A/B testing is widely regarded as the gold standard for data-driven product decisions. Teams trust it to reveal what users prefer and to guide design improvements objectively. Yet many mature products slowly become more addictive, less satisfying, and harder to use—despite constant experimentation. This article examines a specific, structural problem in A/B testing: why it systematically favors short-term behavioral exploitation over long-term user value, even when experiments are run correctly.
AI
How Recommendation Systems Quietly Reduce Exploration and Lock Users Into Narrow Behavior Patterns
Recommendation systems are designed to help users discover relevant content efficiently. Over time, however, many users notice that platforms begin to feel repetitive, predictable, and surprisingly narrow. This is not an accident or a flaw in implementation—it is a structural outcome of how modern recommendation systems are optimized. This article examines the specific mechanisms that cause exploration to shrink, how feedback loops form, and why these systems naturally converge toward behavioral lock-in.