Episode
Does AI Make Communism Feasible? (A Far Right Debate)
- Published
- Jun 3, 2026
- Duration seconds
- 3165
- Processing state
not_requested
Actions
POST https://stenobird.com/v1/public/podcasts/based-camp-simone-malcolm-collins-6378161/episodes/does-ai-make-communism-feasible-a-far-right-debate/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/based-camp-simone-malcolm-collins-6378161/does-ai-make-communism-feasible-a-far-right-debate.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
In this episode of Based Camp, Malcolm and Simone Collins tackle one of the most provocative questions in the age of AI: Does artificial intelligence finally make communism feasible? They explore the structural failures of historical communism (incentives, power consolidation, information problems, and catastrophic mismanagement), why small-scale communism works (families, kibbutzim) but large-scale versions collapse, and whether AI-driven post-scarcity could solve these issues or simply replicate the same human problems of bad actors, bureaucracy, and distorted incentives. Topics include: * The Sam Altman UBI study and why unconditional cash transfers often fail * Why Soviet science succeeded in some areas but governance always failed * Power vacuums in anarcho-communism vs. centralized systems * The future of “techno-fiefdoms,” AI-managed communities, and human reserves for those left behind by AI disruption * Demographic collapse and the likely rise of religious/techno-puritan movements A raw, nuanced debate that challenges both right-wing and left-wing assumptions about economics, human nature, and the coming AI era. Show Notes Why Implementations Fail * Economic calculation problem ( Ludwig von Mises, 1920 ): * Without private property and market prices, planners lack information on relative scarcity/costs. * You can’t rationally allocate steel, labor, or grain. * Attempts at “material balances” or cybernetic planning (e.g., Soviet OGAS —an attempted nationwide information network) failed repeatedly. * HOW AI CAN FIX THIS * Adequately and dynamically track supply and demand * Incentive and knowledge problems ( Hayek ): * People respond to incentives. * Common ownership dilutes responsibility (”tragedy of the commons”). * Local knowledge is dispersed; central decre…