Paper Type
Complete
Abstract
Generative AI platforms face a “Trainer’s Dilemma”: free trials are essential to convert inefficient novice users into low-cost experts, yet the transferability of prompt-engineering skills exposes platforms to strategic trial-hopping. Unlike SaaS settings with negligible marginal costs, inference in Generative AI can consume 50%–70% of revenue, making monetization a first-order operational concern. We develop a game-theoretic framework that explicitly links user learning to compute costs, yielding three results. First, optimal trial duration follows an inverted-U in the learning rate, and higher compute costs call for longer, not shorter, trials. Second, data spillovers from early usage partially offset signaling costs, enabling high-quality platforms to subsidize more aggressively than classical theory predicts. Third, competition induces defensive over-subsidization, pushing trial lengths beyond the monopoly benchmark as platforms race to lock in users before rivals can poach them. Together, these results explain the aggressive cash-burn strategies observed across the generative AI industry.
Paper Number
1356
Recommended Citation
Hu, Jingyun, "The Trainer’s Dilemma: Operational Investment and Competitive Subsidization in Generative AI Platforms" (2026). AMCIS 2026 Proceedings. 1.
https://aisel.aisnet.org/amcis2026/scuidt/scuidt/1
The Trainer’s Dilemma: Operational Investment and Competitive Subsidization in Generative AI Platforms
Generative AI platforms face a “Trainer’s Dilemma”: free trials are essential to convert inefficient novice users into low-cost experts, yet the transferability of prompt-engineering skills exposes platforms to strategic trial-hopping. Unlike SaaS settings with negligible marginal costs, inference in Generative AI can consume 50%–70% of revenue, making monetization a first-order operational concern. We develop a game-theoretic framework that explicitly links user learning to compute costs, yielding three results. First, optimal trial duration follows an inverted-U in the learning rate, and higher compute costs call for longer, not shorter, trials. Second, data spillovers from early usage partially offset signaling costs, enabling high-quality platforms to subsidize more aggressively than classical theory predicts. Third, competition induces defensive over-subsidization, pushing trial lengths beyond the monopoly benchmark as platforms race to lock in users before rivals can poach them. Together, these results explain the aggressive cash-burn strategies observed across the generative AI industry.
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