GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

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Introduction

Large Language Models (LLMs) like GPT-4 have made significant strides in recent years, enabling a wide range of applications in various industries. However, as LLMs and LLM-powered software continue to evolve, their impact on the labor market remains a subject of considerable interest and debate. In this blog post, we explore the potential labor market implications of LLMs based on recent research findings.

Key Findings

1. Higher education levels equate to higher exposure: Occupations requiring advanced degrees show the highest exposure to LLMs and LLM-powered software. Those requiring some college or an associate's degree also exhibit relatively high exposure, while occupations requiring no formal educational credential or only a high school diploma show the lowest exposure.

2. Occupations with the most training face significant exposure: Survey researchers, writers and authors, interpreters and translators, public relations specialists, and animal scientists show very high exposure across all metrics. Mathematicians also exhibit maximum exposure according to the models.

3. High-exposure occupations span various fields: Accountants and auditors, blockchain engineers, court reporters and simultaneous captioners, proofreaders and copy markers, and correspondence clerks all show high exposure, between 90-100% according to the models.

4. "Fully exposed" occupations: The 15 occupations labeled as "fully exposed" by humans include mathematicians, tax preparers, writers and authors, interpreters and translators, public relations specialists, animal scientists, blockchain engineers, court reporters and simultaneous captioners, financial quantitative analysts, and web and digital interface designers.

5. Broad workforce impact: LLMs and LLM-powered software could potentially impact around 80% of the U.S. workforce, with 66% of workers facing 10-50% of their tasks being impacted, and 19% facing over 50% of tasks impacted. The technology could also significantly accelerate 47-56% of worker tasks in the U.S. without compromising quality.

6. Wage levels: Impacts are projected across all wage levels, but higher-income occupations may face greater exposure in terms of the share of tasks that could be impacted. The findings suggest LLMs and associated technologies may significantly transform work at all skill levels, augmenting human capabilities beyond just automating routine tasks.

7. Timeline: The results do not predict the timeline of these technological impacts, as they depend on technology development, economic, policy, and social factors.

Selected Papers and Takeaways

Recent studies highlight risks, ethical concerns, and potential economic benefits of LLMs. Some key points from these papers include:

1. Risks and ethical concerns: Tolan et al. (2022) and Weidinger et al. (2021) discuss privacy and security threats, as well as job disruption and social and economic impacts.

2. AI and the labor market: Webb (2020) reviews wage inequality, economic growth, and job polarization and disruption, arguing that AI could increase wage inequality in the short term, with uncertain long-term effects.

3. AI adoption by firms: Brundage et al. (2022) and Kroff et al. (2022) find that firms adopting AI technologies tend to be larger, more productive, and pay higher wages, while also posing risks of job disruption.

4. AI risk taxonomy: Pesole et al. (2022) propose a taxonomy of AI risks, calling for proactive identification and management of these risks to ensure safe and ethical AI development.

5. AI, economic growth, and productivity: Zolas et al. (2021) find that AI investment correlates with higher growth and productivity, suggesting competitive advantages for AI leaders, while also highlighting the need to manage risks related to job displacement and inequality.

   Conclusion

   Large Language Models and LLM-powered software hold immense potential to reshape the labor market, affecting a significant portion of the workforce. While these advanced technologies may bring about increased efficiency and productivity, they also present challenges in terms of job disruption, wage inequality, and ethical concerns. The timeline for these impacts remains uncertain, as it depends on various factors, including technology development and societal adoption.

   To ensure the safe and fair development and use of LLMs, researchers and policymakers must proactively address the potential risks and ethical concerns associated with these technologies. This will involve ongoing dialogue, collaboration, and the development of robust policies that mitigate negative consequences while maximizing the benefits that LLMs can offer across industries and skill levels. As we continue to explore the potential of LLMs and LLM-powered software, it is crucial that we strike a balance between innovation and responsible, equitable development.

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