WenJun Zhang School of Life Sciences, Sun Yat-sen University, Guangzhou, China Selforganizology ISSN 2410-0080 http://www.iaees.org/publications/journals/selforganizology/online-version.asp 2027 14 3-4 34 46 International Academy of Ecology and Environmental Sciences Hong Kong 21 April 2026 24 April 2026 1 December 2027 artificial consciousness generative AI large language models (LLMs) hard problem of consciousness computational functionalism Integrated Information Theory agnosticism semantic pareidolia The rapid advancement of generative artificial intelligence, particularly large language models, has reignited a profound and longstanding debate: can machines be conscious? This paper presents a comprehensive cross-disciplinary literature review and a critical analysis addressing whether generative AI can produce consciousness. The review synthesizes foundational theories of consciousness - including the hard problem, Integrated Information Theory, Global Workspace Theory, Higher-Order Theories, and Predictive Processing - alongside the current capabilities and development trajectories of generative AI. It systematically examines both optimistic and skeptical empirical and theoretical positions on machine consciousness, revealing a deeply fragmented and inconclusive landscape. Drawing on this review, I critically analyze the core issue from multiple dimensions. I argue that equating intelligent behavior with subjective experience conflates the easy and hard problems of consciousness, and that computational functionalism, while offering a logically coherent possibility, relies on an unproven metaphysical premise that bypasses the explanatory gap. I further identify a structural asymmetry in the evidence: behavioral indicators are multiply interpretable, while direct internal-state analyses consistently fail to find markers of consciousness, and the profound disunity among leading theories undermines checklist-based approaches. Given the absence of a deep explanation for consciousness, I defend agnosticism as the most epistemically honest stance and challenge the emergentist claim that scaling alone will bridge the gap, highlighting the potential necessity of continual learning and embodiment. Finally, I advocate for an ethical precautionary framework that recognizes the non-negligible probability of machine consciousness and urges governance structures that proceed responsibly under fundamental uncertainty. http://www.iaees.org/publications/journals/selforganizology/articles/2027-14(3-4)/can-generative-AI-produce-consciousness.pdf