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. This paper 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. I identify 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, Matthias Qian find 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.