AI translators — multidisciplinary experts who bridge business and technology expertise — reduce the coordination costs that arise with the difficulties in the communication of hyperspecialized workers who engage in the division of labor to redesign systems of decision making. The organizational inertia of incumbent firms reduces their adoption of AI translators, increasing the risk of failed AI investments and of their creative destruction. Matthias Qian asks if AI translators are the basis for the successful entry of new firms and how these VC-funded startups shape the future of work. He identifies 14 million AI translator job postings using natural language processing of over one billion task descriptors extracted from the full vacancy text of the near universe of the past decades’ US online job ads. Using a sample of 11,810 venture-capital-funded US startups, he finds a positive effect of AI translator use on startup performance, including on successful initial public offerings. These scaled startups rely heavily on AI translators as intermediaries: they post over four times as many AI translator job postings as incumbent firms. The lack of intellectual property protections on the task composition of jobs contributes to strong local knowledge spillover effects that explain the growing importance of AI translators in the labor market.
About the speaker
Matthias Qian’s research is in the field of Entrepreneurship, and in his work, he considers the effect of founders’ decision making on startup performance and the future of work. As an empirical researcher, he distinguishes himself through his deep knowledge of machine learning. His work spans two areas: first, he considers why some startups succeed and others fail, emphasizing the importance of strategic alignment of decisions. The goal is to guide entrepreneurs to exploit the interactions among their decisions so that their combined effect is greater than the sum of their individual effects. Second, he considers how surviving startups shape the future of work. In the context of the gig economy and AI, he aims to understand the ways in which technological innovations depend on changes in organizational practice. He holds a Ph.D. in Economics from the University of Oxford.
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