InvestMacro

By Theodore J. Kury, University of Florida 

Many major tech companies have pledged to pay their fair share of the costs associated with generating and transmitting more electricity to serve large data centers. But ratepayers across the United States are worried about the potential costs they might have to bear. That’s because it’s not immediately clear how the cost of data centers’ energy will be calculated. The effects of price increases are likely just beginning, and their full effects may not be felt for years.

For example, a recent report by the organization that monitors the PJM market, an area that encompasses all or part of 14 mid-Atlantic and Midwest states, concluded that expected power demand from data centers was a primary reason for US$23 billion in customer price increases that will last until at least the end of 2028.

I have studied the programs states have launched to address the needs of these large electricity customers. Prices are set by state utility commissions, who determine which customers’ rates will increase by how much to pay for new investments in electricity infrastructure. It’s not simple.

The complexity of setting prices

Setting a price for electricity is straightforward in principle but complicated in execution. Regulators identify the costs to provide service, allocate the costs to customers and design prices to recover those costs.

First, regulators identify the costs that a utility company incurs to provide service. Regulators look at the value of the assets the utility company invests in, such as power plants, transmission lines and substations, as well as its day-to-day operating expenses, such as salaries, fuel, replacement parts and electricity it purchases from other sources. Then these costs are allocated to categories of customers, such as residential, commercial and industrial.

Ideally, costs are allocated to the customers who cause them, but that can be complicated to determine. For example, imagine a data center is built in an area that lacks existing power lines and is located 50 yards from a nearby electric substation. It’s clear that the data center should pay to run a 50-yard power line from the substation to the data center.

But what if the power company needs to upgrade the substation to handle the increased needs of the data center? Or secure additional sources of electricity? In these cases, the investments are part of the electricity grid that everyone uses. These costs will likely be shared among all customers.

Cost analysts review each line of a utility company’s costs, often thousands of items, and determine how each cost will be allocated. Each decision incorporates one basic idea: What’s your share?

For instance, if a group of customers uses 20% of the electricity delivered by the utility, they would be allocated 20% of the costs associated with energy delivery. Other cost items may be allocated based on the number of customers or how much electricity customers use at particular points in time, but the idea is the same.

Finally, the analysts set prices that are designed to recover the costs allocated to each customer group. So, the costs that are allocated to you are directly reflected in the electricity prices that you pay.

Flexibility and a potential loophole

One common criterion for figuring out how much a customer should pay is based on what is called “coincident peak demand” – the amount a customer group uses at the moment when all customers are collectively using the largest amount of electricity. Costs associated with overall peak usage are typically split proportionally – but this opens an opportunity for data centers to exploit the system.

Data centers often are able to fine-tune their electricity consumption, using more one minute and less another, in ways that residential users can’t easily replicate. Computerized systems can automatically adjust the amount of work a data center is doing, while a homeowner would either have to race around shutting off appliances to meaningfully reduce the amount of power their home was using or invest in a device that does.

Their flexibility means data centers may be able to learn to predict when system loads will peak and consume little to no power in just the right period to avoid contributing to peak loads, as has happened with cryptocurrency-mining operations in Texas. So when regulators look at their usage to determine prices, data centers may be able to avoid paying any costs allocated through coincident peak demand, even if they use large amounts of electricity at other times.

Who speaks for you?

When utility regulators decide how costs should be allocated to each customer group, they solicit input from different groups. The utility company initially submits its own proposal for how it thinks costs should be allocated across its system.

Large industrial customer groups representing customers such as factories will also submit their own proposals for how to allocate costs and set rates. Retail customer groups representing large and small stores will submit theirs. And large data centers, with the resources to hire experts in cost allocation, will submit theirs as well. Some states have specific state-government agencies to do some of this work on behalf of particular commercial groups, such as Pennsylvania’s Office of Small Business Advocate.

Regulators don’t always get a good sense of residential customers’ voices, though. Every state except Georgia, Idaho and Louisiana has an office of the consumer advocate that represents customer interests in proceedings before the state utility regulator. But they are often charged with representing all customers in the state without bias, meaning they cannot advocate for outcomes that would impose costs on one group of customers in favor of another.

So while every state’s consumer advocate is concerned with keeping the utility’s costs as low as possible, they may be barred by law from adopting a position on how those costs should be allocated. This lack of representation in this aspect of rate-setting for average households may lead to situations where the data centers’ advocates argue for minimal costs to be allocated to them – but nobody advocates on behalf of residents to examine or refute that argument.

Citizens left holding the bag

There are other risks for residential customers, too. Utilities’ investments in electricity infrastructure last for many years. But not every proposed data center will get built, and some may use less energy than originally projected. Technology may even change, making some data centers obsolete after a year or two of operations.

If those events happen, then any costs the utility company incurred to provide enough electricity will be spread among all the other customers.

The allocation process may be even more complicated for municipal utilities regulated by city councils or independent boards, or cooperative utilities regulated by elected boards in rural communities. These groups may not have full-time staff who are utility or regulatory experts, yet they face the same decision-making challenges as trained professionals and might have to retain outside experts to aid in the process.

Consumers need to be aware of the importance of cost allocation and how it affects their electricity rates. I believe they should provide public comments to the regulators and speak during open hearings, as there may not be anyone else effectively advocating for their interests.

About the Author:

Theodore J. Kury, Director of Energy Studies, University of Florida

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Rachel Mural, Harvard Kennedy School 

As the race to build data centers across the United States accelerates, local governments worry that the tech industry mantra of “move fast and break things” means their communities are at risk of being broken.

I’m a Harvard researcher studying the relationship between data centers and energy. I’ve closely monitored how local governments respond to proposals or even just concerns about the potential for data centers in their communities. What I’ve found is a complex story of community needs, political tensions and corporate power – all interacting with local, state and national democratic processes.

Promises and potential

Technology companies stay competitive by being ready to provide data and communications services even before customer demand rises. Data centers already power online communications, shopping and banking systems. Now, expanding demand for artificial intelligence has led to over 1,000 pending data center proposals across the country.

Federal actions also drive development. The Trump administration has identified data center build-out as a strategic priority. The administration has promoted data center capacity as a measure of American strength and signaled that federal regulations on data centers may be eased.

At the community level, technology companies claim that data centers bring jobs, economic revitalization, digital connectivity and economic growth to local communities.

Not great neighbors

So far, however, data centers’ benefits are overshadowed by more visible harms.

Nearby residents experience higher air pollution and excess noise. Data processing also uses a lot of water to cool the buildings and their equipment.

Simultaneously, electricity prices continue to outpace inflation, burdening families across the country. These trends reflect, in part, the costly infrastructure investments needed to power data centers.

The local movement

My research has found that local governments across the U.S. are trying to avoid or reduce these harms.

Some counties and cities that don’t have specific zoning rules and regulations for data center development are using short-term moratoriums. These pauses in data center permitting and construction give communities time to consider how to define new laws and regulations about the facilities’ location, electricity use, water conservation and noise buffering.

Speaking about his town’s decision to impose a one-year data center moratorium, Rick Bella, the town council president in Merrillville, Indiana, about 40 miles southeast of Chicago, stressed a desire to “evaluate real-world impacts and learn from a project developing right next door before determining what may or may not be appropriate for Merrillville.”

Other places want to block data centers altogether. In April 2026, for example, the Ypsilanti Community Utilities Authority near Detroit, Michigan, passed a yearlong halt to the “delivery, commitment, reservation, extension, or approval of water and sewer services” for data centers. The move blocks data centers, including one under development by the University of Michigan and Los Alamos National Laboratory, from getting the water they need to operate.

Separately, towns across Ohio, Wisconsin, Maryland, Nevada and California have put questions related to data centers on their local ballots. Through these referendums, voters can weigh in on construction bans, tax incentives and zoning ordinances.

Power struggles

While public attitudes around data centers have remained largely nonpartisan, local and state officials don’t always see eye to eye.

Officials in Hood County, Texas, for example, rejected a proposal for a six-month moratorium after a state senator urged the Texas attorney general to intervene and prevent the measure.

In 2025, West Virginia passed a bill that reduces local governments’ zoning and regulatory powers in relation to data centers and microgrids. A similar bill in New Hampshire’s legislature was defeated in May 2026.

Tech companies are also flexing their legal and financial muscles. For example, data center developers sued Saline Township, Michigan, and Chatham County, North Carolina, seeking to overturn their local zoning decisions, to be able to proceed with data center construction.

Changing tides

Local pushback comes at a pivotal moment for artificial intelligence technology itself.

As seen in objections to the internet’s expanding AI “slop,” backlash over AI-generated Super Bowl ads, worries about an AI-related financial bubble and complaints about Google’s pivot to AI-directed search, Americans are reckoning with AI’s role in society.

Further, many people are questioning the role of technology broadly. Increasing numbers of teens and adults are addicted to their smartphones, emotionally and psychologically dependent on their availability. Parents and teachers are questioning the usefulness of various types of digital technologies in classrooms. Even the pope has warned that technology must serve humanity – and not the other way around.

Americans are responding to this moment through the power of their voices and votes.

Technology companies may view moratoriums and new regulations as delays in project development. But the town hall discussions, community coalitions, public petitions and even farmers’ unions reflect American democracy at work.

In Sunbury, Ohio, local officials considered a moratorium only after witnessing the scope of public protest over a proposed data center.

In April 2026, voters in Festus, Missouri, removed several City Council members after they supported a new data center despite resident pushback.

The question of whether a community wants or should have a data center does not have a universal answer. I believe it’s a question that deserves deliberate processes, transparency and consideration.

To me, these local-level actions reflect a desire to slow down. There is little question that data centers and AI will be part of our collective future. Today, communities are asking for a fair say in what their futures will be.

About the Author:

Rachel Mural, Senior Research Associate in Environment and Natural Resources and Science, Technology, and Public Policy, Harvard Kennedy School

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Alex Krasnok, Florida International University 

Every bridge has parts that drivers never see: steel buried in concrete, welds tucked under girders, and soil packed around foundations below the waterline. A bridge can look fine from the road while rust spreads around steel hidden inside concrete. A small fatigue crack can lengthen. A flood can wash soil away from a pier. By the time cracks, loose concrete or lane closures appear, the cheapest repair window may already have closed.

When it comes to these damaged bridges, this problem is national. The United States has more than 624,000 highway bridges. About 220,000 need major repair or replacement, and 41,677 are rated poor, also called structurally deficient. While “poor” does not mean unsafe, it does mean at least one key bridge element received a poor rating, indicating deterioration or cracking that will require significant repair.

As a researcher who studies photonics and quantum sensing, I work on devices that measure faint or hidden signals. My lab applies physics to develop devices, including quantum sensors. Advanced sensors of this type might one day be able to help engineers pinpoint where to look to determine whether hidden damage in infrastructure is worsening. However, they cannot replace human inspectors.

The Dames Point Bridge spans a river in Jacksonville, Fla.
Jonathan Zander/Wikimedia Commons, CC BY

Inspections keep bridges safe, but are snapshots

Federal bridge inspections – rooted in National Bridge Inspection Standards mandated by Congress in 1968 – exist because past failures showed that small defects can threaten large structures.

Under current federal rules, many bridges must be inspected in, at most, 24-month intervals. Higher-risk bridges, such as those carrying heavy interstate traffic, those with aging structures or known defects, or those built over saltwater, may require shorter intervals. Lower-risk bridges with lighter traffic and sound materials may qualify for longer intervals.

Those inspections remain essential, but they are snapshots. A bridge may change during the months between visits. Corrosion can spread below a deck that looks sound. A small crack can sit inside a weld. A river can displace soil from a foundation while the roadway above looks unchanged. Sensors extend inspections by tracking these change that form between scheduled checks.

Hidden damage can grow quietly

The three common hidden threats to bridges are corrosion, fatigue and scour. Corrosion begins when water, oxygen and salts reach steel. A concrete layer usually protects steel, but cracks, salt spray and chloride ions from seawater or deicing salts can break that protection. The rust then expands, much like ice widening a crack in a sidewalk. It pushes the concrete outward and can cause the material to come loose or the layers to separate.

Fatigue damage is the bridge version of bending a paper clip back and forth. Just as a paper clip eventually snaps after repeated bending, a bridge’s steel components weaken and break down under continuous cycles of stress. Thousands of heavy vehicles can make tiny cracks grow near welds, bolted connections or older steel details.

Scour damage is different: Moving water removes soil around the bridge’s foundations. The bridge above can look stable, while the support below loses the ground it needs.

Waiting costs more

The earlier engineers can identify damage to aging bridges, the more time and options they have to fix them. The average U.S. bridge is about 47 years old. Many bridges are near or past the 50-year life they were designed for, and about 45% have exceeded their planned design lives.

Typically, it’s less costly to preserve bridges in fair condition than those already in poor condition. Making all the identified necessary U.S. bridge repairs would cost about US$467 billion.

Past failures show why small details matter. As one example, the 2007 I-35W bridge collapse in Minneapolis was partially due to undersized gusset plates – steel plates that connect the intersecting beams in a bridge’s structural framework – along with added weight and construction loads. The collapse killed 13 people and injured 145.

Sensors alone are not a cure for such failures, but better measurements can help engineers notice when important details are changing.

Sensors help engineers look, listen and scan

Sensor systems are easiest to categorize based on what they do.

Some sensors see: Drones can photograph cracks and loose concrete, infrared cameras can show heat patterns linked to damaged deck zones, and LiDAR, short for light detection and ranging, can build three-dimensional maps.

Some sensors listen: Ultrasonic testing and impact-echo probes send sound waves into concrete or steel, acoustic emission sensors listen for active cracking, and accelerometers track how a bridge vibrates.

Some sensors scan below the surface. Specialized radio tools try to locate hidden steel, trapped moisture, empty pockets or crumbling layers inside the concrete. Meanwhile, magnetic and electrical instruments attempt to guess whether that buried steel is rusting away.

The value of sensors often comes from combining methods. One bridge deck inspection robot uses subsurface radar, electrical tools that measure moisture, and a standard camera to collect data. It then builds simple visual maps showing the exact health of the bridge deck. Fiber-optic sensing could be another route. Researchers have shown that existing telecommunication cables can record bridge vibration signatures.

Sensors are evidence, not verdicts

While instruments provide crucial clues about a structure’s condition, they do not automatically dictate the solution. Engineers still need to examine the bridge design, inspection history, traffic loads, weather, material condition and measurement uncertainty before deciding whether to repair, restrict traffic or close a bridge.

Field data is messy. Wet concrete can blur radar results. Traffic, wind and temperature can mask vibration changes.

The best systems answer narrow questions: Where is the concrete deck beginning to split into horizontal layers underneath the surface? Is this crack actively widening? Is a suspension cable losing its structural strength because its inner steel wires are rusting away? Is the fast-moving water washing away the critical soil supporting the bridge’s underwater foundations after a storm?

Quantum sensors are a frontier

Quantum sensors may help when the signs of structural distress are weak, buried or noisy. These devices use quantum systems, such as atoms or electron spins, as highly sensitive probes.

By measuring how these atomic properties shift in response to extremely subtle changes in gravity, motion or magnetic fields, the sensors can detect flaws that traditional instruments miss.

For bridges, the nearest-term opportunity is likely magnetic inspection. My team and I co-authored a review, which has not yet been peer-reviewed, on quantum magnetometers for infrastructure inspection. These sensors identify signals from induction responses, magnetic flux leakage, stress, corrosion and operational currents.

In plain terms, these sensors may help map weak magnetic fields near steel, cables or electrical conductors. Changes or disruptions in these local magnetic fields can reveal hidden rust, snapped wire strands inside a thick suspension cable, or abnormal stress points in the steel before a crack even forms.

The hard part is not building a record-setting sensor in a quiet lab, but rather making a device that works on a noisy bridge, near traffic, weather, steel and electrical interference. Quantum sensors will matter only where they beat cheaper classical tools in real inspection conditions.

The goal is not to make every bridge smart. The goal is to make damage harder to hide. Sensors give engineers more ways to see inside concrete, steel, soil and water, turning some surprise closures into repairs planned months earlier.

The public may never notice the best use of bridge sensors. That is the point: The safest infrastructure technology often works before a problem becomes visible from the road.

About the Author:

Alex Krasnok, Assistant Professor of Electrical & Computer Engineering, Florida International University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Matt Motta, Boston University and Robert Ralston, University of Birmingham 

Arrests for betting on the U.S. military operation that removed Venezuelan leader Nicolás Maduro. Death threats from gamblers to a journalist reporting on an Iranian missile attack on Israel. Fears of government officials manipulating world events – including the Iran war – to make a quick buck.

These are some of many concerns that experts have raised about how prediction markets – online marketplaces that allow people to bet on world events – might be affecting national security in the U.S. and abroad.

But prediction markets may not be only influencing international affairs. They could also affect the 2026 midterm elections.

We are social scientists who study gambling, public policy and national security. Here are four things you need to know about how prediction markets may be changing American politics:

Prediction markets turn politics into a game

Prediction markets offer people the opportunity to bet on political events by purchasing “shares” – like stock in a company – of different potential outcomes. If an outcome takes place, the market pays out for each share purchased by those who guessed correctly. More betting activity in favor of an outcome raises its price and lowers its payout, and vice versa.

Prediction markets are different from casinos and online sportsbooks because there is no “house” – like a casino – that determines the size of the payout for correctly guessing who will win or lose a sporting event. In a prediction market, players “bet” against one another, not the house. The markets make money by charging transaction fees on each trade.

Betting on prediction markets allows users to turn many aspects of U.S. politics into a game. For example, betting on election outcomes is very popular on prediction markets. Kalshi – a popular prediction market platform – has a portion of its site specifically designated for election-related markets. That includes the chance to bet on the eventual winner of the 2028 presidential election, the margin of victory in the 2026 South Dakota primary elections and which of two Dan Sullivans could become Alaska’s next senator.

Kalshi also offers opportunities to bet on nonelection outcomes, like whether or not the Supreme Court will ban transgender girls and women from competing on “female sports teams,” or whether the government will confirm before September 2026 that aliens exist.

The gamification of politics through prediction market betting is not new. Predictit, a self-described “political prediction market,” has been operating in the U.S. for over a decade.

What has changed in recent years, however, is that prediction markets are no longer an obscure pastime enjoyed by political junkies. Prediction markets have become quite popular, and media organizations are even integrating betting market data in their political analysis. For example, Kalshi is CNN’s “official prediction markets partner.” In a segment called “The Odds,” CNN commentators often use Kalshi data to make predictions about candidates’ electoral performance.

Insider trading could affect US elections

Insider trading on prediction markets occurs when people with nonpublic information – like internal polling, military intelligence, etc. – place wagers on events. While some prediction markets are trying to crack down on the practice, insider trading could already be affecting the upcoming U.S. midterm elections.

In spring 2026, for example, NPR documented several cases where campaign staffers working on statewide campaigns admitted to using inside information about candidates’ performance in the polls to “buy low” on their candidate’s electoral prospects prior to the release of favorable polling data. Additionally, although prediction markets usually prohibit betting on one’s own campaign, both Democrats and Republicans running for political office have come under fire for betting on their own campaigns.

Betting on one’s own campaign could create a scenario where a candidate’s electoral performance seems more robust than it actually is to prediction market users or watchers, including media organizations who report on prediction market data.

This may in turn generate more favorable media coverage, which could affect public sentiment toward the candidate. Unlike polling, which is not typically prone to the same kind of meddling by campaigns, betting on one’s own campaign could ultimately change voters’ minds regarding the viability of a candidate.

Policymakers are paying attention

Given concerns about insider trading and its potential consequences, we asked Americans whether U.S. government officials should be forbidden from trading on prediction markets. In a nationally representative online survey of 1,000 U.S. adults conducted via the survey platform Verasight in March 2026, we found that nearly 70% supported banning government officials from trading on prediction markets, while 20% supported a more limited trading ban when government officials have “inside” information.

Lawmakers in Washington are beginning to respond to public opinion. The Senate recently banned senators and their staff from trading on prediction markets, although how this policy will be implemented remains uncertain. However, members of the House, employees of the executive branch, military officials and other government employees can still bet on prediction markets.

Some lawmakers have proposed limiting trading when government officials have insider information about an event, such as internal polling or fundraising data that members of the public do not have access to.

Others in Congress have made an effort to ban all trading on “death markets,” which include war, assassinations and related topics. Known as the “DEATH BETS Act” – its title is an acronym that stands for “Discouraging Exploitative Assassination, Tragedy, and Harm Betting in Event Trading Systems Act – the legislation has been introduced but is pending committee review.

State governments are also taking action to regulate prediction markets.

Massachusetts, for example, is suing Kalshi for allowing “backdoor betting” on sports.

Backdoor betting refers to wagering through less regulated channels like prediction markets, rather than highly regulated state casinos and sportsbooks. Backdoor betting has been estimated to cost states over US$1 billion in tax revenue since prediction markets first began allowing sports wagering in early 2025.

Minnesota became the first state to ban prediction markets altogether, while Illinois has sent cease and desist letters to prediction market operators that it claims are operating without adhering to state gambling laws.

Trump wants control over prediction markets

In a recent Truth Social post, President Donald Trump blasted the idea that states should be able to regulate prediction markets. Referencing their recent regulatory actions, Trump referred to Minnesota Governor Tim Walz and Illinois Governor JB Pritzker as “SCUM” in the post.

Trump also expressed enthusiasm for prediction markets in the post, saying that the U.S. is “at the top” of a “new form of Financial Market.” The president and his family have deep financial ties to the industry. For example, Donald Trump Jr. serves as a prediction market adviser to Kalshi and Polymarket and is an investor in Polymarket.

Following Trump’s post, the administration began reviewing a proposal to give the Commodity Futures Trading Commission the exclusive authority to regulate prediction markets.

While the CFTC has repeatedly asserted regulatory authority over prediction markets, some – like former CFTC Chairman Gary Gensler – believe that states, not the CFTC, should be in charge.

About the Authors: 

Matt Motta, Associate Professor of Health Law, Policy and Management, Boston University and Robert Ralston, Lecturer in Political Science and International Studies, University of Birmingham

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Neha Gour, George Mason University; Ed Maibach, George Mason University, and Luis Ortiz, George Mason University 

Every internet search, streamed video and AI-generated response depends on a data center somewhere. Driven by rapid growth in artificial intelligence, cloud computing and cryptocurrency, data centers have become the backbone of the modern digital economy. But though their key role is in enabling virtual and remote experiences, data centers are physical buildings in real communities around the nation and the globe.

The United States hosts more than 4,000 data centers – more than any other country. The U.S. Department of Energy expects that, taken together, all U.S. data centers will consume as much as 12% of all U.S. electricity by 2028. In 2023, data centers consumed about 4.4% of total U.S. electricity – roughly 176 terawatt-hours.

In the U.S., Virginia has more data centers than any other state – over 600, two-thirds of which are in the northern Virginia suburbs of Washington, D.C. In 2023, the state’s data centers consumed about 26% of Virginia’s total electricity supply – a higher share than in any other state.

We study science communication, climate science and public health, so we wanted to understand how data centers in Virginia affect the people who live near them and the broader public.

We found that the data centers that already exist affect nearby residents and the nation as a whole in five main areas: air quality, water quality, noise levels, land use and energy costs.

Air pollution

Data centers generally operate 24/7 and consume enormous amounts of electricity, which must be generated somewhere – either near the data center or farther away.

When fossil fuels are burned to generate that power, they emit a wide range of air pollutants, including those linked to lung disease, cardiovascular disease, stroke and neurological conditions. They also emit heat-trapping pollution that causes global warming and climate change, which, in turn, worsens air pollution further.

Generating power for U.S. data centers in 2023 emitted the equivalent of 2.2% of the nation’s greenhouse gas emissions. Other air pollutants emitted from fossil-fuel combustion are associated with increased risk of ADHD and autism in children and risks of Parkinson’s and Alzheimer’s diseases in older adults.

Unless the energy powering data centers comes from clean energy sources, such as solar, wind or geothermal, generating that electricity also pollutes the air. People who live near fossil-fuel burning power plants, whether in communities that also host data centers or in distant states, are exposed to air pollution. And during electrical outages, on-site diesel generators kick in, releasing large amounts of air pollution that can harm data center employees and nearby residents alike.

Water consumption and pollution

Data centers require vast quantities of water to cool their servers. Globally, they are projected to consume between 4.2 billion and 6.6 billion cubic meters of water annually by 2027. In the United States, data centers already rank among the top 10 industrial water users.

In northern Virginia, data center water use has risen sharply. In Loudoun County alone, just northwest of D.C., potable water use by data centers more than doubled between 2019 and 2023, while facilities across northern Virginia consumed nearly 2 billion gallons of water in 2023.

This demand can strain local rivers, aquifers and municipal water systems, even in regions like the mid-Atlantic that are not usually prone to drought, but especially in regions like the U.S. Southwest that face persistent droughts.

Noise pollution

Data centers’ continuous operation means that cooling systems, including air chillers and cooling fans, generate a persistent humming sound around the clock – as do any generators that are in use to provide power.

In northern Virginia, some residents have complained about an industrial-scale “drone” or “hum.” Measurements at the data centers that were the subject of complaints found noise levels were between 40 and 59 decibels on residential property.

Those noise levels are quieter than a conversation with someone 3 feet away and not loud enough to damage people’s hearing or violate local noise ordinances. But they are close to levels the EPA says reduce people’s ability to work, sleep and exercise. Some people have complained that data center noise has given them trouble sleeping and concentrating, and some have said they avoid using their homes’ outdoor spaces, where the noise is louder.

Land use and community well-being

Data center expansion often targets land near green spaces, agricultural areas or rural communities where developers can secure affordable land with access to existing electricity supplies.

Converting green space into industrial facilities can diminish health benefits associated with being in and near natural environments, including opportunities for physical activity and improved mental well-being.

In Virginia, residents living near data center construction have reported increased exposure to truck traffic and diesel exhaust, which can contribute to respiratory and cardiovascular health risks, especially in children and older adults. While these effects are typical of large construction projects, they can be amplified when several data centers are clustered together.

In places like Prince William County, Virginia, developers have proposed data centers on roughly 2,400 acres of undeveloped land in the Rural Crescent, an area designated by the county’s planners to remain relatively undeveloped. Those data centers could transform open space and rural farmland into industrial zones, disrupting communities with long-standing ties to the land.

Rising energy costs

As data centers increase electricity demand, they put upward pressure on energy prices across the grid. A 2024 Virginia legislative report found that the state’s typical residential electricity bill could rise by $14 to $37 per month by 2040 because of grid strain tied to data center growth – a 9% to 25% increase over current average bills, and a figure that does not factor in potential inflation.

These higher costs are paid by all consumers, but they place a greater burden on families that are most economically distressed, who also tend to have more health problems. Lower-income families spend a higher share of their budget on electricity, and when bills rise, the consequences can include reduced access to adequate heating and cooling, increased risks of heat-related illness and cold-related cardiovascular stress, as well as difficult choices between paying for energy and food or healthcare.

What can be done

Many of these health harms can be mitigated through better planning and design.

Increasing the share of renewable energy used to power data centers would help reduce air pollution and associated health harms.

Using recycled water in targeted systems that cool individual server rows or racks rather than whole buildings can significantly reduce cooling energy demand, with some studies estimating reductions of up to 29%.

On noise, a Leesburg, Virginia, data center reduced low-frequency tonal noise by reengineering its fan mounts.

And on energy costs, requiring large-scale data centers to cover more of the grid costs they create could help protect residential customers from higher electricity bills.

The world’s digital infrastructure runs through data centers, and that is not changing. We believe that expanding this infrastructure without protecting the health of surrounding communities is an unacceptable option.

About the Authors:

Neha Gour, Ph.D. Candidate in Science Communication, George Mason University; Ed Maibach, Distinguished University Professor Emeritus of Communication, George Mason University, and Luis Ortiz, Assistant Professor of Atmospheric, Oceanic and Earth Sciences, George Mason University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Ji Y. Son, California State University, Los Angeles and Alice Xu, University of California, Los Angeles 

The nonprofit ARC Prize Foundation on May 1, 2026, released the results of a new benchmark: a test of an AI system’s ability to solve a game. The results were striking – humans scored 100%, while the most advanced AI systems scored under 1%.

At first glance, this may be surprising to users of AI who are impressed by its polished essays, codebases and multistep projects generated in seconds. How can these brilliant AI systems struggle with these simple Tetris-shape puzzles?

That confusion points to a risk: AI is becoming integrated into everyday life faster than people can make sense of it.

We are cognitive psychologists who study how to teach difficult concepts. To recognize the limits and risks of today’s AI agent systems, it’s important for people to grasp that the systems can both accomplish superhuman feats and make mistakes few humans would. To that end, we propose a new way to think about AIs: as button-pushing explorers.

Mental models for AI

We teach college students, a group rapidly incorporating AI tools into their daily routines. That gives us regular opportunities to ask what they think is going on with AI. The answers vary widely. One student said that someone at OpenAI or Anthropic is reading and approving every response the system generates. Another, more succinctly, said, “It’s magic.”

These responses illustrate two tempting ways of making sense of AI. At one extreme, AI is treated as an inscrutable black box – a powerful but ultimately mysterious force. At another, people explain it using the same assumptions they use to understand other humans: that its outputs reflect reasoning or judgment.

The worry is that these misinterpretations don’t go away as users gain more experience interacting with AI, and they might get reinforced. When AI performs well, its output can feel like evidence of understanding or confirmation that it really is something like magic. That apparent success makes it harder to question what the system is actually doing. Biases can seem logical or inevitable; harmful behavior can look like a deliberate choice or even fate, as if it could not have gone any other way.

Saying that AI models are shaped by patterns in data, training processes and system design is true, but that’s too abstract to tell people when to trust the systems’ outputs or when they might fail. To help people avoid misplaced trust in AI, AI literacy efforts will need to include some mechanistic understanding of what produces their behavior – explanations that are perhaps not perfectly accurate but useful. Statistician George Box once wrote, “All models are wrong, but some are useful.”

Researchers have come up with several mental models for large language models. One is “stochastic parrot,” which shows that the models use statistical methods – stochastic refers to probabilities – to mimic responses with no understanding of meaning. Another is “bag of words,” which emphasizes that the models are collections of words – for example, all English words found on the internet – with a mechanism for giving you the best set of words based on your prompt.

These ways of thinking about large language models were never meant to be complete accounts of the systems. But the metaphors serve an important cognitive purpose: They push back against the idea that fluent language is necessarily caused by humanlike understanding.

But as the AI systems people use are increasingly powerful agents capable of stringing together actions on their own, it’s important for people to have a different kind of mental model: one that explains how they act. One place to find such a model is in earlier research on AI systems that learned to play Atari 2600 games. These systems didn’t understand the games the way humans do, but they still managed to rack up a lot of points.

The simple loop: Act, observe, adjust

Imagine a neural network, a relatively simple kind of AI model, placed into a video game it has never seen before. It does not “understand” the game like a human would. It has no idea whether it’s shooting space invaders or navigating an ancient pyramid. It doesn’t know the goals or rules.

Instead, it learns to play through a simple loop: Take an action – move left, jump, shoot – observe what changes, and then adjust. If an action leads to a good outcome, such as gaining points, it adjusts to become more likely to take similar actions in similar situations. If it leads to a bad outcome, such as losing a life, it adjusts in the opposite direction.

Even this simple mechanism can produce surprisingly capable behavior. Over time, by repeating this loop, the neural networks learned to play a wide range of Atari games – but not all games.

There is one game that famously stumped these early neural networks: Montezuma’s Revenge. To make progress, a player must carry out a long sequence of actions – climbing ladders, avoiding obstacles, retrieving keys – before receiving any reward at all. Unlike simpler games, most actions offer very little immediate feedback. The game required something like goal-directed, long-term planning.

Early neural networks would try a few actions, receive no reward and fail to make further progress through Montezuma’s underground pyramid. From the system’s perspective, all actions looked equally useless. But researchers made a breakthrough by changing the feedback signal. Instead of rewarding only success, they also rewarded the system for doing something new. The rewards were for visiting parts of the game it had not seen before or trying actions it had not previously taken. This tweak encouraged exploration.

With that change, performance improved dramatically. The neural network began navigating obstacles, taking multiple steps toward goals and adapting when things went wrong. From the outside, this kind of behavior can look like planning or problem-solving. But what looks like planning was not caused by sophisticated planning abilities. The underlying mechanism is still the same simple loop: act, observe, adjust.

This kind of system isn’t a stochastic parrot or a bag of words. It’s closer to a button-pushing explorer: something that doesn’t understand the world in a human sense but moves forward by pushing buttons, seeing what happens and adjusting what it does next.

From video games to modern AI agents

Today’s AI systems can do far more than play games like Montezuma’s Revenge. They can coordinate tools, write and run code, and carry out multistep projects. The range of possible actions is much larger, and the environments in which they operate are increasingly complex.

But these agents are still fundamentally button-pushing explorers. The behavior can be sophisticated, but the process that produces it is not. Humans can often infer how a new environment works after just a few observations. Systems that rely on these feedback loops cannot. They need to try many actions and see what happens before they can make progress.

This helps explain both the strengths of these AI systems and some of their most concerning failures. What these agents learn depends on what is being rewarded. And in real-world systems, those reward signals are often imperfect.

AI systems that conduct negotiations aim to maximize their client’s interests, sometimes with deceptive tactics. Rental pricing software used by landlords ends up price fixing. Marketing tools generate persuasive but misleading reviews.

These systems aren’t trying to be evil or greedy. They are adjusting to the signals they are given. From the button-pushing explorer perspective, these failures are downright predictable.

Effective AI literacy means holding two ideas at once: These systems can do surprisingly complex things, and they are not doing them the way humans do. If AI is seen as humanlike or magical, its outputs feel authoritative. But if it is understood, even imperfectly, as a button-pushing explorer shaped by feedback, people are likely to ask better questions: Why is it doing this? What shaped this behavior? What might it be missing?

That’s the difference between being impressed by AI and being able to reason about it.

About the Author:

Ji Y. Son, Professor of Psychology, California State University, Los Angeles and Alice Xu, Ph.D. Student in Developmental Psychology, University of California, Los Angeles

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Mohammad Ahmad, West Virginia University 

The cybersecurity community went on alert when Anthropic announced on April 7, 2026, that its latest and most capable general-purpose large language model, Claude Mythos Preview, had demonstrated remarkable – and unintended – capabilities. The artifical intelligence system was able to find and exploit software vulnerabilities – the most serious type of software bugs – at a rate not seen before.

The news ignited concern among the public, world governments and the information technology sector about the capabilities of today’s AI to undermine cybersecurity, with some people framing the model as a global cybersecurity threat.

Claiming that it would be too risky to release the model, and that the company had the moral responsibility to disclose these vulnerabilities, Anthropic said it would not immediately offer the model to the public. Instead, it granted exclusive access to tech giants to test the model’s capabilities, a process Anthropic dubbed Project Glasswing.

As a cybersecurity researcher, I think Mythos’ capabilities are impressive, but the AI system does not represent a radical departure. Mythos is less a new threat than a mirror reflecting how people behave and how fragile modern systems already are.

What Mythos did

During a controlled evaluation, engineers with minimal security experience prompted Mythos to scan thousands of software codebases for vulnerabilities. The model showed striking capabilities in conducting multistep, autonomous attacks that take experts weeks or even months to put together. Mythos was not only able to discover 271 vulnerabilities in Mozilla’s Firefox, it also developed exploits to take advantage of 181 of those.

Overall, Anthropic’s red team, which takes on the role of an attacker to test defenses, and the United Kingdom’s AI Security Institute reported that Mythos found thousands of zero-day, or previously unreported, vulnerabilities in major operating systems, web browsers and other applications – software flaws that have not yet been patched and can be turned into exploits immediately. National Security Agency officials testing Mythos have been impressed by the tool’s speed and efficiency in finding software vulnerabilities, according to a news report.

Among the most widely reported were Mythos’ ability to identify a dormant 27-year-old security flaw in OpenBSD, a security-focused operating system, and a 16-year-old bug in FFmpeg, a video/audio processing tool. Some of these flaws allow unauthenticated users to gain control of the machines hosting these applications.

Even more striking, the relatively inexperienced engineers running Mythos’ evaluations were able to use Mythos to complete attacks overnight, from finding vulnerabilities to exploiting them – something that can take human experts weeks to do. The model’s ability to chain multiple steps is what surprised Anthropic and organizations that tried it. In an evaluation by the AI Security Institute, Mythos was able to take over a simulated corporate network in three out of 10 tries, the first AI model to succeed at the task.

These results are real. They also paint an incomplete picture in ways that matter.

Where is the breakthrough?

At first glance, Mythos’ breakthrough sounds novel and could signal a new class of cyber threats. However, a closer look suggests something different. The vulnerabilities Mythos found are not new in nature. They generally don’t belong to unknown security flaws, and in many cases they are variations of well-known and well-understood classes of software vulnerabilities.

In cybersecurity, finding new instances of known types of flaws is not unusual. The most successful attacks rely on known, well-defined vulnerabilities that stay overlooked or unpatched. What concerned the researchers was not Mythos changing the nature of finding and exploiting vulnerabilities, but rather the intense scale and speed with which it was able to find and exploit those vulnerabilities.

This is not a breakthrough per se but rather a result of decades of research in both cybersecurity and AI. In that sense, Mythos is the natural – and expected – result of powerful automation and AI integration because it follows the same fundamental procedures used in standard offensive cybersecurity practices. These include scanning for vulnerabilities, identifying patterns and testing exploitability. Mythos and similar emerging models make it possible to chain these steps together at a speed that is hard to fathom.

So why were these vulnerabilities missed in the first place?

It is crucial to understand that not all vulnerabilities are cost effective to fix, and not all vulnerabilities are a priority. Mythos did not discover a new kind of weakness – it exposed the limits of how cybersecurity practitioners search for them.

New tech, age-old dynamic

Mythos highlights an important fact about the reality of cybersecurity threats. System defenders are always at a disadvantage because they need to always succeed. Attackers, however, need to succeed only once to break the security of a system. This cat-and-mouse game will always be the same, and Mythos does not change that – it simply reinforces it.

Mythos follows a familiar dynamic: A tool created to protect can also be used to attack and harm.

“The same improvements that make the model substantially more effective at patching vulnerabilities also make it substantially more effective at exploiting them,” Anthropic officials wrote in a blog post about Mythos.

What once may have required highly specialized skills can now be achieved with significantly less effort, which raises the most important question: Who will benefit first by using tools like Mythos – defenders or attackers?

About the Author:

Mohammad Ahmad, Assistant Professor of Management Information Systems, West Virginia University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Brian Jay Tang, University of Michigan and Kang G. Shin, University of Michigan 

Hundreds of millions of people consult artificial intelligence chatbots on a daily basis for everything from product recommendations to romance, making them a tempting audience to target with potentially below-the-radar advertising. Indeed, our research suggests AI chatbots could easily be used for covert advertising to manipulate their human users.

We are computer scientists who have been tracking AI safety and privacy for several years. In a study we published in an Association for Computing Machinery journal, we found that chatbots trained to embed personalized product ads in replies to queries influenced people’s choices about products. And most participants didn’t recognize that they were being manipulated.

These findings come at a pivotal moment. In 2023, Microsoft started running ads in Bing Chat, now called Copilot. Since then, Google and OpenAI have experimented with advertisements in their own chatbots. Meta has started to send people customized ads on Facebook and Instagram based on their interactions with Meta’s generative AI tools.

The major companies are competing for an edge: In late March, OpenAI lured away Meta’s longtime advertising executive, Dave Dugan, to lead OpenAI’s advertising operations.

Tech companies have made ads part of nearly every large free web service, video channel and social media platform. But the latest AI models could take this practice to a new level of risk for consumers.

People don’t simply use chatbots to search for information and media or to produce content. They turn to the bots for a great variety of tasks, as complex as life advice and emotional support. People are increasingly treating chatbots as companions and therapists, with some users even developing deep relationships with AI.

In these circumstances, people can easily forget that companies ultimately create chatbots to turn a profit. And to that end, AI companies are motivated to thoroughly profile users so ads become more effective and profitable.

Chatbot ads have added power

A single prompt to a chatbot can reveal a lot more about a user than the person might expect.

A 2024 study showed that large language models can infer a wide range of personal data, preferences and even a person’s thinking patterns during routine queries. “Help me write an essay on the history of American fiction” could indicate that the user is a high school student. “Give me recipe suggestions for a quick weeknight dinner” could indicate that the user is a working parent. A single conversation can provide a surprising amount of detail. Over time, a full chat history could create a remarkably rich profile.

To show how this might happen in practice, we built a chatbot that quietly wove ads into its conversations with people, suggesting products and services based on the conversation itself. We asked 179 people to complete everyday online tasks using one of three chatbots: one typical of those on the web today, one that slipped in undisclosed ads and one that clearly labeled sponsored suggestions. Participants didn’t know the experiment was about advertising.

For example, when participants asked our chatbot for a diet and exercise plan, the ad version would suggest using a specific app for tracking calories. It presented that sponsored content as an unbiased recommendation, even though it was meant to manipulate people. Many participants indicated that they had been influenced by the AI and that it had affected their decisions. Some participants even said they had completely “outsourced” their decision-making to the chatbot.

Half of the participants who received sponsored and disclosed ads indicated they did not notice the presence of advertising language in the responses they received. This led to a concerning result: Although ads made the chatbot perform 3% to 4% worse on many tasks, numerous users indicated they preferred the advertising chatbot responses over the nonadvertising responses. They even said the ad-infused responses felt more friendly and helpful.

Knowing you to persuade you

This kind of subtle influence can have larger consequences when it arises in other areas of life, such as political and social views. Profiling users, and using psychology to target them, has been part of social media algorithms and web advertising for more than a decade.

But in our view, chatbots are likely to deepen these trends. That’s because the first priority of social media algorithms is to keep you engaged with the content. They personalize ads based on your search history.

Chatbots, however, can go further by trying to persuade you directly, based on your expressed beliefs, emotions and vulnerabilities. And chatbots that can reason and act on their own are far more effective than conventional algorithms at autonomously soliciting information from users. A chatbot with a purpose can keep probing someone until it gets the information it wants, resulting in a more accurate profile of them.

This type of autonomous interrogation is feasible, aligns with AI companies’ business models and has raised concern among regulators. Right now OpenAI is rolling out ads in ChatGPT, but the company said that it will not allow ad placement to alter the AI chatbot’s replies.

But permitting personalized ads within chatbot responses is just a step away. Our research suggests that if AI companies take that step, many human users may not even recognize when it happens.

Here are some steps you can take to try to detect AI chatbot advertising.

• Look for any disclosure text – words such as “ad,” “advertisement” and “sponsored” – even if it is faint or otherwise hard to see. These are mandatory under Federal Trade Commission regulations. Amazon, Google and other major online platforms have these as well.
• Think about whether that product or brand mention makes sense and is widely known. AI learns from text and images on the internet, so popular brands are likely to be ingrained in the models. If it’s a new product or small-name product, it is more likely that it could be advertising.
• An unusual shift in intent or tone is a potential sign of an advertisement. An analogy to this on YouTube is the often abrupt or jarring transition to a sponsored section on videos made by content creators.

About the Author:

Brian Jay Tang, Ph.D. Candidate in Computer Science and Engineering, University of Michigan and Kang G. Shin, Emeritus Professor of Computer Science, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Anne Toomey McKenna, Penn State 

On a Saturday morning, you head to the hardware store. Your neighbors’ Ring cameras film your walk to the car. Your car’s sensors, cameras and microphones record your speed, how you drive, where you’re going, who’s with you, what you say, and biological metrics such as facial expression, weight and heart rate. Your car may also collect text messages and contacts from your connected smartphone.

Meanwhile, your phone continuously senses and records your communications, info about your health, what apps you’re using, and tracks your location via cell towers, GPS satellites and Wi-Fi and Bluetooth.

As you enter the store, its surveillance cameras identify your face and track your movements through the aisles. If you then use Apple or Google Pay to make your purchase, your phone tracks what you bought and how much you paid.

All this data quickly becomes commercially available, bought and sold by data brokers. Aggregated and analyzed by artificial intelligence, the data reveals detailed, sensitive information about you that can be used to predict and manipulate your behavior, including what you buy, feel, think and do.

Companies unilaterally collect data from most of your activities. This “surveillance capitalism” is often unrelated to the services device manufacturers, apps and stores are providing you. For example, Tinder is planning to use AI to scan your entire camera roll. And despite their promises, “opting out” doesn’t actually stop companies’ data collection.

While companies can manipulate you, they cannot put you in jail. But the U.S. government can, and it now purchases massive quantities of your information from commercial data brokers. The government is able to purchase Americans’ sensitive data because the information it buys is not subject to the same restrictions as information it collects directly.

The federal government is also ramping up its abilities to directly collect data through partnerships with private tech companies. These surveillance tech partnerships are becoming entrenched, domestically and abroad, as advances in AI take surveillance to unprecedented levels.

As a privacy, electronic surveillance and tech law attorney, author and legal educator, I have spent years researching, writing and advising about privacy and legal issues related to surveillance and data use. To understand the issues, it is critical to know how these technologies function, who collects what data about you, how that data can be used against you, and why the laws you might think are protecting your data do not apply or are ignored.

Big money for AI-driven tech and more data

Congressional funding is supercharging huge government investments in surveillance tech and data analytics driven by AI, which automates analysis of very large amounts of data. The massive 2025 tax-and-spending law netted the Department of Homeland Security an unprecedented US$165 billion in yearly funding. Immigration and Customs Enforcement, part of DHS, got about $86 billion.

Disclosure of documents allegedly hacked from Homeland Security reveal a massive surveillance web that has all Americans in its scope.

DHS is expanding its AI surveillance capabilities with a surge in contracts to private companies. It is reportedly funding companies that provide more AI-automated surveillance in airports; adapters to convert agents’ phones into biometric scanners; and an AI platform that acquires all 911 call center data to build geospatial heat maps to predict incident trends. Predicting incident trends can be a form of predictive policing, which uses data to anticipate where, when and how crime may occur.

DHS has also spent millions on AI-driven software used to detect sentiment and emotion in users’ online posts. Have you been complaining about Immigration and Customs Enforcement policies online? If so, social media companies including Google, Reddit, Discord, and Facebook and Instagram owner Meta may have sent identifying data, such as your name, email address, phone number and activity, to DHS in response to hundreds of DHS subpoenas served on the companies.

Meanwhile, the Trump administration’s national policy framework for artificial intelligence, released on March 20, 2026, urges Congress to use grants and tax incentives to fund “wider deployment of AI tools across American industry” and to allow industry and academia to use federal datasets to train AI.

Using federal datasets this way raises privacy law concerns because they contain a lifetime of sensitive details about you, including biographical, employment and tax information.

Blurring lines and little oversight

In foreign intelligence work, the funding, development and controlled use of certain AI-driven gathering of data makes sense. The CIA’s new acquisition framework to turbocharge collaboration with the private sector may be legal with proper oversight. But the line between collaborating for lawful national security purposes versus unlawful domestic spying is becoming dangerously blurred or ignored.

For example, the Pentagon has declared a contractor, Anthropic, a national security risk because Anthropic insisted that its powerful agentic AI model, Claude, not be used for mass domestic surveillance of Americans or fully autonomous weapons.

On March 18, 2026, FBI Director Kash Patel confirmed to Congress that the FBI is buying Americans’ data from data brokers, including location histories, to track American citizens.

As the federal government accelerates the use of and investment in AI-driven spy tech, it is mandating less oversight around AI technology. In addition to the national AI policy framework, which discourages state regulation of AI, the president has issued executive orders to accelerate federal government adoption of AI systems, remove state law AI regulation barriers and require that the federal government not procure the use of AI models that attempt to adjust for bias. But using advanced AI systems is risky, given reports of AI agents going rogue, exposing sensitive data and becoming a threat, even during routine tasks.

Your data

The surveillance capitalism system requires people to unwittingly participate in a manipulative cycle of group- and self-surveillance. Neighborhood doorbell cameras, Flock license plate readers and hyperlocal social media sites like Nextdoor create a crowdsourced record of all people’s movements in public spaces.

Sensors in phones and wearable devices, such as earbuds and rings, collect ever more sensitive details. These include health data, including your heart rate and heart rate variability, blood oxygen, sweat and stress levels, behavioral patterns, neurological changes and even brain waves. Smartphones can be used to diagnose, assess and treat Parkinson’s disease. Earbuds could be used to monitor brain health.

This data is not protected under HIPAA, which prohibits health care providers and those working with them from disclosing your health information without your permission, because the law does not consider tech companies to be health care providers nor these wearables to be medical devices.

Legal protections

People have little choice when buying devices, using apps or opening accounts but to agree to lengthy terms that include consent for companies to collect and sell their personal data. This “consent” allows their data to end up in the largely unregulated commercial data market.

The government claims it can lawfully purchase this data from data brokers. But in buying your data in bulk on the commercial market, the government is circumventing the Constitution, Supreme Court decisions and federal laws designed to protect your privacy from unwarranted government overreach.

The Fourth Amendment prohibits unreasonable search and seizure by the government. Supreme Court cases require police to get a warrant to search a phone or use cellular or GPS location information to track someone. The Electronic Communications Privacy Act’s Wiretap Act prohibits unauthorized interception of wire, oral and electronic communications.

Despite some efforts, Congress has failed to enact legislation to protect data privacy, the use of sensitive data by AI systems or to restore the intent of the Electronic Communications Privacy Act. Courts have allowed the broad electronic privacy protections in the federal Wiretap Act to be eviscerated by companies claiming consent.

In my opinion, the way to begin to address these problems is to restore the Wiretap Act and related laws to their intended purposes of protecting Americans’ privacy in communications, and for Congress to follow through on its promises and efforts by passing legislation that secures Americans’ data privacy and protects them from AI harms.

This article is part of a series on data privacy that explores who collects your data, what and how they collect, who sells and buys your data, what they all do with it, and what you can do about it.

About the Author:

Anne Toomey McKenna, Affiliated Faculty Member, Institute for Computational and Data Sciences, Penn State

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Christos Makridis, Arizona State University; Institute for Humane Studies 

Forecasts of the impact of artificial intelligence range from the apocalyptic to the utopian. An October 2025 report from Senate Democrats, for example, predicted AI will destroy millions of U.S. jobs. A couple of years earlier, consultant company McKinsey forecast AI will add trillions to the global economy, while emphasizing job losses can be mitigated by training workers to do new things.

The problem is that many of these claims are based on projections, overly simplified surveys or thought experiments rather than observed changes in the economy. That makes it hard for the public, and often policymakers, to know what to trust.

As a labor economist who studies how technology and organizational change affect productivity and well-being, I believe a better place to start is with actual data on output, employment and wages – which are all looking relatively more hopeful.

AI and jobs

In one of my new research papers with economist Andrew Johnston, we studied how exposure to generative AI affected industries across America between 2017 and 2024, using administrative data that covers nearly all employers. Our analysis covered a crucial period when generative AI use exploded, allowing us to analyze the effect within businesses and industries.

We measured AI exposure using occupation-level task data matched to each industry and state’s occupational workforce mix prior to the pandemic. A state and industry with more workers in roles requiring language processing, coding or data tasks scored higher on exposure, for example, compared with one with more plumbers and electricians.

We then took that exposure ranking by occupation and looked at changes in the standard deviation in occupational exposure, comparing that with labor market and GDP across states and industries from 2017 to 2024.

Think of a standard deviation as roughly the gap between a paramedic – whose work centers on physical assessment, emergency response and hands-on care that AI cannot easily replicate – and a public relations manager, whose work involves drafting communications, analyzing sentiment and synthesizing information that AI tools handle well. That gap in AI exposure is roughly what we’re measuring when we ask: Does being on the higher-exposure side of that divide change your industry’s trajectory?

This data allowed us to answer two questions: When AI tools became widely available following the public release of ChatGPT in late 2022, did states and industries that were more exposed to generative AI become more productive, and what happened to workers?

Our answers are more encouraging, and more nuanced, than much of the public debate suggests.

We found that industries in states that were more exposed to AI experienced faster productivity growth beginning in 2021 – before ChatGPT reached the public – driven by enterprise tools already embedded in professional workflows, including GitHub Copilot for software development, Jasper for marketing and content writing, and Microsoft’s GPT-3-powered business applications. In 2024, for example, industries whose AI exposure was one standard deviation higher saw gains of 10% in productivity, 3.9% in jobs and 4.8% in wages than comparable industries in the same state.

Those patterns suggest that, at least so far, AI has acted as a productivity-enhancing tool that boosts employment and wages rather than a simple substitute for labor.

Augmentation versus displacement

A crucial distinction in the data is between tasks where AI works with people and tasks where AI can act more independently. In sectors where AI mainly complements workers – think marketing, writing or financial analysis – our data show that employment rose by about 3.6% per standard deviation increase in exposure.

In sectors where AI can execute tasks more autonomously – including basic data processing, generating boilerplate code, or handling standardized customer interactions – we found no significant employment change, though workers in those roles saw slower wage growth.

What these findings suggest is that when AI lowers the cost of completing a task and raises worker productivity, companies expand output enough to increase their demand for labor overall — the same logic that explains why power tools didn’t eliminate construction workers.

The economic question is not whether any given task disappears. It is whether businesses and workers can reorganize fast enough to create new productive combinations. And so far, in most sectors, our evidence suggests they can.

But state policies also matter: These benefits were concentrated in the states with more efficient labor markets, meaning that the impact of generative AI on workers and the economy also depends on the types of policies and institutions of the local economy.

Importantly, these findings hold beyond occupational exposure. In additional work with co-authors at the Bureau of Economic Analysis, we found a similar effect on GDP and employment when looking at actual AI utilization — that is how often workers use AI. Drawing on the Gallup Workforce Panel, we measured workers actively using AI daily or multiple times a week. We found that each percentage-point increase in the share of frequent AI users in a state and industry is associated with roughly 0.1% to 0.2% higher real output and 0.2% to 0.4% higher employment.

To put that in context: The share of frequent AI users across all occupations rose from about 12% in mid-2024 to 26% by late 2025, a shift our estimates suggest corresponds to roughly 1.4% to 2.8% higher real output – or about 1 to 2 percentage points of annualized growth over that period.

New technologies rarely leave work untouched. But they also rarely eliminate the need for human contribution altogether. Instead, they change the composition of work, as our research shows. Some tasks shrink. Others expand. New ones emerge that were previously too costly or too hard to perform at scale. Put simply, some occupations might go away, but most of them just change.

If anything, the trends documented here are likely to strengthen rather than fade. Not only are generative AI tools rapidly improving, but also the experimentation and research and development that many workers and companies are engaging in are likely to pay large dividends. These investments – often referred to as intangible capital – tend to get unlocked a few years after a technology comes onto the scene, once complementary investments have been made.

The role of companies and managers

Whether AI leads to anxiety or adaptation for workers depends in part on what happens inside organizations. Using additional data collected over many years in the Gallup Workforce Panel covering more than 30,000 U.S. employees from 2023 to 2026, I found in a 2026 paper that workplace adoption of generative AI rose quickly over the period, with the share of workers using AI often increasing from 9% to 26%.

But the more important finding is that adoption was far more common where workers believed their organization had communicated a clear AI strategy and where employees said they trust leadership. This suggests that growing adoption and effective use of AI depends not only on the availability of the technology but on whether managers make its use clear, credible and safe.

Where that clarity exists, frequent AI use is associated with higher engagement and job satisfaction, and it even reverses the burnout penalties that appear elsewhere.

In other words, the broader economic effects of AI depend not only on how sophisticated the tools are but on whether companies and managers create environments where workers can experiment, reorganize tasks and integrate new tools into productive routines. That is, if employees do not feel the psychological safety to experiment, they are less likely to use AI, and they are especially less likely to use it for higher-value work.

That is precisely the kind of adaptation that I believe makes labor markets more resilient than the most alarmist forecasts suggest.

About the Author:

Christos Makridis, Associate Research Professor of Information Systems, Arizona State University; Institute for Humane Studies

This article is republished from The Conversation under a Creative Commons license. Read the original article.