UK AI Minister Concedes Economic Boost From Technology Is Lower Than Google-Backed Estimate

The UK government has admitted that its official projections for the economic boost provided by artificial intelligence are significantly lower than a £400 billion estimate that ministers cited for nearly two years [1]. AI minister Kanishka Narayan revealed that the government's central estimate stands at £120 billion, with potential additions ranging from £55 billion to £140 billion by 2030 [1].

Origins of the £400 Billion Figure

For two years, under two separate administrations, UK government departments and agencies utilized a £400 billion economic benefit figure to address public concerns regarding potential job losses and economic disruption from artificial intelligence [1]. However, a parliamentary answer provided by AI minister Kanishka Narayan disclosed that this assessment originated entirely from a publicly available 2023 report commissioned by Google and produced by the British consultancy firm Public First, now known as PF Global [1].

When questioned in Parliament, Narayan stated that current government models place the projected economic contribution between £55 billion and £140 billion by 2030, with a central estimate of £120 billion [1]. Narayan characterized even this updated figure as a heavily uncertain estimation [1]. Records indicate that ministers had access to these lower estimates since at least November 2025, though government agencies such as Skills England continued publishing the older, higher figure months later [1].

Explaining the Disparity

Responding to inquiries regarding the variance between the two projections, PF Global head of economics Jonathan Dupont pointed to the UK's slower rate of AI adoption [1]. Dupont explained that AI is a fast-moving field requiring regular model updates, and that government models assumed a much slower adoption rate compared to the firm's original 2023 assumptions [1].

PF Global emphasized that its research remained independent, with all judgments and assumptions made by the consultancy [1]. The firm suggested that increased top-down adoption and integration of AI into civil service workflows could improve overall take-up, noting that growth is expected to ramp up in subsequent years [1].

Parliamentary and Expert Reactions

Public officials and policy experts have raised concerns regarding the prolonged reliance on industry-funded projections. Dame Chi Onwurah, Labour MP and chair of the Commons Science, Innovation and Technology Committee, warned that overstating technological benefits introduces digital transformation hurdles and risks undermining the case for change [1]. Conservative peer Lord Holmes of Richmond criticized the delay in clarifying the figures, noting that robust data is critical when public funds and policy decisions depend on it [1].

Sumedha Deshmukh of the Ada Lovelace Institute stated that precision in AI figures often masks underlying industry incentives and assumptions [1]. Deshmukh urged authorities to independently vet data, fund independent research, and evaluate whether projected productivity gains materialize before formulating policies [1].

Governments worldwide frequently evaluate the macroeconomic impact of emerging technologies like artificial intelligence to shape policy, public communications, and labor strategies. Industry stakeholders and independent research consultancies often produce economic projections to quantify potential growth across public and private sectors.

The reliance of public institutions on industry-funded research highlights broader questions regarding transparency, technological hype, and the formulation of public policy based on economic projections.

Governments may face increased scrutiny when utilizing industry-sponsored research for macroeconomic planning, prompting demands for greater reliance on independent academic and public sector data.

Inaccurate or overly optimistic economic modeling can lead to misallocated public funds, mismanaged digital transformation initiatives in public services, and diminished public trust in technological forecasts.