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Automation-Induced Innovation Shift / Lin William Cong, Yao Lu, Hanqing Shi, Wu Zhu.
- Format:
- Book
- Author/Creator:
- Cong, Lin William.
- Series:
- Working Paper Series (National Bureau of Economic Research) no. w34240.
- NBER working paper series no. w34240
- Language:
- English
- Physical Description:
- 1 online resource: illustrations (black and white);
- Place of Publication:
- Cambridge, Mass. National Bureau of Economic Research 2025.
- Summary:
- We study how exposure to automation affects the nature and level of corporate innovation, which informs how innovation begets innovation. We document that firms with high robot exposure alter their technological focus over time and shift innovative activities towards AI which automation naturally complements through data accumulation. The shift is more pronounced for firms with greater data generation or prior AI-related research experience. Because AI patents are more costly (e.g., in labor input), albeit more general and original, firms with high automation experience a significant rise in R&D expenditure but an initial drop in patent quantity, before benefiting--an innovation "J-Curve." Our findings not only resolve the puzzle that globally firms invent less despite the greater research effort amidst rising automation, but also provides insights on the heterogeneous paths of innovation, all of which we rationalize in a parsimonious dynamic equilibrium model.
- Notes:
- September 2025.
- Print version record
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