July 10, 2026, (Inside AI) — The rapid integration of agentic AI into work and life is following a dangerous pattern previously seen in management consulting, where short-term delegation erodes internal capability until reversal becomes impossible. A new analysis warns that individuals, organizations, and governments are ceding critical thinking to AI systems that deliver polished but often generic or flawed outputs, creating a structural dependency that benefits vendors more than users.
The warning comes from a detailed synthesis published on Towards Data Science, drawing parallels between the consulting industry's "Big Con" and the current AI boom. The author, reflecting on advice from a 2009 programming lecturer at Imperial College London to sketch solutions by hand before coding, argues that the deliberate cognitive struggle is essential for deep understanding—a process AI now threatens to bypass.
At the individual level, the cost is insidious. MIT professor Micah Nathan recounted in The Guardian in May 2026 how a student's use of AI for a creative writing workshop escalated from grammar checks to full rewrites, a surrender of authorship he likened to "an addict's descent." He stressed that "Writing isn't just the production of sentences — it's the training of endurance by way of sustained attention. It's a way of learning what one thinks by attempting to say it." Recent studies back this up: randomized trials show even brief AI assistance impairs subsequent independent performance, and researchers have coined the term cognitive debt for the atrophy of independent thinking.
Organizations face a parallel hollowing out. As AI agents draft contracts, close books, and generate strategy, institutional memory thins. The economics are already jarring. Microsoft cut internal AI coding licenses in 2026 after costs spiraled; Uber exhausted its annual AI coding budget in four months; Salesforce expects to pay Anthropic roughly $300 million this year. An Nvidia vice president admitted "the cost of compute is far beyond the costs of the employees." A survey of nearly 2,500 companies found that for every dollar spent on AI tokens, only 18 cents generated user-facing value, while 44 cents went to fixing AI-caused bugs.
The societal stakes are even higher. When schools, courts, or governments substitute AI for judgment, accountability dissolves. The term "human in the loop" often becomes an accountability sink, where people rubber-stamp AI outputs under time pressure. Geopolitically, the U.S. and China control roughly 90% of global compute and 70-80% of AI investment. In June 2026, the U.S. government ordered Anthropic to suspend access to its Fable 5 and Mythos 5 models for all foreign nationals, citing national security. Anthropic, forced to comply, described the directive as based on "verbal evidence of a narrow, non-universal" vulnerability, exposing how dependency on a single provider can cripple operations overnight.
The analysis proposes a third path between outright bans and uncritical adoption. Individuals should use AI as a sparring partner but retain judgment; organizations must treat institutional memory as a strategic asset and diversify suppliers; and public policy should demand that AI deployments increase local agency and avoid permanent external dependency. As the author notes, the consulting industry is now being disintermediated by AI itself—share prices of firms like Accenture have fallen sharply—but substituting one dependency for another may only deepen lock-in.
The core lesson echoes that programming advice from 2009: do the thinking first. Whether societies apply that principle deliberately, or let short-term convenience dictate the future, remains an open and urgent question.
