Artificial intelligence (AI) is revolutionizing computational chemistry by enhancing quantum chemistry calculations and molecular modeling. Foundational principles of computational chemistry, including the hierarchical structure of methods across length and time scales, frame the discussion of challenges in accurately characterizing complex systems. AI offers a solution to the computational cost and complexity of traditional quantum mechanical approaches, enabling innovations such as artificial intelligence potentials and the acceleration of molecular dynamics simulations. Key applications of AI in computational chemistry include enhanced sampling techniques, generative models for compound design, and reaction pathway predictions. Despite these advancements, challenges remain in incorporating physical meaning into AI models, improving data quality, and exploring broader chemical spaces. By integrating AI with computational chemistry, researchers can achieve more efficient simulations, produce high-quality datasets, and gain novel insights into molecular and material systems. Advancing this field will require interdisciplinary collaboration and the development of new AI algorithms to address the existing limitations and unlock further potential.