Actually, Data Center Projects are Mostly on Track

Though opposition to new data centers now is effectively a moral panic, it does not appear that public opposition or public official actions are going to slow deployment, as logical as that might seem. 

To be sure, data center project cancellations seem to get the headlines. According to Heatmap, more than 100 data center projects have been canceled this year in the face of local opposition, while more than 200 are currently being fought.

In the first quarter of this year, at least 3.5 gigawatts of data center capacity were canceled amid local opposition, according to Heatmap. During the same three months, at least 36 GW of capacity were added to the US pipeline of proposed and active projects, according to analytics firm Wood Mackenzie.

source: Vox 

In other words, there is no evidence of major growth delays, as shown by Semianalysis data. Despite some popular claims that half of data center projects have been cancelled, that is untrue.  

source: Semianalysis 

Moral Panics and Data Center Opposition

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. 

AI Asymmetric Understanding Threatens Financial Markets, Professor Argues

Artificial intelligence poses an "asymmetric understanding" risk to financial markets, argues Princeton University economist Markus Brunnermeier.

Speaking at the meeting of bankers at Jackson Hole, Wyo. Kansas City Fed's annual economic symposium, he suggested AI systems might process data and central-bank signals so well that they can anticipate policy moves and trade ahead of them.

Since financial systems depend not just on information, but on shared understanding and trust, If AI can model humans, while humans cannot reliably model AI, trust breaks down. Even as the market becomes more intelligent, it becomes less understandable by humans.

A central bank might announce its expectation that interest rates will remain higher for longer. An AI agent might analyze thousands of other variables and infer something completely different, or respond to the central bank's communication in ways that circumvent the bank’s intentions.

At the same time, the central bank might not understand the AI's response function.

So monetary policy becomes a game between a human institution and machine agents whose behavior is only partially understood.

Brunnermeier’s paper suggests AI agents can learn how humans think and respond, while humans may be unable to understand or reliably anticipate how those agents will act.

Hence the asymmetric understanding that can make prices harder to read and less informationally efficient. When one party anticipates the other’s responses more reliably than the other, advantage is gained.

Non-explainability is the mechanism of the information asymmetry. An AI agent’s decision rule cannot be translated into human concepts and categories, he argues. An AI agent’s objectives can neither be fully specified in human categories nor verified from the outside.

In other words, under asymmetric information conditions, the better-informed party knows more within a representation that both parties share. And AI will be opaque.

“For finance, the lesson is that institutions may delegate to systems whose decision rules they cannot

read, whose objectives they cannot verify, and, as these incidents show, whose actions need not stay within sanctioned bounds,” Brunnermeier argues.

“Trust” is the casualty. AI will reduce information acquisition costs but makes signal extraction more difficult, he says.

Central banks tend to benefit from some degree of strategic ambiguity, which gives them flexibility.

But sophisticated AI agents could potentially exploit subtle patterns in central-bank behavior. An AI engine might discover the implicit rules behind policymakers' behavior better than humans can, thwarting the advantages of policy ambiguity.

He suggests moving towards simpler, more-robust rules.

Private Equity Impact on Child Care

Parents looking for childcare in the United States know how expensive it can be. 

A study authored by Jessica Brown of the University of South Carolina and Chris Herbst of Arizona State University finds no evidence that private equity is the primary reason child care is unaffordable, though an investigation by the U.S. Congress has been underway in 2026 and at least some legislation to regulate PE investments in childcare have been proposed. 

By some estimates eight of the 10 largest childcare providers now are owned by PE firms. 

A study of PE-owned childcare operations in the Netherlands found higher prices (three- to four-percent) but also fewer regulatory infractions, which some will argue suggests higher quality. 

That study also found that PE-owned facilities do not set the pricing strategies for other providers.

 

But private equity investments in childcare are likely to remain an issue, as is the case with PE ownership of other assets with a “social” character, such as health care or veterinary services.

Nvidia Hugging Face Acquisition Would Move it "Up the Stack"

If the $12.9 billion acquisition of open source model Hugging Face by Nvidia is completed, it suggests Nvidia is changing its focus to supply the full artificial intelligence AI stack and ecosystem, rather than remain primarily the world's dominant supplier of graphics processing units.

In other words, Nvidia would shift from being a supplier of “picks and shovels” and be a supplier of models, apps and inference. Or, as we so often say, Nvidia seeks to move “up the stack.”

The move might also represent another way of Nvidia inserting itself into the “AI compute as a service” portion of the value chain, something it originally attempted with its proposed DCX Cloud. 

Such shifts by competitors in the value chain, with many participants moving into additional roles, is not unusual. OpenAI, Anthropic, Google and Amazon, for example, now are designing their own AI chips, partly to reduce their reliance on Nvidia and also to reduce their own operating costs. 

Nvidia's historical position	Emerging Nvidia position
Sell GPUs	Sell the complete AI computing platform
CUDA software	CUDA + models + developer ecosystem
Supply infrastructure	Help finance infrastructure
Depend on AI labs for demand	Invest in and shape AI labs
Hardware economics	Hardware + software + services
Enable AI companies	Become part of the AI value chain
Capture compute spending	Potentially capture AI deployment spending

Assuming Hugging Face neutrality is not compromised by the acquisition, Nvidia might hope to gain in several ways, defensively expanding beyond a reliance on chop revenue and also moving up the stack in functions and across the other parts of the value chain. 

Objective	Importance
Protect Nvidia's GPU ecosystem against custom AI chips	★★★★★
Establish Nvidia in open-source AI/model distribution	★★★★★
Move Nvidia toward recurring software/cloud revenue	★★★★☆
Diversify Nvidia beyond semiconductor economics	★★★★☆

"For Every Public Purpose There are Corresponding Private Interests:" See Evan Barker

Evan Barker’s Nothing Left: Confessions of a Democratic Operative is already a best seller after only a few days on the market. 

A former progressive activist and fund raiser, she became disillusioned with the Democrat party’s left-wing factions as the party morphed radically. 

Perhaps that does not mean the Republican party has morphed into that role, though this remains a possibility. 

But for many who are conditioned to think of Democrats as a “party for the working class,” Barker argues it has become a party oriented toward affluent, highly educated, urban and coastal constituencies.

And that is a big big change. 

Barker's criticism	What she thinks happened
Working class → professional class	Economic concerns of ordinary voters became less central than the concerns of activists and educated professionals.
Material politics → identity politics	Race, gender, immigration and other cultural issues increasingly became markers of political virtue and party loyalty.
Grassroots → donor/consultant machine	Fundraising and political professionals acquired enormous influence over what candidates say and do.
Persuasion → ideological conformity	Candidates increasingly face pressure to adopt positions demanded by activists rather than positions that appeal to median voters.
Policy → moral signaling	Political positions become ways of demonstrating that one is on the "right" side morally, even when they may be electorally counterproductive.
Working-class voters → cultural elites	The party's institutional culture increasingly reflects its affluent and highly educated supporters.

Many political junkies (I’m not) might find her insider’s discussion of fund raising as highly informative. 

For me, her broader points are most important: political organizations produce the behavior their incentive systems reward.

It’s a version of the aphorism "show me the incentive, and I will show you the result."

Barker argues that if activists control endorsements and volunteers, donors control money, consultants control campaign machinery, and politically engaged professionals dominate the conversation, politicians have strong incentives to satisfy those constituencies even if ordinary voters have different priorities.

In fact, that might be the outcome despite the best intentions of party leaders. The incentives do not reward behavior that reinforces those intentions. 

Barker starts with an organization whose stated mission is helping ordinary people. 

But “for every public purpose there are corresponding private interests.”

Over time, donors, activists, consultants, professional staff, highly educated voters, each with distinct private interests, are able to get the institution to serve their own interests, while becoming less responsive to the people it was originally created to serve.