Person:

Fagan, Mark

7:00 AM, the alarm goes off. 8:00 AM, you have a cup of coffee, scan the news, and check your email on your phone. During that single hour you have interacted with artificial intelligence (AI) numerous times. The coffee beans were likely harvested based on an AI algorithm. The news feed…curated by AI. The ads that came with the news…AI. The facial recognition to open your phone…AI. And more. AI is everywhere in your personal life. It is also increasingly used by your government.

From the RMV to the IRS to the TSA to the FDA, government agencies seek to provide quality services to its constituents, efficiently and with equity. Analytical tools have been introduced over the past 50 years to facilitate achieving this objective. These tools support an array of government functions from benefits distribution to public safety and security to financial policy. Based on the success of these efforts and the rapid development of AI technology, agencies are turning to AI to accelerate creating value for the public.

Artificial intelligence (AI) is the news topic of the 2020s. From ChatGPT writing essays to chatbots answering questions to algorithms identifying cancers, AI is impacting and often disrupting how we live our lives. AI is proposed or in place in every domain and touches every function. In government, AI is relevant for departments from education and transportation to national security and public safety. Within these organizations, AI can facilitate the hiring of new employees, providing interactive responses with constituents, planning workloads, identifying anomalies, and forecasting natural disasters, to name a few applications.

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.