Key facts:
1. Priya is a biomedical data curator in her mid-20s from a poor background in Uttar Pradesh, India. 2. She has a bachelor's degree in Biotechnology and her job involves annotating RNA sequencing data from scientific papers. 3. The author tried using GPT-4 to perform Priya's job and achieved the correct result in less time and at a lower cost. 4. The author speculates that Priya may lose her job within six months due to automation. 5. The author expresses concern about their own long-term career prospects in software engineering because of GPT-4.
Logical fallacies: 1. Hasty Generalization: The author assumes that GPT-4 will make Priya's job obsolete based on a single successful trial. 2. Slippery Slope: The author assumes that GPT-4's impact on Priya's job will lead to her losing her job and moving back home, and potentially to the decline of the author's own career prospects in software engineering.
Counter arguments:
1. GPT-4 may not be able to handle all aspects of Priya's job or maintain consistent quality, which could still necessitate human intervention. 2. The advent of GPT-4 could lead to new job opportunities that require both domain expertise and an understanding of the technology. 3. As technology progresses, there is potential for job retraining and upskilling to adapt to new demands in the workforce.
