Will New Technologies Decrease the Skills Gap?
Many believed that the rise of technology in the late 80s and 90s would lead to a shift in power, with nerds and geeks replacing the jocks as the dominant force in society. And they were right. Over the past three decades, the nerds have won the economic competition, with knowledge-based industries like IT, finance, and bio taking the lead. As a result, being exceptional in areas like programming and math has become a pathway to wealth and success.
However, this increasing emphasis on human capital has also widened the gap between the skilled and the average worker. The wealth and status that come with being a techie have created an unequal society. But is this the inevitable consequence of technological advancement?
Generative AI, a technology that is currently generating a lot of excitement, may actually help bridge this skills gap. Studies have shown that AI tools disproportionately benefit low performers, allowing them to improve their productivity and catch up to their higher-skilled counterparts. For customer support staff, the use of AI tools led to a 35 percent increase in resolutions per hour for lower-skilled workers, while the most skilled workers saw no increase in productivity.
Similar patterns were observed in other professions. Educated professionals using generative AI for writing tasks saw a reduction in productivity inequality, with lower-scoring participants benefiting more from AI assistance. Programmers of lesser experience or older age also experienced greater benefits with AI tools. Even in law and creative writing, AI assistance had an equalizing effect, benefiting those at the bottom of their class or with less inherent creativity.
These findings suggest that new technologies like generative AI have the potential to decrease the skills gap and create a more equitable society. While the impact of technology on inequality cannot be ignored, it’s important to consider how these new tools can be leveraged to level the playing field. With further research and implementation, AI may just prove to be the equalizer we need.
– Brynjolfsson, Li, and Raymond (2023)
– Noy and Zhang (2023)
– Peng et al. (2023)
– Choi and Schwarcz (2023)
– Doshi and Hauser (2023)