As AI capabilities advance rapidly, we face unprecedented economic questions: How will transformative AI reshape labor markets and productivity? What distributional effects might emerge, and how can we ensure broad-based prosperity? What governance structures and incentive systems might guide beneficial AI development? These questions demand rigorous economic analysis from diverse perspectives.

Organized by Apart Research in collaboration with BlueDot Impact, this sprint builds on BlueDot's "Economics of Transformative AI" course, providing participants with an opportunity to apply economic theory to practical research questions. While the course provides a foundation, the sprint welcomes all economists with strong analytical backgrounds interested in applying their expertise to AI safety challenges.

The sprint follows a three-phase structure: pre-event development of open research questions, an intensive weekend of collaborative research, and post-event opportunities for exceptional projects to receive continued support through our fellowship program. Join us in developing the economic tools and frameworks needed to navigate one of this century's most consequential transitions.

Why This Matters

Critical Economic Questions Demand Answers

The economic implications of transformative AI will reshape markets, institutions, and societies. Without rigorous economic analysis, we risk navigating this transition blindfolded. Traditional economic models may fail to capture the unique dynamics of transformative AI systems, including recursive self-improvement, exponential productivity growth, and unprecedented labor market effects.

Economists Bring Essential Perspectives to AI Safety

The field of AI safety has been led primarily by computer scientists and alignment researchers. Economic expertise is crucial but underrepresented. Economists bring vital methodological tools, including causal inference, game theory, mechanism design, and welfare analysis that can inform both technical alignment work and governance frameworks.

Bridging Research and Policy

Economic insights will directly inform policy responses to AI development. Your research contributions could influence governance frameworks, regulatory approaches, and international coordination efforts around AI. By developing robust economic analyses now, we can shape more effective policies before transformative capabilities emerge.

Challenge Tracks

Distribution Track

How will the benefits and risks of transformative AI be distributed across society? This track examines the distributional implications of advanced AI systems:

• Labor Market Transitions: Analyze potential employment disruptions, wage effects, and skill premium dynamics as AI capabilities expand across domains

• Wealth and Income Distribution: Model how capital concentration, data ownership, and algorithmic deployment might affect inequality

• Geographic Disparities: Examine how AI impacts may differ across regions, countries, and urban/rural divides

• Policy Interventions: Design and evaluate potential policies for ensuring broad-based prosperity, including UBI, skill transition programs, and data dividends

Distribution Track Project Ideas:

• Distribution of AI's Economic Gains: Analyze how the "AI dividend" might be split among stakeholders - companies, workers, consumers, and society.

• Labor Market Simulation: Model which occupations are most vulnerable to AI automation versus which might grow, and simulate the timeline of these transitions.

• UBI in an AI Economy: Evaluate universal basic income as a policy response to AI-driven labor market disruptions, determining economic feasibility and effects.

Growth Track

How might transformative AI reshape economic growth trajectories and productivity? This track explores macroeconomic implications:

• Productivity Modeling: Develop frameworks to predict and measure AI-driven productivity changes across sectors

• Growth Dynamics: Explore scenarios for economic growth under different AI development trajectories

• Innovation Systems: Analyze how AI might transform R&D processes, knowledge creation, and technological diffusion

• Transition Management: Identify potential economic instabilities during rapid technological transition and design stabilizing mechanisms

Growth Track Project Ideas:

• AI-GDP Growth Modeling: Build a model to project GDP growth under different AI progress assumptions, comparing scenarios of slow vs. fast automation to identify conditions for explosive economic growth.

• Automation Bottlenecks & Baumol's Cost Disease: Examine how "automation-resistant" sectors might impact overall economic growth despite AI advances in other areas.

• AI and Innovation: Investigate how AI systems as research tools might accelerate technological progress, potentially breaking through diminishing returns in R&D.

Market Dynamics Project Ideas:

• AI Industry Structure Analysis: Examine whether the AI sector will tend toward oligopoly or remain competitive, based on factors like compute costs and network effects.

• Algorithmic Collusion Simulation: Create a market simulation to test if AI pricing algorithms naturally learn to collude, threatening consumer welfare.

• Data Markets in the AI Economy: Investigate emerging markets for data as a critical AI input, including potential market failures and governance mechanisms.

Three-Phase Structure

1. Pre-Event: Open Problems in AI Economics

Before the sprint weekend, participants gain access to:

• A comprehensive paper on open problems in AI economics developed by our area chairs

• Curated reading materials (5-20 pages) highlighting key concepts and current research

• The Hackbook template to help structure research approaches

• Discussion forums to connect with other participants and begin forming teams

We recommend reviewing these materials before the event to maximize your productive research time during the weekend.

What to Expect

Previous Apart Research sprints have attracted 300-350 participants, and we anticipate even more for this economics-focused event. You'll join interdisciplinary teams of 3-5 members based on shared research interests.

Our hackathons foster a collaborative atmosphere where participants can:

• Develop novel research insights in a compressed timeframe

• Receive feedback from leading experts in economics and AI safety

• Network with researchers, policymakers, and industry professionals

• Produce work that contributes to the growing field of AI economics