arXiv:2308. 05201v4 Announce Type: replace Abstract: Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape labor market dynamics.
By Jin Liu, Xingchen Xu, Xi Nan, Yongjun Li, Yong Tan
arXiv:2606. 29111v1 Announce Type: new Abstract: When firms deploy autonomous AI, they must decide how much work to leave to the system and how much to keep workers engaged.
By Simrita Singh, Naireet Ghosh, Tinglong Dai
arXiv:2606. 26359v1 Announce Type: new Abstract: Ray Kurzweil described a thesis of accelerating returns, which is the most influential narratives in discussions of technological progress.
By Guojun Liao (Department of Mathematics, The University of Texas at Arlington)
arXiv:2604. 03501v5 Announce Type: replace-cross Abstract: Experimental evidence suggests that AI tools raise worker productivity, but also that sustained use can erode the expertise on which those gains depend.
By Michael Caosun, Sinan Aral
A full-stack approach to making advanced AI more capable, more affordable, and more widely useful.
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:2412. 19754v4 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) is transforming the nature of work, yet there is limited empirical evidence on how it affects demand for human skills.
By Elina M\"akel\"a, Matthew Bone, Mareike Sehrer, Farah Nanji, Fabian Stephany
arXiv:2512. 04988v2 Announce Type: replace-cross Abstract: Emerging agentic marketplaces provide the economic infrastructure for matching and coordinating the large amounts of AI agents used in agentic swarms.
By Christopher Chiu, Simpson Zhang, Mihaela van der Schaar
arXiv:2412. 08610v3 Announce Type: replace-cross Abstract: Recent evidence, both in the lab and in the wild, suggests that the use of generative artificial intelligence reduces the diversity of content produced.
By Manish Raghavan
How OpenAI deep research helps Bain & Company understand complex industry trends.
The article titled "The Work Now Within Reach" discusses how increasingly capable and affordable AI technologies can broaden the range of tasks that individuals and businesses can perform. It highlights the potential for AI to enhance productivity and enable more efficient, cost-effective growth. The piece emphasizes the expanding opportunities for leveraging AI to achieve greater work outcomes.