Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.
Imagine still not realizing what a useful skill bullshitting is. Literally, hundreds of millions of people are professional bullshitters. So many people go do bullshit every day, all day. Having a machine that can produce the same or better bullshit than them frees them from suffering through doing all that bullshit. I can’t think of something that is more bullshit than pretending like there is no benefit from automating the bullshit out of our lives.
