Token expenditure index plunges nearly 20%; raises questions about AI infrastructure capex sustainability and ROI.

The capital bonanza in the artificial intelligence industry is facing its most direct test yet. A key indicator tracking user willingness to pay has plunged nearly 20% over the past month-plus, sparking deep concerns about whether the wave of over $700 billion in capital expenditure can generate reasonable returns.

According to market sources, the "Silicon Data LLM Token Expenditure Index," which tracks fees users pay for AI tokens, has fallen nearly 20% from its May high after nearly doubling since its launch last December. The index is viewed as the clearest observational tool currently available for measuring how much real momentum the massive capital expenditure boom underpinning the entire industry's heavy development has actually generated.

For equity investors, the softening index is flashing a warning signal: facing increasingly cost-sensitive customers, AI companies may be losing pricing power, and market expectations that the AI industry will eventually unleash a profit surge may be premature. Veteran investor Louis Navellier said, "More and more reports indicate that users of token-billed AI solutions are being forced to limit usage due to high costs. OpenAI is reportedly postponing its IPO timeline to next year, which is widely interpreted as a sign that current profitability remains a major challenge."

It is important to clarify that a decline in the index does not equate to AI usage becoming cheaper. The index blends two factors—pricing and usage volume—meaning a drop could represent vastly different scenarios: official list price reductions, a demand shift toward lower-priced models, or an actual loosening in the fees buyers are willing to bear. Silicon Data, the company that constructed the index, specifically cautions that it should be viewed as a proxy indicator for "marginal willingness to pay," not a simple price tag.

Optimists point out that while token prices have collapsed more than 90% since 2023, total spending has roughly doubled compared to last year. Cheaper tokens have instead expanded the market size. Under this logic, the index's temporary stall is merely a digestion and consolidation phase, with real demand remaining robust and the massive capital expenditure well justified. The bull thesis for Nvidia, memory manufacturers, and data center-related stocks is built precisely on this foundation.

However, pessimists issue a stern warning. Research firm Allianz points out that there is a nearly 46% growth gap between AI investment and sales—a situation even more severe than the 32% divergence seen when the 2001 telecom bubble burst. This means the enormous capital currently being deployed faces an unprecedented gap on the path to converting into actual revenue.

Catalyst Funds Senior Portfolio Manager David Miller offered a more positive view: "During the model training phase, AI infrastructure and token generation costs are extremely high, but now that we have entered the inference phase, the economic benefits have clearly improved. Overall, practical AI applications still deliver positive return on investment for enterprises, at least over the long term."

Beyond pricing power concerns, new bearish factors have recently emerged on the demand side—namely, a clear increase in regulators' willingness to intervene in this critical industry. The US government just this week lifted restrictions on overseas use of Anthropic's Fable 5 model, after regulators had previously required OpenAI to roll out its upcoming new version in stages.

Meanwhile, the European Union's AI Act imposes mandatory assessments and stringent transparency requirements on cutting-edge models. While these regulations do not directly control pricing, they create deployment and compliance burdens for major mainstream platforms, while smaller but equally capable systems face no equivalent pressure. This could prompt corporate CFOs to steer workloads toward lower-priced models, further eroding premium pricing power in the high-end market.

This trend does not mean chips will face an oversupply. Orders for top-tier GPUs and High Bandwidth Memory are booked through 2026, with real supply relief not expected until 2028. However, a subtle shift in demand structure—gradually moving from high-end training GPUs toward inference-optimized chips—is redrawing the winner's map in this race.

The strategy team at German asset manager DWS remains cautious on this front, stating they will closely monitor for signs of excessive valuation stretching, and naming intensifying competition from China and rising price sensitivity as key concerns.

For the bulls, the good news is that the recent downward trend has temporarily halted. While a single week of flat movement is still insufficient to declare a market bottom, it is enough to keep the possibility of a rebound alive. Nasdaq-100 Index futures, dominated by tech stocks, rebounded 1.2% on Friday, indicating that market sentiment has not yet completely collapsed.

Market sources summarize that this "token chart" presents a dual-sided interpretation, and the market should keep both scenarios in play. If the flat trajectory since late June can be sustained, and the recent pullback is merely a digestion process caused by product mix adjustments, then lower-cost tokens will continue to expand the application market, capital expenditure remains justifiable, and the bull thesis stays intact.

But if the opposite is true—if this moment marks the peak of customer willingness to pay, while regulatory pressure simultaneously begins pushing demand toward the lower end of the market—then the most expensive, most premium-dependent segments of this trade will be the first to show cracks. This is because the core foundation supporting capital expenditure that could reach $1 trillion by 2027 is not chip supply capacity itself, but pricing power. Once this foundation wavers, the entire logic of the AI capital feast will face re-examination.