Does the Bank of England assess risks from the AI investment boom?

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1. 1

   Is AI a Bubble That Is About to Burst? A Systematic Review of Financial and Economic Evidence

   INNOVAPATH (Rao)Published Nov 24, 2025Checked Oct 4, 2026

   “Macroeconomic and consulting studies project sizable, long-run productivity and GDP gains from AI, although meta-analytic evidence finds no robust relationship between AI adoption and aggregate productivity to date. Primary market analyses acknowledge bubble-like features, rapid valuation growth, circular spending, and concentration, but argue that fundamentals and current earnings distinguish AI from prior purely speculative episodes. Conclusions:The evidence supports a hybrid view: segments of AI-exposed equity markets display speculative excess. They are vulnerable to correction, whereas AI, as a general-purpose technology, has credible long-run economic value. A complete collapse akin to a pure asset bubble appears unlikely; a pattern similar to the dot-com cycle, over-valuation, correction, and subsequent consolidation with lasting productivity effects is more consistent with the available data. For policymakers, firms, and investors, the key challenge is not to bet for or against “AI” in the abstract, but to distinguish bubble-like exposures from durable, productivity-enhancing applications.”

2. 2

   Over-accumulating AI: Rationale and fault lines beyond the AI investment boom

   Archive ouverte UNIGE (University of Geneva) (Valenti & Durand)Published Jan 1, 2026Checked Oct 4, 2026

   “This contribution proposes examining the surge in the financial value of AI-related stocks and capital expenditures in AI-related assets through the lens of the crisis theory of overaccumulation and discusses the specificities of this AI rush. After documenting the extent of the AI bubble, we argue that crisis dynamics emerge from the competitive, compulsory drive to invest, which coerces firms' behavior without ensuring the macro conditions for the sustainability of the aggregate investment boom. We then distinguish the vulnerabilities arising from the surplus of capital invested in AI relative to valorization opportunities. Following the Anna Karenina principle, we show that there is one path toward the sustained expansion of the AI sector (a rosy scenario) but three paths to crisis that interact with one another, related to mounting financial fragilities, coordination failures, and labor productivity disappointments. In light of the debate on techno-feudalism, we then discuss the broader implications of this likely macro disruption.”

3. 3

   Financial Stability Implications of Generative AI: Taming the Animal Spirits

   Finance and Economics Discussion Series (Hansen & Lee)Published Sep 1, 2025Checked Oct 4, 2026

   “This paper investigates the impact of the adoption of generative AI on financial stability. We conduct laboratory-style experiments using large language models to replicate classic studies on herd behavior in investment decisions. Our results show that AI agents make more rational decisions than humans, relying predominantly on private information over market trends. Increased reliance on AI-powered investment advice could therefore potentially lead to fewer asset price bubbles arising from animal spirits that trade by following the herd. However, exploring variations in the experimental settings reveals that AI agents can be induced to herd optimally when explicitly guided to make profit-maximizing decisions. While optimal herding improves market discipline, this behavior still carries potential implications for financial stability. In other experimental variations, we show that AI agents are not purely algorithmic, but have inherited some elements of human conditioning and bias.”

4. 4

   Financial Risk Management and Explainable, Trustworthy, Responsible AI

   Frontiers in Artificial Intelligence (Fritz-Morgenthal et al.)Published Feb 28, 2022Checked Oct 4, 2026

   “The same type of model is also successful in areas unrelated to risk management, such as sales optimization, customer lifetime value considerations, robo-advisory, and other fields of applications. The paper refers to recent related publications from central banks, financial supervisors and regulators as well as other relevant sources and working groups. It aims to give practical advice for establishing a risk-based governance and testing framework for the mentioned model types and discusses the use of recent technologies, approaches, and platforms to support the establishment of responsible, trustworthy, explainable, auditable, and manageable AI/ML in production. In view of the recent EU publication on AI, also referred to as the EU Artificial Intelligence Act (AIA), we also see a certain added value for this paper as an instigator of further thinking outside of the financial services sector, in particular where "High Risk" models according to the mentioned EU consultation are concerned.”

5. 5

   Artificial Intelligence as a Strategy in the British Economic Field.

   The British journal of sociology (Atkinson)Published Apr 29, 2025Checked Oct 4, 2026

   “Drawing on the sociology of Pierre Bourdieu, this paper conceives the adoption and development of artificial intelligence by businesses as a strategy within the economic field. Using a survey of over 2000 businesses in the UK and tools of geometric data analysis, I construct a model of the British economic field and project into it indicators of past, present and intended AI adoption. This provides a sense of the correspondences between the structure of the field, the temporal order of strategies, and perceptions of the possible and necessary among its agents. Dominant players within the field have clearly led and will lead the AI 'revolution', rendering AI a tool for perpetuating intra-field domination and reproduction, but others below them seem set to pursue emulation strategies to keep up. These conservation strategies may also contain, however, an internal difference between innovation and dependence corresponding with the new and the old within the field.”