The popularization of frontier scientific achievements plays a pivotal role in enhancing public understanding and engagementwith science. With the widespread application of generative artificial intelligence（GenAI），its potential performance of popularizing frontier scientific achievements has become a critical topic. Unlike the generation of general science popularization content，GenAI encounters distinct challenges when addressing topics related to frontier scientific achievements. Firstly，there is a paucity of sufficient online textual data for GenAI training.Secondly，these topics often entail relatively higher uncertainty，necessitating a more cautious approach to content formulation. This study conducts a comparative analysis of science popularization content on frontier scientific achievements generated by traditional science popularizers and that produced by GenAI models. The results indicate no significant disparities between the two in terms of scientific accuracy and source reliability. However，the content generated by GenAI models tends to exhibit a more positive emotional tone compared to that produced by traditional science popularizers. Based on these results，this study suggests that while the risk of GenAI disseminating misinformation may be limited，it has the potential to reinforce existing stereotypes. Consequently，GenAI can not entirely supplant traditional science popularizers within the current framework of science popularization.