Position: Humanity Faces Existential Risk from Gradual Disempowerment

Jan Kulveit, Raymond Douglas, Nora Ammann, Deger Turan, David Krueger, David Duvenaud
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:81678-81688, 2025.

Abstract

This paper examines the systemic risks posed by incremental advancements in artificial intelligence, developing the concept of ‘gradual disempowerment’, in contrast to the abrupt takeover scenarios commonly discussed in AI safety. We analyze how even incremental improvements in AI capabilities can undermine human influence over large-scale systems that society depends on, including the economy, culture, and nation-states. As AI increasingly replaces human labor and cognition in these domains, it can weaken both explicit human control mechanisms (like voting and consumer choice) and the implicit alignments with human preferences that often arise from societal systems’ reliance on human participation to function. Furthermore, AI systems may amplify existing misalignments with human preferences by optimizing these systems more powerfully. These distortions across domains may be mutually reinforcing: economic power shapes cultural narratives and political decisions, while cultural shifts alter economic and political behavior. We argue that this dynamic could lead to an effectively irreversible loss of human influence over crucial societal systems, precipitating an existential catastrophe through the permanent disempowerment of humanity. This analysis suggests the need for both technical research and governance approaches that specifically address the risk of incremental erosion of human influence across interconnected societal systems.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-kulveit25a, title = {Position: Humanity Faces Existential Risk from Gradual Disempowerment}, author = {Kulveit, Jan and Douglas, Raymond and Ammann, Nora and Turan, Deger and Krueger, David and Duvenaud, David}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {81678--81688}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/kulveit25a/kulveit25a.pdf}, url = {https://proceedings.mlr.press/v267/kulveit25a.html}, abstract = {This paper examines the systemic risks posed by incremental advancements in artificial intelligence, developing the concept of ‘gradual disempowerment’, in contrast to the abrupt takeover scenarios commonly discussed in AI safety. We analyze how even incremental improvements in AI capabilities can undermine human influence over large-scale systems that society depends on, including the economy, culture, and nation-states. As AI increasingly replaces human labor and cognition in these domains, it can weaken both explicit human control mechanisms (like voting and consumer choice) and the implicit alignments with human preferences that often arise from societal systems’ reliance on human participation to function. Furthermore, AI systems may amplify existing misalignments with human preferences by optimizing these systems more powerfully. These distortions across domains may be mutually reinforcing: economic power shapes cultural narratives and political decisions, while cultural shifts alter economic and political behavior. We argue that this dynamic could lead to an effectively irreversible loss of human influence over crucial societal systems, precipitating an existential catastrophe through the permanent disempowerment of humanity. This analysis suggests the need for both technical research and governance approaches that specifically address the risk of incremental erosion of human influence across interconnected societal systems.} }
Endnote
%0 Conference Paper %T Position: Humanity Faces Existential Risk from Gradual Disempowerment %A Jan Kulveit %A Raymond Douglas %A Nora Ammann %A Deger Turan %A David Krueger %A David Duvenaud %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-kulveit25a %I PMLR %P 81678--81688 %U https://proceedings.mlr.press/v267/kulveit25a.html %V 267 %X This paper examines the systemic risks posed by incremental advancements in artificial intelligence, developing the concept of ‘gradual disempowerment’, in contrast to the abrupt takeover scenarios commonly discussed in AI safety. We analyze how even incremental improvements in AI capabilities can undermine human influence over large-scale systems that society depends on, including the economy, culture, and nation-states. As AI increasingly replaces human labor and cognition in these domains, it can weaken both explicit human control mechanisms (like voting and consumer choice) and the implicit alignments with human preferences that often arise from societal systems’ reliance on human participation to function. Furthermore, AI systems may amplify existing misalignments with human preferences by optimizing these systems more powerfully. These distortions across domains may be mutually reinforcing: economic power shapes cultural narratives and political decisions, while cultural shifts alter economic and political behavior. We argue that this dynamic could lead to an effectively irreversible loss of human influence over crucial societal systems, precipitating an existential catastrophe through the permanent disempowerment of humanity. This analysis suggests the need for both technical research and governance approaches that specifically address the risk of incremental erosion of human influence across interconnected societal systems.
APA
Kulveit, J., Douglas, R., Ammann, N., Turan, D., Krueger, D. & Duvenaud, D.. (2025). Position: Humanity Faces Existential Risk from Gradual Disempowerment. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:81678-81688 Available from https://proceedings.mlr.press/v267/kulveit25a.html.

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