mlsanityData to 14 Oct 2026

The complementary contributions of academia and industry to AI research

arXiv 2401.10268 ↗First posted 4 Jan 2024cs.CY cs.AI cs.SIcited by 4

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Abstract (arXiv)

Artificial intelligence (AI) has seen fast paced development in industry and academia. However, striking recent advances by industry have stunned the field, inviting a fresh perspective on the role of academic research on this progress. Here, we characterize the impact and type of AI produced by both environments over the last 25 years and establish several patterns. We find that articles published by teams consisting exclusively of industry researchers tend to get greater attention, with a higher chance of being highly cited and citation-disruptive, and several times more likely to produce state-of-the-art models. In contrast, we find that exclusively academic teams publish the bulk of AI research and tend to produce higher novelty work, with single papers having several times higher likelihood of being unconventional and atypical. The respective impact-novelty advantages of industry and academia are robust to controls for subfield, team size, seniority, and prestige. We find that academic-industry collaborations produce the most impactful work overall but do not have the novelty level of academic teams. Together, our findings identify the unique and nearly irreplaceable contributions that both academia and industry make toward the progress of AI.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 7 of its 51 references that are in the map. First posted on arXiv 4 Jan 2024.

Builds on

7 of 51 references.

References · 7

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  4. 2020Language Models are Few-Shot Learnerscited by 32,419
  5. 2014Growth rates of modern science: A bibliometric analysis based on the number of publications and cited referencescited by 64
  6. 2022Machine Learning Model Sizes and the Parameter Gapcited by 39
  7. 2023The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Researchcited by 27

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.