The Agricultural Viability Frontier: When AI Expands What Farming Can Economically Do

Artificial intelligence in agriculture is commonly evaluated through changes in yield, input use, labor cost, profitability, sustainability, or adoption. These measures are necessary, but they can miss a distinct economic effect: a technology may change whether a production configuration is economically viable at all. This paper develops the Agricultural Viability Frontier framework by formalizing the concept of an agricultural frontier introduced in Kao (2026). Building on research on digital agriculture, technology adoption, intensive and extensive supply responses, farm viability, and the economics of autonomous machinery, the paper distinguishes optimization from frontier expansion. Optimization improves a crop-location-scale-production configuration that was already viable; frontier expansion changes membership in the viable set. The framework defines a production configuration, an economically viable set conditioned on technology and the surrounding economic environment, and frontier-expanding technological change. It further proposes a Frontier Expansion Test centered on four questions: whether the activity would occur in substantially the same configuration without the technology, which constraint is binding on viability, whether the technology materially relaxes that constraint, and whether complementary conditions remain sufficient. Recent evidence on autonomous field machinery, labor scarcity, robotic harvesting, and autonomous greenhouse control illustrates why technical success or positive return on investment is not by itself evidence of frontier expansion. The contribution is integrative rather than a claim that viability boundaries or extensive-margin effects are new: the paper extends a prior geographic use of a farm viability frontier into a technology-conditioned, configuration-level framework for evaluating when AI makes agriculture better and when it makes additional agriculture economically possible.