Decoding AI Diffusion: Mapping the path of transformative AI across industries
Matthew Hodak, Mishaal Lakhani
Mentored by Deric Cheng, Justin Bullock
Working report from the SPAR program. May not reflect the authors' current views.
Abstract
In this report, we develop an organizing framework to assess the diffusion potential of transformative AI (TAI) across different sectors of the economy. We argue that many existing methodologies for assessing TAI’s potential to augment and automate cognitive labor oversimplify the structural, cultural, and technological differences between industries which will impact their susceptibility to disruption from AI. Drawing upon TAI literature and historical diffusion patterns of past digital general purpose technologies, we identify the sectoral factors most likely to impact the breadth and speed of TAI diffusion. We propose a five-category framework to organize these factors: 1) technological readiness, 2) workforce and human capital, 3) investment, 4) markets and competition, 5) regulation and oversight. This structure can be applied sectorally, leveraging qualitative and quantitative indicators for a given sector in order to determine the likely path and speed of TAI within that sector, or to compare TAI’s potential impact across different sectors. We aim to provide policymakers, business leaders, and researchers with a tool to assist more nuanced foresight, enabling stakeholders to better anticipate labor market shifts from AI.