Rising AI Opposition: Issues for Firms

Public opposition to AI-related development is beginning to shape policy at all levels of government as well as the midterm elections. This increasingly fragmented policy environment could affect operations for both AI developers and companies using AI, requiring attention from executives and engagement with policymakers and the public.

• Conflicting policy actions across the Federal, state, and local levels are increasing policy volatility requiring careful monitoring from executives to understand business impacts and compliance risks. AI-related policy issues are emerging as central concerns in the current campaign season in a number of states.
• Data centers have become a flashpoint in the AI debate, and public opposition could significantly delay or derail development plans, particularly as states take action against data centers. Early engagement with communities and efforts to address concerns should be a core component of any development plan. This year’s elections may also shift the policy landscape at all levels of government.
• Firms should invest in workforce development and training to address concerns about employment impacts. Collaboration with local educational institutions should be central to this strategy. Firms should also carefully evaluate security plans to address ongoing threats to executives and facilities.

A Growing Trend

Over the last year, public opposition to AI-related development has been building across the country. During commencement addresses this Spring, mentions of AI elicited boos from graduating students. In city council meetings and in state government, residents are expressing opposition to data center construction. Violent threats targeted at tech executives and employees have required firms to increase security. This trend reflects a range of concerns that, while not unique to AI, have been heightened by the technology’s possible widespread economic and social impacts. These include the potential for significant workforce disruptions, concerns about environmental impacts, and safety and privacy fears.

As a result, policymakers – particularly at the state and local levels – have begun taking actions intended to slow or reshape AI-related development and deployment. These measures include moratoria and stricter permitting requirements for data centers, new rules governing energy and water use, and proposed safeguards for automated decision-making, privacy, and consumer protection. For businesses, this evolving policy landscape could affect the cost and timing of AI investments and raise compliance concerns warranting attention from executives.

Policy Landscape

US policymaking related to AI is fragmented and contested, with, in some cases, competing policy priorities at different levels of government.

Federal policy has generally prioritized rapid AI development, including a July 2025 executive order directing agencies to expedite data center and related energy projects through accelerated environmental reviews and permitting, making certain Federal lands available for development, and providing financial support for qualifying projects.¹ The Federal Energy Regulatory Commission has also engaged in the debate about the impact of data center electricity demands. In June 2026, it ordered six regional grid operators to explain how they will meet rising energy needs without passing costs on to ratepayers.² The Administration has also released recommendations for a legislative framework that calls for Federal preemption of state laws regulating AI and formed a task force to challenge state AI laws.  

Meanwhile, states have generally taken a more cautious approach. For example, several states have enacted laws establishing restrictions on certain AI uses and protections against certain potential harms. Texas prohibits AI systems developed with the intent of encouraging users to harm themselves or others, engage in criminal activity, or certain other uses. Texas has also recently imposed a moratorium on data center approvals and connections pending a state audit. California requires developers of certain AI systems to publish frameworks outlining how they manage potential risks and report safety incidents. Other state measures provide protections against deepfakes or provide guidelines for the use of AI in certain contexts such as education and healthcare.

Data Centers

Though AI raises a wide range of policy concerns, data center construction has emerged as a key battleground. Proponents argue that data centers are critical infrastructure for the modern economy, supporting cloud computing, communications networks, and the computing capacity to develop and operate AI systems. Data center construction delays, they argue, would inhibit AI innovation, jeopardizing US global economic competitiveness and national security. Proponents also point to local economic benefits, including employment opportunities for skilled trades during data center construction and some ongoing employment for maintenance and operations. Data centers could also potentially provide significant tax revenue for localities.

Opponents counter that data center construction is straining aging power generation and distribution systems and raising costs for ratepayers, in some cases significantly. This has become a political issue in some states such as Virginia. Potential solutions here include having the facilities provide their own power (including from dedicated sources such as nuclear) or pay for the additional capacity required so that the surrounding communities do not have rate increases. 

Opponents also raise concerns about large water consumption, noise generation, and pollution. However, while data centers do use significant amounts of water for cooling purposes, they do not consume the water. Opponents also question whether the promised economic benefits justify the costs and criticize the non-disclosure agreements often used to keep details about projects from public view.³ There are also many who favor temporary pauses on construction or approvals to give policymakers and the public time to consider tradeoffs and negotiate favorable deals with tech firms but do not categorically oppose data center construction in the long term.

Notably, these debates have not cut cleanly along partisan lines. The Republican Governor of Texas, for example, recently directed the state’s public utility commission to increase scrutiny of data center projects to evaluate anticipated grid demand, onsite generation, water use and cooling systems, tax incentives, ownership, and efforts to mitigate effects such as noise as well as a moratorium on approvals and connections to the grid pending a state audit. Likewise, the Democratic Governor of New York recently signed legislation imposing the first state-wide moratorium on data center construction, pausing construction of large data centers using more than 20 megawatts of electricity for one year. As of July 2026, at least 15 states have considered similar prohibitions.⁴

Local governments also play an important role in data center construction through their zoning and land-use authorities. By one estimate, at least 100 localities have approved pauses on data center development.⁵ Questions have also been raised as to the extent to which significant anti-data center activity actually reflects public opinion or whether it is simply being amplified. For instance, the platform X announced that it performed a probe "into suspected Chinese inauthentic accounts involved in influence operations" and in that probe uncovered some anti-data center accounts within a bot farm.⁶

Implications for CEOs

Given the growing importance of AI within firms and across the economy, executives should prepare for increased scrutiny from policymakers and the public.

Policy Volatility

Businesses should prepare for increased policy volatility related to AI and the infrastructure that supports it, including data centers, electricity generation, transmission systems, and water infrastructure. State and local officials may continue to implement moratoria, permitting restrictions, utility requirements, or sector-specific safeguards while the Federal government seeks to block these measures. These competing approaches could produce rapid changes in policy and a fragmented compliance environment.

Companies should monitor developments beyond legislation formally labeled as “AI regulation.” Relevant policy decisions may arise through utility commission proceedings, zoning rules, environmental reviews, tax-incentive policies, consumer protection enforcement, employment law, privacy requirements, antitrust litigation, and sector-specific regulation. Firms operating nationally should determine whether their governance and compliance systems can accommodate different state and local requirements without creating separate operating models for every jurisdiction.

Assess Constraints

Firms should assess how constraints on data center, power-plant, transmission, or other infrastructure construction could affect their operations. Moratoria, permitting delays, grid-connection backlogs, water restrictions, or requirements that large users fund additional infrastructure could slow the expansion of computing capacity and increase its cost. The effects would extend beyond technology companies and data-center developers. Businesses that rely on cloud services, advanced AI models, or large-scale data processing could face higher computing costs, capacity limitations, longer implementation timelines, or greater dependence on a small number of suppliers.

Invest in Employee Training and Workforce Development

Businesses should invest in employee skills development as an integral part of AI adoption to ensure their workforces are ready to leverage AI and reassure those who fear employment disruptions from AI implementation. Executives should identify which tasks are likely to change, which occupations face the greatest disruption, and which new capabilities employees will need. Training should be tied to actual business processes and job requirements rather than offered as a stand-alone benefit with limited connection to employees’ future roles. Firms should also consider how AI may affect entry-level positions, apprenticeship pathways, and the development of future managers and technical experts.

Engage Communities and Other Stakeholders Early

Companies developing data centers or other AI-related infrastructure should treat community acceptance as a material project risk and an essential aspect of the project. Developers should provide clear information about expected electricity and water use, infrastructure requirements, tax incentives, construction and permanent employment, environmental effects, and responsibility for project-related costs including for ratepayers. Where a project creates identifiable local burdens, developers should consider actions to address those impacts – for example, by funding grid upgrades, reducing freshwater use, supporting workforce training, or providing transparent monitoring of noise and resource consumption. These efforts become even more important to the degree that opposition to data center projects may be amplified by bot activity that can generate further opposition.

Companies should also consider whether nondisclosure agreements are appropriate during data-center siting and development. Limited confidentiality may be justified to protect proprietary technology, commercially sensitive negotiations, security information, or details about sites still under consideration. Broad or long-running NDAs, however, can restrict public officials’ ability to discuss a project’s electricity and water demands, infrastructure costs, tax incentives, environmental impacts, employment commitments, and impact on ratepayers. These NDAs can limit meaningful public participation and reinforce perceptions that important decisions have been made before residents have an opportunity to weigh in.

Review Executive and Facility Security

The most serious manifestations of the AI backlash have included isolated threats against technology executives and companies associated with AI development. Businesses with highly visible leaders, controversial AI initiatives, or major infrastructure projects should determine whether existing security plans account for technology-related grievances. Relevant measures may include threat monitoring, updated executive travel and event protocols, facility-access controls, procedures for handling threatening communications, and coordination among security, human resources, legal, communications, and local law enforcement. Companies should also provide employees with clear channels for reporting concerning behavior or communications.

Endnotes

1. https://www.whitehouse.gov/presidential-actions/2025/07/accelerating-federal-permitting-of-data-center-infrastructure/
2. https://www.ferc.gov/news-events/news/ferc-launches-aggressive-targeted-action-speed-large-load-integration
3. https://www.nbcnews.com/tech/tech-news/data-center-ai-google-amazon-nda-non-disclosure-agreement-colossus-rcna236423
4. https://www.ncsl.org/fiscal/which-states-are-banning-data-centers
5. https://www.brookings.edu/articles/data-center-moratoriums-are-not-a-substitute-for-oversight/
6. https://www.foxbusiness.com/fox-news-tech/x-reveals-foreign-adversary-behind-fake-accounts-disseminating-anti-data-center-messaging