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"There is growing concern among policymakers, international organisations, and even big-tech Chief Executive Officers (exhibits I, II and III) that the current artificial intelligence (AI) boom features valuations increasingly detached from fundamentals. The Bank’s February 2026 Monetary Policy Report noted that an asset price correction is a key risk to the global economy, while the Bank’s July 2026 Financial Stability Report presented a scenario for how an AI correction could unfold. In this post, we study how negative big-tech earnings news transmits to global markets, which informed discussions around this scenario. We find that the effects ripple far beyond tech: equity indices decline, credit spreads widen and the US dollar depreciates. This last result, together with the limited response of Treasury yields, suggests muted flight-to-safety dynamics, unlike other financial stress episodes."

"Unlike previous waves of automation, machine learning and generative AI (Gen AI) technologies can perform non-routine cognitive tasks, such as those involving written or spoken language, and have the potential to affect a wider range of occupations. By augmenting or replacing workers in these tasks, these technologies promise to deliver significant productivity gains. AI adoption, while still limited, seems to be linked to productivity gains across industries in the US, although it can only explain a small fraction of the aggregate pick up in US productivity. This post examines the emerging evidence from UK industry data and finds some indication that AI is contributing to productivity growth following a similar pattern to previous key technologies."

"From Silicon Valley executives promising to automate white-collar work to headlines claiming AI is foreclosing the graduate entry market, the strained ‘low fire, low hire’ environment has increasingly been ascribed to technological transformation. UK vacancies nearly halved since their 2022 peak – a contraction so sustained it has reshaped the British hiring market for the better part of three years. This post examines how evidence of AI-driven transformation at the hiring margin is proving considerably more tenuous than the headlines suggest."

"Who are the UK’s mortgage borrowers, and how do their characteristics differ? Despite extensive literature on mortgage profiles, loan-level segmentation remains limited, existing work relies on aggregates or predefined categories. I address this gap by applying unsupervised machine learning to 20 years of data, allowing the model determine segments without prior assumptions. Three clusters emerge: one with low leverage, and two with high leverage but notably different income profiles. Lending composition has shifted gradually. High leverage, high-income borrowers now account for a larger market share, and first-time buyers increasingly fall into more leveraged segments. Machine learning is crucial for financial stability, revealing concentrations of characteristics, and trends, that aggregates and simple splits cannot, offering richer and earlier indications of potential vulnerabilities."

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