Every large language model you have used, from GPT to Llama to Claude, writes text the same way: one token at a time, left to right. To produce 128 tokens it runs the network 128 times in a row, and each step has to wait for the previous one. That sequential loop is the single biggest reason text generation is slow and expensive.

In October 2026, Stanford's Hazy Research group published Next-Concept Modeling (NCM) , a model that writes text a completely different way: it sketches the whole passage first, then refines it level by level, predicting every position of a level in parallel. The paper reports generation that is 10x to 31x faster than diffusion language models at comparable quality.

Social media quickly turned that into "NCM kills LLMs". That is not what the paper measured.…