Origins of the Boom and Historical Optimism Cycles
Recent market turbulence offers a sharp reminder that the mechanics here are driven by high stakes and heavy capital demands. Artificial intelligence, defined as the capability of computational systems to perform tasks typically associated with human intelligence such as learning, reasoning, problem-solving, perception, and decision-making, has reshaped corporate strategies across global markets. It is a field of research in engineering, mathematics, and computer science that develops and studies methods and software enabling machines to perceive their environment and use learning and intelligence to take actions that maximise their chances of achieving defined goals. High-profile applications include advanced web search engines, chatbots, virtual assistants, autonomous vehicles, play and analysis in strategy games like chess and Go, and content generation spanning text, images, audio, and videos.
The traditional goals of AI research include learning, reasoning, knowledge representation, planning, natural language processing, and perception, alongside support for robotics. To reach these goals, researchers use techniques including state space search, mathematical optimisation, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. The field also draws upon psychology, linguistics, philosophy, neuroscience, and other disciplines. Companies such as OpenAI, Google DeepMind, and Meta aim to create artificial general intelligence, which is AI that can complete nearly any cognitive task at least as well as a human. The 2020s AI boom was spurred on largely by advances in generative intelligence that can create and modify media.
Follow the money back through history, however, and the catch is that the industry has weathered multiple contractions. Artificial intelligence was founded as an academic discipline in 1956. The field went through multiple cycles of optimism throughout its history, followed by periods of disappointment and loss of funding, known as AI winters. Funding and interest increased substantially after 2012, when graphics processing units started being used to accelerate neural networks, and deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with the introduction of the transformer architecture. In addition to AI safety and unintended consequences and harms from the use of AI, ethical concerns, AI’s long-term effects, environmental effects, and potential existential risks have prompted extensive discussions of regulation.
Evaluating Risk and the Limits of Institutional Frameworks
As financial institutions assess technology adoption risks, regulatory bodies attempt to corral mounting corporate exposures through formal governance standards. According to the Financial Stability Board, which published its final report on Sound Practices for Responsible Adoption of Artificial Intelligence in October 2026 following public feedback received on a consultative version published in June 2026, financial systems face distinct oversight challenges. The FSB report identifies 12 sound practices to help financial institutions harness the benefits of AI while managing its risks responsibly, in ways that support the resilience of the financial system. On closer reading, these measures focus heavily on procedural control rather than the underlying capital viability of the physical infrastructure supporting the technology.
The 12 sound practices are organised into two distinct groups. The first group emphasises the importance of organisation-wide AI governance as financial institutions adopt AI, highlighting the board and senior management’s role in setting and overseeing alignment between a financial institution’s AI adoption and its business model, risk appetite, and strategy. It also stresses maintaining an appropriate governance framework with well-defined roles and responsibilities, incorporating AI risks into the risk management framework alongside effective documentation of AI use, and undertaking continuous learning and adaptation. According to the FSB, these sound practices strongly encourage the board and senior management of financial institutions to reference the guidelines when developing and refining their institution’s AI governance, risk management, and control frameworks.
The second group sets out a comprehensive framework for managing risks throughout the AI lifecycle from development and deployment. This focuses on effective and systematic assessment of the materiality and risks associated with AI use cases at inception and thereafter, the selection of appropriate AI technologies, appropriate data governance, the explainability of various types of AI alongside consideration of compensating controls, and the evaluation of the performance of AI use cases. Additional provisions cover appropriate and effective human oversight, the management of AI-related cyber and information and communication technology risks, and the management of risks from third-party AI dependencies. The sound practices serve as a practical toolkit that financial institutions can tailor to their specific context and risk profile and integrate into existing frameworks, including those for cyber and third-party risk, while promoting coordination, cooperation, and information-sharing among financial institutions, supervisors, and other stakeholders within and across jurisdictions.
Infrastructure Realities and Corporate Balance Sheet Pressures
Despite the structural clarity offered by regulatory bodies, institutional governance frameworks largely overlook the mounting capital constraints and viability issues of scaling artificial intelligence data centers. Early researchers developed algorithms that imitated step-by-step reasoning that humans use when solving puzzles or making logical deductions. By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics. However, many of these algorithms were insufficient for solving large reasoning problems because they encountered severe computational bottlenecks.
The current push to scale infrastructure faces similar barriers on a much larger financial scale. Building and maintaining the massive data centers required to train and run advanced neural networks demand continuous capital expenditure that strains corporate balance sheets. While technology firms project rapid scaling to achieve artificial general intelligence, the financing required for massive power consumption, specialized hardware, and real estate development introduces profound market vulnerabilities. Financial stability standards establish clear rules for internal risk management, but they offer little defense against the macroeconomic fallout if heavy infrastructure investments fail to generate commensurate commercial returns.
Sources
- Artificial intelligence
- FSB finalises sound practices for the responsible adoption of artificial intelligence (AI)
- What My Son Is Learning From Artificial Intelligence
Photo: Ishanjyotibora (CC BY 4.0)
