When Education Serves the Machine: The Technocapitalist Capture of Universities
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By Bruna Damiana Heinsfeld
On the first day of “AI Camp” at California State University, students were welcomed not by teachers but by branding. Amazon notebooks. AWS Jam. A motto borrowed from the corporate playbook: Think Big (Singer, 2025). One student, interviewed by The New York Times, compared the experience to “[...] a timeshare presentation. [...] You get the vacation — but you also have to sit through the propaganda.”
This was not simply an event. It was a performance of alignment: a multimillion-dollar gesture signaling that the largest public university system in the United States had chosen its future, one written in the language of corporate partnership. Through a $16.9 million contract with OpenAI, Cal State committed to distributing ChatGPT Edu licenses to more than half a million students and staff, in what the company proudly described as “the world’s largest rollout of ChatGPT to date” (Singer, 2025).
What unfolds here is not the integration of AI into education, but the integration of education into AI: the reconfiguration of the university as infrastructure for technocapitalism.
The gold rush of alignment
From California to Ohio, universities are rushing to declare themselves “AI-ready,” “AI-empowered,” “AI-fluent.” Institutes, minors, and certification tracks multiply, accompanied by announcements from Microsoft, Google, and Amazon promising “democratized access” to cutting-edge tools. Yet beneath the rhetoric of modernization and employability lies a quieter narrative: one of anxiety. As enrollment drops and public trust wanes, institutions seek salvation through technological branding, mistaking procurement for progress.
California State University now markets itself as the nation’s “first and largest AI-empowered university system,” rolling out ChatGPT Edu to more than 500,000 students and faculty (California State University, 2025). Public records and independent reporting place the contract value at roughly $17 million over 18 months (Barajas, 2025).
The scramble to adopt AI is less a pedagogical transformation than an economic defense mechanism, where “innovation” becomes both shield and symptom of structural precarity (Raden, 2025).
The curriculum beneath the curriculum
Every technology that enters the classroom teaches twice: once through its explicit content and again through its implicit ideology. Today’s AI initiatives teach efficiency as virtue, scalability as destiny, and data as truth, the operational grammar of technocapitalism, a logic described as instrumental rationality (Feenberg, 1999).
At Cal State, Amazon engineers did not merely instruct students on coding or model deployment; they modeled a worldview where “Think Big” and “Customer Obsession” masquerade as pedagogical values. The lesson is subtle but profound: to see technology not as a sociocultural artifact but as an inevitable horizon of progress (Selwyn, 2022).
This quiet recoding of education into the language of the market reflects a technocapitalist drift of education (Suarez-Villa, 2009). Public institutions are recalibrated toward private accumulation, their boundaries with industry dissolving in the name of innovation. Each technological “revolution”, from the teaching machine to the MOOC, has promised democratization while deepening systems of control (Cuban, 1986; Reich, 2020; Watters, 2021; Weller, 2020).
A history that repeats itself
The excitement surrounding AI today echoes earlier moments of industrial faith. In the early twentieth century, Ford’s efficiency doctrine re-imagined schooling as a production line. Mid-century, IBM’s testing machines turned learning into data. The 2000s crowned the learning-management system as the new assembly belt, translating classroom rhythms into dashboards and metrics.
Each wave announced “innovation.” Each delivered an even more profound alignment with corporate industrial logics (Heinsfeld, in press). The current rhetoric of AI literacy, with its promises of personalization and scale, continues this lineage. Under the guise of preparing students for a technological world, literacy becomes the language of employability: measured not by understanding technology’s implications, but by fluency in using corporate tools. The new vocabulary conceals an old belief: that education’s legitimacy depends on its proximity to the market.
The rhetoric of inevitability
A recent Nature feature describes a global acceleration: Tsinghua onboarding students via AI agents; Ohio State mandating AI courses; Sydney reverting to handwritten exams. “The rate of adoption,” the article warns, “has been accelerating too fast for institutional policies, pedagogies, and ethics to keep up” (Pearson, 2025).
Acceleration has become an alibi. “Like the rise of the internet,” Cal State officials insist, “artificial intelligence is another technological revolution, and higher education can’t simply stand by and watch” (Singer, 2025). But inevitability is not a law of nature; it is a rhetorical technology. It converts choice into destiny, recasting compliance as courage. When universities say they cannot “stand by,” what they often mean is that they cannot afford to resist.Resistance risks appearing outdated to legislators and donors; it jeopardizes already-precarious enrollments, funding streams, and the promise of institutional relevance. In this calculus, compliance feels safer than sovereignty.
From public mission to platform logic
AI’s arrival in universities extends an older pattern of privatization through partnership. Foundations and corporations fund “innovation centers” that bypass faculty governance; vendors integrate their products into the infrastructures that manage courses, identities, and data. The CSU-OpenAI collaboration exemplifies this entanglement: a pedagogical tool framed as a market triumph (Raden, 2025).
OpenAI’s own blog celebrated the rollout as “the largest deployment to date,” a phrase revealing more about the company’s metrics than the university’s mission (OpenAI, 2025). In the same month, OpenAI announced a $38 billion cloud-computing partnership with Amazon, a reminder that “AI for education” is inseparable from the infrastructures of profit that sustain it (Metz, 2025). When scale becomes the measure of success, students become units of measurement.
Reclaiming education
To question this trajectory is not to reject technology; it is to reject technological destiny. The task is not to teach students to use AI but to understand what AI does: economically, politically, epistemologically.
Education should remain the space where we confront the architectures of our tools: the labor they obscure, the inequities they reproduce, the futures they foreclose. Instead, it risks becoming the laboratory of the very systems it should critique.
Every technological promise in education carries a story about control, about who designs, who decides, and who benefits. If universities continue to align their futures with corporate platforms, they may soon discover that it is not they who chose to work with Big Tech, but Big Tech that chose to work through them.
Dr. Bruna Damiana Heinsfeld is an Assistant Professor of Learning Technologies at the University of Minnesota, Twin Cities. Her research is rooted in the Critical Studies of Education and Technology (CSET), focusing on critically examining the complex intersections of technology, society, and education. Her work seeks to illuminate the ideological, cultural, and economic forces influencing these dynamics, fostering a deeper understanding of their implications for education.
References
Barajas, J. (2025, August 19). Inside Cal State’s big $17 million bet on ChatGPT for all. LAist. https://laist.com/news/education/chatgpt-california-state-university-csu-ai-deal
California State University. (2025, February 4). CSU announces landmark initiative to become nation’s first and largest AI-empowered university system [Press release]. https://www.calstate.edu/csu-system/news/Pages/CSU-AI-Powered-Initiative.aspx
Cuban, L. (1986). Teachers and machines: The classroom use of technology since 1920. Teachers College Press.
Feenberg, A. (1999). Questioning technology. Routledge.
Heinsfeld, B. D. (in press). Tethered futures: The technocapitalist shaping of educational imagination. Critical Education.
Metz, C. (2025, November 3). OpenAI signs $38 billion cloud computing deal with Amazon. The New York Times. https://www.nytimes.com/2025/11/03/technology/openai-amazon-cloud-computing.html
OpenAI. (2025). OpenAI and the California State University system bring AI to 500,000 students and faculty [Blog post]. https://openai.com/index/openai-and-the-csu-system/
Pearson, H. (2025). Universities are embracing AI: Will students get smarter or stop thinking? Nature, 646(8086), 788–791. https://doi.org/10.1038/d41586-025-03340-w
Raden, J. (2025, October 23). Higher ed’s rush to adopt AI is about so much more than AI. Defector. https://defector.com/higher-eds-rush-to-adopt-ai-is-about-so-much-more-than-ai
Reich, J. (2020). Failure to disrupt: Why technology alone can’t transform education. Harvard University Press.
Selwyn, N. (2022). Education and technology: Key issues and debates (3rd ed.). Bloomsbury.
Singer, N. (2025, October 26). Big tech makes Cal State its A.I. training ground. The New York Times. https://www.nytimes.com/2025/10/26/technology/cal-state-ai-amazon-openai.html
Suarez-Villa, L. (2009). Technocapitalism: A critical perspective on technological innovation and corporatism. Temple University Press.
Watters, A. (2021). Teaching machines: The history of personalized learning. MIT Press.
Weller, M. (2020). 25 years of ed tech. Athabasca University Press. https://read.aupress.ca/projects/25-years-of-ed-tech