--- title: "Deep Dive into Goldman Sachs' Trading Desk: Behind the AI Capital Boom, Monetization Challenges Are Quietly Emerging" type: "News" locale: "en" url: "https://longbridge.com/en/news/289213666.md" description: "Goldman Sachs points out that AI investment is shifting from a \"race for capabilities\" to a \"battle for monetization.\" The hurdle rate implied by the nearly $1 trillion in annualized capital expenditure has exceeded historical levels. Open-source models and local inference solutions are compressing AI economics, raising companies' motivation to defer spending. Meanwhile, approximately $100 billion in leveraged exposure in global semiconductor and hardware-related stocks, combined with retail investor participation, constitutes tail risk" datetime: "2026-06-09T15:45:01.000Z" locales: - [zh-CN](https://longbridge.com/zh-CN/news/289213666.md) - [en](https://longbridge.com/en/news/289213666.md) - [zh-HK](https://longbridge.com/zh-HK/news/289213666.md) --- # Deep Dive into Goldman Sachs' Trading Desk: Behind the AI Capital Boom, Monetization Challenges Are Quietly Emerging The wave of AI capital expenditure nearing $1 trillion is facing severe monetization dilemmas, with cracks beginning to appear in valuation logic. Rich Privorotsky, head of the One-Delta trading desk at Goldman Sachs, pointed out in his latest client briefing that **the AI narrative is shifting from a "race for capabilities" to a "battle for monetization."** The investment hurdle rate implied by current AI capital expenditure has surpassed the actual levels achieved in any comparable technological cycle in history. Meanwhile, the rapid evolution of open-source models and local inference solutions is compressing the space for AI economics, leading to a rising rational motive for companies to defer spending. For the market, the risk transmission path is clear: **expectations will come under pressure first, subsequently impacting the revenue growth of companies currently benefiting from the AI construction boom.** There is approximately $100 billion in leveraged fund exposure in global semiconductor and hardware-related stocks, coupled with significant retail investor participation. Once market sentiment reverses, these sectors will face intense deleveraging risks. ## From Capabilities to Monetization: AI Investment Hurdle Rates and Deferral Risks The core contradiction in AI investment is shifting from "can it be done" to "can it make money." Citing recent research from the Wharton School, Privorotsky noted that the productivity gains required to reasonably support the current AI capital expenditure cycle are historically rare—even compared to the IT boom era, the results achieved within the same timeframe remain far out of reach. **He emphasized that the specific figures are not key; the truly noteworthy information is that the investment hurdle rate implied by current spending is extremely high.** Currently, annualized AI capital expenditure in the private sector is approaching $1 trillion. This spending is creating jobs, supporting economic growth, and generating economic spillover effects far beyond AI itself. However, as the narrative shifts from "every company must experiment immediately" to "returns remain difficult to quantify," the likelihood of companies further deferring spending is rising. A signal worth noting is that the Silicon Data Token Spending Index has begun to soften mildly. Meanwhile, increasingly powerful open-source models and local inference solutions are significantly compressing the space for AI economics. **Once companies generally form the expectation that "AI costs will drop significantly in a year," deferring current spending becomes a rational choice—and this is precisely the core risk facing current valuation multiples.** ## Leverage Risk: A Convexity Double-Edged Sword Changes on the supply side are becoming a new source of market pressure. Privorotsky pointed out that the large-scale stock issuances expected by the market for the second half of the year have begun to materialize, which has always been a key variable testing the market's absorption capacity. Meanwhile, approximately $100 billion in leveraged fund exposure in global semiconductor and hardware-related stocks, combined with substantial retail investor participation, constitutes tail risk that cannot be ignored. He described the current landscape as a "convexity double-edged sword": **when the direction is favorable, capital flows can bring significant upside potential; once sentiment reverses, the deleveraging process will also be extremely violent. Part of last week's market volatility was a manifestation of this mechanism.** From a broader macro perspective, the fundamental pillars of AI investment remain intact—the power and infrastructure gaps are still significant, and the scale of capital expenditure is massive. However, as expectations heat up for new releases of frontier models, market catalysts and valuation pressures will simultaneously test investors' resolve. ### Related Stocks - [SOXL.US](https://longbridge.com/en/quote/SOXL.US.md) ## Related News & Research - [Forget AI Models. This Company Sells What Every AI Giant Needs.](https://longbridge.com/en/news/289086501.md) - [NAVER Expands AI Infrastructure With NVIDIA to Serve Surging Global AI Demand](https://longbridge.com/en/news/288974522.md) - [The trader who sparked the Beyond Meat short-squeeze unpacks why he's betting against 3 top AI stocks](https://longbridge.com/en/news/288891866.md) - [Cisco agentic AI security push faces enterprise trust gap](https://longbridge.com/en/news/288788229.md) - [08:05 ETSEI and Accenture Release AI Adoption Maturity Model to Help Organizations Scale AI with Predictable Outcomes](https://longbridge.com/en/news/289052634.md)