Hybrid Intersection: Navigating Context and Constraint in AI for Social Good Among Thailand’s Smallholder Farmers

Artificial intelligence is increasingly deployed as a vehicle for “social good” in agriculture, ostensibly advancing the UN Sustainable Development Goals whilst uplifting smallholders. This study examines how such claims materialise through a selective case study analysis of eleven Thai Agricultural AI providers, analysing governance practices and impact framing. The research develops the “hybrid intersection” concept as an analytical lens for understanding how Agricultural AI simultaneously delivers genuine social benefits whilst reproducing structural constraints that limit transformative change. Findings reveal that “social good” becomes operationalised primarily through economic gains, reflecting farmers’ immediate financial predicament and market-driven innovation constraints. Governance practices prioritise functional trust over procedural safeguards, reflecting institutional pressures to demonstrate immediate value. The study reveals two systemic tensions: data commodification models enabling free farmer access whilst extracting behavioural surplus for third-party monetisation, and market optimisation approaches delivering incremental improvements whilst leaving structural challenges unaddressed. Thailand’s Agricultural AI landscape thus embodies a “hybrid intersection” where genuine social good coexists with constrained transformation, providing analytical tools for understanding similar patterns in other Southern contexts.