This paper challenges the common interpretation of AI models' performance on abstract reasoning benchmarks like ARC, hypothesizing that visual perception limitations, not reasoning deficiencies, are the primary bottleneck. It introduces a two-stage pipeline to separate perception and reasoning, revealing that most model failures stem from perception errors and demonstrating significant performance improvements.