# The complementary contributions of academia and industry to AI research

arXiv: https://arxiv.org/abs/2401.10268 · HTML: https://mlsanity.com/paper/2401.10268/

## 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 (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014)
- [Machine Learning Model Sizes and the Parameter Gap](https://mlsanity.com/paper/2207.02852/) (2022)
- [The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research](https://mlsanity.com/paper/2305.02797/) (2023)

## Built on by (most cited first, 4 of 4)

- [Real-World Gaps in AI Governance Research](https://mlsanity.com/paper/2505.00174/) (2025)
- [From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review](https://mlsanity.com/paper/2602.22438/) (2026)
- [Exploring Novelty Differences between Industry and Academia: A Knowledge Entity-centric Perspective](https://mlsanity.com/paper/2603.19319/) (2026)
- [Beyond Algorithms: Conceptual Innovation in Medical Imaging AI](https://mlsanity.com/paper/2606.19270/) (2026)

## 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.