Algorithmic Alertness and Imprinted Exits: Artificial Intelligence, Founder Characteristics, Opportunity Recognition, and Exit in South America

Gabriel da Silva, Mateo González, Kavin Alves Santos and Sofía Quispe

No pvfjy_v1, SocArXiv from Center for Open Science

Abstract: Artificial intelligence (AI) is reshaping how new ventures are conceived, built, and ultimately exited, yet most theorizing about these shifts has been developed in North American and European settings where capital markets are deep and institutions comparatively stable. This essay develops a conceptual framework that links founder characteristics, AI-enabled opportunity recognition, and entrepreneurial exit in South America, a region marked by institutional voids, concentrated family ownership, rapid consumer adoption of AI, and thin, contracting equity markets. Drawing on upper echelons and imprinting theories, the cognitive literature on opportunity recognition, and an emerging view of AI as an entrepreneurial actor rather than a passive tool, we argue that AI does not so much substitute for founder characteristics as re-weight them. Algorithmic search lowers the cost of identifying opportunities but raises the premium on founder judgment, entrepreneurial drive, and political skill, which govern whether AI-surfaced opportunities can be legitimized and exploited where formal institutions are weak. At the exit stage, founder imprints formed under the region's recurrent macroeconomic crises shape not only whether founders sell or list, but whether they later reverse a public listing altogether, a pattern currently visible in the wave of delistings on Brazil's B3. The essay advances six propositions and outlines an agenda for research on AI-augmented entrepreneurship in emerging economies.

Date: 2026-10-29
References: Add references at CitEc
Citations:

Downloads: (external link)
https://osf.io/download/6ae231f903196c9fda0ec75d/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:pvfjy_v1

DOI: 10.31235/osf.io/pvfjy_v1

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().