Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. We'll demonstrate several examples how a deep neural network trained on quantum mechanical (QM) DFT calculations can learn an accurate and fully transferable potential for organic molecules and materials. In a recent paper, (1) we introduced ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies), or ANI for short. ANI is a new method designed with the intent of developing fully transferrable neural network potentials that utilize symmetry functions to build single-atom atomic environment vectors as a molecular representation. Through a series of case studies, we'll show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems than those included in the training dataset, with root mean square errors as low as 0.56 kcal/mol. As the results clearly show, the ANI method is a potential game changer for molecular simulation.