Platform Competition and Market Tipping with Preference Alignment

The growing economic relevance of platforms has intensified interest in the conditions under which platform markets tip toward dominance. We study platform competition in a setting where two platforms strategically invest to influence and align user preferences with their offerings, capturing strategies, such as persuasive advertising, or Artificial Intelligence (AI) generated persuasive content. We develop an analytical model showing that market tipping can arise even when symmetric platforms would coexist in the absence of such investments, indicating that preference-alignment fundamentally alters standard tipping conditions. We complement the analytical framework with an agent-based model that captures dynamic adoption and asymmetric competitive scenarios, providing additional insights into tipping dynamics. Our results show that platforms invest in preference-alignment primarily under competitive pressure, and that tipping depends jointly on investment intensity, the strength of network effects, and investment costs. Crucially, investment levels alone do not predict market outcomes: identical investments can lead to opposite market structures across environments with different network effects and cost parameters. Technologies, such as AI, that lower the cost of influence and persuasion, increase the likelihood of tipping by lowering the network effect threshold required for dominance. Overall, the findings identify new ex-ante and ex-post conditions for platform market tipping and highlight the strategic importance of preference influence and alignment in platform competition.