Schulte-Althoff, Matthias, Gene Moo Lee, Hannes Rothe, Robert Kauffman, Daniel Fuerstenau. “Does Higher Scaling Intensity Fuel AI Start-up Firm Growth?”. Working Paper. [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 enhance their capabilities and grow. Economists argue that AI innovation-related complementarities have not diffused widely enough to yield higher productivity 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, including start-ups that use AI, and platform and service start-ups. We use propensity score matching and a quasi-experimental research design to predict the scaling of start-ups. We obtained evidence for sublinear scaling intensity for revenue as a function of labor. Our study provides similar results for AI and service start-ups, while platform start-ups produced higher scaling intensity. We show an increase in employee counts compared to category averages, which increased average platform revenue the most, while increases were modest for service and AI start-ups. We also compare the AI boutique model with the AI factory model. The former includes hybrid start-ups that run a service- and AI-based business model, while the latter is represented by AI and platform hybrids. AI boutiques had a scaling intensity between the service and AI start-ups, which suggests they may not yet have achieved scaling benefits because adoption requires human experts. AI factories, in contrast, evidenced higher scaling intensity between the AI and platform start-ups. We also interpret the start-ups’ economies of scale, scope, and learning.