We propose a responsibility-anchored employment theory framework grounded in endogenous responsibility costs. When AI systems replace human labor, runaway risks (e.g., explosions in chemical plants, misdiagnoses by medical robots) create irreversible responsibility gaps; individual corporate owners, neither cognitively nor financially equipped to bear such liabilities, face catastrophic societal damage (environmental destruction, casualties, etc.). By anchoring the balance between automation levels and responsibility-bearing capacity, the model identifies a critical threshold: surpassing the AI-to-labor ratio threshold triggers an responsibility vacuum. A dual institutional mechanism is proposed to resolve this vacuum: governments impose an automation responsibility tax on threshold-breaking firms while redistributing tax revenues as social insurance subsidies to sectors retaining human oversight. Numerical simulations demonstrate that this policy pre-emptively internalizes societal risks, allowing economies to harness AI-driven productivity gains while cushioning the shocks of rapid labor displacement. Specifically, the introduction of the Automation Responsibility Tax would alleviate 30% of unemployment without compromising productivity in the short term following AI-driven replacement. In the long term after AI impacts, under mechanisms addressing responsibility vacuums and social security deficits, it would enhance overall social welfare by 20% at the cost of a 6% reduction in total production. Furthermore, comparative analysis with alternative policies—such as direct taxation on automation volume and government-funded labor training programs—demonstrates the unique effectiveness of the Automation Responsibility Tax framework. This study proves that institutionalized responsibility redistribution is not only an essential pillar of societal stability but also a potential foundational framework for sustainable human–AI coexistence.