Resistance to New Data Centers Might be a Developing "Moral Panic"

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.