A recent paper published in Nature highlights that larger and more instructable language models (LLMs) become less reliable as they are scaled up. Amid the chaos at OpenAI, the study finds that these models, which are trained on more data, tend to attempt more questions they cannot answer accurately, increasing the likelihood of errors. Unlike earlier models that avoided questions they couldn't answer, newer models are more prone to fabricating responses. This phenomenon underscores the need for a shift in AI design towards enhancing reliability. Experts, including Carissa Veliz, note that large language models do not know the limits of their own knowledge.