Executive summary: This exploratory post outlines a broad set of concrete research directions stemming from the “Gradual Disempowerment” (GD) paper, aiming to help others productively investigate how AI might diminish human influence over time and what strategies could prevent this—emphasizing breadth over depth and offering mentorship to those who take on the work.
Key points:
Integrated AI x-risk dynamics: The author encourages research into how GD interacts with other AI-related risks (like misalignment, coup risk, or recursive self-improvement), including mapping tradeoffs and exploring solution robustness across multiple failure modes.
Counterarguments and their assumptions: Several objections to GD—such as the strategy-stealing assumption, aligned AI interventions, or natural societal adaptations—deserve fuller exploration, ideally resulting in a fair synthesis of competing views.
Beyond competition: GD is not solely about competitive pressures; it also involves emergent influence patterns and internal dynamics that can lead to human disempowerment even in the absence of direct competition, warranting deeper conceptual analysis.
Describing and aiming for positive futures: Clarifying what “good outcomes” look like—both long-term and within the next few years—is a central priority, including discussions of paternalism, cultural evolution, and potential relationships between humans and AGI.
Social science and historical grounding: Suggested projects include reassessing the robustness of societal fundamentals (e.g., property rights, human agency) and drawing insights from historical transitions and technologies to better understand power dynamics and cultural shifts.
Indicators and practical policy levers: Developing measurable indicators for GD and actionable policy interventions—particularly short-term “red tape” solutions—is seen as a highly impactful yet currently neglected area.
Technical research areas: Promising directions include simulating civilizations, studying AI cognition and agency, formalizing civilizational alignment, and advancing differential empowerment mechanisms that support beneficial governance structures.
Complementarity over replacement: The post advocates for orienting AI development toward human-AI complementarity (e.g., cyborg evaluations, better interfaces), to avoid defaulting to a replacement paradigm that risks disempowering humans further.
Call to action with mentorship: The author offers personalized feedback to those who pursue these research directions, particularly encouraging undergraduates or early-career thinkers to engage with low-barrier entry points.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: This exploratory post outlines a broad set of concrete research directions stemming from the “Gradual Disempowerment” (GD) paper, aiming to help others productively investigate how AI might diminish human influence over time and what strategies could prevent this—emphasizing breadth over depth and offering mentorship to those who take on the work.
Key points:
Integrated AI x-risk dynamics: The author encourages research into how GD interacts with other AI-related risks (like misalignment, coup risk, or recursive self-improvement), including mapping tradeoffs and exploring solution robustness across multiple failure modes.
Counterarguments and their assumptions: Several objections to GD—such as the strategy-stealing assumption, aligned AI interventions, or natural societal adaptations—deserve fuller exploration, ideally resulting in a fair synthesis of competing views.
Beyond competition: GD is not solely about competitive pressures; it also involves emergent influence patterns and internal dynamics that can lead to human disempowerment even in the absence of direct competition, warranting deeper conceptual analysis.
Describing and aiming for positive futures: Clarifying what “good outcomes” look like—both long-term and within the next few years—is a central priority, including discussions of paternalism, cultural evolution, and potential relationships between humans and AGI.
Social science and historical grounding: Suggested projects include reassessing the robustness of societal fundamentals (e.g., property rights, human agency) and drawing insights from historical transitions and technologies to better understand power dynamics and cultural shifts.
Indicators and practical policy levers: Developing measurable indicators for GD and actionable policy interventions—particularly short-term “red tape” solutions—is seen as a highly impactful yet currently neglected area.
Technical research areas: Promising directions include simulating civilizations, studying AI cognition and agency, formalizing civilizational alignment, and advancing differential empowerment mechanisms that support beneficial governance structures.
Complementarity over replacement: The post advocates for orienting AI development toward human-AI complementarity (e.g., cyborg evaluations, better interfaces), to avoid defaulting to a replacement paradigm that risks disempowering humans further.
Call to action with mentorship: The author offers personalized feedback to those who pursue these research directions, particularly encouraging undergraduates or early-career thinkers to engage with low-barrier entry points.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.