Short-Term Gain, Long-Term Fragility: AI Labor Substitution and the Erosion of Sustainable Capability

• URL: http://arxiv.org/abs/2605.27399v1
• Date: Thu, 23 Apr 2026 03:11:27 GMT
• Title: Short-Term Gain, Long-Term Fragility: AI Labor Substitution and the Erosion of Sustainable Capability
• Abstract summary: This paper develops a mechanism of capability masking and capability erosion under AI labor substitution.<n> AI-generated output can create the appearance that organizational capability has been replaced, even when dependence on skilled human labor remains.<n>Evidence from AI-assisted coding shows that generated output still requires substantial human verification and remains uneven in correctness, maintainability, and security.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: What looks like acceleration can be a quiet transfer of burden from the present to the future. Attempts to replace human labor with AI systems are often presented as rational responses to technological progress, but that view is often structurally short-sighted. Across software development and adjacent knowledge industries, AI is increasingly attractive because it appears to reduce labor costs, speed output, and improve short-term metrics. Yet those gains may be achieved by drawing down human capabilities that are slow to build and difficult to restore. This paper develops a mechanism of capability masking and capability erosion under AI labor substitution. AI-generated output can create the appearance that organizational capability has been replaced, even when dependence on skilled human labor remains. That appearance can support hiring restraint while slower costs accumulate in the background. Evidence from AI-assisted coding shows that generated output still requires substantial human verification and remains uneven in correctness, maintainability, and security. Repository-level studies also suggest limits in handling broader codebase context. More broadly, labor-market, political-economy, and industrial-strategy evidence suggests that substitution pressures are being driven by managerial cost incentives and national competition while increasing risks of concentration and platform control. The result is a system that may look more efficient in the short term while becoming more fragile over time.

Related papers

• When Does AI Augment Work? A Workflow-Level Framework for Human-Agent Collaboration [78.16115745870405]
  We aim to characterise the value of artificial intelligence in the workplace.<n>Current studies largely measure this value in terms of the current automation capabilities and public adoption of AI.<n>To account for this, we must expand the scope of our analysis beyond atomised tasks of today.
  arXiv  Detail & Related papers  (2026-09-11T06:29:34Z)
• Cheap, Fallible Cognition and the Political Economy of Expertise [0.0]
  A job is not an indivisible object, and machine cognition is not a uniform substitute for human labor.<n>This paper develops a task-based framework for analyzing generative AI as cheap, scalable, and fallible cognition.<n>It concludes that AI's labor-market destiny is neither mechanical unemployment nor automatic abundance.
  arXiv  Detail & Related papers  (2026-08-11T23:55:35Z)
• The Boundaries of Automation: A Theory of Persistent Human Participation [107.84677954791773]
  We argue that human participation may persist even with highly capable AI systems.<n>In some activities, the target is not fully specified in advance but instead emerges through the interaction itself.<n>This perspective has important implications for the limits of automation.
  arXiv  Detail & Related papers  (2026-07-23T17:33:43Z)
• Managing the Human Fallback: Skill Investment Under Improving AI and Worker Mobility [0.0]
  We show that when firms deploy autonomous AI, they must decide how much work to leave to the system and how much to keep workers engaged.<n>We develop a parsimonious two-period model in which AI may outperform the worker when it functions, but may fail with positive probability.<n>A firm chooses worker engagement; engagement lowers current output for below-benchmark workers, but changes future skill through learning and erosion.
  arXiv  Detail & Related papers  (2026-06-27T23:34:23Z)
• Generative Models Erode Human Temporal Learning Through Market Selection [0.0]
  We argue that modern generative models create structural risks for knowledge and cultural production at current, sub-AGI capability levels.<n>We define Human Temporal Learning as path-dependent knowledge accumulation through sustained engagement with problems over time.
  arXiv  Detail & Related papers  (2026-06-04T17:59:06Z)
• The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading [0.0]
  Experimental evidence confirms that AI tools raise worker productivity, but that sustained use can erode the expertise on which those gains depend.<n>We develop a dynamic model in which a decision-maker chooses AI usage intensity for a worker over time.<n>We decompose the tool's productivity effect into two channels, one independent of worker expertise and one that scales with it.
  arXiv  Detail & Related papers  (2026-04-03T22:50:32Z)
• Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering [59.18634614089481]
  We present ML-Master 2.0, an autonomous agent that masters ultra-long-horizon machine learning engineering (MLE)<n>By reframing context management as a process of cognitive accumulation, our approach introduces Hierarchical Cognitive Caching (HCC)<n>HCC allows agents to decouple immediate execution from long-term experimental strategy.<n>In evaluations on OpenAI's MLE-Bench under 24-hour budgets, ML-Master 2.0 achieves a state-of-the-art medal rate of 56.44%.
  arXiv  Detail & Related papers  (2026-01-15T13:52:04Z)
• Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2) [49.142289900583705]
  We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability.<n>We discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI.<n>Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI.
  arXiv  Detail & Related papers  (2025-05-22T12:52:34Z)
• AI Safety Should Prioritize the Future of Work [13.076075926681522]
  Current efforts in AI safety prioritize filtering harmful content, preventing manipulation of human behavior, and eliminating existential risks in cybersecurity or biosecurity.<n>While pressing, this narrow focus overlooks critical human-centric considerations that shape the long-term trajectory of a society.
  arXiv  Detail & Related papers  (2025-04-16T23:12:30Z)
• Will Neural Scaling Laws Activate Jevons' Paradox in AI Labor Markets? A Time-Varying Elasticity of Substitution (VES) Analysis [0.0]
  We analyze whether neural scaling laws in artificial intelligence will activate Jevons' Paradox in labor markets.<n>We establish analytical conditions under which AI systems transition from complementing to substituting for human labor.
  arXiv  Detail & Related papers  (2025-03-04T16:55:30Z)
• Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI [67.58673784790375]
  We argue that the 'bigger is better' AI paradigm is not only fragile scientifically, but comes with undesirable consequences.<n>First, it is not sustainable, as, despite efficiency improvements, its compute demands increase faster than model performance.<n>Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate.
  arXiv  Detail & Related papers  (2024-09-21T14:43:54Z)
• Scenarios for the Transition to AGI [0.24664305327044286]
  We analyze how output and wages behave under different scenarios for technological progress. We assume that human work can be decomposed into atomistic tasks that differ in their complexity.
  arXiv  Detail & Related papers  (2024-03-17T22:22:28Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)

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.