At a micro level, case studies pointing to a sharp increase in productivity from the use of generative AI are juxtaposed with those predicting job displacement as a natural consequence of this breakthrough technology. Macro studies largely deliver optimistic outcomes, with rosy estimates of productivity improvement.

What type of framework is needed to accurately evaluate the complex outcomes of a transition of economic activity across sectors and economic blocs into macro productivity? How do we recalibrate labor displacement toward upskilling and labor redeployment? What regulatory and policy initiatives must fall into place to help turn the great productivity promise of generative AI into reality?

In this paper, we start by providing an overview of the economic outcomes from prior technological breakthroughs, highlighting the determinants of success and failure of these innovations. We then demonstrate how generative AI stands alone as a truly groundbreaking innovation, primarily due to its accessibility and versatility — features that give it the potential to be truly transformational.

Assessing the economic impact at a macro level requires a robust framework. Based on the work of the International Monetary Fund (2024), we show that the two key vectors of this framework are “exposure” of the economic activity to generative AI and “complementarity” of the economic activity with others that are created, modified and deployed as a result of generative AI. Using this framework, one can assess the impact of generative AI at a sector or country level in an intuitive way.

Advancing the productivity promise of generative AI requires a rational policy and governance framework. We provide the canons of such a framework and conclude that this revolutionary technology holds great potential.