The paper introduces the Agentic Adoption Index (AAI), a new measure of delegated exposure that captures whether workers actually commit tasks to AI within structured workflows. Using semantic embeddings of 888,000 agent skill specifications from GitHub and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those most vulnerable to pre-AI automation, that AAI correlates more with technical capability than with current LLM use, and that for lower‑educated occupations AAI rises with wages while it falls for higher‑educated, high‑earning workers. These patterns also appear in an independent corpus from the Manus Skills Marketplace.
By Hyeongjae Lee, Jihyang Cheon, Lanu Kim
arXiv:2603. 14805v2 Announce Type: replace Abstract: Enterprise software organizations accumulate critical institutional knowledge - architectural decisions, deployment procedures, compliance policies, incident playbooks - yet this knowledge remains trapped in formats designed for human interpretation.
By Gal Bakal
The article examines the psychological costs that software professionals face when organizations adopt artificial intelligence (AI) in software engineering workflows. Through a case study involving 21 interviews at a large software development services company, the authors identify several negative impacts—accountability anxiety, craft identity disruption, erosion of meaning and satisfaction, increased cognitive load and workload, and uncertainty distress. They also describe how practitioners cope by restoring control, adopting protective adaptations, or absorbing the costs, arguing that AI adoption should be viewed as a human transition rather than merely a technological or organizational change.
By Adam Alami, Elda Paja, Abhishek Tiwari
arXiv:2608. 07779v1 Announce Type: new Abstract: Artificial intelligence is changing the task composition of computing work faster than curricula and training typically adapt.
By Majid Memari, George Rudolph
The paper introduces the Agentic Adoption Index (AAI), a new metric that captures whether workers actually delegate tasks to AI within their workflows, rather than merely measuring potential AI applicability. Using 53,000 agent skill specifications and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those previously deemed most at risk, that AAI aligns more closely with AI’s capabilities than current usage, and that adoption peaks at mid‑wage, bachelor’s‑level occupations while declining at both ends of the wage and education spectrum. The study highlights that technical availability explains much of the variation, but other factors—such as resistance to specification or professional discretion—also influence who adopts AI.
whyItMatters":"The findings suggest that actual AI adoption patterns differ from prior risk assessments, indicating that factors beyond technical feasibility shape who delegates to AI, which has implications for workforce planning and policy."
By Hyeongjae Lee, Jihyang Cheon, Lanu Kim
arXiv:2606. 05770v1 Announce Type: cross Abstract: AI is changing how software engineers work, but it often comes with hidden burdens and costs.
By Vahid Garousi
The study examines how back‑office employees at a large firm use generative AI (genAI) and finds that senior staff use it more sophisticatedly, likely because of their domain expertise. Sophistication differs across functions, peaking in Strategy, Digital Innovation, and Project Management—areas focused on firm‑wide strategic initiatives. The research also shows that sophistication does not improve over time or as a result of formal AI training, indicating that advanced use is hard to change.
By Nicholas J. Hallman, Zachary T. Kowaleski, Anu Puvvada, Jaime J. Schmidt
arXiv:2601. 16700v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) tools have seen rapid adoption among software developers.
By Ludwig Felder, Tobias Eisenreich, Mahsa Fischer, Stefan Wagner, Chunyang Chen
arXiv:2609.22249v1 Announce Type: new
Abstract: This paper treats prompt engineering as a discipline for turning informal human intent into structured AI work specifications. It develops the practice...
By Erfan Loweimi, Hadi Daneshvar, Samira Loveymi, Samir Ouelha, Zhengjun Yue, Hajar Mozaffar, Saturnino Luz
arXiv:2606. 17887v1 Announce Type: cross Abstract: Generative AI (GenAI) deployment in the workplace is accelerating rapidly.
By Dalia Ali, Maria Jos\'e Rodr\'iguez Vel\'azquez, Manoel Horta Ribeiro, Vera Liao, Orestis Papakyriakopoulos
arXiv:2505. 10300v2 Announce Type: replace-cross Abstract: Responsible AI (RAI) efforts increasingly emphasize the importance of addressing potential harms early in the AI development lifecycle through social-technical lenses.
By Muzhe Wu, Yanzhi Zhao, Shuyi Han, Michael Xieyang Liu, Hong Shen
The paper titled "Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025" examines how experienced developers employ AI agents in software development. Through field observations and surveys, it finds that developers value agents for productivity but maintain control over design and implementation to ensure quality. They use agents as collaborative tools rather than full delegation, selecting tasks based on suitability and leveraging their expertise to guide agent behavior.
By Ruanqianqian Huang, Avery Reyna, Sorin Lerner, Haijun Xia, Brian Hempel