AI Insights

• A positive contemporaneous shock to potential output always has a positive effect, while its persistence effect is made of three parts: the demand channel, the substitution effect, and the feedback effect of monetary policy. [3]
• Government debt in deviation positively responds to all model shocks except those hitting potential output, whose reduced-form coefficient is negative. [3]
• Job-separation intensity in deviation refers to the rate at which jobs are separated from their workers due to various economic factors. [3]
• The Taylor principle states that monetary policy should be more aggressive when inflation is above target, and less aggressive when inflation is below target. [3]
• The model's shocks have various effects on job-separation intensity in deviation, depending on the specific shock and its persistence. [3]
• Government debt in deviation responds positively to most model shocks, except those hitting potential output. [3]
• Positive news shocks about the current and future values of the fundamentals worsen debt sustainability due to inflationary pressures. [3]
• The model's assumptions may not accurately reflect real-world economic conditions. [3]
• The model's shocks have various effects on job-separation intensity in deviation. [2]

Abstract
Departing from the dominant approach focused on individual and meso-level determinants, this paper develops a macroeconomic formalization of job insecurity within a New Keynesian framework in which the standard IS-NKPC-Taylor rule block is augmented with labor-market frictions. The model features partially informed private agents who receive a noisy signal about economic fundamentals from a fully informed public sector. When monetary policy satisfies the Taylor principle, the equilibrium is unique and determinate. However, the release of news about current or future fundamentals can generate a "Paradox of Transparency" through general-equilibrium interactions between aggregate demand and monetary policy. When the Taylor principle is violated, belief-driven equilibria may emerge. Validation exercises based on the Simulated Method of Moments support the empirical plausibility of the model's key implications.

Why we are recommending this paper?
Due to your Interest in: Changes in the Labor Market

This paper directly addresses concerns about job displacement and economic instability, aligning with your interest in changes within the labor market. The macroeconomic modeling provides a framework for understanding the potential impacts of automation and technological shifts.

AI Insights

• Automation increases productivity and substitution effects, leading to an increase in wages up to a certain point, after which they decrease to zero. [3]
• The model provides insights into the impact of automation on labor markets, capital allocation, and economic output, but leaves open questions regarding the interaction of automation, AI, and R&D as co-determinants of economic output. [3]
• Automation has both productivity and substitution effects, leading to an increase in wages up to a certain point, after which they decrease to zero. [3]
• The task-based framework is a foundational model for understanding the impact of automation on economic output, labor markets, and capital allocation. [2]

Abstract
The Fourth Industrial Revolution commonly refers to the accelerating technological transformation that has been taking place in the 21st century. Economic growth theories which treat the accumulation of knowledge and its effect on production endogenously remain relevant, yet they have been evolving to explain how the current wave of advancements in automation and artificial intelligence (AI) technology will affect productivity and different occupations. The work contributes to current economic discourse by developing an analytical task-based framework that endogenously integrates knowledge accumulation with frictions that describe technological lock-in and the burden of knowledge generation and validation. The interaction between production (or automation) and growth (or knowledge accumulation) is also described explicitly. To study how automation and AI shape economic outcomes, I rely on high-throughput calculations of the developed model. The effect of the model's structural parameters on key variables such as the production output, wages, and labor shares of output is quantified, and possible intervention strategies are briefly discussed. An important result is that wages and labor shares are not directly linked, instead they can be influenced independently through distinct policy levers.

Why we are recommending this paper?
Due to your Interest in: Changes in the Labor Market

Given your interest in AGI and its applications, this paper’s focus on automation and economic growth is highly relevant. The modeling approach offers insights into how technological advancements could reshape industries and employment patterns.

AI Insights

• The proposed defence-in-depth model consists of three layers: Market Design, Baseline Agent Safety, and Monitoring and Oversight. [2]

Abstract
AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an eventual emergence of a monolithic Artificial General Intelligence (AGI). The alternative AGI emergence hypothesis, where general capability levels are first manifested through coordination in groups of sub-AGI individual agents with complementary skills and affordances, has received far less attention. Here we argue that this patchwork AGI hypothesis needs to be given serious consideration, and should inform the development of corresponding safeguards and mitigations. The rapid deployment of advanced AI agents with tool-use capabilities and the ability to communicate and coordinate makes this an urgent safety consideration. We therefore propose a framework for distributional AGI safety that moves beyond evaluating and aligning individual agents. This framework centers on the design and implementation of virtual agentic sandbox economies (impermeable or semi-permeable), where agent-to-agent transactions are governed by robust market mechanisms, coupled with appropriate auditability, reputation management, and oversight to mitigate collective risks.

Why we are recommending this paper?
Due to your Interest in: AGI

Coming from Google DeepMind, this paper directly addresses the critical safety concerns surrounding the development of AGI. It’s a key consideration given your interest in AGI research and its potential impacts.

AI Insights

• ORKG (Open Research Knowledge Graph): A large-scale knowledge graph that integrates various sources of research information. [3]
• The paper discusses the development of an AI-supported research platform called Tib Aissistant, which aims to facilitate research across various life cycles. [2]
• Tib Aissistant's architecture is based on a modular design, with components for prompt engineering, tool integration, and knowledge graph-based search. [1]

Abstract
The rapid advancements in Generative AI and Large Language Models promise to transform the way research is conducted, potentially offering unprecedented opportunities to augment scholarly workflows. However, effectively integrating AI into research remains a challenge due to varying domain requirements, limited AI literacy, the complexity of coordinating tools and agents, and the unclear accuracy of Generative AI in research. We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery, with AI assistants supporting tasks across the research life cycle. The platform offers modular components - including prompt and tool libraries, a shared data store, and a flexible orchestration framework - that collectively facilitate ideation, literature analysis, methodology development, data analysis, and scholarly writing. We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.

Why we are recommending this paper?
Due to your Interest in: AGI Research

This paper explores the use of AI to augment research workflows, a topic of significant interest considering your broader interest in AGI development. The discussion of integrating AI into scholarly processes is particularly pertinent.

AI Insights

• Temporary employment is a significant aspect of modern labor markets. [2]

Abstract
This paper examines the role of employment flexibility in enhancing the competitiveness of firms using temporary staffing services, with empirical evidence from Poland. The study focuses on how flexible employment arrangements influence operational efficiency, cost reduction, workforce scalability, market responsiveness, and client satisfaction. A quantitative survey was conducted among managers and owners of Polish enterprises that cooperate with temporary staffing agencies, using purposeful sampling to capture informed managerial perspectives. The findings show that employment flexibility significantly reduces downtime, accelerates onboarding processes, and lowers personnel and recruitment costs. Flexible staffing enables rapid workforce scaling during demand fluctuations and facilitates access to specialized skills without long-term commitments. The results also indicate that employment flexibility enhances organizational responsiveness, improves profitability in short-term projects, and strengthens resilience to seasonal and market volatility. Additionally, flexibility is identified as a key determinant of client satisfaction and loyalty toward staffing service providers. The study demonstrates that employment flexibility is not merely a cost-control mechanism but a strategic human resource capability that supports competitiveness, operational adaptability, and sustainable performance in dynamic labor markets.

Why we are recommending this paper?
Due to your Interest in: Job Displacement

This research examines the impact of flexible employment arrangements, a factor likely to be influenced by automation and changes in the labor market. The study’s focus on Poland provides a specific case study relevant to understanding labor market dynamics.