Mapping the Frontier AI Ecosystem: Organizational Productivity, Frontier Compute, and the Industry-Academia Divide
Hajar Ait Lamkademe
Journal of Information Organization, Vol. 16, No. 2, 2026.
DOI: https://doi.org/10.6025/jio/2026/16/2/45-58
Link: https://www.dline.info/jio/fulltext/v16n2/jiov16n2_1.pdf
Abstract:
The rapid proliferation of Large Language Models (LLMs) and multimodal AI systems has created an urgent
need for systematic, analyzable resources to track architectural innovation, organizational strategy, and
computational scaling trends. This paper presents a structured analysis of the "LLMs & Frontier AI Models
Dataset" (Kaggle, 2026), a curated tabular resource documenting metadata for 47 attributes across notable
AI models released through mid-2026. We examine organizational productivity patterns, frontier model
compute allocations, strategic divergences between closed and open weight development paradigms, and
the shifting balance between industrial and academic contributions. Our findings reveal a highly concentrated
ecosystem dominated by a small cohort of well-resourced laboratories, with distinct strategic specializations
emerging across geographic and organizational boundaries. We discuss implications for research
reproducibility, policy development, and future dataset curation practices, while acknowledging limitations
inherent to static, crowd-sourced metadata collections.