Nvidia's latest financial results, with nearly £71 billion in revenue up 106% year-over-year, demonstrate the ongoing boom in artificial intelligence and the world's increasing demand for intelligent systems. AI pioneer Ken Mulvany observed that these figures indicate a market eager for more intelligence than current infrastructure can supply, driving substantial investment.
Mulvany, who has spent over a decade building AI systems, noted the dramatic decrease in the cost of machine intelligence, making capabilities once requiring significant research budgets now accessible at a low cost. He drew parallels to historical technological shifts like the railway boom and the dot-com crash, where infrastructure investments, though costly, ultimately enabled subsequent digital and industrial revolutions.
He cautioned that Britain must proactively adapt to this AI-driven transformation. "The machines being installed at enormous expense today will be second-rate within a handful of years," Mulvany stated, emphasizing the rapid pace of AI hardware development. He explained that while this rapid obsolescence is a challenge for buyers, it also means intelligence becomes progressively cheaper, acting as a powerful economic input.
Potential applications of this cheaper intelligence include providing GPs with vast medical knowledge, enabling scientists to test hypotheses rapidly, and equipping small manufacturers with analytical capabilities comparable to multinational corporations. Mulvany suggested that widespread AI adoption could drive the productivity growth Britain has sought since the financial crisis, leading to higher wages and improved public services.
However, Mulvany identified several key areas where UK policy is currently hindering this potential. He highlighted issues with electricity costs and grid capacity, stating that Britain's industrial electricity is among the most expensive in the developed world, and its power grid was not designed for the demands of data centers. He criticized the long delays in grid connections, noting that a decade-long wait for a connection renders data center construction timelines impractical and effectively acts as a veto on critical infrastructure.
Planning processes also present a significant obstacle. Mulvany argued that a planning process taking three years for machinery that becomes obsolete in three years is counterproductive. He called for a presumption in favor of development with statutory deadlines, rather than lengthy evaluation periods.
Regarding the National Health Service (NHS), Mulvany pointed out that its scale and data resources could make it a leader in adopting medical AI. However, he described the NHS as one of the slowest major purchasers of medical intelligence, with individual trusts undertaking their own lengthy evaluations and data agreements. He advocated for a unified national procurement framework and standardized data rules to expedite the deployment of effective AI solutions.
Finally, Mulvany addressed the issue of capital investment. He expressed concern that British AI companies often seek funding in the U.S. due to insufficient domestic capital. He suggested that reforms to the tax treatment of investments, particularly for pension funds, are necessary to encourage investment in domestic growth companies. He also noted the disadvantage of a 0.5% tax on UK share purchases compared to overseas shares, advocating for its reduction to signal a preference for domestic investment.
Mulvany concluded that Britain possesses the fundamental strengths—world-class science, deep capital markets, strong universities, and expertise in fields like medicine—to capitalize on the AI revolution. He stressed that success does not require out-competing chip manufacturers but rather leveraging existing assets to solve complex problems. The core requirement, he stated, is for the government to remove barriers between willing markets and customers. The global AI engine is being built, and Britain's prosperity will depend on its ability to seize this as an economic opportunity.