Firms adjust to generative AI by reallocating hiring (52% of exposure decline) and redesigning tasks within jobs (39.5%), with senior roles shifting earlier via reallocation and junior roles using mixed channels.
arXiv preprint arXiv:2503.04761 , year=
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Generative models privatize social relations by automating social capacities into synthetic forms owned by private companies.
Large-scale analysis of wild LLM chat logs finds that user interaction patterns stabilize quickly after initial use and correlate with long-term outcomes like retention, creating an agency paradox of limited exploration in unconstrained systems.
Large-scale log study of 14M+ agentic searches finds short sessions, intent-specific repetition patterns, and that 54% of new query terms trace to prior retrieved evidence.
Proposes a three-step benchmark design method (define work activity, specify tested setting, score work product) derived from work studies and O*NET, demonstrated via three case analyses.
Preregistered behavioral study identifies a speedup illusion where users overestimate time savings from AI assistance on cognitive tasks despite no actual difference in completion times.
Three pre-registered studies with 2691 participants show people underestimate their AI usage rate and overestimate efficiency gains on simple tasks, with prior use entrenching further adoption.
Freelancers use generative AI to support exploratory skill acquisition but not as their main resource due to reliability issues, leading to a shift toward survival-oriented upskilling and the emergence of invisible competencies that lack market validation.
AI writing distorts perceived writer personas across 29 dimensions in large experiments, and reward-model mitigation reduces but does not eliminate user preference for the AI.
LLMs corrupt an average of 25% of document content during long delegated editing workflows across 52 domains, even frontier models, and agentic tools do not mitigate the issue.
LLMs drop 39% in performance during multi-turn conversations due to premature assumptions and inability to recover from early errors.
Generative AI adoption in Europe ranges from under 3% to 25%, is steeper for skilled workers in abstract-task jobs and in digitally advanced countries with training, shows a gender gap in exposed roles, and has produced no detectable shift in reported task content so far.
Generative AI may break the education-based recovery mechanism for technological displacement, as evidence shows performance gains without learning gains and current measurements miss the knowledge dimension of cognition.
Benchmarking four LLMs on O*NET skills yields SAFI scores showing mathematics and programming as most automatable while active listening and reading comprehension are least, with 78.7% of real AI interactions being augmentation rather than replacement.
Interviews reveal a four-stage vibe coding workflow that accelerates prototyping while introducing tensions between quick efficiency and reflective design intention, plus asymmetries in trust and ownership.
LLMs relocate rather than eliminate trade-offs among generality, accuracy, and simplicity, shifting complexity to infrastructure, compliance, and expertise and redefining competitive advantage around managing that shift.
citing papers explorer
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Generative AI and the Reorganization of Labor Demand
Firms adjust to generative AI by reallocating hiring (52% of exposure decline) and redesigning tasks within jobs (39.5%), with senior roles shifting earlier via reallocation and junior roles using mixed channels.
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Synthetic Sociality: How Generative Models Privatize the Social Fabric
Generative models privatize social relations by automating social capacities into synthetic forms owned by private companies.
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Priming, Path-dependence, and Plasticity: Understanding the molding of user-LLM interaction and its implications from (many) chat logs in the wild
Large-scale analysis of wild LLM chat logs finds that user interaction patterns stabilize quickly after initial use and correlate with long-term outcomes like retention, creating an agency paradox of limited exploration in unconstrained systems.
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Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests
Large-scale log study of 14M+ agentic searches finds short sessions, intent-specific repetition patterns, and that 54% of new query terms trace to prior retrieved evidence.
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Design and Report Benchmarks for Knowledge Work
Proposes a three-step benchmark design method (define work activity, specify tested setting, score work product) derived from work studies and O*NET, demonstrated via three case analyses.
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Cognitive offloading and the speedup illusion in human-AI interaction
Preregistered behavioral study identifies a speedup illusion where users overestimate time savings from AI assistance on cognitive tasks despite no actual difference in completion times.
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The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks
Three pre-registered studies with 2691 participants show people underestimate their AI usage rate and overestimate efficiency gains on simple tasks, with prior use entrenching further adoption.
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Upskilling with Generative AI: Practices and Challenges for Freelance Knowledge Workers
Freelancers use generative AI to support exploratory skill acquisition but not as their main resource due to reliability issues, leading to a shift toward survival-oriented upskilling and the emergence of invisible competencies that lack market validation.
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Measuring and Mitigating Persona Distortions from AI Writing Assistance
AI writing distorts perceived writer personas across 29 dimensions in large experiments, and reward-model mitigation reduces but does not eliminate user preference for the AI.
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LLMs Corrupt Your Documents When You Delegate
LLMs corrupt an average of 25% of document content during long delegated editing workflows across 52 domains, even frontier models, and agentic tools do not mitigate the issue.
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LLMs Get Lost In Multi-Turn Conversation
LLMs drop 39% in performance during multi-turn conversations due to premature assumptions and inability to recover from early errors.
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From Exposure to Adoption: Generative AI in European Workplaces
Generative AI adoption in Europe ranges from under 3% to 25%, is steeper for skilled workers in abstract-task jobs and in digitally advanced countries with training, shows a gender gap in exposed roles, and has produced no detectable shift in reported task content so far.
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Can the Recovery Mechanism Survive AI? Skill Formation, Labor, and What Current Measurement Misses
Generative AI may break the education-based recovery mechanism for technological displacement, as evidence shows performance gains without learning gains and current measurements miss the knowledge dimension of cognition.
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The AI Skills Shift: Mapping Skill Obsolescence, Emergence, and Transition Pathways in the LLM Era
Benchmarking four LLMs on O*NET skills yields SAFI scores showing mathematics and programming as most automatable while active listening and reading comprehension are least, with 78.7% of real AI interactions being augmentation rather than replacement.
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Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaboration
Interviews reveal a four-stage vibe coding workflow that accelerates prototyping while introducing tensions between quick efficiency and reflective design intention, plus asymmetries in trust and ownership.
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From Model Design to Organizational Design: Complexity Redistribution and Trade-Offs in Generative AI
LLMs relocate rather than eliminate trade-offs among generality, accuracy, and simplicity, shifting complexity to infrastructure, compliance, and expertise and redefining competitive advantage around managing that shift.