Report: Hugging Face Business Breakdown & Founding Story

## Key Risks ### Biases and Limitations in Datasets AI models, particularly NLP, have long struggled with biases in datasets used to build them. Human biases such as overgeneralization, stereotypes, prejudice, selection bias, and the halo effect are prevalent in the real world. Large language models are trained with vast volumes of data, often scraped from the internet, that could contain some of these biases. For instance, researchers found that men are over-represented in online news articles and on Twitter conversations. So, machine learning models trained on such datasets could have implicit gender biases. … Hugging Face acknowledged the issue and even showed how some models in its library, such as BERT, contain implicit biases. It put some checks and fine-tuning in place, including the Model Card feature intended to accompany every model on the platform and highlight its potential biases. However, these measures may not be enough since they warn users but do not fully tackle them. ### Trends to Commercialize Language Models Hugging Face hosts over 2 million models as of January 2026. However, some popular architectures like GPT-3, Jurassic-1, and Megatron-Turing NLG are not available in the company’s library because companies, such as OpenAI and AI21 Labs, began commercializing their proprietary models. Commercialized models usually contain more parameters than open-source models and can perform more advanced tasks. If the commercialized model trend continues, some of the content in Hugging Face’s library could become obsolete. Models they can host would become less accurate, have fewer parameters, and could not perform advanced tasks as well as commercialized models, driving users away from the platform.