paper-with-me

홈 › Papers

The AI Pyramid A Conceptual Framework for Workforce Capability in the Age of AI

2026-01-10 · Alok Khatri, Bishesh Khanal arxiv

Artificial intelligence (AI) represents a qualitative shift in technological change by extending cognitive labor itself rather than merely automating routine tasks. Recent evidence shows that generative AI disproportionately affects highly educated, white collar work, challenging existing assumptions about workforce vulnerability and rendering traditional approaches to digital or AI literacy insufficient. This paper introduces the concept of AI Nativity, the capacity to integrate AI fluidly into everyday reasoning, problem solving, and decision making, and proposes the AI Pyramid, a conceptual framework for organizing human capability in an AI mediated economy. The framework distinguishes three interdependent capability layers: AI Native capability as a universal baseline for participation in AI augmented environments; AI Foundation capability for building, integrating, and sustaining AI enabled systems; and AI Deep capability for advancing frontier AI knowledge and applications. Crucially, the pyramid is not a career ladder but a system level distribution of capabilities required at scale. Building on this structure, the paper argues that effective AI workforce development requires treating capability formation as infrastructure rather than episodic training, centered on problem based learning embedded in work contexts and supported by dynamic skill ontologies and competency based measurement. The framework has implications for organizations, education systems, and governments seeking to align learning, measurement, and policy with the evolving demands of AI mediated work, while addressing productivity, resilience, and inequality at societal scale.

📄 PDF Abstract BibTeX arXiv:2601.06500

Code (0)

등록된 구현이 없습니다.

Tasks

Decision Making

Similar Papers 제목 키워드 기반

WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning

2025-05-28 · Yuchen Zhuang, Di Jin, Jiaao Chen, Wenqi Shi 외

Large language models (LLMs)-empowered web agents enables automating complex, real-time web navigation tasks in enterprise environments. However, existing web agents relying on supervised fine-tuning (SFT) often struggle…

Pyramid Scene Parsing Network

2016-12-04 · CVPR 2017 7 · Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang 외

Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid p…

Dichotomous Image SegmentationImage ClassificationLesion SegmentationReal-Time Semantic Segmentation+4

SkillChain-Gym: A Benchmark for Reskilling-Aware Production-Inventory Control under Disruptions

2026-06-15 · Carlos Eduardo Sanoja arxiv

Production planning increasingly has to treat workforce capability as a decision variable: certifications lapse when skills are not maintained, new products require skills the current workforce does not hold, and reskill…

The Architecture of AI Transformation: Four Strategic Patterns and an Emerging Frontier

2025-09-02 · Diana A. Wolfe, Alice Choe, Fergus Kidd arxiv

Despite extensive investment in artificial intelligence, 95% of enterprises report no measurable profit impact from AI deployments (MIT, 2025). In this theoretical paper, we argue that this gap reflects paradigmatic lock…

PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents

2025-12-11 · Yuqun Zhang, Yuxuan Zhao, Sijia Chen arxiv

This paper proposes PyFi, a novel framework for pyramid-like financial image understanding that enables vision language models (VLMs) to reason through question chains in a progressive, simple-to-complex manner. At the c…

Visual Reasoning