Policies Decided by Legislators

Taxing AI model inference to create a revenue stream growing automatically with AI adoption

Higher effective tax rates on the largest multinational enterprises through coordinated global frameworks.

An annual tax on accumulated net assets rather than income.

Broad-based taxes on spending, that maintain revenue as labor income declines.

Taxes that remove fiscal advantages for automation or directly levy AI and robotic deployment.

Taxes on AI companies paid via equity stakes rather than cash.

Direct ownership stakes in AI infrastructure and the broader economy distributed to all citizens.

State-owned investment vehicles for AI infrastructure and upstream supply chains.

Payments to individuals for the use of their data in training AI models.

Public investment in sectors delivering public value and supporting employment

Fiscal incentives for employers who invest in retraining or hire displaced workers.

Government funding for compute infrastructure or energy to increase competitiveness or democratize AI access.

Modernizing unemployment insurance to expand eligibility and duration.

Free or heavily subsidized access to essential services such as healthcare, education, housing, or transportation.

Governments functioning as an employer of last resort for all willing individuals.

Temporary income supplements to displaced workers who accept reemployment at lower pay.

Public ownership stakes whose profits are distributed as cash payments to all citizens.

Unconditional, regular cash payments to all citizens regardless of employment status.

Scaling-up tax credit programs to reach a broader set of workers.

Adapted copyright and patent frameworks for AI training data and AI-generated outputs.

Dedicated bodies with technical capacity to monitor high-risk AI systems.

Legal frameworks assigning financial responsibility for AI-caused harms.

Legal recognition of AI systems as taxable entities with financial obligations.

Common carrier and universal access obligations applied to AI infrastructure and services.

Competitive markets of licensed private regulators selling compliance services to AI companies.

Pre-legislated fiscal rules that activate automatically when economic indicators cross defined thresholds.

Corporate structures requiring directors to balance societal benefit alongside shareholder returns.

Legal duties requiring AI developers to act in users' and society's interests.

Reserved board seats at AI companies for workers or public interest representatives.

Non-controlling government equity stakes in AI companies and critical technology firms.

Public purchasing rules that shape AI markets and attach public benefit obligations.

Structured government-private collaborations to co-finance and co-govern AI infrastructure.

Reduced working hours to distribute work more evenly and convert productivity gains into leisure.

Strengthened collective bargaining giving workers a voice in AI adoption decisions.

Tax incentives and subsidies connecting displaced workers to skills development.

Expanded pathways for displaced workers to start businesses or become self-employed.

Comprehensive AI literacy embedded across all education levels.

Continuous learning systems replacing traditional front-loaded education.

Expanded technical education and apprenticeships for skilled trades resilient to automation.