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

Daniel Ostry, Roger Vicquéry and Emilio Zaratiegui

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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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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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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Generative AI: degenerative for jobs?

Edward Egan

Headlines warn of a looming ‘jobpocalypse’, but the reality is more complex. Rather than simply causing a wave of job losses, the economic literature suggests generative AI could influence the labour market through several – potentially offsetting – channels: productivity gains, job displacement, new job creation, and compositional shifts. The balance between these effects, rather than displacement alone, will shape AI’s aggregate impact on employment. The latest research suggests that overall effects remain limited so far, but there are some early signs of AI’s impact. I find that, since mid-2022, new online vacancies in the most AI-exposed roles have decreased by more than twice as much as the least exposed group. This highlights the need for ongoing monitoring as AI adoption accelerates.

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The gathering swarm: emergent AGI and the rise of distributed intelligence

Mohammed Gharbawi

Rapid advances in artificial intelligence (AI) have fuelled a lively debate on the feasibility and proximity of artificial general intelligence (AGI). While some experts dismiss the concept of AGI as highly speculative, viewing it primarily through the lens of science fiction (Hanna and Bender (2025)), others assert that its development is not merely plausible but imminent (Kurzweil (2005); (2024)). For financial institutions and regulators, this dialogue is more than theoretical: AGI has the potential to redefine decision-making, risk management, and market dynamics. However, despite the wide range of views, most discussions of AGI implicitly assume that its emergence will be as a singular, centralised, and identifiable entity, an assumption this paper critically examines and seeks to challenge.

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Bias, fairness, and other ethical dimensions in artificial intelligence

Kathleen Blake

Artificial intelligence (AI) is an increasingly important feature of the financial system with firms expecting the use of AI and machine learning to increase by 3.5 times over the next three years. The impact of bias, fairness, and other ethical considerations are principally associated with conduct and consumer protection. But as set out in DP5/22, AI may create or amplify financial stability and monetary stability risks. I argue that biased data or unethical algorithms could exacerbate financial stability risks, as well as conduct risks.

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Opening the machine learning black box

Andreas Joseph

Machine learning models are at the forefront of current advances in artificial intelligence (AI) and automation. However, they are routinely, and rightly, criticised for being black boxes. In this post, I present a novel approach to evaluate machine learning models similar to a linear regression – one of the most transparent and widely used modelling techniques. The framework rests on an analogy between game theory and statistical models. A machine learning model is rewritten as a regression model using its Shapley values, a payoff concept for cooperative games. The model output can then be conveniently communicated, eg using a standard regression table. This strengthens the case for the use of machine learning to inform decisions where accuracy and transparency are crucial.

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Is the economy suffering from the crisis of attention?

Dan Nixon

Smartphone apps and newsfeeds are designed to constantly grab our attention. And research suggests we’re distracted nearly 50% of the time. Could this be weighing down on productivity? And why is the crisis of attention particularly concerning in the context of the rise of AI and the need, therefore, to cultivate distinctively human qualities?

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New machines for The Old Lady

Chiranjit Chakraborty and Andreas Joseph

Rapid advances in analytical modelling and information processing capabilities, particularly in machine learning (ML) and artificial intelligence (AI), combined with ever more granular data are currently transforming many aspects of everyday life and work. In this blog post we give a brief overview of basic concepts of ML and potential applications at central banks based on our research. We demonstrate how an artificial neural network (NN) can be used for inflation forecasting which lies at the heart of modern central banking. We show how its structure can help to understand model reactions. The NN generally outperforms more conventional models. However, it struggles to cope with the unseen post-crises situation which highlights the care needed when considering new modelling approaches.

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Should economists be more concerned about Artificial Intelligence?

Mauricio Armellini and Tim Pike.

This post highlights some of the possible economic implications of the so-called “Fourth Industrial Revolution” — whereby the use of new technologies and artificial intelligence (AI) threatens to transform entire industries and sectors. Some economists have argued that, like past technical change, this will not create large-scale unemployment, as labour gets reallocated. However, many technologists are less optimistic about the employment implications of AI. In this blog post we argue that the potential for simultaneous and rapid disruption, coupled with the breadth of human functions that AI might replicate, may have profound implications for labour markets. We conclude that economists should seriously consider the possibility that millions of people may be at risk of unemployment, should these technologies be widely adopted.

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