The recent launch of the Chinese AI model DeepSeek-V3 caused alarm in tech circles, suggesting China was months closer to achieving artificial general intelligence than had been previously thought. Tech stocks tumbled and President Trump described it as a “wake-up call” for America’s AI industry.
More intriguingly, however, is this month’s comparatively quiet debut of Manus, a Chinese general-purpose AI agent. The demo for this fully autonomous agent shows it carrying out a range of tasks, including buying property, booking holidays and developing video games. Analysts have dismissed it as “merely a wrapper” because it builds off models by Western AI companies like Anthropic. Yet, as the AI researcher Dean Ball has observed, Manus represents the state-of-the-art in agentic product development, whereas Deepseek remains inferior to cutting-edge Western models across most key performance metrics.
This highlights a blind spot in much of the policy and media discourse on AI. Our conversations overwhelmingly focus on innovation and capability breakthroughs, while neglecting the equally vital challenge of actually delivering AI solutions to businesses and consumers. We risk winning the research race but losing the implementation marathon.
The importance of technology diffusion – not simply invention – is illustrated throughout economic history. Jeffrey Ding’s comprehensive analysis of past “general-purpose-technologies” like steam and electricity demonstrates that the speed of adoption, not invention alone, determines the economic impact of these technologies.
We see this theory play it out in Britain’s own history. James Watt created his steam engine in the 1770s, but it took nearly half a century to translate this breakthrough into the factory systems that propelled the UK to world power status. Yet another half a century later, Britain lost this advantage to the United States, not because Americans had better technology, but because they excelled at integrating electricity into new business models and industrial processes at a pace we couldn’t match.
The parallels to today’s AI landscape are striking. The UK has contributed significantly to foundational AI research, but excellence alone won’t secure our economic future if we can’t effectively deploy these technologies across our economy.
What are the implications for policy? We need to stop thinking about AI as merely an important industry in its own right and start recognising it as the essential building block for virtually every other industry over the next decade. AI will only meaningfully transform the UK’s productivity and growth prospects when it moves from research labs to shop floors, construction sites, hospitals and offices across the country. This requires policy that prioritises adoption and integration, and not just research and development.
The Government will soon be launching an Industrial Strategy designed to support the sectors of the economy which offer the highest opportunity for growth. To make the most of the moment, it will need to identify how AI can enhance the sectors where Britain already is already world class – like finance, professional services and life sciences. It should support sub-sectors like cybersecurity where AI is already proving decisive in helping organisations meet the challenge of novel threats in a more volatile world. And it will then need to deeply partner with industry to improve secure access to sector-specific data, address skills gaps to effective deployment and human-AI teaming, and derisk investment.
The countries that master AI diffusion, not just AI invention, will define the economic landscape of this century. Britain has the foundations to excel, if we learn the lessons from our own history.
Ben Lyons is Director of Policy and Public Affairs at Darktrace
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