"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets

• URL: http://arxiv.org/abs/2308.05201v3
• Date: Wed, 18 Jun 2025 16:05:10 GMT
• Title: "Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets
• Authors: Jin Liu, Xingchen Xu, Xi Nan, Yongjun Li, Yong Tan,
• Abstract summary: Large Language Model (LLM)-based generative AI systems, such as ChatGPT, demonstrate zero-shot learning capabilities across a wide range of downstream tasks.<n>These systems are poised to reshape labor market dynamics.<n>However, predicting their precise impact is challenging, given AI's simultaneous effects on both demand and supply.
• Score: 4.955822723273599
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Model (LLM)-based generative AI systems, such as ChatGPT, demonstrate zero-shot learning capabilities across a wide range of downstream tasks. Owing to their general-purpose nature and potential to augment or even automate job functions, these systems are poised to reshape labor market dynamics. However, predicting their precise impact \textit{a priori} is challenging, given AI's simultaneous effects on both demand and supply, as well as the strategic responses of market participants. Leveraging an extensive dataset from a leading online labor platform, we document a pronounced displacement effect and an overall contraction in submarkets where required skills closely align with core LLM functionalities. Although demand and supply both decline, the reduction in supply is comparatively smaller, thereby intensifying competition among freelancers. Notably, further analysis shows that this heightened competition is especially pronounced in programming-intensive submarkets. This pattern is attributed to skill-transition effects: by lowering the human-capital barrier to programming, ChatGPT enables incumbent freelancers to enter programming tasks. Moreover, these transitions are not homogeneous, with high-skilled freelancers contributing disproportionately to the shift. Our findings illuminate the multifaceted impacts of general-purpose AI on labor markets, highlighting not only the displacement of certain occupations but also the inducement of skill transitions within the labor supply. These insights offer practical implications for policymakers, platform operators, and workers.

Related papers

• Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment [49.81946749379338]
  This work seeks to analyze the capacity of Transformers-based systems to learn demographic biases present in the data.<n>We propose a privacy-enhancing framework to reduce gender information from the learning pipeline as a way to mitigate biased behaviors in the final tools.
  arXiv  Detail & Related papers  (2025-06-13T15:29:43Z)
• Small Language Models are the Future of Agentic AI [24.712103135486984]
  We lay out the position that small language models (SLMs) are sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems.<n>We discuss the potential barriers for the adoption of SLMs in agentic systems and outline a general LLM-to-SLM agent conversion algorithm.
  arXiv  Detail & Related papers  (2025-06-02T18:35:16Z)
• The Real Barrier to LLM Agent Usability is Agentic ROI [110.31127571114635]
  Large Language Model (LLM) agents represent a promising shift in human-AI interaction.<n>We highlight a critical usability gap in high-demand, mass-market applications.
  arXiv  Detail & Related papers  (2025-05-23T11:40:58Z)
• Incentivizing Multimodal Reasoning in Large Models for Direct Robot Manipulation [89.5123417007126]
  We show how to make Large Multimodal Models (LMMs) understand the spatial action space.<n>We also show how to fully exploit the reasoning capacity of LMMs in solving these tasks.<n>Our resulting reasoning model built upon a 7B backbone, named ReasonManip, demonstrates three notable advantages.
  arXiv  Detail & Related papers  (2025-05-19T06:00:14Z)
• Few-Shot Vision-Language Action-Incremental Policy Learning [55.07841353049953]
  Transformer-based robotic manipulation methods utilize multi-view spatial representations and language instructions to learn robot motion trajectories. Existing methods lack the capability for continuous learning on new tasks with only a few demonstrations. We develop a Task-prOmpt graPh evolutIon poliCy (TOPIC) to address these issues.
  arXiv  Detail & Related papers  (2025-04-22T01:30:47Z)
• An Overview of Large Language Models for Statisticians [109.38601458831545]
  Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI) This paper explores potential areas where statisticians can make important contributions to the development of LLMs. We focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation.
  arXiv  Detail & Related papers  (2025-02-25T03:40:36Z)
• From Occupations to Tasks: A New Perspective on Automatability Prediction Using BERT [19.75055647648098]
  We propose a BERT-based classifier to predict the automatability of tasks in the forthcoming decade. Our findings indicate that approximately 25.1% of occupations within the O*NET database are at substantial risk of automation.
  arXiv  Detail & Related papers  (2025-02-13T07:18:57Z)
• Exploring the Implementation of AI in Early Onset Interviews to Help Mitigate Bias [0.0]
  This paper investigates the application of artificial intelligence (AI) in early-stage recruitment interviews. Results indicate that AI effectively minimizes sentiment-driven biases by 41.2%.
  arXiv  Detail & Related papers  (2025-01-17T00:40:35Z)
• TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks [52.46737975742287]
  We build a self-contained environment with data that mimics a small software company environment. We find that with the most competitive agent, 24% of the tasks can be completed autonomously. This paints a nuanced picture on task automation with LM agents.
  arXiv  Detail & Related papers  (2024-12-18T18:55:40Z)
• Generative AI Impact on Labor Market: Analyzing ChatGPT's Demand in Job Advertisements [0.9886108751871759]
  This study examines the demand for ChatGPT-related skills in the U.S. labor market. Using text mining and topic modeling techniques, we extracted and analyzed the Gen AI-related skills that employers are hiring for.
  arXiv  Detail & Related papers  (2024-12-09T23:03:20Z)
• Follow the money: a startup-based measure of AI exposure across occupations, industries and regions [0.0]
  Existing measures of AI occupational exposure focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility. We introduce the AI Startup Exposure (AISE) index-a novel metric based on occupational descriptions from O*NET and AI applications developed by startups. Our findings suggest that AI adoption will be gradual and shaped by social factors as much as by the technical feasibility of AI applications.
  arXiv  Detail & Related papers  (2024-12-06T10:25:05Z)
• Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs [10.844598404826355]
  One-third of U.S. employment is highly exposed to AI, primarily in high-skill jobs. This exposure correlates positively with employment and wage growth from 2019 to 2023.
  arXiv  Detail & Related papers  (2024-07-27T08:14:18Z)
• Robotic Control via Embodied Chain-of-Thought Reasoning [86.6680905262442]
  Key limitation of learned robot control policies is their inability to generalize outside their training data. Recent works on vision-language-action models (VLAs) have shown that the use of large, internet pre-trained vision-language models can substantially improve their robustness and generalization ability. We introduce Embodied Chain-of-Thought Reasoning (ECoT) for VLAs, in which we train VLAs to perform multiple steps of reasoning about plans, sub-tasks, motions, and visually grounded features before predicting the robot action.
  arXiv  Detail & Related papers  (2024-07-11T17:31:01Z)
• WESE: Weak Exploration to Strong Exploitation for LLM Agents [95.6720931773781]
  This paper proposes a novel approach, Weak Exploration to Strong Exploitation (WESE) to enhance LLM agents in solving open-world interactive tasks. WESE involves decoupling the exploration and exploitation process, employing a cost-effective weak agent to perform exploration tasks for global knowledge. A knowledge graph-based strategy is then introduced to store the acquired knowledge and extract task-relevant knowledge, enhancing the stronger agent in success rate and efficiency for the exploitation task.
  arXiv  Detail & Related papers  (2024-04-11T03:31:54Z)
• Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing [0.7865191493201839]
  In mobile crowdsourcing, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully. We propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing.
  arXiv  Detail & Related papers  (2024-01-23T16:00:45Z)
• AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform [0.13124513975412255]
  We investigate how AI influences freelancers across different online labor markets (OLMs) To shed light on the underlying mechanisms, we developed a Cournot-type competition model. We find that U.S. web developers tend to benefit more from the release of ChatGPT compared to their counterparts in other regions.
  arXiv  Detail & Related papers  (2023-12-07T10:06:34Z)
• Combatting Human Trafficking in the Cyberspace: A Natural Language Processing-Based Methodology to Analyze the Language in Online Advertisements [55.2480439325792]
  This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques. We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models. A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement.
  arXiv  Detail & Related papers  (2023-11-22T02:45:01Z)
• Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules [0.9790236766474201]
  We show the effectiveness of prompt-based tuning of pre-trained language models (PLM) in labour market specific applications. Our results indicate that cost-efficient methods such as PTR and instruction tuning without exemplars can significantly increase the performance of PLMs.
  arXiv  Detail & Related papers  (2023-08-31T14:47:00Z)
• Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models [98.72986679502871]
  Chat systems powered by large language models (LLMs) emerge and rapidly become a promising direction to achieve artificial general intelligence (AGI) But the path towards AGI in computer vision (CV) remains unclear. We imagine a pipeline that puts a CV algorithm in world-scale, interactable environments, pre-trains it to predict future frames with respect to its action, and then fine-tunes it with instruction to accomplish various tasks.
  arXiv  Detail & Related papers  (2023-06-14T17:15:01Z)
• The Future of ChatGPT-enabled Labor Market: A Preliminary Study in China [22.120632047061235]
  We study the future of ChatGPT-enabled labor market from the view of Human-AI Symbiosis instead of Human-AI Confrontation. Results indicate that about 28% of occupations in the current labor market require ChatGPT-related skills. Additional 45% occupations in the future will require ChatGPT-related skills.
  arXiv  Detail & Related papers  (2023-04-14T06:56:35Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI Inequality [16.121867459980137]
  We argue that a bottom-up approach that empowers individual workers to access AI-enabled work planning support and share data among a group of workers is a practical way to bridge AI inequality in gig work under the current paradigm of privately owned platforms. This position paper articulates a set of research challenges, potential approaches, and community engagement opportunities, seeking to start a dialogue on this important research topic in the interdisciplinary CHIWORK community.
  arXiv  Detail & Related papers  (2022-04-29T01:30:30Z)
• Hierarchical Few-Shot Imitation with Skill Transition Models [66.81252581083199]
  Few-shot Imitation with Skill Transition Models (FIST) is an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks. We show that FIST is capable of generalizing to new tasks and substantially outperforms prior baselines in navigation experiments.
  arXiv  Detail & Related papers  (2021-07-19T15:56:01Z)
• Learning Occupational Task-Shares Dynamics for the Future of Work [5.487438649316376]
  Big data and AI have risen significantly among high wage occupations since 2012 and 2016. We build an ARIMA model to predict future occupational task demands.
  arXiv  Detail & Related papers  (2020-01-28T21:20:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.