Economists often remain skeptical about the potential for AI to drive rapid economic growth, despite widespread optimism in the tech sector. This gap in perspective could lead to misaligned policy, investment, and long-term planning decisions. A deeper exploration of why economists hold these views—whether due to methodological constraints, historical biases, or differing definitions of growth—could help bridge the divide and provide clarity for decision-makers.

Understanding the Skepticism

One way to investigate this discrepancy is by analyzing existing research and engaging directly with economists. A structured review of key debates, such as those between researchers like Erdil, Besiroglu, and Clancy, could reveal recurring themes in their arguments. Interviews with economists could further clarify whether their skepticism stems from modeling limitations, institutional resistance, or other factors. This analysis could be compiled into a white paper or article, offering a synthesized view of the disconnect.

Potential Beneficiaries and Stakeholder Engagement

This exploration could help:

• Economists refine their models by acknowledging biases or overlooked possibilities.
• Tech leaders and investors better understand economic pushback, helping align expectations.
• Policymakers design AI governance frameworks that account for both optimistic and cautious perspectives.

Engaging economists could involve framing discussions as collaborative rather than confrontational. Incentives, such as co-authorship or recognition, might encourage participation. Meanwhile, funding could come from research institutions or tech firms interested in AI policy.

Execution and Testing Assumptions

To test whether economists' skepticism is rooted in methodological habits or institutional inertia, the project could:

1. Compare responses across economists of different career stages or subfields (e.g., macro vs. behavioral economics).
2. Analyze citation patterns in economic literature to see if skepticism correlates with entrenched viewpoints.

An initial phase could focus on summarizing existing debates, followed by interviews and a final synthesis. This structured approach could uncover deeper insights than isolated discussions while remaining adaptable as AI's economic impact evolves.

By systematically examining economist skepticism, this project could contribute to more informed debates about AI’s role in growth, benefiting researchers, industry leaders, and policymakers alike.