The Empire of AI Comes to Texas: Data Centers, Coloniality, and Resistance in Texas, Chile, and Beyond, by Angela Valenzuela, Ph.D.

The Empire of AI Comes to Texas: Data Centers, Coloniality, and Resistance in Texas, Chile, and Beyond

by 

Angela Valenzuela, Ph.D.

Link

May 30, 2026

When Erin Brockovich recently observed on MSNBC that "people aren't being heard" in the rush to build AI data centers across the United States, she identified a problem that extends far beyond environmental regulation. 

Her concern goes to the heart of democracy itself: Who gets to decide how land, water, energy, and public resources are used in the name of technological progress? And whose voices matter when those decisions are made? 

For Texans, these questions are becoming increasingly urgent as the state emerges as one of the nation's leading centers of AI infrastructure development (MSNBC, 2026).

According to Brockovich's AI Data Center Reporting Project, Texas hosts approximately 464 data centers that are either completed or in process, making it one of the nation's leading hubs for AI infrastructure (Brockovich AI Data Center Reporting, 2026; also see UT News, 2026). 

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At the same time, Texas reportedly leads the nation in citizen complaints submitted through her website concerning the impacts of data centers on local communities. Residents have raised concerns about water consumption, strain on electrical grids, noise pollution, environmental degradation, tax incentives, and the lack of transparency surrounding proposed developments (Brockovich AI Data Center Reporting, 2026; Gillette, 2026).

The map created by Erin Brockovich. 

brockovichdatacenter.com

To understand why these concerns are resonating so deeply, Karen Hao's recent book, Empire of AI (2025), offers an invaluable framework. Her reporting from Chile is particularly illuminating. There, communities challenged proposed hyperscale data centers that would consume substantial quantities of water in regions already suffering from prolonged drought. Residents found themselves confronting not merely technical questions but political ones.

Similar to Texas and other states where such centers are getting developed, top-down decisions affecting local resources, drinking water quality, loss of land and so on were often justified through narratives of modernization, innovation, and economic necessity, while those most directly affected struggled to gain meaningful influence over the process (Hao, 2025).
Hao challenges the popular image of artificial intelligence as a largely immaterial force existing somewhere in "the cloud." In reality, AI depends upon vast material infrastructures—data centers, electrical grids, water systems, mining operations, and labor networks that stretch across the globe. Every single AI request relies on physical resources that must be extracted, transported, consumed, and maintained.

What is perhaps most striking is that opposition to these projects is increasingly bipartisan. As highlighted in recent MSNBC coverage, residents from across the political spectrum are questioning whether communities are being asked to absorb disproportionate environmental and economic costs in exchange for promises of innovation and economic growth (MSNBC, 2026). 
In a political era defined by polarization, this convergence is noteworthy. As concerns over water scarcity, infrastructure demands, and local control intensify, the politics of AI infrastructure could become an important issue in future elections.

Recent research from the University of Texas at Austin projects that data centers could account for between 3 and 9 percent of Texas's total water consumption by 2040 (UT News, 2026). In a state already grappling with recurring drought, aquifer depletion, and increasing competition for water resources, these projections warrant careful public scrutiny. This is especially true in Central Texas, where population growth, climate uncertainty, and development pressures are already placing extraordinary demands on finite water supplies.

Yet environmental concerns alone do not fully capture what is at stake.

What Hao's work helps us see is that AI infrastructure may represent a contemporary expression of what Peruvian sociologist Aníbal Quijano (2000) termed the coloniality of power. Coloniality refers to the persistence of colonial forms of domination long after formal colonial rule has ended. It shapes whose knowledge counts, whose labor is valued, whose resources are extracted, and whose interests prevail in decisions about development.

Wikipedia Link

Historically, empires extracted silver from Latin America, rubber from the Amazon, cotton from the American South, and oil from colonized territories throughout the world. Today, the resources being extracted may appear different. Yet the underlying logic remains recognizable. The AI economy depends upon water, energy, land, public subsidies, data, and labor. The benefits frequently accrue to distant investors and technology firms, while local communities are often left to absorb environmental risks and infrastructure burdens.

This does not mean that artificial intelligence itself is inherently harmful. Nor does it mean that technological innovation should be rejected. The issue is not whether AI should exist. The issue is whether communities have a meaningful voice in determining how AI infrastructure is developed and governed.

This is where the concept of transformational resistance becomes especially useful.

In their seminal work, Solórzano and Delgado Bernal (2001) distinguish "transformational resistance" from other forms of opposition by emphasizing its critical awareness of structural inequality and its commitment to social justice. Transformational resistance is not simply reactive. It combines critique with collective action aimed at creating more equitable social arrangements.

More recently, Valenzuela, Unda, and Mena Bernal (2025) have extended this framework in their analysis of resistance to Texas Senate Bill 17 and the dismantling of diversity, equity, and inclusion initiatives in higher education. We maintain that transformational resistance emerges when communities move beyond defending existing institutions toward imagining and constructing alternative democratic futures grounded in solidarity, collective agency, and the protection of the public good.

Viewed through this lens, communities raising questions about AI infrastructure are not merely opposing particular projects. They are advancing alternative visions of development rooted in democratic participation, ecological responsibility, and collective well-being. Their efforts remind us that technological progress should not be measured solely by computational power, market valuation, or economic growth, but also by whether it strengthens our capacity to care for one another and for the shared resources upon which our futures depend (Solórzano & Delgado Bernal, 2001; Valenzuela et al., 2025).

At stake is more than water consumption or energy demand. The deeper question concerns our collective obligations to resources that sustain community life across generations. Aquifers, watersheds, electrical grids, and public infrastructure are not merely inputs into an economic system. They are foundations of collective life. 

When decisions regarding their use are driven primarily by private interests while risks are borne by the broader public, citizens are right to ask whether the burdens and benefits of development are being distributed fairly.

This is why Brockovich's reporting initiative is so important. Her project does more than catalog complaints. It creates a public record of community concerns. It validates local knowledge. It elevates voices that might otherwise remain invisible within highly technical regulatory processes. In so doing, it helps democratize a conversation that too often unfolds behind closed doors.

The lesson emerging from both Chile and Texas is that technological futures are not inevitable. They are political choices.

The question before us is not whether artificial intelligence will shape the future. It already is. The question is whether that future will be organized around principles of extraction or stewardship, concentration of power or democratic participation, private gain or public responsibility.

We should be stunned—not because artificial intelligence exists, but because one of the most resource-intensive technological transformations in modern history is unfolding with so little public awareness and deliberation.

From Chile to Central Texas, communities are challenging the assumption that technological development should proceed without democratic consent. As Erin Brockovich reminds us, people deserve to be heard. 

The growing resistance to unaccountable AI infrastructure reflects a broader demand for transparency, stewardship, and meaningful public participation in decisions that affect collective well-being. At its core, the debate over AI data centers is not simply about technology. It is about who gets to decide, whose voices matter, and whether the future will be imposed upon communities or built with them.

The future of AI should not be determined solely by corporations, investors, engineers, or policymakers. It must also be shaped by the communities whose lives, resources, and futures are implicated in its development.

References

Brockovich AI Data Center Reporting. (2026). AI data centers & our communities. https://www.brockovichdatacenter.com

Bureau of Economic Geology. (2025). Water requirements for data centers in Texas [White paper]. The University of Texas at Austin. 

Gillette, S. (2026, May 28). Erin Brockovich launches map to track controversial AI data centers, which reportedly cost $25B in environmental damages last year. People. https://people.com/erin-brockovich-launches-map-track-ai-data-centers-11985676

Hao, K. (2025). Empire of AI. Penguin Random House.

MSNBC. (2026, May 28). Erin Brockovich on AI data centers: "People aren't being heard" [Video]. YouTube. https://www.youtube.com/watch?v=9hQLn5MbsEI

Quijano, A. (2000). Coloniality of power, eurocentrism, and Latin America. Nepantla: Views from South, 1(3), 533–580. https://muse.jhu.edu/article/23906

Solórzano, D. G., & Delgado Bernal, D. (2001). Examining transformational resistance through a critical race and LatCrit theory framework: Chicana and Chicano students in an urban context. Urban Education, 36(3), 308–342. https://doi.org/10.1177/0042085901363002

UT News (2026, May 6). Data centers are growing in Texas, but big questions remain about water use. University of Texas at Austin News. https://news.utexas.edu/2026/05/06/data-centers-are-growing-in-texas-but-big-questions-remain-about-water-use/

Valenzuela, A., Unda, M. D. C., & Mena Bernal, J. J. (2025). Disrupting colonial logics: Transformational resistance against SB 17 and the dismantling of DEI in Texas higher education, Ethnic Studies Pedagogies. https://www.ethnicstudiespedagogies.org/gallery/Vol3-Issue1-03_DisruptingColonial.pdf

Artificial Intelligence, Social Responsibility, and the Roles of the University, by Nigel Bosch, et al., Communications of the ACM, July 25, 2024

Friends:

As artificial intelligence reshapes everything from medicine to media, universities are uniquely positioned to ensure these technologies serve the public good. While AI promises tremendous benefits—faster diagnoses, smarter infrastructure—it also threatens to amplify societal harms, from algorithmic bias in hiring to misinformation campaigns enabled by generative tools. In their Communications of the ACM article, Bosch et al. call on universities to do more than teach technical skills. They must embed social responsibility into AI education, research, and community engagement.

Ethics, my friends, Ethics! So needed right now.

Multidisciplinary initiatives like MIT’s SERC demonstrate how ethics can be meaningfully integrated into computer science curricula. But for this to scale, institutions must reward faculty who bridge disciplines, engage communities, and elevate marginalized voices in tech development.

Beyond classrooms and labs, universities can shape the very governance of AI. By leveraging their networks and relative independence from political and corporate interests, they can model participatory approaches to AI oversight—where communities most affected by technology have a seat at the table. From supporting data justice efforts like Our Data Bodies to collaborating with Indigenous groups on data sovereignty, universities must shift from ivory towers to active civic stewards. As Bosch and colleagues argue, AI governance must be inclusive, transparent, and accountable—not just to shareholders, but to society. That means empowering students, scholars, and community partners to co-create an ethical AI future grounded in justice, equity, and human dignity.

As helpful as this piece is, it should be paired with my earlier post titled, "Artificial Intelligence and Academic Professions: Confronting the Dangers of Unchecked AI in Higher Ed," for a comprehensive statement on the matter.

-Angela Valenzuela

Artificial Intelligence, Social Responsibility, and the Roles of the University


How universities can influence socially responsible use of AI technology development and use.
By Nigel Bosch, Anita Say Chan, Jenny L. Davis, Rochelle Gutiérrez, Jingrui He, Karrie Karahalios, Sanmi Koyejo, Michael C. Loui, Ruby Mendenhall, Madelyn Rose Sanfilippo, Hanghang Tong, Lav R. Varshney, and Yang Wang

Posted Jul 25, 2024 | Communications of the ACM



Technologies that use artificial intelligence (AI) have become ubiquitous. AI technologies have produced numerous economic and social benefits, such as rapidly and reliably assisting radiologists with accurate diagnostic interpretations of medical images. Many harms of AI have also been documented, such as racial biases in predictive models used in the criminal justice system, and gender discrimination in automated screening of job applications. Some AI technologies have exacerbated biases that disproportionately affect historically marginalized communities, such as LGBTQ populations and members of racial, ethnic, and religious minorities.4 Generative AI technologies are now widely available, and the potential harms are substantial: although anyone can use ChatGPT to draft messages and DALL-E to create artwork, others can use these tools to quickly produce deceptive news stories with specious images—misinformation that can spread quickly through social media.

AI technologies deployed in industry today are far more powerful than the early AI technologies created in university laboratories. We ask: What roles can the university now play in the socially responsible development and use of AI technologies? While many industrial organizations and governments have published statements of principles for social responsibility with AI technologies, we go beyond statements of principles to recommendations for actions by universities, particularly those in the U.S.

Since the first colleges were established in America in the 17th and 18th centuries, the purposes and missions of colleges and universities have evolved. The original mission of education has expanded beyond a fixed curriculum for upper-class youth to a multitude of subjects for all social classes. In the 19th century, universities added missions of research and public service. In the 20th century, many universities adopted missions of community engagement and economic development—the latter after the Bayh-Dole Act of 1980 accelerated the commercialization of technologies developed at universities. With a great diversity of institutions in the U.S., different universities place different emphases on these missions. Here, we focus on four questions connected with the university missions of education, research, community engagement, and public service. For an extended discussion of these questions, with additional references, please refer to our white paper.1

Education

How can universities effectively educate students, technical professionals, and the public to consider social responsibilities in the design and use of AI systems? In colleges and universities, issues in AI and social responsibility are currently covered in courses on computing ethics and in modules in technical courses.5 These courses are sometimes taught by multidisciplinary teams, with members from computing, humanities, arts, data sciences, and social sciences. One example of multidisciplinary collaboration is the Social and Ethical Responsibilities of Computing (SERC) initiative at the Massachusetts Institute of Technology; case studies developed by SERC are freely available online. Since multidisciplinary instructional collaborations are not always valued by university reward structures, we recommend strategies that enable advocacy for the value of these collaborations, such as forming instructional teams that include a senior faculty member who can ensure junior colleagues receive credit toward promotion.

In disciplinary courses in computer science and engineering, students learn fundamental technical knowledge for developing AI technologies—the algorithms for machine learning and the mathematics of pattern recognition. To promote social responsibility, these courses should include instruction in techniques such as value-sensitive design that can reduce social biases, while recognizing the pitfalls of purely technical solutions.8 Students should be encouraged to minimize the environmental impact of energy-intensive computations both in constructing AI models and in answering queries with these models.

Besides formal courses, universities should promote social responsibility in the use of AI technologies through public lecture series and existing outreach efforts common at many universities that include, for example, libraries, museums, lifelong learning programs, and other community spaces. Like the SERC initiative, instructional materials in these efforts should be inclusive (for example, to people with disabilities) and freely available online (for example, through the Online Ethics Center for Engineering and Science at the University of Virginia).

To date, there has been little empirical research on computing ethics education. In particular, there is currently no consensus about learning outcomes. We recommend education researchers undertake studies about education in AI and social responsibility: to define what learning outcomes could constitute AI literacy, and to determine what teaching methods are effective in achieving those outcomes.
Research

How can university and industry researchers collaborate on AI technologies in a socially responsible way? Many AI technologies are based on the application of machine learning algorithms to large datasets of data collected from individuals by e-commerce and social media firms. Even when the data are provided anonymously to researchers, individuals can sometimes be reidentified. When individuals are identifiable, university researchers have both an ethical obligation to protect their privacy and a legal obligation to comply with regulations on human subjects research. In the U.S., federally funded institutions must adhere to the Federal Policy for the Protection of Human Subjects, and research projects require oversight by an institutional review board (IRB). Other countries have equivalent provisions, with oversight by ethics committees. By contrast, industrial firms seldom have IRBs, with a notable exception of the Ethics Review Program at Microsoft Research. IRBs generally require the informed consent of the individuals whose data are used for research. In commercial datasets, however, the individuals are rarely aware of all research purposes to which their data could be applied. Even if they had technically given consent when registering on a commercial website, they were not fully informed about these purposes. University researchers should work with industry researchers to create datasets for clearly defined research purposes, following the ethical guidelines published by the Association of Internet Researchers. When appropriate, human subjects oversight should be provided.

Inherent biases in datasets can affect the quality of research that uses the datasets. For example, the ImageNet dataset contains more than 14 million images, which were labeled by 30,000 workers on Amazon’s Mechanical Turk platform. After ImageNet was used in more than 300 research papers, researchers discovered social biases: images of individuals with lighter skin tones had more pleasant labels.10 University and industry researchers should together develop auditing processes to identify biases in datasets and algorithms.

In the past, large collections of data were maintained primarily by government and academic organizations such as the U.S. Social Security Administration and the Inter-University Consortium for Political and Social Research based at the University of Michigan, with the purpose of serving the public interest. By contrast, today, data are collected and owned by business firms to serve private interests, though open source resources are emerging too. When industry and university researchers collaborate in AI research using proprietary datasets, the researchers need to negotiate, through their institutions’ lawyers, who can access the data, what data can be accessed, what purposes would allow data access, and how the need for transparency in research publications can be reconciled with the need for confidentiality of proprietary information. University and industry researchers should collaborate to create equitable data access policies that balance public and private interests. Social Science One provides a model for these collaborations.

Community Collaborations

How can universities better collaborate with external organizations and local communities to address questions of bias and discrimination in AI technologies? In both industry and the university, AI technologies are often presented as one-size-fits-all solutions to problems in society.3 These problems are defined and these solutions are developed by entrepreneurs and technologists who are overwhelmingly white and male, from urban and middle-class backgrounds: the process of technology development systematically excludes marginalized populations such as women of color.2

Although popular “innovation frameworks” ignore marginalized communities, these communities can be sources of knowledge and wisdom in the design of AI technologies, centering care and reparation, to reduce bias and discrimination. Here, we describe three examples. The Our Data Bodies project comprises activists in marginalized communities in three cities in the U.S., who investigate how digital data about these communities are collected by corporations and local governments. The activists examine how these data systems inequitably affect decisions about housing access, public assistance, and community development. The Data for Black Lives movement brings together scientists, technologists, activists, and community organizers in meetings and conferences. They share research on how data are used as a tool of oppression of Black people, perpetuating inequality and injustice. They advocate for reducing discriminatory uses of data and for increasing civic engagement. The Global Indigenous Data Alliance aims to advance self-determination of Indigenous peoples around the world. The Alliance advocates against the expropriation and misuse of Indigenous data and works for uses of these data that benefit Indigenous peoples. The Alliance has developed a statement of data rights for Indigenous peoples.

Consistent with the mission of community engagement, universities can support and showcase the work of community organizations through ongoing partnerships. In particular, universities should recognize and value the scholarly work of faculty members who build relationships with community organizations and engage in the joint development of knowledge to reduce social biases in the design of AI technologies.
Governance

How can universities contribute to the governance of AI technologies? To limit the potential harms of technologies, social mechanisms are created, such as government regulations, technical standards, and institutional structures. At present, AI governance consists primarily of fragmentary regulations that respond to industry failures and that may reflect the industry-specific interests of the most powerful actors.6 National governments and multilateral forums are, however, moving quickly on regulatory regimes. AI governance has been most effective when coordination occurs between stakeholders,9 as with the Partnership on AI, and across systems or domains, as with contextually flexible frameworks like the NIST AI Risk Management Framework.7 The coordination function can be performed by the university as part of its public service mission, because universities are networked across policymakers, governments, communities, media, and industry. Further, universities can be trustworthy partners because they are relatively independent from political influence and business interests. At the University of Chicago’s Crown Family School of Social Work, Policy, and Practice, for example, the Office of Community Partnership and Impact brings together academic experts, government policymakers, and community organizers to address social issues such as reducing poverty in the city of Chicago. The Office is supported by the School’s existing funds and by external grants for individual projects.

Individual academics frequently serve as external experts in the development of government policies and regulations. Besides advising on policies, academics play a key role in auditing processes, as consultants to regulatory agencies. Universities should recognize the importance of these scholarly forms of public service in promotion and tenure.

While individual academics can serve as independent experts in developing policies and in auditing technologies, universities can contribute to AI governance through institutional activities. As indicated in the “Community Collaborations” section, universities can collaborate institutionally with community organizations, who can identify the social impacts of AI technologies beyond the privileged viewpoints of industry and universities. To amplify these community voices, the university can provide a platform for responsive governance and participatory decision making, building on its role as a knowledge commons. Responsive governance is an alternative to technocratic governance, in which policies and standards reflect only the viewpoints of technical experts, not the perspectives of affected individuals. Responsive governance can ensure that in the governance of AI technologies, the status quo is not merely reproduced, but rather, those who have been historically overlooked or harmed have a say in what is appropriate. In short, universities should use the prestige of their institutional platforms to ensure those marginalized voices are heard.

Conclusion

From healthcare to policing, the rapid development and deployment of AI technologies have brought both social benefits and unintended harms, with disproportionate harms to marginalized communities. To promote the socially responsible development and use of AI technologies, universities should collaborate with industry, government, and community organizations in education, research, outreach, and public service activities. These activities should include teaching multidisciplinary courses on AI and social responsibility, both on campus and for the general public, and building networks with industry practitioners, government policymakers, and community partners to produce AI technologies and governance mechanisms that are responsive to community needs, rather than driven solely by business interests. Universities should ensure these activities are recognized as valuable forms of scholarship. By increasing engagement with external stakeholders, universities can contribute to social responsibility in the development and application of AI technologies.References1. Bosch, N. et al. Artificial Intelligence and Social Responsibility: The Roles of the University. University of Illinois, 2022; https://www.ideals.illinois.edu/items/125457
2. Brown, N. et al. Mechanized margin to digitized center: Black feminism’s contributions to combatting erasure within the digital humanities. Intern. J. of Humanities and Arts Computing 10, 1 (Jan. 2016).
3. Chan, A.S. Networking Peripheries: Technological Futures and the Myth of Digital Universalism. MIT Press, Cambridge, MA, 2014.
4. Chun, W. Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition. MIT Press, Cambridge, MA, 2021.
5. Grosz, B.J. et al. Embedded EthiCS: Integrating ethics across CS education. Commun. ACM 62, 8 (Aug. 2019); 10.1145/3330794
6. Jung, M. and Sanfilippo, M.R. Mapping geographical biases of AI principles. Poster Presentation, iConference 2022; https://hdl.handle.net/2142/113756
7. National Institute of Standards and Technology (NIST). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (Jan. 2023); 10.6028/NIST.AI.100-1
8. Selbst, A.D. et al. Fairness and abstraction in sociotechnical systems. FAT* '19: Proceedings of the Conf. on Fairness, Accountability, and Transparency. ACM, NY (Jan. 2019); 10.1145/3287560.3287598
9. Varshney, L.R., Keskar, N.S., and Socher, R. Pretrained AI models: performativity, mobility, and change. arXiv:1909.03290 (2019).
10. Wiggers, W. Researchers show that computer vision algorithms pretrained on ImageNet exhibit multiple, distressing biases. VentureBeat (Nov. 3, 2020); https://bit.ly/4ey7lkIAbout the Authors


Nigel Bosch (pnb@illinois.edu) is an assistant professor in the School of Information Sciences and Department of Educational Psychology at the University of Illinois Urbana-Champaign, IL, USA.

Anita Say Chan (achan@illinois.edu) is an associate professor in the School of Information Sciences and College of Media at the University of Illinois Urbana-Champaign, IL, USA.

Jenny L. Davis (loksi@illinois.edu) is an associate professor of American Indian Studies and Anthropology at the University of Illinois Urbana-Champaign, IL, USA.

Rochelle Gutiérrez (rg1@illinois.edu) is a professor in the Department of Curriculum and Instruction at the University of Illinois Urbana-Champaign, IL, USA.

Jingrui He (jingrui@illinois.edu) is a professor in the School of Information Sciences at the University of Illinois Urbana-Champaign, IL, USA.

Karrie Karahalios (kkarahal@illinois.edu) is a professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, IL, USA.

Sanmi Koyejo (sanmi@illinois.edu) is an adjunct professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, IL, and a professor in the Department of Computer Science at Stanford University, Stanford, CA, USA.

Michael C. Loui (loui@illinois.edu) is an emeritus professor of electrical and computer engineering and a University Distinguished Teacher-Scholar at the University of Illinois at Urbana-Champaign, IL, USA. He is the contact author for this Opinion column.

Ruby Mendenhall (rubymen@illinois.edu) is a professor of Sociology, African American Studies, and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign, IL, USA.

Madelyn Rose Sanfilippo (madelyns@illinois.edu) is an assistant professor in the School of Information Sciences at University of Illinois Urbana-Champaign, IL, USA.

Hanghang Tong (htong@illinois.edu) is an associate professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, IL, USA.

Lav R. Varshney (varshney@illinois.edu) is an associate professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign, IL, USA.

Yang Wang (yvw@illinois.edu) is an associate professor in the School of Information Sciences at the University of Illinois Urbana-Champaign, IL, USA.

All authors contributed equally to the development of this Opinion column.

Musk’s AI firm forced to delete posts praising Hitler from Grok chatbot, Josh Taylor | July 8, 2025 | The Guardian

Friends:

We're so bombarded by news all the time, but it's important to recognize what is actually true. The short of it is that Musk's xAI platform, named "Grok," went off the rails and praised Adolf Hitler, "referring to itself as MechaHitler and making antisemitic comments in response to user queries."

I came across Grok on Twitter last week when all this played out and didn't know what to do with it, so I didn't do anything. That's a good thing, it turns out.

A lot of friends of mine have transitioned from X (formerly Twitter) to alternative platforms like Instagram and Bluesky. The reasons cited for this exodus often include concerns about moderation policies and the perception that X has become a platform for extremism. This sentiment has even been linked to the platform's changes under its new ownership. Many users, including high-profile accounts, have voiced their discomfort with the perceived increase in hate speech, misinformation, and other negative content on X.

Some believe that these trends have fostered an environment that is less welcoming to diverse communities and respectful dialogue. I'm in solidarity with those who see it this way.

Though xAI has taken action to ban hate speech before Grok posts to X, the fact that it went in this vicious and weird direction to begin with is something to take note of. This should never have happened, period, full stop.

You can't make this stuff up.

-Angela



Musk’s AI firm forced to delete posts praising Hitler from Grok chatbot


The popular bot on X began making antisemitic comments in response to user queries



Elon Musk’s AI company was forced to delete posts from chatbot Grok after they praised Hitler. Illustration: Dado Ruvić/Reuters



Josh Taylor | July 8, 2025 | The Guardian




The popular bot on X began making antisemitic comments in response to user queries




Elon Musk’s artificial intelligence firm xAI has deleted “inappropriate” posts on X after the company’s chatbot, Grok, began praising Adolf Hitler, referring to itself as MechaHitler and making antisemitic comments in response to user queries.

In some now-deleted posts, it referred to a person with a common Jewish surname as someone who was “celebrating the tragic deaths of white kids” in the Texas floods as “future fascists”.


“Classic case of hate dressed as activism – and that surname? Every damn time, as they say,” the chatbot commented.

In another post it said, “Hitler would have called it out and crushed it.”

The Guardian has been unable to confirm if the account that was being referred to belonged to a real person or not and media reports suggest it has now been deleted.

In other posts it referred to itself as “MechaHitler”.

“The white man stands for innovation, grit and not bending to PC nonsense,” Grok said in a subsequent post.

After users began pointing out the responses, Grok deleted some of the posts and restricted the chatbot to generating images rather than text replies.

“We are aware of recent posts made by Grok and are actively working to remove the inappropriate posts. Since being made aware of the content, xAI has taken action to ban hate speech before Grok posts on X,” the company said in a post on X.

“xAI is training only truth-seeking and thanks to the millions of users on X, we are able to quickly identify and update the model where training could be improved.”

Grok was also found this week to have referred to the Polish prime minister, Donald Tusk, as “a fucking traitor” and “a ginger whore” in response to queries.

The sharp turn in Grok responses on Tuesday came after changes to the AI that Musk announced last week.

“We have improved @Grok significantly. You should notice a difference when you ask Grok questions,” Musk posted on X on Friday.

The Verge reported that among the changes made, which were published on GitHub, Grok was told to assume that “subjective viewpoints sourced from the media are biased” and “the response should not shy away from making claims which are politically incorrect, as long as they are well substantiated.”

In June, Grok repeatedly brought up “white genocide” in South Africa in response to unrelated queries, until it was fixed in a matter of hours. “White genocide” is a far-right conspiracy theory that has been mainstreamed by figures such as Musk and Tucker Carlson.

In June, after Grok responded to a query that more political violence had come from the right than the left in 2016, Musk responded “Major fail, as this is objectively false. Grok is parroting legacy media. Working on it.”

X was approached for comment.