The Cooked Curve Hypothesis (travishiggins.substack.com)

The "Cooked Curve Hypothesis" explores the relationship between automation and job transformation, suggesting that as technologies like large language models (LLMs) increasingly automate tasks, jobs become "fully cooked," no longer requiring human involvement. This phenomenon accelerates the migration of human workers to new opportunities or industries, with LLMs already covering around 75% of tasks typically performed by computer programmers. Historical examples, such as the transition from manual farming to tractors and the automation of switchboard operators, illustrate the hypothesis: while automation displaces some jobs, it often creates new roles by re-bundling remaining tasks. This hypothesis is significant for the AI/ML community, as it underscores the rapid pace at which technology reshapes labor dynamics and the need for businesses and workers to adapt strategically. By understanding their position on the "cooked curve," professionals can better navigate their career paths, focusing on tasks that machines cannot easily replicate, thereby maintaining human relevance amid evolving technological landscapes. As AI continues to advance, awareness of these shifts will be crucial for optimizing resources, investments, and workforce allocations to foster resilience in an automated future.