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When AI Models Go Wrong: Understanding Model Drift and Data Decay in Real-World Systems

AI models inevitably lose accuracy over time due to model drift, which stems from concept drift and data drift as real-world data diverges from training data. Data decay—degradation of data quality—further accelerates this degradation, affecting everything from call‑spam detection to generative language models. Scaling compute or adding more data centers does not solve the problem because reality changes faster than datasets can be refreshed. Organizations must adopt monitoring, retraining, and techniques like retrieval‑augmented generation to keep AI reliable.