🥼 Mann Virdee, Head of Science and Technology
Graphene was first isolated in 2004 in a lab at the University of Manchester, work that won Andre Geim and Konstantin Novoselov the Nobel Prize in Physics. But within a decade, more than 7,500 graphene patents had been filed worldwide — only about 50 of them from the UK. By contrast, more than 2,200 were from China and over 1,100 were from South Korea, with Samsung alone holding hundreds. The Chancellor at the time, George Osborne, insisted that technology invented in the UK should also be developed here. It’s a problem that keeps repeating.
It’s that kind of failure that sits at the heart of a new report, Science: A New Golden Age, published last week by President Trump’s science and technology adviser Michael Kratsios. It’s the boldest attempt to reimagine the US research ecosystem since Vannevar Bush’s famous 1945 report Science: The Endless Frontier.
One of the core arguments in the report is that discovery without domestic manufacturing can leave countries paying for the research while others capture the returns. Or, as I wrote for this series last year:
“process knowledge is being lost by offshoring supply chains, which in turn harms countries’ entrepreneurial ecosystems. The process of building, iterating, innovating, and improving manufacturing gets lost, and it’s just as important as the ‘Eureka’ moment in the lab.”
Britain could view the report as a mirror of what has happened in our own science and tech ecosystem.
Another key part of the report is its selection of the funding-mechanism experiments that Britain’s own metascience community has championed for years. That includes golden tickets (letting one reviewer back one idea that a review panel has rejected), fast grants decided in 48 hours, and portfolio-based allocation run like a venture fund. The report calls for empowered metascience units with the authority to run randomised trials on grantmaking, and – interestingly – holds up the UK’s own Metascience Unit, established in 2024, as a model to emulate.
Alongside this, there are proposals for increased direct-to-individual funding that sidelines universities, pressure to reduce research overheads, an AI-first mandate, and a proposed rule giving political appointees more sway over grants. But it’s a tough environment for such proposals; A New Golden Age arrives at a time when the trust between scientists and government has frayed, amid grant terminations, funding cuts, and reductions in agencies such as the National Science Foundation and the National Oceanic and Atmospheric Administration.
🔧 Ian Ng, Researcher
“Can AI build a train?” asked Andy Burnham in his latest push for technical careers. Since ChatGPT took off in 2022, plenty of research has looked at AI’s impact on the labour market, and the common assumption has been that skilled trades are more immune to it than desk jobs. However, Google’s latest research complicates the picture.
Google’s first AI & Economy ATLAS report looks into how users interact with its AI tools and finds that AI use is not limited to desk-based jobs. It also assists workers in physical and manual occupations on adjacent tasks, acting as a “hands-on collaborator” for diagnostics, troubleshooting and real-time learning, with multimodal AI use over two times higher than the overall usage rate among automotive service technicians and mechanics.
The report compares AI usage shares to employment shares across major occupation groups. As you’d expect, AI adoption is the most over-represented (relative to employment share) in “computer and mathematical” and “business and financial operations”. While a correlation can be drawn between higher manual task shares and less AI usage, some high-manual occupations still stand out where AI usage was observed — in tasks complementary to manual work. For example, 44% of industrial machinery mechanics’ tasks are manual, but AI use is still observed in non-routine cognitive tasks such as analysing test results and machine error messages. That’s the key distinction — the routine, hands-on part of manual work is largely untouched by AI so far, but the non-routine analytical part of the same job isn’t.
It’s not the doom and gloom of AI replacing human beings either. Britain faces a well-documented labour shortage in key sectors crucial to the net-zero transition, in particular among automotive technicians and mechanics. Automotive technicians and mechanics are in mostly manual jobs, and the report observed AI being used for testing vehicle components and inspecting parts for wear. It happens to be one of the sectors where Britain faces difficulty in filling vacancies, as the lack of EV-qualified technicians is stalling Britain’s transition to EVs. The training pipeline is not keeping pace with the technology, and instead of replacing technicians, AI could actually assist technicians with diagnostic work. Another occupational group where high AI usage was observed is electrical and electronics repairers, recently included in the Migration Advisory Committee’s Temporary Shortage List.
Burnham is right that Britain undervalued technical routes. But the fix shouldn’t rest on the idea that trades are somehow immune to AI — they’re not, even if the disruption looks nothing like the white-collar version. AI was not built to plug Britain’s skills gap, but we could use some AI to plug that gap and help train the next generation of technicians.
🎢 Philip Salter, Founder
There’s no getting away from it: being an entrepreneur is a risky business. Fewer than four in ten new UK businesses make it to their fifth birthday.
But we know that society benefits from this risk-taking. Entrepreneurship is one of the most powerful engines of productivity and progress. Throughout history, entrepreneurial ventures have transformed societies, lifting people from subsistence to prosperity.
Nordhaus estimated that innovators capture only about 2.2% of the social surplus their innovations create. The rest flows to everyone else. But we aren’t the best in the world at building a society around this fact. As Richard Browning, founder of Gravity Industries, argues:
“Culturally, we are far more risk-averse than places like California, often pricing ambition out of existence before it even starts. To compete globally, we must shift from an insular mindset to a more outward-looking, non-ministerial view of global opportunity.”
If Browning is right that we price ambition out too early, it’s worth asking how early. Writing today, Elle Smith argues that risk aversion starts in childhood — and that a freer upbringing might be what produces the risk-takers an economy needs. So what does the evidence suggest?
Ross Levine and Yona Rubinstein have found that it is the combination of high cognitive ability and a streak of teenage rule-breaking — skipping school, minor mischief, a taste for risk — that predicts who becomes an entrepreneur and who earns more for it. A Swedish cohort tracked for nearly four decades replicated the pattern, though only for modest rule-breaking rather than serious crime.
The strongest signal of all is having an entrepreneurial parent, which makes a child around 60% more likely to follow suit; and because the effect shows up even among adopted children raised apart from their biological parents, the evidence points to role-modelling, not genes alone.
What we can’t yet say is that giving children more freedom makes them entrepreneurs. The logic is inviting: free and risky play builds resilience, self-regulation and self-efficacy, while over-protective parenting seems to erode them. Self-efficacy is one of the best-evidenced ingredients of entrepreneurial intention we have. But no one has yet traced a free-range childhood through to who actually starts a business — the direct evidence isn’t there yet.
I don’t want to rest too heavily on anecdata, but in a recent chat with a large group of some of the most impressive people I know, nearly all of them said they’d had a great deal of freedom growing up. It’s a small sample and correlation isn’t causation and all that, but it deserves more thinking about.