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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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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Agentic commerce and the battleground for new payments infrastructure

Prem Munday

Agentic commerce, where artificial intelligence (AI) systems act on behalf of users to find products, negotiate purchases, and execute payments, is developing rapidly. This creates shared responsibility: developers must build legally sound systems, while regulators and infrastructure operators must consider how existing frameworks apply and where new approaches may be needed. The Bank of England operates, oversees and is co-ordinating the design of payment systems as part of its statutory responsibilities. Emerging agent‑based payments can have implications for how the private sector safely innovates and how regulators and payment infrastructure providers adapt. This post explores how agentic commerce could reshape future payment design.

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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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Could financial infrastructure be used to govern AI agents?

Peter Denton

AI systems are becoming increasingly capable of pursuing sophisticated goals without human intervention. As these systems begin to be used to make economic transactions, they raise important questions for central banks, given their role overseeing money, payments, and financial stability. Leading AI researchers have highlighted the importance of retaining governance control over such systems. In response, AI safety researchers have proposed developing infrastructure to govern AI agents. This blog explores how financial infrastructure may emerge as a particularly viable governance tool, offering pragmatic, scalable, and reversible chokepoints for monitoring and controlling increasingly autonomous AI systems.

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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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Leveraging language models for prudential supervision

Adam Muhtar and Dragos Gorduza

Imagine a world where machines can assist humans in navigating across complex financial rules. What was once far-fetched is rapidly becoming reality, particularly with the emergence of a class of deep learning models based on the Transformer architecture (Vaswani et al (2017)), representing a whole new paradigm to language modelling in recent times. These models form the bedrock of revolutionary technologies like large language models (LLMs), opening up new ways for regulators, such as the Bank of England, to analyse text data for prudential supervision and regulation.

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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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