weightless. Answers appear in seconds. The classroom experience can seem as simple as a Google search.

But for districts trying to plan beyond the first pilots, that moment hides a harder reality: the work that makes generative AI possible sits far away in massive data centers, powered and cooled by systems students will never see—and paid for in ways schools may not be ready to forecast.

A growing body of research is pushing educators to look past what AI does in lessons and toward what it costs to run.

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.

There is another strand of inquiry, though, examining the infrastructure that makes these tools possible—and it points to an uncomfortable truth for school leaders: generative AI is not just software. It is software and hardware that requires robust infrastructure to support and scale.

Data center expansion increasingly shapes land use. energy systems. local planning decisions and community development. according to research by Xiaofan Liang. PhD on data centers. And 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 looking at how data center growth affects electricity demand, water consumption, electrical grid capacity, and environmental sustainability.

In the United States, the scale is already visible in energy figures. Estimates cited by the Congressional Research Service say 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 amount is described as enough electricity to power nearly 17 million American homes for a year.

Those numbers connect classroom decisions to the physical world. The same line of research also draws attention to where the country sits in the world’s energy picture, reflecting why AI’s growing appetite for power matters.

Traditionally, districts have purchased educational technology—learning management systems, assessment platforms and instructional software—through licensing agreements that can often be forecast years into the future.

Generative AI behaves differently.

Unlike traditional software that becomes cheaper to distribute as it scales. generative AI continues generating costs each time users engage with the system. Industry observers increasingly point to “inference costs,” the computing resources required to generate responses. For schools. the question becomes practical and immediate: how does a district plan for costs that repeat with every interaction. especially if usage grows beyond early assumptions?.

Put simply, it’s unclear whether generative AI is financially feasible for schools. Many districts are experimenting through pilot programs, limited licenses or AI features embedded within existing products, but there are few examples of what universal access would cost.

What would it mean for every student and their teachers to have access to generative AI every day? Before that figure can even be discussed, schools have another variable to account for: 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 strengthen governance, but they also require additional investment. Student data privacy becomes as much an infrastructure and policy issue as it is a classroom one. The more control schools want over data. the more likely they are to face costs related to storage. cybersecurity. hardware. networking and technical expertise.

Even as districts debate how to integrate AI, the market around them keeps shifting.

OpenAI, Anthropic and other major AI companies are still competing to define the commercial landscape. Product offerings change frequently, pricing models continue to evolve, and infrastructure investments remain enormous. The result is an ecosystem with long-term economics that remain uncertain at precisely the moment schools are being encouraged to integrate AI more deeply into teaching and learning.

That uncertainty lands in a difficult time 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 also responding to staffing shortages. student mental health concerns. and academic recovery efforts post-COVID-19 school shutdowns.

In that environment, AI procurement is not just a budgeting problem—it’s a commitment problem. Districts are being asked to consider what they may be locking themselves into when AI becomes embedded in curriculum, assessment and daily operations.

There is one more cost factor that often feels distant from classroom planning but is increasingly tied to it: community impact around data centers.

Data centers are expanding rapidly across the United States. Local governments and residents are debating the benefits and tradeoffs associated with new facilities. and questions about energy demand. water consumption. environmental exposure and land use have become common features of public meetings and planning discussions.

For educators, those debates may not begin in lesson plans. But every discussion about AI in schools depends on the infrastructure being built in communities across the country.

Schools are therefore caught between two realities. They are 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 clearer answers about how much it costs—and whether it’s feasible to maintain the systems that make an AI-ready classroom possible.

generative AI schools data centers inference costs AI governance UNESCO Stanford review ESSER funding data privacy cybersecurity electricity demand water consumption