ChatGPT in Deep Time: Technology and Temporality in Kate Mildenhall's The Hummingbird Effect

In the last decade, developments in the fields of machine learning and natural language processing have resulted in the production of increasingly complex text generation programs like OpenAI’s ChatGPT and Anthropic’s Claude. These programs, often referred to as large language models (LLMs), are driven by complex layers of algorithms that are trained upon enormous sets of textual data to create statistical models that can output text which closely resembles the human writing they have been trained upon. While the outputs of LLMs are growing more and more sophisticated, the algorithms they are built on inherently lack direct access to embodied experience and have no understanding of what it means to operate in human time. As a result, LLMs struggle with temporality when producing narratives and their texts often exhibit temporal disorder and lack progression and causality. Several recent novels have experimented with incorporating text generated by LLMs, including Kate Mildenhall’s The Hummingbird Effect (2023), in which she weaves conversations with a language model, generated through experimentation with ChatGPT, amongst multiple interconnected timelines. Mildenhall uses these conversations—and the novel as a whole—to explore temporality, connectedness, and the potential of new innovations to impact the world. These explorations with the experience of time are framed by the deep time perspective of an ever-present river. This paper examines how the narrative structure of The Hummingbird Effect incorporates machine-generated text to investigate human understanding of time in the twenty-first century, and the ways in which technology impacts literary temporality.

N/A

InASA 2025 Biennial Conference: Australian Studies in the 21st Century: Human and More-Than-Human Worlds

N/A

N/A

N/A

N/A

1