# The Privatization of AI Research(-ers): Causes and Potential Consequences -- From university-industry interaction to public research brain-drain?

arXiv: https://arxiv.org/abs/2102.01648 · HTML: https://mlsanity.com/paper/2102.01648/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2021.

## Built on by (most cited first, 10 of 10)

- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023)
- [Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act](https://mlsanity.com/paper/2506.01931/) (2025)
- [Analyzing the Impact of Companies on AI Research Based on Publications](https://mlsanity.com/paper/2310.20444/) (2023)
- [Questionable practices in machine learning](https://mlsanity.com/paper/2407.12220/) (2024)
- [On Tables with Numbers, with Numbers](https://mlsanity.com/paper/2408.06062/) (2024)
- [Big Tech influence over AI research revisited: memetic analysis of attribution of ideas to affiliation](https://mlsanity.com/paper/2312.12881/) (2023)
- [Irresponsible AI: big tech's influence on AI research and associated impacts](https://mlsanity.com/paper/2512.03077/) (2025)
- [Social and environmental impact of recent developments in machine learning on biology and chemistry research](https://mlsanity.com/paper/2210.00356/) (2022)
- [Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias](https://mlsanity.com/paper/2512.05714/) (2025)
- [AI Patents in the United States and China: Measurement, Organization, and Knowledge Flows](https://mlsanity.com/paper/2604.10529/) (2026)

## Abstract (arXiv)

The private sector is playing an increasingly important role in basic Artificial Intelligence (AI) R&D. This phenomenon, which is reflected in the perception of a brain drain of researchers from academia to industry, is raising concerns about a privatisation of AI research which could constrain its societal benefits. We contribute to the evidence base by quantifying transition flows between industry and academia and studying its drivers and potential consequences. We find a growing net flow of researchers from academia to industry, particularly from elite institutions into technology companies such as Google, Microsoft and Facebook. Our survival regression analysis reveals that researchers working in the field of deep learning as well as those with higher average impact are more likely to transition into industry. A difference-in-differences analysis of the effect of switching into industry on a researcher's influence proxied by citations indicates that an initial increase in impact declines as researchers spend more time in industry. This points at a privatisation of AI knowledge compared to a counterfactual where those high-impact researchers had remained in academia. Our findings highlight the importance of strengthening the public AI research sphere in order to ensure that the future of this powerful technology is not dominated by private interests.
