AI has made it dramatically cheaper and faster to produce convincing misinformation at scale. Large language models can generate plausible-sounding articles, social media posts, and comments on any topic. Image generators can create photorealistic images of events that never happened. These tools lower the barrier to entry for anyone who wants to flood information environments with false or misleading content - whether for political manipulation, financial fraud, or simple attention-seeking. The challenge isn't just individual pieces of false content but the broader effect on the information ecosystem. When anyone can produce unlimited convincing content, the signal-to-noise ratio deteriorates for everyone. Trust in all content - including authentic reporting and genuine evidence - erodes. This "liar's dividend" means that even real evidence can be dismissed as AI-generated. Detection tools exist but face a fundamental asymmetry: generation is getting cheaper and more convincing faster than detection is improving. Provenance solutions - tracking where content came from and how it was created - offer a more promising long-term approach than trying to detect fakes after the fact. For businesses that create, distribute, or rely on content, the degradation of information integrity is both a reputational risk and an operational challenge that requires active management.