Who controls artificial intelligence, who benefits from it, and who pays the cost — in jobs, energy, creative work, and existential risk?
Each issue breaks into the specific questions Congress actually fights over. Read each position, then head to the interactive version of this issue to mark which reflects your view and build a message to your representatives.
AI is displacing workers at scale with no meaningful safety net in place. Congress should mandate that AI companies contribute to a federal workforce transition fund — modeled on Trade Adjustment Assistance — providing two years of fully funded retraining, wage insurance, and relocation support for workers in affected industries. The Workforce Innovation and Opportunity Act should be amended to create an AI-displaced worker category with enhanced benefits.
The scale of AI-driven displacement is uncertain but real enough to warrant action before it peaks. A bipartisan AI Workforce Transition Fund with corporate contributions scaled to revenue and verified displacement metrics — phased in over five years — could fund community college retraining and apprenticeship partnerships without the inefficiencies of a fully government-managed program. The Trade Adjustment Assistance model and the FUTURE Act (2019) provide the legislative template.
Government-managed retraining funds have a poor track record — Trade Adjustment Assistance had low completion rates and high administrative overhead. Tax incentives for companies that voluntarily fund worker retraining are more efficient than mandated levies, which will be passed to consumers and slow the AI adoption that ultimately creates new jobs. Market-driven solutions, not government-directed programs, produce better outcomes.
The potential harms of advanced AI — autonomous weapons, systems that could eventually outpace human control — are documented concerns raised by the researchers who build them. Congress should establish a mandatory AI safety authority modeled on the FDA or FAA with power to certify high-risk systems before deployment. A moratorium on frontier AI development above a defined capability threshold deserves serious consideration before the decision point passes.
Advanced AI risk is real and deserves serious policy — not panic or dismissal. Strengthen the NIST AI Safety Institute with authority to certify AI in healthcare, critical infrastructure, and national security applications; require red-teaming and incident reporting; and coordinate internationally through G7 channels to prevent a regulatory race to the bottom. The Advancing American AI Act (2022) and NIST AI Risk Management Framework are the right foundations to build from.
Existential risk projections are speculative and should not drive preemptive regulation that cedes AI leadership to China. Liability law — companies bear legal responsibility for harms their systems cause — creates strong market incentives for safety without government micromanagement. The competitive risk of falling behind a less safety-conscious rival is itself an existential concern that heavy regulation accelerates.
AI data centers are among the fastest-growing sources of electricity demand in the U.S. — straining grids, driving up household utility rates, and burning fossil fuels to power the race to build bigger models. Congress should require 100% renewable energy for AI data centers by 2030, impose carbon disclosure requirements tied to compute consumption, and mandate water use reporting in drought-affected regions.
AI's energy footprint is real and growing — the Department of Energy projects data center electricity demand could double by 2028. Require public disclosure of energy and water use per major AI model and mandate efficiency standards for new data center construction under existing DOE authority. Work with FERC and state utility commissions on grid capacity planning. The CHIPS and Science Act's manufacturing incentive model is the right legislative template.
Power costs are the single largest data center expense — companies have overwhelming market incentives to improve efficiency without mandates. AI will be a net climate positive: it optimizes energy grids, accelerates battery and materials research, and improves climate modeling. Heavy-handed energy mandates will drive data centers offshore to countries with weaker standards, achieving the opposite of their stated goal.
AI companies built billion-dollar systems by ingesting the life's work of artists, writers, musicians, and journalists — without consent, credit, or compensation. Congress should require opt-in consent and royalty payments for copyrighted works used in commercial AI training, establish a federal licensing registry, and mandate labeling of all AI-generated content. Active litigation includes NYT v. OpenAI and Getty Images v. Stability AI.
Copyright law was written before AI existed and needs modernization — not wholesale revision. A workable framework distinguishes transformative research use (fair use, no payment required) from commercial training on copyrighted works (licensing required). An optional federal registry where creators set their own licensing terms, paired with mandatory disclosure of training data categories and labeling of AI-generated content, protects creators without shutting down development.
AI learns from creative works the same way humans do — by reading, observing, and building on what came before. Copyright law has never required students to pay royalties for books they study; it should not require AI companies to pay for training data they process. Existing copyright law applied through the courts is sufficient; new restrictions will drive AI development offshore and harm U.S. competitiveness.
AI systems already make consequential decisions about housing, credit, healthcare, and parole — with little transparency or accountability. Congress should establish a dedicated AI regulatory agency with enforcement authority, require algorithmic impact assessments before deployment in sensitive domains, ban facial recognition in public spaces, and mandate explainability for any AI decision that affects individual rights. The EU AI Act (2024) demonstrates comprehensive regulation is achievable.
A new AI-only agency risks bureaucratic bloat and turf battles. The better model empowers existing agencies with AI-specific authority — FDA for healthcare AI, FTC for consumer AI, CFPB for financial AI — coordinated through an interagency AI council. Codify the NIST AI Risk Management Framework as the required baseline for high-risk AI in federal procurement. Prioritize federal preemption of conflicting state laws to prevent a compliance patchwork that harms smaller companies most.
A new federal AI agency would be slow, politically captured, and technologically obsolete before it opens. Existing agencies already have authority to address AI-caused harm — the problem is capacity and will, not law. A light federal framework that prevents a patchwork of conflicting state laws while preserving liability as the primary accountability mechanism maintains U.S. innovation advantage without command-and-control overreach.