How Costs Influence Preferences for Control in Generative Artificial Intelligence (GenAI): Human-Guided vs. GenAI-Based Delegated Search
Lei Wang, Ho Cheung Brian Lee · Information Systems Research · 2026
As generative artificial intelligence (GenAI) platforms transition to paid models, concerns grow that usage costs will diminish service value. However, our study of 1.8 million prompts shows that economic constraints actually change how users search solutions with AI. We distinguish between GenAI-based delegated search, which relies on probabilistic sampling, and human-guided delegated search, where users exert active control through refined prompting. We find that salient costs drive users to prioritize controllability over simple cost-minimization. Instead of settling for lower quality, users adapt by crafting precise, detailed prompts and actively “controlling” the AI. This strategic shift increases more purposeful exploration, leading to higher satisfaction and superior outcomes. Ultimately, our work shows that charging for AI usage transforms users into more purposeful, deliberate cocreators with AI, indirectly enhancing the service value of AI platforms.