Schulte-Althoff, Matthias, Gene Moo Lee, Hannes Rothe, Robert Kauffman, Daniel Fuerstenau. “What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups”. Under Review. [ResearchGate]
- Presented at HICSS 2021 (SITES mini-track), Copenhagen Business School 2021, FU Berlin 2021, University of Cologne 2021, University of Bremen 2021, Humboldt Institute for Internet and Society 2021, University of British Columbia 2022.
AI technologies automate ever more complex tasks and promise new efficiencies for firms to provide new market offerings and grow. Economists argue that complementarities from AI innovations have not diffused widely enough to yield higher productivity yet though. We examine how firm revenue scales with labor for revenue-per-employee (RPE) and is moderated by firm-level AI investment. We compare AI start-ups, in which AI provides a competitive advantage, with digital platform and service start-ups. We use propensity score matching (PSM) to explain the scaling of start-ups and find evidence for sublinear scaling intensity for revenue as a function of labor. Surprisingly, our study suggests similar scaling intensities between AI and service start-ups, while platform start-ups produce higher scaling intensities. We show that an increase in employee counts is associated with major increases in revenue for platform start-ups, while increases were modest for service and AI start-ups. We also consider AI-enabled service start-ups that incorporate both service and AI-based business models and AI-enabled platform start-ups that combine AI and platform business models. AI-enabled service start-ups have a scaling intensity between service and AI start-ups, so they may not yet have achieved scaling benefits because AI adoption requires manual work from human experts. AI-enabled platform start-ups, in contrast, have a higher scaling intensity. Our study provides new perspectives on the role of AI as an emerging technology resource that supports economies of scale and scope for start-ups.