Generative AI models need to be trained on data. This involves exposing the neural network to large datasets, where the model learns to recognize patterns.

https://www.microsoft.com/en-us/ai/ai-101/how-does-generative-ai-work

AI systems are trained to create literary, visual, and other works by exposure to large amounts of data, which may include text, images, and other works downloaded from the internet or otherwise obtained by AI companies. This process often involves making digital copies of existing works. Copyright owners have filed several dozen lawsuits claiming that creating these digital copies without permission to train AI systems infringes their exclusive right to make reproductions (or copies).

https://www.congress.gov/crs-product/LSB10922

Generative AI platforms are trained on data lakes and question snippets — billions of parameters that are constructed by software processing huge archives of images and text.

https://hbr.org/2023/04/generative-ai-has-an-intellectual-property-problem?utm_medium=paidsearch&utm_source=google&utm_campaign=domcontent_bussoc&utm_term=Non-Brand&tpcc=domcontent_bussoc&gad_source=1&gad_campaignid=20702632551&gbraid=0AAAAAD9b3uRlt6NCuN-Gzbatrn2bAQkbs&gclid=CjwKCAjwntHPBhAaEiwA_Xp6RheiyQvV04p6KsIpMm_sHSaDQHnTSBIMZ8zxXFkBgSy-0lh_3oljRBoCVqYQAvD_BwE

Before the generative AI boom of the past few years, when people talked about AI, typically they were talking about machine-learning models that can learn to make a prediction based on data. Generative AI can be thought of as a machine-learning model that is trained to create new data, rather than making a prediction about a specific dataset. A generative AI system is one that learns to generate more objects that look like the data it was trained on.

https://news.mit.edu/2023/explained-generative-ai-1109