Contrastive learning has advantages for non-parallel voice conversion, but the previous conversion results could be better and more preserved. In previous techniques, negative samples were randomly selected in the features vector from different locations. A positive example could not be effectively pushed toward the query examples. We present contrastive learning in non-parallel voice conversion to solve this problem using hard negative examples. We named it CNEG-VC. Specifically, we teach the generator to generate negative examples. Our proposed generator has specific features. First, Instance- wise negative examples are generated based on voice input. Second, when taught with an adversarial loss, it can produce hard negative examples. The generator significantly improves non parallel voice conversion performance. Our CNEG-VC achieved state-of-the-art results by outperforming previous techniques.