Introduction
On July 23, 2025, the White House Office of Science and Technology Policy (OSTP) released a new national AI Action Plan during an event titled “Winning the AI Race.” The White House described the plan as a broad strategy to “usher in a new golden age of human flourishing, economic competitiveness, and national security for the American people”. (White House) With more than 90 proposed actions and three accompanying executive orders, the plan represents a significant federal push to shape both the domestic and global AI landscape.
While the initiative is national in scope, the policies it introduces will ultimately be implemented and felt at the local level. This raises an important question for engineers and scientists across the country: How will these top-down priorities affect local communities, and what can we do to help guide their impact?
What’s in the AI Action Plan?
The plan includes three pillars:
Across the various agencies, more than 90 actions have been identified. This comprises everything from procurement and evaluation sandboxes to international export programs.
The main actions comprising local impacts are:
In addition to the pillars, the plan also maps out three key executive orders:
The plan also calls for workforce and innovation investments, including the establishment of an AI Workforce Research Hub through the Department of Labor. The goal of the Hub is to assess workforce impacts and the American worker. (US DOL) Alongside this goal is the promotion of partnerships with state and local governments to deliver AI training, offer apprenticeships, and build AI-literate workforce pipelines. (White & Case)
Local Consequences of the AI Action Plan
To understand where national policy meets community, let’s break it down into its components.
Impact to the Environment and Public Health
Streamlined permitting will likely accelerate large-scale data center projects, with minimal environmental review or time for community input. A growing number of studies point to the environmental and health consequences of rapid data center growth. For example, in Santa Clara County, public health damage in the form of noise and toxic air pollution (particulate matter) is projected to increase, with noisy, energy- and water-intensive facilities that are backed by tax and utility incentives (San Francisco Chronicle). Similarly, in Memphis, AI data centers are reported to emit elevated levels of NO2, which has been shown to impact respiratory health in an already marginalized community (Time). Nationwide, it’s estimated that data centers could consume more electricity than all of Poland, with $5-9 billion in annual public health costs and heavy water usage (Business Insider). The overall environmental footprint of AI includes rising carbon emissions due to fossil fuel–based electricity, along with surges in energy and water consumption to power and cool data centers.
Impacts to the Power Grid
In addition to site-level concerns about data center usage, a critical but often overlooked piece of this infrastructure equation is the transmission system. As data center proposals surge, especially in regions vying for AI infrastructure investment, the demand for electricity is outpacing the capacity of existing power grids. Transmission line upgrades and new placements are not keeping up with demand, which creates bottlenecks that affect grid reliability and equity in siting decisions.
In northern Virginia, the heart of the nation’s largest data center corridor, this imbalance has already caused rolling brownouts and is forecast to cause even more frequent power outages by 2030 due to insufficient energy supply (Northern Virginia Magazine).
In the Atlanta metro area, which is now the second-fastest-growing market for data center proposals, new projects are following existing transmission corridors. As a result, approvals are clustering in counties like Fulton, Gwinnett, and Douglas, areas with significant Black, Asian, Latinx, and immigrant/refugee populations. These communities often bear the brunt of siting decisions while benefiting the least from the resulting economic growth or technological access. (Government Technology)
Compounding these concerns, utility companies in the Southeast are using the increased electricity demand as a rationale to keep fossil fuel plants online and, in some cases, expand coal and natural gas generation and purchasing. Public Service Commissions have approved many of these plans with only modest increases in renewable energy investments, which undermines existing emissions reduction and public health commitments. These decisions are also projected to drive residential energy costs sharply upward, with double-digit increases in energy burden expected in many areas, especially where wage growth is unlikely to keep pace.
Impact of AI to Equity in Public Services
Another component to consider at the local level is equity and bias risks of AI in public services. Because of federal mandates to avoid DEI language and bias considerations, oversight is weakened in areas like unexamined algorithmic decisions related to hiring, policing, and services. Tech Policy Press commentary warns that AI systems are “bias engines,” often reflecting disparities baked into training data, and removing DEI safeguards puts underserved communities at risk (Tech Policy Press.) The plan’s rejection of DEI ideology undermines previous bipartisan efforts like the Digital Equity Act, and it’s often local communities who must be the first to avert inequities, with scientists and engineers stepping up to help bridge those gaps through taking action like conducting a local bias audit, providing public education, and offering guidance about ethical procurement.
The plan also raises questions about what happens to workers, especially those at risk of displacement. Although the Workforce Hub is promising, local upskilling still depends on proper funding and outreach. This means that state and local agencies must align rapidly. The risk of not keeping up may lead to unchecked AI growth risks replacing public sector or tech jobs without equitable transition planning.
There’s also growing concern that federal moves could override local values on issues like data ownership and transparency. For instance, the federal government’s push for American technical dominance may clash with local values surrounding hot-button issues like open source, transparency, and data sovereignty. It may in the future require local government action to assert standards or resist vendor lock-in, despite this clashing with the national agenda.
Positive Impacts of the AI Impact Plan
It should be noted that certain potential positive impacts exist as well. For example, faster innovation means economic growth through deregulation, streamlined permitting, and AI infrastructure acceleration. At the local level, this means regions with tech clusters or universities, like Pittsburgh and Durham, may see increases in investment and startup ventures. Infrastructure investments also include a need for building out data centers and semiconductor fabrication plants, which will yield short-term job creation and potentially longer-term infrastructure upgrades like power grid modernization.
What Local Leaders Can Do Now
State and local governments have options when making decisions about adoption of AI by their governments and when overseeing procurement and AI infrastructure proposals, including the following:
1. Insist on transparency in selecting vendors and community impact assessments.
2. Require explainable AI that includes clarity on decision-making.
3. Prioritize open-source, interoperable tools to resist vendor lock-in and retain local control.
4. Establish AI oversight bodies or ethics boards that comprise a mix of technologists and local advocates.
5. Protect environmental and equity standards amid a climate of federal deregulation, including pushes for local review boards, conditional approvals, and sustainability guarantees. (NLC)
Sidebar:
Questions to Ask Your Local Government About AI
Empower your town or city to make smarter, more equitable tech decisions:
1. What criteria guide our selection of AI systems or services?
Is there a clear, public evaluation of the tool’s fairness, environmental footprint, and vendor terms?
2. How do we assess AI’s effects on equity, accessibility, and accountability in our services?
Are bias audits, impact reports, or public consultations part of procurement?
3. Who is responsible for AI oversight, and how can science and engineering professionals get involved?
Is there an AI ethics board, or room to liaise in planning and zoning meetings?
