ch is crucial to make the scientific enterprise act in concert with the diverse needs of the broader public. This is especially true in science, which continues to suffer from pervasive inequalities across gender, race and socio-economic and cultural differences.5
Need for alternative vision

This is why scientists, academics and policymakers should pay more attention to how AI research is organized and led, especially as the technology becomes essential across scientific disciplines. Used well, AI can support a more equitable scientific enterprise by empowering junior researchers who currently have access to few resources.

Instead, some of today?s wealthiest scientific institutions might think that they can deploy the same strategies as the tech industry uses and compete for top talent on financial terms -- perhaps by getting funding from the same billionaires who back big tech. Indeed, wage inequality has been steadily growing within academia for decades.6 But this is not a path that science should follow.

The ideal model for science is a broad, diverse ecosystem in which researchers can thrive at every level. Here are three strategies that universities and mission-driven labs should adopt instead of engaging in a compensation arms race.

First, universities and institutions should stay committed to the public interest. An excellent example of this approach can be found in Switzerland, where several institutions are coordinating to build AI as a public good rather than a private asset. Researchers at the Swiss Federal Institute of Technology in Lausanne (EPFL) and the Swiss Federal Institute of Technology (ETH) in Zurich, working with the Swiss National Supercomputing Centre, have built Apertus, a freely available large language model. Unlike the controversially-labelled ?open source? models built by commercial labs -- such as Meta?s LLaMa, which has been criticized for not complying with the open-source definition (see go.nature.com/3o56zd5) -- Apertus is not only open in its source code and its weights (meaning its core parameters), but also in its data and development process. Crucially, Apertus is not designed to compete with ?frontier? AI labs pursuing superintelligence at enormous cost and with little regard for data ownership. Instead, it adopts a more modest and sustainable goal: to make AI trustworthy for use in industry and public administration, strictly adhering to data-licensing restrictions and including local European languages.7

Principal investigators (PIs) at other institutions globally should follow this path, aligning public funding agencies and public institutions to produce a more sustainable alternative to corporate AI.

Second, universities should bolster networks of researchers from the undergraduate to senior-professor levels -- not only because they make for effective innovation teams, but also because they serve a purpose beyond next quarter?s profits. The scientific enterprise galvanizes its members at all levels to contribute to the same projects, the same journals and the same open, international scientific literature -- to perpetuate itself across generations and to distribute its impact throughout society.

Universities should take precisely the opposite hiring strategy to that of the big tech firms. Instead of lavishing top dollar on a select few researchers, they should equitably distribute salaries. They should raise graduate-student stipends and postdoc salaries and limit the growth of pay for high-profile PIs.

Third, universities should show that they can offer more than just financial benefits: they must offer distinctive intellectual and civic rewards. Although money is unquestionably a motivator, researchers also value intellectual freedom and the recognition of their work. Studies show that research roles in industry that allow publication attract talent at salaries roughly 20% lower than comparable positions that prohibit it (see go.nature.com/4cbjxzu).

Beyond the intellectual recognition of publications and citation counts, universities should recognize and reward the production of public goods. The tenure and promotion process at universities should reward academics who supply expertise to local and national governments, who communicate with and engage the public in research, who publish and maintain open-source software for public use and who provide services for non-profit groups.

Furthermore, institutions should demonstrate that they will defend the intellectual freedom of their researchers and shield them from corporate or political interference. In the United States today, we see a striking juxtaposition between big tech firms, which curry favour with the administration of US President Donald Trump to win regulatory and trade benefits, and higher-education institutions, which suffer massive losses of federal funding and threats of investigation and sanction. Unlike big tech firms, universities should invest in enquiry that challenges authority.

We urge leaders of scientific institutions to reject the growing pay inequality rampant in the upper echelons of AI research. Instead, they should compete for talent on a different dimension: the integrity of their missions and the equitableness of their institutions. These institutions should focus on building sustainable organizations with diverse staff members, rather than bestowing a bounty on science?s 1%.
References

Jurowetzki, R., Hain, D. S., Wirtz, K. & Bianchini, S. AI Soc. 40, 4145 -- 4152 (2025).
Larivi?re, V., Gingras, Y., Sugimoto, C. R. & Tsou, A. J. Assoc. Inf. Sci. Technol. 66, 1323 -- 1332 (2015).
Aksnes, D. W. & Aagaard, K. J. Data Inf. Sci. 6, 41 -- 66 (2021).
Li, J., Yin, Y., Fortunato, S. & Wang, D. J. R. Soc. Interface 17, 20200135 (2020).
Graves, J. L. Jr, Kearney, M., Barabino, G. & Malcom, S. Proc. Natl Acad. Sci. USA 119, e2117831119 (2022).
Lok, C. Nature 537, 471 -- 473 (2016).
Project Apertus. Preprint at arXiv https://doi.org/10.48550/arXiv.2509.14233 (2025).

This essay was written with Nathan E. Sanders, and originally appeared in Nature.

** *** ***** ******* *********** *************
Upcoming Speaking Engagements

[2026.03.14] This is a current list of where and when I am scheduled to speak:

I?m giving the Ross Anderson Lecture at the University of Cambridge?s Churchill College at 5:30 PM GMT on Thursday, March 19, 2026.
I?m speaking at RSAC 2026 in San Francisco, California, USA, on Wednesday, March 25, 2026.
I?m part of an event on ?Canada and AI Sovereignty,? hosted by the University of Toronto?s Munk School of Global Affairs & Public Policy, which will be held online via Zoom at 4:00 PM ET on Monday, March 30, 2026.
I?m speaking at DemocracyXChange 2026 in Toronto, Ontario, Canada, on April 18, 2026.
I?m speaking at the SANS AI Cybersecurity Summit 2026 in Arlington, Virginia, USA, at 9:40 AM ET on April 20, 2026.
I?m speaking at the Nemertes [Next] Virtual Conference Spring 2026, a virtual event, on April 29, 2026.
I?m speaking at RightsCon 2026 in Lusaka, Zambia, on May 6 and 7, 2026.

The list is m
--- FMail-lnx 2.3.2.6-B20251227
* Origin: TCOB1 A Mail Only System (21:1/229)