Recognized as an emerging discipline in the 1970s, computational chemistry aids hands-on experience in concretizing abstract quantities in chemistry, such as reaction barrier, electron density, and wavefunction. However, due to the organization of chemistry curricula, chemistry students typically lack a strong programming background by the time they finish chemistry-focused classes. Thanks to their natural language interface, LLMs can bridge this gap for chemistry students, alleviating the burden of coding for students while still delivering the central knowledge and hands-on experience of computational chemistry. Similar to how students and researchers in the 1970s could not foresee the growth of computational chemistry over the next 50 years, we are only scratching the surface of use cases for LLMs in computation. In the future, we envision LLMs cultivating a new generation of chemists more capable of computation on daily experiments validation empowered by LLMs.