The initiative, known as NJSCI, is designed to establish a shared supercomputing resource for researchers, students, entrepreneurs, businesses and organizations throughout New Jersey. The announcement described access to advanced computing as a potential bottleneck for ideas that require more computational power than ordinary laboratory, classroom or commercial systems can provide. The planned resource is expected to support work including cancer research and flood-risk mapping, as well as other research and training activities that benefit from large-scale calculations.

According to the announcement, NJSCI is expected to double the supercomputing capacity currently available across New Jersey’s research universities. That capacity increase is a planned feature rather than a completed expansion. The initiative is also intended to broaden education and training opportunities, connect university expertise with statewide needs and help prepare students and working professionals for roles in fields where artificial intelligence and data-intensive computation are increasingly important.

The partnership is backed by more than $125 million in public and private investment. The funding announced includes a $75 million personal gift from NVIDIA co-founder Chris Malachowsky and his wife, Melody, a $25 million investment from SHI International and a $25 million state investment made through the New Jersey Economic Development Authority. The announcement did not provide a detailed schedule for procurement, installation or allocation of computing resources, so the timetable and technical specifications of the supercomputer remain to be defined through the initiative’s implementation.

NJIT Interim President John A. Pelesko said the initiative could reduce the barrier between promising ideas and the computing power needed to test them. He described researchers and students as having good ideas that may wait because the necessary machine is inaccessible. NJIT’s role in NJSCI will be to help ensure that shared access is translated into research opportunities and graduates with relevant experience, according to the release. The announcement did not report discoveries, completed research projects or measured outcomes resulting from the partnership.

The institute’s participation will extend beyond its own researchers. Jamie Payton, dean of NJIT’s Ying Wu College of Computing, serves on the NJSCI Governance, Allocation and Access Working Group and the NJSCI Education and Workforce Collaborative. The collaborative is working on curriculum, faculty professional development and workforce-development efforts. Payton said the initiative represents an “AI+X” approach, combining artificial intelligence with subject-matter expertise so that complex challenges can be addressed across disciplines rather than through computation alone.

David Bader, a distinguished professor of data science, associate dean for research in the Ying Wu College of Computing and director of NJIT’s Institute for Data Science, has participated in AI supercomputer working sessions involving the Governor’s Office, NVIDIA, SHI and partner universities. He said the restriction faced by New Jersey’s AI community is often less about the availability of talent than access to computing resources at scale. The initiative is intended to give faculty and students an opportunity to work with serious hardware that is not normally available through laptops or free cloud-service tiers.

Bader also described the partnership as a model for sustained cooperation among state government, technology companies and higher education. NJIT established a Department of Data Science and an Institute for Data Science to develop expertise for a data- and AI-intensive economy, and the supercomputer initiative is expected to extend that training into courses using high-performance resources. The approach would allow students to run models at scales unavailable on personal devices, giving them practical experience that Bader said employers increasingly expect from AI specialists.

Access rules, technical architecture and the distribution of computing time were not specified in the release. The initiative does, however, include governance and allocation structures intended to determine how the shared resource can be used. As a statewide partnership involving multiple universities and agencies, NJSCI may differ from computing projects housed by a single institution, with access and priorities potentially shaped through the working groups and collaborative activities described in the announcement.

Sherrill said the initiative builds on New Jersey’s history of scientific and industrial innovation, citing Thomas Edison, Bell Labs and other examples of research and invention associated with the state. She said the investment is intended to strengthen the use of emerging technologies in scientific discovery, expand students’ access to advanced skills, widen workforce opportunities and improve communities’ capacity to address challenges such as disease and environmental risk. These benefits are stated goals of the launch; the release does not yet document outcomes from the new system.

Malachowsky, whose personal gift forms the largest component of the announced investment, said New Jersey holds special significance because it was where his story began. He said the initiative could unlock talent, transform education and business, support the workforce and bring institutions together around demanding computational problems. With its announced funding, university partnerships and workforce-development activities, NJSCI has been set up as a statewide platform for advanced computing, while its practical effects will depend on the completion and use of the shared supercomputer.

Subject of Research: New Jersey’s statewide initiative to expand shared supercomputing capacity for research, education, workforce development and AI applications.

Article Title: NJIT partners on New Jersey Supercomputer Initiative to drive statewide innovation

Article References: NJIT partners on New Jersey Supercomputer Initiative to drive statewide innovation. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: New Jersey Supercomputer Initiative, NJIT, high-performance computing, AI research, workforce development, public-private partnership, artificial intelligence, supercomputing capacity, research universities, NVIDIA, partners, Jersey

The study, published in the journal Global Public Policy and Governance, was conducted by Shaleen Khanal of the Centre for Trusted Internet and Community at the National University of Singapore and Hongzhou Zhang of the S. Rajaratnam School of International Studies at Nanyang Technological University. Motivated by the theoretical framing of power-knowledge, the idea, associated with Michel Foucault, that the production of knowledge and the exercise of power are deeply intertwined, the authors set out to measure whether the concentration of innovation inputs in corporate hands is translating into a concentration of scientific output and agenda-setting power. The stakes, they argue, extend far beyond academic sociology: whoever controls the production of knowledge in critical technologies ultimately shapes the direction, safety and governance of technologies that will define economic and geopolitical power for decades.

To move beyond anecdote, the researchers assembled an unusually large evidence base. Using OpenAlex, an open-access index of scholarly works, they identified 3.28 million publications on critical and emerging technologies published between 2001 and 2024, spanning fields such as artificial intelligence, quantum computing and biotechnology. They then documented the share of these outputs authored or co-authored by scientists affiliated with large technology firms, using author-level affiliation data rather than publication-level labels alone, a methodological choice that allowed them to capture researchers who move between universities and industry. Within this corpus, they drilled down into artificial intelligence, the technology where concerns about corporate capture have been loudest, to assess both the extent and the character of Big Tech’s influence.

The headline result is mixed evidence. Across the full landscape of critical and emerging technologies, Big Tech’s footprint is significant but far from dominant, contradicting the strongest versions of the epistemic capture thesis. Public research institutions remain central to knowledge production in most critical fields, and corporate publishing varies widely across domains, reflecting the different economics of basic and applied research in each area. In artificial intelligence, however, the picture is different: the study documents an unmistakable and increasing presence of Big Tech-affiliated scientists over the two decades covered by the data, consistent with a broader literature showing that industry’s share of leading AI papers has climbed steeply since the deep learning revolution made massive datasets and computing clusters decisive inputs.

That escalation is unsurprising given the underlying resource asymmetry. Training frontier AI models requires computing infrastructure, specialized hardware and capital budgets that few universities can match. Previous scholarship has described a compute divide in which academic labs are progressively squeezed out of state-of-the-art AI research, and high-profile controversies, including the departure of prominent ethics researchers from major labs, have fed concerns that corporate priorities shape not only what gets researched but what may be published. Yet the new analysis complicates the gloomiest narratives. The authors note that emerging evidence suggests Big Tech’s influence on research has in some respects been overtly exaggerated, and their own data show that corporate-affiliated scientists have not colonized critical technology research wholesale, even as their presence in AI grows year after year.

One of the study’s most striking findings concerns collaboration. Publications co-authored by Big Tech researchers and other knowledge institutions, including universities and public research organizations, are associated with higher research impact than papers produced in isolation. This suggests that the flow of industrial resources, data and engineering expertise into joint projects is not merely extracting value from academia but genuinely raising the scientific quality and visibility of the resulting work. Collaborative papers also show a higher probability of including keywords related to trustworthy AI, indicating that corporate-academic partnerships may, at least in bibliometric terms, be pulling attention toward questions of safety, fairness and accountability rather than away from them. The finding challenges a simple capture story in which industry involvement necessarily narrows the research agenda toward commercially lucrative topics.

At the same time, the authors are careful not to declare the concerns unfounded. The theoretical framework of power-knowledge implies that influence operates through channels that bibliometrics can only partially reveal: the setting of technical standards, the funding of labs, the revolving door of personnel between firms and governments, lobbying networks, and the strategic control of research infrastructures such as cloud computing platforms. Other work by the same research team has documented how Big Tech firms act as super policy entrepreneurs, increasing their power in the policy process around generative AI, and how public-private relations in areas such as defense and security governance are being restructured around corporate technological capabilities. The new bibliometric evidence should therefore be read as calibrating the debate rather than closing it.

The findings carry direct implications for technological sovereignty, a concern that has moved to the center of policy debates in the United States, Europe, China and beyond. Governments increasingly treat critical and emerging technologies as strategic assets, screening foreign investment, restricting exports and funding national research programs precisely because control over knowledge production is understood as control over future power. If a small number of private firms, rather than public institutions, dominate the production of knowledge in these fields, then national strategies aimed at sovereignty must grapple not only with foreign rivals but with domestic corporate concentration. The study’s evidence that Big Tech is most entrenched in AI, arguably the most consequential critical technology, sharpens this dilemma: policymakers who depend on corporate labs for national competitiveness may find that the same dependence erodes public capacity for independent assessment, regulation and basic research.

For universities and research funders, the message is double-edged. The positive association between Big Tech collaboration and research impact creates real incentives to deepen industrial partnerships, and such partnerships have produced celebrated scientific advances, from protein structure prediction to large-scale language models. But the study’s framing also echoes calls from prominent university leaders that academia must reclaim independent capacity in AI research for the public good, ensuring that questions about societal risk, ethics and long-term safety are pursued even when they offer no commercial return. The risk identified in the broader literature is that corporate funding and infrastructure dependency can produce insularity, recency bias and quiet suppression of unfavorable findings, dynamics that citation counts alone will not capture.

Ultimately, the research offers an evidence-based middle position in a debate that has often been driven by rhetoric. Big Tech has not captured the production of scientific knowledge across critical technologies, but its presence in artificial intelligence research is growing steadily and its collaborative ties amplify both its scientific contribution and its agenda-setting influence. The privatization of critical knowledge infrastructure, the authors conclude, remains a live geopolitical risk, one that demands governance responses calibrated to the actual scale and character of corporate power rather than to either panic or complacency. As data, compute and capital continue to concentrate, the question is no longer whether Big Tech matters for frontier science, but how democratic societies can harness its capabilities while preserving the independence of the knowledge systems on which they depend.

Subject of Research: Big Tech's influence on scientific research in critical and emerging technologies such as artificial intelligence

Article Title: Power-knowledge and strategic control: how Big Tech is reshaping research in critical technologies

Article References: Khanal, S., & Zhang, H. (2026). Power-knowledge and strategic control: how Big Tech is reshaping research in critical technologies. Global Public Policy and Governance, 6(2), 199-226. https://doi.org/10.1007/s43508-026-00150-2

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00150-2

Keywords: Big Tech, artificial intelligence, AI research, critical and emerging technologies, power-knowledge, bibliometrics, technological sovereignty, trustworthy AI, epistemic capture, industry-academia collaboration, strategic, control