Data Integrity as the Terminal Constraint in AI-Driven Advertising: An Information-Theoretic Analysis of Conversion Fraud and Agentic Threat Evolution

Modern AI-driven advertising systems, including Google Performance Max and Meta Advan-tage+, rely on conversion event data as their primary training signal. This paper demonstrates that adversarial contamination of these signals constitutes the binding constraint on algorithmic advertising performance, a constraint that no amount of computational sophistication can overcome. We develop an information-theoretic framework showing that the data processing inequality imposes a hard ceiling on optimization quality: when conversion data is poisoned, algorithmic decisions inherit and amplify the corruption. We formalize a four-generation taxonomy of advertising fraud evolution, from volumetric bot trac (Generation 1) through sophisticated invalid trac (Generation 2), conversion-level data poisoning (Generation 3), to an emerging class of autonomous agent collusion (Generation 4). Applying results from PAC learning theory, we prove that at empirically observed fraud rates of 25% in lead generation (rising to 6785% in audience networks), sample complexity inates by 4x to over 100x, pushing many campaigns past the threshold of learnable signal. Economic analysis reveals that the total cost extends well beyond the estimated $84 billion in direct annual losses (projected to $172 billion by 2028): corrupted optimization data creates a compounding feedback loop in which platforms allocate increasing budget to fraudulent sources, producing a measurable divergence between reported and veried returns. We propose signal hygiene, a verication paradigm that validates the event rather than surveilling the user, as the structurally privacy-compatible alternative to surveillancebased fraud detection. This paradigm aligns with the data quality foundations established in the M.A.T.H. Framework [1] and resolves the regulatory paradox between privacy compliance and fraud mitigation.