Policy Options for Addressing AI’s Impact on Employment: A Scenario Planning Approach

Abstract

Generative artificial intelligence is transforming computer programming into a frontier case of high-skill automation. Recent advances in large language models and agentic systems allow automation of many coding tasks previously performed exclusively by highly educated professionals. Early evidence shows substantial productivity gains from AI coding tools and declining demand for entry-level programming roles, even as official projections continue to forecast robust aggregate growth for software developers. This paper applies a structured scenario-planning framework to analyze how AI capability growth and organisational adoption speed jointly shape four plausible futures for programming employment: AI Dominates, Wake Up, Dressed but Nowhere to Go, and AI What? Stand Still. Drawing on labour-economics theory, international statistics, and developer-level evidence, it quantifies potential impacts on employment, wages, skill formation, and public finances, and derives a portfolio of general and scenario-specific policy responses. The analysis emphasises that while the magnitude of disruption is uncertain, the direction is clear: routine programming tasks are increasingly automated, apprenticeship ladders are under strain, and returns to complementary skills in system design, AI governance, and socio-technical integration are rising. The paper concludes with concrete recommendations for policymakers—including AI-augmentation training programmes, curriculum reform, scenario-based career guidance, conditional AI-adoption incentives, and early-warning indicators to trigger larger-scale transition measures.