Report on Policy Frameworks for Artificial Intelligence and Sustainable Development

This report analyzes potential economic policy responses to the proliferation of advanced Artificial Intelligence (AI) systems, with a specific focus on aligning these responses with the United Nations Sustainable Development Goals (SDGs). As AI transitions from a collaborative tool to an autonomous agent for completing full tasks, its impact on labor markets and economic structures necessitates proactive policy formulation to ensure progress towards global sustainability targets.

Aligning AI Economic Policy with SDG 8: Decent Work and Economic Growth

The core challenge posed by AI is its potential disruption to labor markets, directly impacting the achievement of SDG 8. Policies must be developed to foster inclusive growth, full and productive employment, and decent work for all in an AI-driven economy. These policies are categorized based on the anticipated velocity of AI-driven economic change.

Policies for All Scenarios: Foundational Support for SDG 8 and SDG 4

These proposals are designed to strengthen the workforce and economy, contributing to SDG 8 (Decent Work and Economic Growth) and SDG 4 (Quality Education) regardless of the pace of AI adoption.

• Workforce Training Grants: Investing in upskilling through government subsidies to employers for on-the-job training programs. This directly supports SDG 4’s target for lifelong learning and SDG 8’s goal of productive employment.
• Tax Incentive Reforms: Reforming tax codes to favor human capital investment over physical capital. This includes removing caps on tax-free educational assistance and allowing immediate expensing for all job-related training, incentivizing employers to retrain rather than displace workers.

Policies for Moderate Disruption Scenarios: A Just Transition Framework

In scenarios with measurable job losses, policies must provide a robust social safety net to ensure a just transition, in line with the principles of SDG 8 and SDG 10 (Reduced Inequalities).

• Automation Adjustment Assistance (AAA): Adapting models like Trade Adjustment Assistance (TAA) to support workers displaced by AI. This “AI insurance” provides skills training and financial support, directly mitigating the negative social impacts of automation and reducing inequality.

Fostering Innovation and Resilient Infrastructure: Achieving SDG 9

The development and deployment of AI are contingent on robust physical infrastructure. Policy reforms in this area are critical for achieving SDG 9 (Industry, Innovation, and Infrastructure), which is a prerequisite for leveraging AI for broader economic and social benefit.

Accelerating AI Infrastructure Development

To unlock investment, job creation, and productivity gains, regulatory processes must be streamlined to support the buildout of essential AI infrastructure.

1. Permitting Reform: Accelerating land use and environmental approvals for data centers, transmission lines, and power generation facilities.
2. Regulatory Streamlining: Expediting state reviews for transmission projects and approvals for interconnecting facilities to the electric grid.
3. NEPA Modernization: Reforming the National Environmental Policy Act (NEPA) to speed up reviews for critical infrastructure projects.

Ensuring Fiscal Stability and Reducing Inequality: Upholding SDG 10 and SDG 16

As AI potentially shifts the distribution of wealth from labor to capital, fiscal policies must be adapted to prevent rising inequality (SDG 10) and ensure governments have the resources to function effectively (SDG 16: Peace, Justice, and Strong Institutions).

Modernizing Revenue Generation for an AI Economy

New and reformed tax structures are required to capture value from a changing economic landscape and fund essential public services.

• Closing Corporate Tax Loopholes: Implementing reforms to prevent tax avoidance by large businesses, particularly those with digital and intangibles-based models, ensuring they contribute to public revenues.
• Value-Added Taxes (VAT): Adopting or modernizing consumption-based taxes like a VAT, which may become more stable revenue sources if labor’s share of income declines.
• Taxes on AI Activity: Studying taxes on compute, token generation, or digital services to provide revenue for fiscal programs that support displaced workers and reduce inequality.

Policies for Rapid Transformation: Addressing Systemic Inequality (SDG 10)

In scenarios involving dramatic job losses and worsening inequality, more ambitious proposals are necessary to redistribute the gains from AI and ensure broad-based prosperity.

1. National Sovereign Wealth Funds: Creating funds to give citizens and governments direct equity stakes in AI-related assets, enabling a more equitable distribution of AI-derived wealth.
2. New Revenue Structures: Exploring novel taxes, such as a low-rate business wealth tax, to complement income taxes and create a more resilient system for capturing revenue from highly profitable AI-driven enterprises.

Conclusion: A Call for Multi-Stakeholder Partnerships (SDG 17)

The economic effects of AI remain uncertain, necessitating a proactive and collaborative approach. Achieving the Sustainable Development Goals in an age of AI requires robust partnerships between researchers, policymakers, and the technology industry, as outlined in SDG 17 (Partnerships for the Goals). Continued research and open debate on these policy ideas are essential to prepare for a range of futures and ensure that technological advancement promotes inclusive and sustainable development for all.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

1. SDG 8: Decent Work and Economic Growth
   • The article’s central theme is the economic impact of AI on the workforce. It discusses potential “wage declines and job losses for large portions of the workforce,” which directly challenges the goal of achieving full and productive employment. Conversely, it also explores how AI can “improve their productivity,” aligning with the goal of higher economic productivity. Policy ideas like “workforce training grants” and “worker retention and retraining” are aimed at mitigating negative impacts on employment.
2. SDG 10: Reduced Inequalities
   • The article explicitly raises concerns about AI leading to “worsening inequality.” It proposes policy solutions designed to counteract this, such as creating “sovereign wealth funds to give citizens stakes in AI revenues” and other tax reforms to “distribute AI-derived wealth more equitably.” These measures directly address the goal of reducing income and wealth disparities.
3. SDG 9: Industry, Innovation, and Infrastructure
   • The article highlights the critical need for infrastructure to support AI development. It calls for “reforming permitting processes to enable the construction of energy and computing infrastructure,” specifically mentioning “large-scale data centers, transmission infrastructure, and power generation facilities.” This aligns with the goal of developing reliable and resilient infrastructure to support economic development and innovation.
4. SDG 4: Quality Education
   • While not focused on traditional education, the article heavily emphasizes lifelong learning and skills development. Proposals to “invest in upskilling through workforce training grants” and “reform tax incentives for… retraining” are directly related to providing adults with relevant technical and vocational skills for employment in an AI-driven economy.

2. What specific targets under those SDGs can be identified based on the article’s content?

1. Under SDG 8 (Decent Work and Economic Growth):
   • Target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading and innovation. The article discusses how employers adopt AI “to improve their productivity” and how failing to build AI infrastructure will “slow productivity and job growth.”
   • Target 8.5: By 2030, achieve full and productive employment and decent work for all. The article’s focus on potential “job losses,” “wage declines,” and the need for policies like “Trade Adjustment Assistance for AI displacement” directly relates to the challenge of maintaining full employment.
   • Target 8.6: Substantially reduce the proportion of youth not in employment, education or training. The proposal for a “Workforce Training Grant” to create “formal trainee positions with structured training programs” is a direct mechanism to address this target by integrating people into the workforce through on-the-job training.
2. Under SDG 10 (Reduced Inequalities):
   • Target 10.4: Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equality. The article is rich with examples of such policies, including closing “corporate tax loopholes,” implementing “taxes on compute or token generation,” adopting “value-added taxes,” and creating “sovereign wealth funds” to ensure AI revenues are distributed more equitably.
3. Under SDG 9 (Industry, Innovation, and Infrastructure):
   • Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure… to support economic development and human well-being. The call to “accelerate permits and approvals for AI infrastructure” such as “data centers, transmission infrastructure, and power generation facilities” is a direct effort to achieve this target.
4. Under SDG 4 (Quality Education):
   • Target 4.4: Substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurship. The proposals to “invest in upskilling,” provide “workforce training grants,” and reform tax incentives for “worker… retraining” are all aimed at equipping the workforce with the necessary skills to adapt to the AI economy.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

1. For SDG 8 Targets:
   • Implied Indicator: Rate of job displacement or creation due to AI. The article’s concern over “dramatic job losses” implies that tracking employment figures in sectors affected by AI is a key metric.
   • Implied Indicator: Change in average wages for the workforce. The mention of potential “measurable wage declines” suggests that wage levels are a critical indicator of AI’s economic impact.
   • Implied Indicator: Number of participants in employer-led training programs. The “Workforce Training Grant” proposal for “formal trainee positions” implies a need to measure the uptake and success of such programs.
2. For SDG 10 Targets:
   • Implied Indicator: Government revenue from AI-related taxes. Proposals for “taxes on compute or token generation” and modernizing “value-added taxes” imply that the revenue generated would be a measure of successful fiscal policy adaptation.
   • Implied Indicator: Distribution of assets in sovereign wealth funds. The idea to “give citizens stakes in AI revenues” through these funds suggests that the value and distribution of these stakes would be a key indicator of progress in sharing wealth.
   • Implied Indicator: Measures of income and wealth inequality. The fear of “worsening inequality” implies that standard economic indicators like the Gini coefficient would be used to track the success of mitigation policies.
3. For SDG 9 Targets:
   • Implied Indicator: Time required for infrastructure project approval. The article notes that regulatory reviews can cause delays of “10 years or more” and interconnection approvals take “4-6 years,” indicating that a reduction in these timelines would be a measure of success for permitting reform.
   • Implied Indicator: Investment in AI-related infrastructure. The call to unlock “investment, economic growth, and job creation” through infrastructure development implies that the total capital invested in data centers and energy facilities is a key progress indicator.
4. For SDG 4 Targets:
   • Implied Indicator: Public and private expenditure on worker retraining. The proposals for government-funded grants and tax incentives for employers imply that tracking the total investment in upskilling is a relevant metric.
   • Implied Indicator: Number of workers benefiting from educational assistance programs. The proposal to eliminate the “$5,250 cap on tax-free educational assistance” suggests that the number of employees utilizing such benefits would be an indicator of policy effectiveness.

4. Summary Table of SDGs, Targets, and Indicators

Source: anthropic.com