The rapid advancement of artificial intelligence technologies presents both unprecedented opportunities and challenges for the global economy. As AI capabilities continue to expand, there is an urgent need to better understand and prepare for the potential economic impacts these technologies may have across society. However, there are currently significant gaps in our understanding of how AI might reshape economic systems, particularly under scenarios of rapid capability advancement.

Three key factors motivated the organization of Threshold 2030:

First, there is a notable lack of concrete research examining the economic impacts of specific AI scenarios, particularly those involving rapid timelines. While considerable work has explored general AI impacts on labor markets and productivity, few studies have created detailed models explaining potential economic futures if no significant governance interventions occur. This gap in understanding makes it difficult for policymakers and other stakeholders to make informed decisions about AI governance and economic policy.

Second, the absence of concrete scenarios has contributed to insufficient public awareness regarding the urgency of evaluating potential AI economic outcomes. Most proposals to improve economic futures remain well outside mainstream policy discussions, despite the potentially transformative nature of AI technologies. By developing more concrete scenarios and economic models, we can better communicate both the opportunities and risks of AI systems.

Third, scenario modeling presents a compelling and underutilized methodology for producing better predictions about uncertain futures. This approach offers several key advantages: