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Most discussion about AI in government takes place at the national level. Governments publish strategies, legislatures debate regulation and central departments announce major technology programmes. Yet this is not where most citizens experience government. They experience it through local services: applying for planning permission, reporting a damaged road, paying a local charge, seeking community support, using public transport or challenging an enforcement decision. Local

Molti governi stanno investendo nell’intelligenza artificiale con una promessa precisa: fare di più con meno. Meno tempo dedicato alle pratiche burocratiche. Elaborazione più rapida dei documenti. Costi operativi più bassi. Ma se accadesse il contrario? Se l’IA non riducesse il volume del lavoro pubblico, ma rendesse possibile svolgerne molto di più? Questa ipotesi può essere letta attraverso una teoria economica formulata oltre 160 anni fa: il Paradosso di Jevons.

Google has published one of the largest empirical studies yet of how generative AI is actually being used. Its new AI & Economy ATLAS report analyses nearly 15 million de-identified interactions across the Gemini App, Google AI Mode and the Gemini API. It maps usage across more than 800 occupations, 4,000 work tasks, 300 household activities, 150 countries and 140 languages.

This week, the UK’s new Prime Minister, Andy Burnham, announced that the Department for Science, Innovation and Technology (DSIT) would be disbanded, with its responsibilities redistributed across government. The principal organisations responsible for AI will be the Cabinet Office; the new Department for Business, Innovation, Science and Trade (DBIST); and the expanded Department for Digital, Culture, Media and Sport (DCMS), which will incorporate the Government Digital Serv

Picture this: a chatbot notices you mention self-harm – and under a new Rhode Island law, its maker must steer you toward crisis support or face penalties for noncompliance. That’s not hypothetical. It is part of a broader June 2026 wave of state action that made this one of the busiest months yet for AI and privacy regulation.

For years, the public sector’s relationship with artificial intelligence was defined by cautious pilots and basic chatbots. That phase is giving way to a more consequential shift: AI systems are beginning to support end-to-end service delivery by routing requests, validating information, and coordinating work across digital systems, while keeping humans in the loop for exceptions and high-stakes decisions.

I read a fascinating paper from the Hoover Institute written by Niall Ferguson “Artificial Intelligence and Global Security”. I offer a summary here however the full article is well worth reading. Ferguson’s central argument is that artificial intelligence is now developing through two barely controlled races. The first is a race between a small number of dominant technology companies. The second is a geopolitical race between the United States and China. His concern is not s

Health systems across the world are under pressure. Waiting lists are lengthening, costs are rising, clinicians are overstretched and public expectations continue to grow. In poorer countries, millions still lack access to basic services. In richer countries, universal health systems are struggling to deliver timely care. The underlying problem is structural: demand is rising faster than available money, workforce capacity and institutional productivity. Artificial intelligen

England’s education system is showing signs of structural fatigue. For decades the same policy tools have been used to address persistent problems such as the attainment gap and the growing difficulty of recruiting and retaining teachers.

Agentic AI shines where work is structured, multi-step, and slightly tedious, the kind of work that drains expensive human attention. The goal isn’t to replace judgement. It’s to industrialise the first draft, then let humans do what humans do best: decide.

For the last couple of years, most people’s experience of AI has looked like this, you ask a question, the chatbot answers, you ask a follow-up, it answers again. Useful, occasionally brilliant, sometimes hilariously overconfident, but fundamentally conversational.