For years, progress in artificial intelligence was closely tied to scaling laws, where increasing model size, dataset size, and compute power led to consistent performance improvements. Large-scale systems like GPT-4 and architectures such as Transformer architecture demonstrated that bigger models could achieve remarkable capabilities across language, vision, and multimodal tasks. However, recent developments suggest that simply increasing model size is no longer the most efficient or reliable path to better performance. The primary reason is that model performance is fundamentally constrained by the quality of the data it is trained on. High-quality datasets provide clear, relevant, and diverse signals that allow models to generalize effectively. In contrast, noisy, biased, or redundant data introduces ambiguity, leading to poor learning outcomes. Even the largest models struggle when trained on low-quality data because they tend to memorize noise rather than extract meaningful patterns.…