<p>This article examines how artificial intelligence restructures the opportunity architecture of tax evasion in emerging economies, creating conditions that systematically overwhelm traditional financial control systems. Drawing on Routine Activity Theory and Rational Choice Theory, the analysis demonstrates that AI does not produce novel forms of tax crime but transforms the speed, scale, anonymity, and adaptability of existing evasion strategies. Three illustrative scenarios, constructed from documented enforcement patterns and secondary evidence — AI-assisted invoice manipulation, algorithmic shell company networks, and cross-border digital income masking — reveal a structural mismatch between adaptive AI-enabled offender strategies and the reactive, rule-based control systems deployed by tax authorities. A structured comparison of a developed-economy benchmark (the United States) and a large emerging economy (China) demonstrates that the entry points for AI-enabled evasion, and the regulatory responses available to counter it, diverge systematically with tax structure and administrative capacity. The article concludes that effective responses demand a shift from static compliance frameworks to adaptive, AI-integrated monitoring, cross-jurisdictional data-sharing, and regulatory redesign grounded in real-time risk assessment.</p>