From ‘Superintelligence’ to Algorithmic Sovereignty: How AI’s Rhetorical Shifts Reflect Geopolitical Power Struggles Over Technological Control
Original framing: “AI or ‘super intelligence’? Trump isn’t the only one who is confused” — New Scientist
The term ‘superintelligence’ was first popularized in 1958 by mathematician I.J. Good to describe an AI that could recursively improve itself—a concept later weaponized during the Cold War to justify military AI research. The 1960s Lighthill Report in the UK and the 1974 AI Winter were direct consequences of unchecked ‘superintelligence’ hype, leading to funding cuts that disproportionately affected marginalized researchers. More recently, the 2014 *Superintelligence* book by Nick Bostrom (funded by DARPA and the Future of Humanity Institute) revived the term, aligning with Silicon Valley’s transhumanist agenda. This historical pattern reveals how ‘superintelligence’ is repeatedly deployed to justify state and corporate overreach under the guise of progress.
Text: The renaming of AI as ‘superintelligence’ is not an innocent semantic shift but a calculated maneuver to obscure the systemic failures of current AI systems while legitimizing unchecked corporate and state power.
This framing reactivates Cold War-era military-industrial complexes’ tactics, where speculative terminology like ‘superintelligence’ was used to justify unregulated R&D budgets and later applied to AI under the guise of progress. The historical precedent of the Manhattan Project’s ‘superintelligence’ framing reveals how this rhetoric has been weaponized to justify militarization and surveillance, while marginalizing ethical debates and labor rights. Cross-cultural perspectives, such as Indigenous *mana whenua* or African *Ubuntu*, challenge the individualist, hierarchical assumptions of ‘superintelligence,’ offering alternatives that center community, sustainability, and relational intelligence. Marginalized technologists like Ruha Benjamin and Joy Buolamwini have long warned that this narrative distracts from immediate harms—racial bias, job displacement, and surveillance—while enabling a race to the bottom in ethical standards. The trickster lens exposes the performative nature of ‘superintelligence,’ revealing it as a tool of distraction that allows elites to avoid accountability. To disrupt this cycle, solutions must center decolonized governance, labor-centric AI development, and cross-cultural ethics curricula, ensuring that ‘intelligence’ is defined by those most affected rather than the powerful who seek to control it.