As wealth from artificial intelligence surges, a new political fight is brewing over whether to tax AI and how to share its gains. Democratic congressional candidate Alex Bores is pushing for an AI tax, while a tax expert warns the policy could backfire. The discussion reflects growing concern that AI profits are being concentrated among investors and large firms, not workers.
“Should we tax AI?”
The question lands as companies pour billions into AI systems and infrastructure. Supporters say new taxes could fund worker training, protect wages, and support communities facing disruption. Critics counter that a poorly designed levy could slow useful adoption, hurt smaller firms, and be hard to enforce.
Rising Wealth, Uneven Gains
“The AI race has made a lot of people richer … but most of those gains seem to have gone to the wealthy while everyday workers aren’t seeing their incomes go up.”
That claim mirrors a broader trend in recent tech cycles. Capital owners gain quickly from surging valuations and new products. Productivity benefits for workers tend to appear later, if at all. Economists disagree on how fast AI will change the job market, but many expect pressure on certain roles, from customer support to routine office work.
Governments have weighed similar ideas before. The European Parliament debated a “robot tax” several years ago but stopped short of adoption. In 2017, South Korea trimmed tax incentives for automation, a move often described as a limited form of robot tax. Those episodes show interest in capturing some gains from automation without blocking innovation.
Bores’ Case for an AI Tax
Bores argues that AI is creating new wealth streams and public policy should direct some of those gains to workers and local services. He frames the idea as a fairness issue and as a way to prepare for disruption.
Supporters outline several goals for an AI levy:
- Funding worker training and apprenticeships. Support mid-career transitions into higher-wage roles.
- Strengthening the safety net. Backstop communities during rapid change.
- Sharing gains. Ensure windfalls benefit more than a small investor class.
Policy architects are exploring ways to target the tax at the sources of value in AI: compute, data, and software models. They also want to avoid penalties for small developers or open research.
How a Tax Could Work
Several models are under discussion in policy circles. Each has trade-offs on fairness, growth, and ease of enforcement.
- Compute or energy levies: Fees on high-end chips or data center power use above defined thresholds.
- Windfall profits tax: A surtax on extraordinary AI-related profits at large firms.
- Automation offset: Employer contributions when AI replaces roles, akin to payroll taxes that fund training or wage insurance.
- Model licensing fees: Charges on frontier systems that exceed safety or capability thresholds.
States could pilot narrow measures first, such as reporting rules for large training runs or minimal fees on megawatt-scale AI clusters, before wider adoption.
The Expert’s Reservations
A tax specialist interviewed for the discussion expressed skepticism. She questioned whether policymakers can pinpoint what counts as “AI” for tax purposes, given that many tools are embedded across workflows. She also warned about offshoring risk if costs rise domestically.
Her critiques focus on design and practical enforcement:
- Measuring job displacement is hard and contested.
- Taxes on compute or energy could hit research and small firms more than giants.
- Companies might reclassify spending or shift activity abroad.
- Existing tools—corporate taxes, capital gains, antitrust, and training credits—might be better levers.
She suggested lawmakers test targeted credits and transparency rules first. Clear reporting on AI-driven productivity and layoffs could guide later action.
What Comes Next
Several paths could move in parallel. Congress could explore hearings on AI’s labor impact and ask for agency studies on displacement metrics. States might try pilot fees or disclosure mandates tied to large-scale training runs. International talks, such as those shaped by the OECD, could reduce risks of tax arbitrage.
For voters, the question is where to place the policy lens: the tools that enable AI, the profits they create, or the outcomes in labor markets. Bores’ call puts that choice on the table. The tax expert’s caution highlights the stakes of getting the details right.
The debate is set to intensify as AI spending grows and election season heats up. Expect proposals that start small, measure results, and adjust. The central test will be whether policy can channel AI’s gains to workers without stalling progress or pushing investment offshore.