Generative AI in the Post-Transformer Era: Advances, Ethical Dilemmas, and Future Directions

Abstract

Generative Artificial Intelligence (GenAI) has significantly influenced the advancement of machine learning and deep learning, particularly through Transformer-based models that have dominated the field over the past few years. These models, like GPT and BERT, revolutionized tasks such as text generation, summarization, and translation. However, as the limitations of Transformers—such as computational inefficiency, hallucinations, and lack of reasoning—become more evident, researchers are exploring alternative architectures and hybrid models. The emergence of State Space Models, Diffusion Models, and retrieval-augmented techniques marks the onset of the post-Transformer era. This shift is not merely architectural but also ethical and practical.

As generative models grow in capability and societal impact, critical concerns around misinformation, bias, environmental cost, and intellectual property have surfaced. Addressing these challenges requires innovations in ethical AI design, mechanisms for hallucination mitigation, energy-efficient computation, and strategies to foster collaborative human-AI creativity. This review paper provides an overview of these recent advances beyond Transformer architectures, examines the pressing ethical dilemmas posed by GenAI, and outlines future research directions. The goal is to inform a responsible, sustainable evolution of generative systems that balances innovation with safety, inclusiveness, and interpretability.