The accelerating deployment of agentic AI is triggering a structural dependency crisis that mirrors the historical over-reliance on management consulting. As organizations and individuals rapidly outsource cognitive tasks to automated systems, a pattern of strategic atrophy is emerging. Short-term efficiency gains are masking a deeper erosion of institutional memory, independent judgment, and strategic autonomy, creating a lock-in effect that will prove costly and difficult to reverse. At the individual level, cognitive delegation is fostering what researchers term cognitive debt. When users consistently defer analytical and creative tasks to AI, the mental discipline required for independent problem-solving degrades. Studies indicate that even brief AI assistance can impair subsequent unaided performance, replacing deliberate intellectual struggle with passive consumption of algorithmic outputs. This normalization of automated reasoning risks producing a workforce unable to critically evaluate or override machine-generated work. Corporate operations face parallel vulnerabilities. The initial subsidy-driven pricing of AI services encourages rapid integration, but token consumption rates are outpacing cost reductions, leading to spiraling expenditures. Major technology firms have already curtailed AI coding licenses after budgets were exhausted, while token spend frequently fails to deliver commensurate productivity, partly due to inefficient metric tracking and the overhead of human oversight. As vendors shift toward profitability, organizations that hollowed out internal expertise during the subsidy phase will face steep switching costs and compromised governance. The oversight tax compounds this risk, as human reviewers increasingly serve as compliance checkpoints rather than active arbiters, surrendering conceptual integrity to the system. The geopolitical implications are equally consequential. Compute capacity and frontier model development remain heavily concentrated in the United States and China, creating a hierarchical access structure. Recent government directives suspending access to advanced models for foreign users demonstrate how single-vendor dependency can instantly constrain national and corporate strategic options. This centralization echoes historical technology transfer failures, where foreign systems generated output without building local maintenance capacity, ultimately transferring control to external providers. Rather than pursuing prohibition or uncritical adoption, stakeholders must implement deliberate governance frameworks. Individuals should treat AI as a sparring partner rather than a substitute for judgment, preserving the cognitive struggle that builds expertise. Organizations must protect institutional memory through mandatory mentorship, structured staff rotation, and diversified vendor architectures that combine proprietary models with locally operable alternatives. Public policy should enforce rigorous human-in-the-loop standards requiring reviewers to substantiate AI outputs, while investing in open-weight ecosystems and shared compute infrastructure to mitigate geopolitical vulnerability. The trajectory of agentic AI adoption will determine whether technology amplifies human agency or automates it away. By establishing clear operational boundaries, maintaining internal competency, and treating AI integration with the same regulatory discipline as industrial tools, the technology sector can capture efficiency gains without surrendering strategic control. The window to institutionalize these safeguards before dependency becomes irreversible remains narrow.