Test: 15% of Americans say they would work for AI boss
Test summary
In Brief
Posted:
4:41 PM PDT · March 30, 2026
Image Credits:id-work / Getty Images
Would you trade your manager for a chatbot? A growing number of Americans are saying yes.
According to a Quinnipiac University poll published Monday, 15% of Americans say they’d be willing to have a job where their direct supervisor was an AI program that assigned tasks and set schedules. Quinnipiac surveyed 1,397 adults in the United States and conducted the poll — which included questions about AI adoption, trust, and job fears — between March 19 and 23, 2026.
Of course, the majority of respondents said they wouldn’t be willing to swap their human boss for an AI people manager. But the use of AI as a supervisor is gaining in popularity, even if one isn’t directly in charge of steering entire teams of people.
Companies like Workday have launched AI agents that can file and approve expense reports on employees’ behalf. Amazon has deployed new AI workflows to replace some of the responsibilities of middle management, laying off thousands of managers in the process. Engineers at Uber even built an AI model of CEO Dara Khosrowshahi to field pitches before meetings with their actual boss.
Across organizations, AI is being used to replace layers of management in what some are calling “The Great Flattening.” Soon, we may start to see entire billion-dollar companies of one, with fully automated employees and executives.
Americans are wary about what that means for their job prospects. The majority of respondents in Quinnipiac’s survey — 70% — said they believe advances in AI will lead to a decrease in the number of job opportunities for people. Among employed Americans, 30% were either very concerned or somewhat concerned that AI would make their job specifically obsolete.
Topics
Subscribe for the industry’s biggest tech news
Latest in AI
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
aiwork
Vega: Learning to Drive with Natural Language Instructions
arXiv:2603.25741v2 Announce Type: replace-cross Abstract: Vision-language-action models have reshaped autonomous driving to incorporate languages into the decision-making process. However, most existing pipelines only utilize the language modality for scene descriptions or reasoning and lack the flexibility to follow diverse user instructions for personalized driving. To address this, we first construct a large-scale driving dataset (InstructScene) containing around 100,000 scenes annotated with diverse driving instructions with the corresponding trajectories. We then propose a unified Vision- — Sicheng Zuo, Yuxuan Li, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu
Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models
arXiv:2603.25716v2 Announce Type: replace-cross Abstract: Video world models have shown immense potential in simulating the physical world, yet existing memory mechanisms primarily treat environments as static canvases. When dynamic subjects hide out of sight and later re-emerge, current methods often struggle, leading to frozen, distorted, or vanishing subjects. To address this, we introduce Hybrid Memory, a novel paradigm requiring models to simultaneously act as precise archivists for static backgrounds and vigilant trackers for dynamic subjects, ensuring motion continuity during out-of-vie — Kaijin Chen, Dingkang Liang, Xin Zhou, Yikang Ding, Xiaoqiang Liu, Pengfei Wan, Xiang Bai
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!