Nvidia’s Jensen Huang is right that fear keeps UK business small, columnist argues

Nvidia’s Jensen Huang contends the United Kingdom suffers from a lack of entrepreneurial spirit, a diagnosis supported by the increasing outflow of its science and technology firms. Oxford Ionics was acquired by US-based IonQ for $1 billion and OrganOx by Japanese medtech group Terumo for $1.5 billion, demonstrating a clear trend of successful UK companies being bought by international entities.

Even Wise, a company founded in the UK, shifted its primary listing to the US, signaling a broader lack of confidence in the UK market; Huang believes the country “tends to find the one reason not to do it.”

Huang Diagnoses UK Entrepreneurship as Hampered by Risk Aversion

The United Kingdom’s financial structures actively discourage the kind of ambitious investment needed for high-growth companies, contributing to an outflow of promising science and technology firms, according to Nvidia’s Jensen Huang. In 2025, UK pensions held only 25% of their assets in equities, a figure significantly lower than the 43% held in 2014 and the lowest proportion among the world’s seven largest pension markets. This preference for bonds, representing 56% of assets, demonstrates a systemic aversion to the risk associated with supporting innovative ventures and instead prioritizes secure, albeit limited, returns.

Huang’s assessment, delivered to a gathering of politicians and investors, points to a broader cultural issue hindering the UK’s entrepreneurial potential. This risk-averse environment extends beyond financial institutions, manifesting in local council policies that prioritize maintaining the status quo over enabling growth.

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The reluctance of councils to permit new building, driven by concerns about spoiling views or attracting unwanted demographics, constrains investment and contributes to a chronic shortage of funds for businesses seeking to expand. Any project that does proceed faces a complex network of well-intentioned regulations, further stifling innovation and increasing costs. The result, Huang implies, is not merely economic stagnation but a perceptible decline in the nation’s overall prosperity, with councils, businesses and politicians all exhibiting a fear of change or potential legal challenges.

Huang believes the UK’s current predicament represents a stark departure from its historical spirit of innovation and empire-building, questioning how a nation that pioneered advancements from football to steam engines has become preoccupied with identifying reasons not to pursue ambitious projects, Nvidia says. “A procession of promising science and technology companies” is leaving the UK, as a House of Lords committee observed, with Ionics and OrganOx serving as recent examples of firms acquired by US and Japanese entities, respectively.

The issue extends beyond the technology sector, manifesting as a “pathological aversion to risk” that permeates the political and economic culture. Disability payments, first designed for victims of industrial accidents, are today claimed by about 7 million people, more than one in ten.

Huang’s critique isn’t simply about the absence of risk, but about the active fear of it becoming the nation’s organizing principle. He suggests that the UK’s historical strength, its embrace of insurance as a means of protecting against risk, has paradoxically morphed into a defining characteristic that now hinders its progress. Nobody ever wants anything to change, on the off chance it could be dangerous.

This fear, he argues, doesn’t eliminate risk; it merely shifts it onto other segments of the population, potentially leading to instability. The current situation, he implies, is unsustainable, and a fundamental shift in mindset is needed to restore the UK’s entrepreneurial spirit and secure its future economic prosperity.

Offered a million ways to win, he says, it tends to find “the one reason not to do it.”

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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