Pawel Gmyrek

Artificial Intelligence (AI) and particularly generative AI (genAI) is a general-purpose technology with the potential to reshape economies and societies, through its effects on productivity, labor markets, and production structures. Unlike previous technologies, AI extends automation beyond physical and routine tasks to cognitive, linguistic, and decision-making functions. White-collar tasks face growing displacement pressure as capabilities expand into language and reasoning. Value creation is increasingly concentrated in intangible assets - software, research and development (R&D) and organizational capital - as reflected in their rising share of business investment and firm valuations across advanced economies. Depending on the pace and extent of AI diffusion, these effects can translate into economy-wide implications for growth, jobs, inequality, and structural transformation.

This paper investigates whether access to a general-purpose large language model (LLM) improves physicians’ clinical reasoning across diverse healthcare contexts, as well as the possible implications of using an LLM in healthcare settings. Using a randomized controlled trial with 249 physicians in Indonesia, Kenya, and the Netherlands, the study finds that LLM access enhances performance on standardized clinical vignettes in all three countries. The magnitude of improvement varies, with the largest gains observed in Kenya (+18%), followed by Indonesia (+10.7%) and the Netherlands (+7.2%).The results, however, reveal substantial heterogeneity. Performance distributions overlap, and some physicians with LLM access perform worse than those without, indicating that access alone does not guarantee improvement. Higher usage is associated with better outcomes, and less specialized physicians appear to benefit more, implying that LLMs may help reduce skill gaps. Importantly, the findings emphasize that LLMs function as complements rather than substitutes for clinical expertise. However, the study identifies important risks, including automation bias, hallucinations, and context misalignment, underscoring the importance of careful integration, training, and governance.The paper concludes that while LLMs can enhance clinical reasoning, their effectiveness depends critically on how they are implemented within healthcare systems. The paper recommends that policymakers prioritize structured integration of LLMs as decision-support tools, combined with targeted training, local validation, and safeguards against automation bias rather than relying on access alone. It also emphasizes the need for investment in infrastructure, continuous monitoring, clear liability frameworks, and inclusive governance to ensure equitable, safe, and context-appropriate deployment. It argues for the importance of social dialogue in managing the process.

Despite initial research about the biases and perceptions of large language models (LLMs), we lack evidence on how LLMs evaluate occupations, especially in comparison to human evaluators. In this paper, we present a systematic comparison of occupational evaluations by GPT-4 with those from an in-depth, high-quality and recent human respondents survey in the UK. Covering the full ISCO-08 occupational landscape, with 580 occupations and two distinct metrics (prestige and social value), our findings indicate that GPT-4 and human scores are highly correlated across all ISCO-08 major groups. At the same time, GPT-4 substantially under-or overestimates the occupational prestige and social value of many occupations, particularly for emerging digital and stigmatized or illicit occupations. Our analyses show both the potential and risk of using LLM-generated data for sociological and occupational research. We also discuss the policy implications of our findings for the integration of LLM tools into the world of work.