The Autonomy Paradox: AI, Runaway Monopolization, and the Optimal Taxation of Capital

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Abstract This paper develops a dynamic equilibrium model to analyze the economic consequences of self-improving, autonomous capital - i.e. AI. This new kind of capital makes a new, radical economic growth framework necessary which bears significant implications. I postulate a paradigm shift where the classical principal-agent problem is driven away wich engenders several consequences. The agent´s incentive and participation constraint vanish due to fierce competition with a new, super productive autonomous capital. Thus the agent´s effort is only determined by a new constraint - the \textbf{survival constraint}. If the economic AI productivity surpasses that of humans, agents drop out of the market. Former principals become now agents producing goods with autonomous capital. Due to potential non diminishing returns to scale at a certain point these new agents can not compete with even larger companys. A \textbf{runaway to monopolization} takes place. Hence the theoretical model features endogenous growth driven by capital accumulation with potentially non-diminishing returns, leading to runaway monopolization where large firms grow faster than small ones. This concentration, coupled with the erosion of labor's marginal product due to more and more productive autonomous capital, threatens to spur mass unemployment below a socially-defined survival consumption level. I characterize the government's problem of designing an \textbf{optimal profit tax} that redistributes income to ensure survival, while internalizing the tax's negative effect on investments and thus long-run growth. Using full dynamic optimization and numerical methods calibrated to OECD data, I derive the optimal tax path and identify precise technological conditions for runaway monopolization. The framework provides a unified basis for antitrust, redistribution, and growth policy in the age of AI, with extensions addressing political economy, international tax competition and robustness. JEL Codes: D21, D25, E24, E62, O33, O41
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The Autonomy Paradox: AI, Runaway Monopolization, and the Optimal Taxation of Capital | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Autonomy Paradox: AI, Runaway Monopolization, and the Optimal Taxation of Capital Johannes Maria Posselt This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8222735/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper develops a dynamic equilibrium model to analyze the economic consequences of self-improving, autonomous capital - i.e. AI. This new kind of capital makes a new, radical economic growth framework necessary which bears significant implications. I postulate a paradigm shift where the classical principal-agent problem is driven away wich engenders several consequences. The agent´s incentive and participation constraint vanish due to fierce competition with a new, super productive autonomous capital. Thus the agent´s effort is only determined by a new constraint - the \textbf{survival constraint}. If the economic AI productivity surpasses that of humans, agents drop out of the market. Former principals become now agents producing goods with autonomous capital. Due to potential non diminishing returns to scale at a certain point these new agents can not compete with even larger companys. A \textbf{runaway to monopolization} takes place. Hence the theoretical model features endogenous growth driven by capital accumulation with potentially non-diminishing returns, leading to runaway monopolization where large firms grow faster than small ones. This concentration, coupled with the erosion of labor's marginal product due to more and more productive autonomous capital, threatens to spur mass unemployment below a socially-defined survival consumption level. I characterize the government's problem of designing an \textbf{optimal profit tax} that redistributes income to ensure survival, while internalizing the tax's negative effect on investments and thus long-run growth. Using full dynamic optimization and numerical methods calibrated to OECD data, I derive the optimal tax path and identify precise technological conditions for runaway monopolization. The framework provides a unified basis for antitrust, redistribution, and growth policy in the age of AI, with extensions addressing political economy, international tax competition and robustness. JEL Codes: D21, D25, E24, E62, O33, O41 Artificial Intelligence Autonomous Capital Endogenous Growth Monopolization Optimal Taxation Survival Constraint Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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