AI has created more winners than losers in the job market, but attention must be given to displaced workers.
Highly paid professions like data scientists and mathematicians are more exposed to the potential augmentation of their tasks by LLMs.
Deep dives
The Impact of AI on Jobs
There is concern that AI may replace jobs in the future. A research paper titled 'GPTs are general purpose technologies' explores the impact of large language models (LLMs) on the labor market. The paper's co-author, Daniel Rock, discusses the professions that may be affected by the proliferation of LLMs.
AI's Historical Impact on the Job Market
Previous waves of AI and ML have impacted the job market, but not through wholesale automation. Winners tend to outnumber losers, but attention must be given to displaced workers. The market still goes through transformations, creating both challenges and opportunities.
Exposure of Jobs to LLMs
Exposure to LLMs varies across different professions. Highly paid jobs such as data scientists, blockchain engineers, and mathematicians are more exposed due to the potential of augmenting their tasks. Clerical roles and switchboard operators also show high exposure to automation.
Risks, Future Outlook, and Policy Considerations
While the future impact of LLMs is uncertain, there are potential risks such as algorithmic bias and misinformation generation. It is important to hold people responsible for the output of LLMs and establish regulations to prevent harmful use. Long-term policy implications and integration of LLMs in education are areas of interest for further research.
On today’s episode, we have Daniel Rock, an Assistant Professor of Operations Information and Decisions at the Wharton School of the University of Pennsylvania. Daniel’s research focuses on the economics of AI and ML, specifically how digital technologies are changing the economy.
Daniel discussed how AI has disrupted the job market in the past years. He also explained that it had created more winners than losers.
Daniel spoke about the empirical study he and his coauthors did to quantify the threat LLMs pose to professionals. He shared how they used the O-NET dataset and the BLS occupational employment survey to measure the impact of LLMs on different professions. Using the radiology profession as an example, he listed tasks that LLMs could assume.
Daniel broadly highlighted professions that are most and least exposed to LLMs proliferation. He also spoke about the risks of LLMs and his thoughts on implementing policies for regulating LLMs.
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