Even super giants like Microsoft, who have mines at home and unlimited cloud resources, have stopped all Claude Code authorizations for internal use because they cannot afford electricity bills! Microsoft has officially cancelled the authorization to use Claude Code under Anthropic internally. The core reason for this decision is not the productivity output issue of the tool itself, but rather the fact that the actual computing power consumption cost based on token billing has exceeded the financial limit of this tech giant. Almost at the same time, Uber's Chief Technology Officer (CTO) issued an internal memo warning that the company had already exhausted its full year AI budget by the first four months of 2026. Affected by this macro trend, AI software pricing in the US market has generally rebounded by 20% to 37%, and Microsoft's GitHub is also completely phasing out flat rate subscriptions and shifting towards a usage based tiered billing model. This chain reaction marks the official end of the "era of AI industry subsidies at a loss". For a long time, the market has formed a blind technological optimism narrative, believing that as models iterate, inference and token costs will follow some kind of hyper Moore's Law and collapse infinitely. However, real commercial data and the cost of replacing mental labor indicate that when enterprise level customers attempt to fully integrate Large Language Models (LLMs) into high-frequency production lines, the marginal cost not only does not decrease, but also increases exponentially with the surge in call frequency and context length. The chart clearly illustrates this financial anomaly: while the Per Seat Revenue remains constant, the steep increase in Per Token AI Compute Cost directly leads to a black hole collapse of the company's profit margin. From the essence of macro capital distribution and valuation models, the deterioration of this unit economy model has pushed the top AI laboratories in the primary market (such as OpenAI, Anthropic, Google) into a zero sum game of two losses. In order to maintain the valuation myth of hundreds of billions of dollars before the IPO, the laboratory has indirectly increased the actual unit price through various mechanisms in the past six months. And this directly led to the strategic contraction of AI application scale by enterprise customers after budget cuts, thereby blocking the revenue growth of AI manufacturers. If the laboratory chooses to reduce prices and supplement, its own cash flow loss rate will not be able to sustain the construction of the next generation super cluster. This pricing mismatch between the supply and demand sides indicates that the false prosperity of the computing power dividend era has peaked, and the industry is about to face large-scale asset impairment and mental restructuring.