Artificial Intelligence In: Knowledge Economy, Financial Sector, Macroeconomic Productivity, and Banking

1. Artificial Intelligence in the Knowledge Economy

The rise of Artificial Intelligence (AI) has the potential to fundamentally reshape the knowledge economy by solving problems at scale. This paper introduces a framework to study this transformation, incorporating AI into an economy where humans form hierarchical firms: Less knowledgeable individuals become “workers” solving routine problems, while more knowledgeable individuals become “solvers,” assisting workers with exceptional problems. We model AI as a technology that transforms computing power into “AI agents,” which can either operate autonomously (as co-workers or solvers/co-pilots) or non-autonomously (only as co-pilots). We show that basic autonomous AI displaces humans towards specialized problem solving, leading to smaller, less productive, and less decentralized firms. In contrast, advanced autonomous AI reallocates humans to routine work, resulting in larger, more productive, and more decentralized firms. While autonomous AI primarily benefits the most knowledgeable individuals, non-autonomous AI disproportionately benefits the least knowledgeable. However, autonomous AI achieves higher overall output. These findings reconcile seemingly contradictory empirical evidence and reveal key tradeoffs involved in regulating AI autonomy (arXiv).

2. Regulating AI in the financial sector: recent developments and main challenges

This paper explores the potential transformative impact of artificial intelligence (AI) on the financial sector, focusing on operational efficiency, risk management and customer experience in banking and insurance. It delves into the widespread adoption of AI technologies including generative AI (gen AI) and examines the associated risks and regulatory implications. While AI exacerbates existing risks such as model risk and data privacy, it does not introduce fundamentally new risks apart from gen AI, which may give rise to hallucination and anthropomorphism risks. Most financial authorities have not issued AI regulations specific to financial institutions as existing frameworks already address most of these risks. Nevertheless, some areas require further regulatory attention, including governance, expertise and skills, model risk management, data governance, non-traditional players in the financial sector, new business models and third-party AI service providers (BIS).

3. Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence

The paper studies the expected macroeconomic productivity gains from Artificial Intelligence (AI) over a 10-year horizon. It builds a novel micro-to-macro framework by combining existing estimates of micro-level performance gains with evidence on the exposure of activities to AI and likely future adoption rates, relying on a multi-sector general equilibrium model with input-output linkages to aggregate the effects. Its main estimates for annual aggregate total-factor productivity growth due to AI range between 0.25-0.6 percentage points (0.4-0.9 pp. for labour productivity). The paper discusses the role of various channels in shaping these macro-level gains and highlights several policy levers to support AI's growth-enhancing effects (OECD).

4. Extracting value from AI in banking: Rewiring the enterprise

To gain material value from AI, banks need to move beyond experimentation to transform critical business areas, including by reimagining complex workflows with multiagent systems (McKinsey).

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