Models of generative artificial intelligence are becoming more and more available to business and private users every day and, both experimentally and with the aim of creating products for the end user, they are also starting to be used in the creation of literary works. The views of experts from the fields of computer science, philology and various artistic fields are divided regarding the future impact of artificial intelligence on creativity. The aim of this paper is to analyze the ways in which generative artificial intelligence models process and generate natural language in order to determine the limits of their possibilities and potential threats to author's work, rights and ethical obligations towards end users. The paper provides an overview of the development of technology called artificial intelligence and its main mechanisms of data processing, namely data mining and text mining, and recurrent neural networks whose non-linear data processing enabled the creation of higher quality output. As the focus of the research is on literature, special attention is paid to computational linguistics and work with natural language in terms of the possibility of generation, translation, analysis and stylistic features of the text that the artificial intelligence model can offer. The processes of processing, understanding and creation in natural language are accompanied by analysis and comparisons with the ways in which people learn, adopt and use language, as well as how ideas are created and considerations of what creativity actually is. Regardless of the quality of the offered results, the problem of copyright arises for the data used for training and the generated texts. Currently, attempts are being made to devise fair solutions for the distribution of rights or compensation to sources and users, and this includes ethical issues of fair use, misuse of models or results. Generative artificial intelligence models are capable of offering texts that are linguistically meaningful but artistically worthless. The speed of text generation is profitable if the readership does not require the author's intention, which is rarely the case, and the whole process of setting the goal, directing and correcting the results still requires human supervision.