The wrong kind of AI? Artificial intelligence and the future of labour demand

By Daron Acemoglu, P. Restrepo. 2019

514 citations recorded by the source corpus.

Artificial intelligence (AI) is set to influence every aspect of our lives, not least the way production is organised. AI, as a technology platform, can automate tasks previously performed by labour or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labour can be productively employed. The consequences of this choice have been stagnating labour demand, declining labour share in national income, rising inequality and lowering productivity growth. The current tendency is to develop AI in the direction of further automation, but this might mean missing out on the promise of the ‘right’ kind of AI, with better economic and social outcomes.

Subfields: Development, Economics and Econometrics, Economics & Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Development

1. Kun Yang
2. Liang Liu
3. Jun Liu
4. Hidemichi Fujii
5. Daniel S. Schiff
6. David H. Autor

In Economics and Econometrics

1. Silvia Massini
2. Kun Yang
3. Martin Kenney
4. Ilya Sutskever
5. Pamela Mishkin
6. Liang Liu
7. Jun Liu
8. Lucia Piscitello

In Economics & Operations Research

1. Kun Yang
2. Martin Kenney
3. Pamela Mishkin
4. Katya Klinova
5. Liang Liu
6. Jun Liu
7. Silvia Massini
8. Lucia Piscitello

Citations in the corpus

• Evaluating Large Language Models Trained on Code (2021, background): “Furthermore, as with many tools that substitute investments in capital for investments in labor (or increase the productivity of labor) (Frey, 2019; Acemoglu & Restrepo, 2020a;b), more sophisticated future code generation tools could potentially contribute to the displacement of some programmer or…”
• The Labor Market Impacts of Technological Change: From Unbridled Enthusiasm to Qualified Optimism to Vast Uncertainty (2022, background): “…we blunt or reshape the commercial incentives to use AI for socially counterproductive objectives such as displacing wo rkers, preying upon people’s cognitive and emotional frailties, or consolidating the power of governments or corporations to exercise social control (Acemoglu and Restrepo, 2020)?”
• Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies (2021, background): “Researchers, policymakers, and the public have become increasingly aware of issues related to labor displacement (Acemoglu and Restrepo 2019), autonomous vehicles (Bagloee et al. 2016), algorithmic bias (Caliskan et al. 2017), and privacy (Manheim and Kaplan 2018), amongst other issues (Mittelstadt…”
• AI and Shared Prosperity (2021, background): “AI is expected to continue this trend and may even accelerate it [3].”
• Digital technologies, innovation, and skills: emerging trajectories and challenges (2021, background): “, digital technologies will mainly lead to the elimination of some jobs (Acemoglu and Restrepo, 2019; Frey and Osborne, 2017; Korinek and Stiglitz, 2017); others are more optimistic, i.”
• Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel (2021, methodology): “…of machine learning, deep learning and natural language processing, existing studies commonly use industrial robots as a proxy for AI, possibly owing to robots’ physical properties, which makes them easier to track over time and space (Acemoglu and Restrepo, 2020b; Liu et al., 2020, 2021).”