The core variable in AI trading is shifting from "technical feasibility" to "cost affordability." As Token prices decline rapidly, the market is beginning to reassess the relationship between AI inference demand, corporate return on expenditure, and the valuation of related assets.

Rich Privorotsky, head of Goldman Sachs' One-Delta department, noted in his latest trading perspective that DeepSeek reportedly reduced Token pricing by 75%, while Xiaomi's MiMo slashed prices by nearly 99%. The issue of cost compression within the AI ecosystem has become difficult to ignore. He believes this shift could trigger a "price war" dynamic akin to post-subsidy competition.

This change is reshaping market judgments on AI infrastructure. He pointed out that infrastructure bottlenecks will eventually ease, and the market should not pay excessive premiums for "problems that are about to be solved." Token expenditures continue to rise, interest in open-source models has noticeably increased, and the installation volume of coding assistants has entered a plateau phase.

For investors, the question is no longer whether AI demand is growing, but whether low-cost Tokens might replace high-cost inference services before new application scenarios fully explode, potentially creating a period of revenue pressure. In the short term, month-end liquidity, retail capital, and institutional caution may still support semiconductor stocks and momentum trading, but valuation premiums and concentration risks are accumulating.

**Cost Compression Becomes a New Variable in the AI Narrative**

Over the past six months, the focus of the AI investment narrative has primarily been on technical capability. Now, the market is shifting towards unit economics.

AI technology has proven effective for certain workloads, users, and scenarios. However, the new questions are whether the unit economics hold at an enterprise scale, and whether such improvements can be achieved within the revenue trajectory required by labs and cloud service providers.

An optimistic scenario relies on two conditions: the maturation of orchestration capabilities and significant unit cost improvements within 12 to 18 months. A pessimistic scenario would involve more corporate executives publicly stating that AI spending cannot justify its value and subsequently cutting usage.

This makes "Token economics" a critical variable in AI valuation. In other words, the market must consider not only AI usage volume but also whether each unit of usage can generate sufficient revenue and profit.

**Low-Cost Tokens May Initially Disrupt High-Cost Inference**

The key question raised by Rich Privorotsky is whether the rapid decline in Token costs might temporarily disrupt the Jevons paradox logic of demand expansion.

The Jevons paradox refers to the phenomenon where increased efficiency in resource use leads to an overall increase in consumption. Applied to AI, lower costs could lead to greater long-term demand. The debate, however, centers on whether this demand expansion will occur immediately.

If cheaper Tokens first replace existing, more expensive inference services rather than immediately creating entirely new use cases, a time lag in AI revenue growth could emerge. The market needs to assess the pressure this lag period might exert on cloud service providers, model companies, and AI infrastructure demand.

**Cost Logic Reshapes Procurement**

The rising popularity of open-source models is altering corporate AI procurement logic.

As "achieving 90% of the output for 10% of the cost" becomes increasingly feasible, companies may scrutinize Token expenditures more seriously. The rationalization of Token spending could become a significant board-level issue in the second and third quarters, potentially as important as the AI growth narrative itself.

This implies the AI industry faces not a disappearance of demand, but a change in its structure. Companies may continue using AI but will likely seek to lower inference costs, reduce reliance on expensive cutting-edge models, and opt for lower-cost alternatives for different tasks.

For the market, this shift could affect profit distribution within the AI value chain. High-cost inference, data center expansion, and highly valued infrastructure assets will face stricter return-on-investment scrutiny.

**Semiconductor Momentum Remains Strong, but Fragility Increases**

Despite new valuation concerns arising from the Token price war, Rich Privorotsky believes the short-term market may continue its upward trend.

He noted that month-end liquidity factors might continue to mechanically support momentum trading and the semiconductor sector. Retail capital remains robust, and institutional investors may still be more skeptical than warranted, a skepticism that paradoxically continues to support the market's rise.

Simultaneously, AI-related semiconductor trading has experienced intense short squeezes, with every dip being actively bought. Upside volatility is still sought after, and downside panic has receded, though some structures appear stretched.

This explains the current market contradiction: short-term liquidity and positioning factors still support gains, but changes in Token economics are eroding some long-term valuation assumptions. For investors, the risk is not the end of the AI narrative, but a repricing of profit pools within that narrative.

**Bottlenecks Will Ease, Premiums May Not Be Safe**

Rich Privorotsky does not deny the long-term prospects for AI. He believes AI development may ultimately prove to be correct and could change the world like the internet did.

However, he also cautions that the market should not structurally bet against human creativity. Historically, bottlenecks tend to be solved: memory shortages ease, power shortages attract investment, and constraints gradually relax.

This has a dual implication for the market. On one hand, AI infrastructure bottlenecks may not sustain high prices and profit margins in the long term. On the other hand, cost reductions and efficiency improvements could also unleash greater demand over a longer cycle.

The real question now is whether the market has already paid too high a premium for this process. As the "Token war" begins, investors need to more precisely distinguish between the impacts of AI demand growth and AI cost compression, rather than simplistically viewing both as positive factors.