AI for Productivity Tools
Abstract
This paper investigates how executive demographics particularly age and gender influence artificial intelligence (AI) investment decisions and subsequent firm productivity using comprehensive data from over 500 Japanese enterprises spanning from 2018 to 2023. Our central research question addresses the role of executive characteristics in technology adoption, finding that CEO age and technical background significantly predict AI investment propensity. Employing these demographic characteristics as instrumental variables to address endogeneity concerns, we identify a statistically significant 2.4% increase in total factor productivity attributable to AI investment adoption. Our novel mechanism decomposition framework reveals that productivity gains operate through three distinct channels: cost reduction (40% of total effect), revenue enhancement (35%), and innovation acceleration (25%). The results demonstrate that younger executives (below 50 years) are 23% more likely to adopt AI technologies, while firm size significantly moderates this relationship. Aggregate projections suggest potential GDP impacts of 1.15 trillion JPY from widespread AI adoption across the Japanese economy. These findings provide crucial empirical guidance for understanding the human factors driving digital transformation and inform both corporate governance and public policy regarding AI investment incentives.
This paper investigates how executive demographics particularly age and gender influence artificial intelligence (AI) investment decisions and subsequent firm productivity using comprehensive data from over 500 Japanese enterprises spanning from 2018 to 2023. Our central research question addresses the role of executive characteristics in technology adoption, finding that CEO age and technical background significantly predict AI investment propensity. Employing these demographic characteristics as instrumental variables to address endogeneity concerns, we identify a statistically significant 2.4% increase in total factor productivity attributable to AI investment adoption. Our novel mechanism decomposition framework reveals that productivity gains operate through three distinct channels: cost reduction (40% of total effect), revenue enhancement (35%), and innovation acceleration (25%). The results demonstrate that younger executives (below 50 years) are 23% more likely to adopt AI technologies, while firm size significantly moderates this relationship. Aggregate projections suggest potential GDP impacts of 1.15 trillion JPY from widespread AI adoption across the Japanese economy. These findings provide crucial empirical guidance for understanding the human factors driving digital transformation and inform both corporate governance and public policy regarding AI investment incentives.
Abstract
A survey of 26 CS students reveals that AI coding assistants are mainly used for writing code (second to online searches) while AI chatbots are the top resource for debugging. Participants with different coding experience prefer online help over direct human help from peers and instructors.
A survey of 26 CS students reveals that AI coding assistants are mainly used for writing code (second to online searches) while AI chatbots are the top resource for debugging. Participants with different coding experience prefer online help over direct human help from peers and instructors.
Economics of Productivity
Abstract
Behavioural finance offers a valuable framework for examining foreign exchange (FX) market dynamics, including puzzles such as excess volatility and fat-tailed distributions. Yet, when it comes to their interaction with the `real' side of the economy, existing scholarship has overlooked a critical feature of developing countries. They cannot trade in their national currencies and need US dollars to access modern production techniques as well as maintain consumption patterns similar to those of wealthier societies. To address this gap, we present a novel heterogeneous agents model from the perspective of a developing economy that distinguishes between speculative and non-speculative sectors in the FX market. We demonstrate that as long as non-speculative demand responds to domestic economic activity, a market-clearing output growth rate exists that, in steady-state, is equal to the ratio between FX supply growth and the income elasticity of demand for foreign assets, i.e., a generalised dynamic trade-multiplier. Numerical simulations reproduce key stylised facts of exchange rate dynamics and economic growth, including distributions that deviate from the typical bell-shaped curve. Data from a sample of Latin American countries reveal that FX fluctuations exhibit similar statistical properties. Furthermore, we employ time-varying parameter estimation techniques to show that the dynamic trade-multiplier closely tracks observed growth rates in these economies.
Behavioural finance offers a valuable framework for examining foreign exchange (FX) market dynamics, including puzzles such as excess volatility and fat-tailed distributions. Yet, when it comes to their interaction with the `real' side of the economy, existing scholarship has overlooked a critical feature of developing countries. They cannot trade in their national currencies and need US dollars to access modern production techniques as well as maintain consumption patterns similar to those of wealthier societies. To address this gap, we present a novel heterogeneous agents model from the perspective of a developing economy that distinguishes between speculative and non-speculative sectors in the FX market. We demonstrate that as long as non-speculative demand responds to domestic economic activity, a market-clearing output growth rate exists that, in steady-state, is equal to the ratio between FX supply growth and the income elasticity of demand for foreign assets, i.e., a generalised dynamic trade-multiplier. Numerical simulations reproduce key stylised facts of exchange rate dynamics and economic growth, including distributions that deviate from the typical bell-shaped curve. Data from a sample of Latin American countries reveal that FX fluctuations exhibit similar statistical properties. Furthermore, we employ time-varying parameter estimation techniques to show that the dynamic trade-multiplier closely tracks observed growth rates in these economies.
LLMs for Productivity
Abstract
The remarkable success of Large Language Models (LLMs) in generative tasks has raised fundamental questions about the nature of their acquired capabilities, which often appear to emerge unexpectedly without explicit training. This paper examines the emergent properties of Deep Neural Networks (DNNs) through both theoretical analysis and empirical observation, addressing the epistemological challenge of "creation without understanding" that characterises contemporary AI development. We explore how the neural approach's reliance on nonlinear, stochastic processes fundamentally differs from symbolic computational paradigms, creating systems whose macro-level behaviours cannot be analytically derived from micro-level neuron activities. Through analysis of scaling laws, grokking phenomena, and phase transitions in model capabilities, I demonstrate that emergent abilities arise from the complex dynamics of highly sensitive nonlinear systems rather than simply from parameter scaling alone. My investigation reveals that current debates over metrics, pre-training loss thresholds, and in-context learning miss the fundamental ontological nature of emergence in DNNs. I argue that these systems exhibit genuine emergent properties analogous to those found in other complex natural phenomena, where systemic capabilities emerge from cooperative interactions among simple components without being reducible to their individual behaviours. The paper concludes that understanding LLM capabilities requires recognising DNNs as a new domain of complex dynamical systems governed by universal principles of emergence, similar to those operating in physics, chemistry, and biology. This perspective shifts the focus from purely phenomenological definitions of emergence to understanding the internal dynamic transformations that enable these systems to acquire capabilities that transcend their individual components.
The remarkable success of Large Language Models (LLMs) in generative tasks has raised fundamental questions about the nature of their acquired capabilities, which often appear to emerge unexpectedly without explicit training. This paper examines the emergent properties of Deep Neural Networks (DNNs) through both theoretical analysis and empirical observation, addressing the epistemological challenge of "creation without understanding" that characterises contemporary AI development. We explore how the neural approach's reliance on nonlinear, stochastic processes fundamentally differs from symbolic computational paradigms, creating systems whose macro-level behaviours cannot be analytically derived from micro-level neuron activities. Through analysis of scaling laws, grokking phenomena, and phase transitions in model capabilities, I demonstrate that emergent abilities arise from the complex dynamics of highly sensitive nonlinear systems rather than simply from parameter scaling alone. My investigation reveals that current debates over metrics, pre-training loss thresholds, and in-context learning miss the fundamental ontological nature of emergence in DNNs. I argue that these systems exhibit genuine emergent properties analogous to those found in other complex natural phenomena, where systemic capabilities emerge from cooperative interactions among simple components without being reducible to their individual behaviours. The paper concludes that understanding LLM capabilities requires recognising DNNs as a new domain of complex dynamical systems governed by universal principles of emergence, similar to those operating in physics, chemistry, and biology. This perspective shifts the focus from purely phenomenological definitions of emergence to understanding the internal dynamic transformations that enable these systems to acquire capabilities that transcend their individual components.
Abstract
As Artificial Intelligence (AI), particularly Large Language Models (LLMs), becomes increasingly embedded in education systems worldwide, ensuring their ethical, legal, and contextually appropriate deployment has become a critical policy concern. This paper offers a comparative analysis of AI-related regulatory and ethical frameworks across key global regions, including the European Union, United Kingdom, United States, China, and Gulf Cooperation Council (GCC) countries. It maps how core trustworthiness principles, such as transparency, fairness, accountability, data privacy, and human oversight are embedded in regional legislation and AI governance structures. Special emphasis is placed on the evolving landscape in the GCC, where countries are rapidly advancing national AI strategies and education-sector innovation. To support this development, the paper introduces a Compliance-Centered AI Governance Framework tailored to the GCC context. This includes a tiered typology and institutional checklist designed to help regulators, educators, and developers align AI adoption with both international norms and local values. By synthesizing global best practices with region-specific challenges, the paper contributes practical guidance for building legally sound, ethically grounded, and culturally sensitive AI systems in education. These insights are intended to inform future regulatory harmonization and promote responsible AI integration across diverse educational environments.
As Artificial Intelligence (AI), particularly Large Language Models (LLMs), becomes increasingly embedded in education systems worldwide, ensuring their ethical, legal, and contextually appropriate deployment has become a critical policy concern. This paper offers a comparative analysis of AI-related regulatory and ethical frameworks across key global regions, including the European Union, United Kingdom, United States, China, and Gulf Cooperation Council (GCC) countries. It maps how core trustworthiness principles, such as transparency, fairness, accountability, data privacy, and human oversight are embedded in regional legislation and AI governance structures. Special emphasis is placed on the evolving landscape in the GCC, where countries are rapidly advancing national AI strategies and education-sector innovation. To support this development, the paper introduces a Compliance-Centered AI Governance Framework tailored to the GCC context. This includes a tiered typology and institutional checklist designed to help regulators, educators, and developers align AI adoption with both international norms and local values. By synthesizing global best practices with region-specific challenges, the paper contributes practical guidance for building legally sound, ethically grounded, and culturally sensitive AI systems in education. These insights are intended to inform future regulatory harmonization and promote responsible AI integration across diverse educational environments.
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