The emergence of Artificial Intelligence (AI) and its socio-economic impact has spurred significant academic attention. Despite AI not being a new phenomenon, advances in machine and deep learning have magnified its potential, raising questions about its effects on technological and economic development. This cumulative dissertation explores how General Purpose Technologies (GPTs) and Inventions of a Method of Inventing (IMIs), represented by AI, impact socio-economic systems. The dissertation emphasizes the need to differentiate AI's GPT and IMI components. By analyzing both, this research shows that AI's impact is complex and not universally positive, depending on the type of AI, performance measures, and contextual factors. The results reveal that AI’s GPT characteristics positively influence firms' radical innovation output, while IMI characteristics boost general innovation. Additionally, labor productivity is more closely tied to GPT features. Beyond firm-level analysis, the dissertation investigates AI's effects on regional development and sustainable growth. It finds that AI can exacerbate inequalities between regions and firms, favoring those with advanced knowledge bases. Further, the results highlight AI's potential to both support and hinder sustainable development, depending on regional economic structures. In conclusion, while AI exhibits both GPT and IMI traits, its impact varies across contexts, and it cannot be considered a universal solution for growth. Policymakers and managers must account for firm size, regional factors, and specific goals to harness AI’s benefits. Finally, the dissertation underscores the need for future research to further disentangle AI technologies, expand performance indicators, and consider additional contextual factors at firm, regional and national levels.
Alexander Kopka (Mon,) studied this question.