In recent years, applications for computing degrees in the UK have dropped by about 10%, but one trend stands out: applications for artificial intelligence (AI) programs have surged by 15%. What’s even more striking is the rise in female applicants, who increased their numbers by 15%, outpacing the 12% growth among men. While AI-focused degrees still only make up around 5% of all computing applications, this shift reveals something bigger — students now see AI not just as a tech niche, but as a powerful tool that will shape tomorrow’s world.
This growing interest fits well with the UK government’s recent AI Opportunities Action Plan and broader ambitions to become a global leader in AI. But the big question remains: is the UK truly leading the AI race, or already falling behind?
The answer isn’t simple, but the stakes couldn’t be higher.
The UK undeniably has world-class AI research. Institutions like the Alan Turing Institute, along with university research hubs in Oxford, Cambridge, Edinburgh, and Manchester, keep Britain at the heart of global AI innovation. According to Oxford Insights, the UK ranks fourth worldwide in AI readiness, just behind the US, Singapore, and Finland — no small feat.
From an economic perspective, the prize is massive. PwC estimates AI could add up to £232 billion (around $317 billion) to the UK economy by 2030, with the sector’s value expected to nearly double from £1.36 trillion to £2.4 trillion by 2027. The UK is home to giants like DeepMind and a vibrant startup scene.
Yet, despite these bright headlines, structural challenges loom large. The biggest hurdle? Infrastructure. Compared to powerhouses like the US and China, the UK lags behind in investment in supercomputers, data centers, and national compute capacity. Without a solid digital backbone, scaling advanced AI research and education risks hitting a wall.
Education is another mixed story. While AI interest among students grows, the education system is scrambling to keep pace. Postgraduate AI conversion courses, apprenticeships, and industry partnerships are positive moves, but these scattered initiatives won’t build a robust system alone. Without a unified national AI education strategy, efforts remain fragmented — a risky approach when speed and cohesion are crucial.
Look globally for contrast: China integrates AI education from secondary school through university; Finland’s “Elements of AI” program has trained over 1% of its population, including policymakers and teachers; the US is investing billions into AI research and workforce development, linking academia directly with real-world challenges. Against this backdrop, the UK’s approach feels underpowered and too reliant on market forces.
This gap also plays out in everyday student experiences. Take Emily, a London-based master’s student in AI. She explains, “Our courses are heavy on theory but lack enough hands-on projects with real companies. Often, you have to find your own internships to get practical experience.” Meanwhile, her Finnish friend describes how everyone at their university takes an introductory AI course, regardless of major, making AI accessible and relevant across disciplines. This kind of education breaks down barriers and builds AI awareness widely.
For the UK to truly lead in AI education, three priorities stand out. First, a unified national AI education strategy must be developed, spanning early education through lifelong learning. The AI Safety Summit held at Bletchley Park was historic, but its outcomes need to translate into concrete policies that link government, universities, and industry in a seamless effort.
Second, responsibility and ethics must be baked into AI education. AI isn’t just about code; it raises questions around privacy, fairness, and transparency. The Alan Turing Institute’s “AI Skills for Business Competency Framework” offers a solid foundation. Embedding ethics, data literacy, and sector-specific applications into curricula will prepare graduates to navigate AI’s real-world challenges.
Third, the skills gap needs to be closed systematically. The World Economic Forum predicts 97 million new AI-related jobs worldwide by 2025. The UK’s talent pipeline must expand accordingly, paying special attention to underrepresented groups and regional access. Collaboration among government, academia, and industry is essential to build pathways from school to ongoing professional development.
Encouragingly, the rise in female applicants signals progress on diversity. Women currently make up just 19% of the UK tech workforce, but AI’s interdisciplinary nature attracts a broader range of students. Whether in healthcare, creative industries, or climate science, AI’s reach opens doors for many.
Kate, an environmental science student at a university in Boston, shares, “AI projects let me combine my background with computing skills in ways that feel meaningful and exciting.” This highlights an opportunity for education institutions to offer real-world, cross-disciplinary projects that inspire students and foster a more inclusive, equitable tech future.
Finally, it’s time to rethink how AI is taught. It should no longer be seen as the sole domain of computer science. AI needs to become a contextual skill, woven through fields like law, media, nursing, and governance. Students must understand how AI will reshape their professions and the ethical responsibilities involved.
Project-based learning should be the norm, starting from year one of university through to capstone projects. This approach equips students with the mindset and skills needed to thrive in an AI-driven economy.
The UK has the talent, institutions, and economic incentive to lead in AI. But leadership isn’t guaranteed — it requires strategic investment, a clear vision, and inclusive action. AI is more than a tech trend; it’s a societal transformation. Whether the UK rises to this moment depends on how boldly it can rethink education for the age of intelligence.
If the UK acts quickly and decisively, it won’t just compete in the AI race — it will set the pace.