Beyond job displacement: how AI could reshape the value of human expertise
David Autor & Neil Thompson
This paper introduces an 'expertise framework', arguing that the impact of AI on the labour market depends on whether it automates expert or non-expert tasks within occupations.
The authors examine two scenarios: gradual expertise transitions versus complete human labour obsolescence, and propose policy measures including universal basic capital, wage insurance and AI-powered training innovations.
What's there to fear in a world with transformative AI? With the right policy, nothing.
Betsey Stevenson
The impact of transformative AI on societal well-being hinges on policy choices that address three interconnected challenges: how people can improve their lives if the value of human labour diminishes, how resources will be distributed, and how people will find meaning and purpose. The essay emphasises that productivity gains do not automatically translate into flourishing; societies must actively design policies that ensure AI-enabled prosperity is shared widely rather than being concentrated among capital owners.
Economic possibilities for artificial intelligence
Gabriel Unger
In order to manage the arrival of Transformative AI, the economic profession needs to change its focus from short-term to long-term forecasting. Three essential pillars for this transition have been identified: defining a coherent vision for an AI-driven future, developing new theories of AI-led growth that account for radical labour displacement, and redesigning social institutions to foster human connection.
Preserving fiscal stability in the age of transformative AI
Anton Korinek & Lee Lockwood
The paper addresses the pressing issue of preserving and expanding fiscal resources in order to manage the significant economic shifts brought about by artificial intelligence. The authors contend that, although AI has the potential to stimulate unprecedented growth, it also poses a threat to traditional tax bases and could necessitate substantial public expenditure to mitigate labour displacement and uphold social stability.
Women worry, men adopt: how gendered perceptions shape the use of generative AI
Fabian Stephany & Jędrzej Duszyński
The paper investigates the significant gender gap in the adoption of generative AI, finding that women use these tools less than men due to differing perceptions of societal risks. Using nationally representative UK survey data, the authors demonstrate that, for women, concerns about mental health, privacy and labour displacement are more predictive of non-adoption than digital literacy or educational level. The study suggests that, to ensure equitable participation in this field, it is essential to address ethical and social concerns.