Are young workers canaries in the AI coal mine?

"During the late 19th and early 20th century, coal miners in Europe and North America used canaries as living carbon monoxide alarms. Due to their high metabolism and sensitive respiratory system, these small, yellow songbirds succumbed to the invisible, odorless gas much faster than humans. As soon as the small cages strapped to their tool belts stopped chirping and chattering, the miners knew it was time to head back up."

"The results are, fittingly, rather alarming. So far, the widespread adoption of generative AI appears to be hitting entry-level workers harder than older, more experienced people in senior positions. The study also suggests that AI has a greater effect on employment opportunities for professions where the technology is capable of automating human labor, such as software development and customer service. In professions where AI augments and enhances human labor, on the other hand, employment opportunities have remained stable, or even increased."

"To get a clearer picture of how AI is expected to change the future of work, Big Think sat down with two of the study's three co-authors: Erik Brynjolfsson - Jerry Yang and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-Centered AI - and Bharat Chandar, postdoctoral researcher at the Stanford Digital Economy Lab and Institute for Human-Centered Artificial Intelligence."

Research from the Stanford Digital Economy Lab analyzes employment data to measure generative AI’s effects on the American labor market. Early findings indicate generative AI adoption is reducing employment opportunities more for entry-level workers than for experienced, senior employees. Professions where AI can automate tasks — for example, software development and customer service — show larger employment impacts. Professions where AI primarily augments human labor display stable or increased employment opportunities. The historical metaphor of coal miners using canaries highlights the concept of early warning signs for technological hazards. The research uses data-driven methods to trace these emerging labor-market patterns.

Read at Big Think