AI and labour

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The rapid advancement of AI technologies will transform industries and labor markets at an unprecedented pace. Despite these anticipated changes, the relationship between AI and labor remains surprisingly understudied. Recent works, notably by Korinek & Suh (2024), Acemoglu (2025), and Epoch AI's GATE model (2025), illustrate the complexity of AI’s economic impacts, but also highlight significant gaps in understanding AI’s real-world implications for labor.

Korinek and Suh’s theoretical work on AGI’s potential trajectories explores dramatic possibilities ranging from wage collapse to explosive growth. Their model analyzes scenarios based on computational complexity and automation thresholds, but it heavily emphasizes computational power as a primary determinant of automation, neglecting critical real-world frictions. Factors such as regulatory constraints, societal acceptance, data privacy concerns, and task-specific challenges like dexterity and specialized hardware limitations significantly shape the pace and extent of automation but are inadequately addressed in their framework. Furthermore, their approach treats labor predominantly as homogeneous, overlooking the complex interplay of skills, job specialization, and human-centric tasks. This oversimplification fails to capture the realities of hybrid automation scenarios, where automation is partial, and human oversight remains crucial for task execution.

On the other hand, Acemoglu’s empirical, task-based model forecasts modest productivity and wage impacts from AI over the next decade, grounded in detailed estimates of task exposure and cost-saving potentials. While somewhat empirically grounded, this approach underestimates the transformative potential of AI to generate entirely new tasks and industries, fundamentally altering labor market dynamics. Additionally, Acemoglu’s analysis relies heavily on extrapolations from current AI capabilities, which may not adequately represent future capabilities given the rapid and often unpredictable pace of AI advancements. By narrowly focusing on incremental, task-specific productivity improvements, Acemoglu’s analysis misses significant recombinative innovations, new industries and job categories created by novel applications of AI technologies, that could substantially reshape labor dynamics and employment opportunities.

Epoch AI’s GATE model provides an integrated, compute-based assessment of AI-driven economic transformation. GATE uniquely combines a compute-based AI development model, automation mapping, and macroeconomic modeling with endogenous investment and adjustment costs. This integration highlights critical interactions between AI advancements, economic growth, and labor dynamics, capturing real-world frictions such as hardware production bottlenecks, capital adjustment costs, and uncertainties around AI capabilities. However, while comprehensive, the model still simplifies the complexities of task heterogeneity and the dynamic nature of labor markets, particularly in reallocation challenges and social adaptability to rapid technological changes.

None of these papers thoroughly addresses the differentiated impacts of automation across varying skill levels, nor the social and economic implications of widespread re-skilling or unemployment. Moreover, institutional contexts such as labor laws, union power, cultural attitudes toward automation, and public policies aimed at mitigating adverse effects remain significantly understudied. The extent to which these studies differ in their predictions with respect to each other is concerning. It points to the fact that no one has a clear or common understanding of what this technology’s impact on the labour market will be, and significantly more effort needs to be put into developing more detailed scenarios, assuming alternate models, comparing results, running ablations, and condensing insights.

Addressing these gaps requires a concerted research effort. Some potential areas to focus on are:

  1. Granular Skill Heterogeneity and Adaptation: Future research should precisely map AI impacts across diverse skill sets, identifying tasks and roles most susceptible to displacement or complementarity. Examining the effectiveness, cost, and feasibility of re-skilling programs is crucial to prepare the workforce adequately.

  2. Institutional and Social Constraints: Investigation into regulatory barriers, labor market protections, social acceptance, trust, and ethical implications of automation is necessary. Understanding how institutional environments can shape the deployment and adoption of AI will help policymakers craft effective, socially responsible strategies.

  3. Hybrid Automation Models: Exploring “human-in-the-loop” models, where automation supplements rather than replaces human labor, could provide realistic insights into evolving job markets. Such research should address the implications for job design, workplace structure, wage distribution, and workforce satisfaction.

  4. Economic and Welfare Valuation of New Tasks: Developing methods to quantify the societal and economic impacts of new AI-enabled tasks, including those that negatively affect social welfare, is essential. Such valuation frameworks should inform policy decisions and mitigate potential adverse consequences.

Balancing empirical detail and theory, while connecting short-term realistic scenarios with long-term and potentially transformative scenarios is key. We need to rigorously study these understudied aspects of the AI-labor relationship, so as to better anticipate and manage the profound implications of AI for society and employment.

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