- Labor’s AI divide: Building-trades unions increasingly support data-center construction, while professional unions often seek to slow AI adoption that could displace or downgrade members’ work.
- Construction boom: Data centers are generating substantial demand for electricians, pipefitters, welders, and other skilled trades, including major increases in union work hours and apprenticeship enrollment.
- Political support: Building-trades unions have opposed data-center moratoriums proposed or adopted by at least 15 states and more than 200 localities, and may withhold support from candidates who oppose such projects.
- Professional resistance: National Nurses United, the American Association of University Professors, faculty unions, and public-sector unions have backed construction limits, AI-use restrictions, or bargaining rights over workplace deployment.
- AFL-CIO balancing act: The labor federation supports data centers built with union labor while also seeking worker bargaining power over how AI is introduced and used.
- Distinctive technology effects: Unlike earlier automation, which disproportionately harmed routine industrial workers while complementing professionals, generative AI can reduce the skills advantage in professional occupations.
- Risk of protectionism: New York’s LOADinG Act expanded from an AI oversight measure into broad public-sector employment protections that could prevent agencies from replacing or reassigning workers to automated tools, limiting potential taxpayer savings and service improvements.
While organized labor is usually united, artificial intelligence is increasingly dividing it. In just eight days, a split between professional unions and building-trades unions surfaced in two quite distinct places. The Wall Street Journal reported that unions and construction trade groups are threatening to withhold support from candidates who oppose data-center projects. And Jacobin published an article on how “The Fight Over Data Centers Is Dividing the Labor Movement.”
That Jacobin and the Journal converged on the same story is telling. The new divide runs between unions whose members build AI’s physical infrastructure and unions whose members’ jobs the technology could make redundant. One side is organizing to accelerate data-center construction; the other is trying to slow AI’s rollout inside their own workplaces.
The building trades have become a powerful organized constituency for AI infrastructure. Take IBEW Local 26, the electricians’ union covering Washington, D.C., Maryland, and Virginia. Its members worked 28 million hours last year, double what they did a decade ago. Data centers account for at least half that work and 600 new apprentices last year alone.
Elsewhere, building-trades unions are expanding apprenticeship classes and training centers to meet demand. Data centers alone consume an estimated 40 percent of member hours for the Columbus–Central Ohio Building and Construction Trades Council, for example.
It’s thus not a surprise that these groups are fighting back against political attacks on data centers. At least 15 states and more than 200 localities have proposed or adopted data-center moratoriums. Building-trades unions have fought most of them.
But while blue collar workers go one way, professional unions are moving in the opposite direction. National Nurses United and the American Association of University Professors have endorsed legislation introduced by Sen. Bernie Sanders and Rep. Alexandria Ocasio-Cortez that would halt new AI data-center construction. Faculty unions have fought AI vendor contracts and negotiated use restrictions. Public-sector unions are pushing for bargaining rights over whether and how agencies deploy AI technologies.
The American Federation of Labor and Congress of Industrial Organizations, the country’s largest labor federation, is trying to straddle the divide. It supports building data centers with union labor while insisting that workers have bargaining power over how AI is introduced and used on the job.
This blue-collar versus white-collar divide reflects a deeper political-economy dynamic. The more data centers get built, the more work there is for electricians, pipefitters, welders, and other skilled trade workers.
But Generative AI can also reduce skills inequality in professional roles, for example enabling less-experienced workers to perform tasks that once required greater skills and expertise. That can give professional groups a guild-like interest in limiting AI’s spread. Professional associations and unions have long used collective bargaining to protect their interests. AI now gives incumbents an incentive to use those same tools to slow its adoption or reserve certain tasks for existing workers.
This makes AI different from other technologies. The effects of postwar automation, for example, fell most on factory and other routine workers, while computerization generally complemented highly educated professionals. Industrial robots reduced employment and wages in manufacturing and other sectors.
The result is that AI labor politics that does not fit neatly within conventional partisan categories. Organized labor cannot simply be assigned to the anti-AI camp. Data centers give one segment a direct stake in deployment, while diffusion threatens the rents of another.
Those competing interests can also distort AI policy into protection for incumbent workers. Take New York, where the Legislative Oversight of Automated Decision-Making in Government (LOADinG) Act, began as a conventional AI-governance proposal focused on public transparency and oversight. But as the bill moved through the legislature in Albany, lawmakers added sweeping public-sector employment protections. Those protections bar agencies from displacing employees or shifting their duties to automated tools while also extending civil-service and bargaining protections to any job that uses AI, regardless of the cost savings.
Government exists to serve citizens, not to maximize public-sector employment. If AI can reduce backlogs, process claims, detect fraud, or deliver services with fewer inputs, rules that prevent agencies from realizing those gains turn AI regulation into job protection, with taxpayers bearing the cost. If AI is to deliver on its promise, policymakers should resist that protectionist impulse.
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