In this talk, we will rethink language generation beyond the autoregressive paradigm. We will introduce diffusion LLMs as a new family of models that generate through iterative denoising instead of strict left-to-right decoding. We will explain how masked diffusion corrupts text, learns to recover it, and enables parallel token generation. We will compare diffusion decoding with autoregressive decoding across speed, infilling, controllability, and planning. We will examine the practical challenges, including length control, denoising schedules, and blockwise generation. We will end by asking whether the future of language models is autoregressive, diffusion-based, or a hybrid of both.