Moral panics sometimes occur when a society has anxieties about modernization, shifting social roles or perhaps new technologies.
Opposition to the building of high-performance data centers supporting artificial intelligence operations provides a possible case in point.
Yes, there are land use, electricity and water consumption issues. But the actual effects or impacts often are exaggerated.
Data-center electricity use is rising fast because of AI, to be sure.
Globally it was about 1.5% of electrical demand in 2024 (about 415 TWh) and is projected to roughly double to three percent or so by 2030 under IEA base cases, with AI-focused facilities grow faster.
And data center power demand represents a large fraction of incremental demand growth.
Electricity growth is still a minority of overall demand growth globally and competes with air conditioning, electric vehicle charging, industry, and electrification in general.
Category | Approximate Annual Electricity Use (US) | Notes / Equivalence |
Data centers | ~176 TWh (2023); ~180–192 TWh (2024) | ~4.4–4.7% of total US electricity. Rising rapidly with AI; projections for late 2020s/2030 often in the 300–800+ TWh range depending on scenario (9–15%+ of US total in higher cases). |
Residential / Households | ~1,400–1,500 TWh (order of magnitude) | Average US household ~10,000–11,000+ kWh/year. Data centers currently comparable to the electricity use of roughly 15–18 million average households. |
Water use for cooling is a big issue in drought-stressed or aquifer-dependent areas. And noise, land conversion, diesel backup generators, and rate impacts (when grid upgrades or generation are socialized) add to the list of issues.
On the other hand, U.S. data-center water use remains a small fraction of total freshwater (perhaps half a percent) and is dwarfed by water used for golf courses and agriculture.
Category | Approximate Annual Water Use (US) | Notes / Equivalence |
Data centers (direct consumption) | 17–17.4 billion gallons (2023) | Lawrence Berkeley National Laboratory and related analyses. Projected to rise (e.g., 38–73 billion gallons by 2028 in some scenarios). Mostly evaporative cooling. Nationally <0.5% of freshwater use. Equivalent to roughly 160,000–580,000 households (varies by per-household assumption). |
Golf courses | ~531 billion gallons (2024) | 1.63 million acre-feet (GCSAA survey). Down ~31% since 2005 due to efficiency and fewer courses. Roughly 30× data-center direct use. |
Agriculture / Irrigation | ~26.4 trillion gallons (2023) | 81.0 million acre-feet applied (USDA NASS 2023 Irrigation and Water Management Survey). By far the largest category; irrigation accounts for roughly 40–50% of total US freshwater withdrawals in recent USGS data. Orders of magnitude larger than data centers. |
Residential / Households | Roughly 10–15 trillion gallons (order of magnitude) | Public-supply domestic deliveries are a major share of the ~35–39 billion gallons/day public-supply total. Average household often cited around 100,000–120,000 gallons/year (varies widely by region, family size, outdoor use). Data centers equal a very small fraction of total residential use. |
So some might view the concerns as legitimate, but wildly overblown, especially considering all the other value AI and high-performance computing might represent across the whole economy in reducing resource impact.
Water use for cooling is a big issue in drought-stressed or aquifer-dependent areas. And noise, land conversion, diesel backup generators, and rate impacts (when grid upgrades or generation are socialized) add to the list of issues.
On the other hand, U.S. data-center water use remains a small fraction of total freshwater (perhaps half a percent) and is dwarfed by water used for golf courses and agriculture.
And such concerns do not include benefits such as reduced resource consumption in all other areas of the economy affected by AI and high-performance computing.
Sector / Domain | AI / HPC Application | Mechanism of Resource Reduction | Illustrative Potential Impacts |
Agriculture | Precision irrigation, nutrient management, crop monitoring (sensors + satellite + ML models) | Apply water, fertilizer, and pesticides only where and when needed; detect stress early | Water savings commonly 20–50%; fertilizer reductions ~25–30%; higher yields on same or less land |
Energy systems / Grids | Demand forecasting, renewable integration, predictive maintenance, flexible load management | Better match supply and demand; reduce curtailment of renewables; shift or curtail flexible loads; avoid unnecessary generation and infrastructure | Lower peak demand, higher renewable utilization, reduced need for peaker plants and excess capacity |
Manufacturing | Digital twins, process optimization, predictive maintenance, quality control | Simulate and optimize processes before physical runs; minimize scrap, downtime, and energy waste; right-size material and energy inputs | Energy reductions of 10–30% in optimized plants; lower material waste and fewer defective parts |
Buildings & HVAC | Occupancy-based control, predictive climate control, fault detection | Heat/cool only occupied spaces; anticipate weather and usage; detect inefficient equipment early | Significant cuts in heating, cooling, and lighting energy (often 15–30% in smart buildings) |
Transportation & Logistics | Route optimization, traffic management, demand prediction, autonomous systems | Reduce empty miles, congestion, and unnecessary trips; improve vehicle utilization and fuel efficiency | Lower fuel/energy use per ton-mile or passenger-mile; fewer vehicles needed for same service |
Materials & Chemistry | Accelerated materials discovery and catalyst design (HPC simulation + ML) | Design better batteries, insulation, catalysts, and lightweight materials with fewer experiments | Higher-efficiency products (e.g., better batteries, lower-energy chemical processes) reduce lifetime resource intensity |
Water systems | Leak detection, treatment optimization, demand forecasting | Identify and prioritize pipe leaks; optimize chemical dosing and energy in treatment plants | Reduced non-revenue water losses; lower energy and chemical use in water treatment |
Supply chains & Circular economy | Demand forecasting, inventory optimization, computer-vision sorting for recycling | Cut overproduction and excess inventory; improve recovery of materials from waste streams | Less waste, lower storage/transport energy, higher material circularity |
So a case can be made that current data center expansion concerns are akin to a developing moral panic.
Moral panics are:
marked by an increasing concern about a topic
characterized by a growing hostility toward the cause of the concern
marked by a consensus in the public debate about the nature of the threat
led by threats that are disproportionately larger than reality suggests.
Sociologist Stanley Cohen created the phrase. Moral panic is a widespread and exaggerated fear that an evil person, group, or entity threatens a community or society.
Panic | Time Period | Underlying Cultural Anxiety | The "Folk Devil" or Target |
The Salem Witch Trials | 1692–1693 | Religious anxiety, border warfare, and communal instability in colonial Massachusetts. | Marginalized women and community outsiders accused of witchcraft. |
The Comic Book Panic | Late 1940s–1950s | Post-WWII anxiety over juvenile delinquency and the corruption of youth culture by mass media. | Comic book publishers (specifically horror and crime genres) and teenage readers. |
Dungeons & Dragons and Satanic Panic | 1980s | Fear of changing family structures, secularism, and the rise of youth fantasy subcultures. | Role-playing gamers, heavy metal music fans, and alleged secret satanic cults. |
The "Super-Predator" Moral Panic | Mid-1990s | Fear of escalating urban crime rates and changing racial demographics in major cities. | Inner-city youth, particularly young Black males framed as remorseless criminals. |
Such panics are relatively short-lived, but intense in the short term, in part because some have incentives to intensify the issue. Politicians; environmentalists; journalists and industry opponents are among them.
The point is that, in such instances, the threats are exaggerated, often wildly.
And if concern about data centers does develop into something like a moral panic, history suggests that concern will dissipate.
The concerns are legitimate, but simply overblown.
