Abstract
While conventional software is programmed for specific tasks, AI learns and refines its capabilities through a two-stage process: pre-training and fine-tuning. A central feature of this process is the AI adaptive learning embedded in the fine-tuning stage, whereby user interactions refine the AI’s capabilities initially developed from publicly available data during pre-training. This feature reshapes the firm’s freemium decision. We develop an analytical model to examine how AI adaptive learning affects firms’ optimal freemium strategies and find that while a free version can expand the user base and generate learning data, it also intensifies cannibalization of demand and increases the service-cost burden. We demonstrate that highly effective adaptive learning can paradoxically harm profitability by excessively enhancing the free version’s appeal and sharply cannibalizing premium demand. We find further that firms may optimally avoid offering a free version even when service costs are minimal, relying instead on reductions to the price of premium service to expand the paid user base. Narrowing the base capability gap between versions can also increase profitability by strengthening adaptive learning despite the increased cannibalization. Finally, results show that introducing a free version does not necessarily increase consumer surplus, because improved AI capabilities may allow firms to raise premium prices. These results are robust across several model extensions and inform both firms’ monetization strategies and policy discussions of consumer welfare in generative AI markets.