For the first time in the history of technology, the biological processor in a skin suit has proven more cost-effective than silicon-based software. While the industry is caught in a fit of "token-maximization," attempting to overwhelm every task with raw compute, reality has delivered a gut punch: high-quality AI inference currently costs businesses more than a human performer. We are entering a phase where AI doesn't shrink the workforce but bloats it, creating a desperate need for total oversight of "digital employees" who churn out errors at industrial speeds.
The situation suspiciously mirrors the U.S. railroad crisis of the mid-19th century. In the 1830s, track mileage exploded 120-fold, yet the system remained a loss-making chaos lacking any coherent management vertical. On October 5, 1841, two Western Railroad trains collided head-on in Massachusetts simply because the engineers couldn't agree on priorities. Railway empires only became profitable not when they bought more cars, but when they implemented reporting hierarchies and appointed local dispatchers. Today, business is back on the same tracks: autonomous agents have replaced trains, and endless loops of inefficiency have replaced profit.
A Million Bad Employees
The mechanics of modern AI implementation represent an attempt to storm the breach by throwing tokens at it. We have granted every manager an unlimited staff of "digital slaves" while forgetting to teach them delegation skills. Consequently, 80% of tokens today produce nothing but entropy, much like the bloated staff of a zombie corporation. This is a new form of bureaucratic obesity: tokens beget tokens, and employees unable to articulate a task clearly force neural networks to idle.
"Tokens behave like contract laborers. Once you start viewing them as headcount, the promises of AI collapse: they are faster than humans, but speed means nothing if the system requires 100 attempts for a single task."
The vast majority of employees cannot provide models with proper context. Out of a hundred white-collar workers, barely one can articulate a business process without logical gaps. The rest create infinite loops where an agent calls itself, trying to fix its own hallucinations caused by a botched prompt. This is the digital equivalent of meetings for the sake of meetings. We are paying for software to read tea leaves, wasting budgets to fix what should never have broken in the first place.
The Economy of the 100x Token
The myth that AI is "set it and forget it" is officially dead. A neural network can be more accurate than an expert, but only in a sterile context. In reality, it hallucinates with confidence and flawless formatting. In our view, CEO focus must shift from the tired idea of "replacing people" to building management systems for digital labor. We should not be looking for a mythical "job killer," but for methods to scale scenarios where a single token yields a hundredfold return thanks to a rigid control architecture.
"Humans are generally cheaper than tokens, but high-quality tokens are cheaper at scale. The management task is to transform one into the other."
The problem is exacerbated by quiet sabotage. Employees have already moved toward "context hoarding"—hiding professional tricks from models, realizing that the total digitization of their expertise makes them redundant. This creates a massive political risk. Without strict reporting standards for agents, businesses will inherit millions of unmanaged performers whose systemic failures will wipe out any paper savings. The responsibility for a digital collision on the scale of 1841 will fall not on software developers, but on the executives who confused buying tokens with managing a business.