Image: Kiki Chen

It’s 2026, and people who have never used AI software are mythical creatures. To survive, we rely on these technologies to navigate our daily habits, social lives, and careers. However, this increased incorporation of AI brings increased demands with it. When efficiency is prioritized over quality and computing power holds precedence over sustainability, we are forced to reconsider the tradeoffs we’re willing to accept. 

Panelists Victoria Copeland, Kim Fernandes, and Shin Yang, in conversation with M. Remi Yergeau,* discussed tactics for resisting oppressive technology during the DISCO Network** event “Against Surveillance and Spectacle: Building Global Resistance to Tech-Mediated Oppression” on March 10, 2026. 

Copeland explained, “When we talk about tech-mediated oppression, it’s not just that these tools are reliant on underpaid, low-wage, sometimes violent labor across the globe, but that these tools are also being marketed as a fix to problems that are much deeper than what an LLM developer could ever realistically offer up as a solution.”

Artificial intelligence (AI) is marketed as a tool that makes everything easier, a technology that streamlines quick and useful responses. Cutting out human interaction, some turn to AI to diagnose symptoms, become their therapist, and help with everyday tasks and assignments. These actions may have harmless intent, but the consequences cannot be overlooked.

“Systems prioritize efficiency,” said Yang, “and when efficiency becomes the top priority, the nuances get flattened.” We face a constant tradeoff between speed and quality. This exchange is equally as important as the give-and-take between visibility and safety. The more we post on the internet and interact with online technologies, the more personal information we relinquish about ourselves. 

Not to mention AI’s environmental impact—according to a recent study from the United Nations University, ChatGPT alone receives an estimated 2.5 billion prompts daily, consuming an estimated 383 Gigawatt-hours in one year. In 2030, UNU estimates data centers will consume 9.3 trillion litres of water, equal to the basic annual domestic water needs of 1.3 billion people in Sub-Saharan Africa. These statistics only scratch the surface, as we can also take into account AI’s water footprint, land footprint, and data center pollution, which disproportionately impacts marginalized communities. 

The panelists also expressed the need for us to consider the problems we face with emerging technology in a broader context. Our “need” for AI stems from broader problems in the system—one that demands increased efficiency and output. “We’re all working more,” said Fernandes. “We do a whole lot more than we should in terms of labor because of the way that every moment of our lives is calculated toward some kind of addition of value.”

So what can we do to resist and rebuild this system that prioritizes quantity over quality? 

Copeland encourages us to team up with people, rely on interdisciplinarity, and build collaborations. The AI Resist List details a variety of organizations and tools that use tactics of refusal, resistance, and reclamation to fight against AI. For example, the tool Nightshade transforms images and artwork into “poison” samples, so that AI models using those images for training without consent will learn unpredictable behaviors. The No Palantir in the NHS campaign is a grassroots worker, patient, and community campaign to remove Palantir Technologies from NHS data infrastructure. The movement aims to “weaken Palantir, a key supporter and enabler of state violence, as well as resist the ongoing outsourcing, asset stripping and privatisation of the NHS and the expansion of state surveillance technologies in the UK.”

Combined resistance can be effective. We can surround ourselves with people who are also fighting back against the harms of AI, educate others, and opt out of AI use whenever possible. We can challenge demands and renegotiate our relationship with emerging technologies to find a more just and sustainable equilibrium. 

*Victoria Copeland is a disabled organizer and research fellow based at the UCLA Center on Resilience and Digital Justice. 

Kim Fernandes is an assistant professor of sociocultural anthropology at Brown University.

Shin Yang is a digital governance strategist and interdisciplinary researcher working at the intersection of AI-integrated product design, legal frameworks, and community infrastructure. 

M. Remi Yergeau is an associate professor in Communication and Media Studies at Carleton University and the Director of the DISCO Network Lab, Digital Accessible Futures (DAF) Lab.

**The DISCO (Digital Inquiry, Speculation, Collaboration, and Optimism) Network is a collective of interdisciplinary researchers working to envision a new anti-racist and anti-ableist digital future.