As generative large language models (LLMs) such as ChatGPT gain widespread adoption in various domains, their potential to propagate and amplify social biases, particularly in high-stakes areas such as the labor market, has become a pressing concern. AI algorithms are not only widely used in the selection of job applicants, individual job seekers may also make use of generative LLMs to help develop their job application materials. Against this backdrop, this research builds on a novel experimental design to examine social biases within ChatGPT-generated job applications in response to real job advertisements. By simulating the process of job application creation, we examine the language patterns and biases that emerge when the model is prompted with diverse job postings. Notably, we present a novel bias evaluation framework based on Masked Language Models to quantitatively assess social bias based on validated inventories of social cues/words, enabling a systematic analysis of the language used. Our findings show that the increasing adoption of generative AI, not only by employers but also increasingly by individual job seekers, can reinforce and exacerbate gender and social inequalities in the labor market through the use of biased and gendered language.

As artificial intelligence (AI) agents are deployed across economic domains, understanding their strategic behavior and market-level impact becomes critical. This paper puts forward a groundbreaking new framework that is the first to capture the real-world economic forces that shape agentic labor markets: adverse selection, moral hazard, and reputation dynamics. Our framework encapsulates three core capabilities that successful LLM-agents will need: \textbf{metacognition} (accurate self-assessment of skills), \textbf{competitive awareness} (modeling rivals and market dynamics), and \textbf{long-horizon strategic planning}. We illustrate our framework through a tractable simulated gig economy where agentic Large Language Models (LLMs) compete for jobs, develop skills, and adapt their strategies under competitive pressure. Our simulations illustrate how LLM agents explicitly prompted with reasoning capabilities learn to strategically self-improve and demonstrate superior adaptability to changing market conditions. At the market level, our simulations reproduce classic macroeconomic phenomena found in human labor markets, while controlled experiments reveal potential AI-driven economic trends, such as rapid monopolization and systemic price deflation. This work provides a foundation to further explore the economic properties of AI-driven labour markets, and a conceptual framework to study the strategic reasoning capabilities in agents competing in the emerging economy.

Artificial intelligence is simultaneously transforming the production function of human capital in schools and the return to skills in the labor market. We develop a theoretical model to analyze the potential for misallocation when these two forces are considered in isolation. We study an educational planner who observes AI's immediate productivity benefits in teaching specific skills but fails to fully internalize the technology's future wage-suppressing effects on those same skills. Motivated by a pre-registered pilot study suggesting a positive correlation between a skill's "teachability" by AI and its vulnerability to automation, we show that this information friction leads to a systematic skill mismatch. The planner over-invests in skills destined for obsolescence, a distortion that increases monotonically with AI prevalence. Extensions demonstrate that this mismatch is exacerbated by the neglect of unpriced non-cognitive skills and by the endogenous over-adoption of educational technology. Our findings caution that policies promoting AI in education, if not paired with forward-looking labor market signals, may paradoxically undermine students' long-term human capital, such as by crowding out skills like persistence that are forged through intellectual struggle.

Artificial Intelligence is reshaping America's \$9.4 trillion labor market, with cascading effects that extend far beyond visible technology sectors. When AI transforms quality control tasks in automotive plants, consequences spread through logistics networks, supply chains, and local service economies. Yet traditional workforce metrics cannot capture these ripple effects: they measure employment outcomes after disruption occurs, not where AI capabilities overlap with human skills before adoption crystallizes. Project Iceberg addresses this gap using Large Population Models to simulate the human-AI labor market, representing 151 million workers as autonomous agents executing over 32,000 skills and interacting with thousands of AI tools. It introduces the Iceberg Index, a skills-centered metric that measures the wage value of skills AI systems can perform within each occupation. The Index captures technical exposure, where AI can perform occupational tasks, not displacement outcomes or adoption timelines. Analysis shows that visible AI adoption concentrated in computing and technology (2.2% of wage value, approx \$211 billion) represents only the tip of the iceberg. Technical capability extends far below the surface through cognitive automation spanning administrative, financial, and professional services (11.7%, approx \$1.2 trillion). This exposure is fivefold larger and geographically distributed across all states rather than confined to coastal hubs. Traditional indicators such as GDP, income, and unemployment explain less than 5% of this skills-based variation, underscoring why new indices are needed to capture exposure in the AI economy. By simulating how these capabilities may spread under scenarios, Iceberg enables policymakers and business leaders to identify exposure hotspots, prioritize investments, and test interventions before committing billions to implementation

Understanding labor market dynamics is essential for policymakers, employers, and job seekers. However, comprehensive datasets that capture real-world career trajectories are scarce. In this paper, we introduce JobHop, a large-scale public dataset derived from anonymized resumes provided by VDAB, the public employment service in Flanders, Belgium. Utilizing Large Language Models (LLMs), we process unstructured resume data to extract structured career information, which is then normalized to standardized ESCO occupation codes using a multi-label classification model. This results in a rich dataset of over 1.67 million work experiences, extracted from and grouped into more than 361,000 user resumes and mapped to standardized ESCO occupation codes, offering valuable insights into real-world occupational transitions. This dataset enables diverse applications, such as analyzing labor market mobility, job stability, and the effects of career breaks on occupational transitions. It also supports career path prediction and other data-driven decision-making processes. To illustrate its potential, we explore key dataset characteristics, including job distributions, career breaks, and job transitions, demonstrating its value for advancing labor market research.

Artificial intelligence is becoming a permanent element in the world of work, with Silicon Valley calling it the dawning of a new age. Many people are afraid of losing their job, but Germany is well-prepared. In the northern part of the U.S. state of Louisiana, right next to the prison on the outskirts of Shreveport, looms a gigantic building of concrete and steel. Welcome to the future," reads a colorful greeting painted on the wall at the entrance, right next to the obligatory American flag. It is 9:30 a.m., a busy time of day. Yet the halls and corridors of SHV1, as the building is referred to internally, are completely empty of people. A blueprint for the future," as the site manager calls it. The Seattle-based company operates the largest fleet of industrial robots in the world, more than a million of them, and many are outfitted with artificial intelligence, helping them to lift, sort, search, weigh and scan. Guided and directed completely by AI. Without the massive use of this technology," says Aaron Parness, a former NASA aerospace engineer who now heads up the retail giant's AI robotic department, we would be a different company." The article you are reading originally appeared in German in issue 41/2025 (October 2nd, 2025) of DER SPIEGEL. Amazon, though, also employs people. But their role is changing rapidly.

Employee turnover is a critical challenge in financial markets, yet little is known about the role of professional networks in shaping career moves. Using the Hong Kong Securities and Futures Commission (SFC) public register (2007-2024), we construct temporal networks of 121,883 professionals and 4,979 firms to analyze and predict employee departures. We introduce a graph-based feature propagation framework that captures peer influence and organizational stability. Our analysis shows a contagion effect: professionals are 23% more likely to leave when over 30% of their peers depart within six months. Embedding these network signals into machine learning models improves turnover prediction by 30% over baselines. These results highlight the predictive power of temporal network effects in workforce dynamics, and demonstrate how network-based analytics can inform regulatory monitoring, talent management, and systemic risk assessment.

Current efforts in AI safety prioritize filtering harmful content, preventing manipulation of human behavior, and eliminating existential risks in cybersecurity or biosecurity. While pressing, this narrow focus overlooks critical human-centric considerations that shape the long-term trajectory of a society. In this position paper, we identify the risks of overlooking the impact of AI on the future of work and recommend comprehensive transition support towards the evolution of meaningful labor with human agency. Through the lens of economic theories, we highlight the intertemporal impacts of AI on human livelihood and the structural changes in labor markets that exacerbate income inequality. Additionally, the closed-source approach of major stakeholders in AI development resembles rent-seeking behavior through exploiting resources, breeding mediocrity in creative labor, and monopolizing innovation. To address this, we argue in favor of a robust international copyright anatomy supported by implementing collective licensing that ensures fair compensation mechanisms for using data to train AI models. We strongly recommend a pro-worker framework of global AI governance to enhance shared prosperity and economic justice while reducing technical debt.

Current labor markets are strongly affected by the economic forces of adverse selection, moral hazard, and reputation, each of which arises due to $\textit{incomplete information}$. These economic forces will still be influential after AI agents are introduced, and thus, agents must use metacognitive and strategic reasoning to perform effectively. Metacognition is a form of $\textit{internal reasoning}$ that includes the capabilities for self-assessment, task understanding, and evaluation of strategies. Strategic reasoning is $\textit{external reasoning}$ that covers holding beliefs about other participants in the labor market (e.g., competitors, colleagues), making strategic decisions, and learning about others over time. Both types of reasoning are required by agents as they decide among the many $\textit{actions}$ they can take in labor markets, both within and outside their jobs. We discuss current research into metacognitive and strategic reasoning and the areas requiring further development.

The automated future just lurched a few steps closer. Over the past few weeks, nearly all of the major AI firms--OpenAI, Anthropic, Google, xAI, Amazon, Microsoft, and Perplexity, among others--have announced new products that are focused not on answering questions or making their human users somewhat more efficient, but on completing tasks themselves. They are being pitched for their ability to "reason" as people do and serve as "agents" that will eventually carry out complex work from start to finish. Humans will still nudge these models along, of course, but they are engineered to help fewer people do the work of many. Last month, Anthropic launched Claude Code, a coding program that can do much of a human software developer's job but far faster, "reducing development time and overhead."