If AI disappoints? The transmission of US big-tech earnings news

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.

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Is artificial intelligence making us more productive? What the UK industry data show

Sandra Batten

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.

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Canaries in the column? AI exposure and the UK’s hiring slowdown

Haley Schlicht

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.

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Demographics, deposits and data: using machine learning to segment UK mortgages

Joe Grimshaw

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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Credit constraints and housing market access

Belinda Tracey and Neeltje van Horen

The Help-to-Buy (HTB) programme introduced in 2013 reopened the 95% loan to value (LTV) segment of the UK mortgage market, thereby reducing the minimum deposit requirement for many first-time buyers (FTBs) from 10% to 5% (Chart 1). That policy change offers a useful natural experiment to study how deposit constraints shape access to homeownership. We previously demonstrated that this easing of deposit constraints generated a clear increase in local spending. In a recent paper, we show that lowering this constraint increases FTB home purchases, particularly among households without access to external financial support for their deposit.

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When travel restrictions became trade frictions: evidence from Covid-era border closures

John Lewis

Covid travel restrictions limited movement of people but also made cross-border goods trade more difficult. Did this contribute to the fall in global goods trade during the pandemic, and if so by how much? In a recent paper using a structural gravity model on global trade flows with domestic trade, I show that a full closure reduced trade for a typical country pair by around 19%, implying a peak hit to global trade of about 23% in 2020 Q2. Hits were larger for nearby partners, and were concentrated in road and air freight, with seaborne trade unaffected. These differences explain why some countries could close borders with smaller trade hits than others. Trade rebounded as restrictions eased, suggesting no lasting scarring.

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Explainability in machine learning: do popular methods deliver on their promises?

Ivona Cickovic and Andrea Serafino

Machine learning models are increasingly used in organisational decision-making, yet their inner workings often remain opaque. When these systems influence real world outcomes, knowing what they predict is not enough – we also need to understand why. Explainability methods aim to illuminate this ‘black box,’ and feature attribution tools that link predictions to individual inputs are especially popular. They feel intuitive but rely on strict data assumptions that rarely hold, making their outputs unreliable. The 2019 Apple Card case illustrates why this matters: despite gender not being an explicit input, women appeared to receive lower credit limits than men with similar profiles – an outcome attribution methods struggle to explain. This post examines a key assumption underpinning these tools and how it distorts explanations.

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A ‘group washing machine’ and ‘tangled skein’: the failure of Slater Walker

David Rule

In August 1977, the Bank of England purchased the bank Slater Walker Limited, completing its rescue. The bank had been a subsidiary of Slater Walker Securities, controlled by Jim Slater, which also owned an insurer. This post describes how Slater misused depositors’ and policyholders’ funds to finance his wider business interests. The Bank of England sought to protect depositors by supporting the wider group rather than putting the bank into liquidation. The case remains relevant today when banks and insurers continue to be owned by financial and industrial groups, including private equity sponsors, and supervisors must consider how to address conflicts of interest and how far to insulate the bank or insurer from the rest of the group.

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A balancing act in public ownership: the quiet legacy of the Bank of England Act 1946

Andrew Hewitt

Sunday 1 March 2026 was the 80th anniversary of the Bank’s coming into public ownership, following the Bank of England Act 1946. It was the first of eight major nationalisations by the post-war Labour government and the only one not to be later reversed, in whole or in part. Some opponents, at the time, were said to consider it a revolutionary ‘measure of first-class importance’; others considered it inconsequential. Although it was a defining point in UK financial history, it did not feature highly in the public consciousness. Yet it laid enduring foundations for the Bank’s operational and financial independence, carefully balancing powers to act in the public interest with limits on political interference.

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The quantity theory of crypto: what is Bitcoin worth as a medium of exchange?

John Lewis

The recent near halving of Bitcoin’s price has reignited debate about its true value. As a store of value, net present value asset pricing models suggest it should be worth zero because it pays no dividend. Yet its price remains far above zero, and its total value is still large despite recent turbulence. In this post I explore the question: what’s Bitcoin’s value as a means of exchange? I show that using a simple quantity theory of money framework helps explain its extreme volatility, the powerful influence of sentiment, how prices can surge even when transaction usage is low, and – crucially – why innovations by competitors and limited retail payment adoption pose significant downside price risks.

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