In this episode, we dive into the fascinating world of large language models (LLMs) and their potential to revolutionize various professions. Our guest, Daniel Rock, co-author of the paper "GPTs are general-purpose technologies, an early look at the labor market's impact potential for LLMs," shares his insights on the impact of LLMs on the labor market and the implications for different occupations.
Historically, AI and machine learning (ML) have influenced the job market, but the overall effect has tended to create more winners than losers. However, concerns about job displacement and the need to support those affected still persist. We explore the scholarship on measuring the impact of technology on jobs, including the work of Geronath Amoglu and David Otter, who have extensively studied skill bias and technical change.
The paper "GPTs are general-purpose technologies" takes a deep dive into the exposure of different professions to LLMs. The exposure is measured by assessing whether LLMs can double the productivity of a worker in a specific task without compromising quality. We discuss the professions that are most exposed to LLMs, such as data scientists, blockchain engineers, mathematicians, and lawyers. Surprisingly, even clerical roles and switchboard operators are not immune to the potential impact.
On the other hand, we also explore the occupations that are least exposed to LLMs, including skilled trade roles like electricians and plumbers.
While the future impact of LLMs on the job market remains uncertain, there is immense potential for increased productivity and customization in education. We delve into the need for policy and regulation to address risks and ensure responsible use of AI.
Throughout the conversation, Daniel Rock shares his optimism about the potential benefits of AI and LLMs. However, he emphasizes the importance of careful integration and management of these technologies to maximize their positive impact.
Don't miss this thought-provoking episode as we explore the transformative potential of large language models on the labor market. Follow Daniel Rock on Twitter (@DanielRock) and learn more about his generative AI startup, WorkElex.
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.