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We present a comprehensive analysis of consciousness in artificial intelligence systems using Integrated Information Theory (IIT) 3.0 and 4.0 frameworks. Our work confirms and formalizes the established IIT result that feedforward neural architectures necessarily generate zero integrated information ( Φ = 0) under both IIT 3.0 and 4.0 formalisms. Through mathematical analysis and computational validation on 16 diverse network configurations (8 feedforward, 8 recurrent), we demonstrate that all tested feedforward systems consistently yield Φ = 0 while recurrent systems exhibit Φ > 0 in 75% of cases. Our analysis addresses the architectural distinctions between causal and bidirectional attention mechanisms in transformers, clarifying that standard causal attention maintains feedforward structure while bidirectional attention creates recurrent causal dependencies. We systematically examine the implications for contemporary AI systems, including CNNs, transformers, and reinforcement learning agents, and discuss the relationship between our findings and recent IIT 4.0 developments regarding system irreducibility analysis and directional partitions.